Estimating the Unknown: Greater Racial and Ethnic Disparities in COVID-19 Burden After Accounting for Missing Race and Ethnicity Data.
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ABSTRACT: Background
Black, Hispanic, and Indigenous persons in the United States have an increased risk of SARS-CoV-2 infection and death from COVID-19, due to persistent social inequities. However, the magnitude of the disparity is unclear because race/ethnicity information is often missing in surveillance data.Methods
We quantified the burden of SARS-CoV-2 notification, hospitalization, and case fatality rates in an urban county by racial/ethnic group using combined race/ethnicity imputation and quantitative bias analysis for misclassification.Results
The ratio of the absolute racial/ethnic disparity in notification rates after bias adjustment, compared with the complete case analysis, increased 1.3-fold for persons classified Black and 1.6-fold for those classified Hispanic, in reference to classified White persons.Conclusions
These results highlight that complete case analyses may underestimate absolute disparities in notification rates. Complete reporting of race/ethnicity information is necessary for health equity. When data are missing, quantitative bias analysis methods may improve estimates of racial/ethnic disparities in the COVID-19 burden.
SUBMITTER: Labgold K
PROVIDER: S-EPMC8641438 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Black, Hispanic, and Indigenous persons in the United States have an increased risk of SARS-CoV-2 infection and death from COVID-19, due to persistent social inequities. The magnitude of the disparity is unclear, however, because race/ethnicity information is often missing in surveillance data. In this study, we quantified the burden of SARS-CoV-2 infection, hospitalization, and case fatality rates in an urban county by racial/ethnic group using combined race/ethnicity imputation and quantitative bias-adjustment for misclassification. After bias-adjustment, the magnitude of the absolute racial/ethnic disparity, measured as the difference in infection rates between classified Black and Hispanic persons compared to classified White persons, increased 1.3-fold and 1.6-fold respectively. These results highlight that complete case analyses may underestimate absolute disparities in infection rates. Collecting race/ethnicity information at time of testing is optimal. However, when data are missing, combined imputation and bias-adjustment improves estimates of the racial/ethnic disparities in the COVID-19 burden.
| S-EPMC7536882 | biostudies-literature
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| S-EPMC5342943 | biostudies-other
Project description:ObjectiveTo efficiently estimate race/ethnicity using administrative records to facilitate health care organizations' efforts to address disparities when self-reported race/ethnicity data are unavailable.Data sourceSurname, geocoded residential address, and self-reported race/ethnicity from 1,973,362 enrollees of a national health plan.Study designWe compare the accuracy of a Bayesian approach to combining surname and geocoded information to estimate race/ethnicity to two other indirect methods: a non-Bayesian method that combines surname and geocoded information and geocoded information alone. We assess accuracy with respect to estimating (1) individual race/ethnicity and (2) overall racial/ethnic prevalence in a population.Principal findingsThe Bayesian approach was 74 percent more efficient than geocoding alone in estimating individual race/ethnicity and 56 percent more efficient in estimating the prevalence of racial/ethnic groups, outperforming the non-Bayesian hybrid on both measures. The non-Bayesian hybrid was more efficient than geocoding alone in estimating individual race/ethnicity but less efficient with respect to prevalence (p<.05 for all differences).ConclusionsThe Bayesian Surname and Geocoding (BSG) method presented here efficiently integrates administrative data, substantially improving upon what is possible with a single source or from other hybrid methods; it offers a powerful tool that can help health care organizations address disparities until self-reported race/ethnicity data are available.
| S-EPMC2653886 | biostudies-literature
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Project description:Importance: COVID-19 racial disparities have gained significant attention yet little is known about how age distributions obscure racial-ethnic disparities in COVID-19 case fatality ratios (CFR). Objective: We filled this gap by assessing relevant data availability and quality across states, and in states with available data, investigating how racial-ethnic disparities in CFR changed after age adjustment. Design/Setting/Participants/Exposure: We conducted a landscape analysis as of July 1st, 2020 and developed a grading system to assess COVID-19 case and death data by age and race in 50 states and DC. In states where age- and race-specific data were available, we applied direct age standardization to compare CFR across race-ethnicities. We developed an online dashboard to automatically and continuously update our results. Main Outcome and Measure: Our main outcome was CFR (deaths per 100 confirmed cases). We examined CFR by age and race-ethnicities. Results: We found substantial variations in disaggregating and reporting case and death data across states. Only three states, California, Illinois and Ohio, had sufficient age- and race-ethnicity-disaggregation to allow the investigation of racial-ethnic disparities in CFR while controlling for age. In total, we analyzed 391,991confirmed cases and 17,612 confirmed deaths. The crude CFRs varied from, e.g. 7.35% among Non-Hispanic (NH) White population to 1.39% among Hispanic population in Ohio. After age standardization, racial-ethnic differences in CFR narrowed, e.g. from 5.28% among NH White population to 3.79% among NH Asian population in Ohio, or an over one-fold difference. In addition, the ranking of race-ethnic-specific CFRs changed after age standardization. NH White population had the leading crude CFRs whereas NH Black and NH Asian population had the leading and second leading age-adjusted CFRs respectively in two of the three states. Hispanic population’s age-adjusted CFR were substantially higher than the crude. Sensitivity analysis did not change these results qualitatively. Conclusions and Relevance: The availability and quality of age- and race-ethnic-specific COVID-19 case and death data varied greatly across states. Age distributions in confirmed cases obscured racial-ethnic disparities in COVID-19 CFR. Age standardization narrows racial-ethnic disparities and changes ranking. Public COVID-19 data availability, quality, and harmonization need improvement to address racial disparities in this pandemic.
| S-EPMC7536886 | biostudies-literature
Project description:ObjectiveThe purpose of this study was to explore the associations of race/ethnicity and menopausal status to gastrointestinal (GI) symptoms experienced during the menopausal transition while considering multiple factors that could influence the symptoms.MethodsThis secondary analysis was conducted with the data from 1,051 women from 2 Internet-based studies on midlife women's health issues. In the original studies, the data were collected using a dozen questions on sociodemographic and health/menopausal factors and the GI Index for Midlife Women. The data were analyzed utilizing ANOVA, multiple logistic regression, and hierarchical linear regression analyses.ResultsWhen covariates were controlled, being Non-Hispanic (N-H) Asian was a significant factor that influenced the total numbers of GI symptoms (β = -0.26) and total severity scores of GI symptoms (β = -0.26). When covariates were controlled, premenopausal status was the strongest factor that influenced the total numbers of GI symptoms in all participants, Hispanics, N-H Whites, N-H African Americans, and N-H Asians (β = 53, -0.40, -0.77, -0.76, -0.26, respectively) and the total severity scores of GI symptoms in all participants, Hispanics, N-H Whites, N-H African Americans, and N-H Asians (β = -0.50, -0.38, -0.72, -0.75, -0.25, respectively).ConclusionsThis study supports the association of race/ethnicity and menopausal status to GI symptoms experienced during the menopausal transition.
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Project description:BackgroundDementia is often underdiagnosed and this problem is more common among some ethnoracial groups.ObjectiveThe objective of this study was to examine racial and ethnic disparities in the timeliness of receiving a clinical diagnosis of dementia.Research designThis was a prospective cohort study.SubjectsA total of 3966 participants age 70 years and above with probable dementia in the Health and Retirement Study, linked with their Medicare and Medicaid claims.MeasuresWe performed logistic regression to compare the likelihood of having a missed or delayed dementia diagnosis in claims by race/ethnicity. We analyzed dementia severity, measured by cognition and daily function, at the time of a dementia diagnosis documented in claims, and estimated average dementia diagnosis delay, by race/ethnicity.ResultsA higher proportion of non-Hispanic Blacks and Hispanics had a missed/delayed clinical dementia diagnosis compared with non-Hispanic Whites (46% and 54% vs. 41%, P<0.001). Fully adjusted logistic regression results suggested more frequent missed/delayed dementia diagnoses among non-Hispanic Blacks (odds ratio=1.12; 95% confidence interval: 0.91-1.38) and Hispanics (odds ratio=1.58; 95% confidence interval: 1.20-2.07). Non-Hispanic Blacks and Hispanics had a poorer cognitive function and more functional limitations than non-Hispanic Whites around the time of receiving a claims-based dementia diagnosis. The estimated mean diagnosis delay was 34.6 months for non-Hispanic Blacks and 43.8 months for Hispanics, compared with 31.2 months for non-Hispanic Whites.ConclusionsNon-Hispanic Blacks and Hispanics may experience a missed or delayed diagnosis of dementia more often and have longer diagnosis delays. When diagnosed, non-Hispanic Blacks and Hispanics may have more advanced dementia. Public health efforts should prioritize racial and ethnic underrepresented communities when promoting early diagnosis of dementia.
| S-EPMC8263486 | biostudies-literature
Project description:ObjectiveWe aimed to address deficiencies in structured electronic health record (EHR) data for race and ethnicity by identifying black and Hispanic patients from unstructured clinical notes and assessing differences between patients with or without structured race/ethnicity data.Materials and methodsUsing EHR notes for 16 665 patients with encounters at a primary care practice, we developed rule-based natural language processing (NLP) algorithms to classify patients as black/Hispanic. We evaluated performance of the method against an annotated gold standard, compared race and ethnicity between NLP-derived and structured EHR data, and compared characteristics of patients identified as black or Hispanic using only NLP vs patients identified as such only in structured EHR data.ResultsFor the sample of 16 665 patients, NLP identified 948 additional patients as black, a 26%increase, and 665 additional patients as Hispanic, a 20% increase. Compared with the patients identified as black or Hispanic in structured EHR data, patients identified as black or Hispanic via NLP only were older, more likely to be male, less likely to have commercial insurance, and more likely to have higher comorbidity.DiscussionStructured EHR data for race and ethnicity are subject to data quality issues. Supplementing structured EHR race data with NLP-derived race and ethnicity may allow researchers to better assess the demographic makeup of populations and draw more accurate conclusions about intergroup differences in health outcomes.ConclusionsBlack or Hispanic patients who are not documented as such in structured EHR race/ethnicity fields differ significantly from those who are. Relatively simple NLP can help address this limitation.
| S-EPMC6696506 | biostudies-literature
Project description:ImportanceRacial and ethnic disparities in postpartum care access have been well identified in the United States. Such disparities could be exacerbated by the COVID-19 pandemic because of amplified economic distress and compromised social capital among pregnant women who belong to racial or ethnic minority groups.ObjectiveTo examine whether the COVID-19 pandemic is associated with an increase in the existing racial and ethnic disparity in postpartum care access.Design, setting, and participantsThis was a retrospective cohort study using electronic health records data. Multinomial logistic regressions in an interrupted time series approach were used to assess monthly changes in postpartum care access across Asian, Hispanic, non-Hispanic Black (hereafter, Black), non-Hispanic White (hereafter, White) women, and women of other racial groups, controlling for maternal demographic and clinical characteristics. Eligible participants were women who gave live birth at 8 hospitals in the greater Boston, Massachusetts, area from January 1, 2019, to November 30, 2021, allowing for tracking 90-day postpartum access until March 1, 2022.ExposuresDelivery period: prepandemic (January to December 2019), early pandemic (January to March 2020), and late pandemic (April 2020 to November 2021).Main outcomes and measuresPostpartum care within 90 days after childbirth was categorized into 3 groups: attended, canceled, and nonscheduled.ResultsA total of 45 588 women were included. Participants were racially and ethnically diverse (4735 [10.4%] Asian women, 3399 [7.5%] Black women, 6950 [15.2%] Hispanic women, 28 529 [62.6%] White women, and 1269 [2.8%] women of other race or ethnicity). The majority were between 25 and 34 years of age and married and had a full-term pregnancy, vaginal delivery, and no clinical conditions. In the prepandemic period, the overall postpartum care attendance rate was 75.2%, dropping to 41.7% during the early pandemic period, and rebounding back to 60.9% in the late pandemic period. During the months in the late pandemic, the probability of not scheduling postpartum care among Black (average marginal effect [AME], 1.1; 95% CI, 0.6-1.6) and Hispanic women (AME, 1.3; 95% CI, 0.9-1.6) increased more than among their White counterparts.Conclusions and relevanceIn this cohort study of postpartum care access before and during the COVID-19 pandemic, racial and ethnic disparities in postpartum care were exacerbated following the onset of the COVID-19 pandemic, when postpartum care access recovered more slowly among Black and Hispanic women than White women. These disparities require swift attention and amelioration to address barriers for these women to obtain much needed postpartum care during this pandemic.
| S-EPMC9226999 | biostudies-literature
Project description:ObjectiveTo assess the feasibility and implications of imputing race and ethnicity for quality and utilization measurement in Medicaid.Data sources and study setting2017 Oregon Medicaid claims from the Oregon Health Authority and electronic health records (EHR) from OCHIN, a clinical data research network, were used.Study designWe cross-sectionally assessed Hispanic-White, Black-White, and Asian-White disparities in 22 quality and utilization measures, comparing self-reported race and ethnicity to imputed values from the Bayesian Improved Surname Geocoding (BISG) algorithm.Data collectionRace and ethnicity were obtained from self-reported data and imputed using BISG.Principal findings42.5%/4.9% of claims/EHR were missing self-reported data; BISG estimates were available for >99% of each and had good concordance (0.87-0.95) with Asian, Black, Hispanic, and White self-report. All estimated racial and ethnic disparities were statistically similar in self-reported and imputed EHR-based measures. However, within claims, BISG estimates and incomplete self-reported data yielded substantially different disparities in almost half of the measures, with BISG-based Black-White disparities generally larger than self-reported race and ethnicity data.ConclusionsBISG imputation methods are feasible for Medicaid claims data and reduced missingness to <1%. Disparities may be larger than what is estimated using self-reported data with high rates of missingness.
| S-EPMC9643085 | biostudies-literature