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 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: Approach: Use hospital-level hazard models to study relationships between HMO market share and adoption 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 Use patient-level models to study relationship between availability and treatments and outcomes

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

Hospital-Level Data We focus on 2,873 hospitals in MSAs in operation in 1985 We focus on 2,873 hospitals in MSAs in operation in 1985 We use Medicare Claims data from to identify hospitals that adopt these technologies and the year of adoption We use Medicare Claims data from 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 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 Based on patterns in the data, we study 3 adoption states: none, catheterization only, and all techologies

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

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

Hazard Model Results None to Cath Only Standard errors in parentheses. Relative Hazards in brackets. Models include additional covariates. * denotes p<0.05; ** denotes p<0.01

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

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

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

Statistics Estimate individual-level models Estimate individual-level models Control for a range of characteristics 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 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

Index Hospital Capabilities Multinomial 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

Treatments Received Multinomial 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

Treatments and 1-year Mortality Logistic 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

HMO Market Share and 1-Year Mortality Logistic 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

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

Means of Hospital Level Variables

Kaplan-Meier Adoption Probabilities for PTCA and CABG,

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

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

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