4
Communities at Risk

Abstract: When a community has a high rate of uninsurance and subsidies fall short of costs, the financial impact on health care providers may be large enough to affect the availability and quality of local health care services, even for people who are insured. This chapter reviews recent relevant research on this topic. There are stark differences in the burden of uninsurance across communities. Yet the problem of uninsurance may not affect all communities in the same way even when rates of uninsurance are comparable. The dynamics are complex and not well understood. Nevertheless, research on the potential spillover effects of community uninsurance suggests that when local rates of uninsurance are relatively high, insured adults are more likely to have difficulty obtaining needed health care and physicians may be more likely to believe that they are unable to make clinical decisions in the best interest of the patient without losing income. The precise contribution of uninsurance to this dynamic has not been fully defined. Nevertheless, well-documented fault lines in local health care delivery are particularly vulnerable to the financial pressures that may be exacerbated by higher uninsurance. These pressures contribute to the tendency of providers and capital investments in health care facilities and technology to be concentrated in well-insured areas, the reluctance of specialists to assume on-call responsibilities for emergencies, and a cascade of interrelated hospital-based problems such as insufficient inpatient bed capacity, strained emergency services, and barriers to timely trauma care. These problems can only worsen existing disparities between communities in the supply of provider services and other health care resources and may have potentially serious implications for the quality and timeliness of care for insured people, as well as uninsured people, in these communities. Unfortunately, the current economic crisis and associated



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4 Communities at Risk Abstract: When a community has a high rate of uninsurance and subsi- dies fall short of costs, the financial impact on health care proiders may be large enough to affect the aailability and quality of local health care serices, een for people who are insured. This chapter reiews recent releant research on this topic. There are stark differences in the burden of uninsurance across communities. Yet the problem of uninsurance may not affect all communities in the same way een when rates of uninsur- ance are comparable. The dynamics are complex and not well understood. Neertheless, research on the potential spilloer effects of community uninsurance suggests that when local rates of uninsurance are relatiely high, insured adults are more likely to hae difficulty obtaining needed health care and physicians may be more likely to beliee that they are un- able to make clinical decisions in the best interest of the patient without losing income. The precise contribution of uninsurance to this dynamic has not been fully defined. Neertheless, well-documented fault lines in local health care deliery are particularly ulnerable to the financial pres- sures that may be exacerbated by higher uninsurance. These pressures contribute to the tendency of proiders and capital inestments in health care facilities and technology to be concentrated in well-insured areas, the reluctance of specialists to assume on-call responsibilities for emergencies, and a cascade of interrelated hospital-based problems such as insufficient inpatient bed capacity, strained emergency serices, and barriers to timely trauma care. These problems can only worsen existing disparities between communities in the supply of proider serices and other health care re- sources and may hae potentially serious implications for the quality and timeliness of care for insured people, as well as uninsured people, in these communities. Unfortunately, the current economic crisis and associated 

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 AMERICA’S UNINSURED CRISIS growth in unemployment will fuel further decline in the number of people with health insurance and likely intensify financial pressures on local health care deliery. Many of America’s towns and cities have high concentrations of chil- dren and adults under age 65 who lack health insurance (DeNavas-Walt et al., 2008). Thus, two questions arise: What are the implications of high rates of uninsurance for affected communities and for insured people in those communities? Is the financial impact of uninsurance large enough to affect the availability and quality of local health care services for everyone in the community, even for the people who have health insurance? In 2003, an earlier Institute of Medicine (IOM) committee warned of the potential harms of high rates of uninsurance for local health care, including reduced access to clinic-based primary care, specialty services, and hospital-based emergency medical services and trauma care (Box 4-1) (IOM, 2003). Such consequences reduce access to clinic-based primary care, specialty services, and hospital-based care, particularly emergency medical services and trauma care. The prior IOM committee also observed that the evidence available in 2003 was observational and largely cross-sectional in design, making it difficult to infer causal relationships between uninsurance and these harms, and that there was a dearth of systematic data to measure the size, strength, and scope of community effects of uninsurance. Thus, the committee called for additional research to measure the size, strength, and scope of potential adverse community effects of uninsurance. As is explained later on in the chapter, sufficient evidence is still lacking and the methodologic obstacles to unraveling the dynamics of community effects remain very challenging. This chapter reviews what is currently known about the impact of high community-level rates of uninsurance on people who have health insurance in affected communities, using the definition of community used in the 2003 IOM report (Box 4-2). The next section of the chapter provides some context for assessing the consequences of high uninsurance rates in com- munities. The third section discusses challenges in evaluating the impact of high community-level uninsurance rates and presents findings from recent studies of the spillover effects of uninsurance on communities including an analysis commissioned by the committee. The fourth section reviews re- cent evidence on a range of well-documented problems in local health care delivery that are vulnerable to financial pressures and may intensify when a large proportion of the community is uninsured. The final section of the chapter summarizes the committee’s overall conclusions.

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 COMMUNITIES AT RISK BOX 4-1 The IOM’s Previous Findings on the Community Effects of Uninsurance, 2003 One of the reports in the IOM’s earlier series of reports on the consequences of uninsurance was a 2003 report entitled A Shared Destiny: Community Effects of Uninsurance. The following is an excerpt from that report. The committee draws two conclusions based on its expert judgment and the sufficiency of the evidence base: 1. A community’s high uninsured rate has adverse consequences for the community’s health care institutions and providers. These con- sequences reduce access to clinic-based primary care, specialty services, and hospital-based care, particularly emergency medical services and trauma care. 2. Research is needed to more clearly define the size, strength, and scope of adverse community effects that are plausible consequences of uninsurance. These include potentially deleterious effects on access to primary and preventive health care, specialty care, the underlying social and economic vitality of communities, public health capacity, and overall population health. What we don’t know can hurt us. There is much that is not understood about the relationships between health services delivery and financing mechanisms and even less about how the current structure and performance of the American health care enterprise affect communities’ economies and the quality of social and political life in this country. Because policy makers and researchers have not asked or examined these questions through comprehensive and systematic research and analysis, there is a limited body of evidence of mixed quality on community effects. The committee believes, however, that it is both mistaken and dangerous to assume that the prevalence of uninsurance in the United States harms only those who are uninsured. It calls for further research to examine the effects of uninsurance at the community level but nonetheless believes there is sufficient evidence to justify the adoption of policies to address the lack of health insurance in the nation. Rather, the call for more research is to say that, as long as we as a nation tolerate the status quo, we should more fully understand the implications and consequences of our stalemated national health policy. SOURCE: IOM (2003).

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 AMERICA’S UNINSURED CRISIS BOX 4-2 What Is a Community? The term community, as used here, refers to a group of people who: (1) live in a particular geographic area, and (2) have access to a common set of health resources. The term community can describe locations as small as neighborhoods and as large as metropolitan areas. How expansive a community is depends partly on the patterns of social, health care and economic interactions that are being analyzed. Thus, for example, the community that shares primary care resources such as physician practices and clinics may be relatively small and local, while the community sharing an advanced trauma care facility may encompass an entire metropolitan area and adjacent rural communities. The boundaries of a community can extend beyond where its residents live into where its residents work or routinely travel. SOURCE: IOM (2003). CONTExT FOR ASSESSINg COMMuNITy-LEvEL CONSEquENCES OF uNINSuRANCE The burden of Providing Care to uninsured Patients Although there is no definitive accounting of the financial burden of uninsurance at the local or national level, it has been estimated that the annual cost of health services provided to uninsured people in the United States will total about $86 billion in 2008 (Hadley et al., 2008). Uninsured patients will pay approximately $30 billion for these services out of pocket and receive the other $56 billion worth of services as uncompensated care.1 An estimated $43 billion (75 percent) of the $56 billion will be covered through various government subsidies, including Medicare and Medicaid, disproportionate share hospital (DSH) payments, indirect medical educa- tion payments, direct care programs (e.g., community health centers), and state and local tax appropriations. Payments for uncompensated care from the government are not neces- sarily distributed to health care providers in proportion to the uncompen- sated care they provide. Thus, many hospitals and other local providers bear a disproportionate and substantial financial burden due to their inabil- ity to receive adequate payment for the care they provide. Grady Memorial 1 In this analysis, uncompensated care is defined as all care not paid for out of pocket by the uninsured.

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 COMMUNITIES AT RISK BOX 4-3 Challenges at Grady Memorial Hospital in Atlanta Grady Memorial Hospital is the only public hospital in Atlanta, Georgia, and the largest hospital in the state. An estimated one-third of Grady’s patients are uninsured. The hospital receives substantial financial support from local Fulton and DeKalb counties and other public sources, but the subsidies fall short of the hospital’s total costs for uncompensated care. Grady Memorial Hospital has run annual deficits for a decade. When this report was developed, the hospital estimated that its 2008 deficit would total $51 million. Because of continual losses, Grady has delayed capital projects, postponed updating clinical technology, curtailed investment in information technology, and faced difficulties recruiting nurses and pharmacists. The hospital recently re- ported that it needed $370 million to overhaul operations; make capital improve- ments; and purchase basic diagnostic equipment, including X-ray machines, electrocardiogram and ultrasound devices, CT scanners, and MRI machines. In 2006, Grady cared for 24 percent of Georgia’s major trauma cases.* Many insured state residents may go elsewhere for routine health care services. But Grady is one of only four level-1 trauma centers in Georgia, and its service areas include a population of approximately 5.5 million people. Thus, when insured state residents experience severe trauma, they are likely to be transported to Grady. * Personal Communication, G. Bishop, Bishop+Associates, October 29, 2008. SOURCES: American College of Surgeons (2006); The Fulton-Dekalb Hospital Authority (2007); Grady Health System (2008a,b); Greater Grady Task Force (2007); Haley (2008). Hospital illustrates how hospitals may be strained financially by the crisis in uninsurance and how financial burdens may threaten the quality of trauma and other care—even for patients who have health insurance (Box 4-3). The extent to which hospitals’ unreimbursed costs are absorbed by hospitals or passed on in the form of higher charges to insured patients (as many believe to be the case) has not been adequately documented and should be the subject of further research. Differences in Community-Level uninsurance Rates National trends in uninsurance rates, such as those discussed in Chap- ter 2, mask the tremendous variation in uninsurance rates across the United States. In 2007, for example, state-level uninsurance rates among the non- elderly population ranged from as low as 6 percent in Massachusetts to as high as 28 percent in Texas (U.S. Census Bureau, 2008a). Uninsurance rates in different counties within individual states also vary greatly, as shown in Figure 4-1.

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 FIguRE 4-1 Percentage of the nonelderly U.S. population without health insurance, by county, 2005. SOURCE: U.S. Census Bureau (2008b). 4-1 new (broadside maybe)

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 COMMUNITIES AT RISK FIguRE 4-2 Variation in uninsurance rates among communities within Los Angeles County, 2003. SOURCE: Yu et al. (2006). Reprinted, with permission, from UCLA Health Pol- icy Fact Sheet, 2006. Copyright 2008 bynew UCLA Center for Health Policy 4-2 the Research. In fact, even within counties, there are enormous variations in unin- surance rates. Figure 4-2 shows, for example, that across zip codes within Los Angeles county, uninsurance rates among the nonelderly population in 2005 ranged from 6 percent to 45 percent (Yu et al., 2006). HOW THE INSuRED POPuLATION IN A COMMuNITy MAy bE AFFECTED by HIgH RATES OF uNINSuRANCE As noted earlier, there are considerable analytic challenges to evaluating the effects of community-level uninsurance rates on insured populations

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 AMERICA’S UNINSURED CRISIS and health care delivery systems. The dynamics are complex and not well understood, in part because sufficient empirical data are simply not avail- able. Teasing out the impact of uninsurance from other related dynamics is very difficult. Individual and family insurance status changes over time. Employers switch health plans and low-income families cycle in and out of eligibility for public health insurance (Federico et al., 2007). Even minimal disruptions in coverage—such as switching between types of coverage— have been shown to affect use of health care services (Bindman et al., 2008; Lavarreda et al., 2008; Leininger, 2009). In addition, even when community rates of uninsurance are compa- rable, the problem of uninsurance may not affect communities the same way. Different communities’ demographic makeup, employment patterns, residents’ income distribution, and characteristics of the local health care system that may affect community-level health care utilization and out- comes directly, make it difficult to disentangle the spillover effects on health care utilization and outcomes due to uninsurance. And, to complicate mat- ters even further, an extensive body of research has shown wide geographic variation in the quantity and quality of health care services provided even to the insured population, particularly the Medicare population (Fisher et al., 2003; Fuchs, 2004; Wennberg and Wennberg, 2003; Wennberg et al., 2006). Perhaps, as a consequence, the committee found only limited new research to inform its deliberations. Nevertheless, important new data has emerged since the IOM last examined the community consequences of uninsurance—most notably, the survey and site visit data from the Community Tracking Study (CTS) of the Center for Studying Health System Change (HSC). HSC is a policy research organization in Washington, DC, that has been studying changes in the American health care system and the forces driving these changes since 1996 (HSC, 2006). The data that support much of HSC’s research come from the CTS, which consists of periodic surveys of U.S. households and physicians. The household survey, among other things, tracks changes in health care access, utilization, insurance, perceptions of care quality, and problems paying medical bills (HSC, 2008). In the physician survey, practicing physicians across the country provide perspectives on how health care delivery is changing and answer questions about their practice arrange- ments and care practices. Other researchers have also produced valuable community-level research (Cook et al., 2007; Fairbrother et al., 2003; Gusmano et al., 2002; Hicks et al., 2006; Regenstein et al., 2004; Taylor et al., 2006). For the first four rounds of the Community Tracking Household and Physician Surveys, conducted about every 2 years since 1996, the survey samples were clustered in 60 communities, randomly selected to provide a representative profile of change across the United States, and supple-

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 COMMUNITIES AT RISK mented by a national sample (HSC, 2008). Among these communities are 48 “large” metropolitan areas (with populations greater than 200,000). Twelve of these communities (Boston; Miami; Orange County, California; northern New Jersey; Cleveland; Indianapolis; Phoenix; Seattle; Lansing; Greenville; Syracuse; and Little Rock) were randomly selected to be studied in depth using larger survey samples and site visits with health care lead- ers. These data can be used to draw conclusions for the nation and for individual communities. Because of the paucity of research on how the insured population in a community is affected by high community-level rates of uninsurance, the committee asked Mark Pauly and José Pagán to conduct a special analysis of the CTS survey findings. If high rates of uninsurance affect the quality of care provided to the insured population, this should be evident in the CTS survey results (Pauly and Pagán, 2008).2 Pauly and Pagán had previously used data from CTS surveys to develop an analytic model for assessing how the deleterious effects of uninsurance in a community “spill over” into the greater privately insured community (Pagán and Pauly, 2006; Pauly and Pagán, 2007). The findings from the commissioned analysis by Pauly and Pagán, as well as from other studies of the spillover effects of uninsurance in communities with high levels of uninsurance, are presented below. These studies suggest that when local rates of uninsurance are relatively high, insured adults in affected communities are more likely than adults in other communities to have difficulties obtaining needed health care. As noted in Chapter 2, the current economic crisis and associated growth in un- employment will fuel further decline in the number of people with health insurance and likely further intensify financial pressures on local health care delivery. Commissioned Analysis of the Spillover Effects of Community-Level uninsurance In the analysis commissioned by the committee, Pauly and Pagán used data from the 2003 Community Tracking Household Survey to assess the experiences of privately insured, working-age adults ages 18 to 64 in U.S. communities with high rates of uninsurance (Pauly and Pagán, 2008).3 The 2003 CTS is described in Box 4-4. The household survey is particularly 2 The complete text of the commissioned analysis of the effects of uninsurance on privately insured people and local communities by Mark Pauly, Ph.D., and José Pagán, Ph.D., is avail- able on the IOM website for the Health Insurance Status and Its Consequences project at http://www.iom.edu/CMS/3809/54070.aspx. 3 The CTS study population included privately insured, working-age adults ages 18 to 64. References in the text to “adults” refer to adults in this age group.

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00 AMERICA’S UNINSURED CRISIS BOX 4-4 Uninsurance Rates Among Communities in the 2003 Community Tracking Study (CTS) The 2003 CTS contained counties or groups of counties in metropolitan areas and rural areas randomly selected to ensure representation of the U.S. population. The CTS sample of 60 communities included 48 metropolitan areas with a popula- tion of more than 200,000; 3 communities with a population of less than 200,000; and 9 nonmetropolitan areas. Fifteen of the 60 communities with uninsured and underinsured rates in the lower 25th percentile were identified as “low medical cost burden” communities. Fifteen of the 60 communities with uninsured and underinsured rates in the upper 25th percentile were identified as “high medical cost burden” communities. Uninsurance rates (percentage uninsured) among the 60 communities in the 2003 Community Tracking Household Survey ranged from a low of 5.0 percent in Bridgeport, Connecticut, to a high of 30.2 percent in Los Angeles, California. The following 7 communities had uninsurance rates between 20 and 30 percent in 2003: Uninsured rate Community (percentage uninsured) Riverside, California 20.1 Houston, Texas 23.8 Shreveport, Louisiana 23.4 Orange County, California 24.9 West Palm Beach, Florida 26.1 Miami, Florida 26.7 Los Angeles, California 30.2 SOURCE: Cunningham (2007). helpful for assessing the experiences of privately insured adults in com- munities with high rates of uninsurance because it determines respondents’ health insurance status and asks respondents a series of well-validated survey questions related to individuals’ access to care and satisfaction with the health care services they receive.4 Pauly and Pagán used multilevel logistic regression models to assess how higher rates of community-level uninsurance affect access to and sat- 4 In 2007, the Community Tracking Household Survey was renamed the Health Tracking Household Survey. The survey design was scaled back to a national sample as a consequence of reduced funding and the new survey will not support community-level analyses the way the earlier Community Tracking Household Surveys did (personal communication, P. Ginsburg, Center for Studying Health System Change, December 2, 2008).

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0 COMMUNITIES AT RISK isfaction with care among privately insured, working-age adults, controlling for respondents’ health status, age, education, race and ethnicity, marital status, gender, and income/family poverty level. As shown in Table 4-1, the results of this analysis suggest that higher rates of community uninsurance are negatively associated with several well-validated indicators of individu- als’ access to and satisfaction with health care. The access indicators include having a place to go when sick, having a doctor’s visit, visiting a doctor for routine preventive care, and seeing a specialist when needed. The satisfac- tion measures include being very satisfied with the choice of primary care physician, being very satisfied with health care received during the last 12 months, trust that one’s doctors put medical needs above all other consid- erations, and being very satisfied with the choice of specialists. Using the findings of the regression analysis, Pauly and Pagán estimated the impact of a 10-percentage-point increase in community-level uninsur- ance rates (from the sample mean of 15.2 percent to 25.2 percent) on pri- TAbLE 4-1 Estimated Impact of Living in a Higher Uninsurance Community for Working-Age, Privately Insured Adultsa 95% confidence Odds ratiob Effects on access to and satisfaction with care interval Effect on access to care • ave a place to go when sick or in need of advice 0.63c H (0.60-0.68) about health (n = 23,885) • Had doctor’s visit in the past year (n = 23,956) 0.89c (0.85-0.92) • ad doctor’s visit for routine preventive care 0.91c H (0.88-0.94) (n = 23,956) • aw specialist in the last 12 months when 0.85d S (0.76-0.95) needed (n = 9,896) Effect on satisfaction with care • ery satisfied with choice of primary care physician 0.75c V (0.72-0.78) (n = 22,062) • Very satisfied with health care (n = 22,791) 0.90c (0.86-0.94) • Trusts doctors (n = 20,815) 0.93c (0.91-0.96) • ery satisfied with choice of specialist seen 0.87c V (0.82-0.92) (n = 9,586) a The study population included working-age, privately insured adults ages 18 to 64. b Odds ratios less than 1.0 indicate that higher community uninsurance is associated with lower odds of the specific access or satisfaction variable for privately insured adults in the community. Thus, for example, the odds ratio of 0.63 (see above) indicates that privately insured adults in communities with higher uninsurance rates were less likely to “have a place to go when sick or in need of advice.” c Statistically significant difference, p < 0.001. d Statistically significant difference, p < 0.01. SOURCE: Pauly and Pagán (2008).

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0 AMERICA’S UNINSURED CRISIS vately insured adults’ reported access to and satisfaction with health care. The researchers found that higher community-level uninsurance rates were associated with small but significant declines in the measures of the privately insured adult population’s access to care—including a 4.0-percentage-point decline (from 92.0 to 88.0 percent) in the probability of having a place to go when sick and a 2.2-percentage-point decline (from 64.8 to 62.6 percent) in the probability of having a routine preventive care visit. In addition, Pauly and Pagán found that higher community-level uninsurance rates were associated with small but significant declines in the measures of the privately insured adult population’s satisfaction with care—including a 7.1- percentage-point decline (from 62.7 to 55.6 percent) in satisfaction with one’s choice of primary care physician and a 2.7-percentage-point decrease (from 74.5 to 71.8 percent) in satisfaction with one’s choice of specialist. Other Research on Spillover Effects of High Community-Level Rates of uninsurance Other research is consistent with the findings of the 2008 Pauly and Pagán analysis commissioned by the committee. In 2006, Pagán and Pauly used the 2000-2001 Community Tracking Household Survey to assess the impact of community uninsurance on the medical needs of working-age, privately and publicly insured adults. They found that in communities with higher uninsurance rates, insured adults were more likely to report having an unmet medical need in the previous year (Pagán and Pauly, 2006). In- sured adults in such communities were also more likely to report being in only fair to poor health. In 2007, Pagán and colleagues used the 2000-2001 Community Track- ing Physician Survey and the 2000-2001 and the 2003 Community Tracking Household Survey to assess the relationship between community uninsur- ance and primary care physicians’ career satisfaction, perceptions about quality of care, and patients’ trust in their physicians (Pagán et al., 2007).5 The analysis included data from 4,920 primary care (i.e., specialists in in- ternal medicine or general family practice) physician respondents who spent at least 20 hours per week in direct patient care. 5 The 2000-2001 Community Tracking Physician Survey asked physicians about the extent to which they agree with the following statements: (1) I have the freedom to make clinical decisions that meet my patients’ needs; (2) It is possible to provide high quality care to all of my patients; (3) I can make clinical decisions in the best interest of my patients without the possibility of reducing my income; (4) The level of communication I have with specialists about the patients I refer to them is sufficient to ensure the delivery of high-quality care; and (5) It is possible to maintain the kind of continuing relationships with patients over time that promote the delivery of high-quality care. The researchers developed dichotomous variables to capture whether a respondent agreed strongly or somewhat with each of the statements.

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0 COMMUNITIES AT RISK In this analysis, Pagán and colleagues found a significant negative relationship between higher rates of uninsurance and physicians’ career satisfaction. They found that high uninsurance was negatively correlated with physicians’ perceptions about quality of care. Higher community-level uninsurance rates were negatively related to the beliefs among physicians that they were able to make clinical decisions in the best interest of their patient without losing income or that they had sufficient communication with their patients’ specialists. Furthermore, patients in communities with higher uninsurance rates were less likely to report trusting their physicians. In-depth site visits by HSC researchers have underscored these findings (Hurley et al., 2005; Lesser et al., 2005). In a 2008 study, Pagán and colleagues used a multilevel logistic regres- sion model to assess whether higher community-level rates of uninsurance affected mammography screening among insured women age 40 to 69, including not only privately insured women, but also women with Medicare or other sources of coverage (Pagán et al., 2008). They found that higher community-level uninsurance rates were associated with a significant de- cline in the insured women’s access to care. vuLNERAbILITIES IN LOCAL HEALTH CARE DELIvERy Growing economic disparities between U.S. communities with respect to geographic distribution of health care services, including new diagnostic and therapeutic techniques and technology, have been well documented by HSC. As discussed below, the following widespread problems in local health care delivery not necessarily attributable to uninsurance are sensitive to financial pressures and may be exacerbated by higher community-level uninsurance rates: • ealth care providers and capital investment tend to locate in well- H insured areas (and away from high uninsurance communities). It is common for hospitals to focus major investments in more affluent locations with well-insured populations. • hysicians and other health care providers are drawn to newer P facilities with the most up-to-date technologies. This phenomenon makes it especially challenging for financially stressed hospitals in communities with high uninsurance rates to recruit on-call special- ists for emergencies. • range of hospital-based emergency care problems—including A limits on inpatient bed capacity, outpatient emergency services, and timeliness of trauma care—have serious implications for the quality and timeliness of care for insured as well as uninsured patients.

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0 AMERICA’S UNINSURED CRISIS The dynamics in communities with high or increasing uninsurance rates may thus make it increasingly difficult for people in those communities who have health insurance to obtain needed and high-quality health care services, and local primary care providers in those communities may find it more difficult to obtain consultations with specialists for their patients. The quality of care may be undermined not only in individual medical practices in the affected community, but also in the overall local health care delivery system. Consequently, patients in the affected community may lose faith in the ability of their physicians to act in their best interest (Pagán and Pauly, 2006). The precise contribution of uninsurance to this dynamic is neither well understood nor readily measured. Bazzoli and colleagues analyzed reductions in the services that hospitals provided between 1996 and 2002. The researchers found that non-safety net hospitals cut back maternity care, emergency department services, AIDS services, psychiatric emergency care, and substance abuse care—services commonly used by indigent patients (Bazzoli et al., 2005). However this research did not distinguish between high and low uninsurance communities. Relocation of Health Care Services Local health care delivery may be particularly vulnerable to the fi- nancial pressures associated with uninsurance. It is well established that people without health insurance see physicians far less often and use fewer health care services overall than their peers who have health insurance. This report has cited extensive evidence on the differences in health care utilization between insured and uninsured populations (see Chapter 3). The presence of a larger share of “low demanders” for health care services (i.e., uninsured individuals) could ultimately impact the quantity and quality of care available to everyone—including the insured—at the local level (Pauly and Pagán, 2007). Similarly, if a community has a high rate of uninsurance and government or other subsidies do not cover the costs of care provided to the uninsured, related financial pressures may motivate some providers to relocate to communities where patients are more likely to have health insurance. There is growing evidence that hospitals and physicians operating in areas with high rates of uninsurance tend to focus more intensively on revenue-generating activities and to drop or limit unprofitable services (Cunningham et al., 2008). Health care providers can also limit the amount of charity care they provide. In communities with high rates of uninsurance, however, limiting charity care is hard to do. Another alternative for health care providers is to extend or relocate services to more affluent areas where a higher percentage of patients is in-

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0 COMMUNITIES AT RISK BOX 4-5 Denver Hospitals Relocating to More Affluent Areas Hospital relocations in Denver are an example of health care services mov- ing away from high uninsurance communities to areas where a greater share of the patient population has health insurance. In 2004, 23.1 percent of the Denver county population was uninsured. In 2007, two hospitals—the Denver Children’s Hospital and the University of Colorado Hospital—moved to more affluent suburbs. A third Denver hospital, St. Anthony’s Central Hospital, is building a new facility and plans to move out of the city in 2010. The local press has reported that the state’s largest safety net hospital and health care system, Denver Health, is attempting to absorb the patients left be- hind. In August 2007, the hospital reported diverting one of every five ambulances to more remote hospitals for lack of inpatient beds. In November 2008, Denver Health experienced a reported 19 percent increase in emergency department visits by uninsured patients compared to the previous year. SOURCE: Abelson (2008); Colorado Health Institute (2006); Human (2007). sured (Hurley et al., 2005). In 2005, HSC researchers conducted site visits and interviewed more than 1,000 respondents in randomly chosen markets from large, mid-size, and small metropolitan areas (Lesser et al., 2005). These researchers found growing economic disparities with respect to geo- graphic distribution of health care services, including new diagnostic and therapeutic techniques and technology (Hurley et al., 2005). Although some institutions were upgrading existing facilities, many hospitals were focusing major investments in more affluent locations with well-insured populations. The newer facilities with the most up-to-date technologies become a magnet for physicians and other health care providers. Hospital relocations in Denver illustrate this phenomenon of hospitals relocating to more affluent communities where people are more likely to have health insurance (Box 4-5). Such trends can only worsen existing dis- parities in the supply of physician services and other health care resources among U.S. communities. Challenges in Hospitals’ Recruitment of On- Call Specialists for Emergencies Attracting specialty physicians to provide on-call coverage for hospital emergency departments has been a nationwide problem for more than a decade (IOM, 2007; O’Malley et al., 2007). In 2005, the American College

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0 AMERICA’S UNINSURED CRISIS of Emergency Physicians surveyed medical directors of hospital emergency departments. Emergency department directors in large and small hospitals across all geographic regions reported on-call coverage problems. Over- all, almost three-quarters of the survey respondents (73 percent) reported difficulties staffing on-call specialists (American College of Emergency Physicians, 2006). Site visit reports by HSC researchers have documented the problem in hospitals in northern New Jersey, Seattle, Phoenix, Miami, and Little Rock (O’Malley et al., 2005). Although staffing on-call physician specialists for hospital emergency departments may be a challenge faced by hospitals in all types of commu- nities, hospitals already stressed by uninsurance are hard-pressed to cope with the added financial demands. In communities with higher rates of un- compensated care, local physician specialists may be increasingly reluctant to assume on-call responsibilities to cover emergencies. Obstacles to obtaining specialty care on an emergency basis contribute to the crowding of hospital emergency departments and reduce the quality of care for everyone. When backup specialists are unavailable to perform emergency surgeries and provide other forms of definitive care, all patients are affected. Already stressed emergency departments may divert ambu- lances to more distant hospitals, leading to potentially dangerous delays in time-sensitive care for people who have conditions such as major trauma, myocardial infarction (heart attack), or stroke. In fact, it appears that higher rates of uncompensated care associated with uninsurance are a major obstacle to resolving the problem (American Hospital Association, 2006). In many areas of the country, hospitals are able to recruit on-call medical staff only by paying large subsidies, particu- larly for neurosurgeons and orthopedists but also for specialists in other surgical specialties, neurology, and psychiatry (Burt and McCaig, 2006). Thus, for example, a hospital in the Phoenix area reported paying local neurosurgery groups a $10,000 weekly supplement to routine per patient reimbursements in order to ensure trauma care coverage (Hurley et al., 2005); a Little Rock hospital pays trauma surgeons an additional $1,000 per night; and a Miami hospital reports $10 million in annual payments to on-call specialists (O’Malley et al., 2007). In communities with high rates of uninsurance, hospitals may have great difficulty absorbing such costs. Other vulnerabilities in the quality of Emergency Medical Services Other problems in the quality of hospital-based emergency care include the crowding of hospital emergency departments, ambulance diversions, delays in patient care, and the “boarding” of critically ill patients (Olshaker and Rathlev, 2006; Vieth and Rhodes, 2006). “Boarding” occurs when an emergency department patient is admitted to the hospital without an

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0 COMMUNITIES AT RISK available inpatient bed and, as a result, must remain in emergency room hallways or treatment rooms (IOM, 2007). These problems, although not directly attributable to uninsurance, may be exacerbated by high uninsur- ance rates. Recent research has documented that, conventional wisdom to the contrary, uninsurance is not a primary cause of emergency department overcrowding and uninsured patients are not more likely than insured pa- tients to visit emergency departments for convenience and less acute condi- tions (Newton et al., 2008). Financially stressed hospitals often limit the number of inpatient beds available for emergency admissions to minimize overhead costs and to max- imize revenues from more profitable elective surgical admissions (O’Shea, 2007; U.S. Government Accountability Office, 2003). Generally, it is more efficient for hospitals to operate at or near full inpatient capacity than to staff beds for occasional increases in demand. However, shortages of inpa- tient beds, as well as shortages of on-call specialists, contribute to crowd- ing of hospital emergency departments and longer waits for patients, the boarding of patients who need to be admitted, and ambulance diversions (IOM, 2007; O’Malley et al., 2005, 2007). Longer waits are especially perilous for patients with time-sensitive life-threatening conditions, such as acute myocardial infarction, ischemic stroke, and severe sepsis or septic shock. As time elapses, there is a greater risk of irreversible damage and mortality. The Joint Commission has cited emergency department crowding as a cause of treatment delays for 31 percent of sentinel events involving death, serious physical injury including loss of limb or function, or psychological injury (The Joint Commission, 2002). Treatment delays in hospital emergency departments also impede the responsiveness of community emergency medical services. Emergency medi- cal services providers may be required to stay with critical patients waiting for care (Burt et al., 2006). A Los Angeles study, for example, found that ambulance crews had a median waiting time of 27 minutes in the emergency department; the longest wait was 6.75 hours (Eckstein and Chan, 2004). More than one-third of hospitals reported going on ambulance diver- sion status at some point during the past year, according to the 2003-2004 National Hospital Ambulatory Medical Care Survey (Burt and McCaig, 2006). Ambulance diversions occur when hospital emergency rooms are so crowded that they cannot taken any more patients. These also delay patient care. When hospitals are crowded and there are no inpatient beds available, critically ill emergency patients are boarded in emergency department hall- ways and treatment rooms (American College of Emergency Physicians, 2008; O’Shea, 2007; Trzeciak and Rivers, 2003). In particularly busy hospitals, some patients may be boarded for 1 or 2 days (IOM, 2007). The

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0 AMERICA’S UNINSURED CRISIS Memphis Regional Medical Center, for example, has reported boarding patients for up to 48 hours (Wilson et al., 2005). Boarding may be harmful to critically ill patients and also diverts emergency department staff from attending to new incoming patients. The problems of crowding of hospital emergency departments, ambu- lance diversions, delays in patient care, and patient boarding, which may be exacerbated by high uninsurance rates, have potentially grave implications for the quality and timeliness of care not only for uninsured patients, but for insured patients as well. CONCLuSION This chapter has examined what is known about the impact of unin- surance at the community level. There is no definitive accounting of the financial burden of uninsurance at the local or national level, but the total dollar value of services provided to uninsured people without reimburse- ment or subsidies nationwide was estimated to be $56 billion in 2008. In the aggregate, public subsidies cover a large share, but not all the costs of care provided to people without health insurance. Many local hospitals and other health providers who serve uninsured patients continue to bear a disproportionate and substantial burden of these costs. When a community has a high rate of uninsurance and subsidies fall short of costs, the financial impact on providers may be large enough to affect the availability, quality, and cost of local health care services for everyone, even for the people who have health insurance. There are stark differences in community-level uninsurance rates across states, counties, and even areas within counties. In 2007, state-level unin- surance rates among the nonelderly ranged from 6 percent in Massachusetts up to almost 28 percent in Texas. Within Los Angeles county, uninsurance rates in the nonelderly population in 2005 ranged from 6 percent to 45 percent. Evaluating the effects of community-level uninsurance rates on in- sured populations and health care delivery systems is challenging. Even when the rates of uninsurance are comparable, uninsurance may not affect all communities the same way. Community demographic characteristics, employment patterns, income, and the characteristics of the local health care system may affect health care utilization and outcomes directly, mak- ing it difficult to disentangle the spillover effects due to community-level uninsurance. Furthermore, an extensive body of research has shown wide geographic variations in the quantity and quality of health care services provided to the insured population. Nevertheless, an empirical study com- missioned by the committee and other research on the potential spillover effects of community uninsurance suggests that when community-level rates

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0 COMMUNITIES AT RISK of uninsurance are relatively high, insured adults in those communities are more likely to have difficulties obtaining needed health care and to be less satisfied with the care they receive. For example, privately insured, work- ing-age adults in higher uninsurance areas are less likely to report having a place to go when sick, having a doctor’s visit or routine preventive care, and seeing a specialist when needed. They are also less likely to be satisfied with their choice of primary care and specialty physicians or to feel trust in their doctor’s decisions. The Center for Studying Health System Change has documented grow- ing economic disparities among communities with respect to the geographic distribution of health care services. The specific contribution of uninsurance to these problems is not known. The dynamics are complex and not well understood, in part because sufficient empirical data to inform the issue of how uninsurance affects communities are simply not available. But wide- spread problems in health care delivery in local communities, including dis- parities in the supply of physician services and other health care resources that are exacerbated by the burden of uninsurance, have potentially grave implications for the quality and timeliness of care not only for people who lack health insurance, but also for people who have health insurance. REFERENCES Abelson, R. 2008. Uninsured put a strain on hospitals. The New York Times, http://www.nytimes. com/2008/12/09/business/09emergency.html?pagewanted=2&_r=1&ei=5070&emc=eta1 (accessed December 9, 2008). American College of Emergency Physicians. 2006. On-call specialist coverage in U.S. emer- gency departments. ACEP Surey of Emergency Department Directors, April 00, http://www.acep.org/pressroom.aspx?id=25262 (accessed August 28, 2008). ———. 2008. Emergency department crowding: High-impact solutions, http://www.acep. org/workarea/showcontent.aspx?id=37960 (accessed August 28, 2008). American College of Surgeons. 2006. Resources for optimal care of the injured patient, http:// www.facs.org/trauma/hospitallevels.pdf (accessed October 13, 2008). American Hospital Association. 2006. Taking the pulse: The state of America’s hospitals, http://www.aha.org/aha/content/2006/PowerPoint/StateHospitalsChartPack2006.PPT (accessed October 16, 2008). Bazzoli, G. J., R. Kang, R. Hasnain-Wynia, and R. C. Lindrooth. 2005. An update on safety-net hospitals: Coping with the late 1990s and early 2000s. Health Affairs 24(4):1047-1056. Bindman, A. B., A. Chattopadhyay, and G. M. Auerback. 2008. Medicaid re-enrollment policies and children’s risk of hospitalizations for ambulatory care sensitive conditions. Medical Care 46(10):1049-1054. Burt, C. W., and L. F. McCaig. 2006. Staffing, capacity, and ambulance diversion in emergency departments: United States, 2003-04. Adance Data from Vital and Health Statistics No. 376: National Center for Health Statistics. Burt, C. W., L. F. McCaig, and R. H. Valverde. 2006. Analysis of ambulance transports and diversions among U.S. emergency departments. Annals of Emergency Medicine 47(4):317-326.

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