Arts, Faculty of
Sociology, Department of
DSpace
UBCV
Prisk, Dan
Master of Arts - MA
University of British Columbia
Climate change is regarded as the most significant threat to human health in this century. There is a growing consensus that high temperatures in particular have a significant, negative effect on mental health outcomes. Work in environmental justice has shown the unequal distribution of the impacts of climate change, people who are already experiencing social marginalization tend to experience the worst effects. In this thesis, I explore climate change and health in Canada by asking if air temperature affects mental health, and testing for effect variation across three primary dimensions of social marginalizationârace, gender, and income. Using a custom dataset that brings together historical weather data and survey data from the Canadian Community Health Survey, I use multivariate models to estimate the effects of absolute and relative temperature on two measures of mental health. I fail to find robust evidence for a relationship between temperature and mental health, and find none for the differential impact of temperature on mental health by race, gender, or income. These results challenge a growing body of research which shows that temperature is significantly correlated with negative mental health outcomes.
CLIMATE, MARGINALIZATION, AND MENTAL HEALTH IN CANADAbyDan PriskB.A Hons. Simon Fraser University, 2020A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OFMASTER OF ARTSinTHE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES(Sociology)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)August 2024© Dan Prisk, 2024 iiThe following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled:Climate, Marginalization, and Mental Health in Canadasubmitted by Dan Prisk in partial fulfilment of the requirements forthe degree of Master of Artsin SociologyExamining Committee:Ethan Raker, Assistant Professor, Sociology, UBCSupervisor Gerry Veenstra, Professor, Sociology, UBCSupervisory Committee Member iiiABSTRACTClimate change is regarded as the most significant threat to human health in this century. There isa growing consensus that high temperatures in particular have a significant, negative effect on mental health outcomes. Work in environmental justice has shown the unequal distribution of theimpacts of climate change, people who are already experiencing social marginalization tend to experience the worst effects. In this thesis, I explore climate change and health in Canada by asking if air temperature affects mental health, and testing for effect variation across three primary dimensions of social marginalizationârace, gender, and income. Using a custom datasetthat brings together historical weather data and survey data from the Canadian Community Health Survey, I use multivariate models to estimate the effects of absolute and relative temperature on two measures of mental health. I fail to find robust evidence for a relationship between temperature and mental health, and find none for the differential impact of temperature on mental health by race, gender, or income. These results challenge a growing body of research which shows that temperature is significantly correlated with negative mental health outcomes. ivLAY SUMMARYThere is growing evidence that high temperatures lead to worse mental health. Existing research has found higher rates of mental health related deaths and hospitalizations as temperatures rise. This is particularly concerning as climate change continues to raise temperatures globally. However, we do not currently have data on how temperature is impacting mental health in the Canadian population, and if the impact is felt the same by everybody. In this study I use a national survey of population health, with data from weather stations across Canada, to fill this gap. I do not find strong enough evidence to say that temperature is currently impacting mental health in Canada, and find none to say that there is any difference based on a personâs race, gender, or income. This finding does not prove that there is no effect, but rather fails to find evidence to prove there is an effect. vPREFACEThis thesis is the original work of the author, Dan Prisk. Supervisor, Dr. Ethan Raker, provided substantive guidance through the process of analysis and writing. Committee member, Dr. Gerry Veenstra, provided direction and editorial feedback on earlier drafts of this thesis. Survey data used in this analysis was collected and provided by Statistics Canada. Primary weather data was collated by Environment and Climate Change Canada. Dan led the data preparation and analysis with support of the supervisor. This research did not require ethics approval and has not been previously published in whole or in part. viTABLE OF CONTENTS Abstract.........................................................................................................................................iii Lay Summary...............................................................................................................................iv Preface............................................................................................................................................v Table of Contents.........................................................................................................................vi List of Tables..............................................................................................................................viii List of Figures...............................................................................................................................ix Acknowledgements........................................................................................................................x1. Introduction..............................................................................................................................11.1. Temperature and Mental Health.........................................................................................21.2. Environment and Social Marginalization...........................................................................62. Methods.....................................................................................................................................92.1. Data....................................................................................................................................92.2. Variables...........................................................................................................................142.3. Analysis............................................................................................................................202.4. Limitations.......................................................................................................................233. Results.....................................................................................................................................253.1. Bivariate results................................................................................................................253.2. Multivariate results...........................................................................................................274. Conclusion..............................................................................................................................31 References....................................................................................................................................35 Appendices...................................................................................................................................43 vii Appendix A: Codebook............................................................................................................43 Appendix B: Questions from the PHQ-9 used to calculate dependent variable DEP..............44 viiiLIST OF TABLESTable 1: Tests of validity for dependent variables.........................................................................14Table 2: Weighted descriptive data for key variables....................................................................15Table 3: Measures of association between dependent and key independent variables..................25Table 4: Binomial logistic regressions on self-rated mental health and depression......................26Table 5: Binomial logistic regressions on self-rated mental health with interaction effects.........29Table 6: Binomial logistic regressions on self-rated mental health and sleep issues.....................30 ixLIST OF FIGURESFigure 1: Average maximum temperature (tmax) and temperature change (zmean) relative to 1961-1990 average. Mean per census subdivision for period 2015-2018.....................................11 xACKNOWLEDGEMENTSThis thesis little represents the long and challenging journey that has led to it, and I am very grateful to all the people who have helped me along the way. The work in front of you is particularly indebted to my supervisor Dr. Ethan Raker. Your incredible knowledge and support has been invaluable in helping me navigate the challenges of this analysis. I am similarly thankful for my committee member, Dr. Gerry Veenstra, whose critical eye and experience has been crucial to the completion of my project. Also, thank you to Dr. Emily Kennedy for your early support in the graduate program. The intellectual community you built shaped some of my happiest memories of graduate school. I also want to thank the fellow students who were part of that community. In particular Parker Muzzerall, Carly Hamdon, and Emily Van Halemâbut also every other student I have shared classes and conversations with who I can not list hereâyou have all made graduate school the sort of joyful intellectual pleasure I always hoped it could be. Thank you to the staff in the sociology department who do so much to help support those of us going through this process. May Chan, you have been an endlessly supportive presence.I am continually grateful to the faculty at Simon Fraser University who set me on this path, and provided so much support and guidance in getting to graduate school in the first place. For all thedraft reading, strategizing, and support, thank you Dr. Travers, Dr. Nick Scott, and Dr. Cindy Patton. I would not be here without you.And thank you to all the friends and family who have supported me, sat and worked with me, and talked to me about theory and more theory and statistics.11. INTRODUCTIONAs the most significant threat to human health of this century, climate change is impacting health in many different ways (Costello et al., 2009). Major population-level impacts are expected via disease, water and food scarcity, natural disasters, and population displacement (Myers & Patz, 2009). Mortalityincreases (Henderson et al., 2022), sleep is reduced (Minor et al., 2022; Obradovich et al., 2017), mental health is impacted across a variety of outcomes (Bourque & Cunsolo Willox, 2014), with a robust connection seen between excess heat and negative mental health outcomes (Mullins & White, 2019; Obradovich et al., 2018). As of 2018, temperature change due to climate change has been tied to 37% of all heat-related deaths (Vicedo-Cabrera et al., 2021).How climate change may be differentially affecting population subgroups has been a growing concern for scholars and policymakers alike. Interest in the unequal distribution of environmental harms (including climate change) has been substantial enough to develop the field of environmental justice, which has now built a large body of evidence showing that at both the global and national levels environmental harms are more likely in places where marginalized people live (Mohai et al., 2009). Similar concerns have been taken up in health research in sociology, economics, and epidemiology. Here the question has focused on if and how the effects of climate change on health are differentiated across domains of social marginalization. This question is important for two related, but conceptually distinct reasons. First, in terms of direct adaptation actions, better understanding who is impacted by climate change can help channel resources more effectively to mitigate the worst effects. Second, and more indirectly, action on climate change has been limited globally as those countries withmost power to act are also those experiencing the least effects (Roberts & Parks, 2007). If the same patterns are playing out on a national scale then research that highlights this discrepancy can be crucial to understanding some of the limitations to action.2This paper contributes to the fields of environmental sociology and sociology of health by addressing two core questions related to climate change. Specifically, I ask (1) to what extent do extreme temperatures affect mental health in Canada, and (2) How are the effects moderated by social marginalization. I answer these questions through an analysis of a novel dataset that combines repeated,cross-sectional survey data from the Canadian Community Health Survey and historical weather data atthe Census Subdivision level. Empirically, I deploy a series of regression models that adjust for temporal variation in temperature. I fit the models and use two outcome measures of mental health: self-rated mental health and depression (using the PHQ-9). My primary independent variables are two measures of temperature: average maximum temperature experienced in the last 14 days to test for absolute temperature effects, and average variation in temperature from the historical average for the the last 14 days to test for relative temperature effects.Using these data, I fail to find robust evidence for a relationship between temperature and mental health. In particular, I find that significant, adverse consequences of greater temperature on self-rated mental health are explained by spatial variation in average temperatures, and I do not detect effects on depression. Moreover, results from moderation models do not detect significant differential impact of temperature on mental health by social marginalization. These results challenge a growing body of research which indicates that temperature is significantly associated with mental health.1.1. Temperature and Mental HealthThere is a growing body of work showing associations between temperature and mental health (Liu et al., 2021; Thompson et al., 2018, 2023). Across this literature, higher temperatures correspond to a range of negative mental health outcomes: increased hospital and emergency room visits for mental health issues, increased suicide and mortality among those with mental health disorders, and poorer levels of self-reported mental health. The temperature at which mental health begins to be negatively 3affected seems to be somewhat location dependent, with individual studies seeing inflection points as low as 18 degrees Celsius (in the United Kingdom) and as high as 30 degrees Celsius (in the United States) (Obradovich et al., 2018; Page et al., 2012). This may not be a universal rule, however: one international study found that the effects of heat waves on suicide varied by country with some reporting increased suicides, some decreased suicides, and many with no significant effect (Florido Ngu et al., 2021). The effect of cooler weather is less clear still, with some studies finding a linear relationship where mental health issues continue to decline as temperatures fall, and others finding an upside-down u-curve where temperatures below a certain point begin to show a decline in mental health (Baylis, 2020; Li et al., 2020). However, the effects of colder temperatures seem to be less studied than those of hot temperature, with fewer results overall.Much of this work has focused on tracking mental health outcomes via hospitalizations and mortality (Basu et al., 2018; Florido Ngu et al., 2021; Hansen et al., 2008; Mullins & White, 2019; Page et al., 2012; Wang et al., 2014). Such data allow for the tracking of severe mental health outcomes(ranging from those that cause hospitalization to suicide), but are limited in understanding the impact oftemperature on mental health in the wider population. In many cases, these studies are assessing whether heat exacerbates existing mental health disorders. For instance, Page et. al. (2012) use data from general practitioners in the United Kingdom to measure mortality rate among people with primarydiagnoses of psychosis, dementia, or alcohol and substance misuse. While this is important research, it may or may not tell us anything about wider population-level mental health. Due to the nature of the data most of these studies are fairly geographically limited, with many focusing on just a single city (Hansen et al., 2008; Wang et al., 2014). Those that look at larger geographic areas tend to look at moreextreme outcomes, measuring mortality rates associated with mental health disorders (Mullins & White, 2019; Page et al., 2012). 4In order to explore larger spatial and demographic groups, researchers have turned to more subjective measures of mental health. Several studies on the population level in the United States and China have leveraged national-level surveys to explore the impacts of temperature on respondentsâ mental health (Hua et al., 2023; Li et al., 2020; Mullins & White, 2019; Obradovich et al., 2018; Zhanget al., 2023). Measures here follow one of two general patterns: simple self-rated mental health uses some version of a respondentâs own assessment of mental health overall, while depression scales attempt to get at levels of depression by way of a series of questions regarding depressive symptoms. While most of these studies follow the trends described above, findings are not unanimous when looking at subjective mental health. In particular, Xue et al. (2019) found no significant relationship between mean temperature and self reported mental health in China, the only study reviewed that reported a null finding. However, Xue et al. (2019) calculated mean temperature at the annual level, a relatively course measure, while most other work in this area looked at temperature exposure across a range of 7 to 30 days. At a broader level, some interesting new research has used sentiment analysis of social media posts in order to conduct longitudinal analysis of mood across very large samples1 (Baylis, 2020; Baylis et al., 2018). These studies take as their dependent variable the valence of a post, with the aim ofusing the relative levels of positive to negative sentiment to measure the mood of the people posting. While it is still unclear quite how well changes in sentiment measured this way reflect the affect of those posting, the trends follow other research reviewed here: sentiment followed a u-shaped curve such that it was increasingly negative as temperatures increased above 25-30c, and increasingly negative as temperatures declined below 20c.When focused particularly on outcomes within Canada, most research has used the hospitalization model. These studies found significant effects of temperature on mental health 1 On the order of billions of posts.5outcomes at the city level in Toronto (Wang et al., 2014) and the provincial level in Alberta and Quebec(Lavigne et al., 2023; Vida et al., 2012). Bélanger et al. (2014) take a simplified approach and ask respondents in Quebec if the heat affects their mental health, finding nearly 1 in 5 respondents (17.8%) reported negative mental health impacts during very hot and humid summer conditions. No studies to date have looked at the relationship between temperature and mental health at the population level in Canada. This kind of work requires historical weather data at an appropriate level of detail. National-level research in the United States has relied on the high resolution gridded (~4 km) daily weather data from PRISM Climate Group (Baylis et al., 2018; Mullins & White, 2019; Obradovich et al., 2018). Similarly high-resolution data for Canada are not available. While the Canadian Gridded Temperature and Precipitation Anomalies (CANGRD) data makes gridded temperature and precipitation anomalies available, the geographic and temporal resolutions are quite high: data is gridded to 50 km resolution and monthly at minimum (Environment and Climate Change Canada, 2020). Raw weather data as reported by weather stations is available down to the hour, but these are in point form and unevenly spread across the country (Environment and Climate Change Canada, 2023). Existing studies in Canada have made use of a subset of this data by selecting a single weather station in their city of interest (Wang et al., 2014), but such methods do not scale to national-level studies.The work reviewed so far has primarily focused on measuring the impacts of absolute temperature, but what of climate change induced changes to temperature? Work from China has found higher temperature variability within a single year is related to worse mental health outcomes (Xue et al., 2019). A study from Italy found that regional variation in temperature was a stronger indicator of disease mortality and hospital admissions than absolute temperature (Masiero et al., 2022). However, these studies looked at variation from current norms as opposed to variations from historical norms. 6They do not capture, for instance, the psychological impact of experiencing a week of unseasonably hotweather in April as something qualitatively different from experiencing the same temperatures in August. One study conducted in China attempted to capture this via the number of relative exposure days in the prior year (Y. Chen et al., 2023). Here a high exposure day was defined by calculating monthly average and standard deviations for a 30-year window prior to the study period, and extreme temperature days being defined as those that fall more than 1.96 standard deviations from the norm for that month. They found that exposure to either relatively hot or cold extreme temperatures significantlynegatively impacted mental health (Y. Chen et al., 2023, p. 495). 1.2. Environment and Social MarginalizationConceptual insights from two major streams of researchâthe environmental justice and epidemiological paradigmsâinform the present study. Both modalities explore ways that environment and social structures interact to affect human health, but are broadly divided in both unit of study and epistemological orientation. As the name suggests, the environmental justice paradigm takes a normative epistemological approach that is focused on justice. Here the primary unit of study tends to be spatial (e.g., neighbourhood), and the focus is on the unequal distribution of environmental harms along racial and socioeconomic lines. The epidemiological paradigm follows a more positivist epistemology where the key focus of study is on individual-level interactions between characteristics ofthe environment and social determinants of health. Environmental justice work has long documented the unequal spatial distribution of environmental hazards along racial and socioeconomic lines (Mohai & Saha, 2006). Across this body of research, people who already experience social marginalization are more likely to be exposed to environmental hazards, albeit with significant differences in the scale of the disparities found. At a global level, patterns of burden and responsibility follow patterns of colonization: developing nations suffer the greatest burden of climate change while being least 7responsible for greenhouse gas emissions (Roberts & Parks, 2007). At a more localized level, environmental harms such as toxic waste and coal fired power plants are more likely to be found in places where racialized or poor communities live (Mohai et al., 2009). While much localized environmental justice work has focused on the United States, studies in Canada have also highlighted the unequal distribution of air pollution and flood risk along socioeconomic lines (Chakraborty et al., 2020; Jerrett et al., 2001). While the unequal distribution of environmental hazards is well documented,there are significant uncertainties regarding the causes of this distribution: are environmental hazards placed in areas populated by already disadvantaged peoples, or does the presence of environmental hazards reduce the costs of living in a place, thus drawing in already disadvantaged peoples (Mohai et al., 2009)? Such questions are less pertinent to the epidemiological modality.When considering racial and socioeconomic disparities at an epidemiological level the patterns of coincidence are less clear (Gronlund, 2014).2 The impacts of temperature on broad measures of health are moderated by racial and socioeconomic disparities in some studies but not all, and the particular order in which groups are affected is not always consistent. Women tend to experience worseeffects than men (Bakir et al., 2023). Across most studies, extreme cold temperatures affect the mental health of women more acutely than men (Y. Chen et al., 2023; Yang et al., 2021). The effect of extreme heat is more mixed, with some studies finding a greater effect for women, some finding no difference between men and women, and others finding a greater effect for men (N.-T. Chen et al., 2019; Florido Ngu et al., 2021; Wahid et al., 2023). While race is a key form of marginalization studied in environmental justice, it is less often explored in epidemiological work. Here the patterns that do emerge are rather different than those seen in the environmental justice literature. A study of emergencyroom visits in California found that Hispanics and Whites were more likely to be impacted by high 2 For the purposes of this thesis, I focus here on the effect of temperature on mental health in particular.8temperatures than Blacks or Asians (Basu et al., 2018). A small (n=426) study found that White people seem to experience worse impacts to their mental health from extreme weather than other racial groups (Mason et al., 2020). Lower socioeconomic background places populations at particular risk of seeing negative impacts on mental health from climate change (Fatima, 2022). This effect is greater for those with lower income (Bélanger et al., 2014), such that those in the lowest income quartile experience up to 1.6 times the impact of those in the highest quartile (Obradovich et al., 2018).In the epidemiological paradigm the focus is more tightly tied to health outcomes, as opposed tothe social processes focus of the environmental justice paradigm. These differing focuses each have their strengths and limitations: by largely taking exposure to environmental harms as their dependent variable, environmental justice avoids potential issues in the selection and measurement of the dependent variable. However, in doing so it removes the potential to highlight specific impacts that environmental harms can have on individuals. This thesis seeks to contribute to the wider literature by bringing together the environmental justice focus on social processes and the epidemiological focus on specific individual outcomes. In terms of specification this thesis is closest to Obradovich et al. (2018). Similar to that paper, Ilook at nation-wide data, take self-rated mental health as my primary dependent variable, and use short-run maximum temperature as my key independent variable. The major differentiation is in the site of study: while Obradovich et al. (2018) examined the impacts of temperature on mental health in the United States, I look at Canada. My study places a greater focus on differential impacts of temperature on mental health by race, gender, and class, and includes relative measures of temperature as well as absolute measures of temperature in order to explore the potential impact of climate change on mental wellbeing.92. METHODSDrawing on a novel dataset based on the Canadian Community Health Survey, this study explores interactions between mental health, marginalization, and climate change in Canada today. Specifically, I ask if temperature is impacting mental health in Canada, and if so, are these impacts felt more acutely by people who are already experiencing social marginalization? This study focuses on population-level impacts in Canada. While the data on the mental health impacts of temperature is quite compelling, much of the existing research leverages hospital admission data to measure effects, making it hard to extend the findings to the general population. What research there has been at a broader level has explored effects primarily at the national level in the United States and China, where outcomes are potentially particular to their specific social and economic constructions. This study focuses on Canada for three key reasons: extending current national-level research from the US to a close neighbour state allows more space to read across existing studies; Canada has excellent national level data available on health and environment; and, finally, my research is conducted at a university within Canada providing appropriate access to data and contextual knowledge.2.1. DataI conduct this research by bringing together two primary datasets: the Canadian Community Health Survey (CCHS) and a custom generated dataset of daily weather observations for Canadian census subdivisions. The resulting dataset covers the period 2015-2018 inclusive. This time period was chosento provide the most stable recent time frame appropriate when using the CCHS: it covers two complete cycles of the CCHS, immediately precedes the significant health impacts of the COVID-19 pandemic, and follows the major redesign of the CCHS in 2015. 10The CCHS is a cross-sectional survey conducted by Statistics Canada on a rolling two-year cycle that collects information on health determinants and effects from the Canadian population. Each cycle contains a representative sample of approximately 130,000 Canadians aged 13 years and older. Respondents are selected via a multi-stage random sampling strategy, and the final sample is weighted to account for sampling bias. In order to procure fine-grained information on respondent location and interview date for matching to climate data (see below), I used the CCHS micro-data provided by the Statistics Canada Research Data Centre. The two CCHS datasets used in my analysis (2015/2016 and 2017/2018) were vertically stacked (via row binding) in order to create a single CCHS dataset for analysis. Respondents under age 18 were dropped from the dataset.3 The final analytic sample contains a little over 200,000 respondents.4 3 This ensures the analysis is focused on the adult population and does not include complications related to early life. It also accounts for the fact that data collected from respondents under the age of 18 utilized a different sampling methodology than for older respondents. 4 Precise numbers were not able to be released from the RDC without compromising future data releases and potential publication.11Figure 1: Average maximum temperature (tmax) and temperature change (zmean) relative to 1961-1990 average. Mean per census subdivision for period 2015-2018.12Weather data was generated by interpolating from Canada Historical Climate Data (CHCD) to census subdivisions. The CHCD is a repository of raw data from weather stations across Canada maintained by Environment and Climate Change Canada (2023). It contains records extending as far back as 1840. My dataset contained two key variables for each day of the study period for each 2016 census subdivision: the average daily maximum temperature across the last 14 days (tmax14), and the average daily temperature change relative to the 1961-1990 mean across the last 14 days (zmean14). Data preparation entailed four key stages: download, cleaning, interpolation, and calculation. In the first stage, I downloaded daily weather data for all available weather stations for the years 1961-1990 (reference period) and 2014-20185 (study period) inclusive, a total of 307,896 individual files. As these files contain raw reported data from a variety of weather stations across many years the data can be inconsistent. As such, in the second stage a series of tests were run to identify any suspicious temperatures and flag them for manual review. An entry was marked as suspicious if it met any of the following criteria (Vincent et al., 2020): daily minimum temperature was greater than daily maximum; there was a greater than 40c difference in minimum and maximum temperature; any temperature was less than -60c or greater than 50c; or any temperature was more than five standard deviations from the mean of that temperature for the calendar day in question (calculated across all data downloaded). Following manual review of the outcomes from these tests, a total of 1,416 individual records were modified.6 In the third stage, these data were interpolated to 2016 census subdivisions. Interpolation was performed using Kriging with computing resources from UBC Advanced Research Computing (2023). This generated a file containing daily mean and maximum temperatures per record, with a single record for each day in each census subdivision. A summary of these data for the study period can5 This covers the study period (2015-2018) and one year prior in order to include 14 days of weather for those respondents interviewed in the first month of the study period. Daily weather data is accessed as one file for each station / year combination.6 Of these, most readings (1,098) were set to NA, with switching minimum and maximum daily temperatures being the most common change that retained data (~ 200 records). 13be seen in . In the final stage, key variables of interest were calculated. Reference temperatures were calculated as the mean and standard deviation for daily average temperature for each census subdivision on each calendar day in the reference period. These were then used to calculate a z-score for each record in the study period.7 Finally, a 14 day mean of z-score (zmean14) and maximum temperature (tmax14) was calculated for each day of the study period. These represented the average relative and maximum temperatures experienced in each census subdivision for the day of, and 13 daysprior, to the record date.8The resulting custom temperature dataset was merged into the CCHS by matching respondents on census subdivision ID and interview date. This meant the resulting file contained respondent-level data on relative and absolute temperature exposure for the 14 days prior to the interview date. As the 2015/2016 CCHS used 2011 census subdivision IDs, a custom mapping file was generated for this purpose. Shape files for 2011 and 2016 census subdivisions were intersected using the SF library (Pebesma, 2018). Those intersections where >20% of the 2011 census subdivision was contained in a differently coded 2016 census subdivision were retained along with data on the percentage of land area that intersected. These were then manually processed in order to produce a file where each 2011 censussubdivision ID mapped to a single 2016 ID. A total of 173 census subdivisions were modified in this way, and every record in the CCHS was accordingly mapped to an appropriate 2016 census subdivision. 7 As such, these z-scores represent an approximation of the extent to which climate change was experienced on each day in each census subdivision. 8 Following Mullins and White (2019), tmax and zmean were also calculated and tested over 30- and 7-day periods without substantial changes in the results. 14Table 1: Tests of validity for dependent variablesDependent Test Method Statistic p-valuesrmh mental health diagnosis Pearson's X2: Rao & Scott adjustment 9424.57 <0.001dep mood diagnosis Pearson's X2: Rao & Scott adjustment 1161.43 <0.0012.2. VariablesMy analysis looks at two key dependent variables: depression (dep) and self rated mental health (srmh).Both are coded as dichotomous such that a value of true indicates a negative effect on mental health. The depression variable measures a respondentâs level of depression over the last two weeks. The scoreis based on nine questions from the Patient Health Questionnaire (PHQ-9) that, when used in combination with the algorithm provided in Kroenke et al. (2001, p. 607), provide a reliable and valid method to establish diagnoses of depressive disorders. After calculating the depression diagnostic according to Kroenke et al. (2001), I recoded the resulting variable so that any score above zero on the depression diagnostic was true.9 I tested the validity of the resulting variable against a question that asked âDo you have a mood disorder such as depression, bipolar disorder, mania or dysthymia?â The two variables where highly significantly associated (p<0.001, see Table 1: Tests of validity for dependent variables). As the questions in the PHQ-9 specifically target symptoms experienced over the last two weeks (see Appendix B), it is particularly useful for analyzing the impacts of recent temperature events. The weighted population estimate from my sample indicates that 7.75% of the population have some form of depression (see Table 2: Weighted descriptive data for key variables).10 9 The scale runs from 0 to 4, where 0 means no depression, 4 is major depression, and 1-3 indicate other depression (Kroenke et al., 2001, p. 607).10 Due to data privacy requirements from the Research Data Centre, all descriptive statistics reported are weighted population estimates.15However, the PHQ-9 was only included in the CCHS for select provinces and territories11 covering only 1/4 of the sample in a limited set of geographic areas. As such I also draw on the universally applied srmh to test results pertaining to dep against the entire sample.Table 2: Weighted descriptive data for key variablesVariable Estimated mean or %Fair/Poor Self-Rated Mental Health (%) No 92.98 Yes 7.02Depression (%) No 92.25 Yes 7.75Mean 14day max temp mean (sd) 11.7 (11.01)Mean 14 day temperature anomaly mean (sd) 0.27 (0.65)Race (%) White 74.44 Indigenous 3.74 Racialized 21.82Gender (%) Male 49.19 Female 50.81Income CAD$ median 80,000Self-reported mental health offers a subjective measure of a respondentâs overall mental health. The use of srmh follows the well documented strong association between self rated health and mortality. While srmh does not capture specific mental health diagnosesâsuch as depressionâit does have a strong and consistent association with diagnosed mental health morbidities in general that make 11 In the 2015-2016 CCHS the PHQ-9 was included for respondents in Manitoba, Saskatchewan, New Brunswick, Ontario, Nova Scotia, Prince Edward Island, Newfoundland and Labrador, and the Northwest Territories. In the 2017-2018 CCHS the PHQ-9 was included for only the Northwest Territories and Prince Edward Island.16it useful as a general measure of population mental health (Mawani & Gilmour, 2010). In the CCHS, srmh was collected via a question that asked âIn general, would you say your mental health is...?â with five options: excellent, very good, good, fair, and poor. For my analysis I recoded this as a dichotomousvariable where fair/poor is 1 and all other responses are 0. The resulting variable closely mirrors the outcomes used by similar research in the United States (Obradovich et al., 2018). The validity of the resulting variable was tested against a custom variable that was set to true if the respondent indicated they had either a mood or anxiety disorder.12 The two variables were significantly correlated (p<0.001, see Table 1: Tests of validity for dependent variables). The weighted population estimate offers a number very similar to the depression variable above, with 7.02% of the population indicating fair/poorself-rated mental health (see Table 2: Weighted descriptive data for key variables).I bring social marginalization into my analysis via three key concepts: race, gender, and class, measured by income. These represent the big three areas of study in sociology, and are appropriate places to start when considering an initial analysis of interactions with social marginalization such as this study. Race is a challenging concept to represent statistically and is worthy of some discussion. While race is increasingly accounted for in quantitative sociological research,13 this often occurs in a way that effectively treats race as an inherent individual characteristic that is itself causal of the different outcomes being analyzed (Martin & Yeung, 2003; Zuberi, 2008, 2011).14 As such, it implicitly supports a model that is at odds with contemporary understandings of race as a socialânot biologicalâcategory. Pushing against this means focusing analysis not on the category of race itself but rather on12 This drew on two questions: âDo you have a mood disorder such as depression, bipolar disorder, mania or dysthymia?â and âDo you have an anxiety disorder such as a phobia, obsessive-compulsive disorder or a panic disorder?â If either of these were true my validity variable was set to true, otherwiseit was set to false. 13 It appears that much of this increase in the usage of race in sociology may be due to the increasing ease of including it in regression analyses (Martin & Yeung, 2003, p. 531).14 Zuberi (2008, 2011) describes the use of race as a causal factor in statistical models as racial reasoning, a form of reasoning that misunderstands and reifies race.17the social processes of racialization that create and support those categories. Zuberi (2008, p. 133) citesenvironmental justice research as an area that does this effectively, with its focus on the racist practices that determine the locations of environmental hazards.Here, I operationalize race via a custom generated categorical-nominal variable named race. Respondents were grouped into one of: Indigenous, racialized, or White. These three levels were calculated using a combination of the Aboriginal identity and cultural / racial background questions from the CCHS. Respondents who indicated both White and racialized identities were included in the racialized category. The cultural / racial group question was not asked of respondents who answered yes to the Aboriginal identity question. White was used as the reference category as it was expected to have the highest level of mental health, is the largest group, and is distinctly different from the other two groups in its relation to colonialism. The three levels described seek to represent the three core relations to colonialism within Canada: those subject to settler-colonialism within their traditional territories (Indigenous); those subject to colonialism and separated from their traditional lands (racialized); and the perpetrators / benefactors of colonialism (whites). This means that race is not intended to represent characteristics of the people included in the categories. Rather, it seeks to represent the core relations between those people and the processes of colonialism in Canada. These can be thought of as the core threads at the heart of racial formation in settler-colonial Canada.Yet this measure risks falling into racial reasoning (Zuberi, 2011). Despite the intention to focuson the social processes of racialization, the reality is that the data we have does not capture these processes but instead measures the identities of the individuals surveyed. Assigning causality to race as a variable in this instance runs the risk of making arguments akin to saying racial profiling by police is a factor of the skin colour of those targeted and not of the racist attitudes of the police (Zuberi, 2011, p. 1872). However, there is no similar dataset that contains such national-level data, and to build one that also captures all aspects of the active processes of racialization is likely not viable. Rather, in this studyI remain attentive to the ways that I interpret race, and avoid attributing causality to the variable itself. Here, race stands in for a personâs exposure to processes of racialization that are well documented elsewhere.Within the variable itself, the levels chosen inevitably flatten significant differences in experience. In particular, the third category of "racialized" includes many distinct experiences of racialization. This construction mirrors the Government of Canada's use of visible minority, a term that has come under significant criticism, including from the United Nations Committee on the Elimination of Racial Discrimination (2012). However, such flattening is inherent to any quantitative representationof complex social categories such as race. In this instance, I believe that representing the core relations to colonialism within Canada both practically ensures large enough categories for analysis and theoretically captures the overarching processes of racialization as related to place; an important factor when considering experiences of a changing climate. The weighted population estimates from my sample give the following numbers: 74.44% of the population are White; 3.74% are Indigenous; and 21.82% are racialized (see Table 2: Weighted descriptive data for key variables).Gender is operationalized via a dichotomous variable female. While gender is far from a dichotomous variableâwith considerable work showing the many different expressions of gender identityâthe 2015-2018 CCHS did not collect such data.15 I use a variable based on the question âIs [respondent name] male or female?â Despite being labelled âsexâ by Statistics Canada, the question as asked does a reasonable job of assessing respondent gender. The question had a 100% response rate, suggesting thatâdespite its limitationsâit was broadly appropriate for the sample surveyed. I coded 15 Since 2021, Statistics Canada has updated the gender collection process to also include non-binary people. 19the variable female to be true if the respondent answered female. This positions male as the reference category, following the pattern set above of using the most privileged category as the reference such that regression coefficients show the impact of social marginalization. The responses are roughly equally split between categories, with 49.19% male and 50.81% female (weighted population estimates, see Table 2: Weighted descriptive data for key variables).Class is measured via total household income. This variable was calculated from multiple sources: most was drawn from tax records or self reported, with the remaining data imputed by Statistics Canada. This gave an effective 100% response rate. The median income was approximately $80,000 (see Table 2: Weighted descriptive data for key variables). Before modelling, the resulting variable, income, was logged16 in order to account for right skew and stored as inclog. This led to 16 missing records where reported income was below zero. The quadratic term for inclog was also used in modelling to account for a non-linear effect of income on mental health. I include control variables for education (education) and age (age). Education is measured via a categorical variable with three levels representing the highest level of education completed: less than secondary, secondary, and post-secondary. Age is a numerical variable containing the respondentâs age in years. Following recent literature that posits sleep disturbance as the mechanism by which heat affects mental health (Obradovich et al., 2017), I introduce the dichotomous variable sleep that captures whether respondents had any sleep disturbances in the last two weeks. Respondents were asked: âOver the last two weeks, how often have you had trouble falling or staying asleep, or sleeping too much?â I recoded the resulting variable so that responses that indicated any trouble were true and those that indicated no trouble were false. 16 In order to account for records where income was 0 the full calculation was: log(income + 1).202.3. AnalysisI bring all of these variables together to explore the ways relative and absolute temperature impacts mental health in Canada. My analysis proceeds through three stages. First, I estimate the effect of temperature on mental health for each combination of measures of temperature and mental health (a total of four models). I then explore potential differentiation by social marginalization by including interaction effects between temperature and socio-demographic variables. Last, I test for specification errors by reducing my sample to only those with mental health diagnoses and testing sleep issues as a potentially mediating variable explaining effects of temperature on mental health. In order look at the effects of temperature independent of location and season, I specify fixed effects models controlling for census subdivision (csd), month, and year. In the following equations fixed effects are represented as either F1i (equation 1) or F2i (equation 2), where Fxi is the net fixed effects for ith individual, csdi is the census subdivision where the ith individual is located, and monthi and yeari are the month and year in which the interview for the ith individual was conducted. Fi1 =β2 monthi +β3 yeari (1)Fi2=β1csdi +β2 monthi+β3 yeari (2)My primary relationship of interest is the effect of temperature on mental health. As such, I initially specify a series of binomial logistic regression models that test each combination of dependent and temperature variable (four models total). Each model follows the structure seen in equation 3,17 where [y]i represents alternately srmh and dep, and [X]i represents alternately zmean14 and tmax14. Here the parameter of interest is β1: the effect of temperature independent of location, season, and socio-demographic effects. 17 Both education and age were also included in these models as controls but are not represented here.21[ y ]i =F i1 +β1 [ X ]i +β2 racei +β3 femalei +β 4 inclogi +β5 inclogi2+ϵi (3)Any models that found a significant coefficient for temperature were re-run with fixed effects includingcensus sub-division (F2i) to test the effects of controlling for location.I use interaction effects to test for interactions between temperature and social marginalization. Taking the model that found a significant coefficient for temperature from the prior stage, I specify a series of binomial logistic regression models interacting each social marginalization variable independently with temperature. Equation 4 shows this for the interaction with race. This is repeated for female and inclog. For the sake of brevity these equations have not been included here y i =Fi1 +β1 X i +β2 racei +β3 (raceiÃX i )+β4 femalei +β5 inclogi +β6 inclogi2 +ϵ i (4)In the final stage, several checks were run against findings from the literature, first by limiting the sample to those with mental health disorders and then testing for the effect of sleep on mental health. Much of the literature on temperature and mental health draws on hospitalization data for people already experiencing mental health disorders and the effects of heat on mental health are significantly increased for those with existing mental health disorders (Stivanello et al., 2020).18 As such, I rerun the interaction between srmh and tmax14 (from equation 2) while limiting the sample to just respondents who reported a mental health disorder in order to see if the effects of temperature on mental health are only salient among those already experiencing mental health disorders. Following this I run two models testing the effect of sleep. In the first I include sleep as an independent variable inequation (2) to measure the effect of sleep on mental health. Then I run the same model with sleep as the dependent variable, to test for the effects of temperature on sleep disturbance. 18 One suggested mechanism for this is due to treatment via psychotropic drugs that increase vulnerability to heatwaves (Fatima, 2022, p. 96).22Standard errors were clustered at the CSD level for all models. This followed both methodological recommendations and trends in the literature. Methodologically speaking, clustering is recommended when the sampling strategy is not a simple random sample or when the clustered standard errors are substantially larger than the robust standard errors (Abadie et al., 2023). My dataset met both these requirements: the CCHS is sampled via a multi-stage process that uses cluster sampling within the process, and clustering produced substantially larger standard errors than in models with robust standard errors. Where standard errors are discussed in the literature they are almost universally clustered (Baylis, 2020; Li et al., 2020), with the most common level employed being the state level within the United States (Mullins & White, 2019; Obradovich et al., 2018). I tested clustering at the CSD and provincial level without significant shifts in the overall pattern of results, so selected the CSDlevel as the most relevant to my analysis.Sampling weights were applied to all reported data and models. Respondent level sampling weights are supplied by Statistics Canada to account for sampling bias.19All analyses were conducted using R and tidyverse (R Core Team, 2023; Wickham et al., 2019) along with with packages: sf and gstat for managing and interpolating spatial data (Gräler et al., 2016; Pebesma, 2018); survey and srvyr for weighted survey data (Freedman Ellis & Schneider, 2023; Lumley, 2023); data.table for large datasets (Dowle & Srinivasan, 2023); fixest for high-dimensional fixed-effects models (Berge, 2018); future, vctrs, and maxtrixStats for processing speed (Bengtsson, 2021, 2023; Wickham et al., 2023); here for convenience (Müller, 2020); and cowplot for laying out graphics (Wilke, 2020).19 Bootstrap weights are also available, but were not applied in this analysis. As bootstrapping would only increase standard errors, this would not be expected to substantially change the results reported.232.4. Limitations Interpolating temperature data inherently loses some localized variations in weather. The method is built on an assumption of the variables in question being continuous across space, such that the resulting data will show only gradual changes in temperature between areas. While this may broadly beaccurate, it can mean that sharp local changes in temperatureâfor instance the differences between urban neighbourhoods with and without treesâmay be weakened as the temperatures pull one another towards the mean. In areas with sparse weather station coverage, such as the territories, this can also mean that localized weather changes are simply not captured. In this study I have worked to mitigate this by restricting the interpolation process to using only the ten closest weather stations in order to minimize the pull towards the mean while still including enough data for calculation. While the resulting data still suffers from the problems described, by using all available weather station data it produces the best estimates currently possible. This research does not attempt to broadly model intersectionality. The insights that intersectionality offers into the co-constitutive nature of social categories are crucial to our understanding of processes of marginalization and I support calls to engage radical intersectionality in health and climate research (Williams, 2018). However, while interaction effects are well aligned theoretically with intersectional theory (Scott & Siltanen, 2017; Veenstra, 2013), they are challenging to implement in practice. Previous work exploring this via the CCHS has shown that even the significant sample sizes of this survey are insufficient to produce meaningful results across many intersecting identities (Veenstra, 2013). Given a large enough sample size, it is still unclear whether including interaction effects between distinct axes of inequality in a preliminary study of this type is a useful metric on its own. While it may offer further nuance, there are always choices made about whichinteractions to include and the particular intersections studied will always be either privileging or 24masking some interactions. To me, it seems quantitative modelling of intersectionality may be most useful when exploring specific interactions generated from theory and qualitative research. Therefore, in this research I do not analyze intersecting axes of inequality beyond the key interactions with climateon which this paper relies.Important to note for this research is that persons living on reserves and other Indigenous settlements in the provinces are not included in the CCHS. While these represent a small percentage of the overall Canadian population, this is a significant limitation for research exploring the health impacts of climate change as previous research suggests Indigenous reserve populations are likely to besome of the worst impacted. However, as this study seeks to understand the differential health impacts of climate change independent of geography, the exclusion of relatively homogeneous reserve populationsâthat are already well established as experiencing undue impacts from climate changeâdoes not significantly impact the analysis. Further, as a meticulously collected large scale health surveyof the Canadian population, the CCHS represents the best available data for this study.253. RESULTS3.1. Bivariate resultsTable 1: Tests of validity for dependent variables shows the outcome of tests of significance for all combinations of dependent and temperature variables. Only the relationship between srmh and zmean14 is significant, with a p-value of 0.01. No other tests were significant at an alpha of 0.05. It appears from these tests that there is not a robust relationship between the variables. While not includedin this paper,20 these associations were also plotted in order to see how dependent variables varied across levels of independent variables. The resulting plots showed largely flat lines with almost no change in the mean for the dependent variables across the range. There was some variation in means at the upper and lower edges of each graph that was accompanied by significantly increased confidence intervals which are indicative of the lower data density in these regions. These bivariate statistics suggest there is not a robust association between temperature and mental health in my sample.Table 3: Measures of association between dependent and key independent variablesDependent Independent Method Statistic p-valuesrmh tmax14 Design-based t-test -0.45 0.65zmean14 Design-based t-test -2.52 0.01dep tmax14 Design-based t-test 0.17 0.86zmean14 Design-based t-test -0.90 0.3720 It was not possible to produce the graphs in a way that adequately protected respondent anonymity and displayed fine enough detail to be useful.26Table 4: Binomial logistic regressions on self-rated mental health and depressionDependent Var. srmha depbModel 1 Model 2 Model 3 Model 4 Model 5 b (SE) b (SE) b (SE) b (SE) b (SE)Maximum Temp. - 2 prior weeks 0.0115** (0.0041) -0.0027 (0.0036) 0.0036 (0.0062)Relative Temp. - 2 prior weeks 0.0197 (0.0265) 0.0132 (0.0478)Female 0.2094*** (0.0281) 0.2102*** (0.0288) 0.2097*** (0.0280) 0.2352*** (0.0597) 0.2353*** (0.0597)Logged Income 0.4379*** (0.0434) 0.4667*** (0.0449) 0.4380*** (0.0434) 0.6524*** (0.1334) 0.6526*** (0.1337)Square of Logged Income -0.0408*** (0.0028) -0.0431*** (0.0029) -0.0408*** (0.0028) -0.0513*** (0.0078) -0.0513*** (0.0078)Race White (reference) - - - - - Indigenous 0.4035*** (0.0644) 0.3314*** (0.0660) 0.3904*** (0.0638) 0.4221*** (0.0865) 0.4171*** (0.0859) Racialized -0.3200*** (0.0606) -0.4810*** (0.0554) -0.3097*** (0.0624) -0.0689 (0.0822) -0.0661 (0.0833)Age -0.0128*** (0.0015) -0.0128*** (0.0016) -0.0127*** (0.0015) -0.0089*** (0.0021) -0.0089*** (0.0021)Education Post Secondary (reference) - - - - - Secondary 0.2256*** (0.0330) 0.2165*** (0.0344) 0.2253*** (0.0330) 0.3611*** (0.0611) 0.3608*** (0.0610) Less than Secondary 0.4891*** (0.0547) 0.5646*** (0.0554) 0.4828*** (0.0548) 0.5292*** (0.0713) 0.5281*** (0.0709)Fixed-Effects Month Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes CSDc No Yes No No NoS.E.: Clustered by: csd by: csd by: csd by: csd by: csdObservations 194800 186600 194800 53100 53100Squared Cor. 0.03 0.04 0.03 0.03 0.03Pseudo R2 0.04 0.07 0.04 0.04 0.04BIC 26033931.8 25054858.1 26041711.8 7318586 7318718.5Note: Observations rounded to nearest 100 to protect respondent anonymity; asrmh is the variable that measures self-rated mental health; bdep is the variable that measures depression; cCSD stands for census sub-division; * p < 0.05; ** p < 0.01; *** p < 0.001273.2. Multivariate resultsTable 4 describes a series of binomial logistic regression models for differing combinations of key dependent and temperature variables. When excluding census subdivision from the fixed effects, maximum temperature (tmax14) was significantly positively correlated with srmh (model 1), such that a one degree increase in average maximum temperature was associated with a 0.0115 increase in the odds of fair/poor self-rated mental health. As maximum temperature increased, the chance of individuals experiencing worse mental health also increased. This finding was not robust, however. Thecoefficient for tmax14 was no longer significant, and switched signs, when including census subdivision fixed effects (model 2). This suggests that the association seen between temperature and mental health in model 1 can also be explained by location, rather than temperature alone. Although census subdivisions that experience higher average temperatures appear to be associated with worse mental health outcomes, temperature alone does not explain this variation. While this may align with environmental justice work that shows an unequal geographic distribution of environmental harms, the data here cannot speak to that.21 There was no significant coefficient for the effect of relative temperature on srmh, and neither temperature variable showed significant coefficients when regressing on depression. As Model 1 was the only model that showed a significant effect for temperature this is used as the basis for testing interactions with social marginalization. Table 2: Weighted descriptive data for key variables describes a series of binomial logistic regression models on testing interactions between temperature and social marginalization. Here Model 21 It is also possible that this is a fragment of the sampling strategy of the CCHS. While the CCHS attempts to control for seasonal effects within health regions, this is achieved via the seasonal distribution of sampling PSUs that roughly correspond to census dissemination areas. As a CSD is a collection of census dissemination areasâand there is a significant correlation between CSD and interview date within the datasetâit is possible that temperature variation within CSD is too limited to show significant effects. 286 repeats Model 1 from Table 4 and Models 7 to 9 include interactions with social marginalization variables. There were no significant interaction effects found between tmax14 and social marginalization variables. Overall, temperature does not seem to be interacting with social marginalization to affect self-rated mental health: those who are already experiencing social marginalization do not experience significantly different effects from heat on their mental health.Table 6 runs several checks on these findings. Here Model 10 repeats Model 2 from Table 4. When reducing the sample to just those with mental health diagnoses (Model 12) no significant coefficient for tmax14 was found, which does not support the thesis that temperature effects are most salient among people with pre-existing mental health issues. Model 12 includes sleep as an independent variable to test for the effects of sleep on srmh, and Model 13 regresses on sleep to further test the salience of sleep disturbances. A significant coefficient was found for sleep when regressing on srmh, such that experiencing sleep issues in the last two weeks was associated with a 1.661 increase in the odds of fair/poor self-rated mental health. When regressing on sleep no significant coefficient was found for tmax14, suggesting that changes in maximum temperature has no effect on the occurrence of sleep disturbance. 29Table 5: Binomial logistic regressions on self-rated mental health with interaction effectsModel 6 Model 7 Model 8 Model 9b (SE) b (SE) b (SE) b (SE)Maximum Temp. - 2 prior weeks 0.0115** (0.0041) 0.0101* (0.0041) 0.0111** (0.0042) 0.0099 (0.0151)Female 0.2094*** (0.0281) 0.1807*** (0.0360) 0.2095*** (0.0280) 0.2094*** (0.0281)Logged Income 0.4379*** (0.0434) 0.4378*** (0.0434) 0.4385*** (0.0435) 0.4325*** (0.0649)Square of Logged Income -0.0408*** (0.0028) -0.0408*** (0.0028) -0.0409*** (0.0028) -0.0405*** (0.0041)Race White (reference) - - - - Indigenous 0.4035*** (0.0644) 0.4034*** (0.0644) 0.4352*** (0.0877) 0.4035*** (0.0643) Racialized -0.3200*** (0.0606) -0.3200*** (0.0606) -0.3871*** (0.0711) -0.3200*** (0.0606)Age -0.0128*** (0.0015) -0.0128*** (0.0015) -0.0128*** (0.0015) -0.0128*** (0.0015)Education Post Secondary (reference) - - - - Secondary 0.2256*** (0.0330) 0.2256*** (0.0329) 0.2257*** (0.0330) 0.2256*** (0.0329) Less than secondary 0.4891*** (0.0547) 0.4891*** (0.0547) 0.4885*** (0.0544) 0.4891*** (0.0546)Interactions Maximum Temp x Female 0.0025 (0.0026) Maximum Temp x Indigenous -0.0031 (0.0042) Maximum Temp x Racialized 0.0053 (0.0043) Maximum Temp x Logged Income 0.0005 (0.0035) Maximum Temp x Square of Logged Income -0.0000279 (0.0002)Fixed-Effects Month Yes Yes Yes Yes Year Yes Yes Yes YesS.E.: Clustered by: csd by: csd by: csd by: csdObservations 194800 194800 194800 194800Squared Cor. 0.03 0.03 0.03 0.03Pseudo R2 0.04 0.04 0.04 0.04BIC 26033931.8 26033335.4 26031926.5 26033944.8Note: Observations rounded to nearest 100 to protect respondent anonymity;* p < 0.05; ** p < 0.01; *** p < 0.00130Table 6: Binomial logistic regressions on self-rated mental health and sleep issuesDependent Var. srmha sleepbModel 10 Model 11 Model 12 Model 13b (SE) b (SE) b (SE) b (SE)Maximum Temp. - 2 prior weeks -0.0027 (0.0036) -0.0042 (0.0059) -0.0042 (0.0096) -0.0019 (0.0049)Female 0.2102*** (0.0288) -0.1646*** (0.0464) 0.0765. (0.0456) 0.4052*** (0.0346)Logged Income 0.4667*** (0.0449) 0.1819** (0.0554) 0.5390*** (0.0687) 0.1295*** (0.0386)Square of Logged Income -0.0431*** (0.0029) -0.0210*** (0.0036) -0.0468*** (0.0050) -0.0140*** (0.0024)Race White (reference) - - - - Indigenous 0.3314*** (0.0660) -0.0630 (0.1085) 0.3405* (0.1432) 0.2888*** (0.0722) Racialized -0.4810*** (0.0554) 0.1057 (0.1073) -0.2172* (0.0887) -0.4287*** (0.0634)Age -0.0128*** (0.0016) -0.0122*** (0.0014) -0.0044* (0.0022) -0.0135*** (0.0012)Education Post Secondary (reference) - - - - Secondary 0.2165*** (0.0344) 0.1309** (0.0490) 0.2571*** (0.0778) 0.0908. (0.0541) Less than secondary 0.5646*** (0.0554) 0.3517*** (0.0731) 0.6217*** (0.0947) 0.1691** (0.0613)Sleep issues â 2 prior weeks 1.661*** (0.0695)Fixed-Effects CSDc Yes Yes Yes Yes Month Yes Yes Yes Yes Year Yes Yes Yes YesS.E.: Clustered by: csd by: csd by: csd by: csdObservations 186600 26000 47200 50600Squared Cor. 0.04 0.05 0.09 0.04Pseudo R2 0.07 0.06 0.15 0.05BIC 25054858.1 7748771.8 5667050.4 15866534.8Note: Observations rounded to nearest 100 to protect respondent anonymity; asrmh is the variable that measures self-rated mental healthbsleep is the variable the measures sleep issues in the two prior weeks; cCSD stands for census sub-division; * p < 0.05, ** p < 0.01, *** p < 0.001.314. CONCLUSIONThis study asked two research questions: (1) To what extent does temperature affect mental health in Canada and (2) How are these impacts felt differently by people already experiencing social marginalization? I did not find strong evidence to reject the null hypothesis for either question. While temperature was found to have a significant effect on mental health in one of my four core models,22 this effect was not robust when including fixed effects for census subdivision. All three remaining core models reported null findings regardless of the inclusion of census subdivision. None of the interactions between social marginalization and temperature showed significant effects, and including these interactions did not improve the model fit. These results are in tension with much of the existing research on temperature and mental health. While some significant effects of temperature on mental health were found, these findings were far from robust across measures. Temperature did not have robust significant effects on mental health. This conflicts with the weight of published evidence in the field reviewed earlier in this piece. While it is possible that this finding is an error, given the large sample size employed in this study (~200,000) it is quite unlikely for it to be significantly different from the population. Research that has shown relationships between temperature and self-rated mental health was conducted in the United States and China (Hua et al., 2023; Li et al., 2020; Mullins & White, 2019; Obradovich et al., 2018; Zhang et al., 2023). What little research has been conducted in Canada to date has focused on smaller geographic areas and taken hospitalization as its dependent variable (Lavigne et al., 2023; Vida et al., 2012; Wang et al., 2014). As such, it is possible that the effects on self-rated mental health seen elsewhere are not evident in Canada. In the following paragraphs I discuss five possibles explanations for this null finding: publication bias, the large geographic area covered, a focus on average rather than extreme 22 The effect of maximum temperature (tmax14) on self-rated mental health (srmh).32temperatures, potentials for measurement error, and the possibility of Canada being a site of hope for adaptation.Similar null findings are less likely to be published. Across the social sciences, studies that show null findings are significantly less likely to make their way to publication, with most never getting written up (Franco et al., 2014; Gerber & Malhotra, 2008). As such, it makes it difficult to accurately assess the consensus on a topicâit may be that there have been multiple studies run that found similar results but remained in the file drawer (Franco et al., 2014). Indeed, in the one study reviewed that reported a null finding, the effects of temperature on mental health were one of multiple statistics being measured (Xue et al., 2019). As the other statistics were significant this study was less likely to fall foul of publication bias. Given the nature of the political harm that can be caused by studies that erroneously show null effects from climate change I would also be cautious to publish suchfindings without very robust evidence. Unfortunately, due to the nature of the file drawer problem, it is very hard to know anything about unpublished research in order to test this possibility. Averaging temperature across a large geographic area may hide localized effects. My study looks at maximum temperature as a single value across the entire of Canada, a country which includes many widely disparate climates. Prior research has shown significant localized differences in the temperature at which negative mental health effects manifest (Obradovich et al., 2018; Page et al., 2012). As such, it is possible that the localized effects across Canada have enough variation to confound attempts to model at the national level. While I also include relative temperatures, this is complicated by taking account of historical change rather than contemporary conditions.It is plausible that temperatures adversely affect mental health only above some absolute threshold. My study examines average temperatures and temperature anomalies as opposed to effects ofextreme temperatures, which some studies have done. For instance, if negative effects on mental health 33are only evidenced over 30 degrees Celsius this effect may not be significant in models where maximum temperature has a mean of just 11.7 degrees Celsius (as it does here). Future research should continue to deploy a robust set of ways to measure temperature to more thoroughly understand the climatic conditions under which human health is compromised.There are several potential places where measurement error may have confounded my study. First, the sample may be biased to not include those affected. It remains possible that the effects of temperature are primarily experienced by people who are already suffering serious mental health disorders. While I attempted to test for this by restricting my sample to only those who reported mental health disorders, this still differs significantly from the weight of research that focuses on hospitalizations and mortalities: people who are affected enough to be counted in such research would not be at home to complete the CCHS and as such would be missing from my data. Secondly, the use ofa 14 day window may not be capturing the relevant timescale for temperature to affect mental health. While this matches existing research (Mullins and White, 2019), it is possible that the effects of temperature in Canada work on a different time horizon, or with significant lag in their effects that is not captured in this study. But perhaps this is simply a true finding and temperature does not currently have a measurable negative effect on mental health in Canada. This may be a socioeconomic effect (for example, perhaps universal access to health care mitigates the impact of heat or makes it more likely for those experiencing issues to seek care and be excluded from my sample) or an environmental one (Canada may simply not be hot enough often enough for the effects to accumulate). However, the existence of research that shows effects at temperatures as low as 18 degrees Celsius in the United Kingdom suggests that lower temperatures are an unlikely explanation (Page et al., 2012).23 The United Kingdom23 That study looked at mortality and not self-rated mental health. As such it is not directly comparable. 34also has universal access to healthcare that, unlike Canada, includes psychiatric care. As such, more research would be needed to understand what factors cause the effects of temperature to look different in Canada. Rather than refuting the research reviewed earlier in this paper, my study may introduce hope for adaptation: if high temperatures in Canada are not having the same deleterious effect on mental health seen in the United States and China there is potential here to learn the mechanisms that are protecting Canadians and apply them elsewhere. 35REFERENCESAbadie, A., Athey, S., Imbens, G. W., & Wooldridge, J. M. (2023). When Should You Adjust Standard Errors for Clustering?*. The Quarterly Journal of Economics, 138(1), 1â35. https://doi.org/10.1093/qje/qjac038Bakir, M. M. T., Jahan, N. K., & Yap, C. G. (2023). Impact of Climate Change on Womenâs Mental Health: A Narrative Review. 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Used as inclog (calculated as log(income+1)) in analysis to account for skew. race Categorical Respondents self-declared race in three categories: white, Indigenous, and Racialized. Control age Continuous The respondentâs age in years.educ Categorical The respondentâs highest level of education across three categories: less than secondary, secondary, and post secondary.Fixed csduid Categorical The census subdivision in which the respondent lives. month Categorical The calendar month that the interview was conducted in.year Categorical The year that the interview was conducted in.Other sleep Dichotomous True if the respondent experienced any sleep disturbances in the last two weeks. 44Appendix B: Questions from the PHQ-9 used to calculate dependent variable DEPOver the last 2 weeks, how often have you: Not at all Several days More than halfthe daysNearly every dayhad little interest or pleasure in doing things? 0 1 2 3felt down, depressed, or hopeless? 0 1 2 3had trouble falling or staying asleep, or sleeping too much? 0 1 2 3felt tired or had little energy? 0 1 2 3had poor appetite or overate? 0 1 2 3felt bad about yourself, or that you are a failure or have let yourself or your family down?0 1 2 3had trouble concentrating on things, such as reading the newspaper or watching television?0 1 2 3been moving or speaking so slowly that other people could have noticed? Or the opposite, being so fidgety or restless that you have been moving around a lot more than usual?0 1 2 3had thoughts that you would be better off dead, or of hurting yourself? 0 1 2 3
Thesis/Dissertation
eng
Sociology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Raker, Ethan
Climate, marginalization, and mental health in Canada
Text