Abstract

Using a retrospective analysis of data from the 2006 Medical Expenditures Panel Survey (MEPS), we assessed the health status of working-age adults with cognitive limitations in comparison to adults with no disability (unweighted N  =  27,116; weighted N  =  240,343,457). Adults with cognitive limitations had a significantly higher prevalence of diabetes than did adults with no disability (19.4% vs. 3.8%, respectively) and a significantly higher prevalence of six other major chronic conditions. In addition, individuals with cognitive limitations and diabetes were significantly more likely to have multiple (four or more) chronic illnesses. The health disparities we found in this study demonstrate the need to improve disease prevention and education efforts for individuals with cognitive limitations and their health care providers.

Obesity among individuals with intellectual and developmental disabilities has been estimated to be as high as 34.6%, far higher than that for the general population (Yamaki, 2005). The strong link between obesity and the risk for developing major chronic diseases, such as Type II diabetes (Mokdad et al., 2003; Nguyen, Magno, Lane, Hinojosa, & Lane, 2008), suggests that a substantial proportion of adults with intellectual or developmental disabilities are at risk for developing or may have already developed diabetes or other chronic conditions. Few researchers have investigated diabetes among people with disabilities, especially those with intellectual or developmental disability. This information is necessary to raise awareness among health care providers, individuals with intellectual or developmental disabilities and their care providers, public health workers, and advocates about the need for appropriate and sufficient prevention and disease management efforts related to diabetes.

Although diabetes challenges anyone facing it, chronic disease presents substantially greater challenges for people with developmental disabilities or other cognitive limitations. Frequently such individuals are unable to communicate their symptoms effectively, making diagnosis and disease management more difficult but at the same time more important (Baxter & Kerr, 2002; Beange, McElduff, & Baker, 1995). In addition, individuals with developmental disabilities or other cognitive limitations experience many barriers to accessing health care, such as providers who lack education for treating people with disabilities, health insurance coverage, and transportation (Baxter & Kerr, 2002; World Health Organization, 2000). People with intellectual or developmental disabilities also have more limited opportunities for physical activity and healthy diet than do those in the general population (Lennox, 2002; Rimmer & Yamaki, 2006). Moreover, once individuals with intellectual or developmental disabilities are diagnosed with a chronic condition, self-management can be difficult or impossible; their care providers may have limited knowledge for assisting with chronic disease management; and, as a result, the management can be suboptimal (McElduff, 2002; World Health Organization, 2000).

Existing research on the prevalence of diabetes among individuals with intellectual or developmental disabilities is limited, inconclusive, and based on convenience samples. In one recent study Shireman, Reichard, Nazir, Backes, and Greiner (in press) reported a prevalence rate of 11.2% among adults with developmental disabilities supported by Medicaid. In another study Havercamp, Scandlin, and Roth (2004) reported that individuals with developmental disabilities have an increased risk for secondary chronic conditions, such as diabetes. On the other hand, McDermott, Moran, Platt, and Dasari (2006, 2007) found no difference between individuals with disability and those without disability after controlling for obesity.

Given the relative dearth of published studies in which researchers investigated diabetes among people with intellectual or developmental disabilities, the increased risk factors for diabetes, and increased barriers to effective disease management, our primary goal was to help answer the question: How do individuals with intellectual or developmental disabilities and diabetes compare to individuals with no disabilities who have diabetes with regard to prevalence rate, comorbidity, obesity, and health care expenditures? We sought to provide a nationally representative epidemiologic evaluation of individuals with diabetes, both with and without cognitive limitations, because this would allow us to determine whether people with disabilities have higher rates of disease and similar or different risk factors for disease from the general population in order to inform policies, programs, and public health interventions. We also compared the characteristics of diabetic and nondiabetic individuals with cognitive limitations.

An important note: According to the American Association on Intellectual and Developmental Disabilities (2009), an intellectual or developmental disability is “a disability that involves significant limitations both in intellectual functioning and in adaptive behavior, which covers many everyday social and practical skills.” Synonymous with what was formerly referred to as mental retardation, the term intellectual or developmental disability refers to a disability occurring before age 18. Alternately, it is impossible to determine from MEPS data the individuals' age at disability onset. As a result some of the sample likely includes individuals who acquired a disability after age 18. As explained in the Method section, we limited the sample to the greatest extent possible to those with intellectual or developmental disabilities and believe that those who did not meet the age requirement of the definition did meet all other characteristics of the definition. That is, they were cognitively similar to individuals with intellectual and developmental disabilities and, therefore, faced similar health risks, barriers in accessing health care, and challenges to managing chronic disease. For accuracy we have used the term cognitive limitation rather than intellectual or developmental disability to describe the study sample throughout this article.

Method

We compared the health of working age adults (18 to 64 years old) with cognitive limitations with individuals who have no disability using a retrospective analysis of data from the 2006 full-year consolidated data file from the MEPS.

Medical Expenditures Panel Survey

The MEPS is a nationally representative survey that was designed to examine the health care utilization and expenditures of families and individuals in the United States, their medical providers (e.g., doctors, hospitals, pharmacies), and employers. Information about health services use, specific health conditions, cost, and the different coverage methods and payment for health care (Agency for Health Care Research and Quality, 2008) are also collected. Although there are numerous files available through MEPS (e.g., prescribed medicines, dental visits, jobs), we used only the 2006 full-year consolidated file. Individuals with cognitive limitations may be included in the MEPS through proxy response (Agency for Health Care Research and Quality, n.d.).

The sampling frame for the MEPS is the same as the one used for the National Health Interview Survey (NHIS) conducted by the National Center for Health Statistics (NCHS). The unweighted sample size is 34,145. The strength of this survey is that the resulting data enable researchers to examine the relationship between health care utilization and individual characteristics. In addition, each panel includes 2 years of data, and panels can be linked to the NHIS for greater longitudinal capacity and, thus, afford researchers with the ability to analyze change over time. Further, the MEPS serves as the only source that specifies source of payment and paid amounts for health care visits. According to the Agency for Health Care Quality and Research (AHRQ), the MEPS is an important and recommended data source for tracking the provision of health care services and health care utilization of individuals with special health care needs, including those with disabilities (Agency for Health Care Research and Quality, 2009). Several studies have shown that the MEPS is an appropriate data source for analyzing health disparities among different disability groups (Schootman & Jeffe, 2003) and health disparities between individuals with and without disabilities (Mitra, Findley, & Sambamoorthi, 2009; Moon & Shin, 2006; Rasch, Hochberg, Magder, Magaziner, & Altman, 2008; Rasch, Magder, Hochberg, Magaziner, & Altman, 2008; Wei, Findley, & Sambamoorthi, 2006).

Analytic Variables

We classified MEPS respondents into two groups: cognitive limitations and no disability. The cognitive limitations group included anyone who answered yes to the MEPS variables COGLIM31 and COGLIM53. These variables define someone with a cognitive limitation as anyone who “(1) experience[s] confusion or memory loss, (2) [has] problems making decisions, or (3) require[s] supervision for their own safety” (Agency for Health Care Research and Quality, 2008, p. C-38). We excluded those with physical disability, defined using WLKLIMXX (functional limitation) and ADLHLPXX variables (uses assistive device), and we considered all other individuals to have no disability. We further classified these two groups on the basis of diabetes diagnosis, defined by their response to the question of whether they had ever received a diagnosis of diabetes (DIABDX53).

We used the variable RTHLTHXX to examine perceived health status. Although the question for this variable appears in three rounds across the year, we used the last of these rounds. We conducted t-test comparisons of responses between rounds; these revealed no statistically significant changes between them.

Demographics

Using the MEPS data, we described the population on the following basic demographic characteristics: sex, age, race, and education. We calculated the mean age and percentages by groups: 18–34, 35–54, and 55–64 years; those under 18 and over 64 were excluded from the analyses. Race was reported in aggregate as White, Black, and other. It was important to also include an analysis of obesity in our study because of the strong correlation between obesity and diabetes. We reported occurrence of underweight, overweight, and obesity using the standards from the National Heart Lung and Blood Institute (2009). In addition, we examined health insurance status because it provides insurers, both public and private, with an understanding of the prevalence rate of diabetes among the groups they cover.

We measured receipt of health insurance using INSCOV06 and imputing receipt of Medicaid using MCDEV06 and Medicare using MCREV06. The 2006 poverty thresholds established by the U.S. Census Bureau were used to create income categories (U.S. Bureau of the Census, 2008).

Outcome Measures

The main outcome measures were diabetes prevalence, comorbidity, health care expenditures, and quality of care indicators for diabetes-monitoring. Understanding morbidity co-existing or associated with diabetes at the population level helps identify the next steps to take in research for better clinical decision-making: (a) do the two (or more) diseases have a common etiological pathway? or (b) what is the impact of the comorbidity on clinical care? We classified the presence or absence of six major diseases or conditions: asthma, arthritis, cardiac disease, hypertension, high cholesterol, and stroke. To test for differences in the mean number of comorbid conditions, we used a per person count by summing the number of these six conditions experienced by each person.

We also analyzed health care expenditures as a means of determining the health-related economic impact of diabetes and comorbid conditions on the study populations. To examine health care expenditures, we compared total costs incurred for health care using TOTEXP06.

Diabetes can cause serious health problems if it is not properly managed, and several preventive screenings are recommended to ensure proper quality of clinical care. We analyzed the quality of care indicators because understanding them at the population level can demonstrate where greater clinical emphasis is needed. To measure quality of care, we used the HEDIS 2008 diabetes measures that MEPS incorporated in the consolidated household survey, including HbA1c, eye exam, usual source of care, and cholesterol check. Microalbuminaria was not available through MEPS, but we substituted foot check as an additional quality of care indicator. Each of these was classified dichotomously as present or absent.

Statistical Analyses

Descriptive statistics included percentages for categorical variables and means and standard deviations for continuous variables. To examine bivariate associations between disability groups and health outcomes, we used crosstabs and t tests (i.e., perceived health status, health insurance, health care and preventive care use, and chronic disease). We used SPSS 17.0 with the Complex Samples Add-On (2008) to analyze the data. The add-on allows for more accurate estimation of variance for complex sampling designs such as that involved with MEPS. We weighted the percentages presented in all tables using MEPS weights (PERWT06F or DIABDX53) to reflect national estimates.

Results

As can be seen in Table 1, there was a greater proportion of males among diabetics with no disability than among diabetics with cognitive limitations. Those with no disability were a younger group overall, with a mean age more than 2.5 years younger than the cognitive limitations group. Although more than half of the no disability group was between 35 and 54 years of age, more than half of the cognitive limitations group was 55 to 64 years old. The distribution of race between the two groups differed; although the large majority of participants in both groups were White, those with cognitive limitations had a greater proportion of Blacks than those with no disability.

Demographics between diabetic and nondiabetic adults with disabilities were similar, with two exceptions. As can be seen in Table 1, first, the average age for those with diabetes was 8 years older than the average age for those without diabetes, significantly more people without diabetes were in the youngest age group, and significantly more people with diabetes were in the oldest age group. Second, a significantly larger proportion of diabetic adults with disabilities were Black than were nondiabetic adults with disabilities.

Diabetes Prevalence Rates

Adults with cognitive limitations experienced diabetes at a significantly higher prevalence rate than did individuals with no disability (18.5% vs. 3.7%, respectively). The difference in these groups' prevalence rates was increasingly larger with each older age group. For 18 to 29 year olds, the prevalence rate of diabetes for individuals with cognitive limitation was two times that of individuals with no disability; however, for 60 to 64 year olds, it was 3.3 times higher (see Figure 1). In addition, the odds of an adult with cognitive limitations developing diabetes was 2.7 (95% CI  =  1.9–3.58) times that of an adult with no disability. Having diabetes was also correlated with having six other chronic diseases, regardless of disability status (with the exception of asthma for those with no disability). However, individuals with cognitive limitations and diabetes experienced substantially and significantly more chronic diseases than did individuals in the no disability group with diabetes (see Table 2). Moreover, adults with cognitive limitations and diabetes who were over 40 years old reported having four or more of these chronic diseases at prevalence rates up to 19.7 times higher than the no disability group with diabetes in the same age groups (Figure 2).

Factors Associated With Diabetes Prevalence

Several factors were significantly associated with having diabetes. These included health insurance, health care costs, self-reported health status, and obesity.

Health insurance

As can be seen from Table 3, diabetic adults with no disability were more likely to have private insurance than were those with cognitive limitations and diabetes. Individuals with cognitive limitations and diabetes were significantly more likely to receive public health insurance (Medicaid, Medicare or both) (52.8%) than adults with no disability and diabetes (6.9%). Alternately, a significantly higher proportion of individuals with no disability and diabetes had no insurance than did those with a disability and diabetes.

Health care costs

Adults with cognitive limitations had greater expenses than did individuals with no disability, regardless of diabetes status; having diabetes was, however, related to significantly higher average costs. As can be seen in Table 4, individuals with cognitive limitations and diabetes had expenditures 3.7 times higher than those of diabetics with no disabilities. Moreover, diabetic individuals with cognitive limitations incurred average annual health care costs that were $8,358 greater than those of nondiabetics with cognitive limitations. This difference is nearly 3.5 times greater than the difference between the diabetic no disability group and the nondiabetic no disability group.

Health status

Regardless of diabetes status, adults with disabilities rated their health poorer than did adults with no disability (Table 5). Adults without a disability and diabetes rated their health as excellent or very good six times more than did those with cognitive limitations and diabetes. On the other hand, adults with cognitive limitations who did not have diabetes rated their health as excellent or very good four times more than adults with cognitive limitations and diabetes, but still less often than adults with diabetes and no disability.

Obesity

Individuals with cognitive limitations and diabetes were significantly more likely to be obese than were individuals with no disability and diabetes (72.1% vs. 55.8%). Moreover, obese individuals with cognitive limitations had average BMI scores greater than 2 points higher than did obese adults with no disability (37.7 vs. 35.2, respectively) (Table 1).

Discussion

Our purpose here was to examine diabetes among individuals with cognitive limitations in comparison to individuals with no disability. The findings of the present study are important because they are based on a nationally representative baseline measurement of diabetes among working-age adults with cognitive limitations and build upon the paucity of knowledge from nationally representative data for this population.

Comparison of Diabetes Among Individuals With and Without Cognitive Limitations

In the present study we found that compared to individuals with diabetes and no disability, individuals with cognitive limitations had a significantly higher prevalence rate for diabetes as well as a significantly higher prevalence rate for six other major chronic conditions. In addition, individuals with cognitive limitations, especially those in the older age groups, were significantly more likely to have multiple (four or more) chronic conditions. Research has documented that morbidities frequently co-occur with diabetes (Mokdad et al., 2001; Mokdad et al., 2003; Nguyen et al., 2008); however, the rate at which individuals with cognitive limitations and diabetes experience other chronic conditions far exceeds the rate at which individuals with no disability and diabetes experience comorbidity.

Although the high prevalence of obesity in individuals with intellectual disabilities is well-documented (Rimmer & Yamaki, 2006; Sohler, Lubetkin, Levy, Soghomonian, & Rimmerman, 2009; Yamaki, 2005), the level of obesity has not been described. Our results demonstrate that individuals with cognitive limitations not only have a higher prevalence rate of obesity than do individuals with no disability, but that their average BMI level is significantly higher as well. Some evidence indicates that individuals with developmental disabilities may become obese at a younger age than do their peers with no disability (Bandini, Curtin, Hamad, Tybor, & Must, 2005; Emerson, 2009; Takeuchi, 1994). In addition, research about individuals with no disability confirms the relationship between early onset of obesity and the development of subsequent comorbidities (Baker, Olsen, & Sorensen, 2007; Lobstein, Baur, & Uauy, 2004; Must, Jacques, Dallal, Bajema, & Dietz, 1992). Thus, the earlier age at which a person develops obesity and the level of obesity he or she maintains may explain, in part, the higher prevalence of multiple comorbidities among individuals with cognitive limitations and diabetes.

A significantly and substantially greater percentage of the cognitive limitations with diabetes group rated their health status as poor or fair than did the no disability group with diabetes, regardless of the number of chronic diseases they experienced and despite the majority reporting a usual source of care. In addition, individuals with cognitive limitations were more likely to receive public health insurance. Health care expenditures also differed significantly between these two groups. The mean annual costs per person with cognitive limitations and diabetes was significantly higher (nearly four times) than for a person with diabetes in the no disability group.

Comparison of cognitive limitations groups with and without diabetes

In this study we found that individuals with cognitive limitations who develop diabetes have much poorer health than those without diabetes, overall. First, regardless of diabetes diagnosis, individuals with cognitive disabilities experienced unacceptably high prevalence rates of chronic conditions; however, the prevalence rates for these chronic conditions among the diabetic cognitive limitations group was two or three times higher than for the nondiabetic cognitive limitations group. Second, although all people with cognitive limitations had higher health care expenditures than did those without cognitive limitations, the diabetic cognitive limitations group had significantly higher health care costs. In addition, a substantially higher percentage of individuals with cognitive limitations and diabetes compared to those without diabetes rated their health as fair or poor. Individuals with cognitive limitations and diabetes were also more likely to receive public health insurance than those with cognitive limitations without diabetes.

Increased Risk for Diabetes

Numerous determinants influence the health of individuals with cognitive limitations, including social, environmental, and behavioral. First, the risk of developing diabetes results from obesity due to poor diet. Poorer diet among individuals with cognitive limitations may result from uninformed and unrestricted food choices, especially among those living in community settings where individual choice is promoted (Daley, 1996; Rimmer & Yamaki, 2006). Similar to the general population in the United States, individuals with intellectual disability typically favor diets high in calories and fat (Draheim, Williams, & McCubbin, 2002; Rimmer, Braddock, & Fujiura, 1994; Rimmer, Braddock, & Marks, 1995).

Second, people with disabilities experience greater risk for diabetes as a result of limited or no physical activity. Physical inactivity is prevalent among adults with disabilities (McGuire, Strine, Okoro, Ahluwalia, & Ford, 2007); reports citing the 2005 Behavioral Risk Factor Surveillance System (BRFSS) indicate that adults with disabilities are twice as likely to be inactive as those without disabilities (25.6% vs. 12.8%) (Centers for Disease Control and Prevention, 2007). This can be a direct result of the disabling condition itself, physiologic decline, or environmental barriers, such as transportation limitations, a lack of information about accessible exercise programs, lack of accessible equipment in exercise facilities, and the common belief that fitness facilities are not welcoming or equipped to accommodate persons with disabilities (Rimmer, Riley, Wang, Rauworth, & Jurkowski, 2004; Santiago & Coyle, 2004). Conversely, those with disabilities who receive institutional support for healthy living have a decreased risk for secondary health conditions (Janicki et al., 2002; Merrick et al., 2004).

Third, too few people benefit from coverage for disease management services that address diabetes and obesity (Matheson, Wilkins, & Psacharopoulos, 2006). Family members, support staff, and other care providers for individuals with cognitive limitations, therefore, do not receive adequate education concerning the prevention and disease management of diabetes and obesity. Moreover, this lack of knowledge can present as doubly problematic because care providers, like many who receive near-poverty-level wages, are at risk of being overweight or obese and diabetic themselves (Drewnowski & Specter, 2004; PHI National, 2009; Vieweg et al., 2004). In addition, the high rate of turnover among support staff for this population exacerbates the lack of knowledge. Further, very few services are designed to address the unique learning needs of individuals with cognitive limitations to enable them to participate more fully and meaningfully in their own disease management.

Limitations

The data for this study were limited to self-reported data. Information reported on conditions was not verified by clinical records. There is conflicting evidence from national data sets on how well self-reporting of services actually represents the utilization of those services (Blood et al., 2000; King et al., 1990; May et al., 1998). In addition, self-report of laboratory tests or other preventive services may be hindered by respondents' inability to remember exactly which tests were ordered and when they were conducted (Stange et al., 1998). In addition, the design of the MEPS does not allow state-level estimates, only regional or national estimates.

Another major limitation of MEPS data is its inclusion of proxy response. Proxy respondents may have a different perception or experience of individuals' health and health care utilization than the individuals themselves. In addition, proxy respondents may have a different way of storing and retrieving information and a different way of comprehending or wording questions related to disability than do the individuals they are responding for (Hill et al., 2006).

Finally, as we noted in the introduction and Method section, the way the question on the questionnaire was worded made it impossible for us to limit the sample to only those with intellectual and developmental disabilities. Although all members of the group should have similar cognitive characteristics and, thus, likely experience similar barriers to health care, the sample included some proportion of people who acquired their disability after age 18 and may include some who have Alzheimer's or early onset dementia. We could not determine how large or small this proportion was.

Recommendations

In keeping with two recent Surgeon General's reports outlining the importance of improvements in health for people with disabilities in general, and mental retardation specifically (U.S. Department of Health and Human Services, 2002, 2005), we must address the growing problem of diabetes among individuals with cognitive limitations. The first step toward this end is establishing within each state a system of health promotion that educates and supports individuals with cognitive limitations in their effort to make healthy lifestyle choices. Such a system of health promotion will require education programs designed to meet the unique needs of this population as well as policy change among states, public and private health insurance programs, and public health programs.

First, insurance programs must expand coverage for preventive services. In particular, given that Medicaid supports millions of adults with intellectual and developmental disabilities (Kaiser Commission on Medicaid and the Uninsured, 2009), the U.S. Government Accountability Office (2009) recently reported that state and federal Medicaid programs should modify and expand existing policies for preventive services, including diabetes education and management, in an expedited fashion. Medicaid programs, with the assistance of Centers for Medicare & Medicaid Services administrators, should begin to extend preventive services to individuals with disabilities by (a) offering education to health care providers, individuals with cognitive limitations and their care providers, public health workers, and advocates about the need for appropriate and sufficient prevention and disease management efforts related to diabetes and obesity and (b) increasing Medicaid staff resources to monitor utilization of preventive services. One method to support expanded preventive services given funding restraints is for Medicaid programs to use outsourced medical management programs; these have proven to effectively reduce Medicaid spending while enhancing the way health services are delivered to Medicaid populations (A. Lewis, 2004).

In addition, research has shown that the overall cost of care in the general public is lower for those who are a part of a diabetes disease management program versus those who are not a part of such a program (Villagra & Ahmed, 2004). Diabetes management services may provide one means for improving care and lowering costs for individuals with disabilities; however, this will require first conducting rigorous assessment of changes necessary to extend these programs to best serve individuals with disabilities by addressing their needs and preferences that may differ from those of the general population. If diabetes disease management programs are found to be effective for individuals with cognitive limitations, both public (Medicaid and Medicare) and private health insurance programs should adopt policies to support them.

Second, health promotion addressing diabetes and obesity for individuals with cognitive limitations will require changes at the community level. Many advocates argue that dietary selection for this population should emphasize choice and should remain unrestricted. In response to this, we counter that if we do not avail people with cognitive limitations with the opportunity for and knowledge of healthy choices, then we are denying them the option to choose to be healthy; as it stands now, they get to make choices regarding diet and physical activity, but these are uniformed choices. Given that the U.S. Preventive Services Task Force recommends intensive behavioral dietary counseling for all adults who are at risk for hyperlipidemia and other cardiovascular and diet-related conditions (Agency for Health Care Research and Quality, 2003) combined with our findings, it is strongly advisable to provide dietary support and counseling to adults with disabilities.

In addition, at the community level, we must address the health care practices and knowledge of care providers for individuals with cognitive limitations. Support staff members likely lack the knowledge and skills to help those they support to manage diabetes. This lack of knowledge influences the level of disease management for their clients and themselves; their personal poor health that results from inadequate disease management can dramatically affect the quality of support they provide. Worksite-based health promotion programs, when adapted to target the health characteristics and settings of those who care for individuals with cognitive limitations, may help address both of these concerns. It might also help reduce the presence of chronic conditions, health care costs, and worker's compensation claims filed among the workforce. These programs include a wide range of health promotion strategies, including peer coaching, nutrition and exercise counseling, preventive health screenings, motivational interviewing, free access to fitness facilities, and incentives, such as days of paid leave for participation (Hersey et al., 2008).

Research has shown that wellness programming can significantly decrease annual claim costs. For example, in one randomized controlled trial on worksite wellness, the average employee claim cost decreased by 48%, p  =  .002, creating a six-fold return on investment (Milani & Lavie, 2009). Other wellness and health promotion studies have shown improved weight loss as a result of programs that target physical activity, nutrition, or both (Anderson et al., 2009).

A further suggestion for health promotion programming is to add diet and exercise education as an integral part of special education curriculum. Research evidence suggests that young individuals with disabilities are more likely to engage in unhealthy behaviors during adolescence compared to youth of the same age without disability (Everett Jones & Lollar, 2008). There is a tremendous need, then, for special education-based programs that can address healthy lifestyle habits, such as proper nutrition and regular physical activity, at an early age. According to Lennox (2002), such diet and exercise programming must incorporate materials that focus on inclusion, participation, empowerment, skills development, community engagement, the refocusing of health services, and the promotion of environments that enhance health and quality of life (Lennox, 2002). The health behaviors of young people are influenced by a variety of levels including the individual, peers, family members, school, community, and society as a whole (Centers for Disease Control and Prevention, 2010). Due to the scope and variety of health influences on youth behavior, special education programs must engage a variety of partners to improve the health of those with disabilities (Everett Jones & Lollar, 2008).

Conclusion

In the present study adults with cognitive limitations who live in community settings were more likely to have diabetes than were those without disability. Moreover, if they have diabetes, they were more likely to have many other chronic conditions; be obese; and if obese, have a higher BMI than obese diabetics with no disability. Unfortunately, diabetics with cognitive limitations report their health status as fair or poor at significantly higher rates than do peers without disability and nondiabetics with cognitive limitations.

Although many efforts have been implemented to reduce the onset of secondary conditions, such as diabetes and heart disease in the general public, very few preventive efforts have focused on individuals who have a disability. The first step to promoting preventive health efforts in this population is to accurately identify those at risk. However, research indicates that there is an underdiagnosis of chronic conditions in those with disabilities (Beange et al., 1995; Janicki et al., 2002; M. Lewis, Lewis, Leake, King, & Lindemann, 2002; Merrick et al., 2004). Underdiagnosis may be attributable to disparities in primary preventive screening efforts as well as the possibility that individuals in this population experience a decreased capacity to report health issues on their own (Janicki et al., 2002). Therefore, vast improvements are needed in health promotion efforts for people with disabilities in order to reduce the population's prevalence of secondary conditions (Lollar, 2002; Wilber et al., 2002).

To date, very few researchers have investigated the prevalence of diabetes among individuals with cognitive limitations, especially at the national level. The disparities and high prevalence rates that were exposed in this study demonstrate an imperative to increase primary and secondary prevention of diabetes and obesity and improve education and disease management for individuals with cognitive limitations. Prevention of morbidity in this population is vital to preserving individuals' independence, community participation, and quality of life.

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Author notes

Editor-in-Charge: Steven J. Taylor

Amanda Reichard, PhD (e-mail: reichard@ku.edu), Research Director, and Hayley Stolzle, MPH, Research Assistant, University of Kansas, Research and Training Center on Independent Living, 1000 Sunnyside Ave., Suite 4089, Lawrence, KS 66045.