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

Many Americans Are Uninsured 10% of Americans 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.

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

Goals of this Project Determine if Medicare affects the treatment people receive in the hospital Determine if Medicare affects outcomes for people admitted to the hospital

Data California Hospital Discharge Data Census of hospital discharges

Causal Model

Research Design 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

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

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 compute the proportion of admissions for each condition (ICD-9) that occur on the weekend The study will focus on a subset of conditions that have a ratio near 2/7

Study Will Focus on ER Admissions With t-stat <.96 These are the conditions that people are unable to defer No discernable change in admissions –Sorting around the discontinuity will be observable Observable covariates are balanced

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

Hospital Transfers Federal and California State law require that hospitals admit and stabilize patients Hospitals may be discharging uninsured patients earlier than they would discharge an insured patient

Outcomes Readmission to the hospital Within hospital mortality

Summary of Results No evidence that selection is a problem –Covariates balanced –No change in counts of admission at age 65 –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)

Conclusion Medicare increases treatment intensity and probability of transfer within the hospital The increased treatment reduces probability of readmission