1. The Impact of Insurance Status on Hospital Treatment and Outcomes David Card, Carlos Dobkin and Nicole Maestas

2. Many Americans Are Uninsured 10% of Americans 62-64 are uninsured 28% of Minorities with less than a high school education are uninsured Understanding the impact of insurance on treatment and outcomes is important given the number of people uninsured.

3. LITERATURE Cross-Sectional –Levy and Meltzer (2001) Rand Experiment Medicaid –Currie and Gruber (1996) Medicare –Decker and Rapaport (2002) –Decker (2002) –Dow (2004) –McWilliams et al (2003) –Lichtenberg (2001) Car Accidents –Doyle (2005)

5. Goals of this Project Determine if insurance status affects the treatment people receive in the hospital Determine if insurance status affects outcomes for people admitted to the hospital

6. Our Approach Two main difficulties in getting causal estimates –The uninsured are systematically different from the insured –Insurance impacts the probability of admission to the hospital Our solutions –Focus on the abrupt change in insurance induced by Medicare eligibility at age 65 –Focus on severely ill patients that will be admitted to the hospital regardless of insurance status

8. Causal Model

10. Finding an Unselected Group of Admissions Since the probability of hospital admission depends on insurance status the case mix may change discretely at age 65. We focus on admissions that are not deferrable –First we restrict to unplanned admissions through the emergency room –We then focus on a subset of non deferrable conditions

12. Identifying non Deferrable Admissions We focus on unplanned admissions through the Emergency Room Due to staffing constraints hospitals prefer to admit people on weekdays We examine the weekend to weekday ratio of admissions for each condition (ICD-9) Conditions that are equally common on weekends are “non deferrable”

17. Study Will Focus on ER Admissions With t-stat <.96 No discernable change in admissions –Sorting around the discontinuity will be observable Observable covariates are balanced

19. Measures of Treatment Intensity Length of Stay Diagnostic and Surgical Procedures Received Hospital List Charges

27. Hospital Transfers Federal and State law requires that hospitals stabilize patients not cure them Hospitals may be discharging uninsured patients earlier than they would discharge an insured patient

35. Outcomes Readmission to the hospital Within hospital mortality

41. Summary of Results No evidence that selection is a problem –No change in counts of admission at age 65 –Covariates balanced –Regressions not effected by inclusion of covariates –Pattern of outcomes not consistent with selection Find an increase in treatment intensity –4.6% increase in LOS (not statistically significant) –3.9% increase in procedures –2.6% increase in list charges Find increase in transfer probabilities –19% increase in within hospital transfers –5% increase in across hospital transfers (not statistically significant) Outcomes –5.5% reduction in readmissions to the hospital within a month –3.5% decrease in within hospital mortality (not statistically significant)

42. Conclusion Insurance increases treatment intensity and probability of transfer within the hospital The increased treatment reduces probability of readmission