A Study Examining the Impact of County-Level Demographic, Socioeconomic, and Political Affiliation Characteristics on COVID-19 Vaccination Patterns in Indiana.

The COVID-19 vaccination campaign resulted in uneven vaccine uptake throughout the United States, particularly in rural areas, areas with socially and economically disadvantaged groups, and populations that exhibited vaccine hesitancy behaviors. This study examines how county-level sociodemographic and political affiliation characteristics differentially affected patterns of COVID-19 vaccinations in the state of Indiana every month in 2021. We linked county-level demographics from the 2016-2020 American Community Survey Five-Year Estimates and the Indiana Elections Results Database with county-level COVID-19 vaccination counts from the Indiana State Department of Health. We then created twelve monthly linear regression models to assess which variables were consistently being selected, based on the Akaike Information Criterion (AIC) and adjusted R-squared values. The vaccination models showed a positive association with proportions of Bachelor's degree-holding residents, of 40-59 year-old residents, proportions of Democratic-voting residents, and a negative association with uninsured and unemployed residents, persons living below the poverty line, residents without access to the Internet, and persons of Other Race. Overall, after April, the variables selected were consistent, with the model's high adjusted R2 values for COVID-19 cumulative vaccinations demonstrating that the county sociodemographic and political affiliation characteristics can explain most of the variation in vaccinations. Linking county-level sociodemographic and political affiliation characteristics with Indiana's COVID-19 vaccinations revealed inherent inequalities in vaccine coverage among different sociodemographic groups. Increased vaccine uptake could be improved in the future through targeted messaging, which provides culturally relevant advertising campaigns for groups less likely to receive a vaccine, and increasing access to vaccines for rural, under-resourced, and underserved populations.

A Review of the Estimation of Sensitivity and Specificity in the Context of Time-Dependent Outcomes.

Time is an essential element in the field of survival analysis. Time to an event is essential in adjudicating diagnostic utility and determining the effectiveness of treatment. However, time-to-event outcomes-in a sense-are complicated to deal with, since they involve both a binary component, which is the outcome itself (death or recurrence) and a continuous component, which is the time for the occurrence of the outcome. In such scenarios, simpler metrics such as sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve are insufficient to establish the utility of a diagnostic marker. Given the above constraint, this review discusses established ways in which sensitivity and specificity can be determined and are therefore sufficient in establishing utility in the context of time-dependent outcomes. This review also discusses how studies investigating the sensitivity and specificity of a diagnostic marker in the context of time-dependent outcomes can be improved through the use of existing user-friendly statistical software. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 3.