Prehospital Cardiac Arrest Should be Considered When Evaluating Coronavirus Disease 2019 Mortality in the United States - Williams, Nick (Methods of Information in Medicine, 2023)
Background Public health emergencies leave little time to develop novel surveillance efforts. Understanding which preexisting clinical datasets are fit for surveillance use is of high value. Coronavirus disease 2019 (COVID-19) offers a natural applied informatics experiment to understand the fitness of clinical datasets for use in disease surveillance.Objectives This study evaluates the agreement between legacy surveillance time series data and discovers their relative fitness for use in understanding the severity of the COVID-19 emergency. Here fitness for use means the statistical agreement between events across series.Methods Thirteen weekly clinical event series from before and during the COVID-19 era for the United States were collected and integrated into a (multi) time series event data model. The Centers for Disease Control and Prevention (CDC) COVID-19 attributable mortality, CDC's excess mortality model, national Emergency Medical Services (EMS) calls, and Medicare encounter level claims were the data sources considered in this study. Cases were indexed by week from January 2015 through June of 2021 and fit to Distributed Random Forest models. Models returned the variable importance when predicting the series of interest from the remaining time series.Results Model r2 statistics ranged from 0.78 to 0.99 for the share of the volumes predicted correctly. Prehospital data were of high value, and cardiac arrest (CA) prior to EMS arrival was on average the best predictor (tied with study week). COVID-19 Medicare claims volumes can predict COVID-19 death certificates (agreement), while viral respiratory Medicare claim volumes cannot predict Medicare COVID-19 claims (disagreement).Conclusion Prehospital EMS data should be considered when evaluating the severity of COVID-19 because prehospital CA known to EMS was the strongest predictor on average across indices.
Keywords: Random Forest; COVID-19; public health; statistical models; syndromic surveillance