Variations by race/ethnicity and time in Covid-19 testing among Veterans Health Administration users with COVID-19 symptoms or exposure

Differential associations of mask mandates on COVID-19 infection and mortality by community social vulnerability

BACKGROUND

The COVID-19 pandemic in the United States has disproportionately impacted communities deemed vulnerable to disease outbreaks. Our objectives were to test (1) whether infection and mortality decreased in counties in the most vulnerable (highest) tercile of the Social Vulnerability Index (SVI), and (2) whether disparities between terciles of SVI were reduced, as the length of mask mandates increased.

METHODS

Using the New York Times COVID-19 and the Centers for Disease Control and Prevention SVI and mask mandate datasets, we conducted negative binomial regression analyses of county-level COVID-19 cases and deaths from 1/2020-11/2021 on interactions of SVI and mask mandate durations.

RESULTS

Mask mandates were associated with decreases in mid-SVI cases (IRR: 0.79) and deaths (IRR: 0.90) and high-SVI cases (IRR: 0.89) and deaths (IRR: 0.88). Mandates were associated with the mitigation of infection disparities (Change in IRR: 0.92) and mortality disparities (Change in IRR: 0.85) between low and mid-SVI counties and mortality disparities between low and high-SVI counties (Change in IRR: 0.84).

DISCUSSION

Mask mandates were associated with reductions in COVID-19 infection and mortality and mitigation of disparities for mid and high-vulnerability communities.

CONCLUSIONS

Ongoing COVID-19 response efforts may benefit from longer-standing infection control policies, particularly in the most vulnerable communities.

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.

Preferred Language Mediates Association Between Race, Ethnicity, and Delayed Presentation in Critically Ill Patients With COVID-19

Which social factors explain racial and ethnic disparities in COVID-19 access to care and outcomes remain unclear.

OBJECTIVES

We hypothesized that preferred language mediates the association between race, ethnicity and delays to care.

DESIGN SETTING AND PARTICIPANTS

Multicenter, retrospective cohort study of adults with COVID-19 consecutively admitted to the ICU in three Massachusetts hospitals in 2020.

MAIN OUTCOME AND MEASURES

Causal mediation analysis was performed to evaluate potential mediators including preferred language, insurance status, and neighborhood characteristics.

RESULTS

Non-Hispanic White (NHW) patients (157/442, 36%) were more likely to speak English as their preferred language (78% vs. 13%), were less likely to be un- or under-insured (1% vs. 28%), lived in neighborhoods with lower social vulnerability index (SVI) than patients from racial and ethnic minority groups (SVI percentile 59 [28] vs. 74 [21]) but had more comorbidities (Charlson comorbidity index 4.6 [2.5] vs. 3.0 [2.5]), and were older (70 [13.2] vs. 58 [15.1] years). From symptom onset, NHW patients were admitted 1.67 [0.71-2.63] days earlier than patients from racial and ethnic minority groups (p < 0.01). Non-English preferred language was associated with delay to admission of 1.29 [0.40-2.18] days (p < 0.01). Preferred language mediated 63% of the total effect (p = 0.02) between race, ethnicity and days from symptom onset to hospital admission. Insurance status, social vulnerability, and distance to the hospital were not on the causal pathway between race, ethnicity and delay to admission.

CONCLUSIONS AND RELEVANCE

Preferred language mediates the association between race, ethnicity and delays to presentation for critically ill patients with COVID-19, although our results are limited by possible collider stratification bias. Effective COVID-19 treatments require early diagnosis, and delays are associated with increased mortality. Further research on the role preferred language plays in racial and ethnic disparities may identify effective solutions for equitable care.

Racial and ethnic disparities in excess mortality among U.S. veterans during the COVID-19 pandemic

OBJECTIVE

The COVID-19 pandemic disproportionately affected racial and ethnic minorities among the general population in the United States; however, little is known regarding its impact on U.S. military Veterans. In this study, our objectives were to identify the extent to which Veterans experienced increased all-cause mortality during the COVID-19 pandemic, stratified by race and ethnicity.

DATA SOURCES

Administrative data from the Veterans Health Administration's Corporate Data Warehouse.

STUDY DESIGN

We use pre-pandemic data to estimate mortality risk models using five-fold cross-validation and quasi-Poisson regression. Models were stratified by a combined race-ethnicity variable and included controls for major comorbidities, demographic characteristics, and county fixed effects.

DATA COLLECTION

We queried data for all Veterans residing in the 50 states plus Washington D.C. during 2016-2020. Veterans were excluded from analyses if they were missing county of residence or race-ethnicity data. Data were then aggregated to the county-year level and stratified by race-ethnicity.

PRINCIPAL FINDINGS

Overall, Veterans' mortality rates were 16% above normal during March-December 2020 which equates to 42,348 excess deaths. However, there was substantial variation by racial and ethnic group. Non-Hispanic White Veterans experienced the smallest relative increase in mortality (17%, 95% CI 11%-24%), while Native American Veterans had the highest increase (40%, 95% CI 17%-73%). Black Veterans (32%, 95% CI 27%-39%) and Hispanic Veterans (26%, 95% CI 17%-36%) had somewhat lower excess mortality, although these changes were significantly higher compared to White Veterans. Disparities were smaller than in the general population.

CONCLUSIONS

Minoritized Veterans experienced higher rates excess of mortality during the COVID-19 pandemic compared to White Veterans, though with smaller differences than the general population. This is likely due in part to the long-standing history of structural racism in the United States that has negatively affected the health of minoritized communities via several pathways including health care access, economic, and occupational inequities.

Temporal Variation in Individual Social Risk Factors Associated with Testing Positive for SARS-CoV-2 Among Veterans in the Veterans Health Administration

PURPOSE

Marginalized communities have been disproportionally impacted by SARS-CoV-2. How the associations between social determinants of health and the risk of SARS-CoV-2 infection shifted across time is unknown. In this evaluation, we examine individual-level social determinants of health as social risk factors for SARS-CoV-2 infection across the first 12 months of the pandemic among US Veterans.

METHODS

We conducted a retrospective cohort analysis of 946,358 Veterans who sought testing or treatment for SARS-CoV-2 infection in U.S. Department of Veterans Affairs (VA) medical facilities. We estimated risk ratios for testing positive by social risk factors, adjusting for demographics, comorbidities, and time. Adjusted models were stratified by pandemic phase to assess temporal fluctuations in social risks.

RESULTS

Approximately 19% of Veterans tested positive for SARS-CoV-2. Larger household size was a persistent risk factor and this association increased over time. Early in the pandemic, lower county-level population density was associated with lower SARS-CoV-2 infection risk, but between June 1- August 31, 2020, this trend reversed.

CONCLUSIONS

Temporal fluctuations in social risks associated with Veterans' SARS-CoV-2 infection suggest the need for ongoing, real-time tracking as the social and medical environment continues to evolve.

Geographic and temporal variation in racial and ethnic disparities in SARS-CoV-2 positivity between February 2020 and August 2021 in the United States

The coronavirus pandemic has disproportionally impacted racial and ethnic minority communities in the United States. Patterns of these disparities may be changing over time as outbreaks occur in different communities. Utilizing electronic health record data from the US Department of Veterans Affairs (VA), we estimated odds ratios, stratified by time period and region, for testing positive among 1,313,402 individuals tested for SARS-CoV-2 between February 12, 2020 and August 16, 2021 at VA medical facilities. We adjusted for personal characteristics (sex, age, rural/urban residence, VA facility) and a wide range of clinical characteristics that have been evaluated in prior SARS-CoV-2 reports and could potentially explain racial/ethnic disparities in SARS-CoV-2. Our study found racial and ethnic disparities for testing positive were most pronounced at the beginning of the pandemic and decreased over time. A key finding was that the disparity among Hispanic individuals attenuated but remained elevated, while disparities among Asian individuals reversed by March 1, 2021. The variation in racial and ethnic disparities in SARS-CoV-2 positivity by time and region, independent of underlying health status and other demographic characteristics in a nationwide cohort, provides important insight for strategies to prevent further outbreaks.

COVID-19 Infection in the Veterans Health Administration: Gender-specific Racial and Ethnic Differences

PURPOSE

Racial/ethnic minoritized groups, women, and economically disadvantaged groups are disproportionately affected by the COVID-19 pandemic. We investigated racial/ethnic differences by gender in correlates of COVID-19 infection among veterans seeking health care services at the Veterans Health Administration. Little is known about gender-specific factors associated with infection among veterans. This study seeks to fill this gap.

METHODS

The sample was veterans with results from a COVID-19 test (polymerase chain reaction) conducted at Veterans Health Administration facilities between March 1, 2020, and August 5, 2020, and linked to the Centers for Disease Control and Prevention Social Vulnerability Index data (39,223 women and 316,380 men). Bivariate, multivariate logistic, and predicted probability analyses were conducted. All analyses were stratified by gender.

RESULTS

Similar percentages of women and men tested positive for COVID-19 (9.6% vs. 10.0%). In multivariate analysis, compared with non-Hispanic White women, American Indian/Alaska Native, Black, and Hispanic women all had significantly higher odds of infection. Similar racial/ethnic differences were found for men. Both older men and women (>40 years) had lower odds of infection, but the age cut points differed (40 for women, 55 for men). Men 80 years and older had a higher odds than those aged less than 40 years of age. For men, but not for women, being employed (vs. unemployed) was associated with an increased odds of infection, and having comorbidities was associated with decreased odds. There were significant differences within and across gender-by-race/ethnicity in infection, after adjusting for covariates.

CONCLUSIONS

American Indian/Alaska Native, Hispanic, and Black women and men veterans are disproportionately impacted by COVID-19 infection. Widespread testing and tracking, education, and outreach regarding COVID-19 mitigation and vaccination efforts are recommended.

The use of a recurrent neural network model with separated time-series and lagged daily inputs for waste disposal rates modeling during COVID-19

A new modeling framework is proposed to estimate mixed waste disposal rates in a Canadian capital city during the pandemic. Different Recurrent Neural Network models were developed using climatic, socioeconomic, and COVID-19 related daily variables with different input lag times and study periods. It is hypothesized that the use of distinct time series and lagged inputs may improve modeling accuracy. Considering the entire 7.5-year period from Jan 2013 to Sept 2020, multi-variate weekday models were sensitive with lag times in the testing stage. It appears that the selection of input variables is more important than waste model complexity. Models applying COVID-19 related inputs generally had better performance, with average MAPE of 10.1%. The optimized lag times are however similar between the periods, with slightly longer average lag for the COVID-19 at 5.3 days. Simpler models with least input variables appear to better simulate waste disposal rates, and both 'Temp-Hum' (Temperature-Humidity) and 'Temp-New Test' (Temperature-COVID new test case) models capture the general disposal trend well, with MAPE of 10.3% and 9.4%, respectively. The benefits of the use of separated time series inputs are more apparent during the COVID-19 period, with noticeable decrease in modeling error.