Research for Tricare

As part of the Tricare- Tidewater evaluation, CNA fielded a military beneficiary health care survey in the fall of 1992 to collect information on access, satisfaction, health status, and utilization. To supplement these data, and support the Tricare evaluation, we developed an administrative data set using claims data from the Civilian Health and Medical Program for the Uniformed Services (CHAMPUS), Biometrics records, and Medical Expense and Performance Reporting System (MEPRS) data. This administrative data set provides not only cost information but also detailed utilization records, including information about specific diagnoses and treatments. While working with the survey data, we observed internal inconsistencies in how people responded to the utilization section of the survey. These inconsistencies raised concerns about the veracity of the self-reported use measures. Given these concerns and the unique opportunity of having both survey and administrative measures of use for the same population, we thought it was appropriate to attempt to validate the self-reported utilization data against administrative records. In this paper, we compare the self-reported utilization data to the corresponding administrative utilization data, created from the CHAMPUS Quick Response Data File and Biometrics records, for a subset of our survey respondents. We look at incidences of overreporting and underreporting use in the survey data, and we attempt to explain these occurrences and the impact of this misreporting on aggre

To evaluate the TRICARE demonstration project in the Tidewater area of Virginia, we need to collect health care information about military beneficiaries that is not available from administrative records. We chose to acquire information on access, satisfaction, and health status from a mail survey. We conducted a baseline mail survey in the fall of 1992, before the start of TRICARE. We plan to conduct a follow-up survey in the winter of 1995. Our response rate for the baseline survey was lower than expected, with 38 percent of the adult beneficiaries responding. With high levels of nonresponse, one can never be sure that there isn't some degree of bias in the survey responses. Therefore, we feel strongly about trying to improve the response rate for the follow-up survey. We reworked our survey implementation plan and tested it on a small sample of Navy beneficiaries. We find that modifying the survey implementation process has a dramatic effect on the response rate. This paper discusses the modifications we made and how they specifically affect the response rate.