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1.
J Public Health Manag Pract ; 29(4): 572-579, 2023.
Article in English | MEDLINE | ID: covidwho-2283174

ABSTRACT

OBJECTIVE: To examine the association between county-level Black-White residential segregation and COVID-19 vaccination rates. DESIGN: Observational cross-sectional study using multivariable generalized linear models with state fixed effects to estimate the average marginal effects of segregation on vaccination rates. SETTING: National analysis of county-level vaccination rates. MAIN OUTCOME MEASURE: County-level vaccination rates across the United States. RESULTS: We found an overall positive association between county-level segregation and the proportion population fully vaccinated, with a 6.8, 11.3, and 12.8 percentage point increase in the proportion fully vaccinated by May 3, September 27, and December 6, 2021, respectively. Effects were muted after adjustment for sociodemographic variables. Furthermore, in analyses including an interaction term between the county proportion of Black residents and the county dissimilarity index, the association between segregation and vaccination is positive in counties with a lower proportion of Black residents (ie, 5%) but negative in counties with the highest proportions of Black residents (ie, 70%). CONCLUSIONS: Findings highlight the importance of methodological decisions when modeling disparities in COVID-19 vaccinations. Researchers should consider mediating and moderating factors and examine interaction effects and stratified analyses taking racial group distributions into account. Results can inform policies around the prioritization of vaccine distribution and outreach.


Subject(s)
COVID-19 , Social Segregation , Humans , Black People , COVID-19/epidemiology , COVID-19/prevention & control , COVID-19 Vaccines/therapeutic use , United States/epidemiology , Vaccination , White People , Cross-Sectional Studies
3.
Prev Med Rep ; 24: 101588, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1458682

ABSTRACT

BACKGROUND: Racial and ethnic minorities in the US have been disproportionately affected by the COVID-19 pandemic and are at risk for disparities in COVID-19 vaccinations. The H1N1 flu vaccine experience provides lessons learned to address and prevent racial and ethnic disparities in COVID-19 vaccinations. We aim to identify racial/ethnic and geographic disparities in H1N1 vaccinations among Medicaid enrollees to inform equitable COVID-19 vaccination policies and strategies. METHODS: The study population included people under 65 who were continuously enrolled in Medicaid in 2009 and 2010 from 28 states and the District of Columbia. H1N1 vaccinations were identified from Medicaid outpatient claims. Vaccination rates were calculated for the overall sample and subpopulations by race/ethnicity and state. RESULTS: 3,708,894 (12.3%) Medicaid enrollees in the sample were vaccinated for H1N1 in 2009-2010. Race-specific vaccination rates ranged from 8.1% in American Indian/Alaska Native (AI/AN) to 19.8% in Asian/Pacific Islander Medicaid enrollees. NHB enrollees had lower vaccination rates than non-Hispanic White (NHW) enrollees in all states, with the exceptions of Maryland, Missouri, Ohio, and Washington. The largest disparity between NHB and NHW was in Pennsylvania (1.0% vs. 7.0%), while the largest absolute difference between NHB and NHW enrollees was in Georgia (17.4% vs. 30.7%). CONCLUSIONS: Our study found huge variation in H1N1 vaccinations across states and racial/ethnic disparities in H1N1 vaccinations within states. In most states, NHB and AI/AN Medicaid enrollees had lower vaccination rates than Whites. Hispanic and Asian/Pacific Islander Medicaid enrollees in most states had higher vaccination rates than Whites.

4.
Am J Public Health ; 111(6): 1141-1148, 2021 06.
Article in English | MEDLINE | ID: covidwho-1186632

ABSTRACT

Despite growing evidence that COVID-19 is disproportionately affecting communities of color, state-reported racial/ethnic data are insufficient to measure the true impact.We found that between April 12, 2020, and November 9, 2020, the number of US states reporting COVID-19 confirmed cases by race and ethnicity increased from 25 to 50 and 15 to 46, respectively. However, the percentage of confirmed cases reported with missing race remained high at both time points (29% on April 12; 23% on November 9). Our analysis demonstrates improvements in reporting race/ethnicity related to COVID-19 cases and deaths and highlights significant problems with the quality and contextualization of the data being reported.We discuss challenges for improving race/ethnicity data collection and reporting, along with opportunities to advance health equity through more robust data collection and contextualization. To mitigate the impact of COVID-19 on racial/ethnic minorities, accurate and high-quality demographic data are needed and should be analyzed in the context of the social and political determinants of health.


Subject(s)
COVID-19 , Ethnicity/statistics & numerical data , Mandatory Reporting , Mortality/trends , Racial Groups/statistics & numerical data , COVID-19/epidemiology , COVID-19/mortality , Data Collection/standards , Health Status Disparities , Humans , Minority Groups/statistics & numerical data , United States
5.
J Public Health Manag Pract ; 27(3): 268-277, 2021.
Article in English | MEDLINE | ID: covidwho-1150045

ABSTRACT

CONTEXT: There is a need to understand population race and ethnicity disparities in the context of sociodemographic risk factors in the US experience of the COVID-19 pandemic. OBJECTIVE: Determine the association between county-level proportion of non-Hispanic Black (NHB) on county COVID-19 case and death rates and observe how this association was influenced by county sociodemographic and health care infrastructure characteristics. DESIGN AND SETTING: This was an ecologic analysis of US counties as of September 20, 2020, that employed stepwise construction of linear and negative binomial regression models. The primary independent variable was the proportion of NHB population in the county. Covariates included county demographic composition, proportion uninsured, proportion living in crowded households, proportion living in poverty, population density, state testing rate, Primary Care Health Professional Shortage Area status, and hospital beds per 1000 population. MAIN OUTCOME MEASURES: Outcomes were exponentiated COVID-19 cases per 100 000 population and COVID-19 deaths per 100 000 population. We produced county-level maps of the measures of interest. RESULTS: In total, 3044 of 3142 US counties were included. Bivariate relationships between the proportion of NHB in a county and county COVID-19 case (Exp ß = 1.026; 95% confidence interval [CI], 1.024-1.028; P < .001) and death rates (rate ratio [RR] = 1.032; 95% CI, 1.029-1.035; P < .001) were not attenuated in fully adjusted models. The adjusted association between the proportion of NHB population in a county and county COVID-19 case was Exp ß = 1.025 (95% CI, 1.023-1.027; P < .001) and the association with county death rates was RR = 1.034 (95% CI, 1.031-1.038; P < .001). CONCLUSIONS: The proportion of NHB people in a county was positively associated with county COVID-19 case and death rates and did not change in models that accounted for other socioecologic and health care infrastructure characteristics that have been hypothesized to account for the disproportionate impact of COVID-19 on racial and ethnic minority populations. Results can inform efforts to mitigate the impact of structural racism of COVID-19.


Subject(s)
Black or African American/statistics & numerical data , COVID-19/epidemiology , COVID-19/mortality , COVID-19/therapy , Ethnicity/statistics & numerical data , Health Status Disparities , Minority Groups/statistics & numerical data , Adult , Aged , Aged, 80 and over , Female , Humans , Local Government , Male , Middle Aged , Pandemics/statistics & numerical data , Population Surveillance , Risk Factors , SARS-CoV-2 , Socioeconomic Factors , United States/epidemiology
6.
South Med J ; 114(2): 57-62, 2021 02.
Article in English | MEDLINE | ID: covidwho-1063519

ABSTRACT

OBJECTIVES: We hypothesized that the proportion of Black individuals in a county would be associated with higher rates of coronavirus disease 2019 (COVID-19) cases and deaths, even after accounting for other high-risk socioecologic factors such as poverty, population density, and household crowding, and uninsured rates. We also expected that counties designated as primary care health professional shortage areas (PCHPSAs) would be associated with higher COVID-19 death rates, and the lack of primary care access would exacerbate racial disparities in death rates. We undertook this study to test these hypotheses and discern the independent effects of racial composition, socioecologic characteristics, and healthcare system factors on COVID-19 cases and deaths in Georgia counties. METHODS: We used county-level COVID-19 cases and deaths on April 23, 2020 from the Johns Hopkins Coronavirus Resource Center and estimates of 2019 county-level populations from the US Census Bureau to calculate the cumulative event rates for the state of Georgia. We used multiple regression models to examine crude and adjusted associations of socioecologic and health system variables with county-level COVID-19 case and mortality rates. RESULTS: After adjustment, a 1% increase in the proportion of Black people in the county resulted in a 2.3% increase in the county COVID-19 confirmed case rate and a 3.0% increase in the death rate (relative risk 1.03, 95% confidence interval 1.01-1.05, P < 0.001). Primary care shortage areas had a 74% higher death rate (relative risk 1.74, 95% confidence interval 1.00-3.00, P = 0.049). CONCLUSIONS: These results highlight the impact of racial disparities on the spatial patterns of COVID-19 disease burden in Georgia, which can guide interventions to mitigate racial disparities. The results also support the need for robust primary care infrastructure throughout the state.


Subject(s)
Black or African American/statistics & numerical data , COVID-19/ethnology , COVID-19/mortality , Health Services Accessibility/statistics & numerical data , Healthcare Disparities/statistics & numerical data , Primary Health Care/organization & administration , Adult , Aged , COVID-19/therapy , Female , Georgia/epidemiology , Health Status Disparities , Humans , Male , Middle Aged , Socioeconomic Factors
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