1. Demystifying Medicaid Claims Data for Use in Health/Behavioral Health Services Program Evaluation Elspeth M. Slayter, M.S.W., Ph.D. Salem State College School of Social Work Council on Social Work Education Annual Program Meeting San Antonio, Texas November 7, 2009
34. Reframing limitations: Slayter, 2009 Strengths: Limitations: Access to data about low-income populations by key policy categories Generalizable only to people with Medicaid coverage, non-churners Ability to generalize to within-state Medicaid populations In states using local codes, can behard to compare to national studies Ability to generalize to statewide Medicaid populations Can be hard to compare to other states, national studies Data are screened for “a practical level of quality and consistency” by CMS Clinical validity is still questionable at times Good client or population-specific cost estimates Data do not include DSH ot supplemental payments or administrative costs (but could link data sources) Can be linked with other data sources Can be complicated, time-consuming Baseline data from a range of service locations from which to guide smaller, clinical studies Does not provide context of service environment, treatment
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38. Your ideas: How would you use claims data to conduct behavioral health-related research? Slayter, 2009
SAS Version 9.1 and SPSS 12.0 SAS to SPSS: SAS 9.1 datasets can now be read directly into SPSS 12.0. There are two methods to transfer data into SPSS from SAS. These methods only work if the SAS dataset is non-compressed. For "point and click" users, Go to File/Open/Data. Then, in the select box, scroll down to "SAS Long File Name (*.sas7bdat)", select from list and then select the file you'd like to convert, and PRESTO, it's in SPSS !! For syntax users, use the syntax (for example)--- GET SAS DATA='c:\\ecls\\algeria.sas7bdat'. Where the SAS Version 9.1 dataset is algeria.sas7bdat and resides in c:\\ecls. If all goes well, the file will appear in SPSS. SPSS to SAS: To get a SPSS dataset into SAS 9.1, the SPSS dataset must to first saved as a SPSS portable file using SPSS. When the dataset is active in SPSS go to: File/Save as/Portable file (.por). In this example, I saved the file as pcweekly.por in the c:\\ecls subdirectory. Then read the file directly into SAS 9.1 using the code: libname mylib spss 'c:\\ecls\\pcweekly.por'; data one; set mylib._first_; run; proc print data=one; run; SAS gives the SPSS dataset the name _first_ for some reason.
Generalizable to Medicaid beneficiaries only Sometimes a good thing: Major insurance source for people w/MR Dual eligibles – confusing Utilization data not prevalence data Non-Medicaid-funded treatment/services not included State variation Data Strengths The biggest strength of MSIS is its comprehensiveness and, consequently, its adaptability to a wide range of analyses. Even with our source data sets, which are far less detailed than the source MSIS data, it is possible to divide enrollees into many different groupings (e.g., by age, race, or gender, or a combination of these), and then to observe whether they use a particular type of medical service and the aggregate level of payments for those services. With the full range of MSIS data, even more detailed payment analyses are possible. For example, one could observe utilization and payments for specific classes of drugs, or determine spending per day in a hospital or nursing home for people in different groups.9 Data Weaknesses One weakness of MSIS is that its size and intricacy make it susceptible to delays in submission by states and availability from CMS. Historically, statistical information for Medicaid (namely Form HCFA-2082) takes longer to be compiled than expenditure data from Form CMS- 64. Delays lengthened with the switch to MSIS, although longer delays were expected simply due to the considerable logistical challenges to be overcome at both the federal and state levels in implementing such a large data system. MSIS was new to many states in FFY 1999, and even those states that participated in earlier versions of the system had to adjust to the new process. The process has sped up, however, as CMS continues to develop MSIS infrastructure and methods, and the states become more familiar with the system. A second weakness is also related to the comprehensiveness of MSIS data: with so much information to collect and synthesize, there is a greater likelihood that data will be missing or erroneous. CMS and its contractors also run tests to validate data, check for consistency of data elements over different periods, and verify reasonableness of data. However, the general quality of the data is only as reliable as the data submitted to CMS by states. Some states have been unable to provide some data during the first few years under the new system, including FFY 2002. Some payments are known to be missing or inaccurate in our source files and additional errors may be introduced when CMS compiles data into more aggregated data sets or standardized reports for each state (e.g., through an unnoticed mistake in computer programming). As a result, the tabulations shown in the accompanying tables come with an extensive list of caveats, which are posted as a separate document. One last weakness of MSIS that deserves note is that it does not include all types of Medicaid expenditures. This “weakness” is perhaps more appropriately described as a “ substantive difference” between MSIS and Form CMS-64. Total MSIS payments do not agree with the CMS-64 financial figures because they do not include payments made outside the claims processing system, such as DSH payments. Payments to DSH hospitals typically do not appear in MSIS since states directly reimburse these hospitals and there is no fee-for-service billing. Likewise, the supplemental (UPL) payments discussed above are less likely to appear in MSIS. 7 This
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