County-Level Determinants of COVID-19 Testing and Cases: Are there Racial/Ethnic Disparities in Texas?

Beliefs about COVID-19 testing and treatment: A national survey of Black and White adults

Objectives

Knowledge, access, and use of testing and antiviral treatments is critical to managing and mitigating the continuing burden of the novel Corona Virus (COVID-19) in the United States. This study measured knowledge, attitude, behaviors, and self-reported barriers towards COVID-19 testing and outpatient anti-viral medications (OPA) treatments among Black and older individuals who face greater hospitalization and mortality from the disease.

Study design

Cross-sectional structured survey.

Methods

Respondents were randomly selected from an opt-in national panel in December 2022. Equal numbers of Black and White US adults over the age of 40 (n = 1037) completed the 42 item online survey. The main measures were key sociodemographic variables of respondents, race, age, political affiliation and COVID-19 attitudes, beliefs, testing behaviors, and knowledge and barriers to OPA access.

Results

Overall, awareness and knowledge of COVID-19 outpatient treatments was low. Black respondents were more likely to test for COVID-19 than White respondents but less likely to know about OPA treatments. Insurance coverage was a significant factor in use of home tests. Knowledge of OPA treatments was low across groups. White respondents were more likely than Black respondents to be aware of OPA treatments (1.75, 95 % CI [1.31-2.33]) as were higher income respondents (1.13, 95 % CI [1.08-1.17]) and self-identified Liberals (1.79, 95 % CI [1.29-2.49]).

Conclusions

Clinicians should know large numbers of patients may not be testing for COVID-19, nor are they aware of outpatient treatment options and may hold inaccurate beliefs about them. Developing culturally specific patient education materials are warranted to increase testing, utilization of vaccinations and OPAs.

Parental Factors Associated with COVID-19 Vaccine Uptake for Children over 5 Years of Age in Texas

The COVID-19 vaccine is safe and effective for children, yet parental hesitancy towards vaccinating children against the virus persists. We conducted a telephone-administered weighted survey in Texas to examine parents' sociodemographic factors and medical conditions associated with COVID-19 vaccination intention for parents with unvaccinated children ages 5-17 years. We collected responses from 19,502 participants, of which 4879 were parents of children ages 5-17 years. We conducted multiple logistic regression with Lasso-selected variables to identify factors associated with children's vaccination status and parents' intention to vaccinate their children. From the unweighted sample, less than half of the parents (46.8%) had at least one unvaccinated child. These parents were more likely to be White, English-speaking, not concerned about illness, privately insured, and unvaccinated for COVID-19 themselves (p < 0.001). In the adjusted regression model, parents who were unvaccinated (vs. having COVID-19 booster, aOR = 28.6) and financially insecure (aOR = 1.46) had higher odds of having unvaccinated children. Parents who were Asian (aOR = 0.50), Black (aOR = 0.69), Spanish-speaking (aOR = 0.57), concerned about illness (aOR = 0.63), had heart disease (aOR = 0.41), and diabetes (aOR = 0.61) had lower odds of having unvaccinated children. Parents who were Asian, Black, Hispanic, Spanish-speaking, concerned about illness for others, and vaccine-boosted were more likely to have vaccination intention for their children (p < 0.001). Children's vaccination is essential to reduce COVID-19 transmission. It is important to raise awareness about the value of pediatric COVID-19 vaccination while considering parents' sociodemographic and medical circumstances.

COVID-19-related mortality in Texas border counties vs non-border counties

The state of Texas ranked second in total cases of coronavirus disease 2019 (COVID-19) in the United States during the pandemic. Counties near the US-Mexico border were severely impacted by the pandemic. Mortality and long-term consequences from COVID-19 are associated with comorbidities, illness severity, and patient demographics. However, differences in outcomes between border and non-border counties are unknown. In this retrospective observational study, data were obtained for analysis from the Texas hospital inpatient discharge public use data file from 2020 to 2021 for adult patients with COVID-19 based on the associated international classification of disease 10 codes. Patients were categorized into border or non-border counties. The clinical outcomes included mortality, length of stay, mortality risk, illness severity, and intensive care unit (ICU) or critical care unit (CCU) admissions. Cost differences between border and non-border counties were analyzed. Age, gender, race, ethnicity, admission type, location, and year of diagnosis were covariates. A total of 1,745,312 patients were included in this analysis. 25% of COVID-19 patients admitted in Texas were from border counties. Patient mortality was 5.35% in border counties compared to 3.87% in non-border counties (p = 0.003). In border counties, 36.51% and 32.96% of patients required ICU and CCU admissions compared to 32.96% and 10.72%, respectively in non-border counties. Border counties had significantly higher risk of mortality (relative risk (RR) = 1.26; 95% CI: 1.09-1.46, p = 0.002), ICU admission (RR = 1.15; 95% CI: 1.15; 95% CI: 1.01-1.32, p = 0.038), CCU admission (RR = 2.87; 95% CI: 1.93, 4.27, p < 0.001), and ICU/CCU admission (RR = 1.28; 95% CI: 1.10, 1.48, p < 0.001) which reflects health disparities in the management of COVID-19 in border counties of Texas.

Understanding social risk factors of county-level disparities in COVID-19 tests per confirmed case in South Carolina using statewide electronic health records data

BACKGROUND

COVID-19 testing is essential for pandemic control, and insufficient testing in areas with high disease burdens could magnify the risk of poor health outcomes. However, few area-based studies on COVID-19 testing disparities have considered the disease burden (e.g., confirmed cases). The current study aims to investigate socioeconomic drivers of geospatial disparities in COVID-19 testing relative to disease burden across 46 counties in South Carolina (SC) in the early (from April 1, 2020, to June 30, 2020) and later (from July 1, 2020, to September 30, 2021) phases of the pandemic.

METHODS

Using SC statewide COVID-19 testing data, the COVID-19 testing coverage was measured by monthly COVID-19 tests per confirmed case (hereafter CTPC) in each county. We used modified Lorenz curves to describe the unequal geographic distribution of CTPC and generalized linear mixed-effects regression models to assess the association of county-level social risk factors with CTPC in two phases of the pandemic in SC.

RESULTS

As of September 30, 2021, a total of 641,201 out of 2,941,227 tests were positive in SC. The Lorenz curve showed that county-level disparities in CTPC were less apparent in the later phase of the pandemic. Counties with a larger percentage of Black had lower CTPC during the early phase (β = -0.94, 95%CI: -1.80, -0.08), while such associations reversed in the later phase (β = 0.28, 95%CI: 0.01, 0.55). The association of some other social risk factors diminished as the pandemic evolved, such as food insecurity (β: -1.19 and -0.42; p-value is < 0.05 for both).

CONCLUSIONS

County-level disparities in CTPC and their predictors are dynamic across the pandemic. These results highlight the systematic inequalities in COVID-19 testing resources and accessibility, especially in the early stage of the pandemic. Counties with greater social vulnerability and those with fewer health care resources should be paid extra attention in the early and later phases, respectively. The current study provided empirical evidence for public health agencies to conduct more targeted community-based testing campaigns to enhance access to testing in future public health crises.

Nonpharmaceutical Interventions in Georgia: Public Health Implications

OBJECTIVES

As coronavirus disease 2019 (COVID-19) spread, many states implemented nonpharmaceutical interventions in the absence of effective therapies with varying degrees of success. Our aim was to evaluate restrictions comparing two regions of Georgia and their impact on outcomes as measured by confirmed illness and deaths.

METHODS

Using The New York Times COVID-19 incidence data and mandate information from various web sites, we examined trends in cases and deaths using joinpoint analysis at the region and county level before and after the implementation of a mandate.

RESULTS

We found that rates of cases and deaths showed the greatest decrease in acceleration after the simultaneous implementation of a statewide shelter-in-place for vulnerable populations combined with social distancing for businesses and limiting gatherings to <10 people. County-level shelters-in-place, business closures, limits on gatherings to <10, and mask mandates showed significant case rate decreases after a county implemented them. School closures had no consistent effect on either outcome.

CONCLUSIONS

Our findings indicate that protecting vulnerable populations, implementing social distancing, and mandating masks may be effective countermeasures to containment while mitigating the economic and psychosocial effects of strict shelters-in-place and business closures. In addition, states should consider allowing local municipalities the flexibility to enact nonpharmaceutical interventions that are more or less restrictive than the state-level mandates under some conditions in which the data indicate it is necessary to protect communities from disease or undue economic burden.

Improving the representativeness of the tribal behavioral risk factor surveillance system through data integration

BACKGROUND

Previous literature showed significant health disparities between Native American population and other populations such as Non-Hispanic White. Most existing studies for Native American Health were based on non-probability samples which suffer with selection bias. In this paper, we are the first to evaluate the effectiveness of data integration methods, including calibration and sequential mass imputation, to improve the representativeness of the Tribal Behavioral Risk Factor Surveillance System (TBRFSS) in terms of reducing the biases of the raw estimates.

METHODS

We evaluated the benefits of our proposed data integration methods, including calibration and sequential mass imputation, by using the 2019 TBRFSS and the 2018 and 2019 Behavioral Risk Factor Surveillance System (BRFSS). We combined the data from the 2018 and 2019 BRFSS by composite weighting. Demographic variables and general health variables were used as predictors for data integration. The following health-related variables were used for evaluation in terms of biases: Smoking status, Arthritis status, Cardiovascular Disease status, Chronic Obstructive Pulmonary Disease status, Asthma status, Cancer status, Stroke status, Diabetes status, and Health Coverage status.

RESULTS

For most health-related variables, data integration methods showed smaller biases compared with unadjusted TBRFSS estimates. After calibration, the demographic and general health variables benchmarked with those for the BRFSS.

CONCLUSION

Data integration procedures, including calibration and sequential mass imputation methods, hold promise for improving the representativeness of the TBRFSS.

The association of demographic and socioeconomic factors with COVID-19 during pre- and post-vaccination periods: A cross-sectional study of Virginia

Sociodemographic factors have been found to be associated with the transmission of coronavirus disease 2019 (COVID-19), yet most studies focused on the period before the proliferation of vaccination and obtained inconclusive results. In this cross-sectional study, the infections, deaths, incidence rates, case fatalities, and mortalities of Virginia's 133 jurisdictions during the pre-vaccination and post-vaccination periods were compared, and their associations with demographic and socioeconomic factors were studied. The cumulative infections and deaths and medians of incidence rates, case fatalities, and mortalities of COVID-19 in 133 Virginia jurisdictions were significantly higher during the post-vaccination period than during the pre-vaccination period. A variety of demographic and socioeconomic risk factors were significantly associated with COVID-19 prevalence in Virginia. Multiple linear regression analysis suggested that demographic and socioeconomic factors contributed up to 80% of the variation in the infections, deaths, and incidence rates and up to 53% of the variation in the case fatalities and mortalities of COVID-19 in Virginia. The demographic and socioeconomic determinants differed during the pre- and post-vaccination periods. The developed multiple linear regression models could be used to effectively characterize the impact of demographic and socioeconomic factors on the infections, deaths, and incidence rates of COVID-19 in Virginia.

A Novel Bayesian Spatial-Temporal Approach to Quantify SARS-CoV-2 Testing Disparities for Small Area Estimation

Objectives. To propose a novel Bayesian spatial-temporal approach to identify and quantify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing disparities for small area estimation. Methods. In step 1, we used a Bayesian inseparable space-time model framework to estimate the testing positivity rate (TPR) at geographically granular areas of the census block groups (CBGs). In step 2, we adopted a rank-based approach to compare the estimated TPR and the testing rate to identify areas with testing deficiency and quantify the number of needed tests. We used weekly SARS-CoV-2 infection and testing surveillance data from Cameron County, Texas, between March 2020 and February 2022 to demonstrate the usefulness of our proposed approach. Results. We identified the CBGs that had experienced substantial testing deficiency, quantified the number of tests that should have been conducted in these areas, and evaluated the short- and long-term testing disparities. Conclusions. Our proposed analytical framework offers policymakers and public health practitioners a tool for understanding SARS-CoV-2 testing disparities in geographically small communities. It could also aid COVID-19 response planning and inform intervention programs to improve goal setting and strategy implementation in SARS-CoV-2 testing uptake. (Am J Public Health. 2023;113(1):40-48. https://doi.org/10.2105/AJPH.2022.307127).

Social Work and the Next Frontier of Racial Justice: Using COVID-19 as a Vehicle for Healing

The COVID-19 pandemic has put the United States and the world into a state of uncertainty. Before the onset of the coronavirus, awareness of health disparities across cities in the United States was questionable at best. As the world continues to grapple with the fallout of the pandemic and the response to it, several states and developed and developing countries created and implemented response efforts that were used as a guide, which social workers are most qualified to address but have not been a focus on a national nor international stage. This commentary focuses on two American states - Texas and Ohio as well as other global countries, and their responses that gained worldwide attention related to healthcare accessibility, service provision, and the role social workers should play moving forward and beyond the pandemic.

Health Disparities and Climate Change: The Intersection of Three Disaster Events on Vulnerable Communities in Houston, Texas

Although evidence suggests that successive climate disasters are on the rise, few studies have documented the disproportionate impacts on communities of color. Through the unique lens of successive disaster events (Hurricane Harvey and Winter Storm Uri) coupled with the COVID-19 pandemic, we assessed disaster exposure in minority communities in Harris County, Texas. A mixed methods approach employing qualitative and quantitative designs was used to examine the relationships between successive disasters (and the role of climate change), population geography, race, and health disparities-related outcomes. This study identified four communities in the greater Houston area with predominantly non-Hispanic African American residents. We used data chronicling the local community and environment to build base maps and conducted spatial analyses using Geographic Information System (GIS) mapping. We complemented these data with focus groups to assess participants' experiences in disaster planning and recovery, as well as community resilience. Thematic analysis was used to identify key patterns. Across all four communities, we observed significant Hurricane Harvey flooding and significantly greater exposure to 10 of the 11 COVID-19 risk factors examined, compared to the rest of the county. Spatial analyses reveal higher disease burden, greater social vulnerability, and significantly higher community-level risk factors for both pandemics and disaster events in the four communities, compared to all other communities in Harris County. Two themes emerged from thematic data analysis: (1) Prior disaster exposure prepared minority populations in Harris County to better handle subsequent disaster suggesting enhanced disaster resilience, and (2) social connectedness was key to disaster resiliency. Long-standing disparities make people of color at greater risk for social vulnerability. Addressing climate change offers the potential to alleviate these health disparities.

Variations in Presentation and Management of COVID-19 Inpatients by Race and Ethnicity in a Large Texas Metroplex

OBJECTIVE

The aim of this study was to assess variations in presentation and outcomes of coronavirus disease 2019 (COVID-19) across race/ethnicity at a large Texas metroplex hospital.

METHODS

A retrospective cohort study was performed.

RESULTS

Although COVID-19 patients demonstrated significant socioeconomic disparities, race/ethnicity was not a significant predictor of intensive care unit (ICU) admission (P = 0.067) or case fatality (P = 0.078). Hospital admission varied by month, with incidence among Black/African-American and Hispanic/Latino patients peaking earlier in the pandemic timeline (P < 0.001). Patients reporting Spanish as their primary language were significantly more likely to be admitted to the ICU (odds ratio, 1.75; P = 0.007).

CONCLUSIONS

COVID-19 patients do not demonstrate significant racial/ethnic disparities in case fatality, suggesting that state-wide disparities in mortality rate are rooted in infection risk rather than hospital course. Variations in admission rates by race/ethnicity across the timeline and increased ICU admission among Spanish-speaking patients demonstrate the need to pursue tailored interventions on both a community and structural level to mitigate further health disparities throughout the pandemic and after.