Neighborhood Characteristics Associated with Running in Metro Vancouver: A Preliminary Analysis.

Running can improve physical health and psychological wellbeing. However, the characteristics of conducive running environments are relatively unknown. This study determines neighborhood factors that attract running and explores how age and gender mediate built environment preferences. Spatial patterns of runners in Metro Vancouver were identified using crowdsourced fitness data from Strava, a popular application for tracking physical activities. The influence of socio-economic status (SES), green and/or blue space, and urbanicity on route popularity was assessed using a Generalized Linear Model (GLM). The influence of these neighborhood variables was also calculated for runners by age and gender. The results show high neighborhood SES, the presence of green and/or blue space, and high population density are associated with increased running activities in all age and gender groups. This study contributes a novel approach to understanding conducive running environments by demonstrating the utility of crowdsourced data in combination with data about urban environments. The patterns of this large group of runners can be used to inform planning for cities that promote running, as well as seek to encourage equal participation among different ages and genders.

An Exploratory Spatiotemporal Analysis of Socio-Environmental Patterns in Severe Maternal Morbidity.

OBJECTIVES: Severe Maternal Morbidity (SMM) is a group of pregnancy complications in which a woman nearly dies. Despite its increasing prevalence, little research has evaluated geographic patterns of SMM and the underlying social determinants that influence excess risk. This study examined the spatial clustering of SMM across South Carolina, US, and its associations with place-based social and environmental factors. METHODS: Hospitalized deliveries from 2012 to 2017 were analyzed using Kulldorff's spatial scan statistic to locate areas with abnormally high rates of SMM. SMM patients inside and outside risk clusters were compared using Generalized Estimating Equations (GEE) to determine underlying individual and community-level risk factors. RESULTS: GEE models revealed that the odds of living in a high-risk SMM21 (SMM including blood transfusions) cluster was 2.49 times higher among Black patients (p < .001) compared to those outside of a high-risk cluster. Women residing in a high-risk SMM20 (SMM excluding blood transfusions) cluster were 1.38 times more likely to experience the most number of extremely hot days and 1.70 times more likely to present with obesity than women in a low-risk SMM cluster (p < .001). CONCLUSIONS: This study is the first to characterize the geographic clustering of SMM risk in the US. Our geospatial approach contributes a novel understanding to factors which influence SMM beyond patient-level characteristics and identifies the impact of hot ambient temperature on maternal morbidity. Findings address an important literature gap surrounding place-based risk factors by explaining the contextual social and built environmental factors that drive SMM risk.

Spatial Clustering of Adolescent Bereavement in the United States During the COVID-19 Pandemic.

PURPOSE: Few studies have examined grief and bereavement in the context of a pandemic, particularly among young people during the ongoing COVID-19 pandemic. METHODS: The objective of this study is to examine spatiotemporal clustering of bereavement using data from Crisis Text Line, an SMS-based intervention with widespread usage among youth and adolescents in the United States from January 2017 to September 2020. RESULTS: Results found significant spatial clustering of bereavement during the pandemic period in the late summer months compared with the onset of the pandemic. CONCLUSION: Our study provides the first evidence of elevated bereavement in adolescents using a technique for rapidly identifying clusters of bereavement risk among this vulnerable subgroup. Findings can be leveraged for targeted interventions and supportive counseling in geographic hotspots.

Mapping community-level determinants of COVID-19 transmission in nursing homes: A multi-scale approach.

Deaths from the COVID-19 pandemic have disproportionately affected older adults and residents in nursing homes. Although emerging research has identified place-based risk factors for the general population, little research has been conducted for nursing home populations. This GIS-based spatial modeling study aimed to determine the association between nursing home-level metrics and county-level, place-based variables with COVID-19 confirmed cases in nursing homes across the United States. A cross-sectional research design linked data from Centers for Medicare & Medicaid Services, American Community Survey, the 2010 Census, and COVID-19 cases among the general population and nursing homes. Spatial cluster analysis identified specific regions with statistically higher COVID-19 cases and deaths among residents. Multivariate analysis identified risk factors at the nursing home level including, total count of fines, total staffing levels, and LPN staffing levels. County-level or place-based factors like per-capita income, average household size, population density, and minority composition were significant predictors of COVID-19 cases in the nursing home. These results provide a framework for examining further COVID-19 cases in nursing homes and highlight the need to include other community-level variables when considering risk of COVID-19 transmission and outbreaks in nursing homes.

Analyzing the spatial determinants of local Covid-19 transmission in the United States.

The Coronavirus Disease 19 (COVID-19) has quickly spread across the United States (U.S.) since community transmission was first identified in January 2020. While a number of studies have examined individual-level risk factors for COVID-19, few studies have examined geographic hotspots and community drivers associated with spatial patterns in local transmission. The objective of the study is to understand the spatial determinants of the pandemic in counties across the U.S. by comparing socioeconomic variables to case and death data from January 22nd to June 30th 2020. A cluster analysis was performed to examine areas of high-risk, followed by a three-stage regression to examine contextual factors associated with elevated risk patterns for morbidity and mortality. The factors associated with community-level vulnerability included age, disability, language, race, occupation, and urban status. We recommend that cluster detection and spatial analysis be included in population-based surveillance strategies to better inform early case detection and prioritize healthcare resources.