Principle Investigator
Richard Scheffler, PhD
Project Period
July 2, 2020 – May 30, 2022
About the Project
Our study probes into understanding the severity, healthcare costs, and long-term health effects vary across patient groups, based on demographic characteristics and comorbidities. Emphasis will be placed onto interactions among demographic characteristics, comorbidities, and clinical
information to best predict severe COVID-19 complications and mortality. The aims are three-fold, including: (1) identifying combinations of demographic characteristics, comorbidities, and clinical information that predict severe COVID-19 complications; (2) estimating the acute and long-term healthcare expenditures to treat patients with COVID-19; and (3) assessing the prevalence, duration, and type of long-term, adverse health effects of COVID-19.
Phase 1
The first initiative will utilize machine learning to identify interactions among demographic characteristics, comorbidities, and clinical information to best predict severe COVID-19 complications. Our predictive model features will stem from prior literature on demographics, comorbidities, and clinical information of COVID-19 patients.
Phase 2
Next, we will develop a cost model to estimate healthcare expenditures for patients with varying degrees of severity. This will estimate the acute and long-term healthcare expenditures to treat patients with COVID-19, including patients with particular co-morbidities and combinations of risk factors identified in the first phase. With outpatient, inpatient, prescription drug, and rehabilitation costs, we will both isolate expenditures associated with COVID-19 and expenditures from other health conditions exacerbated by COVID-19.
Phase 3
This final inquiry will follow a cohort of COVID-19 patients identified by phase 1 to estimate prevalence, duration, and type of long-term, adverse health effects. Patients with and without comorbid conditions will be studied, as long-term, adverse health effects have been found in both groups.