Introduction

1. Introduction • The Question: Is HMO market share associated with adoption of cardiac-care technologies, and, in turn with treatments and outcomes for heart attack patients • Approach: • Use hospital-level hazard models to study relationships between HMO market share and adoption • Use patient-level models to study relationship between availability and treatments and outcomes

2. Cardiac Care Technologies • We focus on three cardiac technologies • Diagnostic: Cardiac catheterization • Therapeutic: PTCA • Therapeutic: CABG • All involve the adoption of equipment and staff • Catheterization and CABG first developed in the 1960s; PTCA in the 1970s • Catheterization equipment is used to do PTCA • PTCA and CABG are usually adopted together

3. Hospital-Level Data • We focus on 2,873 hospitals in MSAs in operation in 1985 • We use Medicare Claims data from 1985-2000 to identify hospitals that adopt these technologies and the year of adoption • Hospitals with 10 claims for a given service in a calendar year are defined as having the technology in that year • Based on patterns in the data, we study 3 adoption states: none, catheterization only, and all techologies

4. Hospital-Level Data • We classify hospitals according to the average 1990-1999 HMO market share in their MSA • Low: <10% • Medium: 10-30% • High: >30%

5. Hospital-Level Adoption Modeling • Discrete-time hazard models • Competing risks for probability of moving from none to cath only or none to all • Standard hazard model for probability of moving from cath only to all • Controls include a range of potential confounders, including urbanization, demographics, hospital characteristics

6. Hazard Model ResultsNone to Cath Only Standard errors in parentheses. Relative Hazards in brackets. Models include additional covariates. * denotes p<0.05; ** denotes p<0.01

7. Hazard Model ResultsCath Only to All Standard errors in parentheses. Relative Hazards in brackets. Models include additional covariates. * denotes p<0.05; ** denotes p<0.01

8. Technology availability, treatments, and outcomes • HMO activity affects the probability a heart attack patient will be treated in a hospital with the technology • Whether or not the hospital of treatment has the technology affects the probability of actually receiving a treatment • Receiving treatments affects mortality rates

9. Medicare AMI Data • Claims data on a 20% sample of FFS Medicare patients in MSAs with a new AMI between 1996 and 2000 • N=148,170 • Measure technology status of index hospital, treatment receipt within 90 days of initial admission, and 1 year mortality • Data also contain detailed data on comorbidities and other characteristics

10. Statistics • Estimate individual-level models • Control for a range of characteristics • sex; race; age; admission in the prior 2 years for IHD, CHF, VA, or any other cause; conditions at admission: cancer, diabetes, dementia, heart failure, hypertension, stroke, peripheral vascular disease, chronic obstructive pulmonary disease, respiratory failure, renal failure, or hip fracture; area per capita income, total area population and population density; % population graduated high school/college; % of the work force white collar; squared terms for area characteristics; year

11. Index Hospital CapabilitiesMultinomial Logit(results relative to all technologies) Models are multinomial logit regressions and include additional covariates and state dummies as well as interactions between HMO variables and year. * denotes p<0.05; ** denotes p<0.01

12. Treatments ReceivedMultinomial Logit(relative to medical management) Models are multinomial logistic regressions of the probability of receiving cath, PTCA, CABG, or medical management within 90 days of initial hospitalization. Models include additional covariates, state dummies, and interactions between tech variables and year. # denotes p<0.10, * denotes p<0.05; ** denotes p<0.01

13. Treatments and 1-year MortalityLogistic Regression From logistic regression of the probability of 1 year mortality. Models include additional covariates, state dummies, and interactions between tech variables and year. # denotes p<0.10, * denotes p<0.05; ** denotes p<0.01

14. HMO Market Share and 1-Year MortalityLogistic Regression From logistic regression of the probability of 1 year mortality. Models include additional covariates, state dummies, and interactions between HMO variables and year. # denotes p<0.10, * denotes p<0.05; ** denotes p<0.01

15. Conclusions • Managed care activity affected the adoption of cardiac technologies • This could well be associated with worse outcomes for AMI patients • impacts on other patients, and other outcomes, are unknown

16. Means of Hospital Level Variables

17. Kaplan-Meier Adoption Probabilities for PTCA and CABG, 1985-2000

18. Mortality Models are OLS regressions of the probability of 1 year all-cause mortality. Models include additional covariates. * denotes p<0.05; ** denotes p<0.01

19. Predicted PTCA adoption probability in low, medium, and high HMO markets

20. Predicted CABG adoption probability in low, medium, and high HMO markets