A Typology of COVID-19 Data Gaps and Noise From Long-Term Care Facilities: Approximating the True Numbers.

ABSTRACT

Although there is agreement that COVID-19 has had devastating impacts in long-term care facilities (LTCFs), estimates of cases and deaths have varied widely with little attention to the causes of this variation. We developed a typology of data vulnerabilities and a strategy for approximating the true total of COVID-19 cases and deaths in LTCFs. Based on iterative qualitative consensus, we categorized LTCF reporting vulnerabilities and their potential impacts on accuracy. Concurrently, we compiled one dataset based on LTCF self-reports and one based on confirmatory matching with California's COVID-19 databases, including death certificates. Through March 2021, Alameda County LTCFs reported 6663 COVID-19 cases and 481 deaths. In contrast, our confirmatory matching file includes 5010 cases and 594 deaths, corresponding to 25% fewer cases but 23% more deaths. We argue that the higher (self-report) case total approximates the lower bound of true COVID-19 cases, and the higher (confirmed match) death total approximates the lower bound of true COVID-19 deaths, both of which are higher than state and federal counts. LTCFs other than nursing facilities accounted for 35% of cases and 29% of deaths. Improving the accuracy of COVID-19 figures, particularly across types of LTCFs, would better inform interventions for these vulnerable populations.