Journal of urban health : bulletin of the New York Academy of Medicine
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The objective of the present study was to examine the effects of a confluence of demographic, socioeconomic, housing, and environmental factors that systematically contribute to heat-related morbidity in Maricopa County, Arizona, from theoretical, empirical, and spatial perspectives. The present study utilized ordinary least squares (OLS) regression and multiscale geographically weighted regression (MGWR) to analyze health data, U. ⋯ Populations of people aged 65+, Hispanic people, disabled people, people who do not own vehicles, and housing occupancy rate have much stronger local effects than other variables. These findings can be used to inform and educate local residents, communities, stakeholders, city managers, and urban planners in their ongoing and extensive efforts to mitigate the negative impacts of extreme heat on human health in Maricopa County.
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While emerging evidence is highlighting a growing problem of food insecurity among adolescents in disadvantaged neighborhoods, very little is known about the factors that may either protect or place adolescents at higher risk for food insecurity. The primary objective for this analysis, therefore, was to examine the associations between individual-, family-, and neighborhood-level risks and protective factors and food insecurity among 452 adolescents in Baltimore, Maryland. ⋯ Protective factors included perceiving both male and female adult support (OR 0.55 and 0.47, respectively), having a higher sense of community belonging (OR 0.91, 0.32-0.95) and having positive perceptions of their neighborhood's physical environment (OR 0.93, 0.88-0.98). These results suggest that strengthening family and neighborhood relations and resources may promote the health of adolescents in disadvantaged urban areas.
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Retail environments, such as healthcare locations, food stores, and recreation facilities, may be relevant to many health behaviors and outcomes. However, minimal guidance on how to collect, process, aggregate, and link these data results in inconsistent or incomplete measurement that can introduce misclassification bias and limit replication of existing research. We describe the following steps to leverage business data for longitudinal neighborhood health research: re-geolocating establishment addresses, preliminary classification using standard industrial codes, systematic checks to refine classifications, incorporation and integration of complementary data sources, documentation of a flexible hierarchical classification system and variable naming conventions, and linking to neighborhoods and participant residences. ⋯ S. from 1990 to 2014. By incorporating complementary data sources, through manual spot checks in Google StreetView and word and name searches, we enhanced a basic classification using only standard industrial codes. Ultimately, providing these enhanced longitudinal data and supplying detailed methods for researchers to replicate our work promotes consistency, replicability, and new opportunities in neighborhood health research.