Built Environment Exposures Research Articles

Neighborhood built and food environment in relation to glycemic control in people with type 2 diabetes in the moving to health study

ObjectiveTo examine whether built environment and food metrics are associated with glycemic control in people with type 2 diabetes.Research Design and Methods: We included 14,985 patients with type 2 diabetes using electronic health records from Kaiser Permanente Washington. Patient addresses were geocoded with ArcGIS using King County and Esri reference data. Built environment exposures estimated from geocoded locations included residential unit density, transit threshold residential unit density, park access, and having supermarkets and fast food restaurants within 1600-m Euclidean buffers. Linear mixed effects models compared mean changes of HbA1c from baseline at 1, 3 (primary) and 5 years by each built environment variable. ResultsPatients (mean age = 59.4 SD = 13.2, 49.5% female, 16.6% Asian, 9.8% Black, 5.5% Latino/Hispanic, 57.1% White, 20% insulin dependent, mean BMI = 32.7±7.7) had an average of 6 HbA1c measures available. Participants in the 1st tertile of residential density (lowest) had a greater decline in HbA1c (−0.42, −0.43, and −0.44 in years 1, 3, and 5 respectively) than those in the 3rd tertile (HbA1c = −0.37 at 1- and 3-years and −0.36 at 5-years; all p-values <0.05). Having any supermarkets within 1600 m of home was associated with a greater decrease in HbA1c at 1-year and 3-years compared to having none (all p-values <0.05). ConclusionsLower residential density and better proximity to supermarkets may benefit HbA1c control in people with people with type 2 diabetes. However, effects were small and indicate limited clinical significance.

The built environment and cardiovascular disease: an umbrella review and meta-meta-analysis.

To provide a comprehensive overview of the current evidence on objectively measured neighbourhood built environment exposures in relation to cardiovascular disease (CVD) events in adults. We searched seven databases for systematic reviews on associations between objectively measured long-term built environmental exposures, covering at least one domain (i.e. outdoor air pollution, food environment, physical activity environment like greenspace and walkability, urbanization, light pollution, residential noise, and ambient temperature), and CVD events in adults. Two authors extracted summary data and assessed the risk of bias independently. Robustness of evidence was rated based on statistical heterogeneity, small-study effect, and excess significance bias. Meta-meta-analyses were conducted to combine the meta-analysis results from reviews with comparable exposure and outcome within each domain. From the 3304 initial hits, 51 systematic reviews were included, covering 5 domains and including 179 pooled estimates. There was strong evidence of the associations between increased air pollutants (especially PM2.5 exposure) and increased residential noise with greater risk of CVD. Highly suggestive evidence was found for an association between increased ambient temperature and greater risk of CVD. Systematic reviews on physical activity environment, food environment, light pollution, and urbanization in relation to CVD were scarce or lacking. Air pollutants, increased noise levels, temperature, and greenspace were associated with CVD outcomes. Standardizing design and exposure assessments may foster the synthesis of evidence. Other crucial research gaps concern the lack of prospective study designs and lack of evidence from low-to-middle-income countries (LMICs). PROSPERO: CRD42021246580.

Built environment and schizophrenia re-hospitalization risk in China: A cohort study

BackgroundBuilt environment exposure, characterized by ubiquity and changeability, has the potential to be the prospective target of public health policy. However, little research has been conducted to explore its impact on schizophrenia. This study aimed to investigate the association between built environmentand and schizophrenia rehospitalization by simultaneously considering substantial built environmental exposures. MethodsWe recruited eligible schizophrenia patients from Hefei, Anhui Province, China between 2017 and 2019. The main outcome for this study was the time interval until the first recurrent hospital admission occurred within one year after discharge. For each included subject, we estimated the built environment exposures, including population density, walkability, land use mix, green and blue space, public transportation accessibility and road traffic indicator. Lasso (Least Absolute Shrinkage and Selection Operator) analysis was used to select the key variables. Multivariable Cox regression model was applied to obtain hazard ratio (HR) and its corresponding 95% confidence intervals (CI). Further, we also evaluated the joint effects of built environment characteristics on rehospitalization for schizophrenia by Quantile g-computation model. ResultsA total of 1564 hospitalized schizophrenia patients were enrolled, with 347 patients (22.2%) had a rehospitalization within one-year after discharge. Multivariable Cox regression analysis indicated that the re-hospitalization rate for schizophrenia would be higher in areas with a high population density (HR: 1.10, 95%CI: 1.04–1.16). Nonetheless, compared to the reference (Q1), participants who lived in a neighborhood with the highest walkability and NDVI (Normalized Difference Vegetation Index) (Q4) had a 76% and 47% lower risk of re-hospitalization within one year (HR:0.24, 95%CI: 0.13–0.45; and 0.53, 95%CI:0.32–0.85), respectively. Moreover, quantile-based g-computation analyses revealed that increased walkability and green space significantly eliminated the adverse effects of population density increases on schizophrenia patients, with a HR ratio of 0.61 (95%CI:0.48,0.79) per one quartile change at the same time. ConclusionOur study provides scientific evidence for the significant role of built environment in schizophrenia rehospitalization, suggesting that optimizing the built environment is required in designing and building a healthy city.

Measuring and modelling perceptions of the built environment for epidemiological research using crowd-sourcing and image-based deep learning models.

Perceptions of the built environment, such as nature quality, beauty, relaxation, and safety, may be key factors linking the built environment to human health. However, few studies have examined these types of perceptions due to the difficulty in quantifying them objectively in large populations. To measure and predict perceptions of the built environment from street-view images using crowd-sourced methods and deep learning models for application in epidemiologic studies. We used the Amazon Mechanical-Turk crowdsourcing platform where participants compared two street-view images and quantified perceptions of nature quality, beauty, relaxation, and safety. We optimized street-view image sampling methods to improve the quality and resulting perception data specific to participants enrolled in the Washington State Twin Registry (WSTR) health study. We used a transfer learning approach to train deep learning models by leveraging existing image perception data from the PlacePulse 2.0 dataset, which includes 1.1 million image comparisons, and refining based on new WSTR perception data. Resulting models were applied to WSTR addresses to estimate exposures and evaluate associations with traditional built environment measures. We collected over 36,000 image comparisons and calculated perception measures for each image. Our final deep learning models explained 77.6% of nature quality, 68.1% of beauty, 72.0% of relaxation, and 64.7% of safety in pairwise image comparisons. Applying transfer learning with the new perception labels specific to the WSTR yielded an average improvement of 3.8% for model performance. Perception measures were weakly to moderately correlated with traditional built environment exposures for WSTR participant addresses; for example, nature quality and NDVI (r = 0.55), neighborhood area deprivation (r = -0.16), and walkability (r = -0.20), respectively. We were able to measure and model perceptions of the built environment optimized for a specific health study. Future applications will examine associations between these exposure measures and mental health in the WSTR. Built environments influence health through complex pathways. Perceptions of nature quality, beauty, relaxation and safety may be particularly import for understanding these linkages, but few studies to-date have examined these perceptions objectively for large populations. For quantitative research, an exposure measure must be reproducible, accurate, and precise--here we work to develop such measures for perceptions of the urban environment. We created crowd-sourced and image-based deep learning methods that were able to measure and model these perceptions. Future applications will apply these models to examine associations with mental health in the Washington State Twin Registry.

Comparing effects of Euclidean buffers and network buffers on associations between built environment and transport walking: the Multi-Ethnic Study of Atherosclerosis

BackgroundTransport walking has drawn growing interest due to its potential to increase levels of physical activities and reduce reliance on vehicles. While existing studies have compared built environment-health associations between Euclidean buffers and network buffers, no studies have systematically quantified the extent of bias in health effect estimates when exposures are measured in different buffers. Further, prior studies have done the comparisons focusing on only one or two geographic regions, limiting generalizability and restricting ability to test whether direction or magnitude of bias are different by context. This study aimed to quantify the degree of bias in associations between built environment exposures and transport walking when exposures were operationalized using Euclidean buffers rather than network buffers in diverse contexts.MethodsWe performed a simulations study to systematically evaluate the degree of bias in associations between built environment exposures in Euclidean buffers and network buffers and transport walking, assuming network buffers more accurately captured true exposures. Additionally, we used empirical data from a multi-ethnic, multi-site cohort to compare associations between built environment amenities and walking for transport where built environment exposures were derived using Euclidean buffers versus network buffers.ResultsSimulation results found that the bias induced by using Euclidean buffer models was consistently negative across the six study sites (ranging from -80% to -20%), suggesting built environment exposures measured using Euclidean buffers underestimate health effects on transport walking. Percent bias was uniformly smaller for the larger 5 km scale than the 1 km and 0.25 km spatial scales, independent of site or built environment categories. Empirical findings aligned with the simulation results: built environment-health associations were stronger for built environment exposures operationalized using network buffers than using Euclidean buffers.ConclusionThis study is the first to quantify the extent of bias in the magnitude of the associations between built environment exposures and transport walking when the former are measured in Euclidean buffers vs. network buffers, informing future research to carefully conceptualize appropriate distance-based buffer metrics in order to better approximate real geographic contexts. It also helps contextualize existing research in the field that used Euclidean buffers when that were the only option. Further, this study provides an example of the uncertain geographic context problem.

Split and combine simulation extrapolation algorithm to correct geocoding coarsening of built environment exposures.

A major challenge in studies relating built environment features to health is measurement error in exposure due to geocoding errors. Faulty geocodes in built environment data introduce errors to exposure assessments that may induce bias in the corresponding health effect estimates. In this study, we examine the distribution of the measurement error in measures constructed from point-referenced exposures, quantify the extent of bias in exposure effect estimates due to geocode coarsening, and extend the simulation extrapolation (SIMEX) method to correct the bias. The motivating example focuses on the association between children's body mass index and exposure to the junk food environment, represented by the number of junk food outlets within a buffer area near their schools. We show, algebraically and through simulation studies, that coarsening of food outlet coordinates results in exposure measurement errors that have heterogeneous variance and nonzero mean, and that the resulting bias in the health effect can be away from the null. The proposed SC-SIMEX procedure accommodates the nonstandard measurement error distribution, without requiring external data, and provides the best bias correction compared to other SIMEX approaches.

The built environment as determinant of childhood obesity: A systematic literature review.

We evaluated the epidemiological evidence on the built environment and its link to childhood obesity, focusing on environmental factors such as traffic noise and air pollution, as well as physical factors potentially driving obesity-related behaviors, such as neighborhood walkability and availability and accessibility of parks and playgrounds. Eligible studies were (i) conducted on human children below the age of 18 years, (ii) focused on body size measurements in childhood, (iii) examined at least one built environment characteristic, (iv) reported effect sizes and associated confidence intervals, and (v) were published in English language. A z test, as alternative to the meta-analysis, was used to quantify associations due to heterogeneity in exposure and outcome definition. We found strong evidence for an association of traffic-related air pollution (nitrogen dioxide and nitrogen oxides exposure, p < 0.001) and built environment characteristics supportive of walking (street intersection density, p < 0.01 and access to parks, p < 0.001) with childhood obesity. We identified a lack of studies that account for interactions between different built environment exposures or verify the role and mechanism of important effect modifiers such as age.

Natural and built environments and blood pressure of Alpine schoolchildren

BackgroundEarly life environments may influence children's blood pressure (BP), but evidence on the combined effects of natural and built environment exposures is scarce. The present study investigates the associations of natural and built environment indicators, traffic noise, and air pollution with BP in children living in Alpine valleys. MethodsIn 2004/2005, 1251 school children (8–12 years old) were sampled for a cross-sectional survey in several Austrian and Italian mountain valleys. Children's mothers completed a questionnaire. The outcomes of interest were systolic and diastolic BP measured with a calibrated oscillometric device. Indicators of land cover assigned to the residential and school coordinates within 100 and 1000 m included normalized difference vegetation index (NDVI), tree canopy cover, and a broader naturalness indicator titled distance to nature (D2N). The presence of a home garden was also measured via self-report. Imperviousness density served as a proxy for the built environment. Residential air pollution (NO2) and noise (Lden) from traffic were calculated using bespoke modeling. NO2, Lden, physical activity, and body mass index (BMI) were treated as mediating pathways. ResultsHigher NDVI and tree cover levels in residential and school surroundings and home gardens were consistently associated with lower BP. The built environment was associated with higher BP. Counterintuitive inverse associations between NO2 and Lden and BP were also found. Structural equation modeling showed that higher levels of greenspace and presence of a home garden were weakly associated with more outdoor play spaces, and in turn with lower BMI, and ultimately with lower BP. ConclusionsExposure to natural environments may help maintain normal BP in children, while built environment may increase children's BP. Outdoor play and less adiposity in greener areas may mediate some of these associations. Evidence on air pollution and noise remains controversial and difficult to explain.

Associations of the residential built environment with adolescent sleep outcomes.

Over 75% of US high school students obtain insufficient sleep, placing them at risk for adverse health outcomes. Identification of modifiable determinants of adolescent sleep is needed to inform prevention strategies, yet little is known about the influence of the built environment on adolescent sleep. In this prospective study, actigraphy was used to assess sleep outcomes among 110 adolescents for 14 days each in eighth and ninth grades: duration (hours/night), onset and offset, and sleeping ≥8 hours. Home addresses were linked to built environment exposures: sound levels, tree canopy cover, street density, intersection density, population density, and housing density. Mixed-effects regression estimated associations of built environment measures with sleep outcomes, adjusting for sex, race, parent education, household income, household size, grade, weeknight status, and neighborhood poverty. A 1-standard deviation (SD) increase in neighborhood sound was associated with 16 minutes later sleep onset (β = 0.28; 95% confidence interval (CI): 0.06, 0.49) and 25% lower odds of sleeping for ≥8 hours (odds ratio (OR) = 0.75, 95% CI: 0.59, 0.96). A 1-SD increase in neighborhood tree canopy was associated with 18 minutes earlier sleep onset (β = -0.31, 95% CI: -0.49, -0.13) and 10 minutes earlier sleep offset (β= -0.17, 95% CI: -0.28, -0.05). No associations were observed for density-based exposures. Higher neighborhood sound level was associated with lower odds of sufficient sleep, while higher tree canopy cover was associated with more favorable sleep timing. Neighborhood sound levels and tree canopy cover are potential targets for policies and interventions to support healthier sleep among adolescents.

Estimating the Combined Effects of Natural and Built Environmental Exposures on Birthweight among Urban Residents in Massachusetts.

Intrauterine growth has health implications both in childhood and adulthood. Birthweight is partially determined by prenatal environmental exposures. We aim to identify important predictors of birthweight out of a set of environmental, built environment exposures, and socioeconomic environment variables during pregnancy (i.e., fine particulate matter (PM2.5), temperature, greenness, walkability, noise, and economic indices). We included all singleton live births of mothers who resided in urban census block-groups and delivered in Massachusetts between 2001 and 2011 (n = 640,659). We used an elastic-net model to select important predictors of birthweight and constructed a multivariate model including the selected predictors, with adjustment for confounders. We additionally used a weighted quantile sum regression to assess the contribution of each exposure to differences in birthweight. All exposures were selected as important predictors of birthweight. In the multivariate model, lower birthweight was significantly associated with lower greenness and with higher temperature, walkability, noise, and segregation of the “high income” group. Treating the exposures individually, nighttime noise had the highest weight in its contribution to lower birthweight. In conclusion, after accounting for individual confounders, maternal environmental exposures, built environment exposures, and socioeconomic environment during pregnancy were important predictors of birthweight, emphasizing the role of these exposures in fetal growth and development.

Longitudinal impact of changes in the residential built environment on physical activity: findings from the ENABLE London cohort study

BackgroundPrevious research has reported associations between features of the residential built environment and physical activity but these studies have mainly been cross-sectional, limiting inference. This paper examines whether changes in a range of residential built environment features are associated with changes in measures of physical activity in adults. It also explores whether observed effects are moderated by socio-economic status.MethodsData from the Examining Neighbourhood Activity in Built Living Environments in London (ENABLE London) study were used. A cohort of 1278 adults seeking to move into social, intermediate, and market-rent East Village accommodation was recruited in 2013–2015, and followed up after 2 years. Accelerometer-derived steps (primary outcome), and GIS-derived measures of residential walkability, park proximity and public transport accessibility were obtained both at baseline and follow-up. Daily steps at follow-up were regressed on daily steps at baseline, change in built environment exposures and confounding variables using multilevel linear regression to assess if changes in neighbourhood walkability, park proximity and public transport accessibility were associated with changes in daily steps. We also explored whether observed effects were moderated by housing tenure as a marker of socio-economic status.ResultsBetween baseline and follow-up, participants experienced a 1.4 unit (95%CI 1.2,1.6) increase in neighbourhood walkability; a 270 m (95%CI 232,307) decrease in distance to their nearest park; and a 0.7 point (95% CI 0.6,0.9) increase in accessibility to public transport. A 1 s.d. increase in neighbourhood walkability was associated with an increase of 302 (95%CI 110,494) daily steps. A 1 s.d. increase in accessibility to public transport was not associated with any change in steps overall, but was associated with a decrease in daily steps amongst social housing seekers (− 295 steps (95%CI − 595, 3), and an increase in daily steps for market-rent housing seekers (410 95%CI -191, 1010) (P-value for effect modification = 0.03).ConclusionTargeted changes in the residential built environment may result in increases in physical activity levels. However, the effect of improved accessibility to public transport may not be equitable, showing greater benefit to the more advantaged.

0390 Associations of the Neighborhood Built Environment with Adolescent Sleep Outcomes

Abstract Introduction Over 75% of U.S. high school students obtain insufficient amounts of sleep. Identification of modifiable environmental determinants of adolescent sleep is needed to inform interventions and public health strategies, yet little is known about the influence of the built environment on adolescent sleep. We examined associations of the built environment with objectively-measured adolescent sleep outcomes. Methods In this longitudinal, prospective study, we used actigraphy to assess sleep outcomes for 14 days each in 8th grade and 9th grade: duration (hours/night), onset (hours from 00:00), offset (hours from 00:00), and sleeping &amp;gt;8 hours. Home addresses were linked to built environment exposures based on half-mile Euclidian buffers (overall/human-made sound levels, percent tree canopy cover, street density, intersection density) and census block group (population density, housing density). Mixed-effects linear (sleep duration, onset, offset) and logistic (&amp;gt;8 hours) regression estimated associations of each built environment measure with sleep outcomes, adjusting for sex, race, parent education, household income, grade and weeknight status (school or non-school night). Results Among 108 adolescents - 53% female and 25% Black - providing 2,388 nights of sleep data across 8th and 9th grades, a 1-standard deviation increase in neighborhood sound (overall and human-made) associated with 11 minutes later sleep onset (β=0.19; 95% CI: 0.01, 0.38) and 20% lower odds of sleeping for &amp;gt;8 hours (OR=0.80, 95% CI: 0.62, 1.02). A 1-standard deviation increase in neighborhood tree canopy cover associated with 11 minutes earlier sleep onset (β= -0.19, 95% CI: -0.35, -0.03) and 7 minutes earlier sleep offset (β= -0.12, 95% CI: -0.23, -0.02). No associations were observed for “density based” exposures. Conclusion Higher tree canopy cover associated with more favorable sleep timing while higher neighborhood sound level associated with later timing of sleep onset. These modifiable neighborhood built environment factors should be considered when intervening to support healthier sleep among adolescents. Support NIH/NHLBI K01HL123612 (JM) and Sleep Research Society Foundation and K23HD094905 (AAW)

GIS-modelled built-environment exposures reflecting daily mobility for applications in child health research

BackgroundInaccurately modelled environmental exposures may have important implications for evidence-based policy targeting health promoting or hazardous facilities. Travel routes modelled using GIS generally use shortest network distances or Euclidean buffers to represent journeys with corresponding built-environment exposures calculated along these routes. These methods, however, are an unreliable proxy for calculating child built-environment exposures as child route choice is more complex than shortest network routes.MethodsWe hypothesised that a GIS model informed by characteristics of the built-environment known to influence child route choice could be developed to more accurately model exposures. Using GPS-derived walking commutes to and from school we used logistic regression models to highlight built-environment features important in child route choice (e.g. road type, traffic light count). We then recalculated walking commute routes using a weighted network to incorporate built-environment features. Multilevel regression analyses were used to validate exposure predictions to the retail food environment along the different routing methods.ResultsChildren chose routes with more traffic lights and residential roads compared to the modelled shortest network routes. Compared to standard shortest network routes, the GPS-informed weighted network enabled GIS-based walking commutes to be derived with more than three times greater accuracy (38%) for the route to school and more than 12 times greater accuracy (92%) for the route home.ConclusionsThis research advocates using weighted GIS networks to accurately reflect child walking journeys to school. The improved accuracy in route modelling has in turn improved estimates of children’s exposures to potentially hazardous features in the environment. Further research is needed to explore if the built-environment features are important internationally. Route and corresponding exposure estimates can be scaled to the population level which will contribute to a better understanding of built-environment exposures on child health and contribute to mobility-based child health policy.

The combined impact of environmental and built environment exposures on birthweight in an urban population in Massachusetts

The combined impact of environmental and built environment exposures on birthweight in an urban population in Massachusetts

Built environment exposures of adults in the moving to opportunity experiment

This article describes environmental exposures of adult participants in the Moving to Opportunity (MTO) for Fair Housing experiment over a four to seven year period from baseline to the interim evaluation. The MTO experiment randomized participants living in public housing or private assisted housing at baseline into experimental and control groups and provided a housing voucher for experimental group participants to move to neighbourhoods with less than 10% of the population below the poverty line. However, few studies have examined how this move affected exposures to health promoting environments. We used data on residential locations of MTO participants and archival data on the built and food environment to construct environmental exposure variables. MTO participants in the experimental and Section 8 groups lived in neighbourhoods with higher food prices, less high intensity development and more open space relative to the control group. The findings suggest that housing policies can have potential health consequences by altering health-related environmental exposures.

Longitudinal Impact of Changes in the Residential Built Environment on Physical Activity: Findings from the ENABLE London Study

Background: Studies have found associations between features of the residential built environment and physical activity but these have been mainly cross-sectional. In this paper we explore whether longitudinal changes in environmental exposures are associated with changes in physical activity in adults. Methods: Data from the Examining Neighbourhood Activity in Built Living Environments in London (ENABLE London) study were used. Daily steps at follow-up were regressed on daily steps at baseline, change in built environment exposures and confounding variables using multilevel linear regression to assess if neighbourhood walkability, park proximity and public transport accessibility were associated with changes in accelerometer-derived daily steps. We also explored whether observed effects were moderated by housing tenure as a marker of socio-economic status. Findings: Between baseline and follow-up, participants experienced a 1·4 unit (95%CI 1·2,1·6) increase in neighbourhood walkability; a 270m (95%CI 232,307) decrease in distance to their nearest park; and a 0·7 point (95%CI 0·6,0·9) increase in accessibility to public transport. A 1 s.d. increase in neighbourhood walkability was associated with an increase of 302 (95%CI 110,494) daily steps. A 1 s.d. increase in accessibility to public transport was not associated with any change in steps overall, but was associated with a decrease in daily steps amongst social housing seekers (-295 steps (95%CI -595, 3), and an increase in daily steps for market-rent housing seekers (410 95%CI -191, 1010) (P-value for effect modification=0·03). Interpretation: Targeted changes in the residential built environment may result in increases in physical activity levels. However, the effect of improved accessibility to public transport may not be equitable, showing greater benefit to the more advantaged. Funding Statement: This research is being supported by project grants from the UK National Prevention Research Initiative (MR/J000345/1) and the UK National Institute for Health Research (NIHR; 12/211/69). Diabetes and obesity prevention research at St George’s, University of London is supported by the NIHR Collaboration for Leadership in Applied Health Research and Care, South London. CMN is supported by the Wellcome Trust Institutional Strategic Support Fund (204809/Z/16/Z). BR was supported by a PhD studentship from St George’s, University of London. ARC and ASP are supported by the NIHR Biomedical Research Centre at University Hospitals Bristol National Health Service Foundation Trust and the University of Bristol. AE is funded by the Medical Research Council as part of the Neighbourhoods and Communities Programme (MC_UU_12017–10). BG-C is supported by a National Health and Medical Research Council Principal Research Fellowship (1107672). Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: Full ethical approval was obtained from the relevant Multi-Centre Research Ethics Committee (REC Reference 12/LO/1031). All participants provided written informed consent.

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.

Built and natural environment planning principles for promoting health: an umbrella review

BackgroundThe built and natural environment and health are inextricably linked. However, there is considerable debate surrounding the strength and quality of the evidence base underpinning principles of good practice for built and natural environment design in promoting health. This umbrella review aimed to assess relationships between the built and natural environment and health, concentrating on five topic areas: neighbourhood design, housing, food environment, natural and sustainable environment, and transport.MethodsA structured search was conducted for quantitative systematic reviews and stakeholder reviews published between January 2005 and April 2016. Seven databases and the websites of 15 relevant and respected stakeholder organisations known to publish review-level documentation were searched. Searches were limited to English-language publications and duplicate references were removed. Evidence quality and strength was appraised using validated techniques. Findings were used to develop a diagram for each topic area, illustrating relationships between built and natural environment planning principles and health-related outcomes.ResultsA total of 117 systematic reviews and review-level documents were eligible for inclusion. The quality of evidence was mixed; much of the evidence examined relied on findings from cross-sectional studies, making it difficult to draw clear causal links between built environment exposures and health-related impacts and outcomes. Fourteen actionable planning principles associated with positive health-related outcomes were identified across the five topic areas. For example, neighbourhoods that enhanced walkability, were complete and compact in design, and those which enhanced connectivity through safe and efficient infrastructure were associated with better health-related outcomes relating to physical activity, social engagement, mental health, perceptions of crime, and road traffic collisions. Evidence for the effectiveness of planning principles across different topic areas and on reducing health inequalities was sparse and inconclusive.ConclusionsFindings provide an up-to-date overview of relationships between the built and natural environment and health and present logical, evidence-based messages to aid communication between public health and planning professionals.

Residential greenness and adiposity: Findings from the UK Biobank

BackgroundWith the rapid urbanization and prevailing obesity pandemic, the role of residential green exposures in obesity prevention has gained renewed focus. The study investigated the effects of residential green exposures on adiposity using a large and diverse population sample drawn from the UK Biobank. Materials and methodsThis was a population based cross-sectional study of 333,183 participants aged 38–73years with individual-level data on residential greenness and built environment exposures. Residential greenness was assessed through 0.50-metre resolution normalized difference vegetation index (NDVI) derived from spectral reflectance measurements in remotely sensed colour infrared data and measured around geocoded participants' dwelling. A series of continuous and binary outcome models examined the associations between residential greenness and markers of adiposity, expressed as body-mass index (BMI) in kg/m2, waist circumference (WC) in cm, whole body fat (WBF) in kg and obesity (BMI≥30kg/m2) after adjusting for other activity-influencing built environment and individual-level confounders. Sensitivity analyses involved studying effect modification by gender, age, urbanicity and SES as well as examining relationships between residential greenness and active travel behaviour. ResultsResidential greenness was independently and consistently associated with lower adiposity, the association being robust to adjustments. An interquartile increment in NDVI greenness was associated with lower BMI (βBMI=−0.123kg/m2, 95% CI: −0.14, −0.10kg/m2), WC (βWC=−0.551cm, 95% CI: −0.61, −0.50cm), and WBF (βWBF=−0.138kg, 95% CI: −0.18, −0.10kg) as well as a reduced relative risk of obesity (RR=0.968, 95% CI: 0.96, 0.98). Residential greenness was beneficially related with active travel, being associated with higher odds of using active mode for non-work travel (OR=1.093, 95% CI: 1.08, 1.11) as well as doing >30min walking (OR=1.039, 95% CI: 1.03, 1.05). ConclusionResiding in greener areas was associated with healthy weight outcomes possibly through a physical activity-related mechanism. Green allocation and design may act as upstream-level public health interventions ameliorating the negative health externalities of obesogenic urban environments. Further prospective studies are needed to identify potential causal pathways and thereby effectively guide such interventions.

The impact of neighborhood social and built environment factors across the cancer continuum: Current research, methodological considerations, and future directions.

Neighborhood social and built environments have been recognized as important contexts in which health is shaped. The authors reviewed the extent to which these neighborhood factors have been addressed in population-level cancer research by scanning the literature for research focused on specific social and/or built environment characteristics and their association with outcomes across the cancer continuum, including incidence, diagnosis, treatment, survivorship, and survival. The commonalities and differences in methodologies across studies, the current challenges in research methodology, and future directions in this research also were addressed. The assessment of social and built environment factors in relation to cancer is a relatively new field, with 82% of the 34 reviewed articles published since 2010. Across the wide range of social and built environment exposures and cancer outcomes considered by the studies, numerous associations were reported. However, the directions and magnitudes of associations varied, in large part because of the variation in cancer sites and outcomes studied, but also likely because of differences in study populations, geographic regions, and, importantly, choice of neighborhood measures and geographic scales. The authors recommend that future studies consider the life-course implications of cancer incidence and survival, integrate secondary and self-report data, consider work neighborhood environments, and further develop analytical and statistical approaches appropriate to the geospatial and multilevel nature of the data. Incorporating social and built environment factors into research on cancer etiology and outcomes can provide insights into disease processes, identify vulnerable populations, and generate results with translational impact of relevance for interventionists and policy makers.