The Medicare Part A hospital claims data from 1986–2003 were merged with the Medicare Denominator File through 2003 to create a longitudinal cohort of fee-for-service enrollees age 65 or over coded with a new acute myocardial infarction. 6
During 1986–1991 the sample of Part A data was 20 percent of the fee-for-service Medicare population, rising to 100 percent since 1992. Patients with a code of “old MI,” or those identified from the panel data as having had an AMI previously, were excluded from the sample. 7
Overall there were 2,872,050 valid AMI events. For pedagogical reasons, we find it helpful to present our results in terms of survival, or the percentage of AMI patients surviving to one year following their AMI. Expenditure data are available only through the end of 2003, so one-year survival and expenditure data are analyzed from 1986–2002.
The primary analysis follows Cutler and McClellan by using just the Medicare Part A hospital expenditures, correcting for inflation using the US implicit price deflator 8
and expressing all results in 2003 dollars. Both expenditures and survival rates are determined for the same one-year horizon. We also provide secondary analysis using a smaller sample of Part B expenditure data: a 5% sample in 1993–1997 and a 20% sample from 1998–2002.
To adjust for both secular and cross-sectional differences in health status, we adjust survival rates and expenditures for a variety of comorbidities (diabetes, diabetes with complications, pulmonary disease, liver disease, liver disease with complications, dementia, non-metastic cancer, metastatic cancer). Also included were age-sex-race effects consisting of 5 age categories (65–69, 70–74, 75–79, 80–84, and 85+) interacted with sex and with two race variables (black and nonblack), and the type of MI (inferior, anterior, subendocardial, and other).
The regression analysis explains survival and expenditures as a linear function of demographic variables, comorbidities, type of AMI, and year categorical variables. 9
All estimated survival and expenditure measures are expressed in terms of the representative patient with average characteristics during the entire period of analysis using the ADJUST command in STATA 9. Thus the regression adjusts for changes over time in the severity of the disease, demographic changes in the Medicare population, and general increases in the price level.
The regional unit of analysis is the Hospital Referral Region (HRR), which was constructed in the Dartmouth Atlas of Health Care to reflect the actual hospital migration patterns of Medicare patients for tertiary care. 10
There are 306 HRRs in the United States, and each must include at least one hospital that performs cardiac surgery and neurosurgery. Each zip code in the United States is assigned to an HRR depending on what hospital the majority (or in some cases, the plurality) of Medicare enrollees seek their hospital care, so the HRR may cross county or state boundaries. Individuals were assigned to HRRs depending on their zip code of residence, and not whether they were actually admitted to hospitals in those HRRs.
Region-specific measures of annual survival rates and cost measures were constructed from the estimated linear regressions mentioned previously that control for comorbidities and demographics. These region-year-specific measures are interpreted as the risk-adjusted survival rate and expenditures for the representative Medicare AMI patient in that region and year. Regions will differ both with regard to their initial adjusted survival and expenditures, and with respect to changes over time in these variables, but our approach will, as far as possible, ensure that the results reflect regional practice patterns rather than regional differences in patient characteristics. These adjusted measures are used both in the cross-sectional analysis (using just 2002 data) and the longitudinal analysis that examines changes over time 1986–2002.
There are a variety of approaches to treating patients with AMI, and as we show below, regions differed dramatically in their adoption of treatment strategies such as aspirin, β blocker use, and reperfusion, as well as their reliance upon multiple physicians per patient. We hypothesize that regional differences in the diffusion of new treatment strategies will be associated with survival gains and expenditures increases during 1986–2002. Note that we conduct our hypothesis tests at the regional level rather than at the individual patient level. Unobservable aspects of specific patients will lead to unmeasured confounding factors and resulting biases. Differences across region, on the other hand, are small with regard to the average severity of heart attacks, but large with regard to treatment strategy.
In the regression analysis, we focus on two region-level dimensions of care. The first is an index of low-cost highly effective treatments for AMI: aspirin at discharge, β Blockers at discharge, and reperfusion within 12 hours of admission (whether surgical reperfusion or thrombolysis). Aspirin reduces platelet aggregation and is known to reduce the risk of mortality following AMI. 11
Beta blockers are an inexpensive drug that by blocking the beta-adrenergic receptors reduces the demands upon the heart, and have been known since the mid-1980s to be effective in reducing post-AMI mortality by 25 percent or more. 12
Compliance in the use of β Blockers has lagged among many regions, even as late as 2000/2001. 13
Reperfusion encompasses either thrombolytics, “clot-busting” drugs designed to improve blood flow in the blocked arteries, or percutaneous transmural coronary angioplasty (PTCA) within 12 hours of the AMI, again with well-established reductions in the risk of death. 14
Measures are the percentage of patients in each region deemed ideal for treatment who actually did receive treatment, and are based on chart reviews from the Cooperative Cardiovascular Project (CCP) survey, and reported in the Dartmouth Atlas of Cardiovascular Care. 15
These data are available only in 1994/95, but these were years marked by a remarkable divergence in the adoption of these treatments. For example, β Blocker prescription at discharge among ideal heart attack patients was 20 percent in San Antonio, TX, 42 percent in Orange County, CA, and 82 percent in Rochester, NY. Thus 1994/95 data allows us to identify early and laggard adopters. 16
Regional quality is determined by the number of quality measures for which that region is above the national median. The quality measure ranges from 0 (below median for all 3 measures) to 3 (above median for all three measures).
Our second dimension of care is the average number of different physicians treating the patient within one year following the AMI, averaged across all patients in the Hospital Referral Region in 1994/95. This measures both the degree of reliance on specialists, as well as a marker for the continuity of care. There are potential gains from the specialization of medical knowledge, but there are also “network” costs associated with a larger number of interactions necessary among physicians coordinating care. 17
For example, when there are 4 physicians treating a given patient, there are 4×3/2 = 6 possible interactions (whether communication among physicians or potential for drug regimen interactions). With 8 physicians the number of interactions rises to 28, or a 367% increase in the number of potential interactions. Their potential adverse effects are illustrated in a recent Newsweek column written by the wife of a severely ill man:
… the cardiologist told me that Doug was doing reasonably well, and I naively took solace in this mild pronouncement. That is, until a lung specialist zipped into the room, put his stethoscope to Doug’s chest and said ‘He’s not getting any better. He’s worse. He may die. Any questions?’ I was too stunned to be coherent.
Later a nephrologist informed me that Doug’s kidneys were failing and he needed dialysis. I told this doctor what the prior two specialists had said, hoping he could reconcile their conflicting reports. Instead, he plied me with questions about their findings that I could not answer. 18
In other words, the apparent lack of communication among the specialists could attenuate (or even offset) the advantages of specialization. 19
We hypothesize that regions where quality measures were adopted early would experience the greatest improvement in survival with small influences on Medicare expenditures. By contrast, we hypothesize that a larger number of separate physicians should have uncertain effects on survival (depending on whether the network effects overwhelm the gains from specialization) but are likely to be associated with more rapid increases in expenditures during the period. We are not estimating a traditional economic “production function” in part because we do not have sufficient data on inputs in each year 1986–2002. Instead, we test whether characteristics of regions in 1994/95 are predictive of productivity growth; that is, whether survival gains are high relative to expenditure growth over the period 1986–2002.
To quantify the importance of these two region-level measures (quality treatment and number of different physicians), a least-squares regression is estimated at the HRR-level. The dependent variable is the risk-adjusted change, 1986–2002, in one-year survival, one-year expenditures, or one-year log (or proportional) expenditures. The index of quality is entered flexibly with separate categorical variables for the quality index from 0 to 3, and for quartiles (or 25th percentile groupings) of the average number of different physicians per patient, with all estimates reported using the ADJUST command in STATA 9.