Measuring the missing: greater racial and ethnic disparities in COVID-19 burden after accounting for missing race/ethnicity data.
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ABSTRACT:
SUBMITTER: Labgold K
PROVIDER: S-EPMC7536882 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundBlack, 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.MethodsWe 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.ResultsThe 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.ConclusionsThese 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.
| S-EPMC8641438 | biostudies-literature
Project description:Data collection on race and ethnicity is critical in the assessment of racial disparities related to health. Studies comparing clinical and administrative data show discrepancies in race documentation and attribution.Self-reported data from two studies were compared to demographics in the electronic health record (EHR) extracted from the Biomedical Translational Research Information System (BTRIS) repository. McNemar and Bhapkar analyses were conducted to quantify the agreement of ethnicity and race between self-reported and EHR data. Pearson's chi-square tests were used to explore the relationship between acculturation, length of time in the USA, country of residence, and how individuals self-reported their race.The sample (n = 280) was predominantly female (52.1 %), with a mean age of 47 (SD ± 13.74), mean years in the USA were 12.8 (SD ± 11.67) and the majority were born outside of the USA. (55.6 %). Those who self-identified as Hispanic (n = 208) scored a mean of 5.5 (SD ± 3.07) on the short acculturation scale (SAS) that ranges 4 to 20; lower scores indicate less acculturation. A significant difference was found between the way race is reported in the electronic medical record and self-reported data among those people who identified as Hispanic, with significant differences in the white (p < 0.0001) and other (p < 0.0001) categories.The misclassification of race is most frequent in those individuals who self-identified as Hispanic. As the Hispanic population in the USA continues to grow, understanding the factors that affect the way that individuals from this heterogeneous population self-report race may provide important guidance in tailoring care to address health disparities.
| S-EPMC5342943 | biostudies-other
Project description:ObjectiveTo review methods of measuring racial/ethnic health care disparities.Study designIdentification and tracking of racial/ethnic disparities in health care will be advanced by application of a consistent definition and reliable empirical methods. We have proposed a definition of racial/ethnic health care disparities based in the Institute of Medicine's (IOM) Unequal Treatment report, which defines disparities as all differences except those due to clinical need and preferences. After briefly summarizing the strengths and critiques of this definition, we review methods that have been used to implement it. We discuss practical issues that arise during implementation and expand these methods to identify sources of disparities. We also situate the focus on methods to measure racial/ethnic health care disparities (an endeavor predominant in the United States) within a larger international literature in health outcomes and health care inequality. EMPIRICAL APPLICATION: We compare different methods of implementing the IOM definition on measurement of disparities in any use of mental health care and mental health care expenditures using the 2004-2008 Medical Expenditure Panel Survey.ConclusionDisparities analysts should be aware of multiple methods available to measure disparities and their differing assumptions. We prefer a method concordant with the IOM definition.
| S-EPMC3371391 | biostudies-literature
Project description:ObjectiveTo assess whether black-white and Hispanic-white disparities increase or abate in the upper quantiles of total health care expenditure, conditional on covariates.Data sourceNationally representative adult population of non-Hispanic whites, African Americans, and Hispanics from the 2001-2005 Medical Expenditure Panel Surveys.Study designWe examine unadjusted racial/ethnic differences across the distribution of expenditures. We apply quantile regression to measure disparities at the median, 75th, 90th, and 95th quantiles, testing for differences over the distribution of health care expenditures and across income and education categories. We test the sensitivity of the results to comparisons based only on health status and estimate a two-part model to ensure that results are not driven by an extremely skewed distribution of expenditures with a large zero mass.Principal findingsBlack-white and Hispanic-white disparities diminish in the upper quantiles of expenditure, but expenditures for blacks and Hispanics remain significantly lower than for whites throughout the distribution. For most education and income categories, disparities exist at the median and decline, but remain significant even with increased education and income.ConclusionsBlacks and Hispanics receive significantly disparate care at high expenditure levels, suggesting prioritization of improved access to quality care among minorities with critical health issues.
| S-EPMC2754550 | biostudies-literature
Project description:IntroductionEnsuring high-quality race and ethnicity data within the electronic health record (EHR) and across linked systems, such as patient registries, is necessary to achieving the goal of inclusion of racial and ethnic minorities in scientific research and detecting disparities associated with race and ethnicity. The project goal was to improve race and ethnicity data completion within the Pediatric Rheumatology Care Outcomes Improvement Network and assess impact of improved data completion on conclusions drawn from the registry.MethodsThis is a mixed-methods quality improvement study that consisted of five parts, as follows: (1) Identifying baseline missing race and ethnicity data, (2) Surveying current collection and entry, (3) Completing data through audit and feedback cycles, (4) Assessing the impact on outcome measures, and (5) Conducting participant interviews and thematic analysis.ResultsAcross six participating centers, 29% of the patients were missing data on race and 31% were missing data on ethnicity. Of patients missing data, most patients were missing both race and ethnicity. Rates of missingness varied by data entry method (electronic vs. manual). Recovered data had a higher percentage of patients with Other race or Hispanic/Latino ethnicity compared with patients with non-missing race and ethnicity data at baseline. Black patients had a significantly higher odds ratio of having a clinical juvenile arthritis disease activity score (cJADAS10) of ≥5 at first follow-up compared with White patients. There was no significant change in odds ratio of cJADAS10 ≥5 for race and ethnicity after data completion. Patients missing race and ethnicity were more likely to be missing cJADAS values, which may affect the ability to detect changes in odds ratio of cJADAS ≥5 after completion.ConclusionsAbout one-third of the patients in a pediatric rheumatology registry were missing race and ethnicity data. After three audit and feedback cycles, centers decreased missing data by 94%, primarily via data recovery from the EHR. In this sample, completion of missing data did not change the findings related to differential outcomes by race. Recovered data were not uniformly distributed compared with those with non-missing race and ethnicity data at baseline, suggesting that differences in outcomes after completing race and ethnicity data may be seen with larger sample sizes.
| S-EPMC11303283 | biostudies-literature
Project description:PurposeBlack and Hispanic cancer patients experience many worse care quality and health outcomes than non-Hispanic White patients, yet less is known about disparities in caregiving responsibilities and burden among cancer caregivers.MethodsWe analyzed cross-sectional data from Cancer Care Outcomes Research and Surveillance consortium, a large multi-regional, population-based study of colorectal and lung cancer patients and their caregivers. Bivariate and multivariable regression models assessed differences by racial and ethnic groups in caregiving responsibilities and social/emotional, financial, and health burdens. Structural equation models estimated whether sociocultural resources (social support, caregiving preparedness, caregiver-patient communication) mediated racial and ethnic differences in caregiver burden.ResultsCompared with non-Hispanic White caregivers (N = 1,169), Black (N = 220) and Hispanic (N = 84) caregivers spent more time caregiving (18 vs. 26 vs. 26 h/week; P < 0.001), completed more tasks (6.8 vs. 7.6 vs. 8.7; P < 0.05), and reported greater financial burden (P = 0.02). Yet, compared to non-Hispanic Whites, Hispanic caregivers reported similar social/emotional and health burdens, while Black caregivers reported lower levels (P < 0.01). In adjusted models, disparities in financial burden disappeared, and Hispanic caregivers had less health burden than non-Hispanic White caregivers (P = 0.01). Social support and/or caregiving preparedness partially mediated the Black-White gap for all three types of burdens.ConclusionsBlack and Hispanic cancer caregivers perform more caregiving and report greater financial burden than non-Hispanic White caregivers, but experience lower or equivalent social/emotional and health burdens. Racial differences in caregivers' social support and caregiving preparedness levels partially explain Black-White burden differences. Research and policy should address Black and Hispanic caregivers' increased financial burden.
| S-EPMC9633462 | biostudies-literature
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.
| S-EPMC8795485 | biostudies-literature
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