scholarly journals Counties with lower insurance coverage are associated with both slower vaccine rollout and higher COVID-19 incidence across the United States

2021 ◽  
Author(s):  
Emily Lindemer ◽  
Mayank Choudhary ◽  
Gregory Donadio ◽  
Colin Pawlowski ◽  
Venky Soundararajan
Keyword(s):  
Health Insurance ◽  
Insurance Coverage ◽  
Health Insurance Coverage ◽  
The United States ◽  
Vaccination Rate ◽  
Incidence Rates ◽  
P Value ◽  
Spearman Correlation ◽  
Vaccination Rates ◽  
Vaccination Period

Efficient and equitable vaccination distribution is a priority for effectively outcompeting the transmission of COVID-19 globally. A recent study from the Centers for Disease Control and Prevention (CDC) identified that US counties with high social vulnerability according to metrics such as poverty, unemployment, low income, and no high school diploma, have significantly lower rates of vaccination compared to the national average1. Here, we build upon this analysis to consider associations between county-level vaccination rates and 68 different demographic, socioeconomic, and environmental factors for 1,510 American counties with over 228 million individuals for which vaccination data was also available. Our analysis reveals that counties with high levels of uninsured individuals have significantly lower COVID-19 vaccination rates (Spearman correlation: -0.264), despite the fact that the CDC has mandated that all COVID-19 vaccines are free and cannot be denied to anyone based upon health insurance coverage or immigration status. Furthermore, we find that the counties with high levels of uninsured individuals tend to have the highest COVID-19 incidence rates in March 2021 relative to December 2020 (Spearman correlation: 0.388). Among the 68 factors analyzed, insurance coverage is the only factor which is highly correlated with both vaccination rate and change in COVID-19 incidence during the vaccination period (|Spearman correlation| > 0.25). We also find that counties with higher percentages of Black and Hispanic individuals have significantly lower vaccination rates (Spearman correlations: -0.128, -0.136) and lesser declines of COVID-incidence rates (Spearman correlations: 0.334, 0.330) during the vaccination period. Surprisingly however, after controlling for race, we find that the association between lack of insurance coverage and vaccination rate as well as COVID-19 incidence rates is largely driven by counties with a majority white population. Among the counties with high proportions of white residents (top 10% decile), the association between insurance coverage and vaccination rate is significant (Spearman correlation: -0.210, p-value: 0.002), but among counties with low proportions of white residents (bottom 10% decile) this association is not significant (Spearman correlation: 0.072, p-value: 0.088). Taken together, this study highlights the fact that intricate socioeconomic factors are correlated not just to COVID-19 vaccination rates, but also to COVID-19 incidence fluctuations, underscoring the need to improve COVID-19 vaccination campaigns in marginalized communities. The strong positive correlation between low levels of health insurance coverage and low vaccination rates is particularly concerning, and calls for improved public health messaging to emphasize the fact that health insurance is not required to be eligible for any of the FDA-authorized COVID-19 vaccines in the United States.

scholarly journals Higher COVID-19 vaccination rates are linked to decreased county-level COVID-19 incidence across USA

2021 ◽  
Author(s):  
Arjun Puranik ◽  
AJ Venkatakrishnan ◽  
Colin Pawlowski ◽  
Bharathwaj Raghunathan ◽  
Eshwan Ramudu ◽  
...  
Keyword(s):  
Incidence Rate ◽  
Vaccine Efficacy ◽  
Vaccine Dose ◽  
The United States ◽  
Vaccination Rate ◽  
Incidence Rates ◽  
P Value ◽  
County Level ◽  
Vaccination Rates ◽  
The Impact

Real world evidence studies of mass vaccination across health systems have reaffirmed the safety1 and efficacy2,3 of the FDA-authorized mRNA vaccines for COVID-19. However, the impact of vaccination on community transmission remains to be characterized. Here, we compare the cumulative county-level vaccination rates with the corresponding COVID-19 incidence rates among 87 million individuals from 580 counties in the United States, including 12 million individuals who have received at least one vaccine dose. We find that cumulative county-level vaccination rate through March 1, 2021 is significantly associated with a concomitant decline in COVID-19 incidence (Spearman correlation ρ = −0.22, p-value = 8.3e-8), with stronger negative correlations in the Midwestern counties (ρ = −0.37, p-value = 1.3e-7) and Southern counties (ρ = −0.33, p-value = 4.5e-5) studied. Additionally, all examined US regions demonstrate significant negative correlations between cumulative COVID-19 incidence rate prior to the vaccine rollout and the decline in the COVID-19 incidence rate between December 1, 2020 and March 1, 2021, with the US western region being particularly striking (ρ = −0.66, p-value = 5.3e-37). However, the cumulative vaccination rate and cumulative incidence rate are noted to be statistically independent variables, emphasizing the need to continue the ongoing vaccination roll out at scale. Given confounders such as different coronavirus restrictions and mask mandates, varying population densities, and distinct levels of diagnostic testing and vaccine availabilities across US counties, we are advancing a public health resource to amplify transparency in vaccine efficacy monitoring (https://public.nferx.com/covid-monitor-lab/vaccinationcheck). Application of this resource highlights outliers like Dimmit county (Texas), where infection rates have increased significantly despite higher vaccination rates, ostensibly owing to amplified travel as a “vaccination hub”; as well as Henry county (Ohio) which encountered shipping delays leading to postponement of the vaccine clinics. This study underscores the importance of tying the ongoing vaccine rollout to a real-time monitor of spatio-temporal vaccine efficacy to help turn the tide of the COVID-19 pandemic.

scholarly journals Counties with Lower Insurance Coverage and Housing Problems Are Associated with Both Slower Vaccine Rollout and Higher COVID-19 Incidence

Vaccines ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 973
Author(s):  
Gregory Donadio ◽  
Mayank Choudhary ◽  
Emily Lindemer ◽  
Colin Pawlowski ◽  
Venky Soundararajan
Keyword(s):  
Socioeconomic Factors ◽  
Insurance Coverage ◽  
Vaccination Rate ◽  
Incidence Rates ◽  
P Value ◽  
Strongly Correlated ◽  
Marginalized Communities ◽  
Vaccination Rates ◽  
Using Data ◽  
The Impact

Equitable vaccination distribution is a priority for outcompeting the transmission of COVID-19. Here, the impact of demographic, socioeconomic, and environmental factors on county-level vaccination rates and COVID-19 incidence changes is assessed. In particular, using data from 3142 US counties with over 328 million individuals, correlations were computed between cumulative vaccination rate and change in COVID-19 incidence from 1 December 2020 to 6 June 2021, with 44 different demographic, environmental, and socioeconomic factors. This correlation analysis was also performed using multivariate linear regression to adjust for age as a potential confounding variable. These correlation analyses demonstrated that counties with high levels of uninsured individuals have significantly lower COVID-19 vaccination rates (Spearman correlation: −0.460, p-value: <0.001). In addition, severe housing problems and high housing costs were strongly correlated with increased COVID-19 incidence (Spearman correlations: 0.335, 0.314, p-values: <0.001, <0.001). This study shows that socioeconomic factors are strongly correlated to both COVID-19 vaccination rates and incidence rates, underscoring the need to improve COVID-19 vaccination campaigns in marginalized communities.

Data Processing Improvements for Estimates of Health Insurance Coverage in the Current Population Survey Annual Social and Economic Supplement

2021 ◽  
pp. 107755872110008
Author(s):  
Edward R. Berchick ◽  
Heide Jackson
Keyword(s):  
Health Insurance ◽  
Data Processing ◽  
Current Population Survey ◽  
Population Survey ◽  
Insurance Coverage ◽  
Processing System ◽  
Health Insurance Coverage ◽  
The United States ◽  
Health Insurance Data ◽  
Current Population

Estimates of health insurance coverage in the United States rely on household-based surveys, and these surveys seek to improve data quality amid a changing health insurance landscape. We examine postcollection processing improvements to health insurance data in the Current Population Survey Annual Social and Economic Supplement (CPS ASEC), one of the leading sources of coverage estimates. The implementation of updated data extraction and imputation procedures in the CPS ASEC marks the second stage of a two-stage improvement and the beginning of a new time series for health insurance estimates. To evaluate these changes, we compared estimates from two files that introduce the updated processing system with two files that use the legacy system. We find that updates resulted in higher rates of health insurance coverage and lower rates of dual coverage, among other differences. These results indicate that the updated data processing improves coverage estimates and addresses previously noted limitations of the CPS ASEC.

scholarly journals The convergence of racial and income disparities in health insurance coverage in the United States

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
De-Chih Lee ◽  
Hailun Liang ◽  
Leiyu Shi
Keyword(s):  
Health Insurance ◽  
Low Income ◽  
Insurance Coverage ◽  
Health Insurance Coverage ◽  
The United States ◽  
Combined Effect ◽  
Medical Expenditure ◽  
The Us ◽  
Race Ethnicity ◽  
Vulnerability Measure

Abstract Objective This study applied the vulnerability framework and examined the combined effect of race and income on health insurance coverage in the US. Data source The household component of the US Medical Expenditure Panel Survey (MEPS-HC) of 2017 was used for the study. Study design Logistic regression models were used to estimate the associations between insurance coverage status and vulnerability measure, comparing insured with uninsured or insured for part of the year, insured for part of the year only, and uninsured only, respectively. Data collection/extraction methods We constructed a vulnerability measure that reflects the convergence of predisposing (race/ethnicity), enabling (income), and need (self-perceived health status) attributes of risk. Principal findings While income was a significant predictor of health insurance coverage (a difference of 6.1–7.2% between high- and low-income Americans), race/ethnicity was independently associated with lack of insurance. The combined effect of income and race on insurance coverage was devastating as low-income minorities with bad health had 68% less odds of being insured than high-income Whites with good health. Conclusion Results of the study could assist policymakers in targeting limited resources on subpopulations likely most in need of assistance for insurance coverage. Policymakers should target insurance coverage for the most vulnerable subpopulation, i.e., those who have low income and poor health as well as are racial/ethnic minorities.

The Impact of the Affordable Care Act Medicaid Expansions on the Sources of Health Insurance Coverage of Undergraduate Students in the United States

2021 ◽  
pp. 107755872110158
Author(s):  
Priyanka Anand ◽  
Dora Gicheva
Keyword(s):  
United States ◽  
Health Insurance ◽  
Undergraduate Students ◽  
Affordable Care Act ◽  
Insurance Coverage ◽  
Health Insurance Coverage ◽  
The United States ◽  
Percentage Points ◽  
The Affordable Care Act ◽  
Medicaid Expansions

This article examines how the Affordable Care Act Medicaid expansions affected the sources of health insurance coverage of undergraduate students in the United States. We show that the Affordable Care Act expansions increased the Medicaid coverage of undergraduate students by 5 to 7 percentage points more in expansion states than in nonexpansion states, resulting in 17% of undergraduate students in expansion states being covered by Medicaid postexpansion (up from 9% prior to the expansion). In contrast, the growth in employer and private direct coverage was 1 to 2 percentage points lower postexpansion for students in expansion states compared with nonexpansion states. Our findings demonstrate that policy efforts to expand Medicaid eligibility have been successful in increasing the Medicaid coverage rates for undergraduate students in the United States, but there is evidence of some crowd out after the expansions—that is, some students substituted their private and employer-sponsored coverage for Medicaid.

scholarly journals Union Effects on Health Insurance Provision and Coverage in the United States

ILR Review ◽  
2002 ◽  
Vol 55 (4) ◽  
pp. 610-627 ◽  
Author(s):  
Thomas C. Buchmueller ◽  
John Dinardo ◽  
Robert G. Valletta
Keyword(s):  
United States ◽  
Health Insurance ◽  
Survey Data ◽  
Insurance Coverage ◽  
Health Benefits ◽  
Union Density ◽  
Health Insurance Coverage ◽  
The United States ◽  
Health Coverage ◽  
Private Sector Union

During the past two decades, union density has declined in the United States and employer provision of health benefits has changed substantially in extent and form. Using individual survey data spanning the years 1983–97 combined with employer survey data for 1993, the authors update and extend previous analyses of private-sector union effects on employer-provided health benefits. They find that the union effect on health insurance coverage rates has fallen somewhat but remains large, due to an increase over time in the union effect on employee “take-up” of offered insurance, and that declining unionization explains 20–35% of the decline in employee health coverage. The increasing union take-up effect is linked to union effects on employees' direct costs for health insurance and the availability of retiree coverage.

scholarly journals Measurement Error in Health Insurance Reporting

2008 ◽  
Vol 45 (4) ◽  
pp. 422-437 ◽  
Author(s):  
Joanne Pascale
Keyword(s):  
Health Insurance ◽  
Survey Methods ◽  
Insurance Coverage ◽  
Health Insurance Coverage ◽  
The United States ◽  
Household Level ◽  
Testing Protocol ◽  
Life Circumstances ◽  
Rich Data

In the United States, surveys serve as the only source of data for the number of uninsured people; they also provide rich data for exploring the relationships between health insurance coverage and individuals' life circumstances, such as employment, income, and health status, enabling researchers to assess the effectiveness of various aspects of the health care system. The Current Population Survey (CPS) is one of the most influential surveys measuring health insurance, but it is not without critics. To address outstanding questions about the data quality of the CPS health insurance questions, qualitative testing was conducted to assess various aspects of the questionnaire from the respondent's perspective. A testing protocol was developed largely based on previous health survey methods literature, and test subjects were probed about their comprehension of the questions, particular terms and phrases, and their strategies for formulating an answer. Several design features were identified as problematic, including the overall questionnaire structure, the calendar year reference period, the household-level design, and the wording of questions on public coverage.

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