Reaching across the divide: finding solutions to health disparities

Eliminating racial and ethnic disparities in health has become a focal point in the prevention of unnecessary illness, disability, premature death, and the promotion of quality years of life for all persons. The Centers for Disease Control and Prevention (CDC) has responded to disparities in health among racial and ethnic minority populations by launching Racial and Ethnic Approaches to Community Health (REACH). The REACH program is a cornerstone of CDC's efforts to identify, reduce, and ultimately eliminate health disparities. CDC funds REACH communities to address key health areas in which minority groups traditionally experience serious inequities in health outcomes. REACH communities form coalitions that plan, implement, and evaluate strategies to focus on the needs of one or more groups that include African Americans, Alaska Natives, American Indians, Asian Americans, Hispanics/Latinos, and Pacific Islanders. Through REACHing Across the Divide: Finding Solutions to Health Disparities, we are pleased to share with you the successes and lessons learned in eliminating health disparities through the REACH program. The accomplishments highlighted in this book make a powerful case for the importance of working with communities to improve the health and well-being of their members. We now know that we can eliminate health disparities by engaging local leaders, building community partnerships, recognizing cultural influences, creating sustainable programs, leveraging resources, and empowering individuals and communities. The case studies in this book represent only a fraction of the many ways that REACH communities are overcoming barriers to good health. It is inspiring to imagine the possibilities if communities across the country were to put these strategies into practice. Our intent in sharing these innovative strategies and interventions is to assist others in their efforts to successfully close health gaps among racial and ethnic minority groups around the nation. It is a public health imperative that we help people, especially those experiencing the greatest disparities in health, obtain and maintain the highest level of health possible.Alarming facts, unacceptable conditions -- Racial and Ethnic Approaches to Community Health (REACH) -- Health disparities can be overcome -- Nationally demonstrated results -- Impressive community outcomes -- Why REACH works: keys to success -- Spreading the word: sharing principles that work Table of REACH communities (1999-2007) -- Map of REACH communities (1999-2007) -- Selected publications and presentations -- References"REACH, Racial and Ethnic Approaches to Community Health"--Cover.Includes bibliographical references.Centers for Disease Control and Prevention. REACHing Across the Divide: Finding Solutions to Health Disparities. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention; 2007

Comparing policies to tackle ethnic inequalities in health: Belgium 1 Scotland 4

Ethnic-minority health is a public health priority in Europe. This study compares strategies for tackling ethnic inequalities in health from two countries, Scotland and Belgium. Methods: We compared the countries using the Whitehead framework. Official policy documents were retrieved and reviewed and two databases related to immigrant health policies were also used. Ethnic inequalities in health were compared using the UK and Belgian Censuses of 2001. We analysed the recognition of the problem, the policies and the services and described ethnic health inequalities. Results: Scotland has recognized the problem of ethnic inequalities in health, thanks to better data and the Scottish Government has come up with a bold strategy. Belgium is a later starter, unable to properly monitor ethnic inequalities. In addition, there is no clear government commitment to tackling either health inequalities or ethnic inequalities in health. Both countries provide health-care services to ethnic minority groups through the mainstream services, although ethnic minority groups have more choice in Belgium than in Scotland. Overall, ethnic heath inequalities are lower in Scotland than in Belgium. Conclusion: Scotland has provided a more advanced and comprehensive response to tackling ethnic inequalities in health than Belgium. It has acknowledged that discrimination exists and that ethnic minority groups may have different needs. Belgium still assumes non-discrimination in health care and effectively denies the need for policy to tailor services to meet these needs. In Scotland, public organizations have been made accountable for promoting equality in health. This is an important contribution to European health policy

Assessment of the health of Americans: the average health-related quality of life and its inequality across individuals and groups

BACKGROUND: The assessment of population health has traditionally relied on the population's average health measured by mortality related indicators. Researchers have increasingly recognized the importance of including information on health inequality and health-related quality of life (HRQL) in the assessment of population health. The objective of this study is to assess the health of Americans in the 1990s by describing the average HRQL and its inequality across individuals and groups. METHODS: This study uses the 1990 and 1995 National Health Interview Survey from the United States. The measure of HRQL is the Health and Activity Limitation Index (HALex). The measure of health inequality across individuals is the Gini coefficient. This study provides confidence intervals (CI) for the Gini coefficient by a bootstrap method. To describe health inequality by group, this study decomposes the overall Gini coefficient into the between-group, within-group, and overlap Gini coefficient using race (White, Black, and other) as an example. This study looks at how much contribution the overlap Gini coefficient makes to the overall Gini coefficient, in addition to the absolute mean differences between groups. RESULTS: The average HALex was the same in 1990 (0.87, 95% CI: 0.87, 0.88) and 1995 (0.87, 95% CI: 0.86, 0.87). The Gini coefficient for the HALex distribution across individuals was greater in 1995 (0.097, 95% CI: 0.096, 0.099) than 1990 (0.092, 95% CI: 0.091, 0.094). Differences in the average HALex between all racial groups were the same in 1995 as 1990. The contribution of the overlap to the overall Gini coefficient was greater in 1995 than in 1990 by 2.4%. In both years, inequality between racial groups accounted only for 4–5% of overall inequality. CONCLUSION: The average HRQL of Americans was the same in 1990 and 1995, but inequality in HRQL across individuals was greater in 1995 than 1990. Inequality in HRQL by race was smaller in 1995 than 1990 because race had smaller effect on the way health was distributed in 1995 than 1990. Analysis of the average HRQL and its inequality provides information on the health of a population invisible in the traditional analysis of population health

Awareness in Practice: Tensions in Access to Sensitive Attribute Data for Antidiscrimination

Organizations cannot address demographic disparities that they cannot see. Recent research on machine learning and fairness has emphasized that awareness of sensitive attributes, such as race and sex, is critical to the development of interventions. However, on the ground, the existence of these data cannot be taken for granted. This paper uses the domains of employment, credit, and healthcare in the United States to surface conditions that have shaped the availability of sensitive attribute data. For each domain, we describe how and when private companies collect or infer sensitive attribute data for antidiscrimination purposes. An inconsistent story emerges: Some companies are required by law to collect sensitive attribute data, while others are prohibited from doing so. Still others, in the absence of legal mandates, have determined that collection and imputation of these data are appropriate to address disparities. This story has important implications for fairness research and its future applications. If companies that mediate access to life opportunities are unable or hesitant to collect or infer sensitive attribute data, then proposed techniques to detect and mitigate bias in machine learning models might never be implemented outside the lab. We conclude that today's legal requirements and corporate practices, while highly inconsistent across domains, offer lessons for how to approach the collection and inference of sensitive data in appropriate circumstances. We urge stakeholders, including machine learning practitioners, to actively help chart a path forward that takes both policy goals and technical needs into account