Racial and Ethnic Disparities in COVID-19 Infection and Hospitalization in the Active Component US Military.

Objectives. To assess COVID-19 disparities in the active component US military with an emphasis on race and ethnicity. Methods. In this retrospective cohort study, we calculated the incidence of COVID-19 testing, infection, and hospitalization in the active component US military in calendar year 2020. Results. Overall, 61.3 per 100 population per year were tested for COVID-19, 10.4% of tests were positive, and 1.1% of infected individuals were hospitalized. Non-Hispanic Blacks and Hispanics had a rate of testing for COVID-19 similar to that of Whites but had a higher risk of infection (adjusted risk ratio [ARR] = 1.25 and 1.26, respectively) and hospitalization (ARR = 1.28 and 1.21, respectively). Conclusions. Although of lower magnitude than seen in civilian populations, racial and ethnic disparities in COVID-19 infection and hospitalizations exist in the US military despite universal eligibility for health care, similar rate of testing, and adjustment for comorbidities and other factors. Simply making health care coverage available may be insufficient to ensure health equity. Interventions to mitigate disparities in the US military should target the patient, provider, health care system, and society at large. (Am J Public Health. 2021;111(12):2194-2201. https://doi.org/10.2105/AJPH.2021.306527).

Predicting COVID-19 and respiratory illness: results of the 2022-2023 Armed Forces Health Surveillance Division forecasting challenge.

Since 2019, the Integrated Biosurveillance Branch of the Armed Forces Health Surveillance Division has conducted an annual forecasting challenge during influenza season to predict short-term respiratory disease activity among Military Health System beneficiaries. Weekly case and encounter observed data were used to generate 1- through 4-week advanced forecasts of disease activity. To create unified combinations of model inputs for evaluation across multiple spatial resolutions, 8 individual models were used to calculate 3 ensemble models. Forecast accuracy compared to the observed activity for each model was evaluated by calculating a weighted interval score. Weekly 1- through 4-week ahead forecasts for each ensemble model were generally higher than observed data, especially during periods of peak activity, with peaks in forecasted activity occurring later than observed peaks. The larger the forecasting horizon, the more pronounced the gap between forecasted peak and observed peak. The results showed that several models accurately predicted COVID-19 cases and respiratory encounters with enough lead time for public health response by senior leaders.