Medicine, Faculty of
Population and Public Health (SPPH), School of
DSpace
UBCV
Ahmadi, Mohadeseh
Master of Science - MSc
University of British Columbia
Background: Breast cancer is the most common cancer in women. Some established risk factors for breast cancer include obesity, physical activity, food choices, smoking, and alcohol use. With increasing rates of urbanization, there might be certain built environment features, such as traffic-related air pollution (TRAP), greenness, and walkability, that influence breast cancer risk. Objective: To assess the impact of TRAP on the risk of post-menopausal breast cancer by utilizing incident breast cancer data from Alberta’s Tomorrow Project (ATP) cohort linked to geographic datasets from the Canadian Urban Environmental Health Research (CANUE). This study will also assess the impact of the built environment factors on the risk breast cancer. Method: This study entailed a secondary analysis of baseline data from the ATP. Cox proportional hazard regression was used to build four models to assess the association between breast cancer risk and built environment factors. Results: The study included 15,536 participants and 523 incident breast cancer cases. The HR estimate for a 10 ppb increase in NO2 measured at baseline (the year participant completed the UHLQ or CORE survey) was 1.10 (95% CI = 0.90, 1.34). For walkability, the adjusted HRs for quintile 2 through 5, in relation to quintile 1, were 1.07 (95% CI = 0.80-1.44), 1.39 (1.05-1.83), 1.49 (1.13-1.97), and 1.22 (0.90-1.66), respectively. Q1 corresponded to least walkable neighbourhoods and Q5 corresponded to most walkable neighbourhoods. The HR estimate for an interquartile range increase in baseline greenness for this model was 1.05 (95% CI = 0.94, 1.16). For greenness measured within 1000 meters, the HR estimate for an IQR increase was 0.97 (95% CI = 0.88, 1.07). Discussion: We found non-significant associations between NO₂ exposure and post-menopausal breast cancer. For walkability, the least walkable areas had the lowest breast cancer rate, and the most walkable areas corresponded to greater breast cancer risk. Further, higher baseline greenness resulted in greater breast cancer risk, whereas increased greenness within 1000 meters was associated with reduced risk. While the association between NO2 and breast cancer risk has been reasonably studied, research on the association between greenness and walkability, and breast cancer risk is limited.
Association between built environment factors and post-menopausal breast cancer: Alberta’s tomorrow project by Mohadeseh Ahmadi BSc, The University of British Columbia, 2021 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Population and Public Health) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2024 Mohadeseh Ahmadi, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis entitled: Association between built environment factors and post-menopausal breast cancer: Alberta’s tomorrow project submitted by Mohadeseh Ahmadi in partial fulfillment of the requirements for the degree of: Master of Science in Population and Public Health Examining Committee: Dr. Trevor Dummer, Population and Public Health, UBC Supervisor Dr. Maryam Darvishian, Population and Public Health, UBC Supervisory Committee Member Dr. Rachel Murphy, Population and Public Health, UBC Supervisory Committee Member Dr. Chris McLeod, Population and Public Health, UBC Additional Examiner iii Abstract Background: Breast cancer is the most common cancer in women. Some established risk factors for breast cancer include obesity, physical activity, food choices, smoking, and alcohol use. With increasing rates of urbanization, there might be certain built environment features, such as traffic-related air pollution (TRAP), greenness, and walkability, that influence breast cancer risk. Objective: To assess the impact of TRAP on the risk of post-menopausal breast cancer by utilizing incident breast cancer data from Alberta’s Tomorrow Project (ATP) cohort linked to geographic datasets from the Canadian Urban Environmental Health Research (CANUE). This study will also assess the impact of the built environment factors on the risk breast cancer. Method: This study entailed a secondary analysis of baseline data from the ATP. Cox proportional hazard regression was used to build four models to assess the association between breast cancer risk and built environment factors. Results: The study included 15,536 participants and 523 incident breast cancer cases. The HR estimate for a 10 ppb increase in NO2 measured at baseline (the year participant completed the UHLQ or CORE survey) was 1.10 (95% CI = 0.90, 1.34). For walkability, the adjusted HRs for quintile 2 through 5, in relation to quintile 1, were 1.07 (95% CI = 0.80-1.44), 1.39 (1.05-1.83), 1.49 (1.13-1.97), and 1.22 (0.90-1.66), respectively. Q1 corresponded to least walkable neighbourhoods and Q5 corresponded to most walkable neighbourhoods. The HR estimate for an interquartile range increase in baseline greenness for this model was 1.05 (95% CI = 0.94, 1.16). For greenness measured within 1000 meters, the HR estimate for an IQR increase was 0.97 (95% CI = 0.88, 1.07). Discussion: We found non-significant associations between NO2 exposure and post-menopausal breast cancer. For walkability, the least walkable areas had the lowest breast cancer rate, and the most walkable areas corresponded to greater breast cancer risk. Further, higher baseline greenness resulted in greater breast cancer risk, whereas increased greenness within 1000 meters was associated with reduced risk. While the association between NO2 and breast cancer risk has been reasonably studied, research on the association between greenness and walkability, and breast cancer risk is limited. iv Lay summary Breast cancer, the most common cancer among women, is influenced by both modifiable and non-modifiable factors, including age, diet, smoking, alcohol consumption, family history, and genetics. Among these factors, the modifiable ones hold paramount importance from a population and prevention perspective. Our study focused on modifiable factors within the built environment, such as walkability, greenness, and NO2 exposure, which are understudied in breast cancer research. We utilized Alberta's Tomorrow Project data linked to Canadian Urban Environmental Research Consortium dataset to assess the relationship between these built environment factors and breast cancer risk in post-menopausal women. In this study, we found non-significant associations between NO2 exposure and post-menopausal breast cancer. However, increased walkability and NDVI measurements was associated with increased breast cancer risk, suggesting the need for further research. Nonetheless, our findings can still aid policymakers to devise appropriate and effective strategies to reduce cancer risk. v Preface: Ethical approval for this study was granted by the University of British Columbia Behavioral Research Ethics Board (REB) on December 2, 2022 in a Post Approval Activity (H22-03044). The study made use of data sourced from Alberta's Tomorrow Project. This thesis was carried out under the supervision of Dr. Trevor Dummer with committee members Drs. Maryam Darvishian and Rachel Murphy. The literature review and statistical analysis was carried out by Mohadeseh Ahmadi. The ethics application and data request were also completed by Mohadeseh Ahmadi. The data cleaning and analysis was conducted by Mohadeseh Ahmadi under the supervision of the committee members. The thesis was authored by Mohadeseh Ahmadi and underwent review by committee members. vi Table of Contents Abstract ................................................................................................................................. iii Lay summary ......................................................................................................................... iv Preface: .................................................................................................................................. v Table of Contents ................................................................................................................... vi List of Tables .......................................................................................................................... ix List of Figures .......................................................................................................................... x Acknowledgments ................................................................................................................. xi CHAPTER 1: Background, objectives, overview ........................................................................ 1 1.1 Background ................................................................................................................................ 1 1.2 Secondary vs primary prevention ................................................................................................ 1 1.3 Objective and problem statement ............................................................................................... 2 1.4 Study Rationale .......................................................................................................................... 3 1.5 Ethical issues and privacy ............................................................................................................ 5 1.6 Thesis outline ............................................................................................................................. 5 CHAPTER 2: Background and scoping literature review ........................................................... 7 2.1 Breast cancer, overview .............................................................................................................. 7 2.2 The built environment .............................................................................................................. 13 2.3 A scoping review – TRAP, walkability, greenspace, and breast cancer risk ................................... 15 2.3.1 Scoping review outline and methodology ................................................................................................ 15 2.3.2. Scoping review results ............................................................................................................................. 16 2.3.3 TRAP, walkability, greenspace, and breast cancer risk ............................................................................ 22 2.4 Obesity and physical activity ..................................................................................................... 22 2.5 Conclusions .............................................................................................................................. 24 CHAPTER 3: Methods ............................................................................................................ 25 3.1 Study design ............................................................................................................................. 25 3.2 Study population and setting .................................................................................................... 25 3.2.1 Alberta’s Tomorrow Project ..................................................................................................................... 25 3.2.2 Canadian Urban Environmental Health Research Consortium (CANUE) dataset ..................................... 27 3.3 Inclusion and exclusion criteria ................................................................................................. 28 3.4 Data and variable cleaning ........................................................................................................ 31 3.5 Missing data ............................................................................................................................. 32 3.6 Study variables ......................................................................................................................... 32 3.7 Alberta’s Tomorrow Project (ATP) variables ............................................................................... 33 3.7.1. Anthropometric measurements .............................................................................................................. 33 vii 3.7.2 Obesity ..................................................................................................................................................... 33 3.7.3. Physical activity ....................................................................................................................................... 34 3.7.4. Ethnicity ................................................................................................................................................... 34 3.7.5. Diet .......................................................................................................................................................... 35 3.7.6. Women’s health ...................................................................................................................................... 35 3.7.7. Smoking ................................................................................................................................................... 36 3.7.8. Socioeconomic status .............................................................................................................................. 36 3.7.9. Alcohol consumption .............................................................................................................................. 36 3.7.10. Education .............................................................................................................................................. 37 3.8. CANUE data ............................................................................................................................. 37 3.8.1. Greenness ............................................................................................................................................... 37 3.8.2. Traffic-related air pollution ..................................................................................................................... 38 3.8.3. Social and material deprivation ............................................................................................................... 38 3.8.4. Walkability ............................................................................................................................................... 38 3.9 Analysis methods ...................................................................................................................... 39 3.9.1 Descriptive characteristics and bivariate analysis .................................................................................... 39 3.10 Multivariate analysis ............................................................................................................... 40 3.10.1 Cox proportional hazard regression ....................................................................................................... 41 3.11 Model building methods ......................................................................................................... 43 CHAPTER 4: RESULTS ............................................................................................................. 44 4.1 Summary of Alberta’s Tomorrow Project (ATP) .......................................................................... 44 4.2 Missing data ............................................................................................................................. 44 4.3. Descriptive characteristics and summary statistics .................................................................... 45 4.4. Relationship between built environment factors ....................................................................... 52 4.5 Multivariable analysis (Cox proportional hazard models) ........................................................... 53 4.5.1 Greenness and breast cancer ................................................................................................................... 53 4.5.2 Air pollution (TRAP) and breast cancer .................................................................................................... 55 4.5.3 Walkability and breast cancer .................................................................................................................. 56 4.5.4. Deprivation and breast cancer risk .......................................................................................................... 58 CHAPTER 5: Discussion and conclusions ................................................................................ 62 5.1 Summary and interpretation of results ...................................................................................... 62 5.1.1 Greenness and breast cancer risk ............................................................................................................ 62 5.1.2 Traffic related air pollution (TRAP) and breast cancer risk ....................................................................... 64 5.1.3. Walkability and breast cancer risk .......................................................................................................... 67 5.1.4. Deprivation and breast cancer risk .......................................................................................................... 70 5.2 Study strengths and limitations ................................................................................................. 71 5.2.1. Study strengths ....................................................................................................................................... 71 5.2.2 Study limitations ...................................................................................................................................... 71 5.3 Study implications and recommendations ................................................................................. 73 5.4 Conclusions .............................................................................................................................. 77 References: ........................................................................................................................... 79 Appendices ........................................................................................................................... 90 viii Appendix A: Supplementary tables ................................................................................................. 90 Appendix B: Supplementary figures ................................................................................................ 93 ix List of Tables Table 1: Age standardized incidence and mortality rates for breast cancer in women in Alberta in 2010............................................................................................................................................... 10 Table 2: Ethnic makeup of Alberta in 2006 .................................................................................. 27 Table 3: Missing values for variables included in the study ......................................................... 45 Table 4: Baseline participant characteristics ................................................................................. 46 Table 5: Distribution of cases and non-cases within each quintile of walkability ........................ 48 Table 6: Built environment characteristics ................................................................................... 49 Table 7: Spearman correlations between built environment variables ......................................... 52 Table 8: Baseline greenness final model results ........................................................................... 53 Table 9: Average greenness within 1000 m change-in-effect ...................................................... 54 Table 10: NO2 at baseline (the year participant completed the UHLQ or CORE survey) ........... 56 Table 11: Walkability change-in-effect ........................................................................................ 57 Table 12: Social deprivation change-in-effect .............................................................................. 59 Table 13:Material deprivation change-in-effect ........................................................................... 60 Table 14: Change-in-estimate HR ratio percent change for baseline greenness .......................... 90 Table 15: Change-in-estimate HR ratio percent change for average greenness ........................... 90 Table 16: Change-in-estimate HR ratio percent change for NO2 baseline .................................. 91 Table 17: Change-in-estimate HR ratio percent change for walkability ...................................... 91 Table 18: Change-in-estimate HR ratio percent change for social deprivation ............................ 92 Table 19: Change-in-estimate HR ratio percent change for material deprivation ........................ 92 x List of Figures Figure 1: Flow Diagram of Search Strategy ................................................................................. 16 Figure 2: PRISMA diagram of study population .......................................................................... 31 Figure 3: Average air pollution (ppb) at baseline for breast cancer cases and controls ............... 49 Figure 4: Walkability z-score for breast cancer cases and controls .............................................. 50 Figure 5: Baseline greenness, for breast cancer cases and non-cases ........................................... 51 Figure 6: Annual NDVI mean within 1000 meters for breast cases and non-cases ..................... 51 Figure 7: DAG for baseline and average greenness ...................................................................... 93 Figure 8: DAG for NO2 ................................................................................................................ 93 Figure 9: DAG for walkability ...................................................................................................... 94 Figure 10: DAG for social deprivation ......................................................................................... 94 Figure 11: DAG for material deprivation ..................................................................................... 95 xi Acknowledgments First, I would like to thank my supervisor, Dr. Trevor Dummer, for his continuous help and guidance. I would like to also thank Dr. Maryam Darvishian for her assistance on statistical analysis and data cleaning, Dr. Rachel for her review of the thesis, and Alberta’s Tomorrow Project for granting me access to the data. Lastly, I would like to thank my parents for their support throughout the entire process. I would also like to thank my little brother, Amir, for all that he has done for me through my thesis write-up. I love you! Alberta’s Tomorrow Project is only possible because of the commitment of its research participants, its staff and its funders: Alberta Health, Alberta Cancer Foundation, Canadian Partnership Against Cancer and Health Canada, and substantial in-kind funding from Alberta Health Services. Cancer registry data was obtained through linkage with Surveillance & Reporting, Cancer Research & Analytics, Cancer Care Alberta. Environmental exposure data was obtained through linkage with Canadian Urban Environment Health Research. The views expressed herein represent the views of the author(s) and not of Alberta’s Tomorrow Project or any of its funders. NO2 (air pollution) data, indexed to DMTI Spatial Inc. postal codes, were provided by CANUE (Canadian Urban Environmental Health Research Consortium). CANUE is supported by the Canadian Institutes of Health Research. 1 CHAPTER 1: Background, objectives, overview 1.1 Background Breast cancer is the most common cancer in women (1). The World Health Organization (WHO) estimated that in 2020 approximately 2.3 million cases of breast cancer were diagnosed worldwide, of which 685,000 resulted in death, cementing its status as the most prevalent cancer type (1). It had been estimated that in 2022 there will be about 28,600 breast cancer diagnoses among Canadian women, of which 5,500 whom will die from (2). In 2019, there were 264,000 incident breast cancers reported among women in the United States, resulting in approximately 42,000 deaths (3). In the United Kingdom, an estimated 56,000 new breast cancer cases were diagnosed each year between 2016 and 2018 (4). In China, these estimates are striking; in 2022, there were about 430,000 cases of breast cancer documented (5). Breast cancer predominantly affects female biological sex, with less than 1% of all breast cancer cases diagnosed in men (6). Further, breast cancer diagnoses are more common among post-menopausal women compared to pre-menopausal women. In 2018, an estimated 1.4 million cases of post-menopausal breast cancer were diagnosed, of which 490,000 resulted in death (7). On the other hand, only 645,000 cases of pre-menopausal breast cancer were diagnosed worldwide in 2018, with about 130,000 that resulted in death (7). Notably, being overweight or obese has been significantly linked to an increased risk of post-menopausal breast cancer (8,9). In addition, various other risk factors for breast cancer exist, such as genetic factors, as well as modifiable factors related to diet, behavior, and the environment (10,11). 1.2 Secondary vs primary prevention Breast cancer has been shown to be a preventable disease. While genetic factors and inherited mutations account for a small percentage of most cancer cases, with fewer than 10% having a family history of first-degree relatives with breast cancer, the primary causative factors lie in modifiable environmental and lifestyle choices (12). These factors are responsible for nearly 40% of breast cancer cases (13). Major lifestyle factors, such as obesity, physical activity (PA), 2 food choices, smoking, and alcohol use, have a significant impact on the risk of breast cancer (8,14–17). By contrast some modifiable risk factors are not necessarily amenable to individual behaviour changes. For example, traffic-related pollutants (TRAP) (18–21), residential greenness, and walkability are significant characteristics associated with the built environment, and which have been shown to either be risk factors for breast cancer (18–21) or have the potential to impact breast cancer risk. Walkability assesses the ease of using walking as a mode of transportation, while greenness and access to green spaces pertain to areas covered by grass, trees, or other vegetation, which may include parks, schoolyards, or community gardens (22). Moreover, while not directly associated with breast cancer risk, factors such as obesity and physical activity (PA) have been closely connected to key risk factors for breast cancer. Given the extent in which lifestyle factors contribute to the incidence of breast cancer, there is a need to sufficiently characterize and develop appropriate primary interventions to reduce breast cancer incidence. Through building evidence establishing the association between certain built environment features and breast cancer risk, we can further strengthen our understanding of breast cancer burden. This, in turn, can lead to improved identification of population-level strategies to reduce breast cancer risk factors and hence reduce breast cancer incidence. As shown by Ahmadi et al. in a meta-analysis of 22 cross-sectional and prospective studies, where the impact and efficacy of clinical and population approaches on coronary heart disease mortality was evaluated, mass strategies that target low and moderate-risk individuals yield better results compared to clinical strategies where only high-risk individuals are targeted through expensive medical treatments (23). Population strategies are particularly effective for risk factors broadly distributed in the population, such as air pollution (23). 1.3 Objective and problem statement The objective of this study was to evaluate the association between built environment factors and post-menopausal breast cancer risk. To achieve this goal, this study will 1. Evaluate the risk of breast cancer in relation to TRAP – NO2 in cohort of Albertans; 2. Evaluate the risk of breast cancer in relation to walkability, greenness, and socio-economic deprivation in a cohort of Albertans. 3 This study will assess the impact of TRAP on the risk of breast cancer by utilizing incident breast cancer from Albert’s Tomorrow Project (ATP) cohort linked to geographic datasets from the Canadian Urban Environmental Health Research (CANUE). 1.4 Study Rationale In this study, leveraging the comprehensive TRAP data available in CANUE, I conducted a longitudinal analysis of built environment risk factors to explore and understand the association between different markers of the built environment and breast cancer incidence. While some studies from other countries or Canadian provinces have established an association between NO2 exposure and the risk of breast cancer, there have been no studies from Alberta. Furthermore, there is limited data on the association between walkability and greenness and risk of breast cancer, although these factors are associated with other chronic diseases and breast cancer risk factors (22,24). In addition to studying the impact of NO2 on breast cancer risk, this study aims to investigate the connection between other aspects of the built environment, such as walkability and greenness, and breast cancer risk - a dimension that is notably underrepresented in current literature. To the best of our knowledge, this study is one of the first to examine the connection between TRAP and breast cancer risk in Alberta. It also stands as one of the first studies to explore the links between greenness, walkability, and breast cancer risk. Given the global rise in breast cancer rates and its high potential for prevention, delving into the root causes and understanding why certain individuals may be more susceptible than others can provide valuable insights for researchers (1). Furthermore, given the substantial economic strain associated with breast cancer treatments, governments have the potential to achieve cost savings through initiatives aimed at improving local air quality to efficiently reduce breast cancer incidence rates. While some exposures in our study may directly impact cancer risk, for example traffic-related air pollution might potentially be a causal risk factor, others serve as area-level indicators, or proxy indicators, linked to individual-level risk factors affecting cancer risk, for example community level deprivation is a proxy indicator that is associated with socio-economic factors that impact behavioural risk factors. Understanding the causal pathways, both direct and indirect, by which these variables influence breast cancer risk is crucial to help interpret our findings. For 4 instance, in relation to traffic related air pollution measured through NO2 it is reasonable to assume, according to the literature, that NO2 likely exerts a direct causal influence on breast cancer risk, possibly through a biological process. A study with approximately 89,000 women from the Canadian National Breast Screening Study cohort unraveled the impact of NO2 exposure on breast cancer incidence (25). They established positive associations between NO2 concentrations and incident breast cancer in premenopausal women, which persisted even after adjustments for various individual-level risk factors. This again suggests a potential direct causal impact of NO2 on breast cancer risk. Although the biological mechanisms that link NO2 to breast cancer have not yet been fully elucidated, some possible pathways include endocrine disruption, carcinogenic properties of benzene and polycyclic aromatic hydrocarbons, oxidative stress, estrogenic and antiestrogenic effects, methylation effects, DNA damage, and the involvement of nitric oxide (NO) in cancer-related processes (25). Social and material deprivation scores, serving as area-based indicators of socioeconomic status, influence breast cancer risk through a multifaceted interplay of individual and contextual factors. Individuals that reside in socioeconomically deprived areas often exhibit higher rates of established breast cancer risk factors, including smoking, unhealthy diets, and physical inactivity (26,27). Moreover, reduced access to healthcare resources in economically disadvantaged neighborhoods can result in delayed cancer diagnosis and suboptimal treatment, impacting overall cancer outcomes. Environmental exposures, prevalent in deprived areas, may further contribute to breast cancer risk (28). The chronic stress associated with living in economically deprived neighborhoods also plays a role, which influence biological pathways linked to cancer development. Gene-environment interactions and the cumulative impact of socioeconomic conditions across the life course contribute to the complex relationship between social and material deprivation and breast cancer (28). Many studies consider that social and material deprivation scores are area-based indicators of socioeconomic status and associated level risk factors that impact health through area-based effects. It is also important to acknowledge other perspectives. An alternative viewpoint emphasizes that higher mortality rates observed in areas identified as deprived may be attributed to a concentration of socially disadvantaged individuals that reside there (28,29). The study contends that individuals facing social disadvantage experience higher mortality risks regardless 5 of their residential location, which suggests that the focus on area-based measures might not capture the nuanced individual-level disparities (28). The implication is that efforts to reduce premature mortality could potentially be more effective by directly addressing individual-level disadvantage, irrespective of residential context. This perspective challenges the convenience of targeting interventions solely at deprived areas, as it may not address the root causes of mortality disparities. It introduces the idea that reducing health inequalities may be better achieved through interventions tailored to disadvantaged individuals, regardless of their geographical location. Combining these perspectives, our study contributes to the broader discussion on the complex interplay between area-based indicators and individual-level factors in understanding health outcomes (28,30). 1.5 Ethical issues and privacy This study received approval from the UBC Behavioral Research Ethics Board (REB). It involves conducting secondary data analysis using ATP data linked to CANUE data. The ATP is a longitudinal study that has gathered information from more than 55,000 individuals in Alberta between 2000 and 2015. The primary purpose of this database is to facilitate research into the factors that may contribute to elevated risks of specific cancers and chronic diseases in certain population groups. To gain deeper insights into the environmental and social factors which influence breast cancer, this study will analyze a diverse set of variables encompassing lifestyle, behavior, socioeconomic status, and health-related aspects from the ATP database. It is crucial to note that all data used in this study is de-identified, with each participant assigned a unique study ID number to mitigate any potential risks associated with the research. In addition, any elements of data that could potentially reveal the identity of participants, such as postal codes, were removed. Access was granted solely to de-identified data. 1.6 Thesis outline This thesis comprises five distinct chapters: Chapter 1 introduces the study background, objectives and rationale. Chapter 2 delves into the problem at hand, conducts a comprehensive examination that includes a scoping review of the relationship between breast cancer risk and the built environment. 6 Chapter 3 thoroughly details the methodology, which encompasses the study's design, data sources, participant characteristics, and aspects related to the built environment. Chapter 4 presents the study findings. Chapter 5 comprises the discussion, results, implications, and an analysis of the strengths and limitations of this research. 7 CHAPTER 2: Background and scoping literature review 2.1 Breast cancer, overview There are trillions of cells in the human body, which act as the body's basic building blocks and maintain the body's structural integrity. They absorb nutrients from food and convert them into energy. These cells also contain the body's genetic material and execute various vital functions (31). Human cells regularly undergo division to generate new cells to fulfill the needs of the body (31). Normally, a well-regulated process known as apoptosis is responsible for eliminating old or damaged cells (32). When this process malfunctions, it can lead to the uncontrolled growth and division of abnormal or damaged cells (32). Cancer encompasses a group of conditions where cells within the body undergo uncontrolled proliferation and have the potential to metastasize to other organs (31). These abnormal cells that proliferate uncontrollably form tumors (31). Tumours fall into three classes: begin, premalignant, or malignant (31). Benign or noncancerous tumours do not penetrate or spread to neighbouring tissues. They typically do not recur after removal and can occasionally grow to be quite enormous (31). As such, these types of tumours are not considered cancer. Premalignant tumour are tumours that have non-cancerous cells with the potential to develop into malignancy (33). Malignant or cancerous tumors have the capability to disseminate to distant regions of the body which can lead to the formation of new tumors, infiltrate to neighboring tissues, or both, in a process termed metastasis. Malignant tumors also have the potential to recur after surgical removal (31). A malignant tumour is considered in situ when the abnormal cells are localized only in one place where they originally formed. In contrast, metastatic tumours are a lump of abnormal cells that have spread to other places in the body except the lymph nodes close to the original tumour (31) Cancer staging classifies cancer based on its size and its spread to other body parts (34). This staging process provides valuable insights into the severity and prognosis of the cancer. Although several staging systems exist, the TNM (Tumor, Node, and Metastasis) staging method is commonly employed for various types of cancer (35). Typically, cancer staging involves four stages. In this system, each of the three variables— "T" representing the size and extent of the 8 primary tumor, "N" indicating the number of malignant lymph nodes nearby, and "M" signifying the presence or absence of metastases—is assigned a numerical grade ranging from 0 to 4, corresponding to the stage (36). Higher numbers are indicative of more advanced cancer. Stage 0, termed "carcinoma in situ," indicates the presence of abnormal cells that have not yet invaded or spread to neighboring tissues. Stages 1, 2, and 3 encompass cancerous cells. In stage 1, tumors are small and have not extended beyond the original organ (34). Stages 2 and 3 involve the tumor either increasing in size or spreading to nearby tissues, such as the skin or chest wall, from its original location (36). In stage 4, the tumor has undergone metastasis (spread) through the lymphatic or circulatory systems, which relocates from its initial site to other areas of the body, such as the lungs or brain. Breast cancer is the most prevalent cancer among those of female sex, although it can exceptionally occur in men as well (1). It is characterized by the uncontrolled proliferation of cells within the breast tissue (36). Breast cancer manifests in various forms, and the specific type depends on which cell in the breast transforms into a tumor (36). The breast comprises three fundamental components: connective tissues, ducts, and lobules (36). The milk-producing lobules, also referred to as ducts, emanate from the mammary glands and transport milk to the nipple. The most prevalent type of breast cancer, known as ductal carcinoma, develops in the ducts (36). The cells of the lobules are another place where cancer can develop. This type of cancer is known as lobular carcinoma. Both ductal and lobular carcinomas can exist as in situ or invasive forms, which indicates that they have spread into the tissues around them (36). Broadly, there are five distinct subtypes of breast cancer, characterized from an immunohistochemical perspective based on the expression of hormone receptors: estrogen (ER), progesterone (PR), human epidermal growth factor (HER2), and triple negative (TNBC) (37). ER expression is markedly elevated in approximately 70–75% of patients and holds great diagnostic importance in invasive breast carcinomas (37). Furthermore, although HER2 is found in roughly 15–25% of breast tumors, more than 50% of individuals with ER-positive breast cancer also exhibit PR expression (37). 9 Luminal A tumors are characterized by the presence of ER and PR and the absence of HER2 (37). They have a favorable prognosis due to their low grade and slow growth (37). Relapses in this subtype are infrequent, with a higher occurrence in the bone and lower occurrence in the visceral and central nervous systems (CNS) (37). In contrast, Luminal B cancers exhibit faster growth and a worse prognosis than Luminal A tumors (37). They may have a negative PR but a positive ER (37). This group often requires hormone treatment and chemotherapy (37). The third subtype, known as HER2-positive breast cancer, makes up 15% to 20% of cases and exhibits more aggressive biology and clinical behaviour (38). While ER is present in roughly 50% of HER2-positive breast tumours, the ER levels often tend to be lower in these tumours, which have significant proliferative properties (38). Without treatment, HER2-positive tumors progress more rapidly than luminal tumors and have an unfavorable prognosis (38). The fourth subtype is triple-negative or basal-like breast cancer, which accounts for approximately 8% to 37% of all breast cancer cases (38). This subtype is characterized by the absence of ER, PR, and HER2 expression (38). Triple-negative breast cancer is more commonly observed in African women, younger individuals, and those with the BRCA1 mutation (39). Furthermore, triple-negative breast cancer is generally considered to be more aggressive than luminal A or luminal B tumors. The last subtype of breast cancer is termed normal-like. In this subtype, there is positive expression of hormone receptors ER and PR, but negative expression of the HER2 gene. In 2020, the WHO recorded 2.3 million new global cases of breast cancer, with 685,000 of these cases leading to death. This makes it the second leading cause of death in women, following lung cancer (1). The American Cancer Society estimates that a woman's lifetime risk of developing breast cancer is about 13% (40). Likewise, according to estimates from the Canadian Cancer Society, 1 in 8 Canadian women will develop cancer at some point in their lives, while 1 in 34 Canadian women will die from it (41). Earlier data suggests that high-income countries (HIC) had higher breast cancer incidence rates than low-to-middle-income countries (LIMC). Africa and Asia had the lowest rates, while Western Europe and the United States had the highest incidence rates, which may be due to underreporting and skewed prevalence. Remarkably, Israel diverged from this pattern and documented some of the highest incidence rates globally (42). However, recent data supports a different trend: breast cancer incidence are higher in low- and middle-income countries (LMIC) than in high-income countries (HIC). Breast cancer treatments 10 have been proven effective with survival rates that often exceed 90%, in part because of systematic screening programmes that make early detection easier (1). While breast cancer is prevalent in both affluent and less affluent nations, the effectiveness of treatments varies between HIC and LMIC due to differences in healthcare access (1). The high effectiveness of breast cancer treatments in industrialized nations is in large attributable to early detection and time-appropriate treatment initiation (1). According to the WHO, the five-year survival rate for breast cancer is estimated to be 90% in high-income countries but ranges from 40% to 66% in low-income countries (1). In Alberta, an average of six individuals receives a breast cancer diagnosis each day (43). In 2010, there were 2,266 new cases of breast cancer diagnosed in women in Alberta, with an age-standardized incidence rate of 107.8 per 100,000 (44). Among these new cases, 399 resulted in death, with an age-standardized mortality rate of 18.3 per 100,000 (38). In Alberta, the lowest incidence rates were found in the youngest age group (50 years), while the highest incidence rates were observed in the oldest age group (70+ years). The relative 5-year survival rate for breast cancer in Alberta between 2008-2010 was estimated at 89%. This means that about 89% of all women diagnosed with breast cancer during that period would still be alive five years after their initial diagnosis (44). Below is a table summarizing breast cancer incidence and mortality rates in Alberta in 2010. Table 1: Age standardized incidence and mortality rates for breast cancer in women in Alberta in 2010 Age group Number of incident cases Number of deaths Incidence rate* Mortality rate* 0-19 0 0 0 0 20-39 110 9 80.7 6.7 40-59 976 107 752.2 82.6 60-79 932 160 1604.6 293.6 80+ 248 123 1080.6 588.2 Total 2266 399 107.8 18.3 Source: Alberta Cancer Agency (44) * Age standardized rates (per 100,000 women) – the standard population is Canada’s population in 1991 11 Breast cancer risk factors fall into two categories: non-modifiable and modifiable. Non-modifiable risk factors, such as biological sex, breast density, family history of breast cancer, genetic mutations, and aging, are factors that cannot be altered (45). Fibro glandular tissue and fatty tissue make up the majority of the tissue in the breasts; while the former refers to the dense tissue and appear thick on a mammography, the latter are not dense and cannot be observed on a mammography (46). Research indicates that a higher amount of dense tissue in the breast can increase the risk of breast cancer, although this increase in risk is generally slight. In breast cancer cases, less than 10% of patients have a family history of first-degree relatives with the condition, which implies that genetic factors and inherited mutations play a limited role. Among the genetic factors, the breast cancer 1 (BRCA1) and breast cancer 2 (BRCA2) genes are most frequently associated with hereditary breast cancer (47). These genes act as tumor suppressors that help to regulate the growth and division of breast and other cells. When these genes undergo mutations that disrupt their function, the risk of breast cancer increases. Inherited mutations in BRCA1 and BRCA2 genes account for approximately 3% of breast cancer cases, which amounts to about 7,500 women annually (47). Research suggests that individuals with BRCA1 or BRCA2 gene mutations have an 85% lifetime risk of developing breast cancer (48). Furthermore, they are more prone to develop breast cancer at a younger age, often before menopause, in contrast to individuals without these hereditary mutations (48). However, these mutations are relatively rare, as they only affect around 1 in 500 individuals (48). Breast cancer risk increases with age, as the majority of breast cancer diagnoses occur after the age of 50 (47). Furthermore, the age at menstruation and menopause also impacts breast cancer risk. When menstruation starts before the age of twelve and stops (menopause) later in life, generally after the age of 50, the likelihood of a woman to develop breast cancer also rises (46). This increase in risk is largely attributed to estrogen's stimulating effect on the uterus and breast tissue throughout a woman's menstrual cycle (49). This results in extended exposure to estrogen in these tissues as the number of menstrual cycles rises (49). Later onset of menopause implies that a woman has been exposed to hormones for a longer time (49). In addition, the age at which a woman has her first child, and her total number of pregnancies are also factors that affect breast cancer risk. A collaborative international study which examined breast cancer and reproductive experiences across seven different regions 12 revealed a consistent association between breast cancer risk and the age at which a woman has her first child (50). Women who gave birth before the age of 18 had an estimated breast cancer risk that was only about one-third of women who gave birth after the age of 35 (50). Moreover, the total number of pregnancies that a woman has also affect their risk of breast cancer. Women with five or more children are approximately half as likely to develop breast cancer compared to those who have never been pregnant (51). By contrast, modifiable environmental and lifestyle factors play a significant causative role (12), with nearly 40% of breast cancer cases attributable to these factors (13). Obesity, physical activity (PA), diet, and alcohol consumption are some of the major modifiable risk factors that increase breast cancer risk (8,14–17). The prevalence of these risk factors can also vary with their socio-economic status (SES) (52–54). In a meta-analysis of 53 studies, Hamajima et al. demonstrated that the relative risk of breast cancer increased by approximately 32% (95% CI 1.19-1.45) for women who consumed 35-44 g/day, roughly equivalent to 3-4 drinks per day, and by 46% (95% CI 1.33-1.61) for those who consumed 45 g/day of alcohol, which is roughly more than 4 drinks per day (55). Other epidemiological data have also associated 1-2 drinks per day (equivalent to 15-30 grams/day) to 30-50% increase in the risk of breast cancer (56,57). Obesity and PA are also important modifiable breast cancer risk factors; not only does it increase breast cancer risk but also raises the risk of relative mortality (58). Smith et al. has also shown that women residing in areas with higher socio-economic deprivation have a reduced risk of breast cancer but higher mortality rates (59). Socio-economic deprivation refers to an individual's relative disadvantage in terms of their access to and control over economic and social resources. The distribution of specific risk factors that vary with socio-economic status, such as parity (the number of children) and age at first childbirth, can provide insights into the inverse relationship between poor SES status and breast cancer risk (60). It is common for women in more privileged neighborhoods to have fewer children than those in more disadvantaged areas. In addition, deprivation can introduce confounding effects into the relationship between individual level SES and breast cancer, given that SES is associated with both breast cancer risk and characteristics of the built environment (52,53,61). 13 2.2 The built environment Features of the built environment influence human health, including, breast cancer incidence (62,63). In light of the increasing global occurrence of preventable cancers, understanding how the built environment impacts exposure to cancer development and its propagation within a society is of paramount importance. There is substantial evidence indicating that factors related to the built environment and urbanization, such as traffic-related pollution (18–21), are inversely associated with breast cancer incidence. The association between other built environment features, such as residential green spaces and walkability have only been limited or not studied at all. The majority of cancer cases occur in developed countries, which often aligns with trends in urbanisation and economic growth (64). Projections suggest that by 2100, over 90% of the global population will reside in urban areas, whereas currently, about half of the world's population lives in such regions (65). Living in urban areas can offer advantages like improved sanitation, infrastructure, and healthcare access, but it may also expose individuals to challenges such as unhealthy lifestyles and elevated environmental pollution levels (66). Air pollution is a well-documented detriment to human health (23), which exacerbates chronic and fatal conditions like heart disease and cancer (67,68). The gradual alteration in the atmospheric composition over the past century can be attributed to increased fossil fuel combustion and air pollution that manifests in forms such as carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and particulate matter (PM) (69). The International Agency for Research on Cancer (IARC) has categorized outdoor air pollution as a Group 1 carcinogen (70). According to WHO and IARC definitions, a substance is classified in Group 1 when there is substantial evidence to suggest that it can cause cancer or is carcinogenic in humans (64). The primary sources of air pollution, which are often concentrated in urban areas, include stationary-source fuel combustion and vehicle emissions (71). Nitrogen dioxide (NO2) concentrations, typically measured in parts per billion (ppb), are primarily produced through combustion processes and are considered an indicator of traffic-related air pollution (TRAP) (72). Therefore, NO2 levels can be used as a proxy for other TRAP components (73). In this study, we will use NO2 measurements as a representation of TRAP. 14 Different metrics are used to assess TRAP exposure, including proximity to major roads, traffic volume, traffic density, vehicle exhaust emissions density, and pollutant concentrations estimated using dispersion models (74). Different modeling techniques like Land Use Regression (LUR), dispersion models, space-time (geostatistical) models, and hybrid models are often employed to estimate ambient air concentrations (74,75). Particulate matter (PM) is commonly categorized into PM10, which consists of inhalable particles with diameters less than 10 micrometers, and PM2.5, which comprises tiny inhalable particles with diameters less than 2 micrometers. TRAP exposure measures are frequently collected at the area level, which may not accurately represent individual exposure. There are a variety of factors that influence a person’s exposure level, such as the length of time spent indoors and outdoors, air exchange rates in buildings and vehicles, and breathing rates (74). Unfortunately, direct monitoring of exposure to traffic-related air pollutants is seldom feasible due to logistical and financial constraints. To measure walkability, researchers often combine administrative data with geographic information systems (GIS) to gauge the walkability of a specific neighborhood's urban environment. More broadly, factors such as residential density, land use mix, and street connectivity are used to distinguish high and less walkable neighborhoods (76). Street connectivity corresponds to the number of intersections per square kilometre, as well as how efficient and convenient movement is from one place to another. Higher connectivity indicates a more direct route between places. Land use mix describes how physically and operationally integrated various land uses, including residential, commercial, and industrial are (76,77). Residential greenness and access to green spaces refers to land that is covered by grass, trees, or other vegetations; it may include parks, schoolyards, or community gardens. To assess greenness at the neighborhood level, two common methods are employed. One approach utilizes satellite imagery or land cover maps to identify the presence and quantity of various types of green spaces. Alternatively, the normalized difference vegetation index (NDVI) is calculated for green areas near respondents' addresses (78,79). The NDVI assesses vegetation levels by analyzing the energy reflected from the Earth's surface, a process derived from satellite observations. It takes into account various spectral characteristics to identify green vegetation, such as trees. 15 2.3 A scoping review – TRAP, walkability, greenspace, and breast cancer risk 2.3.1 Scoping review outline and methodology A literature review was performed to understand the impact of certain built environment characteristics, specifically TRAP, walkability, and green space, on the risk of breast cancer. We conducted a search in PubMed, MEDLINE, EMBASE, and Google Scholar. In relation to TRAP, we used the following two search strings in PubMed and EMBASE/MEDLINE, respectively: 1. [(“air pollut*” OR TRAP OR “traffic-related air pollut” OR “Air Pollutants” [Mesh]) AND (“breast cancer” OR “breast neoplasm”) AND (“post-menopaus*” OR “postmenopaus*”); 2. [(‘air pollut*’ OR ‘traffic-related air pollution’) AND (‘breast cancer’ OR ‘breast neoplasm’) AND (‘post-menopaus*’ OR ‘postmenopaus*’). In terms of the association between risk of breast cancer and walkability, green space, as well as obesity and PA, seven search terms, “breast cancer”, “post-menopausal”, “urban environment or urbanization”, “walkability”, “physical activity”, “obesity” and “green space” were used in PubMed, MEDLINE, and EMBASE. These search terms were used in different forms to create different search engines using “AND” and “OR” functions. Medical Subject Headings (MeSH) terms and the explode functions were utilized where available to incorporate additional keywords and subtopics where applicable. A flow diagram of search strategy is provided below (figure 1). The search for articles on walkability yielded few results in most search engines. After article identification, their titles and abstracts were assessed for eligibility based on relevance and other inclusion criteria. Studies were included if they met the following criteria: English language; population samples from post-menopausal women and post-menopausal breast cancer; measured exposure in relation to 1. air pollution, such as concentration of nitrogen dioxide and particulate matter, 2. walkability, and 3. green space; and reported either an increase or a decrease in breast cancer risk for each exposure measure. This study specifically focused on traffic-related air pollutants, although in many cases, the term "traffic" was omitted from search engines to ensure a broader selection of studies for analysis. No specify cut-off dates or study design requirements were imposed. The initial scoping review was conducted in December 2022 and repeated in July 2023 to include any new publications. 16 Figure 1: Flow Diagram of Search Strategy 2.3.2. Scoping review results After review, 30 studies that met the indicated inclusion criteria above were identified. The included studies were further broken down into study designs and exposure groups, as depicted in the PRISMA diagram (Figure 1). All the studies were published before 2000, with over 73% of them published since 2015. Majority of the studies concerned TRAP, while some studies focused on more than one topic. There were 13 case-control studies, 11 cohorts, four meta-analysis and reviews, and two cross-sectional studies. Eleven of the studies were based in European countries, seven based in Canada, 10 based in the US, and one in Asia. Of the air pollution studies, eight reported on NO2 exposure only, six reported on NO2 and particulate matter, one on NO2 and ultrafine particles, one on particulate matter only, and three on ultrafine particles only. Of the air pollution studies, all but two studies found that TRAP is associated with increased risk of breast cancer in post-menopausal women. 500 articles identified through database searching 398 exludedDuplication (n=212)Title of article (n= 103)Abstract review (n= 83)102 potentially eligible articles screened for full text72 exluded Not post-menopausal (n= 35)Did not answer research question (n= 37) 30 met the inclusion criteria and included in study3 articles focused on greenness 27 focused on 30 met the inclusion criteria and included in study 17 Only limited research (N=3 studies) was identified on walkability and green space, and their association with breast cancer risk. Among the three studies that focused on green space included in this review, two of them demonstrated a link between neighborhood greenness and a lower breast cancer risk (80,81). The third study, however, did not find any association between green space and breast cancer incidence within a Canadian population (82) Traffic-related air pollution While NO2, recognized as a marker of traffic-related air pollution, has received relatively more attention as a potential risk factor for breast cancer, a few studies have also explored the association between breast cancer risk and exposure to ultrafine particles, polycyclic aromatic hydrocarbons (PAHs), and benzo[a]pyrene (BaP) – which serves as a marker for PAH. These pollutants are known to disrupt endocrine systems and are produced as by-products of incomplete combustion of organic materials (83). However, despite their known hazards to human development and reproduction, there is limited epidemiological evidence regarding the long-term impact of these ambient air pollutants on breast cancer risk. Common sources of PAH in the environment include cigarette smoke, steel mills, vehicles, and coal combustion for electricity generation (84). Another significant ambient source of PAH exposure is vehicular traffic (85). Although research is scant, it has been shown that PAH play a major role in breast cancer etiology, given their ability to bind to deoxyribose nucleic acids (DNA) to form DNA adducts (84,85). DNA adducts form when aromatic chemicals, particularly PAH, covalently bond to DNA (86). When left unpaired, these adducts can alter DNA replication within breast tissue (86). According to previous research where the connection between adult-life exposure to PAHs and breast cancer was investigated, there has been evidence that PAH-DNA adducts in tumour tissue and peripheral blood are often higher in breast cancer cases than in controls (87,88). Breast cancer incidence has been found to be higher in urban than in rural areas (89,90). Breast cancer incidence has increased in tandem with rising traffic emissions over the past 30 years (89,91), which suggests that air pollution may play a role in the aetiology of breast cancer. Estimates suggest that about 10% of Canada's population resides within 100 meters of a major 18 urban road or 500 meters of a highway, which can subject them to substantial levels of traffic-related air pollution (92). Several cohort and case-control studies conducted in Europe and North America have investigated the association between breast cancer risk and exposure to different types of TRAP, particularly nitrogen dioxide (NO2) and particulate matter (PM2.5 or PM10) (20,75,89,91,93–103). Most of these studies established an increased risk of breast cancer in post-menopausal women as result of increased TRAP exposure. While many of these studies utilized NO2 as a proxy for TRAP, some also examined the concentrations of PM2.5 and PM10 in relation to elevated breast cancer risk. However, despite some studies that reported positive associations between air pollutant exposure and breast cancer, the results have been inconsistent. Many of these studies have stressed the importance of further research to confirm their findings, as some focused solely on air pollution measured over a short period of time preceding diagnosis, and others were based on relatively small sample sizes (94,104). Several case-control studies have established an association between NO2 exposure and an increased breast cancer risk (89,93,94,99). For instance, Lemarchand et al. conducted a case-control study to examine the relationship between air pollution and breast cancer risk, as well as whether the risk varied by menopausal status and tumor subtype (93). They assessed air pollution levels in the ten years leading up to diagnosis using data from a population-based case-control study in France (93). The results indicated that NO2 exposure, a marker of TRAP, might be associated with an increased risk of breast cancer, particularly in ER+/PR+ tumor subtypes (93). In this study, Lemarchand et al. reported an odds ratio (OR) of 1.11 (95% CI, 0.98, 1.26) for every 10ug/m3 increase in NO2 exposure (93). Similarly, three Canadian case-control studies also found a statistically significant association between breast cancer risk and NO2 exposure in post-menopausal women. Hystad et al. reported odds ratios for breast cancer of 1.10 per 10 ppb increase in NO2 exposure (99), while Crouse et al. found odds ratios of 1.31 per 5 ppb increase in NO2 exposure (94) Similarly, Goldberg et al. observed a modest non-significant increase of breast cancer incidence per interquartile range, or 3.75 ppb increase in NO2 exposure (OR 1.08, 95% CI, 0.92, 1.2) (98) . A stronger association was noted when they restricted their analysis to women who had lived at their current residence for a decade or more (OR 1.17, 95% CI: 0.93–1.46; IQR=3.84 ppb) (98). 19 Two cohort studies, one in Catalonia and another in Canada, reported similar findings (96,97). In the Canadian National Breast Screening Study, breast cancer incidence was positively associated with NO2 levels, with no association observed in post-menopausal cohorts. For a menopausal status cut-off of 50 and 52 years, a 9.7 ppb increase in NO2 exposure or interquartile range increase was linked to rate ratios (RR) of 1.13 and 1.17 (97). The Catalonia study, on the other hand, reported a hazard ratio (HR) of 1.05 (95% CI, 1.02, 1.08) per interquartile range or 27.6 ug/m3 increase in NO2 exposure in post-menopausal women (96). Within the French E3N cohort study, Amadou and colleagues conducted a nested case-control study and reported an OR of 1.07 (95% CI: 0.98, 1.15) for each interquartile range, or 17.8 ug/m3 increase in NO2 exposure (89). Furthermore, NO2 was identified as a risk factor for breast cancer in several meta-analysis and cohort studies (75,95,100,105). In a pooled analysis of 13 research studies where the relationship between air pollution and breast cancer risk was evaluated, it was established that every 10 ug/m3 increase in exposure levels of NO2 resulted in a meta-analytical RR of 1.027 (95% CI: 1.009, 1.047) (95). Similarly, Keramatinia et al. investigated the relationship between exposure to NO2 and breast cancer risk via a meta-analysis of five observational studies, three ecological studies and two individual based studies (100). Their analysis revealed a subtle correlation between breast cancer risk and individual-level exposure to NO2 in ambient air, although a more notable association was observed at the aggregate level. In the European cohort study known as ESCAPE, which integrated findings from nine prospective European cohorts, researchers found evidence to suggest that the majority of exposures to ambient air pollutants were positively associated to an elevated risk of post-menopausal breast cancer. However, only the association with NOx was found statistically significant (HR = 1.04) (75). While the association with NO2 did not reach statistical significance, the aggregate HR was similar to that of NOx (HR = 1.02) per 10 ug/m3 (75) . In a large US-based Sister’s study cohort that consisted of 47,591 women, of which 1,749 developed breast cancer during a 5-year follow-up period, no overall increased risk of breast cancer was observed (relative risk = 1.10, 95% CI: 0.97, 1.02 for an interquartile range difference of 5.8 parts per billion) (105). In contrast to other cohort studies, no associations were found for incident breast cancer and NO2 exposure in a Danish cohort study (102). This study employed an atmospheric chemistry transport model and reported a RR of 0.99 per 7.4 g/m3 20 (95% CI: 0.93, 1.05) (96). However, a case-control study in Denmark did find an association between NO2 exposure and incident breast cancer (103). The odds ratio for incident breast cancer with every 10 ug/m3 increase in NO2 exposure was 1.03 (95% CI: 1.01, 1.06) (97). Various studies have presented different findings regarding the association between NO2 exposure and breast cancer risk in relation to menopausal status. Some studies have reported a stronger positive association in post-menopausal women compared to pre-menopausal women (89,96). In contrast, other studies have found a higher association in premenopausal women (95,97,99), while a few studies have indicated no difference in risk associated with NO2 exposure in either pre-menopausal or post-menopausal women (93). The impact of particulate matter on risk of breast cancer has also been studied in seven studies. While some studies noted positive association, others did not. For instance, in a French case-control study, neither PM2.5 nor PM10 was associated with breast cancer risk (with odds ratios of 1.02 and 1.01 per 1 ug/m3 increase, respectively) (93). In a meta-cohort study conducted in Catalonia, the authors observed hazard ratios of 1.03 (95% CI, 1.01, 1.04; for a 1.8 ug/m3 increase) and 1.03 (95% CI, 1.01, 1.05; for a 7.9 ug/m3 increase) per interquartile range increment in PM2.5 and PM10, respectively, among post-menopausal women (96). In a large European cohort study by Anderson et al., a positive but statistically non-significant association was observed between PM2.5 (HR = 1.08, 95% CI: 0.77, 1.51) and PM10 (HR = 1.07, 95% CI: 0.89, 1.30) (75). The Sister’s cohort study also did not find a significant association between PM and breast cancer (105). An interquartile range (or 3.6 ug/m3) increase in PM exposure was associated with rate ratio of 1.03 (95% CI: 0.96, 1.11) (105). In addition, no statistically significant association was found between incidence breast cancer and PM in the Nurses' Health Study II, although subjects who lived within 50 metres of major roadways had increased risk of breast cancer incidence (101). Moreover, in the Danish Nurse’s Cohort study, no associations were reported for PM2.5 (RR = 0.99 per 3.3 μg/m3; 95% CI: 0.94, 1.10) or PM10 (RR = 1.02 per 2.9 μg/m3; 95% CI: 0.94, 1.10) (102). In a case-control study in Denmark, Poulsen and colleagues reported 21 an association between breast cancer incidence and PM2.5 (OR = 1.21 for every increase of 10 ug/m3 in exposure) (103). To summarize, the existing literature on air pollution and breast cancer presents different results. Notably, NO2 and NOx have demonstrated a more robust and statistically significant association with breast cancer risk compared to PM. However, it is important to acknowledge that the epidemiological evidence on the association between traffic-related air pollutants (TRAP) and breast cancer remains both scarce and inconsistent. Therefore, further research is essential to replicate the findings from the aforementioned studies and establish more conclusive conclusions. Walkability and green space The association between green spaces and breast cancer has been scarcely studied. While no study has yet been conducted on walkability and breast cancer risk, several case-control and cohort studies analyzed the association between green space and breast cancer incidence. For instance, in a cohort study conducted in Catalonia, Spain, researchers investigated the impact of green spaces on breast cancer risk (96). Their findings revealed a negative association between urban green spaces or NDVI (Normalized Difference Vegetation Index) and breast cancer risk among post-menopausal women. This association, while not statistically significant, was observed in women who had been followed for at least three years (HR = 0.82) and in those who did not change their place of residence during the follow-up period (HR = 0.84) (96). In addition, a case-control study from Spain investigated the relationship between breast cancer risk and urban green areas, agricultural areas, and surrounding greenness (80). After adjusting for age, socioeconomic status at the individual and area levels, education, and the number of children, the presence of urban green spaces or green space within 300 meters was found to be associated with a lower risk of breast cancer (OR = 0.65; 95% CI: 0.49-0.86) (80). In contrast, the presence of agricultural areas within 300 meters from the residence and the increased greenness surrounding the neighbourhood were linked to a higher risk of breast cancer with an adjusted OR of 1.33 (95% CI: 1.07-1.65) and 1.27 (95% CI: 0.92-1.77), respectively (80). None of these associations were mediated by physical activity or air pollution (80). 22 In a French longitudinal cohort study, a protective association was observed between residential greenspace (within 100 meter) and breast cancer incidence (HR = 0.82; 95% CI: 0.68, 0.99) (81). However, given the small sample size available for site-specific cancer analysis, these relationships did not approach statistical significance. In turn, this requires cautious interpretation of these findings. On the other hand, in a case-control study in Montreal, Canada, Wang and colleagues found no association between exposure to NDVI and risk of post-menopausal breast cancer (82). Given the scarcity of research on the relationship between green space, walkability, and breast cancer risk, further investigations into the association between walkability and green space are warranted. 2.3.3 TRAP, walkability, greenspace, and breast cancer risk The studies included in this scoping review shed light on the potential impact of TRAP, particularly NO2, on breast cancer risk. While many of these studies reported positive associations, these findings were often statistically insignificant, underscoring the need for future research to confirm these results, considering the limitations inherent in each study (93,99). While no study has been conducted to assess the impact of walkability on breast cancer risk, four studies linked green space to breast cancer risk. The findings presented in these studies along with a few studies that report association between breast cancer risk and walkability and greenspace demonstrate the need for further research. 2.4 Obesity and physical activity While not within the scope of the scoping review protocol, it is crucial to acknowledge the indirect connection between elements of the built environment, such as walkability and green space, and breast cancer risk through modifiable behaviours. Research has indicated that the relationship between the built environment and breast cancer might be influenced by physical activity and obesity. For example, residing near green spaces or in more walkable communities may potentially lower breast cancer risk because greenspaces or walkability may promote increased physical activity, which, in turn, reduces the risk of obesity. 23 Research has predominantly focused on the indirect relationship mediated by obesity and physical activity, rather than directly measuring how walkability and green spaces influence breast cancer risk. It is well-established that both PA and obesity are significant risk factors for post-menopausal breast cancer (106–108). Majority of the research that demonstrated clear connections between walkability, green space, physical activity, and obesity has been conducted in industrialized countries. For instance, a study in the United States investigated the association between the built environment and active transportation among adolescents. It found that more livable built-environment features, like increased street connectivity, higher walkability, and residential density, were linked to greater active transportation (109). Similarly, a cross-sectional study in British Columbia, Canada, uncovered a direct relationship between walkability, park availability, physical activity (highest vs. lowest quintile OR = 1.15), and obesity (highest vs. lowest quintile OR = 0.58) (110). These findings were reinforced by a French cohort study (111), which also discovered that individuals living in neighborhoods with high green space accessibility and dense cycle paths were more inclined to walk or cycle when compared to areas with limited access to green spaces and facilities, even after adjusting for individual and neighborhood sociodemographic factors (OR = 2.5; 95% CI: 1.4-4.6). Recent research has shown that cities with better walkability, which encompasses factors like walking distance, tend to have lower obesity rates (112). In an observational study conducted in San Diego, the physical activity and weight status of residents in highly walkable neighborhoods were compared to those in less walkable neighborhoods (113). The study revealed that residents in highly walkable areas reported higher physical activity, averaging 70 minutes, which included activities like walking for errands and during breaks at school or work. In addition, higher obesity rates and higher BMIs were observed among residents of less walkable areas compared to those of more walkable areas. Higher neighborhood green space resulted in a higher physical activity. An Australian study investigated the relationship between neighborhood walkability and the walking habits of Australian adults (114). They discovered a statistically significant connection between a neighborhood's objective walkability score and the weekly frequency of walking for transportation purposes, even after adjusting for various factors like age, gender, socioeconomic 24 status, and gender (114). Frank et al. found that greater land-use mix which represents buildings, blocks, and neighborhoods built in a dense area to encourage walking, greater distance walked daily, and less time spent in a car can strongly is associated with lower body mass index (BMI) (115) . Each quartile increase in land-use mix was associated with 12.2% lower likelihood of obesity, while each additional hour spent in car and each additional kilometer walked per day was associated with 6% increase and 4.8% decrease in likelihood of obesity, respectively. The amount of time spent driving and walking was strongly associated with walkability. Findings from a US study examined the association between green space and adult obesity. The study found that forests, public recreational areas, and accessible outdoor recreation resources were significantly associated with lower BMI and lower obesity rates (24). 2.5 Conclusions To summarize, this chapter highlighted the recent trend in breast cancer risk between LMIC and HIC, with higher rates observed in LMIC than in HIC, as well as the role of modifiable and non-modifiable risk factors in breast cancer incidence. While only about less than 10% of breast cancer cases are attributable to non-modifiable risk factors, more than 40% of all breast cancer cases result from modifiable risk factors which mainly consist of lifestyle and environment factors. In addition, findings from literature on the association between NO2, greenness, and walkability were discussed. While there is a reasonable amount of research conducted on the association between NO2 and breast cancer risk, where mostly positive association between the two were established, studies conducted on the association between greenness and breast cancer risk was only limited. To date, there has not been any study to analyze the association between walkability and breast cancer risk. 25 CHAPTER 3: Methods 3.1 Study design The relationship between breast cancer risk and features of the built environment was assessed through a prospective cohort study design. This study entailed the secondary analysis of baseline data from Alberta’s Tomorrow Project (ATP), a longitudinal cohort study that is linked with CANUE urban environment data. Given the longitudinal nature of ATP, the study was able to identify individuals who developed breast cancer after their enrollment (i.e., incident breast cancer cases). To evaluate the association between the built environment factors—specifically TRAP, walkability, greenness, and socio-economic deprivation —and the risk of breast cancer in Alberta, Cox proportional hazard models were employed. The entire data analysis process was conducted in R software, including model construction, statistical testing, and the creation of visual statistics. Excel was employed solely for data cleaning purposes. 3.2 Study population and setting 3.2.1 Alberta’s Tomorrow Project ATP represents a comprehensive province-wide prospective cohort study primarily focused on chronic diseases and cancer. This project encompasses Albertan residents aged 35 to 69 who have no prior history of malignant cancer, except for non-melanoma skin cancer. Participants were enrolled into the ATP cohort through one of two phases. Phase I, conducted between 2000 and 2008, employed a two-stage telephone random digit dialing (RDD) approach to recruit participants. In this case, the RDD was mapped to Alberta Regional Health Authorities (RHA) to select households with eligible residents. One or two residents from each household were then invited for participation in the study. Phase II, spanning from 2009 to 2015, enrolled participants through two methods: first, it extended invitations to individuals already involved in Phase I, and second, via volunteer sampling. The recruitment process for Phase II did not employ RHA-based RDD due to low response rate and high cost. Baseline information for individuals recruited during Phase I was collected through the Health and Life Questionnaire (HLQ), Canadian 26 Dietary History Questionnaire (CDHQ), and Physical Activity Questionnaire (PAQ). For participants recruited during Phase II, the same baseline data were collected using the Updated Health and Life Questionnaire (UHLQ) and Physical Activity and Nutrition Survey (PANS). Beginning in January 2011, the UHLQ and PANS were replaced by the Canadian Partnership of Tomorrow Project (CPTP) CORE Questionnaire. Consequently, participants who joined the study in 2011 or later exclusively completed the CORE Questionnaire. The questionnaires employed in this study collectively gathered a comprehensive range of information encompassing various socio-demographic, health-related variables, and behavioral factors. Specifically, the HLQ, CDHQ, and UHLQ questionnaires captured data regarding personal medical history, family health history, reproductive health, tobacco use, smoking habits, demographics, employment history, residential history, and body measurements, including height, weight, waist, and hip circumference. The PAQ and PANS questionnaires were focused on dietary history, daily and weekly fruit, and vegetable intake, providing valuable insights into participants' eating habits. While most participants contributed baseline bio samples, these samples were not utilized in this study. The ATP research initiative has been thoughtfully designed with a follow-up period spanning at least 50 years. This extended duration allows the cohort to observe and document the natural progression of cancer and various other chronic conditions over an extended period. Furthermore, participants recruited during both Phase I and Phase II of the study provided consent for their data to be linked to administrative healthcare databases. This linkage facilitated the identification of incident breast cancer cases for the purposes of our analysis. Alberta is the fourth most populous province of Canada. Based on Statistics Canada, the population of the province was approximately 4.26 million, representing an increase of around 4.8% since 2016 (116). Of the overall population, approximately 2.2 million individuals were women, with approximately 865,000 within the age range of 35 to 64 (116). Nearly 81% of the population in Alberta resides in urban areas, while only 19% resides in rural areas. 27 Table 2: Ethnic makeup of Alberta in 2006 Ethnicity Population Percent of Total Chinese 122,100 3.7% South Asian 105,600 3.2% Filipino 52,800 1.6% Black 46,200 1.4% Southeast Asian 29,700 0.9% Latin American 26,400 0.8% Arab 26,400 0.8% Korean 13,200 0.4% West Asian 9,900 0.3% Japanese 9,900 0.3% White 2,649,900 80.3% Source: Statistics Canada (117) 3.2.2 Canadian Urban Environmental Health Research Consortium (CANUE) dataset The Canadian Urban Environmental Health Research Consortium (CANUE) dataset compiles and generates standardized environmental data at the area level related to air pollution, green and natural spaces, and built environment characteristics such as walkability and deprivation. Following data compilation, CANUE then facilitates the integration of this information into existing Canadian cohort studies and administrative health databases, including the ATP database. To establish these connections, CANUE data is linked to ATP participants via the postal codes provided by participants during their enrollment in the ATP study. In this study, we utilized three distinct datasets from the CANUE database. Our selection of these datasets was deliberate, aiming to encompass various facets of the built environment that we hypothesized could potentially relate to breast cancer risk. These datasets were TRAP (NO2), the Canadian Active Living Environments Index (CAN-ALE, i.e., walkability index), and the Normalized Difference Vegetation Index (an indicator of greenness). Our choice was informed by the established connections between these variables and the primary risk factors for breast cancer, such as obesity and physical activity. By including these specific variables in our analysis, we aimed to represent diverse aspects of the built environment in our study. 28 3.3 Inclusion and exclusion criteria Participants were enrolled in this study between 2000 and 2016. The sample (about 25,000) we received was less than the total ATP population, because ATP applied exclusion criteria prior to sending the sample to us. The sample that we received consisted of women of any age, participants who had consented to data linkage (which was required to ensure the study had access to CANUE data), the participants had to have CANUE data available, and they had to have completed a baseline health and lifestyle questionnaire (UHLQ/PANS or CORE). In addition, we applied certain exclusion criteria to arrive to our final population. Our study focused on post-menopausal breast cancer cases. Women were considered post-menopausal in two ways. First, based on their response to the ATP questionnaire. In ATP questionnaire, menopausal status was defined as whether an individual’s menstrual period stopped for one year and did not restart. Participants could select from one of the following response options: 1. Yes, natural menopause 2. Yes, other reasons (surgery, chemotherapy, medication) 3. No 4. Don’t know Further, in cases where the participant answered “no” and were over the age of 55, then the age cut-off of 60 was used. In cases where the participant answered “don’t know” or did not answer the question, then the age cut-off of 55 years was used to ascertain menopausal status (118). Individuals were considered eligible for inclusion if they were either post-menopausal at the time of enrollment or transitioned into the post-menopausal stage during the follow-up period. These menopausal status transitions were identified through the questionnaires that participants completed. The outcome variable was breast cancer, and exposure variables were the four built environment features – NO2, walkability, greenness, and socio-economic deprivation. Cases were defined as participants who were cancer-free at baseline but developed breast cancer during the follow-up period and were post-menopausal at the time of diagnosis. Controls, on the other hand, were individuals who remained cancer-free throughout the study and were post-menopausal at the end of the follow-up period. This procedure is explained in detail below and it is also well-depicted in the flow-chart in section 4.1. 29 The initial exclusion criteria for study participants centered on the presence of any pre-existing chronic condition or cancer. In cases where participants had multiple incident diagnoses, our analysis primarily focused on the primary diagnosis. For example, if a participant had been diagnosed with multiple types of cancer, our focus was on the first diagnosis, while subsequent diagnoses were treated as duplicates and not considered in our analysis. Prevalent cancers, including prevalent breast cancer were excluded for two reasons. First, ample evidence indicates that secondary malignancies can elevate the risk of developing breast cancer (119–123). For instance, Ramin et al. conducted a study spanning a median follow-up period of 6.2 years, during which 1,562 women experienced a second cancer. They found that breast cancer survivors exhibited a 70% increased risk of developing any cancer (95% CI = 1.62-1.79) and a 45% increased risk of non-breast cancer (95% CI = 1.37-1.54) compared to the general population (117). Second, individuals who have previously been diagnosed with a prevalent cancer often undergo various treatments, such as chemotherapy or radiation, which have been associated with a heightened risk of developing a subsequent malignancy (124,125). The only exception to the exclusion of cancer types was nonmelanoma skin cancer, and this decision was based on two primary reasons. First, skin cancers are predominantly benign and relatively prevalent (126). Second, unlike many other cancer types, the primary treatment for skin cancer involves surgery, which eliminates the potential risk of secondary breast cancer often associated with chemotherapy and radiation therapy (127). Data on prevalent cancer were provided through survey questionnaire, as well as linked data to the Alberta Cancer registry (ACR). The second exclusion criterion was related to the menopausal status of the participants. Previous research has demonstrated that exposure to various environmental and occupational chemicals can increase the risk of breast cancer in women, with premenopausal women being particularly susceptible to these effects (128–131). To ensure an accurate assessment of the impact of exposure variables on the study's outcome, it was crucial to differentiate between post-menopausal and pre-menopausal women. This is due to the fact that post-menopausal women are different from pre-menopausal women in several ways. The most notable difference between 30 post-menopausal and pre-menopausal women is the variation in hormonal exposure, which increases their risk of cancer. The former group typically have higher estrogen levels and experience a greater number of ovulations (49). In addition, post-menopausal women are exposed to higher levels of environmental factors, specifically TRAP, because by the time women reach menopause, they have had spent more time outdoor, which implies that they were exposed to more air pollution overall. In turn, this makes the influence of the built environment on their health outcomes more significant, a factor that our study aimed to capture and analyze. Menopausal status within the ATP was defined as whether the individuals’ menstrual periods ceased for at least one year and did not resume. In our study, menopausal status was defined in two ways: first, based on the information provided in the ATP questionnaire as previously described, and second, based on the participants' age at the time of enrollment. On the questionnaire, participants could either select Yes- natural menopause or Yes – other reasons (i.e., chemotherapy, surgery, or medication). For individuals who had responded "Don't Know" or did not provide information about their menopause status in the questionnaire, an age cut-off of 55 years or older (for those who answered “Don’t Know”) or 60 years or older (for those who answered “No”) was used to determine their menopause status. Based on current epidemiological literature, an age cut-off of 55 is commonly employed to determine menopausal status (118). Women who answered “No” to the menopause questionnaire, and women who were not 60 years or older were excluded from the study. Only person-years of follow-up for participants classified as menopausal, either directly through the questionnaire or by applying the age cut-off, were considered in the study. Cases in this study were defined as post-menopausal individuals who had incident breast cancer, regardless of whether it was in situ or invasive. The ACR identified and provided us with information on these cases from the ATP dataset. Post-menopausal participants who had been diagnosed with breast cancer prior to their enrollment in the study were excluded. Non-cases were described as post-menopausal women who did not develop breast cancer during the follow-up period. In addition, participants with a follow-up duration of less than one year were excluded from the analysis. This exclusion criterion meant that, in some instances, incident breast cancer cases diagnosed within one year of enrollment were also removed from the analysis. In instances where participants had two breast cancer diagnoses at different time points, we only retained the 31 first diagnosis. Therefore, person-years follow-up period associated with the first diagnoses was only accumulated towards the study. Figure 2: PRISMA diagram of study population Abbreviations: ATP: Alberta’s Tomorrow Project 3.4 Data and variable cleaning Excel and R software were used to clean the data and variables (132,133). When necessary, continuous variables were converted into binary, quintile, or categorical variables. To achieve the analytical goals, categorical variables were also used to create new categorical variables. NA Total ATP participants n = 24,889 Participants without prevalent cancer n = 22,947 Participants with prevalent cancer (excluding skin cancer) n = 1,942 Non-menopausal women n = 7,411 Non-cases n = 15,013 ATP Cancer Menopause Post-menopausal women n = 15,536 Incident cases n = 523 32 was used to replace missing values and entries coded as -999, -9999, 888, 111, 777, 7777, 88, 99, 3, or 9. The NAs were then replaced by an unknown category for each categorical variable. 3.5 Missing data The number and percentage of missing values were calculated and reported for each variable included in the study. However, complete case analysis was employed, meaning that the study excluded participants with missing exposure data. Only participants who had missing data for the exposure or outcome variable were excluded from analysis. To account for high missingness in non-exposure variables, or the covariates, another category – named unknown – was created. 3.6 Study variables The variables included in the analysis are listed in Table 3. Some variables were included as both quantitative (i.e., quintiles or numeric) and qualitative (i.e., categorical, or binary). For analytical purposes, whichever type fulfilled the purpose given were then utilized. For model building, variable choices may have varied. This is further described in later sections. A detailed description of each variable, based on what was provided in ATP and CANUE, is provided below. Table 3: Study variables CANUE Built environment variables ATP variables Greenness (NDVI) Average baseline Average of annual mean within 1000 meter Age Alcohol consumption Contraceptive use Fruit consumption Walkability Given quintiles Calculated quintiles Education BMI Ethnicity Traffic-related air pollution (NO2 in ppb) Average baseline Maternal breast cancer history Smoking Physical activity 33 Diet consumption Material deprivation Social deprivation Income Parity Abbreviation: NDVI: normalized difference vegetation index; NO2: nitrogen dioxide; ppb: parts per billion; BMI: body mass index 3.7 Alberta’s Tomorrow Project (ATP) variables It is important to note that the UHLQ/PANS package and CORE package that we received are very similar. However, ATP has only harmonized the CORE package with other CanPath cohorts such as BCGP. Since we only utilized ATP data, this was not an issue. We have also received the same variable from different questionnaires; therefore, the CanPath harmonization was not necessary in this analysis. 3.7.1. Anthropometric measurements The fundamental components of anthropometry include height, weight, head circumference, body mass index (BMI), and measurements of body circumferences (such as waist, hip, and limbs). ATP provided BMI, height, and weight measurements. Height and weight measurements were given in cm and kg, respectively. Height represented the distance from the foot to the head of the participant when standing. Weight was measured using SECCA 770 scale. Although we had access to these data, we did not need to utilize them as BMI measurements were already accessible. 3.7.2 Obesity Obesity, as defined by the WHO, refers to the excessive or abnormal accumulation of body fat (134). Body Mass Index (BMI) serves as a method to categorize adults into distinct weight classifications, which include underweight, normal weight, overweight, and obese categories. This categorization is determined by considering an individual's height and weight measurements. In our study, self-reported BMI was provided for all participants, and we did not 34 rely on self-reported weight and height data to calculate BMI. The BMI values were further categorized into the following classifications: 1. Underweight: BMI less than 18.5 kg/m^2 2. Normal weight: BMI between 18.5 and 24.9 kg/m^2 3. Overweight: BMI between 25 and 29.9 kg/m^2 4. Obese: BMI greater than or equal to 30 kg/m^2 (134,135). In addition, the continuous BMI variable was transformed into a categorical variable, with normal weight serving as the reference category for the purpose of model construction. 3.7.3. Physical activity Physical activity was assessed using metabolic equivalents (MET). The ATP had provided us with calculated MET minutes per week for both moderate and vigorous-intensity physical activity. The WHO recommends 6000 MET minutes per week of moderate and vigorous-intensity physical activity for adults (136). Based on this recommendation and the data available on ATP, a binary variable was created to determine whether participants met the recommended physical activity per week. For model building purposes, not meeting the recommendation was selected as the reference category. Calculated moderate and vigorous activities in minutes per week were also provided. The WHO recommends at least 150 minutes of moderate-intensity of aerobic physical activity or at least 75 minutes of vigorous-intensity of aerobic physical activity per week. To address these recommendations, we generated two additional binary variables. One variable determined whether participants met the WHO's recommended level of moderate physical activity, while the other variable determined whether they met the recommended level of vigorous physical activity per week. In both cases, "not meeting the recommendation" served as the reference category. 3.7.4. Ethnicity ATP collected self-reported information on race/ethnicity to determine the participants' background origin. The list of ethnicities that participants could select from included Aboriginal, Arab, Black, East Asian, Filipino, Jewish, South Asian, South East Asian, West Asian, Latin American Hispanic, White, or Other. Given that most participants were of White origin, a binary 35 variable was created to represent those of White ethnicity vs all other races. For model building purposes, White was selected as the reference category. 3.7.5. Diet Participants were asked to report the total number of servings of fruits and vegetables they consumed in a typical day as part of the dietary-related information. Two variables were created to represent the consumption of vegetables and fruits: one categorical variable and one numerical variable. The categorical variable had categories of zero, one, two, three, four, and five or more servings, with five or more serving as the reference category. For model building purposes, the categorical variable was used. 3.7.6. Women’s health Variables used to describe women’s health in this dataset include menopausal status, number of births, and contraceptive use. While contraceptive use was recorded as a binary variable (never and ever), number of births was measured as a numerical variable. For analysis purposes, number of births were converted into a categorical variable, with categories of two or less, 3-4, or greater than four. Furthermore, two definitions for menopausal were applied to this study. First, women were considered post-menopausal based on their response to the ATP questionnaire. In ATP questionnaire, menopausal status was defined as whether an individual’s menstrual period stopped for one year and did not restart. Participants could select from one of the following response options: 5. Yes, natural menopause 6. Yes, other reasons (surgery, chemotherapy, medication) 7. No 8. Don’t know In cases where the participant answered “don’t know” or did not answer the question, then the age cut-off of 55 years (for those who answered “Don’t know”) or 60 years (for those who answered “No”) was used to ascertain menopausal status (118). 36 3.7.7. Smoking Cigarette smoking was divided into three categories: never smokers, occasional smokers, and daily smokers. Never-smokers encompassed individuals who either had not smoked at all in the past 30 days or had not smoked a minimum of 100 cigarettes in their lifetime (137). Occasional smokers consisted of individuals who had smoked at least one cigarette in the past 30 days but not daily. Daily smokers included individuals who had smoked at least one cigarette every day for the past 30 days. For model building purposes never-smoker category was used as the reference category. 3.7.8. Socioeconomic status To assess the socioeconomic status of participants, their total household income from all sources before taxes in the previous year was collected. A categorical variable was created with four levels: "less than $50,000," "$50,000-$100,000," "$100,000-$150,000," and "more than $150,000." The category "less than $50,000" was chosen as the reference category for model building purposes and analysis. 3.7.9. Alcohol consumption Three variables were created to represent alcohol consumption. Participants were asked whether they ever consumed alcohol. A binary variable was created to determine whether individuals were never or ever alcohol drinkers. Participants were further asked, on average, over the last year, how often they drank alcohol. This was used to create the second variable for alcohol consumption based on the frequency of alcohol consumed. The six categories were never, less than once a month, once to three times a month, once per week, two to five times a week, and more than six times a week. For these two variables, never drinkers were used as the reference category for model building purposes. In addition, participants were asked about their average weekly consumption of beer, liquor/spirits, red wine, white wine, and other types of alcohol. To derive the third numerical variable, the responses to these questions were aggregated, which resulted in the total weekly alcohol consumption for each participant. 37 3.7.10. Education As part of demographic information, participants were asked to answer a questionnaire related to the highest level of education they had completed. Participants were provided with multiple options to indicate their level of education, including elementary school, high school, trade/technical/vocational school, diploma from a community college, university certificate below bachelor's level, bachelor's degree, and graduate degree. To represent the different levels of education completed, a categorical variable was created. The categories were defined as follows: Category A represented no formal education, elementary school, or high school; Category B included trade/technical or vocational school, a diploma from a community college, or a university certificate below bachelor's level; Category C comprised a bachelor's degree or a graduate degree. For analysis purposes, Category A was chosen as the reference category. Further, participants were also asked how old they were when they completed their highest level of education. 3.8. CANUE data 3.8.1. Greenness Greenness was measured by the Normalized Difference Vegetation Index (NDVI). The NDVI is a measurement that quantifies vegetation by calculating the difference between near-infrared light and red light. Near-infrared light is where vegetation strongly reflects, whereas red light is where vegetation absorbs or has low reflectance. CANUE data on NDVI measurements that are available on ATP are since 1984. In this study, we utilized annual mean NDVI for each participant at postal code (baseline) and mean of annual mean within 1000 meters. The NDVI is scaled between -1 and +1, but there are no fixed thresholds that determine specific land cover types. Typically, negative NDVI values are often associated with water bodies, while NDVI values close to +1 suggest the presence of dense green vegetation, and NDVI values close to 0 illustrates the absence of green vegetation, which could include urban areas. NDVI is calculated by taking the difference between red and near-infrared light and dividing this by the sum of the two intensities. The following formula depicts this: 38 Equation 1: Normalized Difference Vegetation Index NDVI = (NIR – RED) / (NIR + RED) Abbreviations: NIR: near-infrared wavelength; NDVI: normalized difference vegetation index; RED: red wavelength NDVI provides a standardized measure to assess the health and vitality of vegetation. For this reason, low NDVI values are indicative of moisture-stressed vegetation, while higher values suggest a greater density of green vegetation. 3.8.2. Traffic-related air pollution NO2 concentration measurements were utilized as a proxy for TRAP. The initial land-use regression (LUR) annual average concentrations at the postal code level was provided in units of parts per billion (ppb). 3.8.3. Social and material deprivation CANUE had accessed the National Material and Social Deprivation Indices (MSDI) for all Canadian Census enumeration areas, now known as dissemination areas. Social and material deprivation scores were created to address and monitor social inequalities in health. CANUE's deprivation scores were linked to the postal codes of each participant at enrollment. These scores were based on social and material factors and were measured at various geographical levels such as dissemination areas, Census Metropolitan Areas (CMAs), provinces, regions, and zones. In this study, we focused on the region variable. The deprivation scores were categorized into quintiles, ranging from 1 (least deprived) to 5 (most deprived). The lowest quintile was chosen as the reference category for analysis purposes. 3.8.4. Walkability The Canadian Active Living Environments (Can-ALE) database is a collection of geographic measures that assess the extent to which Canadian communities promote active living. In other words, it aims to explore the connection between physical activity levels of Canadians and how communities in which they reside are designed. In this study, the Can-ALE index was used as a 39 proxy for what is widely referred to as the "walkability" of an area. The Can-ALE index is calculated by summing the z-scores of four built environment measures that are related to walking rates and active transportation: intersection density, dwelling density, points of interest, and transit stops. Can-ALE measures have been developed and are accessible at ATP for census dissemination areas for both the 2006 and 2016 Census. In this study, only data for 2006 were utilized because it represented a long-term exposure which is key to consider for latency of breast cancer. The Can-ALE score is a summation that indicates the attitude towards active living in a specific Dissemination Area (DA) compared to all DAs in Canada. Negative values signify below-average walkability, while positive values indicate above-average walkability. A Can-ALE score near zero suggests that the location is relatively similar to the Canadian average concerning the quality of the active living environment. Further, the ATP dataset included a categorical value that characterizes the favourability of the Active Living Environment (ALE) on a scale from very low (1) to very high (5). In addition, we constructed another categorical variable using quintiles to represent the Can-ALE. Quintile 1 represented the least walkable areas, whereas quintile 5 denoted the most walkable areas. For both categorical variables, quintile 1 was chosen as the reference category. Each participants Can-ALE score was determined based on their residential location at the time of study enrollment. 3.9 Analysis methods 3.9.1 Descriptive characteristics and bivariate analysis All of the study's variables were subjected to descriptive statistics, which gave an overview of how different variables, such as behavioural, social, demographic, and health indicators, as well as environmental information on air pollution, walkability, greenness, and socio-economic deprivation were distributed. The descriptive statistics helped to summarize and understand the distribution of these variables within the study population. In order to understand and describe a dataset, descriptive statistics are crucial. Their primary objective is to provide concise summaries that highlight the key characteristics of numerical and 40 categorical data. Descriptive statistics of the dataset allowed us to gain insights into the fundamental elements of the data by examining variables such as central tendency, variability, and distribution. For categorical variables like smoking, alcohol consumption, education, counts and percentages were calculated. Continuous variables, on the other hand, were represented using means and standard deviations. Statistical tests such as ANOVA tests, chi-square, and t-tests were utilized to assess statistical differences between cases and non-cases, with a significance level set at 0.05. In cases where it was appropriate, descriptive statistics for specific variables were visually presented through boxplots or histograms. When parametric assumptions were not satisfied, alternative non-parametric statistical tests were applied. Furthermore, Spearman correlation coefficients were computed to assess the association between different metrics of the built environment. This analysis helped to understand the relationships between these three parameters. By calculating Spearman correlations, we conducted a comprehensive examination of the built environment variables in the study to determine the strength and direction of the associations between these variables. 3.10 Multivariate analysis In this study, multivariable cox-proportional hazard regression models were used. The association between breast cancer and each built environment factors – greenness, walkability, TRAP, and socio-economic deprivation were modelled separately. Two models were created for greenness (baseline NDVI and mean annual average within 1000 meters), one for walkability (ALE-index), one for TRAP (NO2 at baseline), one for social deprivation, and another one for material deprivation. First, fully adjusted models with all variables mentioned in previous section included were created. The change-in-estimate approach was applied to further select possible confounders to ultimately build the final model. The details of change-in-estimate model building is described in section 3.6.2. Different statistical measures such as hazard ratios (HRs), 95% confidence intervals (CIs), p-values (p), concordance and Schoenfeld’s tests were provided for each model. For the built environment measures of walkability, greenness, and socio-economic deprivation where quintile variables were used to categorize their distribution, trend p-values were calculated to assess any linear trends across the quintiles. To facilitate this analysis, the quintile categories were converted into numerical values as follows: 1 represented the low 41 quintile, 2 represented the lower-middle quintile, 3 represented the middle quintile, 4 represented the upper-middle quintile, and 5 represented the high quintile. 3.10.1 Cox proportional hazard regression Cox PH, or Cox Proportional Hazard analysis, is a survival analysis method used to investigate the association between the occurrence of an event, represented by the hazard function, and a set of covariates. In the context of this study, the outcome variable was breast cancer, while the explanatory variables included the four environmental factors: walkability, greenness, TRAP, and socio-economic deprivation. The outcome variable was categorized as follows: 1 denoted cases of breast cancer, while 0 indicated individuals who remained breast cancer-free until the end of the follow-up period or who experienced censoring events such as death. Other cancer diagnoses were excluded from the analysis, and therefore, these excluded cancer diagnoses were not treated as censored events in our analysis. In addition, time was quantified and recorded as a numeric variable, expressed in years. Cox regression, also referred to as proportional hazards regression, is a statistical technique employed to assess how various factors influence the duration until a specific event happens. In this analysis, the coefficients derived from Cox regression indicate the impact on the hazard rate. If a coefficient is positive, it suggests a higher hazard rate, or a worse prognosis associated with the corresponding variable. In contrast, a negative coefficient signifies a protective effect, or a lower hazard rate linked to the variable. The hazard rate represents the likelihood of experiencing the event, such as a breast cancer diagnosis, considering that individuals have survived up to a particular point in time (138). Equation 2: Cox regression equation h (t, X) = ho(t) . e B1X1 + B2X2 + … + BkXk The hazard at time t, denoted as h(t), is determined by a set of explanatory variables x1, x2, …, xK. The coefficients B1, B2, …, Bk associated with these variables are the effect size which are 42 estimated from the data using a statistical package in R software. The baseline hazard, h0(t), represents the hazard when all the explanatory variables are zero. The hazard rate represents the risk of experiencing the event of interest, given that the participant has survived up to a certain time. It is important to note that probabilities must always fall within the range of 0 to 1. In the Cox proportional hazards regression model, the estimated coefficients, such as b1, indicate the expected change in the logarithm of the hazard ratio with a one-unit change in X1, while keeping all other predictors constant. Furthermore, taking the antilog of an estimated regression coefficient, expressed as exp(bi), provides an estimate for the hazard ratio. This hazard ratio is instrumental in understanding the impact of each variable on the hazard or risk of the event occurring. If a predictor is binary (e.g., X1 indicates contraceptive use or male sex), then exp(b1) represents the hazard ratio, comparing the risk of the event for participants with X1=1 (e.g., indicates contraceptive use or male sex) to participants with X1=0 (e.g., no contraceptive use or female sex). If the hazard ratio for a predictor is approximately 1, it suggests that the predictor has no significant effect on survival. A hazard ratio less than 1 indicates a protective effect, meaning that the predictor is associated with improved survival. In contrast, a hazard ratio greater than 1 indicates an increased risk or decreased survival associated with the predictor. If a predictor is continuous, exp(b1) represents the hazard ratio when the variable increases by 1-unit, keeping other variables constant. The Cox proportional hazards model is considered a semi-parametric model because it does not make assumptions about the shape of the baseline hazard function. However, it does rely on certain assumptions, as summarized below: - The Cox proportional hazards model assumes that the survival times of distinct individuals in the sample are statistically independent of each other - Non-informative censoring, meaning that participants drop from the study at random, and for reasons unrelated to the study - The Cox proportional hazards model assumes a constant hazard ratio over time, meaning that the relative risk of the event of interest remains consistent over the entire duration of the study 43 3.11 Model building methods The model-building approach employed in this study followed the change-in-estimate method, as well as DAGs created based on current literature and our conceptual understanding. The outcome variable represented post-menopausal breast cancer diagnosis, with a value of 1 indicating a breast cancer case and 0 for a non-case. The explanatory variables consisted of four built environmental factors: walkability, greenness, TRAP, and socio-economic deprivation. In total, seven models were constructed: one for air pollution (baseline average), two for greenness (one using baseline NDVI measurements and one for the mean of annual average NDVI measurements within 1000 meters), one for walkability, one for material deprivation, and another one for social deprivation. While air pollution and greenness were measured and recorded as continuous variables for analysis purposes, HR associated with these models represented the increase in hazard for one-unit increase in air pollution (measured in ppb) and greenness. Walkability measurements were provided as z-score and were therefore recorded and used as quintile categorical variables for model building purposes. The social and material deprivations were also provided in quintiles. The HR associated with a specific level in these models represented the increase in risk in that level compared to the reference category (first quintile). We employed the change-in-estimate approach method in our model building process to identify the appropriate confounders. In this approach, an outcome model was first fitted; this model incorporated the exposure variable and accounted for all conceivable confounders determined in section 3.5.2. These included age, BMI, physical activity, alcohol, contraceptive use, income, birth, material deprivation, social deprivation, education, vegetable consumption, fruit consumption, smoke, ethnicity, and maternal breast cancer history. These 15 variables were included in the model because their association with breast cancer has been described in other studies. Lastly, we conducted additional analyses by collapsing categories to assess whether it had any impact on the effect estimates in the models. However, no change on the effect estimates was observed. 44 CHAPTER 4: RESULTS 4.1 Summary of Alberta’s Tomorrow Project (ATP) The ATP supplied data for 24,889 women who met all the specified criteria in this study. From the 24,899 participants, 1,942 individuals were diagnosed with prevalent cancers, excluding nonmelanoma skin cancer. After this exclusion, the study population reduced to 22,947. Further, 7,411 research participants who remained premenopausal throughout the follow-up period were excluded from the analysis. This left a final study population of 15,536 participants, as illustrated in Figure 2 of the PRISMA diagram. During the follow-up period, there were 523 incident breast cancer diagnoses, accounting for 3.4% of the participants. The remaining 15,013 individuals, making up 96.6% of the participants, did not receive a breast cancer diagnosis and were classified as non-cases. The study population comprised of 20.9% of the post-menopausal individuals under the age of 44; within the cases about 11.5% of the population under the age of 44 were defined as post-menopausal through their response to the menopause question completed at baseline. Further, 28.7% were between the ages of 45-54, 33.8% between the ages of 55-64, and 16.5% over the age of 65. Only participants that completed the CORE questionnaire (17,001) answered the ethnicity questionnaire. From this population, 94% were of White descendent. The study population were demonstrated to have a notable level of education, with about 40.3% individuals having attained university-level education. 4.2 Missing data Both counts and relative percentage of missing values for all variables included in this study are outlined in Table 3. The variable with highest percentage of missing values was physical activity at 69%, while the variable with lowest percentage of missing values was education and contraceptive use at less than 1%. The majority of the remaining variables displayed missing values below 15%, except for ethnicity, maternal history of breast cancer, and smoking, all registering around 30-40% in missing data. Notably, age was the only variable without any 45 missing values. To handle high missingness, we categorized missing data into an "unknown" category. Table 3: Missing values for variables included in the study. Other Variables Count of missing values Percent of total population Age 0 0% Alcohol consumption 809 5% Birth 2116 14% BMI 1188 8% Ethnicity 4856 31% Education -- -- Contraceptive use -- -- Deprivation score – material 751 5% Deprivation score – social 751 5% Diet – fruit consumption 74 0% Diet – veg consumption 86 0% Income 1186 8% Maternal history of breast cancer 5754 37% Physical activity 10759 69% Smoking 6738 43% Built environment variables Count of missing values Percent of total population Air pollution (ppb NO2) – baseline 485 3% Walkability (ALE-index) 503 3% Greenness (NDVI) – baseline -- -- Greenness (NDVI) – average -- -- Abbreviations: BL: baseline; NO2: nitrogen dioxide; NDVI: normalized difference vegetation index; BMI: body mass index; PA: physical activity; ppb: parts per billion; Veg: vegetable, --: Participant counts <10 suppressed. 4.3. Descriptive characteristics and summary statistics The study population consisted of 523 incident breast cancer cases and 15,013 non-cases, for a total of 15,536. Detailed descriptive statistics are presented in Table 4 below. These participants were followed for an average of 12.6 years, with a standard deviation of 4.2 years. Specifically, cases were followed for an average of 7.9 years, while non-cases had an average follow-up time of 12.8 years. The mean age at baseline for the study population was 53.5 years, with a standard deviation of 9.6 years. The study population were on average mostly educated, with around 40% that held university-level education. In addition, about 95% of them were of White descendent. Approximately, 41% of participants had an income of $100,000 or more. Regarding BMI, among 46 those with reported data, 1.0% were classified as underweight, 38% were classified as normal weight category, and 30% and 23% were categorized as overweight and obese, respectively. Alcohol consumption was relatively infrequent, with about 50% of participants reporting consumption less than three times per month. In terms of physical activity, 27% of participants did not meet the MET-guidelines set by WHO, while only about 3% met the guidelines. Smoking was uncommon – slightly over 49% of participants never smoked. Further, nearly 89% of study population had history of contraceptive use. In terms of fruit and vegetable consumption, about 43% and about 59% consumed fruits and vegetables at least 3 servings per week. Variables that did not exhibit statistically significant differences between cases and non-cases included alcohol consumption, number of births, education, contraceptive use, social deprivation score, material deprivation score, fruit consumption, vegetable consumption, income, and smoking status. In contrast, variables that showed statistical significance between cases and non-cases included age (p < 0.05), ethnicity (p = 0.002), maternal history of breast cancer (p < 0.05), physical activity (p < 0.05), and BMI (p < 0.05). Table 4: Baseline participant characteristics Overall Cases Non-cases P Variables N or mean % or SD N or mean % or SD N or mean % or SD Number of participants 15536 100% 523 3.4% 15013 96.6% Follow-up time (years) 12.6 4.2 7.9 4.9 12.8 4.1 Age at baseline 53.5 9.6 56.9 8.7 53.4 9.6 <0.05 Alcohol consumption Never Less than once per month 1-3 times per month Once per week 2-5 times per week More than 6 times per week Unknown 1127 2964 3652 1623 4229 1132 809 7.3 19.1 23.5 10.4 27.2 7.3 5.2 39 107 114 47 151 43 22 7.5 20.1 20.5 9.0 28.9 8.2 4.2 1088 2857 3538 1576 4078 1089 787 7.3 19.0 23.6 10.5 27.2 7.3 5.2 0.60 Birth Two or less 3-4 Greater than 4 Unknown 8729 4272 419 2116 56.2 27.5 2.7 13.6 270 162 15 76 51.6 31.0 2.9 14.5 8459 4110 404 2040 56.4 27.4 2.7 13.6 0.19 Ethnicity* White Other 10140 540 94.9 5.1 312 12 96.3 3.7 9828 528 94.9 5.1 0.002 47 Unknown 4856 31.3 199 38.0 4657 31.0 Education High school or below Diploma or trade University-level education Unknown 3697 5565 6266 -- 23.8 35.8 40.3 -- 131 198 193 -- 25.0 37.9 36.9 -- 3566 5367 6073 -- 23.8 35.8 40.5 -- 0.20 Contraceptive use Ever Never Unknown 13825 1704 -- 89.0 11.0 -- 467 56 -- 89.3 10.7 -- 13358 1648 -- 89.0 11.0 -- 0.87 Deprivation score - social Q1 (least deprived) Q2 Q3 Q4 Q5 (most deprived) Unknown 2698 2809 2956 3337 2985 751 17.4 18.1 19.0 21.5 19.2 4.8 92 97 95 115 88 36 17.6 18.5 18.2 22.0 16.8 6.9 2606 2712 2861 3222 2897 715 17.4 18.1 19.1 21.5 19.3 4.8 0.25 Deprivation score – material Q1(least deprived) Q2 Q3 Q4 Q5 (most deprived) Unknown 4321 3572 2832 2807 1550 751 27.8 23.0 18.2 18.1 10.0 4.8 133 124 95 83 52 36 25.4 23.7 18.2 15.9 9.9 6.9 4188 3448 2737 2427 1498 715 27.9 23.0 18.2 16.2 10.0 4.8 0.31 Fruit consumption, servings/day Zero One Two Three Four Five or more Unknown 578 3137 5107 3606 1915 1119 -- 3.7 20.2 32.9 23.2 12.3 7.2 -- 21 95 190 121 58 37 -- 4.0 18.2 36.3 23.1 11.1 7.1 -- 557 3042 4917 3485 1857 1082 -- 3.7 20.3 32.8 23.2 12.4 7.2 -- 0.60 Vegetable consumption, servings/day Zero One Two Three Four Five or more Unknown -- 1862 4210 3918 2842 2450 -- -- 12.0 27.1 25.2 18.3 15.8 -- -- 74 128 141 95 81 -- -- 14.1 24.5 27.0 18.2 15.5 -- -- 1788 4082 3777 2747 2369 -- -- 11.9 27.2 25.2 18.3 15.8 -- 0.33 Income Less than 50,000 $50,000-$100,000 $100,000-$150,000 Greater than $150,000 Unknown 3046 4889 3125 3290 1186 19.6 31.5 20.1 21.2 7.6 123 169 99 92 40 23.5 32.3 18.9 17.6 7.6 2923 4720 3026 3198 1146 19.5 31.5 20.2 21.3 7.6 0.09 Maternal history of breast cancer Yes No Unknown 4023 5759 5754 25.9 37.1 37.0 171 195 157 32.7 37.3 30.0 3852 5564 5597 25.7 37.1 37.3 <0.05 Physical activity Met Not Met Unknown 514 4263 10759 3.3 27.4 69.3 15 104 404 2.9 19.9 77.2 499 4159 10355 3.3 27.7 69.0 <0.05 Smoking Never Occasionally 7674 -- 49.4 -- 279 -- 53.3 -- 7395 -- 49.3 -- 0.14 48 Daily Unknown 870 6738 6.0 43.4 30 210 5.7 40.2 840 6528 6.0 43.5 BMI Underweight Healthy Overweight Obese Unknown -- 5969 4697 3523 1188 -- 38.4 30.2 22.7 7.6 -- 168 152 142 58 -- 32.1 29.0 27.2 11.1 -- 5801 4545 3381 1130 -- 38.7 30.3 22.5 7.5 <0.05 Abbreviations: BMI: body mass index; p: p-value; PA: physical activity; MET: metabolic equivalent time; SD: standard deviation; *Proportion of white/other was calculated using white+other (10680) as denominator, and proportion of unknown was calculated as using total as denominator, --: Participant counts <10 suppressed. The statistics for the three built environment factors are presented in Table 6. The average NO2 level at the postal code was 10.3 for cases and 10.1 for non-cases. In terms of greenness, the annual average NDVI at baseline was 0.29 for cases and 0.28 for non-cases. The annual average NDVI within a 1000-meter radius was 0.37 for both cases and non-cases. Regarding walkability, when considering the quintiles, the average was 3.90 for cases and 3.81 for non-cases. The average social deprivation score for cases and non-cases were 3.23 and 3.22, respectively. Lastly, the mean material deprivation score for cases and non-cases were 2.82 and 2.72, respectively. The distribution of cases and non-cases within each quintile of walkability, social deprivation, and material deprivation are provided in table 5. Table 5: Distribution of cases and non-cases within each quintile of walkability Walkability quintiles Cases Non-cases Q1 (least walkable) 89 2860 Q2 92 2952 Q3 120 2916 Q4 121 2871 Q5 (most walkable 92 2920 Social deprivation Q1 (least deprived) 92 2606 Q2 97 2712 Q3 95 2861 Q4 115 3222 Q5 (most deprived) 88 2897 Unknown 36 715 Material deprivation Q1 (least deprived) 133 4188 Q2 124 3448 Q3 95 2737 Q4 83 2427 Q5 (most deprived) 52 1498 Unknown 36 715 49 Table 6: Built environment characteristics Overall Cases Non-cases Variables Mean SD Mean SD Mean SD Air Pollution Annual average NO2 ppb at baseline 10.1 4.4 10.3 4.4 10.1 4.4 Greenness Annual average NDVI at baseline Average of annual average NDVI within 1000 m 0.28 0.37 0.13 0.09 0.29 0.37 0.13 0.08 0.28 0.37 0.13 0.09 Walkability Baseline Z-score 3.81 -0.15 1.7 1.16 3.90 -0.11 1.6 1.07 3.81 -0.15 1.7 1.17 Social deprivation 3.22 1.50 3.23 1.53 3.22 1.50 Material deprivation 2.72 1.50 2.82 1.55 2.72 1.50 Average NO2 at baseline was 10.3 (SD = 4.4) for cases and 10.1 (SD = 4.4) for non-cases (Figure 3). The difference in average NO2 concentration between cases and non-cases was not statistically significant, with a p-value of 0.43. The coefficient of variation for this variable, a measure of dispersion level around the mean, was 43%. Overall population Controls vs Breast cancer cases Figure 3: Average air pollution (ppb) at baseline for breast cancer cases and controls To measure walkability, the 2006 ALE-index from the CANUE dataset was utilized. The z-score from this dataset compared walkability nation-wide. The average walkability z-score for the overall population was -0.15 (SD = 1.16). For cases and non-cases, the average walkability z-score was -0.11 (SD = 1.07) and -0.15 (SD = 1.17), respectively. The difference in walkability z-score between cases and non-cases was statistically non-significant, with a p-value of 0.40. For 50 the purposes of constructing models, we computed quintiles to evenly distribute the data among each quintile. Overall population Controls vs Breast cancer cases Figure 4: Walkability z-score for breast cancer cases and controls Greenness was measured using annual NDVI at postal code. Boxplots representing NDVI distribution for cases and non-cases, as well as for the overall population are presented below. The annual average NDVI for the study population was 0.28 (SD = 0.13). When stratified by cases and non-cases, the annual average NDVI at baseline was 0.29 (SD = 0.13) and 0.28 (SD = 0.13), respectively. The difference in annual average NDVI between cases and non-cases was also not statistically significant with a p-value of 0.37. The coefficient of variation for baseline NDVI was 47%. 51 Overall population Controls vs breast cancer cases Figure 5: Baseline greenness, for breast cancer cases and non-cases We also developed another variable, which represents the mean annual NDVI within a 1000-meter radius (139,140). Across the entire population, this averaged at 0.37, displaying a standard deviation of 0.09. Upon stratifying by cases and non-cases, the average remained consistent at 0.37. For cases, the standard deviation was 0.08, while for non-cases, it was 0.09. Notably, the distinction in average NDVI within 1000 meters did not yield a statistically significant result, with a p-value of 0.98. The coefficient of variation for this variable was 23.2%. Overall population Controls vs breast cancer cases Figure 6: Annual NDVI mean within 1000 meters for breast cases and non-cases 52 4.4. Relationship between built environment factors Table 7 provides detailed descriptions of the relationship between built environment factors. The correlation, while slightly weaker, remained significant between baseline NO2 and baseline walkability (r = 0.61, p < 0.05) and between baseline NO2 and walkability (r = 0.61, p < 0.05). In contrast, there was a weak but positive correlation observed between average greenness within 1000 meters and baseline greenness, with a correlation value of 0.29 (p < 0.05), social deprivation and baseline NO2 (r = 0.12, p < 0.05), and social deprivation and walkability (r = 0.31, p < 0.05). Furthermore, moderately weak correlations were noted between average greenness within 1000 meters and baseline NO2 (r = -0.40, p < 0.05), and average greenness within 1000 meters and baseline walkability (r = -0.46, p < 0.05). Lastly, very weak positive correlations were identified between baseline NO2 and baseline greenness (r = 0.095, p < 0.05), material deprivation and average greenness within 1000 meters (r = 0.03, p < 0.05), social deprivation and material deprivation (r = 0.06, p < 0.05), and baseline walkability and baseline greenness (r = 0.04, p < 0.05). Table 7: Spearman correlations between built environment variables Baseline greenness Average greenness 1000 m Baseline NO2 Baseline walkability Social deprivation Material deprivation Baseline greenness 1 0.29 (<0.05) 0.095 (<0.05) 0.040 (<0.05) -0.04 (0.015) -0.02 (0.011) Average greenness 1000 m 0.29 (<0.05) 1 -0.40 (<0.05) -0.46 (<0.05) -0.20 (<0.05) 0.03 (<0.05) Baseline NO2 0.095 (<0.05) -0.40 (<0.05) 1 0.61 (<0.05) 0.12 (<0.05) -0.26 (<0.05) Baseline walkability 0.040 (<0.05) -0.46 (<0.05) 0.61 (<0.05) 1 0.31 (<0.05) -0.09 (<0.05) Social deprivation -0.04 (<0.05) -0.20 (<0.05) 0.12 (<0.05) 0.31 (<0.05) 1 0.06 (<0.05) Material deprivation -0.02 (0.011) 0.03 (<0.05) -0.26 (<0.05) -0.09 (<0.05) 0.06 (<0.05) 1 53 4.5 Multivariable analysis (Cox proportional hazard models) 4.5.1 Greenness and breast cancer To assess the relationship between baseline greenness (measured by NDVI) and breast cancer risk, a model was constructed. Once again, the change-in-effect estimate approach was employed to identify significant covariates for inclusion in the final model (see Table 8); the following variables were retained in the model. In the full model, which encompassed all covariates retained in the final model, the HR estimate for a 1-IQR increase in baseline greenness was 1.029 (95% CI = 0.92, 1.15). Notably, none of the covariates met the threshold to be included in the model, as detailed in Table 14 in the appendix. However, certain variables such as smoking and social deprivation were considered important via DAGs and were thus incorporated into the final model. The HR estimate for a1-IQR increase in baseline greenness for this model was 1.05 (95% CI = 0.94, 1.16). The concordance value for the final model was 0.55. There was a total of 15528 observations, of which 521 were breast cancer cases. A total of 8 observations (2 breast cancer cases) were excluded due to missingness. Based on the Schoenfeld’s test, the proportional hazard assumption was met. The global p-value, as well as the p-values for baseline greenness and other explanatory variables were greater than 0.05. Table 8: Baseline greenness final model results Variable Baseline greenness* HR 95% CI lower bound 95% CI upper bound p-value 1.05 0.94 1.16 0.43 Social deprivation** [cases/participant] Q1 [91/2696] (least deprived) Q2 [97/2809] Q3 [95/2956] Q4 [115/3337] Q5 [87/2982] (most deprived) Unknown [36/748] 1.0 0.99 0.91 0.98 0.83 1.29 - 0.74 0.68 0.75 0.62 0.88 - 1.31 1.21 1.30 1.12 1.90 - 0.92 0.52 0.91 0.23 0.20 Smoking*** A (Never) [278/7669] 1.0 - - - 54 [cases/participant] B (Daily) [30/870] C (Occasionally) [--254] D (Unknown) [209/6735] 0.87 0.43 0.82 0.59 0.16 0.69 1.27 1.16 0.98 0.46 0.10 0.04 Abbreviations: HR: hazard ratio; CI: confidence interval; Q: quintile; *Adjusted for smoking and social deprivation; **Adjusted for baseline greenness and smoking; ***Adjusted for baseline greenness and social deprivation. --: Participant counts <10 suppressed. To determine the association between average greenness within 1000 meters and breast cancer risk, another model was built. In the full model where all the covariates were included the HR estimate for a 1-IQR increase in average greenness was 0.97 (95% CI = 0.88, 1.08). Again, none of the covariates met the threshold to be included in the model, as described in Table 15 in appendix. However, certain variables such as smoking and social deprivation were included in the final model because they were identified as important through the DAGs built. In this final model, the HR estimate for a 1 IQR increase in average greenness was 0.97 (95% CI = 0.88, 1.07). The concordance for this model was 0.542. There was a total of 15530 observations, of which 522 were breast cancer cases. A total of 6 observations (1 breast cancer cases) were excluded due to missing data. The PH assumption for the adjusted model was met. The global p-value was 0.05, and the p-values for the remaining variables in the model were all above 0.05. Table 9: Average greenness within 1000 m change-in-effect Variable Baseline greenness* HR 95% CI lower bound 95% CI upper bound p-value 0.97 0.88 1.07 0.55 Social deprivation**** [cases/participant] Q1 [92/2698] (least deprived) Q2 [97/2809] Q3 [95/2956] Q4 [115/3337] Q5 [87/2982] (most deprived) Unknown [36/748] 1.0 0.97 0.89 0.96 0.81 1.28 - 0.73 0.67 0.73 0.60 0.87 - 1.29 1.19 1.27 1.09 1.88 - 0.85 0.44 0.78 0.16 0.22 55 Smoking****** A (Never) [278/7670] B (Daily) [30/870] C (Occasionally) [--/254] D (Unknown) [210/6736] 1.0 0.87 0.43 0.82 - 0.60 0.16 0.69 - 1.27 1.56 0.99 - 0.46 0.09 0.03* Abbreviations: HR: hazard ratio; CI: confidence interval; Q: quintile; *Adjusted for smoking and social deprivation; **Adjusted for baseline greenness and smoking; ***Adjusted for baseline greenness and social deprivation. --: Participant counts <10 suppressed. 4.5.2 Air pollution (TRAP) and breast cancer To determine the association between NO2 concentration at baseline and breast cancer risk, another model was built. The change-in-effect estimate approach was used to identify key covariates to be included in the final model. In the full model where all the covariates (age, BMI, smoking, vegetable and fruit consumption, alcohol consumption, physical activity, education, number of births, income, social and material deprivation, ethnicity, maternal history of breast cancer, and contraceptive use were included the HR estimate for a 10 ppb increase in NO2 was 1.14 (95% CI = 0.90, 1.45). Based on a significance threshold of 5%, several covariates, including age, education, number of births, fruit and vegetable consumption, income, physical activity, BMI, and smoking, met the criteria for inclusion in the model. In this adjusted model, the HR for a 10 ppb increase in NO2 was 1.01 (95% CI = 0.82, 1.24). The model had a concordance value of 0.64. Based on the DAG, only covariates such as social deprivation, material deprivation, income, education, and age acted as confounders. Since income, social deprivation, and material deprivation essentially measure the same effect, we retained only social deprivation in the final model. Therefore, the adjusted final model comprised education, social deprivation, and age. In this final model, the HR estimate for a 10 ppb increase in NO2 was 1.10 (95% CI = 0.90, 1.34). The concordance value for the final model was 0.62. There was a total of 15051 observations, of which 514 were breast cancer cases. A total of 485 observations (9 breast cancer cases) were excluded due to missingness. Based on the Schoenfeld’s test, the proportional hazard assumption was met. The global p-value was at 0.08, and the NO2 p-value 56 was at 0.20, with p-values of greater than 0.05 for all other variables, except for age category at 0.006. Table 10: NO2 at baseline (the year participant completed the UHLQ or CORE survey) Variable NO2 per 10 ppb increase* HR 95% CI lower bound 95% CI upper bound p-value 1.10 0.90 1.34 0.36 Age** [cases/participant] Less than 44 [59/3073] 45-54 [112/4325] 55-64 [216/5121] More than 65 [127/2532] 1.0 1.49 2.88 3.47 - 0.94 1.86 2.20 - 2.36 4.44 5.47 - 0.09 <0.05 <0.05 Education*** A (High school or below) [128/3592] B (Diploma or trade) [195/5366] C (University-level education) [190/6085] D (Unknown) [--/--] 1.0 1.13 1.13 2.64 - 0.92 0.89 0.37 - 1.40 1.42 18.90 - 0.24 0.31 0.33 Social deprivation**** Q1 [91/2607] (least deprived) Q2 [95/2660] Q3 [91/2853] Q4 [113/3263] Q5 [88/2965] (most deprived) Unknown [36/703] 1.0 1.10 0.87 1.04 0.90 1.31 - 0.82 0.65 0.79 0.67 0.90 - 1.47 1.17 1.38 1.21 1.93 - 0.52 0.36 0.77 0.50 0.16 Abbreviations: NO2: nitrogen dioxide; HR: hazard ratio; CI: confidence interval; ppb: parts per billion, Q: quintile *Adjusted for age, education, and social deprivation; **Adjusted for NO2, education, and social deprivation ***Adjusted for NO2, age, and social deprivation; ****Adjusted for NO2, age, and education. --: Participant counts <10 suppressed. 4.5.3 Walkability and breast cancer To assess the relationship between walkability and breast cancer risk, we categorized the walkability variable into quintiles, ranging from the least walkable (quintile 1) to the most walkable (quintile 5) areas. The final model selection was once again guided by the change-in-effect approach. Initially, a full model, where all covariates were included, was constructed. 57 Subsequently, variables were iteratively removed, with only those meeting the 5% threshold included in the final model. In the full model, the hazard ratios (HRs) for quintiles 2 through 5, relative to quintile 1, were 1.056, 1.368, 1.469, and 1.216, respectively. However, the final model did not include any covariates, as none met the inclusion threshold (Table 11). However, based on literature and our conceptual understanding, we proposed that certain variables such as social deprivation, material deprivation, income, age, and BMI. Again, given that social deprivation, material deprivation, and income measure the same impact, only social deprivation was retained. The final model was built with these covariates included. The adjusted HR for quintile 2 through 5, in relation to quintile 1, were 1.07, 1.39, 1.49 and 1.22, respectively. There was a total of 15,033 observations, of which 514 were breast cancer cases. A total of 503 observations (9 breast cancer cases) were excluded due to missingness. Table 11: Walkability change-in-effect Variable HR 95% CI lower bound 95% CI upper bound p-value Walkability* [cases/participant] Q1 [89/2949] (least walkable) Q2 [92/3044] Q3 [120/3036] Q4 [121/2992] Q5 [92/3012] (most walkable) 1.0 1.07 1.39 1.49 1.22 - 0.80 1.05 1.13 0.90 - 1.44 1.83 1.97 1.66 - 0.64 0.02* 0.01* 0.19 Social deprivation** Q1 [91/2602] (least deprived) Q2 [95/2647] Q3 [91/2853] Q4 [113/3263] Q5 [88/2965] (most deprived) Unknown [36/703] 1.0 0.97 0.81 0.86 0.73 1.19 - 0.73 0.60 0.65 0.54 0.81 - 1.29 1.08 1.14 0.99 1.75 - 0.83 0.15 0.28 0.04* 0.38 Age*** Less than 44 [59/3062] 45-54 [112/4321] 1.0 1.35 - 0.99 - 1.85 - 0.06 58 55-64 [216/5120] More than 65 [127/2530] 2.38 2.81 1.79 2.06 3.18 3.82 <0.05* <0.05* BMI**** A (Underweight) [--/155] B (Healthy) [162/5770] C (Overweight) [151/4533] D (Obese) [140/3408] E (Unknown) [58/1167] 0.73 1.0 1.09 1.41 1.26 0.23 - 0.87 1.12 0.93 2.28 - 1.37 1.76 1.71 0.58 - 0.44 0.003* 0.13 Abbreviations: HR: hazard ratio; CI: confidence interval; Q: quintile; *Adjusted for age, BMI, and social deprivation; **Adjusted for walkability, age, BMI; ***Adjusted for walkability, social deprivation. and BMI; ****Adjusted for walkability, social deprivation, and age. --: Participant counts <10 suppressed. The results of the Schoenfeld’s test indicated that the proportional hazard assumption was met for all variables except for the age variable. The global p-value was 0.27, with p-values for all other variables exceeding 0.05, except for age, which had a p-value of 0.01. 4.5.4. Deprivation and breast cancer risk To assess the relationship between social deprivation and breast cancer risk, we utilized the social deprivation score provided to us in quintiles, ranging from the least deprived (quintile 1) to the most deprived (quintile 5) areas. To identify the final model, we used the change-in-effect approach. Initially, a full model, where all covariates were included, was constructed. Then, variables were removed one by one, with only those meeting the 5% threshold included in the final model. In the full model, the hazard ratios (HRs) for quintiles 2 through 5, relative to quintile 1, were 0.99, 0.87, 0.95, and 0.81, respectively. Based on change-in-estimate effect, none of the covariates met the inclusion threshold (Table 12). However, based on literature and our conceptual understanding, we proposed that certain variables such as age and income may be important. Thus, the final model included these two covariates. The adjusted HR for quintile 2 through 5, in relation to quintile 1 for this model, were 0.97, 0.88, 0.95, and 0.81, respectively. There was a total of 14,785 observations, of which 487 were breast cancer cases; 751 observations were deleted due to missingness. The proportional hazard assumption was met for all variables except for age. The global p-value was 0.10, with p-values for all other variables over 0.05, except for age with a p-value of 0.01. 59 Table 12: Social deprivation change-in-effect Abbreviations: HR: hazard ratio; CI: confidence interval; Q: quintile; *Adjusted for age and income; **Adjusted for social deprivation and income; ***Adjusted for social deprivation and age To assess the relationship between material deprivation and breast cancer risk, we use the data provided to us in quintiles, ranging from the least deprived (quintile 1) to the most deprived (quintile 5) areas. The final model selection was once again guided by the change-in-effect approach. Initially, a full model, where all covariates were included, was constructed. Variables were then removed one at a time, with only those meeting the 5% threshold included in the final model. In the full model, the hazard ratios (HRs) for quintiles 2 through 5, relative to quintile 1, were 1.14, 1.06, 1.05, and 0.995, respectively. However, the final model did not include any covariates, as none met the inclusion threshold (Table 13). Based on literature and our Variable HR 95% CI lower bound 95% CI upper bound p-value Social deprivation* [cases/participant] Q1 [92/2698] (least deprived) Q2 [97/2809] Q3 [95/2956] Q4 [115/3337] Q5 [88/2985] (most deprived) 1.0 0.97 0.88 0.95 0.81 - 0.73 0.66 0.72 0.60 - 1.30 1.17 1.26 1.10 - 0.85 0.37 0.71 0.17 Age** Less than 44 [55/3104] 45-54 [109/4279] 55-64 [206/4992] More than 65 [117/2410] 1.0 1.48 2.69 3.26 - 1.07 1.99 2.33 - 2.05 3.64 4.55 - 0.017* <0.05* <0.05* Income*** A (Less than 50,000) [112/2858] B ($50,000-$100,000) [159/4642] C ($100,000-$150,000) [94/3017] D (Greater than $150,000) [86/3152] E (Unknown) [36/1116] 1.0 1.07 1.18 1.11 0.95 - 0.84 0.89 0.82 0.65 - 1.37 1.58 1.50 1.38 - 0.57 0.26 0.50 0.78 60 conceptual understanding, we proposed that variables like number income, education, BMI, and age were important to be included. The final model was built with these covariates included. The adjusted HR for this model for quintile 2 through 5, in relation to quintile 1, were 1.15, 1.07, 1.03, and 0.98, respectively. The proportional hazard assumption was met. The global p-value was 0.14, with p-values for all other variables also above over 0.05, except for age with a p-value of 0.01. Table 13:Material deprivation change-in-effect Variable HR 95% CI lower bound 95% CI upper bound p-value Material deprivation* Q1 [133/4321] (least deprived) Q2 [124/3572] Q3 [95/2832] Q4 [83/2510] Q5 [52/1550] (most deprived) 1.0 1.15 1.07 1.03 0.98 - 0.90 0.82 0.78 0.71 - 1.47 1.40 1.36 1.37 - 0.27 0.60 0.84 0.92 Income** A (Less than 50,000) [112/2858] B ($50,000-$100,000) [159/4642] C ($100,000-$150,000) [94/3017] D (Greater than $150,000) [86/3152] E (Unknown) [36/1116] 1.0 1.10 1.23 1.18 0.98 - 0.86 0.92 0.86 0.67 - 1.41 1.64 1.61 1.42 - 0.47 0.17 0.30 0.91 Education*** A (High school or below) [121/3482] B (Diploma or trade) [182/5296] C (University-level education) [183/5999] D (Unknown) [--/--] 1.0 1.14 1.13 3.52 - 0.90 0.88 0.49 - 1.43 1.44 25.44 - 0.28 0.33 0.21 BMI**** A (Underweight) [--/148] B (Healthy) [159/5694] C (Overweight) [142/4477] D (Obese) [130/3349] 0.52 1.0 1.04 1.32 0.13 - 0.83 1.05 2.10 - 1.30 1.68 0.36 - 0.75 0.02* 61 E (Unknown) [54/1117] 1.27 0.93 1.74 0.13 Age***** Less than 44 [55/3104] 45-54 [109/4279] 55-64 [206/4992] More than 65 [117/2410] 1.0 1.50 2.71 3.34 - 1.08 2.00 2.28 - 2.07 3.68 4.67 - 0.02* <0.05* <0.05* Abbreviations: HR: hazard ratio; CI: confidence interval; Q: quintile; * Adjusted for income, education, BMI, and age; **Adjusted for material deprivation, income, education, BMI, and age; *** Adjusted for material deprivation, income, BMI, and age; ****Adjusted for material deprivation, income, education, and age; *****Adjusted for material deprivation, income, education, and BMI. --: Participant counts <10 suppressed. 62 CHAPTER 5: Discussion and conclusions 5.1 Summary and interpretation of results In this study, we utilized data from Alberta’s Tomorrow Project to evaluate the influence of TRAP, walkability, greenness, and socio-economic deprivation on post-menopausal breast cancer risk. The study encompassed 15,536 participants, consisting of 523 post-menopausal breast cancer cases and 15,013 post-menopausal non-breast cancer individuals. We developed a total of six models: one for NO2 (measured at baseline), two for greenness (measured at baseline and within 1000 meters of residence), one for walkability, one for social deprivation, and one for material deprivation. Notably, none of these models yielded statistically significant hazard ratios (HRs). While there exists a substantial body of research on the relationship between NO2 and breast cancer risk, the literature exploring the associations between walkability, greenness, socio-economic deprivation, and breast cancer risk is relatively sparse. Therefore, while we can draw comparisons between our findings regarding NO2 and existing literature, we cannot compare our findings for the other measures of the built environment – walkability, greenness, and socio-economic deprivation. 5.1.1 Greenness and breast cancer risk In our analysis, the adjusted HR for baseline greenness was 1.04 (95% CI = 0.94, 1.16) for each one IQR increase in NDVI. For average greenness measured within 1000 meters, the adjusted HR was 0.76 (95% CI = 0.88, 1.07) for each one IQR increase in NDVI. Although, both findings were statistically non-significant, the HR from baseline greenness indicates that increase in greenness results in increased risk of breast cancer, which is counter-intuitive; the HR from average greenness measurement implies that increase in greenness is associated with decreased risk in breast cancer. The association between greenness and breast cancer risk has received limited attention, with only a few studies exploring this relationship. Two case-control studies, one conducted in Spain and the other in Montreal, Canada, investigated whether past exposure to residential greenness is associated with post-menopausal breast cancer risk. In the Montreal study, which involved 63 approximately 700 cases and 600 controls, Wang et al. discovered that the age-adjusted and fully adjusted odds ratios (ORs) for maximum NDVI, measured with a 250 m buffer around residences, were 0.95 [95% CI = 0.86, 1.04] and 1.00 [95% CI = 0.84, 1.11] per increase in interquartile range (IQR) of 0.13, respectively (139). They also found that for NDVI measured using a 1000 m buffer, the ORs for age-adjusted and fully adjusted models were 0.98 [95% CI = 0.94, 1.02] and 0.99 [95% CI = 0.95, 1.03], respectively (139). These results suggest that there is no association between greenness and incident post-menopausal breast cancer. In this study, the authors used NDVI data measured at baseline or recruitment address for the years 1992-1998 – this represents about 15 years before diagnosis of cases, which reasonably accounts for the latency of breast cancer. Another multicenter case-control Spanish study by O’Callaghan also investigated the association between presence of surrounding green areas, presence of agricultural areas, and urban green space and breast cancer risk. They included about 1000 cases and 1500 controls, including pre-menopausal and post-menopausal, from ten provinces in Spain (80). The study's findings revealed a significant association between the presence of urban green space within 300 meters and a reduced breast cancer risk, with an odds ratio (OR) of 0.65 [95% CI = 0.49, 0.86] (80). The statistical model was adjusted for factors such as age, socioeconomic status at the individual and social levels, education, and the number of children. On the other hand, the presence of agricultural areas and surrounding green areas were associated with increased risk of breast cancer with an adjusted ORs of 1.33 [95% CI = 1.07, 1.65] and 1.27 [95% CI = 0.92, 1.77], respectively (80). Collectively, the finding of this study implies that the association between green spaces and risk of breast cancer depends on land-use. Our study revealed an increase in breast cancer risk for baseline NDVI measurements and a reduced risk for NDVI measurements within 1000 meters. However, our study did not specify the type of greenness measurements, such as whether they were urban greenspaces, agricultural areas, or surrounding greenspaces around residences. Given that the results of this study varied for each type of green space, it is crucial to differentiate between them in future research. This study also investigated whether the association between breast cancer risk and greenspace is mediated by PA or air pollution. They found that PA did not mediate this association; in our study, we also found that PA was not a significant confounder in our model, and we therefore decided to exclude it from the model. In 64 terms of the association between greenspace and air pollution, they found that there was no difference in NO2 exposure for those living close to or further away from greenspace. In our paper, we found a correlation of 0.06 between baseline greenness and baseline air pollution level, which implies no connection between these two measures. On the other hand, we observed a correlation of -0.390 between average greenness within 1000 meters and baseline NO2 measurement. This implies that those living in areas with high NDVI measurements were exposed to less air pollution, and vice versa. 5.1.2 Traffic related air pollution (TRAP) and breast cancer risk To our understanding, this study is the first study to analyze ATPs data in relation to certain built environment factors – NO2, greenness, walkability, and socio-economic deprivation – and breast cancer risk. Specifically, we assessed the association between NO2 and post-menopausal incident breast cancer. The adjusted HR for NO2 concentration at baseline was 1.10 (95% CI = 0.90, 1.34) for every 10 ppb increase in NO2. The association between NO2 and breast cancer risk has been reported repeatedly in several studies, mostly nested case-control studies, or cross-sectional studies. Given the diversity of geographic locations and populations of these studies, our findings may or may not align with their outcomes. While certain investigations have reported positive and significant results, others, like ours, have found non-significant associations. In terms of international studies, there were four relevant studies chosen to be discussed in our paper. A French nested case-control study where the association between long-term exposure to NO2 and breast cancer risk was investigated, the authors found that greater mean NO2 exposure was significantly associated with increased breast cancer risk (89). They identified about 5000 breast cancer cases over 20 years of follow-up and matched them with about 5000 controls. The odds ratio (OR) for adjusted model and unadjusted (or fully adjusted) model was 1.09 (95% CI: 1.01, 1.18) and 1.07 (0.98, 1.15), respectively (89). These results were somewhat in line with our findings regarding the post-menopausal population, although our adjusted model yielded non-significant effects. Notably, among the study population, post-menopausal cancer cases exhibited a higher proportion of alcohol intake and educational attainment compared to the controls. 65 Association between exposure to air pollution and increased risk of breast cancer was also studied in the Nurses’ Health Study II Prospective Cohort in the United States. No association was observed between air pollution exposure and breast cancer risk in the multivariable adjusted model; an increased and non-significant risk – 1.60 [95% CI: 0.80-3.21], however, an association was indeed observed among women living in closer (<50m) proximity to the roads compared to those living more than 200 meters away (101). The models were adjusted for age, race, and other established breast cancer risk factors. The measure of effect was much higher in this study compared to our finding, with much narrower confidence interval. Another study examined the association between air pollution and breast cancer risk in a nationwide prospective cohort of Black women. This study aimed to confirm the hypothesis that higher air pollution exposure among Black women might increase their risk of breast cancer. With approximately 18 years of follow-up and approximately 2000 confirmed cases, the authors were unable to establish that air pollutants are a significant risk factor of breast cancer (141). Similar to our study, two studies reported non-significant associations. A US-based cohort which assessed NO2 exposure using LUR modeling observed positive but not significant for all participants. However, stratified analysis by distance to major roads suggested a positive and significant association for participants that lived within 500 meters of major roads (combination of <200 m and 200-<500 m categories) with an HR of 1.26 [95% CI: 1.0-1.59] (142). The models in this study were adjusted for a comprehensive set of factors, including age, ethnicity, BMI, family history, number of children, PA, alcohol use, smoking, education, and neighborhood SES (142). Long-term exposure to air pollution and breast cancer in post-menopausal women was assessed among 15 European cohorts from 9 European countries (75). Over about 12 years of follow-up, nearly 3,600 developed breast cancer from 75,000 post-menopausal women were developed. Similar to previous findings, positive but non-significant association between breast cancer and NO2 with a HR of 1.02 per 10 ug/m^3 increase [95% CI: 0.98, 1.07] was observed (75). This model adjusted for age, smoking, alcohol consumption, PA, BMI, education, contraceptive use, SES, employment, breastfeeding, number of children, age at first childbirth (75). The magnitude of effect in these two studies was somewhat similar to our findings. However, it is important to note that we adjusted for significantly fewer covariates in our models compared to these two comprehensive studies. 66 Findings from several Canadian studies were consistent with our study. A Canadian case-control study conducted in Montreal, Quebec found a positive and significant association between air pollution and incident breast cancer with an OR of 1.31 [95% CI: 1.00, 1.71] per 5 ppb increase in NO2 (94). Variables adjusted for in this model include age at diagnosis, family history, education, ethnicity, alcohol consumption, smoking, BMI as far as it was relevant to the variables included in our study (94). In contrast, a Canadian National Breast Screening Study conducted by Goldberg et al. yielded different results. This study found no association between NO2 exposure and the incidence of breast cancer among post-menopausal women, with HRs of 1.0 and 1.01 for age cut-offs of 52 years and 50 years, respectively. However, positive association was observed among pre-menopausal women with HR at 1.17 [95% CI: 1.00, 1.38] and 1.13 [95% CI: 0.94, 1.37] for age 52 and 50 cut-off, respectively. This model had adjusted for education, BMI, smoking, contraceptive use, and family history (97). In contrast to our finding, both studies had presented significant and relatively higher magnitude of effect compared to our result. This discrepancy in result could be attributed to differences in study design and biases present in these study designs, as well as different covariates included in our adjusted models. In another Canadian case-control study which included eight Canadian provinces, an investigation into the association between air pollution and breast cancer risk was conducted. The study revealed a positive yet non-significant association between NO2 exposure and incident breast cancer among post-menopausal women. The hazard ratio (HR) was 1.07 [95% CI: 0.86, 1.32] for every 10 ppb increase in NO2 (99). Notably, the association appeared somewhat stronger and approached significance for pre-menopausal women. In this analysis, the model adjusted for multiple covariates, including age, BMI, smoking, alcohol consumption, household income, education level, meat and vegetable consumption, physical activity, and SES deprivation index. For pre-menopausal women, the association remained non-significant but exhibited a slightly higher HR of 1.28 per 10 ppb increase in NO2 (99). Furthermore, Goldberg et al. conducted a study investigating the association between incident post-menopausal breast cancer and street-level NO2 concentrations (104). The findings indicated an increased risk for all subjects, with a more pronounced association observed among women residing at their addresses for over 10 years. The adjusted odds ratio (OR) for every increase in the interquartile range, 67 equivalent to a 3.84 ppb increase in NO2, was 1.11 [95% CI: 0.87, 1.41] (98). In contrast, for women who did not reside at their addresses for 10 years or more, the adjusted OR for every 3.65 ppb increase in NO2 was 0.93 [95% CI: 0.64, 1.36] (104) . These models were adjusted for various factors, including age, family history, education level, ethnicity, contraceptive use, BMI, smoking, and alcohol status, as well as median household income. For similar reasons as mentioned earlier, the effect sizes observed in these two studies closely resembled our findings. 5.1.3. Walkability and breast cancer risk To our understanding, this is the first study to evaluate the association between walkability and breast cancer risk. The walkability model had HRs of 1.07, 1.39, 1.49, and 1.22, for quintiles 2 through 5, respectively. The highest HR was observed for quintile 4 at 1.49 [95% CI: 1.13, 1.97]. The HR for quintile 2 was slightly above 1, but quintiles 3 though 5 had HRs close to 1.5. This implies that quintile 1 and 2 – the least walkable areas – had the lowest breast cancer rate. Higher quintiles (more walkable areas) corresponded to greater breast cancer risk. The result we have obtained is counter-intuitive to what has been repeatedly established in the literature regarding the association between walkability and chronic diseases (143–145). As discussed in chapter two, which includes the background and scoping review, no prior research has explored the potential link between neighborhood walkability and breast cancer risk. Nonetheless, there was one study that did explore the concept of walkability in relation to breast cancer, although it primarily focused on breast cancer mortality. In this study, the authors established a connection between walkability and breast cancer mortality. It also revealed that regions that were heavily reliant on automobile use, thus lacked walkability attributes, exhibited higher rates of breast cancer mortality (146). Although this study did not directly investigate the relationship between walkability and breast cancer risk, it provided valuable insights into the potential association between these factors. Given that the article is written in Farsi, we could not read and understand the details of the paper entirely. Although assessing walkability directly can be challenging, various approaches exist to construct indices that gauge the walkability of a neighborhood. These methodologies encompass the Walk Score, the Vancouver Walkability index (WI), the neighborhood Destination Accessibility index 68 (NDAI), the Pedestrian Index of the Environment (PIE), and the Pedestrian Potential Index (PPI) (147). In our study, we employed the Can-ALE index to measures walkability. CANUE provided two measurements, one from 2006 and another from 2016. Due to alterations in derivation methods and changes in census boundaries between these two years, we refrained from conducting longitudinal analyses. Instead, we exclusively utilized the 2006 measurement for two primary reasons. First, employing the earlier timepoint measurement offered a more robust representation of long-term exposure, a critical aspect when assessing the latency of breast cancer. Second, it reasonably mirrored the residential locations of the participants at the time of enrollment. The sole potential drawback of using the 2006 measurement is its potential limitation in fully capturing participants' lifetime exposure, particularly if they relocated to areas with varying levels of walkability during the follow-up period. As mentioned previously, although there is no study yet conducted on the association between walkability and breast cancer risk, there has been a decent amount of research performed on the association between walkability and certain chronic diseases, such as obesity. For example, Colley et al. in an attempt to understand the association between walkable neighborhoods and obesity utilized the 2016 Can-ALE database and the Canadian Health Measures Survey. They found that the proportion of participants aged 40-59 classified as overweight or obese were by approximately 30% lower in the most walkable neighborhoods compared to the least walkable category with a p-value of less than 0.0125 (148). This positive correlation suggests that a person's place of residence, in terms of walkability, has a general impact on their health. Another US-based examined the relationship between the built environment around participants' residences, travel patterns (walking and time spent in a car), BMI, and obesity (149). In this paper, it was reported that built environment and travel patterns are strong predictors of obesity. Specifically, while each quartile increase in land-use mix is associated with about 12% reduction in obesity with an OR of 0.88 [95% CI: 0.84, 0.92], each additional hour spent in car is associated with 6% increase in risk of obesity with an OR of 1.00 [95% CI: 1.001, 1.002] (149). In contrast, each kilometer spent walking per day is associated with 5% reduction in likelihood of obesity with an OR of 0.95 [95% CI: 0.91-0.99] (149). 69 Although it is conceptually sound that people are more likely to engage in PA, whether via walking or spending less time in the car, there are also studies that have confirmed this association. Again, another study conducted in the US looked at how community or neighborhood design impact physical activity and body weight of elderly individuals. They too found that increased walkability encouraged more walking (OR of 2.02), reduced the time spent travelling in a car (OR of 0.53), and reduced the odds of overweight or obesity (OR of 0.68) (150). In addition, they found that individuals with one car or no car at all were more likely to walk (OR of 2.9), and participants that lived in medium walkable neighborhoods were 32% less likely to be overweight compared to those residing in lower walkable neighborhood (OR of 0.51) (150). Although we did not explore the direct link between walkability and individual walking behavior or the extent of their physical activity engagement within our dataset, several independent studies have examined this relationship. All three of these studies have consistently found that individuals residing in high-walkability neighborhoods tend to be more physically active than those in low-walkability areas. Although we did not explore the direct link between walkability and individual walking behavior or the extent of their physical activity engagement within our dataset, several independent studies have examined this relationship. All three of these studies have consistently found that individuals residing in high-walkability neighborhoods tend to be more physically active than those in low-walkability areas. Further, in an Australian study, the neighborhood walkability and walking behavior of Australian adults were assessed (114). They found a statistically significant association between neighborhood’s objective walkability score and weekly walking frequency, after controlling for several covariates, including age, sex, SES, and gender (114). In addition, Dyck et al. also investigated the relationship between SES and walkability to PA among Belgian adults. This study also confirmed a positive connection between walkability and PA. Specifically, those that lived in high-walkable areas spent more time on accelerometer-based minutes of moderate-to-vigorous PA (38.6 minutes per day) versus those that lived in low-walkable areas spent less time comparatively at 32 minutes per day – with a p-values less than 0.001 (151). In addition, high-walkable area was associated with less motorized transport (p<0.01) and more cycling for transport (p<0.001) (151). 70 It is also important to note how walkability and air pollution are related. In our study, we found a positive association between increased air pollution and increased walkability (BL correlation = 0.628, p < 0.05; file year correlation = 0.61, p <0.05). A similar study conducted in Vancouver, British Columbia also investigated how neighborhood walkability and air pollution exposure may be related. They found evidence that the distribution of air pollution had an urban-rural gradient – high walkability and NO2 concentration was present close to the city centre and high SES neighborhoods (110)(152). Although we did not establish statistically significant findings in our study, these observations underscore the need for further research in this domain. Future studies should place a greater emphasis on exploring the impact of walkability on breast cancer risk. 5.1.4. Deprivation and breast cancer risk Again, to our understanding, this is the first study to evaluate the association between socio-economic deprivation and breast cancer risk. The social deprivation model had adjusted HRs of 0.97, 0.88, 0.95, and 0.81, for quintile 2 through 5, in relation to quintile 1, respectively. The HR for quintile 3 and quintile 5 were lower compared to quintiles 2 and 4 which had HRs close to 1. This implies that quintile 2 – the least deprived areas – had the highest breast cancer rate, and quintile 5 – the most deprived area had the lowest breast cancer rate. Higher quintiles (more socially deprived areas) corresponded to lower breast cancer risk, and vice versa. The material deprivation adjusted HRs for this model for quintile 2 through 5, in relation to quintile 1, were 1.15, 1.07, 1.03, and 0.98, respectively. Quintile 2 had the highest HR at 1.15 [95% CI: 0.90, 1.47]. The HRs for quintile 2 through 4 were above 1, and quintile 5 – the most deprived area had the lowest HR at below 1. This implies that least deprived areas had highest breast cancer rate, and the most deprived area had the lowest breast cancer risk. While the association between socio-economic deprivation and breast cancer risk has not been studied yet, two studies investigated the relationship between area-level deprivation and breast cancer screening uptake and breast cancer-specific survival. The study on breast cancer-survival revealed that, among about 5,000 patients with breast cancer, those that live in deprived areas or those who were exposed to neighborhood disadvantage had a shorter breast cancer-specific survival (153). Similarly, a systematic review on the association between area-level deprivation 71 and breast cancer screening uptake in Europe established a negative association between area-level deprivation and screening uptake. Women living in higher socio-economically deprived area were less likely to access and utilize breast cancer screening (154). 5.2 Study strengths and limitations 5.2.1. Study strengths This study has a number of important strengths. Unlike previous studies where the association between these built environment factors and risk of breast cancer were investigated via case-control or cross-sectional studies, our study benefited from a longitudinal cohort study. This design enabled us to track and record the occurrence of breast cancer over an extended period. Furthermore, a key asset of our study was the integration of ATP data with environmental exposure information, precisely linked to each participant's residential postal code at baseline. Moreover, the incident cancer cases were linked to Alberta's cancer registry, which ensured the accuracy and validity of case measurements. Another advantage we had in this study was that we had access to deprivation scores – material and social – from CANUE linked to each participant in ATP. While our change-in-estimate method did not definitively validate these measures as confounders in the relationship between environmental exposure and incident breast cancer, we did confirm their influence and potential to confound the association between built environment factors and breast cancer risk. For this reason, we decided to include either social deprivation or material deprivation in the final models. Since we had access to this measure, which is certainly not easy to attain, we were able to utilize it in our analysis and establish a more valid linkage between the explanatory variable and the outcome. 5.2.2 Study limitations This study has several notable limitations. First, it could not comprehensively assess participants' total environmental exposure because it only measured and provided exposure data for their current residences. This means that exposure at their workplaces or schools, where they potentially spent a significant portion of their weekdays, could not be accounted for. This limitation may introduce bias into our results. Another limitation was the absence of data on average NO2 concentration and NDVI. We were only provided with measurements at baseline 72 and at the final year of the study. In cases where a participant changed their place of residence between the baseline year and the final year, we could not accurately capture their overall or average exposure level. We also lacked information on place of residence of each participant at certain time points and could therefore not calculate average exposure level. In other words, historical addresses were not available. The same type of information was missing for walkability measurements. The only measurement provided for walkability was taken at the baseline. As mentioned previously, baseline measurement cannot fully capture the lifetime exposure of participants. This can potentially lead to bias if participants relocated to areas with different levels of walkability during the follow-up period. In addition, it is important to note the difference in exposure levels of TRAP between our study and studies where significant association between TRAP and breast cancer risk was found. In our study, the mean TRAP level at baseline was 13.1 ppb, whereas the average TRAP level at file year was 10.1 ppb. The US-based study where significant association between NOx and NO2 and breast cancer risk was established, the average NOx level reported was 70 ppb in 1993, 57 ppb in 2003, and 35 ppb in 2010 (142). The fact that we could not find any significant association between NO2 and breast cancer risk may be due to very low TRAP levels in Alberta. Overall, measurement of exposure levels should be considered cautiously as it did not capture the lifetime or average exposure of each participant for each environmental factor during their follow-up period. It's possible for someone living in a highly walkable area to choose not to walk on a given day due to various reasons, and conversely, someone in a low-polluted area might spend a significant portion of their day in a highly polluted environment. In other words, individuals may not consistently reside in the same residence throughout their lives, and their outdoor activities may extend beyond the boundaries of their local green spaces. Unfortunately, these dynamics were not accounted for and could potentially introduce significant bias into our findings. This exposure misclassification is likely to be non-differential, which can lead to a bias towards the null hypothesis, attenuating our results. Another potential limitation is our inability to accurately classify the menopausal status of participants. The questionnaire regarding menopause status was only administered once at the baseline and was not reassessed during the follow-up period. To address this, we relied on an age cut-off to classify participants who responded with "no" or "don't know" as either menopausal or not based on their age at baseline. 73 However, any misclassification related to menopausal status is also likely to be non-differential and may bias our results towards the null hypothesis. In addition, there's a limitation concerning the ethnicity information provided to us. Roughly 60% of participants did not respond to the ethnicity questionnaire, which could impact the generalizability of our findings. 5.3 Study implications and recommendations About one-third of the breast cancer cases are attributable to known or accepted risk factors, such as family history of breast cancer, genetic causes, alcohol consumption, and other lifestyle factors (14–17). While there were studies that could establish a significant association between TRAP and breast cancer risk, in our study, we could not establish significant results for any of the built environment factors. Despite some limitations in exposure measurements, our work can serve as a valuable foundation and guide for future research examining the relationship between built environment factors and breast cancer risk. Given more precise measurements, there exists significant potential to establish a meaningful and substantial association between breast cancer risk and environmental exposure. Although the association between certain built environment factors - TRAP, walkability, and greenness – and various chronic conditions, mental health, obesity, diabetes, and other cancers have been investigated and established to an extent, the association between these environmental measures and breast cancer has only been minimally studied. Based on existing finding regarding the association between built environment measures and other conditions, it is evident that creating healthier cities and neighborhoods positively influences physical and mental well-being. Consequently, it becomes increasingly clear that the health of a population on a societal scale is inherently intertwined with the health of its environment and neighborhoods. To contribute to the growing body of literature and complement the findings of this study along with previous research, further investigations are warranted in this field. These studies will serve to establish a more robust and definitive association between these environmental measures and the risk of breast cancer. 74 The three environmental measures explored in this paper have both direct and indirect effects on health. TRAP influences an individual’s transportation choices, as well as neighborhood planning and the availability of transportation options like walkability. The more walkable a neighborhood, the more likely residents of that area to adopt walkability-related behaviors, including walking, cycling, biking, jogging, etc. This relationship between physical activity and a reduced risk of chronic conditions contributes to overall better health. The same principle applies to the greenness and greenspaces within a neighborhood. A greater presence of greenspaces encourages people to spend more time outdoors engaging in social activities and physical exercise. This not only helps lower the risk of chronic diseases but also enhances the mental well-being of both individuals and the entire community (155). Furthermore, it is important to note that these three built environment measures are interrelated. As mentioned previously, there are studies that have also established these associations. For example, in a study by Frank et al. it was found that increase in land-use mix, and subsequently a decrease in time spent in car strongly reduces body mass index (BMI) (149). Specifically, it was observed that with each quartile increase in land-use mix, obesity reduced by 12.2%; and with each additional hour spent in car and each additional kilometer walked per day likelihood of obesity decline by 6% increase and 4.8%, respectively. Another US-based study also found that presence of forests, public recreation lands, and publicly accessible outdoor recreation resources significantly reduced BMI and obesity rates (24). In another study by Frank et al., the authors aimed to explore the association between land development, neighborhood design, and modes of transportation, including walking or using public transit (156). They also found that a 5% increase in walkability was associated with approximately a 32% increase in physically active travel, a 6.5% decrease in vehicle miles traveled, and a 5.6% reduction in nitrogen oxide (NOx) emissions. Collectively, these findings demonstrate that efforts to improve at least one of these built environmental measures in order to enhance public health can ultimately have positive impacts on other aspects of these environmental measures. When it comes to devising and implementing policies to enhance public health, actions can be undertaken on both a governmental (macro-level) and community level. At the community level, policy options might involve ensuring affordable housing in highly walkable areas with cleaner 75 air, constructing schools and daycare facilities in close proximity to residential areas to encourage walking or biking as practical commuting options over driving. By providing these amenities nearer to people's homes, even if individuals still choose to use motor vehicles for travel, they will need to cover shorter distances, thus reducing the emissions of pollutants like NO2. On a larger scale, policy considerations may encompass pricing and regulatory measures. Alterations in gasoline prices, coupled with regulations aimed at reducing or eliminating the use of carbon-based vehicles, can have a significant impact on environmental pollutant levels, including NO2. Furthermore, governments can explore the implementation of pollution abatement measures on existing gasoline-dependent vehicles to discourage their usage. In an attempt to investigate the association between built environment measures and cancer prevention, Wray et al. established that features of the built environment do indeed impact air quality, substance use, dietary choices, physical activity, and screening (157). Although the association between certain built environment factors and breast cancer risk was not significant in our study, there is a considerable amount of existing evidence to support the association between healthy cities/neighborhoods and healthy population. In selecting NO2, walkability, greenness, and social and material deprivation as exposures in the study on breast cancer risk, several factors were considered. First, existing literature has demonstrated associations between air pollution, specifically NO2, and breast cancer risk, emphasizing the need to explore this relationship further (25,99,104). The impact of neighborhood walkability on overall health, including potential effects on cancer risk, has also been a topic of interest in epidemiological research. Greenness, as a measure of access to natural environments, has been linked to mental and physical well-being, with some evidence suggesting a potential role in mitigating cancer risks (29). Moreover, social and material deprivation, serving as area-based indicators of socioeconomic status, have been consistently associated with disparities in health outcomes, making them relevant factors to investigate in the context of breast cancer (28). While the observed associations were non-significant in some instances, the decision to include these exposures was motivated by the established findings in the literature. It is important to acknowledge that certain limitations, such as small sample sizes and imprecise measures of exposures, could contribute to non-significant results. Despite the lack of statistical 76 significance, exploring these exposures remains valuable, aligning with the direction of existing research and providing insights into potential relationships with breast cancer risk. The study's findings underscore the importance of considering the environmental factor of NO2 in breast cancer risk management. Given the consistent positive associations observed, policymakers should recognize the potential direct causal impact of NO2 on breast cancer incidence. Incorporating such evidence into policy considerations may involve revisiting air quality standards and urban planning regulations to mitigate NO2 exposure. Mechanistic studies exploring the biological pathways linking NO2 exposure to breast cancer development could further inform targeted policies and interventions. While the current study primarily establishes an association, the implications for public health are substantial when considering evidence from other studies (75,94,96,99,142) , warranting a proactive approach to reduce NO2 exposure and potentially decrease breast cancer risk. Policymakers should consider these findings as part of a broader strategy aimed at creating healthier environments and minimizing potential environmental contributors to breast cancer incidence. When considering the implications for policy and practice, the complex interplay between individual and neighborhood factors in breast cancer risk necessitates a comprehensive approach. Policies that address structural determinants of health inequality, including initiatives to improve socioeconomic conditions and healthcare accessibility, are crucial for reducing breast cancer disparities. While interventions that focus on individual-level behaviors remain important, a broader strategy that addresses neighborhood-level determinants is equally essential. Policies targeting social and material deprivation at the community level can create environments that support healthier lifestyles and reduce environmental exposures linked to breast cancer. Ultimately, an integrated approach recognizing both individual and neighborhood factors is imperative for comprehensive and effective breast cancer prevention strategies. Incorporating both individual and area-based socioeconomic factors in a model offers a more comprehensive view of how socioeconomic status influences health. While our findings suggest a potential link between NO2 exposure and breast cancer risk, it is crucial for policymakers to consider the broader context of environmental exposures and their 77 interconnected relationships with individual behaviors and socioeconomic conditions. The non-significant associations in certain cases highlight the complexity of these interactions, emphasizing the importance of context-specific approaches to policy development. Walkability and access to green spaces emerge as potential protective factors, urging policymakers to prioritize urban planning strategies that promote active lifestyles and enhance greenspace availability. Access to greenspace is associated with increased physical activity, lowering the risk of various health issues, including colon and breast cancer (29). Access to greenspace is particularly crucial for low-income households facing financial barriers to exercise facilities, impacting both physical and mental health positively (29). Further, encouraging active transportation options, such as walking and cycling, not only aligns with public health recommendations but also addresses issues like obesity and cardiovascular diseases. Policymakers can enhance urban design by fostering walkable neighborhoods, incorporating green spaces, and implementing transportation infrastructure that promotes physical activity. These holistic approaches align with established health guidelines and have the potential to significantly reduce the prevalence of various health issues, including cancer, while fostering a healthier and more vibrant urban environment. Future research is needed to establish a firm association between each of these built environment measures and breast cancer risk. Given the latency period of breast cancer, it is critical to understand and work with longitudinal and lifetime exposure data to be able to truly capture the effect of TRAP on the risk of breast cancer. More research is warranted to understand the relationship between walkability and greenness and breast cancer. To better understand the relationship between these two measures of the built environment and breast cancer risk, longitudinal data is clearly needed. Information on where each person lives and the duration in which they live in that area is key to understand their exposure or accessibility to greenspace and walkable neighborhoods. 5.4 Conclusions In this research, we studied the association between five built environment factors – greenness, TRAP, walkability, and socio-economic deprivation – and breast cancer risk. Repeated findings have established the role of certain built environment features on health. Compared to greenness 78 and walkability, the association between NO2 and other chronic conditions and cancers has been reasonably studied. Similarly, while the association between NO2 and other chronic conditions have been established, its relationship with breast cancer is still only suggestive. More research in this area is warranted. While we could not establish a statistically significant finding for walkability and greenness, the association between NO2 and breast cancer risk reached statistically significant for NO2 concentration measured at file year. The magnitude and direction of association for NO2 were similar to what has been established with other studies. This study is one of the first to investigate the association between greenness and walkability and breast cancer risk in post-menopausal women in Alberta. Our findings necessitate replication, particularly across diverse climate conditions and variations in greenness levels. 79 References: 1. World Health Organization. Breast Cancer [Internet]. 2023 [cited 2023 Dec 8]. Available from: https://www.who.int/news-room/fact-sheets/detail/breast-cancer?gclid=CjwKCAiAvdCrBhBREiwAX6-6UhdWtsqj8xVgHhKsw_6hHGTw2pxkeftFBDgoKGd0-Shg6AzIiovcKRoCiL4QAvD_BwE 2. Government of Canada. Breast Cancer [Internet]. 2022 [cited 2023 Dec 10]. 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John Wiley and Sons Inc.; 2019. p. 3299–311. 90 Appendices Appendix A: Supplementary tables Table 14: Change-in-estimate HR ratio percent change for baseline greenness Variables removed New HR Original HR Change-in-effect % Alcohol categories 1.029 1.029 0 Contraceptive use 1.0295 1.029 0.05 Age categories 1.0412 1.0295 1.14 Education categories 1.0409 1.0412 -0.03 Birth categories 1.0421 1.0409 0.12 Social deprivation 1.0499 1.0421 0.75 Ethnicity categories 1.0491 1.0499 -0.08 Material deprivation 1.0534 1.0491 0.41 Fruit categories 1.0546 1.0534 0.12 Vegetable categories 1.0545 1.0546 -0.01 Income categories 1.0555 1.0545 0.09 Mother cancer history 1.0536 1.0555 -0.18 MET 1.0562 1.0536 0.25 Smoking categories 1.056 1.0562 -0.02 BMI categories 1.055 1.056 -0.10 Table 15: Change-in-estimate HR ratio percent change for average greenness Variables removed New HR Original HR Change-in-effect % Alcohol categories 0.9736 0.9735 0.01 Contraceptive use 0.9728 0.9736 -0.08 Age categories 0.9695 0.9728 -0.34 Education categories 0.9683 0.9695 -0.12 Birth categories 0.9676 0.9783 -1.09 Social deprivation 0.9863 0.9676 1.93 Ethnicity categories 0.9886 0.9863 0.23 Material deprivation 0.9883 0.9886 -0.03 Fruit categories 0.9877 0.9883 -0.06 Vegetable categories 0.9897 0.9877 0.20 Income categories 0.9886 0.9897 -0.11 Mother cancer history 0.9931 0.9886 0.46 MET 0.9861 0.9931 -0.70 Smoking categories 0.9907 0.9861 0.47 BMI categories 0.9911 0.9907 0.04 91 Table 16: Change-in-estimate HR ratio percent change for NO2 baseline Variables removed New HR Original HR Change-in-effect % Alcohol categories 1.149 1.143 0.52 Contraceptive use 1.1491 1.149 0.01 Age categories 1.2334 1.1491 7.3 Education categories 1.2407 1.1491 8.0 Birth categories 1.2517 1.1491 8.9 Social deprivation 1.1969 1.1491 4.2 Ethnicity categories 1.1452 1.1041 3.7 Material deprivation 1.1325 1.0813 4.7 Fruit categories 1.1358 1.0547 7.7 Vegetable categories 1.1326 1.0547 7.4 Income categories 1.1233 1.0547 6.5 Mother cancer history 1.0781 1.0547 2.2 MET 1.1168 1.0132 10.2 Smoking categories 1.094 1.0132 8.0 BMI categories 1.084 1.0132 7.0 Table 17: Change-in-estimate HR ratio percent change for walkability Variables removed New HR Original HR Change-in-effect % Alcohol categories 1.068 1.067 0.10 Contraceptive use 1.0679 1.068 -0.01 Age categories 1.0829 1.0679 1.5 Education categories 1.0840 1.0829 0.10 Birth categories 1.0847 1.0840 0.06 Social deprivation 1.0641 1.0847 -2.2 Ethnicity categories 1.0610 1.0641 -0.29 Material deprivation 1.0558 1.0610 -0.49 Fruit categories 1.0565 1.0558 0.07 Vegetable categories 1.0549 1.0565 -0.15 Income categories 1.0552 1.0549 0.028 Mother cancer history 1.0529 1.0552 -0.22 MET 1.0570 1.0529 0.39 Smoking categories 1.055 1.0570 -0.19 BMI categories 1.053 1.055 -0.19 92 Table 18: Change-in-estimate HR ratio percent change for social deprivation Variables removed New HR Original HR Change-in-effect % Alcohol categories 0.9555 0.9553 0.02 Contraceptive use 0.9545 0.9555 -0.10 Age categories 0.9492 0.9545 -0.56 Education categories 0.9487 0.9492 -0.05 Birth categories 0.9510 0.9487 0.24 Ethnicity categories 0.9517 0.9510 0.07 Material deprivation 0.9503 0.9517 -0.15 Fruit categories 0.9498 0.9503 -0.05 Vegetable categories 0.9477 0.9498 -0.22 Income categories 0.9578 0.9477 1.06 Mother cancer history 0.9592 0.9578 0.15 MET 0.9582 0.9592 -0.10 Smoking categories 0.9584 0.9582 0.02 BMI categories 0.965 0.9584 0.69 Table 19: Change-in-estimate HR ratio percent change for material deprivation Variables removed New HR Original HR Change-in-effect % Alcohol categories 0.9930 0.9991 -0.61 Contraceptive use 0.9919 0.9930 -0.11 Age categories 0.9752 0.9919 -1.68 Education categories 0.9720 0.9752 -0.33 Birth categories 0.9703 0.9720 -0.17 Social deprivation 0.9712 0.9703 0.09 Ethnicity categories 0.9727 0.9712 0.15 Fruit categories 0.9724 0.9727 -0.03 Vegetable categories 0.9723 0.9724 -0.01 Income categories 0.9808 0.9723 0.87 Mother cancer history 0.9834 0.9808 0.27 MET 0.9822 0.9834 -0.12 Smoking categories 0.9833 0.9822 0.11 BMI categories 0.9933 0.9833 1.02 93 Appendix B: Supplementary figures Figure 7: DAG for baseline and average greenness Figure 8: DAG for NO2 94 Figure 9: DAG for walkability Figure 10: DAG for social deprivation 95 Figure 11: DAG for material deprivation
Thesis/Dissertation
eng
Population and Public Health
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Dummer, Trevor J. B.
Association between built environment factors and post-menopausal breast cancer : Alberta’s tomorrow project
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