Making cities mental health friendly for adolescents and young adults.

Urban life shapes the mental health of city dwellers, and although cities provide access to health, education and economic gain, urban environments are often detrimental to mental health1,2. Increasing urbanization over the next three decades will be accompanied by a growing population of children and adolescents living in cities3. Shaping the aspects of urban life that influence youth mental health could have an enormous impact on adolescent well-being and adult trajectories4. We invited a multidisciplinary, global group of researchers, practitioners, advocates and young people to complete sequential surveys to identify and prioritize the characteristics of a mental health-friendly city for young people. Here we show a set of ranked characteristic statements, grouped by personal, interpersonal, community, organizational, policy and environmental domains of intervention. Life skills for personal development, valuing and accepting young people's ideas and choices, providing safe public space for social connection, employment and job security, centring youth input in urban planning and design, and addressing adverse social determinants were priorities by domain. We report the adversities that COVID-19 generated and link relevant actions to these data. Our findings highlight the need for intersectoral, multilevel intervention and for inclusive, equitable, participatory design of cities that support youth mental health.

Building running-friendly cities: effects of streetscapes on running using 9.73 million fitness tracker data in Shanghai, China.

The association between built environment and physical activity has been recognized. However, how and to what extent microscale streetscapes are related to running activity remains underexplored, partly due to the lack of running data in large urban areas. Moreover, few studies have examined the interactive effects of macroscale built environment and microscale streetscapes. This study examines the main and interactive effects of the two-level environments on running intensity, using 9.73 million fitness tracker data from Keep in Shanghai, China. Results of spatial error model showed that: 1) the explanatory power of microscale streetscapes was higher than that of macroscale built environment with R2 of 0.245 and 0.240, respectively, which is different from the prior finding that R2 is greater for macroscale built environment than for microscale streetscape; 2) sky and green view indexes were positively associated with running intensity, whereas visual crowdedness had a negative effect; 3) there were negative interactions of land use Herfindahl-Hirschman index with sky and green view indexes, while a positive interaction was observed for visual crowdedness. To conclude, greener, more open and less visually crowded streetscapes, can promote running behavior and enhance the benefits of land use mix as well. The findings highlight the importance of streetscapes in promoting running behavior, instead of a supplement to macroscale built environment.

Correlation analysis of the urban community environment and health promotion among adults aged ≥ 55 years: the mediating role of physical activity.

BACKGROUND: Physical health (PH), including muscle strength, endurance, and the ability to perform daily physical tasks, gradually declines with increasing age, leading to disability and an increased disease burden. Both the urban community environment (UCE) and physical activity (PA) were linked to PH. METHODS: A total of 625 participants aged 55 years and older from Haidian and Chaoyang Districts in Beijing, China, were included in the study from September to December 2023.PH was assessed by a combined score of four tests, including BMI, chronic disease, depression and self-rated health.The Neighborhood Environment Walkability Scale-Abbreviated (NEWS-A), Center for Epidemiological Survey-Depression Scale (CES-D), Physical Activity Scale for Elderly (PASE) and Social Support Rate Score (SSRS) were collected. The analysis was carried out with SPSS v.20 and Amos 24, and the results were validated via structural equation modeling (SEM). RESULTS: The urban community environment was positively associated with the health level of older adults. Specifically, the standardized path coefficients indicate that the influence of the built environment (0.72-0.88) was greater than that of the social environment (0.43-0.55) and personal attributes (0.22-0.37) on older adults' health. Physical activity demonstrated both a mediating effect and an indirect effect, highlighting its significant value as a mediating variable. CONCLUSIONS: The urban community environment has a positive impact on promoting the physical health of older adults, with the influence of the built environment being greater than that of the social environment and personal attributes on their physical health. Physical activity acts as a mediator between the urban community environment and the physical health of older adults.

Spatial analysis of the impact of urban built environment on cardiovascular diseases: a case study in Xixiangtang, China.

BACKGROUND: The built environment, as a critical factor influencing residents' cardiovascular health, has a significant potential impact on the incidence of cardiovascular diseases (CVDs). METHODS: Taking Xixiangtang District in Nanning City, Guangxi Zhuang Autonomous Region of China as a case study, we utilized the geographic location information of CVD patients, detailed road network data, and urban points of interest (POI) data. Kernel density estimation (KDE) and spatial autocorrelation analysis were specifically employed to identify the spatial distribution patterns, spatial clustering, and spatial correlations of built environment elements and diseases. The GeoDetector method (GDM) was used to assess the impact of environmental factors on diseases, and geographically weighted regression (GWR) analysis was adopted to reveal the spatial heterogeneity effect of environmental factors on CVD risk. RESULTS: The results indicate that the built environment elements and CVDs samples exhibit significant clustering characteristics in their spatial distribution, with a positive correlation between the distribution density of environmental elements and the incidence of CVDs (Moran's I > 0, p < 0.01). Further factor detection revealed that the distribution of healthcare facilities had the most significant impact on CVDs (q = 0.532, p < 0.01), followed by shopping and consumption (q = 0.493, p < 0.01), dining (q = 0.433, p < 0.01), and transportation facilities (q = 0.423, p < 0.01), while the impact of parks and squares (q = 0.174, p < 0.01) and road networks (q = 0.159, p < 0.01) was relatively smaller. Additionally, the interaction between different built environment elements exhibited a bi-factor enhancement effect on CVDs. In the local analysis, the spatial heterogeneity of different built environment elements on CVDs further revealed the regional differences and complexities. CONCLUSIONS: The spatial distribution of built environment elements is significantly correlated with CVDs to varying degrees and impacts differently across regions, underscoring the importance of the built environment on cardiovascular health. When planning and improving urban environments, elements and areas that have a more significant impact on CVDs should be given priority consideration.

The moderating role of the built environment in prenatal lifestyle interventions.

This study examined whether the neighborhood built environment moderated gestational weight gain (GWG) in LIFE-Moms clinical trials. Participants were 790 pregnant women (13.9 weeks' gestation) with overweight or obesity randomized within four clinical centers to standard care or lifestyle intervention to reduce GWG. Geographic information system (GIS) was used to map the neighborhood built environment. The intervention relative to standard care significantly reduced GWG (coefficient = 0.05; p = 0.005) and this effect remained significant (p < 0.03) after adjusting for built environment variables. An interaction was observed for presence of fast food restaurants (coefficient = -0.007; p = 0.003). Post hoc tests based on a median split showed that the intervention relative to standard care reduced GWG in participants living in neighborhoods with lower fast food density 0.08 [95% CI, 0.03,0.12] kg/week (p = 0.001) but not in those living in areas with higher fast food density (0.02 [-0.04, 0.08] kg/week; p = 0.55). Interaction effects suggested less intervention efficacy among women living in neighborhoods with more grocery/convenience stores (coefficient = -0.005; p = 0.0001), more walkability (coefficient -0.012; p = 0.007) and less crime (coefficient = 0.001; p = 0.007), but post-hoc tests were not significant. No intervention x environment interaction effects were observed for total number of eating establishments or tree canopy. Lifestyle interventions during pregnancy were effective across diverse physical environments. Living in environments with easy access to fast food restaurants may limit efficacy of prenatal lifestyle interventions, but future research is needed to replicate these findings.

Longitudinal association of built environment pattern with DXA-derived body fat in elderly Hong Kong Chinese: a latent profile analysis.

BACKGROUND: One major limitation of prior studies regarding the associations between built environment (BE) and obesity has been the use of anthropometric indices (e.g., body mass index [BMI]) for assessing obesity status, and there has been limited evidence of associations between BE and body fat. This study aimed to explore the longitudinal association between BE and body fat in a cohort of elderly Hong Kong Chinese and examine whether the BE-body fat associations differed by BMI categories. METHODS: Between 2001 and 2003, 3944 participants aged 65-98 years were recruited and followed for a mean of 6.4 years. BE characteristics were assessed via Geographic Information System. Body fat (%) at whole body and regional areas (trunk, limbs, android, and gynoid) were assessed by dual energy X-ray absorptiometry at baseline and three follow-ups. Latent profile analysis was used to derive BE class, and linear mixed-effects models were used to investigate the associations of BE class with changes in body fat. Stratified analyses by BMI categories were also conducted. RESULTS: Three BE classes were identified. Participants in Class 2 (characterized by greater open space and proportion of residential land use) had a slower increase in whole body fat (B = -0.403, 95% confidence interval [CI]: -0.780, -0.014) and limbs fat (-0.471, 95% CI: -0.870, -0.071) compared with participants in Class 1 (characterized by high proportion of commercial land use). There were significant interactions of BE class with BMI, and participants in Class 2 had a slower increase in whole body fat and regional fat compared with participants in Class 1 (B ranging from -0.987 [limbs] to -0.523 [gynoid]) among overweight and obese participants only. CONCLUSIONS: We found that those who resided in the areas characterized by greater open space and proportion of residential land use had a slower body fat increase.

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.

Identifying urban built environment factors in pregnancy care and maternal mental health outcomes.

BACKGROUNDS: Risk factors related to the built environment have been associated with women's mental health and preventive care. This study sought to identify built environment factors that are associated with variations in prenatal care and subsequent pregnancy-related outcomes in an urban setting. METHODS: In a retrospective observational study, we characterized the types and frequency of prenatal care events that are associated with the various built environment factors of the patients' residing neighborhoods. In comparison to women living in higher-quality built environments, we hypothesize that women who reside in lower-quality built environments experience different patterns of clinical events that may increase the risk for adverse outcomes. Using machine learning, we performed pattern detection to characterize the variability in prenatal care concerning encounter types, clinical problems, and medication prescriptions. Structural equation modeling was used to test the associations among built environment, prenatal care variation, and pregnancy outcome. The main outcome is postpartum depression (PPD) diagnosis within 1 year following childbirth. The exposures were the quality of the built environment in the patients' residing neighborhoods. Electronic health records (EHR) data of pregnant women (n = 8,949) who had live delivery at an urban academic medical center from 2015 to 2017 were included in the study. RESULTS: We discovered prenatal care patterns that were summarized into three common types. Women who experienced the prenatal care pattern with the highest rates of PPD were more likely to reside in neighborhoods with homogeneous land use, lower walkability, lower air pollutant concentration, and lower retail floor ratios after adjusting for age, neighborhood average education level, marital status, and income inequality. CONCLUSIONS: In an urban setting, multi-purpose and walkable communities were found to be associated with a lower risk of PPD. Findings may inform urban design policies and provide awareness for care providers on the association of patients' residing neighborhoods and healthy pregnancy.

Built environment, physical activity, and obesity of adults in Pingshan District, Shenzhen City in Southern China.

BACKGROUND: The relation between neighbourhood built environment and obesity has been described as both nuanced and complex. AIM: The objective of this study was to examine the relationship between the built environment, physical activity, and obesity in a rapidly urbanised area of China. SUBJECTS AND METHODS: This is a cross-sectional study. Descriptive statistics were used to describe the socio-demographic variables, physical activity levels and BMI status. Multivariable logistic regression models were used to examine the association between neighbourhood environment, the likelihood of engaging in different types of physical activity, and BMI. RESULTS: A total of 842 respondents completed the questionnaires and were included (84.1% response rate). Among them, 56.4% reported meeting high physical activity levels, while 40.7% were overweight or obese. Multivariable regression analysis showed that better road conditions (ß = 0.122, t = 2.999, p = 0.003) and access to physical activity facilities (ß = 0.121, t = 3.193, p = 0.001) were significantly associated with higher levels of physical activity. Physical activity levels were inversely associated with the likelihood of being overweight (OR = 0.565, 95%CI: 0.3 4 9-0.917) or obese (OR = 0.614, 95%CI: 0.3 9 0-0.966). CONCLUSION: The built environment has an important impact on physical activity. However, the direct impact of leisure physical activity on BMI is not significant. This research provides a summary of recent evidence in Pingshan District on built environments that are most favourable for physical activity and obesity.

Development and Validation of a Google Street View Pedestrian Safety Audit Tool.

BACKGROUND: Assessing aspects of intersections that may affect the risk of pedestrian injury is critical to developing child pedestrian injury prevention strategies, but visiting intersections to inspect them is costly and time-consuming. Several research teams have validated the use of Google Street View to conduct virtual neighborhood audits that remove the need for field teams to conduct in-person audits. METHODS: We developed a 38-item virtual audit instrument to assess intersections for pedestrian injury risk and tested it on intersections within 700 m of 26 schools in New York City using the Computer-assisted Neighborhood Visual Assessment System (CANVAS) with Google Street View imagery. RESULTS: Six trained auditors tested this instrument for inter-rater reliability on 111 randomly selected intersections and for test-retest reliability on 264 other intersections. Inter-rater kappa scores ranged from -0.01 to 0.92, with nearly half falling above 0.41, the conventional threshold for moderate agreement. Test-retest kappa scores were slightly higher than but highly correlated with inter-rater scores (Spearman rho = 0.83). Items that were highly reliable included the presence of a pedestrian signal (K = 0.92), presence of an overhead structure such as an elevated train or a highway (K = 0.81), and intersection complexity (K = 0.76). CONCLUSIONS: Built environment features of intersections relevant to pedestrian safety can be reliably measured using a virtual audit protocol implemented via CANVAS and Google Street View.

Cardiometabolic Profiles and Change in Neighborhood Food and Built Environment Among Older Adults: A Natural Experiment.

BACKGROUND: The association between neighborhood environment and health may be biased due to confounding by residential self-selection. The displacement of disaster victims can act as a natural experiment that exposes residents to neighborhood environments they did not select, allowing for the study of neighborhood effects on health. METHODS: We leveraged data from a cohort of older adults 65 years of age or older living in Iwanuma, Japan, located 80 km west of the 2011 Great East Japan Earthquake and Tsunami. Surveys were conducted 7 months before the disaster, as well as 2.5 and 5.5 years afterward, and linked with medical records. We classified each individual's type of exposure to neighborhood environment based on proximity to local food and recreation destinations and walkability. RESULTS: Fixed-effect models indicated that change in the exposure type from low to high urban density was associated with increased body mass index (0.46 kg/m; 95% confidence interval [CI] = 0.20, 0.73), waist circumference (1.8 cm; 95% CI = 0.56, 3.0), low-density lipoprotein cholesterol (11 mg/dl; 95% CI = 5.0, 17), and decreased high-density lipoprotein cholesterol (-3.1 mg/dl; 95% CI = -5.0, -1.3). We observed similar trends when we analyzed only the individuals who experienced postdisaster relocation to temporary homes. CONCLUSIONS: Increased proximity to food outlets was simultaneously correlated with greater walkability and accessibility to recreational destinations; however, any protective association of physical activity-promoting built environment appeared to be offset by proximity to unhealthy food outlets, especially fast-food restaurants and bars.

Different neighborhood walkability indexes for active commuting to school are necessary for urban and rural children and adolescents.

BACKGROUND: Literature focusing on youth has reported limited evidence and non-conclusive associations between neighborhood walkability measures and active commuting to and from school (ACS). Moreover, there is a lack of studies evaluating both macro- and micro-scale environmental factors of the neighborhood when ACS is analyzed. Likewise, most studies on built environment attributes and ACS focus on urban areas, whereas there is a lack of studies analyzing rural residential locations. Moreover, the relationship between built environment attributes and ACS may differ in children and adolescents. Hence, this study aimed to develop walkability indexes in relation to ACS for urban and rural children and adolescents, including both macro- and micro-scale school-neighborhood factors. METHODS: A cross-sectional study of 4593 participants from Spain with a mean age of 12.2 (SD 3.6) years was carried out. Macro-scale environmental factors were evaluated using geographic information system data, and micro-scale factors were measured using observational procedures. Socio-demographic characteristics and ACS were assessed with a questionnaire. Several linear regression models were conducted, including all the possible combinations of six or less built environment factors in order to find the best walkability index. RESULTS: Analyses showed that intersection density, number of four-way intersections, and residential density were positively related to ACS in urban participants, but negatively in rural participants. In rural children, positive streetscape characteristics, number of regulated crossings, traffic calming features, traffic lanes, and parking street buffers were also negatively related to ACS. In urban participants, other different factors were positively related to ACS: number of regulated crossings, positive streetscape characteristics, or crossing quality. Land use mix acted as a positive predictor only in urban adolescents. Distance to the school was a negative predictor on all the walkability indexes. However, aesthetic and social characteristics were not included in any of the indexes. CONCLUSIONS: Interventions focusing on improving built environments to increase ACS behavior need to have a better understanding of the walkability components that are specifically relevant to urban or rural samples.

Health Disparities, Transportation Equity and Complete Streets: a Case Study of a Policy Development Process through the Lens of Critical Race Theory.

Historic disinvestment in transportation infrastructure is directly related to adverse social conditions underlying health disparities in low-income communities of color. Complete Streets policies offer a strategy to address inequities and subsequent public health outcomes. This case study examines the potential for an equity-focused policy process to address systemic barriers and identify potential measures to track progress toward equity outcomes. Critical race theory provided the analytical framework to examine grant reports, task force notes, community workshop/outreach activities, digital stories, and stakeholder interviews. Analysis showed that transportation inequities are entrenched in historically rooted disparities that are perpetuated in ongoing decision-making processes. Intentional efforts to incorporate equity into discussions with community members and representatives contributed to explicit equity language being included in the final policy. The potential to achieve equity outcomes will depend upon policy implementation. Concrete strategies to engage community members and focus city decision-making practices on marginalized and disenfranchised communities are identified.

Determinants of Residential Preferences Related to Built and Social Environments and Concordance between Neighborhood Characteristics and Preferences.

We explored associations between residential preferences and sociodemographic characteristics, the concordance between current neighborhood characteristics and residential preferences, and heterogeneity in concordance by income and race/ethnicity. Data came from a cross-sectional phone and mail survey of 3668 residents of New York City, Baltimore, Chicago, Los Angeles, St. Paul, and Winston Salem in 2011-12. Scales characterized residential preferences and neighborhood characteristics. Stronger preferences were associated with being older, female, non-White/non-Hispanic, and lower education. There was significant positive but weak concordance between current neighborhood characteristics and residential preferences (after controlling sociodemographic characteristics). Concordance was stronger for persons with higher income and for Whites, suggesting that residential self-selection effects are strongest for populations that are more advantaged.

Health and the built environment in United States cities: measuring associations using Google Street View-derived indicators of the built environment.

BACKGROUND: The built environment is a structural determinant of health and has been shown to influence health expenditures, behaviors, and outcomes. Traditional methods of assessing built environment characteristics are time-consuming and difficult to combine or compare. Google Street View (GSV) images represent a large, publicly available data source that can be used to create indicators of characteristics of the physical environment with machine learning techniques. The aim of this study is to use GSV images to measure the association of built environment features with health-related behaviors and outcomes at the census tract level. METHODS: We used computer vision techniques to derive built environment indicators from approximately 31 million GSV images at 7.8 million intersections. Associations between derived indicators and health-related behaviors and outcomes on the census-tract level were assessed using multivariate regression models, controlling for demographic factors and socioeconomic position. Statistical significance was assessed at the α = 0.05 level. RESULTS: Single lane roads were associated with increased diabetes and obesity, while non-single-family home buildings were associated with decreased obesity, diabetes and inactivity. Street greenness was associated with decreased prevalence of physical and mental distress, as well as decreased binge drinking, but with increased obesity. Socioeconomic disadvantage was negatively associated with binge drinking prevalence and positively associated with all other health-related behaviors and outcomes. CONCLUSIONS: Structural determinants of health such as the built environment can influence population health. Our study suggests that higher levels of urban development have mixed effects on health and adds further evidence that socioeconomic distress has adverse impacts on multiple physical and mental health outcomes.

Road network intersection density and childhood obesity risk in the US: a national longitudinal study.

OBJECTIVES: Road intersection density is an important indicator of walkability. The objectives of this study were to examine the trends in intersection density in the US from 2007 to 2011 and assess the associations between intersection density and childhood obesity risk at the state level. STUDY DESIGN: Longitudinal analyses were conducted to assess the spatial-temporal changes of population-weighted intersection density in relation to the risk of childhood obesity in the US. METHODS: Road network data from the Topologically Integrated Geographic Encoding and Referencing (TIGER) (2007-2011), the prevalence of overweight and obesity data from the National Survey of Children's Health (NSCH) (2007-2011), and the American Community Survey (ACS) (2011) were used. Geographic information system (GIS) visualization and spatial and regression analyses were conducted. Mixed-effect models were fit to assess the longitudinal relationship between intersection density and childhood obesity. RESULTS: Between 2007 and 2011, population-weighted intersection density remained relatively stable in most states. Low-intersection-density states were clustered in the Southeastern region in both 2007 and 2011. The high-intersection-density states were clustered in the Middle Atlantic Division. California and Nevada also were identified as high-intersection-density clusters in 2011. States with lower road intersection density corresponded with states with higher childhood obesity prevalence. Our mixed-effect model estimates suggested that increased intersection density was associated with decreased obesity prevalence. CONCLUSIONS: This study provided empirical evidence for longitudinal associations between neighborhood intersection density and childhood obesity prevalence based on national data and offered a new perspective of the role that road network plays in childhood obesity prevention.

Estimating neighbourhood-level prevalence of adult obesity by socio-economic, behavioural and built environment factors in New York City.

OBJECTIVES: Some studies reveal that socio-economic status, behavioural factors, accessibility to supermarket or food store, are associated with the prevalence of obesity and overweight. In this study, we aimed to examine to what extent socio-economic, behavioural and built environment characteristics can contribute to spatial disparities in adult obesity. STUDY DESIGN: The spatial analysis was undertaken to understand the association of spatial disparities in adult obesity and spatial disparities in socio-economic, behavioural and built environment characteristics. METHODS: A spatial regression model which can remove the impact of auto-correlation in the residuals of conventionally regression models was applied to modelling local-scale rate of adult obesity (N = 59). RESULTS: Owing to the presence of residual spatial auto-correlation in the non-spatial regression model estimated, a spatial regression model was set up successfully to model local-scale rate of adult obesity across New York City (R2 = 0.8353, N = 59). Compared with socio-economic and built environment factors, behavioural factors make statistically significant contributions to spatial disparities in the prevalence of adult obesity (POAO). Particularly, two behavioural factors ('sugary drinks consumption' and 'fruits and vegetable consumption') can explain more than 70% of the variance of POAO (adjusted R2 = 0.7323, N = 59). Surprisingly, physical activity prevalence (percent of physically active adults) makes no statistically significant contributions. CONCLUSIONS: The results further suggest that the reduction of adult obesity prevalence could benefit more from decreasing intake of sugary drinks than increasing physical activity. The local government and policy are advised to prioritise decreasing exposure of residents to sugary drinks through restricting advertising or increasing taxes rather than increasing neighbourhoods' walkability through urban planning.

Using open-source data to explore distribution of built environment characteristics across Kerala, India.

BACKGROUND: Built environment characteristics in the neighborhood are of utmost priority for a healthy lifestyle in the fast-urbanizing countries. These characteristics are closely linked to the disease burden and challenges in low- and middle-income countries (LMICs), which have been unexplored using open-source data. The present technology offers online resources and open source software that enable researchers to explore built environment characteristics with health and allied phenomena. OBJECTIVES: This article intends to delineate methods to capture available and accessible objective built environment variables for a state in India and determine their distribution across the state. METHODS: Built environment variables such as population density and residential density were collated from the Census of India. Safety from crime and traffic were captured as crime rates and pedestrian accident rates, respectively, acquired from State Crime Records Bureau. Greenness, built-up density, and land slope were gathered from open-source satellite imagery repository. Road intersection density was derived from OpenStreetMap. Processing and analysis differed for each dataset depending on its source and nature. RESULTS: Each variable showed a distinct pattern across the state. Population and residential density were found to be closely related to each other across both districts and subdistricts. They were both positively related to crime rates, pedestrian accident rates, built-up density, and intersection density, whereas negatively related to land slope and greenness across the subdistricts. CONCLUSION: Delineating the distribution of built environment variables using available and open-source data in resource-poor settings is a first in public health research among LMICs. Cost-effectiveness and reproducible nature of open-source solutions could equip researchers in resource-poor settings to identify built environment characteristics and patterns across regions.