Medicine, Faculty of
Population and Public Health (SPPH), School of
DSpace
UBCV
Li, Yannan
Master of Science - MSc
University of British Columbia
Canadians have been reported to have poor dietary intake, with a high consumption of ultra-processed food (UPF), which are energy-dense foods that are typically industrially formulated and contain high levels of added sugar, sodium, and saturated fat while offering minimal nutritional value. UPF is a major contributor to chronic diseases and inversely correlated with overall diet quality. While individual-level factors influencing diet are well-documented, the role of neighborhood characteristics in shaping dietary intake remains underexplored. The retail food environment (RFE), mainly referring to where people can purchase food (e.g., supermarkets), is one such neighborhood-level factor that may affect dietary intake. This research aimed to characterize dietary intake of a contemporary cohort of British Columbians and examine the associations between features of RFE and UPF onsumption, accounting for neighbourhood level characteristics. Using data from BC Generations Project and its sub-study, with a linkage to the Canadian Food Environment Dataset (Can-FED) and Urban Environmental Health Research Consortium (CANUE), this research assessed dietary intake via a validated past year food frequency questionnaire and measured the RFE through absolute and relative densities of food outlets within 1-km radius of participants' residences. UPF intake was classified using the NOVA system and expressed as energy per 1,000 kcal/day. Findings showed that UPFs accounted for about 38% of the daily energy intake among 2,586 cohort participants, with nutrient profiles characterized by high saturated fat, sodium, and added sugars, and insufficient fiber, calcium, and vitamin D. Living in areas with higher densities of fast-food outlets was significantly associated with greater UPF intake, after adjusting for age, sex, body mass index (BMI), education, annual household income, and neighbourhood level characteristics including walkability, marginalization status, and urban sprawl. Additionally, the ecological analysis of the RFE across census subdivisions in BC revealed substantial regional variation in access to different food outlets. This research underscores that dietary intake is not solely an individual choice but is shaped by neighbourhood-level factors. The findings highlight the need for public health interventions and urban planning strategies that limit the overconcentration of fast-food outlets, aiming to support healthier dietary patterns and reduce diet-related chronic disease risk in British Columbia.
CHARACTERIZING THE FOOD ENVIRONMENT AND ITS ASSOCIATION WITH ULTRA-PROCESSED FOOD INTAKE IN BRITISH COLUMBIA, CANADA by Yannan Li M.B.B.S., The Southern Medical University 2016 M.P.H., Icahn School of Medicine at Mount Sinai, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Population and Public Health) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) May 2025 © Yannan Li, 2025 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Characterizing the food environment and its association with ultra-processed food intake in British Columbia, Canada submitted by Yannan Li in partial fulfilment of the requirements for the degree of Master of Science in Population and Public Health Examining Committee: Rachel Murphy Ph.D., Associate Professor, School of Population and Public Health, UBC Supervisor Trevor Dummer Ph.D., Associate Professor, School of Population and Public Health, UBC Supervisory Committee Member Tamara R Cohen Ph.D., R.D., Assistant Professor, Faculty of Land and Food Systems, UBC Supervisory Committee Member iii Abstract Canadians have been reported to have poor dietary intake, with a high consumption of ultra-processed food (UPF), which are energy-dense foods that are typically industrially formulated and contain high levels of added sugar, sodium, and saturated fat while offering minimal nutritional value. UPF is a major contributor to chronic diseases and inversely correlated with overall diet quality. While individual-level factors influencing diet are well-documented, the role of neighborhood characteristics shaping dietary intake remains underexplored. The retail food environment (RFE), mainly referring to where people can purchase food (e.g., supermarkets), is one such neighborhood-level factor that may affect dietary intake. This research aimed to characterize dietary intake of a contemporary cohort of British Columbians and examine the associations between features of RFE and UPF consumption, accounting for neighbourhood level characteristics. Using data from BC Generations Project and its sub-study, with a linkage to the Canadian Food Environment Dataset (Can-FED) and Urban Environmental Health Research Consortium (CANUE), this research assessed dietary intake via a validated past year food frequency questionnaire and measured the RFE through absolute and relative densities of food outlets within 1-km radius of participants' residences. UPF intake was classified using the NOVA system and expressed as energy per 1,000 kcal/day. Findings showed that UPFs accounted for about 38% of the daily energy intake among 2,586 cohort participants, with nutrient profiles characterized by high saturated fat, sodium, and added sugars, and insufficient fiber, calcium, and vitamin D. Living in areas with higher densities of fast-food outlets was significantly associated with greater UPF intake, after adjusting iv for age, sex, body mass index (BMI), education, annual household income, and neighbourhood level characteristics including walkability, marginalization status, and urban sprawl. Additionally, the ecological analysis of the RFE across census subdivisions in BC revealed substantial regional variation in access to different food outlets. This research underscores that dietary intake is not solely an individual choice but is shaped by neighbourhood-level factors. The findings highlight the need for public health interventions and urban planning strategies that limit the overconcentration of fast-food outlets, aiming to support healthier dietary patterns and reduce diet-related chronic disease risk in British Columbia. v Lay Summary What people eat is shaped by personal choices and the communities they live in. This research examined the nutrient intake of BC adult residents and found that many people consume too much saturated fat, sugars, and sodium while not getting enough dietary fibre, calcium, and vitamin D. This research then studied how the types and numbers of food stores in British Columbia (BC), such as fast-food outlets, are linked to people's consumption of ultra-processed foods (UPF)—such as packaged snacks, sodas, and frozen meals. The results found that people living in areas with more fast-food outlets ate more UPF. Additionally, differences were found across BC for the number and types of food stores in each neighbourhoods. By showing how neighborhood food environments shape what people eat, this research highlights the need for healthier community planning that limits access to fast foods to support better eating habits for everyone. vi Preface This thesis is an original intellectual product of the author, Yannan Li. As the author of this thesis, I affirm my substantial contribution to each research stage, including identifying research questions, developing the study design, and conducting the research with careful attention to detail. I am responsible for comprehensively analyzing the research results and ensuring meaningful interpretations. The study was approved by the University of British Columbia—BC Cancer Research Ethics Board (REB numbers H23-02251 and H22-01995). Generative AI was not used for any aspects of the work. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ................................................................................................................................ xi List of Figures ............................................................................................................................. xiii List of Abbreviations ................................................................................................................. xiv Acknowledgements .................................................................................................................... xvi Dedication .................................................................................................................................. xvii Chapter 1: Introduction ................................................................................................................1 1.1 Dietary intake .................................................................................................................. 1 1.2 Environments and dietary intake .................................................................................... 3 1.2.1 Food environment ....................................................................................................... 3 1.2.2 Built environment ....................................................................................................... 6 1.3 Framework ...................................................................................................................... 7 1.4 Study statement ............................................................................................................... 9 1.5 Research objectives ....................................................................................................... 10 Chapter 2: Literature Review .....................................................................................................11 2.1 Dietary intake ................................................................................................................ 11 2.1.1 Total daily energy and macronutrient intake ............................................................ 14 2.1.2 Micronutrient intake ................................................................................................. 15 viii 2.1.3 UPF intake ............................................................................................................... 16 2.1.3.1 Classfication of UPF ......................................................................................... 17 2.1.3.2 Current evidence of UPF in dietary intake ....................................................... 19 2.2 Dietary data collection tools ......................................................................................... 20 2.3 Environmental factors that influence dietary intake ..................................................... 22 2.3.1 Food environment ..................................................................................................... 22 2.3.2 Built environment ..................................................................................................... 26 2.3.2.1 Active living environment (also known as 'walkability') ................................. 26 2.3.2.2 Can-Marg index ................................................................................................ 27 2.3.2.3 Urban sprawl ..................................................................................................... 28 2.4 Current gaps and challenges in dietary research in Canada .......................................... 29 Chapter 3: Methods .....................................................................................................................32 3.1 Study design .................................................................................................................. 32 3.2 Data sources and instruments ....................................................................................... 32 3.2.1 BCGP and the BIRDEE sub-study ........................................................................... 32 3.2.2 The Canadian urban environmental health research consortium .............................. 34 3.2.3 The Canadian food environment dataset .................................................................. 35 3.3 Variables ....................................................................................................................... 36 3.3.1 Dietary intake ............................................................................................................ 36 3.3.2 Dependent variable for aim 1 .................................................................................... 37 3.3.3 Variables used as independent variables for aim 1 and for analysis in aim 2 .......... 38 3.3.4 Covariates ................................................................................................................. 40 3.4 Sample size ................................................................................................................... 43 ix 3.5 Statistical analysis and software ................................................................................... 44 3.5.1 Missing data .............................................................................................................. 44 3.5.2 Data cleaning ............................................................................................................ 44 Descriptive Analyses…………………………………………………………………….45 3.5.3 Model building for aim 1 .......................................................................................... 46 3.5.5 Sensitivity analysis for aim 1 .................................................................................... 47 3.5.6 Statistical software and significance threshold ......................................................... 47 Chapter 4: Results........................................................................................................................48 4.1 Aim 1: dietary intake in BC and associations with food environment ......................... 48 4.1.1 Descriptive analysis for dietary intake ...................................................................... 48 4.1.2 Bivariate analysis of UPF and food environment ..................................................... 56 4.1.3 Linear regression analysis for UPF and food environment ...................................... 57 4.2 Aim 2: summary of retail food environment characteristics in BC .............................. 60 4.2.1 Modified retail food environment (mRFEI) ............................................................. 63 4.2.2 Fast-food restaurant mix (Rmix) ............................................................................... 64 4.2.3 Absolute density distribution for different food outlets across CSDs ...................... 64 4.2.3.1 CSDs with greater availability to outlets selling UPF ..................................... 68 4.2.3.2 CSDs with with a balanced mix of food outlets .............................................. 73 Chapter 5: Discussion ..................................................................................................................79 5.1 Aim 1: Dietary intake in BC and associations with food environment ........................ 78 5.1.1 Descriptive analysis for dietary intake ...................................................................... 80 5.1.2 The relationship between retail food environment and UPF intake ......................... 86 5.2 Aim 2: The retail food environment in BC ................................................................... 89 x 5.2.1 Fast food and convenience stores ............................................................................. 91 5.2.2 Regional exceptions .................................................................................................. 93 5.3 Strengths and limitations .............................................................................................. 93 5.3.1 Strengths ................................................................................................................... 93 5.3.2 Limitations and next steps ........................................................................................ 95 5.3.2.1 Study design and generalizability .................................................................... 95 5.3.2.2 Variable selection, data availability and quality .............................................. 96 5.3.2.3 Classification for UPF ..................................................................................... 98 5.4 Impact ......................................................................................................................... 100 5.5 Conclusion .................................................................................................................. 102 Bibliography ...............................................................................................................................103 Appendices ..................................................................................................................................138 Appendix A ............................................................................................................................. 138 xi List of Tables Table 1. Individual and neighbourhood level characteristics of the BIRDEE participants (N = 2,586) ....48 Table 2. Usual daily dietary intake of BIRDEE participants (N = 2,586) based on CDHQ III data using the nutrient density method ……..……………………………………………………………………..….51 Table 3. Usual daily UPF intake in BIRDEE participants (N = 2,586) using the nutrient density method……………………………………………………………………………………………………..54 Table 4. Bivariate analysis of RFE by UPF intake (N = 2,586)………………………………….……….55 Table 5. Mean differences (95% CI) in UPF intake by food environment characteristics (N = 2,586)......58 Table 6. RFE characteristics among 7,532 DAs in BC included in Can-FED...………………….............61 Table 7. RFE characteristics by classes of absolute density in Abbotsford-Mission (N = 265) ………….69 Table 8. RFE characteristics by classes of absolute density in Kelowna (N = 254) …………………..….69 Table 9. RFE characteristics by classes of absolute density in Chilliwack (N = 175) ……………….….70 Table 10. RFE characteristics by classes of absolute density in Kamloops (N = 163) ……….….……….70 Table 11. RFE characteristics by classes of absolute density in Prince George (N = 155) …..………..…70 Table 12. RFE characteristics by classes of absolute density in Courtenay (N = 97) …………..…...…...71 Table 13. RFE characteristics by classes of absolute density in Penticton (N = 83) ………………..……71 Table 14. RFE characteristics by classes of absolute density in Campbell River (N = 74) ………………71 Table 15. RFE characteristics by classes of absolute density in Quesnel (N = 59) ………...…….………72 Table 16. RFE characteristics by classes of absolute density in Williams Lake (N = 48) ……………….72 Table 17. RFE characteristics by classes of absolute density in Fort St. John (N = 47) ………...……..…73 xii Table 18. RFE characteristics by classes of absolute density in Terrace (N = 36) ……………..….……..73 Table 19. RFE characteristics by classes of absolute density in Dawson Creek (N = 22) ……………….73 Table 20. RFE characteristics by classes of absolute density in Vancouver (N = 3,449) ……….……..…75 Table 21. RFE characteristics by classes of absolute density in Victoria (N = 574) …………………..…75 Table 22. RFE characteristics by classes of absolute density in Nanaimo (N = 181) ………..….……….75 Table 23. RFE characteristics by classes of absolute density in Vermon (N = 104) ……….…...……..…76 Table 24. RFE characteristics by classes of absolute density in Duncan (N = 83) ………….………..…..76 Table 25. RFE characteristics by classes of absolute density in Port Alberni (N = 53) …….……………76 Table 26. RFE characteristics by classes of absolute density in Cranbrook (N = 51) ………….……...…77 Table 27. RFE by classes of absolute density in Parksville (N = 51) ………………….…...…….………77 Table 28. RFE characteristics by classes of absolute density in Powell River (N = 34) …….………...…77 Table 29. RFE characteristics by classes of absolute density in Prince Rupert (N = 29) ….……………..77 Table 30. RFE characteristics by classes of absolute density in Nelson (N = 35) ………………..…....…78 Table 31. RFE characteristics by classes of absolute density in Salmon Arm (N = 33) ……..…….…..…78 Table 32. RFE characteristics by classes of absolute density in Squamish (N = 31) ……………….....…78 xiii List of Figures Figure 1.1. The Social Ecological Model (SEM) ……………………………………………………… 8 Figure 2.1. DAG illustrates the relationship between the retail food environment (independent variable, green) and UPF consumption (dependent variable, blue), with confounders (white) and unobserved variables (gray).……………………………… ………………………………...……………………… 39 Figure 4.1 The mean distribution of carbohydrates, protein, and fat as a percentage of total daily energy intake, stratified by sex: (a) Males; (b) Females...……………………………………………………… 53 Figure 4.2. A map of 26 included census subdivisions in BC………………….………………..……….62 Figure 4.3. Proportional distribution of modified RFEI levels across CSDs …………………….…...…63 Figure 4.4. Proportional distribution of Rmix levels Across CSDs in BC ..……..………………............64 Figure 4.5. Proportional distribution of food outlet density across CSD by type and classification …….67 xiv List of Abbreviations AI: Adequate Intake AMDR: Acceptable Macronutrient Distribution Range ATP: Alberta's Tomorrow Project BC: British Columbia BCGP: British Columbia Generations Project BIRDEE: BIoRepository and Data Expansion Enterprise BMI: Body Mass Index BRFSS: Behavioral Risk Factor Surveillance System CAN-Marg: Canadian Marginalization Index CANUE: Canadian Urban Environmental Health Research Consortium Can-FED: Canadian Food Environment Dataset CanPath: Canadian Partnership for Tomorrow’s Health CARTaGENE: Cohort name; Quebec-based population health cohort, part of CanPath CCHS-Nutrition: Canadian Community Health Survey - Nutrition CDHQ: Canadian Diet History Questionnaire CDHQ-I: Canadian Diet History Questionnaire, version I CDHQ-II: Canadian Diet History Questionnaire, version II CDHQ-III: Canadian Diet History Questionnaire, version III CDRR: Chronic Disease Risk Reduction (intake level) CFG: Canada Food Guide CI: Confidence Interval CRA: Canada Revenue Agency CSD: Census Subdivision CVD: Cardiovascular Disease xv DA: Dissemination Area DRI: Dietary Reference Intakes EAR: Estimated Average Requirement FFQ: Food Frequency Questionnaire FSA: Forward Sortation Area F/V: Fruit and Vegetable IARC: International Agency for Research on Cancer IVM: Immigration and Visible Minority MAR: Missing at Random MCAR: Missing Completely at Random MICE: Multivariate Imputation by Chained Equations MSDI: Material and Social Deprivation Index mRFEI: Modified Retail Food Environment Index MUPs: Markers of Ultra-Processing NFA: Not Further Assigned (in the context of food categories) NOVA: Food classification system based on food processing OR: Odds Ratio RFE: Retail Food Environment Rmix: Fast Food Restaurant Mix SEM: Social Ecological Model SSB: Sugar-Sweetened Beverage(s) UL: Tolerable Upper Intake Level UPF: Ultra-Processed Foods U.S.: United States xvi Acknowledgements I would like to express sincere gratitude to my supervisor, Dr. Rachel Murphy, for her generosity in providing resources and mentorship in shaping my work. Her keen insights and constructive feedback have greatly contributed to the development of this research, and I deeply appreciate the time and effort she has dedicated to this process. I am truly grateful for the opportunity to conduct my research under her supervision. Also, I would like to thank my committee members, Drs. Tamara Cohen and Trevor Dummer, for sharing their expertise and offering thoughtful guidance. I acknowledge and deeply appreciate the financial support and access to data provided through Dr. Rachel Murphy, including CANUE, Can-FED, and BCGP data sources, which were essential to this research. These resources were key in making this work possible. I also want to extend my gratitude to my research group, especially Ash, Marco, and Fidela, for their camaraderie, insightful discussions, and support throughout this process. I sincerely thank Dr. Hui Xie, soon-to-be Dr. Naren Gao, and Dr. Jiayin Liu, and many other friends of mine for their continuous encouragement and support for my academic journey and personal growth over the years. Lastly, a special thanks to my parents, whose unwavering support has been my greatest source of strength and resilience, helping me reach where I am today. xvii Dedication I dedicate this work to everyone striving for better health and well-being through food, especially those navigating the complexities of cancer survivorship. This research is for those who seek to make informed choices without judgment in a world where food environments and circumstances often shape what we eat. May it contribute to a future where healthy eating is promoted through empowerment and environment, not blame. 1 Chapter 1: Introduction 1.1 Dietary intake The 2024 Global Burden of Disease report highlights the significant impact of poor-quality diets on health outcomes, including high fasting plasma glucose and high body mass index (BMI), key drivers of type 2 diabetes mellitus (1,2). Extensive previous research has established the link between poor quality diets and chronic diseases, such as cardiovascular disease and cancer (3,4). The World Health Organization (WHO) and others suggest that nearly one-third of all cancer cases, over 90% of type 2 diabetes cases, 80% of heart disease cases, and 70% of stroke cases could be prevented by improving dietary intake and other health behaviours that tend to cluster with dietary intake: healthy body weight, physical activity, smoking and alcohol consumption (5,6). In 2024, over 3.6 million Canadians were diagnosed with type 2 diabetes, with 11 million – 1 in 3 Canadians - living with diabetes or prediabetes (7). Meanwhile, one in every 12 Canadian adults aged 20 and older is living with heart disease, which is responsible for 14 deaths every hour (8). In 2024, the number of new cancer cases and deaths from cancer are expected to reach 247,100 and 88,100, respectively (9). The economic impact of chronic diseases, including heart disease, type 2 diabetes, and cancer, was estimated to exceed $44 billion annually by 2020, imposing a significant strain on the healthcare system (10). In Canada, public health initiatives that promote healthy eating are generally underpinned by the dietary guidance in Canada’s Food Guide, which emphasizes consuming a variety of vegetables, fruits, whole grains, and protein-rich foods, along with healthy eating behaviours, such as limiting highly processed foods and awareness of the influences from marketing to food choices (11,12). Despite these efforts, poor quality diets that do not align with dietary guidance 2 have been consistently observed in Canadians (13). Canadians generally consume adequate intake of macronutrients such as protein but exceed recommendations for total fat (14). Inadequate intakes of essential nutrients like dietary fibre, calcium, and vitamin D are prevalent, alongside excessive consumption of sodium, saturated fats, and added sugars (14,15). A significant contributor to poor quality diets is the high intake of ultra-processed foods (UPF) (16). Since mid-20th century, Canadian food purchases shifted from unprocessed or minimally processed foods to UPF (17). Common UPF includes most sugar-sweetened beverages (SSBs), packaged snacks and confectionery, refined breakfast cereals and energy bars, reconstituted meat and seafood products, instant and ready-to-eat meals, industrial baked goods and desserts, and alcohol (18). Data from the first national survey from the Canadian Community Health Survey (CCHS) in 2004 revealed that UPF accounted for about half of total energy intake in Canada (19). More recent data from 2015 CCHS-Nutrition shows that UPF continues to dominate the Canadian diet, contributing to over 45% of daily energy intake (20). Since refined grains, sodium, added sugars, and saturated fats—common components of UPF—are also used as 'moderation' factors in diet quality scoring, the association between higher UPF consumption and poorer diet quality is expected, as UPFs inherently contribute to a lower diet quality score (21,22). Elizabeth et al. further highlighted that excessive UPF consumption is linked to negative health outcomes, including increased risks of obesity, metabolic disorders, and cardiovascular disease (23). Hence, reduction of UPF intake is essential for improving overall diet quality and lowering the risk of chronic diseases. Individual-level factors, such as age, sex, body composition, educational attainment, income, and marital status may shape dietary intake (24-28). Broader structural and environmental factors have also been recognized as influences on dietary intake; neighbourhood 3 characteristics such as food and built environment factors may also shape dietary intake, yet these remain underexplored in the Canadian context (29-31). Expanding research on dietary intake beyond individual determinants is essential for developing comprehensive public health strategies that promote higher quality diets. 1.2 Environments and dietary intake Neighbourhood characteristics such as the food environment (e.g., access to grocery stores and fast-food outlets) and the built environment (e.g., material deprivation, urban sprawl) have been identified as potential factors contributing to dietary intake (31-37). In Canada, these studies generally assessed neighborhood-level characteristics using geographic units from the census, such as dissemination areas (DAs) and census subdivisions (CSDs) (31,38). DAs, the smallest standard geographic unit in Canadian census data, typically include 400 to 700 residents and provide a detailed perspective on local environments (39). CSDs, which represent larger areas such as municipalities, cities, and towns, were used to assess broader food environment characteristics (40). Using neighborhood-level data in addition to individual-level risk factors for poor quality diets provides a more comprehensive understanding of the factors that shape dietary intake. 1.2.1 Food environment The food environment—encompassing accessibility, availability, and affordability—has been studied in relation to individuals’ dietary intake and is often assessed through the neighborhood retail food environment, most commonly measured using Geographic Information Systems (GIS)-based methods such as proximity to stores and store density (41). The retail food 4 environment is typically defined by the types and distribution of food outlets within a given geographic range, measuring geographic access to food (41,42). It usually includes chain supermarkets and grocery stores that primarily offer fresh, minimally processed foods, as well as convenience stores and fast-food restaurants, which often provide foods high in sodium, saturated fats, and added sugars. Restaurants are also part of the retail food environment, offering food that varies widely in nutritional content. The relationship between the retail food environment, dietary intake, and diet-related health outcomes remains inconclusive and yields inconsistent findings, with most studies conducted in developed countries (33,41,43,44). A 2012 systematic review reported that 13 out of 20 studies worldwide using GIS-based methods to assess the retail food environment found significant associations between geographic food availability and dietary outcomes, where most of the included studies were from the United States (U.S.). For instance, one U.S. study found that greater distance to the nearest supermarket, measured as the population-weighted mean distance at the county level, was associated with lower fruit and vegetable consumption. However, this association was not observed among individuals living outside metropolitan areas (45). In contrast, Canadian studies have produced mixed findings. The only Canadian study included in this 2012 review reported null associations with dietary outcomes (46). A more recent systematic review focusing on 17 Canadian studies similarly found no clear associations between the retail food environment and diet quality and only modest relationship of that and BMI (44). However, some localized research suggests that improvements in the food environment may benefit dietary intake. For example, a study in Waterloo (Ontario, Canada) found that males with closer proximity to supermarkets or better access to fresh produce tended to have healthier diets, although this association was not observed among females (47). 5 Furthermore, food environment interventions illustrate the complexity of these relationships; for instance, introducing a new neighborhood grocery store increased awareness of food access but did not change dietary intake, indicating that structural changes alone may not directly lead to improved diets (48). These discrepancies underscore the need to clarify the relationship between the retail food environment and dietary intake in Canada. Future studies need to determine if these associations are present and, if they are, identify the specific contexts—such as distinct geographic regions or particular features of the food and built environments—in which these relationships are most significant. These nuanced understandings are essential for developing effective public health strategies that align with Canada’s Food Guide healthy eating strategy, which emphasizes creating environments that help make the healthy choice the easy choice (49). Lastly, research on the food environment in Canada has been hampered not only by limited data availability—with many studies focusing on specific geographic areas or sectors—but also by inconsistent methodologies for measuring the retail food environment. These methodological differences make it challenging to compare findings across studies and draw conclusive evidence on the role of the food environment in shaping dietary intake. The development of the Canadian Food Environment Database (Can-FED) has begun to address these gaps by offering a more comprehensive resource on the density of various food outlets at the postal code level (50). Can-FED supports broader and more detailed assessments of the Canadian food environment, which could ultimately help strengthen research and inform policy efforts aimed at promoting healthier dietary intake. 6 1.2.2 Built environment The built environment is defined as human-made surroundings where people live, work, and play on a day-to-day basis, which includes buildings, parks, transportation systems, water supplies, and energy networks such as gas and electricity (51). Many measurements can be used to assess built environment. For example, walkability measures how easily people can walk or bike to their points of interest, or access transit stops as well as parks and fitness facilities, playing a key role in shaping the individual physical activity levels. The levels of physical activity, in turn, impact lifestyle behaviors and, indirectly, dietary intake, both of which contribute to diet-related health outcomes (52-54). Previous studies have shown an inconsistent relationship between the built environment and dietary intake with respect to deprivation. A recent provincial study in BC, Canada, found that living in areas with greater material and social deprivation was associated with lower odds of meeting fruit and vegetable intake recommendations (>5 servings per day), while higher neighborhood greenness was linked to a greater likelihood of meeting these recommendations (31). Studies in the UK and Scotland have observed a ‘concentration effect,’ where fast-food and takeaway outlets cluster in more deprived neighborhoods (32). However, a study in Montreal, Canada, found that socially deprived areas were actually well-served by supermarkets (55). Other aspects of the built environment such as urban sprawl and car-dependent neighbourhoods can create additional barriers to eating a healthy diet, particularly for individuals without private vehicles (56). A review article highlighted that limited public transit access might exacerbate dietary inequities by restricting mobility to grocery stores while increasing reliance on convenience stores and fast-food outlets, which are often more prevalent than supermarkets in urban neighbourhoods (33). 7 Additionally, socioeconomic factors embedded within the built environment, such as housing affordability and participating in the labor market, may indirectly affect dietary intake by influencing access to food outlets and other resources. For example, a study in U.S. found that families allocating more than 30% of their income to housing expenses are more likely to experience food insecurity, as the high housing costs limit their ability to afford sufficient and nutritious food (57). Similarly, a study in Scotland reported that people lived in socioeconomically disadvantaged areas had lower adherence to healthy eating guidelines, particularly in the consumption of fresh fruits and vegetables (58). The Canadian Urban Environmental Health Research Consortium (CANUE) provides a comprehensive dataset for studying neighbourhood-level characteristics across Canada (59). It integrates high-resolution data from Statistics Canada, Environment and Climate Change Canada, the Canadian Institute for Health Information (CIHI), and municipal sources to assess factors of interest such as walkability, urban sprawl, and various material and social deprivation indices. 1.3 Framework To support public health approaches to improve dietary intake, evidence that moves beyond individual-level determinants, to a broader approach that considers the environmental, structural, and policy factors shaping food access is essential. This study adopts the Social-Ecological Model (SEM) - a well-established behavioural science and public health framework that considers multiple levels of influence, including individual, settings, sectors, and societal levels, to shape dietary intake (60,61). 8 The SEM model emphasizes that individuals are embedded within broader social and environmental contexts that significantly shape their behaviour toward achieving optimal health. Based on the principles of health promotion, the SEM is grounded in two key concepts: (i) behaviour is influenced by and, in turn, influences multiple levels of the environment, and (ii) individual behaviour and the social environment interact through reciprocal causation. This multidimensional approach offers a foundation for designing targeted policies and interventions to improve population health and reduce health disparities (62). The SEM provides a comprehensive approach that examines factors across multiple levels of influence. Besides education, income, and other individual-level factors, environmental influences like the density of grocery stores and fast-food outlets and neighbourhood characteristics such as material deprivation also shape dietary intake at the settings level. Beyond these, sectoral and societal factors, such as government policies, cultural norms, and systemic inequalities, may also contribute to dietary intake. This integrated approach offers a robust framework for designing targeted interventions and policies to promote healthier dietary intake (Figure 1.1). Figure 1.1 The Social Ecological Model (SEM) (60) 9 1.4 Study statement Monitoring dietary intake at the population level is an essential part of nutrition surveillance, which includes assessing trends in dietary intake and informing preventive health policies. However, existing national surveys in Canada, such as the CCHS-Nutrition, infrequently assess dietary data- most recently in 2015, and before that, 2004, limiting the ability to capture contemporary dietary intake (63). More frequent national-level surveillance (i.e. the annual component of CCHS) only captures data on fruit and vegetable intake (64). This lack of timely, detailed dietary data hinders effective public health planning and prevention strategies aimed at improving dietary intake in Canada. Research on dietary risk factors has traditionally focused on individual-level determinants, mainly socioeconomic factors (65). Disparities in diet quality may be further pronounced in rural, remote, and marginalized communities (66,67). Structural barriers such as a scarcity of grocery stores, elevated food prices from inflation, and greater prevalence of convenience stores and fast-food outlets may all contribute to poor quality diets (67). Evidence suggests that the broader environment in which people live may play a role in shaping dietary intake (68). Neighbourhood-level factors, including the food environment; the types and density of food stores in a given neighbourhood, deprivation, and walkability, for example, may influence dietary intake (69-71). However, research examining the relationship between neighbourhood factors and dietary intake in Canada remains scarce, highlighting the need for further investigation. In summary, given the multi-level influences on diet, dietary intake and diet quality, is not simply a matter of personal choice but may be influenced by structural and environmental conditions. Understanding associations between multidimensional factors including the food 10 environment and dietary intake is needed to inform the development of targeted, evidence-based interventions that promote healthy eating and reduce the burden of diet-related chronic diseases. 1.5 Research objectives The current research explores dietary intake and evaluates the associations between UPF intake and the food environment in a sample of British Columbians. This research undertook two studies. The first study aims to provide a comprehensive understanding of overall dietary intake among a population cohort of adults residing in BC and subsequently examine the associations between UPF consumption and features of the food environment, adjusting for both individual factors and neighbourhood built environment factors. The second study aims to provide a detailed overview of the food environment in BC by summarizing features of the retail food environment across census subdivisions in BC. 11 Chapter 2: Literature Review 2.1 Dietary intakeIn Canada, nutrition surveillance primarily relies on the Canadian Community Health Survey (CCHS), a national survey that provides valuable insights into health determinants, health status, and health system utilization (72). The CCHS includes data on both adults and children, capturing data from males and females across the country. It consists of two main components: 1) an annual health survey, which covers some food and nutrition topics such as food insecurity, fruit and vegetable consumption, and dietary supplement use; 2) periodically, it conducts a more detailed nutrition survey using 24-hour dietary recalls, a method that captures comprehensive information on all foods and beverages consumed within the previous 24 hours (63). This provides a snapshot of dietary intake at the population level. However, this method is resource-intensive and requires substantial time commitments from participants, posing challenges for regular surveillance (63,73). As a result, dietary intake data collected within population-based cohorts is an essential complement to national surveys, offering additional insights into the dietary intake of Canadians while allowing for further targeted investigations into specific populations and health outcomes (74,75). Canada’s Dietary Guidelines offers evidence-based dietary recommendations to promote health and reduce the risk of nutrition-related chronic diseases (76). In addition to advocating for eating a variety of healthy foods every day, it stresses the importance of limiting UPF, as well as foods high in added sugars, saturated fats, and sodium. To assess adherence to the guidelines, researchers have used data from the CCHS-Nutrition data to calculate the Healthy Eating Food Index (HEFI), which assigns a diet quality score where higher scores indicate greater alignment 12 with Canada's Dietary Guidelines (21,77,78). Analysis of the 2004 CCHS-Nutrition survey found that the average diet quality score among Canadians aged 2 and older was 58.8 out of 100. Additionally, snacks—most of which are classified as UPFs—accounted for up to 23% of daily energy intake. Sugar-sweetened beverages (SSBs) were the most commonly consumed and biggest source of daily energy among the "other foods" category—those not counted as fruits and vegetables, dairy, protein foods, or grains (77). While data from the 2015 CCHS-Nutrition survey suggests a slight decline in overall UPF consumption, intake remains high (20). The most updated diet quality score using HEFI-2019 with the 2015 CCHS-Nutrition data showed a score of 43.1 out of a total 80 points, equivalent to 53.9 out of 100, indicated similar with previous studies, reinforcing the need for continued public health efforts to improve diet quality nationwide (21,77,78). Consumption of free sugar is a public health concern in Canada and a key component of the HEFI. Free sugar includes added sugars—such as those found in soft drinks, desserts, and flavoured yogurts—as well as naturally occurring sugars in fruit juices, honey, and syrups. Total sugars encompass both free sugars and naturally occurring sugars found in whole foods like fruits, vegetables, and dairy products. The recommended limit for total sugar intake is 10% of total energy intake or 50 grams per day based on a 2,000-calorie diet (79). The 2015 CCHS-Nutrition data showed that most Canadians exceed the recommended intake of added and free sugars, consuming an average of 57.1 grams per day of added sugars (11.1% of total daily energy) and 105.6 grams per day of free sugars (21.6% of total daily energy) (80). Diet quality was found to be different between sexes, because of their biological differences in metabolism and nutritional requirements (81). Generally, females require less kilocalories per day than males although this varies depending on age, body composition/size 13 and physical activity levels. Previous literature has shown that diet quality scores were higher in females than males throughout adulthood (77). Another study using UK Biobank data showed that females were more likely to exceed recommended intakes for total fat, saturated fat, and total sugars, while males were less likely to meet the minimum recommended intakes for protein, polyunsaturated fat, and total carbohydrates (25). Age is a significant factor influencing diet quality. Analysis of the 2004 CCHS-Nutrition data revealed that younger children (0–13 years) exhibited the highest diet quality scores, while older adults (≥51 years) had intermediate diet quality. In contrast, adolescents and adults aged 14–50 years demonstrated the lowest diet quality, indicated by a decline in dietary adherence during adolescence and early-to-mid adulthood (77). Moreover, dietary intake and overall diet quality have been found to differ by education and household income. A Canadian study found that higher educational attainment is consistently associated with greater fruit and vegetable consumption across all intake levels. In contrast, household income appears to influence fruit and vegetable intake mainly among individuals with lower overall consumption (82). Additionally, findings from Project EAT, a longitudinal cohort study that tracked a diverse sample of 2,287 adolescents from 1999 to 2009, suggest that among adolescents from low socioeconomic backgrounds, higher educational attainment in young adulthood was associated with a lower frequency of fast-food consumption (83). A systematic review found that higher UPF intake was more likely among people who were single, separated, or divorced versus people who were married/coupled (84). Another study specifically examined the relationship between marital status and food and beverage consumption and found that unmarried females generally had higher frequency of less healthy food than females who were married (85). 14 Lastly, previous literature has shown that BMI can both influence and be influenced by dietary intake. A previous study in Australia found that an energy-dense dietary pattern that is high in free sugars and saturated fats and low in fiber was associated with increased odds of obesity in young adults (86). Another study in Mexico found that individuals with higher BMI were more likely to follow a 'Western' dietary pattern, characterized by higher intakes of white bread, fried potatoes, and red and processed meats, while consuming fewer fruits and vegetables. In contrast, individuals with lower BMI tended to follow a 'Prudent' dietary pattern, which included higher consumption of fruits, vegetables, and fish (87). 2.1.1 Total daily energy and macronutrient intake Estimates from the 2004 CCHS-Nutrition survey indicated that 50% of females and 70% of males consumed energy in excess of their estimated energy needs based on their BMI (14). Overall, Canadians’ macronutrient intakes were within the acceptable macronutrient distribution range (AMDR) (14). However, data from the 2015 CCHS-Nutrition showed that 32% of males and 21% of females had carbohydrate intakes below the AMDR of 45-65% of total daily calories, whereas 25% of males and 23% of females had total fat intake that exceeded the AMDR for total fat intake (25-35% of total daily calories). In contrast, all adults had protein intakes within the AMDR (10-35% of total daily calories) (88). The WHO recommends that no more than 10% of total energy should come from saturated fat, however, over 62% of Canadian adults exceed this limit in the 2015 CCHS-Nutrition data (88). In this study, monounsaturated fats contributed 12-13% of total energy intake, while polyunsaturated fats accounted for 5.5-5.7%. 15 Dietary fiber intake was notably inadequate in both the 2004 and 2015 CCHS-Nutrition surveys, with fewer than 25% of females and 10% of males meeting the Adequate Intake (AI), of 14g per 1,000 kcal per day for individuals aged one year and older (14, 88). 2.1.2 Micronutrient intake Excessive sodium intake is a recognized public health concern in Canada (89). Data from the 2004 CCHS-Nutrition survey indicated that 78% of Canadian adults consumed sodium in excess of the Tolerable Upper Intake Level (UL) of 2,300 mg per day. Intake was particularly high among males aged 19–30, with a median sodium intake of 4,046 mg per day—nearly 2,000 mg above the level at which the risk of adverse health effects begins to increase (14). Findings from the 2015 CCHS-Nutrition survey suggest a modest decline in sodium intake; however, levels remained elevated. The median sodium intake for Canadian males aged 19 and older was 3,048 mg per day, significantly higher than that of females, who had a median intake of 2,244 mg per day. Despite this reduction, sodium intake continued to exceed recommended levels, with 75% of males and 49% of females aged 19+ surpassing the UL (88). Conversely, on average, consumption of certain micronutrients among Canadians are below the estimated average requirement (EAR). In the 2015 CCHS, 94% of adults had vitamin D intake levels below EAR (400 micrograms per day), which was potentially attributed to low fluid milk consumption which is fortified with vitamin D (88). Nearly half (44%) of adults had calcium intake level below EAR (800-1,000 milligrams per day in adults depending on age and sex), with inadequate intakes more likely among older adults (88). The prevalence of inadequate micronutrient intake, including vitamin D and calcium is greater among females, older adults and vulnerable populations, such as people living in low income households (90,91). 16 2.1.3 UPF intake Although there is no universal definition, UPF’s are typically characterized by a high degree of industrial processing (i.e., additives) and low nutritional value. Overall, UPFs tend to be energy-dense, high in free sugars, saturated fats, and sodium, and low in protein, fiber, and essential vitamins and minerals (16). In addition to the properties of UPF, other aspects may contribute to the high consumption of UPF. For instance, UPF requires little to no preparation and are often engineered to be hyper-palatable and shelf-stable, promoting snacking over regular meals (92). The convenience, generally lower cost, aggressive marketing, and potentially addictive properties may all contribute to excessive intake of UPFs (93, 94). Since the second half of the twentieth century, food systems in high-income countries such as Canada, Australia, the United States, and the United Kingdom have become increasingly dominated by packaged, ready-to-consume UPF (95). In middle-income countries, UPF is also displacing traditional food cultures at a growing rate. This global shift has been driven by urbanization, industrialization, and changing economic and work patterns, which have significantly transformed food systems and increased reliance on UPF (96). Economic pressures, such as longer working hours and changing work environments, have further contributed to shifts in dietary habits, increasing the consumption of UPFs. For example, research from the UK suggests that longer working hours are associated with greater consumption of red and processed meats, as well as alcohol, which are often classified as UPF (97). Consumption of UPF in Canada is among the highest in the world (98). The 2001 Food Expenditure Survey conducted in Canada by the Income Statistics Division of Statistics Canada showed that 67.1% of dietary energy came from UPF using a classification system before NOVA 17 was developed. In 2004, UPFs accounted for 48% of total energy intake among Canadians. By 2015, this proportion decreased slightly to 45.7%; however, UPF still contributed more than half of the total daily energy intake among children and adolescents, reaching 55% of total energy intake (20). This consistently high consumption spans all socioeconomic groups, with the highest intake observed among children, adolescents, non-immigrants, and individuals living in food-insecure households, highlighting the pervasive nature of UPF in the Canadian diet (19,20). 2.1.3.1 Classification of UPF The NOVA food classification system is the most widely used framework for classifying foods and beverages based on the level of processing. It was developed in 2009 by Carlos Monteiro and colleagues at the University of São Paulo, Brazil, in response to growing concerns about the role of food processing in public health (99). The NOVA system was designed to provide a standardized method of classifying food based on the extent and purpose of their processing, with the goal of distinguishing between minimally processed foods and ultra-processed food products. The NOVA food classification system categorizes foods into four groups: 1) unprocessed or minimally processed foods by cleaning, peeling, or cooking, such as fresh fruits, vegetables, whole grains; 2) processed culinary ingredients that are substances extracted from natural foods used for cooking, such as oils, fats, sugars, and salt; 3) processed foods that are modified through canning, pickling, or baking, including canned vegetables, cheeses, and freshly baked pieces of bread; and 4) UPF which are industrial formulated and composed mainly of refined substances, additives, and artificial ingredients (e.g. soft drinks, chips, instant noodles, reconstituted meat products like chicken nuggets, and mass-produced bread). 18 Many studies have established the construct validity of the NOVA classification system (100,101). One study using data from the European Prospective Investigation into Cancer and Nutrition study demonstrated high UPF intake classified using NOVA was positively associated with high circulating levels of industrial elaidic acid and a syringol metabolite—biomarkers for food processing—compared to diets high in minimally processed foods (102). The finding provided evidence that reinforcing the use of NOVA in nutritional research. NOVA has been widely adopted in epidemiological research, policy recommendations, and global nutrition guidelines, including those by the WHO, the Food and Agriculture Organization, and the Pan American Health Organization (103-107). In Canada, the consumption of UPF has been studied mainly using the NOVA classification system (108-113). For example, applying NOVA in a study of First Nations populations across British Columbia, Alberta, Manitoba, and Ontario revealed dietary disparities, with higher UPF consumption associated with lower diet quality and reduced intake of traditional foods (111). Another study using NOVA found that greater UPF consumption was linked to higher disease activity in individuals with inflammatory bowel disease, suggesting that UPFs may be a dietary risk factor for worsening symptoms (112). Similarly, a cohort study of Canadian children demonstrated that higher UPF intake, as classified by NOVA, was positively associated with increased BMI, waist-to-height ratio, subscapular and triceps skinfold thickness, and greater odds of overweight or obesity (113). Despite its widespread use, NOVA has been criticized for subjectivity in classification which may limit comparisons across studies. NOVA also does not consider nutrient content in the classification of foods. For instance, whole wheat bread made in a commercial bakery and soda are both classified as UPFs, where whole wheat bread provides dietary fibre and essential nutrients, and soda is primarily a source of added sugars with minimal nutritional value (114). 19 Despite these criticisms, the ease of the NOVA application and epidemiological support make it the most widely used system for classifying food according to processing. 2.1.3.2 Current evidence of UPF in dietary intake Recent studies have highlighted increasing concerns about UPF consumption and a wide range of chronic diseases and poor health outcomes. An umbrella review provided convincing evidence that high UPF intake is directly associated with increased risks of cardiovascular disease-related mortality (RR: 1.50), type 2 diabetes (RR: 1.12), and both anxiety (OR: 1.48) and depression ((OR: 1.48 and 1.53, respectively). All-cause mortality, heart disease-related mortality, depressive outcomes, adverse sleep outcomes, wheezing, and obesity were also linked to UPF intake, with risk estimates ranging from 1.22 to 1.66 (115). Another systematic review suggested cancer (all types, HR: 1.23), postmenopausal breast cancer (HR: 1.38), and irritable bowel syndrome with concomitant functional dyspepsia (OR: 1.25) are also associated with UPF intake (116). The risk of being classified as having an overweight condition or obesity with greater consumption of UPF is well established (1,115,117). With the well-documented health risks associated with UPF consumption, there is a need to investigate contributors to UPF intake and develop strategies to reduce the consumption of UPF. High UPF intake has also been linked to insufficient intake of key nutrients such as dietary fibre, vitamin D, calcium, and potassium, highlighting the negative impact of UPF on overall diet quality (22,118,119). While fruit and vegetable consumption has traditionally been the most common indicator of diet quality, UPF consumption is increasingly recognized as a complementary and underexplored indicator for diet quality in dietary research (120-122). 20 Most research on UPF consumption has focused on individual-level factors, such as socioeconomic status, education, and lifestyle behaviors (28,84,109,123,124). A secondary analysis of the Foodbook Study (2014–2015), which included 6,062 Canadian adults, found that 99% of participants consumed UPFs at least once per week, with chocolate, chips/pretzels, breakfast cereals, and fast food being the most common foods consumed. Additionally, greater UPF diversity—referring to the variety of UPF types consumed—was significantly associated with younger age, higher BMI, and male sex, highlighting sociodemographic variations in UPF consumption across Canada (109). Another prospective cohort study of 3-5-year-old children in New Hampshire (n=624) found that UPF accounted for 67.6% of total energy intake, and higher UPF consumption was positively associated with age, and sedentary physical activity (123). However, the influence of neighborhood-level factors on UPF intake remains largely unknown, despite growing evidence that food environments shape dietary choices (125,126). For example, a study in Brazil found that 29.5% of participants consumed ultra-processed drinks, with higher intake associated with greater density of stores selling UPF (OR: 2.34) and lower fruit and vegetable availability (OR: 0.49) in their neighborhoods. (127). Given the rising prevalence of UPF in modern diets, understanding how food and built environments contribute to UPF consumption is helpful for developing efficient public health interventions and possibly improving overall dietary quality. 2.2 Dietary data collection tools Food frequency questionnaires (FFQs) are extensively utilized in population studies to collect data on individuals' usual dietary intake over extended periods, typically ranging from weeks to a year (128). Participants are asked to report the frequency and amounts of foods and 21 beverages consumed from a predefined list. FFQs are a cost-effective method for collecting detailed dietary information in large populations, versus other methods, such as 24-hour dietary recall, that are more time- and resource- intensive (129). However, due to the pre-defined list of foods, FFQs do not capture the totality of dietary intake, including, for example, all dietary sources of UPF (128,129). In Canada, various dietary assessment tools are employed to capture population-level dietary intake. FFQs have been widely used in regional cohorts within the Canadian Partnership for Tomorrow’s Health (CanPath). For instance, Alberta’s Tomorrow Project and Quebec’s CARTaGENE utilized the Canadian Diet History Questionnaire II (C-DHQ II), an FFQ adapted from the U.S. DHQ-II with modifications to reflect the Canadian food supply (74,130). The C-DHQII, a past-year food frequency questionnaire, asks participants to recall their intake of over 140 foods and beverages, including typical portion sizes and frequency of consumption. On average, completion takes 45–60 minutes (131). It has been validated in previous studies (132,133). Other commonly used dietary or nutritional data collection methods include 24-hour dietary recalls and dietary screeners (129). The CCHS-Nutrition uses 24-hour dietary recalls. Respondents are prompted to report on foods and drinks consumed the previous day, including portion size (63). Since 24-hour dietary recalls capture short-term intake, they are subject to day-to-day variability, requiring repeated measures and statistical modeling to estimate habitual intake (134). Meanwhile, FFQs, though prone to systematic error when compared to recovery biomarkers, are better suited for assessing long-term dietary intake (134, 135). Additionally, dietary screeners—used in population-level studies such as the Canadian Longitudinal Study on Aging (CLSA) and CanPath regions including Atlantic PATH—offer a 22 more time-efficient alternative but lack detailed information on portion sizes, meal patterns, food preparation methods, and total energy intake (136). Short dietary screeners require less participant time and effort but provide limited dietary insights, as they do not collect quantitative data on portion sizes and only focus on a narrow selection of foods or specific nutrients, rather than capturing an individual's overall diet quality and nutrient intake (136,137). Other approaches to large-scale dietary assessment with minimal participant burden include targeted dietary questions, such as those measuring fruit and vegetable consumption. This approach was used at baseline in the CanPath and the annual CCHS survey (72,130). 2.3 Environmental factors that influence dietary intake 2.3.1 Food environment The neighbourhood food environment primarily refers to the retail food environment, which includes supermarkets, grocery stores, convenience stores, fast-food outlets, and other food retailers where residents purchase food (138). Food environments are commonly characterized using three key measures: 1) proximity, which refers to the distance to food outlets; 2) density, which represents the number of food outlets within a given area; and 3) proportion or relative density, which indicates the percentage of a specific type of food outlet in relation to others within the same area (59). Stores are typically identified through business registry data, geographic surveys, field audits, and commercial databases, providing comprehensive insights into geographical food environments (139-142). Some research has found associations between neighbourhood food environment with certain characteristics and dietary intake. In a U.S.-based study using data from the 23 Atherosclerosis Risk in Communities study, each additional supermarket within a census tract was associated with an 11%-32% increase in fruit and vegetable intake (143). Another study suggested that greater distances to supermarkets were linked to lower fruit and vegetable consumption and higher obesity rates in metropolitan areas in the U.S. (45). Conversely, retail food environments dominated by fast-food outlets and convenience stores have been associated with higher consumption of UPF and calorie-dense foods (144). Some studies found that the closer proximity and density of grocery stores and supermarkets were linked to higher fruit and vegetable intake and better health outcomes. A literature review concluded that residents of neighbourhoods with greater access to supermarkets and fewer convenience stores - tend to have healthier diets along with lower obesity rates (33). A study analyzing data from the 2007-2016 National Health and Nutrition Examination Survey indicated that individuals were more likely to consume higher amounts of UPF if they resided in areas with limited access to healthy food retailers, especially if they lived in food-insecure households (145). However, a study in Mexico found that when both nutrient-dense (healthier) and energy-dense (less healthy) food were abundant, individuals were more likely to choose food with high added sugar, fats, and salt over healthier alternatives (146). This highlights the hyper-palatable and highly appealing nature of UPF and underscores the need to not only improve access to healthy food but also to incentive healthy options both of which influence the food environment and overall diet quality. The concentration of fast-food outlets in a neighborhood has been associated with higher UPF intake. One study found that greater fast-food outlet density was linked to higher fast-food consumption and poorer diet quality, with each standard deviation increase in fast-food exposure associated with 11-61% higher odds of fast-food consumption and 3-17% lower odds of a 24 healthy diet (147). Similarly, a UK study found that higher fast-food outlet exposure was associated with greater fast-food consumption, though this association was only significant among individuals with lower education (OR: 2.05) (148). Studies have also found that the density of convenience stores contributes to poorer diet quality. A small study focusing on African American adults suggests that a higher concentration of convenience stores with 2 mils of residential areas may be linked to lower consumption of fruits, vegetables, and dietary fibre (149). As food environments are increasingly studied in North America for their associations with dietary intake and health outcomes, research on Canadian retail food environments remains limited (150,151). Distinct neighborhood food environment patterns have emerged in Canada compared to the US. While the U.S. has well-documented food deserts—areas with limited access to nutritious food—these are less prevalent in Canada (152-155). Instead, food swamps, low-income neighborhoods with greater access to unhealthy food retailers, appear to be more common (156). Additionally, evidence on the food environment in Canada, both in general (i.e., types and outlets of stores in specific areas) and in relationship to dietary intake, is sparse. Studies in Canada have largely been conducted within a single city or region, limiting their generalizability to other areas. For example, a study examining grocery store accessibility in London, Ontario, found that residents of low socioeconomic neighborhoods had the poorest access to supermarkets. Over time (1961–2005), these inequalities widened, further limiting food access for these disadvantaged neighborhoods (153). Similar results were found in Saskatoon, Saskatchewan, but the authors stated that as a smaller city, the impact of food store distribution there may not be as severe as in high-poverty areas of larger urban centers (157). These studies suggest that neighbourhood-level characteristics, such as socioeconomic status, influence food 25 environments and hence potentially impact dietary intake, although that was not assessed in either of the studies. In Vancouver, BC, a study reported that higher-income neighbourhoods lacked food outlets, which they hypothesized was because of zoning restrictions for predominately residential land use (158). The findings suggest limited food access, although given the neighbourhoods were high-income neighbourhoods, with likely high car ownership, the implications for dietary intake are unclear. Overall, these findings suggest that the neighbourhood built environment influences the retail food environment and may shape dietary intake. However, there is limited research directly assessing such relationships in Canada. Moreover, most Canadian food environment studies were conducted over a decade ago and may not reflect recent changes. The current Canadian food environment is dominated mainly by fast-food chains and restaurant franchises, which primarily sell UPF (159). A study in Saskatoon showed that 75% of people live within one kilometer of a fast-food outlet compared to comparatively only 45% of people of a grocery store (160). A longitudinal study using data from the Framingham Heart Study (1971–2008) found that proximity to and density of fast-food restaurants, full-service restaurants, convenience stores, and bakeries increased over time, while access to grocery stores declined (161). These findings highlight shifts in food environments that may influence dietary intake and health outcomes. However, the lack of comprehensive research at the provincial or national level in Canada limits our understanding of regional disparities and broader trends (155). 26 2.3.2 Built environment 2.3.2.1 Active living environment (also known as ‘walkability’) The accessibility of built environments, including sidewalks, parks, public transit, and community resources, plays a vital role in fostering active living conditions by increasing opportunities for physical activity and potentially reducing the rates of being classified as living with an overweight condition or obesity and diabetes prevalence (162-165). The Canadian Active Living Environment dataset (Can-ALE) were calculated and derived on DA-level within CANUE in 2006 and 2016, providing geographic-based measures that represent the active living friendliness of Canadian communities. It captures 3 core characteristics that support active living in neighborhoods: street connectivity (e.g., intersection density), population and residential density, and the quantity and diversity of nearby destinations (e.g., businesses, services and programs, parks, transit stops) (166). No studies have directly explored the association between active living environments and UPF intake in North America, highlighting a critical gap in literature. Neighbourhoods with robust active living environments often provide a greater variety of food outlets, potentially shaping dietary intake and UPF intake, as noted in Section 2.3. Current research on active living environments has predominantly focused on its association with physical activity and body composition, both of which are indirectly linked to dietary intake including UPF intake (86,87,113). For example, a study found that residents in high-walkability neighborhoods were more likely to accumulate 150+ minutes of weekly physical activity and had lower BMIs (OR: 0.76), compared to residents in areas with limited sidewalks, transit options, and high traffic or crime (52). Moreover, physical activity has been found to partially mediate the walkability–BMI 27 association, explaining about 25% of this relationship. However, the strength of associations between physical activity and active living environment features varies by neighborhood socioeconomic status, suggesting that the role (if any) of active living environments on food access and physical activity differs across populations (71). 2.3.2.2 Can-Marg index The Canadian Marginalization Index (CAN-Marg) is a tool developed to assess marginalization at the neighborhood level, which was created at the DA level and census tract level. It integrates various socioeconomic indicators—including income, education, employment, housing quality, and social exclusion—to provide a composite measure of marginalization (168). Marginalization is broadly understood as the systematic exclusion of individuals from meaningful participation in economic, social, political, and cultural activities, limiting their ability to engage in society fully (169). This exclusion is not random but influenced by various factors, such as race, ethnicity, gender, income level, sexual identity, and disability status (169). CAN-Marg encompasses four key dimensions of marginalization, offering a comprehensive framework that enables users to analyze various aspects of deprivation and social exclusion from multiple perspectives (168). Each dimension is illustrated below. Residential instability, often assessed through factors such as homeownership rates, frequency of relocation, and housing conditions (e.g., major structural deficiencies), may exacerbate food insecurity (170). Individuals experiencing food insecurity are more likely to depend on ultra-processed foods due to their affordability, convenience, and extended shelf life, potentially contributing to adverse health outcomes (145). 28 Material resources and labor force participation are two of the domains in Can-Marg that often depend on the proximity to key institutions such as employment centers and independent businesses (168). A study utilizing data from the Fragile Families and Child Well-Being Study found that neighbourhood economic circumstances are related to new mothers’ employment status, beyond their individual socioeconomic characteristics (171). Higher levels of neighbourhood material deprivation—primarily linked to lower household income—pose additional barriers to consuming high-quality diets, particularly among socioeconomically disadvantaged populations (172). Although people living in lower-income households may travel further to purchase groceries, the ability to do so depends in part on access to a car/public transit (173,174). These findings highlighted the importance of considering neighbourhood level material deprivation in the study of dietary intake and food environments. Ethnic concentration within neighbourhoods, specifically visible minorities and immigrant populations, is another dimension of the built environment that may shape dietary intake (175). Neighbourhoods with high proportions of new immigrants often face structural barriers such as economic constraints, language difficulties, and limited access to culturally appropriate food (176). 2.3.2.3 Urban sprawl Urban sprawl, characterized by low-density, car-dependent development, has been linked to reduced opportunities for physical activity, increased reliance on automobiles, and limited food access (177,178). This pattern of development often expands into previously rural areas, leading to housing growth without corresponding infrastructure and essential resources (179). As a result, residents may face greater distances to grocery stores and reduced access to healthy food 29 options, particularly in areas with limited public transportation, further exacerbating food accessibility challenges (152, 180). Collectively, these factors may contribute to poor diet quality, particularly among socioeconomically disadvantaged populations (181). 2.4 Current gaps and challenges in dietary research in Canada In summary, evidence on dietary intake and its relationship with neighborhood-level factors in Canada remains limited. The existing literature highlights several key gaps and challenges in the current research landscape. A major gap is lack of contemporary Canadian data that captures detailed dietary intake. The CCHS-Nutrition component, last conducted in 2015, remains the most comprehensive dietary dataset but was conducted 10 years ago. More recent sources, such as Canadian Health Measures Survey (CHMS) and Canadian Longitudinal Study on Aging (CLSA), provide some dietary insights but with limited scope—CHMS focuses mainly on fruit and vegetable consumption, while CLSA collects only frequency data for selected food items. This lack of detailed, contemporary data prevents a comprehensive assessment of macronutrient, micronutrient, and UPF consumption, as well as their associations with built and food environments. Plus, regular dietary monitoring is essential for aligning with Canada’s Food Guide recommendations, identifying nutrient intake gaps, and assessing changes in the food supply, especially with the increasing prevalence of UPF intake. Addressing this gap is crucial for developing targeted public health interventions to promote healthier dietary intake among Canadians. Another critical gap is the limited integration of food environment data with built environment factors. Most research isolates food environment characteristics—such as grocery 30 store or fast-food outlet density—without accounting for broader factors like walkability, housing stability, and socioeconomic conditions, which also shape dietary intake. Food environment measures alone provide valuable insights, but a more holistic approach is needed to capture the complex interplay between food and living environments. Moreover, Canadian dietary studies have largely been confined to specific cities or neighborhoods, limiting the generalizability of findings across different regions and population groups. This study directly addresses these evidence gaps by leveraging contemporary data from British Columbia, integrating detailed dietary intake assessments with geospatial measures of the food and built environment. By combining these elements, this research provides a comprehensive analysis of associations between neighborhood and built environment factors and dietary intake in Canada. 2.5 Conceptual frameworks development To guide the analytical approach in this study, a directed acyclic graph (DAG) was developed to visually represent the theoretical framework underlying the relationships between key variables identified in the literature. Informed by the Social Ecological Model (SEM), Figure 2.1 illustrates the hypothesized association between UPF intake and the retail food environment, alongside multiple levels of influencing factors. These include individual characteristics such as age and sex; neighbourhood-level variables such as walkability and material or social marginalization; and broader, unmeasured structural influences, such as cultural preferences. Variables were selected and positioned within the DAG based on both theoretical reasoning and empirical evidence regarding their relationships with UPF intake and 31 the retail food environment, reflecting underlying assumptions about potential confounding, mediating, and moderating factors that may influence these associations. Figure 2.1. This DAG illustrates the relationship between the retail food environment (independent variable, green) and UPF consumption (dependent variable, blue), with confounders (white) and unobserved variables (gray) (200). 32 Chapter 3: Methods 3.1 Study design This research includes two aims: Aim 1 involves a cross-sectional secondary data analysis, while Aim 2 is structured as an ecological study. The University of British Columbia—BC Cancer Research Ethics Board granted ethics approval for the collection of data and this analysis (REB numbers H23-02251 and H22-01995). 3.2 Data sources and instruments This research employed three primary data sources: British Columbia Generation Project (BCGP) and the BIoRepository and Data Expansion Enterprise (BIRDEE) sub-study, Canadian Food Environment Dataset (Can-FED), and the Canadian Urban Environmental Health Research Consortium (CANUE). The Can-FED and CANUE datasets were linked to the 6-digit postal codes of BCGP participants' home addresses. 3.2.1 BCGP and the BIRDEE sub-study As one of the regional cohorts in the CanPath, the BCGP is a prospective, population-based cohort study that aims to examine the impacts of environmental exposures, lifestyle factors, and genetics on chronic diseases, including cancer (182). At the study baseline, the BCGP enrolled residents of British Columbia aged 35–69 between 2009 and 2016. Eligibility criteria were purposely broad and only included the ability to complete questionnaires in French or English. The cohort is intended to be followed for up to 50 years. Active follow-up includes consent to be re-contacted for additional studies. Passive follow-up involves linking participant 33 data to provincial cancer registries, administrative health databases, and other national-level health datasets. At baseline, participants provided comprehensive information on their health, diet, lifestyle, and medical and family history. A proportion also provided biospecimens such as blood and urine, as well as physical measurements at in-person assessment centres. A detailed description of BCGP has been published (182). BIRDEE is an ancillary sub-study that collected additional data from a sample of BCGP participants. Participants were eligible to participate if they provided biospecimens at the BCGP baseline and were cancer-free (except for non-melanoma skin cancer) at the time of serial biospecimen collection (2022–2023) (unpublished). Approximately 3,800 males and females were purposively sampled for BIRDEE, with sampling designed to represent BMI categories: one-third of participants with a BMI < 25 kg/m2 and two-thirds with a BMI ≥ 25 kg/m2. At BIRDEE baseline, participants completed a demographic and health survey, provided a blood specimen, and filled out a past-year food frequency questionnaire (CDHQ III). Optional components included stool specimen collection and two 24-hour dietary recalls (ASA24-Canada) to minimize participant burden. For data not captured in BIRDEE (education attainment, income, and marital status), baseline BCGP data were used. The BIRDEE study utilized the Canadian DHQ-III, a modified version of the U.S. National Institutes of Health DHQ-III. The Canadian adaptation was tailored to reflect the Canadian food supply and fortification practices, ensuring relevance to the Canadian population (183). The questionnaire has been validated and demonstrates comparable or superior performance to other FFQs in assessing nutrient intake, making it a reliable tool for dietary assessment (131). The adaptation of the CDHQ III was informed by data from the 2015 CCHS—Nutrition (132). 34 The CDHQ III is a past-year food frequency questionnaire that captures detailed dietary intake information by asking participants to report their consumption of over 140 commonly consumed foods and beverages. It includes data on portion sizes and frequency of consumption over the previous year. The CDHQ III is linked to the Canadian Nutrient file, which contains the nutritional content of foods (184). Nutrient estimates are derived from participants’ responses using the Diet*Calc Analysis software (183). This software calculates nutrient intake based on the food frequency data recorded in the questionnaire, generating a nutrient profile for each participant. 3.2.2 The Canadian urban environmental health research consortium The built environment data for this study were obtained from the CANUE, which provides comprehensive measures of environmental exposures across Canada (185,186). Neighbourhood-level metrics in CANUE are derived from geospatial data collected through satellite data and routine surveys by Statistics Canada. Data used for this research was derived from the Canadian Census, conducted every five years using long- or short-form questionnaires. CANUE provides data at the DA level for the years 2006 to 2016. The 2016 census had a response rate exceeding 95% (187). CANUE datasets include the CAN-Marg, which comprises four measures designed to study marginalization in Canada, and the Can-ALE index, which reflects the active living friendliness of Canadian communities, also known as walkability. Additionally, CANUE provides variables reflecting air quality, weather, and greenness. This study focuses specifically on CAN-Marg, Can-ALE, and urban sprawl, as these neighbourhood-level factors have been shown to correlate with dietary intake from previous research (31,188,189). 35 3.2.3 The Canadian food environment dataset The food environment data for this study were obtained from the Can-FED to facilitate research on the relationship between local food environments, dietary intake, and health outcomes (50). Derived from the 2018 Statistics Canada Business Register, which is based on mandatory tax filings collected by the CRA, Can-FED provides detailed measures of retail food environments at the DA level across Canada. The dataset covers a total of 56,312 DAs aligned with 2016 census boundaries. It includes density measures calculated within 1-kilometer (used in this study) and 3-kilometer network buffers around each DA. Density measures include absolute density, which represents the total number of specific food retail outlets within the defined buffer, and relative density, which standardizes this count by area (e.g., outlets per square kilometer) to account for differences in DA sizes and provide a comparable measure of food environment accessibility. The publicly available version of Can-FED (used in this study) includes eight variables related to the food environment. Six of these variables measure the absolute density of specific food outlet types: fast food stores, convenience stores, chain supermarkets (referred to herein as “supermarkets”), non-chain grocery stores (referred to herein as “grocery stores”), fruit and vegetable markets, and restaurants. Additionally, the dataset provides two relative density variables: the modified Retail Food Environment Index (mRFEI) and the fast-food restaurant mix (Rmix). The mRFEI is calculated as the ratio of fruit and vegetable stores to all food outlets within a given area. The Rmix indicates the proportion of fast-food outlets relative to the total number of fast food and full-service restaurants. 36 3.3 Variables 3.3.1 Dietary intake Participants' dietary intake, including total daily energy intake, was measured in kilocalories (kcal). The nutrient density method was employed to ensure comparability across participants. This method calculates the amount of a specific nutrient consumed per 1,000 kcal of total daily energy intake, offering a standardized measure for analysis (190). Macronutrient intake was measured in grams per 1,000 kcal. The macronutrient components analyzed included carbohydrates, protein, total fat, total saturated fat, total unsaturated fat, monounsaturated fat, polyunsaturated fat, dietary fiber, and total sugar. Fatty acid ratio, a key measure of dietary fat quality and a core component of the HEFI-2019 were calculated as the sum of monounsaturated and polyunsaturated fats (unsaturated fats) divided by the total saturated fat and expressed as a percentage (21). Of the various dietary measures assessed by the Food Frequency Questionnaire (FFQ), this study focuses on selected micronutrients that are particularly relevant to public health, based on established evidence of nutritional inadequacies or excessive consumption within the Canadian population (14,88). This included sodium, calcium, and vitamin D. Limits (< 2,300 mg per day) on sodium intake are strongly emphasized in Canada’s Food Guide (76). Similarly, calcium and Vitamin D, which are vital for bone health, have a high prevalence of inadequate dietary intake (14,89). This study measured sodium and calcium intake in milligrams per 1,000 kcal, and vitamin D in micrograms per 1,000 kcal. Additionally, alcohol intake, sugar and sugar-sweetened beverages (SSB) were included in the analysis due to their significant public health implications (193-196). Alcohol 37 consumption was measured using the standardized nutrient density model, expressed as intake in grams per 1,000 kcal. SSB intake was derived from the CDHQ III food list, which is provided in the Appendix A Table A2, using kcal per 1,000 kcal after adjusting for energy density. 3.3.2 Dependent variable for the aim 1 The dependent variable in the aim 1 of this study is UPF intake, derived from self-reported data using CDHQ III. The food items were classified according to the NOVA food classification system based on the extent and purpose of their processing. Food items that met characteristics of Class 4 were considered UPF, which is industrially formulated and composed mainly of refined substances, additives, and artificial ingredients (197). The details of the NOVA system have been described in Chapter 2, section 2.3 UPF. Classification of foods into NOVA categories was performed manually and independently by myself, one of my lab members, and my supervisor, according to the established NOVA food classification system. Any discrepancies were highlighted and discussed until a consensus was reached. For example, there was debate over whether gravy should be classified as a Group 3 (processed food) or Group 4 (ultra-processed food) item. After reviewing its typical sources, the team agreed that gravy is most commonly purchased in packaged, ready-to-use forms rather than being homemade. Therefore, it was classified as a Group 4. The list of all foods categorized as UPF (group 4) was provided in the Appendix A Table A1. UPF intake was quantified in kilocalories per 1,000 kcal of total usual daily energy intake using the nutrient-density method and also expressed as a percentage of total usual daily caloric intake. For analytical purposes, UPF intake was treated as a continuous variable (kcal/1,000 kcal per day). This approach aligns with established methodologies used in other Canadian cohort studies, such 38 as CARTaGENE and the Canadian Healthy Infant Longitudinal Development cohort, ensuring consistency and comparability in evaluating UPF consumption and its potential health implications (113,198). 3.3.3 Variables used as independent variables for aim 1 and for analysis in aim 2 Of the Can-FED measures, aim 1 for this study focused on four key independent variables related to the retail food environment on the basis of potential relevance to UPF, while eight variables were identified for analysis in aim 2. The absolute density of food outlets was determined using a classification reflecting the number of specific food outlets per square kilometer (1 kilometer proximity) (199). In the public use dataset, classifications are pre-defined and grouped into classes 0 through 4. Class zero (class 0) indicates no access, and class 1-4 was derived using a k-medians clustering approach, where class 1 represents the lowest densities of an outlet type that is not zero and class 4 represents the highest densities of an outlet type. Due to sample size limitations, the classifications were consolidated into three levels: zero, low (classes 1 and 2 combined), and high (classes 3 and 4 combined). Further details on the measurement of each variable are provided below: a) The mRFEI measures the proportion of food outlets selling fruits and vegetables relative to all food outlets and restaurants per square kilometer. This metric reflects the access to healthy food in a given area, where “zero” indicates no access, and “low” and “high” indicate relatively worse and better access. 39 Calculated as: b) The Rmix quantifies the prevalence of fast-food restaurants relative to the total number of fast-food and full-service restaurants. This metric reflects the access to fast food in a given area, where “zero” indicates either no fast-food outlets in this given area. “low” and “high” indicate relatively lower and higher density of fast-food outlets, respectively. Calculated as: c) The density of fast-food outlets is measured by the number of stores per square kilometer. This metric reflects the concentration of fast-food establishments in a given area, where “zero” indicates none, “low” and “high” indicate relatively lower and higher density of fast-food outlets. Similarly, the density of the following types of food outlets was quantified as the number of this type of food outlets per square kilometer. This metric reflects the concentration of every type of food outlet in a given area, where “zero” indicates none, “low” and “high” indicate relatively lower and higher densities of the given type: d) The absolute density of convenience stores (zero, low and high); e) The absolute density of chain supermarkets (zero, low and high); f) The absolute density of grocery stores (zero, low and high); 40 g) The absolute density of fruit and vegetables markets (zero, low and high); h) The absolute density of restaurants (zero, low and high); 3.3.4 Covariates The selection of covariates was guided by existing literature reviewed in Chapter 2 and the conceptual framework illustrated in the directed acyclic graph (DAG) presented in Figure 2.1. All covariates were treated as confounders, under the assumption that each independently influences both the retail food environment and UPF intake without laying on the causal pathway between them. These variables were included to control for broader contextual differences, while acknowledging that some may plausibly act as mediators or effect modifiers—a possibility further considered in the Discussion section (Chapter 5). Covariates from the BCGP-BIRDEE sub-study included: a) Age: Treated as a continuous covariate. b) Sex: Based on self-reported data, categorized as female and male. c) BMI: calculated by self-reported height and weight. BMI was then categorized into three groups: normal weight (BMI < 25 kg/m2), overweight (BMI 25-29.9 kg/m2), or obese (BMI ≥ 30 kg/m2). Covariates from BCGP baseline data: d) Education was categorized into a binary variable: "< university," represents participants with education levels including no formal schooling, some primary school, completed primary school, some high school, completed high school, and technical school or 41 diploma. The second category, "≥ university," represents individuals with a university degree or graduate school. e) Income: Analyzed as a categorical variable, divided into three categories. The first category, "< $75,000," represents households with an income less than $75,000. The second category includes households with incomes between $75,000 and $99,999. The third category includes households with an income of $100,000 or more. f) Marital status: Recategorized into two groups: "coupled," including married or partnered individuals, and "not-coupled," which includes divorced, separated, widowed, and single individuals. Covariates from CANUE g) The Can-ALE index utilizes data within one-kilometer Euclidean buffers centered on DA centroids to assess features such as intersection density, dwelling density, points of interest, and transit stop availability (166). The index is computed by summing the standardized z-scores of these measures, resulting in a relative ranking of each DA across Canada. Initially, the Can-ALE Index was categorized into five classes (1 = very low to 5 = very high). However, due to sample size limitations, the classification was recategorized into three levels: low (class 1 & 2), moderate (class 3), and high (class 4 & 5), with high being the reference group. h) The urban sprawl score evaluates urban sprawl across four key dimensions widely recognized in North America: density, mixed-use development, street connectivity, and centering (201). This continuous variable produces a composite score for each census tract, calculated as the posterior mean of a Bayesian model. The score is standardized to 42 have a mean of 0 and a standard deviation of 1, ensuring a consistent and quantifiable measure of sprawl that allows for comparisons across tracts. Four covariates, that comprise the Canadian Marginalization Index, were used to measure deprivation across distinct dimensions, each reflecting a different aspect of social and economic life (168). These covariates were selected over alternative measures of social and material deprivation for several reasons: 1) they have less missing data, improving the robustness of the analysis; 2) they help account for the absence of individual-level racial or ethnicity data, ensuring a more comprehensive understanding of social and economic factors; and 3) it has been shown to be stable across different time periods and geographic regions (e.g urban vs. rurual), providing a reliable measure for comparison (168). Variables were originally organized into quintiles, where 1 indicates the least deprived and 5 indicates the most deprived for each dimension. In this study, due to sample size limitations, the classification was recategorized into three levels: low (class 1 & 2), moderate (class 3), and high (class 4 & 5). The four dimensions and their respective indicators are described as follows: i) Households and Dwellings: Measures housing conditions and reflects the stability and quality of living environments, which are key indicators of material deprivation. The detailed indicators included the proportion of dwellings that are apartment buildings, the proportion of individuals living alone, homeownership rates, mobility within the last five years, marital status, and median household income of the DA. j) Material Resources capture economic deprivation and the ability to afford essential goods and services. This neighbourhood-level factor includes indicators that measure 43 access to and attainment of basic material needs, such as unemployment and the percentage without a high school degree in the given DA. k) Age and Labour Force provides economic activity and dependency information within a given DA. This domain includes indicators to describe % seniors (65+), the dependency ratio (the ratio of seniors and children to the population 15-64), and % not participating in the labour force. l) Immigration and Visible Minorities provides neighbourhood-level information about the living conditions and potential barriers for immigrants and visible minorities that reflect social deprivation and systemic inequities. This domain includes indicators to describe % recent immigrants and % who self-identify as a “visible minority” in the given DA (202). 3.4 Sample size For Aim 1, a total of 3,840 participants consented to participate in BCGP-BIRDEE, of whom 3,354 started the CDHQ-III, and 3,119 fully completed it. Fewer participants (~1,100) completed the optional 24-hour dietary recalls, as such, this study focuses on dietary data from the CDHQ-III. Participants' six-digit postal codes of residence were linked to CANUE and Can-FED. The number of participants eligible for final analysis was 2,586 for the complete case analysis, with the rationale of case deletion to address missingness described below in section 3.5.1. For Aim 2, a total of 7,532 DA in BC were included in the Can-FED database to analyze the food environment at the neighbourhood level. 44 3.5 Statistical analysis and software 3.5.1 Missing data Missing data in the BCGP-BIRDEE sample is likely not missing at random (MNAR) due to factors such as preference bias, optional responses, and social desirability bias in dietary data collection process (203). Multiple imputation assumes missing at random (MAR), which may not hold in this case, as the probability of missingness is not only related to observed data but also to the missing values themselves. Hence, such a procedure would potentially introduce additional bias. Therefore, a complete case analysis was conducted instead. To ensure transparency, missing data for each variable was reported, and sensitivity analyses using the residual model were performed to assess the robustness of the findings. 3.5.2 Data cleaning For both aims 1 and 2, variables were recoded to ensure consistency in handling and presenting missing data. This included standardizing missing values across variables by coding all missing responses as 'NA'), collapsing categorical variables with less than 100 counts to improve statistical power, and ensuring uniform data formats to facilitate analysis. Missing patterns were assessed using the “VIM” package in R, which provides tools for visualizing and analyzing missing data patterns (204). Outliers were identified using boxplots and histograms, to evaluate the distributions of key variables. The dataset was further checked for internal consistency, ensuring that variables conformed to their predefined definitions and that no contradictory or implausible data points were present. Examples of inconsistencies included six records where sex values in BIRDEE did not match baseline BCGP data. These records were 45 cross-referenced with the original data sources, and the BIRDEE-reported value was retained if consistency was observed in other variables (e.g., age, FSA code), accounting for potential natural transitions. For the usual total daily energy intake, both lower and upper intake ranges were examined, with upper-bound values determined to be plausible. However, a lower bound exclusion was not applied, as CDHQ-III does not fully capture total food intake, making strict biological plausibility thresholds less appropriate. Other dietary variables were adjusted for energy density to ensure comparability across participants. For aim 2, four zones were also included in the CanFED document that reflect aggregated dissemination areas (DAs) that do not belong to a specific census subdivisions (CSD). These DAs thus reflect disparate CSDs that are not meaningful for understanding small area food environments and were thus not included in aim 2 when interpreting the results. Descriptive Analyses Descriptive were used to summarize demographic, dietary intake, food and built environment data for aim 1, based on participant-level information. For aim 2, food environment data were presented at the DA level, reflecting the smallest geographic area used to share census data. For continuous variables, medians with interquartile ranges (1st quartile, 3rd quartile) were reported, as these measures are robust to skewed distributions often seen in dietary data (205). This approach avoids transformations, which can be complex and may not improve interpretability, especially when dealing with skewed data where outliers may carry meaningful information (205, 206). For categorical variables, frequencies and percentages are presented. Analyses were stratified by sex to account for known differences in dietary intake (25,81). 46 To assess differences in dietary intake across sex in aim 1, statistical comparisons were performed using appropriate tests based on data characteristics and details provided in the table footnotes. Statistical significance was set at p<0.05. For categorical variables, chi-square tests (or Fisher's exact tests where applicable) were used to evaluate differences in proportions. For continuous variables, the Wilcoxon rank-sum test, a non-parametric method, was employed to account for potential non-normality. 3.5.3 Model building for aim 1 Multiple linear regression models were employed to examine the relationships between UPF intake and each characteristic of the food environment. Initially, Model 1 was constructed with UPF intake as the dependent variable and a given food environment variable as the independent variable. Model 1 adjusted for individual-level factors, including age, sex, education attainment, income level, marital status, and BMI. Subsequently, a fully adjusted model, Model 2, was developed to adjust for neighbourhood-level factors. The covariates included urban sprawl, the four domains of the Can-Marg Index, and the Can-ALE Index. Separate models were developed for each food environment variable (four models in total) to examine the specific association of each variable with UPF intake. This approach was used to assess the independent contribution of each food environment variable and to address potential multicollinearity, as the food environment variables are not independent of one another. 3.5.4 Sensitivity analysis for aim 1 To address potential confounding by total energy intake, the residual method was employed to examine absolute nutrient intake (190). Nutrient intake was regressed on total 47 energy intake, and the residuals from this regression were used as an adjusted measure of nutrient intake, independent of total energy. This method allowed for the evaluation of nutrient intake patterns without the influence of caloric variation. By isolating nutrient intake from energy consumption, the residual method facilitates meaningful comparisons across individuals with varying total energy intakes, ensuring robustness in observed associations (207). 3.5.5 Statistical software and significance threshold For both aims 1 and 2, all statistical analyses were performed using RStudio (version 4.3.1; R Foundation for Statistical Computing, Vienna, Austria). A significance level of α < 0.05 was applied. 48 Chapter 4: Results 4.1 Aim 1: dietary intake in BC and associations with food environment 4.1.1 Descriptive analysis for dietary intake Table 1 displays the individual and neighbourhood characteristics of the study participants, stratified by sex. Of the participants, 69.6% identified as female. The mean age was younger among females (66.3, SD=7.8) than males (67.9, SD=7.9). The BMI distribution differed between male and female participants, where females were more likely to have a normal BMI (37.5%) compared to males (24.9%), and males were more likely to be classified as overweight (57.6% vs. 40.0% for females). However, obesity was more prevalent among female participants (22.5%) than males (17.5%). Approximately 75% of participants were living with a partner, while the remaining 25% reported being single, separated, divorced, or widowed. Females were more likely to report being single compared to males (27.6% vs. 14.3% of males). Over 56% of participants attended university. A higher proportion of males (63.0% compared to 53.4% of females) obtained a university degree or higher. Significant differences were observed in household income, with nearly 40% of females reporting a household income below $75,000 annually, whereas over half of the males reported an annual household income exceeding $100,000. Neighbourhood-level characteristics where males and females lived were generally similar when compared by distributions. Overall, nearly half of the participants were living in areas with a low walkability, characterized by low population and dwelling density, fewer 49 intersections in the neighbourhood, and fewer points of interest such as parks, businesses, transit stops, and government institutions, reducing opportunities for regular physical activity. Almost one in four participants were living in neighbourhoods with high material deprivation. One in five participants lived in neighbourhoods that had a high labor force deprivation with more seniors or non-labor force participants relative to the working-age population, suggesting fewer employment opportunities and a greater sensitivity to financial constraints. Over 60% of participants resided in neighborhoods with higher proportions of recent immigrants and visible minorities, suggesting a need for multilingual and culturally tailored services in healthcare, food access, education, and other community programs. On average, participants’ neighbourhoods exhibited low urban sprawl (mean=0.11, SD=0.58). Table 1. Individual and neighbourhood level characteristics of the BIRDEE participants (N = 2,586) Variables a Male Female Total N = 787 (30.4%) N = 1,799 (69.6%) N = 2,586 (100%) b Individual-Level Age (year) * Mean (±SD) 67.9 (7.9) 66.3 (7.8) 66.8 (7.9) BMI (kg/m²) * Normal Weight 196 (24.9) 675 (37.5) 871 (33.7) Overweight 453 (57.6) 720 (40.0) 1173 (45.4) Obese 138 (17.5) 404 (22.5) 542 (21.0) Marital Status * Living with a partner 675 (85.7) 1302 (72.4) 1977 (76.5) Living alone 112 (14.3) 497 (27.6) 609 (23.5) Education attainment * < University 291 (37.0) 838 (46.6) 1129 (43.7) > University 496 (63.0) 961 (53.4) 1457 (56.3) 50 Annual household income * < $75,000 209 (26.6) 677 (37.6) 886 (26.6) $75,000-100,000 151 (19.2) 343 (19.1) 494 (19.1) > $100,000 427 (54.3) 779 (43.3) 1206 (46.6) Neighbourhood-Level Active living environment High 188 (23.9) 475 (26.4) 663 (25.6) Moderate 242 (30.7) 523 (29.1) 765 (29.6) Low 357 (45.4) 801 (44.5) 1158 (44.8) Urban sprawl score Mean (±SD) 0.10 (0.58) 0.11 (0.58) 0.11 (0.58) (Can-Marg) Residential instability (household and dwelling) * Low 345 (43.8) 674 (37.5) 1019 (39.4) Moderate 155 (19.7) 376 (20.9) 531 (20.5) High 287 (36.5) 749 (41.6) 1036 (40.1) Material deprivation Low 438 (55.7) 1294 (57.9) 1480 (55.7) Moderate 162 (20.6) 376 (17.9) 484 (20.6) High 187 (23.8) 508 (24.2) 622 (23.8) Economic dependency (age and labor force) Low 478 (60.7) 1048 (58.3) 1526 (59.0) Moderate 154 (19.6) 371 (20.6) 525 (20.3) High 155 (19.7) 380 (21.1) 535 (20.7) Immigration and visible minority Low 106 (13.5) 223 (12.4) 329 (12.7) Moderate 164 (20.8) 370 (20.6) 534 (20.6) High 517 (65.7) 1206 (67.0) 1723 (66.6) a Significance levels for comparisons between males and females: p < 0.05; b Statistical comparisons of categorical variables were conducted using Chi-square tests or Fisher’s exact tests, as appropriate. For continuous variables, the Wilcoxon rank-sum test was applied to account for potential deviations from normality. The percentages may not sum to 100% due to rounding. 51 Table 2 shows the results from the descriptive analysis of dietary intake, providing estimates of usual daily intake. Overall, male participants reported significantly higher usual daily energy intake compared to female participants, with a median intake of 1,852.7 kcal (IQR: 1,395.6–2,321.7) versus 1,457.3 kcal (IQR: 1,124.1–1,847.3) in females. The carbohydrates consumed in grams per 1,000 kcal showed minimal variation across sex, with a median total carbohydrate intake of 115.5 (IQR: 103.5, 127.4), and a mean of 115.2 (SD=19.4). The total protein intake was slightly higher in females (p = 0.045), with a median total protein intake of 40.1 grams per 1,000 kcal with an IQR of 35.8 to 44.8 and a mean of 40.6 (SD=7.4). The total fat intake was significantly lower in males compared to females, with a median total fat intake of 38.7 grams per 1,000 kcal (IQR: 34.3–43.2) for males and 40.1 grams per 1,000 kcal (IQR: 35.5–44.9) for females. Figure 4.1 shows the distribution of carbohydrates, protein, and fat as a percentage of total daily energy intake among BCGP-BIRDEE participants by sex. Total saturated fat intake was consistent across males and females, with a median intake of 12.4 grams per 1,000 kcal (IQR: 10.5–14.7) and a mean (±SD) intake of 12.8 (± 3.5) grams per 1,000 kcal. Both monounsaturated and polyunsaturated fat intakes were slightly lower in males compared to females. On average, male participants reported 0.5 grams per 1,000 kcal less monounsaturated fat intake than their female counterparts (14.9 ± 3.4 grams per 1,000 kcal). Additionally, female participants had a significantly higher fatty acid ratio, reflecting greater saturated fat intake relative to unsaturated fat intake compared to males, with a mean ratio of 2.0 (SD=0.6) in females versus 1.9 (± 0.5) in males. The median intake of dietary fibre was 11.8 grams per 1,000 kcal (IQR: 9.5–14.1) for males and 13.1 grams per 1,000 kcal (IQR: 10.7–15.8) for females. Total sugar intake (grams per 1,000 kcal) was higher in females compared to males; median 47.3 (IQR: 38.7-57.5) versus 44.1 (IQR: 35.7-54.3). 52 In terms of micronutrients, the usual daily intake of sodium did not differ between males and females, with a mean intake of 1,291 mg per 1,000 kcal for both males and females (SD=258.6). Female participants had higher calcium intake compared to males, with a median intake of 522.4 mg per 1,000 kcal (IQR: 438.0–626.6) versus 474 mg per 1,000 kcal (IQR: 400.1–569.3) in males. Vitamin D intake showed no significant differences between males and females, with a median of 2.4 mcg per 1,000 kcal (IQR: 1.8–3.4) and a mean of 2.8 ± 1.5 mcg per 1,000 kcal. On average, males consumed nearly twice as much alcohol as females (p < 0.001). The median alcohol intake was higher in males at 4.5 grams per 1,000 kcal (IQR: 1.0–10.9) compared to females, who had a median intake of 2.2 grams per 1,000 kcal (IQR: 0.5–7.6). Sugar-sweetened beverage (SSB) consumption was higher in male participants, with a median intake of 14.7 kcal per 1,000 kcal (IQR: 4.8–36.2), compared to 8.8 kcal per 1,000 kcal in female participants (IQR: 3.1–26.7). There was considerable variation, with a range of 0–409.4 kcal per 1,000 kcal in females and 0–392.3 kcal per 1,000 kcal in males (data not shown). Table 2. Usual daily dietary intake of BIRDEE participants (N = 2,586) based on CDHQ III data using the nutrient density method a Variables b Males Females Total N = 787 (30.4%) N = 1,799 (69.6%) N = 2,586 (100%) Energy Total daily calories (kcal) * Median (1st, 3rd) 1852.7 (1395.6, 2321.7) 1457.3 (1124.1, 1847.3) 1561.8 (1185.9, 2003.3) Mean (+-SD) 1928.2 (751.9) 1544.4 (616.4) 1661.2 (683.7) Macronutrients 53 Carbohydrates (g/1,000 kcal) Median (1st, 3rd) 115.3 (104.2, 127.2) 115.6 (103.3, 127.5) 115.5 (103.5, 127.4) Mean (+-SD) 115.4 (19.4) 115.0 (19.4) 115.2 (19.4) Protein (g/1,000 kcal) * Median (1st, 3rd) 39.8 (35.4, 44.4) 40.3 (35.9, 45.1) 40.1 (35.7, 44.8) Mean (+-SD) 40.2 (7.4) 40.8 (7.4) 40.7 (7.4) Total fat (g/1,000 kcal) * Median (1st, 3rd) 38.7 (34.3, 43.1) 40.1 (35.5, 44.9) 39.7 (35.2, 44.5) Mean (+-SD) 38.7 (7.0) 40.5 (7.5) 40.0 (7.4) Saturated fat (g/1,000 kcal) Median (1st, 3rd) 12.3 (10.6, 14.5) 12.4 (10.5, 14.8) 12.4 (10.5, 14.7) Mean (+-SD) 12.7 (3.3) 12.9 (3.6) 12.8 (3.5) Monounsaturated fat (g/1,000 kcal) * Median (1st, 3rd) 14.3 (12.3, 16.1) 14.6 (12.7, 16.6) 14.5 (12.6, 16.4) Mean (+-SD) 14.4 (3.2) 14.9 (3.4) 14.8 (3.3) Polyunsaturated fat (g/1,000 kcal) * Median (1st, 3rd) 8.2 (6.9, 9.5) 8.8 (7.5, 10.6) 8.6 (7.3, 10.3) Mean (+-SD) 8.3 (2.2) 9.1 (2.6) 8.9 (2.5) Fatty acid ratios (%) * Median (1st, 3rd) 1.8 (1.5, 2.2) 1.9 (1.6, 2.3) 1.9 (1.5, 2.2) Mean (+-SD) 1.9 (0.5) 2.0 (0.6) 1.9 (0.6) Micronutrients Sodium (mg/1,000 kcal) Median (1st, 3rd) 1269.2 (1111.6, 1437.5) 1281.2 (1132.2, 1447.1) 1277.0 (1125.8, 1445.6) Mean (+-SD) 1291.3 (274.3) 1290.4 (251.4) 1290.7 (258.6) Calcium (mg/1,000 kcal) * Median (1st, 3rd) 474.4 (400.1, 569.3) 522.4 (438.0, 626.6) 505.8 (424.9, 610.5) Mean (+-SD) 501.0 (160.1) 548.8 (164.0) 534.2 (164.2) Vitamin D (mcg/1,000 kcal) Median (1st, 3rd) 2.4 (1.8, 3.2) 2.4 (1.8, 3.5) 2.4 (1.8, 3.4) Mean (+-SD) 2.7 (1.5) 2.8 (1.5) 2.8 (1.5) 54 Other nutrients Dietary fibre (g/1,000 kcal) * Median (1st, 3rd) 11.8 (9.5, 14.1) 13.1 (10.7, 15.8) 12.6 (10.2, 15.3) Mean (+-SD) 12.1 (3.8) 13.4 (4.0) 13.0 (4.0) Total Sugar (g/1,000 kcal) * Median (1st, 3rd) 44.1 (35.7, 54.3) 47.3 (38.7, 57.5) 46.6 (37.7, 56.6) Mean (+-SD) 46.0 (15.4) 48.8 (14.6) 47.9 (14.9) Beverages Alcohol (g/1,000 kcal) * Median (1st, 3rd) 4.5 (1.0, 10.9) 2.2 (0.5, 7.6) 2.9 (0.6, 8.9) Mean (+-SD) 7.9 (9.7) 5.7 (8.4) 6.4 (8.9) Sugar-Sweetened Beverages (kcal/1,000 kcal) * Median (1st, 3rd) 14.7 (4.8, 36.2) 8.8 (3.1,26.7) 10.2 (3.5, 29.9) Mean (+-SD) 28.5 (40.7) 22.9 (36.8) 24.6 (38.1) a The nutrient density method (190) was used to standardize intake to nutrient units per 1,000 kcal; Statistical comparisons were done using Wilcoxon rank-sum test to account for potential deviations from normality. b Significance levels: * p<0.05. Figure 4.1. The mean distribution of carbohydrates, protein, and fat as a percentage of total daily energy intake, stratified by sex: (a) Males; (b) Females. 55 Table 3 presents a descriptive summary of the usual daily UPF intake. The nutrient density-adjusted UPF intake expressed as a percentage of total daily energy intake, reported a median of 39.1% (IQR: 31.6–46.7) and a mean of 39.4 ± 11.4%. UPF consumption was significantly higher in males than in females, with a median intake of 41.0% (IQR: 33.7–48.4) among males compared to 37.8% (IQR: 31.0–46.0) among females. A summary of UPF intake was calculated using the residual method and provided in Appendix Table A3 (190). Table 3. Usual daily UPF intake in BIRDEE participants (N = 2,586) using the nutrient density method a Variables b Male Female Total N = 787 (30.4%) N = 1,799 (69.6%) N = 2,586 (100%) c Nutrient density-adjusted UPF (kcal/1,000 kcal) * Median (1st, 3rd) 409.5 (337.0, 483.8) 378.2 (310.3, 459.9) 390.9 (316.4, 467.3) Mean (+-SD) 411.1 (113.2) 378.2 (113.0) 394.3 (113.6) UPF (% of total daily energy) * Median (1st, 3rd) 41.0 (33.7, 48.4) 37.8 (31.0, 46.0) 39.1 (31.6, 46.7) Mean (+-SD) 41.1 (11.3) 37.8 (11.3) 39.4 (11.4) Nutrient density-adjusted UPF tertiles * Low 205 (26.0) 657 (36.5) 862 (33.3) Medium 272 (34.6) 590 (32.8) 862 (33.3) High 310 (39.4) 552 (30.7) 862 (33.3) a The nutrient density method (190) was used to standardize intake to nutrient units per 1,000 kcal; b Significance levels: * p<0.05. c Statistical comparisons of categorical variables were conducted using Chi-square tests or Fisher’s exact tests, as appropriate. For continuous variables, the Wilcoxon rank-sum test was applied to account for potential deviations from normality. The percentage might not add up to 100% due to rounding. 56 4.1.2 Bivariate analysis of UPF and food environment Table 4 presents the results of the bivariate analysis examining the relationship between UPF intake (both in kcal/1,000 kcal and categorized as low, medium, or high intake) and food environment characteristics. Across categories of food environment measures, mean UPF intake ranged from 387.3 to 401.5 kcal per 1,000 kcal per day. The prevalence of low, medium and high UPF intake was relatively consistent across food environment characteristics. Table 4. Bivariate analysis of RFE by UPF intake (N = 2,586) Food Environment Variables UPF intake (kcal/1,000 kcal) UPF intake (Tertiles) Total Mean (SD) Low (N = 862) Medium (N = 862) High (N = 862) N = 2,586 (100%) d Modified Retail Food Environment Index (mRFEI) Zero a 398.0 (113.0) 312 (36.2) 327 (37.9) 340 (39.4) 979 (37.9) Low b 395.4 (113.8) 240 (27.8) 253 (29.4) 244 (28.3) 737 (28.5) High c 389.0 (113.9) 310 (36.0) 282 (32.7) 278 (32.3) 870 (33.6) Fast food restaurant mix (Rmix) Zero 395.2 (115.5) 398 (46.2) 393 (45.6) 408 (47.3) 1199 (46.4) Low 387.8 (112.5) 260 (30.2) 253 (29.4) 233 (27.0) 746 (28.8) High 399.9 (111.0) 204 (23.7) 216 (25.1) 221 (25.6) 786 (24.8) Fast food density Zero 395.2 (115.5) 398 (46.2) 393 (45.6) 408 (47.3) 1199 (46.4) Low 390.8 (111.6) 360 (41.8) 360 (41.8) 334 (38.7) 1054 (40.8) High 401.5 (112.7) 104 (12.1) 109 (12.6) 120 (13.9) 333 (12.9) Convenience store density Zero 397.2 (112.5) 347 (40.3) 373 (43.3) 366 (42.5) 1086 (42.0) Low 393.3 (115.1) 404 (46.9) 392 (45.5) 398 (46.2) 1194 (46.2) High 387.3 (111.4) 111 (12.9) 97 (11.3) 98 (11.4) 306 (11.8) a "Zero" refers to no access to the given retail food environment feature. b "Low" represents class 1–2, c "High" represents class 3-4. d The proportion might exceed 100% due to rounding. 57 4.1.3 Linear regression analysis for UPF and food environment Table 5 shows the results from the multiple linear regression analysis of mean differences in UPF intake by food environment characteristics, adjusted for individual- and neighborhood-level factors. In model 1, participants living in neighborhoods with some access to food outlets that included chain supermarkets, grocery stores, and fruit and vegetable markets (i.e., those with low or high levels of mRFEI), tended to have lower UPF intake compared to participants living in neighborhoods with no access (i.e., mRFEI of zero). Specifically, relative to participants living in neighborhoods with an mRFEI of zero, mean UPF intake was lower in participants living in low-mRFEI neighborhoods (-3.7 kcal per 1,000 kcal; 95% CI: -14.4, 7.1) and in high-mRFEI neighborhoods (-7.2 kcal per 1,000 kcal; 95% CI: -17.5, 3.0). However, these differences were not statistically significant. When further adjusting for neighborhood-level factors in model 2, the magnitude of associations was attenuated. A statistically significant trend was observed across increasing levels of mRFEI (p-trend = 0.033) in model 2, indicating an overall decrease in UPF intake with increasing mRFEI. Analysis of Rmix showed no statistically significant differences in UPF intake among participants living in neighborhoods with low or high Rmix values compared to those living in neighborhoods with no access to any fast food outlets after adjusting for individual factors alone and adjusting for both individual- and neighborhood-level factors. For example, in model 2, which accounted for both individual- and neighborhood-level factors, participants living in low-Rmix neighborhoods had a 3.8 kcal per 1,000 kcal higher UPF intake (95% CI: -9.0, 16.7) compared to those in areas with no fast-food outlets. Similarly, participants in high-Rmix neighborhoods had a 5.4 kcal per 1,000 kcal higher UPF intake (95% CI: -5.6, 16.5) compared to the reference group. However, these differences were not statistically significant. p-trend 58 analysis (p = 0.059) suggests a borderline positive trend in UPF intake across increasing levels of Rmix. When comparing UPF intake across different levels of fast-food outlet density, similar results were observed in model 1. In model 2, which adjusted for both individual- and neighborhood-level factors, a significant association was observed with higher UPF intake among participants living in neighborhoods with high fast-food outlet density, with a mean increase of 16.0 kcal per 1,000 kcal (95% CI: 0.5, 31.4), compared to participants in neighborhoods with no fast-food outlets (p < 0.0001). However, no significant association was found for participants in low-density fast-food neighborhoods compared to those who lived in neighbourhoods that had no fast-food outlets. P-trend analysis for the density fast-food outlets indicated a statistically significant positive trend in UPF intake across increasing levels of fast-food outlet density (p = 0.011). When considering the density of convenience stores, model 1, which adjusted for individual-level factors, showed that participants living in neighborhoods with high convenience store density had significantly lower UPF intake compared to those in neighborhoods with no convenience stores (-14.7 kcal per 1,000 kcal; 95% CI: -29.1, -0.4; p < 0.05). However, after further adjusting for neighborhood-level factors in model 2, this association was no longer statistically significant. No significant trend was observed for convenience store density (p = 0.081). 59 Table 5. Mean differences (95% CI) in UPF intake by food environment characteristics (N = 2,586) Variable Category Mean Difference (kcal/1,000 kcal) d Model 1 e Model 2 f Modified retail food environment index (mRFEI) Zero a Ref Ref Low b -3.7 (-14.4, 7.1) 5.5 (-6.8, 17.8) High c -7.2 (-17.5, 3.0) 1.0 (-10.3, 12.3) Fast food restaurant mix (Rmix) Zero Ref Ref Low -8.5 (-18.8, 1.9) 3.8 (-9.0, 16.7) High 1.9 (-8.8, 12.6) 5.4 (-5.6, 16.5) Density of fast-food outlets Zero Ref Ref Low -5.3 (-14.6, 3.9) 2.6 (-7.5, 12.7) High 2.6 (-11.2, 16.4) 16.0 (0.5, 31.4) * Density of convenience stores Zero Ref Ref Low -6.1 (-15.3, 3.1) -2.6 (-12.5, 7.4) High -14.7 (-29.1, -0.4) * -6.1 (-22.1, 9.9) a "Zero" refers to no access to the given retail food environment feature. b "Low" represents class 1–2, c "High" represents class 3-4. d Significance levels: * p<0.05. e Model 1 was adjusted for individual-level factors, including age, sex, education attainment, annual household income, marital status, and BMI. f Model 2 additionally adjusted for neighbourhood-level factors: active living environment index, urban sprawl score, and 4 domains of Can-Marg: 1) Housing and dwellings; 2) Material deprivation; 3) Age and labor force; and 4) Immigration and visible minority. 4.2 Aim 2: summary of retail food environment characteristics in BC Figure 4.2 shows the geographical relations between the 26 census subdivisions included in this study. Table 7 provides a detailed overview of the number and percentage of dissemination areas (DAs) across different categories of food environment characteristics, illustrating the distribution of food outlets across BC. 60 Among the 7,532 DAs in BC that were included in CanFED, 53.4% were classified as class zero for mRFEI, indicating no access to supermarkets, grocery stores, or fresh food markets. Over 60% of DAs had no grocery stores, and nearly 80% had no supermarkets. In comparison, 37.8% of DAs had no restaurants, while 57.0% had no fast-food outlets. 11.7% of DAs were classified in the highest density category (class 4) for Rmix, whereas only 6.7% of DAs were in the highest category for mRFEI. Table 6. RFE characteristics among 7,532 DAs in BC included in Can-FED Variable Classes a N (%); Total N = 7,532 Class 0 (zero) Class 1 Class 2 Class 3 Class 4 mRFEI b 4022 (53.4%) 733 (9.7%) 1020 (13.5%) 1250 (16.6%) 507 (6.7%) Rmix c 4294 (57.0%) 825 (11.0%) 715 (9.5%) 816 (10.8%) 882 (11.7%) Fast food outlets 4294 (57.0%) 1551 (20.6%) 978 (13.0%) 556 (7.4%) 153 (2.0%) Convenience stores 3774 (50.1%) 1653 (22.0%) 1264 (16.8%) 716 (9.5%) 125 (1.7%) Chain supermarkets d 5933 (78.8%) 797 (10.6%) 410 (5.4%) 242 (3.2%) 150 (2.0%) Grocery stores d 4539 (60.3%) 1248 (16.6%) 876 (11.6%) 623 (8.3%) 246 (3.3%) Fruit and vegetables markets 6407 (85.1%) 534 (7.1%) 345 (4.6%) 153 (2.0%) 93 (1.2%) Restaurants 2843 (37.8%) 2304 (30.6%) 1473 (19.6%) 769 (10.2%) 143 (1.9%) a Five classes were created for each measurement: Class 0 (zero) and Classes 1 to 4. Class 0 represents no access to a given outlet type in an area; 1 represents the lowest densities of an outlet type that is not zero, and 4 represents the highest densities of an outlet type. More detailed descriptions for each variable can be found here (199); 61 b mRFEI: Modified Retail Food Environment Index, a relative density measurement that indicates the proportion of food outlets selling fruits and vegetables relative to all food outlets and restaurants per square kilometer; c Rmix: Fast Food Restaurants Mix, a relative density measurement that indicates the proportion of fast-food and full-service restaurants that are fast-food; d Chain supermarkets are identified from NAICS code 445110 using a predefined list of chain names, while (non-chain) grocery stores include all remaining stores under the same NAICS code. 62 Figure 4.2 A map of census subdivisions in BC. (Boundaries and correspondence with Census Geograph, (82-402-X). Originally produced by the Statistical Registers and Geography Division for the Health Statistic Division, 2015 and has been revised for this thesis.) 63 4.2.1 Modified retail food environment (mRFEI) Figure 4.3 illustrates the proportional distribution of mRFEI classes (Class 0 to Class 4) and reflecting distinct retail food environment across CSDs in British Columbia. Class 0 (red) was the most prevalent of all CSDs except for Vancouver, Prince Rupert, and Victoria. Across all CSDs, Dawson Creek had the highest proportion of Class 1 mRFEI (orange, 31.8%), while Prince Rupert had the largest share of Class 2 (yellow, 41.4%). Vancouver had the highest proportion of Class 3 mRFEI (green, 28.1%), whereas Parksville had the largest share of Class 4 (blue, 19.6%). Both Vancouver and Victoria exhibited notable diversity in mRFEI classes, which was expected given their population and geographical size and urban infrastructure. Interestingly, Prince Rupert, a smaller CSD with a population of approximately 12,000 (187), also showed diversity in mRFEI classes, though it lacked representation in the highest density category. Figure 4.3. Proportional distribution of modified RFEI levels across CSDs 64 4.2.2 Fast-food restaurant mix (Rmix) Vancouver, Fort St. John, and Dawson Creek each had over 50% of DAs classified as Class 1 or higher. In Vancouver, the distribution of Rmix classes was relatively equal and followed a descending pattern, with Class 0 being the most common, followed by Class 1, 2 & 3, and the least representation in Class 4 (Figure 4.4). Prince George, Terrace, and Dawson Creek had the highest proportions of DAs in Class 4 (all over 27%), indicating greater fast-food access in these areas. In contrast, Parksville and Nelson had no DAs classified as Class 4, reflecting lower fast-food access. Although Quesnel also had limited fast-food outlets (4 in 55 DAs), but among the four DAs that contained fast-food outlets, three (75%) fell into the highest density category (Class 4, red), indicating a concentrated distribution of fast-food access where present. Figure 4.4. Proportional distribution of Rmix levels Across CSDs in BC 4.2.3 Absolute density distribution for different food outlets across CSDs Examining the absolute density of various food outlet types reveals substantial heterogeneity in their distribution across CSDs, indicating variations in food environment characteristics across BC (Figure 4.5). 65 Absolute density of fast-food outlets. Similar to the distribution of Rmix, over half of the DAs in Vancouver, Fort St. John, and Dawson Creek had fast-food outlets presented. The highest proportions of Class 4 (red), indicating the highest fast-food outlet density, were observed in Vancouver (4.1%) and Kelowna (2.8%). While Dawson Creek and Prince Rupert had relatively high proportions of DAs with fast-food outlets, neither of them had Class 4 DAs. In contrast, only 4 out of 55 DAs in Quesnel and 5 out of 46 DAs in Parksville had fast-food outlets. Among them, 3 out of 4 of the DAs with fast-food outlets in Quesnel were classified as Class 3 (yellow), whereas all the DAs with fast-food outlets in Parksville fell under Class 1 (blue) and Class 2 (green). Absolute density of convenience stores. Dawson Creek, Vancouver, and Prince Rupert had the highest proportions of DAs (~75%) with some convenience stores Class 1 and higher. However, the distribution of convenience store density varied across these areas. In Dawson Creek, most DAs with convenience stores were classified as Class 1 (14.5%, blue), whereas in Prince Rupert, Class 2 (37.9%, green) was the most prevalent. Vancouver, Victoria, and Abbotsford-Mission were the only CSDs in British Columbia with DAs classified as Class 4 (red), though this category accounted for no more than 5% of all DAs. In contrast, Quesnel and Parksville had the lowest proportions of DAs with convenience stores, at 15.3% and 21.6%, respectively. Absolute density of chain supermarkets. Over 33% of DAs in Vancouver had access to chain supermarkets, followed by Dawson Creek (27.3%) and Port Alberni (22.6%). However, Dawson Creek had low density of chain supermarkets (only Class 1 (blue)), while 11 out of 12 DAs in Port Alberni were class 1 as well. Notably, no DA in Salmon Arm had a chain supermarket. 66 Absolute density of (non-chain) grocery stores. Vancouver and Prince Rupert had over 50% of their DAs with grocery stores, while Victoria approached 50%. However, only Vancouver (6.8%) and Victoria (1.7%) had DAs in the highest density category for grocery stores (Class 4, red). Port Alberni followed, with 34% of its DAs having grocery stores, but all of these were in the lowest density category (Class 1). Powell River had had the most DAs with Class 0, with only 2 out of 34 DAs having grocery stores. Absolute density of fruit and vegetable markets. Among the 26 CSDs, 12 (46.2%) had no fruit and vegetable markets. Vancouver had the highest proportion of DAs with fruit and vegetable markets (27.1%), nearly twice that of Penticton (15.7%), which had the second-highest proportion, followed closely by Abbotsford-Mission (14.0%). In contrast, Duncan and Courtenay each had only one DA with fruit and vegetable markets, despite having approximately 90 DAs in their respective CSDs. Absolute density of restaurants. Vancouver had the highest density of restaurants, with 84.7% of DAs having at least one restaurant. Only Vancouver and Victoria had DAs classified in the highest density category (Class 4, red) for restaurants. Prince Rupert and Dawson Creek also had high proportions of DAs with restaurants (approximately 75%), though most were classified as low density (Class 1), particularly in Dawson Creek (68.2%). In contrast, Terrace (25.0%) and Quesnel (18.6%) had the lowest proportions of DAs with restaurants. 67 68 Figure 4.5. Proportional distribution of food outlet density across CSD by type and classification 4.2.3.1 CSDs with greater availability of food outlets selling UPF Fourteen CSDs, including Abbotsford-Mission, Kelowna, Chilliwack, Kamloops, Prince George, Courtenay, Penticton, Campbell River, Quesnel, Williams Lake, Fort St. John, Terrace, and Dawson Creek (ranked by the number of DAs; see Table 8 to 21), had a significantly higher presence of fast-food outlets and convenience stores compared to chain supermarkets, grocery stores, and fruit and vegetable markets. In Campbell River, Williams Lake, and Terrace, fewer than 10% of DAs had any chain supermarkets or grocery stores, compared to more DAs having fast-food outlets and convenience stores in each CSD. In addition, Kamloops had a higher proportion of DAs with fast-food outlets (36.8%) than convenience stores (32.5%), a unique characteristic among this group of CSDs. In comparison, Quesnel, Parksville, and Dawson Creek had nearly twice as many DAs with convenience stores as those with fast-food outlets. Distribution within a food outlet type also differed. Kamloops, Quesnel, Parksville, Terrace, and Dawson Creek had more DAs with Class 3 density for fast-food outlets than Class 2, indicating that when fast-food outlets were present in these CSDs, they tended to be located in 69 close proximity to one another. For example, in Kamloops, 24 DAs were classified as Class 3 (9.8%), while only 10 DAs fell into Class 2 (6.1%). Even in CSDs with a high presence of chain supermarkets and grocery stores, such as Kelowna, Chilliwack, Courtenay, and Penticton, there were in every class where food outlets are present (Classes 1–4), fast-food outlets and convenience stores consistently outnumber chain supermarkets, grocery stores, and fruit and vegetable markets. For example, in Kelowna, fast-food outlets (9.8%) and convenience stores (15.0%) were prevalent in more DAs than chain supermarkets (7.5%), grocery stores (3.9%), and fruit and vegetable markets (4.7%). Table 7. RFE characteristics by classes of absolute density in Abbotsford-Mission (N = 265) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 170 (64.2%) 40 (15.1%) 26 (9.8%) 24 (9.1%) 5 (1.9%) Convenience stores 131 (49.4%) 42 (15.8%) 49 (18.5%) 35 (13.2%) 8 (3.0%) Chain supermarkets 244 (92.1%) 4 (1.5%) 12 (4.5%) 3 (1.1%) 2 (0.8%) Grocery stores 213 (80.4%) 22 (8.3%) 15 (5.7%) 15 (5.7%) 0 (0.0%) Fruit & Vegetable markets 228 (86.0%) 13 (4.9%) 16 (6.0%) 7 (2.6%) 1 (0.4%) Restaurants 127 (47.9%) 97 (36.6%) 33 (12.5%) 8 (3.0%) 0 (0.0%) a N represents the total number of DAs in Abbotsford-Mission, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. Table 8. RFE characteristics by classes of absolute density in Kelowna (N = 254) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 160 (63.0%) 39 (15.4%) 25 (9.8%) 18 (7.1%) 12 (4.7%) Convenience stores 148 (58.3%) 58 (22.8%) 38 (15.0%) 10 (3.9%) 0 (0.0%) Chain supermarkets 211 (83.1%) 13 (5.1%) 19 (7.5%) 10 (3.9%) 1 (0.4%) Grocery stores 205 (80.7%) 34 (13.4%) 10 (3.9%) 5 (2.0%) 0 (0.0%) Fruit & Vegetable markets 222 (87.4%) 15 (5.9%) 12 (4.7%) 3 (1.2%) 2 (0.8%) Restaurants 119 (46.9%) 84 (33.1%) 41 (16.1%) 10 (3.9%) 0 (0.0%) a N represents the total number of DAs in Kelowna, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. 70 Table 9. RFE characteristics by classes of absolute density in Chilliwack (N = 175) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 115 (65.7%) 36 (20.6%) 13 (7.4%) 5 (2.9%) 6 (3.4%) Convenience stores 112 (64.0%) 27 (15.4%) 31 (17.7%) 5 (2.9%) 0 (0.0%) Chain supermarkets 145 (82.9%) 13 (7.4%) 13 (7.4%) 4 (2.3%) 0 (0.0%) Grocery stores 141 (80.6%) 23 (13.1%) 9 (5.1%) 2 (1.1%) 0 (0.0%) Fruit & Vegetable markets 160 (91.4%) 10 (5.7%) 3 (1.7%) 1 (0.6%) 1 (0.6%) Restaurants 82 (46.9%) 57 (32.6%) 33 (18.9%) 3 (1.7%) 0 (0.0%) a N represents the total number of DAs in Chilliwack, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. Table 10. RFE characteristics by classes of absolute density in Kamloops (N = 163) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 103 (63.2%) 33 (20.2%) 10 (6.1%) 16 (9.8%) 1 (0.6%) Convenience stores 110 (67.5%) 35 (21.5%) 9 (5.5%) 9 (5.5%) 0 (0.0%) Chain supermarkets 139 (85.3%) 14 (8.6%) 7 (4.3%) 1 (0.6%) 2 (1.2%) Grocery stores 128 (78.5%) 16 (9.8%) 15 (9.2%) 4 (2.5%) 0 (0.0%) Fruit & Vegetable markets 157 (96.3%) 5 (3.1%) 1 (0.6%) 0 (0.0%) 0 (0.0%) Restaurants 81 (49.7%) 50 (30.7%) 25 (15.3%) 7 (4.3%) 0 (0.0%) a N represents the total number of DAs in Kamloops, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. Table 11. RFE characteristics by classes of absolute density in Prince George (N = 155) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 103 (66.5%) 18 (11.6%) 25 (16.1%) 8 (5.2%) 1 (0.6%) Convenience stores 84 (54.2%) 40 (25.8%) 28 (18.1%) 3 (1.9%) 0 (0.0%) Chain supermarkets 135 (87.1%) 14 (9.0%) 5 (3.2%) 1 (0.6%) 0 (0.0%) Grocery stores 128 (82.6%) 20 (12.9%) 7 (4.5%) 0 (0.0%) 0 (0.0%) Fruit & Vegetable markets 150 (96.8%) 4 (2.6%) 1 (0.6%) 0 (0.0%) 0 (0.0%) Restaurants 86 (55.5%) 61 (39.4%) 6 (3.9%) 2 (1.3%) 0 (0.0%) a N represents the total number of DAs in Prince George, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. 71 Table 12. RFE characteristics by classes of absolute density in Courtenay (N = 97) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 76 (78.4%) 8 (8.2%) 11 (11.3%) 2 (2.1%) 0 (0.0%) Convenience stores 57 (58.8%) 18 (18.6%) 15 (15.5%) 7 (7.2%) 0 (0.0%) Chain supermarkets 91 (93.8%) 1 (1.0%) 2 (2.1%) 3 (3.1%) 0 (0.0%) Grocery stores 73 (75.3%) 19 (19.6%) 5 (5.2%) 0 (0.0%) 0 (0.0%) Fruit & Vegetable markets 96 (99.0%) 0 (0.0%) 1 (1.0%) 0 (0.0%) 0 (0.0%) Restaurants 57 (58.8%) 30 (30.9%) 7 (7.2%) 3 (3.1%) 0 (0.0%) a N represents the total number of DAs in Courtenay, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. Table 13. RFE characteristics by classes of absolute density in Penticton (N = 83) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 54 (65.1%) 19 (22.9%) 5 (6.0%) 4 (4.8%) 1 (1.2%) Convenience stores 36 (43.4%) 27 (32.5%) 15 (18.1%) 5 (6.0%) 0 (0.0%) Chain supermarkets 70 (84.3%) 3 (3.6%) 4 (4.8%) 5 (6.0%) 1 (1.2%) Grocery stores 61 (73.5%) 21 (25.3%) 1 (1.2%) 0 (0.0%) 0 (0.0%) Fruit & Vegetable markets 70 (84.3%) 10 (12.0%) 1 (1.2%) 2 (2.4%) 0 (0.0%) Restaurants 35 (42.2%) 21 (25.3%) 20 (24.1%) 7 (8.4%) 0 (0.0%) a N represents the total number of DAs in Penticton, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. Table 14. RFE characteristics by classes of absolute density in Campbell River (N = 74) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 55 (74.3%) 16 (21.6%) 2 (2.7%) 1 (1.4%) 0 (0.0%) Convenience stores 47 (63.5%) 17 (23.0%) 8 (10.8%) 2 (2.7%) 0 (0.0%) Chain supermarkets 70 (94.6%) 0 (0.0%) 2 (2.7%) 1 (1.4%) 1 (1.4%) Grocery stores 66 (89.2%) 7 (9.5%) 1 (1.4%) 0 (0.0%) 0 (0.0%) Fruit & Vegetable markets 74 (100.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Restaurants 31 (41.9%) 34 (45.9%) 9 (12.2%) 0 (0.0%) 0 (0.0%) a N represents the total number of DAs in Campbell River, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. 72 Table 15. RFE by classes of absolute density in Quesnel (N = 59) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 47 (79.7%) 4 (6.8%) 0 (0.0%) 8 (13.6%) 0 (0.0%) Convenience stores 49 (83.1%) 3 (5.1%) 1 (1.7%) 6 (10.2%) 0 (0.0%) Chain supermarkets 53 (93.2%) 1 (1.7%) 3 (5.1%) 0 (0.0%) 0 (0.0%) Grocery stores 53 (93.2%) 3 (5.1%) 1 (1.7%) 0 (0.0%) 0 (0.0%) Fruit & Vegetable markets 59 (100.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Restaurants 48 (81.4%) 7 (11.7%) 4 (6.8%) 0 (0.0%) 0 (0.0%) a N represents the total number of DAs in Quesnel, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. Table 16. RFE characteristics by classes of absolute density in Williams Lake (N = 48) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 36 (75.0%) 7 (14.6%) 4 (8.3%) 1 (2.1%) 0 (0.0%) Convenience stores 33 (68.8%) 9 (18.8%) 4 (8.3%) 2 (4.2%) 0 (0.0%) Chain supermarkets 46 (95.8%) 1 (2.1%) 0 (0.0%) 1 (2.1%) 0 (0.0%) Grocery stores 44 (91.7%) 3 (6.2%) 1 (2.1%) 0 (0.0%) 0 (0.0%) Fruit & Vegetable markets 48 (100.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Restaurants 33 (68.8%) 13 (27.1%) 2 (4.2%) 0 (0.0%) 0 (0.0%) a N represents the total number of DAs in Williams Lake, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. Table 17. RFE characteristics by classes of absolute density in Fort St. John (N = 47) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 19 (40.4%) 14 (29.8%) 11 (23.4%) 2 (4.3%) 1 (2.1%) Convenience stores 18 (38.3%) 13 (27.7%) 13 (27.7%) 3 (6.4%) 0 (0.0%) Chain supermarkets 38 (80.9%) 5 (10.6%) 4 (8.5%) 0 (0.0%) 0 (0.0%) Grocery stores 39 (83.0%) 7 (14.9%) 1 (2.1%) 0 (0.0%) 0 (0.0%) Fruit & Vegetable markets 47 (100.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Restaurants 17 (36.2%) 16 (34.0%) 14 (29.8%) 0 (0.0%) 0 (0.0%) a N represents the total number of DAs in Fort St. John, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. 73 Table 18. RFE characteristics by classes of absolute density in Terrace (N = 36) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 25 (69.4%) 6 (16.7%) 2 (5.6%) 3 (8.3%) 0 (0.0%) Convenience stores 25 (69.4%) 5 (13.9%) 6 (16.7%) 0 (0.0%) 0 (0.0%) Chain supermarkets 33 (91.7%) 1 (2.8%) 1 (2.8%) 1 (2.8%) 0 (0.0%) Grocery stores 33 (91.7%) 3 (8.3%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Fruit & Vegetable markets 36 (100.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Restaurants 27 (75.0%) 5 (13.9%) 4 (11.1%) 0 (0.0%) 0 (0.0%) a N represents the total number of DAs in Terrace, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. Table 19. RFE characteristics by classes of absolute density in Dawson Creek (N = 22) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 11 (50.0%) 8 (36.4%) 1 (4.5%) 2 (9.1%) 0 (0.0%) Convenience stores 6 (27.3%) 10 (45.5%) 5 (22.7%) 1 (4.5%) 0 (0.0%) Chain supermarkets 16 (72.7%) 6 (27.3%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Grocery stores 18 (81.8%) 4 (18.2%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Fruit & Vegetable markets 22 (100.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Restaurants 6 (27.3%) 15 (68.2%) 1 (4.5%) 0 (0.0%) 0 (0.0%) a N represents the total number of DAs in Dawson Creek, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. 4.2.3.2 CSDs with a balanced mix of food outlets The rest of the CSDs in BC, including Vancouver, Victoria, Nanaimo, Vermon, Duncan, Port Alberni, Cranbrook, Parksville, Powell River, Prince Rupert, Nelson, Salmon Arm, and Squamish (ranked by the number of DAs; see Table 22-33), had a more balanced distribution of fast-food outlets and convenience stores—where UPFs are typically purchased—alongside chain supermarkets, grocery stores, and fruit and vegetable markets, which are more likely to sell 74 healthy food options. For example, in Vancouver, 6.8% of DAs were in Class 4 for grocery stores, which exceeded the number of DAs with class 4 of fast-food outlets (6.3%) and convenience stores (3.2%). The number of DAs in Class 3 for grocery stores also exceeded DAs in Class 3 for fast-food outlets (11.6%) and convenience stores (15.1%). Most of the listed CSDs had more grocery stores than chain supermarkets, except for Powell River and Prince Rupert, where chain supermarkets were more prevalent in DAs compared to grocery stores and fruit and vegetable markets. In Powell River, 5 out of 34 DAs contained chain supermarkets, with 3 classified as Class 3, indicating a greater concentration of them. Meanwhile, only 2 out of 34 DAs in Powell River had grocery stores. In Nelson, Salmon Arm, and Squamish, grocery stores were more prevalent than chain supermarkets, with Salmon Arm having no chain supermarkets at all. In Nelson, although only 4 DAs contained chain supermarkets. In Squamish, grocery stores were more common than fast-food outlets and convenience stores, with approximately 3 in every 5 DAs containing a grocery store. Table 20. RFE characteristics by classes of absolute density in Vancouver (N = 3,449) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 1267 (36.7%) 953 (27.6%) 613 (17.8%) 399 (11.6%) 217 (6.3%) Convenience stores 1059 (30.7%) 949 (27.5%) 810 (23.5%) 521 (15.1%) 110 (3.2%) Chain supermarkets 2306 (66.9%) 577 (16.7%) 266 (7.7%) 174 (5.0%) 126 (3.7%) Grocery stores 1360 (39.4%) 708 (20.5%) 619 (17.9%) 526 (15.3%) 236 (6.8%) Fruit & vegetable markets 2516 (72.9%) 422 (12.2%) 292 (8.5%) 133 (3.9%) 86 (2.5%) Restaurants 528 (15.3%) 1122 (32.5%) 988 (28.6%) 677 (19.6%) 134 (3.9%) a N represents the total number of DAs in Vancouver, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. 75 Table 21. RFE characteristics by classes of absolute density in Victoria (N = 574) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 311 (54.2%) 13(22.8%) 72 (12.5%) 56 0 (9.8%) 4 (0.7%) Convenience stores 278 (48.4%) 161 (28.0%) 87 (15.2%) 41 (7.1%) 7 (1.2%) Chain supermarkets 451 (78.6%) 75 (13.1%) 27 (4.7%) 16 (2.8%) 5 (0.9%) Grocery stores 293 (51.0%) 150 (26.1%) 81 (14.1%) 40 (7.0%) 10 (1.7%) Fruit & vegetable markets 524 (91.3%) 40 (7.0%) 8 (1.4%) 1 (0.2%) 1 (0.2%) Restaurants 153 (26.7%) 255 (44.4%) 133 (23.2%) 25 (4.4%) 8 (1.4%) a N represents the total number of DAs in Victoria, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. Table 22. RFE characteristics by classes of absolute density in Nanaimo (N = 181) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 120 (66.3%) 45 (24.9%) 13 (7.2%) 3 (1.7%) 0 (0.0%) Convenience stores 93 (51.4%) 48 (26.5%) 27 (14.9%) 13 (7.2%) 0 (0.0%) Chain supermarkets 164 (90.6%) 7 (3.9%) 8 (4.4%) 1 (0.6%) 1 (0.6%) Grocery stores 120 (66.3%) 41 (22.7%) 16 (8.8%) 4 (2.2%) 0 (0.0%) Fruit & vegetable markets 181 (100.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Restaurants 77 (42.5%) 80 (44.2%) 19 (10.5%) 5 (2.8%) 0 (0.0%) a N represents the total number of DAs in Nanaimo, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. Table 23. RFE characteristics by classes of absolute density in Vermon (N = 104) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 80 (76.9%) 12 (11.5%) 7 (6.7%) 5 (4.8%) 0 (0.0%) Convenience stores 78 (75.0%) 11 (10.6%) 11 (10.6%) 4 (3.8%) 0 (0.0%) Chain supermarkets 90 (86.5%) 9 (8.7%) 5 (4.8%) 0 (0.0%) 0 (0.0%) Grocery stores 84 (80.8%) 14 (13.5%) 6 (5.8%) 0 (0.0%) 0 (0.0%) Fruit & vegetable markets 98 (94.2%) 5 (4.8%) 1 (1.0%) 0 (0.0%) 0 (0.0%) Restaurants 60 (57.7%) 28 (26.9%) 11 (10.6%) 5 (4.8%) 0 (0.0%) a N represents the total number of DAs in Vermon, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. 76 Table 24. RFE characteristics by classes of absolute density in Duncan (N = 83) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 72 (86.7%) 3 (3.6%) 4 (4.8%) 4 (4.8%) 0 (0.0%) Convenience stores 57 (68.7%) 12 (14.5%) 5 (6.0%) 9 (10.8%) 0 (0.0%) Chain supermarkets 77 (92.8%) 2 (2.4%) 2 (2.4%) 2 (2.4%) 0 (0.0%) Grocery stores 59 (71.1%) 15 (18.1%) 7 (8.4%) 2 (2.4%) 0 (0.0%) Fruit & vegetable markets 82 (98.8%) 0 (0.0%) 1 (1.2%) 0 (0.0%) 0 (0.0%) Restaurants 51 (61.4%) 18 (21.7%) 11 (13.3%) 3 (3.6%) 0 (0.0%) a N represents the total number of DAs in Duncan, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. Table 25. RFE characteristics by classes of absolute density in Port Alberni (N = 53) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 41 (77.4%) 11 (20.8%) 1 (1.9%) 0 (0.0%) 0 (0.0%) Convenience stores 25 (47.2%) 19 (35.8%) 9 (17.0%) 0 (0.0%) 0 (0.0%) Chain supermarkets 41 (77.4%) 11 (20.8%) 1 (1.9%) 0 (0.0%) 0 (0.0%) Grocery stores 35 (66.0%) 18 (34.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Fruit & vegetable markets 53 (100.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Restaurants 26 (49.1%) 22 (41.5%) 5 (9.4%) 0 (0.0%) 0 (0.0%) a N represents the total number of DAs in Port Alberni, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. Table 26. RFE characteristics by classes of absolute density in Cranbrook (N = 51) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 38 (74.5%) 6 (11.8%) 5 (9.8%) 2 (3.9%) 0 (0.0%) Convenience stores 32 (62.7%) 10 (19.6%) 6 (11.8%) 3 (5.9%) 0 (0.0%) Chain supermarkets 40 (78.4%) 6 (11.8%) 3 (5.9%) 2 (3.9%) 0 (0.0%) Grocery stores 40 (78.4%) 10 (19.6%) 1 (2.0%) 0 (0.0%) 0 (0.0%) Fruit & vegetable markets 46 (90.2%) 4 (7.8%) 1 (2.0%) 0 (0.0%) 0 (0.0%) Restaurants 30 (58.8%) 15 (29.4%) 6 (11.8%) 0 (0.0%) 0 (0.0%) a N represents the total number of DAs in Cranbrook, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. 77 Table 27. RFE by classes of absolute density in Parksville (N = 51) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 41 (80.5%) 7 (13.7%) 3 (5.8%) 0 (0.0%) 0 (0.0%) Convenience stores 31 (60.7%) 14 (27.5%) 6 (11.8%) 0 (0.0%) 0 (0.0%) Chain supermarkets 48 (94.1%) 2 (3.9%) 0 (0.0%) 1 (2.0%) 0 (0.0%) Grocery stores 30 (58.8%) 11 (21.6%) 8 (15.9%) 2 (3.9%) 0 (0.0%) Fruit & Vegetable markets 49 (96.1%) 0 (0.0%) 2 (3.9%) 0 (0.0%) 0 (0.0%) Restaurants 25 (49.0%) 16 (31.4%) 10 (19.6%) 0 (0.0%) 0 (0.0%) a N represents the total number of DAs in Parksville, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. Table 28. RFE characteristics by classes of absolute density in Powell River (N = 34) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 29 (85.3%) 2 (5.9%) 3 (8.8%) 0 (0.0%) 0 (0.0%) Convenience stores 23 (67.6%) 5 (14.7%) 4 (11.8%) 2 (5.9%) 0 (0.0%) Chain supermarkets 29 (85.3%) 2 (5.9%) 0 (0.0%) 3 (8.8%) 0 (0.0%) Grocery stores 32 (94.1%) 1 (2.9%) 1 (2.9%) 0 (0.0%) 0 (0.0%) Fruit & vegetable markets 34 (100.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Restaurants 14 (41.2%) 17 (50.0%) 3 (8.8%) 0 (0.0%) 0 (0.0%) a N represents the total number of DAs in Powell River, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. Table 29. RFE characteristics by classes of absolute density in Prince Rupert (N = 29) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 17 (58.6%) 6 (20.7%) 3 (10.3%) 3 (10.3%) 0 (0.0%) Convenience stores 9 (31.0%) 4 (13.8%) 11 (37.9%) 5 (17.2%) 0 (0.0%) Chain supermarkets 23 (79.3%) 1 (3.4%) 2 (6.9%) 3 (10.3%) 0 (0.0%) Grocery stores 13 (44.8%) 9 (31.0%) 7 (24.1%) 0 (0.0%) 0 (0.0%) Fruit & vegetable markets 29 (100.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Restaurants 7 (24.1%) 15 (51.7%) 5 (17.2%) 2 (6.9%) 0 (0.0%) a N represents the total number of DAs in Prince Rupert, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. 78 Table 30. RFE characteristics by classes of absolute density in Nelson (N = 35) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 29 (82.9%) 3 (8.6%) 2 (5.7%) 1 (2.9%) 0 (0.0%) Convenience stores 23 (65.7%) 6 (17.1%) 5 (14.3%) 1 (2.9%) 0 (0.0%) Chain supermarkets 31 (88.6%) 1 (2.9%) 1 (2.9%) 1 (2.9%) 1 (2.9%) Grocery stores 23 (65.7%) 6 (17.1%) 4 (11.4%) 2 (5.7%) 0 (0.0%) Fruit & vegetable markets 32 (91.4%) 3 (8.6%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Restaurants 21 (60.0%) 7 (20.0%) 4 (11.4%) 3 (8.6%) 0 (0.0%) a N represents the total number of DAs in Nelson, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. Table 31. RFE characteristics by classes of absolute density in Salmon Arm (N = 33) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 25 (75.8%) 4 (12.1%) 3 (9.1%) 1 (3.0%) 0 (0.0%) Convenience stores 24 (72.7%) 4 (12.1%) 4 (12.1%) 1 (3.0%) 0 (0.0%) Chain supermarkets 33 (100.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Grocery stores 26 (78.8%) 4 (12.1%) 2 (6.1%) 1 (3.0%) 0 (0.0%) Fruit & vegetable markets 33 (100.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Restaurants 21 (63.6%) 6 (18.2%) 5 (15.2%) 1 (3.0%) 0 (0.0%) a N represents the total number of DAs in Salmon Arm, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. Table 32. RFE characteristics by classes of absolute density in Squamish (N = 31) a Absolute Density Class 0 (zero) Class 1 Class 2 Class 3 Class 4 Fast-food outlets 22 (71.0%) 2 (6.5%) 5 (16.1%) 2 (6.5%) 0 (0.0%) Convenience stores 23 (74.2%) 3 (9.7%) 3 (9.7%) 2 (6.5%) 0 (0.0%) Chain supermarkets 25 (80.6%) 1 (3.2%) 4 (12.9%) 0 (0.0%) 1 (3.2%) Grocery stores 19 (61.3%) 7 (22.6%) 2 (6.5%) 3 (9.7%) 0 (0.0%) Fruit & vegetable markets 31 (100.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) Restaurants 17 (54.8%) 8 (25.8%) 6 (19.4%) 0 (0.0%) 0 (0.0%) a N represents the total number of DAs in Squamish, and percentages indicate the proportion of DAs within each food outlet category at different density levels. RFE: retail food environment. 79 Chapter 5: Discussion This thesis examined the dietary intake as well as the association between UPF intake in relation to retail food environment characteristics in a cohort of males and females in BC, and further described the retail food environment in this region at the DA level. In general, the sample of BCGP-BIRDEE participants had an excessive intake of total fat, total sugar, and sodium, alongside an inadequate intake of dietary fiber, calcium and vitamin D. The patterns of intake are consistent with other cohorts of CanPath in Alberta and Quebec that used earlier versions of the C-DHQ (74,208). UPF consumption as a proportion of energy intake in BIRDEE was reported to be 41% of usual total energy intake, on average, which is lower than previously reported in the CCHS-Nutrition data (14,88). However, due to the limited food list in the CDHQ-III versus the diet recall method in CCHS-Nutrition, underestimation of UPF intake is likely, and thus differences in mean intake of UPF between studies should be interpreted with caution. Higher fast food outlet density was associated with greater UPF intake, but the effect size was modest. No other retail food environment features were associated with UPF intake in this population. In terms of the broader retail food environment, one-third of DAs across BC lacked any food outlets, suggesting that walking to purchase food may not be feasible in these areas, necessitating reliance on public transit or private vehicles. The characteristics of retail food environment varied significantly across CSDs, possibly reflecting differences in geographic location, level of urbanization, and economic activities. 80 5.1 Aim 1: dietary intake and its relation to retail food environments 5.1.1 Findings on dietary intake UPF intake. This study found that 41% of the usual daily energy intake came from UPF in males and they had a higher intake than females, which is consistent with previous research. A study of 5,872 adults aged 18 years and older from the 2019 International Food Policy Study, reported UPF intake contributed to 49.4% and 47.6% of total daily energy intake in males and females (108). Given that BCGP-BIRDEE participants are generally more educated and have higher household incomes — factors typically associated with healthier dietary choices — it is notable the mean UPF intake was lower. This aligns with findings from CARTaGENE, where similar levels of UPF intake were observed in relation to mental health outcomes (198). High UPF consumption has been consistently associated with poor diet quality, including low intake of essential nutrients such as fibre, vitamins, and minerals, as well as high intake of added sugars, sodium, and unhealthy fats (22,120,121). Previous studies have linked higher UPF consumption to lower adherence to dietary guidelines and increased risk of obesity, cardiometabolic diseases, and other adverse health outcomes (111,118,119). In this study, the high UPF intake observed suggests suboptimal diet quality, although that was not directly measured, as the components of the Canadian healthy food index have not yet been integrated into the version of the CDHQ that was used in BIRDEE at the time of the study. The overall dietary intake of BCGP-BIRDEE participants is discussed in relation to dietary requirements and population-level data on dietary intake in Canada. Note that while population-level dietary intake recommendations and dietary reference intakes are meant to serve as a general guide for adequacy for the majority of the population. Individual nutritional 81 requirements can vary significantly depending on their health, life stage and metabolic needs. For example, a young adult who engages in high-intensity athletic training may require significantly higher protein and energy intake (209) than what is recommended by standard dietary guidelines. Conversely, an older adult with a more sedentary lifestyle may have lower energy needs but higher requirements for specific nutrients such as calcium, vitamin D, and B12 due to age-related changes in absorption and metabolism (210). Population health studies such as BCGP rarely have direct measures of energy expenditure and metabolic requirements to understand deviations of individual nutrient requirements from general reference intakes. Hence, the findings in this proposal regarding the prevalence of nutrient shortfalls, should be interpreted in light of the limitations of the data. Future research should consider incorporating physical activity level into the existing models to differentiate nutritional needs by capturing the effect of energy expenditure in a population and potentially assessing the interaction with other factors between the association of retail food environment and UPF, which would lead to a more nuanced understanding for dietary intake on the population level. Direct comparisons with the CCHS-Nutrition survey data are limited due to differences in dietary assessment tools (24-hour recall vs FFQ) and distinct population demographics. The CCHS-Nutrition includes a larger age range starting from age 2 and up, while BCGP-BIRDEE cohort primarily consists of older adults and had purposive study inclusion criteria (e.g. no incident cancer). Findings from BCGP-BIRDEE are contextualized within previous studies from other CanPath regional cohorts—Alberta Tomorrow’s Project (ATP) and CARTaGENE (from Quebec)—both of which gathered dietary data from subsets of their participants using earlier versions of the CDHQ (CDHQ-I and CDHQ-II), which includes 151 food items (74,208). While BCGP-BIRDEE used the most updated version, CDHQ-III, which contains 303 food items. 82 However, participants in BCGP-BIRDEE differ from those in ATP and CARTaGENE, as BCGP-BIRDEE participants were, on average, had higher education attainment, reported higher annual household income levels compared to CARTaGENE, and a larger proportion of participants lived in urban areas, compared to ATP, where over half of ATP participants reported residing in rural areas, which may have differential effects on dietary intake (74,208). Macronutrients. Consistent with findings from CARTaGENE, a significant proportion of individuals in this study fell outside the acceptable macronutrient distribution range (AMDR) range for at least one macronutrient (74). Compared to participants in ATP and CARTaGENE, BCGP-BIRDEE participants tended to have higher total fat intake and lower carbohydrate intake, with most participants meeting the protein intake requirements (74,208). About 60% of participants in BCGP-BIRDEE had carbohydrate intake that met the daily recommended intake (DRI), compared to ~60 % in male participants and 67.5% in female participants in CARTaGENE (74). This suggests persistently low carbohydrate intake. This finding aligns with the continued popularity of low-carbohydrate diets in favor of higher protein and fat intake, for example the ketogenic diet is low in carbohydrates (typically below 20-50 grams per day), which induces ketosis, a metabolic state where the body burns fat for fuel instead of carbohydrates (211, 212). Previous studies suggest that lower carbohydrate intake may reflect a decrease in low-quality, refined carbohydrates (213). However, lower carbohydrate consumption was also associated with insufficient intake of nutrients fortified in grains, such as folic acid, iron, and B vitamins, as well as nutrients like dietary fiber (214). Nearly all participants had protein intake within the AMDR, consistent with CARTaGENE data (74). Females had slightly higher intake than males (~0.5–0.6 g/1,000 kcal) 83 for protein in BCGP-BIRDEE, however, the residual model showed no significant difference, indicating that this variation was largely driven by total energy intake (35). More than half of the participants in this study exceeded the upper limit of the AMDR for total fat (38.9 g per 1,000 kcal), which is consistent with findings from CARTaGENE and ATP (74,208). Saturated fat intake was within the adequate intake, which is similar to CARTaGNE and ATP. Females had higher unsaturated fat intake than males and a higher unsaturated-to-saturated fatty acid ratio, consistent with previous studies (25). This finding reflects differences in dietary intake that may be explained by greater health consciousness among females (215). Dietary fibre intake. Females in this study had higher total dietary fibre intake than males; however, fewer than half of the females and only one in four males met the recommended intake level based on the adequate intake of 14 grams per 1,000 kcal. This finding is aligned with the overall lower carbohydrate intake and consistent with results from the Health and Retirement Study – Health Care and Nutrition Survey, which examined a nationally representative sample of older Americans, as well as a study across six European populations (216). Data from ATP and CARTaGENE also showed similar findings (74,208). These results may be attributed to higher fruit and vegetable consumption among females, as both are generally good sources of fibre (215). Low fibre intake is a concern, as it increases the risk of constipation, digestive issues, and chronic diseases such as cardiovascular diseases, colorectal cancer, and type 2 diabetes (217,218). Given the significant fibre inadequacy among males, targeted interventions may be needed to promote greater consumption of high-fibre foods, such as whole grains, legumes, fruits, and vegetables (219). Micronutrients. Both sexes in this study had high sodium intake, that is common across the Canadian population (74,88,208). Nearly 7 out of 10 participants exceeded the upper limit for 84 sodium based on the Chronic Disease Risk Reduction (CDRR) recommendation (193). This finding is consistent with results from the CARTaGENE cohort (74). The high sodium levels observed in this study reinforced the identification of sodium as a key nutrient of public health concern, highlighting the critical need for sodium reduction strategies (89). Strategies, like those outlined in Health Canada's 2020 voluntary sodium reduction targets, emphasize collaboration with the food industry to reduce sodium levels in processed foods, although the voluntary nature of the strategy, has led to minimal impacts on sodium in the food supply (220). This study found that only half of female participants met the estimated average requirement (EAR) for calcium, while 4 in 10 males met the EAR. This difference may be attributed to the higher consumption of calcium-rich foods, such as leafy greens, which are more commonly consumed by females (221). Previous studies from both the U.S. and Canada have consistently reported inadequate calcium intake among a significant portion of the population (74, 88, 222). Notably, calcium deficiency in this current study was more pronounced compared to the CARTaGENE data, where 80% of females in their cohort met the recommended calcium intake (74). However, ATP participants appeared to have lower calcium intake compared to those in CARTaGENE, suggesting there might be potential provincial differences within Canada or differences based in rural/urban and age. Vitamin D intake was low in both sexes, underscoring persistent concerns about insufficient dietary sources of vitamin D among adults, particularly in older populations (74,88,208,224). In this study, 92% of participants had an inadequate intake, similar to the 94% reported in the 2015 CCHS-Nutrition data despite using different dietary assessment tools. Both ATP and CARTaGENE dietary data included dietary supplements in their diet assessments, yet their findings were similar to those observed in this study, indicating that even with supplementation, vitamin D intake continues to fall short of the 85 recommended levels. This highlights the urgent need for more effective strategies and targeted interventions to improve vitamin D intake. Total Sugar and SSB intake. In this study, female participants had higher total sugar intake compared to males after adjusting for energy, which aligns with findings from a 2017 European study indicating that sugar contributes more to energy intake in females than in males (226). Most participants, especially females, consumed nearly twice the recommended intake of sugar in this study, which raises significant public health concerns. Excessive sugar consumption is associated with an increased risk of obesity, type 2 diabetes, cardiovascular disease, and dental issues (79, 80). The higher SSB consumption reported by males in this study is consistent with a 2012 study, which found that males across various states in the U.S. consumed more SSBs than females (227). Studies on personal food choices suggest that younger individuals, those with a high school education or less, and those who perceive their diet as unhealthy are more likely to consume SSBs (228). Beyond SSBs, the prevalence of added sugars in processed and packaged foods remains a major concern. Sweet baked goods, such as cakes, pies, cookies, brownies, doughnuts, and pastries, continue to be significant contributors to excessive sugar intake (229). These findings emphasize the need for strategies to address sugar consumption. Alcohol consumption. In this study, males consumed approximately two standard drinks per week (14 g of alcohol per drink), while females consumed about one standard drink per week. Males reported nearly twice the alcohol intake of females, a pattern consistent with previous studies from Canada and other regions globally (74, 230). Notably, alcohol consumption in females has been increasing in recent years, raising concerns about its potential health impacts. Evidence suggests that females experience greater harm from lower levels of alcohol intake compared to males (231). For example, heavy drinking increases the risk of liver 86 disease more in females than in males (231, 232). Although per capita alcohol consumption in Canada is lower than in many other developed countries, a 2019 report found that 56% of Canadians engaged in binge drinking at least once in the past year, an increase from 44% in 2004 (233, 234). Alcohol use is also linked to various chronic diseases, including liver disease, heart disease, and certain cancers, with alcohol consumption specifically associated with an increased risk of breast cancer in females (235). Given these concerns, there is a pressing need to strengthen female-centered alcohol policies and prevention efforts to mitigate alcohol-related health risks and address this growing public health issue. 5.1.2 The relationship between retail food environment and UPF intake In this study, UPF consumption remained relatively consistent across different retail food environment categories. This may, in part, reflect limited variation in UPF intake, which could make it difficult to detect statistically significant associations unless effect sizes are large. However, while most associations did not reach statistical significance, many of the effect estimates were moderate in size, suggesting that meaningful relationships may exist but were not detectable within the current sample. In this study, the linear regression models were applied to examine the relationship between UPF intake and food environment measures, ensuring a comprehensive assessment of potential associations. Linear regression provides more statistical power captures possible dose-response relationships and subtle variations across the full range of consumption. However, treating neighbourhood-level covariates solely as confounders may be insufficient and overly simplistic. The geographic variables derived from CANUE represent complex, composite indices that capture multiple domains in one variable, each with context-dependent implications. These 87 variables may not only confound the relationship between the retail food environment and UPF intake, but could also interact with or mediate these effects. For example, urban sprawl may moderate the influence of fast-food outlet density on UPF intake—individuals in neighbourhoods with greater sprawl may be more reliant on private vehicles and have limited food options due to transportation constraints, amplifying the effect of the neighbourhood retail food environment. As such, relying solely on confounding control may be insufficient to capture the full complexity of real-world dynamics. Future analyses should consider testing for effect modification and explore alternative pathways to better reflect the role of geographic-based factors in shaping dietary intake. The null findings between most retail food environment measures and UPF intake in this study contributes to a growing yet limited body of research exploring how neighborhood food environments shape dietary intake. Although many studies have examined the association between the retail food environment and overall diet quality or specific dietary components such as fruit and vegetable intake, few studies have directly investigated the relationship between retail food environment and UPF consumption, particularly in the Canadian context. One important consideration is that UPFs are widely available across a broad spectrum of food outlets — including convenience stores, fast food outlets, supermarkets, and even some grocery stores — making it difficult to link UPF consumption to specific types of retailers. This differs from other dietary exposures, such as fruit and vegetable intake, which are more closely tied to access to grocery stores and produce markets. As a result, the omnipresence of UPFs in the modern food supply may dilute the influence of specific food outlet types on overall UPF consumption. 88 Moreover, evidence suggests that when both nutrient-dense (healthy) and energy-dense (unhealthy) foods are available, individuals tend to favor UPFs, underscoring that the presence of healthier options does not necessarily displace unhealthy choices (146). Thus, simply increasing the proportion of healthy food retailers may not be sufficient to meaningfully influence UPF intake, as individual preferences, marketing, pricing, and other factors also play critical roles in shaping dietary behaviors (238). Beyond the characteristics of the retail food environment, dietary intake is shaped by a complex interplay of factors operating across multiple levels, as outlined in the Social Ecological Model framework adopted in this study. While this study focused on neighborhood retail food environment, many other influences — including individual-level preferences, socio-economic constraints, social and cultural norms, and broader policy-level factors — likely contribute to UPF consumption. Additionally, the study was limited to retail food environment measures around participants' home addresses, but people often purchase and consume food in other locations, such as near workplaces or while commuting, which were not captured in this study. Emerging alternative ways of acquiring food such as using online grocery shopping, takeout platforms or monthly meal subscription services are increasingly shaping dietary behavior by extending access beyond the immediate physical food environment. Future research should seek to integrate additional sources of food purchases to more accurately capture contemporary patterns of dietary intake. In addition, individual patterns of mobility and transportation access—such as reliance on public transit or car ownership—may mediate associations between the retail food environment and dietary intake. Factors, including store operating hours or shift work schedules, may also impact when and where individuals purchase food. These dimensions are currently underrepresented in the literature and warrant further investigation. Future studies should explore multi-level interventions, including school-89 based or workplace-based food access strategies, to understand how different settings contribute to dietary intake across the life course. Furthermore, Furthermore, adopting longitudinal or experimental study designs would allow for a clearer understanding of the causal relationship between changes in the food environment and shifts in population dietary intake over time, providing stronger evidence to disentangle this dynamic relationship and inform future public health interventions and policy. A weak positive association was observed between fast food outlet density and UPF intake in linear regression models. This finding was cohesive with another study that found greater density of fast-food outlets associated with lower likelihood of meeting recommendations for fruit and vegetable consumption (38). Since fruit and vegetable intake tends to be inversely associated with UPF consumption, this provides indirect support to this study. Given the potential impact of fast-food outlet density on UPF intake, urban planning and public health policies could focus on regulating fast food outlet proliferation and promoting healthier food environments through zoning laws and financial incentives for healthier food retailers. However, other findings related to the retail food environment and UPF in this study reinforce the complexity of UPF intake and indicate the needs for interventions that consider factors beyond the retail food environment. 5.2 Aim 2: retail food environment in BC In general, the retail food environment across BC exhibited significant variation by economic factors, levels of urbanization, and other geographical elements. Previous studies have highlighted disparities in food access across urban, suburban, and rural areas, with urban centers generally having greater access to grocery stores and supermarkets, while rural communities 90 have more convenience stores and small retailers that often have fewer fresh and nutritious food options (239). These disparities in food availability may contribute to differences in diet quality and disproportionately affect low-income and underserved populations (239). Small towns often have highly concentrated retail food environments, where food outlets cluster within a limited number of DAs. For instance, Quesnel and Salmon Arm had dense clustering of food outlets in select locations. Such patterns reflect zoning bylaws, regional economic structures, population density, and urbanization (67). As BC’s largest urban hubs, Vancouver and Victoria exhibited a diverse and dense retail food environment with high prevalence of a range of food outlets, reflecting their roles as the province’s economic centre and capital, respectively. However, there was a limited presence of fruit and vegetable markets that specialize in unprocessed or minimally processed foods which was stated in a previous report (240). Based on this report on the economy of local food in Vancouver, several structural and economic factors contribute to this phenomenon. Dense urban landscapes present challenges for local food production, leading to greater dependence on international supply conglomerates. This reliance is driven by an unstable local food supply across BC, as farming is often fragmented, consisting primarily of small-scale, family-owned farms that lack collaboration, infrastructure, and the capacity to reach critical production levels. Additionally, high land costs and seasonal growing conditions further hinder local agricultural viability, making it difficult to sustain year-round supply. Market regulations and the dominance of mainstream supply chains further marginalize local producers, restricting their ability to scale up and distribute through conventional retail channels (241,242). Despite these constraints, some efforts have been made to integrate urban farming into city landscapes. Initiatives such as Sole Food Street Farms in Vancouver have repurposed 91 underutilized spaces to create productive urban farms, improving food access and employment opportunities for the local communities (243). Previous study did not suggest fruit and vegetable markets improve dietary intake at the population level because they tend to be expensive and out of reach for most - —initiatives like these may help shift that dynamic by increasing affordability and community engagement, potentially making healthy food options more accessible to broader populations (244). Tourism also plays a significant role in shaping the food environments of Vancouver and Victoria. Research has indicated that tourism-heavy cities tend to have a greater density of food service establishments catering to transient populations (245). Victoria has cultivated a small-scale, farm-to-table sustainable food environment, with initiatives like Ecogastronomy Research Group emphasizing direct producer-to-consumer connections, local food systems, and sustainable agricultural practices (246). A qualitative study on destination restaurants highlighted how the concept of locality and regional identity was leveraged in food marketing and tourism promotion, reinforcing the economic and cultural value of such food environments (247). These models highlight potential strategies to enhance urban food environments, especially targeting fresh and minimally processed produce like fruits and vegetables. 5.2.1 Places with more fast food and convenience stores Smaller metro areas such as Abbotsford-Mission, Chilliwack, and Kelowna exhibited distinct retail food environments. These areas had a disproportionately high prevalence of fast food outlets and convenience stores relative to grocery stores, which might create barriers to accessing minimally processed foods. This pattern could be explained by the following several 92 factors. The proximity to major transportation routes, as many smaller metro regions served as key transit corridors and highway junctions, where fast food outlets and convenience stores are strategically located to cater to commuters, long-haul truck drivers, and travelers (248). Research suggests that areas near major roads and highways tend to have a higher density of fast food outlets due to the demand for quick, accessible meals, and more frequent fast food outlet visits were linked to slower traffic on the route (248). Areas further away from the metropolitan centres, including CSDs like Dawson Creek, and Fort St. John, may have limited access to efficient food distribution networks for fresh produce, making it easier and more cost-effective for chain fast food outlets and convenience stores to establish their presences (249). Unlike urban centres and smaller cities, rural areas have much lower population densities, making it less profitable for fruit and vegetable markets, grocery stores and supermarkets to operate (250). These factors make fresh food logistics even more difficult, increasing reliance on non-perishable, highly processed foods. As a result, convenience stores and fast food chains, which require less infrastructure and inventory management, dominate the food landscape. In contrast, Prince Rupert had a mixed food environment with both high mRFEI and grocery store density and high density of fast-food outlets and restaurants. These results could inform targeted policies that prioritize increasing accessibility and affordability of fresh and nutritious food in these regions to support healthier dietary intake and reduce reliance on UPF. 93 5.2.2 Regional exceptions Smaller coastal communities such as Squamish and Parksville tended to have relatively low access to fast food outlets and better access to grocery stores and restaurants. Squamish has a sustainable-tourism economy with a focus on outdoor recreation that specifically draws health-conscious demographic, which includes outdoor enthusiasts, artists, and individuals that may prioritize sustainable food choices (251,252). Meanwhile, Parksville, a common retirement community, has a food environment with higher accessibility to grocery stores and fewer fast-food outlets, likely reflecting the needs and preferences of its predominantly older population (253). 5.3 Strengths and limitations 5.3.1 Strengths There are several strengths to this research. This study cohort was nested in a deeply phenotype cohort of male and female participants in the BCGP, permitting consideration of multi-dimensional confounders and exploration of geospatial retail food environments linked to six-digit postal codes. BCGP-BIRDEE is, to date, the only study in Canada to use the updated CDHQ-III FFQ which provides a more contemporary estimate of dietary intake than prior tools. Although smaller in size, the study provides more recent estimates of dietary intake in Canada than the CCHS, which is over a decade old and may not reflect current intake. The CDHQ-III is a validated and comprehensive dietary assessment tool that captures long-term dietary patterns. Compared to dietary screeners, such as those used in Atlantic PATH and the Canadian 94 Longitudinal Study on Aging (254,255), which assess only a limited set of foods, the CDHQ-III provides a more detailed and accurate reflection of habitual dietary intake. Another strength of this study is the adjustment for neighborhood-level confounders, including Can-ALE, Can-Marg, and urban sprawl, rather than solely adjusting for individual-level factors. This approach recognizes the important role of the environments in which people live, work, and play in shaping dietary intake. The retail food environment measures used in this study are also notably comprehensive, capturing multiple types of food outlets — including fast-food restaurants, convenience stores, supermarkets, grocery stores, and fruit and vegetable markets — that went beyond the simplistic measures focused only on grocery stores, which were commonly used in prior studies. Additionally, the use of Business Registry data to derive geospatial measures of the retail food environment enhances the accuracy and reliability of food outlet identification. Another major advantage of this study is the use of the Can-FED, one of the most detailed and comprehensive datasets available on the Canadian food environment. Can-FED provides systematically derived, validated measures of food outlet density and relative access at the dissemination area level, enabling consistent and accurate characterization of the retail food environment across diverse geographic settings. As outlined in their methods paper (50), Can-FED offers several methodological strengths, including a rigorous classification system and use of national business data, which address limitations seen in studies relying on less comprehensive commercial sources. 95 5.3.2 Limitations and next steps 5.3.2.1 Study design and generalizability Several limitations in study design and generalizability should be acknowledged. The cross-sectional design limits the ability to infer causality between retail food environment characteristics and UPF intake. Although, the retail food environment tends to change slowly, with outlets often replaced by others of the same type (e.g., fast food with fast food). This relative stability suggests that current measures of the food environment likely reflect past exposures relevant to dietary intake, thereby reducing the likelihood of reverse causality. Future research should use longitudinal study designs to capture changes in UPF intake and dietary quality in relation to changes in the retail food environment. The planned update for Can-FED in 2025 may facilitate such studies in the near future. Although this study provides an overview of the dietary intake and retail food environment across BC, the BCGP-BIRDEE population was predominately in the lower Mainland of BC. Participants also had a higher-than-average education and income level compared to the general BC and Canadian population, which may affect the generalizability of the findings (74, 88). Additionally, participants were selected based on BMI categories (1/3 normal weight and 2/3 overweight), health status (no prevalent cancer), and were predominantly white which could introduce bias and limit the applicability of results to the broader population. Furthermore, the analysis of dietary intake and the retail food environment did not have sufficient sample size to examine rural and remote areas, where the retail food environment may have a greater influence on dietary intake due to limited food accessibility and availability relative to urban areas. Future research should consider oversampling rural and remote areas to 96 improve data representativeness and better capture regional disparities, more accurately reflect the general population's diversity in socioeconomic status, education, and BMI, thereby enhancing the generalizability of the findings. Lastly, this study focused on the geographical availability of food outlets based on participants’ residential locations. However, data on food affordability and economic access — critical determinants of dietary intake — were not available within this dataset. Additionally, information on food purchased or consumed outside of participants' residential neighborhoods (e.g., near workplaces or during commuting, or online purchases) was not captured. These limitations may have affected the ability to fully assess the relationship between the retail food environment and dietary intake. Future research that integrates measures of geographic access alongside affordability, availability, and other relevant factors from the Social Ecological Model including individual preferences, cultural influences, and social norms — would provide a more comprehensive understanding of how the retail food environment may contribute to dietary intake. Such multidimensional, mixed-method approaches are essential for identifying the pathways through which neighborhood environments influence eating behaviors and for informing effective public health interventions. 5.3.2.2 Variable selection, data availability and quality Limitations related to dietary assessment and the evaluation of the retail food environment should also be considered. Self-reported dietary intake is subject to social desirability bias, particularly for intake in UPF, added sugar, and alcohol intake, which may lead to underreporting or misclassification of dietary intake (256,257). The CDHQ-III has inherent limitations, such as recall bias and reliance on a predefined food list that may not capture all 97 foods consumed, and under estimation of total energy intake. Use of both CDHQ-III and multiple 24-hour food recalls has been shown to minimize bias inherent in the two methodologies and could be used in the future to provide more precise estimates of dietary intake (258). Although, in the BCGP-BIRDEE cohort, the number of participants who completed both the CDHQ-III and ASA24 was modest, highlighting the participant burden with lengthy dietary tools. This thesis focused on nutrients of public health concern, but note that with the availability of dietary data in BCGP-BIRDEE, additional nutrients such as iron, potassium, magnesium, and folate could be investigated in the future. The retail food environment data from Can-FED were collected around 2018, which is disconnected from the timing of dietary assessment. However, given that zoning regulations and food retail infrastructure typically evolve gradually, the overall classification of food environments should remain largely unchanged. While chain supermarkets and grocery stores are typically classified as sources of healthy food, products sold in them may still be categorized as UPF. Similarly, fast-food restaurants and convenience stores may also sell minimally processed foods. This highlights a critical limitation in relying solely on food outlet classification, as the availability of healthier food options does not necessarily translate into healthier dietary choices (259). In addition, the study primarily focuses on physical retail food environments but does not account for alternative food procurement methods, such as online grocery shopping, meal delivery services, or institutional food sources (e.g., schools, workplaces, hospitals). Additionally, key food retail categories such as liquor stores were missing due to public access data limitations. Several food retail sources, including cafes, bakeries, fish and seafood markets, and specialty food stores, were not included in this analysis. This may have underestimated the 98 diversity of food retail options available to consumers. Future research should consider expanding the scope of food environment measures to incorporate alternative procurement methods and additional food retail categories, thereby offering a more comprehensive understanding of consumer access in an evolving food landscape. Moreover, the way neighbourhoods are defined in the study may have influenced the results. Neighborhood boundaries in the model are defined based on geographical census data, but these boundaries might not align with how residents perceive or experience their neighborhood. People may consider a different set of locations or areas as part of their "neighborhood" based on personal, social, or economic factors (260). The disconnect between measured and perceived retail food environment may introduce measurement error. For instance, the use of geographically defined neighborhoods may not reflect the actual areas people interact with or consider when purchasing food, leading to potential under- or over-estimations of the relationship between neighborhood food environment and dietary intake. To improve the model, future research could consider incorporating individual perceptions of neighborhood food access alongside objective measures, or use more nuanced definitions of neighborhood boundaries that align with how residents experience their environment. 5.3.2.3 Classification for UPF Another significant challenge in this study and overall dietary research is the classification and assessment of UPF intake. Most dietary tools lack specific prompts for UPFs, making it difficult to differentiate between minimally processed, processed, and ultra-processed foods. For instance, we could not distinguish between storebought bread (category 4) and 99 homemade bread (category 3) in the CDHQ-III and only conduct the categorization based off predominant patterns in Canada (i.e. most foods are purchased vs. homemade). The NOVA classification system, which was used in this study, has been widely applied in epidemiological research; however, it has also received criticism for being too broad in its definitions, considering food processing but not nutritional content (114). Alternative food processing classification systems have been developed to refine UPF categorization, although none are currently widely used. One such alternative is the Siga, a food and beverage classification system, developed by a French research group based on NOVA’s processing framework. This system further considers: 1) the impact of processing on the food/ingredient matrix; 2) the levels of added salt, sugar, and fat; 3) the number and nature of ultra-processing markers; 4) the presence of at-risk food additives. The Siga classification ultimately divides foods into nine categories and provides a rating system aimed at encouraging product reformulation, guiding packaging design, and improving supermarket food categorization (261). Similarly, the International Agency for Research on Cancer (IARC) has proposed another three-tiered food classification system, which includes: 1) non-processed foods; 2) moderately processed foods (e.g., packaged salads, frozen food ingredients, extra virgin olive oil, fruits and vegetables canned in water/brine); 3) highly processed foods, defined as industrially prepared foods requiring little to no preparation beyond heating (262). Various other classification systems have also been developed globally, though they are not commonly applied in large-scale dietary studies (263). The lack of a universally accepted definition of UPF and the absence of standardized classification frameworks have limited the ability to generate consistent epidemiological 100 evidence. Hence, future research should work towards establishing a gold standard for UPF classification, to enhance comparability across studies. Lastly, while UPF are often associated with poor dietary quality and adverse health outcomes, not all UPFs are nutritionally equivalent. Some UPFs, such as fortified wholewheat breads, plant-based milks, or nutrient-enriched cereals, can provide meaningful nutritional contributions to dietary intake, including dietary fibre, vitamins, and minerals. This complexity highlights a key limitation of using the NOVA classification to assess the degree of food processing. 5.4 Impact The detailed analysis of dietary intake and UPF consumption highlights the ongoing need for nutritional interventions and public health campaigns aimed at reducing excessive intake of total fat, sugar, and sodium, while addressing deficiencies in calcium, vitamin D, and dietary fiber. The dietary intake data also provides a baseline for monitoring trends in intake over time for British Columbians, given that BCGP-BIRDEE is embedded in a longitudinal cohort study. This may be informative for evaluating the effectiveness of public health initiatives and policy changes in the future. The identification of associations between fast food outlet density and UPF provides evidence that the retail food environment may be an actionable target to reduce UPF intake. For example, zoning policies and regulations on the number and location of fast-food outlets may promote healthier food environments for BC residents. Although, caution is warranted, as UPF tends to be more affordable than fresh foods (264), and interventions reducing availability of 101 outlets selling UPFs without substitution of affordable alternatives could have unintended consequences. For instant, in rural areas, solely restricting fast food restaurants could inadvertently exacerbate food insecurity by reducing access to affordable food options. Alternatively, a more comprehensive approach could involve providing tax incentives to encourage the establishment of supermarkets, grocery stores, fruit and vegetable markets, urban farms, and farm-to-table establishments—particularly in materially and socially deprived areas. These interventions may help counterbalance the dominance of fast-food outlets and improve access to fresh, minimally processed foods. In rural or remote regions, improving public transit connections to grocery stores or supporting mobile markets may enhance geographic healthy food access. In addition to modifying the physical food environment, policy efforts should consider regulating the marketing and promotion of UPF, especially in areas with high density of fast food outlets that identified in this study such as Dawson Creek, Fort St. John, and Abbotsford-Mission areas. Given the complex and interconnected influences on dietary intake, effective interventions should to be coordinated across levels of the Social-Ecological Model (SEM) framework to support structural and systematic changes (60). At the societal level, policy changes such as subsidies for healthier foods and regulations on the marketing of ultra-processed foods can help shape the broader food environment. At the sectors level, collaboration across healthcare, education, and food industries can support the development and delivery of culturally appropriate dietary programs. Within settings such as schools, workplaces, and community centers, targeted initiatives can improve access to healthy foods and promote social cohesion around shared food practices. Finally, at the individual level, enhancing food literacy—such as understanding nutrition labels and making informed choices—can empower people to adopt and 102 maintain healthier diets. Together, these coordinated actions across all levels create a more supportive and equitable food environment. 5.5 Conclusions In summary, this study highlights persistent nutrient gaps among the British Columbians, including excessive intake of total fat, sugar, sodium, and UPFs and insufficient consumption of calcium, vitamin D, and dietary fibre. The analysis of the retail food environment and UPF intake revealed the density of fast-food outlets in neighbourhoods where people lived was positively associated with UPF intake. The retail food environment across BC is highly variable, shaped by urbanization, geography, and economic forces. Urban centers such as Vancouver and Victoria had diverse food retail options but lacked specialized outlets for fresh, minimally processed foods. In contrast, rural and small urban areas had more fast-food outlets and convenience stores. Regional exceptions, such as certain coastal and retirement communities, suggest that local demographics and economic conditions may influence characteristics of retail food environment. 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NOVA food classification for CDHQ-III food list # consensus classification * CDHQ-III food group 1 group 4 Milk, evap/cond, in cereal, 2 group 4 Milk, soy, in cereal 3 group 4 Milk, rice, in cereal 4 group 4 Milk, almond, in cereal 5 group 4 Milk, coconut, in cereal 6 group 4 Milk, evap/cond, in coffee or tea 7 group 4 Milk, soy, in coffee or tea 8 group 4 Milk, rice, in coffee or tea 9 group 4 Milk, almond, in coffee or tea 10 group 4 Milk, coconut, in coffee or tea 11 group 4 Milk, soy, to drink 12 group 4 Milk, rice, to drink 13 group 4 Milk, almond, to drink 14 group 4 Milk, coconut, to drink 15 group 4 Milk/ chocolate, whole, to drink 16 group 4 Milk/ chocolate, reduced fat, to drink 17 group 4 Non-dairy crm, powdrd, reg in coffee or tea 18 group 4 Non-dairy crm, powdrd, diet in coffee or tea 19 group 4 Non-dairy crm, liquid, reg in coffee or tea 20 group 4 Non-dairy crm, liquid, diet in coffee or tea 21 group 4 Milkshakes/sodas 22 group 4 Fruit Smoothie 23 group 4 Meal replacement, liquid 24 group 4 Whey Protein Powder 25 group 4 Yogourt, whole milk 26 group 4 Yogourt, lowfat/nonfat 27 group 4 Yogourt, Greek, whole milk 28 group 4 Yogourt, Greek, lowfat/nonfat 29 group 4 Cheese sauce 30 group 4 White sauce 31 group 4 Cream cheese, reg 139 32 group 4 Cream cheese, lowfat 33 group 4 Cream, reg or 1/2&1/2 in coffee or tea 34 group 4 Cream, reg, whipped 35 group 4 Cream sub, whipped 36 group 4 Ice cream, reg 37 group 4 Ice cream/ice milk, lowfat 38 group 4 Frozen yogourt, ices, sorbet, etc 39 group 4 Puddings/custards 40 group 4 Beef stews/pot pies/mixtures 41 group 4 Beef, burgers, w bun 42 group 4 Beef/ burgers/ cheeseburgers/ w bun 43 group 4 Sausage, reg 44 group 4 Sausage, turkey/lowfat 45 group 4 Hot dogs (reg/turkey/lowfat), Hot dog mixed dis 46 group 4 Cold cuts, other 47 group 4 Cold cuts, poultry 48 group 4 Ham, cold cut/ lunch meat 49 group 4 Chicken, fr, light NS 50 group 4 Chicken, fr, light WS 51 group 4 Chicken, fr, dark NS 52 group 4 Chicken fr, dark WS 53 group 4 Tofu 54 group 4 Soy burger or meat substitute 55 group 4 Tuna salad 56 group 4 Fish, coated/ fish sticks 57 group 4 Eggs (whites/substitutes/NFA) 58 group 4 Eggs, salad 59 group 4 Soups, broth w ndles/rice 60 group 4 Soups, w veggies 61 group 4 Soups, bean-type 62 group 4 Soups, creamed 63 group 4 Eng muf/bagel, white 64 group 4 Eng muf/bagel, whole gr 65 group 4 Breads/rolls, white 66 group 4 Bread/rolls whole gr 67 group 4 Croissants 68 group 4 Crackers 69 group 4 Cornbread/muffins 70 group 4 Biscuits, all 71 group 4 Donuts, swt rolls, danish, pop tarts 72 group 4 Muffins/dessert breads 73 group 4 Cookies, brownies 74 group 4 Granola bars 140 75 group 4 Cakes 76 group 4 Cheesecake 77 group 4 Pies, fruit 78 group 4 Pies, crm/custrd/othr 79 group 4 Pies, pumpk/swt pot, etc 80 group 4 Pies, pecan 81 group 4 Crisps/cobblers 82 group 4 Potato chips 83 group 4 Corn chips 84 group 4 Pretzels, all 85 group 4 Pancake, waff, Fr toast 86 group 4 Meal replacement bar 87 group 4 Hot breakfast cereals, NFA 88 group 4 RTE cereal, hi-fiber 89 group 4 RTE cereal, highly fort 90 group 4 RTE cereal, good fiber 91 group 4 RTE cereal, other 92 group 4 Chow mein noodles 93 group 4 Pasta, NFA 94 group 4 Macaroni and cheese 95 group 4 Pasta salad 96 group 4 Pizza, with meat 97 group 4 Pizza, without meat 98 group 4 Egg rolls, all 99 group 4 Mexican mixtures/ burrito 100 group 4 Mexican mixtures/ taco 101 group 4 Mexican mixtures/ not burrito or taco 102 group 4 Fruit salads/other fruits 103 group 4 Orange/grpfrt jce, calcium fortified 104 group 4 Potatoes, fried 105 group 4 Potato salads 106 group 4 Tomato catsup 107 group 4 Coleslaw 108 group 4 Margarine, reg on bread 109 group 4 Margarine, diet on bread 110 group 4 Margarine, reg on vegetables 111 group 4 Margarine, diet on vegetables 112 group 4 Margarine, reg on pancakes or waffles 113 group 4 Margarine, diet on pancakes or waffles 114 group 4 Margarine, reg on potatoes 115 group 4 Margarine, diet on potatoes 116 group 4 Margarine, other uses, reg 117 group 4 Margarine, other uses, diet 141 118 group 4 Salad drsg, reg on salad or veg 119 group 4 Salad drsg, lowfat/fat free on salad or veg 120 group 4 Mayonnaise, reg on sandwich 121 group 4 Mayonnaise, diet on sandwich 122 group 4 Mayonnaise, reg on salad 123 group 4 Mayonnaise, diet on salad 124 group 4 Gelatins 125 group 4 Candy, chocolate 126 group 4 Candy, not chocolate 127 group 4 Jams, jelly, reg 128 group 4 Soft drinks, reg, caff 129 group 4 Soft drinks, reg, decaf 130 group 4 Soft drinks, diet, caff 131 group 4 Soft drinks, diet, decaf 132 group 4 Energy drink, reg, caff 133 group 4 Fruit drinks, reg 134 group 4 Frt drinks, diet 135 group 4 Sports drinks 136 group 4 Water, bottled, sweetened 137 group 4 Water bottled, fortified 138 group 4 Alc bev, liquor 139 group 4 Gravy 140 group 4 Sauces (excluding gravy and tomato sauce) 141 group 4 Artificial sweet./ saccharin, not in coffee/tea 142 group 4 Artificial sweet./ aspartame, not in coffee/tea 143 group 4 Artificial sweet./ sucralose, not in coffee/tea 144 group 4 Artificial sweet./ stevia, not in coffee/tea 145 group 4 Artificial sweet./ other, not in coffee/tea 146 group 4 Artificial sweet./ saccharin, in coffee/tea 147 group 4 Artificial sweet./ aspartame, in coffee/tea 148 group 4 Artificial sweet./ sucralose, in coffee/tea 149 group 4 Artificial sweet./ stevia, in coffee/tea 150 group 4 Artificial sweet./ other, in coffee/tea 151 group 3 Cottage/ricotta cheese 152 group 3 Sour cream, reg 153 group 3 Sour cream, lowfat 154 group 3 Cheese, reg 155 group 3 Cheese, lowfat 156 group 3 Cheese mixtures 157 group 3 Beef, roast 158 group 3 Roast beef in sandwich 159 group 3 Beef, burgers , reg 160 group 3 Beef, burgers, lean 142 161 group 3 Beef, gr, meatballs/loaf/mixtures 162 group 3 Ham, not luncheon 163 group 3 Bacon, regular 164 group 3 Bacon, lean/Canadian 165 group 3 Liver, liverwurst 166 group 3 Meat based snacks (jerky) 167 group 3 Chicken, light NS 168 group 3 Chicken, light WS 169 group 3 Chicken, dark NS 170 group 3 Chicken, dark WS 171 group 3 Chicken, mixtures 172 group 3 Tuna canned (oil/water pack) 173 group 3 Salted fish 174 group 3 Shellfish, fried 175 group 3 Fish mixed dishes 176 group 3 Egg mixed dishes 177 group 3 Stuffing/dumplings, all 178 group 3 Popcorn 179 group 3 Sushi 180 group 3 Pasta, meatless red sauce 181 group 3 Pasta, meat/fish sauce 182 group 3 Lasagna, rav, shells, etc 183 group 3 Poutine 184 group 3 Potato Dishes (stuffed, au gratin, scalloped) 185 group 3 Pickled veg/frt 186 group 3 Tomato salsa 187 group 3 Coffee, regular, cream/sugar 188 group 3 Coffee, decaff, cream/sugar 189 group 3 Tea, regular, cream/sugar 190 group 3 Tea, decaff, cream/sugar 191 group 3 Coffee drinks, regular, cream/sugar 192 group 3 Coffee drinks, decaff, cream/sugar 193 group 3 Beer 194 group 3 Wine, not red 195 group 3 Red wine 196 group 2 Butter on pancakes or waffles 197 group 2 Butter on potatoes 198 group 2 Butter, other uses 199 group 2 Oils, olive 200 group 2 Nuts/seeds, butters 201 group 2 Butter on bread 202 group 2 Butter on vegetables 203 group 2 Oils, canola 143 204 group 2 Oils, other 205 group 2 Maple syrup on pancakes, etc 206 group 2 Syrups and toppings 207 group 2 Sugars/honey, all not in coffee/tea 208 group 2 Sugars/honey, all in coffee or tea 209 group 1 Water, tap 210 group 1 Milk, whole, in cereal 211 group 1 Milk, 2%, in cereal 212 group 1 Milk, 1%, in cereal 213 group 1 Milk, nonfat/skim, in cereal 214 group 1 Milk, whole, in coffee or tea 215 group 1 Milk, 2%, in coffee or tea 216 group 1 Milk, 1%, in coffee or tea 217 group 1 Milk, nonfat/skim, in coffee or tea 218 group 1 Milk, whole, to drink 219 group 1 Milk, 2%, to drink 220 group 1 Milk, 1%, to drink 221 group 1 Milk, nonfat/skim, to drink 222 group 1 Milk, other, to drink 223 group 1 Veal, lamb, plain 224 group 1 Beef, steaks, reg 225 group 1 Beef, steaks, lean 226 group 1 Pork 227 group 1 Pork, neck, feet,etc 228 group 1 Shortribs/spareribs 229 group 1 Game foods 230 group 1 Chicken/turkey ground 231 group 1 Turkey 232 group 1 Duck 233 group 1 Fish, not fried, NFA 234 group 1 Salmon 235 group 1 Fresh tuna, trout, mackerel, herring 236 group 1 Shellfish, not fried 237 group 1 Eggs, regular 238 group 1 Nuts, whole, peanuts 239 group 1 Nuts, whole, almonds 240 group 1 Nuts, whole, other 241 group 1 Flaxseeds 242 group 1 Other seeds 243 group 1 Nut/seed mixtures 244 group 1 Rice/grains, white NFA 245 group 1 Rice/grains, whlgrn NFA 246 group 1 Oranges, tangelo etc 144 247 group 1 Grapefruit, all 248 group 1 Apples 249 group 1 Applesauce/ckd apples 250 group 1 Pears 251 group 1 Peaches/nectarines/plums 252 group 1 Avocado, guacamole 253 group 1 Bananas 254 group 1 Plantains 255 group 1 Cantaloupe 256 group 1 Other melon 257 group 1 Strawberries 258 group 1 Blueberries 259 group 1 Other berries 260 group 1 Grapes, all 261 group 1 Dried fruit 262 group 1 Pineapple 263 group 1 Mangos 264 group 1 Orange/grapefruit juice, all 265 group 1 Other juice 266 group 1 Tomato/vegetable juice 267 group 1 Beans/dried peas/lentils, NFA 268 group 1 Baked beans 269 group 1 Chili 270 group 1 Potatoes, white, NFA 271 group 1 Sweet potatoes, NFA 272 group 1 Lettuce, not dark green 273 group 1 Lettuce/ dark green 274 group 1 Lettuce mixtures 275 group 1 Raw spinach/greens 276 group 1 Ckd spinach/greens, NFA 277 group 1 Broccoli, NFA 278 group 1 Carrots, NFA 279 group 1 Tomatoes, raw 280 group 1 Tomatoes, cooked, NFA 281 group 1 String beans, NFA 282 group 1 Cabbage/sauerkraut 283 group 1 Peas, NFA 284 group 1 Corn, NFA 285 group 1 Cauliflower, NFA 286 group 1 Brussel Sprouts, NFA 287 group 1 Peppers, NFA 288 group 1 Peppers, hot 289 group 1 Onions, NFA 145 290 group 1 Olives, all 291 group 1 Veg med, NFA 292 group 1 Asparagus, NFA 293 group 1 Winter squash, NFA 294 group 1 Mushrooms, NFA 295 group 1 Vegetable mixtures 296 group 1 Garlic 297 group 1 Other vegetables, NFA 298 group 1 Coffee, reg, no cr/sug 299 group 1 Coffee, decaf, no cr/sug 300 group 1 Tea, reg, no cr/sug 301 group 1 Tea, decaf, no cr/sug 302 group 1 Hot/cold herbal tea 303 group 1 Water, bottled, unsweet * Classification was made based on NOVA, where group 1 is non-processed or minimal processed foor, group 2 is processed culinary ingredients, group 3 is processed foods, and group 4 is ultra-processed foods (UPFs). Table A2. Sugar-Sweetened Beverages (SSB) food list from CDHQ-III # CDHQ-III food group 1 Milkshakes/sodas 2 Other juice 3 Soft drinks, reg, caff 4 Soft drinks, reg, decaf 5 Water, bottled, sweetened 6 Fruit drinks, reg 7 Coffee drinks, regular, cream/sugar 8 Coffee drinks, decaff, cream/sugar 9 Orange/grpfrt jce, calcium fortified 10 Coffee, regular, cream/sugar 11 Coffee, decaff, cream/sugar 12 Tea, regular, cream/sugar 13 Tea, decaff, cream/sugar 14 Energy drink, reg, caff 15 Sports drinks 16 Fruit Smoothie 146 Table A3. The usual daily UPF intake in BCGP-BIRDEE participants (N = 2,586) using the residual method a Variables b Male Female Total N = 787 (30.4%) N = 1,799 (69.6%) N = 2,586 (100%) Adjusted UPF using residual method (kcal) * Median (1st, 3rd) 22.0 -13.4 -3.3 Mean (±SD) 26.9 (240.8) -11.8 (189.4) 0 (207.1) a The residual method (190) was used to adjust nutrient intakes for total energy intake. b Significance levels: * p<0.05. Table A4. Bivariate analysis of retail food environment by UPF intake using the residual method (N = 2,586) a Food Environment Variables UPF intake (kcal) Mean (SD) Modified retail food environment index (mRFEI) Zero b 5.8 (199.6) Low c 4.0 (199.1) High d -9.9 (221.5) Fast food restaurant mix (Rmix) Zero 1.3 (209.0) Low -8.9 (207.5) High 7.8 (203.1) Fast food density Zero 1.3 (209.0) Low -5.2 (209.5) High 11.7 (192.3) Convenience store density Zero 6.2 (215.7) Low -3.2 (199.1) High -9.7 (206.9) a The residual method (190) was used to adjust nutrient intakes for total energy intake. b "Zero" refers to no access to the given retail food environment feature. c "Low" represents class 1–2, d "High" represents class 3-4. 147 Table A5. Mean differences (95% CI) in UPF intake by retail food environment characteristics using the residual method (N = 2,586) a Variable Category Mean Difference (95% CI) (kcal/1,000 kcal) d Model 1 e Model 2 f Modified Retail Food Environment Index (mRFEI) Zero a Ref Ref Low b -3.9 (-23.7, 15.8) 15.3 (-7.1, 37.8) High c -13.5 (-32.2, 5.2) 4.1 (-16.7, 24.8) Fast food restaurant mix (Rmix) Zero Ref Ref Low -13.0 (-32.0, 5.9) 12.7 (-10.8, 36.2) High 2.5 (-17.1, 22.1) 10.7 (-9.5, 31.0) Density of fast-food outlets Zero Ref Ref Low -8.3 (-25.2, 8.7) 7.9 (-10.6, 26.4) High 3.1 (-22.1, 28.4) 29.2 (0.9, 57.5) * Density of convenience stores Zero Ref Ref Low -12.8 (-29.6, 4.1) -5.3 (-23.5, 12.9) High -24.7 (-50.9, 1.6) -7.0 (-36.3, 22.4) a The residual method (190) was used to adjust nutrient intakes for total energy intake. b "Zero" refers to no access to the given retail food environment feature. c "Low" represents class 1–2, d "High" represents class 3-4. e Significance levels: * p<0.05. f Model 1 was adjusted for individual-level factors, including age, sex, education attainment, annual household income, marital status, and BMI. g Model 2 additionally adjusted for neighbourhood-level factors: active living environment index, urban sprawl score, and 4 domains of Can-Marg: 1) Housing and dwellings; 2) Material deprivation; 3) Age and labor force; and 4) Immigration and visible minority.
Thesis/Dissertation
eng
Population and Public Health
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Murphy, Rachel Anne
Characterizing the food environment and its association with ultra-processed food intake in British Columbia, Canada
Text