Road Traffic Injuries and the Built Environment in Bogotá, Colombia, 2015-2019: A Cross-Sectional Analysis.

Nine in 10 road traffic deaths occur in low- and middle-income countries (LMICs). Despite this disproportionate burden, few studies have examined built environment correlates of road traffic injury in these settings, including in Latin America. We examined road traffic collisions in Bogotá, Colombia, occurring between 2015 and 2019, and assessed the association between neighborhood-level built environment features and pedestrian injury and death. We used descriptive statistics to characterize all police-reported road traffic collisions that occurred in Bogotá between 2015 and 2019. Cluster detection was used to identify spatial clustering of pedestrian collisions. Adjusted multivariate Poisson regression models were fit to examine associations between several neighborhood-built environment features and rate of pedestrian road traffic injury and death. A total of 173,443 police-reported traffic collisions occurred in Bogotá between 2015 and 2019. Pedestrians made up about 25% of road traffic injuries and 50% of road traffic deaths in Bogotá between 2015 and 2019. Pedestrian collisions were spatially clustered in the southwestern region of Bogotá. Neighborhoods with more street trees (RR, 0.90; 95% CI, 0.82-0.98), traffic signals (0.89, 0.81-0.99), and bus stops (0.89, 0.82-0.97) were associated with lower pedestrian road traffic deaths. Neighborhoods with greater density of large roads were associated with higher pedestrian injury. Our findings highlight the potential for pedestrian-friendly infrastructure to promote safer interactions between pedestrians and motorists in Bogotá and in similar urban contexts globally.

A tale of many neighborhoods: Latent profile analysis to derive a national neighborhood typology for the US.

INTRODUCTION: Neighborhoods are complex and multi-faceted. Analytic strategies used to model neighborhoods should reflect this complexity, with the potential to better understand how neighborhood characteristics together impact health. We used latent profile analysis (LPA) to derive a residential neighborhood typology applicable for census tracts across the US. METHODS: From tract-level 2015-2019 American Community Survey (ACS) five-year estimates, we selected five indicators that represent four neighborhood domains: demographic composition, commuting, socioeconomic composition, and built environment. We compared model fit statistics for up to eight profiles to identify the optimal number of latent profiles of the selected neighborhood indicators for the entire US. We then examined differences in national tract-level 2019 prevalence estimates of physical and mental health derived from CDC's PLACES dataset between derived profiles using one-way analysis of variance (ANOVA). RESULTS: The 6-profile LPA model was the optimal categorization of neighborhood profiles based on model fit statistics and interpretability. Neighborhood types were distinguished most by demographic composition, followed by commuting and built environment domains. Neighborhood profiles were associated with meaningful differences in the prevalence of health outcomes. Specifically, tracts characterized as "Less educated non-immigrant racial and ethnic minority active transiters" (n = 3,132, 4%) had the highest poor health prevalence (Mean poor physical health: 18.6 %, SD: 4.30; Mean poor mental health: 19.6 %, SD: 3.85), whereas tracts characterized as "More educated metro/micropolitans" (n = 15, 250, 21%) had the lowest prevalence of poor mental and physical health (Mean poor physical health: 10.6 %, SD: 2.41; Mean poor mental health: 12.4 %, SD: 2.67; p < 0.001). CONCLUSION: LPA can be used to derive meaningful and standardized profiles of tracts sensitive to the spatial patterning of social and built conditions, with observed differences in mental and physical health by neighborhood type in the US.

Associations Between Neighborhood Opportunity and Indicators of Physical Fitness for New York City Public School Youth.

Background: Fewer than 1/4th of US children and adolescents meet physical activity (PA) guidelines, leading to health disparities that track into adulthood. Neighborhood opportunity may serve as a critical modifiable factor to improve fitness attainment and reduce these disparities. We drew data from the Child Opportunity Index to examine associations between neighborhood indicators of opportunity for PA and multiple fitness indicators among New York City public school youth. Methods: Multilevel generalized linear mixed models were used to estimate the overall and sex-stratified associations between neighborhood indicators (green space, healthy food, walkability, commute time) and indicators for physical fitness [curl-ups, push-ups, Progressive Aerobic Cardiovascular Endurance Run (PACER), sit-and-reach] using the New York City FITNESSGRAM data set. Results: The analytic sample [n = 299,839; median (interquartile range) age = 16 (12-17)] was 50.1% female, 37.5% Hispanic, 26.2% non-Hispanic Black, and most (69.5%) qualified for free/reduced price school meals. Neighborhood indicators were positively associated with higher values of indicators for physical fitness. The strongest associations were observed between walkability and both BMI and PACER, and commute time with BMI, push-ups, and PACER. For example, walkability had the greatest magnitude of effects for BMI and muscular strength and endurance (BMI: ß: -0.75, 95% confidence interval, CI: -1.01 to -0.49; PACER: ß: 1.98, 95% CI: 1.59 to 2.37), and particularly for girls compared with boys (BMI, girls: ß: -0.91, 95% CI: -1.22 to -0.66); BMI, boys: ß: -0.56, 95% CI: -0.86 to -0.25); PACER, girls: ß: 2.11, 95% CI: 1.68 to 2.54; push-ups, boys: ß: 1.71, 95% CI: 1.31 to 2.12). Conclusion: Neighborhood indicators were associated with multiple measures of youth fitness. Continued research on neighborhood opportunity and youth fitness may better inform place-based public health interventions to reduce disparities.

Longitudinal Associations Between Neighborhood Child Opportunity and Physical Fitness for New York City Public School Youth.

Neighborhood environments can support fitness-promoting behavior, yet little is known about their influence on youth physical fitness outcomes over time. We examined longitudinal associations between neighborhood opportunity and youth physical fitness among New York City (NYC) public school youth. The Child Opportunity Index (COI), a composite index of 29 indicators measuring neighborhood opportunity at the census-tract level, along with scores on 4 selected COI indicators were linked to NYC FITNESSGRAM youth data at baseline. Fitness outcomes (measured annually, 2011-2018) included body mass index, curl-ups, push-ups, and Progressive Aerobic Cardiovascular Endurance Run (PACER) laps. Unstratified and age-stratified, adjusted, 3-level generalized linear mixed models, nested by census tract and time, estimated the association between COI and fitness outcomes. The analytical sample (n = 204,939) lived in very low (41%) or low (30%) opportunity neighborhoods. Unstratified models indicated that overall COI is modestly associated with improved youth physical fitness outcomes. The strongest opportunity-fitness associations were observed for PACER. Stratified models show differences in associations across younger vs. older youth. We find that neighborhood factors are associated with youth fitness outcomes over time, with the strength of the associations dependent on age. Future implications include better informed place-based interventions tailored to specific life stages to promote youth health.

The association between urban greenspace and psychological health among young adults in Addis Ababa, Ethiopia.

INTRODUCTION: Psychological disorders are emerging as health priorities in Sub-Saharan Africa, specifically Ethiopia. Urban greenspace - parks, trees, and other vegetation integrated into urban form - may facilitate population psychological health, but is largely understudied outside high-income countries. We explore greenspace in relation to psychological health among young adults in Addis Ababa, Ethiopia. METHOD: Greenspace exposure was calculated using the normalized difference vegetation index (NDVI) derived from publicly available satellite imagery (2018-2019). We used tests of spatial clustering to characterize greenspace distribution. Derived NDVI values were linked to Positive Outcomes for Orphans study participants to explore cross-sectional associations between greenspace exposure and psychological health (measured 2019-2021). Two continuous scores of psychological health were examined: total difficulties from the Strengths and Difficulties Questionnaire and depressive symptoms from the 8-item Patient Health Questionnaire. Multilevel generalized linear regression, nested by administrative units, was used to estimate the association between greenspace and psychological health. We also explored effect modification by gender and having income. RESULTS: We found greenspace is spatially clustered in Addis Ababa, with high greenspace density in the northeast region and low greenspace density in the center of the city. Our findings suggest residing in greener areas is associated with fewer emotional and behavioral difficulties (ß = -1.89; 95% CI: -3.50, -0.29), but not significantly associated with depressive symptomology (ß = -0.61; 95% CI: -2.33, 1.11). We observed stronger associations between greenspace and total difficulties among those reporting any income and among males, and for the association between greenspace and depression symptomology among males. CONCLUSION: We offer initial exploration into the role of greenspace in psychological well-being in Addis Ababa, with potential implications for urban communities across Sub-Saharan Africa. Further research should continue to explore how the built and natural environment could be leveraged in similar settings to promote population psychological health.