Continuation of Research on Consumer Directed Health Plans: HSA Simulation Model Refinement . A. Methods

01/21/2007

There are three major components to our methodological approach:  1) Model Estimation; 2) Choice Set Assignment and Prediction; and 3) Policy Simulation.  As illustrated in Figure 1, often more than one database was required to complete the task.  Integral to this analysis was the use of consumer directed health plan data from four large employers working with the study investigators.


Figure 1

Figure 1 shows three major components to our methodological approach.

Figure 1 illustrates the three major components to our methodological approach: 1) Model Estimation; 2) Choice Set Assignment and Prediction; and 3) Policy Simulation. This particular model shows that more than one database was required to complete the task. Several steps were need for this model estimation.


The model estimation had several steps.  As a first step, we pooled the data from the four employers offering CDHPs to estimate a conditional logistic plan choice model similar to our earlier work (Parente, Feldman and Christianson, 2004).  In the second step we used the estimated choice-model coefficients to predict health plan choices for individuals in the MEPS-HC.  In order to complete this step, it was necessary first to assign the number and types of health insurance choices that are available to each respondent in the MEPS-HC.  For this purpose we turned to the smaller, but more-detailed MEPS Household Component-Insurance Component linked file, which contained the needed information.  The third step was to generate 2006 HSA premiums and benefit designs.  The final step was to apply plan choice models coefficients to the MEPS data with premium information as well as 2006 State of union tax treatment adjustments to get final estimates of take up and subsidy costs.

This process was similar to our previous work and described in more detail in the appendix. Two of the most substantial of several changes were inclusion chronic illness into the plan choice model and generation of premiums through an iterative process using prior years claims data to create actuarially fair estimates of premium.  Below we describe in more detail specific issues that we addressed in our current analysis.

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