DOES HEALTH INSURANCE STATUS AFFECT HEALTH STATUS FOR DC RESIDENTS?

Transcription

1 DOES HEALTH INSURANCE STATUS AFFECT HEALTH STATUS FOR DC RESIDENTS? A Thesis submitted to the Faculty of the Graduate School of Arts and Sciences at Georgetown University in partial fulfillment of the requirements for the degree of Master of Public Policy in Public Policy By Tamar Zaidenweber, B.A. Washington, D.C. April 15, 2011

2 Copyright 2011 by Tamar Zaidenweber All Rights Reserved ii

3 ABSTRACT DOES HEALTH INSURANCE STATUS AFFECT HEALTH STATUS FOR DC RESIDENTS? Tamar Zaidenweber, B.A. Advisor: Jean M. Mitchell, PhD Context: In this changing health insurance environment, it is important to understand the effects of different insurance programs on one s health status. The Affordable Care Act expands Medicaid substantially, though it has been shown previously to be associated with worse health statuses for its participants. This study looks at the pre-reform environment in the District of Columbia and provides insight into the changes being made. Objective: To examine the effect of insurance status on two measures of health for residents of the District of Columbia. Design, Setting, and Participants: the Urban Institute conducted The DC Health Insurance Study in They surveyed 4699 individuals on a variety of topics relating to health insurance, demographic characteristics and health status. The households in this survey were selected at random using two methodologies: random-digit dial telephone sample and an address-based random household sample. iii

4 Main Outcome Measures: The outcomes of interest are two measures of health status. The first is a self-reported status ranging from 1 (Poor or Fair) to 4 (Excellent). Individuals were asked to rate their health statuses on this scale. The second is a dummy variable indicating the presence of an activity limitation. Activity limitation is defined as a physical, mental or emotional problem that limits an individual in any way. These two measures are estimations for the individual s actual health status. Results: The study finds that health insurance does not have a significant effect on health status. Medicare and Medicaid participants are significantly more likely to have an activity limitation than beneficiaries of other insurance and the uninsured. Instead, income, age, education and race all have significant effects on health status. The study finds that higher incomes and educational attainment are associated with higher health statuses. Furthermore, as expected, older individuals are more likely to be in poorer health than younger persons. Finally, minorities are significantly more likely to have worse health than their white counterparts. Conclusions: The findings do not support the argument that policies increasing enrollment in health insurance will improve health status. Rather, the results suggest that policies intended to reduce income inequality, eliminate health disparities across racial lines and increase educational attainment would have greater effects on health status. iv

5 ACKNOWLEDGEMENTS I would like to thank my thesis advisor, Jean Mitchell, PhD, for her invaluable support and insight throughout this process. Jean, your guidance and assistance made this endeavor possible; I am extremely grateful. I have Julie Hudman, PhD, and the staff of the DC Department of Health Care Finance, to thank for providing me with access to the 2009 DC Health Insurance Survey. This survey data was the basis of my research, and without Dr. Hudman s help and confidence, would have been out of reach. Finally, I would like to thank my family for their encouragement throughout my education. It has been quite a journey and I would not have made it through without them. v

6 TABLE OF CONTENTS I. Introduction II. Literature Review III. Conceptual Framework and Model Specification A. Conceptual Framework B. Model Specification IV. Data Description V. Empirical Results A. Characteristics of the Sample B. Ordinal Probit Results for Health Status C. Probit Results for Activity Limitation VI. Discussion Tables References vi

7 I. INTRODUCTION Today, the majority of Americans have health insurance through their employer, the individual market, or a public program such as Medicare or Medicaid. Our society promotes the purchase of health insurance because it provides access to health care that otherwise would be unattainable. In theory, access to care should result in better health for the participants. But, we also have a significant portion of our population who are uninsured, and who access care in acute circumstances, often in the emergency room. The cost of this uncompensated care is borne by those with private insurance and taxpayers. Many incentives currently exist to promote private-market insurance uptake, including the preferential tax treatment that employer-sponsored insurance benefits receive. Beyond this preference, Americans are designing new ways to increase participation in the insurance market because it is expected to make its participants healthier. The Patient Protection and Affordable Care Act greatly expands eligibility for the Medicaid program, alters payment mechanisms in Medicare and more efficiently pools the market for individual and small group insurance into Exchanges. As the policies enacted by our legislature alter the incentives for participating in different types of health insurance coverage, it is important to understand the effect of that participation on beneficiary health. When we pass reforms to increase insurance coverage, are doing so to improve health? In reality, though, the effect of health insurance coverage on health status is not so clear-cut. Numerous studies have attempted to quantify the effect of either being insured, 1

8 or the type of insurance one has, on an individual s health status. The results of these studies are mixed. The most notable study, the RAND Health Insurance Experiment (1978), found that different cost-sharing structures directly affected the amount of care one sought and received. Others, including Ross and Mirowsky (2000), argue that it is not insurance status, but socioeconomic status, that causes variations in health status. This study is an effort to understand the effects of both insurance and socioeconomic status on health status for residents of the District of Columbia, a unique population. Not only is the District the only sole-urban state, but it also has a large minority population, vast income inequality and generous public health insurance programs. The 2009 DC Health Insurance Study found that the District has the second lowest rate of uninsured in the United States, after Massachusetts, where residents are required to purchase insurance. The results of this study offer some insights regarding the effect of insurance on beneficiary health. A finding that health insurance does not result in better health may be attributable to several other factors. For example, there are several potential omitted variables that could explain this lack of association ie. weight, smoking and other unhealthy behaviors. 2

9 II. LITERATURE REVIEW A. Background Researchers have used a variety of methods to estimate the effects of insurance status on health status. These have included self-reported health status, mortality risk, the ability to maintain or control chronic conditions, access to care when needed, and clinical outcomes. These measures differ from each other and researchers have employed a variety of methods to analyze them. These extensive variations result in a very compartmentalized and inconclusive literature. Below I review this literature. Many have examined the effects of health insurance on various health status indicators, including comparisons of having insurance versus no insurance, as well as the effects of different types of health coverage. The most frequently referenced study, the RAND Health Insurance Experiment (1978), investigated the effects of differing types of health coverage on the use of personal medical care services, quality of care, satisfaction with care, and health status. The RAND researchers studied approximately 8000 individuals (2750 families) for periods of either three or five years, and measured health status for each individual upon entry, annually, and upon completion of the experiment. The results show that patients who were required to pay for greater portions of their care spent less on medical services, but that the lower use of services and expenditures did not have significant adverse effects on health. 3

10 B. Self-Reported Health Status Quesnel-Vallée (2004) examined the extent to which private or public insurance coverage affects adult health. Quesnel-Vallée controlled for the effects of unobserved factors shared by siblings, such as parental genetics, to reduce selection bias. The author found that the negative relationship between public health insurance and health is not causal, but resulting from prior health and socioeconomic status. Nonetheless, a lack of health insurance had a significant negative impact on health status. Baker et al. (2006) investigated the association between lack of insurance coverage and the risk of decline in overall health and death among individuals aged over the time period Baker et al. found that those who were uninsured had a 35% higher risk-adjusted mortality from 1992 to 2002 than those privately insured, and over two-year intervals were 1.43 times as likely to have a major decline in overall health. The study found that having private insurance did affect mortality, as compared to someone who was uninsured. The authors concluded that the absence of health insurance results in a higher long-term mortality, which they attribute to the erosion of health status over time. C. Mortality Researchers commonly use mortality to measure the health status of survey respondents. Over the past decade a number of researchers have examined the question of whether insurance status affects mortality, and their results have been mixed. 4

11 Franks, Clancy and Gold (1993) examined the relationship between lacking health insurance and the risk of subsequent mortality, in adults over 25 years of age who reported that they were uninsured or privately insured in the 1971 National Health and Nutrition and Examination Survey. They resurveyed the individuals through 1987 and retained 4694 (of 5161) respondents. Franks et al. estimated a proportional hazard survival analysis, including adjustments for gender, race, and baseline age, education, income, employment status, the presence of morbidity on examination, self-rated health, smoking status, leisure exercise, alcohol consumption, and obesity. Further, Franks et al. estimated the effects of interactions between insurance and all other baseline variables. The authors found that 9.6% of the insured and 18.4% of the uninsured died by the end of the follow-up period. The hazard ratio for lacking insurance was 1.25 (95% confidence interval (CI), 1.00 to 1.55), allowing the authors to conclude that no insurance is linked to higher mortality. The authors also found that the effects of education, income and selfrated health were comparable to the effect of insurance status on mortality. McWilliams et al. (2004) examined a cohort of near-elderly people (51-61 years) and compared mortality rates for those who were insured and those who were uninsured, controlling for race, income, and the presence of diabetes, hypertension and heart disease. McWilliams et al. concluded that for the near elderly, lacking health insurance was associated with substantially higher adjusted mortality among adults who were white and that expanding health insurance coverage for the near elderly could greatly improve health outcomes. 5

12 Todd et al. (2006) examined morbidity, mortality, and charges for hospitalized children with public or no health insurance in comparison the children with private insurance coverage. After controlling for age, race/ethnicity and disease grouping, Todd et al. found that children who were uninsured or had public insurance had significantly higher hospital admission rates and higher mortality rates compared to those with private insurance. Wilper et al. (2009) conducted comparisons of uninsured and privately insured adults ages 17 to 64 years who participated in the 1993 National Health and Nutrition Examination Survey. Wilper et al. predicted mortality dependent on whether the individual had insurance, and found that among all participants, 3.1% died. The age and gender adjusted mortality hazard among uninsured compared to the insured was With further adjustments for race/ethnicity, income, education, self- and physician-rated health status, body mass index, leisure exercise, smoking, and regular alcohol use, the uninsured were more likely to die with a hazard ratio of Wilper et al. concluded that being uninsured is associated with higher mortality. Kronick (2009) followed 643,001 respondents ages who reported being uninsured or privately insured in the National Health Interview Survey from 1986 to through 2002 for prospective mortality. Kronick estimated a survival model with adjustments for demographic, health status, and health behavior characteristics, and found that the risk of subsequent mortality is no different for uninsured respondents than for those covered by employer-sponsored group insurance at baseline (hazard ratio 1.03, 6

13 95 percent confidence interval [CI], ). In a model that omitted health status, smoking status and body mass index, the hazard ratio increased to 1.20 (95 percent CI, ). The finding of no difference in mortality between uninsured and privately insured individuals may be attributable to the long follow up period. During this time period some respondents may have had intermittent insurance coverage. Kronick concluded that expanding insurance to cover all adults would not significantly reduce mortality in the United States. LaPar et al (2010) examined nearly 900,000 surgical outcomes from the National Inpatient Sample (NIS) database, and found that unadjusted mortality for patients covered by Medicaid and the Uninsured were statistically significantly higher than for patients with private insurance. Allen et al (2011) examined the effects of health insurance on long-term survival for lung transplant patients. The study found no statistically significant difference in 30-day, 90-day and 1 year survival rates, but did note that Medicare and Medicaid patients had decreased survival rates than privately insured individuals 3 years and beyond. Similarly, Gaglia et al (2011) studied the probability of having a major cardiac event after an invasive cardiac procedure, by insurance type. The study found that Medicare, Medicaid and uninsured patients were were associated with greater rates of adjusted major adverse cardiac events at 1 year compared with private insurance. While many have investigated the relationship between health insurance status and health status or mortality, as Jack Hadley notes in Sicker and Poorer The Consequences of Being Uninsured: A Review of the Research on the Relationship 7

14 between Health Insurance, Medical Care Use, Health, Work, and Income, the literature's broad range of conditions, populations, and methods makes it difficult to derive a precise quantitative estimate of the effect of having health insurance on the uninsured's health. Hadley notes that studies that analyzed the effects of insurance on mortality reduction vary anywhere from 4% to 25%. Nonetheless, there exists some consistency across studies: Uninsured persons receive fewer preventive and diagnostic services, they have greater severity of illness upon diagnosis, and they receive less therapeutic care. D. Health Indicators and Chronic Conditions Another common measure researchers use to estimate the effect of insurance status on health status is the presence or absence of chronic conditions, such as heart disease or diabetes. Others have relied on health behaviors, such as smoking or alcohol consumption. Baker et al. (2001) investigated the effects of being uninsured on developing physical difficulties for a sample of near elderly. Baker et al. analyzed data from the Health and Retirement Study in 1992 to evaluate the risks of a major decline in overall health and the development of new physical difficulties for the continuously uninsured, the intermittently uninsured, and the continuously insured through After adjusting for socio-demographic factors, preexisting conditions, and types of health related behavior (smoking, alcohol used), Baker et al. found that the continuously uninsured and intermittently uninsured participants were more likely than the 8

15 continuously insured participants to have a major decline in overall health between 1992 and The adjusted relative risk of a major decline in overall health was 1.63 for continuously uninsured participants and 1.41 for intermittently uninsured participants, as compared with continuously insured participants. Baker et al also measured whether participants developed a new difficulty in walking or climbing stairs and found similar results. They concluded that lack of health insurance is associated with an increased risk of a decline in overall health among adults 51 to 61 years old. Wilper et al (2009) examined whether uninsured persons with three chronic conditions (hypertension, diabetes, and elevated cholesterol) were less likely than the insured to be aware of their illness or to have it controlled. Findings revealed the uninsured with diabetes and elevated cholesterol were less likely to be diagnosed than their insured counterparts, and that the uninsured with hypertension and elevated cholesterol were more likely to have poorly managed chronic conditions than their insured counterparts. Furthermore, the uninsured were more likely to have undiagnosed and uncontrolled chronic illnesses than the insured. E. Access and Outcomes Researchers have also estimated the effects of insurance status on health status by examining the relationship between access to care and clinical outcomes. In response to concerns regarding the quality of care provided by managed care plans, Lee-Feldstein, Feldstein, Buchmueller and Katterhagen (2000) investigated the relationship between 9

16 health insurance and delivery to (1) stage at diagnosis of breast cancer, (2) treatment selected, and (3) survival. Lee-Feldstein et al compared the effects of having fee-forservice (FFS), group-model HMO, non-group HMO, public insurance, and being uninsured. After controlling for other confounding factors, Lee-Feldstein et al found that both publicly insured and uninsured patients were more likely to be diagnosed at advanced stages (III or IV). On the other hand, early detection was similar for those with FFS, group HMO and non-group HMO. The authors also found that mortality was higher for those with either public insurance or no insurance (1.46 risk ratio) as compared to private FFS, group or non-group HMO. Finally, they found that treatment selection was related to hospital type but not insurance coverage. Kelz et al (2004) analyzed patients who underwent surgery for colorectal carcinoma to evaluate whether uninsured and underinsured patients suffered impaired access to care, delayed treatment or received substandard care. The authors found that uninsured and publicly insured (Medicaid) patients suffered more emergent admissions and had more comorbid diseases compared to privately insured patients. Further, uninsured and underinsured patients had higher rates of postoperative complications and in-hospital death compared to those with private insurance. The authors concluded, the uninsured and Medicaid populations are at greater risk of developing postoperative complications and dying than the privately insured population. Matthews, Anderson and Nattinger (2005) examined the effect of insurance status (whether one was insured or uninsured) on the probability that participants (all over 50 10

17 years of age) received a colorectal screening test (an access measure). They found that overall 39% of the sample received the test; among those with insurance coverage the rate was 77% compared to 33% among those without insurance. They concluded that having health insurance coverage is a significant determinant of whether individuals undergo colorectal screening tests. F. This Study This study will contribute to the existing research by analyzing data from a new, recent survey conducted in the District of Columbia. Most studies that have looked at the effects of insurance on health status have focused on specific age groups, primarily the elderly, near-elderly or children. This study looks at a random sample of individuals residing in a large urban area, where there exist significant educational and income differences across the population. The study will offer new insights regarding whether health status is better for someone with insurance, as opposed to being uninsured> Controlling for type of insurance coverage will provide further insights on the ongoing debate regarding whether Medicaid is inferior coverage compared to private insurance. With this new data we can investigate the effects of other factors such as age, income or education on self reported health status. Results suggesting that either being insured or participating in public or private insurance rather that being uninsured improves health status, provides further evidence to support policies to improve insurance take-up rates. 11

18 III. CONCEPTUAL FRAMEWORK AND MODEL SPECIFICATION A. Conceptual Framework Many studies attempt to determine the causal relationship between health insurance status and health status. This body of research, based on numerous studies that found being insured leads to better health, has been the basis for US health insurance policies. But, as Levy and Meltzer (2001) note, few studies have been able to establish a causal relationship between health insurance status and health. As Levy and Meltzer (2001) explain, defining a causal relationship between insurance status and health status is difficult for a number of reasons. First, health insurance is a complex, multi-dimensional good. By this, the authors mean that different insurance plans have very different effects. They present two plans: the first is a firstdollar coverage option and the second is catastrophic-only. As illustrated in the RAND health insurance study, these plans have vastly different effects on health. Levy and Meltzer argue that without a more uniform definition of health insurance, measuring its effects will be exceptionally difficult. Second, health is a complex, multi-dimensional product, and our ability to measure it is imperfect. In this study, I will measure health in two ways: self-reported health and whether the individual has an activity limitation. But other measures of health include mortality rates, appropriate access to care and surgical outcomes, to name a few. As Levy and Meltzer note, our ability to measure health is far from perfect, and thus our ability to quantify the effects of various factors on health is lacking. 12

19 Finally, Levy and Meltzer note that the most plausible pathway through which health insurance may have a causal effect on health is through improved access: having health insurance increases the quality and/or quantity of medical care, which in turn improves health. The challenge is that medical care is impossible to properly quantify, so developing a causal link between health insurance and health remains difficult. Hahn (1992) postulated a theoretical hypothesis regarding the effect of health insurance on health status; insurance will have [both] a positive direct and indirect effect on self-reported health. The foundation of her hypothesis rests in three theories: 1) the total effect of insurance on health will not be explained in full by direct paths linking insurance to health as well as indirect paths through use of medical services, 2) the effect of insurance will be different depending on whether medical services are reactive or proactive, and 3) the effect of insurance will be different depending on whether it is public or private, and full year or part year coverage. The reasons why different types of insurance have diverse effects on health all vary based on the type of coverage. Hahn cites benefit variation, including the distinction between the benefit designs of insurance programs, and how much coverage is provided for proactive (well) versus reactive (sick) care. She argues that plans that do not cover proactive (preventive) care, which is associated with better health outcomes, may have adverse effects on the health of their participants. Reimbursement rates vary by type of health plan coverage and this may likewise affect health status. Decker (2007) found that lower Medicaid payment rates were 13

20 associated with reduced access to physicians and medical services. Private insurance, available through ones employer or from the individual market, typically pays higher reimbursement rates to doctors than Medicare or Medicaid. A 2007 GAO report on Medicare payment rates for anesthesia services found that Medicare paid 67% lower rates than the private market for the same services. 1 Though many argue that socioeconomic status is the primary reason for variations in health status, Andrulis (1998) theory argues that in fact, the association of poverty with poor health is due to lack of insurance. 2 Andrulis theory is supported by the fact that having health insurance ensures access to doctors and medical care, which has been shown to improve one s self-reported health (Cunningham 1998). Cunningham found that for individuals with HIV, health-related quality of life outcomes were significantly better for those with higher access [to care]. Access is a combination of the ability to make an appointment, the likelihood that services provided will be paid for, and the quality and appropriateness of services provided. All of these factors are a function of the type of insurance one has, which differs considerably. B. Model Specification The dependent variable is health status. In this study I will examine the effect of insurance status on two different measures of self-reported health status. The first is an ordinal scale with values 1 to 4 where the individual was asked to rate their health status; 1 GAO "Medicare Physician Payments: Medicare and Private Payment Differences for Anesthesia Services." 2 Ross and Government Mirowsky 2000 Accountability Office, July < 14

21 1 = Poor/Fair, 2 = Good, 3 = Very Good, 4 = Excellent. The dependent variable in the second model is a dichotomous variable indicating whether the individual has an activity limitation, such as a disability. Other socio-demographic characteristics have been shown in previous studies to have an effect on health status. This model includes independent variables that indicate socioeconomic characteristics, citizenship status and homeownership status. Socioeconomic variables include level of educational attainment, age, race, percent of the federal poverty level, gender, ward of residence and marital status. Previous research has found that more educated persons are more efficient producers of health. Thus, it is expected that more highly educated individuals are in better health. Education is measured as a set of dummy variables indicating the highest level of education attained. The levels include: less than high school, high school graduate, some college, college graduate, and post-graduate education. Age is likely to be a significant determinant of health status, as those who are older are expected to have more health conditions and visit the doctor more frequently. Thus, older persons are expected to be in poorer health than younger persons. In the District of Columbia, the vast majority of the population is either black (54.0%) or white (40.6%) 3. This model controls for race using variables for white, black and other (any individual who is not either white or black), to measure the different 3 Source: US Census Data. District of Columbia 2009 QuickFacts. 15

22 effects on health status. It is expected that individuals who identify as black are in poorer health relative to their white counterparts. The RAND Health Insurance Study found that individuals with lower incomes had poorer health status than wealthier individuals. This study measures income as a percent of the federal poverty level 4. It is expected that the as income rises, health status will improve. The reasons for this are many, including that higher income individuals likely have better diets, live in cleaner areas and have disposable income with which to purchase health-producing goods. Because this study is DC-specific, the model will also control for the ward of residence. The eight wards in the District have differing socioeconomic status and not surprisingly, substantial variation in access to health care providers (physicians, hospitals, etc), and possibly some effect on health status as a proxy of income 5. For both models, to predict the effect of insurance status on health status, I will estimate probit regressions; for the self-reported health status model the regression is an ordered probit, while the model predicting activity limitation is the binary probit. The regression model is indicated below: Health Status i = βo + β 1 High School Grad + β 2 Some College + β 3 College Grad + β 4 Post Grad Education + β 5 Employer Sponsored Insurance + β 6 Medicaid + β 7 Alliance + β 8 NonGroup Insurance + β 9 Medicare + β 10 Other Insurance + β 11 Age + β 12 Income Less Than 200% Federal Poverty Level + β 13 Male + β 14 Ward 1 + β 15 Ward 2 + β 16 Ward 3 + β 17 Ward 4 + β 18 Ward 5 + β 19 Ward 6 + β 20 Ward 7 + β 21 U.S. Citizen + β 22 Homeowner + β 23 Married + β 24 Cohabitate + β 25 4 See: Table 7 5 See: Table 8 16

23 Divorced/Widowed/Separated + β 26 Marital Status Missing + β 27 Black + β 28 Other Race + u IV. DATA DESCRIPTION The data for this study comes from the 2009 District of Columbia Health Insurance Survey (DC HIS). The District of Columbia's Department of Health Care Finance (DHCF) commissioned the Urban Institute to perform a survey of District residents to document health insurance options and coverage and access to and use of health care for the non-institutionalized population in the District. 6 The data from this survey was released in two formats: the first is a person-level dataset, which includes data on 9,700 individuals who reside in 4,699 households; the second is a target-level dataset, which includes 4,699 responses, one from each target individual 7. Self-reported health is only available in the target-level data. Households were randomly selected using two approaches: random-digit dial telephone sample and an address-based random household sample. Historically, the telephone sample selection method is desirable, but in an attempt to capture cell-phone 6 Ormond, Barbara A., Timothy Triplett, Sharon K. Long "2009 District of Columbia Health Insurance Survey: Methodology Report." The Urban Institute, 26 Apr For target respondents under the age of 18, the parent or legal guardian answered the survey on their behalf. 17

24 only households (which represent approximately 22.7% of households 8 ), the Urban Institute also included an address-based random sample. Survey questions included health status, ward of residence, education, age, the household's federal poverty level, marital status, race, citizenship, homeownership and insurance status. The question on insurance status asked whether or not the individual had each type of insurance (ie. Do you have employer-sponsored insurance, yes or no?) and not what the primary source of insurance was. Thus, there were a significant number of people who had multiple insurance coverage sources. This idiosyncrasy in the data collection made it impossible to determine the primary insurance coverage of each respondent. Income is measured non-traditionally in this study. Here, the respondents selected from income ranges that corresponded to percentages of the Federal Poverty Level (FPL). So, if an individual's household made any amount within 0-100% of the federal poverty level, their income is coded as a dummy variable indicating the appropriate category. V. EMPIRICAL RESULTS A. Characteristics of the Sample Overall Sample Descriptive statistics for study participants are presented in Table 2. 8 The Data Methodology report states that 22.7% of households are cell phone only in the first-half of 2009, up from 20.2% in the second-half of The report cites Blumberg and Luke

25 The study's overall sample is 4700 with 4648 reporting their health status. Of the 4648 respondents, 28.8% reported excellent health, 32.9% responded with very good health, 24.1% had good health, and 14.2% had fair or poor health. The average age of the sample was Males comprised 43.2% of the sample. Interestingly, 95.7% of the sample reported having insurance. Those with insurance were divided into categories representing the source of insurance. 55.4% reported having employer-sponsored insurance, 6.6% had Medicaid, and 3.9% were insured by the DC Healthcare Alliance (the non-medicaid public program in the District). Furthermore, 23.6% had Medicare, 4.7% had non-group insurance, such as an individual policy, and 1.4% responded other. Over 19% of the sample also reported having a secondary source of insurance. Educational attainment was measured by highest degree achieved. Only 6.7% of the sample did not graduate from high school, 14.8% achieved a high school diploma, while about 15% attended some college but did not graduate. Over 50% of the sample graduated from college, and over half of those received post-graduate education. Educational attainment was not reported for about 9% of the sample. The public health insurance programs in the District cover individuals up to 200% of the Federal Poverty Level, which included 28.5% of the sample. Additionally, 44.6% of the sample had incomes above 500% of the FPL, which in 2009 was $54,150 for an individual. 19

26 In the District of Columbia, the census reports that the vast majority (94.6%) 9 of residents identify as black or white, while less than 6% identify as another race. In this sample, race is reported as the primary race the individual identifies with. Of 4648 respondents to this question, 45.5% were black, 44.1% were white, and the remaining 10.4% were another race. The survey also asked respondents in which of the eight wards of the city they reside. Almost 13% resided in ward 1, 12.0% in ward 2, 17.2% in ward 3, 12.1% in ward 4, 11.9% in ward 5, 14.8% in ward 6, 10.5% in ward 7, and 9.3% in ward 8. The median family income in in wards 5, 7 and 8 was less than $80,000 while in wards 1, 2, 3, 4 and 6 the median family income exceeded $80,000. In wards 2 and 3 the median family incomes were well in excess of all other wards, at $190,692 and $257,386 respectively. Ward of residence is a proxy for availability (supply) of health care providers. Health Status Columns 3-6 of Table 2 show the demographic and socioeconomic characteristics controlling for self-reported health status. About 14% of respondents reported Poor/Fair health status, while 24% indicated their health was Good. Nearly onethird reported Very Good health and close to 29% indicated their health was Excellent. For Poor/Fair and Good health statuses, the uninsured comprised over 5% of respondents, 9 Source: US Census Data. District of Columbia 2009 QuickFacts. 20

27 while for Very Good and Excellent, the uninsured comprised just over 3% of respondents. Those who were insured either through employer-sponsored insurance or on the non-group market comprised greater percentages of the Very Good and Excellent (63% and 89.7% respectively) than the Poor/Fair and Good respondents (25.2% and 46.2% respectively). Medicaid and Alliance beneficiaries, on the other hand, comprised larger percentages of the lower two health status categories (11.4% (Poor/Fair) and 8.3% (Good)) than the higher two categories (4.2% (Very Good) and 6% (Excellent)). Medicare beneficiaries accounted for nearly 50% of the respondents in the Poor/Fair category but only 11.1% of respondents reported Excellent health. Members of the Alliance program and individuals who responded that they have Non-Group or Other coverage each comprised less than 10% of each health status category. Of the individuals with insurance, many reported having supplemental insurance. Interestingly, the percent of individuals with supplemental insurance was higher for those with poor health statuses (37.6% Poor/Fair and 24.9% Good). In contrast, 16.5% of those who reported Very Good health and about 10% of persons who had Excellent health had supplemental coverage. Educational attainment follows the expected pattern: greater levels of education are associated with higher health status. Individuals with college degrees and postgraduate education comprised over 50% of the respondents who report Very Good or Excellent health, while individuals with education less than a college degree accounted for over 50% of those in Poor or Fair health and about 46% of persons in Good health. A similar story exists for race and health status, where black respondents accounted for 21

28 about 74% of individuals with Poor/Fair health but only 27.6% of those in Excellent health. Individuals who were white, on the other hand, comprised less than 19% of those in Poor/Fair health but more than 60% of those in Excellent health. The results for income are also in line with expectations. Individuals with incomes less than 200% FPL comprised larger percentages of persons in Poor/Fair or Good health, 56.7% and 34.6% respectively. Conversely, the same cohort accounted for 21.2% and 18.2% of persons in Very Good and Excellent health. The reverse pattern characterizes those with incomes in excess of 400% of the FPL. This cohort comprised smaller percentages of those in Poor/Fair or Good health (21% and 41.6%), but larger percentages of persons with Very Good or Excellent health, 59.1% and 67.1% respectively. Age has the anticipated negative effect on health status. The average age of the Poor/Fair respondents was 59.3 years while the mean age for Excellent respondents was only 36.6 years. Activity Limitation Table 3 contains summary statistics for the sample stratified by activity limitation. Of the overall sample, 20.5% of individuals had an activity limitation; close to 4% of those who reported an activity limitation are uninsured, while the uninsured comprised 4.4% of respondents who said they had no activity limitation. When comparing those with private (ESI and non-group) versus public (Medicare, Alliance, Medicaid) insurance, those with public insurance comprised higher percentage (60.2%) 22

29 of those with an activity limitation than those without (27.3%). The reverse pattern emerges for those with private insurance. In addition, persons with an activity limitation were more likely to have supplemental insurance (34.5%) versus less than 16% for those without an activity limitation. As with health status, those with higher levels of education were less likely to have an activity limitation. Persons without a college degree (less than high school, high school grad and some college) comprised almost 55% of those with an activity limitation, but less than 32% of those without a limitation. Minorities were in poorer health than whites. Blacks were twice as likely as whites to have an activity limitation. Individuals with higher incomes were less likely to have an activity limitation than individuals with lower incomes. Those with incomes less than 200% FPL comprised close to half of the population with an activity limitation, but less than one-quarter of those without an activity limitation. Conversely, those with incomes above 400% FPL represented close to 57% of those without an activity limitation and only 32.5% of those with a limitation. Persons with an activity limitation were about 12 years older, on average, than those without an activity limitation. B. Ordinal Probit Results for Health Status Table 4 reports the ordinal probit estimates for the models predicting selfreported health status. In the Insured model, being insured or having secondary insurance did not significantly affect health status, and in the Insurance type model, only having 23

30 non-group insurance had a positive effect on one s predicted health. No other type of coverage has an effect. Both models showed that as one s educational attainment rises, so does their predicted health status. Additionally, compared to whites, blacks and other minorities were predicted to have lower health status. Income also has the expected positive effect on health. The effect of age also follows the pattern of previous studies: older individuals were predicted to have poorer health than their younger counterparts. Because the probit coefficients only indicate the direction of the effect of each independent variable, one must calculate marginal impacts to assess the magnitude of the effect. Tables 5 and 6 reports the marginal effects from the ordinal probit models predicting self-reported health status. Insured Model Table 5 describes the effects of the independent variables of interest on selfreported health status, where insurance coverage is measured as a binary indicator. Having either primary or supplemental insurance does not have a statistically significant effect on self-reported health status. This is not surprising because only a small percentage of the sample reported no insurance. Conversely, education has a statistically significant effect on one s health status. Higher levels of education reduced the likelihood of reporting Poor/Fair or Good health, but increased the probability of reporting either Very Good or Excellent health. For example, an individual with some college was 2.6 percentage points less likely than an 24

31 individual with less than a high school education to report Poor/Fair health, but was 4.4 percentage points more likely than those with a high school diploma to report being in Excellent health. Furthermore, an individual with post-graduate education was 5.4 percentage points less likely than an individual without a high school diploma to report Poor/Fair health, and 11.3 percentage points more likely than this cohort to report Excellent health. Race has the expected effect on self-reported health status. Black persons were 5.8 percentage points more likely than whites to report Poor/Fair health and around 10 percentage points less likely than whites to report Excellent health. Persons of other races were 2.4 percentage points more likely than whites to report Poor/Fair health and about 4 percentage points less likely than whites to report Excellent health. Consistent with expectations, higher income is linked to better self-reported health. Individuals with incomes 200% - 249% FPL were 2.7 percentage points less likely than someone with income less than 100% FPL to report Poor/Fair health, and 5.8 percentage points more likely to report Excellent health. The magnitude of the effect for higher income cohorts was much larger. Compared to those with incomes below 100% FPL, individuals with income greater than 500% FPL were 8.2 percentage points less likely to report Poor/Fair health and 15.8 percentage points more likely to report Excellent health. 25

32 Age also has the expected effect on health status. Compared to an individual with age at the mean (46.3 years), someone age 56.3 was 2.9 percentage points more likely to report Poor/Fair health and 6 percentage points less likely to report Excellent health. Insurance Type Model Table 6 contains the marginal effects of having different types insurance (and other independent variables) on self-reported health status. Only having Non-Group insurance had a significant effect on health status. An individual who had non-group insurance (typically individual-market coverage) was 2.9 percentage points less likely to report being in Poor/Fair health than someone who was uninsured and 6.5 percentage points (marginally significant) more likely to report being in Excellent health. Having supplemental insurance did not have a significant effect on ones health status. Here again, the results show that educational attainment is liked to better health. Individuals with a college education were 3 percentage points less likely than individuals with less than a high school education to report having Poor/Fair health but 6.2 percentage points more likely to report Excellent health. Compared to whites, blacks were 5.5 percentage points more likely to report Poor/Fair health and 9.9 percentage points less likely to report Excellent health. Income, again, has a significant effect on health status, but only at the higher levels (above 350% FPL). For example, an individual with income greater than 500% FPL was 7.1 percentage points less likely than an individual with income less than 100% 26

33 FPL to report Poor/Fair health and 14 percentage points more likely to report Excellent health. Age also has a significant effect on health status. Compared to the average individual (age 46.3 years), someone age 56.3 was about 3 percentage points more likely to report Poor/Fair health and 5 percentage points less likely to report Excellent health. C. Probit Results for Activity Limitation Table 7 reports the marginal impacts derived from the probit models predicting the probability the individual is limited in the activities he/she can perform. In model 1, which includes a binary indicator of insurance coverage, having insurance did not have a significant effect on the probability of having an activity limitation. In contrast, supplemental insurance increased the likelihood of reporting an activity limitation by 3.3 percentage points. Model 2 controls for type of insurance coverage. For an individual with Medicaid the probability of having an activity limitation was 14.9 percentage points more likely than an uninsured person. Medicare beneficiaries were 14.2 percentage points more likely than uninsured persons to have activity constraints. None of the other insurance categories influence the probability of having an activity limitation in a significant way. Income, represented as a percentage of the federal poverty level, had a significant effect on the probability that one has an activity limitation in both the Insured and 27

34 Insurance Type models. The results show that as income levels increased, the probability that one has an activity limitation decreased. In the Insurance Type model, individuals with household incomes between 150% and 199% of the federal poverty level were only 6.9 percentage points less likely than individuals with incomes below 100% of the federal poverty level to have an activity limitation, while individuals with incomes 500% and above were 17 percentage points less likely. A similar pattern emerges for the Insured model. In both models, individuals who did not report their income were 5.8 percentage points less likely to have an activity limitation than an individual with income below the poverty level. In Model 1, individuals age 56.3 (10 years above the mean) were 4 percentage points more likely than individuals age 46.3 to have an activity limitation. In Model 2, on the other hand, individuals 10 years older than the mean were only 3 percentage points more likely to have an activity limitation than average individuals. Neither race nor education appears to have significant effects on the probability of having an activity limitation. VI. DISCUSSION Implications of Findings Consistent with the findings from many prior studies, my results show that insurance status does not have a significant effect on health status. With the exception of 28

35 the model predicting the probability that an individual has an activity limitation, neither being insured nor the type of insurance coverage one has significantly affects one s selfreported health status. In contrast, educational attainment, income, race and age do have significant effects on health status and results are in line with expectations. There are likely a variety of reasons for these findings, which are discussed at length in the article by Levy and Meltzer (2001). Both health insurance and health status are complex, multi-dimensional goods and our ability to measure them is imperfect, at best. Furthermore, the expected effect of health insurance on health results from increased access to medical care. This study cannot quantify the amount, quality or type of medical care received by participants, and thus fails to establish a causal relationship between health insurance and health. While the analysis could not directly quantify the effect of health insurance on health, it does shed light on the effect of some of the related socioeconomic factors on health. Policies are currently being adopted to increase enrollment in Medicaid and expanding the non-group market, with the expectation that such expansions will improve health. This study also examined the effects of income, race, education and age on health and the findings suggest that efforts to increase enrollment in insurance programs may not be the only mechanism that will lead to improvements in health. The implications of this research on policy decisions are vast. In the District of Columbia, the rate of uninsurance is lower than every state except Massachusetts. As health reform policies become effective and expand access to insurance, reducing the 29