The Affordability and Financial Sustainability of Medicare After researching the affordability and financial sustainability of the Medicare program, discuss whether or not the

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Journal of Health Economics 60 (2018) 75–89

Contents lists available at ScienceDirect

Journal of Health Economics

jo ur nal homep age: www.elsev ier .com/ lo cate /econbase

ffects of Medicare coverage for the chronically ill on health nsurance, utilization, and mortality: Evidence from coverage xpansions affecting people with end-stage renal disease�

artin S. Andersen epartment of Economics, UNC Greensboro, 516 Stirling Street, Greensboro, NC 27412, USA

r t i c l e i n f o

rticle history: eceived 21 June 2017 eceived in revised form 1 June 2018 ccepted 4 June 2018 vailable online 18 June 2018

EL classification: 13 18 51

a b s t r a c t

I study the effect of the 1973 expansions of Medicare coverage among individuals with end-stage renal disease (ESRD) on insurance coverage, health care utilization, and mortality. I find that the expansions increased insurance coverage by between 22 and 30 percentage points, in models that include trends in age, with the increase explained by Medicare coverage, and increased physician visits by 25–35 percent. These expansions also decreased mortality due to kidney disease in the under 65 population by between 0.5 and 1.0 deaths per 100,000. Lastly, I provide evidence for two mechanisms that affected mortality: an increase in access to and use of treatment, which may be due to changes in insurance coverage; and an increase in entry of dialysis clinics and transplant programs.

© 2018 Elsevier B.V. All rights reserved.

eywords: nsurance

ortality idney disease ealth

ealth insurance

. Introduction

The United States has typically expanded public insurance pro- rams by providing coverage to distinct demographic groups. For xample, the introduction of Medicare and Medicaid in 1966 pro- ided insurance coverage to people who were 65 and older or had ow income. However, several expansions of these programs have efined eligibility based in part on the presence of medical condi- ions (e.g. long-term disabled, people with end-stage renal disease, regnant women, and women diagnosed with breast or cervical

ancer). By selecting on ill-health, the effects of a disease-specific nsurance expansion on insurance coverage, health care utilization,

� Aaron Ladd, James Frizzell, and Mohamad Noureddine provided excellent esearch assistance. Dr. Glenn Gee provided helpful insight into the coding and epi- emiology of kidney disease. I thank the Editor, Kitt Carpenter, three anonymous eferees, and participants at the UNCG Brown Bag, the NBER Health Economics pro- ram meeting, and the Southern Economic Association, American Society of Health conomists, AEA/ASSA Annual Meetings for helpful comments. As usual, any errors nd omissions are my own.

E-mail address: [email protected]

ttps://doi.org/10.1016/j.jhealeco.2018.06.002 167-6296/© 2018 Elsevier B.V. All rights reserved.

and health outcomes may differ considerably from the effects of more broad-based expansions.

Previous studies of the Medicare and Medicaid programs have demonstrated that Medicare may reduce mortality (Card et al., 2009; Chay et al., 2017), increase health care utilization (Card et al., 2008), and improve financial risk protection (Barcellos and Jacobson, 2015; Engelhardt and Gruber, 2011), while the intro- duction of state Medicaid programs reduced infant mortality (Goodman-Bacon, 2017). More recent evidence from an expansion of Medicaid for pregnant women demonstrates improvements in infant health outcomes (Currie and Gruber, 1996) and a related expansion affecting children improved their health and increased health care utilization (Currie and Gruber, 1996). A recent ran- domized study of the Oregon Medicaid program (Finkelstein et al., 2012) also demonstrated greater health care utilization and better self-rated physical and mental health among people randomized to receive Medicaid coverage, although there was no statistically significant difference in mortality.

There is, to my knowledge, no empirical evidence of the effects

of three other disease-specific insurance expansions that provided insurance coverage for women with breast or cervical cancer, the long-term disabled, and people with end-stage renal disease

7 ealth E

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6 M.S. Andersen / Journal of H

ESRD). In this paper, I examine the effect of a 1973 Medicare expan- ion that provided coverage to two groups of people: long-term eneficiaries of the Social Security Disability Insurance (SSDI) pro- ram and people who are undergoing dialysis or have received a idney transplant due to having end-stage renal disease. The focus f this paper is the effect of the expansion on people with kidney isease who became eligible for Medicare coverage through either he ESRD route, if they were not already receiving SSDI payments, or ue to SSDI receipt. Consistent with prior practice of the Medicare rogram itself, I consider both sets of enrollees as being enrolled in he ESRD program.1

These expansions are attractive to study for several reasons. irst, the introduction of the program was, for the most part, unan- icipated so that there is unlikely to be any significant anticipatory ffects (Ball, 1973). Second, people with kidney disease tend to be n extremely poor health, so insurance is likely to have unusually arge effects on health. Third, because for most people treatment

as unaffordable prior to the expansion and insurance typically did ot cover treatment (Rettig, 2011; Congressional Research Service, 971), these results provide some insight into the welfare conse- uences of moral hazard induced spending since the bulk of any

ncrease in utilization can be attributed to ex-post moral hazard. he ESRD program is also worthy of study on the basis of the size f the program. In 2015 the United States spent over $30 billion o treat 500,000 Medicare beneficiaries with ESRD, which repre- ents 1% of all Medicare beneficiaries and 5% of Medicare spending. n other words, the ESRD program is almost as large as the entire

edicaid program in the state of Texas, which is the third largest edicaid program (by spending) in the country. In order to identify the effect of the ESRD program, I estimate

riple-difference models that compare outcomes for people over 5, who were always eligible for Medicare coverage, versus those nder 65, before versus after the expansion took effect, with versus ithout ESRD. However, due to the expansion of Medicare coverage

o the long-term disabled, the triple difference estimate is biased. ence, I also estimate difference-in-differences models that con- ition on having ESRD, which yields unbiased estimates as long as here is no selection into treatment, i.e. as long as ex-ante moral azard is small. These two estimators will yield similar results as

ong as either the effect of Medicare eligibility is small in the non- SRD group or the share of people eligible for Medicare coverage n that group is small.

In this paper I document three main facts about the ESRD pro- ram. First, I demonstrate that the ESRD expansion significantly ncreased insurance coverage among people under 65 years of ge with kidney disease. Close to the traditional Medicare eligi- ility threshold of 65, I find a 22.6–29.6 percentage point increase

n the probability of any insurance coverage among people with idney disease. I find somewhat larger increases in Medicare cover- ge (26.0 and 33.9 percentage points, respectively), indicating that ome people would have had insurance coverage in the absence of he expansion.

Second, I find that the ESRD expansion increased physician visits y 25–35 percent for people with kidney disease below 65 years of ge. The increase in physician visits is consistent with my results on ealth insurance coverage and implies that a 10 percent increase

n the share of the population with insurance increases physician

isits by about eight percent. Because of the wording of the survey uestion that I use to assess physician visits, it is also possible that

1 The Medicare Trustees’ Reports from this period all pool the ESRD population nd the disabled with ESRD populations because the disabled population with ESRD s more similar to the non-disabled ESRD population than the rest of the disabled opulation.

conomics 60 (2018) 75–89

the increase in physician visits represents an increase in visits to, among other things, dialysis clinics.

Third, I document a significant reduction in mortality due to kidney disease of between two and seven log points, depending on specification and definition of kidney disease. I am able to replicate this finding in cross-national comparisons as well that allow me to control for innovations in kidney disease treatment across coun- tries. My results imply that the program averted between 174 and 325 deaths per year for whites between 45 and 64 years of age (my estimation sample). Assuming that the entire change in mortality arose among people who gained insurance coverage, then my mor- tality and insurance results imply that the probability of dying in the coming year of kidney disease fell by 0.2–0.5 percentage points.

I am also able to provide evidence in support of two mech- anisms by which the ESRD expansion affected health. First, the state-specific effect of the ESRD expansion on kidney disease mor- tality was larger in states that had more treatment facilities per capita in 1971. One interpretation of this result is that the presence of treatment facilities reduced mortality by increasing access to treatment. This interpretation is also consistent with the increase in physician visits. Second, I document an increase in the number of dialysis clinics and transplant programs per capita from 1971 to 1975 in states that had a higher under 65 mortality rate due to kidney disease, which is consistent with a demand side shock encouraging entry of new treatment facilities.

My mortality estimates also allow me to extrapolate to changes in survival and imply that the expansion saved between 2000 and 14000 life years, based on the change in survival for 45 year olds. This range encompasses some values where, using a value of $100,000 per statistical life year, the cost of the program are out- weighed by the survival benefits. However, these estimates ignore other costs that the program imposes on society (e.g. increased dis- ability insurance payments) but also ignores the value of spillover effects on to people 65 and older.

The remainder of the paper proceeds as follows. Section 2 pro- vides background information on end-stage renal disease, discusses the role that the federal government has played in the treatment of ESRD, and describes the 1973 Medicare expansions that I study. Sections 3 describes the data that I use for my analyses and the empirical approach that I take, while |Sections 4 present my main results from the Medicare expansion in 1973. Section 5 presents potential mechanisms behind my results. Section 6 discusses the welfare implications of my results. Section 7 concludes.

2. Background

End-stage renal disease (ESRD) is the end result of a progressive decline in kidney function due to chronic kidney disease. Leading causes of ESRD and chronic kidney disease include chronic kidney disease include diabetes, hypertension, glomerulonephritis, poly- cystic kidney disease, kidney stones, urinary tract infections, and various congenital defects (National Kidney Foundation, 2009).2

The loss of kidney function that characterizes ESRD leads to a rapid buildup in toxins and disregulation of potassium and sodium levels in the blood that, left unchecked, rapidly leads to death.

Treatment for ESRD emphasizes restoring or augmenting the body’s ability to filter out toxins and maintaining electrolyte levels

either by transplanting a functioning kidney from either a living or cadaveric donor or by externally filtering blood using a dial- ysis machine. There were significant scientific advances affecting

2 Appendix Table A lists the ICDA-8 codes that I use to identify deaths with these underlying cause of death codes. In analyses using the National Health Interview Survey, I also include data that uses ICD-7 and ICD-9 codes, which are also identified in the appendix table.

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they were not eligible for the ESRD program. I code each death as being a kidney disease death, or not, based

on either the underlying cause of death, which the World Health

M.S. Andersen / Journal of H

oth forms of treatment in the late 1950s through the 1960s. The rst successful kidney transplant was performed in 1956 with the ubsequent decade leading to slow, but steady, improvements in ransplantation (Congressional Research Service, 1971) so that by 971 there were 1172 kidney transplants performed (Rettig, 1976). hroughout this period, kidney transplantation was a costly proce- ure with the Congressional Research Service (1971) estimated that idney transplantation had a nominal one-time cost of $10,000 to 20,000 ($59,000 to $117,000 in 2015) and maintenance costs of 1,000 per year ($5,900 in 2015).

Chronic dialysis, which is what is necessary to treat ESRD, was ot feasible until 1960. Furthermore, at its inception, dialysis was xtremely costly leading to rationing at the first dialysis clinic in he United States (Alexander, 1962). In July of 1972, there were 786 living dialysis patients in the United States (Rettig, 1976, p. 00) and the Congressional Research Service (1971) estimated that he annual cost of dialysis was $15,000 in 1971 (nominal dollars, 85,000 in 2015 using the CPI-U).

Despite the availability of treatment modalities in the late 960s and early 1970s, the Congressional Research Service (1971) eported that most health insurance plans did not cover treatment or ESRD.

During the 1960s, the federal government took an active role in romoting the diffusion of treatments for ESRD as well as funding esearch and development of new treatments. In 1963, the Vet- ran’s Administration began to open dialysis clinics in its hospitals cross the country and, by 1971, there were 40 dialysis facilities and 5 transplant programs open at VA and military hospitals across he U.S. Beginning in 1964, the National Institutes of Health started rograms to study transplant immunology, which was intended to

ncrease the number of successful kidney transplants. In 1965, the ublic Health Service started the Kidney Disease Control Program, hich provided start-up grants to open a dozen dialysis centers

Rettig, 1991). The federal government, through the Bureau of the udget, also began examining the fiscal implications of the growth

n ESRD and the advent of new methods to treat ESRD, although hese discussions ultimately did not appear to have affected federal olicy (for further discussion see Rettig, 1991).

In 1972 Congress, for the first time, passed a law expanding eli- ibility for Medicare coverage, with the expansion taking effect on uly 1, 1973. Congress did so by declaring that two groups were eli- ible for coverage people who: had been eligible for Social Security isability Insurance (SSDI) benefits for more than 24 months; or ave received three, or more, months of renal dialysis with cover- ge extending up to twelve months after a person received a kidney ransplant.

Neither component of the expansion was truly universal since in oth cases, only individuals who were eligible for insurance under he Social Security program became eligible. Collectively, these two rograms increased Medicare enrollment by 1.7 million people, of hom 6,371 were eligible solely due to the ESRD in the first year of

he program. By 1978, there were almost 44,000 Medicare benefi- iaries with ESRD, of whom almost 35,000 were under 65 years of ge, with per capita spending of almost $65,000 (in 2015 dollars).

The ESRD component of the expansion (which includes long- erm disabled with ESRD), which was initially expected to enroll 5,000 people and cost $1 billion (nominal) per year, rapidly bal-

ooned in size, covering more than 50,000 people and costing over 1 billion per year in 1979 (Table 1). In 2013, the ESRD program overed almost half a million people at a cost of $30 billion, which epresents approximately 1% of Medicare enrollees and 5% of Medi- are spending.

conomics 60 (2018) 75–89 77

3. Data and empirical framework

3.1. Data

I use data from a variety of sources to measure insurance cov- erage, health care utilization, and mortality in my main analyses as well as data on potential mechanisms and confounding factors. In this subsection, I describe each of these data sources.

3.1.1. Insurance coverage and health care utilization The National Health Interview Survey asked respondents about

insurance coverage in even numbered years beginning in 1968, although the specific wording and universe for various questions has changed over time. In 1968 the NHIS inquired about health insurance generically and did not differentiate between public and private coverage and it was not until 1978 that the NHIS inquired about Medicare coverage for people under 65 years of age. In the 1974 and 1976 waves of the survey individuals with only Medicare coverage were instructed to respond that they were uninsured. As a result, I present results using data from 1968, 1970, 1972, 1978, and 1980 for most insurance outcomes (I include data on private insur- ance coverage in 1974 and 1976). I define an individual as having private insurance coverage based on whether or not an individual reported having private hospital coverage (as in Finkelstein, 2007) and define Medicare coverage in a comparable manner.

The NHIS also included questions on the number of doctor vis- its in the prior year beginning in 1969, which I use to measure health care utilization. Because the NHIS questions refer to treat- ment received over the prior year, I omit people 65 years of age from the utilization analysis and all data from July 1973 through June 1974, the 12 months following the implementation of the Medicare expansion.

I use the condition inventory and the list of conditions that caused the interviewee to miss days from work or access health care services to construct indicators for the presence of kidney dis- ease. The coding is based on the codes for the broad definition, but incorporating the NHIS omissions, listed in Appendix Table A. In total, out of 371,181 people in the NHIS, I identified 1644 peo- ple between 45 and 84 years of age with kidney disease using the broad definition. Despite the small sample size, the ESRD expan- sion is likely to have led to large changes in insurance coverage, hence I remain sufficiently powered to identify effects of the ESRD expansion on insurance coverage. For the utilization analyses, it is possible that I will be underpowered to detect effects if the increase in physician visits from the expansion is small.

3.1.2. Mortality I use the Multiple Cause of Death files from the National Center

for Health Statistics’ (NCHS) for the years from 1968 through 1978 (United States Department of Health and Human Services, 2007, 2007). These data provide the state and county of residence, race, gender, age, underlying cause of death and all other diagnoses listed on the death certificate for all deaths in the United States, except in 1972, when the NCHS was only able to process half of the submit- ted death certificates.3 Preliminary analyses of the distribution of deaths by age indicated significant excess mass at five-year inter- vals of age for non-white individuals, which was also reported in Honoré and Lleras-Muney (2006), so I omit non-whites from my mortality analyses. I also drop deaths to non-U.S. residents since

3 Since I use functions of the count of deaths in a given demographic-time cell as my dependent variable, I multiply the count of deaths in 1972 by 2.

78 M.S. Andersen / Journal of Health Economics 60 (2018) 75–89

Table 1 Enrollment, spending, and utilization in the ESRD program.

Year Enrollment Kidney Deaths Spending Utilization

Total Under 65 Under 65 65 and Over Total Per enrollee Transplants Dialysis

1971 5335 7534 1974 15993 4633 8949 1050.3 65673 1975 22674 12702a 4540 9491 1545.6 68164 1976 28941 14721a 4532 10597 2086.9 72110 1977 35889 16514a 4345 11008 2449.2 68243 1978 43482 34828 4498 11973 2804.6 64500 1979 52636 43031 3761 11966 3126.4 59397 4189 45565 1981 61930 47520 3761 13703 3723.7 60127 4898 58924 1986 93197 59570 3914 17851 6786.7 63646 8948 90886 1991 142510 83443 3395 17963 9704.2 56844 10037 144175 1996 255578 3433 20869 14141.8 55333 12219 215557

Source—Greenbook (various years), Annual Statistical Supplement to the Social Security Bulletin (various years), Multiple Cause of Death files, 1971–1996. a Enrollees eligible solely due to ESRD.

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otes—Enrollment based on enrollment in Medicare Part A, expenditures are for M orkers. Utilization data are the number of transplants and number of enrollees dial

he coding of kidney deaths changed between 1978 and 1979.

rganization defines as “the disease or injury that initiated the train f events leading directly to death, or the circumstances of the acci- ent or violence which produced the fatal injury,” or using any of he diagnosis codes listed on the death certificate. For each source f cause of death diagnosis codes, I defined a death as due to kidney isease using three sets of diagnosis codes. First, I defined a “nar- ow” definition of kidney disease, which did not restrict to only hronic disease, but is generally based on the “renal failure” codes n the ICDA-8. Second, I created a “chronic” definition by restrict- ng the narrow definition to deaths due to chronic causes. Lastly, I reated a “broad” definition, which was based on the codes used by he Kidney Disease Program in tracking kidney disease mortality Kidney Disease Program, 1971). Appendix Table A lists the ICDA-8 iagnosis codes for the three cause of death groupings that I use.

I combine the mortality data with population data from the SEER rogram and the U.S. Census Bureau in order to adjust for changes in he size of the population over time, which also affects the expected umber of deaths due to kidney disease. Because these data do not reak out population figures for individuals 85 and over, I restrict y analysis to deaths to individuals who are 84 or younger.

.1.3. Mechanisms and confounders In my discussion of mechanisms and potential confounders,

elow, I rely on data from three other datasets. I collected data n the geographic distribution of treatment facilities in 1971 from he publication “Kidney disease services, facilities, and programs n the United States” (Kidney Disease Program, 1971), which lists reatment facilities by state. Based on the name of the facility, I also lassified these facilities into Veteran’s Administration/Military vs. ivilian categories since access to the former may be restricted. Data n treatment facilities in 1975 came from the 1977 Annual Sta- istical Supplement to the Social Security Bulletin, which lists the umber of hospital transplant programs, hospital-based dialysis linics, and free-standing dialysis clinics by state.

I collected data on the share of people in an age-gender-state ell who receive income from either Social Security or the Supple- ental Security Income program from the March CPS supplements

or 1977–1979 (spanning 1976–1978).

.2. Empirical approach

.2.1. Identification

My data includes three sources of variation that I could use to

dentify the effect of the Medicare ESRD program on insurance cov- rage, health care utilization, and kidney disease mortality. First, here are differences over time in Medicare eligibility for individu-

re Parts A and B. Spending data have been inflated to 2015 using the CPI for urban respectively. Kidney deaths are based on chronic coding only, see Appendix Table A;

als of the same age and disease status. Second, there are differences by age in eligibility for Medicare for individuals in the same year and disease status. Third, there are differences by disease status in eligi- bility for Medicare coverage for individuals in the same year and of the same age. In principle, these three sources of variation would justify a triple difference estimator assuming that potential out- comes between these groups satisfy a “parallel trends” assumption (Lee and Kang, 2006). However, in my setting the parallel trends assumption is unlikely to hold because the SSDI expansion means that there is partial takeup of Medicare coverage in one of the com- parison groups. The structure of the problem, allows me to identify the source of any bias from these comparisons and identify a solu- tion that leads to unbiased estimates of the intent-to-treat effect of the Medicare expansion on people with kidney disease.

To demonstrate the bias and identify situations in which it does not affect my results, let Ye

akt denote the potential outcome for

someone in age group a (a = 1 for people under 65) who has kidney disease if k = 1, in time period t (t = 1 in the post period), and is either eligible (e = 1) or ineligible (e = 0) for Medicare coverage. Assume that there is a probability ˛akt that a person is eligible for Medicare in each akt cell and define Yakt = ˛aktY

1 akt

+ (1 − ˛akt)Y0 akt

. Ignoring the fact that some people 65 and older are not eligible for Medi- care, Medicare program rules imply that ˛0kt = 1 for all k, t ∈ {0, 1} and ˛1k0 = 0 for k ∈ {0, 1}. Finally, because (almost) everyone with kidney disease is automatically eligible for Medicare coverage, but only some people without kidney disease are eligible for Medicare coverage, we also have ˛111 > ˛101.

Then the triple-difference estimator can be written as:

DDD = ˛111 (

Y1 111 − Y0

111

) − ˛101

( Y1

101 − Y0 101

) +

[ ( Y0

111 − Y0 110

) −

( Y1

011 − Y1 010

) −

( Y0

101 − Y0 100

) +

( Y1

001 − Y1 000

) ]

The “parallel trends” assumption can be stated as the assumption that the terms in the large brackets in the previous equality vanish and that ˛101

( Y1

101 − Y0 101

) = 0. While it is plausible that the second

and third terms vanish, the ˛101 (

Y1 101 − Y0

101

) , which reflects the

effect of the expansions on the disabled without ESRD, is unlikely to vanish. Therefore DDD is biased by partial takeup of treatment (˛111 < 1) and the fact that some people without kidney disease are also treated (˛101 > 0). However, the bias can be signed if one assumes that the sign of the treatment effect is the same regard-

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