Dataset Information

Collection of Data on Race, Ethnicity, Language, and Nativity by US Public Health Surveillance and Monitoring Systems: Gaps and Opportunities.

ABSTRACT: OBJECTIVE:Despite increasing diversity in the US population, substantial gaps in collecting data on race, ethnicity, primary language, and nativity indicators persist in public health surveillance and monitoring systems. In addition, few systems provide questionnaires in foreign languages for inclusion of non-English speakers. We assessed (1) the extent of data collected on race, ethnicity, primary language, and nativity indicators (ie, place of birth, immigration status, and years in the United States) and (2) the use of data-collection instruments in non-English languages among Centers for Disease Control and Prevention (CDC)-supported public health surveillance and monitoring systems in the United States. METHODS:We identified CDC-supported surveillance and health monitoring systems in place from 2010 through 2013 by searching CDC websites and other federal websites. For each system, we assessed its website, documentation, and publications for evidence of the variables of interest and use of data-collection instruments in non-English languages. We requested missing information from CDC program officials, as needed. RESULTS:Of 125 data systems, 100 (80%) collected data on race and ethnicity, 2 more collected data on ethnicity but not race, 26 (21%) collected data on racial/ethnic subcategories, 40 (32%) collected data on place of birth, 21 (17%) collected data on years in the United States, 14 (11%) collected data on immigration status, 13 (10%) collected data on primary language, and 29 (23%) used non-English data-collection instruments. Population-based surveys and disease registries more often collected data on detailed variables than did case-based, administrative, and multiple-source systems. CONCLUSIONS:More complete and accurate data on race, ethnicity, primary language, and nativity can improve the quality, representativeness, and usefulness of public health surveillance and monitoring systems to plan and evaluate targeted public health interventions to eliminate health disparities.

SUBMITTER: Rodriguez-Lainz A

PROVIDER: S-EPMC5805104 | biostudies-other | 2018 Jan/Feb

REPOSITORIES: biostudies-other

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Collection of Data on Race, Ethnicity, Language, and Nativity by US Public Health Surveillance and Monitoring Systems: Gaps and Opportunities.

Rodriguez-Lainz Alfonso A McDonald Mariana M Fonseca-Ford Maureen M Penman-Aguilar Ana A Waterman Stephen H SH Truman Benedict I BI Cetron Martin S MS Richards Chesley L CL

Public health reports (Washington, D.C. : 1974) 20171220 1

<h4>Objective</h4>Despite increasing diversity in the US population, substantial gaps in collecting data on race, ethnicity, primary language, and nativity indicators persist in public health surveillance and monitoring systems. In addition, few systems provide questionnaires in foreign languages for inclusion of non-English speakers. We assessed (1) the extent of data collected on race, ethnicity, primary language, and nativity indicators (ie, place of birth, immigration status, and years in th ...[more]

PMID: 29262290

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Project description:ImportanceEmergency department (ED) triage substantially affects how long patients wait for care but triage scoring relies on few objective criteria. Prior studies suggest that Black and Hispanic patients receive unequal triage scores, paralleled by disparities in the depth of physician evaluations.ObjectivesTo examine whether racial disparities in triage scores and physician evaluations are present across a multicenter network of academic and community hospitals and evaluate whether patients who do not speak English face similar disparities.Design, setting, and participantsThis was a cross-sectional, multicenter study examining adults presenting between February 28, 2019, and January 1, 2023, across the Mass General Brigham Integrated Health Care System, encompassing 7 EDs: 2 urban academic hospitals and 5 community hospitals. Analysis included all patients presenting with 1 of 5 common chief symptoms.ExposuresEmergency department nurse-led triage and physician evaluation.Main outcomes and measuresAverage Triage Emergency Severity Index [ESI] score and average visit work relative value units [wRVUs] were compared across symptoms and between individual minority racial and ethnic groups and White patients.ResultsThere were 249 829 visits (149 861 female [60%], American Indian or Alaska Native 0.2%, Asian 3.3%, Black 11.8%, Hispanic 18.8%, Native Hawaiian or Other Pacific Islander <0.1%, White 60.8%, and patients identifying as Other race or ethnicity 5.1%). Median age was 48 (IQR, 29-66) years. White patients had more acute ESI scores than Hispanic or Other patients across all symptoms (eg, chest pain: Hispanic, 2.68 [95% CI, 2.67-2.69]; White, 2.55 [95% CI, 2.55-2.56]; Other, 2.66 [95% CI, 2.64-2.68]; P < .001) and Black patients across most symptoms (nausea/vomiting: Black, 2.97 [95% CI, 2.96-2.99]; White: 2.90 [95% CI, 2.89-2.91]; P < .001). These differences were reversed for wRVUs (chest pain: Black, 4.32 [95% CI, 4.25-4.39]; Hispanic, 4.13 [95% CI, 4.08-4.18]; White 3.55 [95% CI, 3.52-3.58]; Other 3.96 [95% CI, 3.84-4.08]; P < .001). Similar patterns were seen for patients whose primary language was not English.Conclusions and relevanceIn this cross-sectional study, patients who identified as Black, Hispanic, and Other race and ethnicity were assigned less acute ESI scores than their White peers despite having received more involved physician workups, suggesting some degree of mistriage. Clinical decision support systems might reduce these disparities but would require careful calibration to avoid replicating bias.

Project description:BackgroundHealthcare disparities are well documented across multiple subspecialties in orthopaedics. The widespread implementation of telemedicine risks worsening these disparities if not carefully executed, despite original assumptions that telemedicine improves overall access to care. Telemedicine also poses unique challenges such as potential language or technological barriers that may alter previously described patterns in orthopaedic disparities.Questions/purposesAre the proportions of patients who use telemedicine across orthopaedic services different among (1) racial and ethnic minorities, (2) non-English speakers, and (3) patients insured through Medicaid during a 10-week period after the implementation of telemedicine in our healthcare system compared with in-person visits during a similar time period in 2019?MethodsThis was a retrospective comparative study using electronic medical record data to compare new patients establishing orthopaedic care via outpatient telemedicine at two academic urban medical centers between March 2020 and May 2020 with new orthopaedic patients during the same 10-week period in 2019. A total of 11,056 patients were included for analysis, with 1760 in the virtual group and 9296 in the control group. Unadjusted analyses demonstrated patients in the virtual group were younger (median age 57 years versus 59 years; p < 0.001), but there were no differences with regard to gender (56% female versus 56% female; p = 0.66). We used self-reported race or ethnicity as our primary independent variable, with primary language and insurance status considered secondarily. Unadjusted and multivariable adjusted analyses were performed for our primary and secondary predictors using logistic regression. We also assessed interactions between race or ethnicity, primary language, and insurance type.ResultsAfter adjusting for age, gender, subspecialty, insurance, and median household income, we found that patients who were Hispanic (odds ratio 0.59 [95% confidence interval 0.39 to 0.91]; p = 0.02) or Asian were less likely (OR 0.73 [95% CI 0.53 to 0.99]; p = 0.04) to be seen through telemedicine than were patients who were white. After controlling for confounding variables, we also found that speakers of languages other than English or Spanish were less likely to have a telemedicine visit than were people whose primary language was English (OR 0.34 [95% CI 0.18 to 0.65]; p = 0.001), and that patients insured through Medicaid were less likely to be seen via telemedicine than were patients who were privately insured (OR 0.83 [95% CI 0.69 to 0.98]; p = 0.03).ConclusionDespite initial promises that telemedicine would help to bridge gaps in healthcare, our results demonstrate disparities in orthopaedic telemedicine use based on race or ethnicity, language, and insurance type. The telemedicine group was slightly younger, which we do not believe undermines the findings. As healthcare moves toward increased telemedicine use, we suggest several approaches to ensure that patients of certain racial, ethnic, or language groups do not experience disparate barriers to care. These might include individual patient- or provider-level approaches like expanded telemedicine schedules to accommodate weekends and evenings, institutional investment in culturally conscious outreach materials such as advertisements on community transport systems, or government-level provisions such as reimbursement for telephone-only encounters.Level of evidenceLevel III, therapeutic study.

Project description:ImportanceHealth care disparities are well-documented among children based on race, ethnicity, and language for care. An agenda that outlines research priorities for disparities in pediatric emergency care (PEC) is lacking.ObjectiveTo investigate research priorities for disparities in PEC among medical personnel, researchers, and health care-affiliated community organizations.Design, setting, and participantsIn this survey study, a modified Delphi approach was used to investigate research priorities for disparities in PEC. An initial list of research priorities was developed by a group of experienced PEC investigators in 2021. Partners iteratively assessed the list through 2 rounds of electronic surveys using Likert-type responses in late 2021 and early 2022. Priorities were defined as achieving consensus if they received a score of highest priority or priority by at least 60% of respondents. Asynchronous engagement of participants via online web-conferencing platforms and email correspondence with electronic survey administration was used. Partners were individuals and groups involved in PEC. Participants represented interest groups, research and medical personnel organizations, health care partners, and laypersons with roles in community and family hospital advisory councils. Participants were largely from the US, with input from international PEC research networks.OutcomeConsensus agenda of research priorities to identify and address health care disparities in PEC.ResultsPEC investigators generated an initial list of 27 potential priorities. Surveys were completed by 38 of 47 partners (80.6%) and 30 of 38 partners (81.1%) in rounds 1 and 2, respectively. Among 30 respondents who completed both rounds, there were 7 family or community partners and 23 medical or research partners, including 4 international PEC research networks. A total of 12 research priorities achieved the predetermined consensus threshold: (1) systematic efforts to reduce disparities; (2) race, ethnicity, and language data collection and reporting; (3) recognizing and mitigating clinician implicit bias; (4) mental health disparities; (5) social determinants of health; (6) language and literacy; (7) acute pain-management disparities; (8) quality of care equity metrics; (9) shared decision-making; (10) patient experience; (11) triage and acuity score assignment; and (12) inclusive research participation.Conclusions and relevanceThese results suggest a research priority agenda that may be used as a guide for investigators, research networks, organizations, and funding agencies to engage in and support high-priority disparities research topics in PEC.

Project description:ImportanceWithout knowledge of the degree of misattribution in racial and ethnic designations in data, studies run the risk of missing existing inequities and disparities and identifying others that do not exist. Further, accuracy of racial and ethnic designations is important to clinical care improvement efforts and health outcomes.ObjectiveTo determine the error rate of racial and ethnic attribution in the electronic medical records (EMRs) across the 3 largest pediatric health systems in Michigan.Design, setting, and participantsThis cross-sectional study collected race and ethnicity data from parents in outpatient clinics, emergency departments, and inpatient units at the 3 largest pediatric health systems in Michigan. A total of 1594 parents or guardians participated at health system A, 1537 at health system B, and 1202 at health system C from September 1, 2023, to January 31, 2024. Parent or guardian report of race and ethnicity for a child was used as the gold standard for comparison with the designation in the EMR.ExposureRace and ethnicity designations in the EMR. Options for race designation across the health systems ranged from 6 to 49; options for ethnicity, from 2 to 10.Main outcomes and measuresMatching occurred in 3 stages. First, the exact racial and ethnic designations made by parents for their child were compared with what was found in the EMR. Second, for any child whose parent selected more than 1 racial category or for whom more than 1 appeared in the EMR, the designation of a minoritized racial group was used for matching purposes. Third, starting with the product of stage 2, racial designations were combined or collapsed into 6 (health systems A and C) or 5 (health system B) designations.ResultsA total of 4333 survey responses were included in the analysis. The greatest error rate across the health systems occurred with the exact match of parental report of racial designation with the EMR, which ranged from 41% to 78% across the health systems. Improvement in the matching rate for each health system occurred with consolidation of race options provided. Differences between the health systems narrowed at the final consolidation to varying from 79% to 88% matching. Ethnicity matching between the EMR and the parental report ranged from 65% to 95% across the health systems. Missing race or ethnicity data in the EMR was counted as a nonmatch. Rates of missing racial data varied across the health systems from 2% to 10%. The health system with the greatest number of options for race and ethnicity had the highest error rates.Conclusions and relevanceAlthough there will always be some misattribution of race and ethnicity in the EMR, the results of this cross-sectional study suggest that significant error in these data may undermine strategies to improve care. It is unclear whether those in an organization who determine the number of potential categories are the same persons who use those data to investigate potential disparities and inequities.

Dataset Information

Collection of Data on Race, Ethnicity, Language, and Nativity by US Public Health Surveillance and Monitoring Systems: Gaps and Opportunities.

Publications

Collection of Data on Race, Ethnicity, Language, and Nativity by US Public Health Surveillance and Monitoring Systems: Gaps and Opportunities.

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