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Inequitable Access to Health Services for Older Adults with Diabetes: Potential Solutions on a State Level ab

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Anna C. Faul PhD , Pamela A. Yankeelov PhD & Laneshia R. McCord PhD

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Professor, Kent School of Social Work, University of Louisville, Louisville, Kentucky, USA b

Research Fellow, Department of Social Work, University of the Free State, Bloemfontein, South Africa c

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Assistant Professor, Department of Social Work, University of North Carolina Charlotte, Charlotte, North Carolina, USA Accepted author version posted online: 09 Oct 2014.Published online: 13 Jan 2015.

To cite this article: Anna C. Faul PhD, Pamela A. Yankeelov PhD & Laneshia R. McCord PhD (2015) Inequitable Access to Health Services for Older Adults with Diabetes: Potential Solutions on a State Level, Journal of Aging & Social Policy, 27:1, 63-86, DOI: 10.1080/08959420.2015.969114 To link to this article: http://dx.doi.org/10.1080/08959420.2015.969114

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Journal of Aging & Social Policy, 27:63–86, 2015 Copyright © Taylor & Francis Group, LLC ISSN: 0895-9420 print/1545-0821 online DOI: 10.1080/08959420.2015.969114

Inequitable Access to Health Services for Older Adults with Diabetes: Potential Solutions on a State Level

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ANNA C. FAUL, PhD Professor, Kent School of Social Work, University of Louisville, Louisville, Kentucky, USA, and Research Fellow, Department of Social Work, University of the Free State, Bloemfontein, South Africa

PAMELA A. YANKEELOV, PhD Professor, Kent School of Social Work, University of Louisville, Louisville, Kentucky, USA

LANESHIA R. McCORD, PhD Assistant Professor, Department of Social Work, University of North Carolina Charlotte, Charlotte, North Carolina, USA

Diabetes is a serious global public health challenge. The cost for health services for diabetes care has increased 41% over the past 5 years. Despite escalating health expenditure, the United States continues to have higher rates of diabetes than many other developed countries. There is a need for health care reform in the United States not only in reducing health care costs but also in improving the quality of preventative care. This study presents the testing of a multilevel model investigating variables on the individual and state levels to develop a better understanding of the most important contextual pathways that can lead to providing older adults (50+) with type 2 diabetes with the recommended preventative quality care they require. The model was tested using a three-level repeated cross-sectional design with data from various existing data sources, using a national sample of 181,870 individuals aged 50 years and older. Results showed that differences in state health care systems contributed to inequitable access. Specifically, in a state where there was a higher percentage of adults 65 and older coupled with a shortage of health care professionals, the likelihood Received September 5, 2012; revised October 7, 2013; accepted January 16, 2014. Address correspondence to Anna C. Faul, PhD, Kent School of Social Work, University of Louisville, 2217 South 3rd Street, Louisville, KY 40292, USA. E-mail: [email protected] 63

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of receiving the recommended preventative quality care decreased. Also, older adults living in states with a higher percentage of people with diagnosed diabetes but with a lower-than-average annual per capita health care expenditure fared worse in receiving quality preventative care. Last, older adults in wealthy states with higher percentages of uninsured people had the lowest odds of receiving quality preventative care. Health care reform, similar to what is currently promoted by the Patient Protection and Affordable Care Act of 2010, is recommended to improve the performance of all health care systems in all states.

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KEYWORDS diabetes, health care reform, inequitable access to care, multilevel modeling, older adults

INTRODUCTION The International Diabetes Federation (IDF; 2010) labels diabetes as a global public health challenge and estimates that diabetes affects over 300 million people globally, with a projection of 500 million by 2050. In the United States, 21 million individuals currently live with diabetes, with a projected prevalence of 39 million by the year 2050. The prevalence of diabetes increases with age and affects close to 26% of older adults 65 and older, with type 2 diabetes accounting for 90% of these cases (Centers for Disease Control and Prevention [CDC], 2014). The cost of health services for diabetes care increased by 41% over the past 5 years (Diabetes Care, 2013) with global health care spending estimated at $378 billion, equivalent to 12% of all global health care expenditure (IDF, 2010). In the United States, direct medical cost for diabetes is estimated at $176 billion, and indirect cost (disability, work loss, premature death) at $69 billion. Two-thirds of these expenses are for patients 65 or older (CDC, 2014). Although the United States spends more on health care than any country in the world, it still has higher rates of diabetes than most other developed countries (Kaiser Family Foundation, 2011), indicating the urgent need for health care reform in reducing costs and improving the quality of preventative care. The International Charter of Rights and Responsibilities for people with diabetes emphasizes the rights of patients to act as equal partners with health care providers and governments. Specifically, affordable access to care and treatment, regardless of race, ethnicity, gender, and age, is emphasized (IDF, 2010). The American Diabetes Association (ADA; 2010) established medical standards of care that include recommended composite measures of quality care identified as “best practice” in the treatment or prevention of complications associated with diabetes. These composite measures represent

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various dimensions of quality care used to prevent unnecessary disability and aid in the management of diabetes. Higher utilization scores on these recommended quality care services have been associated with higher patient satisfaction, better self-management (Heisler, Smith, Hayward, Krein, & Kerr, 2003), higher perceived quality of care (Ackermann et al., 2006), better perceived mental health, improved low-density lipoprotein levels, and greater glucose control (Harman et al., 2010; Persell et al., 2004). Ensuring that patients receive the recommended quality care, due to their monitoring and prevention potential, is indicative of the strength of commitment of the health care system to a patient’s disease management (U.S. Department of Health and Human Services [DHHS], 2012). Based on 2009 national data from the Agency for Healthcare Research and Quality, a range of half to nearly three-quarters of adults (aged 40+) with diabetes reported receiving at least some of the recommended preventative quality care services annually: two hemoglobin A1C tests (69%), a dilated eye examination (72.4%), a foot examination (73.1%), and an influenza vaccination (58.6%). However, only 1 in 5 adults aged 40+ (21%) received all four of the recommended quality care services. States also differ in the rates of their residents receiving the quality care services, ranging between a low of 51% in some states for an annual influenza vaccination to a high of 88% in other states for an annual foot examination (DHHS, 2011). Glasgow et al. (1999) championed the notion that diabetes is a public health problem, and they delineated a multilevel systems approach for diabetes management and prevention of complications that recognized the socioenvironmental influences at multiple levels (e.g., personal, family, health care team, work, environmental, community).

PURPOSE OF STUDY Although literature exists that establishes the link between individual- and community-level predictors for other leading causes of death (i.e., cancer), there have been no studies to our knowledge that have simultaneously explored the individual-level influences, as well as contextual influences such as state-level influences and state health care system characteristics, on the utilization patterns of quality preventative care services for adults with diabetes. This study will present a multilevel model aimed at investigating variables on individual and state levels with the goal of developing a better understanding of the most important contextual pathways leading to receiving the recommended quality preventative care services for older adults (50+) with diagnosed type 2 diabetes. With the increased focus on health care policy reform and the increased cost of diabetes, exploration beyond the individual-level variables to state-level differences in utilization

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patterns could inform the discussion on how to help patients successfully manage their diabetes in the most cost-effective manner. A modified version of Andersen’s behavioral model of health utilization (Figure 1) drove this study (Andersen & Davidson, 2007). In the model, the different individual characteristics, as well as state contextual characteristics affecting the receipt of the recommended quality care services for type 2 diabetes, were investigated. According to Andersen and Davidson (2007), equitable access occurs when demographic and need variables account for most of the variance in receiving quality care services, while inequitable access occurs when social structure and enabling resources determine who

STATE MODERATION EFFECTS State Health Care System Characteristics Enabling Resources Shortage of health care professionals Annual per capita health care expenditure % people annually uninsured

INDIVIDUAL CONTROLS Individual Characteristics Predisposing Characteristics Demographics (Age; Gender) Social structure (Education; Ethnicity; Employment; Marital Status) Enabling Resources Income, Health insurance, Existence of regular source of care, Cost as a barrier to care. Need Self-rated health

STATE PREDICTORS State Characteristics Predisposing Characteristics Demographics (% adults 65+) Social structure (% minorities) Enabling Resources % people annually in poverty Need % people annually with diabetes

MEDICAL CARE SERVICES

Preventative Quality Diabetes Care Services Annual dilated eye exam, Annual foot exam, Annual (at least) two hemoglobin A1C tests, Annual Influenza vaccination.

FIGURE 1 Hypothetical model of access to medical care services for older adults with type 2 diabetes. Inequitable access predictors are in italics.

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receives the recommended quality care services. In our modified version of the model, we separated state characteristics from state health care system characteristics that could be influenced by health care policy change. These state health care system characteristics were treated as moderation effects in the sense that they could change the relationship between the state characteristics and the likelihood of receiving the quality care services.

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Hypotheses Defined for This Study In light of our modified conceptual model, the main focus of this study was to determine whether we had equitable access to health care on a state level, based on the demographics (% adults age 65+) and need within a state (% people annually with diabetes), after controlling for any potential individual differences in a state. If this was not the case, and access was driven by social structure characteristics (% minorities) and enabling resource characteristics in a state (% people annually in poverty), as well as the moderation effect of state health care system characteristics on the state characteristics (also enabling resources), then we would have to conclude that inequitable access to health care is prevalent in the United States. Based on this focus, the following hypotheses were defined for this study (in all hypotheses, the individual characteristics were controlled for): H1: If the demographics of a state (specifically having more adults aged 65+) and need characteristics of a state (specifically having more people with diabetes) drove favorable access to the recommended quality care services for its citizens with diabetes, we could assume equitable access to health care in the United States. H2: If negative state health care system characteristics (specifically a shortage of health care professionals, less annual per capita health care expenditure, and fewer people insured) moderate the main positive effect of demographics (having more adults aged 65+) and need characteristics (having more people with diabetes) in a state on the access to the recommended quality care services for its citizens, we could assume movement to inequitable access to health care in the United States. H3: If the social structure of a state (specifically having fewer minorities) and the enabling resources in a state (specifically having fewer people in poverty) drove favorable access to the recommended quality care services for its citizens with diabetes, we could assume inequitable access to health care in the United States. H4: If negative state health care system characteristics (specifically greater shortage of health care professionals, less annual per capita health care expenditure, and fewer people insured) moderate the main positive effect of social structure (having fewer minorities) and enabling

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resources (having fewer people in poverty) in a state on the access to the recommended quality care services for its citizens, we could also assume inequitable access to health care in the United States (see Figure 1).

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Variation in the Hypothesized Model Among Individuals The National Healthcare Quality and Disparities Reports noted significant variations in the receipt of the four recommended preventative quality care services based on the individual social structure and enabling resources in the model (DHHS, 2011). These reports provide a dark picture of the disparities that currently exist in the health care of adults with diabetes. Only 18.3% of adults with diabetes with less than a high school diploma, 14.4% of non-Hispanic Blacks, 17.6% of unemployed adults, 16.5% of poor and low-income adults, and 14.3% of uninsured adults received the four recommended preventative quality care services for diabetes, as compared to 22.9% with a college education, 23.1% of non-Hispanic Whites, 20.4% of employed adults, 26.6% of high-income adults, and 22.9% of publicly insured adults (DHHS, 2011). The report does not compare the receipt of the recommended quality care services among different marital status groups. However, Kirby (2010) found that the risk of losing health insurance coverage for those who experience marital disruption is 2.20 times greater than for those who stay married. Demographic characteristics and need also drive access with the reports showing that 29% of adults aged 60+, 22.4% of females, and 22.2% of adults in poor health receive all four recommended preventative quality care services as compared to only 16% of adults between the ages of 40 and 59, 19.5% of males, and 20.9% of adults in good health (DHHS, 2011).

Variation in the Hypothesized Model Among States State characteristics tested in the hypothesized model show great variations among states. Specifically, in terms of state variables driving equitable access, there are differences among states in the percentage of older adults and the percentage of people living with diabetes in each state. Proportionally, most adults aged 65+ are concentrated in the Northeastern part of the United States (14.1%) as compared to Western states, which have the lowest percentage of older adults (11.9%; U.S. Census Bureau, 2010). With older adults using more health care per capita than any other age groups, states with more older adults can have a disproportional strain on their health care system, even with the Medicare safety net guaranteeing care for adults aged 65+. Over the years, the nature of health care in the United States has changed dramatically with longer life spans and greater prevalence of the five major chronic conditions for adults 65+: high blood pressure, high cholesterol, heart disease, arthritis, and diabetes (Centers for Medicare and Medicaid Services, 2012). The prevalence of diabetes alone has more than tripled from

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5.6 million in 1980 to 20.9 million in 2010 (CDC, 2011). This has placed demanding challenges on state health care systems, particularly an increased need for treatment of ongoing illnesses. State variables driving inequitable access are the percentage of minorities living in each state as well as the percentage of people living in poverty. Currently, 35.5% of the U.S. population is classified as belonging to a minority race or ethnic group. Minority groups have become the majority race or ethnicity in some states, for example, Hawaii, (77.3%) and in the District of Columbia (65.2%). In contrast, there are still a few states in the United States with less than 10% minorities, for example, Maine (5.5%) and Vermont (5.7%; U.S. Census Bureau, 2010). Studies have shown that racial and ethnic minorities tend to receive lower-quality health care than non-minorities, even when access-related factors, such as insurance status and income, are controlled. The sources for these disparities are complex and rooted in historic and contemporary inequities related to stereotyping and biases (Smedley, Stith, & Nelson, 2003). States where minority race groups have a strong presence may have a health care system where these historic and contemporary inequities result in less than optimal care for its citizens living with diabetes. Apart from these differences, wealth is not equally distributed in the United States, with Mississippi having the highest concentration of poverty (28.2%) and New Hampshire the lowest concentration (9.8%; U.S. Census Bureau, 2010). Poorer states cannot spend as much on health care as states with more wealth, resulting in more strain on the health care system. State health care system characteristics that can moderate some of the main state characteristics effects and lead to inequitable access to health care are shortage of health care professionals, per capita health care expenditure, and the percentage of people without health insurance. The Health Resources and Services Administration has designated 6,391 areas across America as health provider shortage areas (HPSAs). These areas are underserved in terms of the availability of health care providers and experience strain due to limited resources. In 2009, national health care expenditure was $7,578 per capita and accounted for 16.2% of the nation’s gross domestic product. Health expenditure ranges between a high of $8,295 in the District of Columbia to a low of $3,972 in Utah. “Uninsurance” rates in the United States range from 6.1% in Massachusetts to 27.7% in Texas (Kaiser Family Foundation, 2009). When states are burdened by a high shortage of health care professionals and high numbers of uninsured individuals, it is difficult to provide adequate care for its citizens living with diabetes. Considering such variation in states’ predisposing characteristics, enabling resources, and need characteristics and the differential strain on state health care systems to provide needed services to adults with diabetes, it appears to be worthwhile to explore the relationship between these state characteristics and the receipt of the four recommended preventative quality care services, after controlling for individual-level characteristics. The State

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Scorecard on Health System Performance (The Commonwealth Fund, 2009) indicates that if we could improve the performance of all health care systems in all states to the levels achieved by the best states, four million more diabetes patients would receive basic services to help avoid complications such as blindness, kidney failure, or limb amputations. With the increased focus on health care policy reform, exploration beyond the individual-level differences to state-level differences in utilization could inform the discussion regarding which state-level initiatives should become the focus to help patients be successful in managing their diabetes in the most cost-effective manner.

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METHODS Design A three-level repeated cross-sectional design with data from existing data sources was used, where states were the highest level (level 3), the repeated state measures between 2003 and 2010 were on the middle level (level 2; henceforth referred to as state cohorts), and the individuals who were aged 50+ in each state surveyed in a particular year were on the lowest level (level 1). This design is different from a true panel design. The same individuals are not measured over time, but cohorts in each year (level 2) are seen as repeated measures of higher level state units (Jones, 2010).

Data Sources The data used in this study came from a combination of sources. All data on individual characteristics and recommended preventative quality care services were retrieved from the Behavioral Risk Factor Surveillance System (BRFSS), a national telephone health survey jointly sponsored by the Division of Adult and Community Health and the CDC (2011). The response rate for the BRFSS differed by state and ranged between 39% (Oregon) and 69% (Nevada) over the time period of the study (CDC, 2011). For individual responses from the BRFSS to be included in this study in a specific year, the individuals had to be aged 50+ with a diabetes diagnosis and living in states where the diabetes module of the BRFSS was administered. The individual control variables focused on the age of the individual; his/her gender, race, and ethnicity; level of education completed; labor force status; marital status; income; health care access (having health insurance, having a personal doctor, experience cost as a barrier to care); and perceived general health status as measured on an ordinal scale from 1 (excellent) to 5 (poor). Data on state characteristics were collected from the U.S. Census Bureau (2010), the Current Population Survey (CPS), and the Annual Social and

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Economic Supplement maintained by the U.S. Census Bureau (2012), and the CDC (2011). Data on the state health care system characteristics were collected from the U.S. DHHS, Health Resources and Services Administration (2006), and the National Health Expenditure Data Set maintained by the CMS (2012). The state variables of interest were the percentage of adults aged 65+ in each state, the % minorities in each state (defined as those who are nonWhite Non-Hispanic), the % of people in a state in a given year who were living in poverty, and the % of people in a state in a given year who had diabetes. The state health care system characteristics of interest were the mean HPSA shortage value in each state (the number of full-time equivalent practitioners needed for the HPSA to reach a target ratio), the total per capita state health care expenditure per year, as well as the annual % of people in a state being uninsured.

Sample The data from this study represented 181,870 individuals aged 50 years and older1 with complete data on all variables, gathered from all 50 states and Washington, DC, over 8 years (2003 to 2010). States varied in how many times they participated during the 8 years, with 22 states that participated all 8 years and three states that participated only once, for a total of 314 state cohorts over the study period. In Table 1 the distribution of the individuals is shown by state. The table also shows how many times each state participated in the study.

Analysis The outcome variable in this study was a count of the four recommended quality preventative care services for diabetes (a dilated eye exam, a foot exam, at least two hemoglobin A1C tests, and an influenza vaccination) received by an individual in a given year. Due to the limited frequency of individuals receiving zero (3%) or one (10%) preventative quality care service, we combined these counts with those who received two preventative quality care services (21%) to simplify the analysis. Therefore, three ordinal response variables were created, namely receiving zero to two preventative quality care services, receiving three preventative quality care services, and receiving four preventative quality care services (the reference group). We removed from the study any individuals with missing data on the outcome variable or any of the predictors. We conducted a nonlinear analysis using an ordered multinomial response model with a log-link function (Raudenbush & Bryk, 2002). Model fit was accomplished with Bayesian modeling using Markov chain Monte Carlo (MCMC) estimation (Browne, Kelly, & Pillinger, 2009), with the

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TABLE 1 Sample Distribution by State

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State Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming Total

Total 4811 870 3548 1912 1673 1635 3081 2785 1514 13639 4928 2263 2839 1056 4977 3373 246 6338 4573 2018 502 3189 1496 2579 3419 3079 3426 292 2135 3025 4519 4238 1625 11080 2330 4281 2660 1619 7652 238 7405 3642 3771 4157 3307 3376 3772 11601 3640 2591 3145 181870

% of Total 2.65 0.48 1.95 1.05 0.92 0.90 1.69 1.53 0.83 7.50 2.71 1.24 1.56 0.58 2.74 1.85 0.14 3.48 2.51 1.11 0.28 1.75 0.82 1.42 1.88 1.69 1.88 0.16 1.17 1.66 2.48 2.33 0.89 6.09 1.28 2.35 1.46 0.89 4.21 0.13 4.07 2.00 2.07 2.29 1.82 1.86 2.07 6.38 2.00 1.42 1.73 100.00

Years in Study 8 8 8 5 5 5 7 8 5 8 8 5 7 2 8 8 1 8 7 7 2 2 2 8 4 7 8 1 8 7 5 8 4 8 7 6 4 4 8 1 8 7 8 6 8 8 8 7 8 6 8

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software package MLwiN, version 2.24 (Rasbash, Browne, Healy, Cameron, & Chartlon, 2011). All the variables on the state and state cohort level were centered on the grand mean, and age and perceived health of participants were centered on the state mean. Centering was done to control for potentially troublesome correlations among random components (Bickel, 2007). The model was allowed to vary on the intercept (state cohort and state level) and slope (state level). The proportional odds assumption was met for all predictors, resulting in the use of a common coefficient for all predictors. Model-fit was accomplished by first estimating the unconditional means model (Model A) that simply described and partitioned the outcome variation. Time was then added to the model to estimate the unconditional growth model (Model B) where change in the outcome variable was described over time. Then the growth model with individual controls was estimated (Model C), followed by the growth model with state predictors and state moderator main effects (Model D), and last the growth model with the interaction between the state predictors and state moderators (Model E).

RESULTS Sample Description Table 2 provides a description of the sample, including a description of the sample as a function of receiving all four recommended preventative quality care services based on the individual and state characteristics used in the analysis. In terms of recommended preventative quality care services, 72.7% of the sample received an eye examination annually; 73.2% had their feet checked annually by a health care professional; 70.7% had their A1C levels checked twice within a year; and 66.5% had an influenza vaccine annually. Only 3.1% of the sample did not received any of the recommended preventative quality care services, with 9.6% receiving one quality care service, 20.9% receiving two quality care services, 33.7% receiving three quality care services, and 32.7% receiving all four quality care services.

Model Results Analysis of the unconditional means model with no predictors (Model A in Table 3) showed that without any predictors, the log-odds of receiving two or fewer quality care services in a given year was −0.6947, which corresponds with a probability of exp (−0.6947)/[1+exp(−0.6947) = 0.33. The log-odds of receiving three or fewer quality care services in a given year was 0.7298, which corresponds with a probability of 0.67. Unique probabilities for the three ordinal response variables, calculated from the cumulative probabilities, showed that the probability of receiving all four quality care services in a given year was 0.34.

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Overall Individual controls Age (Mean = 66.67; SD = 9.69) Between 50 and 64 Between 65 and 79 80 and older Gender Males Females Education Less than 12 years 12th grade College Ethnicity White Non-Hispanic African American Non-Hispanic Hispanic Other Employment Employed Self employed Unemployed Homemaker or student Retired Unable to work 25.8 31.2 36.4 33.5 28.7 27.3 34.5 30.4 26.2 24.0 29.4 36.5 28.4

76.7 12.4 5.0 5.9 20.5 4.6 3.3 6.4 49.5 15.7

34.1 31.6

40.9 59.1 18.1 35.5 46.4

28.7 36.2 34.1

44.3 44.5 11.2

32.7

% Receiving All % of Four Quality Care Sample Services

35.5 30.0

30.8 34.2

33.8 30.6

34.1 31.6

35.0 30.5

34.6 30.7

32.5 32.9

% Receiving All % of Four Quality Care Sample Services State predictors % 65 and older (Mean = 5.78%); SD = 0.75) Less than 5.78% 49.3 More than 5.78% 40.7 % minorities (Mean = 29.37%; SD = 16.18) Less than 29.37% 51.2 More than 29.37% 48.8 % in poverty (Mean = 12.59%; SD = 3.31) Less than 12.59% 47.8 More than 12.59% 52.2 Diabetes rate (Mean = 7.83; SD = 1.61) Less than 7.83% 43.8 More than 7.83% 56.2 State moderators HPSA shortage (Mean = 3.32; SD = 2.53) Less than 3.32 64.9 More than 3.32 35.1 Health expenditure (Mean = $12,318.67; SD = $2,212.60) Less than $12,318.67 45.4 More than $12,318.67 54.6 Percentage uninsured (Mean = 15.81%; SD = 4.37) Less than 15.81% 49.0 More than 15.81% 51.0

TABLE 2 Description of Sample by Percentage of People Receiving All Four Quality Care Services

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Marital status Married Divorced or separated Widowed Not married Income Below $25,000 Between $25,000 and $50,000 Between $50,000 and $75,000 More than $75,000 Health plan Yes No Personal doctor Yes No Medical cost problems Yes No General health 1 Excellent 2 Very good 3 Good 4 Fair 5 poor 34.5 28.2 32.4 31.9 29.5 34.4 36.9 37.6 33.8 17.9 33.3 18.6 20.0 34.3 29.7 32.2 33.8 32.9 31.2

49.8 18.3 25.6 6.3 46.6 34.1 9.7 9.7 93.1 6.9 95.6 4.4 11.3 88.7 2.9 13.9 34.5 29.8 18.9

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Fixed effects∗   ≤0–2 Quality care services  γ0jkl  ≤3 Quality care services γ1jkl Time (β4l .01kl ) Age β5 .01jkl  Male β6 .01jkl   Less than 12th  grade  β7 .01jkl 12th grade β8 .01jkl   African American  Non-Hispanic β9 .01jkl Hispanic β10 .01jkl Other race or multiracial Non-Hispanic (β11 .01jkl )  Employed β12 .01  jkl  Self-employed β13 .01jkl  Unemployed β14 .01jkl   Homemaker or student  β15 .01jkl Unable to work β16 .01jkl  Divorced and β17 .01jkl  separated  Widowed β18  .01jkl  Not married β19 .01jkl   Earning less than $25,000 β20 .01jkl Earning between $25,000 and $50,000   β21 .01jkl Earning between $50,000 and $75,000   β22 .01jkl   No health plan β23 .01jkl No personal doctor (β24.01jk1 )  Medical cost problems β25 .01jkl  Self-rated health β26 .01jkl

Parameter

TABLE 3 Comparison of All Models Model B

Model C

(0.014) (0.023) (0.027) (0.020) (0.015) (0.013) (0.012) (0.020) (0.018) (0.017)

0.4474∗∗∗ 0.6057∗∗∗ 0.4813∗∗∗ −0.0846∗∗∗

(0.014) (0.022) (0.027) (0.020) (0.015) (0.013) (0.012) (0.019) (0.019) (0.017)

(0.049) (0.049) (0.009) (0.001) (0.010) (0.014) (0.010) (0.015) (0.022) (0.020)

(0.014) (0.022) (0.026) (0.019) (0.015) (0.013) (0.012) (0.019) (0.018) (0.017)

(0.045) (0.045) (0.009) (0.001) (0.010) (0.013) (0.010) (0.015) (0.022) (0.020)

(0.019) (0.023) (0.015) (0.005)

0.0460∗ (0.020)

0.2409∗∗∗ 0.4603∗∗∗ 0.2853∗∗∗ 0.1897∗∗∗ 0.0802∗∗∗ 0.1765∗∗∗ 0.1359∗∗∗ 0.0749∗∗∗ 0.2462∗∗∗ 0.1677∗∗∗

−1.3479∗∗∗ 0.1291∗∗ 0.0141 −0.0105∗∗∗ 0.0005 0.4414∗∗∗ 0.2213∗∗∗ −0.0862∗∗∗ −0.0103 −0.1375∗∗∗

Model E

(0.020) 0.4474∗∗∗ (0.023) 0.6078∗∗∗ (0.015) 0.4811∗∗∗ (0.005) −0.0848∗∗∗

0.0460∗ (0.020)

0.2408∗∗∗ 0.4607∗∗∗ 0.2845∗∗∗ 0.1893∗∗∗ 0.0803∗∗∗ 0.1766∗∗∗ 0.1362∗∗∗ 0.0752∗∗∗ 0.2469∗∗∗ 0.1681∗∗∗

−1.3533∗∗∗ 0.1239∗ 0.0082 −0.0105∗∗∗ 0.0008 0.4416∗∗∗ 0.2211∗∗∗ −0.0871∗∗∗ −0.0108 −0.1371∗∗∗

Model D

(0.020) 0.4475∗∗∗ (0.023) 0.6075∗∗∗ (0.015) 0.4810∗∗∗ (0.005) −0.0848∗∗∗

0.0462∗ (0.020)

0.2400∗∗∗ 0.4611∗∗∗ 0.2853∗∗∗ 0.1888∗∗∗ 0.0796∗∗∗ 0.1757∗∗∗ 0.1355∗∗∗ 0.0745∗∗∗ 0.2469∗∗∗ 0.1679∗∗∗

−0.6948∗∗∗ (0.061) −0.6469∗∗∗ (0.037) −1.2976∗∗∗ (0.044) 0.7298∗∗∗ (0.061) 0.7778∗∗∗ (0.037) 0.1797∗∗∗ (0.044) −0.0165∗∗ (0.006) −0.0067∼ (0.004) −0.0105∗∗∗ (0.001) 0.0007 (0.009) 0.4413∗∗∗ (0.013) 0.2213∗∗∗ (0.010) −0.0864∗∗∗ (0.014) −0.0087 (0.021) −0.1369∗∗∗ (0.020)

Model A

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77 0.0788∗∗∗ (0.019) −0.0029 (0.002) 0.0012∗∗∗ (0.000) 0.0072∗∗∗ (0.001) 396,528.49 197.01 51.00 314.00 181870.00 363740.00

0.0666∗∗∗ (0.015)

0.0110∗∗∗ (0.002) 396,529.18 206.52 51.00 314.00 181870.00 363740.00

Note. Standard errors are in parentheses. ∼p ≤ .10; ∗ p ≤ .05; ∗∗ p ≤ .01; ∗∗∗ p ≤ .001. DIC = diagnostic information criterion; pD = estimated degrees of freedom. ∗ The coefficients represent the log-odds of receiving four quality of care services.

% 65 and older (β30 .01l ) % minorities (β29 .01l ) % people in poverty (β31 .01kl ) % people with diabetes (β33 .01kl ) HPSA shortage value (β28 .01l ) Per capita health expenditure (β27 .01kl ) % people uninsured (β32 .01kl ) % 65 and older ∗ HPSA shortage value (β35 .01kl ) % with diabetes ∗ health care expenditure (β34 .01kl ) % people in poverty ∗ % people uninsured (β36 .01kl ) Random parameters Level: State Constant.01/Constant.01 (σf22 ) Time.01/Constant.01 (σf 24 ) Time.01/Time.01 (σf24 ) Level: State Cohort Constant.01/Constant.01 (σv22 ) DIC: pD: Units: State Units: State cohort Units: Individual Units: Response 0.0080∗∗∗ (0.001) 388,115.97 216.24 51.00 314.00 181870.00 363740.00

0.0628∗∗∗ (0.014) −0.0012 (0.001) 0.0002∼ (0.000)

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0.0080∗∗∗ (0.002) 388,120.42 217.99 51.00 314.00 181870.00 363740.00

0.0372∗∗∗ (0.011) −0.0015 (0.001) 0.0004∼ (0.000)

(0.032) (0.002) (0.006) (0.010) (0.012) (0.000) (0.005) (0.011)

0.0075∗∗∗ (0.001) 388,115.84 215.25 51.00 314.00 181870.00 363740.00

0.0288∗∗∗ (0.009) −0.0015 (0.001) 0.0004∗ (0.000)

−0.0019∗ (0.001)

0.00001∗∗ (0.000)

−0.0347 (0.036) −0.0643∗ 0.0023 (0.002) 0.0018 −0.0047 (0.006) 0.0007 0.0162 (0.010) 0.0159 0.0238∼ (0.013) 0.0238∗ −0.00004∗∗ (0.000) −0.0001∗∗∗ 0.0073 (0.005) 0.0064 0.0241∗

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Time 2003 2010 Individual controls Age 54 years (10th percentile) 80 years (90th percentile) Gender Male Female (R) Education Less than 12th grade 12th grade Some college (R) Ethnicity White Non-Hispanic (R) Black Non-Hispanic Hispanic Other race Employment Employed Self-employed Unemployed Homemaker or student Retired (R) Unable to work 0.31 0.33∗∗∗ 0.32 0.34∗∗∗ 0.29∗∗∗ 0.25∗∗∗ 0.28∗∗∗ 0.30∗∗∗ 0.34 0.33∗∗∗

0.32 0.33∗∗∗ 0.32 0.35∗∗∗ 0.29∗∗∗ 0.25∗∗∗ 0.28∗∗∗ 0.30∗∗∗ 0.34 0.33∗∗∗

0.32 0.32

0.33 0.33 0.26∗∗∗ 0.31∗∗∗ 0.36

0.29∗∗∗ 0.35∗∗∗

0.29∗∗∗ 0.35∗∗∗

0.26∗∗∗ 0.31∗∗∗ 0.35

0.33 0.31

Model D

0.32∼ 0.33∼

Model C

0.29∗∗∗ 0.24∗∗∗ 0.28∗∗∗ 0.30∗∗∗ 0.34 0.32∗∗∗

0.31 0.33∗∗∗ 0.31∗∗∗ 0.34∗∗∗

0.26∗∗∗ 0.30∗∗∗ 0.35

0.31 0.31

0.29∗∗∗ 0.34∗∗∗

0.33 0.31

Model E Self-rated health Very good Poor State predictors % Adults 65+ 11.1 (10th percentile) 14.9 (90th percentile) % Minorities 12.2 (10th percentile) 44.2 (90th percentile) % People annually in poverty 9.2 (10th percentile) 17.3 (90th percentile) % People annually with diabetes 6.4 (10th percentile) 10.4 (90th percentile) State moderators HPSA shortage value 1.00 (10th percentile) 7.00 (90th percentile) Health care expenditure $10,178 (10th percentile) $14,967 (90th percentile) % People annually uninsured 10.7 (10th percentile) 23.2 (90th percentile)

TABLE 4 Probability of Receiving All Four Quality Care Services for Model C–E

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0.29∗∗∗ 0.35∗∗∗

Model C

0.31 0.31 0.32 0.31 0.33∗ 0.30∗ 0.29∗∗∗ 0.34∗∗∗ 0.32 0.31

0.32 0.31 0.33∼ 0.30∼ 0.30∗∗ 0.34∗∗ 0.33 0.31

0.32 0.31

0.30∗ 0.32∗

0.29∗∗∗ 0.34∗∗∗

Model E

0.32 0.33

0.33 0.31

0.31 0.33

0.29∗∗∗ 0.35∗∗∗

Model D

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0.33 0.24∗∗∗ 0.33 0.21∗∗∗ 0.33 0.24∗∗∗

0.33 0.24∗∗∗ 0.33 0.21∗∗∗ 0.34 0.24∗∗∗

∗∗

p ≤ .01;

0.31 0.32∗∗∗ 0.35∗ 0.36

∗∗∗

∗∗∗

0.31 0.32∗∗∗ 0.35∗ 0.36

0.33 0.30∗∗∗ 0.31∗∗∗ 0.32∗∗∗

0.34 0.30∗∗∗ 0.31∗∗∗ 0.32∗∗∗

Note. R = reference category; ∼p ≤ .10; ∗ p ≤ .05;

Marital status Married (R) Divorced or separated Widowed Not married Income Below $25,000 Between $25,000 and $50,000 Between $50,000 and $75,000 Above $75,000 (R) Health plan Yes (R) No Personal doctor Yes (R) No Medical cost a problem No (R) Yes

∗∗∗

p ≤ .001.

0.33 0.23∗∗∗

0.32 0.20∗∗∗

0.32 0.23∗∗∗

0.30 0.31∗∗∗ 0.34∗ 0.35

∗∗∗

0.33 0.29∗∗∗ 0.30∗∗∗ 0.31∗∗∗

State interactions between predictors and moderators % Adults 65+ × HPSA shortage value 10th pctl adults 65+ (11.1%),10th pctl HPSA shortage value (1) 10th pctl adults 65+ (11.1%), 90th pctl HPSA shortage value (7) 90th pctl adults 65+(14.9%), 10th pctl HPSA shortage value (1) 90th pctl adults 65+(14.9%), 90th pctl HPSA shortage value (7) % People with diabetes × health care expenditure 10th pctl with diabetes (6.4%), 10th pctl expenditure ($10,178) 10th pctl with diabetes (6.4%), 90th pctl expenditure ($14,967) 90th pctl with diabetes (10.4%), 10th pctl expenditure ($10.178) 90th pctl with diabetes, 90th pctl expenditure ($14,967). % People in poverty × % people uninsured 10th pctl people in poverty (9.2%), 10th pctl people uninsured (10.7%) 10th pctl people in poverty (9.2%), 90th pctl people uninsured (23.3%) 90th pctl people in poverty (17.3%), 10th pctl people uninsured (10.7%) 90th pctl people in poverty (17.3%), 90th pctl people uninsured (23.3%)

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0.31∗

0.31∗

0.29∗

0.33∗

0.35∗∗

0.27∗∗

0.34∗∗

0.31∗∗

0.29∗

0.35∗

0.30∗

0.30∗

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Table 3 shows that the variance at level 3 (state level) was 0.0666, and the variance at level 2 (state cohort level) was 0.0110. Therefore, about 17% of the total variance (0.0666/(3.29+0.0110+0.0666) = 0.1680) was between states, indicating that there were indeed states that performed better in the delivery of quality care services. The proportion of total variance among state cohorts was minimal (0.0028), indicating that not much change took place in the different states between 2003 and 2010 in relation to the receipt of quality care services. The unconditional growth model (where time was added as a predictor and allowed to vary across states; Model B in Table 3) depicts the probability of receiving quality care services over the 8 years across state cohorts. A Wald test showed that the effect of time varied across states, but not across state cohorts; therefore, time was only added as a random effect on the state level. Although time was a significant fixed and random effect in this model, model fit based on the deviance information criterion (DIC) statistic was not significantly improved. In calculating the unique probabilities, the model showed that the probability of receiving four quality care services slightly increased from 0.32 in 2003 to 0.34 in 2010. This indicates a small but significant overall improvement for patients receiving the recommended quality care services. Analysis of the growth model with the individual controls added (Model C in Table 3), showed that most of the individual-level predictors were significant and were able to predict the odds of receiving all four quality care services. Time showed a trend toward the direction of increased probability to receive all four quality care services over time (Table 4).2 The only exceptions were males who were not significantly different from females and Hispanics who were not significantly different from White Non-Hispanics. This was probably due to low Hispanic sample size and lack of power to determine significance. The controls with the strongest negative effect were not having a personal doctor, experiencing medical cost as a barrier to receiving care, not having a health plan, not having at least a high school diploma, and being self-employed (Table 4). Just based on the individual-level controls, it is clear that inequitable access to health services is present nationally because that access is mostly driven by social structure and enabling resource factors. Adding the individual controls to the model resulted in significantly improved model fit. Adding the state predictors (state characteristics) and state moderators (state health care system characteristics) as main effects to the model (Model D in Table 3) did not significantly change the individual-level predictors. The only two state variables that significantly decreased the receipt of quality care services were the enabling resources related to higher shortage of health care professionals and lower annual per capita health care expenditure. Time changed direction showing a decline in probability but was no longer significant, indicating that after the state predictors were added, there was no change in receiving the recommended quality care services between

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2003 and 2010 (Table 4). Model D did not provide a better fit than Model C. However, the DIC fit statistic is more an indication of fit on the lower levels (Browne, Clarke, Jones, Leckie, & Steele, 2012). Just based on the random variance, it can be seen that nearly half of the between-state variance(σf22 ) was explained by adding the state-level variables. In the final model that added moderation effects to the model (Model E in Table 3) the number of people aged 65+ living in a state became a significant predictor alongside the other two significant state health care system characteristics (shortage of health care professionals and lower annual per capita health expenditure). Three significant moderation effects decreasing the receipt of quality care services were also detected: higher percentage of people aged 65+ × higher shortage of health care professionals; higher percentage of people with diabetes × lower annual per capita health care expenditure; and lower percentage of people who annually live in poverty × higher percentage of people annually uninsured (Table 4). These moderation effects did not significantly change the individual-level predictors or the state-level predictors. In Model E the DIC statistic returned to the same level it was in Model C, indicating similar fit for the individual controls. More state variance was explained with the addition of the moderation effects. Variance partition coefficients were calculated for the final model (Model E), showing that the variance at level 3 (state level) dropped from 0.0666 (Model A) to 0.0288. In Model E, the variance left on level 3 was 1% of the total variance left (0.0087). In Model A, the variance on the state level that needed to be explained was 17%, indicating that with all the state characteristics and state health care system characteristics variables added, the final model was able to explain nearly all the state-level variance. The variance between state cohorts remained minimal with Model E (0.0023), although slightly less than what was observed in Model A.

DISCUSSION Although not discussed in detail, the results of this study correspond with the findings of the State Scorecard on Health System Performance (The Commonwealth Fund, 2009), which indicates differences among states in their abilities to provide the recommended quality care services to their older adults with diabetes. Similar to this study, Minnesota and Mississippi showed the best and worst performances, respectively, for persons with diabetes who received at least three of the recommended quality care services. Also, in this study, Vermont and Maine showed better than average odds in 2003 as well as better than average growth over time in their abilities to provide quality care services to their older adults with diabetes. In the scorecard, Vermont and Maine were singled out as states that enacted comprehensive reforms to expand insurance coverage and put initiatives in place to improve

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population health. Minnesota was praised as a leader in bringing public- and private-sector stakeholders together in initiatives to improve the overall value of health care. The scorecard also indicated that differences in how well the health care system functions for people were still based on “equity gap” indicators (The Commonwealth Fund, 2009), similar to what was found in this study. Hypothesis 1 was partially supported, specifically in relation to the amount of older adults aged 65+ living in a state. Guaranteed health insurance, in the form of Medicare, may contribute to equitable access to this part of the population in a state. It is likely that states with larger percentages of older adults would also have a greater number of older adults with diabetes. Perhaps standard practice guidelines become routinized in health organizations when serving a large population with similar health problems, resulting in quality care expanding to more people in the state than just those aged 65+. Yet, the significant moderation effect seems to offer a more complete picture in that the greater the percentage of older adults, coupled with a low shortage of health care professionals at the state level, was needed to increase the odds of receiving all four quality care services. This finding supports hypothesis 2 and suggests that the quantity and quality of care was compromised when living in a state with a large percentage of older adults and a less than optimal health care labor force to meet the needs of the older adult population with diabetes. These results add additional support to some of the provisions in the Patient Protection and Affordable Care Act (PPACA) of 2010, designed to increase the number of providers in medically underserved areas, especially in the area of geriatrics. The PPACA will accomplish workforce development mainly through loan repayment programs, training grants, and expansions in the National Health Service Corps Program (Kaiser Family Foundation, 2010). An additional moderation effect in support of hypothesis 2 was detected, specifically in relation to the percentage of people with diabetes and the annual per capita health expenditure in a state. Older adults living in states with the highest prevalence of diabetes, coupled with the lowest annual per capita health expenditure levels, had the lowest odds of receiving the recommended quality care services. The PPACA’s stronger emphasis on public health initiatives as well as the establishment of coordinated patient-centered medical homes may help states shift spending to more effective, lower-cost, secondary preventative care for older adults with diabetes (HealthCare.gov, 2012). The main effect of health care expenditure showed a positive relationship with receiving the recommended quality care services. However, if compared globally to other high-income countries, the U.S. per capita health care expenditure has increased fivefold in real terms since 1970, with more limited positive impact on chronic diseases like diabetes. According to the Organization for Economic Cooperation and Development (2011), this demonstrates the relative inefficiency of the American health care system,

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despite high health care expenditure. It may be important to investigate closely the outlier states that demonstrate high quality care yet have a lowerthan-average annual per capita health expenditure and a high percentage of individuals living with diabetes to understand better how quality care can be achieved without increased spending (The Commonwealth Fund, 2009). Hypothesis 3 could not be supported, with no significant main effects detected between the percentage of minorities living in a state as well as the percentage of people living in poverty in a state and the odds of older adults receiving the recommended quality care services. However, hypothesis 4 was partially supported by the findings, specifically in relation to the moderation effect detected between the percentage of people not living in poverty and the percentage of people without health insurance. The wealthiest states with the lowest number of uninsured people were also the states where their older adults with diabetes had the highest odds of receiving all four quality care services. However, the same was not true for wealthy states with a high percentage of uninsured people. In these states older adults had the lowest odds of receiving all four quality care services, even lower than poorer states with a high percentage of uninsured people. This result is supported by a study done by Pagan and Pauly (2006), who studied whether the proportion of the local uninsured population is related to the ability of health care professionals to deliver high-quality care to their patients in the local health care markets. They found that spillover effects of community uninsurance result in the reduction of quality health care provided by local medical practitioners, even to individuals who are themselves insured. Alternatively, Baicker and Chandra (2004) found that states with more specialists have lower-quality care than states with more general practitioners. Perhaps wealthier states tend to have more specialists, leading to poorer care for all. Baicker and Chandra’s findings also provide some support for why we found that the poorest states with the most uninsured fared slightly better than the wealthier states with the most uninsured. In areas with a high percentage of poor, uninsured individuals, there may be fewer specialists and more general practitioners serving the area in community health clinics, therefore providing more access to affordable care for low-income adults with diabetes. This finding provides additional support for some of the aforementioned aspects of the PPACA of 2010. Specifically, enabling access to and increasing the emphasis on primary care through an increased work force, medical homes, and greater primary care focus in community health centers may improve the care for all. In the end, reform to the health care system that includes a balanced approach to changing the health care system in terms of health expenditure, improved access to care, and better primary and preventative care, seems to be part of the solution to improve health outcomes for people with diabetes. Reform to the American Health Care system as enforced by the PPACA has as its goal to provide health insurance coverage for more than 94%

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of Americans, while simultaneously working toward more affordable health care. The provision in support of expanding health insurance coverage are estimated to increase the health care expenditure in the U.S. significantly; however, it is also estimated that changes in how care is delivered and how Medicare and Medicaid fraud are reduced by the act will eventually more than offset the cost of the national coverage provisions (Foster, 2010).

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CONCLUSION With such a high prevalence of older adults with diabetes, state-level solutions should be considered to help them manage their illnesses and reduce further disabilities. Clearly, the PPACA includes provisions that would aid states in the rising burden of diabetes prevalence. Given the financial burden of diabetes and the current national health care debate on whether the PPACA will have a positive impact on national health outcomes, data such as these can make a valuable contribution to this dialogue by its exploration of the impact of individual, state, and state health care system characteristics on the receipt of proper care.

FUNDING Funding for this research has been provided by the John A. Hartford Foundation.

NOTES 1. State data focused on adults 65 and older to determine the disproportional strain older adults may put on state health care systems; the individual sample consisted of adults 50 years and older to include older adults without the guaranteed safety net of Medicare. 2. Table 4 shows the results of Models C, D, and E with the log odds exponentiated to show the probability of receiving all four quality care services. To illustrate the impact of continuous variables on the probability of receiving services, we calculated the probability of receiving services for states at the 10th and 90th percentile values of the continuous variables.

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