DOES MEDICARE SAVE LIVES?

1. DOES MEDICARE SAVE LIVES? DAVID CARD CARLOS DOBKIN NICOLE MAESTAS

2. Background • Medicare is the largest health insurance in US, one-fifth of total health care costs in the United States.; virtually free to those aged over 65 • Although existing research has shown that the utilization of health care services increases once people become eligible for Medicare, the health impact of these additional services remains uncertain (e.g., Decker and Rapaport(2002), McWilliams et al.(2003, 2007), Card, Dobkin, and Maestas(2004)).

3. Research Question • Does Medicare improve the patient’s health?

4. Estimation Strategy • Basically the elderly can only enroll into Medicare after turning into 65. This eligibility rule itself creates a opportunity to employ the regression discontinuity (RD) strategy. • It focuses on unplanned admissions through the emergency room for “non-deferrable” conditions – those with similar weekend and weekday admission rates– so that the crucial RD assumption can hold.

5. Main Findings (I) • Admissions: • All causes admissions jump 7% once people reach 65 • Emergency room admissions rise by 3%. • Resources: • A small increase in the number of procedures and total list charges. • A large increase in the chance of being transferred to skilled nursing facilities

6. Main Findings (II) • Readmission: • A reduction in the 28-day readmission rate. • Mortality • 1 % (absolute percentage) lower death rate or roughly a 20 percent • A similar absolute reduction in mortality is registered at 28 days and 90 days • Such the pattern persists for at least two years after admission

7. Regression Discontinuity design • a reduced-form regression–discontinuity (RD) model age. a indicator of policy dummy at age 65, then as the causal effect of Medicare coverage.

8. Regression Discontinuity design RD Scatterplot: With Treatment Effect RD Scatterplot: No Treatment Effect causal effect

9. RD’s model assumption • The key assumption underlying an RD analysis is that assignment to either side of the discontinuity threshold is as good as random. f sufficiently small • If the assumption fails, then we have a non-consistent estimate of the parameter β • Insurance status will lead to the assumption fail, because it affects the probability that a patient is admitted to the hospital. • The onset of Medicare eligibility leads to an increase in hospitalization rates.

10. The Background of Medicare • Medicare coverage: (1) above 65 age (2) have worked at least 10 years in covered employment. • The timing of eligibility: 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.

12. Medicare Enrollment • For 65+: • Any insurance: +10% • Medicare: + 60% • Managed care: -31%

13. Data • California hospital discharges records 1992-2002 • Age, LOS, zip-code, race, gender • ICDs, ER or not, planned or not • Health providers (hospitals) • Mortality records (from social security records) • Select unplanned admissions through the emergency room for “non-deferrable” conditions (10 major diagnoses)

14. Sample Validation • The paper attempts to solve the sample selection problem by focusing on a subset of patients who are admitted through the ER for a relatively severe set of conditions that require immediate hospitalization. • Testing 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.

15. Sample selection: ER v.s Non-ER • Why admissions through the emergency room for “non-deferrable” conditions? Avoid sample selection • ER vs. Non ER admissions: • All causes admissions jump 7% once people reach 65 • Emergency room admissions rise by 3%.

17. Sample selection • Construct pre-tests • Those with similar weekend and weekday admission rates. If admission for a given diagnosis code were equally likely on a weekend and on a weekday, then the proportion of the weekend admissions is equal to 0.29 (2/7). • No significant differences on severity in admissions between 60-64 and 65-70.

19. Compute the fraction of weekend admissions for all ICD-9 codes • Limit the analysis sample to those codes for which the t-statistic for Ho (the proportion of the weekend admissions is equal to 0.29) is less than 0.965. • To test that patients’ inclusion in the non-deferrable admissions subsample is independent of whether they are under or over 65. • There are four groups by t-value: |𝑡|<0.965, 0.965 <t< 2.54, 2.54 <t< 6.62, t< 6.62

23. Also, check • The change in the health characteristics of admissions at age 65 in the nondeferrable sample. (Charlson comorbidity score) • The demographic composition of the nondeferrable subsample at age 65. • The employment status of the patient (retire or not)

24. Charlson Comorbidity Score • There is no discernible evidence of a drop in the severity of comorbidities at age 65 • RD analysis on the age profile indicates a small but statistically insignificant increase in severity

25. Charlson Comorbidity Score

26. Charlson Comorbidity Score

27. Demographic composition • Check for discontinuities in the case mix and demographic composition of the nondeferrable subsample at age 65. • Tests for jumps in the racial composition, sex, and fraction of Saturday or Sunday admissions are all far below critical values

28. Employment status • Ruhm (2000) points that health is affected by employment status. • The paper shows that no discontinuity in the likelihood of working at exactly age 65. • It doesn’t exist the differences in survival between people who return to work and those who do not.

29. The employment status of the patient

30. Medicare’s effect: insurance status • For 65+: • Any insurance: +10% • Medicare: + 60% • Managed care: -31%

33. Medicare’s effect: treatment intensity • A small increase in the number of procedures and total list charges.

37. Medicare’s effect: transfers and readmissions • A large increase in the chance of being transferred to skilled nursing facilities

41. Medicare’s effect: mortality • 1 % (absolute percentage) lower death rate or roughly a 20 percent • A similar absolute reduction in mortality is registered at 28 days and 90 days • Such the pattern persists for at least two years after admission

45. Linear, Quadratic, and Cubic RD models for 28-day mortality • The jump exists in all RD models

47. Robustness of Mortality : Bounding approach (I) • The paper used a simple bounding procedure to obtain lower-bound estimates of the mortality effect of Medicare eligibility on broader samples of hospital admissions. • There are two subgroups of the sample of sick people close to age 65 • Group 1 : The people who enter the hospital regardless of whether they are Medicare-eligible or not. • Group 2 : The people who will only enter the hospital if they are over 65.

48. Robustness of Mortality : Bounding approach (II) • The observed mortality rate of the patient population just over 65 () is an average for groups 1 and 2. Where : The mortality rate of the first group if they enter the hospital just before their 65th birthdays. : The mortality rate if they enter after 65. :The post-65 mortality rate of group 2. : The causal effect for group 1 () : The sample fraction of the second group. (people with non-deferrable conditions , )

49. Robustness of Mortality : Bounding approach (III) • The mortality differential between the post-65 patient population and the pre-65 patient population. • Lower bound on the absolute value of the bias caused by the presence of group 2 in the post-65 patient population This bias tends to 0 as , and is proportional to Bias term