Identifying communities at risk for COVID-19-related burden across 500 U.S. Cities and within New York City (preprint)

Background While it is well-known that older individuals with certain comorbidities are at highest risk for complications related to COVID-19 including hospitalization and death, we lack tools to identify communities at highest risk with fine-grained spatial and temporal resolution. Information collected at a county level obscures local risk and complex interactions between clinical comorbidities, the built environment, population factors, and other social determinants of health. Methods We develop a robust COVID-19 Community Risk Score (C-19 Risk Score) that summarizes the complex disease co-occurrences for individual census tracts with unsupervised learning, selected on their basis for association with risk for COVID complications, such as death. We mapped the C-19 Risk Score onto neighborhoods in New York City and associated the score with C-19 related death. We further predict the C-19 Risk Score using satellite imagery data to map the built environment in C-19 Risk. Results The C-19 Risk Score describes 85% of variation in co-occurrence of 15 diseases that are risk factors for COVID complications among 26K census tract neighborhoods (median population size of tracts: 4,091). The C-19 Risk Score is associated with a 40% greater risk for COVID-19 related death across NYC (April and September 2020) for a 1SD change in the score (Risk Ratio for 1SD change in C19 Risk Score: 1.4, p < .001). Satellite imagery coupled with social determinants of health explain nearly 90% of the variance in the C-19 Risk Score in the United States in held-out census tracts (R 2 of 0.87). Conclusions The C-19 Risk Score localizes COVID-19 risk at the census tract level and predicts COVID-19 related morbidity and mortality.

Rural Disparities in Early Childhood Well Child Visit Attendance (preprint)

Purpose Children should attend well child visits (WCVs) during early childhood so that developmental disorders may be identified as early as possible, and if indicated treatment can begin. The aim of this research was to determine if rurality impacts access to WCV during early childhood, and if altering rurality measurement methods impacts outcomes. Design and Methods We utilized a longitudinal correlational design with early childhood data gathered from the Virginia All Payer Claims Database, which contains claims data from Medicaid and the majority of Virginia commercial insurance payers (n=6349). WCV attendance was evaluated against three rurality metrics: a traditional metric using Rural-Urban Commuting Area codes, a developed land variable, and a distance to care variable, at a zip code level. Results Two of the rurality methods revealed that rural children attend fewer WCVs than their urban counterparts, (67% vs. 50% respectively, using a traditional metric; and a 0.035 increase in WCV attendance for every percent increase in developed land). Differences were attenuated by insurance payer; children with Medicaid attend fewer WCVs than those with private insurance. Conclusions Young children in rural Virginia attend fewer WCVs than their non-rural counterparts, placing them at higher risk for missing timely developmental disorder screenings. The coronavirus disease pandemic has been associated with an abrupt and significant reduction in vaccination rates, which likely indicates fewer WCVs and concomitant developmental screenings. Pediatric nurses should encourage families of young children to develop a plan for continued WCVs, so that early identification of developmental disorders can be achieved.