Racial and ethnic health disparities are a pervasive public health problem. Emerging research finds similar health disparities among people with intellectual and developmental disabilities (IDD) compared to nondisabled adults. However, few studies have examined racial and ethnic health disparities among adults with IDD. Using national data, we examined racial and ethnic disparities in health status among adults with IDD, and investigated differences in health status between adults with IDD and nondisabled adults within each racial and ethnic group. We found that Latino and Black adults with IDD had worse health outcomes compared to White adults with IDD, and Latino and Black adults with IDD had worse health outcomes than nondisabled adults from the same racial and ethnic group.
In this article, we examine racial and ethnic disparities in health status among adults with intellectual and developmental disabilities (IDD) in order to understand whether there are similar disparities among this population as in the general population. It has been long understood that racial and ethnic minorities in the United States, particularly Blacks, Latinos, and Native Americans, fare worse on health status measures compared to White Americans. Emerging research indicates that there are health disparities between people with IDD compared to the general population. However, little is known about the extent of racial and ethnic health disparities among people with IDD.
Healthy People 2020 defines health disparities as differences in health that arise due to social or economic disadvantage and adversely impact groups of people who have experienced marginalization, discrimination and other disadvantages due to their racial, ethnic, religious, sexual orientation, socioeconomic, gender or disability status (U.S. Department of Health and Human Services, 2008). Health disparities are believed to result primarily from social determinants such as income (Adler & Stewart, 2010; Lynch & Kaplan, 2000), education (Beckles & Truman, 2013; Egerter, Braveman, Sadegh-Nobari, Grossman-Kahn, & Decker, 2011), environmental factors such as racial segregation (Pruitt et al. 2015; Rahman & Foster, 2015), and discrimination (Gee, 2002). According to the Institute of Medicine (Smedley, Stith, & Nelson, 2002), these disparities are influenced by determinants at the level of the patient-clinician encounter, as well as the health system as a whole.
Decades of research indicate that racial and ethnic disparities in health status are a pervasive, persistent public health problem (Smedley et al., 2002). More recent research continues to find evidence of these disparities. For example, Black and Latino adults are more likely to report fair or poor health compared to White adults (Subramanian, Acevedo-Garcia, & Osypuk, 2005; Zack, 2013). Obesity, a risk factor for cardiovascular disease, diabetes, and cancer (Kyrou, & Weickert, 2000-2014; Lavie, Milani, & Ventura, 2009; Mokdad et al., 2003), is more prevalent in Blacks and Latinos compared to Whites (Baskin, Ard, Franklin, & Allison, 2005; Dubay & Lebrun, 2012; Pan et al., 2009). Both Blacks and Latinos have higher rates of diabetes than Whites (Centers for Disease Control and Prevention [CDC], 2004, 2013; McBean, Li, Gilbertson, & Collins, 2004). Black women have greater odds of late stage breast cancer diagnosis and mortality after diagnosis compared to White women (Markossian & Hines, 2012). These findings represent a few examples of the overwhelming evidence of racial and ethnic disparities in health status outcomes in the general adult population.
Recent research shows that adults with IDD also experience health disparities compared to those without disabilities. In general, people with disabilities are among the poorest members of U.S. society, and they are disadvantaged on all measures of socioeconomic status compared to the general population (Rehabilitation Research and Training Center on Disability Demographics and Statistics, 2005). Just as with racial disparities, these disparities can be conceptualized as arising from social determinants (Graham, 2005). As such, new research on health disparities between people with IDD and the general population is beginning to emerge. For instance, Rimmer & Hsieh (2012) reported that 51% of adults with IDD had poor or fair health and more than one third of adults with IDD did not have good physical or mental health. This finding is in stark contrast to the 12% of the general U.S. adult population that reports poor or fair health (Blackwell, Lucas, & Clark, 2014). Other studies show similar disparities in self-reported health status (Emerson, 2005; Havercamp, Scandlin, & Roth, 2004). Just as obesity is a growing public health problem in the general population, it is emerging as a critical disparity issue for people with IDD. Rimmer and Wang (2005) reported that adults with IDD had increased odds of overweight (by 25%) and obesity (by 82%), compared to people without disabilities. Other researchers have found similarly elevated rates of obesity among adults with IDD (Anderson et al., 2013; Hsieh, Rimmer, & Heller, 2014; Stancliffe et al., 2011; Yamaki, 2005). In one exception to this trend, a retrospective study found that proportions of obesity between people with and without IDD were similar (Moran et al., 2005).
People with IDD are also disproportionately affected by other chronic conditions. Balogh, Lake, Lin, Wilton, and Lunsky (2015) found markedly higher rates of diabetes among adults with IDD than adults without IDD. In a North Carolina study, adults with IDD had higher rates of arthritis and diabetes than people without disabilities (Havercamp et al., 2004). Reichard, Stolzle, and Fox (2011) similarly found people with IDD had higher rates of asthma, diabetes, and cardiovascular disease compared to people without disabilities. Henderson et al. (2008) reported a higher prevalence of obesity, diabetes, and other coronary heart disease risk factors among adults with IDD compared to those without IDD. Draheim (2006) reported that adults with IDD had a greater prevalence of cardiovascular disease morbidity and mortality than the general population.
For people with IDD, social environmental variables are likely to be important determinants of health disparities just as they are in the general population. Researchers who examined health among people with IDD using the National Core Indicators found that adults with IDD who lived in more supervised environments had lower rates of obesity and greater use of physical activity compared to adults with IDD who live with their families or independently (Hsieh, Heller, Bershadsky, & Taub, 2015).
An unexplored question is the relationship of socioeconomic status to these outcomes among adults with IDD. Some researchers hypothesize that socioeconomic status is the most important cause of health disparities (Link & Phelan, 1995; Phelan, Link, & Tehranifar, 2010). These researchers theorize that socioeconomic status is related to flexible available resources, which are key in accessing important health information, services, and treatments. However, the decades of racial and ethnic disparities research reviewed and synthesized by the Institute of Medicine (Smedley et al., 2002) suggest that racial and ethnic disparities are robust and exist independently of income and other indicators of socioeconomic status. In a more recent review of research on race, ethnicity, and health, Williams and Mohammed (2013) cite numerous studies that converge: minority racial and ethnic status independently predicts poor health. These researchers present evidence of multiple pathways including discrimination, institutional racism, and cultural racism, through which racial and ethnic status leads to poor health.
The focus of this article is on the intersection of race, ethnicity, and IDD as it relates to health status. The research on this intersection is limited, but suggests that there are similar racial and ethnic disparities as those in the general population. In one study, median age of death was lower among Blacks with Down syndrome compared to Whites (Yang, Rasmussen, & Friedman, 2002). Among New York City adults with IDD, Blacks and Latinos had a higher a prevalence of obesity than White adults (Sohler, Lubetkin, Levy, Soghomonian, & Rimmerman, 2009). In another study, Black adults with IDD were much more likely to be obese than White adults (Hsieh et al., 2015).
This study aims to contribute to the sparse literature on the intersection of race, ethnicity, and IDD as they relate to health among adults in the United States. We analyzed data from the National Health Interview Survey (NHIS; National Center for Health Statistics, 2000-2010) merged with the Medical Expenditure Panel Survey (MEPS; Agency for Healthcare Research and Quality, 2002-2011) to answer three questions: (1) Are there racial and ethnic disparities in health status among adults with IDD after adjusting for income and other sociodemographic factors? (2) Are there differences in health status among Latinos with IDD compared to Latinos without IDD? and (3) Are there differences in health status among Blacks with IDD compared to Blacks without IDD? We specifically examined four health outcomes: (a) self-reported health and mental health status, (b) diabetes, and (c) obesity. We hypothesized that racial and ethnic disparities on these outcomes within the IDD population will mirror those found in the general population, and that individuals with IDD within each racial and ethnic group would have worse health outcomes than those without IDD.
Data and Sample
The data used in this study were drawn from linked 2002-2011 MEPS and 2000-2010 NHIS datasets and included 1,131 adults with IDD. The MEPS is a national survey on medical conditions, health status, health care use and expenditures. The NHIS is an annual health survey that collects cross-sectional data from a nationally representative sample of civilian noninstitutionalized population in the United States. The MEPS uses the sampling frame from the previous years' NHIS, typically representing about 3/8 of responding NHIS households (Ezzati-Rice, Rohde, & Greenblatt, 2008). Both NHIS and MEPS only collect information from noninstitutionalized civilian households.
Data from MEPS and NHIS were linked to create the analytical dataset because we examined variables from both surveys. Participants with IDD were identified through a 2-stage process. First, NHIS variables in the Health Status and Limitation of Activity section of the Family Core were used to identify participants with any activity limitations or who needed assistance with daily living activities (eating, bathing, dressing, getting around inside the home) or instrumental activities of daily living (household chores, doing necessary business, shopping, running errands). If participants responded positively, they were provided with flash cards that listed conditions and asked to identify which conditions were responsible for these activity limitations. If respondents chose “intellectual disability” (year 2011) /“mental retardation” (years 2001-2010) or “other developmental problem (e.g., cerebral palsy)” as the primary cause for their limitation(s), the participant would be identified as having IDD. It must be noted that even though any adult (ages 18 or over) members present at the time of the NHIS Family Core interview may participate and respond to survey questions on their own behalf (self-report), it is generally true for both NHIS and MEPS that one knowledgeable adult answers survey questions on behalf of all household members (proxy-report). It is most likely that the person with IDD is not the reporter for the household, and that a family caregiver reported on measures about the adult with IDD. A total of 1,094 adults (ages 18–65 years) with IDD were identified. Second, the MEPS Medical Conditions files (HC-069 to HC-146) were used. If the individual had at least one recorded medical condition with the ICD codes corresponding to “intellectual disability” or “developmental disabilities,” the individual was identified as having IDD. A total of 181 individuals were flagged using this approach, with 90 overlapping with the first stage sample. The final IDD unweighted sample size was 1,131, with a weighted sample size of 972,099.
Age, sex and race/ethnicity are self-reported. Race/ethnicity was structured in four categories: (1) Non-Latino White, (2) Non-Latino Black, (3) Latino, and (4) Non-Latino other. Non-Latino White was the reference group for race/ethnicity in the multivariate analysis. A binary variable of family income was created of total household income < 125% or ≥ 125% federal poverty level. A binary Metropolitan Statistical Area (MSA) status was used to indicate whether the household was urban or rural. Marital status was also binary (“married” vs. “not married”). Education was categorized as “less than high school” versus “graduated high school.” Insurance status was coded as “insured all year” or “not insured all year.”
We examined four binary outcome measures: (1) perceived health status (fair/poor or good/very good/excellent), (2) perceived mental health status (fair/poor or good/very good/excellent, (3) obesity, and (4) diabetes. Obesity was defined as reporting a Body Mass Index (BMI) equal or greater than 30 kg/m2 in the MEPS (“yes/no”). Diabetes status indicates whether an individual has ever been diagnosed with the condition (“yes/no”).
We used survey weights and variance adjustment parameters provided in the MEPS to accurately address survey design. All analyses were conducted using Stata 13 and appropriate survey commands that address clustering and stratification and correct for standard errors. We used chi-square tests and t tests to describe the extent of unadjusted racial and ethnic differences in demographic characteristics and health disparities (Table 1). We then conducted multivariate logistic regression models to estimate odds ratios and 95% confidence intervals for demographic characteristics and each dichotomous health status outcome variable (Table 2). Additionally, we ran separate logistic regression models in which having IDD was a predictor of health outcomes within Latino and Black groups. These latter models adjusted for income, education, age, sex, insurance status, and urban status (Table 3).
In Table 1 we shows bivariate, unadjusted contrasts of demographic and outcome variables by race and ethnicity within the samples with and without IDD. (We note that the population without IDD is representative of the general population.) Consistent with racial and ethnic differences in the general population, Black and Latino adults with IDD had less education and income and were more likely to live in urban environments than White adults with IDD. Latinos tended to be younger than both Blacks and Whites in both the IDD group and the general population. Both Latino and Black adults were less likely to be insured all year than Whites in the general population sample. Black and Latino adults with IDD were less likely to be insured all year and were younger than White adults with IDD, but the difference was borderline significant.
With respect to the bivariate comparisons of health status outcomes, both Black and Latino adults with IDD were more likely to be in fair or poor health compared to White adults with IDD. This finding is similar to the pattern found in the general population sample, however, the percentages of Blacks and Latino adults with IDD with fair or poor health (40.6% and 44.9%, respectively) were considerably higher than those in the general population. A similar pattern was evident among adults with IDD with regard to mental health status. Black and Latino adults with IDD were significantly more likely to have fair or poor mental health than White adults with IDD and these rates were relatively high (37.8% and 42.3% respectively) compared to the general population. In the general population sample, Blacks and Latino adults had significantly higher rates of obesity and diabetes than White adults, and the rates for Black adults were higher than for Latino adults. In the IDD population, there were similar differences, which were not statistically significant; however these findings may well be clinically meaningful. For example, about 40% of Black and Latino adults with IDD had BMIs above 30% compared to about 33% of Whites with IDD.
In Table 2 we present the multivariate regression results that address Research Question 1, whether there are racial and ethnic disparities in health status outcomes among adults with IDD after adjusting for socio-demographic characteristics. Compared to white adults with IDD, Black adults with IDD were 1.72 times more likely to be in fair or poor health, and 1.64 times more likely to be in fair or poor mental health, even after controlling for model covariates (See Table 2). A similar pattern of worse health and mental health was found for Latino adults with IDD compared to White adults with IDD. Latino adults were 2.48 times more likely to be in fair or poor health, 2.2 times more likely to be in fair or poor mental health, and almost 3 times more likely to have diabetes. Latino adults with IDD were 1.72 times more likely to be obese than White adults with IDD, which was marginally significant (p < .10). With respect to other social determinants of health, those with low family income were more likely to be in fair or poor health and mental health.
Research Questions 2 and 3 addressed whether Latino and Black adults with IDD had worse health outcomes within their own racial or ethnic group, in contrast to adults without IDD. After adjusting for socio-demographic variables, Latino adults with IDD were significantly more likely to be in fair and poor health, in fair or poor mental health, obese, and have diabetes than Latino adults without IDD (see Table 3). Compared to Black adults without IDD, Black adults with IDD were significantly more likely to be in fair or poor health and fair or poor mental health. There no significant differences between Black adults with and without IDD in terms of obesity or diabetes.
To the best of our knowledge, this is the first nationally representative investigation of racial and ethnic disparities in the health status of adults with IDD. We found evidence of marked racial and ethnic disparities within the population with IDD. Latino and non-Latino Black adults were more likely than White adults with IDD to be in fair or poor health and fair or poor mental health. These results were robust, even after controlling for a host of possible confounders, including income, education, urban location, marital status, sex, age, and insurance status. After controlling for confounders, Latino adults with IDD were also more likely to be obese and have diabetes than their White counterparts. Disparities in obesity or diabetes were not found between Black and White adults with IDD.
Latino and non-Latino Black adults with IDD were more likely to be in poor health than their counterparts without IDD. After controlling for all model covariates, Latino adults with IDD were significantly more likely to report being in fair or poor health and mental health, be obese, and have diabetes. A similar trend was observed among non-Latino Black adults: Those with IDD were more likely to be in fair or poor health and to be in fair or poor mental health. However, we did not find significant differences in obesity and diabetes within the Black sample. One potential explanation for this is that Black adults had higher rates of diabetes and obesity than Latino adults in the general population. Yet the rates for Latinos and Blacks with IDD were not significantly different from each other. Therefore, there was a greater contrast among Latinos between those with IDD and those without.
Our sample, consistent with the NHIS and MEPS sampling procedures, excluded institutionalized adults. As such, this sample of adults with IDD were living in the community and most likely to be living with family. Hsieh and her colleagues (2014) found that adults with IDD who were living in the community had higher rates of obesity than those living in institutional settings.
There is no research that we could find on social determinants of health, or on health behaviors and health care access among adults with IDD who live in the community. Consequently, this is a critical area for future research. Adults with IDD from racial and ethnic minority backgrounds who live with their families face the same risk factors as racial and ethnic minority adults in the general population, but may be more vulnerable to these risk factors.
Before discussing the study's implications, we must consider its limitations. First, this analysis relied on data from the MEPS and NHIS, which are telephone surveys. Data are obtained from self-report. Self-report data are always subject to error and the extent of these errors is not known. However, there is no evidence that there would be racial or ethnic differences in the reporting of these conditions. Further, past research indicates that adults with IDD are relatively accurate reporters of health events, although they are less accurate about reporting the timing of events (Son, Parish, Swaine, & Luken, 2013). Because adults in these surveys were being asked to report their health status at the time of the survey, and not to recall health events in the past, it is likely that these findings are fairly accurate. Second, the data here were aggregated over the period of 2000-2011. It is possible that changes in the health system influenced outcomes. However, although the insurance coverage of children increased significantly during the early part of this period, largely due to Medicaid and State Children's Health Insurance Program expansions (Dubay & Kenney, 2009), similar expansions have not occurred in the Medicaid adult population. Third, there is considerable state variability in health care access, service utilization, and quality for people with disabilities (Parish, Rose, Yoo, & Swaine, 2012; Parish, Thomas, Rose, Kilany, & Shattuck, 2012). We are unable to analyze state-level determinants of health, because NHIS and MEPS data are not representative at the state level. Finally, we acknowledge that this sample, although large by standards in the intellectual disabilities field, is modest from the standpoint of deriving nationally representative population estimates. Last, the data from NHIS and MEPS likely substantially undercounts the prevalence of IDD. We identified 1,131 adults with IDD which would indicate a prevalence of 6/1000 population. A meta-analysis of population-based studies found a prevalence of intellectual disability as 10.37/1,000 population and this rate does not include other developmental disabilities (Maulik, Mascarenhas, Mathers, Dua, & Saxena, 2011).
Despite these limitations, this study has notable strengths. First, it relies on a nationally representative probability sample of adults with IDD living in noninstitutional settings. This sample is thus not limited by participation in the disability service system and is not limited by the selection bias that emerges with samples that are drawn from registries or recruited from service providers. Second, the measures of health and mental health, as well as obesity and diabetes, are robust and are widely used in numerous American health interview surveys.
These findings suggest that Black and Latino adults with IDD have markedly worse health than both their White counterparts with IDD and nondisabled adults within their racial and ethnic groups. These disparities persist after controlling for a host of demographic characteristics that are potential confounders.
The worse health of Black and Latino adults with IDD in contrast to White adults indicates that racial and ethnic disparities are significant problems, even within the population with IDD. It is more than a decade since the Institute of Medicine (Smedley et al., 2002) published its landmark report, Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. This report aggregated and synthesized decades of evidence and found that racial and ethnic disparities in health and health care persist and constitute a major, costly public health problem that the United States must eliminate. Despite decades of evidence and a number of national initiatives to address this problem, these disparities represent an enduring public health crisis. To the extent that serious efforts are being made to solve this problem, our study finds no evidence that Black and Latino people with IDD are benefiting.
The study's findings indicate that within the population subset of Black and Latino adults, adults with IDD similarly have markedly worse health. The Surgeon General's Closing the Gap report (U.S. Public Health Service, 2002), also published more than a decade ago, argued for aggressive measures to improve the health and well-being of adults with intellectual disabilities. Our findings indicate the troubling gaps in health persist for adults with IDD.
Although the Surgeon General's report did not address racial and ethnic disparities, our findings suggest that Black and Latino adults with IDD incur a sort of double-jeopardy: the intersection of their status as disabled and their racial and ethnic identities contribute to severely compromised health outcomes. Further research is needed to understand the costs of the impact of these social determinants of poor health. It is clear that the existing federal initiatives to address racial- and disability-based health disparities have fallen far short of their targets. Innovative and aggressive new measures are urgently needed to address these disparities.
Using data from linked, aggregated 2000-2011 NHIS and the MEPS, this study found that Latino and non-Latino Black adults with IDD had markedly worse health than both White adults with IDD and nondisabled Latino and Black adults, respectively. Worse outcomes were found on all four measures for Latino adults with IDD: health, mental health, obesity, and diabetes, and for health and mental health for Black adults with IDD. This study offers new evidence, using nationally representative data of important racial and ethnic disparities in the health of individuals with IDD. Further, this study showed disability-based health disparities of an alarming magnitude. Assertive policy measures are necessary to improve the health and well-being of Latino and Black adults with IDD.
The content of this article was developed as part of the UIC Rehabilitation Research and Training Center on Developmental Disabilities and Health under a grant from the United States Department of Health and Human Services, Administration for Community Living (ACL), National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR) Grant # 90RT5020-01-00. However the content does not necessarily represent the policy of the Department of Health and Human Services (DHHS), and you should not assume endorsement by the Federal Government.