DOES MEDICARE SAVE LIVES?^*

Abstract

I. Introduction

Medicare pays nearly one-fifth of total health care costs in the United States. Yet evidence on the health effects of the program is limited. Studies of aggregate death rates before and after the introduction of Medicare show little indication of a program impact (Finkelstein and McKnight 2005). The age profiles of mortality and self-reported health in the population as a whole are likewise remarkably smooth around the eligibility threshold at age 65 (Card, Dobkin, and Maestas 2004; Dow 2004). Although existing research has shown that the utilization of health care services increases once people become eligible for Medicare (e.g., Decker and Rapaport [2002], McWilliams et al. [2003, 2007], Card, Dobkin, and Maestas [2004]), the health impact of these additional services remains uncertain.

This paper presents new evidence on the health effects of Medicare, based on differences in mortality for severely ill people who are admitted to California hospitals just before and just after their 65th birthdays. Specifically, we focus on unplanned admissions through the emergency department (ED) for “non-deferrable” conditions—those with very similar weekend and weekday admission rates. These admissions include patients with diagnoses such as obstructive chronic bronchitis, acute myocardial infarction (AMI), and stroke and represent 12% of hospital admissions for people between the ages of 60 and 70, and 25% of all deaths between these ages. We argue that the decision to present at an ED for these conditions is unlikely to depend on insurance status. Consistent with this assertion, the arrival rate is essentially identical for patients just under and just over age 65. In contrast, admission rates for all causes jump 7% once people reach 65, and total ED admissions rise by 3%.

Focusing on nondeferrable admissions, we turn to an analysis of the age profiles of patient characteristics and outcomes, testing for discontinuities at age 65. The demographic composition and comorbidities of the sample trend smoothly through the age-65 barrier, as would be expected under the assumption of no differential sample selection before and after Medicare eligibility. On the other hand, the fraction of patients with Medicare as their primary insurer rises by about fifty percentage points, whereas the fraction with no insurance drops by eight percentage points.

Associated with these changes in insurance, we find a small but statistically significant increase in the number of procedures performed in the hospital and a 3% rise in total list charges. Using death records matched to our sample of hospital admissions, we find a clearly discernible drop in mortality once people become eligible for Medicare. Relative to people who are just under 65 when admitted, those who are just over 65 have about a 1% lower likelihood of death within a week of admission, or roughly a 20% reduction in 7-day mortality. A similar absolute reduction in mortality is registered at longer horizons and persists for at least 9 months, suggesting that the differential treatment afforded to those with Medicare coverage has an important impact on patient survival.

We conclude by discussing potential channels for the Medicare effect. One mechanism is an increase in services for the relatively small fraction (<10%) of patients who move from uninsured to insured status at age 65. Although previous studies have found evidence of this shift, our estimated mortality effects are too large to be driven entirely by gains for such a small group.¹ The switch to Medicare coverage at 65 could also lead to increased treatments for patients who were previously covered by private insurance or Medicaid.² Though we see modest rises in treatment intensity at age 65, it is unclear whether these changes account for the reduction in mortality, or whether other factors (such as changes in the timeliness of treatment) also play some role.

The next section presents a brief overview of the Medicare program and existing research on its impacts. Section III outlines our regression–discontinuity research design. Section IV describes our procedure for identifying nondeferrable ED admissions and summarizes our tests for differential selectivity between patients just under and just over 65. Section V presents our main analysis of the age profiles of treatment intensity and mortality for the subsample of nondeferrable admissions. Section VI discusses potential channels for the Medicare effect on treatment intensity and heath. Section VII concludes.

II. Medicare: Background and Previous Studies

II.A. Medicare Eligibility and Health Insurance

Medicare coverage is available to people who are 65 or older and have worked at least 10 years in covered employment.³ Medicare is also provided to people under 65 who are receiving Social Security Disability Insurance (DI): currently about 12% of the population are already on the program by the time they reach 65.⁴ Age-eligible individuals can enroll on the first day of the month that they turn 65 and obtain Medicare hospital insurance (Part A) for free. Medicare Part B, which covers doctor bills and some other charges, is available for a modest monthly premium.

The onset of Medicare eligibility leads to sharp changes in health insurance status at age 65. These changes are illustrated in Figure I, which presents data from the 1999–2003 National Health Interview Surveys (NHIS) on four different dimensions of insurance coverage: Medicare coverage; any insurance coverage; coverage by multiple policies; and having primary insurance coverage in a managed care policy. The figure shows the mean of each outcome by age (measured in quarters), as well as the fitted age profiles from a simple regression that includes a quadratic in age, a dummy for age over 65, and interactions of the dummy with age and age-squared.

Open in a separate window

Figure I

Changes in Health Insurance at Age 65 in National Health Interview Survey

Samples are based on data from the National Health Interview Survey, 1999–2003. The estimated discontinuities (and standard errors) at age 65 and the fitted lines are from a regression with a quadratic polynomial in age fully interacted with a dummy for age greater than or equal to 65. Models also include a dummy for people assigned to age 65.0 whose Medicare eligibility status is uncertain. Points represent means for people in each age cell (measured in quarters). Points for peope age 65.0 are not shown in the figure. Estimated fraction who have Medicare includes all forms of Medicare coverage—see text for discussion of underreporting of Medicare coverage in NHIS. Estimated fraction with multiple policies includes people with at least two forms of health insurance coverage (e.g., Medicare and private supplemental coverage, or Medicare and Medicaid).

The data show a 65-percentage-point rise in Medicare coverage at age 65, from 12% to just less than 80%. In fact, the jump may be even larger: a recent study by Cohen and Martinez (2007) analyzes the responses to a probe question added to the 2005 and later NHIS surveys and concludes that Medicare coverage for people over 65 is underreported by roughly eight percentage points in the basic NHIS.⁵ Associated with the rise in Medicare coverage is an increase of about nine percentage points in the fraction of people with any coverage, leaving only about three percent of the population over 65 uninsured, compared with about thirteen percent of those under 65.⁶

The other insurance characteristics shown in Figure I also change sharply at 65. The fraction of the population covered by multiple policies rises by about 45 percentage points, as many of those with private insurance before 65 obtain supplemental policies to “top up” their Medicare coverage.⁷ Conversely, the fraction of people covered by managed care in their primary policies falls by thirty percentage points. This drop reflects the relatively high rate of managed care coverage in the pre-65 insurance market, coupled with the relatively low fraction of Medicare recipients who chose managed care over traditional fee-for-service insurance in the period up to 2002.⁸

Overall, the data in Figure I show striking changes in the heath insurance coverage of the population at age 65. Within a few weeks of becoming eligible for Medicare, at least 80% of the population is enrolled in the program. In the process, about three-fourths of those who were previously uninsured obtain coverage. Many Medicare enrollees who were previously covered by private plans enroll in supplemental policies, creating a sharp rise in the incidence of multiple coverage. And, because most Medicare recipients choose traditional fee-for-service coverage, the fraction of the population with managed care is reduced by half.

III. A Regression Discontinuity Analysis of Health Outcomes

As in earlier studies, we use comparisons across the age threshold for Medicare eligibility to measure the health impacts of the program. Unlike most existing studies, however, we attempt to isolate a subpopulation whose immediate mortality experience is more likely to be affected by differences in health care services provided to people once they are eligible for Medicare. Specifically, we focus on people who are admitted to the hospital through the ED for relatively severe illnesses. Any extra services (or improvement in the quality of services) offered to this subpopulation have at least a plausible chance of affecting short-run mortality. By comparison, Medicare-related changes in health care would have to have a very large impact on mortality to generate a detectable effect on the overall population.¹¹

Our analysis is based on a reduced-form regression–discontinuity (RD) model of the form

where y_i represents a health-related outcome for patient i, a_i represents the patient’s age (measured in days from his or her 65th birthday), f (·) is a function that is continuous at age 65 with parameter vector α (e.g., a flexible polynomial), Post 65_i is an indicator for whether the patient has passed his or her 65th birthday, and ε_i is an error term reflecting the influence of all other factors. In all specifications, we also interact the age polynomial with the Post 65_i indicator to allow the slope of the age profile to vary on either side of the age 65 threshold. If y_i is a measure of health care services provided to patient i, then we interpret β as a scaled estimate of the causal effect of Medicare coverage on the provision of services. As in other “fuzzy” RD designs (Hahn, Todd, and van de Klauuw 2001), the scale factor is just the difference in the probability of treatment on either side of the threshold, although in the case of Medicare, the treatment is potentially multidimensional (see Section VI, below).¹² If y_i is an indicator for mortality over some time horizon, then we interpret β as a scaled estimate of the causal effect of Medicare coverage on the likelihood of death in that time interval.

We defer a detailed discussion of the possible channels leading to the reduced form impact of Medicare coverage on health care services to Section VI. For now, we note that the data in Figure I suggest at least three alternatives: (1) an effect attributable to the increase in the overall fraction of the population with any health insurance; (2) an effect driven by people switching from an insurance carrier other than Medicare to a package that includes Medicare;¹³ and (3) an effect attributable to the change from managed care coverage to indemnity insurance. For example, hospitals may provide extra services if they know that a patient is covered by Medicare and supplemental insurance, rather than being uninsured, or covered by a typical pre-65 policy. Alternatively, there may be a reduction in the delay in verifying insurance status for Medicare patients or in receiving approval for certain procedures that are limited by managed care providers.

As emphasized by Lee (2008), the key assumption underlying an RD analysis is that assignment to either side of the discontinuity threshold (in our context, to being observed just a few weeks older or younger than 65) is as good as random. In the context of equation (1), this requires that

for δ sufficiently small, which ensures that a simple comparison of the mean of y_i on either side of the age-65 threshold yields a consistent estimate of the parameter β.

In a sample of hospital admittees, the assumption that patients close to age 65 are “as good as randomly assigned” to either side of the age threshold may fail if insurance status affects the probability that a patient is admitted to the hospital. Because previous work has found that the onset of Medicare eligibility leads to an increase in hospitalization rates (Card, Dobkin, and Maestas 2008), this is a serious threat to an RD analysis of the health outcomes of patients. Figure II illustrates the difficulty in using counts of hospital admissions based on California discharge records from 1992 to 2002. (The sample is described more precisely below.) At age 65 the number of non-ED admissions jumps by approximately 12%, whereas the number of ED admissions rises by 3%. Assuming that the additional patients are not as sick as those who would enter the hospital regardless of Medicare eligibility, the average health of patients rises discretely at age 65.

Open in a separate window

Figure II

Number of Admissions by Route into Hospital, California, 1992–2002

The lines are fitted values from regressions that include a second-order polynomial in age fully interacted with a dummy for age ≧65 and a dummy variable for the month before people turn 65. The dependent variable is the log of the number of admissions by patient’s age (in days) at admission, for patients between 60 and 70 years of age. The count of admissions is based on hospital discharge records for California and includes admissions from January 1, 1992, to November 30, 2002. The points represent means of the dependent variable for 30-day cells. The age profile for unplanned ED admissions includes admissions that occurred through the emergency department and were unplanned. The category “Other Admissions” includes all other admissions.

In this paper we attempt to solve the sample selection problem by focusing on a subset of patients who are admitted through the ED for a relatively severe set of conditions that require immediate hospitalization. Specifically, we identify a set of admission diagnosis codes with similar ED admission rates on weekdays and weekends.¹⁴ We then test the assumption that there is no remaining selection bias associated with the age-65 boundary by looking for discontinuities in the number of admissions at 65 and the characteristics of patients on either side of the boundary. Importantly, our procedure for identifying an unselected sample is unrelated to the age of patients. Thus, our tests for selection bias are unaffected by “pretest bias” and provide a reasonable degree of confidence in the validity of our inferences.

As a check on inferences from this sample, we also use a simple bounding procedure (Horowitz and Manski 1995) to estimate a lower bound (in magnitude) for the impact of Medicare eligibility on other patient samples, including the overall population of hospital admissions. This bound is fairly tight because the relative size of the group of “extra” patients who only enter the hospital if they are over 65 is modest (at most 12%) and because the gap between actual mortality experience of all patients and the “worst case” bound for the extra patient group is small. For example, the average 28-day mortality rate of all people admitted to the hospital who are just over 65 is 4.6%, whereas the lower bound on the mortality of the extra patients is 0. As we discuss below, this means that the “worst case” bias created by the selective inflow of patients after 65 is −0.3%—a relatively small bias.

Even if there is no differential selection around the discontinuity threshold, inferences from an RD design can be compromised if there are other factors that change at the threshold. One concern is retirement: 65 is a traditional retirement age, and studies have shown that health is affected by employment status (Ruhm 2000). Nevertheless, we believe the confounding effect of retirement is relatively minor. First, as shown in Figure A in the Online Appendix, recent data show no discontinuity in the likelihood of working at exactly age 65.¹⁵ Second, the admission diagnoses included in our nondeferrable sample are relatively severe and would normally preclude an immediate return to work. But the mortality gap we observe in this sample at age 65 emerges within 7 days of initial admission to the hospital, and thus is unlikely to reflect differences in survival between people who return to work and those who do not.

Another concern with the age-65 threshold is that recommended medical practices may change at this age. Until recently, for example, U.S. government agencies recommended different influenza vaccination policies for people over and under 65 (Smith et al. 2006). Again, however, we think this is unlikely to affect the characteristics or treatment of patients admitted through the ED for nondeferrable conditions.

IV. Sample Construction and Validation

Our sample is drawn from the universe of records for patients discharged between January 1, 1992, and December 31, 2002 from hospitals regulated by the state of California. To be included in the sample, patients must have been admitted; thus, those who were sent home after treatment in the ED or who died either en route or in the ED do not appear in the sample.¹⁶ As explained in the Data Appendix (included in the Online Appendix to this paper), we drop discharge records for patients admitted before January 1, 1992, or on or after December 1, 2002, to avoid length-biased sampling problems.

The discharge data set includes basic patient information (month of discharge, age in days at the time of discharge, sex, race/ethnicity, and ZIP code of residence) as well as medical information, including the principal cause of admission,¹⁷ whether the admission was planned or unplanned, the route into the hospital (ED versus non-ED), the patient’s primary health insurance provider, the length of stay, and a list of all procedures performed in the hospital. It also includes a scrambled version of the patient’s Social Security Number, which can be used to track patients who are transferred or readmitted and to link mortality records. Appendix I describes our procedures for consolidating the records for patients who were transferred to new units in the same hospital, or to another hospital. It also describes the linked discharge-mortality file prepared for us by the California Department of Health Services, which we merge with the initial discharge file in order to determine the date of death for patients in the sample.

One limitation of the discharge data is that approximately 5% of 64-year-old patients in our sample have missing Social Security numbers (SSNs), compared with about 4% of those just over 65. Given that the in-hospital mortality rate of 60–64-year-old patients with missing SSNs is much higher than that of patients with valid SSNs (10.4% vs. 6.3%), we believe that the ability to match longer-term mortality outcomes for 1/5 of this group once they reach 65 will tend to bias down any observed mortality improvements at 65. In any case, in Section V.E., we present evidence showing that the mortality effects we estimate are unlikely to have arisen mechanically as a result of merging procedures or selectively missing data.

As discussed in the previous section, a critical step in our analysis is to select a subset of patients whose admission to the hospital is independent of insurance status. We do this by identifying a set of admission diagnosis codes (classified by 5-digit ICD-9) that have similar admission rates through the ED on weekdays and weekends. For example, if admission for a given diagnosis code were equally likely on a weekend and on a weekday, then weekend admissions should constitute 2/7 = 0.29 of total admissions for that diagnosis. We compute the fraction of weekend admissions for all ICD-9 codes and limit our analysis sample to those codes for which the t-statistic for a test that the fraction of weekend admissions = 2/7 is less than 0.965. Figure B in the Online Appendix shows how the distribution of the fraction of weekend admits over the diagnosis codes in our sample centers tightly around 2/7, in contrast to the distributions over all diagnosis codes and those associated with ED admissions (which center around a lower fraction of weekend admits). Arguably, these diagnoses are “nondeferrable,” and patients with these conditions will present at the ED at the same rate just before and just after their 65th birthdays, irrespective of Medicare coverage.

Table I summarizes the ten most common diagnosis codes in this subsample of 425,315 “nondeferrable” admissions. The largest diagnosis category is obstructive chronic bronchitis with acute exacerbation. (This includes patients with chronic obstructive pulmonary disease—a common diagnosis for smokers and ex-smokers.) Acute myocardial infarctions of all forms (i.e., including all 410.xx ICD-9 codes) represent the second most common group of admissions in our sample. The top ten diagnoses also include respiratory failure and two forms of stroke (intracerebral hemorrhage and cerebral artery occlusion).

To test that patients’ inclusion in the “nondeferrable” admissions subsample is independent of whether they are under or over 65, we conducted a regression discontinuity analysis of the count of admissions by age. This procedure is similar to the test of manipulation proposed by McCrary (2008), though we have a discrete running variable (age, measured in days) and we use a parametric rather than a nonparametric approach. Figure III shows the age profiles of the log of the daily admission count for four groups of ED admissions, based on the magnitude of the t-statistic for the test that the fraction of weekend admissions = 2/7. The groups of admission diagnoses with t-statistics in the top two quartiles (t > 6.62 and 2.54 < t < 6.62) show clear evidence of a jump at age 65, whereas the age profile for diagnoses in our preferred group, with |t| < 0.965, shows no visible evidence of an increase in admissions.

Open in a separate window

Figure III

Admissions through the ED by Quartile of t-test for Equality of Weekend and Weekday Admission Rates

See notes for Figure II. In this figure the population of patients with an unplanned admission through the ED is split into four groups based on the primary diagnosis ICD-9 code. Groups are defined by the range of the t-statistic for the test that 2/7 of admissions with the diagnosis occur on the weekend. The y-axis is the log of the number of admissions in the group by patient’s age (in days) at admission, for patients between 60 and 70 years of age. The count of admissions is based on hospital discharge records for California and includes admissions from January 1, 1992, to November 30, 2002.

Formal testing results are summarized in Table II. Each pair of columns in this table presents the estimated discontinuities at age 65 from two alternative RD models for the log of the number of admissions by age (in days) of the admitted patient. We limit the sample to people between the ages of 60 and 70, resulting in 3,652 observations—one for each potential value of age in days. Both specifications include a dummy for age over 65 and a quadratic polynomial fully interacted with the post-65 dummy. We have also fit the models with cubic polynomials and found no significant differences in the estimated values of the post-65 effects (see Table A in the Online Appendix).

Although we know each patient’s exact age (measured in days) at the time of admission, we do not know patients’ birth-dates or the exact admission date.¹⁸ Because Medicare eligibility begins on the first day of the month that a person turns 65, people who are admitted in the period up to 31 days before reaching their 65th birthdays may or may not be eligible for Medicare. Figure C in the Online Appendix shows the fraction of admitted patients in our nondeferrable sample who are recorded as having Medicare as their primary insurance provider, by age in days for a narrow window around age 65. This fraction is relatively flat for people up to a month before their 65th birthdays, and then rises linearly in the 31 days before they reach age 65, as would be expected given Medicare eligibility rules and a uniform distribution of birthdates.

Because we do not know the Medicare eligibility status of patients who are admitted within 31 days of their 65th birthdays, the specifications reported in the even-numbered columns of Table II include a dummy for these observations. The addition of this dummy has relatively little impact on the estimated coefficients.¹⁹ Looking at the models in columns (1)–(4), we estimate that non-ED and planned ED admissions rise by about 12% at 65, whereas unplanned ED admissions rise by 2.6%. The remaining columns report the results for the four quartiles of unplanned ED admissions shown in Figure III. As suggested by the graph, the estimated models for our preferred subgroup of diagnoses (in columns (11) and (12)) show no evidence of a rise in admissions at age 65.

Although the number of admissions in our nondeferrable sample trends smoothly at age 65, we also tested for a change in the health characteristics of admissions at age 65. Specifically, we constructed a Charlson comorbidity score from the secondary diagnoses listed on each discharge record.²⁰ Figure D in the Online Appendix shows the age profile in the Charlson comorbidity scores for the nondeferrable sample. There is no discernible evidence of a drop in the severity of comorbidities at age 65; in fact, formal RD analysis on the age profile indicates a small but statistically insignificant increase in severity (see Table B in the Online Appendix). If we interpret the rise as a true measure of the change in severity (and not a result of upcoding incentives in the Medicare payment system), it suggests that, if anything, our sample becomes slightly less healthy at age 65.

We have also checked for discontinuities in the case mix and demographic composition of the nondeferrable subsample at age 65. Counts of admissions for each primary diagnosis code included in our subsample trend smoothly with age, which suggests that the case mix is very stable through the age 65 boundary. Tests for jumps in the racial composition, sex, and fraction of Saturday or Sunday admissions (available on request) are all far below conventional critical values. To increase the power to detect differences in patient health, we used all the available covariates for an admission (including age, race/ethnicity, sex, year, month, and day of admission, and principal diagnosis fixed effects) to fit linear probability models for mortality over 7, 14, 28, 90, and 365 days. We then took the predicted mortality rates from these models and conducted an RD analysis, looking for any evidence that the predictors of mortality shift at age 65. The age profiles of 7-day and 28-day predicted mortality are shown in Figure E of the Online Appendix. (Results for other follow-up periods are very similar.) These age profiles are extremely smooth, and show no jump at age 65. In an RD specification with a quadratic in age and a dummy for over 65, interacted with the linear and quadratic terms, the t-statistics for the post-65 coefficient are 0.4 (7-day mortality) and 0.25 (28-day mortality), providing no evidence that the observable health of the sample changes at age 65.

A final piece of indirect evidence that patients with severe conditions are not more (or less) likely to present at the ED once they become Medicare-eligible comes from studies of the effect of cost-sharing (i.e., copayments and deductibles) on the use of the ED.²¹ In the RAND health insurance experiment, patients with cost-sharing insurance plans were no less likely to present at the ED for the most serious conditions (chest pain/acute heart disease, surgical abdominal disease, acute eye injuries, second-degree burns) than those with the “free” plan (O’Grady et al. 1985). Similarly, two recent studies of the introduction of cost-sharing for ED visits by patients in a health maintenance organization (HMO) (Selby, Fireman, and Swain 1996; Wharam et al. 2007) conclude that copayments have no large or significant effect on ED visits for patients with severe conditions. The sample sizes in these studies are modest, however, and one certainly could not detect changes on the order of 3% in the use of the ED, which is the magnitude of the jump at age 65 for all unplanned ED admissions in the California discharge data. A fourth HMO-based study by Hsu et al. (2006) has much larger sample sizes and concludes that the introduction of modest copayment requirements reduces ED visits. This study does not break down ED visits by the severity of patients’ conditions, though the authors find that patients with copayment requirements have somewhat better clinical outcomes, which suggests that patients with life-threatening conditions such as AMI, stroke, and chronic obstructive pulmonary disease are obtaining appropriate medical services. Overall we interpret these studies as supporting the hypothesis that people with severe conditions present at the ED independent of their insurance status.

V. Shifts in Insurance, Health Services, and Mortality at 65

V.A. Insurance

We now turn to the impact of the Medicare eligibility age on health-related outcomes. We begin by looking at health insurance coverage. Figure IV shows the age profiles of the fractions of people with various primary insurers (private, Medicaid, Medicare, other, and none) in the nondeferrable admissions subsample. Consistent with the patterns in Figure I for the overall population, we see a big increase in the fraction of patients with Medicare as their primary insurer at 65, coupled with a decline in the fraction with no insurance. RD models for health insurance outcomes are presented in Table III. The models follow the same specification as in Table II, although we now include a set of covariates (year, month and day of admission, race/ethnicity, sex, and admission diagnosis fixed effects) in the specifications shown in even-numbered columns. For reference, the specifications in the odd-numbered columns exclude these controls and also exclude the dummy for admissions in the 31 days before a patient’s 65th birthday.

Open in a separate window

Figure IV

Primary Insurance Coverage of Admitted Patients

See notes for Figure II. In this figure the y-axis represents the fraction of patients with different classes of primary insurance coverage. Sample includes 425,315 patients with nondeferrable primary diagnoses, defined as unplanned admissions through the emergency department for diagnoses with a t-statistic for the test of equal weekday and weekend admission rates of 0.965 or less. Medicare eligibility status of patients within one month of their 65th birthdays is uncertain and we have excluded these observations.

TABLE III

Regression Discontinuity Models for Probability of Different Forms of Primary Insurance Coverage

	Medicare		Private		Medicaid		Uninsured
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Age over 65 (×100)	43.9 (0.4)	47.5 (0.4)	−24.8 (0.4)	−26.8 (0.4)	−10.1 (0.3)	−10.8 (0.3)	−7.4 (0.2)	−8.0 (0.2)
Additional controls	No	Yes	No	Yes	No	Yes	No	Yes
Mean of dependent variable for patients aged 64–65 (×100)	24.0		43.3		43.3		9.7

Open in a separate window

Notes. Standard errors in parentheses. Dependent variable is indicator for type of insurance listed as “primary insurer” on discharge record. Sample includes 425,315 observations on patients between the ages of 60 and 70 admitted to California hospitals between January 1, 1992, and November 30, 2002 for an unplanned admission through the emergency department, with a diagnosis (ICD-9) for which the t-test for equality of weekend and weekday admission rates is less than 0.96 in absolute value. All models include second-order polynomial in age (in days) fully interacted with dummy for age over 65 and are fit by OLS. Models in even-numbered columns include the following additional controls: a dummy for people who are within one month of their 65th birthday; dummies for month, year, sex, race/ethnicity, and admission on Saturday or Sunday; and a complete set of unrestricted fixed effects for each ICD-9 admission diagnosis. In columns (1)–(8) the coefficient on “age over 65” and its standard error have been multiplied by 100.

The regression results confirm the visual impressions conveyed in Figure IV. At age 65, the fraction of patients with Medicare as their primary insurer rises by about 47 percentage points, whereas the fractions with private insurance and Medicaid both fall.²² Note that in the sample of nondeferrable admissions the Medicare coverage rate at age 64 is 24%, substantially higher than in the overall population (shown in Figure I). Presumably this reflects the fact that many of these patients are chronically ill and on DI prior to 65. The percentage with no insurance at 64 is correspondingly a little lower than in the overall population (10% versus about 13%), and the reduction in the rate of noninsurance at 65 is a little smaller (−8 percentage points in the nondeferrable subsample, versus −9.5 percentage points for the population as a whole). Nevertheless, as in the population as a whole, patients with nondeferrable conditions have much different insurance coverage just after age 65 than just before.

V.B. Intensity of Treatment

We have three basic measures of the intensity of treatment offered to patients: the length of their stays in hospital, the number of procedures performed, and total hospital list charges.²³ Figure V shows the age profiles for these measures, whereas Table IV presents RD models similar to the specifications in Table III. The age profile for mean length of stay is somewhat noisier than the other two profiles, but all three profiles suggest an upward jump at 65. The estimation results in Table IV show that mean length of stay increases by 0.4 days (or about 4.5%) at 65, though the estimated gain is not statistically significant. Similarly, the number of procedures jumps by 0.1, or approximately 4% (with a t-ratio around 4), whereas log list charges jump by 2.5% (with a t-ratio of around 2.6).

Open in a separate window

Figure V

Three Measures of Inpatient Treatment Intensity

See notes to Figure IV. Sample includes unplanned admissions through the emergency department for diagnoses with a t-statistic for the test of equal weekday and weekend admission rates of 0.965 or less. In this figure the sample is further restricted to patients with valid SSNs (407,386 observations). Sample for log list charges excludes patients admitted to Kaiser hospitals. Length of stay, number of procedures, and list charges are cumulated over all consecutive hospitalizations. List charges are measured in 2002 dollars.

TABLE IV

Regression Discontinuity Models for Changes in Treatment Intensity

	Length of stay (days)		Number of procedures		Log list charges (×100)
	(1)	(2)	(3)	(4)	(5)	(6)
Age over 65	0.37 (0.24)	0.35 (0.26)	0.09 (0.03)	0.11 (0.03)	2.5 (1.1)	2.6 (1.0)
Additional controls	No	Yes	No	Yes	No	Yes
Mean of dependent variable for patients aged 63 or 64	8.12		2.50		9.87

Open in a separate window

Notes. Standard errors in parentheses. Dependent variable is length of stay in days (columns (1) and (2)), number of procedures performed (columns (3) and (4)), and log of total list charges (columns (5) and (6)). Sample includes 407,386 (352,652 in columns (5) and (6)) observations on patients with valid SSNs between the ages of 60 and 70 admitted to California hospitals between January 1, 1992, and November 30, 2002 for an unplanned admission through the ED. Data on list charges are missing for Kaiser hospitals. See note to Table III for additional details on sample, and list of additional covariates included in even-numbered columns. In columns (5) and (6) the coefficient on “age over 65” and its standard error have been multiplied by 100.

One concern with an RD model for the logarithm of list charges is that the dispersion in charges may increase (or decrease) once patients become Medicare-eligible. If this is true, then the expected level of charges may rise by more (or less) than 3% at age 65 (see Manning [1998] for a general discussion of modeling health spending). To evaluate this concern we fit RD models to the standard deviation of log list charges for patients in each age-at-admission cell (with age measured in days). These models showed no large or statistically significant effect on the dispersion in list charges at age 65. We also fit RD models to the 75th and 90th percentiles of list charges for each age-at-admission cell. These models showed that both the 75th and 90th percentiles of list charges increase by about 2%–3% at age 65. (The regression models, and associated graphs, are presented in the Online Appendix, Table C and Figures F and G.)

Overall, we conclude that there are modest but statistically significant increases in the intensity of treatment at age 65 for patients in our nondeferrable admissions sample, on the order of 3%. These increases are much smaller than the 10%–25% increases in rates of elective procedures such as hip and knee replacements observed in the overall population (Card, Dobkin, and Maestas 2008) but suggest that the availability of Medicare affects the utilization of health care services even for severely ill patients.

We also performed a more detailed analysis of the changes in the use of specific procedures at age 65 for two major sets of diagnoses: obstructive chronic bronchitis with acute exacerbation (the largest ICD-9 in our nondeferrable sample, shown in row (1) of Table I) and acute myocardial infarction (AMI), which combines the various detailed AMI diagnoses in our nondeferrable sample. The results are summarized in Table D of the Online Appendix. For AMI admissions we see a relatively large and precisely estimated increase in the overall number of procedures at age 65 (a rise of 0.44 on a base rate of 5.0 among 64-year-olds, or approximately 9%) and significant increases in the use of several important diagnostic procedures, including coronary arteriography, cardiac catheterization, and angiocardiography.²⁴ In contrast, for obstructive chronic bronchitis patients, we see no change in the overall number of procedures and small increases or decreases in the incidence of specific procedures. This analysis suggests that the relatively small increase in the overall number of procedures for all admission diagnoses in Table IV is masking larger increases for certain “procedure-intensive” diagnoses, such as AMI, and near-constancy for other diagnoses. Unfortunately, the sample sizes for other diagnoses are too small to permit a more extensive investigation. We conclude, however, that the onset of Medicare eligibility is associated with an increase in the use of specific potentially life-saving procedures.

V.D. Mortality

Figure VI plots the age profiles for the probability of death within 7, 14, 28, 90, 180, and 365 days of admission to the hospital, whereas the first two rows of Table V present estimates from RD regression models corresponding to each of these outcomes. Inspection of Figure VI shows that each of the mortality measures has a drop on the order of 1.0 percentage point at age 65. The regression estimates in Table V confirm this: we observe a reduction in 7-day mortality of about 0.7 to 1.0 percentage points that persists over the longer follow-up periods. The effect is relatively precisely measured in the shortest time intervals but has an increasing sampling error as the follow-up window is extended, yielding t-ratios of about 5 at 7 days, about 3 at 28 days, and around 1.8 at 365 days.

Open in a separate window

Figure VI

Patient Mortality Rates over Different Follow-Up Intervals

See notes to Figure IV. Sample includes unplanned admissions through the emergency department for diagnoses with a t-statistic for the test of equal weekday and weekend admission rates of 0.965 or less. In this figure the sample is further restricted to patients with valid SSNs (407,386 observations). Deaths include include in-hospital and out-of-hospital deaths.

TABLE V

Regression Discontinuity Estimates of Changes in Mortality Rates

	Death rate in
	7 days	14 days	28 days	90 days	180 days	365 days
	Estimated discontinuity at age 65 (×100)
Fully interacted quadratic with no additional controls	−1.1 (0.2)	−1.0 (0.2)	−1.1 (0.3)	−1.1 (0.3)	−1.2 (0.4)	−1.0 (0.4)
Fully interacted quadratic plus additional controls	−1.0 (0.2)	−0.8 (0.2)	−0.9 (0.3)	−0.9 (0.3)	−0.8 (0.3)	−0.7 (0.4)
Fully interacted cubic plus additional controls	−0.7 (0.3)	−0.7 (0.2)	−0.6 (0.4)	−0.9 (0.4)	−0.9 (0.5)	−0.4 (0.5)
Local linear regression procedure fit separately to left and right with rule-of-thumb bandwidths	−0.8 (0.2)	−0.8 (0.2)	−0.8 (0.2)	−0.9 (0.2)	−1.1 (0.3)	−0.8 (0.3)
Mean of dependent variable (%)	5.1	7.1	9.8	14.7	18.4	23.0

Open in a separate window

Notes. Standard errors in parentheses. Dependent variable is indicator for death within interval indicated by column heading. Entries in rows (1)–(3) are estimated coefficients of dummy for age over 65 from models that include a quadratic polynomial in age (rows (1) and (2)) or a cubic polynomial in age (row (3)) fully interacted with a dummy for age over 65. Models in rows (2) and (3) include the following additional controls: a dummy for people who are within 1 month of their 65 birthdays, dummies for year, month, sex, race/ethnicity, and Saturday or Sunday admissions, and unrestricted fixed effects for each ICD-9 admission diagnosis. Entries in row (4) are estimated discontinuities from a local linear regression procedure, fit separately to the left and right, with independently selected bandwidths from a rule-of-thumb procedure suggested by Fan and Gijbels (1996). Sample includes 407,386 observations on patients between the ages of 60 and 70 admitted to California hospitals between January 1, 1992, and November 30, 2002, for unplanned admission through the ED who have nonmissing Social Security numbers. All coefficients and their SEs have been multiplied by 100.

We have performed extensive robustness checks to ensure that the mortality results are not an artifact of a particular specification of the RD model. As shown in row (3) of Table V, specifications with a cubic age polynomial yield estimates that are similar to the simpler quadratic models, though typically a little smaller, particularly for the 28-day follow-up window. (Figure J in the Online Appendix compares the fits of linear, quadratic, and cubic RD models for 28-day mortality.) We also refit the models using logits (rather than linear probability specifications) and obtained essentially identical estimates of the change in the probability of death at age 65 (see Table F of the Online Appendix). Finally, we used local linear regression (LLR) models to obtain “nonparametric” estimates of the mortality rates for patients just under and just over 65 (Hahn, Todd, and van der Klaauw 2001; Imbens and Lemieux 2008). Row (4) of Table V presents LLR-based estimates using a triangular kernel and the rule-of-thumb bandwidth selection procedure suggested by Fan and Gijbels (1996). These are very similar to the parametric estimates using a quadratic polynomial but a little more precise. Figure K of the Online Appendix presents LLR-based estimates of the 28-day mortality rate for patients on each side of the age 65 boundary, using all possible bandwidths between 1 month and 5 years.²⁶ The estimated mortality rates for patients just under 65 and patients just over 65 stabilize once the bandwidth reaches about 1 year, centering on values close to the predicted values from our basic quadratic specification.

We used the specification from row (2) of Table V to fit parametric RD models for the probability of death in all possible follow-up windows between 1 day and 2 years. The resulting estimates of the jump in mortality at age 65 are plotted in Figure VII, along with the associated 95% confidence intervals. The data show a robust pattern of reductions in mortality for windows of up to 9 months on the order of 0.8–1.0 percentage points, with somewhat smaller point estimates for windows of 1–2 years.

Open in a separate window

Figure VII

Estimates of the Discontinuity in Mortality Rates at Age 65 over Various Follow-Up Periods

See notes to Figure VI. The estimates in this figure are from a regression with a quadratic polynomial in age fully interacted with a dummy for age over 65. The regressions also include a dummy for patients within one month of their 65th birthdays, month and year dummies, fixed effects for the primary diagnosis, and dummies for race, sex, and admissions on Saturday or Sunday. Regression discontinuities are estimated for probability of death within 7, 14, and 28 days and then at 30-day intervals. The estimates and associated 95% confidence intervals from each regression are then linearly interpolated in the figure.

Our estimates of the mortality effect of Medicare eligibility are relatively large: they represent a 14%–20% reduction in 7-day mortality, a 7%–9% reduction in 28-day mortality, and a 2%–4% reduction in 1-year mortality relative to death rates among 64-year-olds with similar conditions at admission. The emergence of the effect within 7 days of admission suggests that the extra services or changes in the quality of services provided to Medicare-eligible patients have an immediate life-saving impact.

It is also worth noting that the mortality reductions estimated in Table V appear to reflect changes in the treatment of patients with Medicare within the same hospital, rather than patient sorting to higher-quality hospitals at 65. The fractions of patients with nondeferrable conditions entering different kinds of hospitals show only small changes at age 65. The largest change is a reduction of about three percentage points in the fraction entering county hospitals. Interestingly, the 28-day mortality rate for 63–64-year-olds is actually lower at county hospitals (6.8%) than at nonprofit (9.2%), for-profit (9.0%), or district (9.7%) hospitals in our data, so it is implausible that such a small shift in patients out of county hospitals could have much affect on average mortality.²⁷ Thus, it does not appear that Medicare reduces mortality by shifting patients to better hospitals.

VI. Discussion

Our empirical results point to a significant positive effect of Medicare eligibility on the intensity of treatment for acutely ill patients with nondeferrable conditions and a negative effect on patient mortality. In this section we discuss the possible channels for this effect. To aid in this discussion, it is helpful to consider a simplified causal model in which Medicare eligibility affects insurance characteristics, insurance affects health care services, and health services affect mortality. Building on the analysis in Section II, suppose that patient i has a health insurance package with a vector of characteristics z_i, including whether i has any coverage, whether he or she has Medicare or some other form of primary coverage, and (possibly) other characteristics. Assume that the age profile for z_i is generated by a model of the form

where g is a smooth function of age (a_i) with parameters γ_z, υ_zi is an error term that is mean-independent of the dummy Post65_i, and π represents the vector of discontinuities in insurance characteristics at age 65. Suppose that the quality-adjusted health care services delivered to patient i (S_i) depend on age, an error term υ_si, and the characteristics of the insurance package:³¹

Finally, assume that the likelihood of death of patient i (y_i = 1) depends on age and on quality-adjusted health services:

Equations (5), (6), and (7) yield reduced-form models such as equation (1), with a discontinuity in health care services at age 65 equal to

and a discontinuity in mortality equal to

In this simplified setup, each element of the insurance package represents a separate “channel” that contributes additively to the reduced form effects on services and mortality. For example, the kth element of z_i contributes θ_kπ_k to the RD in services and λθ_kπ_k to the RD in mortality. Unfortunately, we have no information on the individual components of θ, and only limited information on the vector π of insurance changes at age 65. For example, we do not observe secondary coverage, or whether the primary insurance is managed care. Nevertheless, it is possible to shed some light on the mortality effect associated with one key insurance characteristic: whether the patient has any insurance coverage or none.

In particular, note that the maximum contribution of the “any coverage” channel cannot exceed π_c (the jump in coverage at 65) times the average mortality rate of uninsured 64-year-olds, because the extension of coverage to the previously uninsured group can only reduce their mortality rate to 0. The average 7-day mortality rate of uninsured patients who are just under 65 years of age in our nondeferrable admission subsample is 0.05, whereas π_c = 0.08 (Table III, column (8)). Thus the maximum reduction in mortality attributable to the reduction in the number of people with no health insurance is 0.004—about 40% of the 7-day mortality effect we estimate. This is an extreme bound because it is based on the assumption that none of the previously uninsured would die if they were covered. A more plausible bound is that insurance coverage reduces the death rate by no more than one-half: in this case the “any coverage” channel can explain at most 20% of the total mortality effect.

In principle we can gain some additional insight by comparing changes in health insurance, the intensity of treatment, and mortality for different subgroups of patients.³² Unfortunately, the limited demographic variables in our discharge data make this a challenging exercise. Comparisons across race/ethnicity groups are uninformative, because the sample sizes for blacks (n = 41,000) and Hispanics (n = 66,280) are too small to obtain useful estimates. We also tried dividing patients into two groups based on the average fraction of 55–64-year-old patients from the same ZIP code who had no insurance coverage. Even here, we were unable to estimate systematic differences in the changes in treatment intensity or mortality outcomes at age 65 between residents from “low-insurance” and “high-insurance” ZIP codes. We do find significant increases in the numbers of procedures and significant reductions in mortality even for patients from the high-insurance ZIP codes, suggesting that an increase in insurance coverage per se is not the explanation for the impacts of Medicare.

An alternative explanation for the measured mortality effects is that for most people Medicare imposes fewer restrictions than private insurance or Medicaid, leading to more (and possibly higher-quality) services to patients over 65 than to those under 65.³³ Card, Dobkin, and Maestas (2008) find clear evidence of this mechanism for a wide range of nonurgent medical procedures, such as surgery to insert a stent in a blocked coronary artery (which rises by 11% at age 65 in California, Florida, and New York), hip and knee replacement surgery (which rises by 23%), and gall bladder removal surgery (which rises by 18%). In fact, Table IV presents evidence of small increases (3%–4%) in the number of procedures and in total list charges at age 65 for patients with nondeferrable conditions. Arguably, however, such small increases in the intensity of treatment are unlikely to generate a one-percentage-point reduction in mortality, though as we noted above, the increase in procedures for AMI cases—which may be more sensitive to medical intervention—is closer to 10%.³⁴ Thus, the precise mechanisms for the mortality effect remain unclear, though we believe the evidence points to a combination of channels.

VII. Summary and Conclusions

A longstanding question in health economics is whether health insurance affects health. This question is particularly relevant for Medicare, the largest medical insurance program in the country, which provides nearly universal coverage to people once they turn 65. We focus on measuring the health effects of Medicare eligibility for a relatively sick population—specifically, people who are admitted to hospitals through the ED with diagnoses that have similar admission rates on weekdays and weekends. In contrast to elective hospitalizations, there is no jump in these “nondeferrable” hospital admissions at age 65. Moreover, the predicted mortality rate of admitted patients (based on demographics and admission diagnoses) trends smoothly. These findings suggest that the underlying health of patients admitted with nondeferrable conditions is very similar whether the patients are just under or just over 65.

In light of this conclusion, we use a regression discontinuity approach to measure the impacts of reaching age 65 on the intensity of treatment in the hospital, and on mortality for up to 2 years after the hospital admission. We find modest but statistically significant increases in measures of treatment intensity at age 65, including the number of procedures performed in the hospital and total list charges. Associated with these changes, we find an important and large reduction in patient mortality at age 65. Medicare eligibility reduces 7-day mortality by about 0.8 to 1.0 percentage points, with similar-sized and statistically significant reductions at windows of up to 9 months. We probe the robustness of these findings by using a bounding procedure to evaluate the lower-bound effect of Medicare eligibility on the entire hospital patient population. The bounds for the overall population are consistent with the magnitude of the effect we estimate for patients with nondeferrable conditions, providing further credence to our basic results.

The magnitude of the estimated mortality effect of Medicare eligibility is too large to be driven solely by changes among the 8% of the patient population who move from no health insurance coverage to Medicare when they reach 65. This is an important distinction between our analysis and existing studies that have attributed much larger mortality gains to insurance status in specialized populations such as auto accident victims (Doyle 2005) and former Medi-Cal recipients (Lurie et al. 1984, 1986). Instead, our findings point to a more widespread effect of Medicare, including an impact on patients who were insured prior to 65. Given the relatively modest increases in the intensity of treatment we measure at age 65, however, we conclude that the actual mechanism for this effect is unclear.

An important limitation of our analysis is that it focuses on just one health outcome, albeit an important one. Certainly, Medicare might affect other dimensions of health and other patient populations, with effects that arise through channels other than hospitalization (e.g., outpatient care and prescription drug use) and that persist over a longer time horizon than that supported by our research design. Nonetheless, our analysis illustrates an important lesson for future research. Any plausible effect of insurance on health status in the general population will likely be small and easily confounded by selection effects in observational settings. Indeed, the only randomized health insurance experiment ever mounted found insignificant impacts of insurance on the health status of the overall population (Newhouse 1993). Further progress on this question will require research designs based on samples larger than those typically available for health services research, along with particular attentiveness to the selection problem.

Supplementary Material

Appendix I

We use annual hospital discharge files from the state of California for the period 1992–2002. These files represent a census of discharges from state-regulated acute care hospitals, collected by OSHPD, the Office of Statewide Health Planning and Development, under the California Code of Regulations, Title 22. The data set excludes people admitted to federally regulated hospitals such as VA hospitals. Our working sample includes people admitted to the hospital between January 1, 1992, and November 30, 2002, whose age at admission was between 60 and 70. The discharge files include the patient’s sex, race/ethnicity, ZIP code of residence, date of admission, source of admission, route of admission (ED or not), type of admission (scheduled or unscheduled), principal diagnosis, other diagnoses present at admission, a list of procedures performed, total list charges, expected payer code, disposition of patient (routine discharge, transfer to another hospital, etc.), and date of discharge. We obtained a restricted use version of the file that also includes the patient’s age in days at admission (calculated from the exact date of admission and exact date of birth). The file also includes a unique patient identifier and an indicator for whether the patient provided a valid SSN.

We also obtained from OSHPD a separate file constructed by OSHPD by matching individuals in the discharge database to state records of death, by name/SSN/date of birth. We merged this file with the discharge record file by patient identifier.

In our analysis we combine all consecutive hospital admissions into a single record defined by the first date of admission (and by the descriptors associated with this first admission). We cumulate the length of stay, the number of procedures, and list charges across all stays.

Footnotes

Contributor Information

David Card, University of California, Berkeley.

Carlos Dobkin, University of California, Santa Cruz.

Nicole Maestas, Rand Corporation.

References