As of 2023, across 70 measures assessing health and healthcare in the U.S., the Black, AI/AN, and Hispanic populations fare worse than the White population. The racial/ethnic disparity was largest comparing Black and White populations. The Black population fared worse than the White population across 55 health and healthcare measures, while they only fared better than the White population for 12 of them.

On the other hand, the Asian population did not fare worse than White people across most examined measures. Nonetheless, these measures cover aspects of health coverage, access, and use; health status, outcomes, and behaviors; and social determinants of health, yet more is needed to provide the full scope of healthcare disparities.

The Overview of Health Disparities analysis is a component of the Healthy People 2020 (HP2020) Final Review. The analysis included 611 objectives in HP2020. This file contains summary level information used for the evaluation of changes in disparities during HP2020, including calculations for the disparities measures and the disparities change categories for all objectives and population characteristics in the analysis. See Technical Notes for the Healthy People 2020 Overview of Health Disparities (https://www.cdc.gov/nchs/healthy_people/hp2020/health-disparities-technical-notes.htm) for additional information and criteria for objectives, data years, and population characteristics included in the analysis and statistical formulas and definitions for the disparities measures. Data for additional years during the HP2020 tracking period that are not included in the Overview of Health Disparities are available on the HP2020 website (https://www.healthypeople.gov/2020/). Note that “rate” as used may refer to a statistical rate expressed per unit population or a proportion, depending on how the HP2020 objective was defined.

Note: This dataset is on hiatus.

CDPH strives to respond equitably to the COVID-19 pandemic and is therefore interested in how different communities are impacted. Collecting and reporting health equity data helps to identify health disparities and improve the state’s response. To that end, CDPH tracks cases, deaths, and testing by race and ethnicity as well as other social determinants of health, such as income, crowded housing, and access to health insurance.

During the response, CDPH used a health equity metric, defined as the positivity rate in the most disproportionately-impacted communities according to the Healthy Places Index. The purpose of this metric was to ensure California reopened its economy safely by reducing disease transmission in all communities. This metric is tracked and reported in comparison to statewide positivity rate. More information is available at https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/CaliforniaHealthEquityMetric.aspx.

Data completeness is also critical to addressing inequities. CDPH reports data completeness by race and ethnicity, sexual orientation, and gender identity to better understand missingness in the data.

Health equity data is updated weekly. Data may be suppressed based on county population or total counts.

For more information on California’s commitment to health equity, please see https://covid19.ca.gov/equity/

According to a 2021 health care systems ranking among selected high-income countries, the United States came last in the overall ranking of its health care system performance. The overall ranking was based on five performance categories, including access to care, care process, administrative efficiency, equity, and health care outcomes. For the category equity, which takes into account income-related disparities in the health system, the U.S. was ranked last again, while Australia took first place. Other disparities of ethnicity, gender, or geography were not included. This statistic present the health care equity rankings of the United States' health care system compared to ten other high-income countries in 2021.

This is a repository to accompany the publication, Mining the Health Disparities and Minority Health Bibliome: A Computational Scoping Review and Gap Analysis of 200,000+ Articles, in Science Advances (doi: 10.1126/sciadv.adf9033) and its associated website, HDMH Monitor.

General Information

Dataset: Mining the health disparities and minority health bibliome: A computational scoping review and gap analysis of 200,000+ articles [Dataset]. Dryad. https://doi.org/10.5061/dryad.vhhmgqp10

Corresponding Author Information Name: Harry Reyes Nieva Institution: Columbia University Address: New York, NY, USA Email: harry.reyes@columbia.edu

Date of data collection: 25 August 2021

Funding sources: National Library of Medicine (NLM) under Award Numbers T15LM007079 and R01LM013043 in addition to a Computational and Data Science Fellowship to Harry Reyes Nieva from the Association for Computing Machinery Special Interest Group in High Performance Computing (ACM SIGHPC).

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The Overview of Health Disparities analysis is a component of the Healthy People 2020 (HP2020) Final Review. The analysis included 611 objectives in HP2020. See Technical Notes for the Healthy People 2020 Overview of Health Disparities (https://www.cdc.gov/nchs/healthy_people/hp2020/health-disparities.htm) for additional information and criteria for objectives, data years, and population characteristics included in the analysis and statistical formulas and definitions for the disparities measures. This file contains estimates and standard errors for the baseline and final years for individual population groups used in the Overview of Health Disparities analysis. The number and definitions of population groups varied across the HP2020 objectives and data sources used. These population groups are shown in the disparities file as originally reported by the data source, rather than the harmonized categories that were used for the HP2020 Progress by Population Group analysis (https://www.cdc.gov/nchs/healthy_people/hp2020/population-groups.htm). Additionally, for any given objective, the baseline and final years used for the disparities analysis do not necessarily correspond to the baseline and final years used to evaluate progress toward target attainment in the HP2020 Final Review Progress Table (https://www.cdc.gov/nchs/healthy_people/hp2020/progress-tables.htm) and Progress by Population Group analysis (https://www.cdc.gov/nchs/healthy_people/hp2020/population-groups.htm). These distinctions should be considered when merging the downloadable Progress Table or Progress by Population Group data files with the Overview of Health Disparities data files, or when integrative analyses that incorporate both disparities and progress data are conducted. Data for additional years during the HP2020 tracking period that are not included in the Overview of Health Disparities are available on the HP2020 website (https://www.healthypeople.gov/2020/).

Contemporary public health and healthcare are navigating a complex landscape marked by limited resources, conflicting individual and collective preferences, and the challenge of improving efficiency while maintaining quality. This scenario raises a multitude of ethical and moral questions, necessitating state intervention through stewardship and governance. Governments worldwide strive to enhance utility, value for money, and health equity, guided by principles of distributive and procedural justice.

The moral underpinnings of public health activities, such as overall benefit, collective efficiency, distributive fairness, and harm prevention, are crucial in addressing global health resource challenges. These considerations encompass efficiency, equity, rights, and other ethical issues. The distribution of resources, whether based on noncorrelative or correlative principles, is a key aspect of justice in public health.

Public health efforts are also focused on mitigating the adverse effects of socio-economic determinants on health outcomes and addressing health disparities. This is particularly vital for vulnerable, high-risk, and marginalized groups who face unique challenges like historic injustices, discrimination, and specific social or physical needs.

The project at hand delves into the concepts outlined by Peragine, focusing on measuring individual opportunity sets, assessing inequality in opportunity distribution, and designing mechanisms to enhance 'opportunity equality'. A representative survey of Vienna's population (N=1411) explores various dimensions:

Socio-demography: This module gathers data on gender, age, education, and migration background. Health: It assesses individual health status, chronic conditions, multimorbidity, and health-related behaviors. Socio-economic status: This includes occupation, net income, asset wealth, and other indicators of social or economic capital. Access to healthcare: Respondents provide insights into their experiences with healthcare access, including barriers and needs. Affordability of healthcare: Questions revolve around health-related expenditures and attitudes towards healthcare coverage and benefits. Provision of healthcare: This focuses on the quality and timeliness of medical interventions and healthcare services. Justice-Fairness attitudes: The survey captures attitudes towards social/distributive justice and fairness in socio-economic and health-related aspects. Preferences for health policy and redistribution: This module explores public vs. private health insurance preferences and allocation preferences for the public health budget. Solidarity & Reciprocity: Estimating solidarity through measures of social trust, cooperative behavior, sharing, helping, and expressions of solidarity. Overall, this comprehensive approach aims to address the intricate interplay of ethical, moral, and practical considerations in public health and healthcare, emphasizing the need for equitable and just solutions in a resource-constrained environment.

Contemporary public health and health care face resource constraints, self-regarding versus other-regarding preferences, and strains to become more efficient at less costs, while maintaining quality. Thus, diverse distinct ethical and moral questions and challenges arise. These concerns inevitably imply some involvement of the state that has to intervene through stewardship and governance. In doing so governments seek to promote (aggregate) utility, increase value for money, and foster health equity, while adhering to principles of distributive and procedural justice (Hecht et al. 2019). Globally nations have found a wealth of ways to reach and improve on these objectives. “Moral justifications for public health activities, including overall benefit, collective efficiency, distributive fairness, and harm prevention, are considered by way of examining global human resources for health, with an eye to efficiency, equity, rights, and other ethical issues” (Merritt & Hyder 2019, p. 109). In striving for justice “we must also consider how to distribute whatever is measured. Noncorrelative principles do not try to correlate how much each individual receives with other facts about that individual, whereas correlative principles do” (Persad 2019, p. 36).

Public health aims to mitigate the negative effects of socio-economic determinants of health outcomes, as well as countering health disparities (Venkatapuram 2019). These patterns and gradients, which harm individual, community and public health, are even exacerbated for vulnerable, high-risk and marginalised populations. Such health “stressors may include historic injustices, discrimination and stigmatization, and unique social or physical needs, limitations, or vulnerabilities. […] Included groups are ageing populations, children and adolescents, persons with mental illness, persons with disabilities, sexual and gender minorities, and immigrants and refugees” (Bernheim &...

The National Veteran Health Equity Report details patterns and provides comparative rates of health conditions for vulnerable Veteran groups. Specifically, this report is designed to provide basic comparative information on the sociodemographics, utilization patterns and rates of diagnosed health conditions among the groups over which the VHA Office of Health Equity (OHE) has responsibility with respect to monitoring, evaluating and acting on identified disparities in access, use, care, quality and outcomes. The report allows the VA, Veterans, and stakeholders to monitor the care vulnerable Veterans receive and set goals for improving their care.

No ethnic/racial groups experienced better access to healthcare (across different access measures from health insurance to usual source of care) compared with non-Hispanic White or White people in 2017, 2018, or 2019. The exception is Asians, where they experienced better access than White population on 2 access measures (or 14 percent) but experienced worse access than White population on 4 measures (or 29 percent). The disparity was largest comparing Hispanic vs. non-Hispanic White population . This statistic depicts the percentage of healthcare access measures for which members of select ethnic groups had better or worse access to care than White population in the U.S. in 2017, 2018, or 2019.

Data set of Health Equity Social Service Contracts and clients who have a better health outcome. Data set displays rate, percentage and number. Data is from the PartnerGrants software used for Social Service Contracts. This data contains quarterly level data of clients served. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/emj9-r2em

Under the Towards Health Equity and Transformative Action on Tribal Health (THETA) study, data were collected from 5 sites which includes two sites in Karnataka (BR Hills and MM Hills), one site in Madhya Pradesh (Kanha Tiger Reserve), one site in Kerala (Wayanad Tiger Reserve), and one site in Arunachal Pradesh (Pakke Tiger Reserve).THETA study protocol is published in Wellcome Open ResearchIndividual identifiers, such as name, address, and phone number, were removed from the dataset in compliance with the ethics committee.

This dataset includes Medicaid Managed Care, Commercial HMO, and Commercial PPO performance data from the Quality Assurance Reporting Requirements (QARR) by member demographic characteristics. QARR is largely based on measures of quality developed and published by the National Committee for Quality Assurance (NCQA) Healthcare Effectiveness Data and Information Set (HEDIS®). Plans are required to submit quality performance data each year. Demographic information analyzed in this report includes members’ sex, age, race/ethnicity, Medicaid aid category, cash assistance status, behavioral health conditions including serious mental illness (SMI) and substance use disorder (SUD), payer status, and region of residence. Measuring the quality of care, and the ability to measure disparities in care is an important first step to a better understanding of the underlying factors that drive differences in care among certain populations within Medicaid Managed Care, Commercial HMO, and Commercial PPO.

The data is published annually for the Medicaid Managed Care Reports for Quality Performance: Health Care Disparities in New York State http://www.health.ny.gov/health_care/managed_care/reports/

Note: This dataset is on hiatus.

CDPH strives to respond equitably to the COVID-19 pandemic and is therefore interested in how different communities are impacted. Collecting and reporting health equity data helps to identify health disparities and improve the state’s response. To that end, CDPH tracks cases, deaths, and testing by race and ethnicity as well as other social determinants of health, such as income, crowded housing, and access to health insurance.

During the response, CDPH used a health equity metric, defined as the positivity rate in the most disproportionately-impacted communities according to the Healthy Places Index. The purpose of this metric was to ensure California reopened its economy safely by reducing disease transmission in all communities. This metric is tracked and reported in comparison to statewide positivity rate. More information is available at https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/CaliforniaHealthEquityMetric.aspx.

Data completeness is also critical to addressing inequities. CDPH reports data completeness by race and ethnicity, sexual orientation, and gender identity to better understand missingness in the data.

Health equity data is updated weekly. Data may be suppressed based on county population or total counts.

For more information on California’s commitment to health equity, please see https://covid19.ca.gov/equity/

The racial/ethnic disparity was largest comparing Black vs. White populations. Black population received worse care than White populations for 43 percent of quality measures, while they only received better care than White population for 11 percent of quality measures. This statistic depicts the percentage of healthcare quality measures for which members of select ethnic groups had better or worse access to care than White people in the U.S. in 2017, 2018, or 2019.

Smoking rates for each Census Tract in Allegheny County were produced for the study “Developing small-area predictions for smoking and obesity prevalence in the United States.” The data is not explicitly based on population surveys or data collection conducted in Allegheny County, but rather estimated using statistical modeling techniques. In this technique, researchers applied the smoking rate of a demographically similar Census Tract to one in Allegheny County to compute a smoking rate.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

This dataset tracks the updates made on the dataset "Social Determinants and Health Equity Resource Guide" as a repository for previous versions of the data and metadata.

The Health Atlas for the City of Los Angeles 2021 presents a data-driven snapshot of health conditions and outcomes in the City of Los Angeles. It illustrates geographic variation in socio-economic conditions, demographic characteristics, the physical environment, and access to support systems and services, and provides a context for understanding how these factors contribute to the health of Angelenos.The data underscore a key issue: where Angelenos live often influences their health and well-being. Los Angeles is a city with great health disparities and the patterns of inequality are reflected in many of the indicators highlighted in the Health Atlas. The spatial characteristics of physical and social determinants of health have roots in structural racism and historic and ongoing discrimination. Historic policies such as redlining have had lasting effects in Los Angeles. The analysis is a first step in understanding the areas of the City burdened with the most adverse health-related conditions in order to improve health outcomes and environmental justice for all Angelenos.The Health Atlas contains 115 maps covering regional context, demographic and social characteristics, economic conditions, education, health conditions, land use, transportation, food systems, crime, housing, and environmental health. In addition to displaying US Census Bureau, City, County, and other data, the Health Atlas contains several indices to facilitate comparisons across the city on subjects including environmental hazards (Map 113: Pollution Burden Index), transportation quality (Map 84: Transportation Index), and economic conditions (Map 19: Hardship Index). The Health Atlas culminates in a Community Health and Equity Index (Maps 114 and 115) which combines many of the above variables into a single index to compare health conditions across the City of Los Angeles. The Community Health and Equity Index can be used to understand the areas of the city with the highest vulnerabilities and cumulative burdens as compared to other portions of the City.The Health Atlas for the City of Los Angeles was originally developed in 2013 as an early step in the process to develop a Health, Wellness, and Equity Element of the General Plan (also known as the Plan for a Healthy Los Angeles). This data set is an update of the Health Atlas, completed in 2021. The Health Element and both editions of the Health Atlas are available as PDFs on the Los Angeles City Planning website, https://planning.lacity.gov.

The data on health care facilities includes the name and location of all the hospitals and primary care facilities in Allegheny County. The current listing of hospitals and primary care facilities is managed by the Allegheny County Health Department and is used in internal reporting and shared for public use.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

Racial/ethnic health disparities are higher rates of serious health conditions or deaths that affect communities of color. These disparities can result in shorter lifespans and lower quality of life, are rooted in inequities in the opportunities and resources needed for good health, such as education, employment, safe and healthy neighborhoods, and access to health care. These inequities are often the result of current and historical institutionalized racism or explicit racial bias.

The National Veteran Health Equity Report details patterns and provides comparative rates of health conditions for vulnerable Veteran groups. The report allows the VA, Veterans, and stakeholders to monitor the care vulnerable Veterans receive and set goals for improving their care.