Invited commentary: Taking advantage of time-varying neighborhood environments

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.

Built environment and health: a systematic review of studies in Germany

Objectives

Empirical research on the relationship between the built environment and health is increasing at a tremendous pace. However, findings from Anglo-American countries may not apply to the European context. Therefore, we systematically reviewed the existing evidence on the role of the built environment for leading risk factors and health behavior in Germany.

Methods

Through an extensive search via PubMed and Web of Science, using predefined selection criteria, two independent reviewers identified 25 empirical studies. We described the main study variables (year of publication, study design, data source, sample characteristics), classified the studies according to their respective operationalization of environmental features and health outcomes, and qualitatively summarized the main results.

Results

The majority of the studies focused on anthropometric measures and physical activity. Access to respective destinations was associated with sports-related physical activity but not with body composition. Also, people living in urban structures tended to show more health-adverse behaviors such as smoking and drinking. Some evidence indicated that higher levels of noise and air pollution were related to higher blood pressure levels. No association was observed between green spaces or street design and health.

Conclusions

Future research should investigate relationships using a sound theoretical basis and research designs that better account for the complex relationship between the built environment and health.

Association between residential self-selection and non-residential built environment exposures

Studies employing 'activity space' measures of the built environment do not always account for how individuals self-select into different residential and non-residential environments when testing associations with physical activity. To date, no study has examined whether preferences for walkable residential neighborhoods predict exposure to other walkable neighborhoods in non-residential activity spaces. Using a sample of 9783 university students from Toronto, Canada, we assessed how self-reported preferences for a walkable neighborhood predicted their exposure to other walkable, non-residential environments, and further whether these preferences confounded observed walkability-physical activity associations. We found that residential walkability preferences and non-residential walkability were significant associated (β = 0.42, 95% CI: (0.37, 0.47)), and further that these preferences confounded associations between non-residential walkability exposure and time spent walking (reduction in association = 10.5%). These results suggest that self-selection factors affect studies of non-residential built environment exposures.

Model-based and design-based inference goals frame how to account for neighborhood clustering in studies of health in overlapping context types

Accounting for non-independence in health research often warrants attention. Particularly, the availability of geographic information systems data has increased the ease with which studies can add measures of the local "neighborhood" even if participant recruitment was through other contexts, such as schools or clinics. We highlight a tension between two perspectives that is often present, but particularly salient when more than one type of potentially health-relevant context is indexed (e.g., both neighborhood and school). On the one hand, a model-based perspective emphasizes the processes producing outcome variation, and observed data are used to make inference about that process. On the other hand, a design-based perspective emphasizes inference to a well-defined finite population, and is commonly invoked by those using complex survey samples or those with responsibility for the health of local residents. These two perspectives have divergent implications when deciding whether clustering must be accounted for analytically and how to select among candidate cluster definitions, though the perspectives are by no means monolithic. There are tensions within each perspective as well as between perspectives. We aim to provide insight into these perspectives and their implications for population health researchers. We focus on the crucial step of deciding which cluster definition or definitions to use at the analysis stage, as this has consequences for all subsequent analytic and interpretational challenges with potentially clustered data.

Is More Area-Level Crime Associated With More Sitting and Less Physical Activity? Longitudinal Evidence From 37,162 Australians

Does a rise in crime result in increased sitting time and a reduction in physical activity? We used unobserved ("fixed")-effects models to examine associations between change in objectively measured crime (nondomestic violence, malicious damage, breaking and entering, and stealing, theft, and robbery) in Australia and measures of sitting time, walking, and moderate-to-vigorous physical activity (MVPA) in a residentially stable sample of 17,474 men and 19,688 women at baseline (2006-2008) and follow-up (2009-2010). Possible sources of time-varying confounding included age, income, economic status, relationship (couple) status, and physical functioning. In adjusted models, an increase in all crimes of 10 counts per 1,000 residents was associated with an increase in sitting time (hours/day) among men (β = 0.21, 95% confidence interval (CI): 0.17, 0.25) and women (β = 0.18, 95% CI: 0.15, 0.22). Counterintuitively, the same increase in crime was also associated with an increase in the weekly number of ≥10-minute walking sessions (men: rate ratio (RR) = 1.01 (95% CI: 1.01, 1.02); women: RR = 1.00 (95% CI: 0.99, 1.01)) and MVPA sessions (men: RR = 1.02 (95% CI: 1.02, 1.03); women: RR = 1.01 (95% CI: 1.00, 1.02)). Similar associations were found for the other area-level crime indicators. While area-level crime prevention may be considered a lever for promoting more active lifestyles, these results suggest that the association is not unequivocal.

Changes in walking, body mass index, and cardiometabolic risk factors following residential relocation: Longitudinal results from the CARDIA study

BACKGROUND

While many studies have found the built environment to be associated with walking, most have used cross-sectional research designs and few have examined more distal cardiometabolic outcomes. This study contributes longitudinal evidence based on changes in walking, body mass index (BMI), and cardiometabolic risk following residential relocation.

METHODS

We examined 1,079 participants in the CARDIA study who moved residential locations between 2000 and 2006 (ages 32-46 in 2000, 49% white/51% black, 55% female). We created a walkability index from measures of population density, street connectivity, and food and physical activity resources, measured at participants' pre- and post-move residential locations. Outcomes measured before and after the move included walking, BMI, waist circumference, blood pressure, insulin resistance, triglycerides, cholesterol, atherogenic dyslipidemia, and C-reactive protein. Fixed effects (FE) models were used to estimate associations between within-person change in walkability and within-person change in each outcome. These estimates were compared to those from random effects (RE) models to assess the implications of unmeasured confounding.

RESULTS

In FE models, a one-SD increase in walkability was associated with a 0.81 mmHg decrease in systolic blood pressure [95% CI: (-1.55, -0.07)] and a 7.36 percent increase in C-reactive protein [95% CI: (0.60, 14.57)]. Although several significant associations were observed in the RE models, Hausman tests suggested that these estimates were biased for most outcomes. RE estimates were most commonly biased away from the null or in the opposite direction of effect as the FE estimates.

CONCLUSIONS

Greater walkability was associated with lower blood pressure and higher C-reactive protein in FE models, potentially reflecting competing health risks and benefits in dense, walkable environments. RE models tended to overstate or otherwise misrepresent the relationship between walkability and health. Approaches that base estimates on variation between individuals may be subject to bias from unmeasured confounding, such as residential self-selection.

Cause and context: place-based approaches to investigate how environments affect mental health

OBJECTIVES

Our surroundings affect our mood, our recovery from stress, our behavior, and, ultimately, our mental health. Understanding how our surroundings influence mental health is central to creating healthy cities. However, the traditional observational methods now dominant in the psychiatric epidemiology literature are not sufficient to advance such an understanding. In this essay we consider potential alternative strategies, such as randomizing people to places, randomizing places to change, or harnessing natural experiments that mimic randomized experiments.

METHODS

We discuss the strengths and weaknesses of these methodological approaches with respect to (1) defining the most relevant scale and characteristics of context, (2) disentangling the effects of context from the effects of individuals' preferences and prior health, and (3) generalizing causal effects beyond the study setting.

RESULTS

Promising alternative strategies include creating many small-scale randomized place-based trials, using the deployment of place-based changes over time as natural experiments, and using fluctuations in the changes in our surroundings in combination with emerging data collection technologies to better understand how surroundings influence mood, behavior, and mental health.

CONCLUSIONS

Improving existing research strategies will require interdisciplinary partnerships between those specialized in mental health, those advancing new methods for place effects on health, and those who seek to optimize the design of local environments.

Walkability and cardiometabolic risk factors: Cross-sectional and longitudinal associations from the Multi-Ethnic Study of Atherosclerosis

We used data from 3227 older adults in the Multi-Ethnic Study of Atherosclerosis (2004-2012) to explore cross-sectional and longitudinal associations between walkability and cardiometabolic risk factors. In cross-sectional analyses, linear regression was used to estimate associations of Street Smart Walk Score® with glucose, triglycerides, HDL and LDL cholesterol, systolic and diastolic blood pressure, and waist circumference, while logistic regression was used to estimate associations with odds of metabolic syndrome. Econometric fixed effects models were used to estimate longitudinal associations of changes in walkability with changes in each risk factor among participants who moved residential locations between 2004 and 2012 (n=583). Most cross-sectional and longitudinal associations were small and statistically non-significant. We found limited evidence that higher walkability was cross-sectionally associated with lower blood pressure but that increases in walkability were associated with increases in triglycerides and blood pressure over time. Further research over longer time periods is needed to understand the potential for built environment interventions to improve cardiometabolic health.

Development and deployment of the Computer Assisted Neighborhood Visual Assessment System (CANVAS) to measure health-related neighborhood conditions

Public health research has shown that neighborhood conditions are associated with health behaviors and outcomes. Systematic neighborhood audits have helped researchers measure neighborhood conditions that they deem theoretically relevant but not available in existing administrative data. Systematic audits, however, are expensive to conduct and rarely comparable across geographic regions. We describe the development of an online application, the Computer Assisted Neighborhood Visual Assessment System (CANVAS), that uses Google Street View to conduct virtual audits of neighborhood environments. We use this system to assess the inter-rater reliability of 187 items related to walkability and physical disorder on a national sample of 150 street segments in the United States. We find that many items are reliably measured across auditors using CANVAS and that agreement between auditors appears to be uncorrelated with neighborhood demographic characteristics. Based on our results we conclude that Google Street View and CANVAS offer opportunities to develop greater comparability across neighborhood audit studies.