Recent years have seen increased efforts to encourage the regular utilization of preventive health care services. A major push was made to increase preventive care service use with the passing of the Affordable Care Act, which will require that health insurance companies cover the full cost of many types of regular preventive care services starting in 2014.
There exists extensive research documenting the disparities in use of preventive health care faced by racial and ethnic minorities,1–4 as well as some research on disparities in preventive health care utilization based on intellectual and developmental disability (ID/DD) status.5–10 What is not known is whether racial and ethnic disparities persist among adults with ID/DD.
The population of individuals with ID/DD is a high-cost and vulnerable group of users of acute and long-term care services. They are disproportionately poor11 and access publicly funded services at a high rate. Much of their medical care is likewise covered by public funds (primarily through Medicaid and Medicare programs). Even so, individuals with ID/DD are documented to be disadvantaged in terms of some preventive health care services. Women with ID/DD are less likely than women without ID/DD to have had breast and cervical cancer screenings or to have ever visited the gynecologist.6 Individuals with ID/DD are less likely to visit the dentist regularly,6,12,13 get eye and hearing tests,14,15 and receive timely vaccines.16
Many of the barriers to the use of preventive care by people with ID/DD have been identified. These barriers include individual factors (fear of specific procedures or of seeing a provider, communication with providers, lack of knowledge about health and prevention), systemic barriers (difficulty accessing specialty care, finding providers experienced in meeting people’s health care needs, and lack of care coordination, continuity, and consistency), and financial factors (people with ID/DD cited cost as a factor in delaying or not accessing care; discrepancies between public and privately funded health care, and lack of insurance).17 Poverty has been documented to influence access to care.18 Medical practitioners inadequately trained to treat individuals with ID/DD and medical facilities that lack accommodations also serve as barriers to care.19 In addition, type of living arrangement has been shown to play a role—individuals living with their families and those living independently are less likely to receive preventive care.5 Other barriers include transportation difficulties, fear, and lack of knowledge about health prevention.5,17
Another population group that is known to be disadvantaged in accessing preventive health care is racial and ethnic minorities. There is extensive literature documenting that African Americans and Hispanics use preventive health services at lower rates than white Americans and these disparities are consistent along a range of services.2,20 In general, African Americans and Hispanic patients visit primary care doctors and have regular dentist visits less frequently than whites.21,22 Furthermore, studies show that they are less likely than whites to receive services such as flu and pneumonia vaccines, colorectal cancer screenings,23 pap tests,24 and mammograms.25
There are several potential explanations for racial and ethnic disparities in preventive care use. Evidence has shown that African Americans and Hispanics have, in general, lower incomes, less education, less insurance coverage, and a higher probability of being underinsured, all of which may affect the ability to access preventive care services.2,18,26 Additional research cites culturally based attitudes toward risk, perception of the health care system and care-seeking behaviors, and other cultural issues.26–28
Potential disparities in use of preventive health care services experienced by individuals of color and ethnic minorities who also have ID/DD are less well understood. Given that adults with ID/DD are disproportionally poor and that as racial/ethnic disparities are often confounded by disparities based on socio-economic status, it is not clear whether the racial and ethnic disparities persist within this group. Another potentially confounding factor to race and ethnicity is availability and type of health insurance; this issue is not likely to be as much of a factor for individuals with ID/DD who receive publicly funded services.
This paper uses a national dataset to investigate whether there are differences by race/ethnicity in receipt of preventive health care and whether the differences persist after considering other demographic factors.
Our study utilized 2011–2012 data from the National Core Indicators (NCI) project. The NCI is a voluntary collaboration between the National Association of State Directors of Developmental Disabilities Services (NASDDDS), the Human Services Research Institute (HSRI), and the state developmental disability agencies of the participating states. The project began in 1997 as an effort to provide states with tools to use in support of their efforts to improve system performance and to better serve people with ID/DD and their families. The current participation in NCI consists of over 30 states, as well as several substate regional entities. There is a common set of data collection protocols to gather information about the performance of service delivery systems for people with ID/DD.
The data used for this study are collected through NCI’s Adult Consumer Survey (ACS)—a survey specifically designed to be administered in a face-to-face interview with adults with ID/DD and people involved in their lives.
The ACS consists of 3 sections—section 1 where only the individual’s responses are allowed, section 2 where proxy responses are also allowed, and the Background Information Section. The Background Information Section collects information on the individual such as basic demographic information, residence type, health and preventive health care, employment, etc. These data are generally derived from existing records and are usually collected by case managers. All the variables utilized in the analyses for this study are from the Background Information Section.
Each participating state is instructed to complete a minimum of 400 surveys with a random sample of individuals over the age of 18 years who are receiving at least one publicly funded service in addition to case management. Most states draw an over-sample greater than 400 in order to account for refusals and surveys that may potentially be deemed invalid. Sample selection is randomized so that every person in the state or service area that meets the criteria has an equal opportunity to be interviewed. There are no prescreening procedures.
Almost all participating states met the goal of collecting 400 interviews in 2011–2012. A few states excluded some segments of the served population, thus not achieving a perfectly random sample of everyone served. When states elected to exclude subsections of the population, it was done by purposefully not including people residing in certain types of living arrangements into the initial sample (eg, institutions). In order to at least partially account for state sampling differences we statistically controlled (adjusted) for state in our models, and for type of living arrangement.
The overall response rate for the Background Section (from which all of the variables of interest for this study were drawn) is almost 100%, though the response rate for any given item may be lower.
Our initial data sample consisted of 12,236 adult surveys from 19 states and 1 substate region (AL, AR, AZ, CT, GA, HI, IL, KY, LA, MA, ME, MI, MO, NC, NJ, NY, OH, PA, SC, and the mid-East Ohio Regional Council).
The Background Section of the ACS contains several questions on receipt of various preventive health care procedures. These questions were used to create 7 dependent variables: person has a primary care doctor (yes/no); person had a physical examination in the past year (yes/no); person had a dentist visit in the past year (yes/no); person had an eye examination/vision screening in the past year (yes/no); person had a hearing test in the past 5 years (yes/no); person had a flu vaccination in the past year (yes/no); person ever had a vaccination for pneumonia (yes/no).
These items are typically collected before the direct interview from administrative records. Some of the items examined for this study had a high number of “don’t know” responses. Percentages of “don’t know” responses ranged from approximately 1% for “person has a primary care doctor” to 48% of responses for “person ever had a vaccination for pneumonia.” We decided to exclude “don’t know” responses from the analyses. That is, when forming the dependent variable, “don’t know” responses were excluded. However, we investigated whether individuals for whom “don’t know” responses were recorded differed from individuals who had valid medical information. We found no significant differences in terms of most of the personal characteristics tested. The one noteworthy difference was that people with “don’t know” responses were more likely to live on their own or with their parent. Furthermore, the rates of “don’t know” responses differed between states. The finding of largely no significant differences in personal characteristics between those who had valid responses for the relevant preventive health indicators and those who had “don’t know” responses recorded provides justification for excluding “don’t know” responses from analyses. However, we performed additional sensitivity analysis. A response of “don’t know” likely indicates a negative response—if there is no record of a procedure, it is likely that the procedure was not performed. Therefore, our sensitivity testing involved equating all “don’t know” responses to the negative response. We performed these analyses on each of the 7 dependent variables. The results of the sensitivity analyses did not change our overall conclusions about the relationship of our primary independent variable to the preventive care outcomes.
Our primary independent predictor of interest was the race/ethnicity variable. This variable was created by combining 2 questions from the Background Information Section of the Adult Consumer Survey: “What is this person’s race?” (American Indian or Alaska Native, Asian, black or African, American, Pacific Islander, white, Other race not listed, or 2 or more races) and “Is this person Spanish/Hispanic/Latino?” [No, not Spanish/Hispanic/Latino and Yes (Mexican, Mexican American, Chicano, Puerto Rican, Cuban, or other Spanish/Hispanic/Latino)]. These 2 items were combined to create a race/ethnicity variable: white non-Hispanic, black non-Hispanic, or Hispanic. Unfortunately, other racial/ethnic combinations yielded numbers too small for analysis.
Other personal-level characteristics were: state of residence, age, sex, primary language, primary means of expression, level of ID, mobility, other diagnoses (in addition to diagnosis of ID/DD), health, residence type, and whether support is needed for behavioral issues. Operational definitions of demographic and other personal characteristics can be found in Table 1. More detailed definitions of constructs used by the survey are available from the authors by request.
After classification into 1 of our 3 racial/ethnic categories, our sample consisted of 11,199 people. The distribution of race/ethnicity was as follows: 75% white non-Hispanic, 20% black or African American non-Hispanic, and 5% Hispanic.
Table 2 presents percentages of receipt of preventive health care procedures by race/ethnicity. White non-Hispanic individuals with ID/DD in our sample are somewhat less likely to have a primary care doctor. In contrast, white non-Hispanics were slightly more likely to have had a dentist visit in the past, a flu vaccination in the past year, and a pneumonia vaccine. Those of Hispanic ethnicity were less likely to have had a physical examination in the past. They seem to be somewhat less likely to have had an eye examination in the past year as, but that difference is not statistically significant. Black non-Hispanic adults with ID/DD were least likely to have had a dentist visit in the past year and a hearing test in the last 5 years, with the difference being only marginally significant.
Table 3 shows how persons of different racial/ethnic backgrounds differ in terms of other personal characteristics. White non-Hispanics are most likely to be female. White non-Hispanics are also slightly more likely to have the label of mild ID than both black non-Hispanics and Hispanics are, and are slightly more likely to have a diagnosis of Down syndrome or mental illness. In contrast, both black non-Hispanics and Hispanics are more likely to have a diagnosis of Autism Spectrum Disorder. Both groups are also significantly younger and more likely to be self-mobile. They are also slightly more likely to use gestures/body language, whereas white non-Hispanics are more likely to use spoken words. Not surprisingly, Hispanics are much more likely to have a language other than English as their primary language. White non-Hispanic individuals with ID/DD are significantly more likely to live in community-based group residence and significantly less likely to live with a parent or a relative. There were no statistically significant differences in the overall health between the 3 groups.
Model 1 in Table 4 presents the results of logistic regression controlling only for state, with race/ethnicity as primary independent variable. The coefficients for state are not shown, but it is a highly statistically significant factor in each model, signifying that there is much state-to-state variation in the rates of the preventive procedures analyzed. White non-Hispanic racial/ethnic group is the reference category, thus the other 2 racial/ethnic groups are compared to being white non-Hispanic. After controlling for state-to-state variation, white non-Hispanics were more likely to have had a physical examination in the past year than both black non-Hispanics and Hispanics, as well as to have had a flu vaccine in the past year and a pneumonia vaccine over the lifetime. They are also more likely than black non-Hispanics to have had a dentist visit in the past year. There is no statistically significant difference among the racial/ethnic groups for having a primary care doctor or for getting an eye or a hearing test.
Model 2 in Table 4 presents logistic regressions where both state and other personal characteristics in addition to race/ethnicity, were adjusted for. Once again, state is a highly statistically significant predictor in each model. Type of residence seems to be the one characteristic that is significant in all models (except for having a primary care doctor). Persons living in institutions are most likely to receive preventive care, and people in a parent’s or relative’s home are least likely. Older age is a significant predictor for having had a physical examination, a flu, or pneumonia vaccine. Younger age is a significant predictor for having had a dentist visit. Being independently mobile is associated with higher likelihood of having had an eye examination, and lower likelihood of having had a flu or a pneumonia vaccine. Not having English as primary language is associated with reduced odds of having had a physical examination or a dentist visit. Using spoken words lowers the odds of having had pneumonia vaccine. Having significant hearing loss is associated with higher likelihood of having a hearing test, as is a diagnosis of Down syndrome. Level of ID does not seem to be a statistically significant predictor, with 1 exception—those not having a label of ID are less likely to have had a flu vaccination than those with the label of profound ID.
In model 2, there is no longer a statistically significant difference between the 3 racial/ethnic groups for having had a physical examination in the past year or having pneumonia vaccination. Furthermore, in model 2 being Hispanic is not associated with lower odds of having visited a dentist in the past year or having had a flu vaccine compared to being white non-Hispanic. However, being black non-Hispanic is still associated with reduced odds of having had a dentist visit or having had a flu vaccine. In addition, after controlling for other characteristics, being black non-Hispanic is now associated with somewhat higher odds of having had an eye examination compared with white non-Hispanics.
This study is one of the first to look at disparities in the receipt of preventive health care at the intersection of race, ethnicity, and ID/DD status. A total of 6 common preventive health care procedures were investigated—physical examination, dentist visit, eye examination, hearing test, flu vaccine, and pneumonia vaccine. In addition, we examined whether having a primary care doctor varied by the race/ethnicity of the individual. An initial investigation of the NCI data indicated that there were statistically significant differences between white non-Hispanic, black non-Hispanic, and Hispanic adults with ID/DD in the rates of having a primary care doctor, and in the rates of having had a physical examination, a dentist visit a flu vaccine, and a pneumonia vaccine, with white non-Hispanics getting the preventive examinations at somewhat higher rates, but being slightly less likely to have a primary care doctor.
There are differences in the rates of the usage of health care and different health care procedures according to the area of the country where individuals reside—both large and small area variation. Therefore, it is important to account for variation due to geographic differences. Although we would have liked to control for smaller geographic units, those data were unavailable to us. We were, however, able to control for state. Controlling for state also had the added effect of mitigating potential differences due to service eligibility criteria, policies, and, at least to some extent, slight sampling differences. After controlling for state of residence, our analyses showed that the differences by race/ethnicity persisted for physical examination, the 2 vaccinations, and to a smaller extent, dentist visit.
There are other personal-level characteristics that may affect whether a person receives preventive health care. Our results indicated that the 3 racial/ethnic groups were fairly heterogenous in terms of diagnoses, mobility, expression, language, type of living arrangement, and age. We therefore controlled for these factors in addition to the state in our analyses. After including demographic and personal-level characteristics in the models, there were no longer statistically significant differences by race/ethnicity for having had a physical examination or a pneumonia vaccination, and only differences between black non-Hispanics and white non-Hispanics in the receipt of a flu vaccine and having had a dentist visit.
Our findings lead us to conclude that, race/ethnicity may not be a very strong factor for adults with ID/DD. Although it may play a role for some of the preventive care examinations and procedures, it seems to be overshadowed by the other. It is important to remember that our sample consisted only of people already receiving long-term care services. Emerson29 found that adults with ID who do not use formal disability services have less access to preventive health services than adults who do use ID services such as supported accommodation. If adults with ID/DD of different racial and ethnic backgrounds are accessing these long-term care services at different rates, the effect of race/ethnicity on receipt of preventive health care may be greater.
Admittedly, our study had important limitations. The accuracy and availability of health care information is a concern. This is not an issue that is unique to our research, but one we attempted to address.
As noted before, some states were less successful at drawing a random sample completely representative of the served population of adults with ID/DD. A few states exclude certain segments of the population, most often those in the institutional settings. We realize this is a potential problem. We sought to control for the sampling differences between states by including state and type of living arrangement in our models. We also performed sensitivity analyses by removing those states that were known to exclude subpopulations from our sample and rerunning the analyses, with no substantive changes in conclusions.
There may be other pertinent demographic and person-level variables that we were unable to include in our analysis. Furthermore, there are other potential factors that our analysis could not address. First is the matter of choice. People of different racial/ethnic backgrounds may be making a conscious choice about not receiving the preventive care. Secondly, as in almost all studies on utilization of health care, there is an inherent difficulty in linking the care with the outcomes. Simply because someone received a vision examination does not necessarily mean that he/she will have better vision outcomes.
Our findings do not suggest that it is not important for health care professionals and policy makers to target individuals of different races and ethnicities who have ID/DD and their caregivers to encourage receipt of preventive care. The interventions need to be culturally sensitive and appropriate. Further analysis is needed into the effect of race and ethnicity on receipt of care, especially among people with ID/DD who may not be accessing formal disability services.
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