Studies of Welfare Populations: Data Collection and Research Issues. Medicaid Data

06/01/2002

Data from Medicaid eligibility, enrollment, and claims and the new state CHIP can be linked to provide longitudinal tracking of a child or family's health care services or lack of services. For example, a state could track the Medicaid or CHIP enrollment of a child whose mother left AFDC. Since it is unlikely that many families leaving TANF will promptly go to jobs with sufficient health benefits or wages above the Medicaid and CHIP guidelines, that is, 200 percent of the federal poverty level in most states but 350 percent of the federal poverty level in some Medicaid expansion programs, lack of Medicaid coverage of a child in an AFDC/TANF leaver family may indicate that the child is at risk of having no health care coverage. If Early and Periodic Screening and Diagnostic Testing (EPSDT) services also are recorded in the Medicaid files, similar linkages with welfare data will allow tracking of the utilization of preventive services for these low-income children. Linked administrative data from AFDC/TANF and Medicaid have also been used as the sample frame for complementary survey research, which can gather indicators of health status or measures of health care utilization and provide more in-depth measures. For example, the Next Generation, a project conducted by Manpower Demonstration Research Corporation (2000), will use survey data from 10 studies to obtain a more comprehensive perspective about the effects of welfare reform on health outcomes. Variables about health will be measured through survey questions, but the project also will include the existing administrative data used in each of the 10 studies.

Using administrative data from Medicaid and CHIP (or other health-related supplemental services such as the Food Stamps Program or WIC) requires attention to a variety of considerations.

One must consider the populations in the data sets in relation to the population of interest for the study. Specifically:

  • Determining what cases are to be included in the population of study. Study populations that can be drawn from Medicaid or CHIP files include: applicants, eligible cases, open cases, closed cases, cases closed with high risk, time-limited or sanctioned cases, or reentry cases.
  • Within the group of eligible children are several subgroups that might be of interest. One group for Medicaid is those children actually enrolled. This subgroup of enrolled children includes a second subgroup of children receiving services. This group is not representative of all children enrolled, or all children eligible, or all low-income children in need of health care.
  • Medicaid data can be used to extend the analysis of the impact of welfare reform beyond the TANF population because the Medicaid eligibility pool is larger than the TANF eligibility pool. For example, California uses data files on Medicaid recipients as the core of its data sharing/data integration initiatives (National Conference of State Legislators, 1999). This strategy can allow evaluators to track service provided across time and programs to low-income children and families.
  • However, Medicaid administrative data can provide data on some of these populations, but not all (i.e. Medicaid administrative data do not represent the entire population of children eligible for Medicaid).
  • Public services data tend to overrepresent families at greatest risk. Findings must be interpreted with this in mind. If a family or child leaves TANF and does not appear on Medicaid enrollment files, this does not necessarily mean the child does not have access to health care as they could be covered by private insurance (Child Trends, 2000a).
  • Beyond data on eligibility and enrollment, the actual Medicaid or CHIP benefits within a state also should be considered part of the evaluation. State CHIP programs can vary by age, geographic area, disability status, or calculation of income.

A thorough understanding of the administrative data being used is necessary.

  • One consideration is whether historical AFDC or Medicaid data were defined the same way across the years.
  • In a cross-state context, one must consider possible differences in programs, definitions of data, caseload characteristics, and take-up rates in each state. Within state differences in each of these are also possible.
  • The dynamics of changing caseloads to determine whether changes are due to differences in entries to health services or differences in lengths of stay in those services need to be clarified (Greenberg, 1998).
  • Administrative data systems for Medicaid often are inadequately automated, even though provision of Medicaid benefits to needy families and children are highly dependent on automated systems. These systems may erroneously terminate a family from Medicaid. Also, eligibility systems typically are not part of the Medicaid division's information system, but reside elsewhere in state government. Because current technology dollars are being spent on TANF automated data systems, there may be some migration away from more archaic Medicaid data (Ellwood, 1999).
  • When designing research about children's access to health care services, it is important to remember that a family or adult parent can be dropped from Medicaid but the child can remain eligible.

Data linkage and confidentiality issues also arise:

  • How cases in the two files are linked requires the establishment of clear decision rules that are appropriate to specific research questions. There are inherent challenges to linking welfare data to Medicaid data because welfare data are case based (and can include a family or group of siblings) and Medicaid data are individual based (Ellwood, 1999).
  • It is useful to maximize the use of common health identifiers. In some states, such as North Carolina, a common health identifying number is used across a range of data sets, from vital statistics to disease registries (North Carolina State Center for Health Statistics, 1997). Where the health identifying number and social services number can be linked together, one can evaluate a child's experiences and outcomes with both health and social service programs.
  • Examining claims data under Medicaid or CHIP requires that issues of confidentiality are responsibly addressed. Many states, such as California, Maryland, Kentucky, and Tennessee, are already addressing these concerns through data sharing and data warehousing projects: (National Conference of State Legislators, 1999).

In addition to these concerns about administrative data, identification of the relevant research questions is critical in guiding the analysis plan and selection of relevant data sets. The question of whether regulations make health care services available to all children who need them could be answered with eligibility data. The question of whether children leaving TANF continue to get needed health services cannot be answered with eligibility or enrollment data. That question only can be answered with service utilization data. The question of whether children exiting TANF are continuing to get timely immunizations could be answered by Medicaid services data or by separate immunization registries within a state (Child Trends, 1999).

Another relevant research question to include would be whether the population of cases had changed since PRWORA was enacted. Will you study AFDC populations before PRWORA, or just those TANF cases after the legislation was implemented? This would require including AFDC and TANF cases in the research. Beyond analysis of the data about AFDC/TANF and Medicaid, the Food Stamps Program, or WIC, research should include questions about barriers to supplemental services for families exiting welfare. One possible barrier is the continued linking of welfare to these supplemental services, despite efforts over the past decade to delink regulations about the programs. In practice, and for individual families, these programs remain interwoven. Another barrier is the complicated eligibility rules for services to support families leaving TANF and the media about the program that might affect whether families think they are eligible or not (Ellwood, 1999). Finally, a research question of interest would be "Upon exiting TANF, do families drop supplemental services, add supplemental services, or maintain existing levels?"

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