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.

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.

• We studied daily temperature and humidity in COVID-19 morbidity. • We used a case-crossover and distributed lag nonlinear model. • We observed non-linear associations with humidity and temperature. • Humidity was the best predictor of COVID-19 transmission. • Results varied across select US cities despite accounting for social distancing measures.

Extreme heat is the leading cause of weather-related mortality in the U.S. Extreme heat also affects human health through heat stress and can exacerbate underlying medical conditions that lead to increased morbidity and mortality. In this study, data on emergency department (ED) visits for heat-related illness (HRI) and other selected diseases were analyzed during three heat events across North Carolina from 2007 to 2011. These heat events were identified based on the issuance and verification of heat products from local National Weather Service forecast offices (i.e. Heat Advisory, Heat Watch, and Excessive Heat Warning). The observed number of ED visits during these events were compared to the expected number of ED visits during several control periods to determine excess morbidity resulting from extreme heat. All recorded diagnoses were analyzed for each ED visit, thereby providing insight into the specific pathophysiological mechanisms and underlying health conditions associated with exposure to extreme heat. The most common form of HRI was heat exhaustion, while the percentage of visits with heat stroke was relatively low (<10%). The elderly (>65 years of age) were at greatest risk for HRI during the early summer heat event (8.9 visits per 100,000), while young and middle age adults (18-44 years of age) were at greatest risk during the mid-summer event (6.3 visits per 100,000). Many of these visits were likely due to work-related exposure. The most vulnerable demographic during the late summer heat event was adolescents (15-17 years of age), which may relate to the timing of organized sports. This demographic also exhibited the highest visit rate for HRI among all three heat events (10.5 visits per 100,000). Significant increases (p < 0.05) in visits with cardiovascular and cerebrovascular diseases were noted during the three heat events (3-8%). The greatest increases were found in visits with hypotension during the late summer event (23%) and sequelae during the early summer event (30%), while decreases were noted for visits with hemorrhagic stroke during the middle and late summer events (13-24%) and for visits with aneurysm during the early summer event (15%). Significant increases were also noted in visits with respiratory diseases (5-7%). The greatest increases in this category were found in visits with pneumonia and influenza (16%), bronchitis and emphysema (12%), and COPD (14%) during the early summer event. Significant increases in visits with nervous system disorders were also found during the early summer event (16%), while increases in visits with diabetes were noted during the mid-summer event (10%).

BackgroundWearable sensors and other smart technology may be especially beneficial in providing remote monitoring of sub-clinical changes in pregnancy health status. Yet, limited research has examined perceptions among pregnant patients and providers in incorporating smart technology into their daily routine and clinical practice.ObjectiveThe purpose of this study was to examine the perceptions of pregnant women and their providers at a rural health clinic on the use of wearable technology to monitor health and environmental exposures during pregnancy.MethodsAn anonymous 21-item e-survey was administered to family medicine or obstetrics and gynecology (n=28) providers at a rural health clinic; while a 21-item paper survey was administered to pregnant women (n=103) attending the clinic for prenatal care.ResultsSmartphone and digital technology use was high among patients and providers. Patients would consider wearing a mobile sensor during pregnancy, reported no privacy concerns, and felt comfortable sharing information from these devices with their physician. About seven out of 10 women expressed willingness to change their behavior during pregnancy in response to receiving personalized recommendations from a smartphone. While most providers did not currently use smart technologies in their medical practice, about half felt it will be used more often in the future to diagnose and remotely monitor patients. Patients ranked fetal heart rate and blood pressure as their top preference for health monitoring compared to physicians who ranked blood pressure and blood glucose. Patients and providers demonstrated similar preferences for environmental monitoring, but patients as a whole expressed more interests in tracking environmental measures compared to their providers.ConclusionsPatients and providers responded positively to the use of wearable sensor technology in prenatal care. More research is needed to understand what factors might motivate provider use and implementation of wearable technology to improve the delivery of prenatal care.