Forestry, Faculty of
DSpace
UBCV
Jarvis, Ingrid
Doctor of Philosophy - PhD
University of British Columbia
Growing research indicates that natural environments – including green and blue spaces – are beneficial for human health. However, evidence gaps remain concerning the health associations with different types of natural environments and forms of human-nature contact, pathways underlying nature-health associations, and health associations across different life stages. To address these gaps, this dissertation examined the relationship between natural environments and human health among residents of Metro Vancouver, Canada. First, a method for estimating and comparing different forms of human-nature contact was developed, specifically access (i.e., living within proximity of a public green space) and exposure (i.e., quantity of natural and non-natural features surrounding residence). Next, these exposure metrics were used to analyze the association between access and exposure to natural environments and self-reported general health, mental health, and common mental disorders among respondents of the Canadian Community Health Survey (CCHS). The exposure metrics were likewise utilized in analyses investigating the association between exposure to natural environments and early childhood development, as measured by teacher-ratings on the Early Development Instrument (EDI). Finally, underlying pathways of nature-health associations were analyzed by evaluating potential mediation by reductions in traffic-related air pollution and noise. In general, natural environments were positively associated with human health. Nature-health associations varied according to land cover type. Specifically, water and some vegetation types were associated with lower odds of poor self-reported health among respondents of the CCHS, while exposure to tree and grass cover was positively associated with children’s EDI scores. Conversely, exposure to paved surfaces was adversely associated with self-reported health and children’s early development. Furthermore, nature-health associations varied according to form of nature contact, with more consistent associations observed for exposure than access. Finally, positive associations between green space exposure and early childhood development were partially explained by reductions in traffic-related air pollution and noise levels. This dissertation provides new empirical evidence of the benefits of natural environments on human health. Results support urban planning and policy frameworks that increase the availability of natural environments to contribute to optimal population health across the life course.
Understanding the human health benefits of urban green space across the life course by integrating epidemiological and novel geospatial approaches by Ingrid Jarvis B.A., The University of British Columbia, 2012 Hon. B.Sc., The University of British Columbia, 2016 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in The Faculty of Graduate and Postdoctoral Studies (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) July 2022 © Ingrid Jarvis, 2022 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Understanding the human health benefits of urban green space across the life course by integrating epidemiological and novel geospatial approaches submitted by Ingrid Jarvis in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Forestry Examining Committee: Matilda van den Bosch, Adjunct Professor, Department of Forest and Conservation Sciences, UBC Co-supervisor Sarah E. Gergel, Professor, Department of Forest and Conservation Sciences, UBC Co-supervisor Mieke Koehoorn, Professor, School of Population and Public Health, UBC Supervisory Committee Member Mariana Brussoni, Associate Professor, School of Population and Public Health, UBC University Examiner Martino Tran, Associate Professor, School of Community and Regional Planning, UBC University Examiner Additional Supervisory Committee Members: Hind Sbihi, Adjunct Professor, School of Population and Public Health, UBC Supervisory Committee Member iii Abstract Growing research indicates that natural environments – including green and blue spaces – are beneficial for human health. However, evidence gaps remain concerning the health associations with different types of natural environments and forms of human-nature contact, pathways underlying nature-health associations, and health associations across different life stages. To address these gaps, this dissertation examined the relationship between natural environments and human health among residents of Metro Vancouver, Canada. First, a method for estimating and comparing different forms of human-nature contact was developed, specifically access (i.e., living within proximity of a public green space) and exposure (i.e., quantity of natural and non-natural features surrounding residence). Next, these exposure metrics were used to analyze the association between access and exposure to natural environments and self-reported general health, mental health, and common mental disorders among respondents of the Canadian Community Health Survey (CCHS). The exposure metrics were likewise utilized in analyses investigating the association between exposure to natural environments and early childhood development, as measured by teacher-ratings on the Early Development Instrument (EDI). Finally, underlying pathways of nature-health associations were analyzed by evaluating potential mediation by reductions in traffic-related air pollution and noise. In general, natural environments were positively associated with human health. Nature-health associations varied according to land cover type. Specifically, water and some vegetation types were associated with lower odds of poor self-reported health among respondents of the CCHS, iv while exposure to tree and grass cover was positively associated with children’s EDI scores. Conversely, exposure to paved surfaces was adversely associated with self-reported health and children’s early development. Furthermore, nature-health associations varied according to form of nature contact, with more consistent associations observed for exposure than access. Finally, positive associations between green space exposure and early childhood development were partially explained by reductions in traffic-related air pollution and noise levels. This dissertation provides new empirical evidence of the benefits of natural environments on human health. Results support urban planning and policy frameworks that increase the availability of natural environments to contribute to optimal population health across the life course. v Lay Summary Natural environments improve the health of urban residents. However, we do not know which types of nature (e.g., trees, grass, water) and forms of nature contact (e.g., neighborhood vegetation, nearby parks) are most beneficial for health, nor do we understand how natural environments are linked to health. This dissertation explores the relationship between natural environments and human health among residents of Metro Vancouver, Canada. This research found that people living in neighborhoods with more vegetation and water had better self-reported health and early childhood development. Conversely, those living in neighborhoods with more pavement had poorer health outcomes. Individuals living within walking distance to a park did not report better health than those living in an area without accessible parks. Neighborhood-level vegetation may support good health by reducing air pollution and noise levels. This research supports planning and policy efforts to increase the availability of urban nature for population health benefits. vi Preface The body of this dissertation consists of four papers written for peer-review for which I am the lead author. The scope of this dissertation was developed through discussions with my supervisory committee, Dr. Matilda van den Bosch, Dr. Sarah E. Gergel, Dr. Mieke Koehoorn, and Dr. Hind Sbihi. Additional research contributions are described below. In Chapter 1, Dr. Matilda van den Bosch, Dr. Sarah E. Gergel, and Dr. Mieke Koehoorn helped me to define the research questions, objectives, and methodology. I was primarily responsible for refining research objectives, performing all data preparation, analyzing the data, and interpretating and presenting the results. Dr. Matilda van den Bosch and I drafted the original manuscript. All authors provided critical revisions to the manuscript. In Chapter 2, Dr. Matilda van den Bosch, Dr. Sarah E. Gergel, and Dr. Mieke Koehoorn helped me to define the research questions, objectives, and methodology. I was primarily responsible for refining research objectives, performing all data preparation, analyzing the data, interpretating and presenting the results, and preparing the original manuscript. All authors provided critical revisions to the manuscript. Data access to the 2013-2014 Canadian Community Health Survey was granted via Statistics Canada’s Research Data Centres (RDC) program, and assistance preparing and vetting the analytic output was provided by RDC analysts Cheryl Fu and Wendy Kei. Although the research and analysis are based on data from Statistics Canada, the opinions expressed in this chapter do not represent the views of Statistics Canada. The work presented in Chapters 4 and 5 were a part of the ‘Born to be Wise’ project. Dr. Matilda van den Bosch conceived of the original research idea, acquired funding, and was responsible for vii project administration. Dr. Matilda van den Bosch, Dr. Hind Sbihi, Dr. Michael Brauer, Dr. Hugh W. Davies, Dr. Martin Guhn, Dr. Michael Jerrett, Dr. Lorien Nesbitt, Dr. Jason Su, and Dr. Tim F. Oberlander contributed to the study design. I prepared and cleaned the data with the help of Zoë Davis and Dr. Hind Sbihi. Dr. Michael Brauer, Dr. Michael Jerrett, Dr. Hugh W. Davies, Dr. Jason Su, and Agatha Czekajlo contributed to the acquisition and application of the environmental exposure data. I was primarily responsible for the data analysis, interpretation and presentation of the results, and preparation of the original manuscripts. All authors provided critical revisions to the manuscripts. Data were made accessible via Population Data British Columbia. Access to data provided by the Data Stewards is subject to approval, but can be requested for research projects through the Data Stewards or their designated service providers. All inferences, opinions, and conclusions drawn in these chapters are those of the authors, and do not reflect the opinions or policies of the Data Stewards. Text and information in this dissertation were derived from the sources listed below. Some content has been moved among chapters to support readability. Chapter 2: Jarvis, I., Gergel, S.E., Koehoorn, M., & van den Bosch, M. (2020). Greenspace access does not correspond to nature exposure: Measures of urban natural space with implications for health research. Landscape and Urban Planning, 194, 103686. https://doi.org/10.1016/j.landurbplan.2019.103686 Chapter 3: Jarvis, I., Koehoorn, M., Gergel, S.E., & van den Bosch, M. (2020). Different types of urban natural environments influence various dimensions of self-reported health. Environmental Research, 186, 109614. https://doi.org/10.1016/j.envres.2020.109614. viii Chapter 4: Jarvis, I., Sbihi, H., Davis, Z., Brauer, M., Czekajlo, A., Davies, H.W., Gergel, S.E., Guhn, M., Jerrett, M., Koehoorn, M., Nesbitt, L., Oberlander, T.F., Su, J., & van den Bosch, M. (2022). The influence of early-life residential exposure to different vegetation types and paved surfaces on early childhood development: a population-based birth cohort study. Environment International, 163, 107196. https://doi.org/10.1016/j.envint.2022.107196 Chapter 5: Jarvis, I., Davis, Z., Sbihi, H., Brauer, M., Czekajlo, A., Davies, H.W., Gergel, S.E., Guhn, M., Jerrett, M., Koehoorn, M., Oberlander, T.F., Su, J., & van den Bosch, M. (2021). Assessing the association between lifetime exposure to greenspace and early childhood development and the mediation effects of air pollution and noise in Canada: a population-based birth cohort study. The Lancet Planetary Health, 5, e709-e717. https://doi.org/10.1016/S2542-5196(21)00235-7 No ethics approval was required or sought for Chapter 2. Ethics approvals for Chapter 3 (certificate H17-01687), Chapter 4 (certificate H18–00908), and Chapter 5 (certificate H18–00908) were obtained from the University of British Columbia’s Behavioral Research Ethics Board. ix Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ......................................................................................................................... ix List of Tables ............................................................................................................................. xvii List of Figures ............................................................................................................................. xix List of Abbreviations ...................................................................................................................xx Acknowledgements ................................................................................................................... xxii Dedication ................................................................................................................................. xxiv Introduction ................................................................................................................1 1.1 Urban living and human health ....................................................................................... 1 1.2 The role of natural environments in supporting human health ....................................... 4 1.3 Theories and pathways underlying nature-health associations ....................................... 7 1.3.1 Restoring capacities ................................................................................................ 8 1.3.2 Building capacities ................................................................................................ 11 1.3.3 Reducing harm ...................................................................................................... 14 1.4 Dissertation rationale and objectives ............................................................................ 16 x 1.4.1 Limitations of current knowledge ......................................................................... 16 1.4.2 Research objectives ............................................................................................... 19 1.5 Dissertation overview ................................................................................................... 21 1.6 Dissertation setting and common methodologies ......................................................... 23 1.6.1 Study area .............................................................................................................. 23 1.6.2 Geographic unit of analysis .................................................................................. 24 1.6.3 Data ....................................................................................................................... 25 1.6.3.1 Land cover data ................................................................................................. 25 1.6.3.2 Land use classification data .............................................................................. 27 1.6.3.3 Spectrally unmixed vegetation data .................................................................. 28 1.6.3.4 Normalized difference vegetation index data ................................................... 30 1.6.3.5 Sociodemographic data ..................................................................................... 31 1.6.3.6 Early Development Instrument ......................................................................... 32 Green space access does not correspond to nature exposure – measures of urban natural environments with implications for health research .......................................34 2.1 Introduction ................................................................................................................... 34 2.2 Materials and Methods .................................................................................................. 37 2.2.1 Access and exposure data ..................................................................................... 38 xi 2.2.1.1 Land cover information ..................................................................................... 38 2.2.1.2 Access ............................................................................................................... 38 2.2.1.3 Exposure ........................................................................................................... 39 2.2.2 Urban-rural gradient data ...................................................................................... 40 2.2.3 Marginalization data ............................................................................................. 42 2.2.4 Statistical analysis ................................................................................................. 43 2.3 Results ........................................................................................................................... 43 2.3.1 Access to public green space and residential exposure to land cover ................... 43 2.3.2 Urban-rural gradient .............................................................................................. 46 2.3.3 Marginalization ..................................................................................................... 48 2.4 Discussion ..................................................................................................................... 50 2.4.1 Access or exposure? A question concerning pathways to good health ................ 51 2.4.2 Exposure, but not access, varies along an urban-rural gradient ............................ 52 2.4.3 Access and exposure are associated with marginalization ................................... 52 2.5 Conclusions ................................................................................................................... 53 Different types of urban natural environments influence various dimensions of self-reported health ......................................................................................................................55 3.1 Introduction ................................................................................................................... 55 xii 3.2 Materials and Methods .................................................................................................. 57 3.2.1 Study sample ......................................................................................................... 57 3.2.2 Assessment of self-reported health ....................................................................... 58 3.2.3 Potential confounders ............................................................................................ 59 3.2.4 Natural environments ............................................................................................ 60 3.2.4.1 Residential exposure to natural and non-natural environments ........................ 60 3.2.4.2 Access to public green space ............................................................................ 61 3.2.5 Statistical analysis ................................................................................................. 62 3.3 Results ........................................................................................................................... 64 3.3.1 Characteristics of the study sample ....................................................................... 64 3.3.2 Self-reported health outcomes in association with natural environment metrics . 66 3.3.3 Analyses stratified by gender ................................................................................ 68 3.3.4 Sensitivity analyses ............................................................................................... 69 3.4 Discussion ..................................................................................................................... 72 3.4.1 Natural environments should be considered individually, rather than as aggregated ‘green’ space ......................................................................................................................... 72 3.4.2 Nature exposure may be more consistently associated with human health outcomes than is access ........................................................................................................ 75 3.4.3 Benefits of human-nature contact may differ between genders ............................ 76 xiii 3.5 Conclusions ................................................................................................................... 77 Various land cover types influence childhood development differently – the relative advantages of vegetation and disadvantages of paved land .......................................78 4.1 Introduction ................................................................................................................... 78 4.2 Materials and Methods .................................................................................................. 79 4.2.1 Study sample ......................................................................................................... 79 4.2.2 Assessment of early childhood development ........................................................ 80 4.2.3 Potential confounders ............................................................................................ 80 4.2.4 Residential exposure to natural and non-natural environments ............................ 82 4.2.5 Statistical analysis ................................................................................................. 83 4.3 Results ........................................................................................................................... 85 4.3.1 Characteristics of the study sample ....................................................................... 85 4.3.2 Childhood development in association with residential environmental exposure 88 4.3.3 Sensitivity analyses ............................................................................................... 89 4.4 Discussion ..................................................................................................................... 90 4.4.1 Lifetime residential exposure to green space may improve childhood development and reduce risk of developmental vulnerability .................................................................... 90 4.4.2 Associations vary depending on vegetation type .................................................. 92 xiv 4.4.3 Lifetime residential exposure to paved surfaces may impair childhood development and increase risk of developmental vulnerability ............................................ 94 4.5 Conclusions ................................................................................................................... 94 The association between lifetime exposure to green space and early childhood development – is the relation mediated by reduction in air pollution and noise? .................96 5.1 Introduction ................................................................................................................... 96 5.2 Materials and Methods .................................................................................................. 97 5.2.1 Study sample ......................................................................................................... 97 5.2.2 Assessment of early childhood development ........................................................ 97 5.2.3 Potential confounders ............................................................................................ 98 5.2.4 Residential environmental exposures ................................................................... 99 5.2.4.1 Green space ....................................................................................................... 99 5.2.4.2 Air pollution .................................................................................................... 100 5.2.4.3 Noise ............................................................................................................... 100 5.2.5 Statistical analysis ............................................................................................... 101 5.3 Results ......................................................................................................................... 103 5.3.1 Characteristics of the study sample ..................................................................... 103 5.3.2 Childhood development in association with residential green space exposure .. 105 5.3.3 The mediation effects of air pollution and noise ................................................ 105 xv 5.3.4 Sensitivity analyses ............................................................................................. 106 5.4 Discussion ................................................................................................................... 107 5.4.1 Lifetime residential exposure to green space may improve overall childhood development ........................................................................................................................ 107 5.4.2 Nature-health associations are partially due to a reduction in air pollution and noise exposure ..................................................................................................................... 109 5.5 Conclusions ................................................................................................................. 111 Conclusion ...............................................................................................................112 6.1 Research summary and contributions ......................................................................... 112 6.2 Key findings ................................................................................................................ 115 6.2.1 Natural environments are beneficial for human health ....................................... 115 6.2.2 Nature-health associations may vary according to natural environment type .... 116 6.2.3 Nature-health associations may vary according to the type of metric used to estimate human contact with nature .................................................................................... 119 6.2.4 Nature-health associations are, in part, explained by reduced exposure to air pollution and noise .............................................................................................................. 120 6.3 Limitations and recommendations for future research ............................................... 122 6.3.1 Study design and methodology ........................................................................... 122 6.3.2 Geographic unit of analysis ................................................................................ 124 xvi 6.3.3 Access and exposure metric ................................................................................ 125 6.3.4 Study generalizability ......................................................................................... 127 6.4 Policy implications ...................................................................................................... 128 6.5 Conclusions ................................................................................................................. 132 References ...................................................................................................................................136 Appendices ..................................................................................................................................211 Appendix A - Supplementary materials for Chapter 2 ........................................................... 211 Appendix B - Supplementary materials for Chapter 3 ............................................................ 213 Appendix C - Supplementary materials for Chapter 4 ............................................................ 218 Appendix D - Supplementary materials for Chapter 5 ........................................................... 226 xvii List of Tables Table 1-1. Land cover types and classification criteria. Table modified from Williams et al. (2018). ........................................................................................................................................... 27 Table 2-1. Mean percentage of the different land cover types within 100-, 250-, 500-, and 1,000 m buffers of six-digit postal code centroids (n=60,782 postal codes). ......................................... 44 Table 2-2. Percentage of postal codes in Metro Vancouver by measures of marginalization (n=57,064). .................................................................................................................................... 48 Table 2-3. Point biserial (rpb) and Pearson correlation coefficients (r) between dimensions of marginalization and access to public green space and exposure to various land cover types (n=57,064 postal codes). ............................................................................................................... 50 Table 3-1. Characteristics of Metro Vancouver respondents to the 2013-2014 CCHS (weighted n=1,960,575). ................................................................................................................................ 65 Table 3-2. Weighted mean percentage of the different land cover types within 250-, 500-, and 1,000 m buffers of six-digit postal code centroids among Metro Vancouver respondents to the 2013-2014 CCHS (weighted n=1,960,575). ................................................................................. 66 Table 3-3. Adjusted associations between access and exposure metrics and self-reported general health, mental health, and common mental disorder among Metro Vancouver respondents to the 2013-2014 CCHS (weighted n=1,960,575). ................................................................................. 68 xviii Table 3-4. Gender-stratified adjusted associations between access and exposure metrics and self-reported general health, mental health, and common mental disorder among Metro Vancouver respondents to the 2013-2014 CCHS (weighted n=1,960,575). ................................................... 70 Table 4-1. Characteristics of cohort of children to examine the association between lifetime residential exposure to distinct land cover types and early childhood development in Metro Vancouver (n=27,539). ................................................................................................................. 87 Table 4-2. Unadjusted and adjusted associations between lifetime residential exposure to distinct land cover types and early childhood development among Metro Vancouver children (n=27,539)........................................................................................................................................................ 89 Table 5-1. Characteristics of cohort of children to examine the association between lifetime residential exposure to green space and early childhood development in Metro Vancouver (n=27,372). .................................................................................................................................. 104 Table 5-2. Adjusted associations and explained percentage of association between lifetime residential exposure to greenspace and total EDI score mediated by NO2, PM2.5 and noise (n=27,372). .................................................................................................................................. 106 xix List of Figures Figure 1-1. Conceptual diagram of the relation between natural environments and health across the life course. ............................................................................................................................... 22 Figure 1-2. Metro Vancouver and boundaries of constituent municipalities. ............................... 24 Figure 1-3. Land cover map of Metro Vancouver. ....................................................................... 26 Figure 1-4. Public green spaces in Metro Vancouver. .................................................................. 28 Figure 1-5. Vegetation in Metro Vancouver. ................................................................................ 29 Figure 1-6. Normalized difference vegetation index in Metro Vancouver. .................................. 31 Figure 2-1. Access and exposure metrics. .................................................................................... 40 Figure 2-2. Urban-rural gradient. .................................................................................................. 42 Figure 2-3. Correlations between access to public green space and exposure to various land cover types. ............................................................................................................................................. 46 Figure 2-4. Urban-rural gradient of mean access to public green space and mean residential exposure to different land cover types. ......................................................................................... 47 xx List of Abbreviations ADHD Attention-deficit/hyperactivity disorder ART Attention restoration theory β coefficient Beta coefficient (i.e., estimate from regression analysis) BC British Columbia CAN-Marg Canadian Marginalization Index CCHS Canadian Community Health Survey CI Confidence interval CMD Common mental disorder DA Dissemination area dB(A) A-weighted decibels DOHaD Developmental origins of health and disease EDI Early Development Instrument GPS Geographical positioning system IQR Interquartile range km2 Square kilometer Lden Day-evening-night noise levels LUR Land use regression m Meter MSP Medical Services Plan NBS Nature-based solutions xxi NCD(s) Non-communicable disease(s) NDVI Normalized difference vegetation index NO2 Nitrogen dioxide OR Odds ratio PM2.5 Fine particulate matter ppb Parts per billion r Pearson correlation coefficient rpb Point biserial correlation coefficient SES Socioeconomic status SRT Stress reduction theory Tb Mann-Kendall rank correlation coefficient μg/m³ Micrograms per cubic meter xxii Acknowledgements I completed this dissertation on UBC’s Point Grey campus, situated on the traditional, ancestral, and unceded territory of the xʷməθkʷəy̓əm (Musqueam) people. I thank those who still live on and care for the land and waters which are central to this research. The work presented in this dissertation was not possible without the support of many individuals. I am aware of and grateful for the privilege I have experienced in pursuing my doctoral degree and am thankful for the guidance I have received throughout the process. First, I would like to express my heartfelt gratitude to my supervisor and mentor, Dr. Matilda van den Bosch. Her commitment to research excellence, passion for inquiry into diverse topics, and compassionate leadership style have been inspirational. Her support was instrumental to this research and my personal growth. I am also very thankful to Dr. Sarah E. Gergel for stepping in as my co-supervisor and welcoming me into the Landscape Ecology Lab partway through my degree. It has been rewarding to work alongside her on this research and on equity, diversity, and inclusion initiatives within the Faculty of Forestry. A special thank you to my committee members, Dr. Mieke Koehoorn and Dr. Hind Sbihi, for their thoughtful feedback, pragmatic insights, and analytical support that helped guide this research. I am grateful for my support network at UBC for cultivating a positive and encouraging environment on campus. A special thank you to past and current office mates, lab mates, collaborators on the Born to be Wise project, members of the Urban Forestry Research Hub, and peers in the Forestry Diversity Crew. Thank you to my friends and family off campus for providing companionship and support, especially Katie, Brynn, and Mandy. xxiii I would like to acknowledge and thank my parents, Esther and Ian Jarvis, for their love, encouragement, and understanding throughout my degree, and always. I could not have accomplished this work without their support. Finally, a very special thank you to my partner, Luke Ankenman. He continued to believe in my potential and provide me with incredible love, support, strength, and patience during even the lowest points of my degree, and for that I will be eternally grateful. This research was supported by the Natural Sciences and Engineering Research Council of Canada (grant reference number 538545) and the Canadian Institutes of Health Research (grant reference numbers 156152 and 165754). xxiv Dedication In loving memory of my grandfather, Ivan Holmgren (1928 – 2021). Thank you for sharing your love of nature and learning with me. I did it! 1 Introduction 1.1 Urban living and human health In recent decades the rate of urbanization has increased dramatically around the globe. Over 55% of the world’s population currently lives in urban areas (United Nations 2018a). In Canada, more than 80% of the population resides in urban environments and it is predicted that this proportion will increase to 87% by the year 2050 (Statistics Canada 2018; United Nations 2018b). This shift from rural to urban living environments affects individuals’ health in many ways. According to the World Health Organization, “health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity” (World Health Organization 2020a: 1). While urban living provides opportunities to support good health, including access to employment, sanitation, nutrition, and healthcare services, urban areas are likewise associated with risk factors of ill health, including increased harmful environmental exposures (e.g., air pollution and noise), crowded and stressful living environments, reduced opportunities for physical activity, increased social disparities, and disconnection from nature (Mouratidis 2021; McCormack et al. 2019; Krefis, Augustin, Heinke Schlünzen, Oßenbrügge & Augustin 2018; Rao, Prasad, Adshead & Tissera 2007). These preceding risk factors, in combination with climate change and increasingly demanding modern lifestyles, create a serious threat for global health. The negative effects of urbanization have been well-documented in relation to non-communicable diseases (NCDs), such as cancer, cardiovascular diseases, chronic respiratory diseases, diabetes, and mental illness. For instance, the prevalence of psychiatric disorders, 2 including mood and anxiety disorders, is significantly higher in cities compared to rural areas (Lederbogen et al. 2011; Peen, Schoevers, Beekman & Dekker 2010). NCDs are the leading cause of global disease burden and mortality in the world, and are expected to increase in prevalence (Vos et al. 2020). In Canada, approximately 30% of adults report having at least one NCD (CCDI Steering Committee 2017). Fortunately, most NCDs can be prevented by reducing common risk factors such as physical inactivity and prolonged stress (World Health Organization 2021). Urbanization also presents unique health challenges for children, including increasing indoor screen time, sedentary behaviors, less outdoor play, and reduced interactions with nature (Radesky & Christakis 2016; Louv 2007). While the full implications of these lifestyle changes on children’s health and development are not adequately understood, preliminary findings suggest that excessive indoor screen time in early childhood may contribute to deficits in cognitive (Lin, Cherng, Chen, Chen & Yang 2015; Tomopoulos et al. 2010), language (Duch et al. 2013; Zimmerman, Christakis & Meltzoff 2007), social (Conners-Burrow, McKelvey & Fussell 2011), and emotional development (Hinkley et al. 2014; Tomopoulos et al. 2007). Concern over the increasing burden of ill-health associated with urbanization has highlighted the need to create healthy cities. In response, research has been conducted across disciplines, including natural sciences, urban planning, and epidemiology, to better understand the features of the urban environment that promote, rather than degrade, population health (Mouratidis 2021; Brook, Setton, Seed, Shooshtari & Doiron 2018; Krefis et al. 2018). Knowledge from these interdisciplinary research efforts is integral to informing urban planners and policy makers on 3 how to improve population health in urban contexts through progressive management and targeted planning strategies. 4 1.2 The role of natural environments in supporting human health Urban natural environments are increasingly recognized for their potential as health promoting settings, and there is growing evidence of and interest in endorsing natural environments for creating healthy, sustainable, and livable cities (World Health Organization 2016; European Commission 2015). Urban natural environments comprise both green spaces (e.g., forests, street trees, gardens, and agricultural crops) and blue spaces (e.g., ponds, lakes, rivers, and coastal waters). Considering urban natural environments as a tool to support improved population health is in alignment with the concept of nature-based solutions (NBS) (van den Bosch & Ode Sang 2017). NBS are “actions which are inspired by, supported by, or copied from nature,” and that are designed to address a range of environmental challenges, while simultaneously providing numerous environmental and societal benefits (European Commission 2015). Urban natural environments are considered a feasible health promotion intervention since they are modifiable (i.e., we can alter the structure, quality, and availability of nature through how we manage existing and create new natural spaces). For example, ecosystem restoration, greening of grey areas (e.g., green roofs, green road boulevards), and increasing residential nature (e.g., street trees, community gardens) are types of NBS that might help promote health and well-being (van den Bosch & Ode Sang 2017). Further research into the health benefits of specific types of natural spaces and forms of nature contact is needed to better inform guidelines concerning how natural environments can be integrated into urban planning and management plans for health promotion. Studies supporting the use of natural environments for improved population health have increased in quantity and quality over the past decade. Recent reviews and meta-analyses have 5 found that natural environments are beneficial for various health outcomes, including reducing the incidence and symptoms of several NCDs (Browning, Rigolon, McAnirlin & Yoon 2022; Davis et al. 2021; Jimenez et al. 2021; Mygind et al. 2021; Nieuwenhuijsen 2021; Rojas-Rueda, Vaught & Buss 2021; Short et al. 2021; Smith et al. 2021; Yang, Zhao, et al. 2021). Natural environments have been observed to reduce the risk of several adverse health outcomes such as cardiovascular disease (Astell-Burt & Feng 2020; Seo, Choi, Kim, Kim & Park 2019), diabetes (Müller, Harhoff, Rahe & Berger 2018; Astell-Burt, Feng & Kolt 2014a), obesity (Almeida, Barreto, Souza, Cardoso & Giatti 2021; Huang, Yang, Yu, et al. 2020; Sarkar 2017), mental health complaints and disorders (White et al. 2021; Alcock, White, Wheeler, Fleming & Depledge 2014; Annerstedt et al. 2012), mortality (Barboza et al. 2021; Crouse et al. 2017), and biological aging (Xu et al. 2021b,a; Miri et al. 2020). Likewise, natural environments may improve physical health and well-being (Huang et al. 2019; Reid, Clougherty, Shmool & Kubzansky 2017) and life satisfaction (Wu, Yao, Song, He & Wang 2021). Though less abundant relative to research on adult populations, there is also evidence that supports positive associations between natural environments and multiple childhood health outcomes, including improved birth outcomes (Villeneuve et al. 2022; Akaraci, Feng, Suesse, Jalaludin & Astell-Burt 2021; Lin et al. 2020; Fong, Kloog, et al. 2018), reduced emotional and behavioral problems (Liao et al. 2020; Madzia et al. 2019; Markevych et al. 2014), improved cognition (Dadvand et al. 2015, 2017), higher academic performance (Kuo, Klein, Browning & Zaplatosch 2021; Sivarajah, Smith & Thomas 2018), and better overall well-being (Tillmann, Clark & Gilliland 2018). Given research on associations between natural environments and human health is rapidly 6 developing (Zhang, Yu, Zhao, Sun & Vejre 2020), the list of the potential health benefits associated with nature exposure will likely grow with further knowledge. The current literature suggests health benefits of natural environments, but there is a need to further develop knowledge and refine research methodologies to gain a better understanding of the evidence level. The following section will summarize the theories and pathways theorized as underlying the observed associations between natural environments and human health to-date. Next, specific evidence gaps to be addressed in the present dissertation will be outlined. 7 1.3 Theories and pathways underlying nature-health associations Natural environments provide urban societies with a wide range of benefits through ecosystem services, which include provisioning (e.g., water and food), supporting (e.g., nutrient cycling, soil formation), cultural (e.g., recreational, aesthetic, spiritual benefits), and regulating (e.g., climate, air quality) services (Millennium Ecosystem Assessment 2005). An ecosystem services perspective may help to explain the ways in which natural environments positively contribute to human health. Several pathways have been proposed to link natural environments to health outcomes, and various papers have offered useful conceptual frameworks of identifying and organizing these pathways (Markevych et al. 2017; Hartig, Mitchell, De Vries & Frumkin 2014); some using an ecosystem system services perspective (Marselle et al. 2021; Bratman et al. 2019; van den Bosch & Ode Sang 2017). Each conceptual framework has a unique theoretical and practical foundation, but they complement each other and share a common aim of further understanding the mechanisms behind nature-health associations to better inform future research and policy interventions. This dissertation adopts the conceptual framework proposed by Markevych et al. (2017) to summarize existing literature and for theoretical purposes to build research hypotheses (see Figure 1-1 in Section 1.5). This conceptual framework organizes pathways into three broad categories: restoring capacities, building capacities, and reducing harm (Markevych et al. 2017). This dissertation only formally investigates the reducing harm pathway (see Section 1.4.2 for rationale), though literature related to restoring capacities and building capacities is included below to provide a summary of current hypothesized pathways underlying nature-health associations. Notably, these hypothesized pathways address several risk factors of NCDs (e.g., physical inactivity, increased stress). For simplicity, each pathway is described 8 individually, but it is important to keep in mind the complexity of nature-health associations and potential for interactions and feedbacks among pathways. The term ‘mediator’ is used below to denote an intervening variable (i.e., pathway) that underlies the observed relationship between natural environments and human health and developmental outcomes (i.e., natural environment influences the mediating variable, which, in turn influences health outcome) (VanderWeele 2016). 1.3.1 Restoring capacities The restoring capacities pathway refers to the recovery of mental capacities that may become diminished through the demands of daily life, which can lead to mental and physical ill health if not restored over time (von Lindern, Lymeus & Hartig 2017). For example, chronic stress is a known risk factor of multiple adverse health outcomes, including mortality, mental disorders, and poor birth outcomes (Ding et al. 2014; Gallo, Fortmann & Mattei 2014; Staufenbiel, Penninx, Spijker, Elzinga & van Rossum 2013). Childhood is a particularly sensitive period to mental stressors, and studies have shown that early-life stress exposure is associated with cognitive and emotional deficits in adulthood (Lupien, McEwen, Gunnar & Heim 2009). Research concerning the restorative capacity of natural environments is largely grounded in three theories: biophilia, stress reduction theory (SRT), and attention restoration theory (ART). The theory of biophilia suggests that people have an innate need to affiliate with and feel connected to the broader natural world, which is a product of, in part, humans evolving in natural landscapes (Wilson 1984). SRT (Ulrich et al. 1991; Ulrich 1983) and ART (Kaplan 1995; Kaplan & Kaplan 1989), two theories within environmental psychology, have built upon biophilia to study the restorative value of natural environments. SRT and ART specify a different 9 antecedent condition from which an individual may require recovery and outline aspects of nature exposure that support the process of restoration (Markevych et al. 2017). SRT maintains that natural environments support good health by facilitating recovery from physiological stress (Ulrich et al. 1991; Ulrich 1983). SRT proposes that exposure to environmental features elicit rapid, unconscious, affective responses (i.e., like/dislike) that initiate subsequent physiological states (Ulrich 1983). Building on evolutionary assumptions of biological prepared learning, SRT suggests that landscapes with natural features encourage more positive affective responses and, thereby, a more relaxed physiological state marked by lower stress levels (Ulrich et al. 1991). Studies guided by SRT, from both controlled laboratory and outdoor settings, have indicated that natural environments may provide more effective restoration from stress than built environments as indicated by, for example, lowered levels of stress hormones (Egorov et al. 2020; Gidlow, Jones, et al. 2016; Gidlow, Randall, Gillman, Smith & Jones 2016; Roe et al. 2013; Lee et al. 2011), blood pressure (Brown, Barton & Gladwell 2013; Li et al. 2011; Park, Tsunetsugu, Kasetani, Kagawa & Miyazaki 2010; Hartig, Evans, Jamner, Davis & Gärling 2003), and heart rate (Liu, Qu, Ma, Wang & Qu 2022; Brown et al. 2013; Lee et al. 2011; Park et al. 2010). Studies have also indicated that stress recovery among children may be associated with outdoor educational settings (Mygind, Stevenson, Liebst, Konvalinka & Bentsen 2018; Dettweiler, Becker, Auestad, Simon & Kirsch 2017; Roe & Aspinall 2011), nature-related activities (Sobko, Liang, Cheng & Tun 2020; Lee, Oh, Jang & Lee 2018), and residential surrounding nature (Van Aart et al. 2018; Wells & Evans 2003). ART, on the other hand, maintains that natural environments support good health by facilitating recovery from attentional fatigue (Kaplan 1995; Kaplan & Kaplan 1989). ART proposes that 10 natural settings can maintain an individual’s attention without effort, thereby allowing a fatigued person to rest and replenish the cognitive mechanisms required to voluntarily direct attention to tasks within their daily lives (Kaplan 1995; Kaplan & Kaplan 1989). According to ART, there are four qualities of natural environments that help to restore attentional fatigue: (i) being away from daily demands and obligations; (ii) a sense of extent or perceived coherent organization of the environment; (iii) effortless fascination with the environment; and (iv) compatibility of the environment with an individual’s interests (Kaplan 1995; Kaplan & Kaplan 1989). Support for attention restoration comes from research that has found evidence of better performance on cognitive tasks (Ohly et al. 2016; Hartig, Mang & Evans 1991; Hartig et al. 2003) and reductions in self-reported rumination and neural activity (Bratman, Hamilton, Hahn, Daily & Gross 2015) following exposure to a natural environment. Among children, studies have shown that contact with nature may improve cognitive performance (Amicone et al. 2018; Schutte, Torquati & Beattie 2017) and reduce symptoms of attention-deficit/hyperactivity disorder (ADHD) (Taylor & Kuo 2011; Taylor, Kuo & Sullivan 2001). The restorative potential of natural environments has been identified as an underlying pathway linking natural environments to health outcomes in the majority of cases in which the perceived environmental restorative quality and cognitive functioning have been considered as mediators (Dzhambov, Browning, Markevych, Hartig & Lercher 2020), particularly for studies relating to mental health and well-being (Dzhambov et al. 2019; Gulwadi, Mishchenko, Hallowell, Alves & Kennedy 2019; Dzhambov, Hartig, Markevych, Tilov & Dimitrova 2018; Dzhambov, Markevych, et al. 2018; Hipp, Gulwadi, Alves & Sequeira 2016; Carrus et al. 2015; Korpela, Borodulin, Neuvonen, Paronen & Tyrväinen 2014). 11 1.3.2 Building capacities The building capacities pathway refers to the strengthening of capabilities that support good health (Marselle et al. 2021). A commonly understood use of natural settings is for physical activity and recreation, including walking, running, cycling, and playing sports. Physical activity is a well-documented protective factor for good health (Abbafati et al. 2020), and natural settings may encourage both children (Molina-García, Menescardi, Estevan & Queralt 2022; Akpinar 2017) and adults (Akpinar 2019; Elliott, White, Taylor & Herbert 2015; Astell-Burt, Feng & Kolt 2014b) to engage in physical activity more often and/or more vigorously. There is also evidence to suggest that performing physical activity in natural environments produces greater psychological and physiological benefits than being physically active in other settings (Wang, Zhou, Zhang & Zhang 2021; Thompson Coon et al. 2011). Natural environments may also be relevant for supporting motor development (Kabisch, Alonso, Dadvand & van den Bosch 2019; Liao, Zhang, et al. 2019) and play (Morrissey, Scott & Rahimi 2017; Grigsby-Toussaint, Chi & Fiese 2011) among children, which are important for healthy social, emotional, and cognitive development (Brussoni et al. 2015; Leonard & Hill 2014; Milteer & Ginsburg 2012). A recent review indicates that physical activity is the most commonly tested mediator of nature-health associations, perhaps due to availability of data (Dzhambov et al. 2020). However, studies investigating whether health benefits of natural environments may be explained by increases in physical activity have mixed findings; with some studies observing evidence of indirect effects (McEachan, Prady & Smith 2016; de Vries, van Dillen, Groenewegen & Spreeuwenberg 2013; Richardson, Pearce, Mitchell & Kingham 2013; Sugiyama, Leslie, Giles-Corti & Owen 2008), while others have not (Dadvand et al. 2016; Triguero-Mas et al. 2015; Astell-Burt, Feng & Kolt 12 2013; Maas, Verheij, Spreeuwenberg & Groenewegen 2008). It may be that the sole presence of natural settings is not sufficient to encourage engagement in physical activity, but rather the type and quality of the space (e.g., safety, amenities). For example, prior research has shown that access to proximate and large public open natural spaces with preferred attributes, such as well-maintained pathways, were associated with higher levels of physical activity among adults compared to more distant, smaller, and less attractive settings (Giles-Corti et al. 2005). In addition to recreational values, natural environments may serve as important settings for contact with community, family, and friends. These interactions may improve the social cohesion of a neighborhood, which refers to the existence of positive relationships and the feeling of solidarity among members of a community (Kawachi & Berkman 2000). Social relationships within a community have been positively associated with health and well-being (Leigh-Hunt et al. 2017) and prior research has indicated that natural settings may encourage people to interact socially with others (Dadvand et al. 2016, 2019; Peters, Elands & Buijs 2010; Maas, Van Dillen, Verheij & Groenewegen 2009; Seeland, Dübendorfer & Hansmann 2008; Sugiyama et al. 2008). Among children, green space exposure has been associated with greater social contacts (Dadvand et al. 2019) and prosocial behavior (Putra, Astell-Burt, Cliff, Vella & Feng 2021), which positively influence childhood developmental trajectories (Flouri & Sarmadi 2016; Flynn, Ehrenreich, Beron & Underwood 2015). However, mixed findings have been reported from studies investigating social cohesion as a mediator of nature and health associations (Georgiou, Morison, Smith, Tieges & Chastin 2021; Zhang, Zhang & Rhodes 2021). Inconsistencies may be attributed to the difficulty in measuring social cohesion (Hartig et al. 2014). Furthermore, like physical activity, specific types of natural settings, such as public parks 13 and community gardens, may be more important for promoting social contact than the overall quantity of natural environments (Fan, Das & Chen 2011). Finally, there is growing interest in the role of natural environments in improving immune system functioning through increased exposure to microbial diversity in the environment. It has been suggested that many of today’s chronic diseases are inflammatory conditions resulting from microbial imbalance (i.e., dysbiosis in microbiota inhabiting, for example, the skin, mouth, and gut), or depletion of healthy microorganisms with which humans coevolved; commonly referred to as ‘old friends hypothesis’ or ‘biodiversity hypothesis’ (Rook 2013). The human microbiome plays an important role in immunoregulation, and the absence of healthy microorganisms can lead to immune dysfunction, inflammation, and adverse health outcomes among children and adults, including allergies, asthma, irritable bowel syndrome, diabetes, and anxiety and depression (Vatanen et al. 2016, 2018; Ruokolainen et al. 2017; Foster & McVey Neufeld 2013; Rook 2013; Hanski et al. 2012; Ege et al. 2011; Kondrashova et al. 2009; Radon et al. 2007). There is emerging evidence suggesting that environmental microbial exposure through, for example, contact with diverse plants or soil particles, is positively associated with skin and gut microbial diversity (Roslund et al. 2021; Parajuli et al. 2020; Pearson et al. 2020; Grönroos et al. 2019). Studies have further indicated that changes in skin and gut microbiota may mediate associations between natural environments and human health outcomes, including immune system functioning and stress (Roslund et al. 2020; Sobko et al. 2020; Ruokolainen et al. 2015; Hanski et al. 2012). 14 1.3.3 Reducing harm The reducing harm pathway refers to the influence of natural environments on health by reducing harm caused by environmental stressors. From an ecosystem services perspective, the capacity of natural environments to reduce harmful environmental stressors may be considered a regulating service, while the restoring and building capacities described above may be considered cultural services. Multiple environmental stressors, including air pollution and noise, are commonly increased in urban settings and are shown to have adverse effects on human health across the life course (Rojas-Rueda, Morales-Zamora, et al. 2021), including deteriorations in cardiovascular health (Requia et al. 2018; Vienneau, Schindler, Perez, Probst-Hensch & Röösli 2015; Brook et al. 2010), cognitive impairments (Sram, Veleminsky & Stejskalová 2017; Tallon, Manjourides, Pun, Salhi & Suh 2017), stress and depression (van den Bosch & Meyer-Lindenberg 2019; Sram et al. 2017; Waye et al. 2002), and impaired birth outcomes (Arroyo et al. 2016; Selander et al. 2016; Dadvand et al. 2013; Brauer et al. 2008). Prior research suggests that air pollution and noise exposure levels are lower in natural environments. Exposure levels of air pollution and noise are probably lower in areas with greater vegetation, in part, because of the absence of air pollution-emitting and noise-emitting sources in these areas (e.g., less vehicle traffic). Vegetation may also directly affect air pollution and noise levels through, for instance, particle deposition, modification, and dispersion (e.g., act as physical barrier or filter) (Diener & Mudu 2021; Van Renterghem et al. 2015). While some research suggests that positive health effects of natural environments are, in part, due to reduced exposure to traffic-related air pollution (Huang, Yang, Yu, et al. 2020; Wang et al. 2020; Liao, Chen, et al. 2019; Liao, Zhang, et al. 2019; Gascon et al. 2018; Shen & Lung 2016, 2017; Thiering et al. 2016), fewer studies have analyzed similar 15 underlying pathways related to reduced exposure to noise (Dzhambov, Markevych, et al. 2018; Gascon et al. 2018; Van Aart et al. 2018). Overall, the evidence base for this pathway remains small and very few studies have assessed the mediation effects of multiple harmful exposures (Klompmaker et al. 2019; Dzhambov, Hartig, et al. 2018; Van Aart et al. 2018). Furthermore, the vast majority of studies are among adult populations and little is known of the potential mediation effects of reduced harmful exposures for linkages between natural environments and childhood health (Dzhambov et al. 2020). Continued global urbanization poses a challenge in addressing health risks related to urban-related stressors and understanding if natural environments may contribute to improved health through reducing levels of these harmful exposures is urgently required for informing urban planning and management strategies. Understanding this pathway may be of heightened importance among children, particularly in consideration of a life course approach, as they are considered particularly vulnerable to the adverse health effects of harmful environmental exposures due to potential interference with sensitive developing systems (Rice & Barone 2000). 16 1.4 Dissertation rationale and objectives 1.4.1 Limitations of current knowledge Although a large and growing body of research has been conducted to understand nature-health relations (Zhang et al. 2020), several recent reviews indicate that findings are inconsistent, study quality is heterogeneous, and overall evidence is limited (Browning et al. 2022; Davis et al. 2021; Jimenez et al. 2021; Mygind et al. 2021; Rojas-Rueda, Vaught, et al. 2021; Smith et al. 2021; Yang, Zhao, et al. 2021; Houlden, Weich, Porto de Albuquerque, Jarvis & Rees 2018; Kondo, Fluehr, Mckeon & Branas 2018). Existing research differs substantially in terms of natural environment assessment, outcome assessment, study design, and study population composition. Moreover, most studies have been conducted in Europe and the United States and, while growing, there is much less research in Canadian populations. This lack of consistency and geographical representation across studies supports a call for further investigation. In particular, further research is required to address important gaps in current knowledge surrounding the characteristics and services of natural settings that influence human health. Addressing these knowledge gaps will be important for advancing the science surrounding nature-health associations and providing an important foundation for future research, as well as contributing to clear recommendations for policy and practice. While evidence in this research field is expanding, there is limited understanding of the relative importance of the structure of natural environments, including how these environments are conceptualized and measured. Existing research has focused primarily on the association between health outcomes and overall quantity of available “green” space, utilizing methods that dichotomize land cover as green/not green (Holland et al. 2021; Labib, Lindley & Huck 2020; 17 Ekkel & de Vries 2017). There is a need to expand upon this research and explore the potential variation in effect depending on the type of natural environments, including, for example, vegetation types. Different vegetation types may vary in their capacity to support different pathways linking natural environments to health outcomes. For instance, trees may provide greater reduction of air pollution and noise levels than do grassy areas, whereas open grass space may encourage greater outdoor social and physical recreational activities than forested areas. It is equally necessary to develop an understanding of the effects of blue spaces on human health. Relatively limited research has differentiated the health benefits of green and blue spaces, as blue spaces are often treated as a component of green space (Georgiou et al. 2021). Though water bodies rarely occur in isolation of vegetation and are hypothesized as promoting human health through similar pathways as green spaces, including reduced stress and the facilitation of social contacts and physical activity (Georgiou et al. 2021; Short et al. 2021; Smith et al. 2021; White et al. 2010; White, Elliott, Gascon, Roberts & Fleming 2020; Gascon, Zijlema, Vert, White & Nieuwenhuijsen 2017), they are separate landscape entities with potentially unique benefits and risks to human health. Moreover, existing research has defined and measured human contact with nature in a variety of ways. Studies typically estimate contact with natural environments using metrics for either access (i.e., spatial proximity of green or blue space to location of interest) or exposure (i.e., physical amount of green or blue space within a defined area) (Holland et al. 2021; Labib et al. 2020; Ekkel & de Vries 2017). Despite supportive evidence for a positive association between aspects of human health and both metrics of nature contact (Dadvand et al. 2014, 2016; Triguero-Mas et al. 2015), little attention has been paid to the potential differences between these 18 metrics and, more importantly, how the choice of metric affects the association with different health outcomes. It is possible that pathways linking natural environments to health may differ depending on the metric used to capture human contact with nature. For example, access to public parks may promote health through providing a space for social interactions and physical activities, whereas exposure to residential nature may contribute to improved health through regulating harmful neighborhood exposures (e.g., reduce air pollution and community noise) and recovery from stress and mental fatigue. A further limitation of the current evidence base is that the pathways underlying nature-health relations are not fully understood. Although there is a convergence of hypothesized pathways linking natural environments to human health, formal investigation of these pathways is lacking (Dzhambov et al. 2020). Of the studies that have empirically investigated pathways linking natural environments to human health, evidence of mediation is limited and quality of analytical approaches are mixed (Zhang et al. 2021; Dzhambov et al. 2020). Moreover, the variety of different exposure measures, mediators, and health outcomes makes it difficult to explain clear pathways (Zhang et al. 2021; Dzhambov et al. 2020; Markevych et al. 2017). Finally, most research on nature-health associations and potential underlying pathways have focused on adult populations, while less is known about the influence on children; though, research specific to children is increasing (Davis et al. 2021; Mygind et al. 2021; Vanaken & Danckaerts 2018). Health benefits of nature exposure and the underlying pathways of this relation may differ in children and adults, in so far that their activity spaces (e.g., near residence versus broader neighborhood) and primary uses (e.g., recreation versus utilitarian) of natural environments are likely dissimilar (Pitt et al. 2021). Furthermore, the theory of developmental 19 origins of health and disease (DOHaD) indicates that exposure to certain environmental influences during critical periods of growth and development in early life can influence lifelong health and disease (Gluckman, Hanson, Cooper & Thornburg 2008). This underscores the importance of better understanding nature-health associations in children to inform policy interventions and prevention tools targeted to improve health early in life, with potential positive effects across the life course (Pitt et al. 2021). 1.4.2 Research objectives Overall, the purpose of this dissertation is to investigate the relationship between natural environments and human health. This dissertation seeks to address the important knowledge gaps in the current literature outlined above using existing data on health and urban environmental exposures and applying an interdisciplinary approach that integrates epidemiological and novel geospatial approaches. Specifically, this dissertation seeks to address the following research objectives: 1. To estimate access and exposure to natural environments and evaluate the relationship between these metrics and human health; 2. To assess potential differences in the relationship between natural environments and human health by type of natural environment; 3. To assess potential differences in the relationship between natural environments and human health by type of human contact with nature; and 4. To evaluate if observed relationships between natural environments and human health are mediated through reduced exposure to traffic-related air pollution and noise. 20 While a variety of potential mediators underlie nature-health associations, this dissertation provides a starting point to better understand these pathways by testing the potential of natural environments to improve health through reducing harmful environmental exposures, specifically traffic-related air pollution and noise. The reducing harm pathway was selected for formal investigation in this dissertation given the limited number of studies that have assessed the mediation effects of multiple harmful environmental exposures on children’s development and potential relevancy of findings to urban planners and policy makers addressing environmental health risks related to urban living. Furthermore, reducing harmful environmental exposure levels may help facilitate the building and restoring capacities pathways, as heightened levels of air pollution and noise may, for example, increase stress and reduce willingness to be physically and/or socially active outdoors. 21 1.5 Dissertation overview This dissertation is made up of six chapters: an introduction, four research chapters, and a conclusion. Chapter 2 addresses Objective 1 by developing a general method for estimating and comparing different forms of human-nature contact, specifically access to public green spaces and residential exposure to different land cover types. This chapter also explores whether access and exposure metrics vary along geographic and socioeconomic gradients to inform measurement of natural environments in future research. Chapter 3 addresses Objectives 1, 2 and 3 by applying the metrics developed in Chapter 2 to investigate the association between access and exposure to natural environments and self-reported general health, mental health, and common mental disorders among adolescent and adult populations. A comparison of the relative strength of association between exposure to distinct land cover types and the health outcomes was conducted to assess potential differences in association depending on the type of natural environment. Chapter 4 addresses Objectives 1 and 2 by investigating the association between early-life residential exposure to natural environments and early childhood development. Like Chapter 3, a comparison of the relative strength of association between exposure to distinct land cover types and early childhood developmental outcomes was conducted to assess potential differences in association depending on the type of natural environment. Chapter 5 addresses Objectives 1 and 4 by investigating the association between early-life residential exposure to green space and early childhood development and evaluated whether the 22 effects of green space exposure on childhood development were mediated through reduced exposure to traffic-related air pollution and noise. Chapter 6 provides a summary and conclusions of the dissertation research. It also discusses research contributions, study limitations and directions for future research, and policy implications. A diagram summarizing the dissertation objectives and chapters relative to the proposed conceptual pathways and evidence needs is shown in Figure 1-1. Figure 1-1. Conceptual diagram of the relation between natural environments and health across the life course. This figure provides an overview of the dissertation objectives and relevant chapters in which the objectives will be addressed. Natural environments may be linked to overall improved health through restoring capacities, building capacities, and reducing harm pathways. As indicated by the double-ended arrows, pathways may be related to one another. This dissertation only tests the reducing harm pathway, while the restoring and building capacities pathways are used for theoretical purposes to develop research hypotheses. Nature-health associations may vary depending on the type of natural environment (e.g., vegetation types, water) and/or form of nature contact (e.g., access, exposure) given these factors may influence the capacity of an environment to support pathways linking nature to health. 23 1.6 Dissertation setting and common methodologies 1.6.1 Study area The study area for this dissertation is Metro Vancouver, located in southwestern British Columbia (BC), Canada (Figure 1-2). Metro Vancouver is the third largest metropolitan area in Canada, comprising over 2.5 million residents (Statistics Canada 2017a). The region covers 2,883 km2 of land area and has a population density of approximately 855 persons/km2 (Statistics Canada 2017a). It is one of the most racially and ethnically diverse cities in Canada, with 41% of the population consisting of immigrants (Statistics Canada 2017b). Approximately one third of the region is designated as urban (e.g., residential, commercial, industrial) and the remaining area is characterized by diverse land use and land cover types, including agricultural crops, forests, and fresh and coastal water (Metro Vancouver 2017). Metro Vancouver is expected to experience substantial population growth over the coming decades, reaching an estimated 3.4 million residents by the year 2040 (BC Stats 2018). This projected growth will put increased pressure on the region’s population and landscape alike. For example, regional tree canopy cover is projected to decrease from 32% to 28% in the coming decades as a result of urban land development and redevelopment of single-family detached homes (Metro Vancouver 2019a). In response to these forthcoming pressures, regional (Metro Vancouver 2016a, 2017) and municipal (City of New Westminster 2021; City of Surrey 2021; City of Vancouver 2012) long-term planning strategies have been created to help achieve sustainable growth. These strategies include, for example, greening initiatives to protect and improve residential access to natural environments (City of Vancouver 2012). 24 Figure 1-2. Metro Vancouver and boundaries of constituent municipalities. 1.6.2 Geographic unit of analysis In all research chapters, data on natural environments and neighborhood sociodemographic information were linked to study participants using six-digit postal codes. In urban areas, six-digit postal codes generally correspond to one side of a city block or a single multi-unit building, with a positional accuracy within approximately 100 – 160 m (Pinault, Khan & Tjepkema 2020; Khan, Pinault, Tjepkema & Wilkins 2018). Six-digit postal codes are the smallest aggregated geographic unit typically applied in Canadian environmental epidemiological studies (Pinault et al. 2020; Khan et al. 2018) and, as such, were used in this dissertation to make the findings applicable to other studies investigating nature-health associations in Canada. 25 1.6.3 Data A brief description of datasets common across multiple research chapters can be found below. Additional datasets used in individual research chapters, as well as how common datasets were applied in analyses, are described in the methods section of each chapter. 1.6.3.1 Land cover data In Chapters 2, 3, and 4, a high spatial resolution (5 m) land cover map of Metro Vancouver (Metro Vancouver 2019b; Williams, Matasci, Coops & Gergel 2018) was used to estimate residential exposure to various natural and non-natural land cover types. The map was derived from 2014 RapidEye satellite imagery and airborne laser scanning data that were combined using an object-based image analysis approach (Williams et al. 2018). The map includes 12 land cover classes (Figure 1-3, Table 1-1) and has an overall accuracy of 88% (kappa 0.87). Tree canopy classes (i.e., coniferous, deciduous, and shrub) had high per-class accuracies, ranging from 83% to 94%, and all individual class accuracies were above 80%, apart from the user’s accuracy for the soil class (77%). Further details on the land cover map are described elsewhere (Williams et al. 2018). 26 Figure 1-3. Land cover map of Metro Vancouver. The map depicts the distribution of land cover classes. The map extent includes a 5 km buffer around Metro Vancouver and the neighboring City of Abbotsford. 27 Table 1-1. Land cover types and classification criteria. Table modified from Williams et al. (2018). Land cover type Description Buildings Houses, garages, warehouses, towers, industrial structures, etc. Paved Asphalt and concrete surfaces, including sidewalks, parking lots and highways Other built Built surfaces that are not concrete/asphalt or building roofs, including sports surfaces (e.g., artificial turf and running tracks) and transit or rail areas. Barren Surfaces that lack vegetation and likely are not soil. Beaches, alpine rock, shoreline rock, etc. Soil Agricultural soils, cleared/open areas where darker colours indicate organic matter is present. Coniferous Predominantly coniferous (> 75%) Deciduous Predominantly deciduous (> 75%) Shrub Woody, leafy, and generally rough-textured vegetation shorter than trees (~ < 3 to 4m) and taller than grass Modified grass-herb Human-modified grass-herb areas, including agricultural crops, golf course greens, city park grass, residential lawns. Natural grass-herb Alpine meadows, near-shore grasses and fine-textured bog/wetland areas Non-photosynthetic vegetation Dead grass, cut block slash and log booms Water Lakes, rivers, inlets, irrigation channels, retention ponds. Shadow Dark pixels with very low reflectance values. 1.6.3.2 Land use classification data In Chapters 2 and 3, land use and land designation data for Metro Vancouver were used to estimate residential access to public green space (Metro Vancouver 2016b). Public green spaces were defined as areas delineated as park, recreation, and protected areas in the land use and land designation layers (Figure 1-4). 28 Figure 1-4. Public green spaces in Metro Vancouver. Public green spaces were defined as area delineated as park, recreation, and protected areas in Metro Vancouver land use and land designation data layers. 1.6.3.3 Spectrally unmixed vegetation data In Chapters 4 and 5, a novel Landsat greenness time series was used to estimate residential exposure to green space (Figure 1-5). Details on the metric are described elsewhere (Czekajlo et al. 2020). In brief, annual Landsat satellite-image composites (30 m spatial resolution) spanning the summer growing period 1984 to 2016 were used to identify three spectral attributes of physical features per each pixel in the study dataset: vegetation (i.e., green vegetation), high-albedo (e.g., impervious surfaces), and dark (e.g., shadows cast by objects, such as buildings). Using the three spectral attributes, a sub-pixel linear spectral unmixing algorithm was applied to 29 each year and the annual percentage of each spectral attribute per pixel in the study area was extracted. Prior research has shown that green space estimated via linear spectral unmixing to be a more sensitive measure of vegetation than commonly employed greenness metrics (e.g., NDVI), particularly in urban settings (Sadeh, Brauer, Dankner, Fulman & Chudnovsky 2021). In Chapters 4 and 5, the annual percentage of the vegetation attribute per pixel was used to represent green space for each year of the study period. In Chapter 4, this metric was used in a sensitivity analysis, while it was used as the main exposure metric in Chapter 5. Figure 1-5. Vegetation in Metro Vancouver. Vegetation was estimated via linear spectral unmixing of annual Landsat satellite image composites. The map depicts the annual percentage of vegetation spectral attributes per pixel for the year 2011. 30 1.6.3.4 Normalized difference vegetation index data In Chapters 3, 4, and 5, sensitivity analyses were conducted for residential green space exposure estimated as the mean of annual mean normalized difference vegetation index (NDVI), derived from Landsat (30 m spatial resolution), RapidEye (5 m spatial resolution), and Planet (3 m spatial resolution) satellite imagery (Canadian Urban Environmental Health Research Consortium 2018, 2019; Gorelick et al. 2017; CanMap 2015; USGS n.d., n.d., n.d., n.d.) (Figure 1-6). NDVI provides an indication of the level of healthy vegetation at a location and is commonly used as a marker of greenness exposure in environmental epidemiological studies (Labib et al. 2020); permitting greater comparability of the results with other literature. NDVI values range from −1 to +1, with higher values indicating more living vegetation and lower values indicating freestanding water. Large water bodies were excluded from NDVI calculations to separate the effects of vegetation from those of water. 31 Figure 1-6. Normalized difference vegetation index in Metro Vancouver. Greenness was estimated using the mean of annual mean NDVI, derived from Landsat (30 m spatial resolution), RapidEye (5 m spatial resolution), and Planet (3 m spatial resolution satellite imagery. NDVI values range from −1 to +1, with higher values indicating more living vegetation and lower values indicating freestanding water. The map depicts NDVI values derived from a Landsat satellite image from July 2014. 1.6.3.5 Sociodemographic data In Chapters 2, 4, and 5, data from the 2006 Canadian Marginalization Index (CAN-Marg) were used to assess socioeconomic marginalization at the neighbourhood level. CAN-Marg is a geographically-derived index comprised of four dimensions – residential instability, material deprivation, ethnic concentration, and dependency – that can be assessed separately or combined as a composite score (Matheson, Dunn, Smith, Moineddin & Glazier 2012). The four dimensions were initially derived from a factor analysis of 42 variables from the 2001 Canadian Census, 32 including indicators of, for example, residential mobility, family size, employment, income, education, and visible minority status (Matheson et al. 2012). CAN-Marg was developed for use in Canadian studies around health inequalities and captures between-place variation in socioeconomic status (SES) (Matheson et al. 2012). It has shown strong stability across time periods and geographical areas (e.g., cities and rural areas) and has been consistently associated with health and behavioural outcomes (White, O’Campo, Moineddin & Matheson 2013; Matheson et al. 2012). Scores for each CAN-Marg dimension are measured at the level of dissemination areas (DA), which are small, relatively stable units composed of 400 to 700 persons (Statistics Canada 2015a). For this dissertation, CAN-Marg data were linked to postal codes of Metro Vancouver. 1.6.3.6 Early Development Instrument In Chapters 4 and 5, early childhood development was assessed using data sourced from the Early Development Instrument (EDI), a population-level tool assessing children’s development in kindergarten (Janus & Offord 2007). Kindergarten teachers complete the EDI for each child in their class in early spring based on classroom observations in the previous 6 months. The EDI consists of demographic information and 103 binary and Likert-scale items assessing a child’s ability to meet age-appropriate developmental expectations in five domains: (1) physical health and well-being; (2) social competence; (3) emotional maturity; (4) language and cognitive development; and (5) communication skills and general knowledge. For each of the five domains, a child receives a domain score, calculated as the mean across all domain items, ranging from 0 (lowest) to 10 (highest). Children also receive a total EDI score, calculated as the sum of the five domain scores, ranging from 0 (lowest) to 50 (highest). Numerous studies have 33 documented the EDI’s validity and reliability as a measure of children’s developmental outcomes (Forer & Zumbo 2011; Janus, Brinkman & Duku 2011; Janus & Offord 2007). 34 Green space access does not correspond to nature exposure – measures of urban natural environments with implications for health research 2.1 Introduction Research on the association between urban natural environments and various health outcomes has increased in both quantity and quality over the last decade. However, heterogeneous measures of natural environments and a lack of consideration of different types of nature in these measures has been a limitation in advancing and evolving knowledge of health impacts, particularly among different populations. These limitations relate to, for instance, measures of accessibility versus exposure, geographical distribution of blue spaces and different vegetation types, and the impact of natural spaces on health among socioeconomically marginalized populations. Pioneering studies on associations between natural environments and health were conducted within the field of environmental psychology (Kaplan 1995; Ulrich et al. 1991; Kaplan & Kaplan 1989; Ulrich 1983), where people’s interactions with, and experiences of, nature were often of interest. Typically, people’s perceptions of, and proximity to, natural spaces (i.e., access) were measured as independent variables in relation to health and well-being outcomes (Ho et al. 2017). Later, studies included measures of general exposure to surrounding nature, including indirect contact with nature (Dadvand, Sunyer, et al. 2012). It is possible that the pathways linking natural environments to health differ between these two different approaches to measuring natural environments. For example, access may promote health through social interactions and physical activity, whereas exposure may contribute to regulating ecosystem 35 services (Millennium Ecosystem Assessment 2005) and thus prevent illness from harmful environmental exposures, such as heat and air pollution. Access measures typically estimate proximity to public natural spaces and rely on various data sources, including local land designation, land use land cover maps (European Environment Agency 2021), audits and expert quality assessments (Rugel, Henderson, Carpiano & Brauer 2017), or self-reported accessibility (Grahn & Stigsdotter 2003). Access is generally determined by distance measures, for example, measuring the distance from a home address to the nearest park. Alternatively, concentric buffers surrounding parks and green spaces may be used to identify the proportion of a population living within various distance zones. Despite lack of scientific consensus as to what constitutes optimal accessibility (Annerstedt van den Bosch et al. 2016), most urban planning recommendations suggest within 250 m to 300 m (World Health Organization 2016). Accessibility measures usually also take size of the natural space into account. For instance, determining the proportion of a population living within 300 m of parks at least one hectare in size (Annerstedt van den Bosch et al. 2016). Such accessibility measures capture important aspects of public availability and access to natural spaces “large enough” for recreation and physical activity, but they do not necessarily account for routine, every day “micro-contacts” with nature, such as exposure to gardens or street trees. In contrast, exposure measures typically derive from remote sensing imagery used in the calculation of metrics, such as the NDVI, to assess surrounding greenness. The main difference from accessibility measures is that rather than a distance-value, exposure is generally quantified as the proportional-value of natural environments within a specified area, for example, a buffer area around a residence (Reid, Kubzansky, Li, Shmool & Clougherty 2018). Various radii are 36 usually evaluated, as there is no scientific consensus as to what buffer size may be most indicative of health effects (Labib et al. 2020; Browning & Lee 2017). Furthermore, the extent of influence is also likely to depend on the health outcome of interest. Buffer areas ranging from approximately 50 m to 1,000 m are typically evaluated under the hypothesis that smaller buffer sizes represent immediate effects related to stress recovery, while larger buffer sizes relate to health benefits from opportunities for physical activity (Markevych et al. 2017). Using land use and land cover maps, exposure measures can also be used to better understand the spatial distribution of various types of natural spaces (e.g., trees, grasses) on a neighbourhood or city level. While a few European studies have assessed both accessibility and exposure in relation to health outcomes (Amoly et al. 2014; Balseviciene et al. 2014; Dadvand, Nazelle, et al. 2012), city-level comparisons are often not considered in public health research. Likewise, there is insufficient knowledge regarding how accessibility and exposure may vary across urban-rural gradients, including assessment of different natural environment types, and how they relate to each other. The relative distribution of natural environments across urban, peri-urban, and rural areas may be important to consider in urban planning and as new residential areas develop on the outskirts of cities. Socioeconomic inequalities and unequal distribution of healthy environments, such as natural spaces, within cities are widely recognized public health problems (World Health Organization 2010). Several studies suggest an association between income and urban vegetation (Nesbitt, Meitner, Girling, Sheppard & Lu 2019) and a recent meta-analysis found evidence of significant income-based inequity in the distribution of urban forest cover (Gerrish & Watkins 2018). 37 However, a variety of factors influence the distribution of urban nature and there are exceptions where green space provisioning does not always favour the wealthy (Mears, Brindley, Maheswaran & Jorgensen 2019; Ngom, Gosselin & Blais 2016). In this context, it is important to also consider quality of green space, since a high exposure or accessibility do not always correspond to, for example, safety or recreational amenities in the green space. Different indicators exist for monitoring area-level socioeconomic marginalization and intra-urban health inequalities (World Health Organization 2010), and one European study suggests including exposure to urban green spaces in these types of indicators (Kabisch, Haase & Annerstedt van den Bosch 2016). Ngom et al. (2016) reported on socioeconomic disparities in green space access in Montreal, but few Canadian studies have looked at the relation between exposure to different types of natural environments and indicators of marginalization. In this study, a general method for estimating and comparing accessibility to public green space and exposure to different land cover types in Metro Vancouver, Canada is developed. This study also determines whether access and exposure vary along geographic and socioeconomic gradients to inform measurement in future research on the relationships between natural environments and health. To do so, first variation in the distribution of access and exposure along an urban-rural gradient is examined, and then correlations between access and exposure and area-level marginalization are assessed. 2.2 Materials and Methods Within Metro Vancouver, analyses were conducted at the six-digit postal code level using the Statistics Canada 2014 Postal Code Conversion File (n=60,782) (Statistics Canada 2014). Six-digit postal codes are the smallest aggregated geographic unit typically used in population health 38 studies in Canada for environmental exposures. As such, this unit was used in the current analyses to make it applicable in future studies on health outcomes related to access or exposure to natural environments in Canada. 2.2.1 Access and exposure data 2.2.1.1 Land cover information To analyze residential exposure to land cover types, a high spatial resolution (5 m) land cover map of Metro Vancouver was used, identifying 12 different land cover classes (Metro Vancouver 2019b; Williams et al. 2018). A main focus of the current study was measuring classes related to natural environments, specifically coniferous trees, deciduous trees, shrubs, modified and natural grass-herbs, and water; but classes related to non-natural areas were also included to achieve an assessment of relative distribution. Two land cover classes were excluded from analyses. ‘Non-photosynthetic vegetation’ was excluded as it represents dead vegetation with no indication of vegetation type, per se. ‘Shadow’ was excluded as it does not represent a true land cover type, but rather a technical consequence of the remote sensing data. 2.2.1.2 Access Access was defined as whether or not a postal code is located within 300 m of a public green space equal to or greater than one hectare. To evaluate public green space accessibility, open source 2011 Land Use and Land Designation data layers were retrieved for Metro Vancouver (Metro Vancouver 2016b). Using these layers in ArcMap 10.4.1, areas delineated as park, recreation and protected areas were identified, and vegetation land cover types located within the boundaries of these spaces were selected to create a final layer representing public green space. 39 Following international recommendations (Annerstedt van den Bosch et al. 2016; World Health Organization 2016), postal codes located within 300 m of public green spaces greater than or equal to one hectare (10,000 m2) in size were considered to have ‘accessible’ green space (Figure 2-1-A). This distance and size were selected to serve as a proxy for walking distance and the minimum area appropriate for health-promoting activities (Annerstedt van den Bosch et al. 2016). A sensitivity analysis was conducted for the presence of a public green space within 400 m of postal codes to align with the city of Vancouver’s Greenest City 2020 Action Plan, which defines 400 m as the maximum distance for accessible public green space (City of Vancouver 2012). 2.2.1.3 Exposure Exposure was defined as the percentage of each land cover class, as well as percentage of aggregated green space, within varying buffer zones of postal code centroids. Surrounding residential exposure to natural and other land cover types was assessed by creating a series of circular buffer zones, with radii spanning 100-, 250-, 500-, and 1,000 m, around each postal code centroid using ArcMap 10.4.1 (Figure 2-1-B). Within the buffers, exposure values were calculated as the percentage of each land cover class (Figure 2-1-B), as well as the percentage of aggregated green space (i.e., combined coniferous, deciduous, shrub, and grass-herbs) to achieve a comparative picture of the full greenness exposure. Multiple buffer sizes were included to reflect the various radii commonly employed in exposure assessment and to align with the hypothesis that smaller buffer sizes relate to immediate effects related to stress relief, while larger buffer sizes relate to opportunities for social cohesion and physical activity (Markevych et al. 2017). 40 Figure 2-1. Access and exposure metrics. Access was calculated as the presence of a public green space (≥ 1 hectare) within 300 m of six-digit postal codes (A). In this example, postal code ‘a’ is categorized as having adequate access to public green space, while postal code ‘b’ is categorized as not having adequate access. Exposure was calculated as the percentage of each land cover class, as well as aggregated green space, within 100-, 250-, 500-, and 1,000 m circular buffer zones of six-digit postal code centroids (B). The pie chart depicts the proportion of each land cover type within a 250 m buffer of a sample residential postal code in an urban area. 2.2.2 Urban-rural gradient data To describe changes in access and exposure along a spatial continuum from urban to rural areas, an urban-rural gradient was applied. The urban-rural gradient approach represents a well-established method of describing differences between urban and rural regions (McDonnell & Hahs 2008). The method has been used to understand the spatial distribution of land use types and ecosystem function (Luck & Wu 2002; McDonnell & Pickett 1990) and allows easy 41 comparison among cities (Jin, Gergel, Lu, Coops & Wang 2019). One approach for examining urban-rural gradients, the concentric ring approach, uses a series of concentric rings that originate in the urban centre and expand outward to rural areas (Kroll & Kabisch 2012). The concentric ring approach assumes that urban expansion exhibits a similar spatial pattern in all directions from a city centre. In North America, urban areas typically have a densely populated urban core surrounded by diminishing population density and landscape modification (Dickinson 1966). To evaluate access and exposure along an urban-rural gradient using the concentric ring method, open source Urban Centre data were retrieved for Metro Vancouver and uploaded in ArcMap 10.4.1 (Metro Vancouver 2018). A series of 1 km concentric rings were delineated around centroids of urban centres (Figure 2-2). To contend with overlap among nearby cities, overlapping rings were dissolved into a single feature (Figure 2-2). Residential postal codes located within the individual rings were identified to characterize access and exposure at varying distances from urban centres. 42 Figure 2-2. Urban-rural gradient. A series of concentric rings of radii of 1 km were delineated around urban centres to describe changes in access to public green space and residential exposure to different land cover types. Base from ArcMap 10.x (Sources: Esri, HERE, Garmin, ©OpenStreetMap contributors, and the GIS user community). 2.2.3 Marginalization data Data from the 2006 CAN-Marg were used to assess socioeconomic marginalization at the neighbourhood level. CAN-Marg is a geographically-derived index comprised of four dimensions – residential instability, material deprivation, ethnic concentration, and dependency – that can be assessed separately or combined as a composite score (Matheson et al. 2012). Scores for each CAN-Marg dimension are measured at the DA-level, but were linked to Metro Vancouver postal codes for this study. 43 2.2.4 Statistical analysis Descriptive statistics were computed to describe access and exposure on a regional scale and along an urban-rural gradient, and to describe marginalization on a regional scale, in the Metro Vancouver study area. Point biserial correlation coefficients (rpb) were calculated to assess correlations between access and exposure and access and marginalization, while Pearson correlation coefficients (r) were calculated to assess correlations between exposure and marginalization. Mann-Kendall tests were performed to detect statistically significant trends for access and exposure measures along an urban-rural gradient. Access was measured as dichotomous variables (yes, no) and exposure as percentage values. For the CAN-Marg data, the standardized factor scores for separate dimensions were used, where higher values represent areas that are the most marginalized. Correlations and Mann-Kendall tests involving exposure were analyzed using the four buffer zones (100-, 250-, 500-, and 1,000 m). The analyses were performed in RStudio version 1.1.456 (R Core Team 2020). P-values < 0.05 were considered for statistical significance. Observations with missing data were excluded from the analyses. 2.3 Results 2.3.1 Access to public green space and residential exposure to land cover Across the Metro Vancouver region, 71% of postal codes had adequate access to public green space, as defined by presence of an area greater than or equal to one hectare at or within 300 m. Results from the sensitivity analysis, defining the maximum distance to a public green space as 400 m, were comparable (see Appendix A). However, as expected, defining access with a longer 44 distance measure resulted in a larger proportion of the population (84%) having adequate access to green space. Across the region, the most dominant land cover exposure classes were built-up and paved areas (Table 2-1). Within the vegetated classes, deciduous land cover was the most abundant exposure, followed by modified grass-herb and coniferous. Exposure to aggregated green space (i.e., combined coniferous, deciduous, shrub, and grass-herbs) varied between 36% and 41% across buffer zones. Exposure to water varied between 0.79% and 4.59%. The remaining exposures, which together accounted for less than 5% of exposure in all buffer zones, consisted of other built, barren, soil, shrub, and natural grass-herb vegetation (Table 2-1). Table 2-1. Mean percentage of the different land cover types within 100-, 250-, 500-, and 1,000 m buffers of six-digit postal code centroids (n=60,782 postal codes). Land cover type Buffer distance (m) from postal code centroid 100 250 500 1,000 Buildings 28.8 (12.5) 27.0 (11.2) 25.4 (10.3) 23.4 (9.4) Paved 32.3 (13.7) 31.0 (11.6) 29.6 (10.4) 28.0 (9.6) Other built 0.04 (0.3) 0.1 (0.3) 0.1 (0.2) 0.1 (0.1) Barren 1.1 (2.9) 1.2 (2.2) 1.6 (1.7) 1.8 (1.4) Soil 0.2 (1.1) 0.3 (1.0) 0.4 (1.0) 0.5 (0.9) Coniferous 6.2 (8.7) 6.6 (8.7) 7.0 (8.8) 7.6 (9.2) Deciduous 17.2 (11.7) 18.0 (10.3) 18.5 (9.3) 18.5 (8.1) Shrub 0.5 (1.4) 0.6 (1.3) 0.7 (1.1) 0.8 (1.0) Modified grass-herb 11.6 (9.5) 12.3 (8.8) 13.0 (8.3) 13.5 (8.3) Natural grass-herb 0.1 (0.4) 0.1 (0.4) 0.1 (0.4) 0.2 (0.7) Water 0.8 (5.8) 1.4 (6.8) 2.6 (8.3) 4.6 (9.7) Aggregated green space 35.6 (18.9) 37.7 (17.9) 39.4 (16.9) 40.1 (16.2) Data presented are mean (SD). Point biserial correlation analyses showed that, for the entire Metro Vancouver region, there were small correlations between access and exposure (Figure 2-3). There was a low positive 45 correlation between access and exposure to aggregated green space, with the rpb value varying between 0.14 and 0.21 (p < 0.001) depending on buffer size, with the strongest positive correlation (rpb = 0.21) for the 500 m buffer zone. Exposure to water was weakly correlated with access across buffer zones (rpb varying from 0.01 to 0.06, p < 0.05). A low negative correlation was observed between access and exposure to buildings (rpb varying from −0.16 and −0.27, p < 0.001) and paved areas (rpb varying from −0.04 to −0.13, p < 0.001). Likewise, Pearson correlation analyses demonstrated strong negative correlations between exposure to aggregated green space and buildings (r varying from −0.67 to −0.74, p < 0.001) and paved surfaces across buffer zones (r varying from −0.72 to −0.77, p < 0.001). The correlations between access and exposure were similar across buffer sizes. 46 Figure 2-3. Correlations between access to public green space and exposure to various land cover types. Correlation values were similar across buffer sixes; this figure presents values for the 500 m buffer zones (n=60,782 postal codes). Correlations with p-values > 0.05 are considered non-significant and cells are left blank. 2.3.2 Urban-rural gradient Examining the distribution of postal codes along the urban-rural gradients, the majority of inhabitants (52%) resided within two to three km from an urban centre. Only 1.8% lived nine km or further from an urban centre. The average percentage of postal codes with accessible public green space remained relatively constant over the urban-rural gradient, varying between 65% and 74% (Figure 2-4). Results from the Mann-Kendall test indicate that there is no trend between access and distance from an urban centre (Tb = −0.02, p = 1.00). The mean percentage of 47 exposure to the various land cover types displayed a similar trend across buffer zones (Figure 2-4). The main pattern was an increase in exposure to water and vegetation, especially coniferous trees, with increasing distance from an urban centre and a corresponding decrease in exposure to buildings and paved areas. Results from the Mann-Kendall tests indicate statistically significant trends (p < 0.01) between mean exposure and distance from an urban centre for all land cover types, except for other built and barren. Trends were similar across buffer distances. Figure 2-4. Urban-rural gradient of mean access to public green space and mean residential exposure to different land cover types. Access is presented as the percentage of postal codes with public green space (≥ 1 hectare) within 300 m. Residential exposure is presented as the mean percentage of each land cover type within 500 m buffer zone (n=60,782 postal codes). Exposure values were similar across buffer sizes. 48 2.3.3 Marginalization Descriptive values for marginalization, as measured by the four dimensions of CAN-Marg 2006, are presented in Table 2-2. After excluding missing data, 57,064 postal codes had valid responses for the CAN-Marg dimensions. In general, there was large variation in the distribution of marginalization dimensions across the study region. More than half of the postal codes belonged to DAs that had a high proportion of the population who were recent immigrants or who identified as a visible minority (defined as the ethnic concentration dimension). Almost one fifth of the postal codes were in the highest quintile for residential instability, while less than one tenth of the postal codes were in the highest quintile for dependency and material deprivation respectively. Table 2-2. Percentage of postal codes in Metro Vancouver by measures of marginalization (n=57,064). CAN-Marg dimension Marginalization level + 1 2 3 4 5 Mean standardized factor score SD (%) (%) (%) (%) (%) Residential instability a 17.6 25.1 23 14.8 19.5 -0.01 1.02 Material deprivation b 20.1 26.5 25.7 18.5 9.2 -0.37 0.71 Dependency c 28.3 25.8 21.6 17.8 6.6 -0.19 0.80 Ethnic concentration d 1.4 4.2 12.1 26.1 56.2 0.89 1.06 + Ranked from 1 to 5, where 1 is the least marginalized and 5 is the most marginalized. a. Indicators: Proportion of population living alone; proportion of the population who are not youth (aged 16+); average number of persons per dwelling; proportion of dwellings that are apartment buildings; proportion of the population that are single/divorced/widowed; proportion of dwellings that are not owned; proportion of the population who moved during the past 5 years. b. Indicators: Proportion of the population aged 20+ without a high school diploma; proportion of families who are lone parent families; proportion of the population receiving government transfer payments; proportion of the population aged 15+ who are unemployed; proportion of the population considered low-income; proportion of households living in dwellings that need major repair. c. Indicators: Proportion of the population who are aged 65 and older; dependency ratio (total population 0-14 and 65+/total population 15 to 64; proportion of the population not participating in labour force (aged 15+). d. Indicators: Proportion of the population who are recent immigrants (arrived in the 5 years prior to 2006 census); proportion of the population who self-identify as a visible minority. 49 Table 2-3 shows correlations between the CAN-Marg dimensions and access to public green space and exposure to various land cover types. A negative correlation was observed between access and residential instability (rpb = −0.16, p < 0.001), material deprivation (rpb = −0.09, p < 0.001), and ethnic concentration (rpb = −0.02, p < 0.001). The correlation values between exposure and marginalization dimensions were similar across buffer sizes. There was a negative correlation between exposure to aggregated green space and the residential instability, material deprivation and ethnic concentration dimensions in all buffer zones (r varying from −0.26 to −0.46, p < 0.001). There was a negative correlation between exposure to aggregated green space and dependency in the 250 m and 500 m buffer zones (r = −0.02, p < 0.001 and r = −0.01, p = 0.01, respectively). There was a positive correlation between exposure to water and residential instability across buffer zones (r varying from 0.03 and 0.22, p < 0.001), apart from the 100 m buffer zone. Ethnic concentration was negatively correlated with exposure to water (r varying from −0.01 and −0.23, p < 0.05). Buildings and paved areas were positively correlated to residential instability, material deprivation, and ethnic concentration (r varying from 0.15 and 0.42, p < 0.001). Using a Bonferroni correction (Bonferroni 1936), a p-value of 0.001 would be required for statistical significance. Under these more conservative conditions, observed correlations between three CAN-Marg dimensions (i.e., residential instability, material deprivation, and ethnic concentration) and access and exposure are maintained (Table 2-3). The application of family-wise control for multiple comparisons (i.e., correlation analyses for access and exposure within all buffers in relation to each CAN-Marg dimension (n=52)) would have only changed certainty around the correlations between access and exposure and area-level 50 dependency; however, low evidence of correlation between this dimension and both metrics was already observed. Table 2-3. Point biserial (rpb) and Pearson correlation coefficients (r) between dimensions of marginalization and access to public green space and exposure to various land cover types (n=57,064 postal codes). Access or exposure Residential instability (r) Material deprivation (r) Dependency (r) Ethnic concentration (r) Access to green space -0.16** + -0.09** + 0.00 + -0.02** + Aggregated green space -0.45** -0.31** -0.01* -0.33** Buildings 0.40** 0.24** -0.02** 0.31** Paved areas 0.23** 0.38** 0.02** 0.35** Other built areas 0.03** 0.06** 0.04** 0.06** Barren areas 0.02** 0.02** 0.00 -0.02** Soil -0.08** -0.02** 0.00 -0.09** Coniferous trees -0.22** -0.24** -0.01 -0.22** Deciduous trees -0.28** -0.24** -0.04** -0.24** Shrubs -0.17** -0.06** -0.02** -0.09** Modified grass-herb -0.34** -0.09** 0.04** -0.13** Natural grass-herb -0.06** -0.06** 0.04** -0.08** Water 0.12** -0.12** 0.03** -0.14** Correlations were similar across buffer sizes. This table presents values for the 500 m buffer zone. + Point biserial correlation coefficient (rpb) * Indicates p-value < 0.05 ** Indicates p-value < 0.001 2.4 Discussion This study explored and compared several dimensions of urban natural environments in Metro Vancouver – access to public green spaces, residential exposure to different land cover types, variation in access and exposure along an urban-rural gradient, and correlations between access and exposure and dimensions of area-level marginalization. The results showed that access to public green space was only weakly positively associated with exposure to various natural land cover types, with correlations similar across buffer zones. Access to public green space remained 51 relatively high throughout the region, while exposure differed across an urban-rural gradient. Exposure to land cover types varied significantly along an urban-rural gradient for all buffer sizes. Specifically, residential exposure to built-up and paved areas declined with increasing distance from urban centres, while exposure to trees, modified grass-herbs, and water increased. Residential instability, material deprivation, and ethnic concentration were negatively associated with both access to public green space and exposure to several natural land cover classes. The marginalization indicator ‘dependency’, which indicates the proportion of the population aged 65 and older or not participating in the labour force (aged 15+), was not correlated with access and only weakly correlated with exposure. 2.4.1 Access or exposure? A question concerning pathways to good health There is no consensus as to what natural environment metric should be used in public health research, nor which would be most strongly associated with various health outcomes. This study shows that access and exposure are only weakly correlated to each other. Thus, it is likely that the choice of either of these metrics would affect the results in analyses of related health outcomes. The hypothesized pathway for a specific study may determine the choice of metric and it is therefore important to a priori determine mechanisms and pathways in the design of studies. For instance, access to public green spaces might be most relevant for recreation and physical activity contributing to improved metabolic health, whereas residential exposure to nature through daily activities may be most relevant for stress reduction contributing to improved mental health. It is important to note, though, that the results are based on the international recommendation of public accessibility (300 m) and the correlation to exposure might vary depending on size of the buffer zone. 52 2.4.2 Exposure, but not access, varies along an urban-rural gradient In Metro Vancouver, access to public green space remained relatively high throughout the region, with the mean percentage of postal codes having accessible green space ranging from 65% to 74%. While a decline in accessibility was observed for postal codes located within five to seven kilometres from an urban centre, this trend was not statistically significant. Conversely, significant trends in the relative proportion of different land cover types along an urban-rural gradient were observed, indicating that specific types of natural environment exposure may depend on distance to urban centres. Most studies investigating the health benefits of nature exposure have used general measures of greenness exposure, derived from remote sensing (e.g., NDVI), without distinguishing between different vegetation types. While evidence is limited, a few studies indicate that health effects differ depending on vegetation type (Reid et al. 2017; Akpinar, Barbosa-Leiker & Brooks 2016; Picavet et al. 2016; Alcock et al. 2015), and that water exposure seems to be particularly important for mental well-being (Nutsford, Pearson, Kingham & Reitsma 2016). If future studies confirm differences in health outcomes by type of natural environment, the urban-rural distribution may be an important aspect to consider when planning for healthy environments across an entire urban area. 2.4.3 Access and exposure are associated with marginalization Finally, this study shows a significant correlation between indicators of marginalization and both access to green space and exposure to different land cover types. This finding is of importance for two major reasons. First, assuming that the evidence on health benefits from natural environments holds true, it is imperative that investments in urban green planning are directed towards marginalized areas that currently have poor provision of green space, while keeping the 53 risks associated with gentrification in mind (Wolch, Byrne & Newell 2014). Second, the current findings call for careful consideration of methods to avoid selection biases whereby observed relationships between natural space and health are explained by already wealthy and healthy individuals choosing to live in areas with an abundance of natural spaces. An unexpected outcome in the analyses was that no correlation was observed between the marginalization indicator ‘dependency’ and access to natural space, while weak correlations were observed with exposure to natural land cover types. CAN-Marg is developed from a pan- Canadian perspective and it is possible that dependency reflects a separate dimension in the Metro Vancouver region. In addition, more than half of the region’s population identify as a recent immigrant or visible minority. As a result, high ethnic diversity in the area may not be associated with socioeconomic marginalization. Living in regions with a higher ethnic density may, in fact, help protect against mental health illness among ethnic minorities (Emerson, Minh & Guhn 2018). 2.5 Conclusions Recently, access and exposure to natural environments have become part of global healthy and sustainable city agendas (Hák, Janoušková & Moldan 2016; World Health Organization 2016), in some cases on par with access to other resources, such as health care and sanitation. The current findings indicate that access and exposure are only weakly correlated, suggesting that the two metrics capture unique estimates of urban natural spaces. Moreover, this study demonstrates a distinct socio-spatial pattern of natural spaces in Metro Vancouver. While accessibility to public green spaces remains relatively high throughout Metro Vancouver, almost one third of the region’s population has inadequate access as suggested by international recommendations 54 (Annerstedt van den Bosch et al. 2016; World Health Organization 2016). The current findings further indicate that residential exposure to nature varies across the region, with the lowest exposure to water and most vegetation types in urban centres. This trend calls for improved urban green planning in combination with considerations of exposure to different vegetation types across an urban-rural gradient. Lastly, the present findings demonstrate significant negative correlations between marginalization and access and exposure to natural spaces. Future studies should include measures of both access and exposure to different types of natural spaces in relation to a broad range of different health indicators, to evaluate if either measure or nature type is more strongly associated with specific outcomes. Aspects of urbanization and marginalization should also be considered to account for geographical and social effects. 55 Different types of urban natural environments influence various dimensions of self-reported health 3.1 Introduction A large and growing body of evidence demonstrates the potential of urban natural environments in tackling a wide variety of public health concerns (Browning et al. 2022; Jimenez et al. 2021; Smith et al. 2021; Houlden et al. 2018; Kondo et al. 2018). Research suggests that urban natural environments can reduce the risk of cardiovascular disease (Donovan, Michael, Gatziolis, Prestemon & Whitsel 2015), cognitive and behavioral disorders (Dadvand et al. 2015), mental health complaints and disorders (Annerstedt et al. 2012), and all-cause mortality (Crouse et al. 2017); though, there is some inconsistency in findings (Zock et al. 2018). Moreover, urban nature can increase the odds of improved self-reported health and well-being (Mears, Brindley, Jorgensen, Ersoy & Maheswaran 2019; Triguero-Mas et al. 2015; Maas, Verheij, Groenewegen, De Vries & Spreeuwenberg 2006; de Vries, Verheij, Groenewegen & Spreeuwenberg 2003). While the majority of findings report positive associations between natural environments and health, several reviews indicate inconsistency in results and insufficient evidence (Browning et al. 2022; Jimenez et al. 2021; Houlden et al. 2018; Kondo et al. 2018; Twohig-Bennett & Jones 2018; van den Bosch & Ode Sang 2017; Gascon et al. 2015). It is plausible that the lack of consistency in findings may in part be due to the simplified characterisation of urban environments and relatively crude green space indicators (e.g., NDVI) that do not take different types of natural environments into account, as well as the range of approaches used to estimate human-nature contact across studies. 56 Though urban natural environments may take many forms, most studies have considered natural environments as aggregated “green” spaces, with few studies exploring potential differences in effect depending on the type of natural environment. Conceivably, different types of natural environments may vary in their capacity to promote health depending on pathways most likely to support a health outcome. For instance, trees provide shade and, thus, likely reduce surrounding air temperatures and heat-related morbidities to a greater extent than do grassy areas, whereas open grass space may encourage group activities and, thus, support social well-being to a greater extent than dense wooded areas. Furthermore, open grass space may be associated with higher presence of recreational spaces and, therefore, encourage greater physical activity than forested areas; though, trees may also provide an attractive setting for physical activity due to shading. Existing research has measured human contact with nature in a variety of ways. Generally, studies use metrics for either access or exposure (Labib et al. 2020; Ekkel & de Vries 2017). Access metrics typically estimate the proximity of an individual’s residence to public natural environments, whereas exposure metrics typically estimate the relative proportion of natural environments surrounding an individual’s residence. Despite evidence suggesting that access to public green space is only weakly correlated with exposure to natural environments (Jarvis, Gergel, Koehoorn & van den Bosch 2020), little attention has been paid to the potential differences between these metrics. Most published work has focused exclusively on evaluating a single metric, without clear consideration of how the choice of metric may affect the association with different health outcomes. Only a limited number of studies have addressed potential gender differences in associations between natural environments and health. The few research findings that exist provide mixed 57 evidence. Some research report stronger beneficial effects of natural environments for women (Reklaitiene et al. 2014; Roe et al. 2013) and others for men (Richardson & Mitchell 2010). On the other hand, van den Berg et al. (2016) found no gender differences in health effects associated with visiting green spaces. Reported variation in health associations between men and women may stem from gender-related differences in perceptions and use of natural space. For instance, studies suggest that women’s engagement with natural environments is more strongly affected by societal norms (e.g., spending more time at home) and perceived barriers to use (e.g., quality and safety), as compared to men (Kavanagh, Bentley, Turrell, Broom & Subramanian 2006; Ward Thompson, Aspinall, Bell & Findlay 2005). The present study evaluates the association between different natural environments and self-reported general health, mental health, and common mental disorders, respectively. It addresses current gaps in the literature by evaluating exposure to different land cover types (both land and water features) and their association to health outcomes. This research also aims to assess potential differences in associations with health outcomes by investigating relationships with access or exposure to nature. Lastly, this study evaluates whether observed associations differ according to gender. 3.2 Materials and Methods 3.2.1 Study sample The study sample was comprised of residents of Metro Vancouver that responded to the 2013–2014 cycle of the Canadian Community Health Survey (CCHS) (weighted n = 2,173,170). The CCHS is an annual cross-sectional survey that collects information on the health status, health 58 care utilization, and health determinants of Canadian citizens who are 12 years of age or older (Statistics Canada 2015b). Responses are collected through in-person or telephone interviews. The questionnaire includes data on sociodemographic characteristics of respondents and residential location at the time of the survey. Residential location is defined on an aggregated six-digit postal code level. To provide reliable estimates across health regions and provinces, a multi-stage sampling strategy is used. Individuals living on First Nations reserves and other Aboriginal settlements, full-time members of the Canadian Forces, and those who are institutionalized at the time of data collection are excluded from the sample population. These groups represent approximately 3% of the Canadian population aged 12 and over (Statistics Canada 2015b). Further details on the survey procedures and methodology can be found via Statistics Canada (Statistics Canada 2015b). 3.2.2 Assessment of self-reported health Self-reported health was examined through three separate outcomes: (1) general health, (2) mental health, and (3) common mental disorder. The outcomes general health and mental health relate to aspects of perceived health, whereas common mental disorder is disease specific (anxiety and/or mood disorder). General and mental health were measured through two items in the CCHS that asked respondents to rate their health along a five-item ordinal scale, with lower values indicating poorer general and mental health (“In general, would you say your [health or mental health] is … poor, fair, good, very good, or excellent?”). Though not a substitute for specific health measures, this metric of self-rated health is a valid measure for monitoring general health and morbidity (Mawani & Gilmour 2010), and has been used in previous studies to monitor population health (Reid et al. 2017; Akpinar et al. 2016). For instance, Mawani and 59 Gilmour (2010) found that respondents with classified mental comorbidities consistently reported lower mean self-rated mental health and had significantly higher odds of reporting poor or fair mental health compared to respondents without classified comorbidities. Common mental disorder was measured through two items in the CCHS that asked respondents if they have a mood and/or anxiety disorder that has been diagnosed by a health professional (“Do you have a mood disorder such as depression, bipolar disorder, mania or dysthymia?” and “Do you have an anxiety disorder such as phobia, obsessive-compulsive disorder or a panic disorder?”). Responses from these two items were combined to create a dichotomous variable indicating whether the respondent reported having none or either of the above-mentioned common mental disorders. 3.2.3 Potential confounders All potential confounders of the associations between natural environments and health were selected a priori using a directed acyclic graph (Textor & Hardt 2011) (see Figure B-1 of Appendix B). The final set of confounders included gender (man, woman), age (12-24 years, 25-44 years, 45-64 years, > 65 years), racial/cultural background (White, East Asian, South Asian, Aboriginal, Black/Latin American/Multiracial/Multiethnic/other), education level (less than secondary education, secondary graduate or some post-secondary education, post-secondary certificate/diploma or university graduate), household income (< 30,000, 30,000-59,999, 60,000-99,999, ≥ 100,000), the number of persons under the age of 18 years in the household (0, 1, 2, ≥ 3), and urbanicity (i.e., distance of residential location from urban centre; < 2.5 km, 2.5-5 km, > 5km). Apart from urbanicity, all confounders were available in the CCHS. The variable urbanicity is a categorical variable that represents the relative distance of the respondents’ six-60 digit postal code to a regional urban centre and was created using an urban-rural gradient approach that was meant to measure urbanicity independent from urban land cover (see description in Section 2.2.2). 3.2.4 Natural environments Data on natural environments were linked to the study participants using six-digit postal codes. Two separate metrics of natural environments – exposure and access – were used to investigate potentially different pathways associated with health outcomes. The development of these metrics is described in detail in Chapter 2 (Section 2.2.1), but brief descriptions are provided below. 3.2.4.1 Residential exposure to natural and non-natural environments As in Chapter 2, a high spatial resolution (5 m) land cover map of Metro Vancouver (Metro Vancouver 2019b; Williams et al. 2018) was used to analyse residential exposure to various natural and non-natural environment types. Residential exposure to seven land cover classes was examined: coniferous, deciduous, shrub, grass-herbs (i.e., combined natural and modified grass-herb), water, buildings, and paved surfaces. Additionally, exposure to aggregated green space (i.e., combined coniferous, deciduous, shrub, natural grass-herb, and modified grass-herb) was estimated. While the main focus was to measure classes related to natural environments, buildings and paved surfaces were also included in separate analyses, as it was hypothesized that these land cover types would be negatively associated with self-reported health outcomes through, for example, association with excessive heat exposure and traffic-related air pollution. 61 Exposure was defined as the percentage of each land cover class, in addition to the percentage of aggregated green space, within varying distances (250-, 500-, and 1,000 m) of a study participant’s six-digit postal code centroid. Smaller distances were excluded to reduce the risk of exposure misclassification associated with positional inaccuracies of geocoded postal codes that may differ from individual’s exact residential location by 80 to 320 m, with a median of 160 m (Khan et al. 2018). Multiple buffers were examined to reflect the relevant spatial scale for different pathways linking natural environments to health outcomes (Markevych et al. 2017). For example, smaller buffer sizes may better represent mental restorative influences, while larger buffer sizes may better represent the potential role of natural environments in promoting physical and social activities. 3.2.4.2 Access to public green space Access was defined as a binary variable indicating whether or not a study participant’s six-digit postal code centroid was located within 300 m of a public green space greater than or equal to one hectare in size. Public green spaces were identified by selecting areas defined as park, recreation, and protected areas in 2011 land use and land designation data layers of Metro Vancouver (Metro Vancouver 2016b). The distance (300 m) and size (one hectare) were selected to conform with international recommendations and serve as a proxy for acceptable walking distance (i.e., 5–10 minute walking time) and the minimum area required to afford health-promoting activities (Annerstedt van den Bosch et al. 2016; World Health Organization 2016). 62 3.2.5 Statistical analysis Logistic regression models were used to estimate the association between the health outcomes and the access and exposure metrics separately (Hosmer, Lemeshow & Sturdivant 2013). Associations with general and mental health were evaluated using multinomial logistic regression models, as the data did not meet the proportional odds assumption for ordinal regression. Associations with a common mental disorder were tested with binary logistic regression models. Each model was regressed on a single natural environment explanatory variable to evaluate the role of each metric independently. Access was modelled as the presence or absence of a public green space within 300 m of a respondent’s six-digit postal code centroid, and exposure as a one percent increase in land cover area within individual buffers surrounding respondent’s six-digit postal code centroid. Correlation coefficients and variance-inflation factor values indicated little evidence of multicollinearity. As such, all seven covariates (i.e., gender, age, racial/cultural background, education level, household income, the number of persons under the age of 18 years in the household, and urbanicity) were controlled for in adjusted models. Stratified analyses were performed to examine whether associations differed by gender. The CCHS includes a variable labelled sex that codes for female and male. Sex is indicative of the biological construct for females and males, but is also a measure for the social construct of gender for women and men. While sex and gender are distinct constructs, they are significantly and complexly interrelated (Sharman & Johnson 2012; Krieger 2003). For ease of communication, gender is referred to in this study. All analyses were conducted within the Statistics Canada Research Data Centre at the University of British Columbia using SAS software, version 9.4 (SAS Institute Inc 2013). Survey sample 63 weights were used to compute point estimates and 500 replicate bootstrap weights provided by Statistics Canada to calculate confidence intervals. P-values < 0.05 for model coefficients were considered as statistically significant. Respondents with missing data for any of the natural environment, outcome or confounder variables were excluded from the study sample. In compliance with Statistics Canada privacy requirements, only weighted values are reported. To test for the robustness of the model findings, associations between general and mental health and the natural environment metrics were also analyzed with binary logistic regression by recoding and dichotomising outcomes to either good (based on categories ‘good’, ‘very good’, and ‘excellent’) or bad (based on categories ‘poor’ and ‘fair’) health. Next, a sensitivity analysis was conducted for exposure estimated as the mean annual NDVI value within the various buffer distances of six-digit postal code centroids, derived from RapidEye (5 m spatial resolution) and Landsat (30 m spatial resolution) satellite imagery (Canadian Urban Environmental Health Research Consortium 2018; CanMap 2015; USGS n.d., n.d.). High spatial resolution RapidEye imagery was included to correspond to the sensor and spatial resolution of the land cover data, and moderate spatial resolution Landsat imagery as it is the most commonly used sensor to derive NDVI values in past studies (Fong, Hart & James 2018). To permit comparability between land cover and NDVI exposure metrics, measures were standardized by transforming continuous values to interquartile ranges (IQR) and evaluated health associations per IQR increase in exposure through logistic regression models. 64 3.3 Results 3.3.1 Characteristics of the study sample After excluding missing data, a weighted sample of 1,960,575 respondents had matched natural environment data and valid responses for the outcome and confounder variables (see Figure B-2 of Appendix B). Descriptive statistics for the study sample and outcomes are presented in Table 3-1. Two-thirds of the sample was 25 to 64 years of age (66.2%). Half of the sample described themselves as white (50.7%) and approximately one third as East Asian (29.5%). The majority of respondents had received a post-secondary certificate, diploma or university degree (59.9%). Approximately half the sample resided near an urban centre (52.4%) and nearly two-thirds lived in a household with no persons under the age of 18 years (64.0%). Response categories of ‘poor’ and ‘fair’ for general health and mental health were recoded into a single category, termed ‘poor or fair’, due to Statistics Canada requirements for the small sample size within the ‘poor’ outcome category. Most respondents reported ‘good’ (32.2%) or ‘very good’ (36.7%) general health, with only 9.8% of respondents reporting the lowest score of ‘poor or fair’. Similar trends were observed for mental health, with most respondents reporting ‘very good’ (37.1%) or ‘excellent’ (30.8%) mental health and only 8.2% reporting ‘poor or fair’. Only 10.7% of respondents reported having a common mental disorder. 65 Table 3-1. Characteristics of Metro Vancouver respondents to the 2013-2014 CCHS (weighted n=1,960,575). Weighted % Demographic variables Gender Women 51.7 Men 48.4 Age 12-24 years 19.0 25-44 years 34.8 45-64 years 31.4 65+ years 14.9 Racial/cultural background White 50.7 East Asian 29.5 South Asian 9.1 Aboriginal 1.5 Black, Latin American, Multiracial/Multiethnic, or Other 9.2 Total household income < 30,000 20.9 30,000-59,999 23.8 60,000-99,999 24.5 ≥ 100,000 30.8 Highest level of education Less than secondary education 15.0 Secondary graduate or some post-secondary education 25.1 Post-secondary certificate/diploma or university graduate 59.9 Number of persons < 18 years of age in household 0 64.0 1 16.8 2 14.7 ≥ 3 4.4 Distance of residential location from urban centre < 2.5 km 52.4 2.5-5 km 39.7 > 5 km 7.9 Outcome variables General health Poor or fair 9.8 Good 32.2 Very good 36.7 Excellent 21.4 Mental health Poor or fair 8.2 Good 23.9 Very good 37.1 Excellent 30.8 Common mental disorder Yes 10.7 No 89.3 66 Approximately 74% of respondents had adequate access to public green space, as defined by the presence of a public green space greater than or equal to one hectare within 300 m of their six-digit postal code centroid. Exposure to aggregated green space varied between 36.0% and 39.2% across buffer sizes (Table 3-2). Within the vegetated classes, deciduous was the most common exposure, followed by grass-herbs, coniferous and shrub. Exposure to water varied between 1.9% and 3.8%. Exposure to buildings and paved surfaces jointly accounted for between 53.8% and 59.3% of exposure across buffer sizes. Table 3-2. Weighted mean percentage of the different land cover types within 250-, 500-, and 1,000 m buffers of six-digit postal code centroids among Metro Vancouver respondents to the 2013-2014 CCHS (weighted n=1,960,575). Land cover type Buffer distance (m) from postal code centroid 250 500 1,000 Buildings 27.8 (0.1) 26.2 (0.1) 24.4 (0.1) Paved 31.5 (0.1) 30.5 (0.1) 29.4 (0.1) Coniferous 5.9 (0.1) 6.3 (0.1) 6.9 (0.1) Deciduous 17.5 (0.1) 17.9 (0.1) 18.2 (0.1) Shrub 0.6 (0.02) 0.7 (0.01) 0.8 (0.01) Grass-herbs 12.0 (0.1) 12.6 (0.1) 13.1 (0.1) Aggregated green space 36.0 (0.2) 37.5 (0.2) 39.2 (0.2) Water 1.9 (0.1) 2.8 (0.1) 3.8 (0.1) Data presented are mean (SE). 3.3.2 Self-reported health outcomes in association with natural environment metrics Results from the fully adjusted logistic regression models are presented in Table 3-3. Findings from the unadjusted models were comparable to fully adjusted models, with slight attenuation in estimated effects and fewer statistically significant findings following the addition of confounders. As such, only findings from the fully adjusted models are reported (see Table B-1 of the Appendix B for unadjusted model output). Access to public green space was not 67 significantly associated with any of the three health outcomes, though the size and direction of the estimated effect suggests that access to public green space was associated with reduced odds of reporting ‘poor or fair’ as compared to ‘excellent’ general health (OR: 0.694, 95% CI: 0.449, 1.074). While exposure to aggregated green space was not significantly associated with the health outcomes, the findings indicate that health associations differ across land cover types. Exposure to water within 1,000 m buffers was associated with lower odds of reporting ‘poor or fair’ as compared to ‘excellent’ general health (OR: 0.982, 95% CI: 0.965, 0.999). Higher exposure to shrubs within all buffer distances was associated with lower odds of reporting ‘poor or fair’ as compared to ‘excellent’ mental health (OR: 0.741, 95% CI: 0.576, 0.953 for 250 m; OR: 0.737, 95% CI: 0.577, 0.942 for 500 m; OR: 0.731, 95% CI: 0.570, 0.938 for 1000 m). While not statistically significant, the size and direction of the estimated effect suggests that higher exposure to shrubs within 1,000 m buffers was associated with reduced odds of reporting ‘poor or fair’ as compared to ‘excellent’ general health (OR: 0.861, 95% CI: 0.699, 1.061). Higher exposure to grass-herbs within 250 m buffers was associated with lower odds of reporting a common mental disorder (OR: 0.980, 95% CI: 0.965, 0.995). Higher exposure to paved surfaces was associated with higher odds of reporting ‘poor or fair’ general health (OR: 1.020, 95% CI: 1.005, 1.036 for 250 m; OR: 1.032, 95% CI: 1.012, 1.051 for 500 m; OR: 1.031, 95% CI: 1.011, 1.051 for 1,000 m) and mental health (OR: 1.025, 95% CI: 1.001, 1.049 for 250 m).68 Table 3-3. Adjusted associations between access and exposure metrics and self-reported general health, mental health, and common mental disorder among Metro Vancouver respondents to the 2013-2014 CCHS (weighted n=1,960,575). Access or exposure Buffer size General Health Mental Health CMD OR (95% CI) a OR (95% CI) a OR (95% CI) b Access c - 0.694 (0.449, 1.074) 0.949 (0.583, 1.546) 0.919 (0.657, 1.288) Buildings d 250 m 0.999 (0.982, 1.016) 1.009 (0.988, 1.030) 1.006 (0.994, 1.018) 500 m 0.996 (0.978, 1.014) 1.009 (0.985, 1.033) 1.002 (0.988, 1.016) 1,000 m 0.994 (0.974, 1.014) 1.003 (0.975, 1.032) 1.005 (0.989, 1.021) Paved d 250 m 1.020 (1.005, 1.036) 1.025 (1.001, 1.049) 1.009 (0.995, 1.023) 500 m 1.032 (1.012, 1.051) 1.011 (0.983, 1.041) 1.001 (0.985, 1.018) 1,000 m 1.031 (1.011, 1.051) 1.007 (0.975, 1.039) 0.998 (0.981, 1.016) Coniferous d 250 m 0.996 (0.969, 1.023) 0.970 (0.933, 1.008) 1.009 (0.991, 1.027) 500 m 0.999 (0.975, 1.024) 0.979 (0.947, 1.012) 1.010 (0.993, 1.026) 1,000 m 0.997 (0.978, 1.017) 0.990 (0.960, 1.020) 1.010 (0.995, 1.027) Deciduous d 250 m 0.994 (0.976, 1.012) 0.989 (0.962, 1.017) 0.992 (0.989, 1.005) 500 m 0.996 (0.976, 1.016) 0.993 (0.963, 1.023) 0.993 (0.979, 1.008) 1,000 m 0.999 (0.978, 1.020) 0.991 (0.955, 1.027) 1.002 (0.984, 1.020) Shrub d 250 m 1.034 (0.858, 1.246) 0.741 (0.576, 0.953) 1.073 (0.948, 1.215) 500 m 0.936 (0.768, 1.142) 0.737 (0.577, 0.942) 1.011 (0.897, 1.140) 1,000 m 0.861 (0.699, 1.061) 0.731 (0.570, 0.938) 0.988 (0.857, 1.139) Grass-herbs d 250 m 1.001 (0.980, 1.021) 1.000 (0.962, 1.039) 0.980 (0.965, 0.995) 500 m 1.008 (0.988, 1.028) 1.006 (0.972, 1.040) 0.985 (0.967, 1.004) 1,000 m 1.009 (0.989, 1.030) 0.995 (0.965, 1.027) 0.985 (0.967, 1.003) Aggregated green space d 250 m 0.997 (0.986, 1.008) 0.991 (0.976, 1.006) 0.994 (0.986, 1.002) 500 m 1.000 (0.988, 1.011) 0.994 (0.979, 1.009) 0.996 (0.988, 1.005) 1,000 m 1.000 (0.989, 1.012) 0.993 (0.977, 1.009) 0.999 (0.990, 1.009) Water d 250 m 0.994 (0.967, 1.021) 0.984 (0.955, 1.014) 0.999 (0.984, 1.015) 500 m 0.982 (0.962, 1.001) 1.002 (0.979, 1.025) 1.007 (0.995, 1.019) 1,000 m 0.982 (0.965, 0.999) 1.012 (0.988, 1.036) 1.002 (0.988, 1.015) Models control for gender, age, racial/cultural background, total household income, education level, number of persons < 18 years of age in the household, and urbanicity. All odds ratios (OR) and 95% confidence intervals are rounded. Bold OR indicates that the association is in hypothesized direction and p-value < 0.05. a. Output modeled as OR of reporting ‘poor or fair’ compared to ‘excellent’ health. b. Output modeled as OR of reporting a common mental disorder (CMD) c. Models based on having access to public green space, defined as six-digit postal code within 300 m of public green space greater than or equal to one hectare. d. Models based on 1% increase in land cover area within buffer. 3.3.3 Analyses stratified by gender Results from fully adjusted logistic regression models stratified by gender are presented in Table 3-4. In summary, access to public green space was significantly associated with lower odds of 69 reporting ‘poor or fair’ general health in men, but not in women. Though not statistically significant, similar trends were observed for mental health and common mental disorder. Estimated effects suggest that higher exposure to shrubs was associated with lower odds of reporting ‘poor or fair’ general and mental health for both genders; however, a statistically significant association was only found among women for mental health. Exposure to water was more strongly associated with positive general health among women as compared to men. Conversely, exposure to paved surfaces was more strongly associated with negative general and mental health among men as compared to women. Exposure to aggregated green space and deciduous trees was associated with lower odds of reporting a common mental disorder among men, while exposure to grass-herbs was associated with lower odds of reporting a common mental disorder among women. 3.3.4 Sensitivity analyses Coding general and mental health as binary outcomes (as opposed to non-binary categorical scales) resulted in comparable findings to the main results (see Table B-2 of Appendix B). Residential exposure measured as mean annual NDVI was not associated with any of the three self-reported health outcomes (see Table B-3 of Appendix B). Additionally, results were robust to the indicator of residential exposure; with neither metric of residential greenness exposure (i.e., exposure estimated as the mean annual NDVI or the percentage of aggregated green land cover) demonstrating stronger associations with the health outcomes (see Table B-4 of Appendix B).70 Table 3-4. Gender-stratified adjusted associations between access and exposure metrics and self-reported general health, mental health, and common mental disorder among Metro Vancouver respondents to the 2013-2014 CCHS (weighted n=1,960,575). Access or exposure Buffer size General Health Mental Health CMD OR (95% CI) a OR (95% CI) a OR (95% CI) b Women Men Women Men Women Men Access c - 1.064 (0.611, 1.855) 0.498 (0.256, 0.972) 1.435 (0.661, 3.116) 0.645 (0.338, 1.229) 1.183 (0.794, 1.761) 0.701 (0.411, 1.295) Buildings d 250 m 0.994 (0.970, 1.018) 1.000 (0.975, 1.027) 1.005 (0.974, 1.038) 1.013 (0.984, 1.042) 0.998 (0.981, 1.015) 1.015 (0.995, 1.036) 500 m 0.986 (0.962, 1.011) 1.002 (0.973, 1.031) 1.013 (0.979, 1.049) 1.003 (0.972, 1.036) 1.001 (0.981, 1.021) 1.005 (0.982, 1.029) 1,000 m 0.983 (0.958, 1.009) 0.999 (0.966, 1.033) 1.014 (0.973, 1.069) 0.989 (0.952, 1.027) 1.006 (0.984, 1.027) 1.005 (0.981, 1.029) Paved d 250 m 0.998 (0.975, 1.021) 1.039 (1.015, 1.063) 1.019 (0.984, 1.056) 1.033 (1.002, 1.065) 1.006 (0.989, 1.024) 1.014 (0.989, 1.040) 500 m 1.006 (0.979, 1.034) 1.055 (1.024, 1.087) 0.990 (0.952, 1.031) 1.035 (0.999, 1.071) 0.996 (0.976, 1.016) 1.011 (0.984, 1.039) 1,000 m 1.013 (0.986, 1.041) 1.048 (1.014, 1.084) 0.998 (0.955, 1.041) 1.017 (0.975, 1.060) 1.001 (0.980, 1.022) 0.997 (0.969, 1.025) Coniferous d 250 m 1.017 (0.980, 1.055) 0.978 (0.933, 1.025) 0.980 (0.925, 1.039) 0.952 (0.901, 1.007) 1.012 (0.988, 1.036) 1.005 (0.978, 1.032) 500 m 1.008 (0.974, 1.044) 0.996 (0.954, 1.039) 0.988 (0.939, 1.039) 0.968 (0.921, 1.016) 1.010 (0.988, 1.033) 1.007 (0.981, 1.034) 1,000 m 1.005 (0.977, 1.033) 1.000 (0.965, 1.036) 0.985 (0.939, 1.034) 1.001 (0.958, 1.046) 1.008 (0.988, 1.029) 1.013 (0.983, 1.043) Deciduous d 250 m 1.018 (0.980, 1.055) 0.973 (0.939, 1.009) 0.981 (0.940, 1.023) 0.995 (0.957, 1.035) 1.001 (0.983, 1.020) 0.978 (0.956, 1.000) 500 m 1.021 (0.995, 1.047) 0.972 (0.938, 1.007) 0.990 (0.943, 1.039) 0.991 (0.957, 1.027) 1.001 (0.918, 1.021) 0.982 (0.957, 1.008) 1,000 m 1.019 (0.992, 1.048) 0.977 (0.942, 1.013) 0.983 (0.925, 1.045) 0.994 (0.957, 1.034) 1.015 (0.991, 1.038) 0.983 (0.953, 1.014) 71 Access or exposure Buffer size General Health Mental Health CMD OR (95% CI) a OR (95% CI) a OR (95% CI) b Women Men Women Men Women Men Shrub d 250 m 0.948 (0.729, 1.232) 1.138 (0.862, 1.501) 0.626 (0.363, 1.078) 0.793 (0.586, 1.072) 1.054 (0.906, 1.228) 1.078 (0.765, 1.519) 500 m 0.937 (0.711, 1.235) 0.921 (0.684, 1.240) 0.663 (0.465, 0.944) 0.746 (0.523, 1.063) 1.002 (0.861, 1.166) 1.005 (0.780, 1.295) 1,000 m 0.828 (0.607, 1.127) 0.860 (0.647, 1.144) 0.578 (0.380, 0.879) 0.813 (0.596, 1.109) 0.959 (0.804, 1.145) 1.015 (0.770, 1.337) Grass-herbs d 250 m 1.004 (0.980, 1.028) 0.996 (0.964, 1.029) 1.010 (0.947, 1.077) 0.982 (0.942, 1.025) 0.980 (0.962, 0.999) 0.976 (0.949, 1.004) 500 m 1.013 (0.988, 1.037) 1.001 (0.968, 1.035) 1.005 (0.953, 1.061) 1.007 (0.963, 1.053) 0.988 (0.969, 1.009) 0.977 (0.941, 1.015) 1,000 m 1.014 (0.990, 1.040) 1.003 (0.971, 1.036) 0.971 (0.936, 1.006) 1.016 (0.971, 1.064) 0.987 (0.969. 1.005) 0.980 (0.941, 1.022) Aggregated green space d 250 m 1.011 (0.996, 1.025) 0.986 (0.968, 1.004) 0.991 (0.969, 1.015) 0.988 (0.969, 1.009) 0.998 (0.987, 1.009) 0.987 (0.974, 1.000) 500 m 1.012 (0.997, 1.037) 0.989 (0.970, 1.008) 0.993 (0.971, 1.016) 0.993 (0.974, 1.013) 1.000 (0.989, 1.011) 0.990 (0.976, 1.005) 1,000 m 1.010 (0.995, 1.024) 0.993 (0.974, 1.012) 0.984 (0.959, 1.009) 1.001 (0.981, 1.022) 1.003 (0.992, 1.014) 0.994 (0.978, 1.011) Water d 250 m 0.971 (0.940, 1.004) 1.003 (0.966, 1.042) 0.983 (0.906, 1.067) 0.984 (0.944, 1.025) 0.998 (0.975, 1.022) 0.997 (0.970, 1.026) 500 m 0.976 (0.954, 0.999) 0.985 (0.955, 1.014) 1.018 (0.982, 1.055) 0.990 (0.959, 1.022) 1.005 (0.989, 1.011) 1.008 (0.986, 1.031) 1,000 m 0.980 (0.958, 1.002) 0.985 (0.957, 1.014) 1.029 (0.991, 1.069) 0.999 (0.967, 1.032) 0.993 (0.973, 1.013) 1.013 (0.991, 1.036) Models control for age, racial/cultural background, total household income, education level, number of persons <18 years of age in the household, and urbanicity. All odds ratios (OR) and 95% confidence intervals are rounded. Bold OR indicates that the association is in hypothesized direction and p-value < 0.05. a. Output modeled as OR of reporting ‘poor or fair’ compared to ‘excellent’ health. b. Output modeled as OR of reporting a common mental disorder (CMD) c. Models based on having access to green space, defined as six-digit postal code within 300 m of public green space greater than or equal to one hectare. d. Models based on 1% increase in land cover area within buffer. 72 3.4 Discussion The present study assessed the relative influence of different types of natural environments and forms of human-nature contact on self-reported general health, mental health, and common mental disorder. The results indicate that associations between exposure and health outcomes varied according to land cover type. Specifically, exposure to water and some vegetation types, in particular shrubs and grass-herbs, was associated with reduced odds of poor self-reported health; the reverse trend was observed for exposure to paved surfaces. No significant associations between self-reported health and access to public green space were observed, though the effect estimates suggest that access to public green space may be beneficial for general health. The findings also suggest that observed associations between human-nature contact and self-reported health differ according to gender. No significant associations were observed between self-reported health and exposure to aggregated green space. 3.4.1 Natural environments should be considered individually, rather than as aggregated ‘green’ space The present study did not detect an association between self-reported health and exposure to aggregated green space. This conflicts with previous studies (Song et al. 2019; de Vries et al. 2003, 2016; Triguero-Mas et al. 2015; van den Berg, Maas, Verheij & Groenewegen 2010; Maas, Verheij, et al. 2009; Maas et al. 2006), yet is similar to Akpinar et al. (2016), Picavet et al. (2016), and Rugel et al. (2019). The conflicting results may indicate differences depending on the population examined or reflect that health may be specifically related to certain landscape characteristics not captured in metrics of exposure to generic “green” space. 73 This study found that associations between exposure to natural environments and self-reported health varied according to land cover type. This finding is supported by a limited body of evidence that has identified differences in health effects depending on the type of natural environment. For instance, consistent with the present findings, positive associations between blue space and self-reported health and well-being have been reported (Mears, Brindley, Jorgensen, et al. 2019; Wheeler, White, Stahl-Timmins & Depledge 2012; Wheeler et al. 2015). Studies have further demonstrated that blue spaces may improve mental health (Völker et al. 2018; Alcock et al. 2015; Huynh, Craig, Janssen & Pickett 2013; White, Alcock, Wheeler & Depledge 2013) and reduce the risk of psychological distress and common mental disorders (de Vries et al. 2016; Nutsford et al. 2016). The association between water exposure and health may be related to the reported preference people have for landscapes with water (White et al. 2010). Additionally, as compared to other settings, blue spaces are particularly associated with feelings of restoration and positive affect (White, Pahl, Ashbullby, Herbert & Depledge 2013). In agreement with the present findings, research from rural England found that higher exposure to grasslands was related to reduced odds of ‘psychiatric caseness’ within-individuals who relocated during the study period (Alcock et al. 2015), while a study from rural Japan found that moderate amounts of neighborhood grassland were associated with lower odds of depression among older adults (Nishigaki, Hanazato, Koga & Kondo 2020). While Metro Vancouver may not be geographically comparable to rural England and Japan, similar associations are observed between exposure to grass-herb vegetation types and reported mental illness in both settings. Previous studies have also shown that exposure to grasslands and agricultural green space is associated with positive perceived health (Wheeler et al. 2015; Maas et al. 2006; de Vries et al. 74 2003), reduced mental distress (Huang, Yang, Jane, Li & Bauer 2020; Tsai et al. 2018), fewer depression complaints (Picavet et al. 2016), and better physical functioning (Picavet et al. 2016; Maas et al. 2008). From a theoretical perspective, grasslands and agricultural green space may improve health as these landscapes afford wide views of distant horizons, which, as described by Prospect-Refuge Theory (Appleton 1975), a pioneering theory in the field of environmental psychology, would have served an adaptive advantage throughout human evolutionary history. Though not supported by the present findings, studies have found that exposure to trees and forests is associated with good self-reported health (Jiang, Larsen & Sullivan 2020; Astell-Burt & Feng 2019; Reid et al. 2017; Wheeler et al. 2015; Maas et al. 2006), fewer mental health complaints (Akpinar et al. 2016), reduced stress (Egorov et al. 2020; Jiang, Li, Larsen & Sullivan 2014), and higher perceived restoration likelihood (Lindal & Hartig 2015). Results from this study suggest that exposure to shrubs is associated with improved general and mental health. Only one known previous study has reported health benefits of specifically shrub vegetation types (Becker, Browning, Kuo & Van Den Eeden 2019). This study found that county-level percentage of shrub and forest cover was inversely associated with healthcare expenditure in the United States. The limited evidence in support of a relationship between shrubs and health outcomes may be due to previous researchers grouping shrubs and trees into a single land cover type. Such grouping could also explain the lack of association between trees and self-reported health found in this study. As described by the ART (Kaplan 1995), environments with more trees and shrubs may promote health as they possess perceptual characteristics that evoke involuntary fascination, contribute to a sense of being away, and support activities compatible with individual’s interests. In line with this notion, research indicates that individuals may prefer 75 landscapes with more tree canopy and other complex vegetation and, furthermore, that these spaces may be important for restoring mental capacities (Veitch et al. 2022; Suppakittpaisarn, Jiang, Slavenas & Sullivan 2019; Wang, Zhao, Meitner, Hu & Xu 2019). For example, a survey among an English population suggested that visits to woodlands and forested areas were associated with higher recalled restoration than visits to more open natural settings (White, Pahl, et al. 2013). Finally, this study found that exposure to paved surfaces was associated with higher odds of ‘poor or fair’ general and mental health. This finding is important, as published work has focused almost exclusively on reporting the health effects of exposure to green and blue space. Only one known study has reported on the association between non-natural land cover types and health outcomes among youth and adults (Larson et al. 2018). This prior study found that the percentage of buildings and paved surfaces within ZIP code areas was associated with increased risk of severe anxiety in youth with autism across the United States. Landscapes with a higher percentage of impervious surface coverage may impair health through association with traffic noise, air pollution, and excessive heat exposure. 3.4.2 Nature exposure may be more consistently associated with human health outcomes than is access In the present study, access to public green space was not significantly associated with any of the three health outcomes in the general population, though estimated effects suggest that access to public green space may be beneficial for general health. Access was, however, significantly associated with lower odds of ‘poor or fair’ general health among men in gender-stratified analyses. While most research has focused exclusively on evaluating a single metric of human-76 nature contact, there are examples of studies that have assessed both access and exposure (e.g., Dadvand, Villanueva, et al. 2014). Many of these studies have, similar to the current study, observed more consistent findings for exposure metrics compared to accessibility metrics, including association to general and mental health (Triguero-Mas et al. 2015) and to perceived restorative quality of respondent’s living environments (Dzhambov, Hartig, et al. 2018). On the other hand, Dadvand et al. (2016) found that both greenness exposure and access to green space were associated with better subjective general health. Altogether, it seems that residential exposure to natural environments may be more consistently associated with health indicators, but evidence is too limited and inconsistent to make conclusive generalizations. 3.4.3 Benefits of human-nature contact may differ between genders Results from the stratified analyses suggest that men may benefit more from access to public green space than women. Findings indicate that access to public green space was significantly associated with reduced odds of ‘poor or fair’ general health among men, but not women. While not statistically significant, this trend was also observed for self-reported mental health and common mental disorder. This finding may in part be explained by research that indicates that men are more likely to use and, therefore, benefit from public green spaces (Richardson & Mitchell 2010). On the other hand, results from the present study suggest that exposure to natural land cover, specifically water and shrubs, was more strongly associated with positive general and mental health respectively for women as compared to men. This tendency for women to benefit more than men from residential exposure to natural environments is consistent with previous findings (Gascon et al. 2018; Triguero-Mas et al. 2015). Gender differences in the health benefits associated with nature exposure may be explained by the social and physical characteristics of 77 residential areas that tend to influence women’s health more strongly than men’s (Stafford, Cummins, Macintyre, Ellaway & Marmot 2005). Research suggests that women may benefit more from residential surrounding nature, as they, due to lingering societal norms, are more likely to spend a greater proportion of their time near the home, for example, supervising children or elderly family, working part time, and conducting domestic chores (Kavanagh et al. 2006). 3.5 Conclusions This study contributes to an improved understanding of the potential role of land cover type in associations with public health outcomes and suggests that exposure to certain types of nature may be more beneficial than access to public green space. The findings also suggest that observed associations between human-nature contact and self-reported health may differ according to gender. Consistent with published work, the strength of observed associations between natural environments and health were generally weak. Nonetheless, small effects distributed across a population may have a substantial influence on public health. If confirmed by other studies, the results suggest that if we continue to plan for and manage urban nature without a consideration of the type of natural environment or the kind of contact residents have with their surrounding environment, we may not be creating cities that optimally benefit public health. 78 Various land cover types influence childhood development differently – the relative advantages of vegetation and disadvantages of paved land 4.1 Introduction Early childhood development is an important predictor for physical, socio-emotional, and cognitive health across the life course (Gluckman et al. 2008). Globally, there is public health concern over the high prevalence of developmental health issues among children, including emotional and behavioral problems (e.g., ADHD) that often impose lasting harmful effects on children’s lives and considerable social and economic burdens (Baranne & Falissard 2018). Previous studies indicate that family risk factors, including SES, are strong predictors of children’s developmental trajectories (Brownell et al. 2016); however, features of the surrounding environment that are associated with child development remain understudied. Growing evidence demonstrates the potential of urban green spaces to positively influence early childhood health and development (Davis et al. 2021; Mygind et al. 2019, 2021; Tillmann, Tobin, Avison & Gilliland 2018; Vanaken & Danckaerts 2018). Prior research has demonstrated positive associations between green space exposure and multiple childhood health outcomes, such as reduced emotional and behavioral problems (Liao et al. 2020; Madzia et al. 2019; Markevych et al. 2014), improved attention and working memory (Dadvand et al. 2015, 2017, 2018), higher academic achievement (Kuo et al. 2021; Sivarajah et al. 2018), and better overall quality of life (Tillmann, Clark, et al. 2018). 79 While a growing amount of work suggests a positive association between green space exposure and indicators of early childhood health and development, the current evidence base is inconsistent and inconclusive (Davis et al. 2021; Mygind et al. 2021). The heterogeneity in findings may be partly due to the unidimensional definitions of green space and overly simplistic exposure measures that do not take the specific type of green space into consideration. Few studies have investigated childhood health associations related to distinct vegetation types (Holland et al. 2021). Different types of vegetation may support different pathways linking green space exposure to health outcomes. Equally, the relative influence of non-vegetated land, such as paved surfaces, has rarely been evaluated in this context (Larson et al. 2018). The present study aims to investigate the association between lifetime residential exposure to green space and childhood development in a population-based birth cohort in Metro Vancouver, Canada (van den Bosch et al. 2018). It addresses current gaps in the literature by evaluating exposure to distinct land cover types and their association to childhood developmental outcomes. 4.2 Materials and Methods 4.2.1 Study sample The study cohort was identified using administrative data from the BC Ministry of Health (British Columbia Ministry of Health 2011b,a; Canadian Institute for Health Information 2011), the BC Vital Statistics Agency (British Columbia Vital Statistics Agency 2011b,a), Perinatal Services BC (Perinatal Services BC 2011), and the Human Early Learning Partnership (Human Early Learning Partnership 2011), through Population Data BC that provided individual-data linkage across data sources. The cohort comprised all live, singleton births to mothers residing in 80 Metro Vancouver from April 2000 to December 2005. To be eligible, children and their mothers had to be registered in the provincial health insurance program, Medical Services Plan (MSP), and have lived in Metro Vancouver for the duration of the exposure period. The MSP is a mandatory universal health insurance program in the province of BC, covering nearly all residents (Chamberlayne et al. 1998). The exposure period was from birth to time of outcome assessment (second half of kindergarten school year). In BC, children can commence full-day kindergarten in the September of the calendar year in which they turn five years old (Government of British Columbia 2021). Children who moved outside of the study region, had a gap in residential history, or who died during the exposure period were excluded from the cohort. 4.2.2 Assessment of early childhood development Early childhood development was assessed via kindergarten teacher ratings on the EDI completed in the 2005/2006 to 2010/2011 academic years (Janus & Offord 2007). As described earlier (see Section 1.6.3.6), the EDI is a population-level dataset that assesses a child’s ability to meet age-appropriate developmental expectations in five domains: (1) physical health and well-being; (2) social competence; (3) emotional maturity; (4) language and cognitive development; and (5) communication skills and general knowledge. For the current study, the total EDI score (ranging from 0 to 50) was used as an indicator of individual children’s overall childhood development, with higher scores corresponding to better developmental outcomes. 4.2.3 Potential confounders All potential confounders of the association between residential exposure and childhood development were selected a priori using a directed acyclic graph (Textor & Hardt 2011) (see 81 Figure C-1 of Appendix C). Individual-level variables for a child’s sex (male, female), season of birth (January to March as winter, April to June as spring, July to September as summer, and October to December as fall), and lone parent household at time of birth (yes, no) were obtained from the BC Perinatal Data Registry. The British Columbia Perinatal Data Registry (BCPDR) contains data abstracted from obstetrical and neonatal medical records on nearly 100% of births in the province of British Columbia from over 60 hospitals as well as births occurring at home attended by BC registered midwives. The BCPDR has collected full provincial data from across BC since April 1, 2000 and currently contains records for nearly 700,000 births and deliveries. Information on a child’s English as a second language status (yes, no) recorded in the EDI was acquired from the Human Early Learning Partnership. Maternal age (< 25 years, 25-40 years, > 40 years) at time of child’s birth was accessed via BC Vital Statistics Agency. Health practitioner Premium Subsidy Codes recorded in the MSP, that indicate the rate of provincial health insurance premium subsidy, were used as a proxy of low household income. Following methodology from previous research (Guhn, Emerson, Mahdaviani & Gadermann 2020), a binary variable (yes, no) was created to indicate if a child received subsidized MSP for their earliest recorded health practitioner visit. The material deprivation dimension score of the 2006 CAN-Marg was used to assess neighborhood-level SES, with higher scores corresponding to areas of greater marginalization (Matheson et al. 2012). The material deprivation dimension indicates education, employment, income, and family structure measures within DAs (Statistics Canada 2015a). Neighborhood-level urbanicity (urban, rural/rural-urban fringe), geocoded to six-digit postal codes, was derived from Statistics Canada (Statistics Canada 2011). Neighborhoods are classified as urban if they 82 are located in regions in which the surrounding census metropolitan area has a population of at least 50,000 individuals (Statistics Canada 2011). Rural-urban fringe includes small urban areas not contiguous with the urban core, while rural is all area not defined as urban core or rural- urban fringe. 4.2.4 Residential exposure to natural and non-natural environments Data on environmental exposures were linked to individual children using six-digit residential postal codes, recorded at each contact with the healthcare system, that were summarized on an annual basis (i.e., single recorded postal code per year). In circumstances where multiple unique postal codes were reported in a single year, the latest recorded postal code for the year was selected. As in Chapters 2 and 3, the environment surrounding children’s residence was characterized using a high spatial resolution (5 m) land cover map of Metro Vancouver (Metro Vancouver 2019b; Williams et al. 2018). Exposure to three vegetated land cover classes was examined: grass cover (i.e., combined modified and natural grass-herb land cover), tree cover (i.e., combined coniferous and deciduous land cover), and aggregated green space (i.e., combined coniferous, deciduous, shrub, modified grass-herb, and natural grass-herb land cover). On average, a large proportion (~75%) of tree cover was comprised of deciduous trees. While the main focus was to measure classes related to green space, paved surfaces were included (i.e., asphalt and concrete) in the analyses, as it was hypothesized that impervious land cover would be negatively associated with childhood development through, for example, association with heat exposure and traffic-related air and noise pollution. The amount of ‘shrub’ and ‘water’ land 83 cover classes was limited near residences and thus excluded as individual exposures from the analyses. Exposure was defined as the percentage of each land cover class within a 250 m buffer of participants’ six-digit residential postal code centroid. A buffer size of 250 m was selected to serve as a proxy for exposure within a walkable distance from home, as it was assumed that prior to entering elementary school children would be spending most of their time at or within close proximity of their residence since they have less independent mobility compared to older, school-aged children (Riazi, Wunderlich, Yun, Paterson & Faulkner 2022). A 250 m buffer also aligns with exposure measures used in prior studies (e.g., Dadvand et al. 2015; Amoly et al. 2014). A time-weighted lifetime exposure estimate was calculated as the mean of annual land cover percentage values across the exposure period (from birth to time of EDI assessment), while accounting for residential address changes reported in the context of a universal healthcare system. Residential mobility was defined as any changes in the child’s recorded six-digit postal code across the exposure period. 4.2.5 Statistical analysis Because of the nested nature of the data (i.e., children are grouped in schools and rated by their kindergarten teachers), multilevel regression models were used to estimate the association between exposure and early childhood development, with total EDI score as the outcome, residential exposure as a fixed effect, and teacher as a random effect. Residual independence was tested with Moran’s I statistic and no evidence of spatial autocorrelation was found, so spatial dependence was not formally included in the statistical analyses. In each model, the outcome was regressed on a single explanatory variable (aggregated green space, tree cover, grass cover, 84 paved surfaces) to evaluate the association with each land cover class independently. The change in outcome score associated with one IQR increase in lifetime mean percent land cover was estimated. The IQR was deemed an appropriate unit, as it closely approximated the standard deviation and is in alignment with previously published environmental health literature (Binter et al. 2022; Bijnens, Derom, Thiery, Weyers & Nawrot 2020; Liao, Zhang, et al. 2019; Dadvand et al. 2015). Multicollinearity among the covariates was tested with generalized variance-inflation factors. With the largest generalized variance-inflation factor of 1.13, little evidence of multicollinearity was found; therefore, for all eight covariates (i.e., sex, birth season, lone-parent household, English as a second language, maternal age, MSP subsidy status, material deprivation, and urbanicity) were controlled for in adjusted models. Potential differences in estimated effects depending on a child’s sex and SES were tested through interaction terms and stratified analyses. Respondents with missing data for any of the outcome, exposure, or confounder variables were excluded from the study sample. P-values < 0.05 for model coefficients were considered as statistically significant. All analyses were performed in R version 4.0.3 (R Core Team 2020) using the ‘lme4’ and ‘lmerTest’ statistical modeling packages (Kuznetsova, Brockhoff & Christensen 2017; Bates, Mächler, Bolker & Walker 2015). While the primary aim was to evaluate the influence of environmental exposure on overall childhood development, as measured by total EDI score, complementary post-hoc analyses were run to evaluate associations between residential exposure and the scores of the five EDI domain separately (range 0-10) to evaluate the consistency of results across dimensions of early childhood development. Several sensitivity analyses were also conducted to test the robustness of the findings to different measures of the outcome and the exposures. First, associations 85 between residential exposure and EDI scores were analyzed with multilevel logistic regression by dichotomizing outcomes to reflect developmental vulnerability (not vulnerable, vulnerable). Using standardized approaches (Janus & Duku 2007), children were considered ‘vulnerable’ if any one of their five EDI domain scores were within the bottom 10th percentile. Second, given varying distributions of land cover types, the change in outcome score associated with a one percent unit increase in lifetime mean land cover was estimated to evaluate trends across land cover types with a comparable unit increase across exposures. Third, the analyses were run with lifetime residential exposure calculated as the mean of annual land cover percentage values within 100- and 500 m buffer zones, corresponding to additional buffer sizes commonly used in the literature that show consistent associations with health outcomes (Davis et al. 2021). Finally, the association with lifetime residential aggregated green space exposure estimated as the mean of annual values for two alternative greenness metrics was evaluated: spectrally unmixed vegetation percentage and NDVI. Vegetation percentage values were derived from linear spectral unmixing of Landsat (30 m spatial resolution) satellite image composites spanning 2000 to 2011 (Czekajlo et al. 2020). NDVI values were derived from 2000 to 2011 Landsat (30 m spatial resolution) and 2017 Planet Scope (3 m spatial resolution) satellite images (Canadian Urban Environmental Health Research Consortium 2019; Gorelick et al. 2017; CanMap 2015; USGS n.d., n.d., n.d., n.d.). 4.3 Results 4.3.1 Characteristics of the study sample The final study sample included 27,539 children (see Figure C-2 of Appendix C). Descriptive and correlation analyses demonstrated no significant differences between the full cohort and the 86 analytical sample (see Table C-1 of Appendix C). Table 4-1 presents descriptive statistics for the sample. Children were on average 5.6 years of age at time of EDI assessment and 48.4% were female. About one third of children were in the English as a second language group (33.6%). Most children were born to mothers aged 25 to 40 years (87%) and in multi-parent households (96.4%). Approximately one fifth of children belonged to low-income families receiving subsidized MSP (20.3%). Almost all children resided in urban neighborhoods (97.9%). Total EDI score was negatively skewed, as most children received high scores. On average, children received a total EDI score of 40.2 (range 0-50). The mean lifetime aggregated green space exposure percentage within a 250 m buffer was 36%. Within the distinct vegetated classes, there was a higher mean lifetime percentage of tree cover exposure (23.2%) compared to grass cover exposure (12.2%). The mean lifetime percentage of paved surfaces exposure was 32.2%. 87 Table 4-1. Characteristics of cohort of children to examine the association between lifetime residential exposure to distinct land cover types and early childhood development in Metro Vancouver (n=27,539). Mean (SD) or number (%). Demographic variables Age at EDI assessment 5.6 (0.3) Sex Female 13,331 (48.4%) Male 14,208 (51.6%) English as a second language Yes 9,241 (33.6%) No 18,298 (66.4%) Season of birth Winter 6,505 (23.6%) Spring 6,932 (25.2%) Summer 7,113 (25.8%) Fall 6,989 (25.4%) MSP subsidy Yes 5,600 (20.3%) No 21,939 (79.7%) Lone-parent household Yes 996 (3.6%) No 26,543 (96.4%) Maternal age < 25 2,832 (10.3%) 25-40 23,948 (87.0%) > 40 759 (2.8%) Neighborhood-level material deprivation -0.3 (0.7) Urbanicity Urban 26,951 (97.9%) rural/rural-urban fringe 588 (2.1%) Outcome variable Overall EDI score 40.2 (7.8) Environmental exposure variables Aggregated green space (%) 36.0 (13.6) Tree cover (%) 23.2 (12.2) Grass cover (%) 12.2 (6.6) Paved surfaces (%) 32.2 (8.9) 88 4.3.2 Childhood development in association with residential environmental exposure Results from the unadjusted and adjusted models of the association between lifetime residential exposure and total EDI score are presented in Table 4-2. In the unadjusted model, for every IQR (17%) increase in exposure to aggregated green space, there was a 0.90 (95% CI: 0.78, 1.02) increase in total EDI score, indicating a weak positive association given the range (0-50) of this score. Positive associations were also observed for exposure to tree cover (β-coefficient: 0.75, 95% CI: 0.64, 0.86) and grass cover (β-coefficient: 0.29, 95% CI: 0.18, 0.40). Conversely, for every IQR (12%) increase in exposure to paved surfaces, there was a 0.90 (95% CI: -1.02, -0.78) decrease in total EDI score. Attenuated associations were obtained from fully adjusted models (β-coefficient: 0.33, 95% CI: 0.21, 0.45 for aggregated green space; β-coefficient: 0.26, 95% CI: 0.15, 0.37 for tree cover; β-coefficient: 0.12, 95% CI: 0.02, 0.22 for grass cover; β-coefficient: -0.35, 95% CI: -0.47, -0.23 for paved surfaces). To test which land cover classes were most strongly associated with total EDI score, the land cover classes were examined using incremental analyses. Post-hoc incremental models were run with aggregated green space and paved land cover, and then with tree and grass cover. Models with all four land cover types were not run given multicollinearity among exposures. Each model was adjusted for potential confounding factors included in the overall adjusted model. Like the main findings, when accounting for paved surfaces, exposure to aggregated green space was positively associated with total EDI score; though, the strength of association was attenuated and weaker relative to paved surfaces (see Table C-2 of Appendix C). In models with tree cover and grass cover, both exposures were positively associated with total EDI score, with a stronger relative association observed for tree cover (see Table C-3 of Appendix C). Analysis of interaction terms between exposure and 89 modifying variable (i.e., child’s sex, MSP subsidy), as well as stratified analyses, indicated that there were no significant differences in estimated associations based on a child’s sex or SES. Table 4-2. Unadjusted and adjusted associations between lifetime residential exposure to distinct land cover types and early childhood development among Metro Vancouver children (n=27,539). Exposure IQR (%) Total EDI score β-coefficient (95% CI) Unadjusted Adjusted Aggregated green space 17 0.90 (0.78, 1.02) *** 0.33 (0.21, 0.45) *** Tree cover 14 0.75 (0.64, 0.86) *** 0.26 (0.15, 0.37) *** Grass cover 7.6 0.29 (0.18, 0.40) *** 0.12 (0.02, 0.22) * Paved surfaces 12 -0.90 (-1.02, -0.78) *** -0.35 (-0.47, -0.23) *** Data presented are β-coefficients (95% confidence interval) with one interquartile range increase in environmental exposure within a 250 m buffer of participants’ six-digit postal code. Adjusted models control for sex, English as a second language, season of birth, lone-parent household, maternal age, MSP subsidy, neighborhood-level material deprivation, and urbanicity. Models include random effect for teacher ID. All estimated effects and 95% confidence intervals are rounded. *p-value < 0.05. ** p-value < 0.01. *** p-value < 0.001. 4.3.3 Sensitivity analyses Consistent with the main findings, complementary analyses indicated that increased residential exposure to vegetated land cover was positively associated with scores across all five EDI domains; a negative association was observed for exposure to paved surfaces (see Table C-4 of Appendix C). Equally, when EDI was measured as a binary indicator in the sensitivity analyses, exposure to vegetated landcover reduced the odds of being vulnerable to poor childhood development, with the strongest association with exposure to aggregated green space (OR: 0.88, 95% CI: 0.84, 0.92) (see Table C-5 of Appendix C). Sensitivity analyses indicated comparable results when modelling changes in outcome with a one percent unit, as opposed to an IQR, increase in land cover exposure (see Table C-6 of Appendix C). The findings were also similar for the 100- and 500 m buffer zones (see Table C-7 of Appendix C) and when aggregated green 90 space exposure was assessed by spectrally unmixed vegetation percentage or NDVI values (see Table C-8 of Appendix C). 4.4 Discussion In this large population-based birth cohort study, a positive association was observed between lifetime exposure to green space and early childhood development, as measured by the EDI. Furthermore, the results demonstrated that associations between lifetime exposure and EDI varied according to land cover type. Specifically, lifetime exposure to tree and grass cover was positively associated with child development outcomes and an inverse relationship was observed for exposure to paved surfaces. The findings also indicated that lifetime exposure to various types of vegetated land cover was associated with reduced odds of being vulnerable to poor childhood development, while exposure to paved surfaces was associated with increased odds. 4.4.1 Lifetime residential exposure to green space may improve childhood development and reduce risk of developmental vulnerability The finding of a positive association between aggregated green space exposure and childhood development is consistent with previous research. In general, earlier studies have reported that residential access and exposure to green space is associated with better socio-emotional (Putra et al. 2021; Andrusaityte, Grazuleviciene, Dedele & Balseviciene 2020; Bell et al. 2020; Van Aart et al. 2018), behavioral (Bijnens et al. 2020; Lee, Kim & Ha 2019), cognitive (Almeida, Barros & Ribeiro 2022; Binter et al. 2022; Lee, Kim, et al. 2021; Bijnens et al. 2020; Dadvand et al. 2015, 2017, 2018), and psychomotor development (Kabisch et al. 2019; Liao, Zhang, et al. 2019) among children; though, some studies have reported null (Hartley et al. 2021; Donovan, Michael, 91 Gatziolis, Mannetje & Douwes 2019) or inverse associations (Nordbø, Raanaas, Nordh & Aamodt 2020; Browning, Kuo, Sachdeva, Lee & Westphal 2018) between green space and childhood development. Studies further indicate that school greenness may be associated with improved academic performance in reading, writing, and mathematics (Kuo et al. 2021; Sivarajah et al. 2018). The present results are consistent with previous research that has shown family- and neighborhood-level socio-economic factors to be more important in explaining variation in EDI scores than characteristics of the surrounding environment, but that the surrounding environment also has an impact (Bell et al. 2020; Christian et al. 2017). Findings from the sensitivity analysis indicated that exposure to vegetated land cover was associated with reduced odds of being vulnerable to poor childhood development. This finding is consistent with those of Bell et al. (2020) who reported that the presence of high-quality neighborhood green space is associated with reduced odds of vulnerability in social development among Australian children. Previous studies have also suggested that residential green space may reduce adverse developmental outcomes among children, including symptoms and incidence of ADHD (Yuchi et al. 2022; Thygesen et al. 2020; Donovan et al. 2019; Markevych et al. 2018), rates of psychiatric disorders (Engemann, Svenning, Arge, Brandt, Erikstrup, et al. 2020; Engemann, Svenning, Arge, Brandt, Geels, et al. 2020), and emotional and behavioral problems (Dockx et al. 2022; Liao et al. 2020; Madzia et al. 2019; Amoly et al. 2014; Flouri, Midouhas & Joshi 2014; Markevych et al. 2014). Risk reduction of poor childhood health and development from green space exposure indicates important public health benefits. This knowledge can inform preventative programs, including urban greening, to be prioritized towards children considered ‘vulnerable’ to poor development, for whom interventions may be most effective. 92 4.4.2 Associations vary depending on vegetation type The results indicated that associations between lifetime exposure to green space and childhood development varied according to vegetation type. This finding is supported by a limited number of studies that have identified differences in developmental outcomes among children depending on the type of green space. For instance, comparable to the present findings, several North American studies have reported positive associations between schoolyard tree cover and academic achievement (Kuo, Browning, Sachdeva, Lee & Westphal 2018; Kuo et al. 2021; Browning & Locke 2020; Donovan, Michael, Gatziolis & Hoyer 2020; Leung et al. 2019; Sivarajah et al. 2018; Tallis, Bratman, Samhouri & Fargione 2018; Kweon, Ellis, Lee & Jacobs 2017). Studies have further demonstrated that higher exposure levels to trees is associated with lower autism prevalence (Wu & Jackson 2017) and conduct problems (Barger et al. 2020), but also to higher quality of life (Kim, Lee & Sohn 2016) and emotional and behavioral regulatory skills (Scott, Kilmer, Wang, Cook & Haber 2018). While the current study found a positive association between exposure to grass cover and childhood development, evidence concerning the childhood health effects of grass cover are relatively limited and inconsistent; some studies have reported positive associations (Wu & Jackson 2017; Taylor & Kuo 2011), while others have found null (Donovan et al. 2020; Kuo et al. 2018; Sivarajah et al. 2018; Kweon et al. 2017) or inverse associations (Browning & Locke 2020; Tillmann, Clark, et al. 2018). Inconsistencies in findings across studies may stem from differences in terms of natural environment assessment, outcome assessment, and study population. For instance, studies may estimate exposure using data sets that define and aggregate land cover classes differently (e.g., grass, meadow, agricultural crops). 93 The current findings indicate that exposure to tree cover had a stronger positive association with early childhood development than exposure to grass cover. This trend has been observed in prior studies of academic achievement among children (Donovan et al. 2020; Kuo et al. 2018; Sivarajah et al. 2018); though, for other developmental outcomes, few studies have compared the effects of multiple vegetation types (Wu & Jackson 2017). Limited existing evidence highlights the need for further research to confirm the relative importance of different vegetation types on health associations. Differences in estimated health effects of vegetation types may, in part, be due to their differing capacities to support pathways of nature-health relations, including regulating and cultural ecosystem services. For instance, areas with more tree cover may provide greater mitigation of air pollution, noise, and heat (Vieira et al. 2017) relative to more open green space, such as grassy fields. Vegetation-related reductions in air pollution, noise, and heat might support improved childhood health and development through subsequent reductions in the adverse health effects associated with these harmful environmental exposures including, for example, central nervous system damage via inflammation and oxidative stress (Lopuszanska & Samardakiewicz 2020; Zare Sakhvidi, Zare Sakhvidi, Mehrparvar & Dzhambov 2018). Moreover, as described by the ART (Kaplan 1995), it has been hypothesized that environments with more trees may promote health and development as they possess perceptual attributes (e.g., ‘fascination’, ‘being away’, ‘extent’, ‘compatibility’) that support restoration from mental fatigue and the capacity for directed attention. Likewise, compared to other vegetation types, trees may encourage greater reduction of stress (Egorov et al. 2020; Jiang et al. 2014), which could otherwise have an adverse effect on childhood health and developmental trajectories (Lupien et al. 2009). 94 4.4.3 Lifetime residential exposure to paved surfaces may impair childhood development and increase risk of developmental vulnerability The study findings indicate that higher lifetime residential exposure to paved surfaces was negatively associated with early childhood development. Furthermore, when both aggregated green space and paved surfaces were accounted for in the same model, exposure to paved surfaces was more strongly associated with total EDI score than exposure to aggregated green space. Very few studies have reported on the association between human health and exposure to non-natural, impervious surfaces. The findings of the present study are consistent with those of Larson et al. (2018) who reported that the percentage of impervious surface (e.g., buildings and paved surfaces) within neighborhood areas was associated with increased risk of severe anxiety among youth with autism. Previous studies have also suggested that exposure to impervious surface cover surrounding a child’s residence or school may be associated with poor self-regulation (Scott et al. 2018), increased autism diagnosis prevalence (Wu & Jackson 2017), and decreased academic test scores (Donovan et al. 2020) among children. 4.5 Conclusions In this population-based birth cohort, a positive association was observed between lifetime residential exposure to vegetated land cover (i.e., aggregated green space, tree cover, grass cover) and early childhood development; a negative association was observed for exposure to paved surfaces. The present findings contribute to an improved understanding of associations between green space exposure and childhood development, particularly because this study was able to assess associations for different vegetation types, suggesting that tree cover may be of more importance than grass cover. The results also indicate that even when accounting for the 95 negative effects of paved surfaces, exposure to vegetation can still have a positive influence on early childhood development. Together, the results suggest that converting paved surfaces to vegetated environments may have important effects on early childhood health and development, as not only will they offer added beneficial services provided by green spaces, but perhaps more so reduce the adverse effects associated with impervious environments. While the strength of observed associations between residential exposure and childhood development was relatively weak, even small individual gains can shift the population distribution of early childhood development to higher levels, resulting in important public health benefits. The findings indicate that urban planning and policy frameworks aimed at increasing the availability of vegetation in neighborhoods may be beneficial for early childhood developmental outcomes. If confirmed by other studies, the present findings can inform urban planners of the types of green space that support optimal early childhood development, with potential positive health benefits across the life course. 96 The association between lifetime exposure to green space and early childhood development – is the relation mediated by reduction in air pollution and noise? 5.1 Introduction Exposure to green space has been proposed to benefit early childhood health and development through several underlying pathways, including increased physical activity, enhanced mental restoration, and improved social cohesion (Mygind et al. 2021). Beneficial effects of green space on childhood health and development may also accrue through the ability of vegetation to reduce levels of air pollution and noise via, for example, direct removal, distance buffering, or source emission reductions (Diener & Mudu 2021; Van Renterghem et al. 2015). Vegetation-related reductions in exposure to air pollution and noise may support improved childhood health and development through subsequent reductions in adverse health effects associated with early life exposure to these harmful environmental factors, including impairments in brain function and cognitive development (Raess et al. 2022; Lopuszanska & Samardakiewicz 2020; Zare Sakhvidi et al. 2018). Exposure to air pollution and noise may adversely affect childhood health and development through, for instance, increased stress, sleep disturbance, and central nervous system damage via inflammation and oxidative stress (Lopuszanska & Samardakiewicz 2020; Zare Sakhvidi et al. 2018). Despite these proposed pathways, few studies have empirically investigated underlying pathways linking green space and developmental outcomes among children (Dzhambov et al. 2020). Some research suggests a positive effect of green space because of reduced exposure to traffic-related 97 air pollution (Asta et al. 2020; Liao, Zhang, et al. 2019; Dadvand et al. 2015), but few studies have analyzed similar underlying mechanisms related to reduced exposure to noise (Van Aart et al. 2018). This study aims to investigate the association between lifetime residential exposure to green space and early childhood development in a large population-based birth cohort in Metro Vancouver, Canada (van den Bosch et al. 2018). In addition, this study evaluates whether beneficial effects of green space exposure on childhood development were mediated through reduced exposure to traffic-related air pollution and noise. 5.2 Materials and Methods 5.2.1 Study sample As in Chapter 4, the study cohort was identified using administrative data accessed through Population Data BC that provided individual-data linkage across data sources (Population Data BC 2021). The cohort comprised all live, singleton births in Metro Vancouver between April 2000 and December 2005, who attended kindergarten in the 2005/2006 to 2010/2011 school years. To be eligible, children and their mothers had to be registered with the mandatory provincial health insurance program, MSP, and have lived within the study area for the duration of the exposure period. The exposure period was from birth until age of outcome assessment (mean age = 5.6 years). 5.2.2 Assessment of early childhood development Early childhood development was assessed using data sourced from the EDI, a population-level tool assessing children’s development in kindergarten (Janus & Offord 2007). For the current 98 study, childhood development was assessed using total EDI scores as the primary outcome variable and mean score for the ‘language and cognitive development’ domain. The ‘language and cognitive development’ domain score, which captures children’s acquisition of literacy, numeracy, and memory skills, was included as a secondary outcome variable as it was hypothesized that this domain would be the most helpful in examining the potential mediation effects of harmful exposures on the association between green space exposure and childhood development, given the adverse effects of traffic-related air pollution and noise exposure on childhood cognition (Lopuszanska & Samardakiewicz 2020; Zare Sakhvidi et al. 2018). 5.2.3 Potential confounders All potential confounders of the association between green space exposure and childhood development were selected a priori using a directed acyclic graph (Textor & Hardt 2011) (see Figure D-1 of Appendix D). All potential confounders and their source dataset are the same as Chapter 4 (see Section 4.2.3 for further details). In brief, individual-level covariates included children’s sex (male, female), season of birth (January to March as winter, April to June as spring, July to September as summer, and October to December as fall), lone-parent household at time of birth (yes, no), child’s English as a second language (ESL) status (yes, no), maternal age (< 25 years, 25-40 years, > 40 years) at time of child’s birth, and MSP subsidy status (no subsidy, subsidy). Neighborhood-level covariates included material deprivation and urbanicity (urban, rural/rural-urban fringe). 99 5.2.4 Residential environmental exposures Environmental exposure variables were assigned to individual children using six-digit residential postal codes, recorded at each contact with the healthcare system, that were summarized on an annual basis (i.e., single recorded postal code per year). For each exposure variable, a time-weighted cumulative lifetime estimate was calculated by averaging annual values over the exposure period (from birth to time of EDI assessment), while accounting for residential address changes of participants. Exposure estimates were linked to all outcome variables and covariates at the six-digit postal code level. 5.2.4.1 Green space Residential green space was estimated using a novel greenness time series developed from linear spectral unmixing of annual Landsat satellite-image composites (30 m spatial resolution) (Czekajlo et al. 2020). In the present study, the annual percentage of the vegetation attribute per pixel was used to represent green space for each year of the study period. Residential exposure to green space was calculated as the mean vegetation percentage values within a 250 m buffer surrounding participants’ six-digit postal code centroid. As in Chapter 4, a buffer size of 250 m was selected to align with prior research (e.g., Dadvand et al. 2015; Amoly et al. 2014) and to serve as a proxy for exposure within a walkable distance from home, assuming that prior to entering elementary school children have less independent mobility and, as such, spend most of their time at or within close proximity of their residence (Riazi et al. 2022). 100 5.2.4.2 Air pollution For traffic-related air pollution, this study focused on residential exposure to nitrogen dioxide (NO2) and fine particulate matter (PM2.5) referenced to postal code centroids. A regional land use regression (LUR) model (adjusted R2= 0.84 and RMSE=1.59 ppb) was applied to estimate residential exposure to NO2 (Su et al. 2020). Details on the measurements and geospatial predictors are provided elsewhere (Wang, Henderson, Sbihi, Allen & Brauer 2013). The LUR model was developed using a mixed effects Deletion/Substitution/Addition machine learning algorithm (Su et al. 2020) that incorporates air pollutant measurements from 128 monitoring locations, as well as geospatial predictor variables including elevation, meteorological conditions, distance to and length of roadways, and land use. Estimates of residential exposure to PM2.5 were provided by the Canadian Urban Environmental Health Research Consortium and were estimated using a national LUR model (CanMap 2015; van Donkelaar, Martin, D Spurr & Burnett 2015; Boys et al. 2014). Ground-level PM2.5 was estimated by combining a 0.01 degree x 0.01 degree resolution optimal estimate-based Aerosol Optical Depth retrieval with aerosol vertical profile and scattering properties (van Donkelaar et al. 2015; Boys et al. 2014). To adjust for residual bias in PM2.5 estimates, geographically weighted regression that incorporates ground-based observations was applied. 5.2.4.3 Noise Residential exposure to community noise was estimated using a deterministic noise prediction model. Details on the noise model are presented elsewhere (Gan, McLean, Brauer, Chiarello & Davies 2012). In brief, annual day-evening-night A-weighted equivalent noise levels (Lden, dB(A)) were estimated using predictors such as street and rail layout, road type, traffic volume, 101 railway frequency, aircraft forecasts, topography, and building heights and footprint. The community noise metric applied a weight to evening (5-dB (A) weighting) and night (10-dB (A) weighting) noise levels to reflect individuals increased sensitivity to noise levels during these times relative to during the day. Annual average A-weighted noise levels were calculated for a 10 m x 10 m grid and then averaged for each 6-digit postal code polygon area. 5.2.5 Statistical analysis Multilevel regression models were used to estimate the association between exposure and early childhood development, with total EDI score as the outcome, residential exposure as a fixed effect, and teacher as a random effect to account for teacher-rater bias. Residual independence was tested with Moran’s I statistic and no evidence of spatial autocorrelation was found, so spatial dependence was not formally included in the statistical analyses. The change in outcome scores associated with one IQR increase in lifetime mean percent vegetation was estimated. Multicollinearity among the covariates was tested with generalized variance-inflation factors. With the largest generalized variance-inflation factor of 1.12, little evidence of multicollinearity was found; therefore, for all eight covariates (i.e., sex, birth season, lone-parent household, English as a second language, maternal age, MSP subsidy status, material deprivation, and urbanicity) were controlled for in adjusted models. Respondents with missing or invalid data for any of the outcome, exposure, or confounder variables were excluded from the study sample. Potential differences in estimated effects depending on a child’s sex and SES (proxied by MSP subsidy status) were tested through interaction terms and stratified analyses. P-values < 0.05 for model coefficients were considered as statistically significant. All analyses were performed in R version 4.0.3 (R Core Team 2020). 102 A causal mediation analysis framework was used to evaluate whether decreased levels of traffic-related air pollution and noise are possible pathways underlying associations between residential exposure to green space and childhood development (Imai, Keele & Tingley 2010). NO2, PM2.5, and noise were specified as mediators in the green space-health association, assuming a causal relation between residential green space and levels of air pollution and noise. The mediating effects were estimated separately for each exposure using the ‘mediation’ package in R (Tingley, Yamamoto, Hirose, Keele & Imai 2014). For each mediation model, the mean total effect, the mean direct effect of green space, and the mean mediated effect of the mediator were estimated. In addition, the percentage of the total association between residential exposure to green space and childhood development explained by the mediator was estimated. Mediator and outcome models controlled for all eight covariates and included teacher as a random effect. 1000 bootstrap simulations were used to obtain confidence intervals. The findings are reported as changes in outcome associated with one IQR increase in the environmental exposure. Although it was hypothesized that the ‘language and cognitive development’ domain would be the strongest domain to investigate mediation effects, complementary post-hoc analyses were run to evaluate associations between residential green space exposure and the scores of the five EDI domains separately to evaluate the consistency of results across dimensions of early childhood development. Several sensitivity analyses were also conducted to test the robustness of the findings to different measures of the outcome and the exposures. First, associations between residential green space exposure and EDI scores were analyzed with multilevel logistic regression by dichotomizing outcomes to reflect developmental vulnerability (not vulnerable, vulnerable). Using standardized approaches (Janus & Duku 2007), children were considered 103 ‘vulnerable’ in ‘language and cognitive development’ if their score in this domain was in the bottom 10th percentile of a provincial distribution; whereas they were considered ‘vulnerable’ in overall EDI if any one of their five domain scores were within the bottom 10th percentile. Second, the main analyses were run with lifetime residential green space exposure calculated as the mean of annual vegetation percentage within 100- and 500 m buffer zones. Finally, the main analyses were run with lifetime residential green space exposure estimated as the mean of annual mean NDVI values within 100-, 250-, and 500 m buffers. NDVI values were derived from 2000 to 2011 Landsat (30 m spatial resolution) and 2017 Planet Scope (3 m spatial resolution) satellite images (Canadian Urban Environmental Health Research Consortium 2019; Gorelick et al. 2017; CanMap 2015; USGS n.d., n.d., n.d., n.d.). 5.3 Results 5.3.1 Characteristics of the study sample The final study sample included 27,372 children (see Figure D-2 of Appendix D). Descriptive and correlation analyses demonstrated no significant differences between the full cohort and the analytical sample (see Table D-1 of Appendix D). Descriptive statistics for the study population are presented in Table 5-1. Children were on average 5.6 years old at time of EDI assessment and 48.3% were female. Most children were born to mothers aged 25-40 years and in multi-parent households. Approximately one-fifth of children belonged to low-income families (with a subsidized MSP) and nearly all children resided in urban areas. EDI scores were negatively skewed, as most children received high (i.e., favorable) scores. On average, children received a total EDI score of 40.1 (range 0 to 50) and a ‘language and cognitive development’ domain score of 8.3 (range 0 to 10). The mean vegetation percentage within a 250 m buffer was 31.3%. The 104 mean concentration of NO2 and PM2.5 were 40.2 ug/m3 and 7.6 ug/m3 respectively, while the mean noise level was 63.5 dB(A). Table 5-1. Characteristics of cohort of children to examine the association between lifetime residential exposure to green space and early childhood development in Metro Vancouver (n=27,372). Mean (SD) or number (%). Demographic variables Age at EDI assessment 5.6 years (0.3) Sex Female 13,225 (48.3%) Male 14,147 (51.7%) Season of birth Winter 6,465 (23.6%) Spring 6,901 (25.2%) Summer 7,069 (25.8%) Fall 6,937 (25.3%) MSP subsidy Yes 5,618 (20.5%) No 21,754 (79.5%) Lone-parent household Yes 992 (3.6%) No 26,380 (96.4%) Maternal age < 25 2,856 (10.4%) 25-40 23,772 (86.8%) > 40 744 (2.7%) Neighborhood-level material deprivation -0.2 (0.7) Urbanicity Urban 26,799 (97.9%) rural/rural-urban fringe 573 (2.1%) Outcome variables Total EDI score 40.1 (7.8) Language and cognition domain score 8.3 (1.8) Environmental variables Green space (250 m) (%) 31.3 (8.1) NO2 (ug/m3) 40.2 (9.1) PM2.5 (ug/m3) 7.6 (1.2) Noise (Lden, dB(A)) 63.5 (4.7) 105 5.3.2 Childhood development in association with residential green space exposure In the unadjusted model, for every IQR (10%) increase in vegetation percentage within 250 m of residential postal code centroid, there was a 0.61 (95% CI: 0.48, 0.73) increase in total EDI score; indicating a weak positive association given the range (0 to 50) of this score. In the fully adjusted model, the positive association between vegetation percentage and total EDI score remained (β-coefficient: 0.16, 95% CI: 0.04, 0.28). Based on the size and confidence interval of the estimated effect, it was determined that there was no evidence of an association between lifetime residential exposure to green space and ‘language and cognitive development’ domain score (β-coefficient: -0.03, 95% CI: -0.06, -0.00). Analysis of interaction terms between exposure and modifying variable (i.e., child’s sex and MSP subsidy status), as well as stratified analyses, indicated that there were no significant sex or subsidy status differences in estimated associations. 5.3.3 The mediation effects of air pollution and noise Traffic-related air pollution and noise were examined as mediators of the relationship between lifetime residential exposure to green space and total EDI score; ‘language and cognitive development’ domain score was excluded as an outcome given the null findings in the main analyses. Moderate-to-high correlations were observed between vegetation percentage within a 250 m buffer and the harmful environmental exposures (r = -0.60 for NO2; r = -0.33 for PM2.5; r = -0.25 for noise). The indirect effect of the association between vegetation percentage and total EDI score through NO2, PM2.5, and noise were 0.16 (95% CI: 0.09, 0.22), 0.05 (95% CI: 0.02, 0.08), and 0.06 (95% CI: 0.04, 0.08), respectively (Table 5-2). The percentage of the total effect of vegetation percentage on the total EDI score accounted for by NO2, PM2.5, and noise were 106 97.1% (95% CI: 43.0%, 396.0%), 29.5% (95% CI: 12.0%, 117.0%;), and 35.2% (95% CI: 17.9%, 139.0%). Table 5-2. Adjusted associations and explained percentage of association between lifetime residential exposure to greenspace and total EDI score mediated by NO2, PM2.5 and noise (n=27,372). Mediator Total Effect Average Causally Mediated Effect Average Direct Effect Percentage of the effect explained by the mediator β-coefficient (95% CI) β-coefficient (95% CI) β-coefficient (95% CI) Percentage (95% CI) NO2 (ug/m3) 0.16 (0.04, 0.28) * 0.16 (0.09, 0.22) *** 0.00 (-0.14, 0.13) 97.1 (43.0, 396.0) * PM2.5 (ug/m3) 0.16 (0.04, 0.29) ** 0.05 (0.02, 0.08) *** 0.11 (-0.01, 0.24) 29.5 (12.0, 117.0) ** Noise (Lden, dB(A)) 0.17 (0.04, 0.29) ** 0.06 (0.04, 0.08) *** 0.11 (-0.02, 0.23) 35.2 (17.9, 139.0) ** Data presented are β-coefficients (95% confidence interval) with one interquartile range increase in environmental exposure. Adjusted models control for sex, English as a second language, season of birth, lone-parent household, maternal age, MSP subsidy, neighborhood-level material deprivation, and urbanicity. Models include random effect for teacher ID. All estimated effects and 95% confidence intervals are rounded. *p-value < 0.05. ** p-value < 0.01.*** p-value < 0.001. 5.3.4 Sensitivity analyses Consistent with the main findings for the ‘language and cognitive development’ domain score, complementary analyses indicated that lifetime residential exposure to vegetation was not associated with scores across all five EDI domains (see Table D-2 of Appendix D). The sensitivity analyses indicated little change in associations between vegetation percentage and EDI scores when EDI scores were measured as binary indicators of vulnerability (see Table D-3 of Appendix D), mean vegetation percentage within 100- and 500 m buffer zones (see Table D-4 of Appendix D), or residential exposure to green space using NDVI values (see Table D-5 of Appendix D). 107 5.4 Discussion Lifetime residential exposure to green space was associated with higher ability to meet age-appropriate developmental expectations, as measured by total EDI scores, among a population-based birth cohort of 27,372 children living in Metro Vancouver. Specifically, an IQR increase in vegetation percentage within 250 m buffers of postal codes was associated with a 0.16 (95% CI: 0.04, 0.28) increase in total EDI score. This association was partially mediated by reductions in NO2, PM2.5, and noise that each explained 97.1%, 29.5%, and 35.2% of the estimated associations between lifetime residential exposure to green space and total EDI scores, respectively. No association was observed between lifetime residential exposure to green space and ‘language and cognitive development’ domain score. 5.4.1 Lifetime residential exposure to green space may improve overall childhood development This study’s finding of a weak positive association between green space exposure and early childhood development is consistent with previous research that reports sociodemographic factors to be more important in explaining variation in EDI outcomes across children than aspects of the physical environment (Bell et al. 2020; Christian et al. 2017). While the estimated effect of green space exposure on EDI may be small, the findings of this study agree with studies that indicate a positive influence of green space on child development outcomes. Previous research has shown that residential green space is associated with better intellectual, behavioral, and psychomotor development among children (Almeida et al. 2022; Lee, Kim, et al. 2021; Bijnens et al. 2020; Liao, Zhang, et al. 2019), while access to neighborhood green space is 108 associated with reduced odds of childhood social vulnerability (Bell et al. 2020). Studies also indicate that greenness may reduce behavioral problems in children (Markevych et al. 2014). This study did not observe an association between lifetime residential exposure to green space and ‘language and cognitive development’ domain score, which captures children’s acquisition of literacy, numeracy, and memory skills. This finding is inconsistent with previous research that indicates that green space exposure is positively associated with aspects of childhood cognition, including attention, working memory, and academic performance (Dockx et al. 2022; Jimenez et al. 2022; Kuo et al. 2021; Flouri, Papachristou & Midouhas 2019; Dadvand et al. 2015, 2017, 2018; Sivarajah et al. 2018). The null association observed in the current study may in part be due to the outcome metric that, in contrast to prior work that primarily utilized measured responses on tasks (e.g., test scores), captured teacher-ratings on children’s skills in cognitive activities. Furthermore, most studies on cognitive outcomes have conducted analyses on samples that include children aged 6 years and over (Kuo et al. 2021; Flouri et al. 2019; Dadvand et al. 2015, 2018), so it may be that associations between green space exposure and childhood cognition are stronger in later childhood. Finally, the null findings may be attributable to the estimate of green space exposure which differed from most prior research in that it utilized a more sensitive measure of urban vegetation and captured life-time exposure. Moreover, green space exposure was estimated surrounding children’s postal codes rather than other potentially important environments, such as daycare and school locations. 109 5.4.2 Nature-health associations are partially due to a reduction in air pollution and noise exposure Results from the mediation analyses suggest that the observed association between lifetime residential exposure to green space and total EDI score was partially mediated through reductions in NO2, PM2.5, and noise exposure levels. Very few studies have estimated the mediation effect of air pollution in associations between green space exposure and childhood development, and only one known study evaluated the mediating role of noise. The current findings are consistent with those of Dadvand et al. (2015) who reported that indoor levels of elemental carbon explained 20-65% of associations between green space exposure and 12-month progress in cognitive functions among school-aged children, and with those of Asta et al. (2020) that reported that reductions in NO2 levels accounted for 7-62% of associations between residential greenness and arithmetic test scores among 7-year-old children. Similarly, Liao et al. (2019) reported that reductions in PM2.5 concentrations explained 13.6-28.1% of the association between green space exposure and neurodevelopment at 24 months of age, while Binter et al. (2022) reported that PM2.5 accounted for 74% of the association between residential greenness and verbal abilities at five-years old. Previous studies have also suggested a potential mediation effect of air pollution on associations between green space exposure and childhood neurobehavioral health, motor development, and psychiatric disorder rates (Engemann, Svenning, Arge, Brandt, Erikstrup, et al. 2020; Kabisch et al. 2019; Lee et al. 2019). The findings on noise are consistent with a study in Belgium that reported noise as a partial mediator in associations between natural landscapes and children’s emotional state (Van Aart et al. 2018). While the estimates of mediation by PM2.5 and noise are within a similar range to previous 110 studies, the estimated mediation effect of NO2 is higher. This finding might, in part, be attributable to reduced misclassification of the exposure associated with the use of NO2 exposure values estimated from high-quality, temporally aligned data. Moreover, in contrast to previous studies (Asta et al. 2020; Liao, Zhang, et al. 2019; Dadvand et al. 2015), this study used a life-course approach to estimate exposure from birth to time of assessment. Finally, nearly all NO2 in Metro Vancouver is locally derived via transportation sources, as there is little industry and no combustion power plants within the region; thus, NO2 concentrations are strongly linked to local road networks that are inversely correlated with green space. Sources of NO2 emissions and subsequent correlations with green space may differ in other geographic contexts. This study found that harmful exposures were not equal in their mediation effects on the association between lifetime residential exposure to green space and total EDI score, with the strongest mediation effects found for NO2 air pollution. For both air pollution and noise, exposure levels are probably lower in areas with greater vegetation, in part, because of the lack of air pollution-emitting and noise-emitting sources in these locations, direct removal (e.g., deposition), and buffering (e.g., a barrier to dispersion). The mediation effects of NO2 may be greater than PM2.5 given that concentrations of NO2 are more spatially variable than PM2.5 (Karner, Eisinger & Niemeier 2010); the same may be true for noise (Gan et al. 2012). Differences in mediation effects between NO2 and PM2.5 might also be attributable to the varying capacity of vegetation to remove each air pollutant (Nowak, Hirabayashi, Doyle, McGovern & Pasher 2018); however, research to support this assumption has been inconclusive (Markevych et al. 2017). The relatively small mediation effect size for noise might be partly due to the greenness metric used in the current study that did not offer information on vegetation 111 characteristics that are important for noise attenuation, including composition, structure, and distance from source (Van Renterghem et al. 2015). 5.5 Conclusions In this population-based birth cohort, evidence was found for a positive association between lifetime residential exposure to green space and early childhood development. The positive association was partially mediated by reductions in traffic-related air pollution and noise exposure levels, and particularly NO2 air pollution. Further studies are needed to confirm these findings, which, if replicated, could have important implications for urban planning and green infrastructure interventions that support childhood health and development across the life course. 112 Conclusion 6.1 Research summary and contributions The overall purpose of this dissertation was to explore the relationship between natural environments and human health. This was accomplished through an interdisciplinary research approach using geospatial and epidemiological analyses that combined administrative, survey, and geospatial data. This dissertation developed and compared metrics for access and exposure to natural environments, analyzed how access and exposure to natural environments were associated with human health, and explored potential underlying pathways of nature-health associations through mediation analyses. In Chapter 2, a general method was developed for estimating and comparing different forms of human-nature contact, specifically access to public green space and residential exposure to different land cover types. The findings indicated that access and exposure were only weakly correlated. Moreover, this study demonstrated a distinct socio-spatial pattern of natural environments in Metro Vancouver. Access to public green space remained relatively high throughout the region, while exposure differed across an urban-rural gradient. Specifically, residential exposure to built-up and paved areas declined with increasing distance from urban centres, while exposure to trees, modified grass-herbs, and water increased. Furthermore, significant negative correlations between neighborhood-level marginalization and access and exposure to natural environments were observed. In Chapter 3, the metrics developed in Chapter 2 were applied to investigate the association between access and exposure to natural environments and self-reported general health, mental 113 health, and common mental disorders among adolescent and adult populations. The results indicated that associations between exposure and health outcomes varied according to land cover type. Specifically, exposure to water and some vegetation types was associated with reduced odds of poor self-reported health and an inverse relationship was observed for exposure to paved surfaces. No statistically significant associations between self-reported health and access to public green space were observed, though the effect estimates suggest that access to public green space may be beneficial for general health. No significant associations were observed between self-reported health and exposure to aggregated green space. In Chapter 4, the association between exposure to distinct land cover types and early childhood developmental outcomes was explored. This chapter found a positive association between early childhood development and lifetime exposure to aggregated green space, using estimates of environmental exposure preceding the outcome. The results demonstrated that associations between lifetime exposure and childhood development varied according to land cover type. Specifically, lifetime exposure to tree and grass cover was positively associated with childhood development outcomes and an inverse relationship was observed for exposure to paved surfaces. In Chapter 5, underlying pathways of nature-health associations were explored. This chapter found that positive associations between lifetime exposure to green space and early childhood development were partially mediated by reductions in NO2, PM2.5, and noise. Together, these four research chapters contribute new empirical evidence of how natural environments are associated with human health. By investigating several environmental exposures and indicators of health and development, this dissertation presents a robust and comprehensive set of analyses on nature-health associations. This dissertation advances existing 114 research methodologies and provides an important foundation for future studies by studying nature-health associations through an interdisciplinary lens that integrates epidemiological and novel geospatial approaches. A search of the relevant literature indicates that this may be the first set of Canadian studies to examine the relationship between natural environments and early childhood development as measured by the EDI. Chapters 4 and 5 utilized a large population-based birth cohort to examine the association between cumulative early-life residential exposure to natural environments (from birth to kindergarten) and early childhood development, while accounting for residential address changes of children. The population-based study design reduces the potential for bias within the sample and is a marked improvement from most prior research that has adopted cross-sectional research designs with much smaller sample sizes (e.g., Lee, Choi, et al. 2021; Andrusaityte et al. 2020; Liao et al. 2020; Lee et al. 2019; Liao, Zhang, et al. 2019; Scott et al. 2018; Kim et al. 2016; Amoly et al. 2014; Taylor & Kuo 2011). This dissertation contributes to ongoing discussions of best practices in environmental exposure assessment by utilizing high quality exposure data and estimating natural environments using several common and novel metrics, including land use and land cover data, NDVI, and spectrally unmixed Landsat imagery. It provides novel insights of the health associations of various natural (e.g., vegetation types, water) and non-natural (e.g., paved surfaces, buildings) land cover types; very few studies have explored the health effects of impervious, built environments. In addition, it contributes to a deeper understanding of the pathways underlying nature-health associations by formally testing the mediation effects of both traffic-related air pollution (NO2 and PM2.5) and noise exposure on associations between natural environments and early childhood development, as opposed to a single harmful exposure like most prior research. By studying nature-health 115 associations in Canada, this dissertation broadens the geographical scope of existing research, which has predominantly taken place in Europe and the United States. Since the environmental exposures studied in this dissertation are modifiable, the results may be used to inform urban planning, policies, and interventions to benefit population health. Overall, this dissertation advances prior research efforts by addressing four gaps in current knowledge: (a) the effect of natural environment type on human health; (b) the effect of type of human-nature contact on human health; (c) pathways underlying nature-health associations; and (d) the effect of natural environments on childhood health and development. 6.2 Key findings Several key findings emerged from the four research chapters. They are presented below by dissertation objective. 6.2.1 Natural environments are beneficial for human health Results from this dissertation indicate that natural environments were beneficial for the health of Metro Vancouver residents. While no statistically significant associations were observed between exposure to aggregated green space and self-reported general health, mental health, and common mental disorder in Chapter 3, positive associations were observed for exposure to individual vegetation types (i.e., shrub and grass-herbs) and water. Likewise, the size and direction of estimated effects suggest that access to public green space may be beneficial for self-reported general health, though results were not statistically significant. Access was, however, significantly associated with lower odds of ‘poor or fair’ general health among men in gender-stratified analyses. In Chapters 4 and 5, lifetime exposure to vegetation was positively associated 116 with early childhood development. Findings in Chapters 4 and 5 were consistent across multiple green space metrics (i.e., vegetated land cover, percent spectrally unmixed vegetation, and NDVI). Results from this dissertation are consistent with recent reviews and meta-analyses that have found that, in general, natural environments are beneficial for human health (Browning et al. 2022; Davis et al. 2021; Jimenez et al. 2021; Mygind et al. 2021; Nieuwenhuijsen 2021; Rojas-Rueda, Vaught, et al. 2021; Short et al. 2021; Smith et al. 2021; Yang, Zhao, et al. 2021). Inconsistencies across research findings may be a result of differences in the exposure metric, outcome, study design, and study sample considered. 6.2.2 Nature-health associations may vary according to natural environment type Findings from Chapters 3 and 4 indicate that associations between exposure to natural environments and human health varied according to land cover type. However, there was no clear trend regarding which natural land cover type was most consistently associated with improved health across the outcomes considered. In Chapter 3, exposure to water was associated with reduced odds of ‘poor or fair’ self-reported general health, while exposure to shrubs and grass-herbs was associated with reduced odds of ‘poor or fair’ mental health and self-reported common mental disorder respectively. In Chapter 4, lifetime exposure to tree and grass cover was positively associated with early childhood development, with stronger associations observed for tree cover relative to grass cover. The findings are supported by a limited number of studies that have identified differences in health outcomes depending on the type of natural environment (Astell-Burt & Feng 2019; Tsai et al. 2018; Wu & Jackson 2017; Akpinar et al. 2016; de Vries et al. 2016; Picavet et al. 2016; Alcock et al. 2015; Wheeler et al. 2015; Nordh, Hartig, Hagerhall & Fry 2009). In general, prior studies have reported that exposure to tree cover may be more 117 strongly and consistently associated with health and development compared to exposure to grass cover (Maes et al. 2021; Donovan et al. 2020; Egorov et al. 2020; Jiang et al. 2020; Becker et al. 2019; Mears, Brindley, Jorgensen, et al. 2019; Kuo et al. 2018; Sivarajah et al. 2018; Tsai et al. 2018; Reid et al. 2017); though findings are mixed and too few studies have compared the health effects of multiple vegetation types to make conclusive generalizations. The heterogeneity in findings may suggest that there is no single vegetation type that is best for improved health across outcomes and populations. Instead, a variety of vegetation types may be required to suit the different needs and activities of users to support various health outcomes. The finding of a positive association of exposure to water on self-reported general health reported in Chapter 3 is consistent with earlier studies that show positive associations between blue space and various physical and mental health outcomes (de Vries et al. 2016; de Vries, Nieuwenhuizen, Farjon, van Hinsberg & Dirkx 2021; McDougall et al. 2021; Mears, Brindley, Jorgensen, et al. 2019; Crouse et al. 2018; Völker et al. 2018; Nutsford et al. 2016; Alcock et al. 2015; Wheeler et al. 2015; White, Wheeler, Herbert, Alcock & Depledge 2014). Similar positive associations have been observed for childhood health and developmental outcomes (Wood et al. 2016; Amoly et al. 2014; Ashbullby, Pahl, Webley & White 2013; Huynh et al. 2013), though the findings from this dissertation cannot confirm these trends as water was excluded as an exposure in Chapter 5 due to limited presence near children’s residences. Findings from Chapter 3 and 4 indicate that exposure to paved surfaces was negatively associated with human health. In Chapter 3, exposure to paved surfaces was associated with higher odds of ‘poor or fair’ general and mental health. In Chapter 4, lifetime exposure to paved surfaces was associated with lower EDI scores and higher odds of developmental vulnerability. 118 The results are consistent with a small number of studies that have reported adverse effects of exposure to non-natural, impervious surfaces on health outcomes, including anxiety (Larson et al. 2018), blood pressure (Dzhambov, Lercher, Markevych, Browning & Rüdisser 2022), self-regulation (Scott et al. 2018), autism prevalence (Wu & Jackson 2017), and academic performance (Donovan et al. 2020). Paved surfaces not only reflect the counterpart of natural environments but may also reflect urbanicity-related stressors that can adversely influence human health, including residential crowding and increased levels of harmful environmental exposures (e.g., air pollution, noise, heat). As such, built environment indicators may be negatively associated with health outcomes beyond the mere lack of natural environments. While the findings of this dissertation suggest exposure to paved surfaces is negatively associated with health outcomes, it is important to acknowledge the potential role paved surfaces play in promoting human health. For instance, roadways are necessary in supporting multi-modal transit systems, providing residents with access to healthcare services and other facilities that are important for health and well-being, such as schools and places of employment (Mayaud, Tran, Pereira & Nuttall 2019). Transit-calmed streets and pedestrian amenities have also been shown to encourage healthy movement behaviors, such as group play (e.g., foursquare) and physical activities (e.g., skateboarding, cycling) among children (Lambert, Vlaar, Herrington & Brussoni 2019). With the projected and ongoing intensification of global urbanization, which has been characterized by expanding impervious surface coverage (Seto, Fragkias, Güneralp & Reilly 2011), it is important to better understand the implications (both positive and negative) these land cover changes will have on human health. 119 Although there is presently no consensus regarding which land cover types are most consistently associated with improved human health, the present findings and previous findings suggest that observed associations between nature exposure and health vary according to the type of natural environment. As such, it is recommended that natural environments be viewed as distinct environment types, rather than as simply “green” spaces. 6.2.3 Nature-health associations may vary according to the type of metric used to estimate human contact with nature Findings from this dissertation suggest that nature-health associations varied according to type of human contact with nature. Findings from Chapter 2 indicate that access to public green space and residential exposure to land cover types were only weakly correlated, suggesting that the two metrics capture unique estimates of human contact with natural environments. Furthermore, results from Chapter 3 suggest that health associations may be more consistent for exposure than access. While most published work has focused exclusively on evaluating a single metric – either access or exposure – there are examples of studies that have included both metrics in analyses of the health effects of urban natural environments. However, with a few exceptions (Seabrook, Smith, Clark & Gilliland 2019; Hystad et al. 2014; Potestio et al. 2009), Canadian research has relied mainly on exposure metrics (Villeneuve et al. 2012, 2022; Crouse et al. 2017, 2019, 2021; Yuchi, Sbihi, Davies, Tamburic & Brauer 2020; McMorris, Villeneuve, Su & Jerrett 2015). Consistent with the present findings, most studies seem to suggest stronger and more consistent health relationships with exposure than with accessibility metrics (Larson, Jennings & Cloutier 2016; Triguero-Mas et al. 2015; Amoly et al. 2014; Dadvand et al. 2014; Hystad et al. 2014); though evidence is too limited and inconsistent to make conclusive generalizations. Thus, when 120 consistent with hypothesized pathways linking natural environments to health outcomes, researchers are recommended to evaluate both access to public green space and residential exposure to surrounding nature in future studies. 6.2.4 Nature-health associations are, in part, explained by reduced exposure to air pollution and noise As outlined in the Introduction (see Section 1.3), several pathways have been proposed to link natural environments and health outcomes, including restoring capacities (e.g., reduced stress, attention restoration), building capacities (e.g., improved physical activity, social cohesion, and immune system functioning), and reducing harm (e.g., reduced air pollution and noise). Consistent with the reducing harm pathway, results from Chapter 5 suggest that the observed association between lifetime exposure to green space and early childhood development was partially mediated through reductions in NO2, PM2.5, and noise exposure levels. The current findings are consistent with the limited number of studies that have estimated the mediation effect of air pollution and noise in associations between green space exposure and childhood developmental outcomes (Binter et al. 2022; Asta et al. 2020; Liao, Zhang, et al. 2019; Van Aart et al. 2018; Dadvand et al. 2015). The results further indicate that harmful exposures were not equal in their mediation effects on the association between lifetime residential exposure to green space and childhood development, with the largest mediation effects observed for NO2, followed by noise and PM2.5 respectively. No known studies have investigated the mediation effects of all three harmful exposures on childhood developmental outcomes, though prior research has observed stronger mediations effects for NO2 (Thygesen et al. 2020) and noise (Van Aart et al. 2018) relative to PM2.5. Additional research is required to confirm these findings. 121 Although mediation analyses were not used in Chapters 3 and 4, research findings from these chapters may offer additional insights on pathways underlying the observed nature-health associations. Limited studies have explored the relative importance of different types of natural environments and forms of nature contact on health associations, so the following conclusions are speculative. Findings from Chapter 3 indicate that residential exposure to natural environments may be more consistently associated with self-reported health among adolescent and adult populations than to access to public green space. The results suggest that pathways supported by residential nature exposure, such as the reduction of harmful exposures and recovery from stress and attentional fatigue, may be more relevant for good self-reported health than pathways supported by accessible public green space, such as the promotion of physical activity and social cohesion. Findings from Chapters 3 and 4 indicate that associations between exposure to natural environments and health varied according to natural environment type. Exposure to water, shrubs, and grass, but not trees, was associated with good self-reported health among adolescents and adults, whereas exposure to trees and grass was associated with improved early childhood development. The heterogeneity in findings suggests that a variety of pathways likely underlie the nature-health associations observed. Areas with more grass may support self-reported health and early childhood development by providing a space for physical and social activities. Conversely, areas with greater tree cover may support early childhood development through mitigating harmful environmental exposures. Harm reduction may be a more important pathway underlying nature-health associations for children given their increased vulnerability to the adverse health effects of air pollution and noise exposure compared to adolescents and adults (Rice & Barone 2000). It is important to note that the restoring and building capacity pathways 122 were not empirically tested in this dissertation, and the reducing harm pathway was only investigated in Chapter 5. As such, readers should interpret these conclusions with caution. Pathways underlying nature-health associations likely differ depending on the exposure, outcome, and population of interest, and more research is needed to better understand these complex relations. 6.3 Limitations and recommendations for future research There are several limitations associated with the research presented in this dissertation. They are discussed below along with some recommendations for possible improvement in future research. 6.3.1 Study design and methodology For all four research chapters, outcome data were only available at one point in time. As such, the studies were unable to infer casual links in the observed associations between natural environments and the health outcomes considered. Some portion of the observed associations could be the result of self-selection, whereby, for instance, neighborhoods with abundant surrounding natural environments attract individuals of higher SES who already have healthy lifestyles and greater support for good health and development. Although, Chapters 4 and 5 were able to demonstrate temporal sequence in the association because exposure data preceding the outcome was accessible. The dominance of observational study designs in research on nature-health associations has been identified as a limitation inhibiting advancement in this field (Browning et al. 2022; Dzhambov et al. 2020; Labib et al. 2020; Twohig-Bennett & Jones 2018; Markevych et al. 2017). Where possible, future research should undertake longitudinal study designs, which would allow for better understanding of the direction of causality and the 123 mechanisms underlying the associations between natural environments and human health. In Canada, this may include the use of existing longitudinal data sources, such as the National Population Health Survey (spanning 1994 to 2010), the Canadian Longitudinal Study on Aging (spanning 2010 onwards), and the CHILD cohort (spanning 2009 onwards), in addition to longitudinal health administrative data. Future studies may also employ innovative experimental, quasi-experimental, or randomized controlled trials to evaluate the association between health and, for example, vacant lot greening and the development of new green spaces. Moreover, the lack of individual- and area-level variables in analyses may have led to residual confounding. In Chapter 3, the study could not control for area-level sociodemographic variables, though any bias on estimated effects was considered to be minimal due to the inclusion of individual-level variables, such as income and education level, that capture components of neighborhood characteristics (Diez-Roux 2000). Chapters 4 and 5 utilized administrative data that are not generally collected for research purposes and, as such, lack numerous individual-level variables. To address this, all available variables were used to operationalize constructs for which direct measures were unavailable (e.g., MSP subsidy as proxy for household income). Adequate control for individual- and area-level confounders is an important consideration for future research and, as highlighted in Chapter 2, SES and urbanicity may be particularly important area-level confounders to include in analyses. Lastly, in Chapter 5 air pollution and noise were specified as mediators of the green space-health association in mediation analyses, assuming a causal relation. While there is supportive evidence for this assumption (Diener & Mudu 2021), it should be acknowledged that the effect of green space on harmful exposures may not be causal and, if so, air pollution and noise would be more 124 appropriately classified as confounders of green space-health relations. Future research should further explore the relationship among these exposures, as well as empirically test potential mediating effects of other pathways of nature-health associations including, for example, stress reduction, physical activity, social cohesion, and heat reduction. Given multiple pathways may work together simultaneously, it will also be important for future research to move beyond single mediation models to consider synergies and trade-offs between mediators of nature-health associations (Dzhambov et al. 2020). A consideration of multiple underlying pathways of nature-health associations may be investigated using an ‘exposome’ approach, which encompasses all internal and external exposures throughout a human lifespan, from conception to death (Wild 2012). The dynamic and complex nature of the exposome presents a challenge in its characterization and measurement; nonetheless, analyses of even a subset of environmental exposures likely provides a more useful framework for understanding how the surrounding natural environment contributes to health outcomes than single mediation models (Stingone et al. 2017; Wild 2012). 6.3.2 Geographic unit of analysis Since data on participants’ residential address were unavailable, environmental exposures were assigned to six-digit postal codes. This may have resulted in some exposure misclassification associated with spatial discrepancy between participants’ postal code and true residential location; however, error is probably minimal given the small size of urban postal codes (Khan et al. 2018). Furthermore, postal codes may not accurately reflect the activity spaces of participants and the environments in which health effects are most likely to accrue. Ignoring individual mobility and assigning exposures to a fixed location may lead to the neighborhood effect 125 averaging problem (Kwan 2018), whereby exposures estimates inaccurately estimate individual’s true exposures obtained from non-residential contexts. This limitation can result in non-differential exposure misclassification, biasing the results toward null (Armstrong 2003). In Chapter 3, a range of buffer sizes (250-, 500-, and 1,000 m) were included to account for this problem, hypothesizing that larger buffers include workplaces, commuting routes, and recreational spaces. For Chapters 4 and 5, it was presumed that environmental exposure was relatively well-represented by the area of residence, as children assumingly spend most of their time at or within proximity of their home prior to entering elementary school. Future research may consider exposure outside of the home, such as workplaces, schools, and daycare locations. In addition, future researchers may take advantage of GPS devices (e.g., smartphones, smartwatches) to estimate exposure by tracking an individual’s geographic location at a high temporal and spatial resolution (Merry & Bettinger 2019). GPS coordinates can be linked with remotely sensed data to more accurately estimate participants exposure to and time spent in nature (Holland et al. 2021). 6.3.3 Access and exposure metric There are several limitations related to the methodology to estimate access and exposure to natural environments. A common issue with studies using access as a natural environment metric is that the value of access can vary depending on the size of the area to which you have access. For example, living within 300 m from a large urban forest, such as Stanley Park in Vancouver, is likely to have a different effect on health than living within 300 m from a smaller natural space. In the development of the access metric, a ‘minimum size’ approach was taken to account for this, estimating accessibility only for areas equal to or greater than one hectare, but it is 126 acknowledged that the value of accessing these spaces may vary. In addition, the access metric did not take into consideration the spatial distribution (e.g., fragmentation) and qualitative attributes (e.g., amenities, maintenance, aesthetic, safety) of public green spaces, as well as community programming available to neighborhood populations. These factors may influence visitation rates and the activities afforded by public green spaces. As such, future studies should consider the relative effect of these factors on health. This dissertation used a variety of data (e.g., land cover, NDVI, spectrally unmixed vegetation percentage) to map residential exposure to the surrounding environment, contributing unique insights on the influence of various forms of natural environments on human health. Noteworthy is the use of a high spatial resolution land cover dataset to map exposure to different types of natural and non-natural land cover types. While this is an improvement to more crude measures (e.g., NDVI) of natural environments, it was not possible to distinguish vegetation to the species level nor types of blue space (e.g., fresh water, salt water), which may matter for differentiating health effects (Beute et al. 2020). Furthermore, future studies may also consider estimating exposure using street view imagery, as opposed to downward-facing aerial imagery, to better capture how people perceive their surrounding environment (Larkin & Hystad 2019). Like most epidemiological studies, Euclidian linear distances were used for exposure buffer zones and buffer zones around public green spaces. While Euclidian linear distance is commonly used as a proxy for access, it does not necessarily approximate the true distances or routes travelled by respondents in their daily lives. More complex spatial modelling may be warranted in future studies, such as networks analyses that take into consideration transportation networks and barriers that prevent access (Frank et al. 2022). 127 Another important aspect that is likely to affect health outcomes and that was not assessed, is time spent in public green spaces and activities performed, sometimes referred to as “dose” (Shanahan et al. 2016). This may also potentially be affected by distance and quality of natural environments (Bijker & Sijtsma 2017). Research has shown that the mere presence of and access to natural environments does not necessarily equate to usage (Tillmann, Clark, et al. 2018). Moving forward, it will be important to apply qualitative research methods to better assess motives for engagement with nature, including willingness to travel (Smith et al. 2021). Lastly, it is important to recognise that current methods to estimate access and exposure may encourage ableist thinking by mainly focusing on walking distance and/or time. These metrics may not consider the forms of access, exposure, and use of natural environments for those living with disabilities (e.g., physical, sensory, cognitive, and mental health-related disabilities), as well as barriers to nature contact. Disability is not a special condition experienced by a few but may affect everyone for some part of their lives (e.g., injury, aging). In Canada, one in five (22%) of the population aged 15 years and over have one or more disabilities (Morris, Fawcett, Brisebois & Hughes 2018). To shift towards more inclusive exposure assessment methodologies, it will be important to inform metric development through user-centred approaches, such as participatory mapping, focus groups, and interviews. 6.3.4 Study generalizability Finally, the present findings are limited in their generalizability. In Chapter 3, the results were limited to respondents of the 2013-2014 CCHS. The study population were largely adults, white, educated, and residents of urbanised areas. In Chapters 4 and 5, the study cohorts were largely comprised of children born to multi-parent households, who spoke English as their first 128 language, and who resided in urbanised neighborhoods. Given prior research has reported that associations between natural environments and human health may be modified by population characteristics (e.g., McEachan et al., 2018) and degree of urbanicity (e.g., Bijnens et al., 2020), the findings may not be generalizable to other sociodemographic groups and rural populations. Moreover, all research chapters were restricted to the Metro Vancouver region. Metro Vancouver is a region with relatively high levels of vegetation cover, so results may not be generalizable to other geographic contexts with less available green space. Though, preliminary research indicates that even within arid regions with desert landscapes, low levels of vegetation may offer health benefits to a degree that is similar to regions with higher baseline levels of vegetation (Olvera-Alvarez, Browning, Neophytou & Bratman 2021). While this dissertation contributed to expanding the geographic scope of existing literature by studying Canadian study populations, future research should focus on different geographic areas and populations, particularly low- and middle-income settings and historically marginalized populations, which are not well-represented in current literature on nature-health associations (Jimenez et al. 2021; Rigolon, Browning, McAnirlin & Yoon 2021; Yang, Zhao, et al. 2021). 6.4 Policy implications The research presented in this dissertation demonstrate that natural environments are beneficial for human health and, as such, justify the consideration of natural environments in urban planning and public health policy frameworks. While the evidence base on nature-health associations may be inconsistent and/or limited for certain health outcomes (Browning et al. 2022; Davis et al. 2021; Jimenez et al. 2021; Mygind et al. 2021; Nieuwenhuijsen 2021; Rojas-Rueda, Vaught, et al. 2021; Short et al. 2021; Smith et al. 2021; Yang, Zhao, et al. 2021), it is 129 important to recognise that the health benefits associated with greening interventions outweigh potential negative consequences (van den Bosch & Nieuwenhuijsen 2017). Though some studies have noted possible adverse health associations of natural environments (e.g., allergic disease from airborne pollen, vector-borne pathogens, damage to people or properties from falling trees and growing roots), most of these risks can be prevented through appropriate land management and policy interventions (e.g., selecting tree species and genotype without allergenic potential, regular monitoring of tree health) (Dunn 2010). Moreover, unlike individual-level health promotion approaches, natural environment interventions help to create a supportive environment with the potential of facilitating population-wide health improvements and long-term effects (Hunter et al. 2019). Beyond improved public health and well-being, natural environment interventions also provide social, economic, and environmental co-benefits, including reduced crime and violence (Hadavi, Rigolon, Gobster & Stewart 2021; Kondo, Han, Donovan & MacDonald 2017; Weinstein et al. 2015), increased property value (Elmqvist et al. 2015; Brander & Koetse 2011), improved temperature regulation and reduced energy costs (Horváthová, Badura & Duchková 2021; Gill, Handley, Ennos & Pauleit 2007; Akbari 2002), maintained urban biodiversity and habitat for wildlife (Haase, Frantzeskaki & Elmqvist 2014; Nielsen, van den Bosch, Maruthaveeran & van den Bosch 2014), and increased carbon sequestration (Zhao & Sander 2015; Nowak & Crane 2002). A key finding from this dissertation was that access to public green space and exposure to natural environments are unique metrics of human contact with nature and, furthermore, that health associations may be more consistent for exposure than for access to nature. The later finding is of relevance as regional densification and increasingly limited available land for public parks and 130 other green spaces suggest that micro-contacts with nature through daily exposure may be particularly important for Metro Vancouver residents in coming decades; this may be true in other urbanizing contexts around the globe as well. As such, planning strategies should aim towards increasing the availability and connectivity of natural environments in residential neighborhoods for daily exposure opportunities, in addition to common policy targets of accessible parks, greenways, and other forms of public natural spaces within walking distance of residential locations (World Health Organization 2016; City of Vancouver 2012). These strategies may include, for example, increasing urban tree canopy cover, upgrading bikeways and roadways into greenways, greening vacant lots and schoolyards, and establishing community gardens. An example of a framework that considers both access and exposure is the 3-30-300 rule (Konijnendijk van den Bosch 2021), which specifies three rules of thumb for urban greening: (i) at least three visible trees from every home; (ii) at least 30 percent canopy cover in every neighborhood; and (iii) a maximum distance of 300 m to the nearest park or green space (of at least 1 hectare in size). The 3-30-300 rule is a straightforward framework that can be applied in cities around the globe to ensure optimal provision of nature-related health benefits (Konijnendijk van den Bosch 2021). Another key finding of this dissertation is that associations between natural environments and health varied depending on land cover type, though there were no clear trends regarding which natural land cover type was most strongly and consistently associated with the health outcomes considered. A better understanding around the health benefits of specific types of natural environments (i.e., vegetation and water body types) could potentially contribute to improved planning and management of public open spaces with incorporation of health promoting natural 131 elements. A noteworthy finding in this dissertation was the adverse associations of exposure to paved surfaces across multiple health outcomes. This suggests that urban planners and managers should aim to minimize the presence of impervious surfaces in residential areas through, for example, conversion of paved areas (e.g., streets and parking spaces) to natural spaces, while ensuring that residents still have adequate transportation infrastructure in place for accessing essential services. Likewise, findings from Chapter 5 indicating that nature-health associations are partially mediated through reduced exposure to air pollution and noise suggest that increasing vegetation cover near roadways may be particularly important for reducing adverse health effects associated with non-natural, impervious environments. While this dissertation focused on access and exposure metrics that encourage landscape changes to increase the availability of urban natural environments, it is likewise important to consider interventions that encourage engagement with natural environments such as nature prescriptions, nature exposure guidelines, and community programming. For example, several Canadian provinces have recently introduced a park prescription initiative, called a Prescription for Nature (PaRx), where health-care providers can prescribe free annual passes to natural parks as a way of encouraging patients to spend time in nature to improve their health (BC Parks Foundation 2022). Prior research indicates that dual approaches, in which physical landscape changes are accompanied alongside social programming, lead to more positive health outcomes than physical changes alone (Hunter et al. 2019). In turn, these interventions may lead to greater nature relatedness, that may in turn encourage greater pro-environmental behavior that indirectly improves public health through healthier ecosystems and reduced climate change impacts (van den Bosch & Ode Sang 2017; Annerstedt van den Bosch & Depledge 2015). 132 Findings from Chapter 2 indicated significant negative associations between indicators of neighborhood-marginalization and both access to public green space and exposure to natural land cover. This finding is consistent with growing environmental justice literature that highlights the unequal distribution of natural environments across SES and ethnic groups (Nowak, Ellis & Greenfield 2022; Kephart 2021; Schüle, Hilz, Dreger & Bolte 2019; Rigolon 2016). While not confirmed by the findings of this dissertation, prior research also indicates that the protective health effects of natural environments may be stronger for marginalized communities (Rigolon et al. 2021), who are more likely to suffer poorer health outcomes compared to more privileged groups (OECD 2019). Interventions may be prioritized in these marginalized neighborhoods to alleviate the ‘double burden’ of socioeconomic deprivation and unavailable nature (Doiron et al. 2020); however, it is imperative to keep the potential risks of ‘green gentrification’ in mind, whereby interventions cause physical and/or psychological displacement of marginalized populations (Wolch et al. 2014). 6.5 Conclusions The relationship between humans and the broader environment has never been more important, and urgent pressures are leading us to re-evaluate the value of urban natural environments. As demonstrated by the United Nations 17 Strategic Development Goals (United Nations 2015), the conservation and restoration of urban nature is increasingly viewed as an important strategy in tackling the social, economic, and environmental challenges of global environmental concerns, like urbanization and climate change. The most recent Lancet Countdown on health and climate change (Watts et al. 2021) and Intergovernmental Panel on Climate Change (IPCC) report (IPCC 2022) have both mentioned urban green space as a tool for enhancing the resilience of 133 communities and increasing the mitigative and adaptive capacity of cities. Increasing urban greenness is proposed as one approach to mitigating the harmful health effects of climate change, including direct health risks related to changes in weather extremes (e.g., flooding, wildfires, droughts, heatwaves) and indirect health risks related to changes in, for example, the distribution of disease vectors, air quality, food availability, and social processes (e.g., migration and conflict) (Watts et al. 2021; Salmond et al. 2016). The potential of urban green space in mitigating climate change-related health risks is exemplified by the unprecedented heat dome experienced in the summer of 2021 in the province of British Columbia, which resulted in a 440% increase in community deaths (Henderson, McLean, Lee & Kosatsky 2022). A recent study exploring the demographic and spatial risk factors of mortality during this event indicated that higher surrounding neighborhood green space was associated with a significantly lower odds of heat dome-related death, even after controlling for neighborhood deprivation (Henderson et al. 2022). This finding underlines the importance of continuing initiatives to increase green space coverage in communities to protect against existing and foreseeable health threats of climate change around the globe. The COVID-19 pandemic has further pushed the relevance of urban natural environments for health promotion to the forefront of urban planning and management objectives. Around the world, national and local governments have sought to limit the spread of COVID-19 by introducing stay-at-home orders and lockdowns, which has resulted in a variety of restrictions for public natural spaces, such as parks (Frumkin 2021). Growing research has arisen to investigate the role of urban natural environments as a coping mechanism to the mental and physical health consequences of the pandemic and as a potential spatial risk factor for transmission (Labib, 134 Browning, Rigolon, Helbich & James 2022). COVID-19 appears to have modified the use of public natural spaces (e.g., parks), with some studies reporting increased visits (Berdejo-Espinola et al. 2021; Lu, Zhao, Wu & Lo 2021; Robinson, Brindley, Cameron, MacCarthy & Jorgensen 2021; Venter, Barton, Gundersen, Figari & Nowell 2021; Volenec, Abraham, Becker & Dobson 2021; Venter, Barton, Gundersen, Figari & Nowell 2020) and others reporting decreased visits (Zhang et al. 2022; Burnett, Olsen, Nicholls & Mitchell 2021; Heo et al. 2021; Larson et al. 2021; Ribeiro et al. 2021; Ugolini et al. 2020) following the onset of the pandemic. Heterogeneity in findings may stem from varying lockdown guidelines, socio-cultural contexts, and regional COVID-19 severity (Labib et al. 2022). On the other hand, other forms of nature contact, including spending time in private gardens and viewing nature through windows, may have increased since the onset of the pandemic (Corley et al. 2021; Pearson, Breeze, Reuben & Wyatt 2021). Nature exposure during the pandemic appears to be important for alleviating stress (Larson et al. 2022; Vos et al. 2022; Pearson et al. 2021; Ribeiro et al. 2021), depression and anxiety (Dzhambov et al. 2021; Lõhmus, Stenfors, Lind, Lauber & Georgelis 2021; Wortzel et al. 2021; Pouso et al. 2020), sleep disturbances (Corley et al. 2021; Spano et al. 2021), loneliness (Soga et al. 2020), and physical inactivity (Astell-Burt & Feng 2021; Yang, Lu, Yang, Gou & Liu 2021), while improving mental health and well-being (Robinson et al. 2021; Soga et al. 2020). Findings are less consistent regarding associations between natural environments and COVID-related health outcomes and transmission risk, as well as modifying effects of demographics and SES (Curtis, Rigolon, Schmalz & Brown 2022; Johnson, Hordley, Greenwell & Evans 2021; Klompmaker et al. 2021; Pan, Bardhan & Jin 2021; Russette et al. 2021; Spotswood et al. 2021; Venter, Sadilek, et al. 2021); though, some studies suggest that COVID-135 19 restrictions may have further exacerbated existing health and green space equity concerns (Burnett et al. 2021; Larson et al. 2021; Pipitone & Jović 2021). The COVID-19 pandemic is viewed as an opportunity for global communities to ‘build back better’ and, considering research pointing to the value of nature during the pandemic, initiatives to utilize urban natural environments for the development of healthy and liveable cities may be accelerated (World Health Organization 2020b). 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Environment International. 121:453–460. doi.org/10.1016/j.envint.2018.09.008. 211 Appendices Appendix A - Supplementary materials for Chapter 2 Table A-1. Point biserial (rpb) correlations between access and exposure to various land cover types when using a 300 m versus a 400 m buffer to define the maximum distance to the public green space. Exposure Access – 300 m buffer Access – 400 m buffer (rpb) (rpb) Buildings -0.27* -0.20* Paved -0.13* -0.05* Other built 0.06* 0.05* Barren 0.04* 0.04* Soil -0.01* -0.04* Coniferous trees 0.16* 0.13* Deciduous trees 0.17* 0.12* Shrubs 0.06* 0.00 Modified grass-herbs 0.06* 0.01* Natural grass-herbs 0.04* 0.02* Water 0.06* 0.04* Aggregated green space 0.21* 0.14* Correlations were similar across buffer sizes. This table presents values for the 500 m buffer zone. * Indicates p-value < 0.001. Table A-2. Percentage of postal codes with access to a public green space (≥ 1 hectare) across an urban-rural gradient when using a 300 m versus a 400 m buffer to define the maximum distance to the public green space. Distance from urban centre Access – 300 m buffer Access – 400 m buffer (%) (%) 1 km 64.6 80.0 2 km 68.8 82.4 3 km 72.2 85.7 4 km 74.5 87.2 5 km 72.9 84.9 6 km 70.5 83.1 7 km 70.9 82.5 8 km 68.0 78.8 9 km 66.2 82.2 ≥ 10 km 74.0 83.1 212 Table A-3. Point biserial (rpb) correlations between dimensions of marginalization and access to a public green space (≥ 1 hectare) when using a 300 m versus a 400 m buffer to define the maximum distance to the public green space. Residential instability Material deprivation Dependency Ethnic concentration Access – 300 m -0.16* -0.09* 0.00 -0.02* Access – 400 m -0.12* -0.06* 0.00 0.01* 213 Appendix B - Supplementary materials for Chapter 3 Figure B-1. Directed acyclic graph (DAG) depicting hypothesized associations between residential access and exposure to natural environments, potential confounding variables, and self-reported health outcomes. 214 Figure B-2. Final data cleaning of linked cohort of Metro Vancouver respondents to the 2013-2014 CCHS. 215 Table B-1. Unadjusted associations between access and exposure metrics and self-reported general health, mental health, and common mental disorder among Metro Vancouver respondents to the 2013-2014 CCHS (weighted n=1,960,575). Access or exposure Buffer size General Health Mental Health CMD OR (95% CI) a OR (95% CI) a OR (95% CI) b Access c - 0.640 (0.417, 0.983) 0.866 (0.532, 1.408) 0.939 (0.676, 1.304) Buildings d 250 m 1.010 (0.995, 1.026) 1.021 (1.002, 1.041) 1.004 (0.992, 1.017) 500 m 1.011 (0.995, 1.027) 1.025 (1.004, 1.047) 1.000 (0.987, 1.014) 1,000 m 1.009 (0.992, 1.026) 1.023 (0.998, 1.048) 1.001 (0.985, 1.017) Paved d 250 m 1.028 (1.014, 1.043) 1.034 (1.013, 1.056) 1.004 (0.990, 1.018) 500 m 1.040 (1.023, 1.057) 1.027 (1.003, 1.052) 0.997 (0.982, 1.012) 1,000 m 1.039 (1.022, 1.057) 1.027 (1.000, 1.054) 0.993 (0.978, 1.008) Coniferous d 250 m 0.981 (0.957, 1.006) 0.953 (0.918, 0.989) 1.012 (0.995, 1.030) 500 m 0.981 (0.959, 1.004) 0.959 (0.927, 0.993) 1.012 (0.995, 1.029) 1,000 m 0.981 (0.962, 1.000) 0.970 (0.940, 1.001) 1.012 (0.996, 1.028) Deciduous d 250 m 0.985 (0.968, 1.002) 0.978 (0.952, 1.005) 0.994 (0.982, 1.007) 500 m 0.984 (0.966, 1.003) 0.980 (0.952, 1.009) 0.995 (0.981, 1.010) 1,000 m 0.984 (0.965, 1.004) 0.976 (0.943, 1.010) 1.002 (0.984, 1.020) Shrub d 250 m 1.033 (0.868, 1.228) 0.715 (0.548, 0.931) 1.100 (0.968, 1.251) 500 m 0.924 (0.770, 1.110) 0.708 (0.553, 0.905) 1.038 (0.923, 1.167) 1,000 m 0.863 (0.708, 1.053) 0.685 (0.531, 0.885) 1.039 (0.896, 1.206) Grass-herbs d 250 m 0.993 (0.973, 1.014) 0.994 (0.954, 1.035) 0.979 (0.964, 0.994) 500 m 1.002 (0.983, 1.021) 0.997 (0.966, 1.029) 0.983 (0.963, 1.004) 1,000 m 1.003 (0.983, 1.023) 0.985 (0.958, 1.011) 0.983 (0.964, 1.003) Aggregated green space d 250 m 0.989 (0.979, 1.000) 0.983 (0.969, 0.997) 0.996 (0.989, 1.004) 500 m 0.991 (0.980, 1.001) 0.984 (0.971, 0.998) 0.998 (0.989, 1.007) 1,000 m 0.991 (0.980, 1.001) 0.982 (0.968, 0.996) 1.000 (0.991, 1.010) Water d 250 m 0.987 (0.963, 1.012) 0.974 (0.945, 1.004) 1.001 (0.989, 1.013) 500 m 0.977 (0.960, 0.995) 0.990 (0.970, 1.011) 1.009 (0.999, 1.019) 1,000 m 0.982 (0.965, 0.998) 1.002 (0.981, 1.025) 1.007 (0.996, 1.019) All odds ratios (OR) and 95% confidence intervals are rounded. Bold OR indicates that the association is in hypothesized direction and p-value < 0.05. a. Output modeled as OR of reporting ‘poor or fair’ compared to ‘excellent’ health. b. Output modeled as OR of reporting a common mental disorder (CMD) c. Models based on having access to public green space, defined as six-digit postal code within 300 m of public green space greater than or equal to one hectare. d. Models based on 1% increase in land cover area within buffer. 216 Table B-2. Adjusted associations between access and exposure metrics and self-reported general health and mental health coded as binary outcomes among Metro Vancouver respondents to the 2013-2014 CCHS (weighted n=1,960,575). Access or exposure Buffer size General Health Mental Health OR (95% CI) a OR (95% CI) a Access b - 0.723 (0.494, 1.058) 0.949 (0.609, 1.481) Buildings c 250 m 0.995 (0.981, 1.009) 1.008 (0.987, 1.029) 500 m 0.992 (0.976, 1.007) 1.008 (0.985, 1.031) 1,000 m 0.992 (0.975, 1.008) 1.005 (0.977, 1.033) Paved c 250 m 1.007 (0.992, 1.021) 1.018 (0.995, 1.041) 500 m 1.015 (0.998, 1.033) 1.003 (0.976, 1.031) 1,000 m 1.015 (0.997, 1.032) 1.002 (0.972, 1.032) Coniferous c 250 m 1.004 (0.980, 1.029) 0.973 (0.937, 1.010) 500 m 1.005 (0.983, 1.027) 0.982 (0.950, 1.015) 1,000 m 1.003 (0.986, 1.021) 0.993 (0.963, 1.023) Deciduous c 250 m 1.001 (0.986, 1.016) 0.990 (0.964, 1.017) 500 m 1.002 (0.986, 1.020) 0.992 (0.964, 1.022) 1,000 m 1.006 (0.988, 1.018) 0.989 (0.955, 1.024) Shrub c 250 m 1.103 (0.919, 1.324) 0.774 (0.605, 0.991) 500 m 1.000 (0.832, 1.201) 0.762 (0.596, 0.973) 1,000 m 0.928 (0.776, 1.110) 0.755 (0.598, 0.952) Grass-herbs c 250 m 0.994 (0.977, 1.011) 0.997 (0.959, 1.037) 500 m 1.000 (0.984, 1.015) 1.002 (0.969, 1.036) 1,000 m 1.003 (0.998, 1.018) 0.992 (0.963, 1.022) Aggregated green space c 250 m 1.000 (0.990, 1.009) 0.991 (0.977, 1.006) 500 m 1.002 (0.992, 1.012) 0.994 (0.979, 1.009) 1,000 m 1.003 (0.994, 1.012) 0.992 (0.977, 1.008) Water c 250 m 1.003 (0.978, 1.029) 0.989 (0.961, 1.018) 500 m 0.991 (0.974, 1.009) 1.008 (0.986, 1.030) 1,000 m 0.989 (0.974, 1.005) 1.015 (0.992, 1.038) Models control for gender, age, racial/cultural background, total household income, education level, number of persons < 18 years of age in the household, and urbanicity. All odds ratios (OR) and 95% confidence intervals are rounded. Bold OR indicates that the association is in hypothesized direction and p-value < 0.05. a. Output modeled as OR of reporting ‘poor or fair’ compared to ‘good, very good, or excellent’ health. b. Models based on having access to public green space, defined as six-digit postal code within 300 m of public green space greater than or equal to one hectare. c. Models based on 1% increase in land cover area within buffer. 217 Table B-3. Adjusted associations between exposure estimated as mean annual NDVI and self-reported general health, mental health, and common mental disorder among Metro Vancouver respondents to the 2013-2014 CCHS (weighted n = 1,960,575). Exposure metric Buffer size General Health Mental Health CMD OR (95% CI) a OR (95% CI) a OR (95% CI) b NDVI - Landsat c 250 m 0.357 (0.018, 7.253) 0.651 (0.020, 21.206) 0.309 (0.043, 2.248) 500 m 1.573 (0.079, 31.416) 0.488 (0.015, 15.484) 0.502 (0.058, 4.338) 1,000 m 0.787 (0.045, 13.811) 0.103 (0.002, 4.398) 0.755 (0.075, 7.632) NDVI - RapidEye c 250 m 0.497 (0.087, 2.822) 0.578 (0.084, 3.968) 0.511 (0.165, 1.588) 500 m 1.092 (0.200, 5.977) 0.531 (0.076, 3.703) 0.700 (0.213, 2.296) 1,000 m 0.752 (0.154, 3.687) 0.258 (0.031, 2.111) 0.903 (0.253, 3.224) Models control for gender, age, racial/cultural background, total household income, education level, number of persons < 18 years of age in the household, and urbanicity. All odds ratios (OR) and 95% confidence intervals are rounded. Bold OR indicates that the association is in hypothesized direction and p-value < 0.05. a. Output modeled as OR of reporting ‘poor or fair’ compared to ‘excellent’ health. b. Output modeled as OR of reporting a common mental disorder (CMD) c. Models based on 1-unit increase in NDVI within buffer. Table B-4. Adjusted associations between exposure estimated as the percentage of aggregated green land cover and mean annual NDVI and the three self-reported health outcomes among Metro Vancouver respondents to the 2013-2014 CCHS (weighted n = 1,960,575). Exposure metric Buffer size General Health Mental Health CMD OR (95% CI) a OR (95% CI) a OR (95% CI) b Aggregated green land cover 250 m 0.868 (0.519, 1.453) 0.622 (0.327, 1.183) 0.780 (0.535, 1.138) 500 m 1.146 (0.654, 2.007) 0.901 (0.472, 1.720) 1.094 (0.720, 1.664) 1,000 m 0.989 (0.573, 1.706) 0.654 (0.339, 1.263) 1.189 (0.783, 1.806) NDVI - Landsat 250 m 1.184 (0.676, 2.074) 1.273 (0.634, 2.559) 0.999 (0.677, 1.474) 500 m 1.087 (0.638, 1.850) 0.870 (0.457, 1.658) 1.088 (0.707, 1.673) 1,000 m 0.778 (0.462, 1.309) 0.570 (0.294, 1.107) 1.288 (0.827, 2.006) NDVI - RapidEye 250 m 1.008 (0.562, 1.809) 0.881 (0.456, 1.703) 0.93 (0.622, 1.388) 500 m 1.282 (0.733, 2.244) 0.765 (0.398, 1.473) 1.086 (0.703, 1.676) 1,000 m 0.852 (0.488, 1.486) 0.924 (0.404, 2.115) 1.245 (0.806, 1.924) Models control for gender, age, racial/cultural background, total household income, education level, number of persons < 18 years of age in the household, and urbanicity. All odds ratios (OR) and 95% confidence intervals are rounded. Bold OR indicates that the association is in hypothesized direction and p-value < 0.05. a. Output modeled as OR of reporting ‘poor or fair’ compared to ‘excellent’ health when comparing quartile four (most green) to quartile one (least green) b. Output modeled as OR of reporting a common mental disorder (CMD) when comparing quartile four (most green) to quartile one (least green) 218 Appendix C - Supplementary materials for Chapter 4 Figure C-1. Directed acyclic graph (DAG) depicting hypothesized associations between residential environmental exposure, potential confounding variables, and early childhood development. The DAG includes potential confounding variables that were excluded from the analyses due to high percentage of missing values (e.g., maternal education, alcohol consumption, and substance use). 219 Figure C-2. Final data cleaning of linked cohort of children to examine the association between lifetime residential exposure to distinct land cover types and early childhood development in Metro Vancouver. 220 Table C-1. Characteristics in the overall cohort and the sample of children with exposure, confounder, and outcome data to examine the association between lifetime residential exposure to distinct land cover types and early childhood development in Metro Vancouver. Entire Cohort (n=36,526) Study sample (n=27,539) P value (Study vs. Cohort) Demographic variables Age at EDI assessment 5.6 (0.3) 5.6 (0.3) 1 No data available 4,827 0 Sex 1 Female 17,659 (48.3%) 13,331 (48.4%) Male 18,867 (51.7%) 14,208 (51.6%) No data available 0 0 English as a second language 1 Yes 10,493 (28.7%) 9,241 (33.6%) No 20,939 (57.3%) 18,298 (66.4%) No data available 5,094 (13.9%) 0 Season of birth 1 Winter 8,836 (24.2%) 6,505 (23.6%) Spring 9,132 (25.0%) 6,932 (25.2%) Summer 9,293 (25.4%) 7,113 (25.8%) Fall 9,265 (25.4%) 6,989 (25.4%) No data available 0 0 MSP subsidy 1 Yes 7,657 (21.0%) 5,600 (20.3%) No 28,494 (78.0%) 21,939 (79.7%) No data available 375 (1.0%) 0 Lone-parent household 1 Yes 1,592 (4.4%) 996 (3.6%) No 34,200 (93.6%) 26,543 (96.4%) Unknown 487 (1.3%) 0 No data available 247 (0.7%) 0 Maternal age 1 < 25 4,450 (12.2%) 2,832 (10.3%) 25-40 31,093 (85.1%) 23,948 (87.0%) > 40 945 (2.6%) 759 (2.8%) No data available 38 (0.1%) 0 Neighborhood-level material deprivation -0.2 (0.7) -0.3 (0.7) 1 No data available 5,455 0 Urbanicity 1 Urban 29,850 (81.8%) 26,951 (97.9%) Rural/rural-urban fringe 797 (2.2%) 588 (2.1%) 9 (data only at DA-level) 829 (2.3%) 0 No data available 5,050 (13.8%) 0 Outcome variables Total EDI score 40.0 (7.9) 40.2 (7.8) 1 No data available 5,198 0 221 Entire Cohort (n=36,526) Study sample (n=27,539) P value (Study vs. Cohort) Environmental variables Aggregated green space (%) 36.0 (13.9) 36.0 (13.6) 1 No data available 4,943 0 Tree cover (%) 23.3 (12.6) 23.2 (12.2) 1 No data available 4,943 0 Grass cover (%) 12.1 ± 6.6 12.2 ± 6.6 1 No data available 4,943 0 Paved surfaces (%) 32.2 ± 9.0 32.2 ± 8.9 1 No data available 4,943 0 Data presented are mean (SD) or number (%). 222 Table C-2. Adjusted model output testing relative association of vegetated and paved land cover on early childhood development among Metro Vancouver children (n=27,539). Exposure IQR (%) Total EDI score β-coefficient (95% CI) Aggregated green space 17 0.17 (-0.01, 0.35) Paved surfaces 12 -0.22 (-0.40, -0.05) * Data presented are β-coefficients (95% confidence interval) with one interquartile range increase in environmental exposure within a 250 m buffer of participants’ six-digit postal code. Model includes both total vegetation and paved surfaces as fixed effects, and controls for sex, English as a second language, season of birth, lone-parent household, maternal age, MSP subsidy, neighborhood-level material deprivation, and urbanicity. Model includes random effect for teacher ID. All estimated effects and 95% confidence intervals are rounded. *p-value < 0.05. ** p-value < 0.01.*** p-value < 0.001. Table C-3. Adjusted model output testing relative association of tree cover and grass cover on early childhood development among Metro Vancouver children (n=27,539). Exposure IQR (%) Total EDI score β-coefficient (95% CI) Tree cover 14 0.28 (0.18, 0.39) *** Grass cover 7.6 0.16 (0.06, 0.26) ** Data presented are β-coefficients (95% confidence interval) with one interquartile range increase in environmental exposure within a 250 m buffer of participants’ six-digit postal code. Model includes both tree cover and grass cover as fixed effects, and controls for sex, English as a second language, season of birth, lone-parent household, maternal age, MSP subsidy, neighborhood-level material deprivation, and urbanicity. Model includes random effect for teacher ID. All estimated effects and 95% confidence intervals are rounded. *p-value < 0.05. ** p-value < 0.01.*** p-value < 0.001. 223 Table C-4. Adjusted associations between lifetime residential exposure to distinct land cover types and various dimensions of early childhood development among Metro Vancouver children (n=27,539). Exposure IQR (%) Physical well-being Social competence Emotional maturity Language and Cognition Communication and General knowledge β (95% CI) β (95% CI) β (95% CI) β (95% CI) β (95% CI) Aggregated green space 17 0.06 (0.04, 0.08) *** 0.08 (0.04, 0.11) *** 0.06 (0.03, 0.08) *** 0.02 (-0.01, 0.05) 0.12 (0.08, 0.16) *** Tree cover 14 0.03 (0.01, 0.05) ** 0.06 (0.03, 0.09) *** 0.04 (0.01, 0.06) ** 0.03 (0.00, 0.06) * 0.10 (0.06, 0.14) *** Grass cover 7.6 0.04 (0.02, 0.06) *** 0.03 (-0.00, 0.05) 0.04 (0.01, 0.06) *** -0.01 (-0.03, 0.02) 0.03 (-0.01, 0.06) Paved surfaces 12 -0.05 (-0.07, -0.02) *** -0.07 (-0.10, -0.04) *** -0.05 (-0.07, -0.02) *** -0.08 (-0.11, -0.05) *** -0.10 (-0.15, -0.06) *** Data presented are β-coefficients (95% confidence interval) with one interquartile range increase in environmental exposure within a 250 m buffer of participants’ six-digit postal code. Models control for sex, English as a second language, season of birth, lone-parent household, maternal age, MSP subsidy, neighborhood-level material deprivation, and urbanicity. Models include random effect for teacher ID. All estimated effects and 95% confidence intervals are rounded. *p < 0.05. ** p < 0.01.*** p < 0.001. Table C-5. Unadjusted and adjusted associations between lifetime residential exposure to distinct land cover types and early childhood developmental vulnerability among Metro Vancouver children (n=27,539). Exposure IQR (%) Total EDI vulnerability (yes versus no) OR (95% CI) Unadjusted Adjusted Aggregated green space 17 0.76 (0.73, 0.79) *** 0.88 (0.84, 0.92) *** Tree cover 14 0.80 (0.77, 0.83) *** 0.91 (0.88, 0.95) *** Grass cover 7.6 0.90 (0.87, 0.93) *** 0.94 (0.90, 0.97) *** Paved surfaces 12 1.25 (1.20, 1.30) *** 1.08 (1.04, 1.13) *** Data presented are odds ratio (95% confidence interval) for developmental vulnerability with one interquartile range increase in environmental exposure within a 250 m buffer of participants’ six-digit postal code. Adjusted models control for sex, English as a second language, season of birth, lone-parent household, maternal age, MSP subsidy, neighborhood-level material deprivation, and urbanicity. Models include random effect for teacher ID. All odds ratios and 95% confidence intervals are rounded. *p < 0.05. ** p < 0.01.*** p < 0.001. 224 Table C-6. Unadjusted and adjusted associations between lifetime residential exposure to distinct land cover types and early childhood development among Metro Vancouver children (n=27,539). Exposure Total EDI score β-coefficient (95% CI) Unadjusted Adjusted Aggregated green space 0.053 (0.046, 0.060) *** 0.020 (0.013, 0.027) *** Tree cover 0.055 (0.047, 0.063) *** 0.019 (0.011, 0.27) *** Grass cover 0.038 (0.024, 0.052) *** 0.016 (0.003, 0.029) * Paved surfaces -0.078 (-0.089, -0.067) *** -0.030 (-0.041, -0.020) *** Data presented are β-coefficients (95% confidence interval) with one percent increase in environmental exposure within a 250 m buffer of participants’ six-digit postal code. Adjusted models control for sex, English as a second language, season of birth, lone-parent household, maternal age, MSP subsidy, neighborhood-level material deprivation, and urbanicity. Models include random effect for teacher ID. All estimated effects and 95% confidence intervals are rounded. *p < 0.05. ** p < 0.01.*** p < 0.001. Table C-7. Unadjusted and adjusted associations between lifetime residential exposure to distinct land cover types within varying buffer zones and early childhood development among Metro Vancouver children (n=27,539). Exposure Buffer IQR (%) Total EDI score β-coefficient (95% CI) Unadjusted Adjusted Aggregated green space 100 m 18 0.72 (0.60, 0.84) *** 0.20 (0.09, 0.32) *** 250 m 17 0.90 (0.78, 1.02) *** 0.33 (0.21, 0.45) *** 500 m 17 0.98 (0.85, 1.10) *** 0.35 (0.22, 0.48) *** Tree cover 100 m 15 0.60 (0.50, 0.71) *** 0.15 (0.04, 0.25) ** 250 m 14 0.75 (0.64, 0.86) *** 0.26 (0.15, 0.37) *** 500 m 13 0.77 (0.66, 0.88) *** 0.25 (0.13, 0.36) *** Grass cover 100 m 8.4 0.25 (0.14, 0.36) *** 0.11 (0.01, 0.22) * 250 m 7.6 0.29 (0.18, 0.40) *** 0.12 (0.02, 0.22) * 500 m 6.8 0.31 (0.20, 0.41) *** 0.13 (0.03, 0.22) * Paved surfaces 100 m 13 -0.71 (-0.83, -0.59) *** -0.27 (-0.39, -0.16) *** 250 m 12 -0.90 (-1.02, -0.78) *** -0.35 (-0.47, -0.23) *** 500 m 11 -1.05 (-1.18, -0.92) *** -0.41 (-0.54, -0.28) *** Data presented are β-coefficients (95% confidence interval) with one interquartile range increase in environmental exposure within a 100-, 250-, and 500 m buffer of participants’ six-digit postal code. Adjusted models control for sex, English as a second language, season of birth, lone-parent household, maternal age, MSP subsidy, neighborhood-level material deprivation, and urbanicity. Models include random effect for teacher ID. All estimated effects and 95% confidence intervals are rounded. *p < 0.05. ** p < 0.01.*** p < 0.001. 225 Table C-8. Unadjusted and adjusted associations between lifetime residential greenness exposure and early childhood development among Metro Vancouver children (n=27,539). Exposure IQR Total EDI score β-coefficient (95% CI) Unadjusted Adjusted Aggregated green space (Land cover) 17% 0.90 (0.78, 1.02) *** 0.33 (0.21, 0.45) *** Vegetation (Landsat) 10% 0.61 (0.49, 0.73) *** 0.17 (0.05, 0.29) ** NDVI (Landsat) 0.1 0.97 (0.84, 1.10) *** 0.34 (0.21, 0.46) *** NDVI (Planet) 0.1 1.04 (0.91, 1.17) *** 0.34 (0.21, 0.47) *** Data presented are β-coefficients (95% confidence interval) with one interquartile range increase in environmental exposure within a 250 m buffer of participants’ six-digit postal code. Adjusted models control for sex, English as a second language, season of birth, lone-parent household, maternal age, MSP subsidy, neighborhood-level material deprivation, and urbanicity. Models include random effect for teacher ID. All estimated effects and 95% confidence intervals are rounded. *p < 0.05. ** p < 0.01.*** p < 0.001. 226 Appendix D - Supplementary materials for Chapter 5 Figure D-1. Directed acyclic graph (DAG) depicting hypothesized associations between residential green space exposure, potential mediators, potential confounding variables, and early childhood development. The double-headed arrow between the potential mediators (e.g., air pollution and noise) indicates a hypothesized correlational relationship. Covariates are hypothesized to be confounders of the association between residential green space exposure and early childhood development, as well as between the potential mediators (e.g., air pollution and noise) and early childhood development (for ease of presentation, only one arrow is shown for the hypothesized relation between potential confounders and mediators). The DAG includes potential confounding variables that were excluded from the analyses due to high percentage of missing values (e.g., maternal education, alcohol consumption, and substance use). 227 Figure D-2. Final data cleaning of linked cohort of children to examine the association between lifetime residential exposure to green space and early childhood development in Metro Vancouver. 228 Table D-1. Characteristics in the overall cohort and the sample of children with exposure, confounder, and outcome data to examine the association between lifetime residential exposure to green space and early childhood development in Metro Vancouver. Entire Cohort (n=36,526) Study sample (n=27,372) P value (Study vs. Cohort) Demographic variables Age at EDI assessment 5.6 (0.3) 5.6 years (0.3) 1 No data available 4,827 0 Sex 1 Female 17,6599 (48.3%) 13,225 (48.3%) Male 18,867 (51.7%) 14,147 (51.7%) No data available 0 0 English as a second language 1 Yes 10,493 (28.7%) 9,276 (33.9%) No 20,939 (57.3%) 18,096 (66.1%) No data available 5,094 (13.9%) 0 Season of birth 1 Winter 8,836 (24.2%) 6,465 (23.6%) Spring 9,132 (25.0%) 6,901 (25.2%) Summer 9,293 (25.4%) 7,069 (25.8%) Fall 9,265 (25.4%) 6,937 (25.3%) No data available 0 0 MSP subsidy 1 Yes 7,657 (21.0%) 5,618 (20.5%) No 28,494 (78.0%) 21,754 (79.5%) No data available 375 (1.0%) 0 Lone-parent household 1 Yes 1,592 (4.4%) 992 (3.6%) No 34,200 (93.6%) 26,380 (96.4%) Unknown 487 (1.3%) 0 No data available 247 (0.7%) 0 Maternal age 1 < 25 4,450 (12.2%) 2,856 (10.4%) 25-40 31,093 (85.1%) 23,772 (86.8%) > 40 945 (2.6%) 744 (2.7%) No data available 38 (0.1%) 0 Neighborhood-level material deprivation -0.2 (0.7) -0.2 (0.7) 1 No data available 5,455 0 Urbanicity 1 Urban 29,850 (81.8%) 26,799 (97.9%) Rural/rural-urban fringe 797 (2.2%) 573 (2.1%) 9 (data only at DA-level) 829 (2.3%) 0 No data available 5,050 (13.8%) 0 Outcome variables Total EDI score 40.0 (7.9) 40.1 (7.8) 1 No data available 5,198 0 Language and cognition domain score 8.3 (1.9) 8.3 (1.8) 1 No data available 5,411 0 229 Entire Cohort (n=36,526) Study sample (n=27,372) P value (Study vs. Cohort) Environmental variables Greenspace (250 m) (%) 31.4 (8.3) 31.3 (8.1) 1 No data available 4,927 0 NO2 (ug/m3) 40.0 (9.2) 40.2 (9.1) 1 No data available 4,860 0 PM2.5 (ug/m3) 7.6 (1.2) 7.6 (1.2) 1 No data available 4,886 0 Noise (Lden, dB(A)) 63.5 (4.8) 63.5 (4.7) 1 No data available 3,164 0 Data presented are mean (SD) or number (%). 230 Table D-2. Adjusted associations between lifetime residential exposure to green space and various domains of early childhood development among Metro Vancouver children (n=27,142). Exposure IQR Physical well-being Social competence Emotional maturity Language and Cognition Communication and General knowledge β (95% CI) β (95% CI) β (95% CI) β (95% CI) β (95% CI) % Vegetation 100 m 11% 0.04 (0.02, 0.06) *** 0.04 (0.01, 0.07) ** 0.05 (0.02, 0.07) *** -0.03 (-0.06, -0.00) * 0.05 (0.01, 0.09) ** 250 m 10% 0.04 (0.02, 0.06) *** 0.04 (0.01, 0.08) ** 0.05 (0.03, 0.08) *** -0.03 (-0.06, -0.00) * 0.06 (0.01, 0.10) ** 500 m 10% 0.04 (0.03, 0.07) *** 0.06 (0.03, 0.09) *** 0.07 (0.04, 0.09) *** -0.01 (-0.04, 0.02) 0.07 (0.03, 0.11) ** Data are β-coefficients (95% confidence interval) with one interquartile range increase in vegetation percentage within a 100-, 250-, and 500 m buffer of participants’ six-digit postal code. Models control for sex, English as a second language, season of birth, lone-parent household, maternal age, MSP subsidy, neighborhood-level material deprivation, and urbanicity. Models include random effect for teacher ID. All estimated effects and 95% confidence intervals are rounded. *p < 0.05. ** p < 0.01.*** p < 0.001. Table D-3. Unadjusted and adjusted associations between lifetime residential exposure to green space and early childhood developmental vulnerability among Metro Vancouver children (n=27,372). Exposure IQR Total EDI vulnerability (yes versus no) OR (95% CI) Language and Cognition vulnerability (yes versus no) OR (95% CI) Unadjusted Adjusted Unadjusted Adjusted % Vegetation 100 m 11% 0.83 (0.80, 0.86) *** 0.91 (0.87, 0.95) *** 0.92 (0.87, 0.98) ** 1.03 (0.96, 1.09) 250 m 10% 0.82 (0.78, 0.85) *** 0.91 (0.87, 0.95) *** 0.91 (0.86, 0.97) ** 1.04 (0.97, 1.11) 500 m 10% 0.79 (0.76, 0.82) *** 0.89 (0.85, 0.93) *** 0.88 (0.83, 0.94) *** 1.01 (0.95, 1.08) Data are odds ratio (95% confidence interval) of developmental vulnerability with one interquartile range increase in vegetation percentage within a 100-, 250-, and 500 m buffer of participants’ six-digit postal code. Adjusted models control for sex, English as a second language, season of birth, lone-parent household, maternal age, MSP subsidy, neighborhood-level material deprivation, and urbanicity. Models include random effect for teacher ID. All odds ratios and 95% confidence intervals are rounded. *p < 0.05. ** p < 0.01.*** p < 0.001. 231 Table D-4. Unadjusted and adjusted associations between lifetime residential exposure to green space within varying buffer zones and early childhood development among Metro Vancouver children (n=27,372). Exposure IQR Total EDI score β-coefficient (95% CI) Language and Cognition score β-coefficient (95% CI) Unadjusted Adjusted Unadjusted Adjusted % Vegetation 100 m 11% 0.53 (0.41, 0.65) *** 0.16 (0.04, 0.27) ** 0.04 (0.01, 0.07) ** -0.03 (-0.06, -0.00) * 250 m 10% 0.61 (0.48, 0.73) *** 0.16 (0.04, 0.28) ** 0.05 (0.02, 0.08) *** -0.03 (-0.06, -0.00) * 500 m 10% 0.73 (0.60, 0.85) *** 0.24 (0.11, 0.36) *** 0.08 (0.05, 0.11) *** -0.01 (-0.04, 0.02) Data are β-coefficients (95% confidence interval) with one interquartile range increase in vegetation percentage within a 100-, 250-, and 500 m buffer of participants’ six-digit postal code. Adjusted models control for sex, English as a second language, season of birth, lone-parent household, maternal age, MSP subsidy, neighborhood-level material deprivation, and urbanicity. Models include random effect for teacher ID. All estimated effects and 95% confidence intervals are rounded. *p < 0.05. ** p < 0.01.*** p < 0.001. Table D-5. Unadjusted and adjusted associations between lifetime residential greenness exposure and early childhood development among Metro Vancouver children (n=27,372). Exposure IQR Total EDI score β-coefficient (95% CI) Language and Cognition score β-coefficient (95% CI) Unadjusted Adjusted Unadjusted Adjusted NDVI - Landsat 100 m 0.1 0.87 (0.74, 1.00) *** 0.29 (0.17, 0.42) *** 0.10 (0.07, 0.13) *** -0.01 (-0.04, 0.02) 250 m 0.1 0.95 (0.82, 1.08) *** 0.32 (0.20, 0.45) *** 0.12 (0.09, 0.15) *** 0.00 (-0.03, 0.03) 500 m 0.1 1.02 (0.89, 1.16) *** 0.36 (0.23, 0.50) *** 0.14 (0.11, 0.17) *** 0.01 (-0.02, 0.04) NDVI - Planet 100 m 0.09 0.97 (0.85, 1.10) *** 0.31 (0.19, 0.44) *** 0.14 (0.11, 0.17) *** 0.02 (-0.01, 0.05) 250 m 0.09 1.03 (0.90, 1.16) *** 0.33 (0.20, 0.46) *** 0.15 (0.12, 0.18) *** 0.02 (-0.01, 0.05) 500 m 0.09 1.10 (0.97, 1.23) *** 0.38 (0.25, 0.51) *** 0.16 (0.13, 0.19) *** 0.03 (-0.00, 0.06) Data are β-coefficients (95% confidence interval) with one interquartile range increase in annual mean NDVI within a 100-, 250-, and 500 m buffer of participants’ six-digit postal code. Adjusted models control for sex, English as a second language, season of birth, lone-parent household, maternal age, MSP subsidy, neighborhood-level material deprivation, and urbanicity. Models include random effect for teacher ID. All odds ratios and 95% confidence intervals are rounded. *p < 0.05. ** p < 0.01.*** p < 0.001.
Thesis/Dissertation
eng
Forestry
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Bosch, Matilda van den
Gergel, Sarah E.
Understanding the human health benefits of urban green space across the life course by integrating epidemiological and novel geospatial approaches
Text