Assessing race and ethnicity data quality across cancer registries and EMRs in two hospitals

Lee, Simon J. Craddock; Grobe, James E.; Tiro, Jasmin A.

doi:10.1093/jamia/ocv156

Cited by 41 publications

(30 citation statements)

References 45 publications

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“…Inconsistencies of race and ethnicity were reported between the birth and death of US infants; there was higher agreement for those who identified as White (1.2 %) and Black (4.3 %) and lower for those identified as Hispanic (30.3 %) [8]. Lee and colleagues analyzed the agreement of race and ethnicity between cancer registries and an electronic medical record and found that there was only complete agreement in 39.2 % of pairs; pairs with “black” data value labels had the highest agreement (95.3 %) and pairs with “other” data value labels had the lowest agreement across sources (11.1 %) [9]. The assessment of the validity of race/ethnicity in Medicare databases found that those who were recorded as White and Black in the Medicare electronic health record had better concordance with their self-report data than those who were identified as Hispanic, Asian, Pacific Islanders, American Indians, and Alaskan Natives (39–60 % agreement) [1].…”

Section: Introductionmentioning

confidence: 99%

Discrepancies in Race and Ethnicity Documentation: a Potential Barrier in Identifying Racial and Ethnic Disparities

Lòpez

Bevans

Wehrlen

et al. 2016

J. Racial and Ethnic Health Disparities

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BackgroundData 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.MethodsSelf-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.ResultsThe 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.ConclusionsThe 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.

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Section: Introductionmentioning

confidence: 99%

Discrepancies in Race and Ethnicity Documentation: a Potential Barrier in Identifying Racial and Ethnic Disparities

Lòpez

Bevans

Wehrlen

et al. 2016

J. Racial and Ethnic Health Disparities

View full text Add to dashboard Cite

show abstract

“…We though believe that our data give a realistic estimate of the order of magnitude of the issue. d. Furthermore, these results may have been influenced by documentation disparities, which have been demonstrated to occur in a significant number of patients in similar studies [9,[42][43][44][45][46][47][48][49]. e. We have no data to estimate to which extent complications emerging in the course of treatment might have influenced the results of this study, though e.g.…”

Section: Discussionmentioning

confidence: 84%

Prescription Rates for Antiplatelet Therapy (APT) in Coronary Artery Disease (CAD) – What Benchmark are We Aiming at in Continuing Medical Education (CME)?

Hagen

Griebenow

2020

Journal of European CME

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Physicians always aim to improve their patients' health. CME should be designed not only to provide knowledge transfer, but also to influence clinical decision-making and to close performance gaps. In aretrospective study we analysed prescription rates for APT in 254,932 CAD patients (male: 64.4%), treated in atotal of 3,405 practices in 2019 in aDMP in the region of North Rhine, Germany. Analyses were run for the whole study population stratified by sex as well as for subgroups of patients suffering from myocardial infarction/acute coronary syndrome, or who have been treated with percutaneous coronary intervention or bypass surgery. Patients mean age was 72.7 ± 11.2 years (mean ± 1SD), mean duration of DMP participation was 7.2 ± 4.7 years, and mean cumulative number of DMP visits was 27 ± 17. APT prescription rates were 85.0% in male and 78.8% in female CAD patients. In subgroups of male CAD patients APT prescription rates were between 89.7% and 92.8%, in the same subgroups of female CAD patients the corresponding rates were between 87.8% and 92.0%. Rates for amissing APT prescription per practice were between .0044% and .0062% for male and female CAD patients, respectively. Rates for amissing APT prescription per practice and DMP visit were .0002% for both sexes. These results suggest that a DMP can achieve high attainment rates for APT in CAD. To further improve attainment rates, consideration of absolute numbers of eligible patients per practice or physician is probably more appropriate than expression of performance as percentage values. This is especially true if attainment rates show substantial variations between subgroups, if subgroups show substantial variation in size, if attainment rates are already in the magnitude of 80% or higher, and if there are disparities in the evidence base underlying treatment recommendations related to subgroups.

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“…37 It was difficult to use EHRs to identify AA patients, as race-ethnicity fields are commonly missing or inaccurate, even in settings that care mainly for minority patients. 38,39 In GUARDD, over 10% of patients told us their listing as Black race was wrong. We were also unable to identify and thus contact AA patients who were among the one-third who met all other EHR criteria for eligibility, but had missing race-ethnicity data.…”

Section: Discussionmentioning

confidence: 99%

Successful recruitment and retention of diverse participants in a genomics clinical trial: a good invitation to a great party

Horowitz

Sabin

Ramos

et al. 2019

Genetics in Medicine

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Purpose: African ancestry (AA) individuals are inadequately included in translational genomics research, limiting generalizability of findings and benefits of genomic discoveries for populations already facing disproportionately poor health outcomes. We aimed to determine the impact of stakeholder-engaged strategies on recruitment and retention of AA adult patients into a clinical trial testing them for renal risk variants nearly exclusive to AAs. Methods: Our academic-clinical-community team developed ten key strategies that recognize AAs' barriers and facilitators for participation. Using electronic health records (EHRs), we identified potentially eligible patients. Recruiters reached out through letters, phone calls, and at medical visits. Results: Of 5481 AA patients reached, 51% were ineligible, 37% enrolled, 4% declined, 7% were undecided when enrollment finished. We retained 93% at 3-month and 88% at 12-month follow-up. Those enrolled are more likely female, seen at community sites, and reached through active strategies, than those who declined. Those retained are more likely female, health-literate, and older. While many patients have low income, low clinician trust, and perceive racism in health care, none of these attributes correlate with retention. Conclusion: With robust stakeholder engagement, recruiters from patients' communities, and active approaches, we successfully recruited and retained AA patients into a genomic clinical trial.

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Assessing race and ethnicity data quality across cancer registries and EMRs in two hospitals

Abstract: To facilitate transparent reporting of healthcare delivery outcomes by race/ethnicity, healthcare systems need to monitor and enforce race/ethnicity data collection standards.

Cited by 41 publications

References 45 publications

Discrepancies in Race and Ethnicity Documentation: a Potential Barrier in Identifying Racial and Ethnic Disparities

Discrepancies in Race and Ethnicity Documentation: a Potential Barrier in Identifying Racial and Ethnic Disparities

Prescription Rates for Antiplatelet Therapy (APT) in Coronary Artery Disease (CAD) – What Benchmark are We Aiming at in Continuing Medical Education (CME)?

Successful recruitment and retention of diverse participants in a genomics clinical trial: a good invitation to a great party

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