DOI: https://doi.org/10.21203/rs.3.rs-40632/v1
Background: As of June 3, 2020, the mortality to incidence ratio (MIR) of COVID-19 was 5.8%. We utilized a longitudinal model-based clustering system based on the disease trajectories over time.
Methods: County-level COVID-19 cases and deaths (March-June 2020), and a set of potential risk factors were collected for 3050 U.S. counties. We used growth mixture models to identify clusters of counties exhibiting similar COVID-19 MIR growth trajectories and risk-factors over time.
Results: We identified clusters 1 (rural-areas of IA, NC, OK, VA, FL, GA, LA, OH states), and 7 (rural-areas of AR, CO, GA, KS, NE, TN, TX states) as the so-called “more vulnerable” clusters. Tuberculosis (OR=1.3), drug use disorder (OR=1.1), and particulate matter (OR=1.1) were significantly associated with increased odds of being in a more vulnerable cluster. Heart complications and cancer were the main risk factors increasing the COVID-19 MIR (range: 0.08%-0.72% MIR↑).
Conclusion: We identified two county-clusters exhibiting the highest COVID-19 MIR trajectories, indicating that enhancing the capacity and access to healthcare resources would be key to successfully manage COVID-19 in these “vulnerable” clusters. These findings provide insights for public health policymakers on the groups of people and locations they need to pay particular attention while managing the COVID-19 epidemic.
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