Abstract

Research has documented disparities in health care and access for people with intellectual and developmental disabilities (IDD) and people in racial and ethnic minority groups. Though both populations are underserved, the additive impact of being both a member of a racial/ethnic minority and having IDD is largely unknown. This study uses data from a nationally representative survey to explore health service utilization among adults with IDD belonging to minority racial/ethnic groups compared to adults with IDD who are White. The results of this study indicated that racial/ethnic minority groups are disadvantaged in several essential areas of health care utilization and that Hispanic Americans are particularly underserved. Additional research is needed to identify and address the factors driving this difference.

Background

The overall health of Americans has improved over the past few decades, but all Americans have not shared equally in these improvements (Agency for Healthcare Research and Quality, 2009). Health disparities can be defined as systematic, socially produced, and important differences in health between groups that are not only unnecessary and avoidable but, in addition, unjust and unfair (Whitehead, 1992). In 1985, the U.S. Department of Health and Human Services (HHS) released a landmark report documenting the existence of health disparities for minorities in the United States. It called such disparities, “an affront both to our ideals and to the ongoing genius of American medicine” (Office of Minority Health, 2011). Although the nation has seen significant improvements in health and healthcare since disparities first drew national attention, health and healthcare disparities continue to exist and, in some cases, grow for racial and ethnic minorities, the poor, and other at-risk populations including people with disabilities.

Despite the Americans With Disabilities Act (ADA) requiring health care providers to give patients full and equal access, people with disabilities experience worse health and poorer access to healthcare than their counterparts without disabilities. People with disabilities are vulnerable to high rates of chronic conditions including diabetes, high blood pressure, arthritis, chronic pain, and heart disease (Havercamp, Scandlin, & Roth, 2004; Pharr & Moonie, 2012). In addition, this population is vulnerable to disability-related health conditions such as pressure ulcers, overuse injuries, dysphagia, and mental health problems (Kinne, Patrick, & Doyle, 2004; Mitra, Wilber, Allen, & Walker, 2005). This phenomenon has been described as producing a “thinner margin of health” for people with disabilities (DeJong, Batavia, & Griss, 1989). Despite this thinner margin, people with disabilities experience decreased access to preventative health care services such as cancer screenings, visits with a dentist, and cholesterol checks (Armour, Swanson, Waldman, & Perlman, 2008; Havercamp et al., 2004; Reichard, Stolzle, & Fox, 2011).

Among people with disabilities, people with intellectual and developmental disabilities (IDD) are perhaps the most underserved. In 2002 the Surgeon General drew attention to this problem with a report titled Closing the Gap: A National Blueprint to Improve the Health of Persons with Mental Retardation. This report addressed the disparities in health care that exist between the general and the IDD population, and also cited a lack of surveillance data for this population (Public Health Services, 2002). Recent research has looked at many different aspects of health utilization, and with few exceptions found that relative to the general population, those with IDD have a limited access to needed healthcare services.

Some studies have reported low rates of primary health care visits despite the fact that many individuals with IDD are covered by Medicaid (Hall, Wood, Hou, & Zhang, 2007). Disparities are also seen in specialized mental health care and diabetes care (McCarthy & Boyd, 2002; Shireman, Reichard, Nazir, Backes, & Greiner, 2010). An Ohio study used electronic health records to examine health service utilization in the IDD population compared to the general population over a 3-year period. The study found that although those with IDD were more likely to have seen a primary care physician, they were significantly less likely than their counterparts to have had a visit in each of these specialties: gastroenterology, pulmonary, cardiology, OB/GYN, general surgery, and pain management. This is particularly troubling in light of the fact that these same participants reported higher levels of constipation, obesity, epilepsy, hyperlipidemia, and osteoporosis (Tyler, Schramm, Karafa, Tang, & Jain, 2011). Additionally, studies have shown that people with IDD have poorer dental health with higher levels of untreated dental problems (Haveman et al., 2010), a greater percentage of untreated but treatable simple medical conditions, and lack of prevention care (e.g., cancer screenings) than that of the general population (Lennox & Kerr, 1997).

People with IDD have similar risks for chronic health conditions such as cancer, diabetes, cardiovascular disease, lung disease, and obesity-related diseases as reported for the general population (Patja, Eero, & Iivanainen, 2001; Shireman et al., 2010). In addition, people with IDD may be at greater risk for weight-related problems, epilepsy, mental illness, and overall poor health (Bhaumik, Watson, Thorp, Tyrer, & McGrother, 2008; Cooper, Smiley, Morrison, Williamson, & Allan, 2007; Havercamp et al., 2004; McGrother et al., 2006; Yamaki, 2005).

Disparities in health and access to care have been well documented in racial and ethnic minority groups. Even when income, health insurance, and access to care are accounted for, disparities remain. Low performance on a range of health indicators—such as infant mortality, life expectancy, prevalence of chronic disease, and insurance coverage—reveal differences between racial and ethnic minority populations and their White counterparts. The Medical Expenditure Panel Survey, a nationally representative health survey, found that from 1977 to 1996, significant and lasting disparities in routine health care were evident between White Americans and Black or Hispanic Americans (Weinick, Zuvekas, & Cohen, 2000). A similar study looked at data from 1996 to 2005 and found that disparities between White-Black Americans remained constant and that White-Hispanic disparities became more pronounced over time (Cook, McGuire, & Zuvekas, 2009). Racial/ethnic disparities have also been found in cardiac care, cancer treatment, mental health treatment, and dental care (Edelstein, 2002; Mayberry, Mili, & Ofili, 2000; McGuire & Miranda, 2008). Though some studies have reported health care disparities may be shrinking, significant and troubling disparities in health care services based upon racial/ethnic factors persist (Dubay & Lebrun, 2012; Mayberry et al., 2000).

Recent research has explored the question of whether people in underserved racial or ethnic groups who also have a disability are at greater disadvantage than those in either group alone (Drum, McClain, Horner-Johnson, & Taitano, 2011). This research promises to advance our understanding of health disparities at the intersection of disability and race. However, the health of people with IDD has not been addressed and very little is known about the potential additive effects of having both IDD and belonging to an ethnic or racial minority group. One study of mortality associated with Down syndrome found that although life expectancy in this population has steadily increased, median age at death was significantly lower among ethnic and racial minorities (Flores, 2010). Recently, a meta-analysis was conducted to review research on ethnic factors and mental health care utilization of people with IDD, but only two U.S. studies were included in the analysis and neither study found any difference in care utilization based upon ethnicity (Durà-Vilà & Hodes, 2012). However, a recent study conducted in a southeastern U.S. state found that among women with IDD, Black women were significantly less likely to have received a mammogram in the past year or over the span of several years when compared to White women with IDD (Parish, Swaine, Son, & Luken, 2013). Analysis of large-scale survey data on children with developmental disabilities found that Black and Hispanic children were less likely than White children to receive quality health care according to their parents (Magana, Parish, Rose, Timberlake, & Swaine, 2012).

This study explored health service utilization among adults with IDD in minority racial/ethnic groups compared to adults with IDD who are White. Previous research has indicated that healthcare utilization is affected by type of residence among adults with IDD, specifically that adults living in more restrictive placements are more likely to receive a variety of health related services (Bershadsky & Kane, 2010; Bershadsky et al., 2012). For this reason, we include covariates such as level of IDD and place of residence in our analyses. We explore factors predicting six different routine health care services and predict that racial and ethnic minorities with IDD have a compounded risk of underutilization of healthcare services beyond the risk associated with IDD.

Method

Measures

The National Core Indicators (NCI) is a quality management protocol for the developmental disability service delivery system directed by the Human Services Research Institute and the National Association of State Directors of Developmental Disabilities Service. This protocol standardizes the instrumentation, methodology, and training for data collectors, which allows states to set standards of care and monitor progress toward goals to improve the quality of care. The NCI Consumer Survey was used in this study. The Consumer Survey gathers data from three separate sources. First, case managers obtain quantitative items that can be accessed through case files, such as demographic information and medical records. Second, adults with disabilities are asked questions in a face-to-face interview where only answers from that individual are accepted. Finally, the third section of the survey can be completed by an interview with the individual with IDD or, if the individual is unable or unwilling to continue, the third section of the survey can be completed by an interview with a family member or someone who knows him/her well. This approach of using records, the individual in question and a third party responder is supported by research done by Lunsky, Emery, and Benson (2002) who found that the most accurate reports of health and health care utilization were obtained when multiple sources were consulted. The NCI looks at a wide variety of services, but for this study the focus will be upon health and healthcare questions.

Participants

This study uses a combined dataset from the 2009–2010 and 2010–2011 NCI cycles. Participants were 20,395 adults with IDD randomly selected from the registry of all individuals receiving state IDD services. Because a new sample is randomly selected every year, it is possible that an individual may have been surveyed twice, once in each year included in this study. Per NCI protocol, randomly selected individuals were invited to participate as part of the IDD services quality management process. This sample includes individuals from 25 states: Alabama, Arizona, Arkansas, California, District of Columbia, Florida, Georgia, Hawaii, Illinois, Kentucky, Louisiana, Maine, Massachusetts, Missouri, New Hampshire, New Jersey, New Mexico, New York, North Carolina, Ohio, Oklahoma, Pennsylvania, South Dakota, Texas, and Washington. A minimum of 400 adults were sampled from each state each year. See Table 1 for demographic information on the sample.

Table 1

Demographic Information

Demographic Information
Demographic Information

The vast majority (87%) of the sample were diagnosed with Intellectual Disability (ID). Other diagnoses included mental illness, Down syndrome, and Autism Spectrum Disorders. The NCI asks two questions regarding race and ethnicity. First, participants are asked if they identify as Hispanic/Latino/Spanish. Secondly, they are asked to self-identify race. For this article participants were grouped into four categories, White, Black, Hispanic, and Other. Participants who endorsed being both Hispanic and a specific race such as Black were placed in the Hispanic group. Analyses were conducted on only the first three groups; White, Black, and Hispanic. The Other group consisted of 4.4% of the sample and contained six different categories. Given this heterogeneity, it would not be feasible to draw any real world conclusions from analyses of this group so it was excluded. See Table 2 for a breakdown of demographic information by race/ethnicity.

Table 2

Demographic Variables by Race/Ethnicity

Demographic Variables by Race/Ethnicity
Demographic Variables by Race/Ethnicity

Analysis

Differences in health utilization by race were tested using six multivariate logistic regression models. Each model represented a routine health service such as cancer screenings, annual doctor visit, dental visit, and flu shot. In each analysis, having received the health service was used as the reference option and the model shows odds of not having received the appropriate services. Similarly, White was chosen as the reference racial group and odds of not receiving service is reported for each racial group compared to White. Covariates included age as a continuous variable, level of ID (ID at the upper end of the spectrum as reference), and place of residence (living with family as reference). Living with family was chosen as the reference group because this was the most frequently reported place of residence. Age, level of ID, and place of residence were entered into the regression model in the first step in order to control for the variance associated with those factors. Race/ethnicity was entered last in order to assess how this variable impacted health services above and beyond demographic variables.

Results

Results of all analyses are reported in Table 3. Age was a significant covariate for most analyses with older adults generally being more likely to receive health care services than their younger counterparts. Level of ID was a significant predictor of women's preventative cancer screening with ID in the lower range of the spectrum being less likely to have received those screenings. For example, women with ID in the lower range of the spectrum had an odds ratio of 2.0 for having received a mammogram in the past 2 years, indicating they were about two times less likely to receive a mammogram compared to women with ID at the upper end of the spectrum. In terms of general preventive care such as flu shots and routine exams, ID in the lower range of the spectrum were associated with increased likelihood of receiving services. Place of residence was a strong predictor of every health care outcome. Adults living with family had the lowest likelihood of receiving adequate health care whereas adults living in more restrictive settings such as group homes and institutions were more likely to have received services.

Table 3

Logistic Regression: Factors Predicting Lack of Health Care Services

Logistic Regression: Factors Predicting Lack of Health Care Services
Logistic Regression: Factors Predicting Lack of Health Care Services

After controlling for covariates including age, level of ID, and place of residence, we found that membership in a minority group was associated with decreased odds of having a recent dentist visit, visit with a general practitioner, and a flu shot. A different pattern was seen in preventive care screenings among racial/ethnic groups. In terms of cancer screening, people with IDD who identified as Black were more likely than either of the other groups to have received cancer screenings. However, Hispanic participants were significantly less likely than White participants to have received cancer screenings. See Figure 1 for a visual breakdown of what percentage of participants had received needed services by racial/ethnic group.

Figure 1

Percentage of sample not having received services by race/ethnicity status.

Figure 1

Percentage of sample not having received services by race/ethnicity status.

Discussion

The main goal of this project was to explore differences in access to healthcare by racial/ethnic groups in adults with IDD. Surprisingly, on cancer screening rates, few differences were found between White and Black participants. This is consistent with research in the general population (Henley, King, German, Richardson, & Plescia, 2010) but is contrary to previous research in the field of IDD which found that when compared to White women, Black women were less likely to receive a mammogram (Parish et al., 2013). The Parish et al. (2013) study was conducted on a smaller sample in one southern U.S. state, and it is possible that access to these screenings in North Carolina differs from other parts of the U.S. In contrast, Hispanic participants were significantly less likely to receive cancer screenings. Additional research is needed to understand the role of race in cancer screening rates among adults with IDD.

Racial/ethnic minority groups were significantly less likely to receive routine care services than their White counterparts. What is especially troubling is that these differences exist in a population of adults with IDD that is already underserved in terms of health care (e.g., Hall et al., 2007). These results confirmed our hypothesis that racial and ethnic minorities have a compounded risk of underutilization in terms of general care services.

It is unclear what may be driving these differences. Research suggests that health disparities for people with IDD may occur for a variety of reasons including limitations in heath care plans, a lack of education in the medical field about caring for patients with IDD, communication barriers, and difficulty with assessments. The vast majority of individuals with IDD are covered by publicly funded health insurance and many doctors' offices may not accept Medicaid or may limit the number of patients with Medicaid that they will accept. A survey given to general practitioners revealed that 90% of practitioners found it more difficult to provide quality care to patients with an intellectual disability and 16% of general practitioners stated that they would prefer not to treat people with intellectual disabilities (Lennox, Diggens, & Ugoni, 2000).

Health disparities in minority groups are also a complicated issue with no clear explanation or easy fix. Factors such as socioeconomic status (SES), language barriers, and availability of health insurance certainly impact utilization of healthcare. However, even after adjusting for factors such as SES and insurance, disparities still remain (Cook et al., 2009). Discrimination may play a role in health and health care; research has indicated that when people have experienced chronic discrimination or health-related discrimination, they are less likely to access some preventative services (Benjamins, 2012). Other factors which may impact racial/ethnic disparities are availability of culturally sensitive providers, job-related barriers, residential patterns, and passive prejudice.

Finding that people with IDD who belong to a minority racial or ethnic group experience less access to health care services is particularly troubling. The data used for this project comes from a served sample so all participants should theoretically have equal access to health insurance and transportation to medical appointments. It may be that stigmatization or passive prejudices are contributing to the results found in this study. People with IDD in racial/ethnic minority groups may be at increased risk for prejudice or discrimination due to their membership in two often marginalized groups. Understanding what may be driving these differences is an important topic for future research.

Generally, adults with ID in the lower range of the spectrum were more likely to receive routine healthcare services. An exception to this trend was noted in women and cancer screenings where the probability of receiving cancer screenings decreased as severity of ID increased. Low rates of cancer screenings are troubling as most studies show that adults with IDD are in no way protected from cancer and early detection is essential for effective cancer treatment (Etzioni et al., 2003) Across racial groups, people living at home with family were the least likely to receive health care services. This is particularly relevant as minority groups were more likely to live with family members than their White counterparts. There may be a variety of barriers to health care unique to individuals living with parents such as a lack of information about proper health care on the part of individuals and their families, difficulty with transportation, or lack of insurance. Additional research is needed to identify and address these barriers.

Limitations and Conclusions

Due to the retrospective nature of the study, we are not able to draw causal conclusions from our results. It should also be noted that report of health care services was obtained through record review and was not verified for accuracy, though there is no reason to believe that accuracy would vary in any specific pattern. Additionally, the NCI data is collected from a population of adults receiving Developmental Disability (DD) services. This study cannot generalize to the many adults in the community who are not receiving services; caution should be used in generalizing the results as it is likely that people who are not served through the DD system experience even worse health care access.

Despite these limitations, we believe that this study contributes important information regarding factors that influence health care utilization in adults with IDD. This study adds to the growing body of literature suggesting that factors such as level of IDD and place of residence impact whether or not people with IDD receive routine health services. In addition, this study found that belonging to a racial/ethnic minority group confers a compound risk in this vulnerable population.

Acknowledgments

This project was determined exempt by the Behavioral and Social Sciences IRB at The Ohio State University

Thanks to HSRI and NASDDDS for the use of the NCI database and all their helpful comments.

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Author notes

Haleigh M. Scott and Susan M. Havercamp, The Ohio State University.