Abstract
OBJECTIVES: The purpose of this study was to employ simulations to model the probability of mortality from COVID-19 (i.e., coronavirus) for older adults in the United States given at best and at worst cases. METHODS: This study first examined current epidemiological reports to better understand the risk of mortality from COVID-19. Past epidemiological studies from severe acute respiratory syndrome were also examined given similar virology. Next, at best and at worst mortality cases were considered with the goal of estimating the probability of mortality. To accomplish this for the general population, microdata from the National Health Interview Survey pooled sample (2016, 2017, and 2018 public-use NHIS with a sample of 34,881 adults at least 60 years of age) were utilized. Primary measures included age and health status (diabetes, body mass index, and hypertension). A logit regression with 100,000 simulations was employed to derive the estimates and probabilities. RESULTS: Age exhibited a positive association for the probability of death with an odds ratio (OR) of 1.22 (p < .05, 95% confidence interval [CI]: 1.05-1.42). A positive association was also found for body mass index (BMI) (OR 1.03, p < .01, 95% CI: 1.02-1.04) and hypertension (OR 1.36, p < .01, 95% CI: 1.09-1.66) for the at best case. Diabetes was significant but only for the at best case. DISCUSSION: This study found mortality increased with age and was notable for the 74-79 age group for the at best case and the 70-79 age group of the at worst case. Obesity was also important and suggested a higher risk for mortality. Hypertension also exhibited greater risk but the increase was minimal. Given the volume of information and misinformation, these findings can be applied by health professionals, gerontologists, social workers, and local policymakers to better inform older adults about mortality risks and, in the process, reestablish public trust.