The Moving to Health (M2H) approach to natural experiment research: A paradigm shift for studies on built environment and health

Associations between Changes in Exposure to Air Pollutants due to Relocation and the Incidence of 14 Major Disease Categories and All-Cause Mortality: A Natural Experiment Study

BACKGROUND

Though observational studies have widely linked air pollution exposure to various chronic diseases, evidence comparing different exposures in the same people is limited. This study examined associations between changes in air pollution exposure due to relocation and the incidence and mortality of 14 major diseases.

METHODS

We included 50,522 participants enrolled in the UK Biobank from 2006 to 2010. Exposures to particulate matter with a diameter ≤ 2.5 μ m (PM 2.5 ), particulate matter with a diameter ≤ 10 μ m (PM 10 ), nitrogen oxides (NO x ), nitrogen dioxide (NO 2 ), and sulfur dioxide (SO 2 ) were estimated for each participant based on their residential address and relocation experience during the follow-up. Nine exposure groups were classified based on changes in long-term exposures due to residential mobility. Incidence and mortality of 14 major diseases were identified through linkages to hospital inpatient records and death registries. Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for incidence and mortality of the 14 diseases of interest.

RESULTS

During a median follow-up of 12.6 years, 29,869 participants were diagnosed with any disease of interest, and 3,144 died. Significantly increased risk of disease and all-cause mortality was observed among individuals who moved from a lower to higher air polluted area. Compared with constantly low exposure, moving from low to moderate PM 2.5 exposure was associated with increased risk of all 14 diseases but not for all-cause mortality, with adjusted HRs (95% CIs) ranging from 1.18 (1.05, 1.33) to 1.48 (1.30, 1.69); moving from low to high PM 2.5 areas increased risk of all 14 diseases: infections [1.37 (1.19, 1.58)], blood diseases [1.57 (1.34, 1.84)], endocrine diseases [1.77 (1.50, 2.09)], mental and behavioral disorders [1.93 (1.68, 2.21)], nervous system diseases [1.51 (1.32, 1.74)], ocular diseases [1.76 (1.56, 1.98)], ear disorders [1.58 (1.35, 1.86)], circulatory diseases [1.59 (1.42, 1.78)], respiratory diseases [1.51 (1.33, 1.72)], digestive diseases [1.74 (1.58, 1.92)], skin diseases [1.39 (1.22, 1.58)], musculoskeletal diseases [1.62 (1.45, 1.81)], genitourinary diseases [1.54 (1.36, 1.74)] and cancer [1.42 (1.24, 1.63)]. We observed similar associations for PM 10 and SO 2 with 14 diseases (but not with all-cause mortality); increases in NO 2 and NO x were positively associated with 14 diseases and all-cause mortality.

CONCLUSIONS

This study supports potential associations between ambient air pollution exposure and morbidity as well as mortality. Findings also emphasize the importance of maintaining consistently low levels of air pollution to protect the public's health. https://doi.org/10.1289/EHP14367.

GIS-Based Assessments of Neighborhood Food Environments and Chronic Conditions: An Overview of Methodologies

The industrial revolution and urbanization fundamentally restructured populations' living circumstances, often with poor impacts on health. As an example, unhealthy food establishments may concentrate in some neighborhoods and, mediated by social and commercial drivers, increase local health risks. To understand the connections between neighborhood food environments and public health, researchers often use geographic information systems (GIS) and spatial statistics to analyze place-based evidence, but such tools require careful application and interpretation. In this article, we summarize the factors shaping neighborhood health in relation to local food environments and outline the use of GIS methodologies to assess associations between the two. We provide an overview of available data sources, analytical approaches, and their strengths and weaknesses. We postulate next steps in GIS integration with forecasting, prediction, and simulation measures to frame implications for local health policies.

Changes in air pollution exposure after residential relocation and body mass index in children and adolescents: A natural experiment study

Air pollution exposure may affect child weight gain, but observational studies provide inconsistent evidence. Residential relocation can be leveraged as a natural experiment by studying changes in health outcomes after a sudden change in exposure within an individual. We aimed to evaluate whether changes in air pollution exposure due to residential relocation are associated with changes in body mass index (BMI) in children and adolescents in a natural experiment study. This population-based study included children and adolescents, between 2 and 17 years, who moved during 2011-2018 and were registered in the primary healthcare in Catalonia, Spain (N = 46,644). Outdoor air pollutants (nitrogen dioxides (NO2), particulate matter <10 μm (PM10) and <2.5 μm (PM2.5)) were estimated at residential census tract level before and after relocation; tertile cut-offs were used to define changes in exposure. Routinely measured weight and height were used to calculate age-sex-specific BMI z-scores. A minimum of 180 days after moving was considered to observe zBMI changes according to changes in exposure using linear fixed effects regression. The majority of participants (60-67% depending on the pollutant) moved to areas with similar levels of air pollution, 15-49% to less polluted, and 14-31% to more polluted areas. Moving to areas with more air pollution was associated with zBMI increases for all air pollutants (β NO2 = 0.10(95%CI 0.09; 0.12), β PM2.5 0.06(0.04; 0.07), β PM10 0.08(0.06; 0.10)). Moving to similar air pollution areas was associated with decreases in zBMI for all pollutants. No associations were found for those moving to less polluted areas. Associations with moving to more polluted areas were stronger in preschool- and primary school-ages. Associations did not differ by area deprivation strata. This large, natural experiment study suggests that increases in outdoor air pollution may be associated with child weight gain, supporting ongoing efforts to lower air pollution levels.

The association between the food environment and adherence to healthy diet quality: the Maastricht Study

OBJECTIVE

The purpose of this study is to determine if healthier neighbourhood food environments are associated with healthier diet quality.

DESIGN

This was a cross-sectional study using linear regression models to analyse data from the Maastricht Study. Diet quality was assessed using data collected with a FFQ to calculate the Dutch Healthy Diet (DHD). A buffer zone encompassing a 1000 m radius was created around each participant home address. The Food Environment Healthiness Index (FEHI) was calculated using a Kernel density analysis within the buffers of available food outlets. The association between the FEHI and the DHD score was analysed and adjusted for socio-economic variables.

SETTING

The region of Maastricht including the surrounding food retailers in the Netherlands.

PARTICIPANTS

7367 subjects aged 40-75 years in the south of the Netherlands.

RESULTS

No relationship was identified between either the FEHI (B = 0·62; 95 % CI = -2·54, 3·78) or individual food outlets, such as fast food (B = -0·07; 95 % CI = -0·20, 0·07) and diet quality. Similar null findings using the FEHI were identified at the 500 m (B = 0·95; 95 % CI = -0·85, 2·75) and 1500 m (B = 1·57; 95 % CI = -3·30, 6·44) buffer. There was also no association between the food environment and individual items of the DHD including fruits, vegetables and sugar-sweetened beverages.

CONCLUSION

The food environment in the Maastricht area appeared marginally unhealthy, but the differences in the food environment were not related to the quality of food that participants reported as intake.

The neighbourhood built environment and health-related fitness: a narrative systematic review

Background

There is increasing evidence demonstrating the importance of the neighbourhood built environment in supporting physical activity. Physical activity provides numerous health benefits including improvements in health-related fitness (i.e., muscular, cardiorespiratory, motor, and morphological fitness). Emerging evidence also suggests that the neighbourhood built environment is associated with health-related fitness. Our aim was to summarize evidence on the associations between the neighbourhood built environment and components of health-related fitness in adults.

Methods

We undertook a systematic review following PRISMA guidelines. Our data sources included electronic searches in MEDLINE, Embase, CINAHL, Web of Science, SPORTDiscus, Environment Complete, ProQuest Dissertations and Theses, and Transport Research International Documentation from inception to March 2021. Our eligibility criteria consisted of observational and experimental studies estimating associations between the neighbourhood built environment and health-related fitness among healthy adults (age ≥ 18 years). Eligible studies included objective or self-reported measures of the neighbourhood built environment and included either objective or self-reported measures of health-related fitness. Data extraction included study design, sample characteristics, measured neighbourhood built environment characteristics, and measured components of health-related fitness. We used individual Joanna Briggs Institute study checklists based on identified study designs. Our primary outcome measure was components of health-related fitness (muscular; cardiorespiratory; motor, and morphological fitness).

Results

Twenty-seven studies (sample sizes = 28 to 419,562; 2002 to 2020) met the eligibility criteria. Neighbourhood destinations were the most consistent built environment correlate across all components of health-related fitness. The greatest number of significant associations was found between the neighbourhood built environment and morphological fitness while the lowest number of associations was found for motor fitness. The neighbourhood built environment was consistently associated with health-related fitness in studies that adjusted for physical activity.

Conclusion

The neighbourhood built environment is associated with health-related fitness in adults and these associations may be independent of physical activity. Longitudinal studies that adjust for physical activity (including resistance training) and sedentary behaviour, and residential self-selection are needed to obtain rigorous causal evidence for the link between the neighbourhood built environment and health-related fitness.

Trial registration

Protocol registration: PROSPERO number CRD42020179807.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12966-022-01359-0.

Differences in Weight Gain Following Residential Relocation in the Moving to Health (M2H) Study

BACKGROUND

Neighborhoods may play an important role in shaping long-term weight trajectory and obesity risk. Studying the impact of moving to another neighborhood may be the most efficient way to determine the impact of the built environment on health. We explored whether residential moves were associated with changes in body weight.

METHODS

Kaiser Permanente Washington electronic health records were used to identify 21,502 members aged 18-64 who moved within King County, WA between 2005 and 2017. We linked body weight measures to environment measures, including population, residential, and street intersection densities (800 m and 1,600 m Euclidian buffers) and access to supermarkets and fast foods (1,600 m and 5,000 m network distances). We used linear mixed models to estimate associations between postmove changes in environment and changes in body weight.

RESULTS

In general, moving from high-density to moderate- or low-density neighborhoods was associated with greater weight gain postmove. For example, those moving from high to low residential density neighborhoods (within 1,600 m) gained an average of 4.5 (95% confidence interval [CI] = 3.0, 5.9) lbs 3 years after moving, whereas those moving from low to high-density neighborhoods gained an average of 1.3 (95% CI = -0.2, 2.9) lbs. Also, those moving from neighborhoods without fast-food access (within 1600m) to other neighborhoods without fast-food access gained less weight (average 1.6 lbs [95% CI = 0.9, 2.4]) than those moving from and to neighborhoods with fast-food access (average 2.8 lbs [95% CI = 2.5, 3.2]).

CONCLUSIONS

Moving to higher-density neighborhoods may be associated with reductions in adult weight gain.

Associations between neighborhood built environment, residential property values, and adult BMI change: The Seattle Obesity Study III

Objective

To examine associations between neighborhood built environment (BE) variables, residential property values, and longitudinal 1- and 2-year changes in body mass index (BMI).

Methods

The Seattle Obesity Study III was a prospective cohort study of adults with geocoded residential addresses, conducted in King, Pierce, and Yakima Counties in Washington State. Measured heights and weights were obtained at baseline (n = 879), year 1 (n = 727), and year 2 (n = 679). Tax parcel residential property values served as proxies for individual socioeconomic status. Residential unit and road intersection density were captured using Euclidean-based SmartMaps at 800 m buffers. Counts of supermarket (0 versus. 1+) and fast-food restaurant availability (0, 1–3, 4+) were measured using network based SmartMaps at 1600 m buffers. Density measures and residential property values were categorized into tertiles. Linear mixed-effects models tested whether baseline BE variables and property values were associated with differential changes in BMI at year 1 or year 2, adjusting for age, gender, race/ethnicity, education, home ownership, and county of residence. These associations were then tested for potential disparities by age group, gender, race/ethnicity, and education.

Results

Road intersection density, access to food sources, and residential property values were inversely associated with BMI at baseline. At year 1, participants in the 3rd tertile of density metrics and with 4+ fast-food restaurants nearby showed less BMI gain compared to those in the 1st tertile or with 0 restaurants. At year 2, higher residential property values were predictive of lower BMI gain. There was evidence of differential associations by age group, gender, and education but not race/ethnicity.

Conclusion

Inverse associations between BE metrics and residential property values at baseline demonstrated mixed associations with 1- and 2-year BMI change. More work is needed to understand how individual-level sociodemographic factors moderate associations between the BE, property values, and BMI change.

•

Strong, inverse cross-sectional relationships between the built environment, residential property values (a proxy for individual socioeconomic status), and measured BMI were observed.

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Measures of the built environment and residential property values showed modest and inconsistent associations with 1- and 2-year BMI change.

•

There was suggestive evidence that age may moderate the association between urban density and 1- and 2-year BMI change while education may moderate the association between residential property values and 2-year BMI change.

The relationship between the built environment and respiratory health: Evidence from a longitudinal study in Indonesia

Multiple studies have discussed the relationship between the built environment and non-infectious diseases, but research involving infectious diseases and the built environment is scarce. How the built environment is associated with infectious diseases varies across areas, and previous literature produces mixed results. This study investigated the relationship between the built environment and infectious diseases in Indonesia, which has different settings compared to developed countries. We combined the longitudinal panel data, Indonesian Family Life Survey (IFLS), and land cover data to examine the relationship between the built environment and the likelihood of contracting respiratory infectious diseases. We focused on the sprawl index to measure the built environment. The study confirmed that a sprawling neighbourhood is linked to lower respiratory infection symptoms by employing a fixed effect method. The association is more evident in urban areas and for females. The results also suggested that the linkage works through housing quality, such as housing crowdedness and ventilation, and neighbourhood conditions like neighbourhood transportation modes and air pollution levels. Thus, our results underlined the need to consider the health consequences of the densification policy and determine the direction of landscape planning and policy.

Implementing a Rural Natural Experiment: A Protocol for Evaluating the Impacts of Food Coops on Food Consumption, Resident's Health and Community Vitality

BACKGROUND

Local food environments are recognized by experts as a determinant of healthy eating. Food cooperatives (coop) can promote the accessibility to healthier foods and thus improve the health of the population, particularly in remote rural communities.

OBJECTIVE

To measure the effects of implementing a food coop in a disadvantaged community with poor access to food. We have two main research questions: (1). Does the establishment of a food coop in rural areas described as food deserts have an impact on accessibility, frequency of use, food consumption, food quality, and ultimately the health of individuals? (2). Does the establishment of a food coop in rural areas described as food deserts have an impact on food security and community vitality?

DESIGN

A natural experiment with a mixed pre/post method will be used. The sample is composed of households that came from geographically isolated communities (population: 215 to 885 inhabitants) which qualified as food deserts and located in rural areas of Quebec (Canada). All communities plan to open a food coop (in the years 2022-2023), and as their opening will be staggered over time, participants from communities with a new food coop (intervention) will be compared to communities awaiting the opening of their food coop (control). Data collection was carried out at three time points: (1) before; (2) 1 to 5 months after; and (3) 13 to 17 months after the opening of the coop. Questionnaires were used to measure sociodemographic variables, dietary intake, residents' health, and community vitality. Semi-structured interviews were conducted with community stakeholders.

RESULTS

Few natural experiments have been conducted regarding the impact of implementing food coops. Gathering concrete data on the effectiveness and processes surrounding these interventions through natural experiments will help to quantify their impact and guide knowledge users and policymakers to make more informed decisions.

The Effects of Urban Neighborhood Environmental Evaluation and Health Service Facilities on Residents' Self-Rated Physical and Mental Health: A Comparative and Empirical Survey

(1) Background: The neighborhood environment has been shown to be an essential factor affecting residents’ quality of life and health, but the relationship between the characteristics of health service facilities and health levels is rarely known. (2) Methods: This study used a representative sample (n = 591, 303 women; 288 men, age 18–85 years, lived in Chengdu for an extensive time) of residents living in Chengdu City, China, and took spatial point data and empirical research data to construct an ordered logistic regression model. We contrastively analyzed the influence of different variables in the neighborhood environment and health service facilities on self-rated physical health (SRPH) and self-rated mental health (SRMH). (3) Results: The frequency of use and accessibility of multiple facilities in the health service facilities were significantly associated with self-rated health (SRH). Significant differences occurred between residents’ perceived accessibility and actual accessibility of facilities in SRH. Comparing the results of SRPH and SRMH revealed that the influencing factors that affect the two vary. The factors that significantly affect SRMH include neighborhood physical environment evaluation; social environmental evaluation; the frequency of use of the parks and squares, and sports zones; and the accessibility of parks and squares, specialized hospitals, community hospitals, and pharmacies. However, the factors that significantly affect SRPH include the frequency of use of sports venues, general hospitals, and pharmacies and the accessibility of general hospitals. The social environment of the neighborhood is also a non-negligible part, and its interaction with the physical environment of the neighborhood affects the outcome of SRH. (4) Conclusions: Neighborhood environmental characteristics and the layout of health service facilities have significant differential effects on people’s physical and psychological health, and this information is of great value in promoting healthy city development and improving the quality of life of urban populations around the world.

Exercise/Physical Activity in Individuals with Type 2 Diabetes: A Consensus Statement from the American College of Sports Medicine

ABSTRACT

This consensus statement is an update of the 2010 American College of Sports Medicine position stand on exercise and type 2 diabetes. Since then, a substantial amount of research on select topics in exercise in individuals of various ages with type 2 diabetes has been published while diabetes prevalence has continued to expand worldwide. This consensus statement provides a brief summary of the current evidence and extends and updates the prior recommendations. The document has been expanded to include physical activity, a broader, more comprehensive definition of human movement than planned exercise, and reducing sedentary time. Various types of physical activity enhance health and glycemic management in people with type 2 diabetes, including flexibility and balance exercise, and the importance of each recommended type or mode are discussed. In general, the 2018 Physical Activity Guidelines for Americans apply to all individuals with type 2 diabetes, with a few exceptions and modifications. People with type 2 diabetes should engage in physical activity regularly and be encouraged to reduce sedentary time and break up sitting time with frequent activity breaks. Any activities undertaken with acute and chronic health complications related to diabetes may require accommodations to ensure safe and effective participation. Other topics addressed are exercise timing to maximize its glucose-lowering effects and barriers to and inequities in physical activity adoption and maintenance.

Differential associations of the built environment on weight gain by sex and race/ethnicity but not age

OBJECTIVE

To explore the built environment (BE) and weight change relationship by age, sex, and racial/ethnic subgroups in adults.

METHODS

Weight trajectories were estimated using electronic health records for 115,260 insured Kaiser Permanente Washington members age 18-64 years. Member home addresses were geocoded using ArcGIS. Population, residential, and road intersection densities and counts of area supermarkets and fast food restaurants were measured with SmartMaps (800 and 5000-meter buffers) and categorized into tertiles. Linear mixed-effect models tested whether associations between BE features and weight gain at 1, 3, and 5 years differed by age, sex, and race/ethnicity, adjusting for demographics, baseline weight, and residential property values.

RESULTS

Denser urban form and greater availability of supermarkets and fast food restaurants were associated with differential weight change across sex and race/ethnicity. At 5 years, the mean difference in weight change comparing the 3rd versus 1st tertile of residential density was significantly different between males (-0.49 kg, 95% CI: -0.68, -0.30) and females (-0.17 kg, 95% CI: -0.33, -0.01) (P-value for interaction = 0.011). Across race/ethnicity, the mean difference in weight change at 5 years for residential density was significantly different among non-Hispanic (NH) Whites (-0.47 kg, 95% CI: -0.61, -0.32), NH Blacks (-0.86 kg, 95% CI: -1.37, -0.36), Hispanics (0.10 kg, 95% CI: -0.46, 0.65), and NH Asians (0.44 kg, 95% CI: 0.10, 0.78) (P-value for interaction <0.001). These findings were consistent for other BE measures.

CONCLUSION

The relationship between the built environment and weight change differs across demographic groups. Careful consideration of demographic differences in associations of BE and weight trajectories is warranted for investigating etiological mechanisms and guiding intervention development.

Does the built environment have independent obesogenic power? Urban form and trajectories of weight gain

OBJECTIVE

To determine whether selected features of the built environment can predict weight gain in a large longitudinal cohort of adults.

METHODS

Weight trajectories over a 5-year period were obtained from electronic health records for 115,260 insured patients aged 18-64 years in the Kaiser Permanente Washington health care system. Home addresses were geocoded using ArcGIS. Built environment variables were population, residential unit, and road intersection densities captured using Euclidean-based SmartMaps at 800-m buffers. Counts of area supermarkets and fast food restaurants were obtained using network-based SmartMaps at 1600, and 5000-m buffers. Property values were a measure of socioeconomic status. Linear mixed effects models tested whether built environment variables at baseline were associated with long-term weight gain, adjusting for sex, age, race/ethnicity, Medicaid insurance, body weight, and residential property values.

RESULTS

Built environment variables at baseline were associated with differences in baseline obesity prevalence and body mass index but had limited impact on weight trajectories. Mean weight gain for the full cohort was 0.06 kg at 1 year (95% CI: 0.03, 0.10); 0.64 kg at 3 years (95% CI: 0.59, 0.68), and 0.95 kg at 5 years (95% CI: 0.90, 1.00). In adjusted regression models, the top tertile of density metrics and frequency counts were associated with lower weight gain at 5-years follow-up compared to the bottom tertiles, though the mean differences in weight change for each follow-up year (1, 3, and 5) did not exceed 0.5 kg.

CONCLUSIONS

Built environment variables that were associated with higher obesity prevalence at baseline had limited independent obesogenic power with respect to weight gain over time. Residential unit density had the strongest negative association with weight gain. Future work on the influence of built environment variables on health should also examine social context, including residential segregation and residential mobility.

Methods to Address Self-Selection and Reverse Causation in Studies of Neighborhood Environments and Brain Health

Preliminary evidence suggests that neighborhood environments, such as socioeconomic disadvantage, pedestrian and physical activity infrastructure, and availability of neighborhood destinations (e.g., parks), may be associated with late-life cognitive functioning and risk of Alzheimer’s disease and related disorders (ADRD). The supposition is that these neighborhood characteristics are associated with factors such as mental health, environmental exposures, health behaviors, and social determinants of health that in turn promote or diminish cognitive reserve and resilience in later life. However, observed associations may be biased by self-selection or reverse causation, such as when individuals with better cognition move to denser neighborhoods because they prefer many destinations within walking distance of home, or when individuals with deteriorating health choose residences offering health services in neighborhoods in rural or suburban areas (e.g., assisted living). Research on neighborhood environments and ADRD has typically focused on late-life brain health outcomes, which makes it difficult to disentangle true associations from associations that result from reverse causality. In this paper, we review study designs and methods to help reduce bias due to reverse causality and self-selection, while drawing attention to the unique aspects of these approaches when conducting research on neighborhoods and brain aging.

Impact of Built Environments on Body Weight (the Moving to Health Study): Protocol for a Retrospective Longitudinal Observational Study

BACKGROUND

Studies assessing the impact of built environments on body weight are often limited by modest power to detect residential effects that are small for individuals but may nonetheless comprise large attributable risks.

OBJECTIVE

We used data extracted from electronic health records to construct a large retrospective cohort of patients. This cohort will be used to explore both the impact of moving between environments and the long-term impact of changing neighborhood environments.

METHODS

We identified members with at least 12 months of Kaiser Permanente Washington (KPWA) membership and at least one weight measurement in their records during a period between January 2005 and April 2017 in which they lived in King County, Washington. Information on member demographics, address history, diagnoses, and clinical visits data (including weight) was extracted. This paper describes the characteristics of the adult (aged 18-89 years) cohort constructed from these data.

RESULTS

We identified 229,755 adults representing nearly 1.2 million person-years of follow-up. The mean age at baseline was 45 years, and 58.0% (133,326/229,755) were female. Nearly one-fourth of people (55,150/229,755) moved within King County at least once during the follow-up, representing 84,698 total moves. Members tended to move to new neighborhoods matching their origin neighborhoods on residential density and property values.

CONCLUSIONS

Data were available in the KPWA database to construct a very large cohort based in King County, Washington. Future analyses will directly examine associations between neighborhood conditions and longitudinal changes in body weight and diabetes as well as other health conditions.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/16787.

Residential mobility and chronic disease among World Trade Center Health Registry enrollees, 2004-2016

Residential mobility is hypothesized to impact health through changes to the built environment and disruptions in social networks, and may vary by neighborhood deprivation exposure. However, there are few longitudinal investigations of residential mobility in relation to health outcomes. This study examined enrollees from the World Trade Center Health Registry, a longitudinal cohort of first responders and community members in lower Manhattan on September 11, 2001. Enrollees who completed ≥2 health surveys between 2004 and 2016 and did not have diabetes (N = 44,089) or hypertension (N = 35,065) at baseline (i.e., 2004) were included. Using geocoded annual home addresses, residential mobility was examined using two indicators: moving frequency and displacement. Moving frequency was defined as the number of times someone was recorded as living in a different neighborhood; displacement as any moving to a more disadvantaged neighborhood. We fit adjusted Cox proportional hazards models with time-dependent exposures (moving frequency and displacement) and covariates to evaluate associations with incident diabetes and hypertension. From 2004 to 2016, the majority of enrollees never moved (54.5%); 6.5% moved ≥3 times. Those who moved ≥3 times had a similar hazard of diabetes (hazard ratio (HR) = 0.78; 95% Confidence Interval (CI): 0.40, 1.53) and hypertension (HR = 0.99; 95% CI: 0.68, 1.43) compared with those who never moved. Similarly, displacement was not associated with diabetes or hypertension. Residential mobility was not associated with diabetes or hypertension among a cohort of primarily urban-dwelling adults.

Evidence from big data in obesity research: international case studies

BACKGROUND/OBJECTIVE

Obesity is thought to be the product of over 100 different factors, interacting as a complex system over multiple levels. Understanding the drivers of obesity requires considerable data, which are challenging, costly and time-consuming to collect through traditional means. Use of 'big data' presents a potential solution to this challenge. Big data is defined by Delphi consensus as: always digital, has a large sample size, and a large volume or variety or velocity of variables that require additional computing power (Vogel et al. Int J Obes. 2019). 'Additional computing power' introduces the concept of big data analytics. The aim of this paper is to showcase international research case studies presented during a seminar series held by the Economic and Social Research Council (ESRC) Strategic Network for Obesity in the UK. These are intended to provide an in-depth view of how big data can be used in obesity research, and the specific benefits, limitations and challenges encountered.

METHODS AND RESULTS

Three case studies are presented. The first investigated the influence of the built environment on physical activity. It used spatial data on green spaces and exercise facilities alongside individual-level data on physical activity and swipe card entry to leisure centres, collected as part of a local authority exercise class initiative. The second used a variety of linked electronic health datasets to investigate associations between obesity surgery and the risk of developing cancer. The third used data on tax parcel values alongside data from the Seattle Obesity Study to investigate sociodemographic determinants of obesity in Seattle.

CONCLUSIONS

The case studies demonstrated how big data could be used to augment traditional data to capture a broader range of variables in the obesity system. They also showed that big data can present improvements over traditional data in relation to size, coverage, temporality, and objectivity of measures. However, the case studies also encountered challenges or limitations; particularly in relation to hidden/unforeseen biases and lack of contextual information. Overall, despite challenges, big data presents a relatively untapped resource that shows promise in helping to understand drivers of obesity.

Impact of Changes in the Food, Built, and Socioeconomic Environment on BMI in US Counties, BRFSS 2003-2012

OBJECTIVE

Researchers have linked geographic disparities in obesity to community-level characteristics, yet many prior observational studies have ignored temporality and potential for bias.

METHODS

Repeated cross-sectional data were used from the Behavioral Risk Factor Surveillance System (BRFSS) (2003-2012) to examine the influence of county-level characteristics (active commuting, unemployment, percentage of limited-service restaurants and convenience stores) on BMI. Each exposure was calculated using mean values over the 5-year period prior to BMI measurement; values were standardized; and then variables were decomposed into (1) county means from 2003 to 2012 and (2) county-mean-centered values for each year. Cross-sectional (between-county) and longitudinal (within-county) associations were estimated using a random-effects within-between model, adjusting for individual characteristics, survey method, and year, with nested random intercepts for county-years within counties within states.

RESULTS

A negative between-county association for active commuting (β = -0.19; 95% CI: -0.23 to -0.16) and positive associations for unemployment (β = 0.17; 95% CI: 0.14 to 0.19) and limited-service restaurants (β = 0.13; 95% CI: 0.11 to 0.14) were observed. An SD increase in active commuting within counties was associated with a 0.51-kg/m² (95% CI: -0.72 to -0.31) decrease in BMI over time.

CONCLUSIONS

These results suggest that community-level characteristics play an important role in shaping geographic disparities in BMI between and within communities over time.

Obesity and the Built Environment: A Reappraisal

The built environment (BE) has been viewed as an important determinant of health. Numerous studies have linked BE exposure, captured using a variety of methods, to diet quality and to area prevalence of obesity, diabetes, and cardiovascular disease. First-generation studies defined the neighborhood BE as the area around the home. Second-generation studies turned from home-centric to person-centric BE measures, capturing an individual's movements in space and time. Those studies made effective uses of global positioning system tracking devices and mobile phones, sometimes coupled with accelerometers and remote sensors. Activity space metrics explored travel paths, modes, and destinations to assess BE exposure that was both person and context specific. However, as measures of the contextual exposome have become ever more fine-grained and increasingly complex, connections to long-term chronic diseases with complex etiologies, such as obesity, are in danger of being lost. Furthermore, few studies on obesity and the BE have included intermediate energy balance behaviors, such as diet and physical activity, or explored the potential roles of social interactions or psychosocial pathways. Emerging survey-based applications that identify habitual destinations and associated travel patterns may become the third generation of tools to capture health-relevant BE exposures in the long term.