US County-Level COVID-19 Vaccine Uptake and Rates of Omicron Cases and Deaths.
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ABSTRACT:
SUBMITTER: McLaughlin JM
PROVIDER: S-EPMC9301577 | biostudies-literature | 2022 Jul
REPOSITORIES: biostudies-literature
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US County-Level COVID-19 Vaccine Uptake and Rates of Omicron Cases and Deaths.
McLaughlin John M JM Wiemken Timothy L TL Khan Farid F Jodar Luis L
Open forum infectious diseases 20220617 7
The population-level impact of vaccination on Omicron-related disease is not well described. We fit negative binomial models to estimate the relationship between US county-level vaccine coverage and rates of coronavirus disease 2019. Increased booster dose uptake was associated with lower rates of Omicron cases and deaths and is critical to combat future severe acute respiratory syndrome coronavirus 2 waves. ...[more]
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Project description:BackgroundOn Dec 14, 2020, the United States initiated a nationwide COVID-19 vaccination campaign. Demonstrating clear population-level impact following vaccine introduction helps to further elucidate and quantify the public-health benefits of vaccination.MethodsUsing a negative binomial regression model we evaluated the ecological association between county-level COVID-19 vaccine uptake and rates of COVID-19 cases and deaths in the United States from April 1, 2021 through October 31, 2021 controlling for a broad set of county-level environmental, sociodemographic, economic, and health-status-related characteristics. County-level data were obtained from several publicly available databases that were merged for analysis.FindingsAfter adjustment for county-level characteristics, US counties with ≥ 80% of their residents ≥ 12 years of age fully vaccinated against COVID-19 had 30% (95% CI: 25-35; P < .001) and 46% (38-52; P < .001) lower rates of COVID-19 cases and deaths, respectively, versus those with <50% coverage (reference group). A dose response was observed: counties with 70-79% uptake had 20% (95% CI: 16-24; P < .001) and 35% (29-40; P < .001) lower rates of cases and deaths, respectively; counties with 60-69% uptake had 8% (5-11; P < .001) and 20% (15-24; P < .001) lower rates; and counties with 50-59% uptake had 2% (0-4; P =.09) and 8% (4-12; P < .001) lower rates. Restricting the analysis to the period when the Delta variant was predominant (June 1, 2021 ‒ October 31, 2021) showed similar findings.InterpretationOur results showed that US counties with higher proportions of persons ≥ 12 years of age fully vaccinated against COVID-19 had substantially lower rates of COVID-19 cases and deaths-a finding that showed dose response and persisted even in the period when Delta was predominant.FundingPfizer.
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Project description:For most of the COVID-19 pandemic, the daily focus has been on the number of cases, and secondarily, deaths. The most recent wave was caused by the omicron variant, first identified at the end of 2021 and the dominant variant through the first part of 2022. South Africa, one of the first countries to experience and report data regarding omicron (variant 21.K), reported far fewer deaths, even as the number of reported cases rapidly eclipsed previous peaks. However, as the omicron wave has progressed, time series show that it has been markedly different from prior waves. To more readily visualize the dynamics of cases and deaths, it is natural to plot deaths per million against cases per million. Unlike the time-series plots of cases or deaths that have become daily features of pandemic updates during the pandemic, which have time as the x-axis, in a plot of deaths vs. cases, time is implicit, and is indicated in relation to the starting point. Here we present and briefly examine such plots from a number of countries and from the world as a whole, illustrating how they summarize features of the pandemic in ways that illustrate how, in most places, the omicron wave is very different from those that came before. Code for generating these plots for any country is provided in an automatically updating GitHub repository.
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Project description:People of different racial/ethnic backgrounds, demographics, health, and socioeconomic characteristics have experienced disproportionate rates of infection and death due to COVID-19. This study tests if and how county-level rates of infection and death have changed in relation to societal county characteristics through time as the pandemic progressed. This longitudinal study sampled monthly county-level COVID-19 case and death data per 100,000 residents from April 2020 to March 2022, and studied the relationships of these variables with racial/ethnic, demographic, health, and socioeconomic characteristics for 3125 or 97.0% of U.S. counties, accounting for 96.4% of the U.S. population. The association of all county-level characteristics with COVID-19 case and death rates changed significantly through time, and showed different patterns. For example, counties with higher population proportions of Black, Native American, foreign-born non-citizen, elderly residents, households in poverty, or higher income inequality suffered disproportionately higher COVID-19 case and death rates at the beginning of the pandemic, followed by reversed, attenuated or fluctuating patterns, depending on the variable. Patterns for counties with higher White versus Black population proportions showed somewhat inverse patterns. Counties with higher female population proportions initially had lower case rates but higher death rates, and case and death rates become more coupled and fluctuated later in the pandemic. Counties with higher population densities had fluctuating case and death rates, with peaks coinciding with new variants of COVID-19. Counties with a greater proportion of university-educated residents had lower case and death rates throughout the pandemic, although the strength of this relationship fluctuated through time. This research clearly shows that how different segments of society are affected by a pandemic changes through time. Therefore, targeted policies and interventions that change as a pandemic unfolds are necessary to mitigate its disproportionate effects on vulnerable populations, particularly during the first six months of a pandemic.
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Project description:BackgroundThere is a need to evaluate how the choice of time interval contributes to the lack of consistency of SDoH variables that appear as important to COVID-19 disease burden within an analysis for both case counts and death counts.MethodsThis study identified SDoH variables associated with U.S county-level COVID-19 cumulative case and death incidence for six different periods: the first 30, 60, 90, 120, 150, and 180 days since each county had COVID-19 one case per 10,000 residents. The set of SDoH variables were in the following domains: resource deprivation, access to care/health resources, population characteristics, traveling behavior, vulnerable populations, and health status. A generalized variance inflation factor (GVIF) analysis was used to identify variables with high multicollinearity. For each dependent variable, a separate model was built for each of the time periods. We used a mixed-effect generalized linear modeling of counts normalized per 100,000 population using negative binomial regression. We performed a Kolmogorov-Smirnov goodness of fit test, an outlier test, and a dispersion test for each model. Sensitivity analysis included altering the county start date to the day each county reached 10 COVID-19 cases per 10,000.ResultsNinety-seven percent (3059/3140) of the counties were represented in the final analysis. Six features proved important for both the main and sensitivity analysis: adults-with-college-degree, days-sheltering-in-place-at-start, prior-seven-day-median-time-home, percent-black, percent-foreign-born, over-65-years-of-age, black-white-segregation, and days-since-pandemic-start. These variables belonged to the following categories: COVID-19 related, vulnerable populations, and population characteristics. Our diagnostic results show that across our outcomes, the models of the shorter time periods (30 days, 60 days, and 900 days) have a better fit.ConclusionOur findings demonstrate that the set of SDoH features that are significant for COVID-19 outcomes varies based on the time from the start date of the pandemic and when COVID-19 was present in a county. These results could assist researchers with variable selection and inform decision makers when creating public health policy.
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Project description:We used the COVID-19 Community Vulnerability Index and 7 theme scores to assess associations between vulnerability and county-level COVID-19 vaccination (n = 2415 counties) through May 25th, 2021. When comparing vaccination rates among quintiles of CCVI scores, Theme 3 (housing type, transportation, household composition, and disability) was associated with the largest disparity, with the least vulnerable counties (Q1) having 33% higher rates of vaccination among individuals aged 18+ (53.5% vs 40.2%) compared to counties with the highest vulnerability (Q5). Using generalized linear models with binomial distributions and log links, we found that a 10-point increase in the CCVI index, socioeconomic vulnerability, housing type and composition, and epidemiological factors were associated with at least a 1.0 percentage point decline in county-level vaccination. The association between community vulnerability and lower vaccination rates suggests the need for continued efforts for equitable COVID-19 vaccination across marginalized communities.
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