County-level longitudinal clustering of COVID-19 mortality to incidence ratio in the United States

Our plain-English summary of County-level longitudinal clustering of COVID-19 mortality to incidence ratio in the United States, by N. Vahabi, M. Salehi, J.D. Duarte, et al (Scientific Reports, 2021).

The study

• Authors: N. Vahabi, M. Salehi, J.D. Duarte, A. Mollalo, G. Michailidis
• Journal: Scientific Reports
• Published: 2021;11(1):3088
• Cited by: 22 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

As of November 12, 2020, the mortality to incidence ratio (MIR) of COVID-19 was 5.8% in the US. A longitudinal model-based clustering system on the disease trajectories over time was used to identify "vulnerable" clusters of counties that would benefit from allocating additional resources by federal, state and county policymakers. County-level COVID-19 cases and deaths, together with a set of potential risk factors were collected for 3050 U.S. counties during the 1st wave of COVID-19 (Mar25-Jun3, 2020), followed by similar data for 1344 counties (in the "sunbelt" region of the country) during the 2nd wave (Jun4-Sep2, 2020), and finally for 1055 counties located broadly in the great plains region of the country 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. The analysis identifies "more vulnerable" clusters during the 1st, 2nd and 3rd waves of COVID-19. Further, 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↑). We identified "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.

In plain English

This was not a study of asbestos. It was a county-level analysis of U.S. COVID-19 deaths in 2020, and the county death rate from mesothelioma was one of 38 county factors. The authors tracked the COVID-19 mortality to incidence ratio (deaths divided by confirmed cases) for 3050 counties in a first wave (March 25 to June 3), 1344 'sunbelt' counties in a second wave, and 1055 counties broadly in the great plains region in a third wave. They grouped counties with similar patterns over time into clusters and compared each cluster with counties that had no COVID-19 deaths. In the first wave, a higher county mesothelioma death rate was associated with a higher ratio (β = 0.58%, P value = 0.031). In the third wave, mesothelioma was associated with higher odds of being in the 'more vulnerable' cluster (OR 9.3) and in another cluster (OR 20.5). The authors concluded that heart complications and cancer were statistically significant pre-existing conditions related to the ratio. They noted that the data were county-level and that untested mild cases biased the estimates.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: County-level longitudinal (ecological) statistical analysis: generalized estimating equations to find factors linked to the COVID-19 mortality to incidence ratio, then latent growth mixture models to cluster counties, with a multinomial logit model comparing clusters
• Who was studied: U.S. counties: 3050 counties in the 1st wave (Mar 25 to Jun 3, 2020), 1344 'sunbelt' counties in the 2nd wave (Jun 4 to Sep 2, 2020) and 1055 counties broadly in the great plains in the 3rd wave (Sep 3 to Nov 12, 2020). No individual patients were studied.
• Exposure: 38 county-level potential risk factors, including county mortality rates of comorbidities such as mesothelioma, plus demographic, social and environmental factors. Asbestos exposure was not measured, and the type of mesothelioma (pleural or peritoneal) was not specified.
• Main results:
  • In the multivariate model for the 1st wave (all U.S. counties), the county mesothelioma death rate was positively associated with the COVID-19 mortality to incidence ratio (β = 0.58%, P value = 0.031)
  • In the 3rd wave, mesothelioma (OR 9.3), along with cardiomyopathy and myocarditis (OR 1.3), diabetes (OR 1.2) and TB (OR 3.2), was associated with higher odds of a county being in cluster 1 (the 'more vulnerable' cluster) rather than cluster 0 (counties with no COVID-19 deaths)
  • In the 3rd wave, mesothelioma (OR 20.5) was also associated with higher odds of a county being in cluster 2 rather than cluster 0
  • In the 1st wave, mesothelioma (OR 3.7) was associated with higher odds of a county being in cluster 8 rather than cluster 0
  • The comparison group for all odds ratios was cluster 0, the counties with no confirmed COVID-19 deaths
• The authors' conclusion: The authors concluded that heart complications and cancer were statistically significant pre-existing comorbidities related to the COVID-19 mortality to incidence ratio across the U.S.
• Limitations the authors state:
  • Mortality and mortality to incidence ratio estimates were biased because most people with mild or no symptoms were not tested
  • COVID-19 reporting differed between regions, adding inaccuracies to the data
  • Disease data were mostly based on ICD9/10 codes, which the authors say can be fairly inaccurate
  • The analysis used county-level data, not individual-level data
  • Some counties, especially in Maine, were excluded because environmental data were not available
  • Testing strategies, re-opening, self-isolation, physical distancing and mask policies could confound the analysis

Topics

Medical terms in this study, explained in our Lexicon: Mesothelioma.

Related studies

• Pleural mesothelioma incidence and use of systemic treatment decreased during the COVID-19 pandemic in The Netherlands — I. Smesseim, L.H. Douma, et al. (2025)
• Mortality rates from asbestos-related diseases in Italy during the first year of the COVID-19 pandemic — L. Fazzo, E. Grande, et al. (2023)
• The impact of COVID-19 on new mesothelioma diagnoses in Italy — L. Mangone, P. Mancuso, et al. (2022)
• A MUC5B Gene Polymorphism, rs35705950-T, Confers Protective Effects Against COVID-19 Hospitalization but Not Severe Disease or Mortality — A. Verma, J. Minnier, et al. (2022)
• COVID-19 in patients with thoracic malignancies (TERAVOLT): first results of an international, registry-based, cohort study — M.C. Garassino, J.G. Whisenant, et al. (2020)
• Lung Cancer Incidence in Nonmetropolitan and Metropolitan Counties - United States, 2007-2016 — M.E. O'Neil, S.J. Henley, et al. (2019)

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