Understanding the factors associated with the relationship between where individuals are shot and where they reside can improve our knowledge of local risk environments and inform violence intervention planning. The objective of this study was to describe the association between residential racialized economic segregation and firearm injury location, conceptualized as spatial networks of shooting incidents within and between neighborhoods. This cross-sectional study linked hospital and death records to examine firearm assaults and homicides in or among residents living within the Boston metro area. Racialized economic segregation was measured using the “Index of Concentration at the Extremes” for race and income at each residential and injury address. For the 802 individuals with gunshot wounds included for analysis, individuals living in more privileged neighborhoods were more likely to be injured: (1) farther from their homes (median [IQR] miles, 3.4 [0.9–9.7] for lowest deprivation tertile [T1] vs 0.7 [0.1–1.9] for highest deprivation tertile [T3], p = 0.0001), (2) outside their residential census tract (T1: 82.8
Soil-transmitted helminth (STH) infections are considered neglected tropical diseases. Prior studies have demonstrated that substandard housing conditions and deficiencies in water, sanitation, and hygiene (WASH) in urban slums significantly contribute to a high prevalence of STH infections among children. This study assessed the difference in WASH practices and STH infections in children aged 3 to 12 living in slums and in situ slum upgradation housing. A cross-sectional study was conducted among urban slum children living in three cities in Odisha, India, using a multi-stage cluster random sampling method. The prevalence of intestinal helminthic parasites—Ascaris lumbricoides (roundworm), Trichuris trichiura (whipworm), Ancylostoma duodenale, and Necator americanus—was assessed in the stool samples using both direct microscopy and the Kato-Katz procedure. The findings showed a significant difference (χ2 = 19.915, p < 0.001) between STH infections in slums (48
When occurring in supportive conditions, active travel is a behavior that benefits both human and planetary health. Technologically assisted tools for measuring travel-related behaviors often lack demographic disaggregation or are not feasible across settings. Direct observation remains a feasible complementary method for assessing active travel patterns in public spaces. We aimed to describe the development, piloting, and validation of the System for Observing Travel-Related Activity on Urban Roads (SOTRAVEL). The System for Observing Play and Recreation in Communities (SOPARC) path form was adapted to focus on active travel behaviors as outcomes, public roads as settings, and rapidly urbanizing mixed urban contexts. SOTRAVEL collects data on sex, age group, and travel mode (pedestrian activity, private cycling, bike-sharing, work-related cycling, other wheeled modes, and sedentary nonmotorized travel). Validation involved 15-min observations on eight street segments with varying traffic flow, using three data collection protocols: (1) single observer, (2) dual observer, and (3) video recordings (reference measure). Spearman correlations assessed validity, and Wilcoxon signed-rank tests evaluated differences between single- and dual-observer counts. Overall, correlations between video recordings and observer-based protocols were strong (r ≥ 0.7) across sex, age, traffic flow, and travel mode groupings, except for minors (r = 0.65–0.67). Strong correlations were observed across sex and travel mode traffic flow levels. No significant differences were found between single- and dual-observer protocols. SOTRAVEL is a valid tool for assessing active travel on urban roads, providing reliable disaggregated data on active travel modes by sex. It may be particularly useful in rapidly urbanizing or high-complexity urban settings requiring contextualized and demographically disaggregated active travel surveillance.
Healthy ageing requires supportive environments beyond individual behaviors. Community building has gained policy attention, but evidence on how municipal progress in community building relates to individual social capital remains limited. We analyzed data from 91,891 older adults aged ≥ 65 years across 49 municipalities in Japan using the Japan Gerontological Evaluation Study (2019–2022). We examined whether municipal community-building progress in 2020 related to subsequent individual structural and cognitive social capital in 2022, after adjusting for individual-level covariates and social capital in 2019 and municipal-level covariates in fiscal year 2019. We applied multilevel linear regression with individuals nested within municipalities and assessed heterogeneity using a moderator-wide approach. Higher preventive care and daily life support scores and a greater number of community gathering places per 1000 older adults related to higher structural social capital. These associations were consistent across most subgroups, although the association between the number of community gathering places per 1000 older adults and structural social capital was stronger in municipalities with smaller older populations. We found no clear overall association between municipal community-building indicators and cognitive social capital. However, higher living support system scores related to higher cognitive social capital among women and adults aged ≥ 75 years. Municipal community-building efforts related differently to structural and cognitive social capital. Participation-oriented measures showed consistent associations with structural social capital, whereas support-oriented measures related to cognitive social capital more selectively. These findings highlight the importance of aligning community-building strategies with both social capital dimensions and population characteristics to promote healthy ageing.
Rapid urbanization has intensified health and social inequities through the uneven distribution of infrastructure, services, and public space. In St. Louis, Missouri (USA), these disparities remain geographically concentrated and rooted in long-standing patterns of racial segregation and disinvestment. This study examines how spatial inequities shape urban livability by integrating scalable artificial intelligence (AI) methods with community-based insights. We applied a vision–language model (VLM), supported by a large language model (LLM), to classify micro-scale built-environment features from 7848 Google Street View segments, and conducted semi-structured interviews with residents and stakeholders. The AI analysis revealed baseline infrastructure such as street lighting (84.9
