Children's Health Insurance Patterns: A Review of the Literature. Chapter VI. Summary and Next Steps


Although there is more agreement about the number and proportion of uninsured children than in the past, there is still a rather substantial difference of opinion. For the most part, estimates differ depending on whether adjustments for Medicaid enrollment, based on HCFA(now known as CMS) administrative data, are made to national survey data. This difference of opinion caused the 1995 estimates of uninsured children under age 18 using CPS data to range from 6.9 million (9.8 percent) according to the Urban Institute (which adjusts for Medicaid under reporting in the CPS) to 9.8 million (13.8 percent) according to EBRI, the Census Bureau, CBO, and others (who do not adjust for Medicaid under reporting in the CPS) -- a difference of about 30 percent, or nearly 3 million children. This issue of Medicaid under reporting exists with both the CPS and the SIPP, the most widely used sources for estimates. For example, unadjusted CPS data showed 4.9 million fewer children (23 percent) enrolled in Medicaid in 1995 than HCFA(now known as CMS) administrative data showed. This difference represents more than half of the children thought to be uninsured according to the CPS, although under reporting of Medicaid can occur among the privately insured as well as the uninsured.

Other issues are also involved in counting uninsured children. There is increased awareness of how critical the time period definition is to estimates. The CPS is designed to measure the number of children uninsured throughout a given year. Yet, most researchers believe the CPS estimates of the uninsured are closer to point in time estimates -- meaning the commonly used annual estimates of the number of uninsured children may be overstated. The extent of churning among uninsured children is not well-understood yet. Some analyses of SIPP data suggest that almost one-third of all children will experience some period of noncoverage over a 2 ½ year period. However, research on the length of spells of noncoverage for children is still in the early stages, and there appear to be some inconsistent results.

Another concern is how the national surveys word their questions about insurance coverage. With both the CPS and the SIPP the number of uninsured must be defined as a residual -- that is, the uninsured are those who do not report receiving coverage of any type. The recent CTS survey asked respondents directly whether they were in fact uninsured, and the study authors believe this was a factor in their estimate of the number of uninsured children nationwide being lower than the CPS. In any event, the problem of asking about coverage, especially public coverage, may get worse before it gets better. With the moves to managed care, state insurance programs for children, and other state health reform efforts, there may be even more confusion in the future as to what types of coverage low-income persons have. This underscores the value of the CTS survey's approach of asking respondents directly whether they are uninsured if they fail to indicate coverage.

In response to the recently passed expansions in insurance coverage for low-income children, states would like more information on counting uninsured children at the state level. However, the national surveys generally lack a sufficient sample size to support state-specific estimates of uninsured children, although some researchers are now combining multiple years of data in order to produce state level estimates.

Most research on the characteristics of the uninsured has used cross-sectional data, without taking into account the potential heterogeneity of the population by length of uninsurance. Nevertheless, most researchers agree on the following overall profile:

  • The uninsured are found in every age group of children, even among the very young supposedly covered by past expansion efforts under Medicaid
  • Over two-thirds of uninsured children live in families with family income less than 200 percent of poverty
  • About 70 percent live in two-parent families; further, 64 percent have one parent working full-year, full-time
  • The vast majority (80 percent) of uninsured children have one parent who is also uninsured
  • Even though a majority of uninsured children are white, minorities (especially Hispanics) are disproportionately represented
  • Uninsured children also include a disproportionate number of noncitizens (10 percent versus 4 percent in the general population of children)
  • There are inconsistencies in the research about how long children are uninsured and the extent to which there is churning among the population

As would be expected (given disagreements about the number of uninsured), researchers do not agree on the number of children who are Medicaid eligible or Medicaid participation rates, although most agree that Medicaid participation rates are lower than participation rates in the AFDC and Food Stamp programs. Some differences in estimating Medicaid eligibles result from the extent to which the simulation models used to develop estimates reflect Medicaid programmatic and state-specific eligibility rules, which can be very complex. However, this source of differences will probably diminish in the future, as expansions to all poverty-related children are implemented.

There is little research about the characteristics of uninsured children who are eligible for Medicaid but not participating. This confounds the development of outreach efforts to increase public coverage for uninsured children.

This literature review was intended to assist the project team in understanding what research has been done to date on uninsured children and help them refine topics for further study. Clearly, many questions remain unanswered about the extent of uninsurance among children and the characteristics of those who are uninsured. Five topics were identified by ASPE as needing further investigation:

  • Insured versus uninsured children. What is distribution of both the number and lengths of spells of uninsurance for children? What factors are associated with short-term and long-term spells? What is the extent of churning or turnover? What are the trigger events leading to uninsurance and then coverage? What are the branching probabilities (i.e. what type of insurance did the child have prior to becoming uninsured and what type of insurance does the child obtain after being uninsured)?
  • Medicaid eligibility among children: population dynamics. What are the implications of churning for Medicaid eligibles? To what extent are there short-term and long-term eligibles and how do these subgroups differ? What are the events triggering Medicaid eligibility and do they differ between short-term and long-term eligibles? What are the characteristics of children leaving Medicaid and what is their insurance status?
  • Medicaid eligibility among children: information from examining alternative data bases and trends over time. What are the alternative ways of defining Medicaid eligibility when examining various data sources? For example, would examining two SIPP waves several years apart provide useful insights (1983-1984 and 1993-1994, or 1986-1988 to include a recession), or is this too complicated? Have participation decisions/rates and the characteristics of Medicaid enrollees changed over time?
  • Medicaid participation among children: rates and dynamics. What are the issues involved in determining the Medicaid participation rate and how do rates differ, dependent on the methodology? Are there events which seem to trigger Medicaid eligibility and participation?
  • Medicaid participation among children: characteristics of participants and comparison with eligible non-participants and other program populations. What factors correlate with Medicaid participation? How do participants and eligible non-participants compare? How do children enrolled in Medicaid compare with children participating in the AFDC and Food Stamp programs?

In addition to these five topics, MPR is responsible for suggesting two additional topics for study. Based on the findings from the literature review, two alternatives include:

  • Medicaid Enrollment Patterns Using HCFA(now known as CMS) Administrative Data. What is the distribution of Medicaid enrollment spells, using linked years of Medicaid person-based data in selected states? What are the characteristics of children with short and long spells (with adjustments for age)?
  • Medicaid Enrollee Characteristics: Comparisons of HCFA(now known as CMS) Administrative Data and SIPP. What are the differences in enrollee characteristics between HCFA(now known as CMS) person-based Medicaid administrative data (in selected states) and SIPP data? This analysis would explore type of eligibility (receiving cash assistance, medically needy, expansion groups), age, race, citizenship status (where available), county of residence. Do any differences provide insights regarding the problem of Medicaid under reporting in the national surveys?

Although beyond the scope of this project, the literature review also points to some other possible avenues for reconciling the CPS and SIPP data on Medicaid participation with HCFA(now known as CMS) administrative data. If resources were available, would it be feasible for the Census Bureau to link CPS and/or SIPP person-based data with person-based Medicaid enrollment data in selected states? Without this type of effort, it seems likely that estimates of uninsured children using national survey data will continue to be problematic. With the recent legislation providing funding to states to expand their coverage of children, more attention than before is likely to be focused on counting children who remain uninsured. If the problem of under reporting enrollment in public insurance program persists, how will these efforts be evaluated?