Association of Social Vulnerability, COVID-19 vaccine site density, and vaccination rates in the United States.
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ABSTRACT:
SUBMITTER: Thakore N
PROVIDER: S-EPMC8450273 | biostudies-literature | 2021 Dec
REPOSITORIES: biostudies-literature
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Association of Social Vulnerability, COVID-19 vaccine site density, and vaccination rates in the United States.
Thakore Nitya N Khazanchi Rohan R Orav E John EJ Ganguli Ishani I
Healthcare (Amsterdam, Netherlands) 20210920 4
The COVID-19 pandemic has disproportionately impacted Americans in socially vulnerable areas. Unfortunately, these groups are also experiencing lower vaccination rates. To understand how strategic vaccine site placement may benefit high vulnerability populations, we extracted vaccine site locations for 26 U.S. states and linked these data to county-level adult vaccination rates and the CDC 2018 Social Vulnerability Index rankings. We fit quasi-Poisson regression models to compare vaccine site de ...[more]
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Project description:Emerging evidence suggests that socially vulnerable communities are at higher risk for coronavirus disease 2019 (COVID-19) outbreaks in the United States. However, no prior studies have examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. The purpose of this study was to examine temporal trends among counties with high and low social vulnerability and to quantify disparities in these trends over time. We hypothesized that highly vulnerable counties would have higher incidence and death rates compared to less vulnerable counties and that this disparity would widen as the pandemic progressed. We conducted a retrospective longitudinal analysis examining COVID-19 incidence and death rates from March 1 to August 31, 2020 for each county in the US. We obtained daily COVID-19 incident case and death data from USAFacts and the Johns Hopkins Center for Systems Science and Engineering. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention in which higher scores represent more vulnerability. Using a Bayesian hierarchical negative binomial model, we estimated daily risk ratios (RRs) comparing counties in the first (lower) and fourth (upper) SVI quartiles. We adjusted for percentage of the county designated as rural, percentage in poor or fair health, percentage of adult smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, and the proportion tested for COVID-19 in the state. In unadjusted analyses, we found that for most of March 2020, counties in the upper SVI quartile had significantly fewer cases per 100,000 than lower SVI quartile counties. However, on March 30, we observed a crossover effect in which the RR became significantly greater than 1.00 (RR = 1.10, 95% PI: 1.03, 1.18), indicating that the most vulnerable counties had, on average, higher COVID-19 incidence rates compared to least vulnerable counties. Upper SVI quartile counties had higher death rates on average starting on March 30 (RR = 1.17, 95% PI: 1.01,1.36). The death rate RR achieved a maximum value on July 29 (RR = 3.22, 95% PI: 2.91, 3.58), indicating that most vulnerable counties had, on average, 3.22 times more deaths per million than the least vulnerable counties. However, by late August, the lower quartile started to catch up to the upper quartile. In adjusted models, the RRs were attenuated for both incidence cases and deaths, indicating that the adjustment variables partially explained the associations. We also found positive associations between COVID-19 cases and deaths and percentage of the county designated as rural, percentage of resident in fair or poor health, and average daily PM2.5. Results indicate that the impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties over time. This highlights the importance of protecting vulnerable populations as the pandemic unfolds.
| S-EPMC7491526 | biostudies-literature
Project description:BackgroundSocially vulnerable communities may be at higher risk for COVID-19 outbreaks in the US. However, no prior studies examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. Therefore, we examined temporal trends among counties with high and low social vulnerability to quantify disparities in trends over time.MethodsWe conducted a longitudinal analysis examining COVID-19 incidence and death rates from March 15 to December 31, 2020, for each US county using data from USAFacts. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention, with higher values indicating more vulnerability. Using a Bayesian hierarchical negative binomial model, we estimated daily risk ratios (RRs) comparing counties in the first (lower) and fourth (upper) SVI quartiles, adjusting for rurality, percentage in poor or fair health, percentage female, percentage of smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, daily temperature and precipitation, and proportion tested for COVID-19.ResultsAt the outset of the pandemic, the most vulnerable counties had, on average, fewer cases per 100,000 than least vulnerable SVI quartile. However, on March 28, we observed a crossover effect in which the most vulnerable counties experienced higher COVID-19 incidence rates compared to the least vulnerable counties (RR = 1.05, 95% PI: 0.98, 1.12). Vulnerable counties had higher death rates starting on May 21 (RR = 1.08, 95% PI: 1.00,1.16). However, by October, this trend reversed and the most vulnerable counties had lower death rates compared to least vulnerable counties.ConclusionsThe impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties and back again over time.
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Project description:IntroductionIn 2021, HHS Office of Minority Health and CDC developed a composite measure of social vulnerability called the Minority Health Social Vulnerability Index (MHSVI) to assess the needs of communities most vulnerable to COVID-19. The MHSVI extends the CDC Social Vulnerability Index with two new themes on healthcare access and medical vulnerability. This analysis examines COVID-19 vaccination coverage by social vulnerability using the MHSVI.MethodsCounty-level COVID-19 vaccine administration data among persons aged ≥18 years reported to CDC from 12/14/20 to 01/31/22 were analyzed. U.S. counties from 50 states and DC were categorized into tertiles of vulnerability (low, moderate, and high) for the composite MHSVI measure and each of the 34 indicators. Vaccination coverage (≥1 dose, primary series completion, and receipt of a booster dose) was calculated by tertiles for the composite MHSVI measure and each indicator.ResultsCounties with lower per capita income, higher proportion of individuals with no high school diploma, living below poverty, ≥65 years of age, with a disability, and in mobile homes had lower vaccination uptake. However, counties with larger proportions of racial/ethnic minorities and individuals speaking English less than "very well" had higher coverage. Counties with fewer primary care physicians and greater medical vulnerabilities had lower ≥ 1 dose vaccination coverage. Furthermore, counties of high vulnerability had lower primary series completion and receipt of a booster dose. There were no clear patterns in COVID-19 vaccination coverage by tertiles for the composite measure.ConclusionResults from the new components in the MHSVI identify needs to prioritize persons in counties with greater medical vulnerabilities and limited access to health care, who are at greater risk for adverse COVID-19 outcomes. Findings suggest that using a composite measure to characterize social vulnerability might mask disparities in COVID-19 vaccination uptake that would have otherwise been observed using specific indicators.
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Project description:ImportanceSelf-injury mortality (SIM) combines suicides and the preponderance of drug misuse-related overdose fatalities. Identifying social and environmental factors associated with SIM and suicide may inform etiologic understanding and intervention design.ObjectiveTo identify factors associated with interstate SIM and suicide rate variation and to assess potential for differential suicide misclassification.Design, setting, and participantsThis cross-sectional study used a partial panel time series with underlying cause-of-death data from 50 US states and the District of Columbia for 1999-2000, 2007-2008, 2013-2014 and 2018-2019. Applying data from the Centers for Disease Control and Prevention, SIM includes all suicides and the preponderance of unintentional and undetermined drug intoxication deaths, reflecting self-harm behaviors. Data were analyzed from February to June 2021.ExposuresExposures included inequity, isolation, demographic characteristics, injury mechanism, health care access, and medicolegal death investigation system type.Main outcomes and measuresThe main outcome, SIM, was assessed using unstandardized regression coefficients of interstate variation associations, identified by the least absolute shrinkage and selection operator; ratios of crude SIM to suicide rates per 100 000 population were assessed for potential differential suicide misclassification.ResultsA total of 101 325 SIMs were identified, including 74 506 (73.5%) among males and 26 819 (26.5%) among females. SIM to suicide rate ratios trended upwards, with an accelerating increase in overdose fatalities classified as unintentional or undetermined (SIM to suicide rate ratio, 1999-2000: 1.39; 95% CI, 1.38-1.41; 2018-2019: 2.12; 95% CI, 2.11-2.14). Eight states recorded a SIM to suicide rate ratio less than 1.50 in 2018-2019 vs 39 states in 1999-2000. Northeastern states concentrated in the highest category (range, 2.10-6.00); only the West remained unrepresented. Least absolute shrinkage and selection operator identified 8 factors associated with the SIM rate in 2018-2019: centralized medical examiner system (β = 4.362), labor underutilization rate (β = 0.728), manufacturing employment (β = -0.056), homelessness rate (β = -0.125), percentage nonreligious (β = 0.041), non-Hispanic White race and ethnicity (β = 0.087), prescribed opioids for 30 days or more (β = 0.117), and percentage without health insurance (β = -0.013) and 5 factors associated with the suicide rate: percentage male (β = 1.046), military veteran (β = 0.747), rural (β = 0.031), firearm ownership (β = 0.030), and pain reliever misuse (β = 1.131).Conclusions and relevanceThese findings suggest that SIM rates were associated with modifiable, upstream factors. Although embedded in SIM, suicide unexpectedly deviated in proposed social and environmental determinants. Heterogeneity in medicolegal death investigation processes and data assurance needs further characterization, with the goal of providing the highest-quality reports for developing and tracking public health policies and practices.
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Project description:PurposeThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began proliferating widely throughout the world in late 2019/early 2020, creating a global pandemic and health crisis. Although vaccines became available to the public approximately 1 year after the onset of the pandemic, there still remains much hesitancy surrounding vaccination. One key concern comes from reports of breakthrough infections among the vaccinated that show comparable levels of peak viral load as the unvaccinated, calling into question the ability of vaccines to prevent transmission. Therefore young, healthy individuals who are at low risk of serious complications themselves have little incentive to receive a vaccine that they are not convinced will protect others around them. To address this important concern, this study aimed to evaluate the extent to which vaccination rates are associated with reduced SARS-CoV-2 transmission among the unvaccinated population.MethodsAn observational study was conducted in the United States of America throughout the months of June through September, 2021. Vaccination rate and incidence of coronavirus disease 2019 (COVID-19) were obtained for each state, along with a number of important control variables. Panel data regression was used to predict incidence among the unvaccinated based on each state's vaccination rate.ResultsStates with a higher proportion of fully vaccinated individuals reported fewer new cases among the remaining unvaccinated population.ConclusionThese data add to accumulating evidence that COVID-19 vaccinations can indeed slow the spread of SARS-CoV-2, and are an important tool in society's arsenal to put this pandemic behind us.
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