Browsing by Subject "infection-hospitalization ratio"

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    How Many SARS-CoV-2–Infected People Require Hospitalization? Using Random Sample Testing to Better Inform Preparedness Efforts
    (Wolters Kluwer, 2021) Menachemi, Nir; Dixon, Brian E.; Wools-Kaloustian, Kara K.; Yiannoutsos, Constantin T.; Halverson, Paul K.; Epidemiology, School of Public Health
    Context: Existing hospitalization ratios for COVID-19 typically use case counts in the denominator, which problematically underestimates total infections because asymptomatic and mildly infected persons rarely get tested. As a result, surge models that rely on case counts to forecast hospital demand may be inaccurately influencing policy and decision-maker action. Objective: Based on SARS-CoV-2 prevalence data derived from a statewide random sample (as opposed to relying on reported case counts), we determine the infection-hospitalization ratio (IHR), defined as the percentage of infected individuals who are hospitalized, for various demographic groups in Indiana. Furthermore, for comparison, we show the extent to which case-based hospitalization ratios, compared with the IHR, overestimate the probability of hospitalization by demographic group. Design: Secondary analysis of statewide prevalence data from Indiana, COVID-19 hospitalization data extracted from a statewide health information exchange, and all reported COVID-19 cases to the state health department. Setting: State of Indiana as of April 30, 2020. Main Outcome Measure(s): Demographic-stratified IHRs and case-hospitalization ratios. Results: The overall IHR was 2.1% and varied more by age than by race or sex. Infection-hospitalization ratio estimates ranged from 0.4% for those younger than 40 years to 9.2% for those older than 60 years. Hospitalization rates based on case counts overestimated the IHR by a factor of 10, but this overestimation differed by demographic groups, especially age. Conclusions: In this first study of the IHR based on population prevalence, our results can improve forecasting models of hospital demand—especially in preparation for the upcoming winter period when an increase in SARS CoV-2 infections is expected.