1. Managed Care, Drug Benefits and Mortality: An Analysis of the Elderly Gautam Gowrisankaran Washington University / NBER gowrisankaran@wustl.edu Robert Town University of Minnesota / NBER rjtown@umn.edu

2. Research Questions What is the population level impact of Medicare+Choice (M+C) enrollment on elderly mortality? What is the population level impact of Medicare+Choice (M+C) enrollment on elderly mortality? What is the impact of prescription drug coverage on elderly mortality? What is the impact of prescription drug coverage on elderly mortality?

3. Data Unit of observation is a county—focus on the approximately 420 counties with over 100,000 population. Unit of observation is a county—focus on the approximately 420 counties with over 100,000 population. Time Frame--1993-2000 Time Frame--1993-2000 Dependent variable is county-level Mortality rate. Dependent variable is county-level Mortality rate.  Mortality rates are calculated from death certificates-- National Vitality Statistics. Variable of interest is the M+C Enrollment in plans with and without drug benefits Variable of interest is the M+C Enrollment in plans with and without drug benefits  Data is from CMS  We also use payments to HMOs from CMS Demographic controls are from Area Resource File. Demographic controls are from Area Resource File.

4. Model Equation of interest Equation of interest m it =  i +  d d it +  h h it +  x x it +e it (1) m it is county-level mortality rate at a given time m it is county-level mortality rate at a given time  i are county fixed effects  i are county fixed effects d it is penetration rate for M+C plans with drug coverage d it is penetration rate for M+C plans with drug coverage h it is penetration rate for M+C plans without drug coverage h it is penetration rate for M+C plans without drug coverage x it captures time-varying SES and health coverage measures (age/sex, income, Medicaid enrollment, medical infrastructure) x it captures time-varying SES and health coverage measures (age/sex, income, Medicaid enrollment, medical infrastructure) e it is residual component of health status e it is residual component of health status

5. Endogeneity and Instruments It is possible (perhaps likely) that the error term in (1) is correlated with M+C enrollment. It is possible (perhaps likely) that the error term in (1) is correlated with M+C enrollment. Instruments are contemporaneous, lead and lagged values of: Instruments are contemporaneous, lead and lagged values of:  Normalized, real (constant dollar) CMS payment rate  Squared and cubed CMS payment rate  4 quintiles of CMS payment rate based on similar size counties  Mean, Min and Max HSA payment rate of adjacent counties Are these good instruments? Are these good instruments? Estimation is done using Forward Mean Differencing IV Estimation is done using Forward Mean Differencing IV

6. Results Table 1 (Table 2 in the paper)– Summary Statistics Table 1 (Table 2 in the paper)– Summary Statistics Table 2 (Table 5 in the paper) Table 2 (Table 5 in the paper)  Enrollment in an HMO without drug benefits increases mortality  Drug coverage impacts mortality  HMO enrollment does not impact mortality.

7. Variable Entire sample 19932000 2000 subsample where M+C HMO drug penetration rate: 2000 subsample where M+C non- drug penetration rate: = 0>0= 0>0 65 and over mortality rate (%) 5.08 (.52) 5.16 (.45) 5.04 (.56) 5.20 (.52) 4.96 (.57) 4.99 (.60) 5.16 (.44) Mortality rate 65-74 (%) 2.53 (.38) 2.64 (.35) 2.41 (.39) 2.53 (.40) 2.35 (.36) 2.42 (.41) 2.39 (.32) 75 and over mortality rate (%) 8.25 (.76) 8.58 (.65) 8.16 (.73) 8.35 (.65) 8.05 (.74) 8.10 (.79) 8.26 (.55) Mortality rate for heart disease (%) 1.73 (.28) 1.83 (.27) 1.64 (.27) 1.62 (.24) 1.64 (.28) 1.61 (.26) 1.70 (.27) Mortality rate for cancer (%) 1.00 (.18) 1.15 (.11) 1.11 (.12) 1.14 (.13) 1.13 (.12) 1.11 (.13) 1.11 (.10) Summary Statistics

8. Dependent variable 65 and over mortality rate (1) Log 65 and over mortality rate (3) 65 and Over Mortality Rate (6) Estimation method Forward mean differenced FE IV Forward mean differenced FE IV Fixed effects least-squares M+C drug penetration rate.00062 (.0020).010 (.039).00097 (.00064) M+C non-drug penetration rate.015 ** (.0051).31 ** (.098).0023 ** (.00078) Log of Mortality Rate 50 to 59 year olds  -.000034 (.000019)  N3,597 R 2 (within) =.23 Main Results

9. Magnitudes 10 percentage point increase in non-drug, M+C enrollment increases mortality rates by.15 or 2.8% 10 percentage point increase in non-drug, M+C enrollment increases mortality rates by.15 or 2.8% An increase of approximately 4 million enrollees in non-drug, M+C plans is expected to cause 51,000 deaths. An increase of approximately 4 million enrollees in non-drug, M+C plans is expected to cause 51,000 deaths. Translates into an economic cost of $1,500 per enrollee Translates into an economic cost of $1,500 per enrollee

10. Are these findings reasonable? We calculated expected mortality for high cholesterol, hypertension and diabetes. We calculated expected mortality for high cholesterol, hypertension and diabetes. ‘Back of the envelope’ calculations using drug elasticities and mortality information from the literature. ‘Back of the envelope’ calculations using drug elasticities and mortality information from the literature. Our calculations result in an expected increase of 21,000 deaths from a 10 percentage point increase in non-drug, M+C enrollment. Our calculations result in an expected increase of 21,000 deaths from a 10 percentage point increase in non-drug, M+C enrollment.

11. Conclusions Enrollment in an HMO without drug benefits increases mortality Enrollment in an HMO without drug benefits increases mortality  Magnitudes are sizable. Drug coverage is the likely reason underlying this relationship Drug coverage is the likely reason underlying this relationship HMO enrollment does not impact mortality HMO enrollment does not impact mortality