{"Affiliation":[{"label":"Affiliation","value":"Medicine, Faculty of","attrs":{"lang":"en","ns":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","classmap":"vivo:EducationalProcess","property":"vivo:departmentOrSchool"},"iri":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","explain":"VIVO-ISF Ontology V1.6 Property; The department or school name within institution; Not intended to be an institution name."},{"label":"Affiliation","value":"Population and Public Health (SPPH), School of","attrs":{"lang":"en","ns":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","classmap":"vivo:EducationalProcess","property":"vivo:departmentOrSchool"},"iri":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","explain":"VIVO-ISF Ontology V1.6 Property; The department or school name within institution; Not intended to be an institution name."}],"AggregatedSourceRepository":[{"label":"Aggregated Source Repository","value":"DSpace","attrs":{"lang":"en","ns":"http:\/\/www.europeana.eu\/schemas\/edm\/dataProvider","classmap":"ore:Aggregation","property":"edm:dataProvider"},"iri":"http:\/\/www.europeana.eu\/schemas\/edm\/dataProvider","explain":"A Europeana Data Model Property; The name or identifier of the organization who contributes data indirectly to an aggregation service (e.g. Europeana)"}],"Campus":[{"label":"Campus","value":"UBCV","attrs":{"lang":"en","ns":"https:\/\/open.library.ubc.ca\/terms#degreeCampus","classmap":"oc:ThesisDescription","property":"oc:degreeCampus"},"iri":"https:\/\/open.library.ubc.ca\/terms#degreeCampus","explain":"UBC Open Collections Metadata Components; Local Field; Identifies the name of the campus from which the graduate completed their degree."}],"Creator":[{"label":"Creator","value":"Lan, Qingyi","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/creator","classmap":"dpla:SourceResource","property":"dcterms:creator"},"iri":"http:\/\/purl.org\/dc\/terms\/creator","explain":"A Dublin Core Terms Property; An entity primarily responsible for making the resource.; Examples of a Contributor include a person, an organization, or a service."}],"DateAvailable":[{"label":"Date Available","value":"2022-09-14T22:34:54Z","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/issued","classmap":"edm:WebResource","property":"dcterms:issued"},"iri":"http:\/\/purl.org\/dc\/terms\/issued","explain":"A Dublin Core Terms Property; Date of formal issuance (e.g., publication) of the resource."}],"DateIssued":[{"label":"Date Issued","value":"2022","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/issued","classmap":"oc:SourceResource","property":"dcterms:issued"},"iri":"http:\/\/purl.org\/dc\/terms\/issued","explain":"A Dublin Core Terms Property; Date of formal issuance (e.g., publication) of the resource."}],"Degree":[{"label":"Degree (Theses)","value":"Master of Science - MSc","attrs":{"lang":"en","ns":"http:\/\/vivoweb.org\/ontology\/core#relatedDegree","classmap":"vivo:ThesisDegree","property":"vivo:relatedDegree"},"iri":"http:\/\/vivoweb.org\/ontology\/core#relatedDegree","explain":"VIVO-ISF Ontology V1.6 Property; The thesis degree; Extended Property specified by UBC, as per https:\/\/wiki.duraspace.org\/display\/VIVO\/Ontology+Editor%27s+Guide"}],"DegreeGrantor":[{"label":"Degree Grantor","value":"University of British Columbia","attrs":{"lang":"en","ns":"https:\/\/open.library.ubc.ca\/terms#degreeGrantor","classmap":"oc:ThesisDescription","property":"oc:degreeGrantor"},"iri":"https:\/\/open.library.ubc.ca\/terms#degreeGrantor","explain":"UBC Open Collections Metadata Components; Local Field; Indicates the institution where thesis was granted."}],"Description":[{"label":"Description","value":"The British Columbia (BC) wildfire seasons of 2017 and 2018 were unprecedented in terms of the area they burned and the smoke they emitted. Wildfire smoke is a complex and dynamic mixture of air pollutants, of which fine particulate matter (PM2.5) is generally recognized as the greatest threat to human health. Very few studies have examined how exposure to PM2.5 influences in utero respiratory tract development processes, so the most concerning prenatal exposure windows remain unknown.  \r\nIn this thesis, all infants in utero during the wildfire seasons (July to September) of 2016-2019 were identified using the BC Perinatal Data Registry (BCPDR).  Residential addresses of the mothers and their infants were used to estimate daily PM2.5 exposures throughout pregnancy and the first year of life using the Canadian Optimized Statistical Smoke Exposure Model (CanOSSEM) at a resolution of 5x5 km\u00b2. Outcomes of interest and potential covariates for each infant in the first year of life were identified through data linkage and compared during critical windows of prenatal respiratory tract development using the Cox proportional hazard model.\r\nWe found that the sensitive windows for respiratory infections and associated amoxicillin dispensations move to the later stages of development as the respiratory infections of interest move from the upper respiratory tract to the lower respiratory tract. Each 1 mg\/m3 increase in PM2.5 exposure was associated with earlier diagnosis of otitis media in the first window (week 0-9) of the Eustachian tube development (HR=1.012, 95%CI: 1.004-1.021). Each 1 mg\/m3 increase in PM2.5 exposure was associated with earlier diagnosis of lower respiratory infections in the Saccular stage (week 28-36) (HR =1.015, 95%CI: 1.010-1.020) and Alveolar stage (week 36 to birth) (HR = 1.008, 95%CI: 1.004-1.012). Similar results were observed for the effect of wildfire-related PM2.5 on amoxicillin dispensations related to respiratory tract infections in the first year of life. The statistically significant associations between wildfire-related PM2.5 and overall amoxicillin dispensation were detected for the later stages of respiratory tract development. Our results suggest that it is necessary to formulate clear public health guidelines for pregnant mothers to avoid being exposed to wildfire during wildfire seasons.","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/description","classmap":"dpla:SourceResource","property":"dcterms:description"},"iri":"http:\/\/purl.org\/dc\/terms\/description","explain":"A Dublin Core Terms Property; An account of the resource.; Description may include but is not limited to: an abstract, a table of contents, a graphical representation, or a free-text account of the resource."}],"DigitalResourceOriginalRecord":[{"label":"Digital Resource Original Record","value":"https:\/\/circle.library.ubc.ca\/rest\/handle\/2429\/82713?expand=metadata","attrs":{"lang":"en","ns":"http:\/\/www.europeana.eu\/schemas\/edm\/aggregatedCHO","classmap":"ore:Aggregation","property":"edm:aggregatedCHO"},"iri":"http:\/\/www.europeana.eu\/schemas\/edm\/aggregatedCHO","explain":"A Europeana Data Model Property; The identifier of the source object, e.g. the Mona Lisa itself. This could be a full linked open date URI or an internal identifier"}],"FullText":[{"label":"Full Text","value":"IN UTERO EXPOSURE TO WILDFIRE SMOKE AND CRITICAL TIME WINDOWS FOR RESPIRATORY OUTCOMES IN THE FIRST YEAR OF LIFE by Qingyi Lan B.S., The University of Illinois at Urbana-Champaign, 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) September 2022  \u00a9Qingyi Lan, 2022   ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled:  In utero exposure to wildfire smoke and critical time windows for respiratory outcomes in the first year of life  submitted by Qingyi Lan  in partial fulfilment of the requirements for the degree of Master of Science in Population and Public Health  Examining Committee: Sarah Henderson, Associate Professor, Population and Public Health, UBC Co-supervisor Kate Weinberger, Assistant Professor, Population and Public Health, UBC Co-supervisor  Sabrina Luke, Epidemiologist, Perinatal Service BC Supervisory Committee Member Karen Bartlett, Professor, Population and Public Health, UBC Additional Examiner    iii Abstract The British Columbia (BC) wildfire seasons of 2017 and 2018 were unprecedented in terms of the area they burned and the smoke they emitted. Wildfire smoke is a complex and dynamic mixture of air pollutants, of which fine particulate matter (PM2.5) is generally recognized as the greatest threat to human health. Very few studies have examined how exposure to PM2.5 influences in utero respiratory tract development processes, so the most concerning prenatal exposure windows remain unknown.   In this thesis, all infants in utero during the wildfire seasons (July to September) of 2016-2019 were identified using the BC Perinatal Data Registry (BCPDR).  Residential addresses of the mothers and their infants were used to estimate daily PM2.5 exposures throughout pregnancy and the first year of life using the Canadian Optimized Statistical Smoke Exposure Model (CanOSSEM) at a resolution of 5x5 km\u00b2. Outcomes of interest and potential covariates for each infant in the first year of life were identified through data linkage and compared during critical windows of prenatal respiratory tract development using the Cox proportional hazard model. We found that the sensitive windows for respiratory infections and associated amoxicillin dispensations move to the later stages of development as the respiratory infections of interest move from the upper respiratory tract to the lower respiratory tract. Each 1 mg\/m3 increase in PM2.5 exposure was associated with earlier diagnosis of otitis media in the first window (week 0-9) of the Eustachian tube development (HR=1.012,  iv 95%CI: 1.004-1.021). Each 1 mg\/m3 increase in PM2.5 exposure was associated with earlier diagnosis of lower respiratory infections in the Saccular stage (week 28-36) (HR =1.015, 95%CI: 1.010-1.020) and Alveolar stage (week 36 to birth) (HR = 1.008, 95%CI: 1.004-1.012). Similar results were observed for the effect of wildfire-related PM2.5 on amoxicillin dispensations related to respiratory tract infections in the first year of life. The statistically significant associations between wildfire-related PM2.5 and overall amoxicillin dispensation were detected for the later stages of respiratory tract development. Our results suggest that it is necessary to formulate clear public health guidelines for pregnant mothers to avoid being exposed to wildfire during wildfire seasons.       v Lay Summary Climate change is expected to increase the frequency and intensity of wildfires in British Columbia and elsewhere over the coming decades. Smoke exposure in utero can potentially affect respiratory health throughout the course of life. This study will help to assess the critical timing for public health intervention to reduce adverse outcomes. This thesis aims to examine whether in utero exposure to wildfire smoke affects the incidence of respiratory health outcomes in the first year of life and to identify the most relevant developmental windows using data from an administrative retrospective cohort of approximately 63,000 infants. Preface The work in this dissertation was designed, carried out, and analyzed by myself, with guidance from supervisor Sarah Henderson, co-supervisor Kate Weinberger, and committee member Sabrina Luke. The exposure data related to the Canadian Optimized Statistical Smoke Exposure Model (CanOSSEM) was generated by Paul Naman. This thesis work received approval from the UBC Research Ethics Board (Certificate number: H20-01077), and it also received permission to access the health data from Population Data BC (approval number: 20-197).                    ii Table of Contents Abstract .......................................................................................................................................... iii Lay Summary .................................................................................................................................. v Preface .............................................................................................................................................. i Table of Contents ............................................................................................................................ ii List of Tables ................................................................................................................................. vi List of Figures .............................................................................................................................. viii List of Abbreviations ..................................................................................................................... ix Acknowledgements ......................................................................................................................... x Chapter 1: Introduction ................................................................................................................... 1 1.1 Wildfires Globally .......................................................................................................... 1 1.2 Wildfire in BC ................................................................................................................. 1 1.3 Global Warming and Wildfire ........................................................................................ 3 1.4 Wildfire Smoke and Air Quality ..................................................................................... 4 1.5 Pm2.5 from Wildfire Smoke ............................................................................................ 4 1.6 Wildfire Smoke and the Developing Fetus ..................................................................... 6 1.7 Study Rationale ............................................................................................................... 7 Chapter 2: Literature Review .......................................................................................................... 9 2.1 Wildfire Smoke and Human Health ................................................................................ 9 2.2 Prenatal Exposure to PM2.5 And Health Outcomes ....................................................... 12 2.3 Prenatal PM2.5 and Respiratory Outcomes in Early Life .............................................. 13 2.4 Sensitive Windows of Exposure ................................................................................... 15 2.4.1 The Respiratory System ........................................................................................ 15   iii 2.4.2 Fetal Upper Respiratory Tract Development ........................................................ 16 2.4.2.1 Fetal Development of Facial Regions ............................................................... 16 2.4.2.2 Fetal Development of Larynx ........................................................................... 17 2.4.2.3 Fetal Development of Eustachian Tube ............................................................ 18 2.4.2.4 Lower Respiratory Tract Development ............................................................ 19 2.4.3 Evidence on Sensitive Windows ........................................................................... 20 2.4.4 Evidence Gaps ...................................................................................................... 21 2.5 Acute Respiratory Infections ........................................................................................ 23 2.6 Amoxicillin ................................................................................................................... 24 2.7 Study Aim ..................................................................................................................... 25 Chapter 3: Method ........................................................................................................................ 27 3.1 Health Data Sources ...................................................................................................... 27 3.1.1 Unconsolidated Registration and Premium Billings (URPB) ............................... 27 3.1.2 Consolidation File ................................................................................................. 28 3.1.3 BC Perinatal Data Registry ................................................................................... 28 3.1.4 Medical Services Plan (MSP) Payment Information File ..................................... 28 3.1.5 Pharmanet ............................................................................................................. 28 3.2 Cohort Construction ...................................................................................................... 29 3.3 Omission of infants due to incomplete maternal residential histories .......................... 29 3.4 Exposure Assessment .................................................................................................... 30 3.5 Outcome Measures ........................................................................................................ 32 3.6 Covariates ..................................................................................................................... 33 3.7 Statistical Analysis ........................................................................................................ 34   iv Chapter 4: Results ......................................................................................................................... 37 4.1 Study Cohort Description ............................................................................................. 37 4.2 Survival Analysis .......................................................................................................... 39 4.2.1 Prenatal development of the eustachian tubes and diagnosis of otitis media in the first year of life .................................................................................................................. 39 4.2.2 Prenatal development of the facial regions and diagnosis of related respiratory infections in the first year of life ....................................................................................... 40 4.2.3 Prenatal development of larynx and diagnosis of related respiratory infections in the first year of life ............................................................................................................ 41 4.2.4 Prenatal development of lower respiratory tract and diagnosis of related respiratory infections in the first year of life .................................................................... 42 4.2.5 Prenatal development of the eustachian tubes and prescription of amoxicillin for any reasons in the first year of life .................................................................................... 43 4.2.6 Prenatal development of facial regions and prescription of amoxicillin for any reasons in the first year of life ........................................................................................... 44 4.2.7 Prenatal development of larynx and prescription of amoxicillin for any reasons in the first year of life ............................................................................................................ 44 4.2.8 Prenatal development of lower respiratory tract and diagnosis of related amoxicillin dispensation for any reasons in the first year of life ...................................... 45 4.2.9 Prenatal development of the eustachian tubes and prescription of amoxicillin related to otitis media in the first year of life .................................................................... 46 4.2.10 Prenatal development of facial regions and related prescription of amoxicillin in the first year of life ............................................................................................................ 47   v 4.2.11 Prenatal development of larynx and related prescription of amoxicillin in the first year of life ......................................................................................................................... 48 4.2.12 Prenatal development of lower respiratory tract and related prescription of amoxicillin in the first year of life .................................................................................... 49 4.3 Analysis By Trimesters ................................................................................................. 50 Chapter 5: Discussion ................................................................................................................... 52 5.1 Summary of Findings .................................................................................................... 52 5.2 Strengths ....................................................................................................................... 54 5.3 Limitations .................................................................................................................... 56 5.4 How to protect pregnant mothers during wildfire events? ............................................ 57 5.5 Directions for Future Research ..................................................................................... 59 5.6 Conclusion .................................................................................................................... 60 Bibliography ................................................................................................................................. 61 Appendices .................................................................................................................................... 86 Appendix A : Statistical Analysis by Trimester Windows ................................................... 86 A.1  Sub-Appendix: Survival analysis for the first diagnosis of respiratory infections by trimester windows ............................................................................................................. 86 A.2  Sub-Appendix: Survival analysis for overall dispensation of antibiotics by trimester windows ............................................................................................................................ 88 A.3  Sub-Appendix: Survival analysis for the first dispensation of amoxicillin related to  respiratory infections by trimester windows ..................................................................... 88    vi List of Tables Table 2.1 Fetal development of facial regions .............................................................................. 17 Table 2.2  Fetal development of larynx ........................................................................................ 18 Table 2.3 Fetal development of the Eustachian tube .................................................................... 19 Table 2.4 Fetal development of lower respiratory tract ................................................................ 20 Table 3.1 ICD-9 codes and respiratory infections ........................................................................ 32 Table 3.2 Development of facial regions & Sino-nasal mucosa ................................................... 35 Table 3.3 Laryngeal development ................................................................................................. 35 Table 3.4 Development of the Eustachian tube ............................................................................ 35 Table 3.5 Development of lower respiratory tract ........................................................................ 36 Table 4.1 Summary characteristics of infants and mothers included in the cohort ...................... 38 Table 4.2 Survival analysis for the first diagnosis of otitis media in the first year and prenatal exposure to wildfire-related PM2.5 ................................................................................................ 40 Table 4.3 Survival analysis for the first diagnosis of upper respiratory infection (facial regions) in the first year and prenatal exposure to wildfire-related PM2.5 .................................................. 41 Table 4.4 Survival analysis for the first diagnosis of upper respiratory infection (larynx) the first year and prenatal exposure to wildfire-related PM2.5 ................................................................... 41 Table 4.5 Survival analysis for the first diagnosis of lower respiratory infection in the first year and prenatal exposure to wildfire-related PM2.5 ........................................................................... 42 Table 4.6 Survival analysis for the first dispensation of amoxicillin in the first year and prenatal exposure to wildfire-related PM2.5 (by Eustachian tubes developmental windows) .................... 43 Table 4.7 Survival analysis for the first dispensation of amoxicillin in the first year and prenatal exposure to wildfire-related PM2.5 (by facial regions developmental windows) .......................... 44   vii Table 4.8 Survival analysis for the first dispensation of amoxicillin in the first year and prenatal exposure to wildfire-related PM2.5 (by larynx developmental windows) ..................................... 45 Table 4.9 Survival Analysis for the first dispensation of amoxicillin in the first year and prenatal exposure to wildfire-related PM2.5 (by lower respiratory developmental windows) .................... 46 Table 4.10 Survival analysis for the first dispensation of amoxicillin associated with otitis media in the first year and prenatal exposure to wildfire-related PM2.5 .................................................. 47 Table 4.11 Survival analysis for the first dispensation of amoxicillin associated with upper respiratory infection (facial regions) in the first year and prenatal exposure to wildfire-related PM2.5 ............................................................................................................................................. 48 Table 4.12 Survival analysis for the first dispensation of amoxicillin associated with upper respiratory infection (larynx) the first year and prenatal exposure to wildfire-related PM2.5 ....... 49 Table 4.13 Survival analysis for the first dispensation of amoxicillin associated with lower respiratory infection in the first year and prenatal exposure to wildfire-related PM2.5 ................ 50    viii List of Figures Figure 1.1 Time series of wildfire smoke in BC from 2016-2019 .................................................. 6     ix List of Abbreviations   BCPDR BC Perinatal Data Registry BC British Columbia CanOSSEM Canadian Optimized Statistical Smoke Exposure Model HR Hazard Ratio ICD-9 International Classification of Diseases, 9th Edition LVP Levetor veli palatini muscle MSP Medical Services Plan \u00b5g\/m3 Micrograms per cubic meter of air 95% CI 95 percent confidence interval PM2.5 Particulate matter smaller than 2.5 micrometers in aerodynamic diameter SES Maternal Socio-economic status SHS Second-hand smoke TVP Tensor veli palatini muscle URPB Unconsolidated Registration and Premium Billings    x Acknowledgements I want to express my gratitude to my supervisor, Sarah Henderson, co-supervisor, Kate Weinberger, and committee member, Sabrina Luke. Thank you for encouraging and guiding me through all the steps in my academic life and for answering my endless questions. It was a great pleasure to work with you all. Your support makes me feel even more determined to forge ahead into a Ph.D. career.  I also want to thank my parents for giving me strong support and inspiring me to be an incredible woman. Thank you to my grandparents, who consistently taught me to learn throughout my lifetime and told me that I am the pride of our family.   I also want to thank everyone in the SPPH programs, my classmates, professors, and Emily, for supporting me during this tough COVID time. I could not imagine that I could complete my master's without encouragement and support from you.   Thank you to my best friend, Hanna Gong, who travelled from the US to be here and cooked delicious food while I was writing this thesis. And thank you to my friend, Nick Chou, who provided me with much mental support when I experienced loneliness in a foreign city.     1 Chapter 1: Introduction 1.1 Wildfires Globally Wildfires are natural disasters that prevail in regions of forests and grassland. In the most recent two years (2019-2020), wildfires made a significant impact on the world. In 2019, the unprecedented wildfires in Australia killed hundreds of people, destroyed thousands of homes, and severely impacted the ecosystem (Linn, 2019). The 2020 wildfires in the western parts of the United States also broke national records and contributed to 3,720 exceedances of the US NAAQS (National Ambient Air Quality Standards) (Y. Li et al., 2021). The prevalence of wildfire will inevitably influence global air quality and pose ongoing challenges for human health. The well-being costs are defined as long-term and intangible health care costs, including psychological disorders, physical injury, and behavioural change. According to a study analyzing the well-being costs of the 2009 Black Saturday Bushfires, the most fatal wildfire event in Australia\u2019s history, the negative well-being cost associated with the wildfire event was determined as A$52,300 for every adult citizen, which is equal to 80% of the annual salary of a full-time employee from the state of Victoria (D. W. Johnston et al., 2021). For people living close to the center of the wildfires, their life satisfaction was significantly reduced and there were long-lasting impacts on their mental health (D. W. Johnston et al., 2021). In Canada, BC is one of the regions with the most frequent wildfires, along with the boreal forest zones of Ontario, Quebec, the Prairie provinces, and the Yukon and Northwest Territories (Canada, Public Safety, 2018).  1.2 Wildfire in BC Two-thirds of BC is forested, which makes the province extremely prone to wildfires (British Columbia & Ministry of Forests and Range, 2007). The wildfire seasons can last from   2 the start of April until the end of September (BC Wildfire Service, 2021b). The central interior region of the province is the most affected by wildfires (Yao, 2019). This thesis will mainly focus on the wildfire seasons in BC from 2016-2020. The 2016 wildfire season was below the average due to high rainfall in June, July, and August, that kept the forests from drying out (BC Wildfire Service, 2021b). Approximately 100 thousand hectares of land burned and $129 million was spent during the 2016 wildfire season (BC Wildfire Service, 2021a). In contrast, the BC wildfire seasons of 2017 and 2018 were unprecedented in terms of the area that burned and the smoke that emitted. Over 1.2 million hectares of land burned and over $649 million was spent on fire suppression during the 2017 wildfire season. Even though the total cost of wildfire suppression was less in 2018 ($615 million), 0.15 million more hectares burned in that season compared with the previous year (BC Wildfire Service, 2021a). Compared with any year since 1959, the wildfire activity in 2017 and 2018 was more than double (R. Murphy & desLibris - Documents, 2020). The 2017 wildfire season also initiated a provincial State of Emergency that lasted for over 70 days, breaking the longest records since 2003 (BC Wildfire Service, 2021b). Responses included banning off-road vehicles, inhibiting full access to remote country regions, and prohibiting use of campfires (BC Wildfire Service, 2021b). In comparison, the 2018 wildfire season had 23 days under a provincial State of Emergency, but it broke the record for total area burned set in 2017. The intense lightning events occurring during July and August, and extreme temperatures starting in late July, contributed to around 400 new wildfires within two weeks in 2018. Compared with the wildfire seasons in 2017 and 2018, the 2019 wildfire season was much less destructive. Only 21 thousand hectares of land burned and the overall fire activity in 2019 was below the 10-year average. The 2020 wildfire season was also below average, with only 15 thousand hectares burned (BC Wildfire Service, 2021a).    3 1.3 Global Warming and Wildfire Rising global temperatures, more heatwaves, and associated droughts leads to hot and dry weather, which increases wildfire risk. Climate warming driven by CO2 (carbon dioxide) emissions could lead to even greater water-soil evaporation, resulting in drier soil and water loss in forest flora, which creates ideal conditions for wildfire ignition (Baker, 2022; Mansoor et al., 2022). The relationship between global warming and wildfire events has also been shown in fossil records, where increased wildfire activity has been associated with greenhouse-induced global warming periods throughout the history of the Earth (Baker, 2022). According to Westerling et.al, global warming seems to increase the duration and intensity of wildfire seasons in the western United States. Wildfire frequency since 1986 has increased by a factor of four, and the total land burned has increased by more than a factor of six, compared with the average from 1970 to 1986. The lengths of wildfire seasons from 1987 to 2003 have increased by up to 64% compared with those from 1970 to 1986. The mean wildfire event duration from 1970 to 1986 was 7.5 days compared to 37.1 days from 1987-2003. (Westerling et al., 2006). While global warming contributes to more wildfires, wildfire events are also contributing to global warming, forming a feedback loop (Mansoor et al., 2022). Wildfires could contribute to the release of stored carbon like CO2, CO (carbon monoxide), CH4 (methane), and aerosols, which can impact the climate. Wildfires can also change the vegetation cover, which leads to changes in the amount of solar radiation absorbed, causing a heating or cooling effect (Oris et al., 2014). More wildfires are expected in the decades ahead. The Fire Weather Index, which is used to rate fire danger, is expected to increase in the future in both Canada and North America due to increased surface temperature, changed precipitation patterns, and comparatively low humidity. Indeed, extreme wildfire conditions have increased by a factor of four across   4 Western Canada in recent years (Coogan et al., 2019). Another study focusing on wildfire weather in 11 Canadian cities found that prolonged wildfire seasons and more frequent fire-prone weather were observed with 2\u2103 and 3.5\u2103 changes in mean global temperature, compared with a historical baseline from 1989 to 2019 (Gaur et al., 2021).   1.4 Wildfire Smoke and Air Quality Wildfire smoke is a complex and dynamic mixture of different air pollutants, including fine particulate matter (PM2.5), carbon monoxide (CO), nitrogen oxides (NOX), methane (CH4), polycyclic aromatic hydrocarbons (PAHs), and volatile organic compounds (VOCs) (Naeher et al., 2007). It can influence air quality in two ways: 1) dispersion of primary pollutants such as PM2.5, CO, and NOX  into the air; and 2) emergence of secondary pollutants such as ozone and secondary organic aerosol (SOA) through photochemical reactions (Dreessen et al., 2016; Urbanski et al., 2008). Air quality can be affected by wildfire smoke dispersion at the local, regional, and continental scales (Urbanski et al., 2008).  Wildfire smoke can travel long distances and affect large populations through the formation of pyro cumulonimbus clouds. Pyro cumulonimbus clouds are created by the wildfires when ash, smoke, and burning material vent into the upper atmosphere. These clouds can form their own weather systems, including lightning strikes that create more fires. Once the wildfire smoke reaches the stratosphere, it can be transported over thousands of kilometers to affect populations that are far from the wildfires (Jenner, 2020). 1.5 PM2.5 from Wildfire Smoke Of all the pollutants in wildfire smoke, PM2.5 is generally recognized as the greatest threat to human health. Wildfire smoke is one of the largest sources of PM2.5 in Canada, contributing to   5 17.1% of the total populated-weighted exposure (Meng et al., 2019). These inhalable fine particles comprise a mixture of solids and liquid droplets with aerodynamic diameters 2.5 micrometers or smaller (US EPA, 2016). The composition of PM2.5 released from wildfire smoke includes organic compounds (>90%), elemental carbon (ca 5%-10% by mass), nitrate (NO3\u2212), potassium ion (K+), chloride (Cl\u2212), ammonium (NH4+), metals, and other elements that constitute only a small percentage (Jaffe et al., 2020). According to Liu & Peng, wildfire specific PM2.5 was different from PM2.5 on days without wildfires, with increased organic carbon and elemental carbon species, and decreased sulfur\/sulfate and crustal species (2019). On average, 71.3% of total PM2.5 was attributed to wildfires from 2004 to 2009 on the days when the PM2.5 concentration was above the US Environmental Protection Agency\u2019s (EPA) regulatory 24-hour standard of 35\u03bcg\/m3 (Liu et al., 2016). Increased emissions of PM2.5 from wildfire smoke are expected as the climate changes, such that more than 82 million US residents may experience an almost 60% increase in frequency and a one-third increase in intensity of smoke waves (two or more consecutive days of smoke exposure) during wildfire seasons from 2046-2051 (Liu et al., 2016).  The composition of PM2.5 during wildfire seasons varies in different ecoregions even when the mixtures originated from the same wildfires. This suggests that the toxicity of PM2.5 mixtures from different ecosystems might also vary (Liu & Peng, 2019). Studies have shown that wildfire-related PM2.5 could be more toxic and harmful to humans compared with ambient PM2.5 from other sources, particularly for respiratory outcomes. A 10 \u03bcg\/m3 increase in wildfire-related PM2.5 has been associated with increases in respiratory hospitalization, ranging from 1.3% to 10%, while a 10 \u03bcg\/m3 increase in PM2.5 from other sources is only associated with increases ranging from 0.67% to 1.3% (Aguilera et al., 2021). Another toxicological study indicated that   6 particulate matter originating from wildfires is four times more toxic to lung macrophages compared with particulate matter from other sources (Franzi et al., 2011).  Average PM2.5 concentrations vary across different wildfire seasons in BC. During the extreme seasons of 2017 and 2018, most regions experienced 24-hour mean concentrations >50 \u03bcg\/m3, while during the more typical wildfire seasons of 2016 and 2019, the 24-hour mean concentrations were mostly <10 \u03bcg\/m3 (Henderson, 2019) (Figure 1.1).    Figure 1.1 Time series of wildfire smoke in BC from 2016-2019 The daily population weighted average PM2.5 during 2016-2019 wildfire seasons, generated from CanOSSEM. The wildfire seasons are indicated by the dash lines (July to September).  1.6 Wildfire Smoke and the Developing Fetus Wildfire smoke exposure during pregnancy has been linked to several negative birth outcomes, including increased infant mortality, lower birth weight, impaired respiratory function, and changes in early immune development (Amjad et al., 2021; Ha, 2022; Kondo et al., 2019).   7 Possible mechanisms explaining how wildfire pollutants directly interact with pregnant mothers include particles dispersing across the tissue barriers or permeating across the cellular membrane (Proietti et al., 2013). The indirect mechanisms include oxidative stress and systemic or inflammatory reactions of the pregnant mothers that can lead to decreased fetal supply of nutrients or oxygen (Korten et al., 2016; Proietti et al., 2013). There is also substantial evidence concluding that the interaction of epigenetic influences and air pollution could also lead to adverse birth outcomes including decreased respiratory function, even if the specific mechanism is not fully understood (Korten et al., 2016). Some psychological mechanisms that initiate stress and other mental health outcomes for pregnant women during wildfire seasons could also impact the developing fetus. The psychological stress is extreme and magnified for pregnant mothers when they are afraid of losing their homes, suffering from power outages, have health symptoms related to wildfire smoke, or have limited access to healthcare resources (Padula & Benmarhnia, 2022).  1.7 Study Rationale Climate change is increasing human exposure to wildfires in BC and around the world. The wildfire-related PM2.5 can be transported over large areas by wind and influence pregnant women miles away from the fire. There is growing concern about the health effects of wildfires, particularly for pregnant women and their developing fetus. Several birth cohort studies have suggested that prenatal exposure to wildfire smoke or particulate matter may be associated with respiratory illness during childhood (Jedrychowski, Perera, Maugeri, Mroz, et al., 2010; Jung et al., 2019; Rivera Rivera et al., 2021; Sbihi et al., 2016). Further evidence also suggests that short-term changes in air pollution caused by wildfires may be correlated with gestational periods critical to the development of the fetus (Padula & Benmarhnia, 2022). Existing birth   8 cohort studies identified the specific windows of in utero lower respiratory tract development, and gestational trimesters are associated with respiratory infections and related medications after births (Dhingra et al., 2022; Willis et al., 2020). However, most of these studies lacked sufficient sample sizes because their participants were identified through active recruitment. Loss of follow-up, recall bias, and selection bias might also arise due to study design. To aim for greater statistical power and avoid certain biases, we conducted a study using population-based administrative data. The BC Perinatal Data Registry provides rich, individual-level information for pregnant mothers and newborns in BC, and it allows for the adjustment for potential confounders in the analysis.   Exposure assessment for wildfire-related PM2.5 is also critical in our research context. In some studies, exposure was defined as living within 15 miles of the fire perimeter. However, the studies failed to address to what extent those pregnant mothers and the newborns were exposed to the wildfire-related PM2.5, and how the exposure varied for pregnant mothers and the newborns (Padula & Benmarhnia, 2022). Our study uses the Canadian Optimized Statistical Smoke Exposure Model (CanOSSEM), which allows us to estimate wildfire-related PM2.5 and identifies individual exposure based on residential addresses. The CanOSSEM model can also precisely capture spatial variations in wildfire-related PM2.5 through a statistical approach, integrating satellite and meteorological data rather than providing imprecise estimates based on proximity to the wildfires or the closest air quality monitoring stations.     9 Chapter 2: Literature Review This chapter reviews what is known about the relationship between wildfire smoke and health, the effects of prenatal wildfire smoke exposure, and developmental windows for the fetal respiratory system.  2.1  Wildfire Smoke and Human Health Exposure to wildfire smoke has been associated with a wide range of health outcomes across all stages of life. Multiple studies have found that wildfire smoke is associated with all-cause premature mortalities (Faustini et al., 2015; F. H. Johnston et al., 2012; Reid et al., 2016). In Canada, Matz et al. (2020) estimated 54-240 premature deaths annually from short-term exposure and 570-2,500 premature deaths annually from long exposure attributed to wildfire PM2.5. The population health impacts were evaluated to have a per-year economic value of $410M - $1.8B for short-term health impacts and $4.3B - $19B for long-term health impacts (Matz et al., 2020).  In several studies, respiratory outcomes such as increased diagnosis of respiratory symptoms, increased risk of hospital visits, emergency department visits, and physician visits for respiratory illness were found to be related to wildfire smoke (Reid et al., 2016). Haikerwal et al. (2016) reported that an 8.6 \u03bcg\/m3 increase in PM2.5 concentration was associated with an increase in emergency department (ED) attendances for asthma by 1.96% during the 2006-2007 wildfire season in Victoria, Australia. A meta-analysis which included 20 studies found that elevated PM2.5 from landscape fire smoke was associated with increased risk of asthma hospitalization and emergency visits (Borchers Arriagada et al., 2019). A longitudinal cohort study also found that every 10 \u03bcg\/m3 increase in the two-year average PM2.5 concentration was associated with decreases in lung function and an increase in the prevalence of poor lung function in children,   10 adolescents, and young adults in Taiwan (Guo et al., 2019). In addition to the epidemiologic evidence, toxicological evidence suggests wildfire-specific particles could induce rapid cytotoxicity in macrophages through oxidative reagents (Williams et al., 2013) and even cause respiratory tract infections by changing the activity of pulmonary macrophages (Migliaccio et al., 2013).  Evidence on the cardiovascular outcomes associated with wildfire smoke is more mixed but growing rapidly. Increases in hospital visits for cardiovascular diseases and cardiac arrest during wildfire events are also suggested in several studies. Prior systematic reviews have found the evidence to be inconclusive (Liu et al., 2016; Reid et al., 2016). However, newer studies continue to detect statistically significant effects of wildfire smoke that are consistent with the cardiovascular effects of PM2.5 from other sources (Chen et al., 2021; Hadley et al., n.d.; Rajagopalan et al., 2020). Several studies have suggested a relationship between wildfire smoke and mental health outcomes. A study focusing on the Camp Fire of 2018, a fatal wildfire event in California, has reported that exposure to wildfire smoke significantly increased the risk for mental health disorders, including depression and post-traumatic stress disorder (PTSD). Another study suggests that patients who attended out-of-office clinics 18 months after the wildfire event would have a higher risk of mental illness and addiction (Moosavi et al., 2019). However, some earlier studies indicate no increase in either mental health or physician visits or hospitalization associated with wildfire-related air pollution (Moore et al., 2006; Duclos et al., 1990).  Exposure to wildfire smoke has been associated with decreased cognitive function in both adults and children. A longitudinal study from the USA which measured cognitive performance through a brain-training game reported that short-term exposure to wildfire-related PM2.5 is   11 associated with reduced attention in adults (Cleland et al., 2022). Another study from Indonesia found prolonged exposure to PM 2.5 in a forest fire-prone province is associated with decreased cognitive scores for children (Jalaludin et al., 2022). The effects of wildfire-related PM2.5 on cognitive function for adults and children are consistent with the effects of PM2.5 from other sources. According to a study from Mexico, children who are exposed to high PM2.5 concentrations are more likely to develop pervasive neuroinflammation, impairment to the neurovascular unit, and autoantibodies targeting tight-junction proteins. These physiological damages can further increase the risk of developing Alzheimer\u2019s disease in the later stage of life (Calder\u00f3n-Garcidue\u00f1as et al., 2012). According to the 2004 Health and Retirement Study, increased residential exposure to PM2.5 is also associated with worse cognitive function for over 13,996 older adults aged over 50 (Ailshire & Clarke, 2015). Another study reported that higher concentrations of PM2.5 is associated with increased hospitalizations related to dementia (M. Lee et al., 2019).   Diabetes is also one of the health outcomes associated with wildfire smoke exposure. Increased PM2.5 during wildfire seasons was associated with ambulance dispatches related to diabetic conditions and the association increased over time (Yao et al., 2020). However, there is limited literature studying the effects of wildfire-related PM2.5 on diabetic conditions. Most of the literature focusses on PM2.5 from other sources. A study from Taiwan found that long-term exposure to PM2.5 could increase the incidences of Type 2 diabetes by 11% (C.-Y. Li et al., 2019). Another study from the US also indicated that a 10 \u03bcg\/m3 increase in PM2.5 exposure was associated with a 1% increase in diabetes prevalence in the country level (Pearson et al., 2010).  In general, literature on wildfire-related PM2.5 is still quite limited compared with the larger literature on PM2.5 from other sources, but the effects of wildfire smoke are consistent with those   12 from other PM2.5 so we can use that much bigger evidence base to help guide research questions about wildfire smoke. 2.2 Prenatal Exposure to PM2.5 and Health Outcomes Research suggests that air pollutants released from wildfire smoke can pass through the placental barrier and disturb fetal-maternal circulation, which may negatively impact the growing fetus (Amjad et al., 2021). Even though the detailed physiological mechanism between wildfire smoke and adverse birth outcomes remains unknown, epidemiologic evidence has shown that prenatal exposure to wildfire smoke is associated with adverse pregnancy outcomes, including preterm birth (PTB), low birth weight, and fetal growth retardation (Amjad et al., 2021).  Prenatal exposure to wildfire smoke has also been associated with congenital disabilities. One study indicates that exposure to wildfire during the first trimester of pregnancy is associated with a significantly higher risk of fetal gastroschisis (Park et al., 2022), a congenital disability in which the intestines develop outside of the body (CDC, 2020). Another study in Brazil involving a total of 7,595 infants with congenital disabilities found that cleft lip or palate, congenital anomalies of the respiratory system, and anomalies of the nervous system were significantly associated with wildfire-related air pollution during pregnancy (Requia et al., 2021). Prenatal exposure to wildfire smoke has also been associated with fetal mortality. After a severe smoke event in 1997, 1.2% of the birth cohorts was missing from the 2000 Indonesian Census. The study found that prenatal exposure to wildfire-related air pollution led to this outcome (Jayachandran, 2009).  The most well-studied constituent of wildfire smoke is PM2.5. Although the literature on wildfire smoke is limited, many more studies have analyzed the relationship between prenatal exposure to PM2.5 and health outcomes after birth. According to Rosa et al., exposure to PM2.5   13 during the second and third trimester was associated with increased blood pressure in early life, which may predispose children to cardiovascular disease during adulthood (Rosa et al., 2020). Studies have also shown that prenatal exposure to PM2.5 is associated with neurodevelopmental outcomes (Okechukwu, 2021) and decreased corpus callosum volume in children aged 8-12 years old, which led to increased behavioural problems (Mortamais et al., 2019). Prenatal exposure to PM2.5, including wildfire smoke, has the potential to affect health in early life and throughout the course of life.      Toxicological studies have indicated that prenatal exposure to PM2.5 can initiate an epigenetic mechanism that alters gene expression and cell function (Gruzieva et al., 2019). DNA methylation, the primary epigenetic mechanism that involves adding a methyl group to cytosine at cytosine-phosphate-guanine (CpG) sites in DNA, is disrupted by persistent prenatal exposure to PM2.5 (Gruzieva et al., 2019; Okechukwu, 2021). A reduction in placental DNA methylation associated with prenatal exposure to PM2.5 was observed in early pregnancy, including at the implantation stage (B. G. Janssen et al., 2013). Another study using a cell line cultured from HRT8 trophoblasts found that prenatal exposure to urban PM2.5 causes cytotoxic effects, hormone imbalance, oxidative stress, inflammation, and mitochondrial disruption that may have long-term health effects (N\u00e4\u00e4v et al., 2020). Both epidemiologic and biological evidence has indicated the importance of studying the prenatal period as a critical time window for exposure. 2.3 Prenatal PM2.5 and Respiratory Outcomes in Early Life Even though some studies have explored the association between wildfire smoke exposure and respiratory outcomes among adults and school-age children (Guo et al., 2019; Haikerwal et al., 2016; Reid et al., 2016), very few studies have examined the effects of in utero exposure on respiratory outcomes in the first year of life.    14 According to toxicological evidence, in utero exposure to wildfire smoke may result in respiratory outcomes in early life because maternal PM2.5 exposure negatively affects fetal lung growth, contributing to respiratory symptoms in early childhood (Jedrychowski, Perera, Maugeri, Mroz, et al., 2010). One study hypothesized that PM2.5 could either impact the fetus directly through the placental barrier or indirectly induce a maternal inflammatory or systematic immune response, eventually leading to adverse consequences in fetal lung development (Kannan et al., 2006). Exposure to PM2.5 in early pregnancy may also interfere with cell differentiation and branching morphology. In contrast, exposure in late pregnancy could interfere with alveolarization, impairing lung development and function soon after birth (Korten et al., 2016).  Willis et al. (2020) reported that a 10 \u03bcg\/m3 increase in mean daily in utero PM2.5 exposure increased reports of runny nose\/cough, wheeze, seeking health professional advice, doctor diagnoses of upper respiratory tract infections, and cold or flu among infants affected by smoke from a coal mine fire. The association between an increase in peak 24-hour PM2.5 and increased use of asthma inhalers was also found for newborns exposed in utero. However, a similar correlation was not reflected in newborns exposed to coalmine fire smoke postnatally. Emerging evidence from a similar birth cohort also suggests that prenatal exposure to wildfire PM2.5 was associated with more frequent episodes of wheezing in the first two years of life (Jedrychowski, Perera, Maugeri, Mrozek-Budzyn, et al., 2010). A previous cohort study in BC found that perinatal exposure to residential wood smoke was associated with increased diagnoses of asthma in the preschool years (Sbihi et al., 2016). These studies show that it is essential to study prenatal or perinatal exposure to wildfire smoke and adverse respiratory outcomes in early life. However, none of these studies assessed the relationship between prenatal exposure to smoke PM2.5 and respiratory outcomes in the first year of life.    15 2.4 Sensitive Windows of Exposure Prior research has also been limited in its assessment of the importance of different prenatal windows as they relate to fetal development. Some studies have looked broadly at the effects of trimesters, but not critically at stages of fetal development and their potential susceptibility to time-specific exposures. To better consider the risks, it is essential to first consider how the upper and lower respiratory tract develop during the embryotic and fetal stages.  2.4.1 The Respiratory System The respiratory system is made up of the lungs and the respiratory airways that pass through the head, neck, and trunk. Core functions of the respiratory system include air exchange between the external environment and the air sacs of the lung, detection of odours, and sound production (McKinley, 2013). The respiratory system is divided into an upper respiratory tract and a lower respiratory tract. The upper respiratory tract comprises regions including the nose, nasal cavity, pharynx, the Eustachian (pharyngotympanic) tube, and larynx. The lower respiratory tract includes the trachea, bronchi, bronchioles, alveolar ducts, and alveoli (Green et al., 2017; Pagano et al., 2019). The respiratory system can also be divided into two functional zones: the conducting zone and the respiratory zone. The conducting zone is made of pathways that convey air running from the nose to the end of the terminal bronchioles and transport air into the body. The respiratory zone is for gas exchange with blood, comprising respiratory bronchioles, alveolar ducts, and alveoli (McKinley, 2013).  Mucosa lining the respiratory passageway produce mucus that secretes mucin, a protein that helps effectively trap inhaled dust, dirt particles, microorganisms, and pollen. Mucin also contains antibacterial enzymes, lysozyme, antimicrobial protein like defensins, and antibodies like immunoglobulin A, which can help the body defend against microbes. The mucosa   16 comprises an epithelium that sits atop a basement membrane and a lamina propria comprised of areolar connective tissues. With a few exceptions, the epithelium thins as it moves from the nasal cavity to the alveoli (McKinley, 2013).  2.4.2 Fetal Upper Respiratory Tract Development The nasal cavity, mouth, nasopharynx, and hypopharynx develop in a distinct environment from the larynx, trachea, bronchi, and lung throughout the embryologic development (Zimmerman et al., 2022). The developmental stages also vary for different parts of the upper respiratory airway. To better outline the fetal development process for the upper respiratory tract, the following sections and tables cover three areas: facial regions, larynx, and eustachian tube.   2.4.2.1 Fetal Development of Facial Regions The facial regions include a significant proportion of the upper respiratory airway, including the nasal cavity, mouth, and pharynx. The early development of facial areas can be observed by the late 3rd and early 4th gestational week. During this period, the oropharyngeal membrane appears, followed by the closing of the anterior neuropore. Rapid development follows in gestational weeks 4-12 (Table 2.1).           17 Table 2.1 Fetal development of facial regions Gestational Week Fetal Development of Facial Regions 4th week  the stomodeum is formed by the early development of the maxillary and mandibular process: the frontonasal process occurs; the oropharyngeal membrane breaks apart; and the nasal placodes appear (Som & Naidich, 2013) 5th week the nasal pit is formed; the olfactory epithelium begins to form in upper nasal cavities; the nasal fin is formed and separates the primitive nasal and oral cavities; the nasal sacs are formed and migrate medially to be more slit-like; and the olfactory nerves and Meckel cartilage also begin (Som & Naidich, 2013) 6th week the primitive nasal septum and primary palate are formed; the maxilloturbinal and remaining nasal turbinal are formed; lips and gums are separated; the lateral palate develops above the tongue; and nasooptic furrow and nasolacrimal duct also appear (Som & Naidich, 2013) 7th week the complete development of philtrum and upper lip; further development of nasal septum; confinement of the upper nasal cavities; full development of the external ear; closure of nostrils by nasal plugs; development of cartilaginous nasal capsule; formation of the secondary palate; completion of lower cheeks (Som & Naidich, 2013) 8th week the lateral nasal wall is fully developed 9th to 10th week the nasal septum begins to merge with the palate; the ossification of the maxilla starts (Som & Naidich, 2013) 10th to 11th week the uncinate process begins with the infundibulum's development (Som & Naidich, 2013) 12th week  the embryological development of facial regions is nearly completed; the ossification centers of all facial bones are present (Som & Naidich, 2013) 13th to 14th week as the cartilaginous capsule recedes, the growing maxilla creates the lateral wall of the inferior meatu (Som & Naidich, 2013) 15th to 16th week the primitive maxillary sinus emerges from the low edge of the infundibulum (Som & Naidich, 2013) 17th to 18th week the ethmoid bulla develops; the mucosa thickens, and vascular channels appear; the early ethmoid sinuses are lined by mature respiratory epithelium with goblet cells (Som & Naidich, 2013; Wake et al., 1994) 20th to 24th week Ethmoid sinus epithelium is well-developed, with a visible distribution of the secretory structure (Som & Naidich, 2013; Wake et al., 1994) 25th week to the end of pregnancy the length of the bottom three turbinate rises gradually and proportionally; however, the supreme turbinate remains at an average length of 5 mm and is present in only 65 percent of fetuses (Som & Naidich, 2013; Wake et al., 1994) 2.4.2.2 Fetal Development of Larynx The larynx is a complicated structure that serves as the trachea\u2019s superior entry. The larynx is mainly composed of cartilage suspended from the hyoid bone and cervical vertebrates by muscle and ligament attachments (Arvedson & Lefton-Greif, 2019). The entrance of the laryngotracheal tube forms the primitive glottis into the pharynx. The 4th and 6th pairs of branchial arches give rise to the laryngeal cartilage and muscles. The basic larynx structure is   18 formed between the 5th to 10th gestational weeks, and it experiences rapid development of cartilage and mucosal glands from the 13th gestational week to the end of pregnancy (Table 2.2): Table 2.2  Fetal development of larynx Gestational Week Fetal Development of Larynx 5th week the paired swellings occur at the cranial end of the laryngotracheal tube (Arvedson & Lefton-Greif, 2019) 6th week the primitive glottis is visible (Arvedson & Lefton-Greif, 2019) 7th week the glottis has a T-shaped aperture as the arytenoid swellings expand towards the tongue (Arvedson & Lefton-Greif, 2019) 8th to 10th week the recanalization of the larynx; the completion of the embryonic phase of larynx development; the larynx is easily recognized with its intrinsic musculature, innervations, blood supply, and cartilages (Arvedson & Lefton-Greif, 2019; Elumalai, 2016) 11th to 12th week the thyroid cartilages laminae fuse into the midline, and the vocal processes arise from the arytenoids (Elumalai, 2016) 13th to 16th week the goblet cells and submucosal glands are visible (Elumalai, 2016) 20th to 28th week the epiglottic cartilages mature and become fibro cartilaginous (Elumalai, 2016) 29th to the end of pregnancy  the corniculate and cuneiform cartilages become visible during this time\u2014coricoid cartilages transition from interstitial to perichondral growth (Elumalai, 2016)  2.4.2.3 Fetal Development of Eustachian Tube The Eustachian tube connects the middle ear to the external environment. It is also part of a system of contiguous organs, including the nose, palate, rhino pharynx, and middle ear cleft (Ars & Dirckx, 2016). The middle ear is ventilated, drained, and protected by the Eustachian tube. Because of the pressure in the surrounding tissue, the lumen of the membrane-cartilaginous section of the Eustachian tube is typically closed during normal function, and the lumen of the membrane-cartilaginous section of the Eustachian tube only opens for a short period of a day. As a result, its primary function is to protect the middle ear from backflow, pathogens, sound pressure, and pharyngeal air pressure fluctuations (Dornhoffer et al., 2014). Otitis media, which is characterized by acute or chronic inflammation of the middle ear and is also the most common upper respiratory illness that requires extensive pediatric care in the early stage of life, could be   19 caused by Eustachian tube dysfunction (Fireman, 1997; Mazer, 2016). There are five critical stages for Eustachian tube (Table 2.3): Table 2.3 Fetal development of the Eustachian tube Gestational Week Fetal Development of the Eustachian Tube  8th week the tubal lumen becomes visible (Dornhoffer et al., 2014) 10th to 12th week the LVP and TVP muscles first appear; the lumen transforms into a pseudostratified columnar epithelium, and the lumen grows considerably in the area of the developing cochlea (Dornhoffer et al., 2014) 13th to 18th week the cartilaginous condensation has spread medially from the pharyngeal area, encompassing a region superior and medial to the lumen; the glandular tissues are also present (Dornhoffer et al., 2014) 19th to 28th week the perichondrium diversified along the entire tubal length; the cartilages and para tubal muscle structure are fully developed; the lumens indicate differentiation of some seromucous glandular extensions (Dornhoffer et al., 2014) 29th to the end of pregnancy the lumen length continues to grow, primarily due to an increase in the size of the osseous component of the tube (Dornhoffer et al., 2014)  2.4.2.4 Lower Respiratory Tract Development Lower respiratory tract development, also known as fetal lung development, is a complex and lengthy process that starts during the embryonic period. There are five critical windows for lower respiratory tract (fetal lung) development: Embryonic stage (0-7 weeks); Pseudoglandular stage (8-17 weeks); Canalicular stage (18-27 weeks); Saccular stage (28-36 weeks); and Alveolar stage (37 weeks-2 years) (Table 2.4).    Failure with lower respiratory tract development at different stages could be associated with abnormalities and respiratory illness after birth. Abnormalities including tracheal stenosis and congenital lung cysts (including bronchogenic cysts) can be formed by the embryonic stage (Biyyam et al., 2010; Kotecha, 2000). If the pleuro-peritoneal membranes cannot be successfully closed by the Pseudoglandular stage, a congenital diaphragmatic hernia will develop (Javors & Mazzie, 2008; Kotecha, 2000). Preterm infants delivered at the canalicular stage are potentially exposed to respiratory distress due to the poorly developed peripheral airways and immature type   20 I and type II cells that prevent them from responding to ambient oxygen (Kotecha, 2000). The impaired development of the alveolar-capillary interface by Alveolar stage can also lead to a series of abnormalities and respiratory distress syndrome even for infants who are not born preterm (Kotecha, 2000).  Table 2.4 Fetal development of lower respiratory tract Gestational Week Fetal Development of Lower Respiratory Tract  Embryonic stage (0-7 week) The trachea and bronchi start to appear; Occurrence of two lungs (McEvoy & Spindel, 2016; Stocks et al., 2013) Pseudoglandular stage (8-17 week) All preacinar airways and blood vessels are formed; The occurrence of centrifugal differentiation of the airway wall structure and epithelium (Stocks et al., 2013) Canalicular stage (18-27 week) Differentiation in type I and type II cells takes place and an alveolar-capillary barrier is formed (Stocks et al., 2013) Saccular stage (28-36 week) Enlargement of the peripheral airways and thinning of the gas-blood barrier (Stocks et al., 2013) Alveolar stage (>36 weeks) Between 100 million and 150 million alveoli are formed; the process of alveologenesis will extend until birth (Stocks et al., 2013)  2.4.3 Evidence on Sensitive Windows Studies on specific fetal impacts of PM2.5 exposure during sensitive windows of development are very limited. According to one study conducted using a mouse model, maternal PM2.5 exposure did not affect lower respiratory tract development in early pregnancy but had a more significant impact in later pregnancy, which could be attributed to the length of exposure and accumulation of PM2.5 (Yue et al., 2020). Even though there are limited observational studies evaluating the effects of prenatal exposure to PM2.5 on fetal lung development at sensitive windows (Veras et al., 2016), several studies are identifying susceptible windows for prenatal exposure to PM2.5 and the respiratory outcomes during the early stage of life. According to Hsu. et al., the mid-pregnancy period (16-25 weeks) was identified as a significant sensitive window of PM2.5 exposure for asthma onset by the age of six based on distributed lag model (Hsu et al., 2015). Another study that used a profile regression model did   21 not identify a specific trimester window for higher risk. However, they did report that a slight increase in PM10 exposure during the first trimester was associated with lower predicted FEV1% (forced expiratory volume) and FVC% (forced vital capacity), which is a critical indication of lung function that measures how much air a person can exhale during a forced breath. However, similar associations were also found in other trimesters at age eight (Cai et al., 2020).  Lin. et al. concluded that the late gestational period and the first year of life would be the sensitive window for allergic rhinitis related to PM2.5 (Lin et al., 2021). Another study found that the second and third trimesters were the most sensitive window for delayed use of upper respiratory medications related to increased wildfire smoke exposure (Dhingra et al., 2022). A study on prenatal exposure to PM2.5 and respiratory symptoms in Mexican children aged 6 to 8 years did not find any sensitive time window if using a modified Poisson regression model but identified a sensitive time range from 14 weeks prenatal to 18 weeks postnatal using distributed lag models (Rivera Rivera et al., 2021).  One of the most recent studies also divided the exposure windows based on fetal development periods. They reported that increased PM2.5 exposure at the Canalicular stage (16-24 weeks) was associated with a higher risk of any asthma and current asthma (Hazlehurst et al., 2021). Another study used the Cox proportional hazards model and found that the Pseudoglandular and Canalicular stages were the most sensitive exposure windows for prenatal PM2.5 exposure and asthma\/wheezing after birth (Chen et al., 2022). 2.4.4 Evidence Gaps Exposure estimates with restricted geospatial information have been used in many epidemiological studies to measure the health effects of PM2.5. Examples include measurements   22 assigned to populations within a specified distance of the monitor. This kind of method can introduce error and bias effect estimates towards the null due to spatial misalignment. Certain tools including LUR (land use regression), and daily satellite measurements of aerosol optical depth (AOD) were applied to generate exposure estimates that account for spatial and temporal variation of wildfire-related PM2.5 (Just et al., 2015). Most of the recent studies have applied validated satellite-based spatiotemporal resolved models for exposure assessment. However, the performance of the models can still be improved. Rivera Rivera et al. used a model that integrates Aerosol Optical Depth (AOD) measurements from MODIS satellites at a spatial resolution of 1x1 km. The AOD-PM2.5 association was calibrated using municipal ground monitors, land use and meteorological features daily, which result in an out-of-sample cross-validation R2 of 0.724 and cross-validated root-mean-squared prediction error (RMSPE) of the model of 5.55 \u03bcg\/m3 (Just et al., 2015). Lin et al. used a novel satelite based hybrid model that also integrated AOD, land use and meterological variables with high temporal resolution. The model has R2 of 0.78 and a root mean-squared error (RMSE) of 8.1 \u03bcg\/m3 at a spatial resolution of 1x1 km for cross-validation (Lin et al., 2021). Cai et al took account of residential mobility and used dispersion modeling to estimate residential exposure to particulate matter from different sources during gestational trimester and infancy. The model has a correlation of 0.71, variance of the modelled values (Varp) of 226.0 and variance of the measured PM10 (Varo) concentration of 220.0. The regression fit line suggests that the concentrations are underestimated and MSE-r2 (the coefficient of determination) is 0.41 (Cai et al., 2020; Gulliver et al., 2018).  Available research on exposure during specific windows of development is very limited, with only a few studies that have examined the effects of trimesters or developmental weeks. Furthermore, only two studies used ICD-9\/ICD-10 diagnosis codes as criteria for identifying   23 respiratory outcomes and used administrative data for larger data pools and more accurate health records. The statistical models for these studies also range from the Poisson regression model, Cox proportional hazard model, profile regression model to the distributed lag model. All of them have quantified estimation of PM2.5 or PM10, though the respiratory-related health outcomes measured for the association are different for each study, ranging from asthma\/wheezing, lung function allergic rhinitis, and upper respiratory medications.  There is value in conducting research on this topic based on a more sophisticated exposure assessment model and clarifying the relationship between prenatal exposure to wildfire and the most relevant time windows during both upper and respiratory tract development periods by using administrative data, which enrich the details on respiratory illness, and physician visits and medications.  2.5 Acute Respiratory Infections       Both upper respiratory infections and lower respiratory infections are classified as acute respiratory infections. Acute respiratory infections are the leading cause for mortality and morbidity in children aged under five. Normally a pre-school aged child experiences three to six episodes of acute respiratory infections on average, regardless of their socio-economic level or residence (Simoes et al., 2006). According to a study conducted by the World Health Organization, approximately 15 million children with severe and very severe acute lower respiratory infections are admitted to hospitals around the world, and 265,000 (95% CI 160,000-450,000) young children died in hospital due to lower respiratory infections (Nair et al., 2013). Another study conducted in British Columbia illustrated that lower respiratory infections can add significant burden to acute care infrastructure and the Canadian medical system. The acute care beds required for pediatric lower respiratory infections ranged from 19.6 to 64.0 on average. The   24 study also found that 47% of hospitalizations related to pediatric lower respiratory infections are attributed to infants within the first year of births. The hospital stay time associated with lower respiratory infections for infants within the first year of life was 3.1 days on average (Santibanez et al., 2012). The burden is expected to increase in the next 20 years based on population projection. Therefore, respiratory infection is a great topic to explore regarding its high prevalence and great health burden. Additionally, respiratory infections in children are associated with antibiotic use among children. Acute otitis media, bronchitis, and upper respiratory tract infections were the most common scenarios with antibiotic treatment (Marra et al., 2006). In this thesis, we can also explore amoxicillin associated with respiratory infections based on the availability of Pharma Net and MSP billing records.  2.6 Amoxicillin      Amoxicillin is devised to combat antibiotic resistance by adding an additional amino group to penicillin to form an amino-penicillin, and it is also one of the most frequently used antibiotics in medical settings (Akhavan et al., 2022). Amoxicillin can bind to penicillin-binding protein and impede the cross-linking process in bacteria\u2019s cell wall synthesis, which further destroys the bacteria cell wall and leads to the death of bacteria cells (Akhavan et al., 2022; Bernatov\u00e1 et al., 2013). According to FDA approved indications, amoxicillin can be used to treat tonsillitis, pharyngitis, lower respiratory tract infections, and otitis media if the infections are due to Streptococcus pneumoniae, Staphylococcus species, or Haemophilus influenza (Akhavan et al., 2022; Berbari et al., 2015; Shulman et al., 2012). A cohort study conducted in Australia has found that 10 and 100\u03bcg m\u22123 increases in average and peak PM2.5 exposure during infancy were associated with an increase in prescriptions of antibiotics after the coal mine fire event, though no statistical significance was found in the group of participants who were exposed to coalmine   25 fire prenatally (Shao et al., 2020). However, there are only 311 participants enrolled in this study and the frequency of antibiotics prescription was generally low among participants. The limited study samples could decrease the statistical power of their analysis to detect significant association and affect the generalizability of their study because it could not represent a wider population. The large administrative database available in our study could further discover whether prenatal smoke exposure is associated with antibiotic use (amoxicillin specifically) in the first year of life and which gestational windows are associated with antibiotic use.  2.7 Study Aim My study aims to investigate two interrelated questions: (1) Is in utero exposure to wildfire smoke associated with upper and lower respiratory infections in the first year of life? (2) Which specific fetal developmental windows during pregnancy are associated with a risk of respiratory infection? Using the best available data sources on (1), the upper and lower respiratory infections in infants who were exposed to wildfire smoke during the gestational period, and (2), wildfire smoke PM2.5 exposures, the research questions will address three specific aims: Aim 1 will create a residential history to assign exposure for the birth cohort. The residential history for the mothers during pregnancy and their babies in the first year of life will be created based on their administrative records. CanOSSEM will be used to generate daily PM2.5 estimates based on residential postal codes from the residential history to assign exposure for the birth cohort.  Aim 2 will identify outcomes of interest and potential covariates through administrative datasets from Population Data BC. The outcomes of interest can be separated into three categories: 1) diagnosis of respiratory infections, 2) overall antibiotic dispensation, and 3)   26 antibiotic dispensation related to respiratory infections. The potential covariates are identified from the literature and will be further adjusted in Aim 3.  Aim 3 will examine the effect of prenatal exposure to wildfire-related PM2.5 on the respiratory infections in the first year of life by critical developmental windows. The exposure, outcomes, and covariates ascertained in Aim 1 and Aim 2 will allow us to establish an adjusted Cox proportional hazard model by different gestational periods in an effort to identify the windows of susceptibility related to wildfire-related PM2.5 and respiratory infections.    27 Chapter 3: Method We constructed a cohort of infants that we followed from conception through the first year of life. To assign exposure in utero we created a residential history for each mother during pregnancy. To assign exposure after birth, we created a residential history for each infant in the first year of life. We then examined the effect of in utero exposures on outcomes in the first year of life, adjusting for exposures after birth. We used survival analysis for the first event of interest.  The event of interest includes three major categories: upper and lower respiratory infections, overall antibiotic use, and antibiotic use associated with upper and lower respiratory infections. The survival analysis was conducted through a Cox proportional model adjusting for covariates including postnatal exposure, sex, maternal age, pre-pregnancy BMI, smoking during pregnancy, preterm births, birth weight at delivery, and SES. In utero exposures were also examined for critical windows of fetal development, and by trimester.  3.1  Health Data Sources We had access to all health data through Population Data BC, which is a multi-university data and education resource facilitating research on determinants of human health, well-being, and development (Population Data BC, 2022). The application for data access was submitted and approved (No: 20-197). Smoke exposure estimates from CanOSSEM were imported into the SRE (Secure Research Environment) with the consent of Population Data BC.  3.1.1 Unconsolidated Registration and Premium Billings (URPB) URPB is collected by the Ministry of Health and contains the start and stop dates of all registrations and changes in registration for the Medical Service Plan (MSP). It also includes   28 demographic information (Population Data BC, 2021). We used these files to create residential histories for mothers during pregnancy and for infants during their first year of life. 3.1.2 Consolidation File The consolidation file contains basic demographic information on study individuals, including age, sex, and location of residence.  3.1.3 BC Perinatal Data Registry The BC Perinatal Data Registry (BCPDR) contains maternal, fetal, and neonatal data for almost all births that occur in BC. It covers information on delivery and postpartum period, and hospital transfer or re-admission within 42 days of birth for both mothers and neonates. The BCPDR includes critical information on covariates in the analysis and provides details on infant birth and conception dates.   3.1.4 Medical Services Plan (MSP) Payment Information File  MSP includes services through MSP (the provincial universal insurance program) by fee-for-service practitioners, including hospital laboratory and diagnostic procedures. Most of the records are submitted electronically to MSP, and MSP conducts a quality check for selected fields (Popdata BC, 2022). Because MSP data are only collected for billing purposes, the reason recorded for an outpatient visit might not provide reliable information.  3.1.5 PharmaNet PharmaNet includes records of all medications dispensed in community pharmacies and hospitals in BC. This dataset is administrated by the Ministry of Health and was developed to support prescription safety and prescription claim processing. It is a centralized database that reflects live transactions of medications which could help reduce inappropriate prescription and dispensation of drugs. (Population Data BC, 2022). However, over-the-counter prescriptions, the   29 third-party paid amounts, medications administrated to hospital patients, and medication costs for federally insured individuals are not captured by PharmaNet (BC government, 2022; Dormuth et al., 2012). Furthermore, it only has details on prescription transactions, and lacks actual information on medication consumptions, which could result in potential sources of inaccuracy when the data is assumed to reflect population or individual drug utilization (Dahri et al., 2008). Some studies have suggested that PharmaNet has low accuracy and should be analyzed with other sources for medication histories for research purposes (Price et al., 2012).  3.2 Cohort Construction The cohort included all singleton live births that were potentially exposed to wildfire smoke because they were in utero between July and September of the 2016-2018 wildfire seasons. Mothers or infants who moved out of BC during pregnancy or the first year of life were excluded from the study. Miscarriage, stillbirths, and abortion were all excluded from the cohort. 3.3 Omission of infants due to incomplete maternal residential histories To assign exposure during pregnancy, it was necessary to create a residential history for each other mother. For every infant, we calculated the pregnancy interval from BCPDR by subtracting the gestational weeks on records from the date of birth. We matched the pregnancy interval with the effective and cancellation dates of MSP registration records and 6-digit residential postal codes from the URBP. Any MSP cancellation date in the URPB can indicate a residential location change in BC or a move out of the study area. If the MSP cancellation date was earlier than the end of the pregnancy, and there were no further MSP registrations, we assumed the mother had moved out of BC and both mother and infant were omitted from the cohort. However, if the cancellation date was earlier than the end of the pregnancy, and there were subsequent MSP registrations with different postal codes, it was assumed that the mother moved   30 between locations during pregnancy. If the cancellation date was later than the end of pregnancy, the end of the pregnancy would be considered the final date of the residential history record, and the related postal code would be regarded as the last residential location during the pregnancy. We also excluded infants with the earliest MSP effective date starting after the conception date because we had no information on prior residential history. In some cases, the URPB indicates a cancellation, and then a new record indicates a new effective date sometime later. As previously stated, if the postal code associated with the new MSP effective date was different from the postal code associated with the previous cancellation date, it indicated a residential change to a new location. However, if the postal code associated with the latest MSP effective date was the same as the one associated with the previous cancellation date, it indicated staying in the same location. If there were overlapping MSP records for the same day with different postal codes, we coded the 6-digit postal codes as missing because we could not be sure which location the mother was at. The infants were excluded from the analysis if they met any of the following criteria: 1) the maternal MSP registration records did not fully cover the pregnancy period; 2) the maternal MSP registration records were discontinuous, defined as a gap in MSP records of greater than seven days; and 3) the maternal MSP registration records had more than 25% missing values for the 6-digit postal codes for the in utero exposure window under consideration.  3.4 Exposure Assessment CanOSSEM is an empirical model that predicts daily population exposure to PM2.5 from biomass burning in Canada from 2010 to 2019 at a 5 km \u00d7 5 km resolution for all populated regions. The model was constructed using measurements of PM2.5 that were from the over 300 air quality monitoring stations in the National Air Pollution Surveillance (NAPS) Program,   31 which covers both rural and urban regions in Canada (Paul et al., 2022). Daily smoke plumes from remote sensing imagery, measurements of Aerosol Optical Depth, measurements of fire radiative power, and meteorological parameters from satellite estimates were all included as potentially predictive variables (Paul et al., 2022).  CanOSSEM has several advantages over other methods, including capturing both short-term and long-term exposure patterns, providing spatial coverage for places near the stations and remote populated regions, and estimating with high accuracy. Overall, the CanOSSEM performed well. The root mean squared error values for the validation and prediction sets were 2.89 and 2.96 \u00b5g\/m3 when using the CanOSSEM primary model for estimate generation. When applying the CanOSSEM secondary model for estimates, the validation and prediction sets\u2019 root mean squared error values were 2.40 and 2.73\u00b5g\/m3. The percentage of datasets within 5 \u00b5g\/m3 of the observed PM2.5 concentration were 97.7 for the validation sets and 96.4% for the prediction sets for the CanOSSEM primary model. For the CanOSSEM secondary model, the percentage of datasets within 5 g\/m3 of the observed PM2.5 concentration was 98.35% for the validation sets and 96.96% for the prediction sets (Paul et al., 2022). The CanOSSEM model has greater precision in exposure prediction which was shown by the lower root mean squared error value compared to 5.55 \u03bcg\/m3 in Just et al., and 8.1 \u03bcg\/m3 in Lin et al. However, CanOSSEM estimates are sensitive to removing extreme biomass burning events such as the Fort McMurray interface fire in May 2016 and the extreme 2017 and 2018 wildfire seasons in BC (Paul et al., 2022). A prior version of CanOSSEM developed for BC has been compared with PM2.5 measurements in epidemiologic analyses. The study concluded that such models can effectively improve epidemiologic effect estimates by reducing exposure misclassification (Yao & Henderson, 2014).    32 Daily estimated PM2.5 concentrations for each postal code were derived from CanOSSEM. The estimates were then matched with postal codes for pregnant mothers and newborns from the URBP to provide prenatal and postnatal PM2.5 exposures for all infants.  3.5 Outcome Measures  Diagnosis of upper and lower respiratory infections was identified from the Medical Services Plan (MSP) Payment Information File based on ICD-9 codes (International Classification of Disease, 9th Revision). We used billings for specific codes to identify middle ear infection, upper respiratory tract infections, and lower respiratory infections in the first year after birth (Table 3.1).   Table 3.1 ICD-9 codes and respiratory infections  ICD-9 Codes Diagnosed Respiratory Illness Relevant Respiratory Regions 381-382 otitis media Eustachian tube 460-463 acute nasopharyngitis infection, acute sinusitis infection, maxillary infection, frontal infection, ethmoidal infection, sphenoidal infection, acute pharyngitis, acuate tonsillitis facial regions  464-465 acute laryngitis and tracheitis, croup, acute laryngopharyngitis larynx 466, 480-487 acute bronchitis and bronchiolitis, pneumonia, influenza lower respiratory tract The use of antibiotics was identified from PharmaNet. We only included dispensations of amoxicillin, which was the most prescribed antibiotic in the first year of life. We examined all dispensations of amoxicillin, and dispensations within seven days of a physician visit for a   33 respiratory infection. To identify the latter, the PharmaNet dataset was joined with the MSP file. As with the MSP analyses, only dispensations within one year of the birth date were included.  3.6 Covariates There are several important maternal covariates to consider in a study like this: age at delivery; BMI; smoking during the current pregnancy; exposure to second-hand smoke; education; and socioeconomic status (SES) have all been identified by prior research (Harskamp-van Ginkel et al., 2015; A. Lee et al., 2018; Soh et al., 2018; Vanker et al., 2017) (Cakmak et al., 2016; Uphoff et al., 2015). All these covariates are available from the BCPDR, but some have a high number of missing values. Both maternal education and exposure to SHS (second-hand smoke) had more than 25% missing values for our data, so these two covariates could not be incorporated into the analysis. Although we did not have individual-level information on SES, analyses were adjusted for neighbourhood SES quintile, which is available from the Consolidation file. There are also important infant covariates to consider including sex, birth weight, and gestational age. According to previous literature, infant birthweight can be affected by wildfire smoke and is directly related to lung development for infants (Hack & Fanaroff, 1999; Holstius et al., 2012). This covariate can also be retrieved from the BCPDR. Preterm birth refers to a newborn with a gestational age of fewer than 37 weeks. Previous literature has identified preterm birth as a mediator, for which analyses should be adjusted (Patella et al., 2018; Priante et al., 2016). This is also available from the BCPDR.  Finally, postnatal exposure to wildfire smoke is also associated with an increased diagnosis of respiratory illness. Thus analyses should account for the average postnatal PM2.5 exposure in the survival analyses (Jung et al., 2019; Lavigne et al., 2021; Ostro et al., 2009). The postnatal   34 PM2.5 exposure was generated by CanOSSEM using the residential history of each infant. The postnatal exposure for infants was excluded from the analysis if they met the following criteria: 1) infant\u2019s first-year MSP registration records did not fully cover the first year period; 2) infant\u2019s first-year MSP registration records were discontinuous (have a gap in MSP records for more than seven days); 3) from birth to the first occurrence of the outcomes we are interested in, more than 25% of the 6-digit postal codes in the MSP registration record of the first year of the baby are \u201cNA\u201ds. The average postnatal PM2.5 concentration was calculated from birth to the first occurrence of the outcomes. 3.7 Statistical Analysis Descriptive statistics of demographic factors and other variables of interest were calculated for the infants included in the cohort. We used a retrospective cohort design to quantify the association between wildfire exposure and respiratory outcomes in the first year of life. In our statistical analysis, daily averaged PM2.5 concentration estimates for each critical prenatal exposure period served as the primary predictive variable. Selected respiratory outcomes, including respiratory infections, overall amoxicillin use, and amoxicillin use related to facial regions, larynx, and lower respiratory tract infections would be used as dependent outcome variables. The critical exposure windows for both upper and lower respiratory were based on previous literature review (Table 3.2, Table 3.3, Table 3.4, Table 3.5).         35 Table 3.2 Development of facial regions & Sino-nasal mucosa 1st stage (0-12 weeks) Finish development of nasal cavities and early face 2nd stage (12-24 weeks)  Development of maxillary sinuses and ethmoid sinuses; goblet cells and mucosa experience significant development  3rd stage (>24 weeks)  The length of the lower 3 turbinate increases in progress in utero  Table 3.3 Laryngeal development 1st stage (0-10 weeks) Formation of the basic larynx structure 2nd stage (11-28 weeks) Goblet cells and submucosal glands become visible; Development of epiglottic cartilage; the corniculate and cuneiform cartilages also become visible 3rd stage (>28 weeks) Cricoid cartilages change from interstitial to perichondral growth   Table 3.4 Development of the Eustachian tube 1st stage (0-9 week) Tubal lumen can be observed 2nd stage (10-12 week) First appearance of the LVP and TVP muscles 3rd stage (13-18 week) From the pharyngeal region medially, the cartilaginous condensations have spread out to include a region superior and medial to the lumen. There are glandular tissues present. Glandular tissues can be seen.  4th stage (19-27 week) The cartilage and paratubal muscle structure are well-formed; the perichondrium developed along the entire tubal length; and the lumens showed differentiation of some seromucous glandular extensions.  5th stage (>28 weeks) Continued expansion of the lumen, which is mostly correlated with an extension of the osseous section of the tube.     36 Table 3.5 Development of lower respiratory tract Embryonic stage (0-7 week) The trachea and bronchi start to appear; Occurrence of two lungs Pseudoglandular stage (8-17 week) All preacinar airways and blood vessels are formed; The occurrence of centrifugal differentiation of the airway wall structure and epithelium  Canalicular stage (18-27 week) Differentiation in type I and type II cells takes place and an alveolar-capillary barrier is formed Saccular stage (28-36 week) Enlargement of the peripheral airways and thinning of the gas-blood barrier Alveolar stage (>36 weeks) Between 100 million and 150 million alveoli are formed; the process of alveologenesis will extend until birth     Analyses followed a time-to-event survival design, based on the first outcome of interest within the first year of life. We conducted survival analysis with a Cox proportional hazards model by each critical developmental window to examine the association between the estimated PM2.5 concentration and health outcomes for the upper or lower respiratory tract. We examined the effect of each critical window and gestational trimester on respiratory infections in the relevant category. All analyses were adjusted for the following covariates, as described in the previous section: postnatal PM2.5 exposure, sex, maternal age, pre-pregnancy BMI, smoking during pregnancy, preterm births, birth weight at delivery, and SES. Only the infants with the complete set of covariates will be included in the model for analysis.     37 Chapter 4: Results 4.1 Study Cohort Description The BCPDR identified total singleton births of 164,415 in BC from 2016-2019. Because the CanOSSSEM exposure data after 2020 were unavailable, we only included infants born before 2019. After applying the exclusion criteria for residential history, the exclusion criteria for exposure assessment, and removing mothers with missing BCPDR data, 62,782 infants remained in the cohort, matched to 60,940 individual mothers. All the characteristics for pregnant mothers were calculated based on unique births.  Of the infants, 51% of the infants were male and 49% were female. Only 5.9% of infants were preterm births. Almost half of the mothers had a normal BMI (45%), and most mothers (88%) had never smoked in their lifetime. The average (standard deviation) maternal age at birth was 32.1 (5.1) years. As expected, the neighborhood socioeconomic status was equally distributed between the quintiles. The average daily PM2.5 concentration exposure during pregnancy was 7.76 \u03bcg\/m3 (Table 4.1).             38 Table 4.1 Summary characteristics of infants and mothers included in the cohort                     Characteristic N = 62,782 Estimated PM2.5 daily mean (standard deviation) during pregnancy (\u00b5g\/m3) 7.76 (1.98) Preterm (%)  5.9%  Infant Discharge Weight (grams) 3,235 (485) Maternal BMI Category (%)  Normal 45%  Obese 12%  Overweight 18%  Underweight 4%  Maternal Smoking (%)  Current Smoker 4.8%  Former Smoker 6.9% Never Smoke 88%  Mean (SD) Maternal Age at Birth (years) 32.1 (5.1) Infant Sex (%)  Female 49%  Male 51%  Maternal Neighborhood SES quintile (%)  1 20%  2 20% 3 21% 4 21% 5 16% Missing 1.3%    39 4.2 Survival Analysis  Since we aimed to answer whether exposure to ambient wildfire smoke during the critical windows of different parts of the respiratory tract is associated with an increased risk of respiratory infections in the first year of life, our study cohorts were constructed based on the completeness of exposure and covariate information for each critical window related to different parts of the respiratory tract.   4.2.1 Prenatal development of the Eustachian tubes and diagnosis of otitis media in the first year of life Infants were omitted from the survival analysis if they did not have 75% or more days with valid CanOSSEM PM2.5 estimates during the critical window of exposure under investigation, or in the postnatal period. As such, a slightly different number of infants was included in each of the models, ranging from 3,625-4,108 (Table 4.2). I tested five different critical windows of Eustachian tube development (Table Previous in methods) and found that PM2.5 exposure during the first window was significantly associated with increased risk of otitis media diagnosis in the first year of life. The crude HR [95% CI] for a 1 mg\/m3 increase in average daily PM2.5 exposure during the first window was 1.010 [1.002, 1.018] and fully adjusted HR was 1.012 [1.004, 1.021]. However, protective effect was shown in the third and fourth stages of the Eustachian tubes development. Estimates for all other critical developmental windows were null (Table 4.2).        40 Table 4.2 Survival analysis for the first diagnosis of otitis media in the first year and prenatal exposure to wildfire-related PM2.5  Eligible infants Infants diagnosed  Infants included in the model Crude HR (95% Cl) Adjusted HR (95% Cl)* Stage 1  (0-9 weeks) 62,523 4,868 4,106 1.010 [1.002, 1.018] 1.012 [1.004, 1.021] Stage 2 (10-12 weeks) 62,538 4,867 4,108 0.999  [0.991, 1.008] 1.001  [0.992, 1.010] Stage 3 (13-18 weeks) 62,558 4,869 4,107 0.989  [0.979, 0.998] 0.990  [0.980, 0.999] Stage 4 (19-28 weeks) 62,529 4,872 3,721 0.989 [0.991, 1.007] 0.988 [0.979, 0.998] Stage 5 (> 28 weeks) 62,236 4,858 3,625 1.001  [0.994, 1.009] 0.999 [0.992, 1.007] * The variables in the adjusted model include sex, maternal age, BMI during pregnancy, smoking during pregnancy, preterm births, discharge birth weight, and SES   4.2.2 Prenatal development of the facial regions and diagnosis of related respiratory infections in the first year of life Models for these analyses included a slightly different number of infants, ranging from 9,842-9,844 (Table 4.3). I tested three different critical windows of facial regions development (Table Previous in methods) and found that PM2.5 exposure during the first and third window was significantly associated with increased risk of upper respiratory tract infection diagnosis (facial regions) in the first year of life. The fully adjusted HR was 1.009 [1.001, 1.018] for a 1 mg\/m3 increase in average daily PM2.5 exposure for the first window. The crude HR [95% CI] for a 1 mg\/m3 increase in average daily PM2.5 exposure was 1.008 [1.001, 1.014] and fully adjusted HR was 1.010 [1.003, 1.016] for the third window. Slight protective effect was shown in the second stage of facial regions development (Table 4.3).       41 Table 4.3 Survival analysis for the first diagnosis of upper respiratory infection (facial regions) in the first year and prenatal exposure to wildfire-related PM2.5  Eligible infants # of infants diagnosed  # of infants included in the model Crude HR (95% Cl) Adjusted HR (95% Cl) * Stage 1  (1-12 weeks) 62,523 11,269 9,842 0.998 [0.991, 1.006] 1.009  [1.001, 1.018] Stage 2 (13-24 weeks) 62,549 11,269 9,842 0.979 [0.970, 0.988] 0.987  [0.978, 0.996] Stage 3 (>24 weeks) 62,393 11,261 9,844 1.008 [1.001, 1.014] 1.010  [1.003, 1.016] * The variables in the adjusted model include sex, maternal age, BMI during pregnancy, smoking during pregnancy, preterm births, discharge birth weight, and SES  4.2.3 Prenatal development of larynx and diagnosis of related respiratory infections in the first year of life There were 16,283-16,299 infants included in the models for analyses (Table 4.4). I tested three different critical windows of larynx development (Table Previous in methods) and found that PM2.5 exposure during the third window was significantly associated with increased risk of upper respiratory tract infection diagnosis (larynx) in the first year of life. The crude HR [95% CI] for a 1 mg\/m3 increase in average daily PM2.5 exposure was 1.011 [1.007, 1.015] and fully adjusted HR was 1.012 [1.008, 1.016]. However, protective effect was shown in the first and second stages of larynx development (Table 4.4).   Table 4.4 Survival analysis for the first diagnosis of upper respiratory infection (larynx) the first year and prenatal exposure to wildfire-related PM2.5  Eligible infants # of infants diagnosed  # of infants included in the model Crude HR (95% Cl) Adjusted HR (95% Cl) * Stage 1  (1-10 weeks) 62,525 18,969 16,299 0.991  [0.986, 0.996] 0.998  [0.992, 1.003] Stage 2 (11-28 weeks) 62,542 18,982 16,312 0.980  [0.974, 0.987] 0.987  [0.981, 0.994] Stage 3 (>28 weeks) 62,236 18,947 16,283 1.011  [1.007, 1.015] 1.012  [1.008, 1.016] * The variables in the adjusted model include sex, maternal age, BMI during pregnancy, smoking during pregnancy, preterm births, discharge birth weight, and SES   42 4.2.4 Prenatal development of lower respiratory tract and diagnosis of related respiratory infections in the first year of life Models for these analyses included 5,708-6,370 infants (Table 4.5). I tested five different critical windows of lower respiratory tract development (Table Previous in methods) and found that PM2.5 exposure during the Saccular stage and Alveolar stage was significantly associated with increased risk of lower respiratory tract infection diagnosis in the first year of life. The crude HR [95% CI] for a 1 mg\/m3 increase in daily average PM2.5 exposure was 1.014 [1.008, 1.019] and fully adjusted HR was 1.015 [1.010, 1.020] for the Saccular stage. The crude HR [95% CI] for a 1 mg\/m3 increase in daily average PM2.5 exposure was 1.006 [1.002, 1.010] and fully adjusted HR was 1.008 [1.004, 1.012] for the Alveolar stage. Slight protective effect was seen in the Pseudoglandular stage. Estimates for all other critical developmental windows were null (Table 4.5).   Table 4.5 Survival analysis for the first diagnosis of lower respiratory infection in the first year and prenatal exposure to wildfire-related PM2.5  Eligible infants # of infants diagnosed  # of infants included in the model Crude HR (95% Cl) Adjusted HR (95% Cl) * Embryonic Stage  (0-7 weeks) 62,505 7,441 6,366 0.994  [0.988, 1.001] 1.002  [0.995, 1.009] Pseudoglandular Stage (8-17 weeks) 62,530 7,446 6,370 0.976  [0.967, 0.985] 0.988  [0.979, 0.997] Canalicular Stage (18-27 weeks) 62,523 7,440 6,364 0.999  [0.991, 1.006] 1.002  [0.995, 1.009] Saccular Stage (28-36 weeks) 62,523 7,413 6,347 1.014  [1.008, 1.019] 1.015  [1.010, 1.020] Alveolar Stage (> 36 weeks) 60,292 7,068 5,708 1.006  [1.002, 1.010] 1.008   [1.004, 1.012] * The variables in the adjusted model include sex, maternal age, BMI during pregnancy, smoking during pregnancy, preterm births, discharge birth weight, and SES    43 4.2.5 Prenatal development of the Eustachian tubes and prescription of amoxicillin for any reasons in the first year of life Models for these analyses included 7,897-7,915 infants, a slightly varying number. (Table 4.6). I tested five different critical windows of Eustachian tube development (Table Previous in methods) and found that PM2.5 exposure during the fifth window was significantly associated with an increased risk of amoxicillin prescription for any reasons in the first year of life. The crude HR [95% CI] for a 1 mg\/m3 increase in average daily PM2.5 exposure was 1.009 [1.004, 1.015] and fully adjusted HR was 1.009 [1.003, 1.014]. Protective effects can be seen in the second, third, and fourth stages of the Eustachian tube development. Estimates for all other critical developmental windows were null (Table 4.6).   Table 4.6 Survival analysis for the first dispensation of amoxicillin in the first year and prenatal exposure to wildfire-related PM2.5 (by Eustachian tubes developmental windows)  Eligible infants # of infants with amoxicillin dispensation # of infants included in the model Crude HR (95% Cl) Adjusted HR (95% Cl) * Stage 1  (0-9 weeks) 62,523 9,223 7,907 1.003 [0.997, 1.009] 1.005 [0.998, 1.011] Stage 2 (10-12 weeks) 62,538 9,224 7,911 0.993  [0.987, 0.998] 0.994  [0.988, 0.999] Stage 3 (13-18 weeks) 62,558 9,231 7,915 0.983  [0.976, 0.990] 0.984  [0.977, 0.992] Stage 4 (19-28 weeks) 62,529 9,227 7,914 0.987 [0.981, 0.994] 0.987 [0.980, 0.994] Stage 5 (> 28 weeks) 62,236 9,207 7,897 1.009  [1.004, 1.015] 1.009 [1.003, 1.014] * The variables in the adjusted model include sex, maternal age, BMI during pregnancy, smoking during pregnancy, preterm births, discharge birth weight, and SES    44 4.2.6 Prenatal development of facial regions and prescription of amoxicillin for any reasons in the first year of life Models were analyzed with 7,908-7,914 infants (Table 4.7). I tested three different critical windows of facial regions development (Table Previous in methods) and found that PM2.5 exposure during the third window was significantly associated with increased risk of amoxicillin prescription for any reasons in the first year of life. The crude HR [95% CI] for a 1 mg\/m3 increase in daily average PM2.5 exposure was 1.007 [1.001, 1.014] and fully adjusted HR was 1.007 [1.000, 1.013]. However, protective effect was shown in the second stage of facial regions development. Estimates for all other critical developmental windows were null (Table 4.7).  Table 4.7 Survival analysis for the first dispensation of amoxicillin in the first year and prenatal exposure to wildfire-related PM2.5 (by facial regions developmental windows)  Eligible infants # of infants with amoxicillin dispensation # of infants included in the model Crude HR (95% Cl) Adjusted HR (95% Cl) * Stage 1  (1-12 weeks) 62,523 9,223 7,908 1.000 [0.993, 1.007] 1.002  [0.996, 1.009] Stage 2 (13-24 weeks) 62,549 9,232 7,916 0.978 [0.970, 0.987] 0.979  [0.970, 0.987] Stage 3 (>24 weeks) 62,393 9,228 7,914 1.007 [1.001, 1.014] 1.007  [1.000, 1.013] * The variables in the adjusted model include sex, maternal age, BMI during pregnancy, smoking during pregnancy, preterm births, discharge birth weight, and SES  4.2.7 Prenatal development of larynx and prescription of amoxicillin for any reasons in the first year of life Models for these analyses included a slightly different number of infants, ranging from 7,897-7,914 (Table 4.8). I tested three different critical windows of larynx development (Table Previous in methods) and found that PM2.5 exposure during the third window was significantly associated with increased risk of amoxicillin prescription for any reasons in the first year of life. The crude HR [95% CI] for a 1 mg\/m3 increase in average daily PM2.5 exposure was 1.009   45 [1.004, 1.015] and fully adjusted HR was 1.009 [1.003, 1.014]. However, protective effect can be seen in the second stage of larynx development. Estimates for all other critical developmental windows were null (Table 4.8).  Table 4.8 Survival analysis for the first dispensation of amoxicillin in the first year and prenatal exposure to wildfire-related PM2.5 (by larynx developmental windows)  Eligible infants # of infants with amoxicillin dispensation # of infants included in the model Crude HR (95% Cl) Adjusted HR (95% Cl) * Stage 1  (1-10 weeks) 62,525 9,223 7,907 1.002  [0.996, 1.009] 1.004  [0.997, 1.011] Stage 2 (13-28 weeks) 62,542 9,229 7,914 0.975  [0.966, 0.985] 0.976  [0.966, 0.985] Stage 3 (>28 weeks) 62,236 9,207 7,897 1.009  [1.004, 1.015] 1.009  [1.003, 1.014] * The variables in the adjusted model include sex, maternal age, BMI during pregnancy, smoking during pregnancy, preterm births, discharge birth weight, and SES  4.2.8 Prenatal development of lower respiratory tract and diagnosis of related amoxicillin dispensation for any reasons in the first year of life Models for these analyses included 7,645-7,912 infants (Table 4.9). I tested five different critical windows of lower respiratory tract development (Table Previous in methods) and found that PM2.5 exposure during the Embryonic, Saccular, and Alveolar stages was significantly associated with increased risk of amoxicillin prescription for any reasons in the first year of life. The crude HR [95% CI] for a 1 mg\/m3 increase in average daily PM2.5 exposure was 1.006 [1.001, 1.012] and fully adjusted HR was 1.006 [1.000, 1.011] for the Saccular stage. The crude HR [95% CI] for a 1 mg\/m3 increase in average daily PM2.5 exposure was 1.005 [1.002, 1.008] and fully adjusted HR was 1.005 [1.002, 1.008] for the Alveolar stage. The adjusted HR [95% CI] for a 1 mg\/m3 increase in average daily PM2.5 exposure was 1.005 [1.000, 1.011] for the Embryonic stage. Protective effect was detected in both the Pseudoglandular and Canalicular stages. (Table 4.9).    46   Table 4.9 Survival Analysis for the first dispensation of amoxicillin in the first year and prenatal exposure to wildfire-related PM2.5 (by lower respiratory developmental windows)  Eligible infants # of infants with amoxicillin dispensation # of infants included in the model Crude HR (95% Cl) Adjusted HR (95% Cl) * Embryonic Stage  (0-7 weeks) 62,505 9,220 7,904 1.004  [0.9985, 1.009] 1.005  [1.000, 1.011] Pseudoglandular Stage (8-17 weeks) 62,530 9,224 7,909 0.986  [0.979, 0.994] 0.988  [0.980, 0.996] Canalicular Stage (18-27 weeks) 62,523 9,226 7,912 0.985  [0.978, 0.992] 0.985  [0.978, 0.992] Saccular Stage (28-36 weeks) 62,523 9,215 7,903 1.006  [1.001, 1.012] 1.006  [1.000, 1.011] Alveolar Stage (> 36 weeks) 60,292 8,914 7,645 1.005  [1.002, 1.008] 1.005  [1.002, 1.008] * The variables in the adjusted model include sex, maternal age, BMI during pregnancy, smoking during pregnancy, preterm births, discharge birth weight, and SES  4.2.9 Prenatal development of the Eustachian tubes and prescription of amoxicillin related to otitis media in the first year of life Models were analyzed with 2,762-2,769 infants (Table 4.10). I tested five different critical windows of the Eustachian tube development (Table Previous in methods) and found that PM2.5 exposure during the first window was significantly associated with an increased risk of amoxicillin prescription associated with otitis media in the first year of life. The crude HR [95% CI] for a 1 mg\/m3 increase in average daily PM2.5 exposure was 1.009 [1.000, 1.018] and fully adjusted HR was 1.010 [1.000, 1.020]. Protective effects can be seen in the third and fourth stages of the Eustachian tube development. Estimates for all other critical developmental windows were null (Table 4.10).     47 Table 4.10 Survival analysis for the first dispensation of amoxicillin associated with otitis media in the first year and prenatal exposure to wildfire-related PM2.5  Eligible infants # of infants with amoxicillin dispensation  # of infants included in the model Crude HR (95% Cl) Adjusted HR (95% Cl) * Stage 1  (0-9 weeks) 62,523 3,266 2,766 1.009 [1.000, 1.018] 1.010  [1.000, 1.020] Stage 2 (10-12 weeks) 62,538 3,264 2,767 1.000  [0.990, 1.010] 1.000  [0.989, 1.010] Stage 3 (13-18 weeks) 62,558 3,247 2,767 0.987  [0.976, 0.998] 0.986  [0.975, 0.998] Stage 4 (19-28 weeks) 62,529 3,267 2,769 0.989 [0.977, 1.000] 0.987  [0.975, 0.998] Stage 5 (> 28 weeks) 62,236 3,261 2,762 1.004 [0.994, 1.013] 1.002  [0.992, 1.012] * The variables in the adjusted model include sex, maternal age, BMI during pregnancy, smoking during pregnancy, preterm births, discharge birth weight, and SES  4.2.10  Prenatal development of facial regions and related prescription of amoxicillin in the first year of life Models for these analyses had 864-866 infants, a slightly varying number (Table 4.11). I tested three different critical windows of the facial regions development (Table Previous in methods) and found that PM2.5 exposure during the first window was significantly associated with an increased risk of amoxicillin prescription associated with facial regions in the first year of life. The fully adjusted HR for a 1 mg\/m3 increase in average daily PM2.5 exposure was 1.016 [1.001, 1.037], while the crude HR for a 1 mg\/m3 increase in PM2.5 exposure was null. Estimates for all other critical developmental windows were also null (Table 4.11).        48 Table 4.11 Survival analysis for the first dispensation of amoxicillin associated with upper respiratory infection (facial regions) in the first year and prenatal exposure to wildfire-related PM2.5  Eligible infants # of infants with amoxicillin dispensation  # of infants included in the model Crude HR (95% Cl) Adjusted HR (95% Cl) * Stage 1  (1-12 weeks) 62,523 983 864 1.013 [0.994, 1.032] 1.016  [1.001, 1.037] Stage 2 (13-24 weeks) 62,549 985 866 1.000 [0.973, 1.028] 1.002  [0.975, 1.029] Stage 3 (>24 weeks) 62,393 982 864 0.994 [0.973, 1.016] 0.992 [0.971, 1.014] * The variables in the adjusted model include sex, maternal age, BMI during pregnancy, smoking during pregnancy, preterm births, discharge birth weight, and SES  4.2.11  Prenatal development of larynx and related prescription of amoxicillin in the first year of life  There were 1,043-1,046 infants included in the models for analyses (Table 4.12). I tested three different critical windows of the larynx development (Table Previous in methods) and found that PM2.5 exposure during the third window was significantly associated with an increased risk of amoxicillin prescription associated with larynx infections in the first year of life. The crude HR [95% CI] for a 1 mg\/m3 increase in average daily PM2.5 exposure was 1.016 [1.001, 1.031] and fully adjusted HR was 1.017 [1.001, 1.032]. However, protective effect was shown in the second stage of larynx development. Estimates for all other critical developmental windows were null (Table 4.12).        49 Table 4.12 Survival analysis for the first dispensation of amoxicillin associated with upper respiratory infection (larynx) the first year and prenatal exposure to wildfire-related PM2.5  Eligible infants # of infants with amoxicillin dispensation  # of infants included in the model Crude HR (95% Cl) Adjusted HR (95% Cl) * Stage 1  (1-10 weeks) 62,525 1,219 1,046 0.991  [0.974, 1.008] 0.991  [0.972, 1.009] Stage 2 (11-28 weeks) 62,542 1,216 1,043 0.958  [0.930, 0.986] 0.955  [0.926, 0.985] Stage 3 (>28 weeks) 62,236 1,216 1,045 1.016  [1.001, 1.031] 1.017  [1.001, 1.032] * The variables in the adjusted model include sex, maternal age, BMI during pregnancy, smoking during pregnancy, preterm births, discharge birth weight, and SES  4.2.12  Prenatal development of lower respiratory tract and related prescription of amoxicillin in the first year of life Models for these analyses had a slightly different number of infants, ranging from 1,238-1,298 (Table 4.13). I tested five different critical windows of the lower respiratory tract development (Table Previous in methods) and found that PM2.5 exposure during the Saccular stage and Alveolar stage was significantly associated with increased risk of amoxicillin prescription related to lower respiratory tract infections in the first year of life. The crude HR [95% CI] for a 1 mg\/m3 increase in average daily PM2.5 exposure was 1.019 [1.006, 1.032] and fully adjusted HR was 1.018 [1.005, 1.031] for the Saccular stage. The crude HR [95% CI] for a 1 mg\/m3 increase in average daily PM2.5 exposure was 1.009 [1.003, 1.015] and fully adjusted HR was 1.011 [1.005, 1.012] for the Alveolar stage. Protective effect can be seen in both the Pseudoglandular and Canalicular Stages. Estimates for all other critical developmental windows were null (Table 4.13).    50 Table 4.13 Survival analysis for the first dispensation of amoxicillin associated with lower respiratory infection in the first year and prenatal exposure to wildfire-related PM2.5  Eligible infants # of infants with amoxicillin dispensation  # of infants included in the model Crude HR (95% Cl) Adjusted HR (95% Cl) * Embryonic Stage  (0-7 weeks) 62,505 1,493 1,297 1.003  [0.991, 1.015] 1.007  [0.994, 1.021] Pseudoglandular Stage (8-17 weeks) 62,530 1,494 1,298 0.982 [0.964, 1.001] 0.988  [0.970, 1.007] Canalicular Stage (18-27 weeks) 62,523 1,492 1,296 0.987  [0.969, 1.006] 0.991  [0.973, 1.009] Saccular Stage (28-36 weeks) 62,523 1,487 1,293 1.019  [1.006, 1.032] 1.018  [1.005, 1.031] Alveolar Stage (> 36 weeks) 60,292 1,424 1,238 1.009  [1.003, 1.015] 1.011  [1.005, 1.012] * The variables in the adjusted model include sex, maternal age, BMI during pregnancy, smoking during pregnancy, preterm births, discharge birth weight, and SES  4.3 Analysis by Trimesters  Because many of the existing epidemiological studies on air pollution and respiratory outcomes performed analyses based on the trimesters, we performed analyses to test the robustness of the results to differing time windows, which is by trimesters.  The results for the survival analysis for the first diagnosis of respiratory infections in the first year by gestational trimesters (Appendix A.1) were consistent with those which separated the windows based on the critical development of the respiratory tracts. Similarly, the results for the survival analysis for the first dispensation of amoxicillin in the first year by gestational trimesters (Appendix A.2) were also consistent with those which separated the windows based on the critical development of the respiratory tracts. However, the results for the survival analysis for the first dispensation of amoxicillin associated with otitis media and upper respiratory tract infections (facial regions) by gestational trimesters were very different from those that separated the windows based on the critical development of prenatal Eustachian tube and facial regions   51 (Appendix A.3). Significant positive associations between wildfire-related PM2.5 and respiratory infections were detected for the first window of prenatal Eustachian tube and facial regions, but similar association was not seen for the first gestational trimester. The discrepancy in the results based on prenatal Eustachian tube developmental windows and gestational trimesters suggests the results could be sensitive to the time windows in the analysis.    52 Chapter 5: Discussion 5.1 Summary of Findings This was the first study that integrated critical windows of prenatal development for different parts of the respiratory tract to examine associations with health outcomes in the first year of life. The study also used CanOSSEM for spatially resolved daily PM2.5 exposure assessment linked to the residential histories of mothers and infants. We found that the sensitive windows for respiratory infections and associated amoxicillin dispensations move to the later stages of development as the respiratory infections of interest move from the upper respiratory tract to the lower respiratory tract. Every 1 \u03bcg\/m3 increase in wildfire-related PM2.5 exposure in utero was associated with earlier diagnosis of otitis media, earlier diagnosis of infections related to facial regions, and earlier dispensation of amoxicillin related to upper respiratory infections in the earlier stages of prenatal Eustachian tube and facial regions development. Increases in wildfire-related PM2.5 exposure in utero were associated with earlier diagnosis of larynx infections, earlier diagnosis of lower respiratory infections, and earlier dispensation of amoxicillin related to lower respiratory infections in later stages of the prenatal larynx and lower respiratory tract development. The sensitive windows for overall amoxicillin dispensations are the later stages of prenatal upper and lower respiratory tract development.  The results indicate that in utero exposure to wildfire-related PM2.5 might interfere with the development of early facial regions and nasal cavities and the LVP (levetor veli palatini muscle) and TVP (tensor veli palatini muscle) muscles of the Eustachian tube before 10-12 weeks. The normal functioning of the TVP muscle is directly associated with the opening and closure of the Eustachian tube. Dysfunction of the TVP muscle can lead to obstruction of the tube, which plays a vital role in the pathogenesis of otitis media (Fireman, 1997). Even though there is no existing   53 literature on the prenatal development of facial regions and nasal cavities and upper respiratory infections after birth, some studies indicate otitis media and upper respiratory infections are highly correlated (Pettigrew et al., 2011). Wildfire smoke-related PM2.5 might also interfere with the enlargement of the peripheral airways and thinning of the gas-blood barrier, thereby impacting the formation of alveoli in lungs, and interfering with perichondral growth of cricoid cartilage after the 28th gestational week. According to Mirabelli et al., exposure to wildfire smoke can reduce airway size in infants (2009). Another study conducted on mice reported that gestational exposure to air pollution led to significantly lower alveolar numbers (de Barros Mendes Lopes et al., 2018).  Our results were consistent with those of a study by Dhingra. et al., which used the Cox proportional hazards model to estimate HRs of 0.95 and 0.935 (no confidence intervals reported) for increased wildfire smoke exposure in the second and third trimesters and the first use of upper respiratory medications. We estimated an adjusted HR of 0.986 [0.975, 0.998] for the third stage (weeks 13 to 18) and an adjusted HR of 0.987 [0.975, 0.998] for the fourth stage (weeks 19 to 28) of prenatal Eustachian tube development for each 1 \u03bcg\/m3 increase in wildfire-related PM2.5 exposure in utero and the first dispensation of amoxicillin related to otitis media. We also found an adjusted HR of 0.955 [0.926, 0.985] for the second stage (weeks 11 to 28) of prenatal upper respiratory tract (larynx) development for each 1 \u03bcg\/m3 increase in wildfire-related PM2.5 exposure in utero and the first dispensation of amoxicillin related to larynx infections.  Chen et al. used the Cox proportional hazards model to estimate the effect of PM2.5 and the risk of childhood asthma\/wheezing. They found that each interquartile range (IQR) increase in PM2.5 exposure (4.8 \u03bcg\/m3) was associated with an HR of 1.10 at the Pseudoglandular (weeks 6-16) stage and 1.13 at the Canalicular stage (weeks 16-24). This result was somewhat inconsistent   54 with ours as we report associations at later stages of lower respiratory tract development. We report an HR of 1.015 [CI: 1.010, 1.020] for the Saccular stage (weeks 28 to 36) and 1.008 [CI: 1.004, 1.012] for the Alveolar stage (weeks 36 to birth) for 1 each \u03bcg\/m3 in wildfire-related PM2.5 exposure and lower respiratory infections, equivalent to 1.058 for the Saccular stage (weeks 28 to 36) and 1.031 for the Alveolar stage (weeks 36 to birth) for a 4.8 ug\/m3 increase. A similar association with hazards ratio of 1.018 [CI: 1.005, 1.031] for the Saccular stage (week 28 to 36) and 1.011 [CI:1.005, 1.012] for the Alveolar stage for 1 \u03bcg\/m3 in wildfire-related PM2.5 exposure and amoxicillin dispensation related to lower respiratory infections were also observed in our study.  5.2  Strengths Many studies have not differentiated between the effects of postnatal and prenatal air pollution exposure, which are highly correlated. Our study achieved this by generating postnatal exposure measurements based on the residential history of the infants. The residential history in our research captured moves during pregnancy and the first year of birth and the associated changes in exposures. The CanOSSEM exposure estimates also have several advantages compared to methods used in other studies. First of all, this model combined multiple remote sensing data sources available for the public and it also spanned a wide temporal range, allowing for the identification of short- and long-term exposure. Furthermore, it covered both locations near the ground monitoring stations and remote areas, taking into consideration the populations that were not covered in the previous research. Last but not least, CanOSSEM was generated based on data reduction, machine learning, and multiple cross-validations, which have improved accuracy and efficiency compared with previous PM2.5 estimation models used in similar research context (Paul et al., 2022).    55 This study captured more specific and accurate respiratory outcomes and critical covariates based on ICD-9 codes from a population-based database. The exposure measurements, based on postal codes, give an estimate based on the individual level and avoid the ecological fallacy, which occurs when making inferences based on measurements from the population level rather than the individual level.  This study used administrative records in BC which are a valuable resource for health research. Previous studies in Canada have assessed the accuracy of coding related to respiratory illness. According to a study conducted in Quebec, the verified asthma diagnoses from administrative datasets are consistent with the diagnoses from chart records, indicating the reliability of billing records in administrative databases as a valid measure of respiratory illness (Annika Clark, 2008; Blais et al., 2006). The administrative databases we used not only reflected the accurate measure of respiratory diseases, but also covered a total of 164,415 singleton births born in BC, which is a large number of study subjects and has greater statistical power to detect small differences between groups.  Finally, the ability to use PharmaNet with MSP billings is an advantage, providing an estimate of medication use related to respiratory illness. Only the diagnosis of respiratory diseases or the medication dispensations can be tracked by using PharamNet and MSP billing records alone. However, we were able to link PharmaNet with the MSP billings and find medications immediately after the disease diagnosis to infer that the medications are related to the disease occurrence. This is an innovative approach to explore the relationship between wildfire-related PM2.5 exposure and medication dispensations related to respiratory illness.    56 5.3  Limitations To better study the effects of wildfire-related PM2.5 on respiratory infections, underlying causes of respiratory outcomes including coughing, runny nose, and wheezing should be included in the analysis. However, the administrative datasets we used lack clinical details and can only capture the diagnosis codes and medication dispensations. Therefore, not including the mild symptoms of respiratory tract infections in the analysis is one of the limitations of our research, which can affect the comprehensiveness of the study findings.  Even if we considered using residential history as an advantage in our study, the residential history might not be the most accurate indicator for exposure measurements. The exposure estimates are generated from residential postal codes, and our approach assumes that pregnant mothers and their infants are at these locations 100% of the time. However, we could not account for the situations where mothers and infants were mobile or staying at a location different from their residential addresses. However, the respiratory outcomes are not influenced by the mobile status of mothers and infants (Annika Clark, 2008). This would lead to non-differential misclassification of exposure and impact the precision of our estimates, which will lead the direction of bias towards null (Annika Clark, 2008). Additionally, the URPB had overlapping postal codes for pregnant mothers and infants on many dates, so it was often challenging to assign exposure estimates for these individuals. Our study set \u201cmissing\u201d exposures to those days with overlapping postal codes. However, this significantly reduced our cohort samples, decreasing the statistical power. Among 164,415 singleton births in BC Perinatal Registry, only 62,782 were included in the cohort for analysis. Future work will evaluate methods that allow us to include these infants.    57 There are also some limitations related to covariates in our study. The SHS and maternal education variable, identified as covariates in the previous literature, were self-reported variables in the BCPDR. Because both variables have more than 25% missing values in the database, we chose not to incorporate them into the analysis. Another limitation is that individual-level data were not available for SES. We are using neighbourhood-level SES data to approximate parental SES. Though evidence shows that neighbourhood SES is highly associated with individual SES, it could still lead to misclassification of income in studies (Dom\u00ednguez-Berj\u00f3n et al., 2006; Krieger, 1992). According to Hanley & Morgan, neighborhood-level SES can smooth the outcome distribution across income decile, and shift the estimates towards null compared to household-level SES (2008).  5.4  How to protect pregnant mothers during wildfire events? Our research found a significant association between wildfire-related PM2.5 exposure during pregnancy and first diagnosis of respiratory infections and related antibiotic dispensations in the first year of life. It is important to make timely decisions during wildfire seasons to avoid exposure to PM2.5 and protect pregnant mothers.  Guidance and preparation for wildfire events is necessary to reduce the effect of wildfire smoke on health outcomes. We need to build resiliency in the population by clear risk communication and simple guidance. Prenatal education can provide an opportunity to teach future parents resilience strategies that can be used during wildfire events (Pike et al., 2022). The risk communications should include useful interventions for pregnant mothers to protect themselves during wildfire seasons. Guideline documents in BC specifically indicate environmental health officers as a resource to identify the location of clean air shelters and their appropriateness before wildfire seasons (Maguet, 2018). Installing portable air filters at home is   58 recommended in most health communications. Still, there is no specific guideline for the appropriate type of units or sizing for the portable filters. Face masking or N95 respirators are not highly recommended in BC policy but seem to be promoted in some other guidelines for when staying outdoors (Maguet, 2018). However, those documents that recommend face masks also emphasize that staying indoors is much more efficient than wearing face masks outdoors. Besides, face masks can be very uncomfortable for pregnant mothers as they already have difficulty breathing due to compression of the lungs. As suggested in BCCDC\u2019s wildfire factsheet, pregnant women should consult their healthcare providers before using masks for wildfire smoke (BC CDC, 2021).  One potential solution to target pregnant mothers during wildfire seasons is to use current communication platforms. SmartMom, a free prenatal education program which can send text messages on important health information during each week of pregnancy, could be a great tool for risk communication during wildfire seasons. The program currently includes health topics on fetal growth and development, preparation for labor and delivery, prenatal screening, vaccination, and mental health. The messages also contain embedded links which could refer pregnant mothers to more inclusive information online. Pregnant mothers can easily get access to the program through the first prenatal visit, local outreach programs, and social media (SmartMom, 2022). A survey on the user experience of the program showed that 99% of the participants reported that they think the program was useful in providing reliable and comprehensive information related to their pregnancy. Significant improvements on knowledge test and certain perinatal health outcomes were also found for the enrolled mothers and their babies (P. Janssen, 2022). Prenatal exposure to wildfire smoke during critical time windows and respiratory outcomes in early childhood can also be included in the content of the SmartMom   59 prenatal education program, with specific guidelines for the pregnant mothers to protect themselves during wildfire seasons.   5.5 Directions for future research Because our research only focuses on the respiratory outcomes in the first year of life, it is worth conducting further research to evaluate whether prenatal exposure to wildfire smoke affects respiratory outcomes in later childhood. The cohort established here can be followed for many more years using updated data from the administrative datasets, including MSP and PharmaNet, if the study subjects stay in BC. However, the residential exposure measurements might lead to more misclassification for school-aged children, who are more mobile than pregnant women and newborns. A stronger association between prenatal exposure to wildfire smoke and respiratory outcomes after birth could be ascertained if researchers can follow up on the current birth cohort. It is also worth exploring biological mechanisms related to prenatal exposure to wildfire smoke. Epigenetics could be a potential mechanism linking exposure to PM2.5, other pollutants of fire emission, and maternal stress from wildfire events to affect the respiratory outcomes after birth. The changes in DNA methylation (DNAm) at CpG sites (regions of DNA in which a cytosine nucleotide is followed by a guanine nucleotide in a 5\u2019 to 3\u2019 direction along the linear sequence) in genes could indicate the epigenetic process related to wildfire smoke exposure (Jang et al., 2017; V. E. Murphy et al., 2021). The oxidative stress from prenatal exposure could also trigger adverse birth outcomes (Nagiah et al., 2015). Increased PM2.5 exposure during the third trimester is associated with decreased mtDNA content, indicating high oxidative stress triggered by prenatal PM2.5 exposure (Rosa et al., 2017). Future research could consider integrating the critical window of respiratory tract development with measurements of DNAm or   60 mtDNA content in blood samples to assess the biological mechanisms behind the epidemiological results.  5.6  Conclusion Our findings suggest that wildfire-related exposure in utero during critical developmental windows of the respiratory tract was associated with earlier diagnosis of respiratory infections, amoxicillin dispensations related to respiratory infections, and overall amoxicillin dispensations. Our work identified potential critical windows of exposure related to respiratory tract development. These results can be used by policymakers, public health officials, and community leaders to devise more valuable and explicit guidelines for decision-making during wildfire events for pregnant women. The future researcher could consider continuing follow-up of the current birth cohort or discover changes in DNAm or mtDNA contents related to prenatal wildfire smoke exposure based on critical time windows of respiratory tract development.       61 Bibliography Aguilera, R., Corringham, T., Gershunov, A., & Benmarhnia, T. (2021). Wildfire smoke impacts respiratory health more than fine particles from other sources: Observational evidence from Southern California. Nature Communications, 12(1), 1493\u20131498. https:\/\/doi.org\/10.1038\/s41467-021-21708-0 Ailshire, J. A., & Clarke, P. (2015). Fine particulate matter air pollution and cognitive function among U.S. older adults. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 70(2), 322\u2013328. https:\/\/doi.org\/10.1093\/geronb\/gbu064 Akhavan, B. J., Khanna, N. R., & Vijhani, P. (2022). Amoxicillin. In StatPearls. StatPearls Publishing. http:\/\/www.ncbi.nlm.nih.gov\/books\/NBK482250\/ Amjad, S., Chojecki, D., Osornio-Vargas, A., & Ospina, M. B. (2021). Wildfire exposure during pregnancy and the risk of adverse birth outcomes: A systematic review. Environment International, 156(Journal Article), 106644\u2013106644. https:\/\/doi.org\/10.1016\/j.envint.2021.106644 Annika Clark, N. (2008). Effect of Ambient Air Pollution on Development of Childhood Asthma. The University of British Columbia. Ars, B., MD, PhD, & Dirckx, J., PhD. (2016). Eustachian Tube Function. Otolaryngologic Clinics of North America, 49(5), 1121\u20131133. https:\/\/doi.org\/10.1016\/j.otc.2016.05.003 Arvedson, J. C., & Lefton-Greif, M. A. (2019). Pediatric Swallowing and Feeding: Assessment and Management, 3rd Edition. ProtoView, 2019(37). https:\/\/go.exlibris.link\/nTFKF6R2 Baker, S. J. (2022). Fossil evidence that increased wildfire activity occurs in tandem with periods of global warming in Earth\u2019s past. Earth-Science Reviews, 224(Journal Article), 103871. https:\/\/doi.org\/10.1016\/j.earscirev.2021.103871   62 BC CDC. (2021). Face Masks for Wildfire Smoke. http:\/\/www.bccdc.ca\/resource-gallery\/Documents\/Guidelines%20and%20Forms\/Guidelines%20and%20Manuals\/Health-Environment\/BCCDC_WildFire_FactSheet_FaceMasks.pdf BC government. (2022). About PharmaNet. https:\/\/www2.gov.bc.ca\/gov\/content\/health\/health-drug-coverage\/pharmacare-for-bc-residents\/pharmanet BC health wildfire smoke response coordination guideline. (2017). Vancouver, BC: BCCDC and the Ministry of the Environment. http:\/\/www.bccdc.ca\/resource-gallery\/Documents\/BC%20Health%20Wildfire%20Smoke%20Response%20%20Coordination%20Guideline%202017.pdf BC Wildfire Service. (2021a). Wildfire Averages\u2014Province of British Columbia. Province of British Columbia. https:\/\/www2.gov.bc.ca\/gov\/content\/safety\/wildfire-status\/about-bcws\/wildfire-statistics\/wildfire-averages BC Wildfire Service. (2021b). Wildfire Season Summary\u2014Province of British Columbia. Province of British Columbia. https:\/\/www2.gov.bc.ca\/gov\/content\/safety\/wildfire-status\/about-bcws\/wildfire-history\/wildfire-season-summary Berbari, E. F., Kanj, S. S., Kowalski, T. J., Darouiche, R. O., Widmer, A. F., Schmitt, S. K., Hendershot, E. F., Holtom, P. D., Huddleston, 3rd, Paul M., Petermann, G. W., Osmon, D. R., & Infectious Diseases Society of America. (2015). 2015 Infectious Diseases Society of America (IDSA) Clinical Practice Guidelines for the Diagnosis and Treatment of Native Vertebral Osteomyelitis in Adults. Clinical Infectious Diseases, 61(6), 859\u2013863. https:\/\/doi.org\/10.1093\/cid\/civ482 Bernatov\u00e1, S., Samek, O., Pil\u00e1t, Z., Ser\u00fd, M., Je\u017eek, J., J\u00e1kl, P., Siler, M., Krzy\u017e\u00e1nek, V., Zem\u00e1nek, P., Hol\u00e1, V., Dvo\u0159\u00e1\u010dkov\u00e1, M., & R\u016f\u017ei\u010dka, F. (2013). Following the mechanisms   63 of bacteriostatic versus bactericidal action using Raman spectroscopy. Molecules (Basel, Switzerland), 18(11), 13188\u201313199. https:\/\/doi.org\/10.3390\/molecules181113188 Biyyam, D. R., Chapman, T., Ferguson, M. R., Deutsch, G., & Dighe, M. K. (2010). Congenital lung abnormalities: Embryologic features, prenatal diagnosis, and postnatal radiologic-pathologic correlation. Radiographics, 30(6), 1721\u20131738. https:\/\/doi.org\/10.1148\/rg.306105508 Blais, L., Lemi\u00e8re, C., Menzies, D., & Berbiche, D. (2006). Validity of asthma diagnoses recorded in the Medical Services database of Quebec. Pharmacoepidemiology and Drug Safety, 15(4), 245\u2013252. https:\/\/doi.org\/10.1002\/pds.1202 Borchers Arriagada, N., Horsley, J. A., Palmer, A. J., Morgan, G. G., Tham, R., & Johnston, F. H. (2019). Association between fire smoke fine particulate matter and asthma-related outcomes: Systematic review and meta-analysis. Environmental Research, 179(Pt A), 108777\u2013108777. https:\/\/doi.org\/10.1016\/j.envres.2019.108777 British Columbia, & Ministry of Forests and Range. (2007). The state of British Columbia\u2019s forests 2006. Ministry of Forests and Range. Cai, Y., Hansell, A. L., Granell, R., Blangiardo, M., Zottoli, M., Fecht, D., Gulliver, J., Henderson, A. J., & Elliott, P. (2020). Prenatal, Early-Life, and Childhood Exposure to Air Pollution and Lung Function: The ALSPAC Cohort. American Journal of Respiratory and Critical Care Medicine, 202(1), 112\u2013123. https:\/\/doi.org\/10.1164\/rccm.201902-0286OC Cakmak, S., Hebbern, C., Cakmak, J. D., & Vanos, J. (2016). The modifying effect of socioeconomic status on the relationship between traffic, air pollution and respiratory health in elementary schoolchildren. Journal of Environmental Management, 177(Journal Article), 1\u20138. https:\/\/doi.org\/10.1016\/j.jenvman.2016.03.051   64 Calder\u00f3n-Garcidue\u00f1as, L., Kavanaugh, M., Block, M., D\u2019Angiulli, A., Delgado-Ch\u00e1vez, R., Torres-Jard\u00f3n, R., Gonz\u00e1lez-Maciel, A., Reynoso-Robles, R., Osnaya, N., Villarreal-Calderon, R., Guo, R., Hua, Z., Zhu, H., Perry, G., & Diaz, P. (2012). Neuroinflammation, hyperphosphorylated tau, diffuse amyloid plaques, and down-regulation of the cellular prion protein in air pollution exposed children and young adults. Journal of Alzheimer\u2019s Disease, 28(1), 93\u2013107. https:\/\/doi.org\/10.3233\/JAD-2011-110722 Canada, Public Safety, P. S. (2018, December 21). Wildfires. https:\/\/www.getprepared.gc.ca\/cnt\/hzd\/wldfrs-en.aspx CDC. (2020, December 28). Facts about Gastroschisis | CDC. Centers for Disease Control and Prevention. https:\/\/www.cdc.gov\/ncbddd\/birthdefects\/gastroschisis.html Chen, G., Guo, Y., Yue, X., Tong, S., Gasparrini, A., Bell, M. L., Armstrong, B., Schwartz, J., Jaakkola, J. J. K., Zanobetti, A., Lavigne, E., Nascimento Saldiva, P. H., Kan, H., Roy\u00e9, D., Milojevic, A., Overcenco, A., Urban, A., Schneider, A., Entezari, A., \u2026 Li, S. (2021). Mortality risk attributable to wildfire-related PM2\u00b75 pollution: A global time series study in 749 locations. The Lancet. Planetary Health, 5(9), e579\u2013e587. https:\/\/doi.org\/10.1016\/S2542-5196(21)00200-X Chen, G., Zhou, H., He, G., Zhu, S., Sun, X., Ye, Y., Chen, H., Xiao, J., Hu, J., Zeng, F., Yang, P., Gao, Y., He, Z., Wang, J., Cao, G., Chen, Y., Feng, H., Ma, W., Liu, C., & Liu, T. (2022). Effect of early-life exposure to PM2.5 on childhood asthma\/wheezing: A birth cohort study. Pediatric Allergy and Immunology, 33(6). https:\/\/doi.org\/10.1111\/pai.13822 Cleland, S. E., Wyatt, L. H., Wei, L., Paul, N., Serre, M. L., West, J. J., Henderson, S. B., & Rappold, A. G. (2022). Short-Term Exposure to Wildfire Smoke and P[M.sub.2.5] and   65 Cognitive Performance in a Brain-Training Game: A Longitudinal Study of U.S. Adults. Environmental Health Perspectives, 130(6). https:\/\/doi.org\/10.1289\/EHP10498 Coogan, S. C. P., Robinne, F.-N., Jain, P., & Flannigan, M. D. (2019). Scientists\u2019 warning on wildfire\u2014A Canadian perspective. Canadian Journal of Forest Research, 49(9), 1015\u20131023. https:\/\/doi.org\/10.1139\/cjfr-2019-0094 Dahri, K., Shalansky, S. J., Jang, L., Jung, L., Ignaszewski, A. P., & Clark, C. (2008). Accuracy of a Provincial Prescription Database for Assessing Medication Adherence in Heart Failure Patients. The Annals of Pharmacotherapy, 42(3), 361\u2013367. https:\/\/doi.org\/10.1345\/aph.1K385 de Barros Mendes Lopes, T., Groth, E. E., Veras, M., Furuya, T. K., de Souza Xavier Costa, N., Ribeiro J\u00fanior, G., Lopes, F. D., de Almeida, F. M., Cardoso, W. V., Saldiva, P. H. N., Chammas, R., & Mauad, T. (2018). Pre- and postnatal exposure of mice to concentrated urban PM2.5 decreases the number of alveoli and leads to altered lung function at an early stage of life. Environmental Pollution (1987), 241(Journal Article), 511\u2013520. https:\/\/doi.org\/10.1016\/j.envpol.2018.05.055 Dhingra, R., Keeler, C., Staley, B. S., Ward-Caviness, C., Rebuli, M. E., Wei, L., Hernandez, M., Chelminski, A., Jaspers, I., Rappold, A., & Hill, C. (2022). Developmental Exposure to Wildfire Smoke: A Study of Insurance Claims Data to Assess First Use of Respiratory Medications. 1. https:\/\/doi.org\/Am J Respir Crit Care Med 2022;205:A5801 Dom\u00ednguez-Berj\u00f3n, F., Borrell, C., Rodr\u00edguez-Sanz, M., & Pastor, V. (2006). The usefulness of area-based socioeconomic measures to monitor social inequalities in health in Southern Europe. European Journal of Public Health, 16(1), 54\u201361. https:\/\/doi.org\/10.1093\/eurpub\/cki069   66 Dormuth, C. R., Miller, T. A., Huang, A., Mamdani, M. M., Juurlink, D. N., Canadian Drug Safety and Effectiveness Research Network, & for the Canadian Drug Safety and Effectiveness Research Network. (2012). Effect of a centralized prescription network on inappropriate prescriptions for opioid analgesics and benzodiazepines. Canadian Medical Association Journal (CMAJ), 184(16), E852\u2013E856. https:\/\/doi.org\/10.1503\/cmaj.120465 Dornhoffer, J. L., Leuwer, R., Schwager, K., & Wenzel, S. (2014). A Practical Guide to the Eustachian Tube. Springer Berlin Heidelberg. https:\/\/doi.org\/10.1007\/978-3-540-78638-2 Dreessen, J., Sullivan, J., & Delgado, R. (2016). Observations and impacts of transported Canadian wildfire smoke on ozone and aerosol air quality in the Maryland region on June 9-12, 2015. Journal of the Air & Waste Management Association (1995), 66(9), 842\u2013862. https:\/\/doi.org\/10.1080\/10962247.2016.1161674 Duclos, P., Sanderson, L. M., & Lipsett, M. (1990). The 1987 Forest Fire Disaster in California: Assessment of Emergency Room Visits. Archives of Environmental Health, 45(1), 53\u201358. https:\/\/doi.org\/10.1080\/00039896.1990.9935925 Elumalai, G. (2016). \u201cLaryngomalacia\u201d Embryological Basis and Its Clinical Significance. Elixir International Journal - Elixir Embryology, 100, 43420\u201343424. Faustini, A., Alessandrini, E. R., Pey, J., Perez, N., Samoli, E., Querol, X., Cadum, E., Perrino, C., Ostro, B., Ranzi, A., Sunyer, J., Stafoggia, M., Forastiere, F., MED-PARTICLES study group, & the MED-PARTICLES study group. (2015). Short-term effects of particulate matter on mortality during forest fires in Southern Europe: Results of the MED-PARTICLES Project. Occupational and Environmental Medicine (London, England), 72(5), 323\u2013329. https:\/\/doi.org\/10.1136\/oemed-2014-102459   67 Fireman, P. (1997). Otitis media and eustachian tube dysfunction: Connection to allergic rhinitis. Journal of Allergy and Clinical Immunology, 99(2), s787\u2013s797. https:\/\/doi.org\/10.1016\/S0091-6749(97)70130-1 Franzi, L. M., Bratt, J. M., Williams, K. M., & Last, J. A. (2011). Why is particulate matter produced by wildfires toxic to lung macrophages? Toxicology and Applied Pharmacology, 257(2), 182\u2013188. https:\/\/doi.org\/10.1016\/j.taap.2011.09.003 Gaur, A., B\u00e9nichou, N., Armstrong, M., & Hill, F. (2021). Potential future changes in wildfire weather and behavior around 11 Canadian cities. Urban Climate, 35(Journal Article), 100735. https:\/\/doi.org\/10.1016\/j.uclim.2020.100735 Green, R. J., SpringerLink ebooks - Biomedical and Life Sciences, & SpringerLink (Online service). (2017). Viral Infections in Children, Volume II. Springer International Publishing. http:\/\/ubc.summon.serialssolutions.com\/2.0.0\/link\/0\/eLvHCXMwfV1LT8MwDLZgu7ATT9ExpogLF4byYkuuVJso4oKEduAStUsqTaAirYPfj5O2Wpkqjnm0dlzV_uzYCYDg93SypxOY1TkaojRHgO6szVyWIpBwnNtVLm2m91J1mtq_Xd9_H77jgukQL0fjpRT-ypXYl-uNP0KfJnWSk4ep1DvfNK7LpnFR2FoGreAnJofoy1FWXYswgEFafqDiQaW0Lf_g0Va9vjdKi2PoO1-pcAIHrjiD20CZ7OiSdUEamnekokeS5ByGi_lb_DTB15k6eGPUDAEcl-ICesVX4S6ByFRqblU-Veg6MWdTRFDScrFSiC50NovgpsWo-fkMu66l8TEe4c93o1qwCEjDvwnjdSqomT_GUj1I9IMiOO1gJIJRu7fZIjOaiamictj50BUccW8NQ-RiBL3t5ttdB7GNg2zH0I9fXt-ffwGqxaSB Gruzieva, O., Xu, C.-J., Yousefi, P., Relton, C., Merid, S. K., Breton, C., Gao, L., Volk, H. E., Feinberg, J. I., Ladd-Acosta, C., Bakulski, K., Auffray, C., Lemonnier, N., Plusquin, M., Ghantous, A., Herceg, Z., Nawrot, T. S., Pizzi, C., Richiardi, L., \u2026 the Biobank-based Integrative Omics Studies (BIOS) Consortium. (2019). Prenatal Particulate Air Pollution   68 and DNA Methylation in Newborns: An Epigenome-Wide Meta-Analysis. Environmental Health Perspectives, 127(5), 57012. https:\/\/doi.org\/10.1289\/EHP4522 Gulliver, J., Elliott, P., Henderson, J., Hansell, A. L., Vienneau, D., Cai, Y., McCrea, A., Garwood, K., Boyd, A., Neal, L., Agnew, P., Fecht, D., Briggs, D., & de Hoogh, K. (2018). Local- and regional-scale air pollution modelling (PM10) and exposure assessment for pregnancy trimesters, infancy, and childhood to age 15\u2009years: Avon Longitudinal Study of Parents And Children (ALSPAC). Environment International, 113(Journal Article), 10\u201319. https:\/\/doi.org\/10.1016\/j.envint.2018.01.017 Guo, C., Hoek, G., Chang, L.-Y., Bo, Y., Lin, C., Huang, B., Chan, T.-C., Tam, T., Lau, A. K. H., & Lao, X. Q. (2019). Long-Term Exposure to Ambient Fine Particulate Matter ( P M 2.5 ) and Lung Function in Children, Adolescents, and Young Adults: A Longitudinal Cohort Study. Environmental Health Perspectives, 127(12), 127008. https:\/\/doi.org\/10.1289\/EHP5220 Ha, S. (2022). The Changing Climate and Pregnancy Health. Current Environmental Health Reports, 9(2), 263\u2013275. https:\/\/doi.org\/10.1007\/s40572-022-00345-9 Hack, M., & Fanaroff, A. A. (1999). Outcomes of children of extremely low birthweight and gestational age in the 1990\u2019s. Early Human Development, 53(3), 193\u2013218. https:\/\/doi.org\/10.1016\/S0378-3782(98)00052-8 Hadley, M. B., Henderson, S. B., Brauer, M., & Vedanthan, R. (n.d.). Protecting Cardiovascular Health from Wildfire Smoke. Haikerwal, A., Akram, M., Sim, M. R., Meyer, M., Abramson, M. J., & Dennekamp, M. (2016). Fine particulate matter (PM2.5) exposure during a prolonged wildfire period and   69 emergency department visits for asthma. Respirology (Carlton, Vic.), 21(1), 88\u201394. https:\/\/doi.org\/10.1111\/resp.12613 Hanley, G. E., & Morgan, S. (2008). On the validity of area-based income measures to proxy household income. BMC Health Services Research, 8(1), 79\u201379. https:\/\/doi.org\/10.1186\/1472-6963-8-79 Harskamp-van Ginkel, M. W., London, S. J., Magnus, M. C., Gademan, M. G., & Vrijkotte, T. G. (2015). A Study on Mediation by Offspring BMI in the Association between Maternal Obesity and Child Respiratory Outcomes in the Amsterdam Born and Their Development Study Cohort. PloS One, 10(10), e0140641. https:\/\/doi.org\/10.1371\/journal.pone.0140641 Hazlehurst, M. F., Carroll, K. N., Loftus, C. T., Szpiro, A. A., Moore, P. E., Kaufman, J. D., Kirwa, K., LeWinn, K. Z., Bush, N. R., Sathyanarayana, S., Tylavsky, F. A., Barrett, E. S., Nguyen, R. H. N., & Karr, C. J. (2021). Maternal exposure to PM2.5 during pregnancy and asthma risk in early childhood. Environmental Epidemiology, 5(2), e130. https:\/\/doi.org\/10.1097\/EE9.0000000000000130 Henderson, S. B. (2019). How Does Wildfire Smoke Affect Unborn and Newborn Babies? University of British Columbia. Holstius, D. M., REID, C. E., JESDALE, B. M., & MORELLO-FROSCH, R. (2012). Birth Weight following Pregnancy during the 2003 Southern California Wildfires. Environmental Health Perspectives, 120(9), 1340\u20131345. https:\/\/doi.org\/10.1289\/ehp.1104515 Hsu, H.-H. L., Chiu, Y.-H. M., Coull, B. A., Kloog, I., Schwartz, J., Lee, A., Wright, R. O., & Wright, R. J. (2015). Prenatal Particulate Air Pollution and Asthma Onset in Urban Children. Identifying Sensitive Windows and Sex Differences. American Journal of   70 Respiratory and Critical Care Medicine, 192(9), 1052\u20131059. https:\/\/doi.org\/10.1164\/rccm.201504-0658OC Iliodromiti, Z., Zygouris, D., Sifakis, S., Pappa, K. I., Tsikouras, P., Salakos, N., Daniilidis, A., Siristatidis, C., & Vrachnis, N. (2013). Acute lung injury in preterm fetuses and neonates: Mechanisms and molecular pathways. The Journal of Maternal-Fetal & Neonatal Medicine, 26(17), 1696\u20131704. https:\/\/doi.org\/10.3109\/14767058.2013.798284 Jaffe, D. A., O\u2019Neill, S. M., Larkin, N. K., Holder, A. L., Peterson, D. L., Halofsky, J. E., & Rappold, A. G. (2020). Wildfire and prescribed burning impacts on air quality in the United States. Journal of the Air & Waste Management Association, 70(6), 583\u2013615. https:\/\/doi.org\/10.1080\/10962247.2020.1749731 Jalaludin, B., Garden, F. L., Chrzanowska, A., Haryanto, B., Cowie, C. T., Lestari, F., Morgan, G., Mazumdar, S., Metcalf, K., & Marks, G. B. (2022). Associations Between Ambient Particulate Air Pollution and Cognitive Function in Indonesian Children Living in Forest Fire\u2013Prone Provinces. Asia-Pacific Journal of Public Health, 34(1), 96\u2013105. https:\/\/doi.org\/10.1177\/10105395211031735 Jang, H. S., Shin, W. J., Lee, J. E., & Do, J. T. (2017). CpG and Non-CpG Methylation in Epigenetic Gene Regulation and Brain Function. Genes, 8(6), 148. https:\/\/doi.org\/10.3390\/genes8060148 Janssen, B. G., Godderis, L., Pieters, N., Poels, K., Kici\u0144ski, M., Cuypers, A., Fierens, F., Penders, J., Plusquin, M., Gyselaers, W., & Nawrot, T. S. (2013). Placental DNA hypomethylation in association with particulate air pollution in early life. Particle and Fibre Toxicology, 10(1), 22\u201322. https:\/\/doi.org\/10.1186\/1743-8977-10-22   71 Janssen, P. (2022). SmartMom: Teaching by Texting. Journal of Obstetrics and Gynaecology Canada, 44(5), 611\u2013611. https:\/\/doi.org\/10.1016\/j.jogc.2022.02.052 Javors, B. R., & Mazzie, J. P. (2008). chapter 115\u2014Applied Embryology of the Gastrointestinal Tract. In R. M. Gore & M. S. Levine (Eds.), Textbook of Gastrointestinal Radiology (Third Edition) (pp. 2179\u20132193). W.B. Saunders. https:\/\/doi.org\/10.1016\/B978-1-4160-2332-6.50120-2 Jayachandran, S. (2009). Air Quality and Early-Life Mortality: Evidence from Indonesia\u2019s Wildfires. The Journal of Human Resources, 44(4), 916\u2013954. https:\/\/doi.org\/10.1353\/jhr.2009.0001 Jedrychowski, W. A., Perera, F. P., Maugeri, U., Mroz, E., Klimaszewska-Rembiasz, M., Flak, E., Edwards, S., & Spengler, J. D. (2010). Effect of prenatal exposure to fine particulate matter on ventilatory lung function of preschool children of non-smoking mothers. Paediatric and Perinatal Epidemiology, 24(5), 492\u2013501. https:\/\/doi.org\/10.1111\/j.1365-3016.2010.01136.x Jedrychowski, W. A., Perera, F. P., Maugeri, U., Mrozek-Budzyn, D., Mroz, E., Klimaszewska-Rembiasz, M., Flak, E., Edwards, S., Spengler, J., Jacek, R., & Sowa, A. (2010). Intrauterine exposure to polycyclic aromatic hydrocarbons, fine particulate matter and early wheeze. Prospective birth cohort study in 4-year olds. Pediatric Allergy and Immunology, 21(4p2), e723\u2013e732. https:\/\/doi.org\/10.1111\/j.1399-3038.2010.01034.x Jenner, L. (2020, January 9). NASA Animates World Path of Smoke and Aerosols from Australian Fires [Text]. NASA. http:\/\/www.nasa.gov\/feature\/goddard\/2020\/nasa-animates-world-path-of-smoke-and-aerosols-from-australian-fires   72 Johnston, D. W., \u00d6nder, Y. K., Rahman, M. H., & Uluba\u015fo\u011flu, M. A. (2021). Evaluating wildfire exposure: Using wellbeing data to estimate and value the impacts of wildfire. Journal of Economic Behavior & Organization, 192(Journal Article), 782\u2013798. https:\/\/doi.org\/10.1016\/j.jebo.2021.10.029 Johnston, F. H., Henderson, S. B., Chen, Y., Randerson, J. T., Marlier, M., Defries, R. S., Kinney, P., Bowman, D. M. J. S., & Brauer, M. (2012). Estimated Global Mortality Attributable to Smoke from Landscape Fires. Environmental Health Perspectives, 120(5), 695\u2013701. https:\/\/doi.org\/10.1289\/ehp.1104422 Jung, C.-R., Chen, W.-T., Tang, Y.-H., & Hwang, B.-F. (2019). Fine particulate matter exposure during pregnancy and infancy and incident asthma. Journal of Allergy and Clinical Immunology, 143(6), 2254-2262.e5. https:\/\/doi.org\/10.1016\/j.jaci.2019.03.024 Just, A. C., Wright, R. O., Schwartz, J., Coull, B. A., Baccarelli, A. A., Tellez-Rojo, M. M., Moody, E., Wang, Y., Lyapustin, A., & Kloog, I. (2015). Using High-Resolution Satellite Aerosol Optical Depth To Estimate Daily PM2.5 Geographical Distribution in Mexico City. Environmental Science & Technology, 49(14), 8576\u20138584. https:\/\/doi.org\/10.1021\/acs.est.5b00859 Kannan, S., Misra, D. P., Dvonch, J. T., & Krishnakumar, A. (2006). Exposures to Airborne Particulate Matter and Adverse Perinatal Outcomes: A Biologically Plausible Mechanistic Framework for Exploring Potential Effect Modification by Nutrition. Environmental Health Perspectives, 114(11), 1636\u20131642. https:\/\/doi.org\/10.1289\/ehp.9081 Kondo, M. C., De Roos, A. J., White, L. S., Heilman, W. E., Mockrin, M. H., Gross-Davis, C. A., & Burstyn, I. (2019). Meta-Analysis of Heterogeneity in the Effects of Wildfire Smoke   73 Exposure on Respiratory Health in North America. International Journal of Environmental Research and Public Health, 16(6), 960. https:\/\/doi.org\/10.3390\/ijerph16060960 Korten, I., Ramsey, K., & Latzin, P. (2016). Air pollution during pregnancy and lung development in the child. Paediatric Respiratory Reviews, 21(Journal Article), 38\u201346. https:\/\/doi.org\/10.1016\/j.prrv.2016.08.008 Kotecha, S. (2000). Lung growth: Implications for the newborn infant. Archives of Disease in Childhood - Fetal and Neonatal Edition, 82(1), F69\u2013F74. https:\/\/doi.org\/10.1136\/fn.82.1.F69 Krieger, N. (1992). Overcoming the absence of socioeconomic data in medical records: Validation and application of a census-based methodology. American Journal of Public Health (1971), 82(5), 703\u2013710. https:\/\/doi.org\/10.2105\/AJPH.82.5.703 Lavigne, \u00c9., Talarico, R., van Donkelaar, A., Martin, R. V., Stieb, D. M., Crighton, E., Weichenthal, S., Smith-Doiron, M., Burnett, R. T., & Chen, H. (2021). Fine particulate matter concentration and composition and the incidence of childhood asthma. Environment International, 152(Journal Article), 106486. https:\/\/doi.org\/10.1016\/j.envint.2021.106486 Lee, A., Leon Hsu, H.-H., Mathilda Chiu, Y.-H., Bose, S., Rosa, M. J., Kloog, I., Wilson, A., Schwartz, J., Cohen, S., Coull, B. A., Wright, R. O., & Wright, R. J. (2018). Prenatal fine particulate exposure and early childhood asthma: Effect of maternal stress and fetal sex. Journal of Allergy and Clinical Immunology, 141(5), 1880\u20131886. https:\/\/doi.org\/10.1016\/j.jaci.2017.07.017 Lee, M., Schwartz, J., Wang, Y., Dominici, F., & Zanobetti, A. (2019). Long-term effect of fine particulate matter on hospitalization with dementia. Environmental Pollution (1987), 254(Pt A), 112926. https:\/\/doi.org\/10.1016\/j.envpol.2019.07.094   74 Li, C.-Y., Wu, C.-D., Pan, W.-C., Chen, Y.-C., & Su, H.-J. (2019). Association Between Long-term Exposure to PM2.5 and Incidence of Type 2 Diabetes in Taiwan: A National Retrospective Cohort Study. Epidemiology (Cambridge, Mass.), 30 Suppl 1(Journal Article), S67\u2013S75. https:\/\/doi.org\/10.1097\/EDE.0000000000001035 Li, Y., Tong, D., Ma, S., Zhang, X., Kondragunta, S., Li, F., & Saylor, R. (2021). Dominance of Wildfires Impact on Air Quality Exceedances During the 2020 Record\u2010Breaking Wildfire Season in the United States. Geophysical Research Letters, 48(21), n\/a-n\/a. https:\/\/doi.org\/10.1029\/2021GL094908 Lin, Y.-T., Shih, H., Jung, C.-R., Wang, C.-M., Chang, Y.-C., Hsieh, C.-Y., & Hwang, B.-F. (2021). Effect of exposure to fine particulate matter during pregnancy and infancy on paediatric allergic rhinitis. Thorax, 76(6), 568\u2013574. https:\/\/doi.org\/10.1136\/thoraxjnl-2020-215025 Linn, R. (2019). Fluid dynamics of wildfires. Physics Today, 72(11), 70\u201371. https:\/\/doi.org\/10.1063\/PT.3.4350 Liu, J. C., Mickley, L. J., Sulprizio, M. P., Dominici, F., Yue, X., Ebisu, K., Anderson, G. B., Khan, R. F. A., Bravo, M. A., & Bell, M. L. (2016). Particulate air pollution from wildfires in the Western US under climate change. Climatic Change, 138(3\u20134), 655\u2013666. https:\/\/doi.org\/10.1007\/s10584-016-1762-6 Liu, J. C., & Peng, R. D. (2019). The impact of wildfire smoke on compositions of fine particulate matter by ecoregion in the Western US. Journal of Exposure Science & Environmental Epidemiology, 29(6), 765\u2013776. https:\/\/doi.org\/10.1038\/s41370-018-0064-7 Mansoor, S., Farooq, I., Kachroo, M. M., Mahmoud, A. E. D., Fawzy, M., Popescu, S. M., Alyemeni, M. N., Sonne, C., Rinklebe, J., & Ahmad, P. (2022). Elevation in wildfire   75 frequencies with respect to the climate change. Journal of Environmental Management, 301(Journal Article), 113769\u2013113769. https:\/\/doi.org\/10.1016\/j.jenvman.2021.113769 Marra, F., Patrick, D. M., Chong, M., & Bowie, W. R. (2006). Antibiotic use among children in British Columbia, Canada. Journal of Antimicrobial Chemotherapy, 58(4), 830\u2013839. https:\/\/doi.org\/10.1093\/jac\/dkl275 Matz, C. J., Egyed, M., Xi, G., Racine, J., Pavlovic, R., Rittmaster, R., Henderson, S. B., & Stieb, D. M. (2020). Health impact analysis of PM2.5 from wildfire smoke in Canada (2013\u20132015, 2017\u20132018). The Science of the Total Environment, 725(Journal Article), 138506. https:\/\/doi.org\/10.1016\/j.scitotenv.2020.138506 Mazer, B. D. (2016). Otitis Media. In D. Y. M. Leung MD PhD FAAAAI, S. J. Szefler MD, F. A. Bonilla MD PhD, C. A. Akdis MD, & H. A. Sampson MD (Eds.), Pediatric Allergy: Principles and Practice (Third, Vol. 1\u2013Book, Section, pp. 219-227.e3). https:\/\/doi.org\/10.1016\/B978-0-323-29875-9.00025-2 McEvoy, C. T., & Spindel, E. R. (2016). Environmental Effects on Lung Morphogenesis and Function: Tobacco Products, Combustion Products, and Other Sources of Pollution. In A. H. Jobe, J. A. Whitsett, & S. H. Abman (Eds.), Fetal and Neonatal Lung Development: Clinical Correlates and Technologies for the Future (pp. 77\u201393). Cambridge University Press. https:\/\/doi.org\/10.1017\/CBO9781139680349.006 McKinley, M. P. (2013). Anatomy & physiology: An integrative approach (2nd ed.). McGraw Hill. https:\/\/go.exlibris.link\/VQpGctR6 Meng, J., Martin, R. V., Li, C., van Donkelaar, A., Tzompa-Sosa, Z. A., Yue, X., Xu, J.-W., Weagle, C. L., & Burnett, R. T. (2019). Source Contributions to Ambient Fine Particulate   76 Matter for Canada. Environmental Science & Technology, 53(17), 10269\u201310278. https:\/\/doi.org\/10.1021\/acs.est.9b02461 Migliaccio, C. T., Kobos, E., King, Q. O., Porter, V., Jessop, F., & Ward, T. (2013). Adverse effects of wood smoke PM2.5 exposure on macrophage functions. Inhalation Toxicology, 25(2), 67\u201376. https:\/\/doi.org\/10.3109\/08958378.2012.756086 Mirabelli, M. C., K\u00fcnzli, N., Avol, E., Gilliland, F. D., Gauderman, W. J., McConnell, R., & Peters, J. M. (2009). Respiratory Symptoms following Wildfire Smoke Exposure: Airway Size as a Susceptibility Factor. Epidemiology (Cambridge, Mass.), 20(3), 451\u2013459. https:\/\/doi.org\/10.1097\/EDE.0b013e31819d128d Moore, D., Copes, R., Fisk, R., Joy, R., Chan, K., & Brauer, M. (2006). Population Health Effects of Air Quality Changes Due to Forest Fires in British Columbia in 2003: Estimates from Physician-visit Billing Data. Canadian Journal of Public Health, 97(2), 105\u2013108. https:\/\/doi.org\/10.1007\/BF03405325 Moosavi, S., Nwaka, B., Akinjise, I., Corbett, S. E., Chue, P., Greenshaw, A. J., Silverstone, P. H., Li, X.-M., & Agyapong, V. I. O. (2019). Mental Health Effects in Primary Care Patients 18 Months After a Major Wildfire in Fort McMurray: Risk Increased by Social Demographic Issues, Clinical Antecedents, and Degree of Fire Exposure. Frontiers in Psychiatry, 10(Journal Article), 683\u2013683. https:\/\/doi.org\/10.3389\/fpsyt.2019.00683 Mortamais, M., Pujol, J., Mart\u00ednez-Vilavella, G., Fenoll, R., Reynes, C., Sabatier, R., Rivas, I., Forns, J., Vilor-Tejedor, N., Alemany, S., Cirach, M., Alvarez-Pedrerol, M., Nieuwenhuijsen, M., & Sunyer, J. (2019). Effects of prenatal exposure to particulate matter air pollution on corpus callosum and behavioral problems in children. Environmental Research, 178(Journal Article), 108734. https:\/\/doi.org\/10.1016\/j.envres.2019.108734   77 Murphy, R. & desLibris - Documents. (2020). Trends in Canadian Forest Fires 1959\u20132019. Fraser Institute. https:\/\/go.exlibris.link\/h4PV4TXd Murphy, V. E., Karmaus, W., Mattes, J., Brew, B. K., Collison, A., Holliday, E., Jensen, M. E., Morgan, G. G., Zosky, G. R., McDonald, V. M., Jegasothy, E., Robinson, P. D., & Gibson, P. G. (2021). Exposure to Stress and Air Pollution from Bushfires during Pregnancy: Could Epigenetic Changes Explain Effects on the Offspring? International Journal of Environmental Research and Public Health, 18(14), 7465. https:\/\/doi.org\/10.3390\/ijerph18147465 N\u00e4\u00e4v, \u00c5., Erlandsson, L., Isaxon, C., \u00c5sander Frostner, E., Ehinger, J., Sporre, M. K., Krais, A. M., Strandberg, B., Lundh, T., Elm\u00e9r, E., Malmqvist, E., & Hansson, S. R. (2020). Urban PM2.5 Induces Cellular Toxicity, Hormone Dysregulation, Oxidative Damage, Inflammation, and Mitochondrial Interference in the HRT8 Trophoblast Cell Line. Frontiers in Endocrinology (Lausanne), 11(Journal Article), 75. https:\/\/doi.org\/10.3389\/fendo.2020.00075 Naeher, L. P., Brauer, M., Lipsett, M., Zelikoff, J. T., Simpson, C. D., Koenig, J. Q., & Smith, K. R. (2007). Woodsmoke Health Effects: A Review. Inhalation Toxicology, 19(1), 67\u2013106. https:\/\/doi.org\/10.1080\/08958370600985875 Nagiah, S., Phulukdaree, A., Naidoo, D., Ramcharan, K., Naidoo, R., Moodley, D., & Chuturgoon, A. (2015). Oxidative stress and air pollution exposure during pregnancy: A molecular assessment. Human & Experimental Toxicology, 34(8), 838\u2013847. https:\/\/doi.org\/10.1177\/0960327114559992 Nair, H., Dr, Sim\u00f5es, E. A., Prof, Rudan, I., Prof, Gessner, B. D., MD, Azziz-Baumgartner, E., MD, Zhang, J. S. F., MPH, Feikin, D. R., MD, Mackenzie, G. A., PhD, Moi\u00efsi, J. C., PhD,   78 Roca, A., PhD, Baggett, H. C., MD, Zaman, S. M., PhD, Singleton, R. J., MD, Lucero, M. G., MD, Chandran, A., MD, Gentile, A., Prof, Cohen, C., MBBCH, Krishnan, A., MD, Bhutta, Z. A., Prof, \u2026 Severe Acute Lower Respiratory Infections Working Group. (2013). Global and regional burden of hospital admissions for severe acute lower respiratory infections in young children in 2010: A systematic analysis. The Lancet (British Edition), 381(9875), 1380\u20131390. https:\/\/doi.org\/10.1016\/S0140-6736(12)61901-1 Okechukwu, C. E. (2021). Neurodevelopmental Consequences of Constant Prenatal Exposure to Particulate Matter 2.5 MICRONS in Diameter. Journal of Paediatrics and Child Health, 57(11), 1837\u20131837. https:\/\/doi.org\/10.1111\/jpc.15552 Oris, F., Asselin, H., Ali, A. A., Finsinger, W., & Bergeron, Y. (2014). Effect of increased fire activity on global warming in the boreal forest. Environmental Reviews, 22(3), 206\u2013219. https:\/\/doi.org\/10.1139\/er-2013-0062 Ostro, B., Roth, L., Malig, B., & Marty, M. (2009). The Effects of Fine Particle Components on Respiratory Hospital Admissions in Children. Environmental Health Perspectives, 117(3), 475\u2013480. https:\/\/doi.org\/10.1289\/ehp.11848 Padula, A. M., & Benmarhnia, T. (2022). Wildfires in Pregnancy: Potential Threats to the Newborn. Paediatric and Perinatal Epidemiology, 36(1), 54\u201356. https:\/\/doi.org\/10.1111\/ppe.12838 Pagano, A. S., M\u00e1rquez, S., & Laitman, J. T. (2019). Reconstructing the Neanderthal Eustachian Tube: New Insights on Disease Susceptibility, Fitness Cost, and Extinction. Anatomical Record (Hoboken, N.J.\u2009: 2007), 302(12), 2109\u20132125. https:\/\/doi.org\/10.1002\/ar.24248 Park, B. Y., Boles, I., Monavvari, S., Patel, S., Alvarez, A., Phan, M., Perez, M., & Yao, R. (2022). The association between wildfire exposure in pregnancy and foetal gastroschisis: A   79 population\u2010based cohort study. Paediatric and Perinatal Epidemiology, 36(1), 45\u201353. https:\/\/doi.org\/10.1111\/ppe.12823 Patella, V., Florio, G., Magliacane, D., Giuliano, A., Crivellaro, M. A., Di Bartolomeo, D., Genovese, A., Palmieri, M., Postiglione, A., Ridolo, E., Scaletti, C., Ventura, M. T., Zollo, A., & Air Pollution and Climate Change Task Force of the Italian Society of Allergology, A. and C. I. (SIAAIC). (2018). Urban air pollution and climate change: \u201cThe Decalogue: Allergy Safe Tree\u201d for allergic and respiratory diseases care. Clinical and Molecular Allergy CMA, 16(1), 20\u201311. https:\/\/doi.org\/10.1186\/s12948-018-0098-3 Paul, N., Yao, J., McLean, K. E., Stieb, D. M., & Henderson, S. B. (2022). The Canadian Optimized Statistical Smoke Exposure Model (Canossem): A Machine Learning Approach to Estimate National Daily Fine Particulate Matter (Pm2.5) Exposure (SSRN Scholarly Paper No. 4098551). https:\/\/doi.org\/10.2139\/ssrn.4098551 Pearson, J. F., Goldfine, A. B., Bachireddy, C., Brownstein, J. S., & Shyamprasad, S. (2010). Association Between Fine Particulate Matter and Diabetes Prevalence in the U.S. Diabetes Care, 33(10), 2196\u20132201. https:\/\/doi.org\/10.2337\/dc10-0698 Pettigrew, M. M., Gent, J. F., Pyles, R. B., MilleR, A. L., Nokso-Koivisto, J., & Chonmaitree, T. (2011). Viral-Bacterial Interactions and Risk of Acute Otitis Media Complicating Upper Respiratory Tract Infection. Journal of Clinical Microbiology, 49(11), 3750\u20133755. https:\/\/doi.org\/10.1128\/JCM.01186-11 Pike, A., Mikolas, C., Tompkins, K., Olson, J., Olson, D. M., & Br\u00e9mault-Phillips, S. (2022). New Life Through Disaster: A Thematic Analysis of Women\u2019s Experiences of Pregnancy and the 2016 Fort McMurray Wildfire. Frontiers in Public Health, 10(Journal Article), 725256\u2013725256. https:\/\/doi.org\/10.3389\/fpubh.2022.725256   80 Popdata BC. (2022). Consolidation file (MSP registration and premium billing) data set. https:\/\/www.popdata.bc.ca\/data\/demographic\/consolidation_file Population Data BC. (2021). Registration and Premium Billings (RBP Lite, unconsolidated) (PopData). https:\/\/www.popdata.bc.ca\/data\/demographic\/consolidation_file Population Data BC. (2022a). Home | Population Data BC. https:\/\/www.popdata.bc.ca Population Data BC. (2022b). PharmaNet data set. https:\/\/www.popdata.bc.ca\/data\/health\/pharmanet Priante, E., Moschino, L., Mardegan, V., Manzoni, P., Salvadori, S., & Baraldi, E. (2016). Respiratory Outcome after Preterm Birth: A Long and Difficult Journey. American Journal of Perinatology, 33(11), 1040\u20131042. https:\/\/doi.org\/10.1055\/s-0036-1586172 Price, M., Bowen, M., Lau, F., Kitson, N., & Bardal, S. (2012). Assessing accuracy of an electronic provincial medication repository. BMC Medical Informatics and Decision Making, 12(1), 42\u201342. https:\/\/doi.org\/10.1186\/1472-6947-12-42 Proietti, E., R\u00f6\u00f6sli, M., Frey, U., & Latzin, P. (2013). Air pollution during pregnancy and neonatal outcome: A review. Journal of Aerosol Medicine and Pulmonary Drug Delivery, 26(1), 9. Rajagopalan, S., Brauer, M., Bhatnagar, A., Bhatt, D. L., Brook, J. R., Huang, W., M\u00fcnzel, T., Newby, D., Siegel, J., Brook, R. D., American Heart Association Council on Lifestyle and Cardiometabolic Health, Council on Arteriosclerosis, T. and V. B., Council on Clinical Cardiology, Council on Cardiovascular and Stroke Nursing, and Stroke Council, On behalf of the American Heart Association Council on Lifestyle and Cardiometabolic Health, Council on Arteriosclerosis, T. and V. B., Council on Clinical Cardiology, Council on Cardiovascular and Stroke Nursing, & and Stroke Council. (2020). Personal-Level   81 Protective Actions Against Particulate Matter Air Pollution Exposure: A Scientific Statement From the American Heart Association. Circulation (New York, N.Y.), 142(23), e411\u2013e431. https:\/\/doi.org\/10.1161\/CIR.0000000000000931 Reid, C. E., Brauer, M., Johnston, F. H., Jerrett, M., Balmes, J. R., & Elliott, C. T. (2016). Critical Review of Health Impacts of Wildfire Smoke Exposure. Environmental Health Perspectives, 124(9), 1334\u20131343. https:\/\/doi.org\/10.1289\/ehp.1409277 Requia, W. J., Kill, E., Papatheodorou, S., Koutrakis, P., & Schwartz, J. D. (2021). Prenatal exposure to wildfire-related air pollution and birth defects in Brazil. Journal of Exposure Science & Environmental Epidemiology, Journal Article. https:\/\/doi.org\/10.1038\/s41370-021-00380-y Rivera Rivera, N. Y., Tamayo-Ortiz, M., Mercado Garc\u00eda, A., Just, A. C., Kloog, I., T\u00e9llez-Rojo, M. M., Wright, R. O., Wright, R. J., & Rosa, M. J. (2021). Prenatal and early life exposure to particulate matter, environmental tobacco smoke and respiratory symptoms in Mexican children. Environmental Research, 192(C), 110365. https:\/\/doi.org\/10.1016\/j.envres.2020.110365 Rosa, M. J., Hair, G. M., Just, A. C., Kloog, I., Svensson, K., Pizano-Z\u00e1rate, M. L., Pantic, I., Schnaas, L., Tamayo-Ortiz, M., Baccarelli, A. A., Tellez-Rojo, M. M., Wright, R. O., & Sanders, A. P. (2020). Identifying critical windows of prenatal particulate matter (PM2.5) exposure and early childhood blood pressure. Environmental Research, 182(Journal Article), 109073\u2013109073. https:\/\/doi.org\/10.1016\/j.envres.2019.109073 Rosa, M. J., Just, A. C., Guerra, M. S., Kloog, I., Hsu, H.-H. L., Brennan, K. J., Garc\u00eda, A. M., Coull, B., Wright, R. J., T\u00e9llez Rojo, M. M., Baccarelli, A. A., & Wright, R. O. (2017). Identifying sensitive windows for prenatal particulate air pollution exposure and   82 mitochondrial DNA content in cord blood. Environment International, 98(Journal Article), 198\u2013203. https:\/\/doi.org\/10.1016\/j.envint.2016.11.007 Santibanez, P., Gooch, K., Vo, P., Lorimer, M., & Sandino, Y. (2012). Acute care utilization due to hospitalizations for pediatric lower respiratory tract infections in British Columbia, Canada. BMC Health Services Research, 12(1), 451\u2013451. https:\/\/doi.org\/10.1186\/1472-6963-12-451 Sbihi, H., Tamburic, L., Koehoorn, M., & Brauer, M. (2016). Perinatal air pollution exposure and development of asthma from birth to age 10 years. The European Respiratory Journal, 47(4), 1062\u20131071. https:\/\/doi.org\/10.1183\/13993003.00746-2015 Shao, J., Zosky, G. R., Wheeler, A. J., Dharmage, S., Dalton, M., Williamson, G. J., O\u2019Sullivan, T., Chappell, K., Knibbs, L. D., & Johnston, F. H. (2020). Exposure to air pollution during the first 1000 days of life and subsequent health service and medication usage in children. Environmental Pollution (1987), 256(Journal Article), 113340\u2013113340. https:\/\/doi.org\/10.1016\/j.envpol.2019.113340 Shulman, S. T., Bisno, A. L., Clegg, H. W., Gerber, M. A., Kaplan, E. L., Lee, G., Martin, J. M., Van Beneden, C., & Infectious Diseases Society of America. (2012). Clinical Practice Guideline for the Diagnosis and Management of Group A Streptococcal Pharyngitis: 2012 Update by the Infectious Diseases Society of America. Clinical Infectious Diseases, 55(10), 1279\u20131282. https:\/\/doi.org\/10.1093\/cid\/cis629 Simoes, E. A. F., Cherian, T., Chow, J., Shahid-Salles, S. A., Laxminarayan, R., & John, T. J. (2006). Acute Respiratory Infections in Children. In D. T. Jamison, J. G. Breman, A. R. Measham, G. Alleyne, M. Claeson, D. B. Evans, P. Jha, A. Mills, & P. Musgrove (Eds.),   83 Disease Control Priorities in Developing Countries (2nd ed.). World Bank. http:\/\/www.ncbi.nlm.nih.gov\/books\/NBK11786\/ SmartMom. (2022). What is SmartMom? https:\/\/www.smartmomcanada.ca\/careprovider.aspx Soh, S.-E., Goh, A., Teoh, O. H., Godfrey, K. M., Gluckman, P. D., Shek, L. P.-C., & Chong, Y.-S. (2018). Pregnancy Trimester-Specific Exposure to Ambient Air Pollution and Child Respiratory Health Outcomes in the First 2 Years of Life: Effect Modification by Maternal Pre-Pregnancy BMI. International Journal of Environmental Research and Public Health, 15(5), 996. https:\/\/doi.org\/10.3390\/ijerph15050996 Som, P. M., & Naidich, T. P. (2013). Illustrated review of the embryology and development of the facial region, part 1: Early face and lateral nasal cavities. American Journal of Neuroradiology\u2009: AJNR, 34(12), 2233\u20132240. https:\/\/doi.org\/10.3174\/ajnr.A3415 Stocks, J., Hislop, A., & Sonnappa, S. (2013). Early lung development: Lifelong effect on respiratory health and disease. The Lancet Respiratory Medicine, 1(9), 728\u2013742. https:\/\/doi.org\/10.1016\/S2213-2600(13)70118-8 Uphoff, E., Cabieses, B., Pinart, M., Vald\u00e9s, M., Ant\u00f3, J. M., & Wright, J. (2015). A systematic review of socioeconomic position in relation to asthma and allergic diseases. The European Respiratory Journal, 46(2), 364\u2013374. https:\/\/doi.org\/10.1183\/09031936.00114514 Urbanski, S. P., Hao, W. M., & Baker, S. (2008). Chapter 4 Chemical Composition of Wildland Fire Emissions. In A. Bytnerowicz, M. J. Arbaugh, A. R. Riebau, & C. Andersen (Eds.), Developments in Environmental Science (Vol. 8, pp. 79\u2013107). Elsevier. https:\/\/doi.org\/10.1016\/S1474-8177(08)00004-1 US EPA, O. (2016, April 19). Particulate Matter (PM) Basics [Overviews and Factsheets]. https:\/\/www.epa.gov\/pm-pollution\/particulate-matter-pm-basics   84 Vanker, A., Gie, R. P., & Zar, H. J. (2017). The association between environmental tobacco smoke exposure and childhood respiratory disease: A review. Expert Review of Respiratory Medicine, 11(8), 661. Veras, M. M., de Oliveira Alves, N., Fajersztajn, L., & Saldiva, P. (2016). Before the first breath: Prenatal exposures to air pollution and lung development. Cell and Tissue Research, 367(3), 445\u2013455. https:\/\/doi.org\/10.1007\/s00441-016-2509-4 Wake, M., Takeno, S., & Hawke, M. (1994). The early development of sino-nasal mucosa. The Laryngoscope, 104(7), 850. Westerling, A. L., Hidalgo, H. G., Cayan, D. R., & Swetnam, T. W. (2006). Warming and earlier spring increase Western U.S. forest wildfire activity. Science (American Association for the Advancement of Science), 313(5789), 940\u2013943. https:\/\/doi.org\/10.1126\/science.1128834 Williams, K. M., Franzi, L. M., & Last, J. A. (2013). Cell-specific oxidative stress and cytotoxicity after wildfire coarse particulate matter instillation into mouse lung. Toxicology and Applied Pharmacology, 266(1), 48\u201355. https:\/\/doi.org\/10.1016\/j.taap.2012.10.017 Willis, G. A., Chappell, K., Williams, S., Melody, S. M., Wheeler, A., Dalton, M., Dharmage, S. C., Zosky, G. R., & Johnston, F. H. (2020). Respiratory and atopic conditions in children two to four years after the 2014 Hazelwood coalmine fire. Medical Journal of Australia, 213(6), 269\u2013275. https:\/\/doi.org\/10.5694\/mja2.50719 Yao, J. (2019). Assessing Sub-Daily Exposure to Wildfire Smoke and Its Public Health Effects in British Columbia. The University of British Columbia.   85 Yao, J., Brauer, M., Wei, J., McGrail, K. M., Johnston, F. H., & Henderson, S. B. (2020). Sub-Daily Exposure to Fine Particulate Matter and Ambulance Dispatches during Wildfire Seasons: A Case-Crossover Study in British Columbia, Canada. Environmental Health Perspectives, 128(6), 67006. https:\/\/doi.org\/10.1289\/EHP5792 Yao, J., & Henderson, S. B. (2014). An empirical model to estimate daily forest fire smoke exposure over a large geographic area using air quality, meteorological, and remote sensing data. Journal of Exposure Science & Environmental Epidemiology, 24(3), 328\u2013335. https:\/\/doi.org\/10.1038\/jes.2013.87 Yue, H., Ji, X., Li, G., Hu, M., & Sang, N. (2020). Maternal Exposure to PM 2.5 Affects Fetal Lung Development at Sensitive Windows. Environmental Science & Technology, 54(1), 316\u2013324. https:\/\/doi.org\/10.1021\/acs.est.9b04674 Zimmerman, J. J., Rotta, A. T., & ClinicalKey Flex. (2022). Fuhrman and Zimmerman\u2019s Pediatric Critical Care. Elsevier. https:\/\/go.exlibris.link\/xnmxtGg4     86 Appendices   Appendix A  : Statistical Analysis by Trimester Windows  A.1 Sub-Appendix: Survival analysis for the first diagnosis of respiratory infections by trimester windows  Survival analysis for the first diagnosis of otitis media in the first year and prenatal exposure to wildfire-related PM2.5  Eligible infants # of infants diagnosed  # of infants included in the model Crude HR (95% Cl) Adjusted HR (95% Cl) Trimester 1  (1-13 weeks) 62,521 4,867 4,106 0.998  [0.990, 1.006] 1.013 [1.002, 1.024] Trimester 2 (14-27 weeks) 62,545 4,873 4,110 0.985  [0.974, 0.996] 0.984  [0.973, 0.995] Trimester 3 (>27 weeks) 62,393 4,862 4,102 1.000  [0.992, 1.008] 0.998  [0.991, 1.006] * The variables in the adjusted model include sex, maternal age, BMI during pregnancy, smoking during pregnancy, preterm births, discharge birth weight, and SES   Survival analysis for the first diagnosis of upper respiratory infections (facial regions) in the first year and prenatal exposure to wildfire-related PM2.5  Eligible infants # of infants diagnosed  # of infants included in the model Crude HR (95% Cl) Adjusted HR (95% Cl) Trimester 1  (1-13 weeks) 62,521 11,269 9,836 0.998 [0.990, 1.006] 1.009  [1.001, 1.018] Trimester 2 (14-27 weeks) 62,545 11,272 9,843 0.981 [0.973, 0.990] 0.988  [0.979, 0.996] Trimester 3 (>27 weeks) 62,393 11,244 9,826 1.010 [1.004, 1.015] 1.011  [1.006, 1.017] * The variables in the adjusted model include sex, maternal age, BMI during pregnancy, smoking during pregnancy, preterm births, discharge birth weight, and SES     87  Survival analysis for the first diagnosis of upper respiratory infection (larynx) the first year and prenatal exposure to wildfire-related PM2.5  Eligible infants # of infants diagnosed  # of infants included in the model Crude HR (95% Cl) Adjusted HR (95% Cl) Trimester 1  (1-13 weeks) 62,521 18,967 16,299 0.987 [0.981, 0.993] 0.996 [0.990, 1.002] Trimester 2 (14-27 weeks) 62,545 18,985 16,312 0.984  [0.978, 0.989] 0.989  [0.983, 0.995] Trimester 3 (>27 weeks) 62,281 18,957 20,043 1.011  [1.007, 1.015] 1.012  [1.008, 1.017] * The variables in the adjusted model include sex, maternal age, BMI during pregnancy, smoking during pregnancy, preterm births, discharge birth weight, and SES    Survival analysis for the first diagnosis of lower respiratory infection in the first year and prenatal exposure to wildfire-related PM2.5  Eligible infants # of infants diagnosed  # of infants included in the model Crude HR (95% Cl) Adjusted HR (95% Cl) Trimester 1  (1-13 weeks) 62,521 7,442 6,358 0.984  [0.976, 0.993] 0.997  [0.988, 1.006] Trimester 2 (14-27 weeks) 62,545 7,443 6,358 0.990  [0.981, 0.999]  0.997 [0.989, 1.006] Trimester 3 (>27 weeks) 62,281 7,413 6,360 1.016   [1.010, 1,002] 1.018  [1.013, 1.024] * The variables in the adjusted model include sex, maternal age, BMI during pregnancy, smoking during pregnancy, preterm births, discharge birth weight, and SES         88 A.2 Sub-Appendix: Survival analysis for overall dispensation of antibiotics by trimester windows  Survival analysis for the first dispensation of amoxicillin in the first year and prenatal exposure to wildfire-related PM2.5  Eligible infants # of infants with amoxicillin dispensation # of infants included in the model Crude HR (95% Cl) Adjusted HR (95% Cl) Trimester 1  (1-13 weeks) 62,521 9,223 7,908 0.999  [0.991, 1.006] 1.000  [0.993, 1.008] Trimester 2 (14-27 weeks) 62,545 9,230 7,915 0.978  [0.970, 0.987]  0.979 [0.970, 0.987] Trimester 3 (>27 weeks) 62,281 9,216 7,905 1.009   [1.003, 1.015] 1.009  [1.003, 1.015] * The variables in the adjusted model include sex, maternal age, BMI during pregnancy, smoking during pregnancy, preterm births, discharge birth weight, and SES   A.3  Sub-Appendix: Survival analysis for the first dispensation of amoxicillin related to respiratory infections by trimester windows  Survival analysis for the first dispensation of amoxicillin associated with otitis media in the first year and prenatal exposure to wildfire-related PM2.5  Eligible infants # of infants with amoxicillin dispensation # of infants included in the model Crude HR (95% Cl) Adjusted HR (95% Cl) Trimester 1  (1-13 weeks) 62,521 3,265 2,766 1.008 [0.997, 1.020] 1.010 [0.997, 1.023] Trimester 2 (14-27 weeks) 62,545 3,269 2,769 0.982 [0.970, 0.996] 0.980  [0.967, 0.994] Trimester 3 (>27 weeks) 62,393 3,265 2,766 1.002  [0.993, 1.013] 1.001  [0.991, 1.011] * The variables in the adjusted model include sex, maternal age, BMI during pregnancy, smoking during pregnancy, preterm births, discharge birth weight, and SES      89 Survival analysis for the first dispensation of amoxicillin associated with upper respiratory infection (facial regions) in the first year and prenatal exposure to wildfire-related PM2.5  Eligible infants # of infants with amoxicillin dispensation  # of infants included in the model Crude HR (95% Cl) Adjusted HR (95% Cl) Trimester 1  (1-13 weeks) 62,521 983 864 1.013 [0.994, 1.033] 1.017  [0.997, 1.038] Trimester 2 (14-27 weeks) 62,545 985 866 0.993 [0.966, 1.021] 0.992 [0.966, 1.020] Trimester 3 (>27 weeks) 62,393 980 862 0.998  [0.979, 1.017] 0.997  [0.978, 1.016] * The variables in the adjusted model include sex, maternal age, BMI during pregnancy, smoking during pregnancy, preterm births, discharge birth weight, and SES   Survival for the first dispensation of amoxicillin associated with upper respiratory infection (larynx) the first year and prenatal exposure to wildfire-related PM2.5  Eligible infants # of infants with amoxicillin dispensation   # of infants included in the model Crude HR (95% Cl) Adjusted HR (95% Cl) Trimester 1  (1-13 weeks) 62,521 1,219 1,046 0.984  [0.964, 1.003] 0.982  [0.961, 1.003] Trimester 2 (14-27 weeks) 62,545 1,216 1,043 0.969  [0.943, 0.995] 0.969 [0.942, 0.997] Trimester 3 (>27 weeks) 62,281 1,215 1,044 1.016  [0.999, 1.032] 1.017  [1.000, 1.034] * The variables in the adjusted model include sex, maternal age, BMI during pregnancy, smoking during pregnancy, preterm births, discharge birth weight, and SES   Survival analysis for the first dispensation of amoxicillin associated with lower respiratory infection in the first year and prenatal exposure to wildfire-related PM2.5  Eligible infants # of infants with amoxicillin dispensation  # of infants included in the model Crude HR (95% Cl) Adjusted HR (95% Cl) Trimester 1  (1-13 weeks) 62,521 1,493 1,297 1.001  [0.985, 1.017] 1.009  [0.992, 1.026] Trimester 2 (14-27 weeks) 62,545 1,492 1,296 0.972  [0.951, 0.994]  0.978 [0.956, 1.000] Trimester 3 (>27 weeks) 62,281 1,487 1,293 1.021   [1.009, 1.034] 1.022  [1.010, 1.035] * The variables in the adjusted model include sex, maternal age, BMI during pregnancy, smoking during pregnancy, preterm births, discharge birth weight, and SES  ","attrs":{"lang":"en","ns":"http:\/\/www.w3.org\/2009\/08\/skos-reference\/skos.html#note","classmap":"oc:AnnotationContainer"},"iri":"http:\/\/www.w3.org\/2009\/08\/skos-reference\/skos.html#note","explain":"Simple Knowledge Organisation System; Notes are used to provide information relating to SKOS concepts. There is no restriction on the nature of this information, e.g., it could be plain text, hypertext, or an image; it could be a definition, information about the scope of a concept, editorial information, or any other type of information."}],"Genre":[{"label":"Genre","value":"Thesis\/Dissertation","attrs":{"lang":"en","ns":"http:\/\/www.europeana.eu\/schemas\/edm\/hasType","classmap":"dpla:SourceResource","property":"edm:hasType"},"iri":"http:\/\/www.europeana.eu\/schemas\/edm\/hasType","explain":"A Europeana Data Model Property; This property relates a resource with the concepts it belongs to in a suitable type system such as MIME or any thesaurus that captures categories of objects in a given field. It does NOT capture aboutness"}],"GraduationDate":[{"label":"Graduation Date","value":"2022-11","attrs":{"lang":"en","ns":"http:\/\/vivoweb.org\/ontology\/core#dateIssued","classmap":"vivo:DateTimeValue","property":"vivo:dateIssued"},"iri":"http:\/\/vivoweb.org\/ontology\/core#dateIssued","explain":"VIVO-ISF Ontology V1.6 Property; Date Optional Time Value, DateTime+Timezone Preferred "}],"IsShownAt":[{"label":"DOI","value":"10.14288\/1.0419388","attrs":{"lang":"en","ns":"http:\/\/www.europeana.eu\/schemas\/edm\/isShownAt","classmap":"edm:WebResource","property":"edm:isShownAt"},"iri":"http:\/\/www.europeana.eu\/schemas\/edm\/isShownAt","explain":"A Europeana Data Model Property; An unambiguous URL reference to the digital object on the provider\u2019s website in its full information context."}],"Language":[{"label":"Language","value":"eng","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/language","classmap":"dpla:SourceResource","property":"dcterms:language"},"iri":"http:\/\/purl.org\/dc\/terms\/language","explain":"A Dublin Core Terms Property; A language of the resource.; Recommended best practice is to use a controlled vocabulary such as RFC 4646 [RFC4646]."}],"Program":[{"label":"Program (Theses)","value":"Population and Public Health","attrs":{"lang":"en","ns":"https:\/\/open.library.ubc.ca\/terms#degreeDiscipline","classmap":"oc:ThesisDescription","property":"oc:degreeDiscipline"},"iri":"https:\/\/open.library.ubc.ca\/terms#degreeDiscipline","explain":"UBC Open Collections Metadata Components; Local Field; Indicates the program for which the degree was granted."}],"Provider":[{"label":"Provider","value":"Vancouver : University of British Columbia Library","attrs":{"lang":"en","ns":"http:\/\/www.europeana.eu\/schemas\/edm\/provider","classmap":"ore:Aggregation","property":"edm:provider"},"iri":"http:\/\/www.europeana.eu\/schemas\/edm\/provider","explain":"A Europeana Data Model Property; The name or identifier of the organization who delivers data directly to an aggregation service (e.g. Europeana)"}],"Publisher":[{"label":"Publisher","value":"University of British Columbia","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/publisher","classmap":"dpla:SourceResource","property":"dcterms:publisher"},"iri":"http:\/\/purl.org\/dc\/terms\/publisher","explain":"A Dublin Core Terms Property; An entity responsible for making the resource available.; Examples of a Publisher include a person, an organization, or a service."}],"Rights":[{"label":"Rights","value":"Attribution-NonCommercial-NoDerivatives 4.0 International","attrs":{"lang":"*","ns":"http:\/\/purl.org\/dc\/terms\/rights","classmap":"edm:WebResource","property":"dcterms:rights"},"iri":"http:\/\/purl.org\/dc\/terms\/rights","explain":"A Dublin Core Terms Property; Information about rights held in and over the resource.; Typically, rights information includes a statement about various property rights associated with the resource, including intellectual property rights."}],"RightsURI":[{"label":"Rights URI","value":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/","attrs":{"lang":"*","ns":"https:\/\/open.library.ubc.ca\/terms#rightsURI","classmap":"oc:PublicationDescription","property":"oc:rightsURI"},"iri":"https:\/\/open.library.ubc.ca\/terms#rightsURI","explain":"UBC Open Collections Metadata Components; Local Field; Indicates the Creative Commons license url."}],"ScholarlyLevel":[{"label":"Scholarly Level","value":"Graduate","attrs":{"lang":"en","ns":"https:\/\/open.library.ubc.ca\/terms#scholarLevel","classmap":"oc:PublicationDescription","property":"oc:scholarLevel"},"iri":"https:\/\/open.library.ubc.ca\/terms#scholarLevel","explain":"UBC Open Collections Metadata Components; Local Field; Identifies the scholarly level of the author(s)\/creator(s)."}],"Supervisor":[{"label":"Supervisor","value":"Henderson, Sarah B.","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/contributor","classmap":"vivo:AdvisingRelationship","property":"dcterms:contributor"},"iri":"http:\/\/purl.org\/dc\/terms\/contributor","explain":"A Dublin Core Terms Property; An entity responsible for making contributions to the resource.; Examples of a Contributor include a person, an organization, or a service."},{"label":"Supervisor","value":"Weinberger, Kate","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/contributor","classmap":"vivo:AdvisingRelationship","property":"dcterms:contributor"},"iri":"http:\/\/purl.org\/dc\/terms\/contributor","explain":"A Dublin Core Terms Property; An entity responsible for making contributions to the resource.; Examples of a Contributor include a person, an organization, or a service."}],"Title":[{"label":"Title ","value":"In utero exposure to wildfire smoke and critical time windows for respiratory outcomes in the first year of life","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/title","classmap":"dpla:SourceResource","property":"dcterms:title"},"iri":"http:\/\/purl.org\/dc\/terms\/title","explain":"A Dublin Core Terms Property; The name given to the resource."}],"Type":[{"label":"Type","value":"Text","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/type","classmap":"dpla:SourceResource","property":"dcterms:type"},"iri":"http:\/\/purl.org\/dc\/terms\/type","explain":"A Dublin Core Terms Property; The nature or genre of the resource.; Recommended best practice is to use a controlled vocabulary such as the DCMI Type Vocabulary [DCMITYPE]. To describe the file format, physical medium, or dimensions of the resource, use the Format element."}],"URI":[{"label":"URI","value":"http:\/\/hdl.handle.net\/2429\/82713","attrs":{"lang":"en","ns":"https:\/\/open.library.ubc.ca\/terms#identifierURI","classmap":"oc:PublicationDescription","property":"oc:identifierURI"},"iri":"https:\/\/open.library.ubc.ca\/terms#identifierURI","explain":"UBC Open Collections Metadata Components; Local Field; Indicates the handle for item record."}],"SortDate":[{"label":"Sort Date","value":"2022-12-31 AD","attrs":{"lang":"en","ns":"http:\/\/purl.org\/dc\/terms\/date","classmap":"oc:InternalResource","property":"dcterms:date"},"iri":"http:\/\/purl.org\/dc\/terms\/date","explain":"A Dublin Core Elements Property; A point or period of time associated with an event in the lifecycle of the resource.; Date may be used to express temporal information at any level of granularity. Recommended best practice is to use an encoding scheme, such as the W3CDTF profile of ISO 8601 [W3CDTF].; A point or period of time associated with an event in the lifecycle of the resource.; Date may be used to express temporal information at any level of granularity. Recommended best practice is to use an encoding scheme, such as the W3CDTF profile of ISO 8601 [W3CDTF]."}]}