RESUMEN
STUDY OBJECTIVE: One in 4 deaths from COVID-19 has been attributed to hospital crowding. We simulated how many ambulances would be required to rebalance hospital load through systematic interhospital transfers. We assessed the potential feasibility of such a strategy and explored whether transfer requirement was a helpful measure and visualization of regional hospital crowding during COVID-19 surges. METHODS: Using data from the United States hospitals reporting occupancy to the Department of Health and Human Services from July 2020 to March 2022 and road network driving times, we estimated the number of ambulances required weekly to relieve overcapacity hospitals. RESULTS: During the peak week, which ended on January 8, 2021, approximately 1,563 ambulances would be needed for 15,389 simulated patient transports, of which 6,530 (42%) transports involved a 1-way driving time of more than 3 hours. Transfer demands were dramatically lower during most other weeks, with the median week requiring only 134 ambulances (interquartile range, 84 to 295) and involving only 116 transports with 1-way driving times above 3 hours (interquartile range, 4 to 548). On average, receiving hospitals were larger and located in more rural areas than sending hospitals. CONCLUSION: This simulation demonstrated that for most weeks during the pandemic, ambulance availability and bed capacity were unlikely to have been the main impediments to rebalancing hospital loads. Our metric provided an immediately available and much more complete measure of hospital system strain than counts of hospital admissions alone.
Asunto(s)
Ambulancias , COVID-19 , COVID-19/epidemiología , Servicio de Urgencia en Hospital , Hospitales , Humanos , Pandemias , Estados Unidos/epidemiologíaRESUMEN
OBJECTIVE: We examined whether stable, county-level, voter preferences were significantly associated with county-level obesity prevalence using data from the 2012 US Presidential election. County voting preference for the 2012 Republican Party presidential candidate was used as a proxy for voter endorsement of personal responsibility approaches to reducing population obesity risk versus approaches featuring government-sponsored, multi-sectoral efforts like those recommended by the Centers for Disease Control Centers for Disease Control (CDC, 2009). METHOD: Cartographic visualization and spatial analysis were used to evaluate the geographic clustering of obesity prevalence rates by county, and county-level support for the Republican Party candidate in the 2012 U.S. presidential election. The spatial analysis informed the spatial econometric approach employed to model the relationship between political preferences and other covariates with obesity prevalence. RESULTS: After controlling for poverty rate, percent African American and Latino populations, educational attainment, and spatial autocorrelation in the error term, we found that higher county-level obesity prevalence rates were associated with higher levels of support for the 2012 Republican Party presidential candidate. CONCLUSION: Future public health efforts to understand and reduce obesity risk may benefit from increased surveillance of this and similar linkages between political preferences and health risks.