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National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Health Care Services; Committee on Health Care Utilization and Adults with Disabilities. Health-Care Utilization as a Proxy in Disability Determination. Washington (DC): National Academies Press (US); 2018 Mar 1.

Cover of Health-Care Utilization as a Proxy in Disability Determination

Health-Care Utilization as a Proxy in Disability Determination.

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2Factors That Affect Health-Care Utilization

The committee was tasked with identifying factors that influence a person's use of health-care services, including poverty and level of urbanization. This chapter will address those factors. The committee has organized the beginning of the chapter around individual and societal determinants of health-care utilization, including factors that affect the need for care, the propensity to use services, and barriers to the use of services. That is followed by a brief overview of disparities in the use of health care that have differentially affected different population groups. Finally, it concludes with a discussion of what is known about the relationship between disability status and use of health-care services.

People use health-care services to diagnose, cure, or ameliorate disease or injury; to improve or maintain function; or to obtain information about their health status and prognosis. Health-care utilization can be appropriate or inappropriate, of high or low quality, and of high or low cost. Types of health-care utilizations are described in detail in Appendix A.

The health-care delivery system has undergone great change over the past few decades. New and improved drugs, devices, procedures, tests, and imaging machinery have changed patterns of care and sites where care is provided (NCHS, 2003). The growth of ambulatory surgery has been influenced by improvements in anesthesia and analgesia and by the development of noninvasive or minimally invasive techniques. New and improved, and less invasive, procedures are available to treat a number of previously untreatable conditions in a variety of new sites of care, or even in physicians' offices. New drugs can cure or lengthen the course of disease, although often at increased cost or increased utilization. Combinations of technologies can be more effective than individual ones, such as the combination of drugs now used to treat HIV/AIDS, combination chemotherapy for many types of cancers, and the recent creation of scanning machines that combine positron emission tomography and computed tomography or positron emission tomography and magnetic resonance imaging. As some technologies become easier to use and less expensive, as equipment becomes more transportable, and as recovery times for procedures are reduced, even complex technologies move out of hospitals and institutional settings and into ambulatory surgery centers, provider offices, outpatient facilities, imaging centers, and patients' homes and become more accessible. The average length of hospitalizations decreased with the diffusion of new technologies until 2010 and has been constant since then (NCHS, 2016).

The availability of newer and improved health-care services, however, does not mean that they are equally available to all Americans. For example, white women are much more likely to have outpatient surgery than women of other races or ethnicities (Salasky et al., 2014) and Medicaid beneficiaries, who are poor and often disabled, are more likely to use emergency departments (EDs) than people who have other coverage, in part because they have less access to ambulatory care (MACPAC, 2016; NCHS, 2017a).

Health-care utilization is determined by the need for care, by whether people know that they need care, by whether they want to obtain care, and by whether care can be accessed. Quality is a construct separate from access and is related to the achievement of favorable outcomes associated with utilization, not to whether health-care utilization occurs at all or to difficulties in obtaining care. In theory, health-care utilization should correlate highly with the need, however defined, for services. But, some services are needed and not obtained, and others are utilized but not clearly indicated, or are indicated only after other protocols are followed (Kale et al., 2013; Kressin and Groeneveld, 2015; Lyu et al., 2017). The committee did not address various technologies and whether they might be useful for disability assessments, because data on technologies are insufficient for such assessments. For example, telehealth is not widely used throughout the country, and the medical field continues to try to determine how it can be used most effectively. However, the committee did examine national data when they were available.

Need for and access to health care are discussed below. Those sections are followed by a discussion of differences in utilization according to selected characteristics. Finally, health-care utilization by people who have disabilities is discussed.

NEED FOR HEALTH-CARE SERVICES

Health status and the need for health-care services to improve or maintain health are major determinants of health-care utilization. The World Health Organization states that health is determined by a person's individual characteristics and behaviors, physical environment, and socioeconomic environment (WHO, 2017). People's individual characteristics include their biology and genetics, such as inherited diseases and conditions that require medical care. The prevalence of those conditions differs by sex, age, race and ethnicity, employment status, and other factors. Physical environments can affect health because of pollutants or other environmental health hazards. Individual behaviors, such as smoking or lack of exercise and overeating, also cause health conditions that require health care (ODPHP, 2017a). Recent attention to social determinants of health, such as education, economic stability, community safety, and availability of adequate housing and healthful food, has shown that they correlate with healthier populations (ODPHP, 2017a). People who have unmet social needs are more likely to be frequent ED users, to have repeat “no-shows” for medical appointments, and to have poorer glycemic and cholesterol control than those who are able to meet their needs (Thomas-Henkel and Schulman, 2017).

How need affects differential health-care utilization by specific populations of interest is discussed below with reference to poverty and its correlates and geographic area of residence, race and ethnicity, sex, age, language spoken, and disability status. Ideally, need should be the major determinant of health-care utilization, but other factors clearly have an effect. One of those factors is the ability to access care—including whether it is available, timely and convenient, and affordable (Figueroa et al., 2017).

ACCESS TO HEALTH CARE

Access to health care is defined as having timely use of personal health services to achieve the best possible health outcome (IOM, 1993). Access requires gaining entry into the health-care system, getting access to sites of care where patients can receive needed services, and finding providers who meet the needs of patients and with whom patients can develop a relationship based on mutual communication and trust (AHRQ, 2010). Clinicians note that timely access to health care is important inasmuch as it might enable patients and physicians to prevent illness, control acute episodes, or manage chronic conditions, any of which could avoid exacerbation or complication of health conditions (NCHS, 2017b).

There are many ways to think of access, and the term access is often used to describe factors or characteristics that influence one's initial contact with or use of services. Anderson and Newman (2005) present a framework of health-care utilization that includes predisposing factors, enabling factors, and magnitude of illness. More recently Levesque et al. (2013) defined access to health care by presenting five dimensions of accessibility: approachability, acceptability, availability and accommodation, affordability, and appropriateness. They saw access as the opportunity to identify health-care needs; to reach, obtain, or use health-care services; and to have the need for services fulfilled. Access can be seen as a continuum: even if care is available, many factors can affect ease of access to it, for example, the availability of providers who will accept a person's insurance (including Medicaid), ease in making an appointment with a given provider, the ability of a patient to pay for care (even if a patient is insured, due to cost-sharing copayments and deductibles), and the difficulty of arranging transportation to and from healthcare facilities (AHRQ, 2010, MACPAC, 2016). Some of those issues are discussed below.

Ability and Propensity to Use Services

People cannot access care if it does not exist in their geographic area, or if providers will not treat them because of insurance or other issues. Rural areas in particular have been identified as lacking a sufficient supply of specialty physicians and, in particular, mental health-care providers (Meit et al., 2014; Douthit et al., 2015).

Assuming that services are available, access to care might be impeded by other barriers. One is inadequate transportation, either because travel time is excessive, because no public transportation is available and the person does not have a car or other alternative transportation, or because the cost of transportation is prohibitive. Providers might refuse to see patients because no appointment times are open, or because they do not accept patients' insurance. Providers might be unable to communicate with patients because of language issues, or their offices might not be accessible to people with disabilities. Excessive wait times to obtain appointments or to see providers at their places of service might also deter use (MACPAC, 2016; NCHS, 2016).

Insurance and Ability to Pay for Services

Access to health care is tied to the affordability of health insurance. Financial barriers to care, particularly among low-income people and the uninsured, have been greater in the United States than in other high-income countries (Davis and Ballreich, 2014; Squires and Anderson, 2015). According to a 2013 Commonwealth Fund survey of adults in 11 high-income countries, the United States ranks last on measures of financial access to care (Schoen et al., 2013). The Kaiser Commission on Medicaid and the Uninsured notes that people who lack insurance coverage have worse access than people who are insured, and 20 percent of uninsured adults in 2015 went without needed medical care because of cost (KFF, 2016). The lack of health insurance has been identified as an important driver of health-care disparities (IOM, 2002).

Uninsured people who are 18–64 years old are more likely than those who have Medicaid or private coverage to report difficulties in affording needed medical care and prescription drugs, and are more likely than the insured to delay or forgo them because of cost (KFF, 2016). During 2004–2014, uninsured adults were 4–5 times more likely than those who had private coverage and 1.5–3.0 times more likely than those who had Medicaid to report difficulty in medical care and prescription access. For adults who had Medicaid, medical care access problems were stable until 2008 and then decreased through 2014. For those who had private insurance, medical care access problems increased until 2009 and then decreased through 2014 (NCHS, 2016). For the uninsured, problems with medical care and prescription access increased until 2010 and 2009, respectively, and then were stable for medical care and decreased through 2014 for prescriptions. Prescription access problems were stable in 2004–2014 for those who had private insurance but decreased for adults who had Medicaid (NCHS, 2016).

Having insurance coverage does not mean that coverage is adequate or is not associated with burdensome cost-sharing through premium payments, copayments, and deductibles (Lavarreda et al., 2011; Fang et al., 2016). Surveys have categorized underinsured people as those who say they have insurance but are worried about medical bills, who are paying them off over time, or who have not obtained selected types of health-care services because of cost.

A study by Magge et al. (2013) estimated that more than one-third of low-income adults were underinsured (defined as spending more than 5 percent of household income on medical care) and that 8 percent and 13 percent deferred or delayed obtaining medical care or prescription medications, respectively (Magge et al., 2013). Avoiding or delaying medical care correlates strongly with coverage under a high-deductible health plan (in which a person must make a substantial out-of-pocket [OOP] contribution before insurance payment begins) and with depression, poor perceived health, or poverty. However, it is relatively independent of the percentage of income spent on OOP costs, so the percentage of income spent on OOP costs by itself is a poor measure of health-care unaffordability. People who spend a small percentage of their income on health care might be extremely burdened by their health-care plan when financial concerns prevent access to health care (Kielb et al., 2017). Current evidence also suggests that high-deductible health plans are associated with lower health-care costs as a result of a reduction in the use of health-care services, including appropriate services (Agarwal et al., 2017).

DIFFERENCES IN HEALTH-CARE UTILIZATION ASSOCIATED WITH SELECTED CHARACTERISTICS

Ideally, utilization of health-care services reflects a need for care, but that is not the case, for several reasons. Many factors affect health-care utilization independently of need and are reflected in differences, some of which are remediable, among population groups. Some of these factors are related to biologic or environmental differences among groups, such as disproportionate residence in polluted environments, access to healthful food and adequate housing, and education associated with effective use of health care. Others are related to differences in access, such as health insurance coverage or income needed to obtain services, ease of obtaining services, and discriminatory practices of providers.

Race and Ethnicity

Racial and ethnic disparities are found in many sectors of American life. Black people, people of Hispanic origin, American Indians, Pacific Islanders, and some Asian Americans might be disproportionately represented in the lower socioeconomic ranks, in lower-quality schools, and in poorer-paying jobs (IOM, 2002). Racial residential segregation is a key mechanism through which racism produces and perpetuates social disadvantage. Black and Latino adults are more likely to live in disadvantaged neighborhoods and to have inadequately resourced schools, which yield lower educational attainment and quality (Braveman et al., 2014). Those factors can result in some racial and ethnic minorities experiencing higher rates of chronic and disabling illnesses, infectious diseases, and higher mortality than white Americans. Minority populations have more difficulty than the majority population in locating a “usual source” of medical care, and black and Hispanic adults report greater difficulty than whites in obtaining medical care at a consistent location (IOM, 2002; AHRQ, 2010).

Black adults have earlier onset of multiple illnesses, greater severity and more rapid progression of diseases, higher levels of comorbidity and impairment throughout the life course, and higher mortality than whites up to the age of 65 years. Similar patterns are evident in American Indians, Pacific Islanders, Asian populations of low socioeconomic status, and US-born Latinos (Williams and Wyatt, 2015).

A recent report by the Kaiser Family Foundation (2016) on health and health care by race and ethnicity found that disparities in health and health care continue to pose a persistent challenge in the United States. Among their findings are the following:

  • Some 41 percent of nonelderly people living in the United States are nonwhite.
  • Nonwhite people1 face substantial disparities in access to and utilization of health care.
  • Black, American Indian, and Alaska Native adults fare worse than whites on most of the measures of health status and outcomes that were examined.
  • Nonelderly Hispanic, black, American Indian, and Alaska Native adults remain much more likely than whites to be uninsured despite coverage gains under the ACA.

Lack of insurance, more than any other demographic or economic barrier, adversely affects the quality of health care received by minority populations (OMH, 2011). In the United States, black and Hispanic people have disproportionately lower income and higher rates of uninsurance, which might result in poorer health outcomes and different health-care utilization trends. In 2015, the percentage of people who lacked health insurance coverage for the entire calendar year was 9.1 percent or 29.0 million (USCB, 2016). Non-Hispanic whites had the lowest uninsured rate, 6.7 percent; blacks and Asian people had rates of 11.1 percent and 7.5 percent, respectively; and people of Hispanic origin had the highest rate, 16.2 percent. From 2014 to 2015, the overall rate of health insurance coverage increased in people of Hispanic origin by 3.6 percentage points, in Asian people by 1.9 percentage points, and in non-Hispanic white people by 0.9 percentage points.

During that period, the trend in lack of coverage varied by racial and ethnic group. From 1999 to June 2015, among people 18–64 years old, people of Hispanic origin had the highest percentage without coverage (27.2 percent in the first 6 months of 2015), and non-Hispanic white adults had the lowest except in the first 6 months of 2015, when non-Hispanic Asian adults had the lowest percentage. The difference between the highest and lowest percentages of people 18–64 years old who did not have health insurance among the four racial and ethnic groups narrowed from 1999 to June 2015. The difference was 24.9 percentage points in 1999 (adults of Hispanic origin compared with non-Hispanic white adults) and 19.9 percentage points in the first 6 months of 2015 (adults of Hispanic origin compared with non-Hispanic Asian adults) (NCHS, 2016).

Chen et al. (2016) examined racial and ethnic disparities in health-care access and utilization after the ACA health insurance mandate was fully implemented in 2014. They used the 2011–2014 National Health Interview Survey (NHIS) to examine changes in health-care access and utilization. They noted that the full implementation of the ACA (year indicator, 2014) was associated with substantial reductions in the probabilities of being uninsured, delaying necessary care, and forgoing necessary care, with an increase in the probability of having physician visits compared with the reference year (2011).

A systematic review of 37 studies examined the extent and measurement of racism in health-care providers (Paradies et al., 2014). A number of databases and electronic journal collections were searched for articles published in 1995–2012 (including Medline, CINAHL, PsycInfo, and Sociological Abstracts). The search included published reports (in English) of empirical studies of any design that measured or discussed racism in health-care providers. The studies used a number of measurement approaches and dealt primarily with physicians in the United States. Of the 37 studies, 26 found statistically significant evidence of racist beliefs, emotions, or practices among health-care providers. A 2015 “Perspective” in the New England Journal of Medicine (Ansell and McDonald, 2015) noted evidence that physicians hold stereotypes that are based on patients' race that can influence clinical decisions. The authors stated that despite physicians' and medical centers' best intentions to be equitable, black–white disparities persist in patient outcomes, medical education, and faculty recruitment.

A 2002 Institute of Medicine report, Unequal Treatment, reviewed hundreds of studies of age, sex, and racial differences in medical diagnoses, treatments, and health outcomes. It concluded that black people received less effective care than white people for each disease studied even after matching for socioeconomic factors and insurance status. It found evidence of the role of bias, stereotyping, and prejudice in perpetuating racial and health disparities. Multiple factors contribute to racial disparities in health-care access and utilization, but unconscious bias on the part of medical professionals might contribute to deficits in the quality of care.

The more recent annual National Healthcare Quality and Disparities Report (QDR)2 provides an overview of the quality of health care received by the US population and highlights continued disparities in care that are experienced by different racial and socioeconomic groups (AHRQ, 2017). The 2016 QDR states that disparities were getting smaller from 2000 through 2014–2015 but that disparities persist, especially for poor and uninsured populations. Furthermore, although 20 percent of measures show disparities decreasing for black people and people of Hispanic origin, most disparities have not changed more than a small amount for any racial and ethnic groups. More than half the measures3 show that poor and low-income households had worse care than high-income households; more than 40 percent of the measures show that middle-income households had worse care than high-income households. Nearly two-thirds of the measures show that uninsured people had worse care than privately insured people.

Sex

Women overall have higher health-care utilization than men. Although it had been thought that women receive health care primarily during child-bearing years for reproductive health, many health-care utilizations occur during and after menopause for such issues as cardiovascular disease and osteoporosis (Owens, 2008). Other studies have shown that women make more primary care visits and receive more diagnostic services, screening services, diet and nutrition counseling, and sexual health care than men even though men generally have higher rates of obesity and cardiovascular problems (Salganicoff et al., 2014).

Among people 18–64 years old, women have higher rates of disability and self-reported fair or poor health status. Among all people 18 years and older, women are more likely to delay or not receive care, or to not receive prescription drugs, because of cost. Women are more likely to have a health-care visit in a given year, to have 10 or more visits, and to have a hospitalization or ED visit (NCHS, 2017b). Those findings indicate that although women utilize health-care resources at greater rates, health-care needs go unmet.

Working Age Adults

The average retirement age in the United States in 2013 was 64 years for men and 62 years for women (Munnell, 2015). However, because of disability or for other reasons, many workers leave the workforce before Social Security retirement age or before they become eligible for pensions or vested savings.

There is no question that increased functional limitations and consequent health-care utilization occurs in people as they age; that is true for working-age people and for older adults (NCHS, 2017b). There has also been an increase in rates of health-care utilization and functional limitation in people 45–64 years old (Freid et al., 2012). About 4 percent of people who were 18–44 years old had self-reported heart disease compared with about 12 percent of people 45–64 years old; corresponding rates of cancer were 2 percent and 7 percent, respectively. Almost one-fourth of people 18–44 years old reported low back pain compared with 35 percent of people 45–64 years old (NCHS, 2017b). Rates of self-reported disability among working-age adults and mental illness among all people 18 years and older have remained stable in recent years (Ahrnsbrak et al., 2017; NCHS, 2017b).

Spoken Language

Many Americans in racial and ethnic minorities experience language barriers and have low or no proficiency in speaking, reading, or comprehending English. In a health-care setting, those barriers can present serious challenges to both patients and providers. A systematic review of studies of language barriers in health care for Latino populations showed that access to care, quality of care, and health status all suffer as a result of language barriers. If a patient does not speak the language of his or her health-care provider, multiple adverse effects on the patient's health care might occur. For example, a patient's inability to understand a provider's diagnosis or treatment plan can lead to poor patient satisfaction, poor compliance, and underuse of services (Timmins, 2002).

Flores (2006) examined language barriers to health care in the United States and noted that some 49.6 million Americans speak a language other than English at home, and 22.3 million have low English proficiency, speaking English less than “very well” according to self-ratings. From 1990 to 2000, the number of Americans who spoke a language other than English at home grew by 15.1 million, and the number with low English proficiency grew by 7.3 million. Many patients who need medical interpreters have no access to them. According to one study, in 46 percent of ED cases that involved patients who had low English proficiency, no interpreter was used (Baker et al., 1996). Few clinicians receive training in working with interpreters; only 23 percent of US teaching hospitals provide any such training, and most of them make it optional (Flores, 2006).

Patients who face language barriers are less likely than their English-speaking counterparts to have a usual source of medical care, receive fewer preventive services, and have a greater likelihood of nonadherence to medication prescriptions. Psychiatric patients who encounter language barriers are more likely than others to receive a diagnosis of a severe psychopathologic condition—but are also more likely to leave the hospital against medical advice. Such patients are less likely than others to return for followup appointments after visits to an ED, and they have higher rates of hospitalization and drug complications (Flores, 2006).

Income and Poverty

Income correlates highly with risk factors for chronic disease: for example, people who have lower family income have higher rates of heart disease, stroke, diabetes, or hypertension, and have four or more common chronic conditions (NCHS, 2017b). People in families whose income is less than 200 percent of the federal poverty level are more likely to be obese and to smoke cigarettes than wealthier people. Adults who live in poverty are also more likely to have self-reported serious psychologic distress, as measured by a series of questions about their perceived mental health.

Economic resources (such as income and wealth) enable access to material goods and services, including health-care services. In 2009–2010, people of all racial and ethnic groups who were 18–64 years old and had a family income below 200 percent of the poverty level were more likely than those who had higher family income to delay seeking or not to receive needed medical care because of cost (NCHS, 2012). From 2000 to 2010, the percentage of people 18–64 years old who did not get or delayed seeking needed medical care during the preceding 12 months because of cost increased in all family income groups. During that period, the percentage who had unmet needs for medical care decreased as family income increased from below 200 percent of the poverty level to 400 percent or more of the poverty level. Failure to receive needed medical care because of cost was equally likely in families below the poverty level and those whose income was 100–199 percent of the poverty level.

More recent findings indicate that in 2014 28.6 percent of adults who were living under the poverty level had one or more ED visits compared with 13.5 percent of adults who were at 400 percent of the poverty level, and 8.1 percent of adults under the poverty level had at least one hospitalization compared with 3.8 percent of adults at 400 percent of the poverty level. Despite the high utilization of health-care services by low-income people, adults under the poverty level reported greater rates of not receiving or of delaying medical care, obtaining prescription drugs, and receiving dental care because of costs than adults who were at 400 percent of the poverty level. Thus, health-care needs of those under the poverty level are still being unmet despite their higher utilization of emergency and hospital services (NCHS, 2017b).

Geography

The extent of urbanization has been shown to be associated with health-care utilization in several ways, including the correlation of residents' sociodemographic characteristics with need, risk factors, and access to care. Communities that differ in urbanization differ in their demographic, environmental, economic, and social characteristics, and those characteristics correlate with the magnitude and types of health problems that the communities face. For example, more-urban counties tend to have more health-care providers per capita, and residents of more-rural counties often live farther from health-care resources.

Most studies have compared aggregated magnitude of urbanization—primarily metropolitan compared with nonmetropolitan areas. However, a few studies have compared utilization by more granular urbanization groupings: metropolitan geographic areas, inner cities of large metropolitan areas, fringes of large cities (sometimes called suburbs), and small metropolitan areas; and two categories of nonmetropolitan areas, namely, large rural areas that contain small population centers and rural areas that contain very small population centers (Ingram and Franco, 2014). Those studies show some differences by magnitude of urbanization, but other differences are more pronounced between suburban fringe areas of cities (which tend to have higher average income) and other areas.

Sociodemographic and Characteristics

Residents of rural areas differ from residents of urban areas in a number of important characteristics that correlate with health-care utilization. Rural residents have low incomes: 17 percent of rural workers earn less than the poverty level ($11,490 per year for an individual) compared with 14.6 percent of urban workers (Mueller et al., 2014). They are more likely to classify themselves as white. The greatest racial and ethnic diversity was found in central counties of large metropolitan areas. In 2010, the population of central counties nationwide was 27 percent Hispanic origin, 17 percent non-Hispanic black, 9 percent non-Hispanic Asian or Pacific Islander, 2 percent people identifying with multiple races, and less than 1 percent non-Hispanic or American Indian or Alaska Native (AI/AN) origin. All those groups except AI/ANs were less likely to live in nonmetropolitan counties than in central counties. Among all regions, fringe counties of large metropolitan areas (suburban areas) had the lowest concentration of people in poverty in 2011 (9–13 percent). The proportions of the populations in poverty in all other urbanization levels were comparable.

Risk Factors

Rural residents have a higher percentage of several risk factors associated with poorer health than urban residents. For example, self-reported obesity rates vary by urbanization and increase with increasing rurality. In 2010–2011, people living in central counties of large metropolitan areas nationwide had the lowest age-adjusted prevalence of obesity, and women in the most rural counties had the highest (Meit et al., 2014). In people 20 years old and over, urbanization patterns in ischemic heart disease (IHD) death rates differed by region. In the South, 2008–2010 IHD death rates were lowest in fringe counties of large metropolitan areas and more than 25 percent higher in the most rural counties. In the Midwest, IHD death rates were highest in the most rural counties.

For the United States as a whole, limitation of activity due to chronic health conditions in adults is more common in nonmetropolitan counties than in large metropolitan counties. In all regions except the Midwest, the rate of activity limitation due to chronic health conditions in both men and women generally increases as rurality increases.

Utilization

Using the more aggregated metropolitan/nonmetropolitan grouping, data show that adults who live in nonmetropolitan areas are more likely to report having 10 or more visits to doctors' offices, visits to EDs, or home visits in the preceding year than are people who live in metropolitan areas (NCHS, 2017b). In particular, they have higher rates of ED visits. Nonmetropolitan residents have a slightly higher rate of at least one hospital stay during the year and a similar rate of two or more hospital stays (NCHS, 2017b). Inpatient hospital discharge rates of people 18–64 years old were higher in small metropolitan counties than in metropolitan and nonmetropolitan counties. The average length of stay was highest in central counties of large metropolitan areas (Meit et al., 2014).

Access

Douthit et al. (2015) conducted a literature review of the provision of health care and access in rural areas of the United States. Their findings indicate a reluctance to seek health care because of cultural and financial constraints, which are often compounded by a scarcity of services, a lack of trained physicians, insufficient public transportation, and poor availability of broadband Internet services. Rural residents were found to have poorer health than urban residents, and rural areas had more difficulty in attracting and retaining physicians than their urban counterparts.

The urbanization pattern of the supply of physicians and other providers depends on specialty. However, physician supply generally decreases steadily as rurality increases both nationally and regionally. The National Center for Health Statistics (2017b) NHIS4 found that in 2015, about 10 percent of people who were 18–64 years old had difficulty in accessing needed medical care, which included experiencing delays or not receiving needed care because of cost. The difficulty varied by region: people in the South and the Midwest had the most difficulty, and those in the Northeast and West had the least (NCHS, 2017b).

Urban and rural locations differ in transit options, which affects whether patients can access care. Syed et al. (2013) studied issues of cost, availability of transit, distance to healthcare providers, and travel burden by time and distance. Their literature review found that transportation barriers affect health-care access in as little as 3 percent or as much as 67 percent of the population, depending on the area. Their findings collectively suggest that lack or inaccessibility of transportation might be associated with less health-care utilization, lack of regular medical care, and missed appointments.

Residents of central counties of large metropolitan areas and nonmetropolitan counties have similarly high percentages of residents who lack health insurance. In general, central counties of large metropolitan areas often have the most adverse health measures in the Northeast and Midwest, and nonmetropolitan counties tend to fare the worst in the South and West (Meit et al., 2014). Including all income levels, fringe counties of large metropolitan areas had the largest proportion of privately insured people less than 65 years old in each of the four regions. The coverage gaps in rural America exist in large part because rural Americans have less access to private insurance. Only 64 percent of rural employers offer their workers health insurance compared with 71 percent in urban areas (Coburn et al., 2009). The prevalence of such jobs as farming and contracting means that self-employment is more prevalent in rural America. Not only are the self-employed in all parts of the country insured at lower rates than wage earners, but the self-employed in rural America are insured at even lower rates than the self-employed in urban areas.

Geographic markets also differ with respect to managed care penetration, payer involvement, and state and local oversight (Horwitz and Nichols, 2011; Colla et al., 2016). Physicians in rural areas receive 20 percent of their revenue from Medicaid compared with 17 percent for physicians in urban areas (Bailey, 2012). Up to 84 percent of rural physicians accept new Medicaid patients compared with 65 percent of urban physicians (UnitedHealth Center for Reform & Modernization, 2011).

Geographic Practice Patterns

Variation in surgical rates by geographic area, particularly hospital referral regions, is high and represents both gaps in outcomes research and poor patient decision quality. Outcomes differ from place to place even when patient differences are controlled for. The Dartmouth Atlas Project has conducted extensive research designed to examine local differences in practice patterns (Goodney et al., 2015). It used the hospital referral regions as the geographic unit of study. In 2007 and 2011, rates of bariatric surgery in Medicare beneficiaries varied by a factor of more than 20 and rates of therapeutic endovascular interventions by a factor of more than 6. The rate of open leg bypass surgery was 4.1 per 1,000 Medicare beneficiaries who had diabetes and peripheral artery disease; this rate varied from under 2 to more than 9 procedures per 1,000 people among hospital referral regions.

In 2013, the Institute of Medicine report Variation in Health Care Spending: Target Decision Making, Not Geography evaluated geographic variation in magnitude and growth of health-care spending by people in the United States who have Medicare, Medicaid, private insurance, or no insurance. The report presented findings of analyses of traditional, fee-for-service Medicare (and to a smaller extent Medicare Advantage, or Part C) and commercial insurance. It noted that because of methodologic challenges and data limitations, it did not include separate analyses of variation in the Medicaid and uninsured populations, although estimates of spending by those two groups are included in the area-wide estimates of total healthcare spending. The report's findings are instructive in examining geographic determinants of health-care use:

  • Geographic variation in spending and utilization persists among geographic units and health-care services and over time.
  • After adjustment for differences in the age, sex, and health status of beneficiaries, geographic variation in spending on both Medicare and commercial insurance is not further explained by other beneficiary demographic factors, insurance plan factors, or market-level characteristics. In fact, after controlling for all factors measurable within the data used for the analysis, much of the variation remains unexplained.
  • Variation in spending and utilization remains as units of analysis get progressively smaller (hospital referral regions, hospital service area, hospital, practice, and individual provider). Hospital referral region-level quality is not consistently related to spending or utilization by either Medicare beneficiaries or the commercially insured.

PEOPLE WITH DISABILITIES

Disability is a multidimensional concept. Some health conditions associated with disability result in poor health and extensive health-care needs, but others do not. The disabled population is clinically diverse. Some members have multiple chronic conditions that are stable with treatment and will persist for years; others have extreme functional limitations. Some have mostly severe, persistent behavioral health challenges; others have conditions that are greatly exacerbated by social factors such as lack of housing, food, and supportive personal relationships (Krahn et al., 2015; Meade et al., 2015; Blumenthal et al., 2016). The committee's use of the word disability will vary with the literature that it reviewed.

The term disability implies that there are limitations in a person's ability to function in one or more ways. But types of impairment also differ substantially and might more or less limit a person's ability to function. They might:

  • Impair a person's body structure or function, or mental functioning; examples of impairment are loss of a limb, loss of vision, and memory loss.
  • Limit activity, such as difficulty seeing, hearing, walking, or problem-solving.
  • Restrict participation in normal daily activities, such as working, engaging in social and recreational activities, and obtaining health-care and preventive services (ODPHP, 2017b).

Disability can be caused by or be related to genetic disorders, injuries, illness, or environmental exposures. Disabilities can be temporary, progressive, or intermittent. Thus, it is difficult to generalize about the relationship between disability and health-care utilization without targeting specific causes, diseases, or conditions. However, people who have disabilities have the same general health-care needs as everyone else, and therefore need access to healthcare services (WHO, 2017).

Some diseases or conditions associated with disability are common; others are rare. For example, in 2015 one-fourth of people who were 18–64 years old reported difficulty in performing a basic activity, and 12.5 percent reported complex-activity limitation (NCHS, 2017b).

Without adjustment, population rates of seven selected disabilities among all adults (not limited to working-age adults) increased substantially from 1998 to 2011. The absolute percentage change was greatest for movement difficulties, from 19.3 percent in 1998 to 23.3 percent in 2011. After separate adjustments for trends in age, race and ethnicity, and body mass index distributions, six disability types continued to show increased rates over time; the exception was sensory disabilities. Poor education, poverty, and unemployment remained associated with disability (Iezzoni et al., 2014). People who report disability are at higher risk for poor health outcomes such as obesity, hypertension, falls-related injuries, and mood disorders, including depression (An et al., 2015; Krahn et al., 2015; Meade et al., 2015; CDC, 2017).

People with disabilities use more health-care services than people who do not, in part because they have greater need for medical and health services to treat their conditions (NCHS, 2017b). One-third of adults who had disabilities had at least one ED visit in 2015, compared to 13 percent of adults who had no disability. Some 29 percent of people who had disabilities had 10 or more visits to doctors or EDs or had home health-related visits compared with 8 percent of adults who had no disability (NCHS, 2017b).

People who have disabilities also face a number of barriers to access to health care that are specific to their individual limitations in function (Lagu et al., 2014; Meade et al., 2015). The barriers include physical access and absence of working elevators or ramps, automatic doors, hallways and doors wide enough to accommodate wheelchairs, and accessible parking; policies that discriminate against people with disabilities and the absence of policies in place and enforced that are designed to accommodate people with disabilities; and lack of accommodation for barriers to communication, including large print materials, sign language interpreters, and staff who are willing to try to communicate with impaired patients during scheduling or other interactions.

SUMMARY AND CONCLUSIONS

The health-care delivery system has undergone much change in the past few decades: new and improved drugs, devices, procedures, tests, and imaging machinery have changed patterns of care and sites where care is provided. The growth in ambulatory surgery has been influenced by improvements in anesthesia and analgesia and by the development of noninvasive or minimally invasive techniques. New and improved, and less invasive, procedures are available to treat people for a number of previously untreatable conditions in a variety of new sites of care, and even in physicians' offices. New drugs can cure disease or prolong survival of disease, although often at increased cost or with increased utilization. However, the availability of newer and improved types of health-care services does not mean that they are equally available to all Americans.

People use health-care services to diagnose, cure, or ameliorate disease or injury; to improve or maintain function; or even to obtain information about their health status and prognosis. Many factors affect health-care utilization, including need. The need for services affects differential use of health utilization for specific populations. Ideally, need is the major determinant of health-care utilization, but other factors clearly have an impact. They include poverty and its correlates, geographic area of residence, race and ethnicity, sex, age, language spoken, and disability status. The ability to access care—including whether it is available, timely and convenient, and affordable—affects health care utilization.

For people who have disabilities, accessing health care can be demanding. Many factors facilitate and hinder access to needed health care. Disability correlates with increased health-care utilization, and people who have disabilities often have worse overall health status, are poorer, and have a higher prevalence of poor health behaviors such as smoking and obesity. They also have poorer overall access because of provider discrimination, provider failure to make accommodations, and inadequate communication with providers.

REFERENCES

Footnotes

1

Defined as people who report as being Asian, Hispanic, black, or American Indian or Alaska Native.

2

The Agency for Healthcare Research and Quality produces the QDR, which is mandated by the US Congress.

3

The report assesses the performance of our health-care system and identifies strengths, weaknesses, and disparities in access to quality of health care. Quality is described in terms of the National Quality Strategy priorities, which include patient safety, person-centered care, care coordination, effective treatment, healthy living, and care affordability. The report is based on more than 250 measures of quality and disparities covering a broad array of health-care services and settings.

4

The National Center for Health Statistics NHIS notes that respondents are civilian noninstitutionalized persons; thus active-duty members of the armed forces, incarcerated persons, and people in long-term care institutions are excluded.

Copyright 2018 by the National Academy of Sciences. All rights reserved.
Bookshelf ID: NBK500097
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