In this section we illustrate how to use our measure. As we noted in the introduction, the data we will use are from an “unmanaged” plan, so the findings can not be regarded as definitive. Our purpose here is to illustrate how to use presently available data to calculate the distortion index. At the same time, the elements that feed into incentives to distort, such as predictability of various services, and correlation among use in various categories of service, are likely to be largely common to managed and unmanaged patterns of care. We believe our findings are therefore of some interest in themselves.
The empirical building blocks for estimation of shadow prices are the expected spending of individuals by service class and the correlation of expected spending across services and under differing information assumptions (see equation (11)). Our estimation strategy is aimed at obtaining estimates of future spending, conditional on the information assumptions, which minimize the forecast error. The performance of a number of estimation strategies for health care spending data have been assessed over the past fifteen years.
Duan et al. (1983, 1984) and Manning et al. (1981) contend that two-part models minimize mean forecast errors under distributional assumptions commonly exhibited by health spending data. Two-part models consist of one equation, typically a logit, for the yes/no decision about use, and a second equation, typically estimated by OLS, describing the extent of use, given some use. We use a two-part model for estimation under differing information assumptions. An “informational assumption” means, operationally, which covariates to include in the models. The pieces of equation (11) are computed from the predicted values generated from these estimated models.
The data are health claims and enrollment files from the Michigan Medicaid program for the years 1991-1993. We chose a subset of the data for application of our model. The sample consists of individual adults who were eligible for Medicaid in 1991 through the Aid to Families with Dependent Children (AFDC) program, and who were continuously enrolled in this or another Medicaid program through the end of 1993. We excluded individuals who joined an HMO during the study time period. The resulting sample consisted of 16,131 individuals, overwhelmingly female (90.5%), with a mean age of 32 years.
There are a variety of approaches one could take to identifying “services,” ranging from very specific treatments, such as angioplasty, to groups of treatments which would be associated with an illness, such as care for hypertension. In this paper we define a “service” as all the treatments received in connection with certain diagnostic classifications. We identify 9 classes of services: 1) birth related, 2) cancer care, 3) gastrointestinal problems, 4) heart care, 5) hypertension, 6) injuries/poisonings, 7) mental health/substance abuse, 8) musculoskeletal problems, and 9) an “all other category.” Each of the services is defined by a grouping of ICD-9-CM diagnostic codes. fast payday loans online
We chose conditions that met several criteria. Significant shares of the enrolled population received treatment for each condition. The categories were broad enough so that at least 7.5% of the population was treated for each condition in a year. We included conditions that were a mix of chronic (cancer, hypertension, metal health care) and acute conditions (gastrointestinal, injuries, and birth-related).