Collection Of Ethnicity Data Research Articles

Health Equity Beyond Data: Health Care Worker Perceptions of Race, Ethnicity, and Language Data Collection in Electronic Health Records.

Recent research and policy initiatives propose addressing the social determinants of health within clinical settings. One such strategy is the expansion of routine data collection on patient Race, Ethnicity, and Language (REAL) within electronic health records (EHRs). Although previous research has examined the general views of providers and patients on REAL data, few studies consider health care workers' perceptions of this data collection directly at the point of care, including how workers understand REAL data in relation to health equity. This qualitative study examines a large integrated delivery system's implementation of REAL data collection, focusing on health care workers' understanding of REAL and its impact on data's integration within EHRs. Providers, staff, and administrators expressed apprehension over REAL data collection due to the following: (1) disagreement over data's significance, including the expected purpose of collecting REAL items; (2) perceived barriers to data retrieval, such as the lack of standardization across providers and national tensions over race and immigration; and (3) uncertainty regarding data's use (clinical decision making vs. system research) and dissemination (with whom the data may be shared; eg, public agencies, other providers, and insurers). Emerging racial disparities associated with COVID-19 highlight the high stakes of REAL data collection. However, numerous barriers to health equity remain. Health care workers need greater institutional support for REAL data and related EHR initiatives. Despite data collection's central importance to policy objectives of disparity reduction, data mandates alone may be insufficient for achieving health equity.

Iwi (tribal) data collection at a primary health care organisation in Aotearoa.

INTRODUCTION Indigenous peoples' rights include the right to self-determine one's identity. For Māori, this includes self-assignment of ethnicity, and traditional identities such as Iwi (tribe). New Zealand's Ministry of Health requires health services to collect ethnicity data using standard protocols. Iwi data are also collected by some health services; however, with no health-specific protocols, little is known about Iwi data collection and quality. The National Hauora Coalition (NHC) Primary Healthcare Organisation (PHO) sought to understand Iwi data collection across its network of primary care providers. AIM To understand Iwi data collection at the NHC PHO; specifically, is it being routinely collected, how is it being collected and what are the results? METHODS In 2017, NHC's general practice clinics were invited to submit their enrolment forms, which capture ethnicity and potentially Iwi information, by e-mail to the audit team. Forms were reviewed to determine whether Iwi information was being collected and if so, what question was being used. Iwi numbers were collated from the annual data extract. RESULTS Thirty-three of a total of 35 clinics (94%) submitted their enrolment forms to the audit team. Nine of the 33 clinics (27%) sought Iwi name/s with a specific question on their enrolment form. Six different 'Iwi' questions were used by the nine clinics. The data extract revealed that the NHC had Iwi data for 13% (2672/20,814) of its Māori enrolments. Ngāpuhi were the largest Iwi group at the NHC. DISCUSSION This is the first study to describe the quantity and quality of Iwi data collection in NZ primary care. Standard procedures for collecting, recording and using Iwi data are being developed by the NHC PHO. These could inform national protocols to optimise the quality of Iwi data.

Combating discrimination through Big Data – future of equality?

The paper aims firstly to assess the future of anti-discrimination measures and policies, above all through the lens of ethnic data utilization. The question posed is not only whether massive collection and usage of such data is viable in relation to its result, but also whether such ethnic data collection is an obligation incumbent upon state authorities, in international and European human rights protection systems in particular. On the other hand, this article aims to compare existence of such obligation with the existing standards on right to privacy and implications for this right stemming from such use of Big Data. The negative impact resulting from such obligation in this regard could weigh heavily on protection of personal data, currently one of the main concerns throughout Europe and the EU.

Examining emergency department inequities: Are outcomes in your emergency department equitable?

Examining emergency department inequities: Are outcomes in your <scp>emergency department</scp> equitable?

Using the National Incident-Based Reporting System (NIBRS) to examine racial and ethnic disparities in cannabis incidents

ABSTRACT Background: Reducing racial and ethnic arrest disparities is one driver of cannabis legalization in the US., but outcomes of cannabis policies and equity provisions remain unknown. Early research finds legalization reduces total cannabis incidents, but disparities persist. In response, novel research approaches are emerging, but a comprehensive assessment of their strengths and limitations is needed, particularly when using data sources developed for other purposes. Objectives: This perspective assesses the National Incident-Based Reporting System (NIBRS), originally developed for crime statistics, as a means to surveil cannabis incident disparities. Methods: Massachusetts NIBRS (2000–2013) illustrates discussion points, including around sample inclusion, data integrity, and missing data. Results: We find that NIBRS is a comprehensive source for state-level monitoring if used with knowledge of its limitations. However, drawing conclusions about disparities requires careful assessment of sample inclusion and any changes to participation rates, data integrity, reporting changes, and missing variables, before analysis and reporting. Conclusion: NIBRS utility would increase with greater or required jurisdiction participation, guidance for collection of racial and ethnic data, and required ethnicity reporting. Despite limitations, cannabis disparity analyses using NIBRS can provide key insights for increasing equity in states considering and enacting cannabis legalization.

Accuracy of the Preferred Language Field in the Electronic Health Records of Two Canadian Hospitals

The collection of race, ethnicity, and language (REaL) data from patients is advocated as a first step to identify, monitor, and improve health inequities. As a result, many health care institutions collect patients' preferred languages in their electronic health records (EHRs). These data may be used in clinical care, research, and quality improvement. However, the accuracy of EHR language data are rarely assessed. This study aimed to audit the accuracy of EHR language data at two academic hospitals in Toronto, Ontario, Canada. The EHR language was compared with a patient's stated preferred language by interview. Language was dichotomized to English or non-English. Agreement between language documented in the EHR and patient-reported preferred language was calculated using sensitivity, specificity, and positive predictive value (PPV). A total of 323 patients were interviewed, including 96 with a stated non-English preferred language. The sensitivity of the EHR for English-language preference was high at both hospitals: 100% at hospital A with a PPV of 88%, and 99% at hospital B with a PPV of 85%. However, the sensitivity of the EHR for non-English preference differed greatly between the two hospitals. The sensitivity was 81% with a PPV of 100% at hospital A and the sensitivity was 12% with a PPV of 60% at hospital B. The accuracy of the EHR for identifying non-English language preference differed greatly between the hospitals studied. Language data must be accurate for it to be used, and regular quality assurance is required.

A case study of the 1115 waiver using population health informatics to address disparities.

As participants in the California Medicaid 1115 waiver, the University of California San Diego Health (UCSDH) used population health informatics tools to address health disparities. This case study describes a modern application of health informatics to improve data capture, describe health disparities through demographic stratification, and drive reliable care through electronic medical record-based registries. We provide a details in our successful approach using (1) standardized collection of race, ethnicity, language, sexual orientation, and gender identity data, (2) stratification of 8 quality measures by demographic profile, and (3) improved quality performance through registries for wellness, social determinants of health, and chronic disease. A strong population health platform paired with executive support, physician leadership, education and training, and workflow redesign can improve the representation of diversity and drive reliable processes for care delivery that improve health equity.

Understanding and Interpreting Law School Enrollment Data

The Law School Admission Council (LSAC) has a long-standing commitment to diversity, equity, and inclusion in legal education and in the legal profession. In line with its mission to promote quality, access, and equity in legal education, LSAC is providing a report, Understanding and Interpreting Law School Enrollment Data: A Focus on Race and Ethnicity, to help law schools, admission professionals, and other legal education stakeholders understand how we are measuring who is in the pipeline. The purpose of the report is to inform conversations about diversity, equity, and inclusion in law school and recruitment efforts. The report outlines the history of the Office of Management and Budget (OMB) data reporting standards, how these differ from LSAC data collection and reporting practices, and the social and cultural implications of different race and ethnicity data collection and reporting methods. The report includes examples of how the different methods affect conclusions that can be drawn from analyses of subgroup trends over time.

Ethnicity recording in health and social care data collections in Ireland: where and how is it measured and what is it used for?

BackgroundIn the European Union (EU), discrimination based on racial and ethnic origin is prohibited under the Racial Equality Directive. Ireland is one of only three EU countries where a legal duty of equality data collection is placed on public bodies. It provides an important context in which to study ethnic equality monitoring; however no systematic mapping of where it occurs in health information systems has been carried out. The aim of this study is to identify all existing national health and social care data collections with information on ethnicity and to explore how this data has been collected and used.MethodsAn electronic search of a national catalogue of health and social care data collections (N = 97) was carried out to identify any collections which contained information on ethnicity. Data dictionaries were searched and key informants contacted. For each of the data collections that collected information on ethnicity, data was extracted on the ethnic categories used and how this data is collected; the completeness of ethnicity recording; and other measures related to ethnicity in the data collection. Relevant outputs for these data collections, related to ethnicity, were identified through key informants and electronic searches.ResultsOf the 97 data collections, 14 (14%) collected information on ethnic or cultural background. Country of birth was collected by 10 of these 14 data collections. Most used the ethnic categories in the Census and recommended that ethnicity should be self-identified and not assigned. Reported rates of identification were generally high (≥90%). Data collections which recorded ethnicity tended to be focused on potentially high-risk populations with no routine recording in primary care. There were some examples of where ethnic equality monitoring had informed targeted interventions e.g. vaccination awareness initiatives or cultural training for healthcare staff.ConclusionsDespite strong policy and legal imperatives, there is limited data collection of ethnicity in health and social care data collections in Ireland. While there are some examples of where differences by ethnicity have been identified and acted upon, a more coordinated and comprehensive approach to the collection, quality and utilization of ethnicity data is needed to promote health equity.

The reporting of race and/or ethnicity in the medical literature: a retrospective bibliometric analysis confirmed room for improvement

ObjectivesAlthough the collection of race and/or ethnicity data is an important way to identify and address inequalities in health care provision and disparities in access to treatment, studies examining the extent to which race and/or ethnicity data are reported in the medical literature, and the quality of these data, are lacking. Therefore, we sought to objectively determine the quality of reporting of race and/or ethnicity in original medical research papers. Study Design and SettingA retrospective bibliometric analysis was used. Two independent investigators analyzed original articles investigating race/ethnicity, published between 2007 and 2018, in the 10 top-ranking academic journals in each of the following categories: general medicine, surgery, and oncology. ResultsA total of 995 original articles were included in our analysis. Only 45 studies (4.52%) provided a formal definition of race/ethnicity, and 8.94% identified the investigator responsible for the classification. While race/ethnicity was a key part of study design in 31.86% of the included investigations, the method used to classify individuals into racial/ethnic groups was described in only 10.25% of articles. In terms of terminology, we identified 81 different race/ethnicity classifications, but these were often imprecise and open to interpretation. ConclusionThere is significant room for improvement in the collection, reporting, and publishing of data describing ethnicity and/or race in the medical literature.

The clinical imperative for inclusivity: Race, ethnicity, and ancestry (REA) in genomics.

The Clinical Genome Resource (ClinGen) Ancestry and Diversity Working Group highlights the need to develop guidance on race, ethnicity, and ancestry (REA) data collection and use in clinical genomics. We present quantitative and qualitative evidence to characterize: (1) acquisition of REA data via clinical laboratory requisition forms, and (2) information disparity across populations in the Genome Aggregation Database (gnomAD) at clinically relevant sites ascertained from annotations in ClinVar. Our requisition form analysis showed substantial heterogeneity in clinical laboratory ascertainment of REA, as well as marked incongruity among terms used to define REA categories. There was also striking disparity across REA populations in the amount of information available about clinically relevant variants in gnomAD. European ancestral populations constituted the majority of observations (55.8%), allele counts (59.7%), and private alleles (56.1%) in gnomAD at 550 loci with "pathogenic" and "likely pathogenic" expert-reviewed variants in ClinVar. Our findings highlight the importance of implementing and supporting programs to increase diversity in genome sequencing and clinical genomics, as well as measuring uncertainty around population-level datasets that are used in variant interpretation. Finally, we suggest the need for a standardized REA data collection framework to be developed through partnerships and collaborations and adopted across clinical genomics.

Heterogeneity/granularity in ethnicity classifications project: the need for refining assessment of health status.

Identifying ethnic inequalities in health requires data with sufficiently 'granular' (fine detailed) classifications of ethnicity to capture sub-group variation in healthcare use, risk factors and health behaviors. The Robert Wood Johnson Foundation (RWJF), in the USA, commissioned us to explore granular approaches to ethnicity data collection outside of the USA, commencing with the European Union. We examined official data sources (population censuses/registers) within the EU-28 to determine the granularity of their approach to ethnicity. When ethnic information was not available, related variables were sought (e.g. country of birth). Within the EU-28, we found 55% of countries collected data on ethnicity. However, only 26% of these countries (England, Wales, Northern Ireland, Scotland, Republic of Ireland, Hungary, Poland and Slovakia) had a granular approach, with half of these being within the UK. Estonia, Lithuania, Croatia, Bulgaria, Republic of Cyprus and Slovenia collected one to six categories. A 'write-in' option only was found in Latvia, Romania and the Czech Republic. Forty-five percent of countries did not collect ethnicity data but collected other related variables. (i) Although there is reasonable attention to the diversity of ethnic groups in data collection, a granular approach does not predominate within EU-28 classifications. (ii) Where ethnicity is collected, it is conceptualized in different ways and diverse terminology is used. (iii) A write-in option provides the most granular approach. (iv) Almost half of the countries did not collect data on ethnicity, but did collect related variables that could be used as a proxy.

PHP162 - Sociodemographic Population and Health Profiles of Self-Identified Middle Eastern/North African Medicaid Beneficiaries in Managed Care

To evaluate the sociodemographic population and health profiles of self-identified Middle Eastern/North African (MENA) Medicaid beneficiaries enrolled in managed care in Michigan (MI), Pennsylvania (PA), and other (SC, IA, FL, DC) markets. AmeriHealth Caritas’ Race, Ethnicity, and Language (REL) data collection efforts recorded MENA as a distinct self-reported REL designation. All analyses were performed with SAS EG 7.1 software (SAS institute, Cary, NC). In the entire MENA population (N=7,356), females modestly outnumbered males both overall (52.2% versus 47.7%, respectively) and in all markets (range, 51.0%–53.3%). A plurality of MENA members self-identified as Arab (overall, 33.9%; range, 25.5%–43.6), followed by Pakistani (overall, 14.5%; range, 8.5%–23.2%), Lebanese (overall, 7.9%; range, 2.0%–15.0%), Egyptian (overall, 7.7%; range, 1.3%–15.0%), Syrian (overall, 7.5%; range, 4.0%–13.2%), and Iraqi (overall, 7.3%; range, 4.3%–11.0%). Respondents primarily self-identified as English-speakers (57.1%), Arabic speakers (25.0%), or other (5.9%). A majority was covered under Temporary Assistance for Needy Families (overall, 51.4%; range, 44.8%–71.6%) and a small minority under Supplemental Security Income (overall, 6.1%; range, 3.7%–9.5%). A plurality was under 18 years (overall, 39.3%; range, 31.4%–50.0%), followed by 25–34 years (14.3%; range, 10.6%–17.5%) and 35–44 years (13.9%; range, 12.0%–14.9%). Integrated care management occurred in the MENA population at relatively low rates, both regarding members “touched” (overall, 8.7%; range, 4.1%–12.3%) and “engaged” (overall, 2.5%; range, 0.77%–4.0%; mean age, 26.9 years) by the health plan. Overall scoring by the Charlson's Cormobidity index was 0.38 (range, 0.30–0.41); the most prevalent comorbid conditions were chronic pulmonary disease (11.2%), diabetes without and with complications (9.1% and 2.7%, respectively), and mild liver disease (2.7%). Understanding the diverse demographic and health profile of our MENA population allows future development of culturally competent programing to effectively serve this Medicaid population.

Doing ethnography among Latinos in London and Barcelona: languages, genders, and ethnicities

Abstract Based on two ethnographic studies of people of Latin American descent in global cities, this article explores how language, gender, and ethnicity shape field relations, community membership, and data collection. It examines some of the implications of being positioned intersectionally as an outsider or insider of the community, and being sexualised by a male gatekeeper. It suggests that gender roles are a powerful aspect of conducting ethnographic research among Latinos, while pointing to the challenge of dealing with, and potentially contributing to, essentialising discourses in the field. It argues that the notion of ‘being Latino’ is imagined and constructed interactionally and contextually, in reaction to social pressures, as well as local and historical narratives.

Race Counts: Racial and Ethnic Data on the U.S. Census and the Implications for Tracking Inequality

This article is a critical review of racial and ethnic categories on the U.S. Census with a focus on how the census categories affect opportunities to track racial and ethnic inequality. The authors summarize how motivations behind the census categories changed from a historical basis in controlling people of color and protecting Whiteness toward a contemporary orientation around equity. Yet, many issues remain that confound the racial and ethnic census data, which are then used in research. A look at these issues and possible changes for the upcoming 2020 census serves as a critical reminder of the limitations of the census data. Taking this information into account, the authors conclude with comments and suggestions on the principles underlying racial and ethnic data collection on the census and the implications for tracking inequality.

Ethnic Differences in Mental Health and Race-Based Data Collection.

There is strong evidence of ethnic disparities in chronic medical conditions, such as diabetes and cardiovascular diseases; however, less is known about ethnic differences in mental illness and health service utilization. Previous studies have shown that Asians are more likely to avoid or delay seeking help for their mental illness. We conducted a population-based study using Ontario health administrative data to examine ethnic differences in mental illness severity at hospital presentation. We found that Chinese and South Asian psychiatric patients were significantly more likely to be involuntarily admitted and exhibited more aggressive behaviours and psychotic symptoms compared to the general population. Our study highlights the need to better understand how individual, family and health-system factors contribute to the observed ethnic disparities. This paper also describes the current status of ethnicity and race-based data collection in Ontario and the benefits of routinely collecting more ethnicity data in our healthcare system to ensure equitable healthcare access and outcomes for all Ontarians.

Primary Care Physicians' Collection, Comfort, and Use of Race and Ethnicity in Clinical Practice in the United States.

Purpose: The clinical utility of race and ethnicity has been debated. It is important to understand if and how race and ethnicity are communicated and collected in clinical settings. We investigated physicians' self-reported methods of collecting a patient's race and ethnicity in the clinical encounter, their comfort with collecting race and ethnicity, and associations with use of race in clinical decision-making.Methods: A national cross-sectional study of 787 clinically active general internists in the United States. Physicians' self-reported comfort with collecting patient race and ethnicity, their collection practices, and use of race in clinical care were assessed. Bivariate and multivariable regression analyses were conducted to examine associations between comfort, collection practices, and use of race.Results: Most physicians asked patients to self-report their race or ethnicity (26.5%) on an intake form or collected this information directly from patients (26.2%). Most physicians were comfortable collecting patient race and ethnicity (84.3%). Physicians who were more comfortable collecting patient race and ethnicity (β=1.65; [95% confidence interval; CI 0.03–3.28]) or who directly collected patients' race and ethnicity (β=1.24 [95% CI 0.07–2.41]) were more likely to use race in clinical decision-making than physicians who were uncomfortable.Conclusions: This study documents variation in physician comfort level and practice patterns regarding patient race and ethnicity data collection. As the U.S. population becomes more diverse, future work should examine how physicians speak about race and ethnicity with patients and their use of race and ethnicity data impact patient–physician relationships, clinical decision-making, and patient outcomes.

Canadian data sources on ethnic classifications: Contemporary and historical developments in heterogeneity

ABSTRACTObjectiveA thorough understanding of the health status of Canadians must take into account their ethnicity, given the genetic and social effects of race and ethnicity on health. Our objective is to describe Canadian data sources that collect ethnicity data and the degree of granularity that exists for ethnic classifications within these sources. We contextualize changes to the collection of ethnic data by considering their historical, social, and political circumstances.&#x0D; ApproachOur methods are informed by environmental scan and history methodology. We searched publicly available government documents, peer-reviewed literature, and contacted key informants to gain a comprehensive understanding of the Canadian sources available for collecting nationally representative ethnicity data. Two investigators, using qualitative content analysis, analyzed these sources independently. We extracted information form sources relating to the ethnic classifications (e.g., race, ethnic origins, colour, ancestry) and constructed a historical timeline of key changes. We mapped these to Canada’s changing social and political landscape and drew on contemporary literature to consider the implications of these changes for population health. The study team met to discuss findings, interpretations, and themes that emerged from these sources.&#x0D; ResultsThere are four main sources of ethnicity data in Canada used for health research: 1) Provincial health insurance registries, 2) Canadian Health Measures Survey, 3) Canadian Community Health Survey, 4) Census. Of these, ethnicity data are most limited in the provincial health insurance registries, flagging only Aboriginal status. The other three data sources are nationally administered, with all asking individuals to select, out of 11 categories, self-identified racial or ethnic groups.&#x0D; Historically, Canada’s changing policies on multiculturalism and immigration have influenced the collection of ethnic data to become more inclusive and granular. Important periods include early attempts at nation-building during the late 19th century, social changes post-WWII and the introduction of multiculturalism into federal policy, and present-day efforts in ethnic classifications for research purposes and preserving cultural diversity.&#x0D; ConclusionsThere is a need for greater granularity in ethnic classifications to reflect the diversity of the Canadian population. Consideration should be made to capture ethnicity as a social, cultural, and historical concept concept, especially in large data sources that influence health decision-making, such as the census. For example, questions on ethnic classification may consider incorporating questions about sense of belonging with the identified ethnic ancestry, rather than relying solely on reported ethnic origin and race.

Seroprevalence of Hepatitis B Infection Among Immigrants in a Primary Care Clinic: A Case for Granular Ethnicity and Language Data Collection.

Chronic hepatitis B virus (HBV) infection is highly prevalent worldwide and is most often diagnosed through screening efforts. In order to identify the specific ethnic groups at greatest risk, it is necessary to go beyond traditional categories. We conducted a retrospective case series in a primary care clinic serving non-English speaking immigrants to determine the prevalence of HBV among patients of various primary spoken languages (used as a proxy for ethnicity). Among the 1378 patients, the overall prevalence of current infection was 8%. HBV infection was markedly higher among Somali, Oromo and Khmer speakerscompared to other groups. This study illustrates the use of granular language data in describing the serologic profiles of HBV infection among non-English speaking patients in primary care setting. The variations in prevalence by language have implications for public health HBV screening efforts, in addition to suggesting potential risk factors for transmission.

Conducting Cancer Research among Culturally and Linguistically Diverse Groups in Australia: A Reflection on Challenges and Strategies.

BackgroundWhile effort has been made to include minority groups in cancer studies in Western countries, overseas experience indicates that recruiting immigrant members to participate in cancer research is challenging. The aim of the paper is to set out the challenges and strategies needed to ensure the success of cancer research among culturally and linguistically diverse (CALD) groups in Australia.DiscussionAuthor suggested that partnership with ethnic community organization, research team, research setting and data collection methods, access to national data in cancer register, the informed consent process, and resources management are important elements to ensure the success of cancer research among CALD groups.SummaryThe paper provides health care professionals with insight into methodological and practical issues needed to plan and conduct cancer research among CALD populations not only in Australia but also other Western countries, where numbers of minority groups are increasing significantly.