Medicine, Faculty of
Population and Public Health (SPPH), School of
DSpace
UBCV
Hu, Alexi Tianyang
Master of Science - MSc
University of British Columbia
Extreme heat events (EHEs) are escalating in frequency, intensity, and duration due to climate change, posing significant health risks. To mitigate their impacts, many countries, including Canada, have established heat-health warning systems (HHWSs). However, these systems often rely on absolute temperature thresholds that require all conditions to be met simultaneously. This rigidity can overlook different sequences of day-night heat that accumulate over multiple days, potentially leading to missed warnings. In this study, seven cumulative temperature metrics were created by summing daily high and overnight low temperatures over one to three days. To assess each metricâs relationship with all-cause mortality, heat-related ED visits, and ambulance dispatches in Western Metro Vancouver (WMV) from 2008-2023, generalized additive models with negative binomial distributions were fitted. The statistical performance of the metrics was evaluated using significance tests, the Akaike Information Criterion (AIC), and forecasting accuracy. All cumulative metrics demonstrated stable and comparable statistical performance. Given its applicability, the High+Low+High (HLH) metricâsumming two daily highs and one overnight lowâwas selected as an example for further analyses. A split value analysis was performed by dividing HLH into one hundred equally spaced intervals and creating a binary variable for each interval, refitting a model to determine the odds ratio (OR) at each split. Points at which the OR for the three outcomes became significantly greater than 1.0 were designated as baseline thresholds. 58°C (all-cause mortality), 47°C (ED visits), and 42°C (ambulance dispatches) were identified as the baseline points. Using the mortality-based threshold as a template, further âseparate baseline thresholdsâ of 51°C and 15°C were identified for two daily highs (HH) and overnight low (L) respectively. Accordingly, an HLH threshold algorithm was formulated such that the OR of mortality exceeds 1.0 when HLH â¥ 58°C and either L â¥ 15°C or HH â¥ 51°C. Subgroup analyses demonstrated elevated risks among individuals with cardiovascular, renal, or respiratory conditions, mental illnesses, multiple comorbidities, or lower socioeconomic status. Overall, this study developed methods for a more dynamic and flexible HHWS framework to issue heat alerts, offering a multi-trigger approach that can better account for cumulative day-night heat sequences. [An errata to this thesis was added on 2025-10-20.]
EVALUATING CUMULATIVE TEMPERATURE INDICATORS FOR HEAT ALERT AND RESPONSE SYSTEMS: A CASE STUDY IN VANCOUVER, BRITISH COLUMBIA by Alexi Tianyang Hu M.A., The University of Victoria, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Population and Public Health) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) February 2025 © Alexi Tianyang Hu, 2025 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Evaluating cumulative temperature indicators for heat alert and response systems: a case study in Vancouver, British Columbia submitted by Alexi Tianyang Hu in partial fulfilment of the requirements for the degree of Master of Science in Population and Public Health Examining Committee: Michael Brauer, Professor, Population and Public Health, UBC Co-Supervisor Sarah Henderson, Professor, Population and Public Health, UBC Co-supervisor Eric Lavigne, Senior Scientist, Health Canada Supervisory Committee Member Rachel White, Assistant Professor, Earth, Ocean and Atmospheric Sciences, UBC Additional Examiner iii Abstract Extreme heat events (EHEs) are escalating in frequency, intensity, and duration due to climate change, posing significant health risks. To mitigate their impacts, many countries, including Canada, have established heat-health warning systems (HHWSs). However, these systems often rely on absolute temperature thresholds that require all conditions to be met simultaneously. This rigidity can overlook different sequences of day-night heat that accumulate over multiple days, potentially leading to missed warnings. In this study, seven cumulative temperature metrics were created by summing daily high and overnight low temperatures over one to three days. To assess each metricâs relationship with all-cause mortality, heat-related ED visits, and ambulance dispatches in Western Metro Vancouver (WMV) from 2008-2023, generalized additive models with negative binomial distributions were fitted. The statistical performance of the metrics was evaluated using significance tests, the Akaike Information Criterion (AIC), and forecasting accuracy. All cumulative metrics demonstrated stable and comparable statistical performance. Given its applicability, the High+Low+High (HLH) metricâsumming two daily highs and one overnight lowâwas selected as an example for further analyses. A split value analysis was performed by dividing HLH into one hundred equally spaced intervals and creating a binary variable for each interval, refitting a model to determine the odds ratio (OR) at each split. Points at which the OR for the three outcomes became significantly greater than 1.0 were designated as baseline thresholds. 58°C (all-cause mortality), 47°C (ED visits), and 42°C (ambulance dispatches) were identified as the baseline points. Using the mortality-based threshold as a template, further âseparate baseline thresholdsâ of 51°C and 15°C were identified for two daily highs (HH) and overnight low (L) respectively. Accordingly, an iv HLH threshold algorithm was formulated such that the OR of mortality exceeds 1.0 when HLH â¥ 58°C and either L â¥ 15°C or HH â¥ 51°C. Subgroup analyses demonstrated elevated risks among individuals with cardiovascular, renal, or respiratory conditions, mental illnesses, multiple comorbidities, or lower socioeconomic status. Overall, this study developed methods for a more dynamic and flexible HHWS framework to issue heat alerts, offering a multi-trigger approach that can better account for cumulative day-night heat sequences. v Lay Summary Traditional heat warning systems often use stringent criteria to issue alerts, which may not fully reflect the dangers of heat exposure. This study looked at how combined temperatures over several days affect health in Western Metro Vancouver. High+Low+High (HLH)âsumming two daily highs with one overnight lowâwas selected as the metric. By analyzing health data from western Metro Vancouver (2008â2023), the study pinpointed key temperature cutoffsâ58°C for deaths, 47°C for emergency visits, and 42°C for ambulance callsâabove which the likelihood of heat-related risks rose notably. Using the cutoff of 58°C for deaths, more detailed thresholds emerged for overnight low (15°C) and daytime highs (51°C) that further clarified how people are affected by intense, multi-day heat. Certain groups, such as individuals with multiple or severe health conditions, older adults, and those facing economic challenges, were at especially high risk. vi Preface All work presented in this thesis was designed, carried out, and analyzed by Alexi Hu, under the guidance of co-supervisors Dr. Sarah Henderson and Dr. Michael Brauer, and committee member Dr. Ãric Lavigne. The external examiner, Dr. Rachel White also reviewed and provided feedback for this thesis. The temperature data were extracted from Environment and Climate Change Canada by Kathleen McLean, a senior epidemiologist at the BC Centre for Disease Control. This thesis received approval from the University of British Columbia Research Ethics Board (Certificate number: H23-01938), and permission to access the health data was granted by the BCCDC PANDA team. Generative artificial intelligence (AI) was used solely for proofreading the draft and was not involved in any other aspect of the work presented. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ................................................................................................................................ v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables .............................................................................................................................. xiv List of Figures .............................................................................................................................. xv List of Equations ........................................................................................................................ xix List of Abbreviations .................................................................................................................. xx Acknowledgements ................................................................................................................... xxii Dedication ................................................................................................................................. xxiii Chapter 1: Introduction and Literature Review........................................................................ 1 1.1 Problem Statement........................................................................................................ 1 1.2 Extreme Heat Events .................................................................................................... 2 1.2.1 Climate Change and EHEs: a Global and Canadian Perspective ............................... 3 1.3 EHEs and Health........................................................................................................... 6 1.3.1 Human Heat Physiology ............................................................................................. 6 1.3.2 Heath Impacts of EHEs ............................................................................................... 8 1.3.2.1 Mortality ............................................................................................................. 8 1.3.2.2 Morbidity .......................................................................................................... 10 viii 1.3.2.3 Susceptibility and Vulnerability to Heat ........................................................... 11 1.3.2.3.1 Susceptibility Factors .................................................................................... 11 1.3.2.3.2 Vulnerability Factors .................................................................................... 13 1.4 Heat Health Warning Systems (HHWSs) ................................................................. 14 1.4.1 An Overview of HHWSs .......................................................................................... 15 1.4.2 Effectiveness of HHWSs .......................................................................................... 19 1.4.3 Heat Alert and Response System (HARS) in BC ..................................................... 21 1.5 Gaps in Knowledge and Study Rationale ................................................................. 22 1.6 Study Aims ................................................................................................................... 23 Chapter 2: Methods .................................................................................................................... 26 2.1 Study Context .............................................................................................................. 26 2.2 Data Sources ................................................................................................................ 26 2.2.1 Temperature .............................................................................................................. 28 2.2.2 Vital Statistics ........................................................................................................... 28 2.2.3 Chronic Disease Registries (CDRs) .......................................................................... 28 2.2.4 National Ambulatory Care Reporting System (NACRS) ......................................... 29 2.2.5 Medical Priority Dispatch System (MPDS) .............................................................. 30 2.2.6 PharmaNet................................................................................................................. 30 2.3 Data Collection and Linkage...................................................................................... 31 2.3.1 Western Metro Vancouver (WMV) Spatial Health Boundaries ............................... 31 2.3.2 Data Linkage Between Vital Statistics and CDR ..................................................... 33 ix 2.3.3 Data Linkage Between NACRS and CDR................................................................ 33 2.3.4 Linking PharmaNet with Vital Statistics and NACRS ............................................. 34 2.4 Data Analysis ............................................................................................................... 35 2.4.1 Objective 1 ................................................................................................................ 35 2.4.1.1 Outcome Measures............................................................................................ 35 2.4.1.2 Exposure Measures ........................................................................................... 35 2.4.1.3 Time Series Covariates ..................................................................................... 36 2.4.1.4 Statistical Analysis ............................................................................................ 37 2.4.1.4.1 Statistical Performance of the Metrics .......................................................... 39 2.4.1.4.2 Operational Considerations ........................................................................... 40 2.4.2 Objective 2 ................................................................................................................ 40 2.4.2.1 Outcome and Exposure Measures ..................................................................... 41 2.4.2.2 Statistical Analysis: ........................................................................................... 41 2.4.3 Objective 3 ................................................................................................................ 44 2.4.3.1 Outcome and Exposure Measures ..................................................................... 44 2.4.3.2 Statistical Analysis ............................................................................................ 44 Chapter 3: Results....................................................................................................................... 47 3.1 Data Summary ............................................................................................................ 47 3.2 Temperature Metric Evaluation ................................................................................ 48 3.2.1 A GAM with Negative Binomial Distribution.......................................................... 54 3.2.2 Selection of Spline Function ..................................................................................... 55 3.2.3 Temperature Metric Selection................................................................................... 55 x 3.2.3.1 Statistical Performance ..................................................................................... 55 3.2.3.2 Operational Considerations ............................................................................... 56 3.2.4 Model diagnostics ..................................................................................................... 57 3.2.5 Sensitivity analysis.................................................................................................... 58 3.3 Identifying HLH Thresholds ...................................................................................... 59 3.4 Mortality-Based Threshold Algorithm: An Example of Threshold Application .. 60 3.4.1 Identifying L Threshold ............................................................................................ 60 3.4.2 Identifying HH Threshold ......................................................................................... 61 3.4.3 Mortality-Based HLH Threshold Algorithm ............................................................ 65 3.4.3.1 Algorithm Sensitivity Analysis 1 ...................................................................... 65 3.4.3.2 Algorithm Sensitivity Analysis 2 ...................................................................... 66 3.5 Extra Split Value Analysis on All-Cause ED Visits and Ambulance Dispatches .. 67 3.6 Application of Mortality-Based HLH Algorithm to Subgroups ............................. 71 3.6.1 Chronic Condition-Specific Outcomes ..................................................................... 71 3.6.2 Condition Groups and Multimorbidity ..................................................................... 72 3.6.3 Demographic-Related Mortality and Morbidity ....................................................... 75 3.6.4 Acuity of Heat-Related ED Visits............................................................................. 77 Chapter 4: Discussion and Conclusions .................................................................................... 78 4.1 Summary of Findings ................................................................................................. 78 4.1.1 Methods to Identify Cumulative Temperature Thresholds ....................................... 78 4.1.2 Health impacts of cumulative temperature on subgroups ......................................... 79 xi 4.2 Comparison With Existing Literature ...................................................................... 80 4.2.1 Alignment With Other Compound Heat Studies ...................................................... 80 4.2.2 Flexibility Versus Rigid Absolute Thresholds .......................................................... 81 4.3 Strengths ...................................................................................................................... 82 4.3.1 Comprehensive Data Linkage ................................................................................... 82 4.3.2 Advanced Methods for Identifying Cumulative Heat-Risk Thresholds ................... 83 4.3.3 Detailed Subgroup Analyses ..................................................................................... 83 4.4 Limitations and Directions for Future Research ..................................................... 83 4.4.1 Misclassification of Incidence and Heat-Related Outcomes .................................... 84 4.4.2 Mortality-Based Algorithm Versus Morbidity Thresholds....................................... 84 4.4.3 Unidentifiable Ambulance Dispatch Data ................................................................ 85 4.4.4 Exposure Misclassification, Geographical Scope, and Climatic Considerations ..... 85 4.4.5 Limited Socioeconomic and Demographic Data ...................................................... 87 4.4.6 Exclusion of Air Pollution ........................................................................................ 87 4.5 Conclusions .................................................................................................................. 88 Bibliography ................................................................................................................................ 89 Appendices ................................................................................................................................. 100 Appendix A Statistical Analysis of Objective 1 .................................................................. 100 A.1 Sub-Appendix: Descriptive Statistics of Daily Maximum and Minimum Temperatures....................................................................................................................... 100 A.2 Sub-Appendix: Descriptive Statistics of Health Outcomes .................................... 103 A.3 Sub-Appendix: AICs of Different Knots for Outcomes ......................................... 107 xii A.4 Sub-Appendix: Model Diagnostics for Outcomes .................................................. 109 A.5 Sub-Appendix: Sensitivity Analysis for Objective 1 .............................................. 115 Appendix B Statistical Analysis of Objective 2 .................................................................. 118 B.1 Sub-Appendix: Mortality-Based HLH Algorithm 1 ............................................... 118 B.2 Sub-Appendix: Mortality-Based HLH Algorithm 2 ............................................... 119 B.3 Sub-Appendix: Threshold Analysis of All-Cause ED Visits .................................. 120 B.4 Sub-Appendix: Threshold Analysis of All-Cause Ambulance Dispatches ............ 121 Appendix C Results of Analyses on Subgroups ................................................................. 122 C.1 Sub-Appendix: Comorbidity-Specific Mortality .................................................... 122 C.2 Sub-Appendix: Multimorbidity-Specific Mortality ................................................ 123 C.3 Sub-Appendix: Cardiovascular-Specific Mortality ................................................ 124 C.4 Sub-Appendix: Mental-Illness-Specific Mortality ................................................. 125 C.5 Sub-Appendix: Other-Chronic-Condition-Specific Mortality ................................ 126 C.6 Sub-Appendix: Age-Specific Mortality .................................................................. 127 C.7 Sub-Appendix: Location of Deaths ........................................................................ 128 C.8 Sub-Appendix: Sex-Specific Mortality................................................................... 129 C.9 Sub-Appendix: PharmaCare-Specific Mortality ..................................................... 130 C.10 Sub-Appendix: Age-Comorbidity-Specific Mortality ............................................ 131 C.11 Sub-Appendix: Age-Multimorbidity-Specific Mortality ........................................ 132 C.12 Sub-Appendix: Comorbidity-Specific Heat-Related ED Visits ............................. 133 C.13 Sub-Appendix: Multimorbidity-Specific Heat-Related ED Visits ......................... 134 C.14 Sub-Appendix: Cardiovascular-Specific Heat-Related ED Visits .......................... 135 xiii C.15 Sub-Appendix: Mental-Illness-Specific Heat-Related ED Visits ........................... 136 C.16 Sub-Appendix: Other-Chronic-Condition-Specific Heat-Related ED Visits ......... 137 C.17 Sub-Appendix: Age-Specific Heat-Related ED Visits ........................................... 138 C.18 Sub-Appendix: Sex-Specific Heat-Related ED Visits ............................................ 139 C.19 Sub-Appendix: PharmaCare-Specific Heat-Related ED Visits .............................. 140 C.20 Sub-Appendix: Acuity-Specific Heat-Related ED Visits ....................................... 141 C.21 Sub-Appendix: Age-Comorbidity-Specific Heat-Related ED Visits ..................... 142 C.22 Sub-Appendix: Age-Multimorbidity-Specific Heat-Related ED Visits ................. 143 C.23 Sub-Appendix: Acuity-Specific Heat-Related Ambulance Dispatches .................. 144 xiv List of Tables Table 1 Examples of different heatwave definitions and heat-health warning systems ............... 17 Table 2 List of comorbidity categories and 25 chronic conditions ............................................... 29 Table 3 Western Metro Vancouver Local Health Areas and Hospitals ........................................ 32 Table 4 Subgroups within each health outcome ........................................................................... 45 Table 5 Descriptive statistics of outcome variables ...................................................................... 49 Table 6 Descriptive statistics of temperature metrics ................................................................... 53 Table 7 Dispersion and normality tests of outcomes .................................................................... 54 Table 8 Results of the temperature metric evaluation .................................................................. 57 Table 10 A.5 AICs of temperature metrics modelled against daily health outcomes ................. 115 xv List of Figures Figure 1 Global average temperature from 1850 â 2023 by Berkeley Earth .................................. 3 Figure 2 Annual average temperature change in Canada between 1948 and 2016 ........................ 5 Figure 3 Current HAAs in the BC HARS and map of WMV ...................................................... 27 Figure 4 Flow chart of data collection .......................................................................................... 32 Figure 5 Inclusion criteria for PharmaCare plans ......................................................................... 34 Figure 6 Schematic of split value analysis .................................................................................... 42 Figure 7 Odds ratios of High-Low-High split values in all-cause mortality ................................ 59 Figure 8 Odds ratios of High-Low-High split values in heat-related ED visits and ambulance dispatches ...................................................................................................................................... 60 Figure 9 Odds ratios of High-Low-High values for all-cause mortality ....................................... 62 Figure 10 Odds ratios of overnight low groups when High-Low-High >= 58, against High-Low-High < 58 ...................................................................................................................................... 63 Figure 11 Odds ratios of daily highs when High-Low-High >= 58 and L < 15, compared with High-Low-High < 58 .................................................................................................................... 64 Figure 12 Comparison of Dates Meeting Absolute and Cumulative High-Low-High Thresholds....................................................................................................................................................... 67 Figure 13 High-Low-High algorithm plot, using observed temperature data .............................. 69 Figure 14 High-Low-High algorithm plot, using observed and forecast temperature data .......... 70 Figure 15 Odds ratios of Morbidity and Mortality Across Chronic Conditions at HLH >= 75°C & (HH >= 51°C | L >= 15°C) ........................................................................................................... 73 Figure 16 Odds ratios of Morbidity and Mortality Across Comorbidity Groups at HLH >= 75°C & (HH >= 51°C | L >= 15°C) ....................................................................................................... 74 xvi Figure 17 Odds ratios of Morbidity and Mortality Across Demographic Groups at HLH >= 75°C & (HH >= 51°C | L >= 15°C) ....................................................................................................... 76 Figure 18 A.1 Histogram of observed daily maximum temperature in WMV ........................... 100 Figure 19 A.1 Boxplot of observed daily maximum temperature in WMV by year .................. 101 Figure 20 A.1 Histogram of observed daily minimum temperature in WMV............................ 101 Figure 21 A.1 Boxplot of observed daily minimum temperature in WMV by year ................... 102 Figure 22 A.2 Histogram of 3-day sum of daily all-cause death count in WMV ....................... 103 Figure 23 A.2 Boxplot of 3-day sum of daily all-cause death count by year ............................. 103 Figure 24 A.2 Decomposed time series of 3-day sum of daily all-cause death count ................ 104 Figure 25 A.2 Histogram of 3-day sum of daily heat-related ED visits in WMV ...................... 104 Figure 26 A.2 Boxplot of 3-day sum of daily heat-related ED visits by year ............................ 105 Figure 27 A.2 Decomposed time series of 3-day sum of daily heat-related ED visits ............... 105 Figure 28 A.2 Histogram of 3-day sum of daily heat-related ambulance dispatches in WMV .. 106 Figure 29 A.2 Boxplot of 3-day sum of daily heat-related ambulance dispatches by year ........ 106 Figure 30 A.2 Decomposed time series of 3-day sum of daily heat-related ambulance dispatches..................................................................................................................................................... 107 Figure 31 A.3 AICs across different knots for all-cause mortality ............................................. 107 Figure 32 A.3 AICs across different knots for heat-related ED visits ........................................ 108 Figure 33 A.3 AICs across different knots for heat-related ambulance dispatches .................... 108 Figure 34 A.4 Residual plot of HLH for all-cause mortality model ........................................... 109 Figure 35 A.4 Residual plot of time for all-cause mortality model ............................................ 109 Figure 36 A.4 QQ plot for all-cause mortality model ................................................................. 110 Figure 37 A.4 Smooth term plots for all-cause mortality model ................................................ 110 xvii Figure 38 A.4 Residual plot of HLH for heat-related ED visits model ...................................... 111 Figure 39 A.4 Residual plot of time for heat-related ED visits model ....................................... 111 Figure 40 A.4 QQ plot for heat-related ED visits model ............................................................ 112 Figure 41 A.4 Smooth term plots for heat-related ED visits model ........................................... 112 Figure 42 A.4 Residual plot of HLH for heat-related ambulance dispatches model .................. 113 Figure 43 A.4 Residual plot of time for heat-related ambulance dispatches model ................... 113 Figure 44 A.4 QQ plot for heat-related ambulance dispatches model ........................................ 114 Figure 45 A.4 Smooth term plots for heat-related ambulance dispatches model ....................... 114 Figure 46 A.5 AICs of temperature metrics using 32, 20, and 28 knots for all-cause mortality, heat-related ED visits, and heat-related ambulance dispatches respectively .............................. 116 Figure 47 A.5 AICs of temperature metrics using a Poisson distribution for all-cause mortality, heat-related ED visits, and heat-related ambulance dispatches .................................................. 117 Figure 48 B.1 HLH algorithm plot, using temperature forecast data made 2 days in advance .. 118 Figure 49 B.2 HLH algorithm plot, using temperature forecast data made 3 days before ......... 119 Figure 50 B.3 ORs of HLH split values in all-cause ED visits................................................... 120 Figure 51 B.4 ORs of HLH split values in all-cause ambulance dispatches .............................. 121 Figure 52 C.1 ORs of mortality across comorbidity groups ....................................................... 122 Figure 53 C.2 ORs of mortality in multimorbidity group ........................................................... 123 Figure 54 C.3 ORs of mortality in heart-related chronic conditions .......................................... 124 Figure 55 C.4 ORs of mortality in schizophrenia and SUD ....................................................... 125 Figure 56 C.5 ORs of mortality among COPD, Parkinsonism, CKD, and other chronic conditions..................................................................................................................................................... 126 Figure 57 C.6 ORs of mortality in age group ............................................................................. 127 xviii Figure 58 C.7 ORs of mortality in location of death .................................................................. 128 Figure 59 C.8 ORs of mortality in sex ........................................................................................ 129 Figure 60 C.9 ORs of mortality in PharmaCare plan.................................................................. 130 Figure 61 C.10 ORs of mortality in age and comorbidity .......................................................... 131 Figure 62 C.11 ORs of mortality in age and multimorbidity ...................................................... 132 Figure 63 C.12 ORs of heat-related ED visits in comorbidity group ......................................... 133 Figure 64 C.13 ORs of heat-related ED visits in multimorbidity group ..................................... 134 Figure 65 C.14 ORs of heat-related ED visits in heart-related chronic conditions .................... 135 Figure 66 C.15 ORs of heat-related ED visits in schizophrenia and SUD ................................. 136 Figure 67 C.16 ORs of heat-related ED visits among COPD, Parkinsonism, CKD, and other chronic conditions ....................................................................................................................... 137 Figure 68 C.17 ORs of heat-related ED visits in age group ....................................................... 138 Figure 69 C.18 ORs of heat-related ED visits in sex .................................................................. 139 Figure 70 C.19 ORs of heat-related ED visits in PharmaCare plan............................................ 140 Figure 71 C.20 ORs of heat-related ED visits in acuity ............................................................. 141 Figure 72 C.21 ORs of heat-related ED visits in age and comorbidity ...................................... 142 Figure 73 C.22 ORs of heat-related ED visits in age and multimorbidity .................................. 143 Figure 74 C.23 ORs of heat-related ambulance dispatches in acuity ......................................... 144 xix List of Equations Equation 1 Generalized additive model formula .......................................................................... 38 Equation 2 Equation of odds ratio ................................................................................................ 38 Equation 3 Root mean square error .............................................................................................. 39 Equation 4 HLH algorithm ........................................................................................................... 43 xx List of Abbreviations AIC: Akaike Information Criterion AMI: Acute Myocardial Infarction BC: British Columbia BC HEAT Committee: British Columbia Health Effects of Anomalous Temperatures Coordinating Committee BCCDC: BC Centre for Disease Control CDR: Chronic Disease Registry CIs: Confidence Intervals CKD: Chronic Kidney Disease COPD: Chronic Obstructive Pulmonary Disease ECCC: Environment and Climate Change Canada ED: Emergency Department EHE: Extreme Heat Event EMPI: Enterprise Master Patient Index GAM: Generalized Additive Model HAA: Heat Alert Area HARS: Heat Alert and Response System HHWS: Heat-Health Warning System LHAs: Local Health Areas MPDS: Medical Priority Dispatch System NACRS: National Ambulatory Care Reporting System NOAA: National Oceanic and Atmospheric Administration xxi OR: Odds Ratio PANDA: PHSA Platform for Analytics and Data PHSA: Provincial Health Services Authority Q-Q plot: Quantile-Quantile Plot RMSE: Root Mean Square Error SES: Socioeconomic Status SUD: Substance Use Disorder WGBT: Wet-Bulb Globe Temperature WHO: World Health Organization WMV: Western Metro Vancouver xxii Acknowledgements I had the privilege of completing this thesis while living, studying, and working on the traditional, ancestral, and unceded territories of the Coast Salish Peoples, specifically the Sḵwx̱wú7mesh, SÉlÌílwÉtaÊ/Selilwitulh, and xÊ·mÉÎ¸kÊ·ÉyÌÉm Nations. As this thesis examined extreme heat in the era of climate change, it is crucial to acknowledge that Indigenous peoples on Turtle Island and across the globe are at the forefront of preserving biodiversity and combating climate change. They are also vulnerable to its impacts due to environmental racism, colonialism, and their traditional ways of living and thriving. I extend my deepest gratitude to my supervisors, Dr. Michael Brauer and Dr. Sarah Henderson. Thank you, Dr. Brauer, for guiding me patiently and kindly throughout my graduate school journey, and for sharing your incredibly broad expertise and academic curiosity. Thank you, Dr. Henderson, for teaching me how to be a proficient data analyst and an applied public health researcher, and for dedicating so much of your time and energy to guiding my research and work. I am also grateful to Dr. Ãric Lavigne for taking time from your busy schedule to provide valuable feedback on this thesis. Special thanks to Kathleen McLean and Dr. Michael Lee from the BCCDC for their support with administrative data linkage. I also appreciate the BC Ministry of Health and the PANDA team for providing access to a world-class health database. Last but not least, I would like to thank my parents, friends, and my partner Sya, for their love, support, and encouragement. I am incredibly fortunate to have you all in my life and I cherish all the moments we share. xxiii Dedication For Zhang Xiaobo and Hu Xinming, my mother and father, who always believe in me, support me, and love me unconditionally. You have taught me to be a kind person whose passion is tied to helping others and contributing to community and society. You have encouraged me to disregard traditional social norms and to follow my heart and dreams. You have trusted and supported me in all my wild and unconventional life decisions and journeys, including the decision to completely switch careers and apply to UBC for a science degree. Thank you.1 Chapter 1: Introduction and Literature Review 1.1 Problem Statement Extreme heat events (EHEs), defined as prolonged excessive heat, are increasing in frequency, intensity, and duration due to climate change, posing significant health risks globally. To help mitigate the impact of EHEs, many countries, including Canada, have implemented heat-health warning systems (HHWSs) based on the link between meteorological parameters and public health impacts1â3. One example is the Heat Alert and Response System (HARS) in British Columbia (BC), which was first established after the catastrophic 2021 EHE3. The province-wide HARS replaced the regional HARS that had been established for Metro Vancouver after a severe EHE in 20093. Many HHWSs rely on fixed absolute temperature thresholds to issue heat alerts, requiring that specific conditionsâsuch as two maximum daytime temperatures and minimum overnight temperature in BC HARSâbe met at the same time3. While straightforward, this rigid approach can fail to capture the cumulative heat load imposed by multiple warm days and nights. For instance, if any single condition is not met, no alert is issued, even if different combinations of temperature sequences lead to comparable levels of risk from the overall heat exposure. Consequently, some hazardous scenarios may go unaddressed because they do not fit a single, static definition of extreme heat. This study examined whether a more dynamic and flexible methodâincorporating the sum of daytime and overnight temperatures and considering the different combinations of temperature sequencesâcould align with real-world health outcomes. This study focused on Western Metro Vancouver (WMV), BCâs most populous region, which was severely impacted during the 2009 and 2021 heatwaves. It evaluated multiple cumulative temperature metrics that 2 sum daily maximum and minimum temperatures over 1â3 days and examined their associations with all-cause mortality, heat-related emergency department (ED) visits, and ambulance dispatches during the summer months from 2008 to 2023. The statistical performance of the metrics was evaluated using significance tests, the Akaike Information Criterion (AIC), and forecasting accuracy. The selection of the metric was not only based on its statistical performance but also with the operational feasibility for local HHWSs. Baseline thresholds for three health outcomes were established using the selected metric where ORs exceeded 1.0. Furthermore, to explore the broader applicability of the cumulative temperature metric, a multi-component threshold algorithm was developed based on all-cause mortality. This algorithm considered the distinct risks associated with cumulative heat exposure and the contributions of different daily highs and overnight low separately, allowing multiple routes to an alert condition if certain cut-offs are reached. The algorithm was subsequently tested across mortality and morbidity subgroups to evaluate its applicability. The findings aim to advance cumulative heat exposure assessment methods and propose a more nuanced and dynamic HHWS framework. This framework could contribute to local heat-health policies in response to rising temperatures and serve as a model for urban areas worldwide facing similar climate challenges. 1.2 Extreme Heat Events EHEs, often referred to as heatwaves, are prolonged periods of excessive heat. Although there is no universally accepted definition of an EHE, it is generally characterized as a period of abnormally elevated ambient temperatures relative to a locationâs typical climate that persists for multiple days 2,4,5. 3 From a meteorological perspective, there are two primary types of EHEs: dry and moist2. Dry EHEs occur under stable weather patterns with high-pressure systems, clear skies, high solar radiation, and stagnant air masses, typically in continental or Mediterranean climates, such as BC, Canada, where air warms adiabatically2,6. On the other hand, moist EHEs involve the advection of persistent hot and humid air, common in mid-latitude temperate and maritime climates2,6,7. 1.2.1 Climate Change and EHEs: a Global and Canadian Perspective Since the Industrial Revolution, human activitiesâmainly fossil fuel combustionâhave significantly altered global weather patterns and temperatures, marking the onset of climate change. Currently, the atmospheric concentration of carbon dioxide (CO2), the primary greenhouse gas driving global warming, is approximately 427 parts per million (ppm), as recorded by the Maun Loa Observatory of the U.S. National Oceanic and Atmospheric Administration (NOAA)8. The last time Earth experienced CO2 concentrations at this level was during the Pliocene Epoch, around 3 to 5 millions years ago, when global average temperatures were roughly 2-3 °C higher than today9. As a result, the ongoing atmospheric changes have already led to an average surface temperature rise of 1.1°C since the late 1800s, reaching a level of warmth unseen in over 100,000 years 10. The global mean temperature in 2023 was 1.54°C above the 1850-1900 average (Figure 1) 11. Figure 1 Global average temperature from 1850 â 2023 by Berkeley Earth 4 * This figure was obtained from Berkeley Earth Global Temperature Report for 2023 under the terms of the Creative Commons BY-NC 4.0 copyright license for any non-commercial purpose. Globally, the increase in temperature has led to a rise in the frequency, intensity, and duration of EHEs, posing significant threats to human society and public health12,13. A global study found that urban exposure to extreme heat surged by nearly 200% from 1983 to 2016, affecting as many as 1.7 billion people14. Over the past two decades, notable EHEs, such as those in Western Europe (2003), India (2010), Russia (2010), and Pakistan-India (2015), have led to tens of thousands of excess deaths15â18 (Error! Reference source not found. Error! Reference source not found.). The escalating incidence of EHEs exacerbates heat-related illnesses and deaths, aggravates pre-existing health conditions, strains healthcare systems, and disrupts daily activities and economic productivity, highlighting the broad societal and health impacts of these events 10,13,14,19,20. These direct and substantial adverse effects position EHEs among the most impactful climate-related hazards on health and society10,19â21. In Canada, temperatures are rising at a rate faster than the global average due to its extensive northern landmass. From 1948 to 2016, the national temperature increased by 1.7°Câ5 roughly double the global averageâshowing significant amplification in warming largely attributed to temperature feedbacks and surface albedo (Figure 2)22â24. During the same period, BC experienced a temperature rise of 1.9°C, with the annual number of days exceeding 30°C having increasing by an average of 3.3 days23. This regional disparity underscores the unique climatic challenges faced by northern latitudes, where warming is intensified by the loss of reflective ice and snow cover, leading to further absorption of solar radiation and continued temperature increases. Figure 2 Annual average temperature change in Canada between 1948 and 2016 * This figure was acquired from Canadaâs Changing Climate Report led by ECCC While the climate of BC has historically been temperate, the province has experienced two deadly heatwaves in the past fifteen years. The 2009 heatwave saw temperatures peak at 34.4°C in Vancouver, resulting in over 100 excess deaths25,26. More recently, the unprecedented 2021 heat dome shattered over 100 temperature records, with a national temperature record of 6 49.6°C in Lytton, which was destroyed by a wildfire the following day27. This catastrophic EHE was associated with an estimated 740 excess deaths across BC over an eight-day period, with the BC Coroners Service attributing 619 deaths directly to heat. Notably, the majority of these deaths occurred in the western and Fraser regions of greater Vancouver27â29. 1.3 EHEs and Health Due to ongoing climate change, EHEs present an urgent and growing societal and public health challenge. Over the last few decades, the most direct health impact of EHEs has been tens of thousands of excess deaths worldwide. Beyond the direct effects of heat, a multitude of health and societal factors exacerbate the impacts of EHEs on public health, such as pre-existing medical conditions, age, acclimatization, the urban heat island effect, income disparities, and social isolation. 1.3.1 Human Heat Physiology Heat illness generally occurs when the body is unable to maintain a core temperature of 37°C through thermoregulation and can rapidly progress to injury and death if the core temperature rises above 39°C due to excessive heat exposure30,31. In fact, the human body is highly sensitive to temperature change; even a 1ºC increase can be immediately detected by thermoreceptors distributed throughout the body32. When the hypothalamic thermoregulatory center receives signals from these thermoreceptors, it activates cutaneous vasodilation and sweating to facilitate heat dissipation32. Cutaneous vasodilation increases blood flow to the skin and boosts cardiac output, putting a strain on the cardiovascular system by redirecting blood from other vascular areas such 7 as the abdominal cavity33. Sweating can produce up to two liters per hour of sweat rich in sodium and potassium, which further stresses the cardiovascular system if fluids are not adequately replenished34. The human body also employs complementary mechanisms to reduce heat gain, such as inhibiting shivering and lowering cellular metabolic heat production34. The human body can acclimatize to a hot environment through physiological adjustments that improve heat tolerance over two to six weeks. Key adaptations include increased stroke volume, decreased heart rate, expanded plasma volume, and improved renal function, all of which reduce cardiac strain34. Additionally, sweating initiates at lower temperatures, in greater volumes, and with less sodium, which improves heat dissipation and reduces dehydration34. These adaptations, however, can diminish after weeks without heat exposure, and complete long-term acclimatization to a hot environment may take years35,36. As a result, the timing of an EHE is critical; early summer events can be more impactful due to insufficient heat acclimatization2,37. Furthermore, from a biological perspective, the total heat load accumulated over time â rather than just peak temperature â plays a crucial role in determining the level of heat stress on the body38. Prolonged exposure to high ambient temperatures forces cells to operate under conditions of increased oxidative stress, wherein the overproduction of reactive oxygen species damages cellular components such as lipids, proteins, and DNA39,40. Simultaneously, excessive heat can prompt protein misfolding and aggregation, overwhelming the cellâs chaperone-mediated repair systems and triggering the heat-shock response41,42. Over time, these insults set off an escalating cascade of events that culminate in a systemic inflammatory response, as pro-inflammatory mediators (e.g., cytokines) are released in an attempt to contain and repair the damage43,44. If heat exposure remains prolonged or is repeated without sufficient recovery 8 periods, this chronic inflammatory state can compromise multiple organ systems, exacerbating injury and potentially leading to irreversible physiological harm32. 1.3.2 Heath Impacts of EHEs EHEs have profound impacts on human health, both directly and indirectly. Direct and immediate health effects of excessive heat exposure include dehydration, heat exhaustion, heat cramps, heat stroke, acute renal failure, cerebrovascular accidents, and even death31,32. EHEs are also associated with heightened risks of mortality and morbidity, particularly among susceptible populations such as the elderly and individuals with pre-existing health conditions. The health impacts of EHEs can vary significantly across different geographical regions, demographic groups, and time periods. This section reviews the impacts of EHEs on mortality and morbidity, as well as the susceptibility and vulnerability factors that contribute to these outcomes, including pre-existing medical conditions and sociodemographic determinants. 1.3.2.1 Mortality EHEs have a significant impact on all-cause mortality, with many studies reporting increased all-cause death rates during such periods. Research on heat-related mortality typically examines the correlation between temperature spikes and all-cause mortality to estimate excess deaths attributable to heat45. Meta-analyses have demonstrated that the relative risks (RRs) for all-cause mortality during EHEs range from 1.03 (95% CI: 1.02â1.04; N = 3) to 1.23 (95% CI: 1.11â1.37; N = 5), varying based on the duration and intensity of the events21,46. Historical EHEs have resulted in substantial increases in mortality. For instance, the 1995 Chicago heatwave and the 2003 Western Europe heatwave, resulted in 692 and 15,000 excess 9 deaths, respectively16,47. In Canada, similar patterns have been observed: the 2009 Vancouver heatwave resulted in an estimated 114 excess deaths, the 2010 Montreal heatwave led to 106 excess deaths, and the 2021 Western North American heat dome caused approximately 740 excess deaths26,48,49. Beyond all-cause mortality, cause-specific mortality also increases during EHEs. Deaths from cardiovascular causes such as ischemic heart disease, arrhythmias, and heart failure rise, likely because cutaneous vasodilation adds strain on the cardiovascular system50â53. Cerebrovascular mortality such as stroke and transient ischemic attacks can also increase due to heat-induced dehydration or thicker blood viscosity50,51,54 . Deaths from acute kidney injury and renal failure also rise in tandem with high temperatures as heat stress compromises renal perfusion through intensified dehydration55. Moreover, EHEs have also been linked to external and injury-related causes like violence, traffic collisions, accidental drownings, and suicides54,56â58. This is attributed to heat potentially increasing aggressive behavior, impairing driver alertness, causing vehicle malfunctions, and leading more peopleâespecially childrenâto engage in water activities54,56â58. It is also worth noting that heat-related mortality is not limited to the immediate period of exposure. Although acute effects often manifest within the first few days, lagged associations of 2â5 daysâand in some cases up to 30 daysâhave been reported in distributed lag models and other time-series analyses59â62. 10 1.3.2.2 Morbidity EHEs also impact morbidity, leading to increases in hospitalizations, ED visits, and ambulance callouts across a wide range of health conditions. These impacts are particularly pronounced for cardiovascular, respiratory, renal, and cerebrovascular conditions, as well as mental health disorders and occupational injuries51,53,55. Multiple studies have consistently shown that EHEs lead to a significant rise in ED visits for cardiovascular and cerebrovascular diseases. For instance, in North Carolina, ED visits for cardiovascular issues, including hypotension and related conditions, increased by 3-8% during EHEs63. Similarly, the 2009 heatwave in Adelaide, Australia saw a significant rise in ED visits for ischemic heart disease among those aged 15-6464. In Atlanta, the risks for ED visits related to acute renal failure and ischemic stroke increased during heatwaves, with RRs reaching up to 1.15 (95% CI: 1.03â1.29) for renal failure and 1.09 (95% CI: 1.02â1.17) for ischemic stroke65. In BC and Ontario, Canada, studies also found significant spikes in heat-related ED visits during EHEs66,67. EHEs also result in substantial increases in hospital admissions, especially for cardiovascular, respiratory, and renal diseases. For cardiovascular conditions such as ischemic heart disease and myocardial infarction, one meta-analysis reported an RR of 1.02 (95% CI: 1.00â1.03; N = 13) per 1ºC rise in temperature53. Similarly, respiratory conditions including asthma and COPD show hospitalization rates rise with RRs up to 1.04 (95% CI: 1.00â1.09; N = 17)51. Hospitalizations for renal diseases have even more pronounced increases, with one meta-analysis reporting an RR of 1.24 (95% CI: 1.19â1.28; N = 16) when using temperature thresholds between the 90th and 99th percentiles55. Cerebrovascular events, including strokes, as 11 well as mental health disorders such as schizophrenia, and complications during childbirth are also notably exacerbated by extreme heat 51,55,68â70. Moreover, the strain on ambulance services during EHEs is significant. In Adelaide, ambulance callouts surged by 10% during the 2008 heatwave and 16% during the 2009 event64. In King County, Washington, extreme heat led to an 8% increase in basic life support calls and a 14% increase in advanced life support calls71. Specific ambulance callouts such as acute renal failure, ischemic stroke, intestinal infection, Alzheimer's disease, mental disorders, bacterial enteritis, and injuries are also heightened by EHEs 65,72â74. Although no study has specifically examined ambulance dispatches during the 2021 heat dome in BC, the Emergency Communications Centre received over 15,000 calls in just two daysâan increase of 55% compared with a typical June weekend75. 1.3.2.3 Susceptibility and Vulnerability to Heat Susceptibility and vulnerability to EHEs are shaped by a complex interplay of intrinsic and extrinsic factors that collectively influence heat-related health outcomes across different populations. Susceptibility refers to the inherent sensitivity of individuals or groups to heat due to physiological and biological characteristics76. In contrast, vulnerability encompasses susceptibility but also includes external factors such as exposure to heat and the capacity to cope with or adapt to extreme temperatures2. 1.3.2.3.1 Susceptibility Factors EHEs significantly elevate mortality and morbidity rates across various comorbidities, particularly impacting the cardiovascular, respiratory, renal, and cerebrovascular systems. 12 Studies have shown that EHEs increase excess mortality and morbidity risks in individuals with conditions such as ischemic heart disease, heart failure, stroke, chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), diabetes, mental illnesses such as schizophrenia, and in those working in high-risk outdoor occupations like agriculture and construction21,37,50â55,66,68,71,72,77â79. During the 2021 heat dome in BC, a study found that individuals with schizophrenia (OR 3.07, 95% CI: 2.39-3.94), chronic kidney disease (CKD) (OR 1.36, 95% CI: 1.18-1.56), and heart disease (OR 1.18, 95% CI: 1.00-1.38) faced a higher mortality risk than others during the event80. Another study on the same EHE identified significantly higher odds of mortality associated with conditions when controlling for income assistance including schizophrenia (OR 1.93, 95% CI: 1.51-2.45), chronic obstructive pulmonary disease (COPD) (OR 1.48, 95% CI: 1.29-1.71), parkinsonism (OR 1.46, 95% CI: 1.03-2.02), heart failure (OR 1.45, 95% CI: 1.25-1.68), CKD (OR 1.36, 95% CI: 1.18-1.56), ischemic stroke (OR 1.32, 95% CI: 1.05-1.66), and substance use disorder (SUD) (OR 1.27, 95% CI: 1.07-1.50) 81. Besides pre-existing medical conditions, age is one of the most critical determinants of susceptibility to EHEs. The elderly are particularly at risk due to compromised thermoregulation resulting from age-related physiological changes. These include declines in skin vascularization, sweat production, autonomic nervous system function, and alterations in body composition, compounded by the prevalence of chronic conditions such as cardiovascular and cerebrovascular diseases82â84. Meta-analyses and observational studies show that the risk of all-cause mortality during EHEs is significantly higher among those aged 65 and older compared with younger populations79,83,85,86. 13 Children and young adults also face heightened risks, particularly for heat, injury, and infection-related morbidity, due to their underdeveloped thermoregulatory systems and greater engagement in outdoor activities74,87,88. 1.3.2.3.2 Vulnerability Factors Socioeconomic status (SES) profoundly impacts vulnerability to EHEs by influencing exposure and adaptive capacity. Individuals with lower SESâincluding those experiencing poverty, systemic racism, and homelessnessâare often more vulnerable due to limited access to resources such as cooling amenities, healthcare, and financial means to mitigate heat exposure83,84,89,90. They are more likely to reside in substandard housing with poor insulation and inadequate ventilation, often located in densely populated urban areas with limited green spaces, where the urban heat island effect intensifies heat exposure84,89â91. Additionally, they may have lower baseline health status and a higher likelihood of engaging in outdoor, heat-exposed occupations54,69,77. Other SES-related factors, such as living alone, being bedridden, or having limited social support further elevate the risk89. Gender difference also contribute to vulnerability. Men may have increased exposure due to higher prevalence in outdoor occupations, while women may face greater physiological risks owing to factors such as a higher surface-to-mass ratio and increased subcutaneous fat, both of which affect heat dissipation efficiency84,91. Moreover, women are more likely to live alone, which can increase vulnerability due to social isolation49,54. Additionally, environmental factors play a significant role in determining vulnerability. Historically hotter regions often exhibit lower heat-related mortality and morbidity, possibly because of physiological acclimatization and behavioural factors, such as the widespread 14 employment of cooling strategies2,37,54,84. Residents in regions without adequate heat-related policies, prevention and mitigation strategies, or heat warning systems are more vulnerable to the adverse effects of extreme heat1,37,92,93. This intricate web of susceptibility and vulnerability factors was starkly illustrated during the 2021 heat dome in British Columbia. Certain demographic and social groups were disproportionately affected. Females, those aged 65 and older, individuals living alone, residents in neighborhoods with less greenery, and those experiencing poverty or other social and material barriers were at significantly higher risk of mortality during this event49,81. This underscores how both intrinsic susceptibility and extrinsic vulnerability factors interplay to influence the impact of EHEs on different populations. 1.4 Heat Health Warning Systems (HHWSs) In response to the significant health risks and growing challenges posed by EHEs, the World Health Organization (WHO) advises the integration of meteorological forecasts with public health evidence to establish and continually enhance HHWSs for effective heat preparedness and response94. As such, many regions, including Canada and BC, have established HHWSs as an essential component of climate adaptation and mitigation strategies. Typically, HHWSs involve a multi-dimensional approach that includes weather forecasting, determination of heat-related health risk thresholds, and dissemination of warnings to the public and relevant stakeholders. These systems are tailored to reflect the specific meteorological, demographic, and urban characteristics of the local context1,2,94. 15 1.4.1 An Overview of HHWSs According to WHO guidelines, HHWSs should be grounded in the direct correlation between heat-related biometeorological parametersâsuch as air temperatureâand health outcomes, particularly mortality, which is widely used as a health indicator due to its standardized and consistent collection across regions2,94. Air temperature is the most broadly used metric for evaluating EHEs, given its simplicity and reliability in forecasting2 (Table 1). Some systems have also adopted apparent or âfeels likeâ temperature metrics such as heat index, humidex, net effective temperature, and wet-bulb globe temperature (WGBT), which consider the combined effects of air temperature, humidity, windspeed, and solar radiation1,2 (Table 1). However, the nonlinear behavior of relative humidity introduces challenges in accurate forecasting, particularly in regions prone to high moisture levels such as BC, which complicates the prediction of heat-related health impacts95. When predicting and assessing EHEs, most HHWSs use either a single-parameter or multiple-parameter method. In some regions in Canada and the USA, both air and apparent temperature metrics are applied, whereas other regions and countries rely on daily maximum temperature or a combination of maximum and overnight temperatures to define EHEs and issue warnings1,2. Further, many HHWSs require all specified conditions to be met simultaneouslyâsuch as exceeding certain daytime highs and overnight lows for consecutive daysâto trigger an alert. Along with duration (commonly 2-3 days of continuous extreme heat) as a defining criterion, HHWSs typically establish alert thresholds either by absolute biometeorological values or by historical percentiles correlated with all-cause mortality1,2. To quantify "excess mortality" attributable to heat and determine baseline mortality outside EHE periods, time-series 16 analyses of death and meteorological data are frequently performed. For instance, countries such as France, the USA, and Canada analyze decades of mortality and weather records to estimate heat-related excess mortality and set corresponding alert thresholds2,3. 17 Table 1 Examples of different heatwave definitions and heat-health warning systems Country Definition of Heatwave Main Indicator (Air Temp or Apparent Temp) Heat-Health Warning System United States96 A heat wave is typically 2+ consecutive days of abnormally hot, often humid weather. Current rule of thumb for a Heat Advisory: max Heat Index â¥100 °F for at least 2 days, with nighttime temps â¥75 °F. Local offices may have additional criteria. Heat Index (apparent temperature combining air temp & humidity) Heat advisory: per the national guideline or local thresholds; Excessive heat watch: Heat Index â¥105 °F possible in next 24â72 hours; Excessive heat warning: â¥105 °F imminent/ongoing for 2+ days. Canada97 2+ consecutive days of daytime maximum temperatures reaching regional thresholds (e.g., 29 °C in BC southwest) and nighttime minimum temperatures exceeding regional thresholds (e.g., 16 °C in BC southwest). Air temperature and/or Humidex (apparent temperature) Heat Warnings: when forecast temps or Humidex reach region-specific thresholds for 2+ days; some cities/provinces also issue Extreme Heat Alerts. United Kingdom98 3+ consecutive days meeting or exceeding regional daily max temperature thresholds (e.g., 25 °C in some areas, 28 °C in southeast England). Air temperature (daily max) Heat Health Alert System (2023): Green/yellow/amber/red based on temperature thresholds and potential health impacts. Higher levels can trigger national-level actions. Australia99 3+ consecutive days of significantly high max and min temperatures relative to local averages Air temperature Use the excess heat factor (EHF) to monitor and issue heatwaves, which combines a comparison of the average temperatures for a 3-day period with what would be considered hot at that location as well as the observed temperatures at that location over the past 30 days Heatwaves are classified as: â¢ Low-intensity: regularly in summer, and most people can cope. â¢ Severe: less frequent and greater risks for vulnerable groups, such as older adults and those with medical conditions. â¢ Extreme heatwaves: rare but dangerous to anyone who fails to stay coolâeven otherwise healthy individuals. Those who work or exercise outdoors face particularly high risks. India100 Max temp â¥ 40 °C in plains (â¥ 30 °C in hills). If the local normal max â¤ 40°C: Air temperature Colour-coded warnings based on severity/duration: â¢ Green: normal temperatures 18 â¢ Heat wave: 5â6°C above normal â¢ Severe heat wave: 7°C or more above normal If the local normal max > 40°C: â¢ Heat wave: 4-5°C above normal â¢ Severe heat wave: 6°C or more above normal â¢ Yellow: Heat alert. Moderate temperature. Heat is tolerable for general public but moderate health concern for vulnerable people. â¢ Orange: severe heat alert. High temperature. Increased likelihood of heat illness symptoms. â¢ Red: extreme heat alert. Extremely high likelihood of developing heat illness and heat stroke in all ages. China101 Commonly 3+ consecutive days with daily max â¥ 35 °C. Some provinces use 37â40 °C for extreme heat. Air temperature Four-tier Color System: Yellow (â¥ 35 °C for 3 consecutive days), Orange (â¥ 37 °C within 24 hours), Orange (~37â39 °C), Red (â¥ 40 °C within 24 hours). Spain102 Often 3+ consecutive days when weather stations exceed the 95th percentile of historical JulyâAug max temps. Varies by region. Air temperature National Plan for Preventive Actions Against High Temperatures: color-coded alerts (green, yellow, orange, red) and health advisories from Health Ministry. France103 3+ consecutive days of daytime max and nighttime min above region-specific thresholds. Air temperature Vigilance map (green/yellow/orange/red). Orange/red indicate significant to extreme heat. Japan104 Heatstroke alerts: the daily maximum WBGT â¥ 33 °C for 2 consecutive days. Apparent temperature Heatstroke alert: â¢ 31 and above: Danger â¢ 28 or more and less than 31: Careful vigilance â¢ 25 or more and less than 28: vigilance â¢ Less than 25: Note South Africa105 A daily max â¥5 °C above the local maximum average of âthe hottest monthâ for 3+ consecutive days. Air temperature Heatwave warnings/advisories. 19 The criteria for issuing warnings vary across different systems and are generally based on the degree to which all-cause mortality rates exceed these baselines during EHEs1. In some cases, thresholds are determined with consideration for vulnerable populations, such as those aged 65 or above, while others may rely on historical temperature percentiles or fixed values2. In many HHWSs, thresholds are standardized at the national level, though localized adjustments are often made to account for regional climate variations and population needs, as seen in Canada, the USA, and France2. Additionally, some systems incorporate seasonal adjustments to account for heat acclimatization throughout the summer3,106. Moreover, impact-based HHWSs, which issue warnings at varying levels such as âwarningâ and âemergencyâ based on the severity and duration of EHEs, are increasingly favored for their capacity to communicate the nonlinear nature of heat risk, deliver more intuitive public messages, and reduce alert fatigue compared with phenomenon-based systems2. 1.4.2 Effectiveness of HHWSs HHWSs have proven generally effective in improving disaster readiness and encouraging behavioral changes107. A systematic review of HHWSs highlighted their role in mitigating health impacts when paired with comprehensive public health interventions and clear and timely communication strategies107. The form and structure of an HHWSâsuch as tailored communication strategies, locally relevant criteria for issuing alerts, and the incorporation of multi-sectoral responsesâplay a critical role in determining its effectiveness107. Evidence also suggests that HHWSs integrated with broader heat adaptation frameworks, including healthcare system preparedness, urban 20 infrastructure (e.g., cooling centers, shaded areas), and social programs, achieve better outcomes than standalone systems107. However, while some evidence supports the effectiveness of well-designed HHWSs, there is limited research that isolates specific design elements or compares alternative forms of HHWSs under similar conditions. For instance, few studies control for variables such as different heat warning criteria, levels of public outreach, or access to resources when evaluating HHWS effectiveness. This limitation makes it challenging to attribute reductions in mortality and morbidity solely to HHWSs, as they often operate within broader public health strategies. For example, a U.S. study found no clear link between heat alerts and reductions in mortality across cities, likely due to limited heat response strategies and variability in public perception of heat warnings at the time108. This study only focuses on the parameter of measuring extreme heat and issuing heat warnings, with the goal of developing a more dynamic and flexible HHWS framework. Although such a focus is only one component of the broader HHWS framework, less rigid and more biologically relevant warning criteria can enable more effective heat response strategies and action plans, ultimately improving public health protection. As Toloo and colleagues emphasized in their study, a tangible decrease in EHE-related mortality and morbidity require evidence-based alert criteria, well-coordinated response, and action plans, along with adequate community resources107. Continuous evaluation and adaptation of these systems are crucial to ensure they meet the evolving challenges posed by climate change. 21 1.4.3 Heat Alert and Response System (HARS) in BC BC has developed its own HHWS following the deadly EHEs in 2009 and 2021. After the 2009 heatwave in Metro Vancouver, the BC Centre for Disease Control (BCCDC), in collaboration with ECCC and local health authorities, developed a data-driven approach to establish temperature thresholds for heat health emergencies. By analyzing environmental and epidemiologic data from 2005 to 2009, they evaluated single-day maximum temperatures and two-day averages of maximum temperatures, identifying a correlation between excess mortality and the two-day average of maximum temperatures26,109. This analysis led to the recommendation that a heat health emergency be declared when the observed temperature at 14:00 combined with the forecasted high for the following day reached or exceeded 29°C on the coast and 34°C inland110. In 2018, the BCCDC expanded the HARS beyond Metro Vancouver to include the Southwest, Southeast, Northwest, and Northeast Heat Alert Areas (HAAs) of BC. This expansion involved analyzing three-day moving average death counts alongside forecasted and observed air temperatures across these regions, using a High-Low-High (HLH) temperature measurement that includes the maximum daily temperature, the overnight low, and the next dayâs maximum110. This model allowed for the establishment of region-specific heat alert thresholds designed to account for the diverse climatic conditions across the province and balance the need to minimize warning fatigue with the evidence-based correlations between extreme heat and mortality3,110. In 2019, the BCCDC further refined HARS by incorporating the Fraser region as a new HAA, which covers Eastern Metro Vancouver, including Coquitlam and Surrey, as well as the Fraser Valley3. This decision was based on the region's hotter and more humid conditions. 22 The current temperature threshold for the Southwest region, where WMV is located, is 29°C-16°C-29°C. When weather forecasts predict that the daily high and overnight low temperatures simultaneously meet all three threshold conditions, the BC HARS issues a Heat Warning. If these conditions are expected to intensify over the next three days, an Extreme Heat Emergency is declared. This two-tiered system also allows for the reduction of Heat Warnings after multiple heat events to account for the population's seasonal temperature adaptation3. 1.5 Gaps in Knowledge and Study Rationale Despite the variability among different HHWSs, most employ a fixed-temperature approach and issue heat alerts based on specific absolute thresholds being met or exceeded. In these fixed-threshold systems, a heat alert is only issued when all predefined conditions are satisfied at the same time. This means that if any one of the criteriaâsuch as the maximum daytime temperature, the minimum overnight temperature, or the duration of the heat eventâis not met, no warning is activated. While such approaches are straightforward, they may be arbitrarily stringent and overlook different sequences of dayânight heat that accumulate over multiple days, potentially leading to missed warnings even if different combinations of temperature sequences lead to comparable levels of risk from the overall heat exposure. For example, a region might experience moderately high daytime temperatures just slightly below the threshold, but even if the overnight lows are extremely high and the diurnal temperature difference is small, the system would not issue an alert. Conversely, moderate temperatures that persist over an extended period might cumulatively stress the population, but without exceeding any single threshold, the system remains inactive. This rigid approach can lead to missed opportunities for intervention. 23 From a biological standpoint as mentioned previously (Section 1.3.1), the total heat load accumulated over time places a greater strain on the bodyâs thermoregulatory systems than a single peak temperature. Sustained exposure prevents adequate recovery, heightening oxidative stress and triggering systemic inflammation. Therefore, focusing on cumulative thermal stress provides a more accurate gauge of heat-related health outcomes. Furthermore, as climate change reshapes the meteorological patterns of EHEs, a growing body of knowledge suggests the duration, frequency, and impact of compound EHEs that combine daytime and nighttime heat are expected to surpass the impacts of singular daytime or nighttime heatwaves alone111,112. Some recent studies suggest that day-night compound EHEs may be associated with higher mortality and morbidity, largely because continuous exposure to high temperatures impedes the body's ability to cool down, negatively affect sleep quality, and impose additional physiological strain compared with the effects of high daytime temperatures alone19,111,113â115. This gap in most HHWS frameworks underscores the need for a more nuanced and dynamic approach to heat warnings. Incorporating the concept of cumulative heat impact into dynamic and flexible temperature thresholds within HHWS frameworks could more accurately reflect the true health risks posed by extended periods of high temperatures. 1.6 Study Aims This study sought to address three key questions: 1. Are cumulative temperature metrics associated with the health outcomes? 24 2. At what thresholds within a pre-selected cumulative temperature parameter does the likelihood of all-cause mortality, heat-related ED visits, and ambulance dispatches significantly increase? 3. Are the cumulative heat thresholds identified for all-cause mortality sensitive to capture risks within subgroups of mortality, morbidity, and ambulance dispatch outcomes? To answer these questions, the study pursued the following aims: â¢ Exposure selection: To determine if a cumulative temperature measure of heat exposureâsumming daily high temperatures and overnight lows over 1-3 daysâis associated with all-cause mortality, heat-related ED visits, and heat-related ambulance dispatches, using historical observed and forecast temperature data, and its statistical performance based on the AIC and root mean square error (RMSE). To then apply a practical metric based on the current BC HARS as an example for further analyses. â¢ Threshold analysis: o Establish the thresholds at which the Odds ratios (ORs) and its 95% confidence intervals (CIs for all-cause mortality, heat-related ED visits, and heat-related ambulance dispatches exceed 1.0 for the selected cumulative temperature measure. o Identify the specific thresholds of daily highs and overnight lows within the selected cumulative temperature measure based on all-cause mortality, recognizing that the cumulative temperature value may represent different 25 combinations of daily highs and overnight lows associated with varying levels of risk. â¢ Subgroup analysis: Evaluate whether the cumulative heat thresholds identified in the threshold analysis are sensitive to capture risks within subgroups of mortality and morbidity outcomes. This includes analysis based on variations in age, location of death, sex, specific chronic conditions, multimorbidity, PharmaCare plans, and acuity levels. 26 Chapter 2: Methods 2.1 Study Context WMV, located on Canada's west coast, has a diverse population of approximately 1.5 million people, as per the 2021 Canadian Census. WMV, with its warm-summer Mediterranean climate, experiences cooler summer highs than other major Canadian cities due to the moderating influence of Pacific onshore breezes, while dry conditions prevail in July and August116. In BC HARS, WMV is part of the Southwest HAA, which includes the City of Vancouver, Burnaby, Electoral Area A (University of British Columbia), New Westminster, Richmond, Delta, West Vancouver, North Vancouver City, and North Vancouver District (Figure 3). Given its large population and high population density, WMV accounts for more than 30% of the province's total population. 2.2 Data Sources The study used temperature data from ECCC and health data from the BC Vital Statistics Agency, the National Ambulatory Care Reporting System (NACRS), the Chronic Disease Registry (CDR), the ambulance services Medical Priority Dispatch System (MPDS), and PharmaNet. The health datasets were linked via the BC Provincial Health Services Authority (PHSA) Platform for Analytics and Data (PANDA) through the approved data mart of âEvaluation of 2021 heat dome and extreme cold impacts in BCâ (nb0107aa). While BC Vital Statistics, NACRS, and CDR datasets were linkable in PANDA, MPDS was not linkable at the time the study was conducted. 27 Figure 3 Current HAAs in the BC HARS and map of WMV 28 2.2.1 Temperature Daily temperature forecasts and observations from 2008 to 2023 were obtained from the ECCC weather station at YVR, with temperatures recorded in integer degrees Celsius. For temperature forecasts, the official ECCC forecast made at 16:00 Pacific Time, 2 and 3 days in advance, was used. 2.2.2 Vital Statistics The primary dataset for this research comprised daily all-cause mortality data from the BC Vital Statistics Agency for the summer months (May through September) between 2008 to 2023. This included anonymized details such as the date and primary cause of death, age, sex, local health area, and location of death for each decedent. 2.2.3 Chronic Disease Registries (CDRs) Comorbidity data were sourced from administrative CDRs, maintained by the BC Ministry of Health (HLTH). The CDRs track the incidence and prevalence of 25 chronic conditions annually (Table 2) using different administrative case definitions for each condition117. In addition to using individual chronic conditions, two derivative groups were created and analyzed from the CDRs â comorbidity categories and multimorbidity. Comorbidity categories included circulatory conditions, respiratory conditions, CKD and diabetes, mental illness and SUD, and other (Table 2). Multimorbidity, defined as the number of chronic conditions in one individual, measured the burden of comorbidities. Currently, the CDR contains data spanning from 1994 to March 30, 2022. This study included registered clients in the CDR who died during the summer months from 2008 to 2023 and those who visited the ED 29 during the summer months from 2014 to 2023 within WMV. These cases were linked to mortality, morbidity, and temperature data to conduct a comorbidity-specific analysis of mortality and morbidity. Table 2 List of comorbidity categories and 25 chronic conditions Comorbidity category Chronic conditions Circulatory Ischemic heart disease, acute myocardial infarction, heart failure, hypertensive diseases, stroke, ischemic stroke, hemorrhagic stroke, transient ischemic attack Respiratory Asthma, chronic obstructive pulmonary disease Chronic kidney disease and diabetes Chronic kidney disease, diabetes mellitus Mental health and substance use disorder Depressive disorders, anxiety and mood disorders, schizophrenia and delusional disorders, substance use disorders Other Epilepsy, multiple sclerosis, Parkinsonâs disease, Alzheimer's and other dementias, gout, osteoarthritis, osteoporosis, rheumatoid arthritis, juvenile idiopathic arthritis 2.2.4 National Ambulatory Care Reporting System (NACRS) Morbidity data were obtained from NACRS, which collects data on all ambulatory care levels, including ED visits, day surgeries, and clinics since 2012. To identify heat-related morbidity, the BC Health Effects of Anomalous Temperatures (BC HEAT) Coordinating Committee uses five validated diagnostic and complaint codes: general weakness, syncope/pre-syncope, altered level of consciousness, heat-related issue, and effects of heat and light81. NACRS has complete WMV hospital data from 2014 to 2023, because not all hospitals started reporting simultaneously. WMV patients recorded in the NACRS who visited EDs located in 30 within or near WMV (including Coquitlam and Surrey) from 2014 to 2023 and are registered in the CDR were included for heat-related and comorbidity-specific morbidity analysis. 2.2.5 Medical Priority Dispatch System (MPDS) Ambulance dispatch data from 2010 to 2023 were sourced from MPDS, an internationally standardized system to prioritize 9-1-1 calls and optimize resource allocation. MPDS data, currently deidentified and un-linkable in the PANDA environment, include information on the date, protocols, response determinant code, and health service location. BC HEAT uses three validated MPDS protocol codes to identify heat-related ambulance dispatches: code 09 (cardiac or respiratory arrest), code 20 (heat & cold exposure), and code 31 (unconscious & fainting). 2.2.6 PharmaNet PharmaNet is a real-time system managed by HLTH, designed to monitor all medications dispensed by community pharmacies and hospital outpatient pharmacies for home use in BC118. The system records data on dispensed medications, PharmaCare claims, patient demographics (including Personal Health Number, name, address, and date of birth), reported drug allergies, drug details, drug interaction assessments, and information on both PharmaCare and patient-paid prescription claims118. PharmaNet data were used in this study to identify patients in the mortality and morbidity datasets who were enrolled in PharmaCare Plan C. This plan fully covers eligible prescription costs for BC residents receiving benefits through the BC Ministry of Social Development and 31 Poverty Reduction or under the care of the BC Ministry of Children and Family Development119. As such, PharmaCare Plan C identifies subjects living below or near the poverty line. 2.3 Data Collection and Linkage Mortality, comorbidity, morbidity, ambulance dispatch, and PharmaCare data from Vital Statistics, CDRs, NACRS, MPDS, and PharmaNet were obtained via the BCCDC data mart of âEvaluation of 2021 heat dome and extreme cold impacts in BCâ (nb0107aa) in PANDA (Figure 4). PANDA is a cloud-based platform created to improve data access for reporting, analytics, and research within PHSA programs. It provides a secure cloud environment for data analytics and governance and integrating advanced tools. Data linkage was facilitated through PANDA's Client Roster data. Vital Statistics, CDRS, NACRS, and PharmaNet contribute anonymous unique identifiers (UID) for the patients to the Client Roster, enabling effective data linkage at the individual level. 2.3.1 Western Metro Vancouver (WMV) Spatial Health Boundaries Data from Vital Statistics, NACRS, and MPDS included information on local health areas (LHAs), which are administrative boundaries for health administration in BC. For Vital Statistics and MPDS, data from 12 LHAs in WMV were included in the study (Table 3). For NACRS, 12 hospitals were selected as WMV or WMV-adjacent hospitals (Table 3). 32 Figure 4 Flow chart of data collection Table 3 Western Metro Vancouver Local Health Areas and Hospitals Dataset Health area code Vital statistics and MPDS Local health area code: 221 New Westminster, 222 Burnaby, 232 Delta, 311 Richmond, 321 Vancouver - City Centre, 322 Vancouver - Downtown Eastside, 323 Vancouver - Northeast, 324 Vancouver - Westside, 325 Vancouver - Midtown, 326 Vancouver - South, 331 North Vancouver, 332 West Vancouver/Bowen Island NACRS Hospital code: 101 Vancouver General Hospital, 102 St. Paulâs Hospital, 105 BC Childrenâs Hospital, 106 Mount Saint Joseph Hospital, 109 Royal 33 Columbian Hospital, 112 Lions Gate Hospital, 116 Surrey Memorial Hospital, 121 Richmond Hospital, 123 UBC Health Sciences Centre, 130 Burnaby Hospital, 131 Peace Arch District Hospital, 134 Delta Hospital 2.3.2 Data Linkage Between Vital Statistics and CDR For comorbidity-specific mortality, UIDs from Vital Statistics served as the linkage identifiers. CDR UIDs corresponding to those in Vital Statistics were included in the comorbidity-specific mortality dataset. A temporal mismatch between the mortality data (2008-2023) and the CDR (1994-2022) needed to be addressed in the inclusion criteria for data linkage. Because the CDR does not have data post-March 30, 2022, patients who died between April 1, 2022, and September 30, 2023, might have developed additional chronic conditions not captured by the CDR. Including these patients with potentially incomplete chronic condition records in the comorbidity-specific mortality analysis might introduce bias. To ensure comparability of comorbidity-specific deaths each summer between 2008 and 2023, chronic conditions registered in the CDR between April 1 of the previous year (Y-1) and September 30 of year Y were excluded (see inclusion formula below). ð¿ððððð ð·ðð¡ðð ðð¡ = â (ðð¢ðððð ð·ððð¡âð ð2023ð=2028â {ð¶âððððð ððððð¡ðððð ðððððððð ððð¡ð¤ððð ð´ðððð 1ð ð¡, (ð â 1) ððð ðððð¡ððððð 30ð¡â, ð}) 2.3.3 Data Linkage Between NACRS and CDR For comorbidity-specific morbidity, NACRS UIDs matching those in the CDR were selected. Specifically, most WMV or WMV-adjacent hospitals started reporting to NACRS in 2012, except for Delta Hospital and Peach Arch District Hospital, which started in 2014. To 34 consider the spatial and demographic variations within WMV, ED visit data from 2014 to 2023 were included in the data linkage. For the CDR data, the same inclusion criteria described in the data linkage between Vital Statistics and CDR were applied when linking to NACRS. For the ED visits in the summer of year Y, chronic conditions registered in the CDR between April 1 of the previous year (Y-1) and September 30 of year Y were excluded. 2.3.4 Linking PharmaNet with Vital Statistics and NACRS All prescriptions dispensed within the one-year period preceding a death or ED visit were extracted from the PharmaNet database and linked to Vital Statistics and the NACRS using UIDs. Individuals with one or more prescriptions covered by PharmaCare Plan C during this period were classified as low-income. Those with no prescriptions dispensed during the year prior to the death or ED visit were classified as having unknown coverage. All others were categorized as not being covered by PharmaCare Plan C (Figure 5). Figure 5 Inclusion criteria for PharmaCare plans 35 2.4 Data Analysis Data analysis was conducted for each objective, focusing on the respective outcomes and exposure measures. Descriptive and inferential statistics were employed to assess the relationships between cumulative temperature metrics and health outcomes. Sensitivity analyses were also performed to ensure the robustness of the results. All data were extracted and analyzed using SQL and R Statistical Software (v4.3.1 2023-06-16)120. 2.4.1 Objective 1 Objective 1 identified a cumulative temperature measure of heat exposure, derived from the combinations of daily highs and overnight lows over 1-3 days, as a practical predictor of all-cause mortality, heat-related ED visits, and heat-related ambulance dispatches. 2.4.1.1 Outcome Measures Daily counts of individual deaths, heat-related ED visits, and heat-related ambulance dispatches were aggregated. These daily health outcome counts were then summed over a 3-day period: â¢ If t is the day of health outcome, then 3-day sum of daily health outcome is ð¶ð¡â1 + ð¶ð¡ + ð¶ð¡+1 2.4.1.2 Exposure Measures Exposure measures consisted of cumulative temperature combinations over 1-3 days using historical observed and forecast temperature data: 36 â¢ If t is the day of health outcome, High is the daily maximum temperature, and Low is the overnight minimum temperature, the exposure measures are: o Hight-1 + Lowt-1:t (HL) o Lowt-1:t + Hight (LH) o Hight-1 + Lowt-1:t + Hight (HLH) o Lowt-2:t-1 + Hight-1 + Lowt-1:t (LHL) o Hight-2 + Lowt-2:t-1 + Hight-1 + Lowt-1:t (HLHL) o Lowt-2:t-1 + Hight-1 + Lowt-1:t + Hight (LHLH) o Hight-2 + Lowt-2:t-1 + Hight-1 + Lowt-1:t + Hight (HLHLH) â¢ When calculating these temperature metrics, day t refers to the day used to sum the temperature combinations. In subsequent analyses, only days t where the temperature metrics exceed the identified thresholds are considered as days of threshold exceedance. 2.4.1.3 Time Series Covariates It is important to control for temporal covariates such as the day of the week, seasonality, long-term trends, delayed exposure effects (lags), and other time-varying factors in time-series studies because the effects of temporality can confound exposure-outcome associations121. The focus of this study was on outcomes during the summer months and, given that cumulative outcomes and exposures were already being considered, it may not be necessary to control for seasonality and delayed exposure effects. However, it is crucial to account for long-term trends, particularly considering that the study period includes the COVID-19 pandemic, 37 which has had a profound impact on health outcomes and healthcare services. Additionally, control is needed for the day of week, because heat exposure levels and health risks due to factors unrelated to exposure may vary according to population-level behaviors that differ on holidays, weekdays, and weekends121. 2.4.1.4 Statistical Analysis Descriptive statistics for both the outcomes and exposures related to Objective 1 were calculated and visualized to provide an initial understanding of the data. The distributions of the three health outcomes were examined, including tests for normality and assessments for overdispersion. Given the non-normal distribution and the presence of overdispersion in the outcomes, a generalized additive model (GAM) with a negative binomial distribution was employed to model the health outcomes. Additional Poisson model was tested and compared, using AIC. The model incorporated a penalized spline function for long-term trends and accounted for the day of the week. Penalized splines in GAMs offer a robust and flexible approach for modeling complex, nonlinear relationships in time-series data, effectively balancing the fit of the model while minimizing the risk of overfitting122. For each health outcome, various numbers of knots for the penalized spline term were tested, with the optimal number identified using the AIC. Subsequently, all exposure measures with a natural cubic spline function were modeled against each health outcome, with time (as a spline) and the day of the week included as covariates. Below is the mathematical expression of the model formula: 38 Equation 1 Generalized additive model formula ð¥ð¨ð (ðð) = ð·ð + ð(ð¬ðððððððð) + ð(ððððð, ð ð = ð)+ â ð·ðð Ã ð«ðð ðð ððð ðððððððð=ð Where: â¢ ðð¡ is the 3-day-sum of daily health outcome count at time t. â¢ log(ðð¡) is the log of the expected value (log-link function for the negative binomial distribution). â¢ ð½0 is the intercept term. â¢ ð (ð¸ð¥ððð ð¢ððð¡) is the exposure variable with a natural cubic spline function. â¢ ð (ð¡ðððð¡ , ðð = ð) is the penalized spline term for the time variable, with k degrees of freedom, which accounts for non-linear trends over time. â¢ ð·ðð¦ ðð ð¡âð ð¤ððððð¡ are the dummy variables for day of the week, where ð½1ð are the coefficients for each day of the week (excluding Monday as the reference category). â¢ The model includes a dispersion parameter θ to account for overdispersion in the count data. In this GAM with a logit link function, the coefficients estimated by the model represent the log-odds of the outcome relative to the exposure. OR measures the strength and direction of the association between an exposure (e.g., cumulative heat metrics) and an outcome (e.g., mortality)123. It is calculated as the ratio of the odds of the outcome occurring in the exposed group to the odds in the non-exposed group123. To calculate the OR from the GAM, the estimated coefficient expressed as log-odds was exponentiated: Equation 2 Equation of odds ratio ðð = ðð¥ð (logð1 â ð) Where: â¢ p is the probability of the outcome occurring when the exposure is present. 39 â¢ logð1âð is the estimated effect of the cumulative temperature metric on the log-odds of the outcome. â¢ OR > 1: Higher cumulative temperature metric is associated with an increased likelihood of the outcome. â¢ OR < 1: Higher cumulative temperature metric is associated with a decreased likelihood of the outcome. â¢ OR = 1: No association between cumulative temperature metric and the outcome. 2.4.1.4.1 Statistical Performance of the Metrics To assess the statistical performance of seven temperature measures, significance tests, model AICs, and RMSEs were evaluated: i. Significance: The statistical significance of the exposure measure was evaluated using p-values to determine whether the association between the metric and health outcomes was meaningful. ii. AIC: AIC values were calculated and compared across exposure measures, with lower AIC values indicating relatively better model fit. iii. RMSE: The RMSE of the forecasted exposure measures in relation to the observed exposure measures were calculated (Equation 3). This criterion ensures that the exposure measure can be forecasted accurately and contributes to the predictive power of the model. Equation 3 Root mean square error ð ððð¸ = â1ðâ(ð¦ð â ?Ì?ð)2ðð=1 Where: 40 â¢ ð¦ð is the observed values, ?Ì?ð is the forecast values, and N the total number of observations. â¢ n is the number of components (or values) used to compute the composite metric. 2.4.1.4.2 Operational Considerations In addition to statistical performance, operational considerations were also applied to identify a practical metric for further analyses: a) Consistency with Current Heat-Health Warning Systems âMetrics were evaluated based on their alignment with established heat-health warning criteria in BC. b) Practical Utility â The ability to accurately forecast and effectively communicate heat-health risks in advance is critical for public health interventions. Sensitivity analyses were also conducted to confirm the robustness of the statistical results. These analyses included using daily health outcomes instead of the 3-day sum and experimenting with different numbers of knots for the spline term and GAMs with a Poisson distribution. By doing so, the study ensured that the findings were consistent and reliable across different modeling approaches. 2.4.2 Objective 2 Objective 2 identified the point at which the selected temperature metric increased the ORs for the health outcomes above 1.0. It also determined thresholds for daily highs and overnight lows within the selected temperature metric using mortality outcome as an example, considering the varying risk levels associated with different temperature combinations. 41 2.4.2.1 Outcome and Exposure Measures The outcome measures in Objective 2 were the same as in Objective 1, which included 3-day sum of daily death counts, heat-related ED visits, and heat-related ambulance dispatches. The exposure measure was the cumulative temperature metric E selected from Objective 1. 2.4.2.2 Statistical Analysis: A split value analysis was conducted to identify the points at which the exposure E was associated with ORs significantly greater than 1.0 for the health outcomes (Figure 6). Specifically, 98 splits (not percentiles) were created at equally spaced intervals between the 1st and 99th percentiles of E. This means each split cuts the temperature range into fixed increments of E, rather than ensuring that each split contains an equal proportion of data points (as would be the case with true percentiles). For each of the 100 splits, a binary variable was assigned: values below the split were given 0, while values equal to or greater than the split were assigned 1. The method of this split value analysis was adopted from a study on the temperature-mortality relationships124. GAMs were then run with temporal variables and each of the 100 binary variables, producing ORs and 95% CIs for values above and below each split. The results for below-split and above-split areas were plotted as two smooth lines on a single graph to identify where a positive relationship existed between the above-split values and the first separation (the single cut point) of the below-split and above-split intervals. 42 Figure 6 Schematic of split value analysis A threshold analysis based on mortality data for specific overnight lows and daily highs was also performed to detect risks driven by different combinations of overnight lows and daily highs above the single cut point. The single cut point B identified from the split value analysis using all-cause mortality was treated as the baseline risk for E. For each value in ð¸ð where ðµ â¤ð â¤ ð¸ððð¥ , a binary variable was created where it had a value of 1 when E was greater than or equal to B, 0 when E was smaller than B, and missing otherwise. Each variation in overnight lows was modelled for each new binary variable in ð¸ð within the range ðµ â¤ ð â¤ ð¸ððð¥, using a GAM. The results were visualized with an error bar plot to identify the specific overnight low value L at which the risk of all-cause mortality increased and its OR exceeded 1.0. To determine the threshold of daily highs, a binary variable was created for each value in ð¸ð where ðµ â¤ ð â¤ ð¸ððð¥ with a value of 1 if E was greater than or equal to B and the overnight low was less than the identified threshold L from previous step, 0 if E was smaller than B, and 43 missing otherwise. Each variation in daily highs was then modeled for each new binary variable in ð¸ð within the range ðµ â¤ ð â¤ ð¸ððð¥, using a GAM. The results were visualized with an error bar plot to identify the specific daily high value HH at which the risk of all-cause mortality increased and its OR exceeded 1. The same techniques for identifying thresholds of overnight lows and daily highs were also applied to the other two outcomes for comparison. After determining the thresholds B, L, and HH, a dynamic and flexible threshold algorithm was established (Equation 4). Each value in ð¸ð within the range ðµ â¤ ð â¤ ð¸ððð¥ and the overnight low combination greater than or equal to L or daily high combination greater than or equal to H was modeled against the baseline value B of the exposure variable. The results for all models were combined and plotted to visualize how ORs changed. Equation 4 HLH algorithm (ð¸ â¥ ðµ) â (ððð¤ð â¥ ð¿ â âððâð â¥ ð»ð») The selected exposure metric E, using forecasted temperature data, was applied to the algorithm as a sensitivity analysis to ensure that the algorithm remains effective when the temperature data are based on weather predictions. Another sensitivity analysis was conducted to compare the dates meeting the HLH absolute thresholds (29°C-16°C-29°C) in WMV under the current BC Heat Alert and Response System (HARS) with the dates meeting the corresponding cumulative threshold algorithm of the selected metric. 44 2.4.3 Objective 3 Objective 3 was a case study for applying the mortality-based threshold algorithm to subgroups of mortality and morbidity causes. This objective explored whether the algorithm was applicable to measure risks among different sub-populations. 2.4.3.1 Outcome and Exposure Measures The subgroups by health outcomes are detailed in Table 4. The exposure variable used was the selected cumulative temperature metric E. 2.4.3.2 Statistical Analysis Descriptive statistics for each sub-group were calculated and presented in a frequency table. Each combination of (ð¸ â¥ ðµ) â (ððð¤ð â¥ ð¿ â âððâð â¥ ð»ð») was modelled against when E < B using a GAM within each subgroup. The results from all subgroups were then combined and plotted to visualize and compare how ORs changed within each subgroup by the temperature metric and how ORs differed across the subgroups. 45 Table 4 Subgroups within each health outcome Mortality-related Morbidity-related Ambulance-dispatch-related Comorbidity categories Respiratory, circulatory, chronic kidney disease and diabetes, mental health and substance use disorder, and all other comorbidities Comorbidity categories Respiratory, circulatory, chronic kidney disease and diabetes, mental health and substance use disorder, and all other comorbidities Acuity High (all advanced life support codes), low (all basic life support codes) Specific chronic conditions Heart failure, ischemic stroke, acute myocardial infarction, schizophrenia, substance use disorder, chronic obstructive pulmonary disease, parkinsonism, chronic kidney disease, and other chronic conditions Specific chronic conditions Heart failure, ischemic stroke, acute myocardial infarction, schizophrenia, substance use disorder, chronic obstructive pulmonary disease, parkinsonism, chronic kidney disease, and other chronic conditions Multimorbidity 3 or more multimorbidity, less than 3 multimorbidity Multimorbidity 3 or more multimorbidity, less than 3 multimorbidity Age Under 65 years old, 65 years old or above Age Aged 65 or above, 40-64, 20-39, under 19 Sex Male, female Sex Male, female 46 Location of death In care (hospitals and long-term care facilities, out of care Location of death In care (hospitals and long-term care facilities, out of care Acuity High (resuscitation and emergency), low (urgent, semi-urgent, and non-urgent) PharmaCare plan Plan C, other plans, unknown plans PharmaCare plan Plan C, other plans, unknown plans 47 Chapter 3: Results 3.1 Data Summary A total of 58,304 deaths occurring in WMV from 2008 to 2023, 123,670 heat-related ED visits from 2014 to 2023, and 93,990 heat-related ambulance dispatches from 2010 to 2023 were included. Because these data were aggregated into daily counts, the number of observations refers to the number of days rather than individual deaths, ED visits, or ambulance dispatches. Specifically, the data of all-cause mortality, heat-related ED visits, and heat-related ambulance dispatches covered 2,448 days, 1,530 days, and 2,210 days respectively. 91.2% of all-cause deaths and 84.9% of heat-related ED visits involved individuals with pre-existing chronic conditions. Among the deceased, 79% had circulatory-related conditions and at least three comorbidities, while 58% had a mental illness or SUD, 45% had CKD or diabetes, and 29% had respiratory conditions. A similar pattern was observed among those with heat-related ED visits: 71% had circulatory-related conditions, 68% had mental illness or SUD, 48% had CKD or diabetes, 31% had respiratory conditions, and 78% presented with three or more comorbidities. In terms of specific chronic conditions among the deceased, 27% had heart failure, 23% had CKD, 21% had COPD, 15% had SUD, 10% had acute myocardial infarction (AMI), 7% had ischemic stroke, 5% had schizophrenia, and 3% had Parkinsonâs. Similarly, among individuals with heat-related ED visits, 28% had CKD, 22% had SUD, 21.8% had heart failure, 17% had COPD, 9% had AMI, 7% had schizophrenia, 6% had ischemic stroke, and 4% had Parkinsonâs. Demographically, 79% of those who died were aged 65 or older, 78% passed away in hospitals or residential care facilities, 47% were male, 53% were female, and 9% were enrolled in PharmaCare Plan C. In the cohort of heat-related ED visits, 56% were aged 65 or older, 50% 48 were female, 11% were on PharmaCare Plan C, and 72% were classified as low-acuity visits. Of those who were identified as heat-related ambulance dispatches, 82% needed advanced life support. More detailed descriptive statistics can be found in Table 5. The average observed daily maximum temperature was 20.3 °C, while the average overnight low was 12.2 °C. Forecasted temperatures were closely aligned, with only minor decimal differences. However, these differences became more pronounced when multiple days of temperatures were aggregated (Table 6). Moreover, no significant temporal trends were observed in daily maximum or overnight low temperatures between 2008 and 2023. Histograms of daily maximum and minimum temperatures, as well as boxplots for each year, can be found in Appendix A.1. 3.2 Temperature Metric Evaluation The evaluation of the temperature metrics involved several steps: assessing the distribution of outcome variables, selecting an appropriate time-series model, choosing the most suitable spline function, identifying a practical temperature metric, and conducting model diagnostics and sensitivity analyses. 49 Table 5 Descriptive statistics of outcome variables Outcomes % of individual observations before aggregation Mean (SD) of 3-day sum Range (Min-Max) of 3-day sum All-cause mortality N = 58,304 71.4 (13.7) 43-311 Age Under 65 20.9% 15.0 (4.6) 5-64 65 or above 79.1% 56.5 (11.2) 30-253 Location Out-of-care 22.4% 16.3 (9.2) 3-218 In-care 77.6% 55.3 (7.7) 34-101 Sex Female 47.4% 33.9 (7.6) 13-167 Male 52.2% 37.2 (8.7) 16-144 PharmaCare Plan C 9.3% 7.5 (3.1) 3-60 Other plans 82.3% 58.8 (3.1) 29-230 Unknown 8.5% 7.1 (3.0) 3-52 Comorbidity-specific all-cause mortality N = 53,166 Respiratory 28.7% 18.8 (5.91) 5-104 Circulatory 79.7% 51.9 (10.9) 28-244 Chronic kidney disease and diabetes 44.5% 29.0 (9.3) 6-156 Mental health and substance use disorder 57.8% 37.6 (10.1) 15-200 Other comorbidity groups 63.1% 41.2 (9.4) 20-196 Less than 3 multimorbidity 20.6% 13.6 (3.8) 4-34 3 or more multimorbidity 79.4% 51.7 (12.2) 30-271 Chronic-condition-specific all-cause mortality N = 53,166 Heart failure 26.8% 17.6 (5.2) 4-89 Ischemic stroke 7.0% 5.9 (1.9) 3-24 Acute myocardial infarction 9.6% 7.2 (2.4) 3-29 50 Schizophrenia 4.5% 4.9 (2.2) 3-41 Substance use disorder 15.3% 10.6 (4.4) 3-60 Chronic obstructive pulmonary disease 20.5% 13.7 (4.6) 3-80 Parkinsonism 3.4% 4.2 (1.2) 3-11 Chronic kidney disease 23.4% 15.6 (7.1) 4-104 Other chronic conditions 36.9% 24.0 (5.5) 9-80 Comorbidity-specific all-cause mortality among those aged under 65 N = 22,184 Respiratory 10.1% 4.7 (1.6) 3-18 Circulatory 16.2% 5.9 (2.1) 3-28 Chronic kidney disease and diabetes 10.6% 4.8 (1.6) 3-24 Mental health and substance use disorder 30.3% 9.4 (3.4) 3-53 Other comorbidity groups 11.0% 4.9 (1.6) 3-19 Less than 3 multimorbidity 14.4% 5.5 (1.7) 3-14 3 or more multimorbidity 24.4% 7.8 (3) 3-50 Comorbidity-specific all-cause mortality among those aged 65 or above N = 111,635 Respiratory 11.0% 16.0 (5.2) 4-86 Circulatory 32.7% 47.3 (10.1) 25-219 Chronic kidney disease and diabetes 18.0% 26.0 (8.3) 5-132 Mental health and substance use disorder 20.1% 29.0 (8.0) 13-149 Other comorbidity groups 26.3% 38.0 (8.9) 17-180 Less than 3 multimorbidity 6.4% 9.8 (3.0) 3-25 3 or more multimorbidity 31.0% 44.8 (10.4) 23-226 All-cause ED visits N = 3,397,259 6660.8 (557.1) 4424-8236 Heat-related ED visits N = 123,670 242.2 (43.5) 154-969 Age Under 19 5.2% 13.0 (4.5) 3-44 20-39 14.9% 36.2 (9.1) 14-119 40-64 24.1% 58.4 (11.3) 28-188 51 65 or above 55.7% 134.7 (27.8) 90-618 Sex Female 49.9% 120.8 (23.9) 64-511 Male 50.0% 121.1 (23.3) 70-457 Acuity level High 27.6% 66.8 (19.3) 37-435 Low 72.1% 174.6 (28.4) 111-532 PharmaCare Plan C 13.9% 33.7 (9.8) 12-135 Other plans 75.6% 183.2 (32.5) 124-733 Unknown 10.5% 25.5 (7.3) 7-101 Comorbidity-specific heat-related ED visits N = 103,772 Respiratory 30.6% 62.2 (13.2) 37-272 Circulatory 71.3% 145.0 (26.8) 89-620 Chronic kidney disease and diabetes 48.0% 97.6 (19.3) 61-416 Mental health and substance use disorder 68.3% 138.8 (25.1) 88-552 Other comorbidity groups 56.8% 115.3 (22.3) 71-517 Less than 3 multimorbidity 21.6% 43.7 (9.8) 24-145 3 or more multimorbidity 78.4% 159.5 (29.9) 105-693 Chronic-condition-specific heat-related ED visits N = 103,772 Heart failure 21.8% 44.5 (10.6) 21-105 Ischemic stroke 6.4% 13.4 (4.4) 4-53 Acute myocardial infarction 8.9% 18.2 (5.3) 6-76 Schizophrenia 6.6% 13.6 (4.7) 5-66 Substance use disorder 22.2% 44.9 (10.5) 20-152 Chronic obstructive pulmonary disease 16.7% 33.9 (8.8) 14-163 Parkinsonism 3.6% 8.0 (3.5) 3-64 Chronic kidney disease 28.3% 57.7 (12.8) 31-267 Other chronic conditions 37.7% 76.6 (15.1) 41-271 52 Comorbidity-specific heat-related ED visits among those aged under 40 N = 29,233 Respiratory 7.7% 9.2 (3.1) 3-29 Circulatory 12.3% 4.5 (1.3) 3-9 Chronic kidney disease and diabetes 8.0% 4.7 (1.0) 1.44-3 Mental health and substance use disorder 23.0% 20.9 (5.7) 7-64 Other comorbidity groups 8.3% 4.9 (1.6) 3-11 Less than 3 multimorbidity 10.9% 14.0 (4.1) 3-41 3 or more multimorbidity 18.5% 11.8 (4.0) 3-37 Comorbidity-specific heat-related ED visits among those aged between 40-64 N = 58,256 Respiratory 3.9% 14.5 (4.4) 3-48 Circulatory 6.2% 27.2 (6.1) 11-76 Chronic kidney disease and diabetes 4.0% 19.0 (4.9) 5-48 Mental health and substance use disorder 11.5% 40.1 (8.7) 18-139 Other comorbidity groups 4.2% 18.9 (5.0) 6-57 Less than 3 multimorbidity 6.6% 13.5 (4.2) 3-46 3 or more multimorbidity 9.3% 35.8 (7.7) 16-115 Comorbidity-specific heat-related ED visits among those aged 65 or above N = 156,490 Respiratory 1.4% 39.2 (9.7) 19-195 Circulatory 2.3% 115.4 (23.7) 74-539 Chronic kidney disease and diabetes 1.5% 75.9 (17.1) 45-370 Mental health and substance use disorder 4.3% 77.9 (16.2) 48-349 Other comorbidity groups 1.6% 93.2 (19.9) 53-457 Less than 3 multimorbidity 2.0% 16.7 (5.7) 5-59 3 or more multimorbidity 3.5% 112.2 (23.6) 71-544 All-cause ambulance dispatches N = 857,812 1164.2 (116.0) 822-1509 Heat-related ambulance dispatches N = 93,990 123.5 (31.1) 64-410 Acuity level High 81.8% 98.6 (29.7) 42-362 53 Low 18.2% 22.9 (5.8) 4-54 Table 6 Descriptive statistics of temperature metrics Temperature measure (°C) Observed (N = 2,448) Forecast â 2 days before (N = 2,448) Forecast â 3 days before (N = 2,448) Mean (SD) Min-max Mean (SD) Min-max Mean (SD) Min-max Daily high 20.4 (3.5) 10.5-34.4 20.14 (3.5) 10.0-34.0 20.16 (3.46) 10.0-32.0 Overnight low 12.2 (2.8) 2.0-22.4 12.77 (2.7) 2.0-24.0 12.92 (2.68) 3.0-24.0 HL 32.6 (5.9) 13.6-56.4 32.90 (5.9) 14.0-58.0 33.07 (5.87) 16.0-56.0 LH 32.6 (5.8) 15.3-56.8 32.91 (5.7) 16.0-55.0 33.08 (5.75) 16.0-54.0 HLH 52.9 (9.0) 26.9-90.8 53.04 (9.0) 27.0-88.0 53.24 (8.99) 29.0-86.0 LHL 44.8 (8.3) 17.8-76.6 45.66 (8.2) 17.0-79.0 45.97 (8.26) 22.0-76.0 HLHL 65.2 (11.4) 29.6-107.7 65.79 (11.5) 29.0-110.0 66.11 (11.47) 34.0-107.0 LHLH 65.2 (11.3) 31.8-111.0 65.80 (11.2) 31.0-109.0 66.14 (11.28) 35.0-106.0 HLHLH 85.6 (14.3) 44.1-141.9 85.93 (14.4) 43.0-140.0 86.28 (14.43) 46.0-137.0 54 3.2.1 A GAM with Negative Binomial Distribution Appendix A.2 presents histograms, boxplots, and decomposition plots of the additive time series for the 3-day sum of daily health outcomes. Visual inspection of these distributions showed varying degrees of non-normality and skewness across all three health outcomes. To further investigate the distributional properties, both dispersion analysis and the Shapiro-Wilk normality test were applied. The results confirmed overdispersion and significant non-normality for all health outcomes (Table 7). In addition, boxplots displaying the outcomes by year and the decomposition plots (Appendix A.2) highlighted evident long-term temporal trends and nonlinearity within the data. Given these findings, a GAM with a negative binomial distribution was selected for all three health outcomes to account for overdispersion and time-dependent variations. The GAM in general provides a flexible framework for modeling complex, nonlinear relationships between the predictors and outcomes, allowing for a more accurate representation of the underlying data patterns122. In addition, the negative binomial distribution is well-suited for non-normal data, particularly when dealing with count data that exhibits overdispersion and offers full likelihood-based inference such as AIC and likelihood ratio tests122,125. Table 7 Dispersion and normality tests of outcomes All-cause mortality Heat-related ED visits Heat-related ambulance dispatches Daily 3-day Daily 3-day Daily 3-day Dispersion 1.7 2.6 3.7 7.8 3.6 7.8 55 Shapiro-Wilk test 0.83*** 0.76*** 0.7*** 0.6*** 0.9*** 0.9*** * Dispersion parameter = ð£ððððððð ððððâ ; the coefficient in Shapiro-Wilk test is W; Significance: *p < 0.05, **p < 0.01, ***p < 0.001 3.2.2 Selection of Spline Function Given the strong long-term trends observed in all three health outcomes, smoothing functions were optimal for capturing complex, nonlinear temporal patterns, reducing short-term noise, and minimizing the risk of overfitting. Within the GAM framework, a penalized spline function was employed for the time variable. This approach allowed for smooth modeling of long-term trends while also preventing overfitting by regulating the modelâs complexity. To determine the optimal degrees of freedom or the number of "knots" for adjusting the spline's smoothness, different knots were tested, ranging from 1 to 5 knots per calendar year across the study periods for the three health outcomes. For all-cause mortality, models with 16, 32, 48, 64, and 80 knots were tested over the 16-year study period, with the model using 80 knots yielding the lowest AIC. Similarly, for heat-related ED visits, models with 10, 20, 30, 40, and 50 knots were tested for the 10-year study period, with the model using 40 knots having the smallest AIC. For heat-related ambulance dispatches, models with 14, 28, 42, 56, and 70 knots were tested over the 14-year study period, with 70 knots providing the best fit based on the AIC. Detailed results of the knot selection process are provided in Appendix A.3. 3.2.3 Temperature Metric Selection 3.2.3.1 Statistical Performance Seven cumulative temperature metrics were evaluated for their statistical performance across all three health outcomes, with daily maximum and minimum temperatures also included 56 as reference metrics. These temperature metrics, using observed data, were modeled against the 3-day sum of the outcomes. The significance test and AIC value for each model were extracted (Table 8). Among the temperature metrics, HLH, LHL, HLHL, LHLH, and HLHLH consistently yielded lower AIC values across 3-day aggregated outcomes compared to the rest, and their inclusion in the models was statistically significant. In addition to AIC comparisons, RMSE values tended to increase as more days were incorporated into the temperature metrics. Overall, all cumulative metrics demonstrated stable and comparable statistical performance. 3.2.3.2 Operational Considerations Among the seven cumulative metrics, HLH aligns closely with the absolute thresholds currently used by BC HARS, which define heat warnings based on HLH cutoffs. This practical compatibility suggests that the implementation of HLH in public health interventions can be streamlined without major adjustments to existing alert systems. The simplicity of HLHâincorporating only three temperature variables (two daily highs plus an overnight low)âenhances not only its day-to-day forecast reliability but also public acceptance of multi-day alerts. Research on weather warnings has shown that shorter lead times in advance tend to minimize âwarning fatigueâ while still providing adequate time to prepare108,126â128. Systems that push warnings too far in advance can erode public compliance if forecast details shift or if repeated alerts do not manifest as predicted126,128,129. By focusing on a shorter but cumulative and actionable window, HLH naturally fits a good balance between forecast confidence and public engagement. 57 HLH was therefore selected as an example of a cumulative temperature metric for subsequent analyses. Table 8 Results of the temperature metric evaluation Temperature indicators P-value AIC - mortality AIC - heat-related ED visits AIC - heat-related ambulance dispatches RMSE: forecast made 2 days before RMSE: forecast made 3 days before Daily maximum < 0.001 17780 (9) 13514 (5) 17495 (5) 1.5 (1) 1.7 (1) Overnight minimum < 0.001 17649 (7) 13623 (7) 17786 (9) 1.6 (2) 1.8 (2) HL < 0.001 17698 (8) 13620 (6) 17675 (6) 2.16 (3) 2.2 (3) LH < 0.001 17582 (5) 13408 (2) 17482 (2) 2.18 (4) 2.3 (4) HLH < 0.001 17626 (6) 13401 (1) 17470 (1) 2.8 (5) 3.3 (5) LHL < 0.001 17485 (3) 13665 (8) 17682 (8) 3.1 (6) 3.5 (6) HLHL < 0.001 17522 (4) 13694 (9) 17680 (7) 3.4 (7) 4.0 (7) LHLH < 0.001 17393 (1) 13414 (3) 17483 (3) 3.5 (8) 4.1 (8) HLHLH < 0.001 17415 (2) 13478 (4) 17494 (4) 3.9 (9) 4.6 (9) * The numbers in the parentheses for AIC and RMSE values are their corresponding rank values. 3.2.4 Model diagnostics For each of the three models, several diagnostic plots were generated to assess the adequacy of model fit and detect potential concerns with residuals or model assumptions. These included smooth term plots for HLH and time, residual plots, and quantile-quantile (Q-Q) plots for deviance residuals (Appendix A.4). The smooth term plots for HLH showed a relatively flat effect across much of the range, with a notable upward trend at higher exposure levels, particularly above 70 in all models. 58 Confidence intervals remained narrow for most of the range but widened at the extremes, indicating increased uncertainty at higher HLH values. The time variable exhibited some fluctuations, especially during the COVID-19 pandemic, but with narrow confidence intervals throughout, indicating a good model fit. In all models, the Q-Q plots demonstrated that the residuals closely followed the theoretical quantiles, suggesting the residuals were approximately normally distributed. Slight deviations were observed at the extremes, particularly at higher HLH levels, possibly due to the two EHEs in 2009 and 2021, which may have introduced outliers. Overall, the diagnostics indicate that all three models fit the data well, with no major violations of model assumptions. 3.2.5 Sensitivity analysis Sensitivity analysis included using daily health outcomes instead of the 3-day sum to evaluate the selection of the temperature metric (Appendix A.5) and testing alternative numbers of knots for the spline terms in the three outcomes. For all-cause mortality, heat-related ED visits, and heat-related ambulance dispatches, 32, 20, and 28 knots were applied, respectively (Appendix A.5). The models with these alternative knots yielded similar results. Furthermore, GAMs with a Poisson distribution, using the same number of knots for the spline functions as specified in Section 3.2.2 Selection of Spline Function, were also examined (Appendix A.5). The results led to the same conclusion. 59 3.3 Identifying HLH Thresholds The overall thresholds of HLH for the three health outcomes were determined using split value analysis. For all-cause mortality (Figure 7), a threshold of 58°C (OR 1.02, 95% CI: 1.00â1.03) was identified as the cut point where a positive relationship emerged between the above-split ORs and the first separation between the below-split and above-split intervals. Another separation point was observed at 44°C for all-cause mortality; however, the above-44 split OR was 0.98 (95% CI: 0.97â0.99), indicating a negative relationship. This negative association was likely attributable to cooler temperatures, as 72% of the cases (108 out of 151) where HLH ranged between 44°C and 46°C occurred in May or September. For heat-related ED visits and ambulance dispatches (Figure 8), the thresholds were identified as 47°C (OR 1.02, 95% CI: 1.00â1.04) and 42 (OR 1.03, 95% CI: 1.00â1.05), respectively. Figure 7 Odds ratios of High-Low-High split values in all-cause mortality 60 Figure 8 Odds ratios of High-Low-High split values in heat-related ED visits and ambulance dispatches 3.4 Mortality-Based Threshold Algorithm: An Example of Threshold Application Using the cut point of 58°C identified for all-cause mortality, each HLH combination ð¸ð â¥ ð, where 58°C â¤ ð â¤ 75°C, was modelled against cases where HLH was below 58°C for all three health outcomes. A consistent pattern of increasing ORs with higher HLH values was observed for all-cause mortality (Figure 9). 3.4.1 Identifying L Threshold Each variation in the overnight low for each HLH combination ð¸ð â¥ ð, where 58°C â¤ð â¤ 75°C, was modelled against cases where HLH was below 58°C to identify the overnight low 61 L threshold driving risk. An overnight low of 15°C was identified as the nadir of the risk curve, serving as the L threshold. Specifically, for all-cause mortality, the OR at 14°C was 0.98 (95% CI: 0.96â1.00), rising to 1.01 (95% CI: 0.99â1.03) when the overnight low reached 15°C, with the OR continuing to increase as the overnight low rose further (Figure 10). The U-shaped pattern observed in the plot suggests that overnight low temperatures â¥ 15°C drive the risk, while daytime high temperatures are the primary risk drivers when overnight lows are < 15°C. 3.4.2 Identifying HH Threshold To determine the threshold HH for two daily high temperatures, each variation in daily highs for each HLH combination ð¸ð â¥ ð, where 58°C â¤ ð â¤ 75°C and the overnight low < 15°C was modelled against cases where HLH was below 58°C. A value of 51°C was identified as the HH threshold, using all-cause mortality. The ORs for daily highs below 51°C remained relatively stable around 1, with narrow CIs. However, at 51°C, the OR was 1.0 (95% CI: 0.94â1.06) and increased to 1.03 (95% CI: 0.91â1.17) when daily high temperatures reached 52°C (Figure 11).62 Figure 9 Odds ratios of High-Low-High values for all-cause mortality Note: Above each range bar is the frequency of HLH values over the entire study period, along with the corresponding OR. °C 63 Figure 10 Odds ratios of overnight low groups when High-Low-High >= 58, against High-Low-High < 58 Odds ratio °C 64 Figure 11 Odds ratios of daily highs when High-Low-High >= 58 and L < 15, compared with High-Low-High < 58 °C 65 3.4.3 Mortality-Based HLH Threshold Algorithm The previously identified baseline risk threshold B for HLH, as well as the cut-off points L and HH for overnight low and daily high temperatures, form the final HLH threshold algorithm: (ð»ð¿ð» â¥ 58°C) â (ð¿ â¥ 15°C â ð»ð» â¥ 51°C). This algorithm reflects a more flexible and dynamic approach to assessing heat-related health risks by incorporating the risk associated with an HLH value of 58°C or greater, and further considering risk when overnight low reaches 15°C or higher, or daily high temperatures exceed 51°C (Figure 13). 3.4.3.1 Algorithm Sensitivity Analysis 1 In the first sensitivity analysis ( Figure 14), temperature forecasts made 2 and 3 days in advance were applied to the mortality-based HLH threshold algorithm for all three health outcomes. Overall, the U-shaped risk patterns derived from the forecasted temperature data were consistent with those based on observed temperature measurements. However, when HLH values were greater than or equal to 73°C, the temperature forecasts (both 2 and 3 days in advance) produced higher ORs than the observed data, leading to an overestimation of risk. Additionally, in the plots in the Appendix (Figure 48 and Figure 49), the frequency of days meeting each threshold combination (expressed as a percentage of total calendar time) is displayed above the 95% CIs. These plots indicated that the forecast temperature data tended to overestimate the frequency of days that met each threshold combination when HLH was below 73°C, while underestimating the frequency when HLH exceeded 73°C. 66 In other words, when HLH was below 73°C, the ORs for each threshold combination were similar between observed and forecasted temperature data, but the forecast data overestimated the number of days that met threshold criteria. On the other hand, when HLH was 73°C or higher, the forecast data underestimated the number of days meeting the threshold criteria and overestimated the associated risk. 3.4.3.2 Algorithm Sensitivity Analysis 2 Another sensitivity analysis compared the dates that met the HLH absolute thresholds with those (Day t) that met the corresponding cumulative thresholds. In Figure 12, dates that fulfill these criteria are coloured green. As shown in the table, the current absolute HLH thresholds (29°Câ16°Câ29°C) used by BC HARS captured only two heat events (in 2009 and 2021), whereas the cumulative threshold algorithm (HLH >= 74°C & (HH >= 51°C | L >= 15°C) identified additional occurrences. A GAM was then fitted to estimate ORs for all-cause mortality across two subsets of dates: (1) those flagged by the absolute thresholds and (2) those missed by the absolute thresholds but still caught by the cumulative threshold algorithm, compared with all HLH values under the 58°C baseline. Compared with HLH < 58°C, the OR of mortality was 3.24 (95% CI: 3.05â3.44) for dates flagged by the absolute thresholds and 1.13 (95% CI: 1.08â1.18) for dates not flagged by the absolute thresholds but detected by the cumulative threshold. These results suggest that while BC HARSâs current absolute thresholds can capture high-risk heat events, they may fail to identify moderate-risk events. 67 Figure 12 Comparison of Dates Meeting Absolute and Cumulative High-Low-High Thresholds * In column 4, the numbers are real HLH temperature readings for the dates in column 2. 3.5 Extra Split Value Analysis on All-Cause ED Visits and Ambulance Dispatches An additional split value analysis was conducted for all-cause ED visits and ambulance dispatches to investigate whether these outcomes displayed different or similar risk patterns compared with their heat-related counterparts (Figure 50 and Figure 51). For all-cause ED visits, no marked increase in the risk curve was observed. In the case of all-cause ambulance dispatches, the first separation point between the 95% CIs for above-split and below-split value 68 was similar to that of heat-related ambulance dispatches, but the risk curve appeared less steep and relatively flat. Unlike mortality data, the volume of ED visits and ambulance dispatches is inherently limited by the capacity of the healthcare system, which can constrain the outcomes themselves. This structural limitation makes it challenging to detect a clear relationship between heat exposure and these all-cause morbidity outcomes, because the number of emergency rooms and ambulances in service may not increase proportionally with rising temperatures.69 Figure 13 High-Low-High algorithm plot, using observed temperature data Odds ratio °C 70 Figure 14 High-Low-High algorithm plot, using observed and forecast temperature data °C 71 3.6 Application of Mortality-Based HLH Algorithm to Subgroups The HLH algorithm was applied to mortality and morbidity subgroups to explore its applicability and whether the algorithm could accurately capture the heat risks experienced by subpopulations extensively studied in prior research. Scenarios ranging from HLH â¥ 58°C to HLH â¥ 75°C while keeping L â¥ 15°C and HH â¥ 51°C fixed were modeled against HLH < 58°C. A comprehensive set of results from these analyses is provided in Appendix C (page122). For simplicity, this section focuses solely on mortality and morbidity outcomes when HLH ï³ 75°C & HH ï³ 51°C | L ï³ 15°C. To provide context, all-cause mortality exhibited an OR of 1.67 (95% CI: 1.57-1.78), while the ORs of heat-related ED visits and ambulance dispatches were 1.99 (95% CI: 1.89-2.11) and 1.75 (95% CI: 1.64-1.87), respectively, at an HLH threshold of 75°C +. 3.6.1 Chronic Condition-Specific Outcomes At an HLH of 75°C +, all-cause mortality among individuals with specific chronic conditions showed notable differences (Figure 15). Schizophrenia was associated with the highest mortality risk, with an OR of 2.61 (95% CI: 2.23-3.07). This was followed by CKD with an OR of 1.95 (95% CI: 1.77-2.16), SUD (OR 1.80, 95% CI: 1.60-2.02), COPD (OR 1.79, 95% CI: 1.59-2.02), other chronic conditions (OR 1.74, 95% CI: 1.61-1.88), heart failure (OR 1.71, 95% CI: 1.53-1.91), ischemic stroke (OR 1.61, 95% CI: 1.33-1.95), AMI (OR 1.48, 95% CI: 1.25-1.76), and Parkinsonâs (OR 1.44, 95% CI: 1.12-1.86). In contrast, the risk profile for heat-related ED visits showed a different hierarchy. Parkinsonâs presented the highest risk, with an OR of 3.24 (95% CI: 2.77-3.78), closely followed by heart failure (OR 2.46, 95% CI: 2.24-2.70). AMI (OR 2.39, 95% CI: 2.12-2.68) and COPD (OR 2.39, 95% CI: 2.15-2.66) had similar odds of ED visits, followed by schizophrenia (OR 72 2.37, 95% CI: 2.10-2.67), CKD (OR 2.29, 95% CI: 2.11-2.49), and ischemic stroke (OR 2.11, 95% CI: 1.83-2.44). Other chronic conditions and SUD posed more moderate risks of ED visits, with ORs of 1.89 (95% CI: 1.75-2.02) and 1.76 (95% CI: 1.60-1.93), respectively. 3.6.2 Condition Groups and Multimorbidity At an HLH of 75°C +, mortality and ED visits for individuals with multiple comorbidities showed slight variations in their risk profiles (Figure 16). For heat-related ED visits, individuals with respiratory conditions and those with CKD and diabetes showed the highest risks, both with an OR of 1.80 (95% CI: 1.62-2.01 for respiratory conditions; 1.65-1.96 for CKD and diabetes). Mental health and SUD (OR 1.73, 95% CI: 1.59-1.88), as well as other comorbidities (OR 1.74, 95% CI: 1.61-1.88) showed moderately elevated risks. Those with circulatory conditions (OR 1.67, 95% CI: 1.55-1.79) had morbidity risks comparable to the average risk of all heat-related ED visits. For mortality outcomes, individuals with respiratory conditions also had the highest odds (OR 2.25, 95% CI: 2.08-2.43), followed by those with other comorbidities (OR 2.24, 95% CI: 2.09-2.40) and circulatory conditions (OR 2.23, 95% CI: 2.09-2.37). CKD and diabetes (OR 2.17, 95% CI: 2.02-2.33) also presented substantial risk. Mental health and SUD, although slightly lower (OR 2.06, 95% CI: 1.93-2.19), still posed a higher risk than the overall odds for all-cause mortality. Furthermore, those with three or more comorbidities faced elevated mortality and morbidity risks, with an OR of 1.71 (95% CI: 1.59-1.84) for heat-related ED visits and an OR of 2.19 (95% CI: 2.06-2.33) for all-cause mortality at HLH 75°C +, exceeding the risk for those with fewer comorbidities. 73 Figure 15 Odds ratios of Morbidity and Mortality Across Chronic Conditions at HLH >= 75°C & (HH >= 51°C | L >= 15°C) 74 Figure 16 Odds ratios of Morbidity and Mortality Across Comorbidity Groups at HLH >= 75°C & (HH >= 51°C | L >= 15°C) 75 3.6.3 Demographic-Related Mortality and Morbidity Demographic factors further stratified the risks of mortality and morbidity at an HLH of 75°C + (Figure 17). Individuals who died out of care experienced the highest mortality risk at an HLH of 75°C +, with an OR of 2.65 (95% CI: 2.36-2.98), much higher than those who died in care settings (OR 1.21, 95% CI: 1.13-1.29), such as hospitals or long-term care facilities. Females had a higher mortality risk compared to males, with an OR of 1.82 (95% CI: 1.67-1.97) versus 1.52 (95% CI: 1.41-1.65). In terms of morbidity, females also had slightly higher odds for ED visits, with an OR of 2.05 (95% CI: 1.92-2.18), compared with males at 1.99 (95% CI: 1.87-2.12). Age-related outcomes indicated that individuals aged 65 and older had a mortality OR of 1.67 (95% CI: 1.56â1.78), higher than those under 65 (OR 1.56, 95% CI: 1.39â1.75). ED visits among younger individuals aged 20â39 had the lowest OR (1.82, 95% CI: 1.66â1.99), while those aged 65 and older had the highest OR (2.25, 95% CI: 2.10â2.40). In the age-specific comorbidity analysis, individuals aged 65 and above had higher ORs for mortality in the respiratory and other comorbidity groups compared to younger age groups. However, this trend was reversed for morbidity, as individuals aged 65 and above consistently showed the highest ORs for heat-related ED visits across all comorbidity group. This pattern was reversed for morbidity, because heat-related ED visits among those aged 65 or above consistently showed the highest ORs across all comorbidity groups. Furthermore, PharmaCare plan coverage significantly influenced mortality and morbidity risks. Individuals on Plan C faced the highest mortality odds (OR 2.38, 95% CI: 2.09â2.71), followed by those with an unknown plan (OR 1.90, 95% CI: 1.62â2.22). Those with other plans had a slightly lower risk (OR 1.53, 95% CI: 1.43â1.64). For heat-related ED visits, individuals 76 with an unknown plan had the highest odds (OR 2.16, 95% CI: 1.95â2.40), followed by Plan C (OR 2.04, 95% CI: 1.84â2.26) and other plans (OR 1.97, 95% CI: 1.86â2.09). Figure 17 Odds ratios of Morbidity and Mortality Across Demographic Groups at HLH >= 75°C & (HH >= 51°C | L >= 15°C) 77 3.6.4 Acuity of Heat-Related ED Visits High-acuity heat-related ED visits had an OR of 2.78 (95% CI: 2.54â3.03), whereas low-acuity visits had a lower OR of 1.80 (95% CI: 1.70â1.90) at an HLH of 75°C +. For heat-related ambulance dispatches, there was little distinction between high-acuity (OR 1.74, 95% CI: 1.62â1.88) and low-acuity (OR 1.73, 95% CI: 1.57â1.91) outcomes.78 Chapter 4: Discussion and Conclusions 4.1 Summary of Findings 4.1.1 Methods to Identify Cumulative Temperature Thresholds Comprehensive methods were applied to identify cumulative temperature exposure and thresholds associated with increased health risks, focusing on all-cause mortality, heat-related ED visits, and heat-related ambulance dispatches in the WMV region from 2008 to 2023. Seven cumulative temperature metrics, involving combinations of daily high and overnight low temperatures over multiple days, were evaluated alongside daily maximum and minimum temperatures as reference metrics. Each metric was modeled against the 3-day sum of health outcomes using observed temperature data. The significance tests, model AIC values, and RMSEs were applied to check the statistical performance of the metrics. All metrics showed stable and comparable statistical performance. The HLH metric was selected as an example for further analysis not only for its statistics, but also its alignment with the existing use of absolute HLH thresholds in BC HARS. Using a split-value analysis, three baseline thresholds for the health outcomes were identified where ORs surpassed 1.0: 58°C for all-cause mortality, 47°C for heat-related ED visits, and 42°C for ambulance dispatches. These results suggest that ED visits and ambulance dispatches may respond to earlier or milder heat exposuresâthus requiring a lower thresholdâwhile mortality appears to escalate at a higher cumulative temperature. Next, the threshold of 58°C identified from all-cause mortality data was used as an example to explore its application in health outcomes. At the same time, limiting the threshold to a mortality-driven metric does mean that non-fatal health outcomes, such as heat-related ED visits or ambulance calls, begin occurring below 58°C (i.e., at 47°C or 42°C). In practice, 79 research aiming to capture all heat impacts may consider exploring the application of morbidity-based thresholds. Subsequent analyses showed that the mortality-based threshold of 58°C could be further refined by separating overnight low (L) and two daytime highs (HH). An overnight low of 15°C emerged as the point above which mortality odds rose significantly, and a sum of two daily highs of 51°C was similarly associated with increased risks. Integrating these, a mortality-based HLH algorithm was formulated: HLH â¥ 58°C and (L â¥ 15°C or HH â¥ 51°C). This multi-trigger structure offers more flexibility than rigid, absolute value cut-offs, as it can capture different patterns of warm days and nights. Applying the HLH threshold algorithm using two- and three-day forecasted temperature data yielded risk patterns consistent with those based on observed temperatures, although forecasted data tended to overestimate risk at higher HLH values (> 73°C) and the frequency of days meeting threshold criteria. Despite minor discrepancies, the HLH metric and threshold algorithm remained robust across different data sources. Additionally, a sensitivity analysis compared the dates caught by the standard HLH absolute thresholds (29°Câ16°Câ29°C) versus those identified by the new cumulative threshold algorithm. Results showed that the absolute thresholds captured only the most extreme eventsânamely those in 2009 and 2021âwhereas the cumulative threshold approach flagged additional periods of moderately high but still hazardous temperatures. 4.1.2 Health impacts of cumulative temperature on subgroups The application of the mortality-based HLH threshold algorithm to subpopulations showed significant disparities in heat-related health outcomes. These findings align with existing 80 literature, demonstrating that the algorithm is sensitive enough to identify different risks across subgroups. Individuals with specific chronic conditions exhibited markedly higher risks. Schizophrenia was associated with the highest mortality during periods of elevated cumulative temperature. Chronic health conditions such as CKD, SUD, COPD, and heart failure also correlated with increased mortality. In terms of morbidity, as measured by heat-related ED visits, the risk profiles altered slightly compared with mortality. Prior diagnosis of Parkinsonâs, heart failure, AMI, COPD, and schizophrenia were linked to substantially higher risks for a heat-related ED visit. The presence of multiple comorbidities also intensified these mortality and morbidity outcomes, as individuals with three or more chronic conditions faced significantly higher heat-related mortality and morbidity risks compared to those with fewer health issues. These results were consistent with the current literature on risk factors for mortality and morbidity outcomes during EHEs49,54,68,72,79â81,83,84. 4.2 Comparison With Existing Literature 4.2.1 Alignment With Other Compound Heat Studies The findings in this research underscore that cumulative temperature metric over two days and a night better aligns with the health burden of extreme heat than metrics based on absolute values. This conclusion aligns with an emerging consensus in the literature that âcompoundâ day-night heat events pose higher mortality and morbidity risks because they offer litter or no relief overnight and increase the total heat load on the human body13,111,112. Without adequate nocturnal cooling, the human body struggles to dissipate stored heat, heightening both cardiovascular and thermoregulatory strain13,130 81 Moreover, the choice of HLH metric aligns with multi-day approaches such as the Excess Heat Factor method used in Australia131. Both frameworks incorporate daytime peaks and overnight lows to capture sustained heat stress over multiple daysâa key factor absent in single-day or single-threshold models. Such approaches better reflect the protracted nature of heat exposure, which has consistently been shown to correlate more strongly with adverse health outcomes than short-lived temperature spikes2,111 4.2.2 Flexibility Versus Rigid Absolute Thresholds Many HHWSs often rely on rigid temperature cut pointsâfor instance, a heat alert is issued if maximum temperature â¥29°C and minimum temperature â¥16°C for two consecutive days1,2. While straightforward, these rigid thresholds treat alerts as all-or-nothing: if one parameter falls marginally below the cut-off, no warning is issued, even if total heat exposure remains dangerously high. In contrast, the HLH algorithm is more dynamic. Rather than fixating on a single pair of temperature cut-offs, the mortality-based threshold algorithm integrates multiple componentsâan HLH of 58°C while an overnight low â¥ 15°C or the sum of two daily highs â¥ 51°C. This structure accommodates diverse temperature patterns that can collectively stress the body. If overnight lows remain moderate, yet two daytime highs in a row are exceptionally warm, HLH still flags elevated risk. Conversely, if daytime highs are only slightly above average but the overnight low stays â¥15°C (eliminating nighttime relief), the algorithm again highlights the heightened biological strain. From a physiological standpoint, this flexibility can improve biological relevance: the bodyâs thermoregulatory burden depends on both the âceilingâ of daily highs and the lack of 82 cooling at night. By capturing those dual pathways to cumulative heat stress, the HLH metric helps reduce false negatives that purely absolute thresholds might miss. Ultimately, it aligns more closely with the real-world thermoregulatory challenges faced by vulnerable individuals, thereby providing a more nuanced and effective decision-making tool for public health interventions. 4.3 Strengths This research developed and tested advanced and innovative methods to assess extreme heat and its health impacts in WMV, addressing critical gaps in existing HHWSs. The study's strengths are multifaceted, including comprehensive data linkage, advanced methods for identifying cumulative heat exposure and thresholds, and detailed subgroup analyses. 4.3.1 Comprehensive Data Linkage A significant strength of this research lies in its integration of diverse and extensive health administrative datasets, thanks to PANDA team. By linking individual mortality data from the BC Vital Statistics Agency with morbidity data from the NACRS, comorbidity information from the CDR, and prescription coverage data from PharmaNet, the study achieved a granular level of analysis. This comprehensive data linkage enabled the examination of health outcomes across multiple dimensionsâtemporal, spatial, and demographicâproviding a rich context for developing cumulative heat thresholds and understanding the multifaceted impacts of EHEs. The ability to analyze individual-level data across different health data enhanced the validity of the findings. It allowed for precise identification of patterns and correlations between cumulative heat exposure and health outcomes, minimizing potential biases associated with 83 aggregated data. Moreover, the inclusion of detailed comorbidity and socioeconomic information facilitated a nuanced understanding of susceptibility and vulnerability factors, which is critical for informing targeted public health interventions. 4.3.2 Advanced Methods for Identifying Cumulative Heat-Risk Thresholds The study employed sophisticated statistical techniques to identify baseline thresholds for both overnight low temperatures and daily high temperatures within HLH, offering a more dynamic framework for HHWSs. This approach could more accurately reflect the true health risks posed by extended periods of high temperatures and capture both moderate and high-risk heat events. 4.3.3 Detailed Subgroup Analyses The detailed subgroup analyses shed light on the disproportionate impact of extreme heat on susceptible populations. Individuals with chronic conditions such as schizophrenia, CKD, heart failure, COPD, and multiple chronic conditions were identified as having markedly higher risks. The exploration of demographic factorsâage, sex, care setting, and socioeconomic indicatorsâhighlighted the intersectionality of susceptibility, emphasizing the need for targeted strategies to protect high-risk groups during extreme heat. 4.4 Limitations and Directions for Future Research While this study provides significant insights into the health impacts of cumulative heat exposure and advances methodologies for identifying heat-health thresholds, certain limitations must be acknowledged. Recognizing these limitations not only contextualizes the findings but 84 also highlights areas where future research can build upon this work to enhance understanding and inform public health strategies more effectively. 4.4.1 Misclassification of Incidence and Heat-Related Outcomes Although this study leveraged administrative data to identify incidence cases in CDR and heat-related ED and ambulance data in NACRS and MPDS, there remains potential for misclassification in these data sources. For instance, incidence cases in the CDR rely on specific billing or diagnostic codes, and patients with late or no contact with the healthcare system might be underrepresented. Moreover, conditions may not be captured in time, leading to delayed or missed registration of new diagnoses. Similarly, heat-related ED visits and ambulance dispatches were identified using a limited set of diagnostic or complaint codes. Patients presenting with non-specific symptoms may not have been flagged as heat-related if clinicians documented different primary reasons for care. Future research should explore enhanced coding protocols, natural language processing of clinical notes, or prospective chart reviews to refine the accuracy of these outcomes and better capture the true burden of heat morbidity. 4.4.2 Mortality-Based Algorithm Versus Morbidity Thresholds This study used the mortality-based (58°C) HLH threshold primarily as the algorithmic example, even though heat-related ED visits (47°C) and ambulance dispatches (42°C) also showed clear thresholds. Morbidity outcomes are important for preventing health-system strain and catching earlier or milder exposures; thus, future research should explore morbidity-focused 85 algorithms and evaluate whether multi-tiered thresholdsâencompassing mortality, ED visits, and ambulance dispatchesâyield more comprehensive HHWS criteria. 4.4.3 Unidentifiable Ambulance Dispatch Data Another limitation of this research is the inability to link individual-level ambulance dispatch data with other health records due to privacy constraints. The MPDS data used were deidentified and could not be connected to patient-specific information such as comorbidities, demographics, or subsequent health outcomes. This limitation restricted the analysis to aggregate-level associations and precluded a detailed examination of how cumulative heat exposure affected individuals requiring emergency services, particularly among susceptible subgroups. Future studies should advocate for policies and protocols that facilitate the secure linkage of ambulance dispatch data with other health records while respecting privacy concerns. Expanding data linkage to include additional health services would capture a broader spectrum of health outcomes and enable a more comprehensive analysis of the acute health impacts of extreme heat. 4.4.4 Exposure Misclassification, Geographical Scope, and Climatic Considerations Another limitation concerns exposure misclassification, particularly given that temperature data were drawn from a single weather station at YVR. Because this station sits on the coast in a relatively unpopulated area, local microclimatesâsuch as denser urban neighborhoods with higher heat-retaining surfacesâmay be under- or overrepresented by these readings. Moreover, populations living further inland can experience significantly warmer 86 conditions and less overnight cooling, introducing spatial heterogeneity in actual exposure. Consequently, the absolute temperature levels used to compute HLH and its thresholds may not perfectly capture on-the-ground temperatures for all WMV residents. In addition, BC HARS uses temperature data from the West Vancouver weather station to forecast conditions for metro Vancouver rather than from YVR, making it difficult to directly compare YVR-derived thresholds with West Vancouverâbased forecasts. The studyâs geographical focus on WMV, characterized by a temperate coastal climate, further limits generalizability to regions with different climatic conditions. The cumulative heat thresholds identified here may not translate well to tropical, arid, or continental climates, where temperature ranges, humidity levels, and baseline population acclimatization differ significantly. Additionally, this study did not incorporate humidity or âapparent temperatureâ metrics like the Humidex, which can strongly influence the physiological impact of heat. This omission means this research cannot fully account for the role of moisture in exacerbating heat stress, a key consideration in many non-temperate regions. Future research should address these limitations by incorporating multi-station networks, satellite data, or personal sensor measurements to reduce exposure misclassification and refine temperature estimates in both coastal and inland areas. Furthermore, conducting comparative studies in diverse regionsâparticularly those with varying levels of humidity, different altitudes, and contrasting urban infrastructuresâwould clarify how HLH metrics and thresholds perform in climates dissimilar to that of southwestern BC. Such efforts could also help develop location-specific HHWSs that capture local meteorological nuances, ensuring that alerts and interventions are appropriately tailored to both local conditions and population vulnerabilities. 87 4.4.5 Limited Socioeconomic and Demographic Data While the study included demographic variables such as age, sex, and a proxy for socioeconomic status through PharmaCare Plan C enrollment, it lacked detailed socioeconomic and racial data. Factors like income, education, occupation, housing conditions, and race or ethnicity are critical determinants of health and potential susceptibility to heat exposure. The absence of granular socioeconomic and demographic data limits the ability to fully assess the social determinants of health that contribute to heat risk and may hinder the identification of at-risk populations requiring targeted interventions. Future research should integrate individual-level socioeconomic data, if possible, to assess their influence on heat risk comprehensively. Collecting and analyzing data on race and ethnicity would help identify and address potential health disparities and environmental justice issues. Using geospatial analyses to examine the effects of urban heat islands and neighborhood characteristics on health outcomes can further elucidate the interplay between environmental and social factors. 4.4.6 Exclusion of Air Pollution The primary focus of this study was on methods development, and it did not account for air pollution, which can mediate the relationship between heat exposure and health outcomes132. High temperatures often coincide with elevated levels of pollutants like ozone and particulate matter, especially during wildfire seasons or in urban areas with significant vehicular emissions. Air pollution can exacerbate respiratory and cardiovascular conditions, potentially amplifying the health impacts of extreme heat. Incorporating air quality data into analyses would control for confounding effects and allow exploration of interactions between heat and pollution. 88 4.5 Conclusions In conclusion, this research advances the understanding of how cumulative heat exposure impacts health outcomes and provides a robust methodological framework and valuable insights for public health policy. The identification and application of dynamic temperature thresholds and the emphasis on cumulative exposure represent significant contributions to heat-health research, offering a more flexible, dynamic, and physiologically relevant basis for HHWSs. The detailed subgroup analyses underscore the importance of tailoring interventions to protect the most susceptible populations during EHEs. 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Epidemiology. 2014;25(2):242-245. doi:10.1097/EDE.0000000000000051 100 Appendices Appendix A Statistical Analysis of Objective 1 A.1 Sub-Appendix: Descriptive Statistics of Daily Maximum and Minimum Temperatures Figure 18 A.1 Histogram of observed daily maximum temperature in WMV °C 101 Figure 19 A.1 Boxplot of observed daily maximum temperature in WMV by year Figure 20 A.1 Histogram of observed daily minimum temperature in WMV °C 102 Figure 21 A.1 Boxplot of observed daily minimum temperature in WMV by year 103 A.2 Sub-Appendix: Descriptive Statistics of Health Outcomes Figure 22 A.2 Histogram of 3-day sum of daily all-cause death count in WMV Figure 23 A.2 Boxplot of 3-day sum of daily all-cause death count by year 104 Figure 24 A.2 Decomposed time series of 3-day sum of daily all-cause death count Figure 25 A.2 Histogram of 3-day sum of daily heat-related ED visits in WMV (year) 105 Figure 26 A.2 Boxplot of 3-day sum of daily heat-related ED visits by year Figure 27 A.2 Decomposed time series of 3-day sum of daily heat-related ED visits (year) 106 Figure 28 A.2 Histogram of 3-day sum of daily heat-related ambulance dispatches in WMV Figure 29 A.2 Boxplot of 3-day sum of daily heat-related ambulance dispatches by year 107 Figure 30 A.2 Decomposed time series of 3-day sum of daily heat-related ambulance dispatches A.3 Sub-Appendix: AICs of Different Knots for Outcomes Figure 31 A.3 AICs across different knots for all-cause mortality (year) 108 Figure 32 A.3 AICs across different knots for heat-related ED visits Figure 33 A.3 AICs across different knots for heat-related ambulance dispatches 109 A.4 Sub-Appendix: Model Diagnostics for Outcomes Figure 34 A.4 Residual plot of HLH for all-cause mortality model Figure 35 A.4 Residual plot of time for all-cause mortality model HLH value (ºC) 110 Figure 36 A.4 QQ plot for all-cause mortality model Figure 37 A.4 Smooth term plots for all-cause mortality model HLH value (ºC) 111 Figure 38 A.4 Residual plot of HLH for heat-related ED visits model Figure 39 A.4 Residual plot of time for heat-related ED visits model HLH value (ºC) 112 Figure 40 A.4 QQ plot for heat-related ED visits model Figure 41 A.4 Smooth term plots for heat-related ED visits model HLH value (ºC) 113 Figure 42 A.4 Residual plot of HLH for heat-related ambulance dispatches model Figure 43 A.4 Residual plot of time for heat-related ambulance dispatches model HLH value (ºC) 114 Figure 44 A.4 QQ plot for heat-related ambulance dispatches model Figure 45 A.4 Smooth term plots for heat-related ambulance dispatches model HLH value (ºC) 115 A.5 Sub-Appendix: Sensitivity Analysis for Objective 1 Table 9 A.5 AICs of temperature metrics modelled against daily health outcomes All-cause mortality Heat-related ED visits Heat-related ambulance dispatches Temperature measure (°C) p-value Above AIC median p-value Above AIC median p-value Above AIC median Daily high <0.001 <0.001 <0.001 Overnight low <0.001 <0.001 <0.001 HL <0.001 <0.001 <0.001 LH <0.001 <0.001 <0.001 HLH <0.001 <0.001 <0.001 LHL <0.001 <0.001 <0.001 HLHL <0.001 <0.001 <0.001 LHLH <0.001 <0.001 <0.001 HLHLH <0.001 <0.001 <0.001 116 Figure 46 A.5 AICs of temperature metrics using 32, 20, and 28 knots for all-cause mortality, heat-related ED visits, and heat-related ambulance dispatches respectively 117 Figure 47 A.5 AICs of temperature metrics using a Poisson distribution for all-cause mortality, heat-related ED visits, and heat-related ambulance dispatches 118 Appendix B Statistical Analysis of Objective 2 B.1 Sub-Appendix: Mortality-Based HLH Algorithm 1 Figure 48 B.1 HLH algorithm plot, using temperature forecast data made 2 days in advance ºC 119 B.2 Sub-Appendix: Mortality-Based HLH Algorithm 2 Figure 49 B.2 HLH algorithm plot, using temperature forecast data made 3 days before ºC 120 B.3 Sub-Appendix: Threshold Analysis of All-Cause ED Visits Figure 50 B.3 ORs of HLH split values in all-cause ED visits ºC 121 B.4 Sub-Appendix: Threshold Analysis of All-Cause Ambulance Dispatches Figure 51 B.4 ORs of HLH split values in all-cause ambulance dispatches ºC 122 Appendix C Results of Analyses on Subgroups C.1 Sub-Appendix: Comorbidity-Specific Mortality Figure 52 C.1 ORs of mortality across comorbidity groups HLH category (ºC) 123 C.2 Sub-Appendix: Multimorbidity-Specific Mortality Figure 53 C.2 ORs of mortality in multimorbidity group HLH category (ºC) 124 C.3 Sub-Appendix: Cardiovascular-Specific Mortality Figure 54 C.3 ORs of mortality in heart-related chronic conditions HLH category (ºC) 125 C.4 Sub-Appendix: Mental-Illness-Specific Mortality Figure 55 C.4 ORs of mortality in schizophrenia and SUD HLH category (ºC) 126 C.5 Sub-Appendix: Other-Chronic-Condition-Specific Mortality Figure 56 C.5 ORs of mortality among COPD, Parkinsonism, CKD, and other chronic conditions HLH category (ºC) 127 C.6 Sub-Appendix: Age-Specific Mortality Figure 57 C.6 ORs of mortality in age group HLH category (ºC) 128 C.7 Sub-Appendix: Location of Deaths Figure 58 C.7 ORs of mortality in location of death HLH category (ºC) 129 C.8 Sub-Appendix: Sex-Specific Mortality Figure 59 C.8 ORs of mortality in sex HLH category (ºC) 130 C.9 Sub-Appendix: PharmaCare-Specific Mortality Figure 60 C.9 ORs of mortality in PharmaCare plan HLH category (ºC) 131 C.10 Sub-Appendix: Age-Comorbidity-Specific Mortality Figure 61 C.10 ORs of mortality in age and comorbidity HLH category (ºC) 132 C.11 Sub-Appendix: Age-Multimorbidity-Specific Mortality Figure 62 C.11 ORs of mortality in age and multimorbidity HLH category (ºC) 133 C.12 Sub-Appendix: Comorbidity-Specific Heat-Related ED Visits Figure 63 C.12 ORs of heat-related ED visits in comorbidity group HLH category (ºC) 134 C.13 Sub-Appendix: Multimorbidity-Specific Heat-Related ED Visits Figure 64 C.13 ORs of heat-related ED visits in multimorbidity group HLH category (ºC) 135 C.14 Sub-Appendix: Cardiovascular-Specific Heat-Related ED Visits Figure 65 C.14 ORs of heat-related ED visits in heart-related chronic conditions HLH category (ºC) 136 C.15 Sub-Appendix: Mental-Illness-Specific Heat-Related ED Visits Figure 66 C.15 ORs of heat-related ED visits in schizophrenia and SUD HLH category (ºC) 137 C.16 Sub-Appendix: Other-Chronic-Condition-Specific Heat-Related ED Visits Figure 67 C.16 ORs of heat-related ED visits among COPD, Parkinsonism, CKD, and other chronic conditions HLH category (ºC) 138 C.17 Sub-Appendix: Age-Specific Heat-Related ED Visits Figure 68 C.17 ORs of heat-related ED visits in age group HLH category (ºC) 139 C.18 Sub-Appendix: Sex-Specific Heat-Related ED Visits Figure 69 C.18 ORs of heat-related ED visits in sex HLH category (ºC) 140 C.19 Sub-Appendix: PharmaCare-Specific Heat-Related ED Visits Figure 70 C.19 ORs of heat-related ED visits in PharmaCare plan HLH category (ºC) 141 C.20 Sub-Appendix: Acuity-Specific Heat-Related ED Visits Figure 71 C.20 ORs of heat-related ED visits in acuity HLH category (ºC) 142 C.21 Sub-Appendix: Age-Comorbidity-Specific Heat-Related ED Visits Figure 72 C.21 ORs of heat-related ED visits in age and comorbidity HLH category (ºC) 143 C.22 Sub-Appendix: Age-Multimorbidity-Specific Heat-Related ED Visits Figure 73 C.22 ORs of heat-related ED visits in age and multimorbidity HLH category (ºC) 144 C.23 Sub-Appendix: Acuity-Specific Heat-Related Ambulance Dispatches Figure 74 C.23 ORs of heat-related ambulance dispatches in acuity HLH category (ºC) EVALUATING CUMULATIVE TEMPERATURE INDICATORS FOR HEAT ALERT AND RESPONSE SYSTEMS: A CASE STUDY IN VANCOUVER, BRITISH COLUMBIA by Alexi Tianyang Hu M.A., The University of Victoria, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Population and Public Health) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) February 2025 © Alexi Tianyang Hu, 2025 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Evaluating cumulative temperature indicators for heat alert and response systems: a case study in Vancouver, British Columbia submitted by Alexi Tianyang Hu in partial fulfilment of the requirements for the degree of Master of Science in Population and Public Health Examining Committee: Michael Brauer, Professor, Population and Public Health, UBC Co-Supervisor Sarah Henderson, Professor, Population and Public Health, UBC Co-supervisor Eric Lavigne, Senior Scientist, Health Canada Supervisory Committee Member Rachel White, Assistant Professor, Earth, Ocean and Atmospheric Sciences, UBC Additional Examiner iii Abstract Extreme heat events (EHEs) are escalating in frequency, intensity, and duration due to climate change, posing significant health risks. To mitigate their impacts, many countries, including Canada, have established heat-health warning systems (HHWSs). However, these systems often rely on absolute temperature thresholds that require all conditions to be met simultaneously. This rigidity can overlook different sequences of day-night heat that accumulate over multiple days, potentially leading to missed warnings. In this study, seven cumulative temperature metrics were created by summing daily high and overnight low temperatures over one to three days. To assess each metricâs relationship with all-cause mortality, heat-related ED visits, and ambulance dispatches in Western Metro Vancouver (WMV) from 2008-2023, generalized additive models with negative binomial distributions were fitted. The statistical performance of the metrics was evaluated using significance tests, the Akaike Information Criterion (AIC), and forecasting accuracy. All cumulative metrics demonstrated stable and comparable statistical performance. Given its applicability, the High+Low+High (HLH) metricâsumming two daily highs and one overnight lowâwas selected as an example for further analyses. A split value analysis was performed by dividing HLH into one hundred equally spaced intervals and creating a binary variable for each interval, refitting a model to determine the odds ratio (OR) at each split. Points at which the OR for the three outcomes became significantly greater than 1.0 were designated as baseline thresholds. 58°C (all-cause mortality), 47°C (ED visits), and 42°C (ambulance dispatches) were identified as the baseline points. Using the mortality-based threshold as a template, further âseparate baseline thresholdsâ of 51°C and 15°C were identified for two daily highs (HH) and overnight low (L) respectively. Accordingly, an iv HLH threshold algorithm was formulated such that the OR of mortality exceeds 1.0 when HLH â¥ 58°C and either L â¥ 15°C or HH â¥ 51°C. Subgroup analyses demonstrated elevated risks among individuals with cardiovascular, renal, or respiratory conditions, mental illnesses, multiple comorbidities, or lower socioeconomic status. Overall, this study developed methods for a more dynamic and flexible HHWS framework to issue heat alerts, offering a multi-trigger approach that can better account for cumulative day-night heat sequences. v Lay Summary Traditional heat warning systems often use stringent criteria to issue alerts, which may not fully reflect the dangers of heat exposure. This study looked at how combined temperatures over several days affect health in Western Metro Vancouver. High+Low+High (HLH)âsumming two daily highs with one overnight lowâwas selected as the metric. By analyzing health data from western Metro Vancouver (2008â2023), the study pinpointed key temperature cutoffsâ58°C for deaths, 47°C for emergency visits, and 42°C for ambulance callsâabove which the likelihood of heat-related risks rose notably. Using the cutoff of 58°C for deaths, more detailed thresholds emerged for overnight low (15°C) and daytime highs (51°C) that further clarified how people are affected by intense, multi-day heat. Certain groups, such as individuals with multiple or severe health conditions, older adults, and those facing economic challenges, were at especially high risk. vi Preface All work presented in this thesis was designed, carried out, and analyzed by Alexi Hu, under the guidance of co-supervisors Dr. Sarah Henderson and Dr. Michael Brauer, and committee member Dr. Ãric Lavigne. The external examiner, Dr. Rachel White also reviewed and provided feedback for this thesis. The temperature data were extracted from Environment and Climate Change Canada by Kathleen McLean, a senior epidemiologist at the BC Centre for Disease Control. This thesis received approval from the University of British Columbia Research Ethics Board (Certificate number: H23-01938), and permission to access the health data was granted by the BCCDC PANDA team. Generative artificial intelligence (AI) was used solely for proofreading the draft and was not involved in any other aspect of the work presented. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ................................................................................................................................ v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables .............................................................................................................................. xiv List of Figures .............................................................................................................................. xv List of Equations ........................................................................................................................ xix List of Abbreviations .................................................................................................................. xx Acknowledgements ................................................................................................................... xxii Dedication ................................................................................................................................. xxiii Chapter 1: Introduction and Literature Review........................................................................ 1 1.1 Problem Statement........................................................................................................ 1 1.2 Extreme Heat Events .................................................................................................... 2 1.2.1 Climate Change and EHEs: a Global and Canadian Perspective ............................... 3 1.3 EHEs and Health........................................................................................................... 6 1.3.1 Human Heat Physiology ............................................................................................. 6 1.3.2 Heath Impacts of EHEs ............................................................................................... 8 1.3.2.1 Mortality ............................................................................................................. 8 1.3.2.2 Morbidity .......................................................................................................... 10 viii 1.3.2.3 Susceptibility and Vulnerability to Heat ........................................................... 11 1.3.2.3.1 Susceptibility Factors .................................................................................... 11 1.3.2.3.2 Vulnerability Factors .................................................................................... 13 1.4 Heat Health Warning Systems (HHWSs) ................................................................. 14 1.4.1 An Overview of HHWSs .......................................................................................... 15 1.4.2 Effectiveness of HHWSs .......................................................................................... 19 1.4.3 Heat Alert and Response System (HARS) in BC ..................................................... 21 1.5 Gaps in Knowledge and Study Rationale ................................................................. 22 1.6 Study Aims ................................................................................................................... 23 Chapter 2: Methods .................................................................................................................... 26 2.1 Study Context .............................................................................................................. 26 2.2 Data Sources ................................................................................................................ 26 2.2.1 Temperature .............................................................................................................. 28 2.2.2 Vital Statistics ........................................................................................................... 28 2.2.3 Chronic Disease Registries (CDRs) .......................................................................... 28 2.2.4 National Ambulatory Care Reporting System (NACRS) ......................................... 29 2.2.5 Medical Priority Dispatch System (MPDS) .............................................................. 30 2.2.6 PharmaNet................................................................................................................. 30 2.3 Data Collection and Linkage...................................................................................... 31 2.3.1 Western Metro Vancouver (WMV) Spatial Health Boundaries ............................... 31 2.3.2 Data Linkage Between Vital Statistics and CDR ..................................................... 33 ix 2.3.3 Data Linkage Between NACRS and CDR................................................................ 33 2.3.4 Linking PharmaNet with Vital Statistics and NACRS ............................................. 34 2.4 Data Analysis ............................................................................................................... 35 2.4.1 Objective 1 ................................................................................................................ 35 2.4.1.1 Outcome Measures............................................................................................ 35 2.4.1.2 Exposure Measures ........................................................................................... 35 2.4.1.3 Time Series Covariates ..................................................................................... 36 2.4.1.4 Statistical Analysis ............................................................................................ 37 2.4.1.4.1 Statistical Performance of the Metrics .......................................................... 39 2.4.1.4.2 Operational Considerations ........................................................................... 40 2.4.2 Objective 2 ................................................................................................................ 40 2.4.2.1 Outcome and Exposure Measures ..................................................................... 41 2.4.2.2 Statistical Analysis: ........................................................................................... 41 2.4.3 Objective 3 ................................................................................................................ 44 2.4.3.1 Outcome and Exposure Measures ..................................................................... 44 2.4.3.2 Statistical Analysis ............................................................................................ 44 Chapter 3: Results....................................................................................................................... 47 3.1 Data Summary ............................................................................................................ 47 3.2 Temperature Metric Evaluation ................................................................................ 48 3.2.1 A GAM with Negative Binomial Distribution.......................................................... 54 3.2.2 Selection of Spline Function ..................................................................................... 55 3.2.3 Temperature Metric Selection................................................................................... 55 x 3.2.3.1 Statistical Performance ..................................................................................... 55 3.2.3.2 Operational Considerations ............................................................................... 56 3.2.4 Model diagnostics ..................................................................................................... 57 3.2.5 Sensitivity analysis.................................................................................................... 58 3.3 Identifying HLH Thresholds ...................................................................................... 59 3.4 Mortality-Based Threshold Algorithm: An Example of Threshold Application .. 60 3.4.1 Identifying L Threshold ............................................................................................ 60 3.4.2 Identifying HH Threshold ......................................................................................... 61 3.4.3 Mortality-Based HLH Threshold Algorithm ............................................................ 65 3.4.3.1 Algorithm Sensitivity Analysis 1 ...................................................................... 65 3.4.3.2 Algorithm Sensitivity Analysis 2 ...................................................................... 66 3.5 Extra Split Value Analysis on All-Cause ED Visits and Ambulance Dispatches .. 67 3.6 Application of Mortality-Based HLH Algorithm to Subgroups ............................. 71 3.6.1 Chronic Condition-Specific Outcomes ..................................................................... 71 3.6.2 Condition Groups and Multimorbidity ..................................................................... 72 3.6.3 Demographic-Related Mortality and Morbidity ....................................................... 75 3.6.4 Acuity of Heat-Related ED Visits............................................................................. 77 Chapter 4: Discussion and Conclusions .................................................................................... 78 4.1 Summary of Findings ................................................................................................. 78 4.1.1 Methods to Identify Cumulative Temperature Thresholds ....................................... 78 4.1.2 Health impacts of cumulative temperature on subgroups ......................................... 79 xi 4.2 Comparison With Existing Literature ...................................................................... 80 4.2.1 Alignment With Other Compound Heat Studies ...................................................... 80 4.2.2 Flexibility Versus Rigid Absolute Thresholds .......................................................... 81 4.3 Strengths ...................................................................................................................... 82 4.3.1 Comprehensive Data Linkage ................................................................................... 82 4.3.2 Advanced Methods for Identifying Cumulative Heat-Risk Thresholds ................... 83 4.3.3 Detailed Subgroup Analyses ..................................................................................... 83 4.4 Limitations and Directions for Future Research ..................................................... 83 4.4.1 Misclassification of Incidence and Heat-Related Outcomes .................................... 84 4.4.2 Mortality-Based Algorithm Versus Morbidity Thresholds....................................... 84 4.4.3 Unidentifiable Ambulance Dispatch Data ................................................................ 85 4.4.4 Exposure Misclassification, Geographical Scope, and Climatic Considerations ..... 85 4.4.5 Limited Socioeconomic and Demographic Data ...................................................... 87 4.4.6 Exclusion of Air Pollution ........................................................................................ 87 4.5 Conclusions .................................................................................................................. 88 Bibliography ................................................................................................................................ 89 Appendices ................................................................................................................................. 100 Appendix A Statistical Analysis of Objective 1 .................................................................. 100 A.1 Sub-Appendix: Descriptive Statistics of Daily Maximum and Minimum Temperatures....................................................................................................................... 100 A.2 Sub-Appendix: Descriptive Statistics of Health Outcomes .................................... 103 A.3 Sub-Appendix: AICs of Different Knots for Outcomes ......................................... 107 xii A.4 Sub-Appendix: Model Diagnostics for Outcomes .................................................. 109 A.5 Sub-Appendix: Sensitivity Analysis for Objective 1 .............................................. 115 Appendix B Statistical Analysis of Objective 2 .................................................................. 118 B.1 Sub-Appendix: Mortality-Based HLH Algorithm 1 ............................................... 118 B.2 Sub-Appendix: Mortality-Based HLH Algorithm 2 ............................................... 119 B.3 Sub-Appendix: Threshold Analysis of All-Cause ED Visits .................................. 120 B.4 Sub-Appendix: Threshold Analysis of All-Cause Ambulance Dispatches ............ 121 Appendix C Results of Analyses on Subgroups ................................................................. 122 C.1 Sub-Appendix: Comorbidity-Specific Mortality .................................................... 122 C.2 Sub-Appendix: Multimorbidity-Specific Mortality ................................................ 123 C.3 Sub-Appendix: Cardiovascular-Specific Mortality ................................................ 124 C.4 Sub-Appendix: Mental-Illness-Specific Mortality ................................................. 125 C.5 Sub-Appendix: Other-Chronic-Condition-Specific Mortality ................................ 126 C.6 Sub-Appendix: Age-Specific Mortality .................................................................. 127 C.7 Sub-Appendix: Location of Deaths ........................................................................ 128 C.8 Sub-Appendix: Sex-Specific Mortality................................................................... 129 C.9 Sub-Appendix: PharmaCare-Specific Mortality ..................................................... 130 C.10 Sub-Appendix: Age-Comorbidity-Specific Mortality ............................................ 131 C.11 Sub-Appendix: Age-Multimorbidity-Specific Mortality ........................................ 132 C.12 Sub-Appendix: Comorbidity-Specific Heat-Related ED Visits ............................. 133 C.13 Sub-Appendix: Multimorbidity-Specific Heat-Related ED Visits ......................... 134 C.14 Sub-Appendix: Cardiovascular-Specific Heat-Related ED Visits .......................... 135 xiii C.15 Sub-Appendix: Mental-Illness-Specific Heat-Related ED Visits ........................... 136 C.16 Sub-Appendix: Other-Chronic-Condition-Specific Heat-Related ED Visits ......... 137 C.17 Sub-Appendix: Age-Specific Heat-Related ED Visits ........................................... 138 C.18 Sub-Appendix: Sex-Specific Heat-Related ED Visits ............................................ 139 C.19 Sub-Appendix: PharmaCare-Specific Heat-Related ED Visits .............................. 140 C.20 Sub-Appendix: Acuity-Specific Heat-Related ED Visits ....................................... 141 C.21 Sub-Appendix: Age-Comorbidity-Specific Heat-Related ED Visits ..................... 142 C.22 Sub-Appendix: Age-Multimorbidity-Specific Heat-Related ED Visits ................. 143 C.23 Sub-Appendix: Acuity-Specific Heat-Related Ambulance Dispatches .................. 144 xiv List of Tables Table 1 Examples of different heatwave definitions and heat-health warning systems ............... 17 Table 2 List of comorbidity categories and 25 chronic conditions ............................................... 29 Table 3 Western Metro Vancouver Local Health Areas and Hospitals ........................................ 32 Table 4 Subgroups within each health outcome ........................................................................... 45 Table 5 Descriptive statistics of outcome variables ...................................................................... 49 Table 6 Descriptive statistics of temperature metrics ................................................................... 53 Table 7 Dispersion and normality tests of outcomes .................................................................... 54 Table 8 Results of the temperature metric evaluation .................................................................. 57 Table 10 A.5 AICs of temperature metrics modelled against daily health outcomes ................. 115 xv List of Figures Figure 1 Global average temperature from 1850 â 2023 by Berkeley Earth .................................. 3 Figure 2 Annual average temperature change in Canada between 1948 and 2016 ........................ 5 Figure 3 Current HAAs in the BC HARS and map of WMV ...................................................... 27 Figure 4 Flow chart of data collection .......................................................................................... 32 Figure 5 Inclusion criteria for PharmaCare plans ......................................................................... 34 Figure 6 Schematic of split value analysis .................................................................................... 42 Figure 7 Odds ratios of High-Low-High split values in all-cause mortality ................................ 59 Figure 8 Odds ratios of High-Low-High split values in heat-related ED visits and ambulance dispatches ...................................................................................................................................... 60 Figure 9 Odds ratios of High-Low-High values for all-cause mortality ....................................... 62 Figure 10 Odds ratios of overnight low groups when High-Low-High >= 58, against High-Low-High < 58 ...................................................................................................................................... 63 Figure 11 Odds ratios of daily highs when High-Low-High >= 58 and L < 15, compared with High-Low-High < 58 .................................................................................................................... 64 Figure 12 Comparison of Dates Meeting Absolute and Cumulative High-Low-High Thresholds....................................................................................................................................................... 67 Figure 13 High-Low-High algorithm plot, using observed temperature data .............................. 69 Figure 14 High-Low-High algorithm plot, using observed and forecast temperature data .......... 70 Figure 15 Odds ratios of Morbidity and Mortality Across Chronic Conditions at HLH >= 75°C & (HH >= 51°C | L >= 15°C) ........................................................................................................... 73 Figure 16 Odds ratios of Morbidity and Mortality Across Comorbidity Groups at HLH >= 75°C & (HH >= 51°C | L >= 15°C) ....................................................................................................... 74 xvi Figure 17 Odds ratios of Morbidity and Mortality Across Demographic Groups at HLH >= 75°C & (HH >= 51°C | L >= 15°C) ....................................................................................................... 76 Figure 18 A.1 Histogram of observed daily maximum temperature in WMV ........................... 100 Figure 19 A.1 Boxplot of observed daily maximum temperature in WMV by year .................. 101 Figure 20 A.1 Histogram of observed daily minimum temperature in WMV............................ 101 Figure 21 A.1 Boxplot of observed daily minimum temperature in WMV by year ................... 102 Figure 22 A.2 Histogram of 3-day sum of daily all-cause death count in WMV ....................... 103 Figure 23 A.2 Boxplot of 3-day sum of daily all-cause death count by year ............................. 103 Figure 24 A.2 Decomposed time series of 3-day sum of daily all-cause death count ................ 104 Figure 25 A.2 Histogram of 3-day sum of daily heat-related ED visits in WMV ...................... 104 Figure 26 A.2 Boxplot of 3-day sum of daily heat-related ED visits by year ............................ 105 Figure 27 A.2 Decomposed time series of 3-day sum of daily heat-related ED visits ............... 105 Figure 28 A.2 Histogram of 3-day sum of daily heat-related ambulance dispatches in WMV .. 106 Figure 29 A.2 Boxplot of 3-day sum of daily heat-related ambulance dispatches by year ........ 106 Figure 30 A.2 Decomposed time series of 3-day sum of daily heat-related ambulance dispatches..................................................................................................................................................... 107 Figure 31 A.3 AICs across different knots for all-cause mortality ............................................. 107 Figure 32 A.3 AICs across different knots for heat-related ED visits ........................................ 108 Figure 33 A.3 AICs across different knots for heat-related ambulance dispatches .................... 108 Figure 34 A.4 Residual plot of HLH for all-cause mortality model ........................................... 109 Figure 35 A.4 Residual plot of time for all-cause mortality model ............................................ 109 Figure 36 A.4 QQ plot for all-cause mortality model ................................................................. 110 Figure 37 A.4 Smooth term plots for all-cause mortality model ................................................ 110 xvii Figure 38 A.4 Residual plot of HLH for heat-related ED visits model ...................................... 111 Figure 39 A.4 Residual plot of time for heat-related ED visits model ....................................... 111 Figure 40 A.4 QQ plot for heat-related ED visits model ............................................................ 112 Figure 41 A.4 Smooth term plots for heat-related ED visits model ........................................... 112 Figure 42 A.4 Residual plot of HLH for heat-related ambulance dispatches model .................. 113 Figure 43 A.4 Residual plot of time for heat-related ambulance dispatches model ................... 113 Figure 44 A.4 QQ plot for heat-related ambulance dispatches model ........................................ 114 Figure 45 A.4 Smooth term plots for heat-related ambulance dispatches model ....................... 114 Figure 46 A.5 AICs of temperature metrics using 32, 20, and 28 knots for all-cause mortality, heat-related ED visits, and heat-related ambulance dispatches respectively .............................. 116 Figure 47 A.5 AICs of temperature metrics using a Poisson distribution for all-cause mortality, heat-related ED visits, and heat-related ambulance dispatches .................................................. 117 Figure 48 B.1 HLH algorithm plot, using temperature forecast data made 2 days in advance .. 118 Figure 49 B.2 HLH algorithm plot, using temperature forecast data made 3 days before ......... 119 Figure 50 B.3 ORs of HLH split values in all-cause ED visits................................................... 120 Figure 51 B.4 ORs of HLH split values in all-cause ambulance dispatches .............................. 121 Figure 52 C.1 ORs of mortality across comorbidity groups ....................................................... 122 Figure 53 C.2 ORs of mortality in multimorbidity group ........................................................... 123 Figure 54 C.3 ORs of mortality in heart-related chronic conditions .......................................... 124 Figure 55 C.4 ORs of mortality in schizophrenia and SUD ....................................................... 125 Figure 56 C.5 ORs of mortality among COPD, Parkinsonism, CKD, and other chronic conditions..................................................................................................................................................... 126 Figure 57 C.6 ORs of mortality in age group ............................................................................. 127 xviii Figure 58 C.7 ORs of mortality in location of death .................................................................. 128 Figure 59 C.8 ORs of mortality in sex ........................................................................................ 129 Figure 60 C.9 ORs of mortality in PharmaCare plan.................................................................. 130 Figure 61 C.10 ORs of mortality in age and comorbidity .......................................................... 131 Figure 62 C.11 ORs of mortality in age and multimorbidity ...................................................... 132 Figure 63 C.12 ORs of heat-related ED visits in comorbidity group ......................................... 133 Figure 64 C.13 ORs of heat-related ED visits in multimorbidity group ..................................... 134 Figure 65 C.14 ORs of heat-related ED visits in heart-related chronic conditions .................... 135 Figure 66 C.15 ORs of heat-related ED visits in schizophrenia and SUD ................................. 136 Figure 67 C.16 ORs of heat-related ED visits among COPD, Parkinsonism, CKD, and other chronic conditions ....................................................................................................................... 137 Figure 68 C.17 ORs of heat-related ED visits in age group ....................................................... 138 Figure 69 C.18 ORs of heat-related ED visits in sex .................................................................. 139 Figure 70 C.19 ORs of heat-related ED visits in PharmaCare plan............................................ 140 Figure 71 C.20 ORs of heat-related ED visits in acuity ............................................................. 141 Figure 72 C.21 ORs of heat-related ED visits in age and comorbidity ...................................... 142 Figure 73 C.22 ORs of heat-related ED visits in age and multimorbidity .................................. 143 Figure 74 C.23 ORs of heat-related ambulance dispatches in acuity ......................................... 144 xix List of Equations Equation 1 Generalized additive model formula .......................................................................... 38 Equation 2 Equation of odds ratio ................................................................................................ 38 Equation 3 Root mean square error .............................................................................................. 39 Equation 4 HLH algorithm ........................................................................................................... 43 xx List of Abbreviations AIC: Akaike Information Criterion AMI: Acute Myocardial Infarction BC: British Columbia BC HEAT Committee: British Columbia Health Effects of Anomalous Temperatures Coordinating Committee BCCDC: BC Centre for Disease Control CDR: Chronic Disease Registry CIs: Confidence Intervals CKD: Chronic Kidney Disease COPD: Chronic Obstructive Pulmonary Disease ECCC: Environment and Climate Change Canada ED: Emergency Department EHE: Extreme Heat Event EMPI: Enterprise Master Patient Index GAM: Generalized Additive Model HAA: Heat Alert Area HARS: Heat Alert and Response System HHWS: Heat-Health Warning System LHAs: Local Health Areas MPDS: Medical Priority Dispatch System NACRS: National Ambulatory Care Reporting System NOAA: National Oceanic and Atmospheric Administration xxi OR: Odds Ratio PANDA: PHSA Platform for Analytics and Data PHSA: Provincial Health Services Authority Q-Q plot: Quantile-Quantile Plot RMSE: Root Mean Square Error SES: Socioeconomic Status SUD: Substance Use Disorder WGBT: Wet-Bulb Globe Temperature WHO: World Health Organization WMV: Western Metro Vancouver xxii Acknowledgements I had the privilege of completing this thesis while living, studying, and working on the traditional, ancestral, and unceded territories of the Coast Salish Peoples, specifically the Sḵwx̱wú7mesh, SÉlÌílwÉtaÊ/Selilwitulh, and xÊ·mÉÎ¸kÊ·ÉyÌÉm Nations. As this thesis examined extreme heat in the era of climate change, it is crucial to acknowledge that Indigenous peoples on Turtle Island and across the globe are at the forefront of preserving biodiversity and combating climate change. They are also vulnerable to its impacts due to environmental racism, colonialism, and their traditional ways of living and thriving. I extend my deepest gratitude to my supervisors, Dr. Michael Brauer and Dr. Sarah Henderson. Thank you, Dr. Brauer, for guiding me patiently and kindly throughout my graduate school journey, and for sharing your incredibly broad expertise and academic curiosity. Thank you, Dr. Henderson, for teaching me how to be a proficient data analyst and an applied public health researcher, and for dedicating so much of your time and energy to guiding my research and work. I am also grateful to Dr. Ãric Lavigne for taking time from your busy schedule to provide valuable feedback on this thesis. Special thanks to Kathleen McLean and Dr. Michael Lee from the BCCDC for their support with administrative data linkage. I also appreciate the BC Ministry of Health and the PANDA team for providing access to a world-class health database. Last but not least, I would like to thank my parents, friends, and my partner Sya, for their love, support, and encouragement. I am incredibly fortunate to have you all in my life and I cherish all the moments we share. xxiii Dedication For Zhang Xiaobo and Hu Xinming, my mother and father, who always believe in me, support me, and love me unconditionally. You have taught me to be a kind person whose passion is tied to helping others and contributing to community and society. You have encouraged me to disregard traditional social norms and to follow my heart and dreams. You have trusted and supported me in all my wild and unconventional life decisions and journeys, including the decision to completely switch careers and apply to UBC for a science degree. Thank you.1 Chapter 1: Introduction and Literature Review 1.1 Problem Statement Extreme heat events (EHEs), defined as prolonged excessive heat, are increasing in frequency, intensity, and duration due to climate change, posing significant health risks globally. To help mitigate the impact of EHEs, many countries, including Canada, have implemented heat-health warning systems (HHWSs) based on the link between meteorological parameters and public health impacts1â3. One example is the Heat Alert and Response System (HARS) in British Columbia (BC), which was first established after the catastrophic 2021 EHE3. The province-wide HARS replaced the regional HARS that had been established for Metro Vancouver after a severe EHE in 20093. Many HHWSs rely on fixed absolute temperature thresholds to issue heat alerts, requiring that specific conditionsâsuch as two maximum daytime temperatures and minimum overnight temperature in BC HARSâbe met at the same time3. While straightforward, this rigid approach can fail to capture the cumulative heat load imposed by multiple warm days and nights. For instance, if any single condition is not met, no alert is issued, even if different combinations of temperature sequences lead to comparable levels of risk from the overall heat exposure. Consequently, some hazardous scenarios may go unaddressed because they do not fit a single, static definition of extreme heat. This study examined whether a more dynamic and flexible methodâincorporating the sum of daytime and overnight temperatures and considering the different combinations of temperature sequencesâcould align with real-world health outcomes. This study focused on Western Metro Vancouver (WMV), BCâs most populous region, which was severely impacted during the 2009 and 2021 heatwaves. It evaluated multiple cumulative temperature metrics that 2 sum daily maximum and minimum temperatures over 1â3 days and examined their associations with all-cause mortality, heat-related emergency department (ED) visits, and ambulance dispatches during the summer months from 2008 to 2023. The statistical performance of the metrics was evaluated using significance tests, the Akaike Information Criterion (AIC), and forecasting accuracy. The selection of the metric was not only based on its statistical performance but also with the operational feasibility for local HHWSs. Baseline thresholds for three health outcomes were established using the selected metric where ORs exceeded 1.0. Furthermore, to explore the broader applicability of the cumulative temperature metric, a multi-component threshold algorithm was developed based on all-cause mortality. This algorithm considered the distinct risks associated with cumulative heat exposure and the contributions of different daily highs and overnight low separately, allowing multiple routes to an alert condition if certain cut-offs are reached. The algorithm was subsequently tested across mortality and morbidity subgroups to evaluate its applicability. The findings aim to advance cumulative heat exposure assessment methods and propose a more nuanced and dynamic HHWS framework. This framework could contribute to local heat-health policies in response to rising temperatures and serve as a model for urban areas worldwide facing similar climate challenges. 1.2 Extreme Heat Events EHEs, often referred to as heatwaves, are prolonged periods of excessive heat. Although there is no universally accepted definition of an EHE, it is generally characterized as a period of abnormally elevated ambient temperatures relative to a locationâs typical climate that persists for multiple days 2,4,5. 3 From a meteorological perspective, there are two primary types of EHEs: dry and moist2. Dry EHEs occur under stable weather patterns with high-pressure systems, clear skies, high solar radiation, and stagnant air masses, typically in continental or Mediterranean climates, such as BC, Canada, where air warms adiabatically2,6. On the other hand, moist EHEs involve the advection of persistent hot and humid air, common in mid-latitude temperate and maritime climates2,6,7. 1.2.1 Climate Change and EHEs: a Global and Canadian Perspective Since the Industrial Revolution, human activitiesâmainly fossil fuel combustionâhave significantly altered global weather patterns and temperatures, marking the onset of climate change. Currently, the atmospheric concentration of carbon dioxide (CO2), the primary greenhouse gas driving global warming, is approximately 427 parts per million (ppm), as recorded by the Maun Loa Observatory of the U.S. National Oceanic and Atmospheric Administration (NOAA)8. The last time Earth experienced CO2 concentrations at this level was during the Pliocene Epoch, around 3 to 5 millions years ago, when global average temperatures were roughly 2-3 °C higher than today9. As a result, the ongoing atmospheric changes have already led to an average surface temperature rise of 1.1°C since the late 1800s, reaching a level of warmth unseen in over 100,000 years 10. The global mean temperature in 2023 was 1.54°C above the 1850-1900 average (Figure 1) 11. Figure 1 Global average temperature from 1850 â 2023 by Berkeley Earth 4 * This figure was obtained from Berkeley Earth Global Temperature Report for 2023 under the terms of the Creative Commons BY-NC 4.0 copyright license for any non-commercial purpose. Globally, the increase in temperature has led to a rise in the frequency, intensity, and duration of EHEs, posing significant threats to human society and public health12,13. A global study found that urban exposure to extreme heat surged by nearly 200% from 1983 to 2016, affecting as many as 1.7 billion people14. Over the past two decades, notable EHEs, such as those in Western Europe (2003), India (2010), Russia (2010), and Pakistan-India (2015), have led to tens of thousands of excess deaths15â18 (Error! Reference source not found. Error! Reference source not found.). The escalating incidence of EHEs exacerbates heat-related illnesses and deaths, aggravates pre-existing health conditions, strains healthcare systems, and disrupts daily activities and economic productivity, highlighting the broad societal and health impacts of these events 10,13,14,19,20. These direct and substantial adverse effects position EHEs among the most impactful climate-related hazards on health and society10,19â21. In Canada, temperatures are rising at a rate faster than the global average due to its extensive northern landmass. From 1948 to 2016, the national temperature increased by 1.7°Câ5 roughly double the global averageâshowing significant amplification in warming largely attributed to temperature feedbacks and surface albedo (Figure 2)22â24. During the same period, BC experienced a temperature rise of 1.9°C, with the annual number of days exceeding 30°C having increasing by an average of 3.3 days23. This regional disparity underscores the unique climatic challenges faced by northern latitudes, where warming is intensified by the loss of reflective ice and snow cover, leading to further absorption of solar radiation and continued temperature increases. Figure 2 Annual average temperature change in Canada between 1948 and 2016 * This figure was acquired from Canadaâs Changing Climate Report led by ECCC While the climate of BC has historically been temperate, the province has experienced two deadly heatwaves in the past fifteen years. The 2009 heatwave saw temperatures peak at 34.4°C in Vancouver, resulting in over 100 excess deaths25,26. More recently, the unprecedented 2021 heat dome shattered over 100 temperature records, with a national temperature record of 6 49.6°C in Lytton, which was destroyed by a wildfire the following day27. This catastrophic EHE was associated with an estimated 740 excess deaths across BC over an eight-day period, with the BC Coroners Service attributing 619 deaths directly to heat. Notably, the majority of these deaths occurred in the western and Fraser regions of greater Vancouver27â29. 1.3 EHEs and Health Due to ongoing climate change, EHEs present an urgent and growing societal and public health challenge. Over the last few decades, the most direct health impact of EHEs has been tens of thousands of excess deaths worldwide. Beyond the direct effects of heat, a multitude of health and societal factors exacerbate the impacts of EHEs on public health, such as pre-existing medical conditions, age, acclimatization, the urban heat island effect, income disparities, and social isolation. 1.3.1 Human Heat Physiology Heat illness generally occurs when the body is unable to maintain a core temperature of 37°C through thermoregulation and can rapidly progress to injury and death if the core temperature rises above 39°C due to excessive heat exposure30,31. In fact, the human body is highly sensitive to temperature change; even a 1ºC increase can be immediately detected by thermoreceptors distributed throughout the body32. When the hypothalamic thermoregulatory center receives signals from these thermoreceptors, it activates cutaneous vasodilation and sweating to facilitate heat dissipation32. Cutaneous vasodilation increases blood flow to the skin and boosts cardiac output, putting a strain on the cardiovascular system by redirecting blood from other vascular areas such 7 as the abdominal cavity33. Sweating can produce up to two liters per hour of sweat rich in sodium and potassium, which further stresses the cardiovascular system if fluids are not adequately replenished34. The human body also employs complementary mechanisms to reduce heat gain, such as inhibiting shivering and lowering cellular metabolic heat production34. The human body can acclimatize to a hot environment through physiological adjustments that improve heat tolerance over two to six weeks. Key adaptations include increased stroke volume, decreased heart rate, expanded plasma volume, and improved renal function, all of which reduce cardiac strain34. Additionally, sweating initiates at lower temperatures, in greater volumes, and with less sodium, which improves heat dissipation and reduces dehydration34. These adaptations, however, can diminish after weeks without heat exposure, and complete long-term acclimatization to a hot environment may take years35,36. As a result, the timing of an EHE is critical; early summer events can be more impactful due to insufficient heat acclimatization2,37. Furthermore, from a biological perspective, the total heat load accumulated over time â rather than just peak temperature â plays a crucial role in determining the level of heat stress on the body38. Prolonged exposure to high ambient temperatures forces cells to operate under conditions of increased oxidative stress, wherein the overproduction of reactive oxygen species damages cellular components such as lipids, proteins, and DNA39,40. Simultaneously, excessive heat can prompt protein misfolding and aggregation, overwhelming the cellâs chaperone-mediated repair systems and triggering the heat-shock response41,42. Over time, these insults set off an escalating cascade of events that culminate in a systemic inflammatory response, as pro-inflammatory mediators (e.g., cytokines) are released in an attempt to contain and repair the damage43,44. If heat exposure remains prolonged or is repeated without sufficient recovery 8 periods, this chronic inflammatory state can compromise multiple organ systems, exacerbating injury and potentially leading to irreversible physiological harm32. 1.3.2 Heath Impacts of EHEs EHEs have profound impacts on human health, both directly and indirectly. Direct and immediate health effects of excessive heat exposure include dehydration, heat exhaustion, heat cramps, heat stroke, acute renal failure, cerebrovascular accidents, and even death31,32. EHEs are also associated with heightened risks of mortality and morbidity, particularly among susceptible populations such as the elderly and individuals with pre-existing health conditions. The health impacts of EHEs can vary significantly across different geographical regions, demographic groups, and time periods. This section reviews the impacts of EHEs on mortality and morbidity, as well as the susceptibility and vulnerability factors that contribute to these outcomes, including pre-existing medical conditions and sociodemographic determinants. 1.3.2.1 Mortality EHEs have a significant impact on all-cause mortality, with many studies reporting increased all-cause death rates during such periods. Research on heat-related mortality typically examines the correlation between temperature spikes and all-cause mortality to estimate excess deaths attributable to heat45. Meta-analyses have demonstrated that the relative risks (RRs) for all-cause mortality during EHEs range from 1.03 (95% CI: 1.02â1.04; N = 3) to 1.23 (95% CI: 1.11â1.37; N = 5), varying based on the duration and intensity of the events21,46. Historical EHEs have resulted in substantial increases in mortality. For instance, the 1995 Chicago heatwave and the 2003 Western Europe heatwave, resulted in 692 and 15,000 excess 9 deaths, respectively16,47. In Canada, similar patterns have been observed: the 2009 Vancouver heatwave resulted in an estimated 114 excess deaths, the 2010 Montreal heatwave led to 106 excess deaths, and the 2021 Western North American heat dome caused approximately 740 excess deaths26,48,49. Beyond all-cause mortality, cause-specific mortality also increases during EHEs. Deaths from cardiovascular causes such as ischemic heart disease, arrhythmias, and heart failure rise, likely because cutaneous vasodilation adds strain on the cardiovascular system50â53. Cerebrovascular mortality such as stroke and transient ischemic attacks can also increase due to heat-induced dehydration or thicker blood viscosity50,51,54 . Deaths from acute kidney injury and renal failure also rise in tandem with high temperatures as heat stress compromises renal perfusion through intensified dehydration55. Moreover, EHEs have also been linked to external and injury-related causes like violence, traffic collisions, accidental drownings, and suicides54,56â58. This is attributed to heat potentially increasing aggressive behavior, impairing driver alertness, causing vehicle malfunctions, and leading more peopleâespecially childrenâto engage in water activities54,56â58. It is also worth noting that heat-related mortality is not limited to the immediate period of exposure. Although acute effects often manifest within the first few days, lagged associations of 2â5 daysâand in some cases up to 30 daysâhave been reported in distributed lag models and other time-series analyses59â62. 10 1.3.2.2 Morbidity EHEs also impact morbidity, leading to increases in hospitalizations, ED visits, and ambulance callouts across a wide range of health conditions. These impacts are particularly pronounced for cardiovascular, respiratory, renal, and cerebrovascular conditions, as well as mental health disorders and occupational injuries51,53,55. Multiple studies have consistently shown that EHEs lead to a significant rise in ED visits for cardiovascular and cerebrovascular diseases. For instance, in North Carolina, ED visits for cardiovascular issues, including hypotension and related conditions, increased by 3-8% during EHEs63. Similarly, the 2009 heatwave in Adelaide, Australia saw a significant rise in ED visits for ischemic heart disease among those aged 15-6464. In Atlanta, the risks for ED visits related to acute renal failure and ischemic stroke increased during heatwaves, with RRs reaching up to 1.15 (95% CI: 1.03â1.29) for renal failure and 1.09 (95% CI: 1.02â1.17) for ischemic stroke65. In BC and Ontario, Canada, studies also found significant spikes in heat-related ED visits during EHEs66,67. EHEs also result in substantial increases in hospital admissions, especially for cardiovascular, respiratory, and renal diseases. For cardiovascular conditions such as ischemic heart disease and myocardial infarction, one meta-analysis reported an RR of 1.02 (95% CI: 1.00â1.03; N = 13) per 1ºC rise in temperature53. Similarly, respiratory conditions including asthma and COPD show hospitalization rates rise with RRs up to 1.04 (95% CI: 1.00â1.09; N = 17)51. Hospitalizations for renal diseases have even more pronounced increases, with one meta-analysis reporting an RR of 1.24 (95% CI: 1.19â1.28; N = 16) when using temperature thresholds between the 90th and 99th percentiles55. Cerebrovascular events, including strokes, as 11 well as mental health disorders such as schizophrenia, and complications during childbirth are also notably exacerbated by extreme heat 51,55,68â70. Moreover, the strain on ambulance services during EHEs is significant. In Adelaide, ambulance callouts surged by 10% during the 2008 heatwave and 16% during the 2009 event64. In King County, Washington, extreme heat led to an 8% increase in basic life support calls and a 14% increase in advanced life support calls71. Specific ambulance callouts such as acute renal failure, ischemic stroke, intestinal infection, Alzheimer's disease, mental disorders, bacterial enteritis, and injuries are also heightened by EHEs 65,72â74. Although no study has specifically examined ambulance dispatches during the 2021 heat dome in BC, the Emergency Communications Centre received over 15,000 calls in just two daysâan increase of 55% compared with a typical June weekend75. 1.3.2.3 Susceptibility and Vulnerability to Heat Susceptibility and vulnerability to EHEs are shaped by a complex interplay of intrinsic and extrinsic factors that collectively influence heat-related health outcomes across different populations. Susceptibility refers to the inherent sensitivity of individuals or groups to heat due to physiological and biological characteristics76. In contrast, vulnerability encompasses susceptibility but also includes external factors such as exposure to heat and the capacity to cope with or adapt to extreme temperatures2. 1.3.2.3.1 Susceptibility Factors EHEs significantly elevate mortality and morbidity rates across various comorbidities, particularly impacting the cardiovascular, respiratory, renal, and cerebrovascular systems. 12 Studies have shown that EHEs increase excess mortality and morbidity risks in individuals with conditions such as ischemic heart disease, heart failure, stroke, chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), diabetes, mental illnesses such as schizophrenia, and in those working in high-risk outdoor occupations like agriculture and construction21,37,50â55,66,68,71,72,77â79. During the 2021 heat dome in BC, a study found that individuals with schizophrenia (OR 3.07, 95% CI: 2.39-3.94), chronic kidney disease (CKD) (OR 1.36, 95% CI: 1.18-1.56), and heart disease (OR 1.18, 95% CI: 1.00-1.38) faced a higher mortality risk than others during the event80. Another study on the same EHE identified significantly higher odds of mortality associated with conditions when controlling for income assistance including schizophrenia (OR 1.93, 95% CI: 1.51-2.45), chronic obstructive pulmonary disease (COPD) (OR 1.48, 95% CI: 1.29-1.71), parkinsonism (OR 1.46, 95% CI: 1.03-2.02), heart failure (OR 1.45, 95% CI: 1.25-1.68), CKD (OR 1.36, 95% CI: 1.18-1.56), ischemic stroke (OR 1.32, 95% CI: 1.05-1.66), and substance use disorder (SUD) (OR 1.27, 95% CI: 1.07-1.50) 81. Besides pre-existing medical conditions, age is one of the most critical determinants of susceptibility to EHEs. The elderly are particularly at risk due to compromised thermoregulation resulting from age-related physiological changes. These include declines in skin vascularization, sweat production, autonomic nervous system function, and alterations in body composition, compounded by the prevalence of chronic conditions such as cardiovascular and cerebrovascular diseases82â84. Meta-analyses and observational studies show that the risk of all-cause mortality during EHEs is significantly higher among those aged 65 and older compared with younger populations79,83,85,86. 13 Children and young adults also face heightened risks, particularly for heat, injury, and infection-related morbidity, due to their underdeveloped thermoregulatory systems and greater engagement in outdoor activities74,87,88. 1.3.2.3.2 Vulnerability Factors Socioeconomic status (SES) profoundly impacts vulnerability to EHEs by influencing exposure and adaptive capacity. Individuals with lower SESâincluding those experiencing poverty, systemic racism, and homelessnessâare often more vulnerable due to limited access to resources such as cooling amenities, healthcare, and financial means to mitigate heat exposure83,84,89,90. They are more likely to reside in substandard housing with poor insulation and inadequate ventilation, often located in densely populated urban areas with limited green spaces, where the urban heat island effect intensifies heat exposure84,89â91. Additionally, they may have lower baseline health status and a higher likelihood of engaging in outdoor, heat-exposed occupations54,69,77. Other SES-related factors, such as living alone, being bedridden, or having limited social support further elevate the risk89. Gender difference also contribute to vulnerability. Men may have increased exposure due to higher prevalence in outdoor occupations, while women may face greater physiological risks owing to factors such as a higher surface-to-mass ratio and increased subcutaneous fat, both of which affect heat dissipation efficiency84,91. Moreover, women are more likely to live alone, which can increase vulnerability due to social isolation49,54. Additionally, environmental factors play a significant role in determining vulnerability. Historically hotter regions often exhibit lower heat-related mortality and morbidity, possibly because of physiological acclimatization and behavioural factors, such as the widespread 14 employment of cooling strategies2,37,54,84. Residents in regions without adequate heat-related policies, prevention and mitigation strategies, or heat warning systems are more vulnerable to the adverse effects of extreme heat1,37,92,93. This intricate web of susceptibility and vulnerability factors was starkly illustrated during the 2021 heat dome in British Columbia. Certain demographic and social groups were disproportionately affected. Females, those aged 65 and older, individuals living alone, residents in neighborhoods with less greenery, and those experiencing poverty or other social and material barriers were at significantly higher risk of mortality during this event49,81. This underscores how both intrinsic susceptibility and extrinsic vulnerability factors interplay to influence the impact of EHEs on different populations. 1.4 Heat Health Warning Systems (HHWSs) In response to the significant health risks and growing challenges posed by EHEs, the World Health Organization (WHO) advises the integration of meteorological forecasts with public health evidence to establish and continually enhance HHWSs for effective heat preparedness and response94. As such, many regions, including Canada and BC, have established HHWSs as an essential component of climate adaptation and mitigation strategies. Typically, HHWSs involve a multi-dimensional approach that includes weather forecasting, determination of heat-related health risk thresholds, and dissemination of warnings to the public and relevant stakeholders. These systems are tailored to reflect the specific meteorological, demographic, and urban characteristics of the local context1,2,94. 15 1.4.1 An Overview of HHWSs According to WHO guidelines, HHWSs should be grounded in the direct correlation between heat-related biometeorological parametersâsuch as air temperatureâand health outcomes, particularly mortality, which is widely used as a health indicator due to its standardized and consistent collection across regions2,94. Air temperature is the most broadly used metric for evaluating EHEs, given its simplicity and reliability in forecasting2 (Table 1). Some systems have also adopted apparent or âfeels likeâ temperature metrics such as heat index, humidex, net effective temperature, and wet-bulb globe temperature (WGBT), which consider the combined effects of air temperature, humidity, windspeed, and solar radiation1,2 (Table 1). However, the nonlinear behavior of relative humidity introduces challenges in accurate forecasting, particularly in regions prone to high moisture levels such as BC, which complicates the prediction of heat-related health impacts95. When predicting and assessing EHEs, most HHWSs use either a single-parameter or multiple-parameter method. In some regions in Canada and the USA, both air and apparent temperature metrics are applied, whereas other regions and countries rely on daily maximum temperature or a combination of maximum and overnight temperatures to define EHEs and issue warnings1,2. Further, many HHWSs require all specified conditions to be met simultaneouslyâsuch as exceeding certain daytime highs and overnight lows for consecutive daysâto trigger an alert. Along with duration (commonly 2-3 days of continuous extreme heat) as a defining criterion, HHWSs typically establish alert thresholds either by absolute biometeorological values or by historical percentiles correlated with all-cause mortality1,2. To quantify "excess mortality" attributable to heat and determine baseline mortality outside EHE periods, time-series 16 analyses of death and meteorological data are frequently performed. For instance, countries such as France, the USA, and Canada analyze decades of mortality and weather records to estimate heat-related excess mortality and set corresponding alert thresholds2,3. 17 Table 1 Examples of different heatwave definitions and heat-health warning systems Country Definition of Heatwave Main Indicator (Air Temp or Apparent Temp) Heat-Health Warning System United States96 A heat wave is typically 2+ consecutive days of abnormally hot, often humid weather. Current rule of thumb for a Heat Advisory: max Heat Index â¥100 °F for at least 2 days, with nighttime temps â¥75 °F. Local offices may have additional criteria. Heat Index (apparent temperature combining air temp & humidity) Heat advisory: per the national guideline or local thresholds; Excessive heat watch: Heat Index â¥105 °F possible in next 24â72 hours; Excessive heat warning: â¥105 °F imminent/ongoing for 2+ days. Canada97 2+ consecutive days of daytime maximum temperatures reaching regional thresholds (e.g., 29 °C in BC southwest) and nighttime minimum temperatures exceeding regional thresholds (e.g., 16 °C in BC southwest). Air temperature and/or Humidex (apparent temperature) Heat Warnings: when forecast temps or Humidex reach region-specific thresholds for 2+ days; some cities/provinces also issue Extreme Heat Alerts. United Kingdom98 3+ consecutive days meeting or exceeding regional daily max temperature thresholds (e.g., 25 °C in some areas, 28 °C in southeast England). Air temperature (daily max) Heat Health Alert System (2023): Green/yellow/amber/red based on temperature thresholds and potential health impacts. Higher levels can trigger national-level actions. Australia99 3+ consecutive days of significantly high max and min temperatures relative to local averages Air temperature Use the excess heat factor (EHF) to monitor and issue heatwaves, which combines a comparison of the average temperatures for a 3-day period with what would be considered hot at that location as well as the observed temperatures at that location over the past 30 days Heatwaves are classified as: â¢ Low-intensity: regularly in summer, and most people can cope. â¢ Severe: less frequent and greater risks for vulnerable groups, such as older adults and those with medical conditions. â¢ Extreme heatwaves: rare but dangerous to anyone who fails to stay coolâeven otherwise healthy individuals. Those who work or exercise outdoors face particularly high risks. India100 Max temp â¥ 40 °C in plains (â¥ 30 °C in hills). If the local normal max â¤ 40°C: Air temperature Colour-coded warnings based on severity/duration: â¢ Green: normal temperatures 18 â¢ Heat wave: 5â6°C above normal â¢ Severe heat wave: 7°C or more above normal If the local normal max > 40°C: â¢ Heat wave: 4-5°C above normal â¢ Severe heat wave: 6°C or more above normal â¢ Yellow: Heat alert. Moderate temperature. Heat is tolerable for general public but moderate health concern for vulnerable people. â¢ Orange: severe heat alert. High temperature. Increased likelihood of heat illness symptoms. â¢ Red: extreme heat alert. Extremely high likelihood of developing heat illness and heat stroke in all ages. China101 Commonly 3+ consecutive days with daily max â¥ 35 °C. Some provinces use 37â40 °C for extreme heat. Air temperature Four-tier Color System: Yellow (â¥ 35 °C for 3 consecutive days), Orange (â¥ 37 °C within 24 hours), Orange (~37â39 °C), Red (â¥ 40 °C within 24 hours). Spain102 Often 3+ consecutive days when weather stations exceed the 95th percentile of historical JulyâAug max temps. Varies by region. Air temperature National Plan for Preventive Actions Against High Temperatures: color-coded alerts (green, yellow, orange, red) and health advisories from Health Ministry. France103 3+ consecutive days of daytime max and nighttime min above region-specific thresholds. Air temperature Vigilance map (green/yellow/orange/red). Orange/red indicate significant to extreme heat. Japan104 Heatstroke alerts: the daily maximum WBGT â¥ 33 °C for 2 consecutive days. Apparent temperature Heatstroke alert: â¢ 31 and above: Danger â¢ 28 or more and less than 31: Careful vigilance â¢ 25 or more and less than 28: vigilance â¢ Less than 25: Note South Africa105 A daily max â¥5 °C above the local maximum average of âthe hottest monthâ for 3+ consecutive days. Air temperature Heatwave warnings/advisories. 19 The criteria for issuing warnings vary across different systems and are generally based on the degree to which all-cause mortality rates exceed these baselines during EHEs1. In some cases, thresholds are determined with consideration for vulnerable populations, such as those aged 65 or above, while others may rely on historical temperature percentiles or fixed values2. In many HHWSs, thresholds are standardized at the national level, though localized adjustments are often made to account for regional climate variations and population needs, as seen in Canada, the USA, and France2. Additionally, some systems incorporate seasonal adjustments to account for heat acclimatization throughout the summer3,106. Moreover, impact-based HHWSs, which issue warnings at varying levels such as âwarningâ and âemergencyâ based on the severity and duration of EHEs, are increasingly favored for their capacity to communicate the nonlinear nature of heat risk, deliver more intuitive public messages, and reduce alert fatigue compared with phenomenon-based systems2. 1.4.2 Effectiveness of HHWSs HHWSs have proven generally effective in improving disaster readiness and encouraging behavioral changes107. A systematic review of HHWSs highlighted their role in mitigating health impacts when paired with comprehensive public health interventions and clear and timely communication strategies107. The form and structure of an HHWSâsuch as tailored communication strategies, locally relevant criteria for issuing alerts, and the incorporation of multi-sectoral responsesâplay a critical role in determining its effectiveness107. Evidence also suggests that HHWSs integrated with broader heat adaptation frameworks, including healthcare system preparedness, urban 20 infrastructure (e.g., cooling centers, shaded areas), and social programs, achieve better outcomes than standalone systems107. However, while some evidence supports the effectiveness of well-designed HHWSs, there is limited research that isolates specific design elements or compares alternative forms of HHWSs under similar conditions. For instance, few studies control for variables such as different heat warning criteria, levels of public outreach, or access to resources when evaluating HHWS effectiveness. This limitation makes it challenging to attribute reductions in mortality and morbidity solely to HHWSs, as they often operate within broader public health strategies. For example, a U.S. study found no clear link between heat alerts and reductions in mortality across cities, likely due to limited heat response strategies and variability in public perception of heat warnings at the time108. This study only focuses on the parameter of measuring extreme heat and issuing heat warnings, with the goal of developing a more dynamic and flexible HHWS framework. Although such a focus is only one component of the broader HHWS framework, less rigid and more biologically relevant warning criteria can enable more effective heat response strategies and action plans, ultimately improving public health protection. As Toloo and colleagues emphasized in their study, a tangible decrease in EHE-related mortality and morbidity require evidence-based alert criteria, well-coordinated response, and action plans, along with adequate community resources107. Continuous evaluation and adaptation of these systems are crucial to ensure they meet the evolving challenges posed by climate change. 21 1.4.3 Heat Alert and Response System (HARS) in BC BC has developed its own HHWS following the deadly EHEs in 2009 and 2021. After the 2009 heatwave in Metro Vancouver, the BC Centre for Disease Control (BCCDC), in collaboration with ECCC and local health authorities, developed a data-driven approach to establish temperature thresholds for heat health emergencies. By analyzing environmental and epidemiologic data from 2005 to 2009, they evaluated single-day maximum temperatures and two-day averages of maximum temperatures, identifying a correlation between excess mortality and the two-day average of maximum temperatures26,109. This analysis led to the recommendation that a heat health emergency be declared when the observed temperature at 14:00 combined with the forecasted high for the following day reached or exceeded 29°C on the coast and 34°C inland110. In 2018, the BCCDC expanded the HARS beyond Metro Vancouver to include the Southwest, Southeast, Northwest, and Northeast Heat Alert Areas (HAAs) of BC. This expansion involved analyzing three-day moving average death counts alongside forecasted and observed air temperatures across these regions, using a High-Low-High (HLH) temperature measurement that includes the maximum daily temperature, the overnight low, and the next dayâs maximum110. This model allowed for the establishment of region-specific heat alert thresholds designed to account for the diverse climatic conditions across the province and balance the need to minimize warning fatigue with the evidence-based correlations between extreme heat and mortality3,110. In 2019, the BCCDC further refined HARS by incorporating the Fraser region as a new HAA, which covers Eastern Metro Vancouver, including Coquitlam and Surrey, as well as the Fraser Valley3. This decision was based on the region's hotter and more humid conditions. 22 The current temperature threshold for the Southwest region, where WMV is located, is 29°C-16°C-29°C. When weather forecasts predict that the daily high and overnight low temperatures simultaneously meet all three threshold conditions, the BC HARS issues a Heat Warning. If these conditions are expected to intensify over the next three days, an Extreme Heat Emergency is declared. This two-tiered system also allows for the reduction of Heat Warnings after multiple heat events to account for the population's seasonal temperature adaptation3. 1.5 Gaps in Knowledge and Study Rationale Despite the variability among different HHWSs, most employ a fixed-temperature approach and issue heat alerts based on specific absolute thresholds being met or exceeded. In these fixed-threshold systems, a heat alert is only issued when all predefined conditions are satisfied at the same time. This means that if any one of the criteriaâsuch as the maximum daytime temperature, the minimum overnight temperature, or the duration of the heat eventâis not met, no warning is activated. While such approaches are straightforward, they may be arbitrarily stringent and overlook different sequences of dayânight heat that accumulate over multiple days, potentially leading to missed warnings even if different combinations of temperature sequences lead to comparable levels of risk from the overall heat exposure. For example, a region might experience moderately high daytime temperatures just slightly below the threshold, but even if the overnight lows are extremely high and the diurnal temperature difference is small, the system would not issue an alert. Conversely, moderate temperatures that persist over an extended period might cumulatively stress the population, but without exceeding any single threshold, the system remains inactive. This rigid approach can lead to missed opportunities for intervention. 23 From a biological standpoint as mentioned previously (Section 1.3.1), the total heat load accumulated over time places a greater strain on the bodyâs thermoregulatory systems than a single peak temperature. Sustained exposure prevents adequate recovery, heightening oxidative stress and triggering systemic inflammation. Therefore, focusing on cumulative thermal stress provides a more accurate gauge of heat-related health outcomes. Furthermore, as climate change reshapes the meteorological patterns of EHEs, a growing body of knowledge suggests the duration, frequency, and impact of compound EHEs that combine daytime and nighttime heat are expected to surpass the impacts of singular daytime or nighttime heatwaves alone111,112. Some recent studies suggest that day-night compound EHEs may be associated with higher mortality and morbidity, largely because continuous exposure to high temperatures impedes the body's ability to cool down, negatively affect sleep quality, and impose additional physiological strain compared with the effects of high daytime temperatures alone19,111,113â115. This gap in most HHWS frameworks underscores the need for a more nuanced and dynamic approach to heat warnings. Incorporating the concept of cumulative heat impact into dynamic and flexible temperature thresholds within HHWS frameworks could more accurately reflect the true health risks posed by extended periods of high temperatures. 1.6 Study Aims This study sought to address three key questions: 1. Are cumulative temperature metrics associated with the health outcomes? 24 2. At what thresholds within a pre-selected cumulative temperature parameter does the likelihood of all-cause mortality, heat-related ED visits, and ambulance dispatches significantly increase? 3. Are the cumulative heat thresholds identified for all-cause mortality sensitive to capture risks within subgroups of mortality, morbidity, and ambulance dispatch outcomes? To answer these questions, the study pursued the following aims: â¢ Exposure selection: To determine if a cumulative temperature measure of heat exposureâsumming daily high temperatures and overnight lows over 1-3 daysâis associated with all-cause mortality, heat-related ED visits, and heat-related ambulance dispatches, using historical observed and forecast temperature data, and its statistical performance based on the AIC and root mean square error (RMSE). To then apply a practical metric based on the current BC HARS as an example for further analyses. â¢ Threshold analysis: o Establish the thresholds at which the Odds ratios (ORs) and its 95% confidence intervals (CIs for all-cause mortality, heat-related ED visits, and heat-related ambulance dispatches exceed 1.0 for the selected cumulative temperature measure. o Identify the specific thresholds of daily highs and overnight lows within the selected cumulative temperature measure based on all-cause mortality, recognizing that the cumulative temperature value may represent different 25 combinations of daily highs and overnight lows associated with varying levels of risk. â¢ Subgroup analysis: Evaluate whether the cumulative heat thresholds identified in the threshold analysis are sensitive to capture risks within subgroups of mortality and morbidity outcomes. This includes analysis based on variations in age, location of death, sex, specific chronic conditions, multimorbidity, PharmaCare plans, and acuity levels. 26 Chapter 2: Methods 2.1 Study Context WMV, located on Canada's west coast, has a diverse population of approximately 1.5 million people, as per the 2021 Canadian Census. WMV, with its warm-summer Mediterranean climate, experiences cooler summer highs than other major Canadian cities due to the moderating influence of Pacific onshore breezes, while dry conditions prevail in July and August116. In BC HARS, WMV is part of the Southwest HAA, which includes the City of Vancouver, Burnaby, Electoral Area A (University of British Columbia), New Westminster, Richmond, Delta, West Vancouver, North Vancouver City, and North Vancouver District (Figure 3). Given its large population and high population density, WMV accounts for more than 30% of the province's total population. 2.2 Data Sources The study used temperature data from ECCC and health data from the BC Vital Statistics Agency, the National Ambulatory Care Reporting System (NACRS), the Chronic Disease Registry (CDR), the ambulance services Medical Priority Dispatch System (MPDS), and PharmaNet. The health datasets were linked via the BC Provincial Health Services Authority (PHSA) Platform for Analytics and Data (PANDA) through the approved data mart of âEvaluation of 2021 heat dome and extreme cold impacts in BCâ (nb0107aa). While BC Vital Statistics, NACRS, and CDR datasets were linkable in PANDA, MPDS was not linkable at the time the study was conducted. 27 Figure 3 Current HAAs in the BC HARS and map of WMV 28 2.2.1 Temperature Daily temperature forecasts and observations from 2008 to 2023 were obtained from the ECCC weather station at YVR, with temperatures recorded in integer degrees Celsius. For temperature forecasts, the official ECCC forecast made at 16:00 Pacific Time, 2 and 3 days in advance, was used. 2.2.2 Vital Statistics The primary dataset for this research comprised daily all-cause mortality data from the BC Vital Statistics Agency for the summer months (May through September) between 2008 to 2023. This included anonymized details such as the date and primary cause of death, age, sex, local health area, and location of death for each decedent. 2.2.3 Chronic Disease Registries (CDRs) Comorbidity data were sourced from administrative CDRs, maintained by the BC Ministry of Health (HLTH). The CDRs track the incidence and prevalence of 25 chronic conditions annually (Table 2) using different administrative case definitions for each condition117. In addition to using individual chronic conditions, two derivative groups were created and analyzed from the CDRs â comorbidity categories and multimorbidity. Comorbidity categories included circulatory conditions, respiratory conditions, CKD and diabetes, mental illness and SUD, and other (Table 2). Multimorbidity, defined as the number of chronic conditions in one individual, measured the burden of comorbidities. Currently, the CDR contains data spanning from 1994 to March 30, 2022. This study included registered clients in the CDR who died during the summer months from 2008 to 2023 and those who visited the ED 29 during the summer months from 2014 to 2023 within WMV. These cases were linked to mortality, morbidity, and temperature data to conduct a comorbidity-specific analysis of mortality and morbidity. Table 2 List of comorbidity categories and 25 chronic conditions Comorbidity category Chronic conditions Circulatory Ischemic heart disease, acute myocardial infarction, heart failure, hypertensive diseases, stroke, ischemic stroke, hemorrhagic stroke, transient ischemic attack Respiratory Asthma, chronic obstructive pulmonary disease Chronic kidney disease and diabetes Chronic kidney disease, diabetes mellitus Mental health and substance use disorder Depressive disorders, anxiety and mood disorders, schizophrenia and delusional disorders, substance use disorders Other Epilepsy, multiple sclerosis, Parkinsonâs disease, Alzheimer's and other dementias, gout, osteoarthritis, osteoporosis, rheumatoid arthritis, juvenile idiopathic arthritis 2.2.4 National Ambulatory Care Reporting System (NACRS) Morbidity data were obtained from NACRS, which collects data on all ambulatory care levels, including ED visits, day surgeries, and clinics since 2012. To identify heat-related morbidity, the BC Health Effects of Anomalous Temperatures (BC HEAT) Coordinating Committee uses five validated diagnostic and complaint codes: general weakness, syncope/pre-syncope, altered level of consciousness, heat-related issue, and effects of heat and light81. NACRS has complete WMV hospital data from 2014 to 2023, because not all hospitals started reporting simultaneously. WMV patients recorded in the NACRS who visited EDs located in 30 within or near WMV (including Coquitlam and Surrey) from 2014 to 2023 and are registered in the CDR were included for heat-related and comorbidity-specific morbidity analysis. 2.2.5 Medical Priority Dispatch System (MPDS) Ambulance dispatch data from 2010 to 2023 were sourced from MPDS, an internationally standardized system to prioritize 9-1-1 calls and optimize resource allocation. MPDS data, currently deidentified and un-linkable in the PANDA environment, include information on the date, protocols, response determinant code, and health service location. BC HEAT uses three validated MPDS protocol codes to identify heat-related ambulance dispatches: code 09 (cardiac or respiratory arrest), code 20 (heat & cold exposure), and code 31 (unconscious & fainting). 2.2.6 PharmaNet PharmaNet is a real-time system managed by HLTH, designed to monitor all medications dispensed by community pharmacies and hospital outpatient pharmacies for home use in BC118. The system records data on dispensed medications, PharmaCare claims, patient demographics (including Personal Health Number, name, address, and date of birth), reported drug allergies, drug details, drug interaction assessments, and information on both PharmaCare and patient-paid prescription claims118. PharmaNet data were used in this study to identify patients in the mortality and morbidity datasets who were enrolled in PharmaCare Plan C. This plan fully covers eligible prescription costs for BC residents receiving benefits through the BC Ministry of Social Development and 31 Poverty Reduction or under the care of the BC Ministry of Children and Family Development119. As such, PharmaCare Plan C identifies subjects living below or near the poverty line. 2.3 Data Collection and Linkage Mortality, comorbidity, morbidity, ambulance dispatch, and PharmaCare data from Vital Statistics, CDRs, NACRS, MPDS, and PharmaNet were obtained via the BCCDC data mart of âEvaluation of 2021 heat dome and extreme cold impacts in BCâ (nb0107aa) in PANDA (Figure 4). PANDA is a cloud-based platform created to improve data access for reporting, analytics, and research within PHSA programs. It provides a secure cloud environment for data analytics and governance and integrating advanced tools. Data linkage was facilitated through PANDA's Client Roster data. Vital Statistics, CDRS, NACRS, and PharmaNet contribute anonymous unique identifiers (UID) for the patients to the Client Roster, enabling effective data linkage at the individual level. 2.3.1 Western Metro Vancouver (WMV) Spatial Health Boundaries Data from Vital Statistics, NACRS, and MPDS included information on local health areas (LHAs), which are administrative boundaries for health administration in BC. For Vital Statistics and MPDS, data from 12 LHAs in WMV were included in the study (Table 3). For NACRS, 12 hospitals were selected as WMV or WMV-adjacent hospitals (Table 3). 32 Figure 4 Flow chart of data collection Table 3 Western Metro Vancouver Local Health Areas and Hospitals Dataset Health area code Vital statistics and MPDS Local health area code: 221 New Westminster, 222 Burnaby, 232 Delta, 311 Richmond, 321 Vancouver - City Centre, 322 Vancouver - Downtown Eastside, 323 Vancouver - Northeast, 324 Vancouver - Westside, 325 Vancouver - Midtown, 326 Vancouver - South, 331 North Vancouver, 332 West Vancouver/Bowen Island NACRS Hospital code: 101 Vancouver General Hospital, 102 St. Paulâs Hospital, 105 BC Childrenâs Hospital, 106 Mount Saint Joseph Hospital, 109 Royal 33 Columbian Hospital, 112 Lions Gate Hospital, 116 Surrey Memorial Hospital, 121 Richmond Hospital, 123 UBC Health Sciences Centre, 130 Burnaby Hospital, 131 Peace Arch District Hospital, 134 Delta Hospital 2.3.2 Data Linkage Between Vital Statistics and CDR For comorbidity-specific mortality, UIDs from Vital Statistics served as the linkage identifiers. CDR UIDs corresponding to those in Vital Statistics were included in the comorbidity-specific mortality dataset. A temporal mismatch between the mortality data (2008-2023) and the CDR (1994-2022) needed to be addressed in the inclusion criteria for data linkage. Because the CDR does not have data post-March 30, 2022, patients who died between April 1, 2022, and September 30, 2023, might have developed additional chronic conditions not captured by the CDR. Including these patients with potentially incomplete chronic condition records in the comorbidity-specific mortality analysis might introduce bias. To ensure comparability of comorbidity-specific deaths each summer between 2008 and 2023, chronic conditions registered in the CDR between April 1 of the previous year (Y-1) and September 30 of year Y were excluded (see inclusion formula below). ð¿ððððð ð·ðð¡ðð ðð¡ = â (ðð¢ðððð ð·ððð¡âð ð2023ð=2028â {ð¶âððððð ððððð¡ðððð ðððððððð ððð¡ð¤ððð ð´ðððð 1ð ð¡, (ð â 1) ððð ðððð¡ððððð 30ð¡â, ð}) 2.3.3 Data Linkage Between NACRS and CDR For comorbidity-specific morbidity, NACRS UIDs matching those in the CDR were selected. Specifically, most WMV or WMV-adjacent hospitals started reporting to NACRS in 2012, except for Delta Hospital and Peach Arch District Hospital, which started in 2014. To 34 consider the spatial and demographic variations within WMV, ED visit data from 2014 to 2023 were included in the data linkage. For the CDR data, the same inclusion criteria described in the data linkage between Vital Statistics and CDR were applied when linking to NACRS. For the ED visits in the summer of year Y, chronic conditions registered in the CDR between April 1 of the previous year (Y-1) and September 30 of year Y were excluded. 2.3.4 Linking PharmaNet with Vital Statistics and NACRS All prescriptions dispensed within the one-year period preceding a death or ED visit were extracted from the PharmaNet database and linked to Vital Statistics and the NACRS using UIDs. Individuals with one or more prescriptions covered by PharmaCare Plan C during this period were classified as low-income. Those with no prescriptions dispensed during the year prior to the death or ED visit were classified as having unknown coverage. All others were categorized as not being covered by PharmaCare Plan C (Figure 5). Figure 5 Inclusion criteria for PharmaCare plans 35 2.4 Data Analysis Data analysis was conducted for each objective, focusing on the respective outcomes and exposure measures. Descriptive and inferential statistics were employed to assess the relationships between cumulative temperature metrics and health outcomes. Sensitivity analyses were also performed to ensure the robustness of the results. All data were extracted and analyzed using SQL and R Statistical Software (v4.3.1 2023-06-16)120. 2.4.1 Objective 1 Objective 1 identified a cumulative temperature measure of heat exposure, derived from the combinations of daily highs and overnight lows over 1-3 days, as a practical predictor of all-cause mortality, heat-related ED visits, and heat-related ambulance dispatches. 2.4.1.1 Outcome Measures Daily counts of individual deaths, heat-related ED visits, and heat-related ambulance dispatches were aggregated. These daily health outcome counts were then summed over a 3-day period: â¢ If t is the day of health outcome, then 3-day sum of daily health outcome is ð¶ð¡â1 + ð¶ð¡ + ð¶ð¡+1 2.4.1.2 Exposure Measures Exposure measures consisted of cumulative temperature combinations over 1-3 days using historical observed and forecast temperature data: 36 â¢ If t is the day of health outcome, High is the daily maximum temperature, and Low is the overnight minimum temperature, the exposure measures are: o Hight-1 + Lowt-1:t (HL) o Lowt-1:t + Hight (LH) o Hight-1 + Lowt-1:t + Hight (HLH) o Lowt-2:t-1 + Hight-1 + Lowt-1:t (LHL) o Hight-2 + Lowt-2:t-1 + Hight-1 + Lowt-1:t (HLHL) o Lowt-2:t-1 + Hight-1 + Lowt-1:t + Hight (LHLH) o Hight-2 + Lowt-2:t-1 + Hight-1 + Lowt-1:t + Hight (HLHLH) â¢ When calculating these temperature metrics, day t refers to the day used to sum the temperature combinations. In subsequent analyses, only days t where the temperature metrics exceed the identified thresholds are considered as days of threshold exceedance. 2.4.1.3 Time Series Covariates It is important to control for temporal covariates such as the day of the week, seasonality, long-term trends, delayed exposure effects (lags), and other time-varying factors in time-series studies because the effects of temporality can confound exposure-outcome associations121. The focus of this study was on outcomes during the summer months and, given that cumulative outcomes and exposures were already being considered, it may not be necessary to control for seasonality and delayed exposure effects. However, it is crucial to account for long-term trends, particularly considering that the study period includes the COVID-19 pandemic, 37 which has had a profound impact on health outcomes and healthcare services. Additionally, control is needed for the day of week, because heat exposure levels and health risks due to factors unrelated to exposure may vary according to population-level behaviors that differ on holidays, weekdays, and weekends121. 2.4.1.4 Statistical Analysis Descriptive statistics for both the outcomes and exposures related to Objective 1 were calculated and visualized to provide an initial understanding of the data. The distributions of the three health outcomes were examined, including tests for normality and assessments for overdispersion. Given the non-normal distribution and the presence of overdispersion in the outcomes, a generalized additive model (GAM) with a negative binomial distribution was employed to model the health outcomes. Additional Poisson model was tested and compared, using AIC. The model incorporated a penalized spline function for long-term trends and accounted for the day of the week. Penalized splines in GAMs offer a robust and flexible approach for modeling complex, nonlinear relationships in time-series data, effectively balancing the fit of the model while minimizing the risk of overfitting122. For each health outcome, various numbers of knots for the penalized spline term were tested, with the optimal number identified using the AIC. Subsequently, all exposure measures with a natural cubic spline function were modeled against each health outcome, with time (as a spline) and the day of the week included as covariates. Below is the mathematical expression of the model formula: 38 Equation 1 Generalized additive model formula ð¥ð¨ð (ðð) = ð·ð + ð(ð¬ðððððððð) + ð(ððððð, ð ð = ð)+ â ð·ðð Ã ð«ðð ðð ððð ðððððððð=ð Where: â¢ ðð¡ is the 3-day-sum of daily health outcome count at time t. â¢ log(ðð¡) is the log of the expected value (log-link function for the negative binomial distribution). â¢ ð½0 is the intercept term. â¢ ð (ð¸ð¥ððð ð¢ððð¡) is the exposure variable with a natural cubic spline function. â¢ ð (ð¡ðððð¡ , ðð = ð) is the penalized spline term for the time variable, with k degrees of freedom, which accounts for non-linear trends over time. â¢ ð·ðð¦ ðð ð¡âð ð¤ððððð¡ are the dummy variables for day of the week, where ð½1ð are the coefficients for each day of the week (excluding Monday as the reference category). â¢ The model includes a dispersion parameter θ to account for overdispersion in the count data. In this GAM with a logit link function, the coefficients estimated by the model represent the log-odds of the outcome relative to the exposure. OR measures the strength and direction of the association between an exposure (e.g., cumulative heat metrics) and an outcome (e.g., mortality)123. It is calculated as the ratio of the odds of the outcome occurring in the exposed group to the odds in the non-exposed group123. To calculate the OR from the GAM, the estimated coefficient expressed as log-odds was exponentiated: Equation 2 Equation of odds ratio ðð = ðð¥ð (logð1 â ð) Where: â¢ p is the probability of the outcome occurring when the exposure is present. 39 â¢ logð1âð is the estimated effect of the cumulative temperature metric on the log-odds of the outcome. â¢ OR > 1: Higher cumulative temperature metric is associated with an increased likelihood of the outcome. â¢ OR < 1: Higher cumulative temperature metric is associated with a decreased likelihood of the outcome. â¢ OR = 1: No association between cumulative temperature metric and the outcome. 2.4.1.4.1 Statistical Performance of the Metrics To assess the statistical performance of seven temperature measures, significance tests, model AICs, and RMSEs were evaluated: i. Significance: The statistical significance of the exposure measure was evaluated using p-values to determine whether the association between the metric and health outcomes was meaningful. ii. AIC: AIC values were calculated and compared across exposure measures, with lower AIC values indicating relatively better model fit. iii. RMSE: The RMSE of the forecasted exposure measures in relation to the observed exposure measures were calculated (Equation 3). This criterion ensures that the exposure measure can be forecasted accurately and contributes to the predictive power of the model. Equation 3 Root mean square error ð ððð¸ = â1ðâ(ð¦ð â ?Ì?ð)2ðð=1 Where: 40 â¢ ð¦ð is the observed values, ?Ì?ð is the forecast values, and N the total number of observations. â¢ n is the number of components (or values) used to compute the composite metric. 2.4.1.4.2 Operational Considerations In addition to statistical performance, operational considerations were also applied to identify a practical metric for further analyses: a) Consistency with Current Heat-Health Warning Systems âMetrics were evaluated based on their alignment with established heat-health warning criteria in BC. b) Practical Utility â The ability to accurately forecast and effectively communicate heat-health risks in advance is critical for public health interventions. Sensitivity analyses were also conducted to confirm the robustness of the statistical results. These analyses included using daily health outcomes instead of the 3-day sum and experimenting with different numbers of knots for the spline term and GAMs with a Poisson distribution. By doing so, the study ensured that the findings were consistent and reliable across different modeling approaches. 2.4.2 Objective 2 Objective 2 identified the point at which the selected temperature metric increased the ORs for the health outcomes above 1.0. It also determined thresholds for daily highs and overnight lows within the selected temperature metric using mortality outcome as an example, considering the varying risk levels associated with different temperature combinations. 41 2.4.2.1 Outcome and Exposure Measures The outcome measures in Objective 2 were the same as in Objective 1, which included 3-day sum of daily death counts, heat-related ED visits, and heat-related ambulance dispatches. The exposure measure was the cumulative temperature metric E selected from Objective 1. 2.4.2.2 Statistical Analysis: A split value analysis was conducted to identify the points at which the exposure E was associated with ORs significantly greater than 1.0 for the health outcomes (Figure 6). Specifically, 98 splits (not percentiles) were created at equally spaced intervals between the 1st and 99th percentiles of E. This means each split cuts the temperature range into fixed increments of E, rather than ensuring that each split contains an equal proportion of data points (as would be the case with true percentiles). For each of the 100 splits, a binary variable was assigned: values below the split were given 0, while values equal to or greater than the split were assigned 1. The method of this split value analysis was adopted from a study on the temperature-mortality relationships124. GAMs were then run with temporal variables and each of the 100 binary variables, producing ORs and 95% CIs for values above and below each split. The results for below-split and above-split areas were plotted as two smooth lines on a single graph to identify where a positive relationship existed between the above-split values and the first separation (the single cut point) of the below-split and above-split intervals. 42 Figure 6 Schematic of split value analysis A threshold analysis based on mortality data for specific overnight lows and daily highs was also performed to detect risks driven by different combinations of overnight lows and daily highs above the single cut point. The single cut point B identified from the split value analysis using all-cause mortality was treated as the baseline risk for E. For each value in ð¸ð where ðµ â¤ð â¤ ð¸ððð¥ , a binary variable was created where it had a value of 1 when E was greater than or equal to B, 0 when E was smaller than B, and missing otherwise. Each variation in overnight lows was modelled for each new binary variable in ð¸ð within the range ðµ â¤ ð â¤ ð¸ððð¥, using a GAM. The results were visualized with an error bar plot to identify the specific overnight low value L at which the risk of all-cause mortality increased and its OR exceeded 1.0. To determine the threshold of daily highs, a binary variable was created for each value in ð¸ð where ðµ â¤ ð â¤ ð¸ððð¥ with a value of 1 if E was greater than or equal to B and the overnight low was less than the identified threshold L from previous step, 0 if E was smaller than B, and 43 missing otherwise. Each variation in daily highs was then modeled for each new binary variable in ð¸ð within the range ðµ â¤ ð â¤ ð¸ððð¥, using a GAM. The results were visualized with an error bar plot to identify the specific daily high value HH at which the risk of all-cause mortality increased and its OR exceeded 1. The same techniques for identifying thresholds of overnight lows and daily highs were also applied to the other two outcomes for comparison. After determining the thresholds B, L, and HH, a dynamic and flexible threshold algorithm was established (Equation 4). Each value in ð¸ð within the range ðµ â¤ ð â¤ ð¸ððð¥ and the overnight low combination greater than or equal to L or daily high combination greater than or equal to H was modeled against the baseline value B of the exposure variable. The results for all models were combined and plotted to visualize how ORs changed. Equation 4 HLH algorithm (ð¸ â¥ ðµ) â (ððð¤ð â¥ ð¿ â âððâð â¥ ð»ð») The selected exposure metric E, using forecasted temperature data, was applied to the algorithm as a sensitivity analysis to ensure that the algorithm remains effective when the temperature data are based on weather predictions. Another sensitivity analysis was conducted to compare the dates meeting the HLH absolute thresholds (29°C-16°C-29°C) in WMV under the current BC Heat Alert and Response System (HARS) with the dates meeting the corresponding cumulative threshold algorithm of the selected metric. 44 2.4.3 Objective 3 Objective 3 was a case study for applying the mortality-based threshold algorithm to subgroups of mortality and morbidity causes. This objective explored whether the algorithm was applicable to measure risks among different sub-populations. 2.4.3.1 Outcome and Exposure Measures The subgroups by health outcomes are detailed in Table 4. The exposure variable used was the selected cumulative temperature metric E. 2.4.3.2 Statistical Analysis Descriptive statistics for each sub-group were calculated and presented in a frequency table. Each combination of (ð¸ â¥ ðµ) â (ððð¤ð â¥ ð¿ â âððâð â¥ ð»ð») was modelled against when E < B using a GAM within each subgroup. The results from all subgroups were then combined and plotted to visualize and compare how ORs changed within each subgroup by the temperature metric and how ORs differed across the subgroups. 45 Table 4 Subgroups within each health outcome Mortality-related Morbidity-related Ambulance-dispatch-related Comorbidity categories Respiratory, circulatory, chronic kidney disease and diabetes, mental health and substance use disorder, and all other comorbidities Comorbidity categories Respiratory, circulatory, chronic kidney disease and diabetes, mental health and substance use disorder, and all other comorbidities Acuity High (all advanced life support codes), low (all basic life support codes) Specific chronic conditions Heart failure, ischemic stroke, acute myocardial infarction, schizophrenia, substance use disorder, chronic obstructive pulmonary disease, parkinsonism, chronic kidney disease, and other chronic conditions Specific chronic conditions Heart failure, ischemic stroke, acute myocardial infarction, schizophrenia, substance use disorder, chronic obstructive pulmonary disease, parkinsonism, chronic kidney disease, and other chronic conditions Multimorbidity 3 or more multimorbidity, less than 3 multimorbidity Multimorbidity 3 or more multimorbidity, less than 3 multimorbidity Age Under 65 years old, 65 years old or above Age Aged 65 or above, 40-64, 20-39, under 19 Sex Male, female Sex Male, female 46 Location of death In care (hospitals and long-term care facilities, out of care Location of death In care (hospitals and long-term care facilities, out of care Acuity High (resuscitation and emergency), low (urgent, semi-urgent, and non-urgent) PharmaCare plan Plan C, other plans, unknown plans PharmaCare plan Plan C, other plans, unknown plans 47 Chapter 3: Results 3.1 Data Summary A total of 58,304 deaths occurring in WMV from 2008 to 2023, 123,670 heat-related ED visits from 2014 to 2023, and 93,990 heat-related ambulance dispatches from 2010 to 2023 were included. Because these data were aggregated into daily counts, the number of observations refers to the number of days rather than individual deaths, ED visits, or ambulance dispatches. Specifically, the data of all-cause mortality, heat-related ED visits, and heat-related ambulance dispatches covered 2,448 days, 1,530 days, and 2,210 days respectively. 91.2% of all-cause deaths and 84.9% of heat-related ED visits involved individuals with pre-existing chronic conditions. Among the deceased, 79% had circulatory-related conditions and at least three comorbidities, while 58% had a mental illness or SUD, 45% had CKD or diabetes, and 29% had respiratory conditions. A similar pattern was observed among those with heat-related ED visits: 71% had circulatory-related conditions, 68% had mental illness or SUD, 48% had CKD or diabetes, 31% had respiratory conditions, and 78% presented with three or more comorbidities. In terms of specific chronic conditions among the deceased, 27% had heart failure, 23% had CKD, 21% had COPD, 15% had SUD, 10% had acute myocardial infarction (AMI), 7% had ischemic stroke, 5% had schizophrenia, and 3% had Parkinsonâs. Similarly, among individuals with heat-related ED visits, 28% had CKD, 22% had SUD, 21.8% had heart failure, 17% had COPD, 9% had AMI, 7% had schizophrenia, 6% had ischemic stroke, and 4% had Parkinsonâs. Demographically, 79% of those who died were aged 65 or older, 78% passed away in hospitals or residential care facilities, 47% were male, 53% were female, and 9% were enrolled in PharmaCare Plan C. In the cohort of heat-related ED visits, 56% were aged 65 or older, 50% 48 were female, 11% were on PharmaCare Plan C, and 72% were classified as low-acuity visits. Of those who were identified as heat-related ambulance dispatches, 82% needed advanced life support. More detailed descriptive statistics can be found in Table 5. The average observed daily maximum temperature was 20.3 °C, while the average overnight low was 12.2 °C. Forecasted temperatures were closely aligned, with only minor decimal differences. However, these differences became more pronounced when multiple days of temperatures were aggregated (Table 6). Moreover, no significant temporal trends were observed in daily maximum or overnight low temperatures between 2008 and 2023. Histograms of daily maximum and minimum temperatures, as well as boxplots for each year, can be found in Appendix A.1. 3.2 Temperature Metric Evaluation The evaluation of the temperature metrics involved several steps: assessing the distribution of outcome variables, selecting an appropriate time-series model, choosing the most suitable spline function, identifying a practical temperature metric, and conducting model diagnostics and sensitivity analyses. 49 Table 5 Descriptive statistics of outcome variables Outcomes % of individual observations before aggregation Mean (SD) of 3-day sum Range (Min-Max) of 3-day sum All-cause mortality N = 58,304 71.4 (13.7) 43-311 Age Under 65 20.9% 15.0 (4.6) 5-64 65 or above 79.1% 56.5 (11.2) 30-253 Location Out-of-care 22.4% 16.3 (9.2) 3-218 In-care 77.6% 55.3 (7.7) 34-101 Sex Female 47.4% 33.9 (7.6) 13-167 Male 52.2% 37.2 (8.7) 16-144 PharmaCare Plan C 9.3% 7.5 (3.1) 3-60 Other plans 82.3% 58.8 (3.1) 29-230 Unknown 8.5% 7.1 (3.0) 3-52 Comorbidity-specific all-cause mortality N = 53,166 Respiratory 28.7% 18.8 (5.91) 5-104 Circulatory 79.7% 51.9 (10.9) 28-244 Chronic kidney disease and diabetes 44.5% 29.0 (9.3) 6-156 Mental health and substance use disorder 57.8% 37.6 (10.1) 15-200 Other comorbidity groups 63.1% 41.2 (9.4) 20-196 Less than 3 multimorbidity 20.6% 13.6 (3.8) 4-34 3 or more multimorbidity 79.4% 51.7 (12.2) 30-271 Chronic-condition-specific all-cause mortality N = 53,166 Heart failure 26.8% 17.6 (5.2) 4-89 Ischemic stroke 7.0% 5.9 (1.9) 3-24 Acute myocardial infarction 9.6% 7.2 (2.4) 3-29 50 Schizophrenia 4.5% 4.9 (2.2) 3-41 Substance use disorder 15.3% 10.6 (4.4) 3-60 Chronic obstructive pulmonary disease 20.5% 13.7 (4.6) 3-80 Parkinsonism 3.4% 4.2 (1.2) 3-11 Chronic kidney disease 23.4% 15.6 (7.1) 4-104 Other chronic conditions 36.9% 24.0 (5.5) 9-80 Comorbidity-specific all-cause mortality among those aged under 65 N = 22,184 Respiratory 10.1% 4.7 (1.6) 3-18 Circulatory 16.2% 5.9 (2.1) 3-28 Chronic kidney disease and diabetes 10.6% 4.8 (1.6) 3-24 Mental health and substance use disorder 30.3% 9.4 (3.4) 3-53 Other comorbidity groups 11.0% 4.9 (1.6) 3-19 Less than 3 multimorbidity 14.4% 5.5 (1.7) 3-14 3 or more multimorbidity 24.4% 7.8 (3) 3-50 Comorbidity-specific all-cause mortality among those aged 65 or above N = 111,635 Respiratory 11.0% 16.0 (5.2) 4-86 Circulatory 32.7% 47.3 (10.1) 25-219 Chronic kidney disease and diabetes 18.0% 26.0 (8.3) 5-132 Mental health and substance use disorder 20.1% 29.0 (8.0) 13-149 Other comorbidity groups 26.3% 38.0 (8.9) 17-180 Less than 3 multimorbidity 6.4% 9.8 (3.0) 3-25 3 or more multimorbidity 31.0% 44.8 (10.4) 23-226 All-cause ED visits N = 3,397,259 6660.8 (557.1) 4424-8236 Heat-related ED visits N = 123,670 242.2 (43.5) 154-969 Age Under 19 5.2% 13.0 (4.5) 3-44 20-39 14.9% 36.2 (9.1) 14-119 40-64 24.1% 58.4 (11.3) 28-188 51 65 or above 55.7% 134.7 (27.8) 90-618 Sex Female 49.9% 120.8 (23.9) 64-511 Male 50.0% 121.1 (23.3) 70-457 Acuity level High 27.6% 66.8 (19.3) 37-435 Low 72.1% 174.6 (28.4) 111-532 PharmaCare Plan C 13.9% 33.7 (9.8) 12-135 Other plans 75.6% 183.2 (32.5) 124-733 Unknown 10.5% 25.5 (7.3) 7-101 Comorbidity-specific heat-related ED visits N = 103,772 Respiratory 30.6% 62.2 (13.2) 37-272 Circulatory 71.3% 145.0 (26.8) 89-620 Chronic kidney disease and diabetes 48.0% 97.6 (19.3) 61-416 Mental health and substance use disorder 68.3% 138.8 (25.1) 88-552 Other comorbidity groups 56.8% 115.3 (22.3) 71-517 Less than 3 multimorbidity 21.6% 43.7 (9.8) 24-145 3 or more multimorbidity 78.4% 159.5 (29.9) 105-693 Chronic-condition-specific heat-related ED visits N = 103,772 Heart failure 21.8% 44.5 (10.6) 21-105 Ischemic stroke 6.4% 13.4 (4.4) 4-53 Acute myocardial infarction 8.9% 18.2 (5.3) 6-76 Schizophrenia 6.6% 13.6 (4.7) 5-66 Substance use disorder 22.2% 44.9 (10.5) 20-152 Chronic obstructive pulmonary disease 16.7% 33.9 (8.8) 14-163 Parkinsonism 3.6% 8.0 (3.5) 3-64 Chronic kidney disease 28.3% 57.7 (12.8) 31-267 Other chronic conditions 37.7% 76.6 (15.1) 41-271 52 Comorbidity-specific heat-related ED visits among those aged under 40 N = 29,233 Respiratory 7.7% 9.2 (3.1) 3-29 Circulatory 12.3% 4.5 (1.3) 3-9 Chronic kidney disease and diabetes 8.0% 4.7 (1.0) 1.44-3 Mental health and substance use disorder 23.0% 20.9 (5.7) 7-64 Other comorbidity groups 8.3% 4.9 (1.6) 3-11 Less than 3 multimorbidity 10.9% 14.0 (4.1) 3-41 3 or more multimorbidity 18.5% 11.8 (4.0) 3-37 Comorbidity-specific heat-related ED visits among those aged between 40-64 N = 58,256 Respiratory 3.9% 14.5 (4.4) 3-48 Circulatory 6.2% 27.2 (6.1) 11-76 Chronic kidney disease and diabetes 4.0% 19.0 (4.9) 5-48 Mental health and substance use disorder 11.5% 40.1 (8.7) 18-139 Other comorbidity groups 4.2% 18.9 (5.0) 6-57 Less than 3 multimorbidity 6.6% 13.5 (4.2) 3-46 3 or more multimorbidity 9.3% 35.8 (7.7) 16-115 Comorbidity-specific heat-related ED visits among those aged 65 or above N = 156,490 Respiratory 1.4% 39.2 (9.7) 19-195 Circulatory 2.3% 115.4 (23.7) 74-539 Chronic kidney disease and diabetes 1.5% 75.9 (17.1) 45-370 Mental health and substance use disorder 4.3% 77.9 (16.2) 48-349 Other comorbidity groups 1.6% 93.2 (19.9) 53-457 Less than 3 multimorbidity 2.0% 16.7 (5.7) 5-59 3 or more multimorbidity 3.5% 112.2 (23.6) 71-544 All-cause ambulance dispatches N = 857,812 1164.2 (116.0) 822-1509 Heat-related ambulance dispatches N = 93,990 123.5 (31.1) 64-410 Acuity level High 81.8% 98.6 (29.7) 42-362 53 Low 18.2% 22.9 (5.8) 4-54 Table 6 Descriptive statistics of temperature metrics Temperature measure (°C) Observed (N = 2,448) Forecast â 2 days before (N = 2,448) Forecast â 3 days before (N = 2,448) Mean (SD) Min-max Mean (SD) Min-max Mean (SD) Min-max Daily high 20.4 (3.5) 10.5-34.4 20.14 (3.5) 10.0-34.0 20.16 (3.46) 10.0-32.0 Overnight low 12.2 (2.8) 2.0-22.4 12.77 (2.7) 2.0-24.0 12.92 (2.68) 3.0-24.0 HL 32.6 (5.9) 13.6-56.4 32.90 (5.9) 14.0-58.0 33.07 (5.87) 16.0-56.0 LH 32.6 (5.8) 15.3-56.8 32.91 (5.7) 16.0-55.0 33.08 (5.75) 16.0-54.0 HLH 52.9 (9.0) 26.9-90.8 53.04 (9.0) 27.0-88.0 53.24 (8.99) 29.0-86.0 LHL 44.8 (8.3) 17.8-76.6 45.66 (8.2) 17.0-79.0 45.97 (8.26) 22.0-76.0 HLHL 65.2 (11.4) 29.6-107.7 65.79 (11.5) 29.0-110.0 66.11 (11.47) 34.0-107.0 LHLH 65.2 (11.3) 31.8-111.0 65.80 (11.2) 31.0-109.0 66.14 (11.28) 35.0-106.0 HLHLH 85.6 (14.3) 44.1-141.9 85.93 (14.4) 43.0-140.0 86.28 (14.43) 46.0-137.0 54 3.2.1 A GAM with Negative Binomial Distribution Appendix A.2 presents histograms, boxplots, and decomposition plots of the additive time series for the 3-day sum of daily health outcomes. Visual inspection of these distributions showed varying degrees of non-normality and skewness across all three health outcomes. To further investigate the distributional properties, both dispersion analysis and the Shapiro-Wilk normality test were applied. The results confirmed overdispersion and significant non-normality for all health outcomes (Table 7). In addition, boxplots displaying the outcomes by year and the decomposition plots (Appendix A.2) highlighted evident long-term temporal trends and nonlinearity within the data. Given these findings, a GAM with a negative binomial distribution was selected for all three health outcomes to account for overdispersion and time-dependent variations. The GAM in general provides a flexible framework for modeling complex, nonlinear relationships between the predictors and outcomes, allowing for a more accurate representation of the underlying data patterns122. In addition, the negative binomial distribution is well-suited for non-normal data, particularly when dealing with count data that exhibits overdispersion and offers full likelihood-based inference such as AIC and likelihood ratio tests122,125. Table 7 Dispersion and normality tests of outcomes All-cause mortality Heat-related ED visits Heat-related ambulance dispatches Daily 3-day Daily 3-day Daily 3-day Dispersion 1.7 2.6 3.7 7.8 3.6 7.8 55 Shapiro-Wilk test 0.83*** 0.76*** 0.7*** 0.6*** 0.9*** 0.9*** * Dispersion parameter = ð£ððððððð ððððâ ; the coefficient in Shapiro-Wilk test is W; Significance: *p < 0.05, **p < 0.01, ***p < 0.001 3.2.2 Selection of Spline Function Given the strong long-term trends observed in all three health outcomes, smoothing functions were optimal for capturing complex, nonlinear temporal patterns, reducing short-term noise, and minimizing the risk of overfitting. Within the GAM framework, a penalized spline function was employed for the time variable. This approach allowed for smooth modeling of long-term trends while also preventing overfitting by regulating the modelâs complexity. To determine the optimal degrees of freedom or the number of "knots" for adjusting the spline's smoothness, different knots were tested, ranging from 1 to 5 knots per calendar year across the study periods for the three health outcomes. For all-cause mortality, models with 16, 32, 48, 64, and 80 knots were tested over the 16-year study period, with the model using 80 knots yielding the lowest AIC. Similarly, for heat-related ED visits, models with 10, 20, 30, 40, and 50 knots were tested for the 10-year study period, with the model using 40 knots having the smallest AIC. For heat-related ambulance dispatches, models with 14, 28, 42, 56, and 70 knots were tested over the 14-year study period, with 70 knots providing the best fit based on the AIC. Detailed results of the knot selection process are provided in Appendix A.3. 3.2.3 Temperature Metric Selection 3.2.3.1 Statistical Performance Seven cumulative temperature metrics were evaluated for their statistical performance across all three health outcomes, with daily maximum and minimum temperatures also included 56 as reference metrics. These temperature metrics, using observed data, were modeled against the 3-day sum of the outcomes. The significance test and AIC value for each model were extracted (Table 8). Among the temperature metrics, HLH, LHL, HLHL, LHLH, and HLHLH consistently yielded lower AIC values across 3-day aggregated outcomes compared to the rest, and their inclusion in the models was statistically significant. In addition to AIC comparisons, RMSE values tended to increase as more days were incorporated into the temperature metrics. Overall, all cumulative metrics demonstrated stable and comparable statistical performance. 3.2.3.2 Operational Considerations Among the seven cumulative metrics, HLH aligns closely with the absolute thresholds currently used by BC HARS, which define heat warnings based on HLH cutoffs. This practical compatibility suggests that the implementation of HLH in public health interventions can be streamlined without major adjustments to existing alert systems. The simplicity of HLHâincorporating only three temperature variables (two daily highs plus an overnight low)âenhances not only its day-to-day forecast reliability but also public acceptance of multi-day alerts. Research on weather warnings has shown that shorter lead times in advance tend to minimize âwarning fatigueâ while still providing adequate time to prepare108,126â128. Systems that push warnings too far in advance can erode public compliance if forecast details shift or if repeated alerts do not manifest as predicted126,128,129. By focusing on a shorter but cumulative and actionable window, HLH naturally fits a good balance between forecast confidence and public engagement. 57 HLH was therefore selected as an example of a cumulative temperature metric for subsequent analyses. Table 8 Results of the temperature metric evaluation Temperature indicators P-value AIC - mortality AIC - heat-related ED visits AIC - heat-related ambulance dispatches RMSE: forecast made 2 days before RMSE: forecast made 3 days before Daily maximum < 0.001 17780 (9) 13514 (5) 17495 (5) 1.5 (1) 1.7 (1) Overnight minimum < 0.001 17649 (7) 13623 (7) 17786 (9) 1.6 (2) 1.8 (2) HL < 0.001 17698 (8) 13620 (6) 17675 (6) 2.16 (3) 2.2 (3) LH < 0.001 17582 (5) 13408 (2) 17482 (2) 2.18 (4) 2.3 (4) HLH < 0.001 17626 (6) 13401 (1) 17470 (1) 2.8 (5) 3.3 (5) LHL < 0.001 17485 (3) 13665 (8) 17682 (8) 3.1 (6) 3.5 (6) HLHL < 0.001 17522 (4) 13694 (9) 17680 (7) 3.4 (7) 4.0 (7) LHLH < 0.001 17393 (1) 13414 (3) 17483 (3) 3.5 (8) 4.1 (8) HLHLH < 0.001 17415 (2) 13478 (4) 17494 (4) 3.9 (9) 4.6 (9) * The numbers in the parentheses for AIC and RMSE values are their corresponding rank values. 3.2.4 Model diagnostics For each of the three models, several diagnostic plots were generated to assess the adequacy of model fit and detect potential concerns with residuals or model assumptions. These included smooth term plots for HLH and time, residual plots, and quantile-quantile (Q-Q) plots for deviance residuals (Appendix A.4). The smooth term plots for HLH showed a relatively flat effect across much of the range, with a notable upward trend at higher exposure levels, particularly above 70 in all models. 58 Confidence intervals remained narrow for most of the range but widened at the extremes, indicating increased uncertainty at higher HLH values. The time variable exhibited some fluctuations, especially during the COVID-19 pandemic, but with narrow confidence intervals throughout, indicating a good model fit. In all models, the Q-Q plots demonstrated that the residuals closely followed the theoretical quantiles, suggesting the residuals were approximately normally distributed. Slight deviations were observed at the extremes, particularly at higher HLH levels, possibly due to the two EHEs in 2009 and 2021, which may have introduced outliers. Overall, the diagnostics indicate that all three models fit the data well, with no major violations of model assumptions. 3.2.5 Sensitivity analysis Sensitivity analysis included using daily health outcomes instead of the 3-day sum to evaluate the selection of the temperature metric (Appendix A.5) and testing alternative numbers of knots for the spline terms in the three outcomes. For all-cause mortality, heat-related ED visits, and heat-related ambulance dispatches, 32, 20, and 28 knots were applied, respectively (Appendix A.5). The models with these alternative knots yielded similar results. Furthermore, GAMs with a Poisson distribution, using the same number of knots for the spline functions as specified in Section 3.2.2 Selection of Spline Function, were also examined (Appendix A.5). The results led to the same conclusion. 59 3.3 Identifying HLH Thresholds The overall thresholds of HLH for the three health outcomes were determined using split value analysis. For all-cause mortality (Figure 7), a threshold of 58°C (OR 1.02, 95% CI: 1.00â1.03) was identified as the cut point where a positive relationship emerged between the above-split ORs and the first separation between the below-split and above-split intervals. Another separation point was observed at 44°C for all-cause mortality; however, the above-44 split OR was 0.98 (95% CI: 0.97â0.99), indicating a negative relationship. This negative association was likely attributable to cooler temperatures, as 72% of the cases (108 out of 151) where HLH ranged between 44°C and 46°C occurred in May or September. For heat-related ED visits and ambulance dispatches (Figure 8), the thresholds were identified as 47°C (OR 1.02, 95% CI: 1.00â1.04) and 42 (OR 1.03, 95% CI: 1.00â1.05), respectively. Figure 7 Odds ratios of High-Low-High split values in all-cause mortality 60 Figure 8 Odds ratios of High-Low-High split values in heat-related ED visits and ambulance dispatches 3.4 Mortality-Based Threshold Algorithm: An Example of Threshold Application Using the cut point of 58°C identified for all-cause mortality, each HLH combination ð¸ð â¥ ð, where 58°C â¤ ð â¤ 75°C, was modelled against cases where HLH was below 58°C for all three health outcomes. A consistent pattern of increasing ORs with higher HLH values was observed for all-cause mortality (Figure 9). 3.4.1 Identifying L Threshold Each variation in the overnight low for each HLH combination ð¸ð â¥ ð, where 58°C â¤ð â¤ 75°C, was modelled against cases where HLH was below 58°C to identify the overnight low 61 L threshold driving risk. An overnight low of 15°C was identified as the nadir of the risk curve, serving as the L threshold. Specifically, for all-cause mortality, the OR at 14°C was 0.98 (95% CI: 0.96â1.00), rising to 1.01 (95% CI: 0.99â1.03) when the overnight low reached 15°C, with the OR continuing to increase as the overnight low rose further (Figure 10). The U-shaped pattern observed in the plot suggests that overnight low temperatures â¥ 15°C drive the risk, while daytime high temperatures are the primary risk drivers when overnight lows are < 15°C. 3.4.2 Identifying HH Threshold To determine the threshold HH for two daily high temperatures, each variation in daily highs for each HLH combination ð¸ð â¥ ð, where 58°C â¤ ð â¤ 75°C and the overnight low < 15°C was modelled against cases where HLH was below 58°C. A value of 51°C was identified as the HH threshold, using all-cause mortality. The ORs for daily highs below 51°C remained relatively stable around 1, with narrow CIs. However, at 51°C, the OR was 1.0 (95% CI: 0.94â1.06) and increased to 1.03 (95% CI: 0.91â1.17) when daily high temperatures reached 52°C (Figure 11).62 Figure 9 Odds ratios of High-Low-High values for all-cause mortality Note: Above each range bar is the frequency of HLH values over the entire study period, along with the corresponding OR. °C 63 Figure 10 Odds ratios of overnight low groups when High-Low-High >= 58, against High-Low-High < 58 Odds ratio °C 64 Figure 11 Odds ratios of daily highs when High-Low-High >= 58 and L < 15, compared with High-Low-High < 58 °C 65 3.4.3 Mortality-Based HLH Threshold Algorithm The previously identified baseline risk threshold B for HLH, as well as the cut-off points L and HH for overnight low and daily high temperatures, form the final HLH threshold algorithm: (ð»ð¿ð» â¥ 58°C) â (ð¿ â¥ 15°C â ð»ð» â¥ 51°C). This algorithm reflects a more flexible and dynamic approach to assessing heat-related health risks by incorporating the risk associated with an HLH value of 58°C or greater, and further considering risk when overnight low reaches 15°C or higher, or daily high temperatures exceed 51°C (Figure 13). 3.4.3.1 Algorithm Sensitivity Analysis 1 In the first sensitivity analysis ( Figure 14), temperature forecasts made 2 and 3 days in advance were applied to the mortality-based HLH threshold algorithm for all three health outcomes. Overall, the U-shaped risk patterns derived from the forecasted temperature data were consistent with those based on observed temperature measurements. However, when HLH values were greater than or equal to 73°C, the temperature forecasts (both 2 and 3 days in advance) produced higher ORs than the observed data, leading to an overestimation of risk. Additionally, in the plots in the Appendix (Figure 48 and Figure 49), the frequency of days meeting each threshold combination (expressed as a percentage of total calendar time) is displayed above the 95% CIs. These plots indicated that the forecast temperature data tended to overestimate the frequency of days that met each threshold combination when HLH was below 73°C, while underestimating the frequency when HLH exceeded 73°C. 66 In other words, when HLH was below 73°C, the ORs for each threshold combination were similar between observed and forecasted temperature data, but the forecast data overestimated the number of days that met threshold criteria. On the other hand, when HLH was 73°C or higher, the forecast data underestimated the number of days meeting the threshold criteria and overestimated the associated risk. 3.4.3.2 Algorithm Sensitivity Analysis 2 Another sensitivity analysis compared the dates that met the HLH absolute thresholds with those (Day t) that met the corresponding cumulative thresholds. In Figure 12, dates that fulfill these criteria are coloured green. As shown in the table, the current absolute HLH thresholds (29°Câ16°Câ29°C) used by BC HARS captured only two heat events (in 2009 and 2021), whereas the cumulative threshold algorithm (HLH >= 74°C & (HH >= 51°C | L >= 15°C) identified additional occurrences. A GAM was then fitted to estimate ORs for all-cause mortality across two subsets of dates: (1) those flagged by the absolute thresholds and (2) those missed by the absolute thresholds but still caught by the cumulative threshold algorithm, compared with all HLH values under the 58°C baseline. Compared with HLH < 58°C, the OR of mortality was 3.24 (95% CI: 3.05â3.44) for dates flagged by the absolute thresholds and 1.13 (95% CI: 1.08â1.18) for dates not flagged by the absolute thresholds but detected by the cumulative threshold. These results suggest that while BC HARSâs current absolute thresholds can capture high-risk heat events, they may fail to identify moderate-risk events. 67 Figure 12 Comparison of Dates Meeting Absolute and Cumulative High-Low-High Thresholds * In column 4, the numbers are real HLH temperature readings for the dates in column 2. 3.5 Extra Split Value Analysis on All-Cause ED Visits and Ambulance Dispatches An additional split value analysis was conducted for all-cause ED visits and ambulance dispatches to investigate whether these outcomes displayed different or similar risk patterns compared with their heat-related counterparts (Figure 50 and Figure 51). For all-cause ED visits, no marked increase in the risk curve was observed. In the case of all-cause ambulance dispatches, the first separation point between the 95% CIs for above-split and below-split value 68 was similar to that of heat-related ambulance dispatches, but the risk curve appeared less steep and relatively flat. Unlike mortality data, the volume of ED visits and ambulance dispatches is inherently limited by the capacity of the healthcare system, which can constrain the outcomes themselves. This structural limitation makes it challenging to detect a clear relationship between heat exposure and these all-cause morbidity outcomes, because the number of emergency rooms and ambulances in service may not increase proportionally with rising temperatures.69 Figure 13 High-Low-High algorithm plot, using observed temperature data Odds ratio °C 70 Figure 14 High-Low-High algorithm plot, using observed and forecast temperature data °C 71 3.6 Application of Mortality-Based HLH Algorithm to Subgroups The HLH algorithm was applied to mortality and morbidity subgroups to explore its applicability and whether the algorithm could accurately capture the heat risks experienced by subpopulations extensively studied in prior research. Scenarios ranging from HLH â¥ 58°C to HLH â¥ 75°C while keeping L â¥ 15°C and HH â¥ 51°C fixed were modeled against HLH < 58°C. A comprehensive set of results from these analyses is provided in Appendix C (page122). For simplicity, this section focuses solely on mortality and morbidity outcomes when HLH ï³ 75°C & HH ï³ 51°C | L ï³ 15°C. To provide context, all-cause mortality exhibited an OR of 1.67 (95% CI: 1.57-1.78), while the ORs of heat-related ED visits and ambulance dispatches were 1.99 (95% CI: 1.89-2.11) and 1.75 (95% CI: 1.64-1.87), respectively, at an HLH threshold of 75°C +. 3.6.1 Chronic Condition-Specific Outcomes At an HLH of 75°C +, all-cause mortality among individuals with specific chronic conditions showed notable differences (Figure 15). Schizophrenia was associated with the highest mortality risk, with an OR of 2.61 (95% CI: 2.23-3.07). This was followed by CKD with an OR of 1.95 (95% CI: 1.77-2.16), SUD (OR 1.80, 95% CI: 1.60-2.02), COPD (OR 1.79, 95% CI: 1.59-2.02), other chronic conditions (OR 1.74, 95% CI: 1.61-1.88), heart failure (OR 1.71, 95% CI: 1.53-1.91), ischemic stroke (OR 1.61, 95% CI: 1.33-1.95), AMI (OR 1.48, 95% CI: 1.25-1.76), and Parkinsonâs (OR 1.44, 95% CI: 1.12-1.86). In contrast, the risk profile for heat-related ED visits showed a different hierarchy. Parkinsonâs presented the highest risk, with an OR of 3.24 (95% CI: 2.77-3.78), closely followed by heart failure (OR 2.46, 95% CI: 2.24-2.70). AMI (OR 2.39, 95% CI: 2.12-2.68) and COPD (OR 2.39, 95% CI: 2.15-2.66) had similar odds of ED visits, followed by schizophrenia (OR 72 2.37, 95% CI: 2.10-2.67), CKD (OR 2.29, 95% CI: 2.11-2.49), and ischemic stroke (OR 2.11, 95% CI: 1.83-2.44). Other chronic conditions and SUD posed more moderate risks of ED visits, with ORs of 1.89 (95% CI: 1.75-2.02) and 1.76 (95% CI: 1.60-1.93), respectively. 3.6.2 Condition Groups and Multimorbidity At an HLH of 75°C +, mortality and ED visits for individuals with multiple comorbidities showed slight variations in their risk profiles (Figure 16). For heat-related ED visits, individuals with respiratory conditions and those with CKD and diabetes showed the highest risks, both with an OR of 1.80 (95% CI: 1.62-2.01 for respiratory conditions; 1.65-1.96 for CKD and diabetes). Mental health and SUD (OR 1.73, 95% CI: 1.59-1.88), as well as other comorbidities (OR 1.74, 95% CI: 1.61-1.88) showed moderately elevated risks. Those with circulatory conditions (OR 1.67, 95% CI: 1.55-1.79) had morbidity risks comparable to the average risk of all heat-related ED visits. For mortality outcomes, individuals with respiratory conditions also had the highest odds (OR 2.25, 95% CI: 2.08-2.43), followed by those with other comorbidities (OR 2.24, 95% CI: 2.09-2.40) and circulatory conditions (OR 2.23, 95% CI: 2.09-2.37). CKD and diabetes (OR 2.17, 95% CI: 2.02-2.33) also presented substantial risk. Mental health and SUD, although slightly lower (OR 2.06, 95% CI: 1.93-2.19), still posed a higher risk than the overall odds for all-cause mortality. Furthermore, those with three or more comorbidities faced elevated mortality and morbidity risks, with an OR of 1.71 (95% CI: 1.59-1.84) for heat-related ED visits and an OR of 2.19 (95% CI: 2.06-2.33) for all-cause mortality at HLH 75°C +, exceeding the risk for those with fewer comorbidities. 73 Figure 15 Odds ratios of Morbidity and Mortality Across Chronic Conditions at HLH >= 75°C & (HH >= 51°C | L >= 15°C) 74 Figure 16 Odds ratios of Morbidity and Mortality Across Comorbidity Groups at HLH >= 75°C & (HH >= 51°C | L >= 15°C) 75 3.6.3 Demographic-Related Mortality and Morbidity Demographic factors further stratified the risks of mortality and morbidity at an HLH of 75°C + (Figure 17). Individuals who died out of care experienced the highest mortality risk at an HLH of 75°C +, with an OR of 2.65 (95% CI: 2.36-2.98), much higher than those who died in care settings (OR 1.21, 95% CI: 1.13-1.29), such as hospitals or long-term care facilities. Females had a higher mortality risk compared to males, with an OR of 1.82 (95% CI: 1.67-1.97) versus 1.52 (95% CI: 1.41-1.65). In terms of morbidity, females also had slightly higher odds for ED visits, with an OR of 2.05 (95% CI: 1.92-2.18), compared with males at 1.99 (95% CI: 1.87-2.12). Age-related outcomes indicated that individuals aged 65 and older had a mortality OR of 1.67 (95% CI: 1.56â1.78), higher than those under 65 (OR 1.56, 95% CI: 1.39â1.75). ED visits among younger individuals aged 20â39 had the lowest OR (1.82, 95% CI: 1.66â1.99), while those aged 65 and older had the highest OR (2.25, 95% CI: 2.10â2.40). In the age-specific comorbidity analysis, individuals aged 65 and above had higher ORs for mortality in the respiratory and other comorbidity groups compared to younger age groups. However, this trend was reversed for morbidity, as individuals aged 65 and above consistently showed the highest ORs for heat-related ED visits across all comorbidity group. This pattern was reversed for morbidity, because heat-related ED visits among those aged 65 or above consistently showed the highest ORs across all comorbidity groups. Furthermore, PharmaCare plan coverage significantly influenced mortality and morbidity risks. Individuals on Plan C faced the highest mortality odds (OR 2.38, 95% CI: 2.09â2.71), followed by those with an unknown plan (OR 1.90, 95% CI: 1.62â2.22). Those with other plans had a slightly lower risk (OR 1.53, 95% CI: 1.43â1.64). For heat-related ED visits, individuals 76 with an unknown plan had the highest odds (OR 2.16, 95% CI: 1.95â2.40), followed by Plan C (OR 2.04, 95% CI: 1.84â2.26) and other plans (OR 1.97, 95% CI: 1.86â2.09). Figure 17 Odds ratios of Morbidity and Mortality Across Demographic Groups at HLH >= 75°C & (HH >= 51°C | L >= 15°C) 77 3.6.4 Acuity of Heat-Related ED Visits High-acuity heat-related ED visits had an OR of 2.78 (95% CI: 2.54â3.03), whereas low-acuity visits had a lower OR of 1.80 (95% CI: 1.70â1.90) at an HLH of 75°C +. For heat-related ambulance dispatches, there was little distinction between high-acuity (OR 1.74, 95% CI: 1.62â1.88) and low-acuity (OR 1.73, 95% CI: 1.57â1.91) outcomes.78 Chapter 4: Discussion and Conclusions 4.1 Summary of Findings 4.1.1 Methods to Identify Cumulative Temperature Thresholds Comprehensive methods were applied to identify cumulative temperature exposure and thresholds associated with increased health risks, focusing on all-cause mortality, heat-related ED visits, and heat-related ambulance dispatches in the WMV region from 2008 to 2023. Seven cumulative temperature metrics, involving combinations of daily high and overnight low temperatures over multiple days, were evaluated alongside daily maximum and minimum temperatures as reference metrics. Each metric was modeled against the 3-day sum of health outcomes using observed temperature data. The significance tests, model AIC values, and RMSEs were applied to check the statistical performance of the metrics. All metrics showed stable and comparable statistical performance. The HLH metric was selected as an example for further analysis not only for its statistics, but also its alignment with the existing use of absolute HLH thresholds in BC HARS. Using a split-value analysis, three baseline thresholds for the health outcomes were identified where ORs surpassed 1.0: 58°C for all-cause mortality, 47°C for heat-related ED visits, and 42°C for ambulance dispatches. These results suggest that ED visits and ambulance dispatches may respond to earlier or milder heat exposuresâthus requiring a lower thresholdâwhile mortality appears to escalate at a higher cumulative temperature. Next, the threshold of 58°C identified from all-cause mortality data was used as an example to explore its application in health outcomes. At the same time, limiting the threshold to a mortality-driven metric does mean that non-fatal health outcomes, such as heat-related ED visits or ambulance calls, begin occurring below 58°C (i.e., at 47°C or 42°C). In practice, 79 research aiming to capture all heat impacts may consider exploring the application of morbidity-based thresholds. Subsequent analyses showed that the mortality-based threshold of 58°C could be further refined by separating overnight low (L) and two daytime highs (HH). An overnight low of 15°C emerged as the point above which mortality odds rose significantly, and a sum of two daily highs of 51°C was similarly associated with increased risks. Integrating these, a mortality-based HLH algorithm was formulated: HLH â¥ 58°C and (L â¥ 15°C or HH â¥ 51°C). This multi-trigger structure offers more flexibility than rigid, absolute value cut-offs, as it can capture different patterns of warm days and nights. Applying the HLH threshold algorithm using two- and three-day forecasted temperature data yielded risk patterns consistent with those based on observed temperatures, although forecasted data tended to overestimate risk at higher HLH values (> 73°C) and the frequency of days meeting threshold criteria. Despite minor discrepancies, the HLH metric and threshold algorithm remained robust across different data sources. Additionally, a sensitivity analysis compared the dates caught by the standard HLH absolute thresholds (29°Câ16°Câ29°C) versus those identified by the new cumulative threshold algorithm. Results showed that the absolute thresholds captured only the most extreme eventsânamely those in 2009 and 2021âwhereas the cumulative threshold approach flagged additional periods of moderately high but still hazardous temperatures. 4.1.2 Health impacts of cumulative temperature on subgroups The application of the mortality-based HLH threshold algorithm to subpopulations showed significant disparities in heat-related health outcomes. These findings align with existing 80 literature, demonstrating that the algorithm is sensitive enough to identify different risks across subgroups. Individuals with specific chronic conditions exhibited markedly higher risks. Schizophrenia was associated with the highest mortality during periods of elevated cumulative temperature. Chronic health conditions such as CKD, SUD, COPD, and heart failure also correlated with increased mortality. In terms of morbidity, as measured by heat-related ED visits, the risk profiles altered slightly compared with mortality. Prior diagnosis of Parkinsonâs, heart failure, AMI, COPD, and schizophrenia were linked to substantially higher risks for a heat-related ED visit. The presence of multiple comorbidities also intensified these mortality and morbidity outcomes, as individuals with three or more chronic conditions faced significantly higher heat-related mortality and morbidity risks compared to those with fewer health issues. These results were consistent with the current literature on risk factors for mortality and morbidity outcomes during EHEs49,54,68,72,79â81,83,84. 4.2 Comparison With Existing Literature 4.2.1 Alignment With Other Compound Heat Studies The findings in this research underscore that cumulative temperature metric over two days and a night better aligns with the health burden of extreme heat than metrics based on absolute values. This conclusion aligns with an emerging consensus in the literature that âcompoundâ day-night heat events pose higher mortality and morbidity risks because they offer litter or no relief overnight and increase the total heat load on the human body13,111,112. Without adequate nocturnal cooling, the human body struggles to dissipate stored heat, heightening both cardiovascular and thermoregulatory strain13,130 81 Moreover, the choice of HLH metric aligns with multi-day approaches such as the Excess Heat Factor method used in Australia131. Both frameworks incorporate daytime peaks and overnight lows to capture sustained heat stress over multiple daysâa key factor absent in single-day or single-threshold models. Such approaches better reflect the protracted nature of heat exposure, which has consistently been shown to correlate more strongly with adverse health outcomes than short-lived temperature spikes2,111 4.2.2 Flexibility Versus Rigid Absolute Thresholds Many HHWSs often rely on rigid temperature cut pointsâfor instance, a heat alert is issued if maximum temperature â¥29°C and minimum temperature â¥16°C for two consecutive days1,2. While straightforward, these rigid thresholds treat alerts as all-or-nothing: if one parameter falls marginally below the cut-off, no warning is issued, even if total heat exposure remains dangerously high. In contrast, the HLH algorithm is more dynamic. Rather than fixating on a single pair of temperature cut-offs, the mortality-based threshold algorithm integrates multiple componentsâan HLH of 58°C while an overnight low â¥ 15°C or the sum of two daily highs â¥ 51°C. This structure accommodates diverse temperature patterns that can collectively stress the body. If overnight lows remain moderate, yet two daytime highs in a row are exceptionally warm, HLH still flags elevated risk. Conversely, if daytime highs are only slightly above average but the overnight low stays â¥15°C (eliminating nighttime relief), the algorithm again highlights the heightened biological strain. From a physiological standpoint, this flexibility can improve biological relevance: the bodyâs thermoregulatory burden depends on both the âceilingâ of daily highs and the lack of 82 cooling at night. By capturing those dual pathways to cumulative heat stress, the HLH metric helps reduce false negatives that purely absolute thresholds might miss. Ultimately, it aligns more closely with the real-world thermoregulatory challenges faced by vulnerable individuals, thereby providing a more nuanced and effective decision-making tool for public health interventions. 4.3 Strengths This research developed and tested advanced and innovative methods to assess extreme heat and its health impacts in WMV, addressing critical gaps in existing HHWSs. The study's strengths are multifaceted, including comprehensive data linkage, advanced methods for identifying cumulative heat exposure and thresholds, and detailed subgroup analyses. 4.3.1 Comprehensive Data Linkage A significant strength of this research lies in its integration of diverse and extensive health administrative datasets, thanks to PANDA team. By linking individual mortality data from the BC Vital Statistics Agency with morbidity data from the NACRS, comorbidity information from the CDR, and prescription coverage data from PharmaNet, the study achieved a granular level of analysis. This comprehensive data linkage enabled the examination of health outcomes across multiple dimensionsâtemporal, spatial, and demographicâproviding a rich context for developing cumulative heat thresholds and understanding the multifaceted impacts of EHEs. The ability to analyze individual-level data across different health data enhanced the validity of the findings. It allowed for precise identification of patterns and correlations between cumulative heat exposure and health outcomes, minimizing potential biases associated with 83 aggregated data. Moreover, the inclusion of detailed comorbidity and socioeconomic information facilitated a nuanced understanding of susceptibility and vulnerability factors, which is critical for informing targeted public health interventions. 4.3.2 Advanced Methods for Identifying Cumulative Heat-Risk Thresholds The study employed sophisticated statistical techniques to identify baseline thresholds for both overnight low temperatures and daily high temperatures within HLH, offering a more dynamic framework for HHWSs. This approach could more accurately reflect the true health risks posed by extended periods of high temperatures and capture both moderate and high-risk heat events. 4.3.3 Detailed Subgroup Analyses The detailed subgroup analyses shed light on the disproportionate impact of extreme heat on susceptible populations. Individuals with chronic conditions such as schizophrenia, CKD, heart failure, COPD, and multiple chronic conditions were identified as having markedly higher risks. The exploration of demographic factorsâage, sex, care setting, and socioeconomic indicatorsâhighlighted the intersectionality of susceptibility, emphasizing the need for targeted strategies to protect high-risk groups during extreme heat. 4.4 Limitations and Directions for Future Research While this study provides significant insights into the health impacts of cumulative heat exposure and advances methodologies for identifying heat-health thresholds, certain limitations must be acknowledged. Recognizing these limitations not only contextualizes the findings but 84 also highlights areas where future research can build upon this work to enhance understanding and inform public health strategies more effectively. 4.4.1 Misclassification of Incidence and Heat-Related Outcomes Although this study leveraged administrative data to identify incidence cases in CDR and heat-related ED and ambulance data in NACRS and MPDS, there remains potential for misclassification in these data sources. For instance, incidence cases in the CDR rely on specific billing or diagnostic codes, and patients with late or no contact with the healthcare system might be underrepresented. Moreover, conditions may not be captured in time, leading to delayed or missed registration of new diagnoses. Similarly, heat-related ED visits and ambulance dispatches were identified using a limited set of diagnostic or complaint codes. Patients presenting with non-specific symptoms may not have been flagged as heat-related if clinicians documented different primary reasons for care. Future research should explore enhanced coding protocols, natural language processing of clinical notes, or prospective chart reviews to refine the accuracy of these outcomes and better capture the true burden of heat morbidity. 4.4.2 Mortality-Based Algorithm Versus Morbidity Thresholds This study used the mortality-based (58°C) HLH threshold primarily as the algorithmic example, even though heat-related ED visits (47°C) and ambulance dispatches (42°C) also showed clear thresholds. Morbidity outcomes are important for preventing health-system strain and catching earlier or milder exposures; thus, future research should explore morbidity-focused 85 algorithms and evaluate whether multi-tiered thresholdsâencompassing mortality, ED visits, and ambulance dispatchesâyield more comprehensive HHWS criteria. 4.4.3 Unidentifiable Ambulance Dispatch Data Another limitation of this research is the inability to link individual-level ambulance dispatch data with other health records due to privacy constraints. The MPDS data used were deidentified and could not be connected to patient-specific information such as comorbidities, demographics, or subsequent health outcomes. This limitation restricted the analysis to aggregate-level associations and precluded a detailed examination of how cumulative heat exposure affected individuals requiring emergency services, particularly among susceptible subgroups. Future studies should advocate for policies and protocols that facilitate the secure linkage of ambulance dispatch data with other health records while respecting privacy concerns. Expanding data linkage to include additional health services would capture a broader spectrum of health outcomes and enable a more comprehensive analysis of the acute health impacts of extreme heat. 4.4.4 Exposure Misclassification, Geographical Scope, and Climatic Considerations Another limitation concerns exposure misclassification, particularly given that temperature data were drawn from a single weather station at YVR. Because this station sits on the coast in a relatively unpopulated area, local microclimatesâsuch as denser urban neighborhoods with higher heat-retaining surfacesâmay be under- or overrepresented by these readings. Moreover, populations living further inland can experience significantly warmer 86 conditions and less overnight cooling, introducing spatial heterogeneity in actual exposure. Consequently, the absolute temperature levels used to compute HLH and its thresholds may not perfectly capture on-the-ground temperatures for all WMV residents. In addition, BC HARS uses temperature data from the West Vancouver weather station to forecast conditions for metro Vancouver rather than from YVR, making it difficult to directly compare YVR-derived thresholds with West Vancouverâbased forecasts. The studyâs geographical focus on WMV, characterized by a temperate coastal climate, further limits generalizability to regions with different climatic conditions. The cumulative heat thresholds identified here may not translate well to tropical, arid, or continental climates, where temperature ranges, humidity levels, and baseline population acclimatization differ significantly. Additionally, this study did not incorporate humidity or âapparent temperatureâ metrics like the Humidex, which can strongly influence the physiological impact of heat. This omission means this research cannot fully account for the role of moisture in exacerbating heat stress, a key consideration in many non-temperate regions. Future research should address these limitations by incorporating multi-station networks, satellite data, or personal sensor measurements to reduce exposure misclassification and refine temperature estimates in both coastal and inland areas. Furthermore, conducting comparative studies in diverse regionsâparticularly those with varying levels of humidity, different altitudes, and contrasting urban infrastructuresâwould clarify how HLH metrics and thresholds perform in climates dissimilar to that of southwestern BC. Such efforts could also help develop location-specific HHWSs that capture local meteorological nuances, ensuring that alerts and interventions are appropriately tailored to both local conditions and population vulnerabilities. 87 4.4.5 Limited Socioeconomic and Demographic Data While the study included demographic variables such as age, sex, and a proxy for socioeconomic status through PharmaCare Plan C enrollment, it lacked detailed socioeconomic and racial data. Factors like income, education, occupation, housing conditions, and race or ethnicity are critical determinants of health and potential susceptibility to heat exposure. The absence of granular socioeconomic and demographic data limits the ability to fully assess the social determinants of health that contribute to heat risk and may hinder the identification of at-risk populations requiring targeted interventions. Future research should integrate individual-level socioeconomic data, if possible, to assess their influence on heat risk comprehensively. Collecting and analyzing data on race and ethnicity would help identify and address potential health disparities and environmental justice issues. Using geospatial analyses to examine the effects of urban heat islands and neighborhood characteristics on health outcomes can further elucidate the interplay between environmental and social factors. 4.4.6 Exclusion of Air Pollution The primary focus of this study was on methods development, and it did not account for air pollution, which can mediate the relationship between heat exposure and health outcomes132. High temperatures often coincide with elevated levels of pollutants like ozone and particulate matter, especially during wildfire seasons or in urban areas with significant vehicular emissions. Air pollution can exacerbate respiratory and cardiovascular conditions, potentially amplifying the health impacts of extreme heat. Incorporating air quality data into analyses would control for confounding effects and allow exploration of interactions between heat and pollution. 88 4.5 Conclusions In conclusion, this research advances the understanding of how cumulative heat exposure impacts health outcomes and provides a robust methodological framework and valuable insights for public health policy. The identification and application of dynamic temperature thresholds and the emphasis on cumulative exposure represent significant contributions to heat-health research, offering a more flexible, dynamic, and physiologically relevant basis for HHWSs. The detailed subgroup analyses underscore the importance of tailoring interventions to protect the most susceptible populations during EHEs. 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Epidemiology. 2014;25(2):242-245. doi:10.1097/EDE.0000000000000051 100 Appendices Appendix A Statistical Analysis of Objective 1 A.1 Sub-Appendix: Descriptive Statistics of Daily Maximum and Minimum Temperatures Figure 18 A.1 Histogram of observed daily maximum temperature in WMV °C 101 Figure 19 A.1 Boxplot of observed daily maximum temperature in WMV by year Figure 20 A.1 Histogram of observed daily minimum temperature in WMV °C 102 Figure 21 A.1 Boxplot of observed daily minimum temperature in WMV by year 103 A.2 Sub-Appendix: Descriptive Statistics of Health Outcomes Figure 22 A.2 Histogram of 3-day sum of daily all-cause death count in WMV Figure 23 A.2 Boxplot of 3-day sum of daily all-cause death count by year 104 Figure 24 A.2 Decomposed time series of 3-day sum of daily all-cause death count Figure 25 A.2 Histogram of 3-day sum of daily heat-related ED visits in WMV (year) 105 Figure 26 A.2 Boxplot of 3-day sum of daily heat-related ED visits by year Figure 27 A.2 Decomposed time series of 3-day sum of daily heat-related ED visits (year) 106 Figure 28 A.2 Histogram of 3-day sum of daily heat-related ambulance dispatches in WMV Figure 29 A.2 Boxplot of 3-day sum of daily heat-related ambulance dispatches by year 107 Figure 30 A.2 Decomposed time series of 3-day sum of daily heat-related ambulance dispatches A.3 Sub-Appendix: AICs of Different Knots for Outcomes Figure 31 A.3 AICs across different knots for all-cause mortality (year) 108 Figure 32 A.3 AICs across different knots for heat-related ED visits Figure 33 A.3 AICs across different knots for heat-related ambulance dispatches 109 A.4 Sub-Appendix: Model Diagnostics for Outcomes Figure 34 A.4 Residual plot of HLH for all-cause mortality model Figure 35 A.4 Residual plot of time for all-cause mortality model HLH value (ºC) 110 Figure 36 A.4 QQ plot for all-cause mortality model Figure 37 A.4 Smooth term plots for all-cause mortality model HLH value (ºC) 111 Figure 38 A.4 Residual plot of HLH for heat-related ED visits model Figure 39 A.4 Residual plot of time for heat-related ED visits model HLH value (ºC) 112 Figure 40 A.4 QQ plot for heat-related ED visits model Figure 41 A.4 Smooth term plots for heat-related ED visits model HLH value (ºC) 113 Figure 42 A.4 Residual plot of HLH for heat-related ambulance dispatches model Figure 43 A.4 Residual plot of time for heat-related ambulance dispatches model HLH value (ºC) 114 Figure 44 A.4 QQ plot for heat-related ambulance dispatches model Figure 45 A.4 Smooth term plots for heat-related ambulance dispatches model HLH value (ºC) 115 A.5 Sub-Appendix: Sensitivity Analysis for Objective 1 Table 9 A.5 AICs of temperature metrics modelled against daily health outcomes All-cause mortality Heat-related ED visits Heat-related ambulance dispatches Temperature measure (°C) p-value Above AIC median p-value Above AIC median p-value Above AIC median Daily high <0.001 <0.001 <0.001 Overnight low <0.001 <0.001 <0.001 HL <0.001 <0.001 <0.001 LH <0.001 <0.001 <0.001 HLH <0.001 <0.001 <0.001 LHL <0.001 <0.001 <0.001 HLHL <0.001 <0.001 <0.001 LHLH <0.001 <0.001 <0.001 HLHLH <0.001 <0.001 <0.001 116 Figure 46 A.5 AICs of temperature metrics using 32, 20, and 28 knots for all-cause mortality, heat-related ED visits, and heat-related ambulance dispatches respectively 117 Figure 47 A.5 AICs of temperature metrics using a Poisson distribution for all-cause mortality, heat-related ED visits, and heat-related ambulance dispatches 118 Appendix B Statistical Analysis of Objective 2 B.1 Sub-Appendix: Mortality-Based HLH Algorithm 1 Figure 48 B.1 HLH algorithm plot, using temperature forecast data made 2 days in advance ºC 119 B.2 Sub-Appendix: Mortality-Based HLH Algorithm 2 Figure 49 B.2 HLH algorithm plot, using temperature forecast data made 3 days before ºC 120 B.3 Sub-Appendix: Threshold Analysis of All-Cause ED Visits Figure 50 B.3 ORs of HLH split values in all-cause ED visits ºC 121 B.4 Sub-Appendix: Threshold Analysis of All-Cause Ambulance Dispatches Figure 51 B.4 ORs of HLH split values in all-cause ambulance dispatches ºC 122 Appendix C Results of Analyses on Subgroups C.1 Sub-Appendix: Comorbidity-Specific Mortality Figure 52 C.1 ORs of mortality across comorbidity groups HLH category (ºC) 123 C.2 Sub-Appendix: Multimorbidity-Specific Mortality Figure 53 C.2 ORs of mortality in multimorbidity group HLH category (ºC) 124 C.3 Sub-Appendix: Cardiovascular-Specific Mortality Figure 54 C.3 ORs of mortality in heart-related chronic conditions HLH category (ºC) 125 C.4 Sub-Appendix: Mental-Illness-Specific Mortality Figure 55 C.4 ORs of mortality in schizophrenia and SUD HLH category (ºC) 126 C.5 Sub-Appendix: Other-Chronic-Condition-Specific Mortality Figure 56 C.5 ORs of mortality among COPD, Parkinsonism, CKD, and other chronic conditions HLH category (ºC) 127 C.6 Sub-Appendix: Age-Specific Mortality Figure 57 C.6 ORs of mortality in age group HLH category (ºC) 128 C.7 Sub-Appendix: Location of Deaths Figure 58 C.7 ORs of mortality in location of death HLH category (ºC) 129 C.8 Sub-Appendix: Sex-Specific Mortality Figure 59 C.8 ORs of mortality in sex HLH category (ºC) 130 C.9 Sub-Appendix: PharmaCare-Specific Mortality Figure 60 C.9 ORs of mortality in PharmaCare plan HLH category (ºC) 131 C.10 Sub-Appendix: Age-Comorbidity-Specific Mortality Figure 61 C.10 ORs of mortality in age and comorbidity HLH category (ºC) 132 C.11 Sub-Appendix: Age-Multimorbidity-Specific Mortality Figure 62 C.11 ORs of mortality in age and multimorbidity HLH category (ºC) 133 C.12 Sub-Appendix: Comorbidity-Specific Heat-Related ED Visits Figure 63 C.12 ORs of heat-related ED visits in comorbidity group HLH category (ºC) 134 C.13 Sub-Appendix: Multimorbidity-Specific Heat-Related ED Visits Figure 64 C.13 ORs of heat-related ED visits in multimorbidity group HLH category (ºC) 135 C.14 Sub-Appendix: Cardiovascular-Specific Heat-Related ED Visits Figure 65 C.14 ORs of heat-related ED visits in heart-related chronic conditions HLH category (ºC) 136 C.15 Sub-Appendix: Mental-Illness-Specific Heat-Related ED Visits Figure 66 C.15 ORs of heat-related ED visits in schizophrenia and SUD HLH category (ºC) 137 C.16 Sub-Appendix: Other-Chronic-Condition-Specific Heat-Related ED Visits Figure 67 C.16 ORs of heat-related ED visits among COPD, Parkinsonism, CKD, and other chronic conditions HLH category (ºC) 138 C.17 Sub-Appendix: Age-Specific Heat-Related ED Visits Figure 68 C.17 ORs of heat-related ED visits in age group HLH category (ºC) 139 C.18 Sub-Appendix: Sex-Specific Heat-Related ED Visits Figure 69 C.18 ORs of heat-related ED visits in sex HLH category (ºC) 140 C.19 Sub-Appendix: PharmaCare-Specific Heat-Related ED Visits Figure 70 C.19 ORs of heat-related ED visits in PharmaCare plan HLH category (ºC) 141 C.20 Sub-Appendix: Acuity-Specific Heat-Related ED Visits Figure 71 C.20 ORs of heat-related ED visits in acuity HLH category (ºC) 142 C.21 Sub-Appendix: Age-Comorbidity-Specific Heat-Related ED Visits Figure 72 C.21 ORs of heat-related ED visits in age and comorbidity HLH category (ºC) 143 C.22 Sub-Appendix: Age-Multimorbidity-Specific Heat-Related ED Visits Figure 73 C.22 ORs of heat-related ED visits in age and multimorbidity HLH category (ºC) 144 C.23 Sub-Appendix: Acuity-Specific Heat-Related Ambulance Dispatches Figure 74 C.23 ORs of heat-related ambulance dispatches in acuity HLH category (ºC) 1 Errata Original Document: âEvaluating cumulative temperature indicators for heat alert and response systems: a case study in Vancouver, British Columbiaâ Link: https://dx.doi.org/10.14288/1.0448095 Author: Alexi Tianyang Hu *** Begin errata. Page vi, line 10: Replace âthe BCCDC PANDA team.â with âthe BCCDC PANDA team and the BC Ministry of Health.â Page vi, line 10: After the sentence âthe BCCDC PANDA team and the BC Ministry of Health.â Add âMost of the materials contained in this thesis were reorganized and published in a preprint on medRxiv (doi: https://doi.org/10.1101/2025.09.05.25333794) in September 2025.â. Page 4, line 10: Remove the error message âError! Reference source not found. Error! Reference source not found.â following âtens of thousands of excess deaths,â and replace it with â(1.3 EHEs and Health).â
Thesis/Dissertation
eng
Population and Public Health
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Brauer, Michael (Of University of British Columbia)
Henderson, Sarah B.
Evaluating cumulative temperature indicators for heat alert and response systems : a case study in Vancouver, British Columbia
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