DOI: https://doi.org/10.21203/rs.3.rs-40632/v2
Background: As of November 12, 2020, the mortality to incidence ratio (MIR) of COVID-19 was 5.8% in the US. We utilized a longitudinal model-based clustering system based on the disease trajectories over time. We aimed to find the so-called “vulnerable” cluster of counties where to dedicate additional resources by the US policymakers.
Methods: County-level COVID-19 cases and deaths (Mar-Nov 2020), and a set of potential risk factors were collected for 3050 U.S. counties during the 1st wave (Mar25-Jun3, 2020), 1344 counties (sunbelt region) during the 2nd wave (Jun4-Sep2, 2020), and 1055 counties (great plains) during the 3rd wave (Sep3-Nov12, 2020). 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 the so-called “more vulnerable” clusters during the 1st, 2nd and 3rd waves of COVID-19. Tuberculosis (OR=1.3-2.1-3.2), drug use disorder (OR=1.1), hepatitis (OR=13.1), HIV/AIDS (OR=2.3), cardiomyopathy and myocarditis (OR=1.3), diabetes (OR=1.2), mesothelioma (OR=9.3) 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%-0.52% MIR↑).
Conclusion: We identified the so-called “more vulnerable” 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 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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