Collection Of Ethnicity Data Research Articles

Prostate cancer-specific proinflammatory cytokines and chemokines impact on cancer stem cell development, lineage plasticity and heterogeneity in an Ancestral/racially diverse population: review.

Since 1976, Surveillance Epidemiology End Results (SEER) began collecting ethnicity data for the National Cancer Institute. The incidence of prostate cancer (PCa) among African American men (AAM) has been 60-70% higher than any other ethnicity and mortality rate 2 to 3 times greater than European American men (EAM), and those data have not changed. We reported in 2010 that PCa grows faster among AAM compared to EAM. In 2013, we utilized bioinformatics and ingenuity gene network analysis and in silico analysis to identify driver genes responsible for "racial" disparity. Genes associated with lipid metabolism were more expressed among EAM and genes associated with inflammation were more expressed among AAM. In 2021, we unraveled the network of the Ingenuity gene analysis and reported that the inflammatory genes, specifically proinflammatory cytokines and chemokines initiated multiple pathways. A literature review of these pathways showed that they induce castrate-resistant PCa (CRPC), metastasis, oxidative stress, DNA damage, cancer stem cells, lineage plasticity, and tumor heterogeneity. These genes and processes will be discussed in detail as to how they are initiated by proinflammatory cytokines and chemokines and how they act in a domino effect. Most importantly, how lineage plasticity changes the chemistry of the cancer stem cells of the original PCa so that it is no longer recognized by current therapy, chemotherapy, and immunotherapy. This suggests a paradigm change of current therapy is necessary to significantly reduce mortality of advanced PCa.

P747: The collection and use of patient sex, gender, race, ethnicity, and ancestry data among US-based clinical genomics laboratories

P747: The collection and use of patient sex, gender, race, ethnicity, and ancestry data among US-based clinical genomics laboratories

Not Indian, Not African: Classifying the East African Asian Population in Aotearoa New Zealand

This paper explores the challenges of measuring and classifying the East African Asian population in Aotearoa New Zealand. As a particularly diverse country, New Zealand has a significant and varied population of immigrants from South Asia, including India, Pakistan and Bangladesh, along with immigrants of South Asian origin, from Fiji, Southeast Asia, the Caribbean and East Africa. New Zealand’s system of ethnic classification relies on self-identification, with a broad definition of ethnicity encompassing heritage, ancestry, culture, language and feelings of belonging. However, the collection of this information at a granularity that enables detailed analysis is constrained for the South Asian population, regardless of origin or identification. People are typically presented with the choice of selecting “Indian” ethnicity as a tick box, or providing ethnicities under “Other” as write-in descriptors, which in turn are coded to a limited set of categories within the classification being used. This practice potentially conceals a diversity of ethnicities, which can only partially be hinted at by responses to questions relating to religions, languages and birthplaces, especially for second or third-generation descendants of migrants. Ethnic classification at the highest level, moreover, includes East African Indians as Asian, rather than African, reflecting diasporic heritage as a shorthand for ancestry and overlooking the relevance of layers of identity associated with the double diaspora. Drawing on Peter J. Aspinall’s work on collective terminology in ethnic data collection and categorizing the “Asian” ethnic group in the UK, this paper looks at the overlaps and disconnects between heritage, ethnicity and national belonging in classifying less clearcut identities. We explore the strengths and limitations of New Zealand’s self-identification approach to ethnic identity, and query what exactly is being asked of groups on the margins: when self-identification does not match external perception, are we looking for geographic, cultural, or genetic origins? A focus on the East African Asian population in Aotearoa highlights the complexity of identity for diasporic groups with distant ancestral links with India, as lived experience of cultural connection extends far beyond the bounds of ethnicity, language and even ancestry.

The journey to building a diverse, equitable, and inclusive American Medical Informatics Association.

The journey to building a diverse, equitable, and inclusive American Medical Informatics Association.

Indigenous governance, ethics and data collection in Australian clinical registries.

To examine Indigenous Governance of Data processes in Australian clinical registries. Audit (via desktop review and interviews) of registries in the Australian Register of Clinical Registries from 17 January 2022 to 30 April 2023. The number of clinical registries collecting ethnicity data, reporting Aboriginal and/or Torres Strait Islander representation on registry governance or steering committees, and reporting human research ethics committee approval. A total of 107 clinical registries were reviewed. Of these registries, 65 (61%) collected ethnicity data; when these were grouped by geographical coverage, those most likely to collect ethnicity data were binational (24/40 [60%]), national (19/26 [73%]) or state based (19/26 [73%]). Of the registries that collected ethnicity data, 29 (45%) classified their ethnicity item as Aboriginal and/or Torres Strait Islander. Only eight clinical registries (7%) reported Aboriginal and/or Torres Strait Islander representation on their governance or steering committees. Human research ethics approval was reported in 94 registries (88%), with only 11 (12%) having Aboriginal human research ethics committee approval. Significant variability is evident in clinical registry recording of Indigenous governance of data, meaning that Aboriginal and Torres Strait Islander communities remain invisible in data which is used to inform policy, clinical models of care, health services and initiatives. Radical change is required to facilitate meaningful change in quality indicators for clinical registries nationally.

HPR58 Mapping Medicaid: A Comparative Analysis of State-Level Racial and Ethnic Data Collection to Federal Guidelines

HPR58 Mapping Medicaid: A Comparative Analysis of State-Level Racial and Ethnic Data Collection to Federal Guidelines

Who Counts in Official Statistics? Ethical‐Epistemic Issues in German Migration and the Collection of Racial or Ethnic Data

ABSTRACTIn European countries (excluding the UK and Ireland), official statistics do not use racial or ethnic categories, but instead rely on proxies to collect data about discrimination. In the German microcensus, the proxy category adopted is ‘migration background’ (Migrationshintergrund): an individual has a ‘migration background’ when one or more of their parents does not have German citizenship by birth. We apply a coupled ethical‐epistemic analysis to the ‘migration background’ category to illuminate how the epistemic issues contribute to ethical ones. Our central claim is that these ethical‐epistemic issues with the ‘migration background’ category are best analysed in terms of Charles Mills's ‘white ignorance’. Appealing to Annette Martín's structural account of white ignorance, we highlight the cyclical reinforcement of ignorance and racial injustice in the use of the ‘migration background’ variable. Colourblind eliminativism about race perpetuates the use of ‘migration background’, which sustains and intensifies racial injustices.

Quality Improvement Intervention Using Social Prescribing at Discharge in a University Hospital in France: Quasi-Experimental Study.

Social prescription is seen as a public health intervention tool with the potential to mitigate social determinants of health. On one side, social prescription is not yet well developed in France, where social workers usually attend to social needs, and historically, there is a deep divide between the health and social sectors. On the other side, discharge coordination is gaining attention in France as a critical tool to improve the quality of care, assessed indirectly using unplanned rehospitalization rates. This study aims to combine social prescription and discharge coordination to assess the need for social prescription and its effect on unplanned rehospitalization rates. We conducted a quasi-experimental study in two departments of medicine in a French university hospital in a disadvantaged suburb of Paris over 2 years (October 2019-October 2021). A discharge coordinator screened patients for social prescribing needs and provided services on the spot or referred the patient to the appropriate service when needed. The primary outcome was the description of the services delivered by the discharge coordinator and of its process, as well as the characteristics of the patients in terms of social needs. The secondary outcome was the comparison of unplanned rehospitalization rates after data chaining. A total of 223 patients were included in the intervention arm, with recruitment being disrupted by the COVID-19 pandemic. More than two-thirds of patients (n=154, 69.1%) needed help understanding discharge information. Slightly less than half of the patients (n=98, 43.9%) seen by the discharge coordinator needed social prescribing, encompassing language, housing, health literacy, and financial issues. The social prescribing covered a large range of services, categorized into finding a general practitioner or private sector nurse, including language-matching; referral to a social worker; referral to nongovernmental organization or group activities; support for transportation issues; support for health-related administrative procedures; and support for additional appointments with nonmedical clinicians. All supports were delivered in a highly personalized way. Ethnic data collection was not legally permitted, but for 81% (n=182) of the patients, French was not the mother tongue. After data chaining, rehospitalization rates were compared between 203 patients who received the intervention (n=5, 3.1%) versus 2095 patients who did not (n=51, 2.6%), and there was no statistical difference. First, our study revealed the breadth of patient's unmet social needs in our university hospital, which caters to an area where the immigrant population is high. The study also revealed the complexity of the discharge coordinator's work, who provided highly personalized support and managed to gain trust. Hospital discharge could be used in France as an opportunity in disadvantaged settings. Eventually, indicators other than the rehospitalization rate should be devised to evaluate the effect of social prescribing and discharge coordination.

Social health and subsequent cognitive functioning in people aged 50 years and older: examining the mediating roles of depressive symptoms and inflammatory biomarkers in two European longitudinal studies

Social health and subsequent cognitive functioning in people aged 50 years and older: examining the mediating roles of depressive symptoms and inflammatory biomarkers in two European longitudinal studies

Implications and Insights on Federal Revisions to Race and Ethnicity Data Collection

Implications and Insights on Federal Revisions to Race and Ethnicity Data Collection

Assessing intersectional gender analysis in Nepal’s health management information system: a case study on tuberculosis for inclusive health systems

BackgroundTuberculosis (TB) remains a major public health problem in Nepal, high in settings marked by prevalent gender and social inequities. Various social stratifiers intersect, either privileging or oppressing individuals based on their characteristics and contexts, thereby increasing risks, vulnerabilities and marganilisation associated with TB. This study aimed to assess the inclusiveness of gender and other social stratifiers in key health related national policies and the Health Management Information System (HMIS) of National Tuberculosis Programme (NTP) by conducting an intersectional analysis of TB cases recorded via HMIS.MethodsA desk review of key policies and the NTP’s HMIS was conducted. Retrospective intersectional analysis utilized two secondary data sources: annual NTP report (2017–2021) and records of 628 TB cases via HMIS 6.5 from two TB centres (2017/18–2018/19). Chi-square test and multi-variate analysis was used to assess the association between social stratifers and types of TB, registration category and treatment outcome.ResultsGender, social inclusion and concept of intersectionality are incorporated into various health policies and strategies but lack effective implementation. NTP has initiated the collection of age, sex, ethnicity and location data since 2014/15 through the HMIS. However, only age and sex disaggregated data are routinely reported, leaving recorded social stratifiers of TB patients static without analysis and dissemination. Furthermore, findings from the intersectional analysis using TB secondary data, showed that male more than 25 years exhibited higher odds [adjusted odds ratio (aOR) = 4.95, 95% confidence interval (CI): 1.60–19.06, P = 0.01)] of successful outcome compared to male TB patients less than 25 years. Similarly, sex was significantly associated with types of TB (P < 0.05) whereas both age (P < 0.05) and sex (P < 0.05) were significantly associated with patient registration category (old/new cases).ConclusionsThe results highlight inadequacy in the availability of social stratifiers in the routine HMIS. This limitation hampers the NTP’s ability to conduct intersectional analyses, crucial for unveiling the roles of other social determinants of TB. Such limitation underscores the need for more disaggregated data in routine NTP to better inform policies and plans contributing to the development of a more responsive and equitable TB programme and effectively addressing disparities.

Making Oneself "Fit": Community Perspectives on Disaggregated Race and Ethnicity Data Collection and Reporting in Healthcare Settings.

Patient race/ethnicity data collection in most U.S. health systems abide by federal standards, determined by the federal Office of Management and Budget. Yet, decades of research show that reliance on these categories alone limits understanding of within-group health disparities, systematically erasing key groups from health data. Because granular race/ethnicity data is complex and patients may be hesitant to disclose this personal information, it is important for health leaders to consider community perspectives when making decisions about race/ethnicity data procedures. As such, this study uses community focus groups to understand: (1) how individuals representing different racial/ethnic identities perceive the collection of race/ethnicity in healthcare settings; (2) differences in opinions between disaggregated race/ethnicity data collection instruments and those using federal standards; and (3) recommended practices for collecting race/ethnicity from patients. Participants self-selected into 13 focus groups and one key informant interview based on the race/ethnicity with which they most closely identified. Audio recordings from these groups were transcribed and evaluated using thematic content analysis. Among the 83 total participants in this study, there was a strong preference for more flexible and specific options for self-identifying race/ethnicity in healthcare settings. Participants also felt comfortable disclosing granular race/ethnicity to health providers but expressed discomfort with disclosing this information for other purposes. Recommendations for healthcare leaders include ensuring patients receive detailed communication about race/ethnicity data use and purpose, allowing multiple category selection, keeping the list of disaggregated response options short so as to not overwhelm patients, and providing a free text option to ensure inclusivity.

Leveraging Ethnic Backgrounds to Improve Collection of Race, Ethnicity, and Language Data.

Healthcare disparities may be exacerbated by upstream incapacity to collect high-quality and accurate race, ethnicity, and language (REaL) data. There are opportunities to remedy these data barriers. We present the Denver Health (DH) REaL initiative, which was implemented in 2021. Denver Health is a large safety net health system. After assessing the state of REaL data at DH, we developed a standard script, implemented training, and adapted our electronic health record to collect this information starting with an individual's ethnic background followed by questions on race, ethnicity, and preferred language. We analyzed the data for completeness after REaL implementation. A total of 207,490 patients who had at least one in-person registration encounter before and after the DH REaL implementation were included in our analysis. There was a significant decline in missing values for race (7.9%-0.5%, p < .001) and for ethnicity (7.6%-0.3%, p < .001) after implementation. Completely of language data also improved (3%-1.6%, p < .001). A year after our implementation, we knew over 99% of our cohort's self-identified race and ethnicity. Our initiative significantly reduced missing data by successfully leveraging ethnic background as the starting point of our REaL data collection.

Lead exposure sources and public health investigations for children with elevated blood lead in England, 2014 to 2022.

Lead exposure at any concentration can adversely impact health, with children being more vulnerable to its effects. In England, children with an elevated blood lead concentration (BLC) are reported to Health Protection Teams (HPTs) for public health investigation. A detailed review of these cases has not yet been conducted. The objectives of this study were to describe the demographics, likely setting and sources of lead exposure, risk behaviours, public health investigations and outcomes for children aged <16 years with a BLC requiring public health action reported to HPTs between 2014-2022 in England. Data were collected via a lookback questionnaire and a live enhanced surveillance questionnaire. Data were deduplicated, cleaned and results summarised as numbers and percentages using R studio. A thematic analysis was conducted on qualitative responses to a question relating to problems experienced during case investigation. There were 340 cases in our study: the majority were aged 1-4 years old (53%) and male (69%). Ethnicity data was poorly recorded. A higher than expected proportion (31%) lived in the most deprived areas. Pica (76%) and learning difficulties (60%) were often present. Cases were primarily exposed to lead in the domestic setting (92%) with paint (43%) and soil (29%) the most common exposures. Most cases lived in rented accommodation (63%), with a higher proportion in social rentals (48%) than privately rented (37%). Case investigations were resource intensive and poor stakeholder engagement/response was most frequently identified as challenging by HPTs. Lead exposure is harmful to children and requires public health and clinical management, which can be complex and challenging. Prevention of lead exposure in children should be the focus of intervention efforts. Outreach, engagement and preventative work should focus on both renters and homeowners. Collecting ethnicity data consistently may enable identification of more specific groups at increased risk of lead exposure in England.

Beyond Information Provision: Analysis of the Roles of Structure and Agency in COVID-19 Vaccine Confidence in Ethnic Minority Communities.

People from Black and Asian backgrounds are more likely to die from COVID-19 but less likely to be vaccinated, threatening to exacerbate health inequalities already experienced by ethnic minority groups. The literature suggests that mistrust rooted in structural inequality (including socioeconomic position and experience of racism) may be a key barrier to COVID-19 vaccine uptake. Understanding and addressing structural inequality is likely to lead to longer-term impacts than information alone. The aim of this study is to draw on health and sociological theories of structure and agency to inform our understanding of how structural factors influence vaccine confidence. We conducted qualitative interviews and focus groups with 22 people from London and the surrounding areas from December 2021 to March 2022. Fourteen participants were members of the public from ethnic minority backgrounds, and seven were professionals working with the public to address concerns and encourage vaccine uptake. Our findings suggest that people from ethnic minority backgrounds make decisions regarding COVID-19 vaccination based on a combination of how they experience external social structures (including lack of credibility and clarity from political authority, neglect by health services, and structural racism) and internal processes (weighing up COVID-19 vaccine harms and benefits and concerns about vaccine development and deployment). We may be able to support knowledge accumulation through the provision of reliable and accessible information, particularly through primary and community care, but we recommend a number of changes to research, policy and practice that address structural inequalities. These include working with communities to improve ethnicity data collection, increasing funding allocation to health conditions where ethnic minority communities experience poorer outcomes, greater transparency and public engagement in the vaccine development process, and culturally adapted research recruitment processes.

Achieving health equity: A patient safety imperative

Achieving health equity requires achieving zero preventable harm for all people. Health inequity must be inextricably linked to safety if all people are to be free from harm. There is no safety without equity and no equity without safety. Health inequity is an unsafe condition. Equity-related near misses, adverse events and sentinel events must undergo a comprehensive systematic analysis. Each root cause should have a strong corrective action(s). Measurable actions might include the collection and stratification of race, ethnicity and language data, or addressing stereotype bias, implicit bias, structural competency and institutional and structural racism. Leadership committed to creating a culture of equity is required. Measures of success should be linked to payment and restoring trust.

Collaborative Learning Among Health Care Organizations to Improve Quality and Advance Racial Equity.

The study examined stakeholder experiences of a statewide learning collaborative, sponsored and led by Blue Cross Blue Shield of Massachusetts (BCBSMA) and facilitated by the Institute for Healthcare Improvement (IHI) to reduce racial and ethnic disparities in quality of care. Interviews of key stakeholders (n=44) were analyzed to assess experiences of collaborative learning and interventions to reduce racial and ethnic disparities in quality of care. The interviews included BCBSMA, IHI, provider groups, and external experts. Breast cancer screening, colorectal cancer screening, hypertension management, and diabetes management were focal areas for reducing disparities. Collaborative learning methods involved expert coaching, group meetings, and sharing of best practices. Interventions tested included pharmacist-led medication management, strategies to improve the collection of race, ethnicity, and language (REaL) data, transportation access improvement, and community health worker approaches. Stakeholder experiences highlighted three themes: (1) the learning collaborative enabled the testing of interventions by provider groups, (2) infrastructure and pilot funding were foundational investments, but groups needed more resources than they initially anticipated, and (3) expertise in quality improvement and health equity were critical for the testing of interventions and groups anticipated needing this expertise into the future. BCBSMA's learning collaborative and intervention funding supported contracted providers in enhancing REaL data collection, implementing equity-focused interventions on a small scale, and evaluating their feasibility and impact. The collaborative facilitated learning among groups on innovative approaches for reducing racial disparities in quality. Concerns about sustainability underscore the importance of expertise for implementing initiatives to reduce racial and ethnic disparities.

Racing the Machine: Data Analytic Technologies and Institutional Inscription of Racialized Health Injustice.

Recent scientific and policy initiatives frame clinical settings as sites for intervening upon inequality. Electronic health records and data analytic technologies offer opportunity to record standard data on education, employment, social support, and race-ethnicity, and numerous audiences expect biomedicine to redress social determinants based on newly available data. However, little is known on how health practitioners and institutional actors view data standardization in relation to inequity. This article examines a public safety-net health system's expansion of race, ethnicity, and language data collection, drawing on 10 months of ethnographic fieldwork and 32 qualitative interviews with providers, clinic staff, data scientists, and administrators. Findings suggest that electronic data capture institutes a decontextualized racialization within biomedicine as health practitioners and data workers rely on biological, cultural, and social justifications for collecting racial data. This demonstrates a critical paradox of stratified biomedicalization: The same data-centered interventions expected to redress injustice may ultimately reinscribe it.

Racial and ethnic disparities in common inpatient safety outcomes in a children’s hospital cohort

BackgroundEmerging evidence has shown racial and ethnic disparities in rates of harm for hospitalised children. Previous work has also demonstrated how highly heterogeneous approaches to collection of race and ethnicity...

DATA PROTECTION IN THE FRAMEWORK OF COMBATING DISCRIMINATION, RACISM AND XENOFOBIA IN THE EUROPEAN UNION

As the EU is based on specific values, including the value of equality, and as one of its objectives is to combat social exclusion and discrimination, this work presents two desiderata that are difficult to reconcile: the need for the collection and analysis of ethnic data - as a tool to combat discrimination and promote equality - and the imperative of its protection. In fact, technological developments allow the use of personal data, many of them classified as “sensitive”, so that their exceptional use requires compliance with strict rules of an ethical nature. Thus, our intention is to assess whether compliance with the rules on the collection and processing of ethnic data by member states can safeguard the fundamental rights of members belonging to minorities, preventing the misuse or abuse of such data.