The Economic Burden of Racial, Ethnic, and Educational Health Inequities in the US.

Importance: Health inequities exist for racial and ethnic minorities and persons with lower educational attainment due to differential exposure to economic, social, structural, and environmental health risks and limited access to health care. Objective: To estimate the economic burden of health inequities for racial and ethnic minority populations (American Indian and Alaska Native, Asian, Black, Latino, and Native Hawaiian and Other Pacific Islander) and adults 25 years and older with less than a 4-year college degree in the US. Outcomes include the sum of excess medical care expenditures, lost labor market productivity, and the value of excess premature death (younger than 78 years) by race and ethnicity and the highest level of educational attainment compared with health equity goals. Evidence Review: Analysis of 2016-2019 data from the Medical Expenditure Panel Survey (MEPS) and state-level Behavioral Risk Factor Surveillance System (BRFSS) and 2016-2018 mortality data from the National Vital Statistics System and 2018 IPUMS American Community Survey. There were 87 855 survey respondents to MEPS, 1 792 023 survey respondents to the BRFSS, and 8 416 203 death records from the National Vital Statistics System. Findings: In 2018, the estimated economic burden of racial and ethnic health inequities was $421 billion (using MEPS) or $451 billion (using BRFSS data) and the estimated burden of education-related health inequities was $940 billion (using MEPS) or $978 billion (using BRFSS). Most of the economic burden was attributable to the poor health of the Black population; however, the burden attributable to American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander populations was disproportionately greater than their share of the population. Most of the education-related economic burden was incurred by adults with a high school diploma or General Educational Development equivalency credential. However, adults with less than a high school diploma accounted for a disproportionate share of the burden. Although they make up only 9% of the population, they bore 26% of the costs. Conclusions and Relevance: The economic burden of racial and ethnic and educational health inequities is unacceptably high. Federal, state, and local policy makers should continue to invest resources to develop research, policies, and practices to eliminate health inequities in the US.

The Importance of Evaluating Health Disparities Research.

Health disparity populations are socially disadvantaged, and the multiple levels of discrimination they often experience mean that their characteristics and attributes differ from those of the mainstream. Programs and policies targeted at reducing health disparities or improving minority health must consider these differences. Despite the importance of evaluating health disparities research to produce high-quality data that can guide decision-making, it is not yet a customary practice. Although health disparities evaluations incorporate the same scientific methods as all evaluations, they have unique components such as population characteristics, sociocultural context, and the lack of health disparity common indicators and metrics that must be considered in every phase of the research. This article describes evaluation strategies grouped into 3 components: formative (needs assessments and process), design and methodology (multilevel designs used in real-world settings), and summative (outcomes, impacts, and cost). Each section will describe the standards for each component, discuss the unique health disparity aspects, and provide strategies from the National Institute on Minority Health and Health Disparities Metrics and Measures Visioning Workshop (April 2016) to advance the evaluation of health disparities research.

Methodological Approaches to Understanding Causes of Health Disparities.

Understanding health disparity causes is an important first step toward developing policies or interventions to eliminate disparities, but their nature makes identifying and addressing their causes challenging. Potential causal factors are often correlated, making it difficult to distinguish their effects. These factors may exist at different organizational levels (e.g., individual, family, neighborhood), each of which needs to be appropriately conceptualized and measured. The processes that generate health disparities may include complex relationships with feedback loops and dynamic properties that traditional statistical models represent poorly. Because of this complexity, identifying disparities' causes and remedies requires integrating findings from multiple methodologies. We highlight analytic methods and designs, multilevel approaches, complex systems modeling techniques, and qualitative methods that should be more broadly employed and adapted to advance health disparities research and identify approaches to mitigate them.

Statistical inferences of extended concentration indices for directly standardized rates.

The relative concentration index (RCI) and the absolute concentration index (ACI) have been widely used for monitoring health disparities with ranked health determinants. The RCI has been extended to allow value judgments about inequality aversion by Pereira in 1998 and by Wagstaff in 2002. Previous studies of the extended RCI have focused on survey sample data. This paper adapts the extended RCI for use with directly standardized rates (DSRs) calculated from population-based surveillance data. A Taylor series linearization (TL)-based variance estimator is developed and evaluated using simulations. A simulation-based Monte Carlo (MC) variance estimator is also evaluated as a comparison. Following Wagstaff's approach in 1991, we extend the ACI for use with DSRs. In all simulations, both the TL and MC methods produce valid variance estimates. The TL variance estimator has a simple, closed form that is attractive to users without sophisticated programming skills. The TL and MC estimators have been incorporated into a beta version of the National Cancer Institute's Health Disparities Calculator, a free statistical software tool that enables the estimation of 11 commonly used summary measures of health disparities for DSRs.

Patterns and Trends in Cancer Screening in the United States.

INTRODUCTION: We examined the prevalence of cancer screening reported in 2015 among US adults, adjusted for important sociodemographic and access-to-care variables. By using data from the National Health Interview Survey (NHIS) for 2000 through 2015, we examined trends in prevalence of cancer screening that adhered to US Preventive Services Task Force screening recommendations in order to monitor screening progress among traditionally underserved population subgroups. METHODS: We analyzed NHIS data from surveys from 2000 through 2015 to estimate prevalence and trends in use of recommended screening tests for breast, cervical, colorectal, and prostate cancers. We used logistic regression and report predictive margins for population subgroups adjusted for various socioeconomic and demographic variables. RESULTS: Colorectal cancer screening was the only test that increased during the study period. We found disparities in prevalence of test use among subgroups for all tests examined. Factors that reduced the use of screening tests included no contact with a doctor in the past year, no usual source of health care, and no insurance coverage. CONCLUSION: Understanding use of cancer screening tests among different population subgroups is vital for planning public health interventions with potential to increase screening uptake and reduce disparities in cancer morbidity and mortality. Overarching goals of Healthy People 2020 are to "achieve health equity, eliminate disparities, and improve the health of all groups." Adjusted findings for 2015, compared with previous years, show persistent screening disparities, particularly among the uninsured, and progress for colorectal cancer screening only.

Assessing disparities in colorectal cancer mortality by socioeconomic status using new tools: health disparities calculator and socioeconomic quintiles.

PURPOSE: Colorectal cancer mortality rates dropped by half in the past three decades, but these gains were accompanied by striking differences in colorectal cancer mortality by socioeconomic status (SES). Our research objective is to examine disparities in colorectal cancer mortality by SES, using a scientifically rigorous and reproducible approach with publicly available online tools, HD*Calc and NCI SES Quintiles. METHODS: All reported colorectal cancer deaths in the United States from 1980 to 2010 were categorized into NCI SES quintiles and assessed at the county level. Joinpoint was used to test for significant changes in trends. Absolute and relative concentration indices (CI) were computed with HD*Calc to graph change in disparity over time. RESULTS: Disparities by SES significantly declined until 1993-1995, and then increased until 2010, due to a mortality drop in populations living in high SES areas that exceeded the mortality drop in lower SES areas. HD*Calc results were consistent for both absolute and relative concentration indices. Inequality aversion parameter weights of 2, 4, 6 and 8 were compared to explore how much colorectal cancer mortality was concentrated in the poorest quintile compared to the richest quintile. Weights larger than 4 did not increase the slope of the disparities trend. CONCLUSIONS: There is consistent evidence for a significant crossover in colorectal cancer disparity from 1980 to 2010. Trends in disparity can be accurately and readily summarized using the HD*Calc tool. The disparity trend, combined with published information on the timing of screening and treatment uptake, is concordant with the idea that introduction of medical screening and treatment leads to lower uptake in lower compared to higher SES populations and that differential uptake yields disparity in population mortality.

Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century.

Addressing minority health and health disparities has been a missing piece of the puzzle in Big Data science. This article focuses on three priority opportunities that Big Data science may offer to the reduction of health and health care disparities. One opportunity is to incorporate standardized information on demographic and social determinants in electronic health records in order to target ways to improve quality of care for the most disadvantaged populations over time. A second opportunity is to enhance public health surveillance by linking geographical variables and social determinants of health for geographically defined populations to clinical data and health outcomes. Third and most importantly, Big Data science may lead to a better understanding of the etiology of health disparities and understanding of minority health in order to guide intervention development. However, the promise of Big Data needs to be considered in light of significant challenges that threaten to widen health disparities. Care must be taken to incorporate diverse populations to realize the potential benefits. Specific recommendations include investing in data collection on small sample populations, building a diverse workforce pipeline for data science, actively seeking to reduce digital divides, developing novel ways to assure digital data privacy for small populations, and promoting widespread data sharing to benefit under-resourced minority-serving institutions and minority researchers. With deliberate efforts, Big Data presents a dramatic opportunity for reducing health disparities but without active engagement, it risks further widening them.

Receipt of mammography recommendations among White and non-White women before and after the 2009 United States Preventive Services Task Force recommendation change.

PURPOSE: Receipt of a mammography recommendation from a physician is a strong predictor of obtaining a mammogram. In 2009, the United States Preventive Services Task Force (USPSTF) recommended routine biennial mammography for women aged 50-74 but not for women aged 40-49. We examined changes in reports of clinician recommendations for mammography among White and non-White women after these age-specific recommendations were issued. METHODS: Data from women aged 40-49 and 50-74 were drawn from the 2008 and 2013 National Health Interview Surveys. We used linear probability models to determine whether the proportions of women reporting a mammography recommendation changed after the USPSTF recommendation was issued and whether any changes observed differed across White and non-White women. All analyses were stratified by age groups and mammography history. RESULTS: Among women without a recent mammogram, reported clinician recommendations did not change for White women, but they decreased by 13-percentage points (95 % CI -0.22, -0.03) among non-White women aged 40-49 (p = 0.01) and increased by 9-percentage points (95 % CI 0.01, 0.17) among non-White women aged 50-74 (p = 0.04). Among women with a mammogram in the past 2 years, reported mammography recommendation from a clinician did not change for White or non-White women. CONCLUSIONS: Recommendations to reduce screening may be differentially implemented across racial/ethnic groups. Changes in reports of mammography recommendation from a clinician after the USPSTF breast cancer screening recommendation change were observed only among non-White women without a recent history of mammography. It is unclear whether these differences are due to the clinician, the women, or both.

Trends in Black-White Disparities in Breast and Colorectal Cancer Screening Rates in a Changing Screening Environment: The Peters-Belson Approach Using United States National Health Interview Surveys 2000-2010.

OBJECTIVE: Cancer screening utilization rates have changed over time and race disparities have persisted. We apply the Peters-Belson (PB) methodology to assess trends in disparities between blacks and whites in breast and colorectal cancer screening rates in the United States from 2000 to 2010. During this time period, the screening environment has changed for these screening modalities. METHODS: Cancer screening data collected in Cancer Control Supplements to the US National Health Interview Survey during 2000, 2003, 2005, 2008, and 2010 were used to estimate disparities between blacks and whites. Using the PB method, logistic regression models with selected covariates were fit to the white sample for each year, sex, and type of screening. The unexplained part of the observed disparity was estimated by the average difference between the expected (from the models) and the observed rates for blacks. Weighted least squares linear regression was used to analyze the trend in unexplained disparities. RESULTS: The black rates were generally lower than white rates for both screening tests. Observed mammogram rates for women ages 50-74 years declined slightly for whites with little trend for blacks. There was no statistical trend in the unexplained disparity. Colorectal cancer screening rates among men and women ages 50-75 years increased for both races. The unexplained disparity decreased over time for women (2.98 to -2.00; P=0.03) and nonsignificantly increased for men (5.1 to 8.6; P=0.62). Higher education, health insurance, and a usual source of care were significantly predictive of cancer screening between 2000 and 2010. CONCLUSIONS: Over the period we studied, screening rates in the United States increased for colorectal cancer but were stable or declining slightly for mammography. Our PB analysis provides evidence that the unexplained disparity in colorectal screening among women decreased between 2000 and 2010. It is important to continue to study trends over time to evaluate whether the Affordable Care Act will reduce the unexplained disparity for cancer screening in subgroups of the population by increasing insurance coverage and usual source of care among all Americans.

US trends in survival disparities among adolescents and young adults with non-Hodgkin lymphoma.

PURPOSE: Improvement in US survival rates among adolescents and young adults (AYAs, ages 15 through 39 years inclusive) diagnosed with non-Hodgkin lymphoma (NHL) has been documented over the last two decades. We examined national trends in survival disparities for AYAs with NHL by race/ethnicity and socioeconomic status (SES, county-level poverty) to further understand NHL and to begin monitoring health outcome disparities for this disease. METHODS: Surveillance Epidemiology and End Results data were used to calculate 5-year relative survival rates of AYAs diagnosed with NHL from 1992 to 2007 and followed through 2011. Absolute and relative disparities were computed using HD*Calc. Whether a significant linear trend was present was evaluated using Joinpoint. Analyses were replicated after excluding individuals with known HIV infection. RESULTS: The study sample included 9,573 total and 7,121 non-HIV cases of NHL. Five-year survival rates improved for all groups over time. Significant decreases were found in absolute disparities for race/ethnicity (non-HIV), in relative disparities for SES (total) and race/ethnicity (total and non-HIV) (all p < 0.05). Survival rates of non-Hispanic Blacks and Hispanics remained below than those of non-Hispanic Whites throughout the time period. CONCLUSION: Absolute and relative disparities in 5-year survival narrowed for AYAs with NHL over the time period. To continue to promote this trend, future research should investigate factors, particularly diagnostic delays and barriers to care, which continue to contribute to SES and racial/ethnic differences in survival. These factors may be particularly relevant to identify given the recent Affordable Care Act, which is designed to increase access to medical services, particularly for young adults.

Asian American women in California: a pooled analysis of predictors for breast and cervical cancer screening.

OBJECTIVES: We examined patterns of cervical and breast cancer screening among Asian American women in California and assessed their screening trends over time. METHODS: We pooled weighted data from 5 cycles of the California Health Interview Survey (2001, 2003, 2005, 2007, 2009) to examine breast and cervical cancer screening trends and predictors among 6 Asian nationalities. We calculated descriptive statistics, bivariate associations, multivariate logistic regressions, predictive margins, and 95% confidence intervals. RESULTS: Multivariate analyses indicated that Papanicolaou test rates did not significantly change over time (77.9% in 2001 vs 81.2% in 2007), but mammography receipt increased among Asian American women overall (75.6% in 2001 vs 81.8% in 2009). Length of time in the United States was associated with increased breast and cervical cancer screening among all nationalities. Sociodemographic and health care access factors had varied effects, with education and insurance coverage significantly predicting screening for certain groups. Overall, we observed striking variation by nationality. CONCLUSIONS: Our results underscore the need for intervention and policy efforts that are targeted to specific Asian nationalities, recent immigrants, and individuals without health care access to increase screening rates among Asian women in California.

Health disparities calculator: a methodologically rigorous tool for analyzing inequalities in population health.

Historically, researchers and policy planners have selected a single indicator to measure trends in social inequalities. A more rigorous approach is to review the literature and data, select appropriate inequality measures to address the research question, compute results from various indices, and graphically compare resulting trends. The Health Disparities Calculator (HD*Calc, version 1.2.4; National Cancer Institute, Bethesda, MD) computes results from different indices and graphically displays them, making an arduous task easier, more transparent, and more accessible.

The biracial Asian population in California: an examination of health profiles and chronic conditions.

OBJECTIVES: To examine health outcomes and chronic conditions for the biracial Asian population in California. We hypothesized that the biracial population will display intermediate (or an average of) outcomes in comparison to their monoracial counterparts. DESIGN: The study was cross-sectional. After adjusting for sociodemographic variables, multivariable regression models predicted health outcomes (ie, diabetes, heart disease, high blood pressure, disability status, BMI, and general health) and compared health outcomes among various (mono- and bi-) racial and ethnic groups. PARTICIPANTS: Data were collected from 238,897 adult (aged ≥ 18 years) respondents after merging iterations of the California Health Interview Survey (CHIS) administered in 2001, 2003, 2005, 2007, and 2009. RESULTS: Multivariate results revealed that Whites reported better health overall than biracial Asians and other monoracial groups. Biracial Asians displayed BMI ranges that were intermediate between their monoracial constituents. CONCLUSIONS: BMI is a more proximal health outcome and is more sensitive to lifestyles and behaviors. As a result, BMI may be a better indicator than chronic diseases in showing that biracial Asians have adopted health behaviors and practices that fall between their mono-racial counterparts. Future epidemiological research should examine the prevalence of more proximal health outcomes among biracial Asians and assess how it differs by developmental age.

[Implicit value judgments in the measurement of health inequalities]. / Juicios de valor implícitos en la medición de las desigualdades en salud.

CONTEXT: Quantitative estimates of the magnitude, direction, and rate of change of health inequalities play a crucial role in creating and assessing policies aimed at eliminating the disproportionate burden of disease in disadvantaged populations. It is generally assumed that the measurement of health inequalities is a value-neutral process, providing objective data that are then interpreted using normative judgments about whether a particular distribution of health is just, fair, or socially acceptable. METHODS: We discuss five examples in which normative judgments play a role in the measurement process itself, through either the selection of one measurement strategy to the exclusion of others or the selection of the type, significance, or weight assigned to the variables being measured. FINDINGS: Overall, we find that many commonly used measures of inequality are value laden and that the normative judgments implicit in these measures have important consequences for interpreting and responding to health inequalities. CONCLUSIONS: Because values implicit in the generation of health inequality measures may lead to radically different interpretations of the same underlying data,we urge researchers to explicitly consider and transparently discuss the normative judgments underlying their measures. We also urge policymakers and other consumers of health inequalities data to pay close attention to the measures on which they base their assessments of current and future health policies.

Independent state health surveys: responding to the need for local population health data.

CONTEXT: There is high demand for local-level population health data. A national system of state and local data collection would help improve both population health and health care delivery. The primary source of state-level population health data for adults is the Behavioral Risk Factor Surveillance System. However, many states need data on children and adolescents, racial and ethnic subpopulations, consistent estimates for localities, or more in-depth information on key topics than the Behavioral Risk Factor Surveillance System provides. Eleven state health surveys (SHSs) have emerged in an effort to address these gaps. DESIGN: Semistructured telephone interviews were conducted in 2009 with representatives of 9 SHSs. The interviews were recorded, and data were transcribed, organized, and analyzed according to the query structure. This analysis identified (1) the core elements of SHS that have been successful in meeting needs for local data and (2) the processes and strategies used by state officials in creating these surveys. RESULTS: Key findings include the following: (1) SHSs provide concrete data on local health issues that meet the needs of policy makers who wish to adopt evidence-based public health policies; (2) data from SHSs allow researchers to identify issues, apply for grants, and evaluate, assess, and track health indicators; (3) a "champion" is required to build the case for a survey and push through barriers to obtain funding and stakeholder buy-in; and (4) SHSs face challenges such as inconsistent funding and lack of uniform standards. CONCLUSION: Opportunities to support SHSs include (1) identifying sustained funding sources; (2) providing technical assistance and facilitating training to foster best practices, quality standards, and comparability across states; and (3) supporting an organization for SHS researchers to share resources, information, and experiences.

Effects of socioeconomic status and health care access on low levels of human papillomavirus vaccination among Spanish-speaking Hispanics in California.

Little is known about the effect of language preference, socioeconomic status, and health care access on human papillomavirus (HPV) vaccination. We examined these factors in Hispanic parents of daughters aged 11 to 17 years in California (n = 1090). Spanish-speaking parents were less likely to have their daughters vaccinated than were English speakers (odds ratio [OR] = 0.55; 95% confidence interval [CI] = 0.31, 0.98). Adding income and access to multivariate analyses made language nonsignificant (OR = 0.68; 95% CI = 0.35, 1.29). This confirms that health care use is associated with language via income and access. Low-income Hispanics, who lack access, need information about free HPV vaccination programs.

National evidence on the use of shared decision making in prostate-specific antigen screening.

PURPOSE: Recent clinical practice guidelines on prostate cancer screening using the prostate-specific antigen (PSA) test (PSA screening) have recommended that clinicians practice shared decision making-a process involving clinician-patient discussion of the pros, cons, and uncertainties of screening. We undertook a study to determine the prevalence of shared decision making in both PSA screening and nonscreening, as well as patient characteristics associated with shared decision making. METHODS: A nationally representative sample of 3,427 men aged 50 to 74 years participating in the 2010 National Health Interview Survey responded to questions on the extent of shared decision making (past physician-patient discussion of advantages, disadvantages, and scientific uncertainty associated with PSA screening), PSA screening intensity (tests in past 5 years), and sociodemographic and health-related characteristics. RESULTS: Nearly two-thirds (64.3%) of men reported no past physician-patient discussion of advantages, disadvantages, or scientific uncertainty (no shared decision making); 27.8% reported discussion of 1 to 2 elements only (partial shared decision making); 8.0% reported discussion of all 3 elements (full shared decision making). Nearly one-half (44.2%) reported no PSA screening, 27.8% reported low-intensity (less-than-annual) screening, and 25.1% reported high-intensity (nearly annual) screening. Absence of shared decision making was more prevalent in men who were not screened; 88% (95% CI, 86.2%-90.1%) of nonscreened men reported no shared decision making compared with 39% (95% CI, 35.0%-43.3%) of men undergoing high-intensity screening. Extent of shared decision making was associated with black race, Hispanic ethnicity, higher education, health insurance, and physician recommendation. Screening intensity was associated with older age, higher education, usual source of medical care, and physician recommendation, as well as with partial vs no or full shared decision making. CONCLUSIONS: Most US men report little shared decision making in PSA screening, and the lack of shared decision making is more prevalent in nonscreened than in screened men. Screening intensity is greatest with partial shared decision making, and different elements of shared decision making are associated with distinct patient characteristics. Shared decision making needs to be improved in decisions for and against PSA screening.

States' use of local population health data: comparing the Behavioral Risk Factor Surveillance System and independent state health surveys.

OBJECTIVES: To identify and compare key features of independent comprehensive state health surveys (SHS) with those of the Behavioral Risk Factor Surveillance System (BRFSS) for addressing the need for statewide and local population health data. METHODS: We developed inclusion criteria, systematically collected information about federal and SHS that met these criteria, and obtained supplemental information from SHS leaders. RESULTS: We identified comprehensive independent SHS in 11 states and BRFSS surveys in all 50 states. The independent SHS meet important statewide and local data needs, filling 3 key health data gaps in the BRFSS: lack of adequate data on special populations such as children, lack of data on specific localities, and limited depth and scope of health topics surveyed on key issues such as health insurance coverage. Unlike BRFSS, independent SHS have limited comparability with each other. CONCLUSIONS: The BRFSS and independent SHS each meet some key state and local data needs but result in data gaps and inefficient use of resources. Surveys could more effectively and efficiently meet future needs for comparable data to monitor health care reform and address health disparities if they were coordinated across states and at the national, state, and local levels.