H E A L T H  T R A C K I N G : 
M A R K E T W A T C H
W E B  E X C L U S I V E 

25 July 2001

Targeting Communities With High Rates
Of Uninsured Children

Despite improved eligibility, enrollment outreach remains
the key to getting children insured.

by Peter J. Cunningham

ABSTRACT:

Data from the first two rounds of the Community Tracking Study household survey show that coverage expansions through the State Children's Health Insurance Program (SCHIP) have virtually eliminated differences across communities in children's eligibility for public or private health coverage. Nevertheless, some communities continue to have very high rates of uninsured children, in large part because of lower participation rates in public programs and higher costs for employer-sponsored coverage. Participation in SCHIP may increase in high-uninsurance communities as the new programs mature, although low participation rates in public programs prior to SCHIP suggest that enrollment barriers may still be greater in such communities.

Large differences in the rate at which children are uninsured persist across the nation.¹ Since uninsured children tend to be disproportionately concentrated in some areas of the country, the success of policies to expand health coverage for children-such as the State Children's Health Insurance Program (SCHIP)-will depend in large part on how successful they are in reducing the number of uninsured children in these high-uninsurance areas.

State policies regarding public programs account for some of the differences in uninsurance rates. However, areas with high uninsurance rates also have large differences in their local economies and population characteristics, which may affect eligibility for employer-sponsored and public coverage, as well as the extent to which eligible children are enrolled in coverage.² Moreover, if low levels of enrollment-or "take-up" of coverage-are an important factor in accounting for high levels of uninsurance in some areas, then it is unlikely that significant reductions in children's uninsurance in these areas will be realized merely by further eligibility expansions.

Using data from the 1996-1997 and 1998-1999 Community Tracking Study (CTS) household surveys, this paper examines differences between areas with high and low rates of uninsurance among children. Specifically, we examine differences in eligibility for employer-sponsored and public coverage, and differences in take-up enrollment rates among eligible children. We also assess the extent of changes in eligibility for and participation in public programs as a result of SCHIP, and whether these changes have been greatest in those areas with the highest levels of uninsured children.

Data And Methods

The CTS is a longitudinal study designed to track changes in the health care system and the effects of these changes on individuals.³ The CTS household survey is designed to produce representative estimates, for the U.S. population and sixty randomly selected communities, of health insurance coverage, access to care, use of services, and perceived quality of care.⁴ The sample for the surveys was obtained primarily through random-digit dialing, supplemented by in-person interviews to represent households without telephones. As of this writing, two rounds of the survey have been completed. The first round of the household survey took place between August 1996 and August 1997, while the second round took place between August 1998 and October 1999.

The timing of the two surveys coincides with the passage and early implementation stages of SCHIP. The field period for Round 1 ended just before passage of the SCHIP legislation in 1997. The field period for Round 2 began after thirty-three states began implementing their SCHIP programs and were enrolling children. These include states with the largest populations, such as New York, California, Illinois, Pennsylvania, Florida, and Texas. In addition, all but five states had implemented their programs by 1 January 1999 (a little before the halfway point of the survey field period). Nevertheless, because the 1998-99 survey began immediately after or during SCHIP implementation in most states, the results reflect the early stages of SCHIP implementation. Enrollment increases in SCHIP after 1999 are not reflected in these results.

Information was obtained on all adults in a family, as well as one randomly selected child. There are a total of about 18,800 children under age eighteen in the sixty-community sample, including 9,600 in the 1996-97 survey and 9,200 in the 1998-99 survey. The overall response rate for both rounds of the survey was 65 percent.⁵

Classification of CTS communities. The sixty CTS communities are classified into three groups based on children's uninsurance rates in those communities in the 1996-97 survey.⁶ These include low-uninsurance communities (children's uninsurance rates of less than 8 percent), moderate-uninsurance communities (rates between 8 and 15 percent), and high-uninsurance communities (rates of 16 percent or higher).⁷ Estimates for each group of sites are weighted so as to be representative of all US communities with low, moderate, or high percentages of uninsured children.

To simplify the presentation of the results, we focus only on differences between high- and low-uninsurance communities. Estimates for moderate-uninsurance communities generally fall in between the estimates for high- and low-uninsurance communities and thus are consistent with our major findings.

Defining eligibility for and take-up of public and private coverage. Eligibility for public coverage is defined as being eligible for Medicaid or other state coverage programs (such as SCHIP). Our determination of whether individual children in the sample were eligible for Medicaid/SCHIP was similar to that used in other studies.⁸ It takes into account state-specific criteria related to age, income, family composition, and labor-force participation of the parents. State-specific criteria as of 1996 were used to determine eligibility for Medicaid and selected other state programs for the 1996-97 survey.⁹ For the 1998-99 survey we used state-specific criteria as of 1998 for Medicaid, as well as the age and income criteria for the SCHIP programs that existed on the date of the interview.¹⁰ Children were considered to be eligible for employer coverage if either parent was offered and eligible for coverage through an employer on the day of the interview (asked in the survey).

Take-up rates reflect the percentage of eligible children who actually enroll in employer-sponsored or public coverage. The denominator for computing take-up rates is based on the number of children eligible for coverage, as defined above. Eligible children who were reported to be enrolled in Medicaid or other state coverage on the day of the interview were considered to have taken up public coverage. Eligible children who were reported to have private insurance obtained through an employer on the day of the interview were considered to have taken up employer coverage.

Study Findings

Geographic concentration. Uninsured children tend to be more heavily concentrated in some areas of the nation. While only one-fourth of all children live in high-uninsurance areas, about 40 percent of all uninsured children live in these areas (Exhibit 1). Conversely, while about 40 percent of all children live in low-uninsurance areas (uninsured rate of less than 8 percent), only about one-fourth of all uninsured children live in these areas.

The percentage of children who were uninsured between the two survey rounds was virtually unchanged across all areas. However, high-uninsurance communities saw an increase in the percentage of children covered by Medicaid and other state programs (from 14 percent to 17 percent).¹¹ At the same time, the percentage of children with private insurance decreased in these communities, although the decrease was statistically significant only at the .10 level. Nevertheless, the decrease was enough to offset the gains in Medicaid and other state coverage for children in high-uninsurance communities, where uninsurance rates are still about 2.5 times higher on average than in low-uninsurance communities.

Eligibility. It is likely that the gains in Medicaid and other state coverage for children in high-uninsurance communities were the result of a dramatic increase in the percentage of children in these areas who became eligible for public programs. Furthermore, coverage expansions through SCHIP have virtually eliminated the difference between high- and low-uninsurance communities in children's eligibility for health coverage.

Just prior to SCHIP (in 1996-97), 91 percent of children in low-uninsurance communities were eligible for employer-sponsored or public coverage, compared with 82 percent of children in high-uninsurance communities (Exhibit 2). These discrepancies reflect both lower eligibility for employer coverage and lower eligibility levels for Medicaid in high-uninsurance communities, especially among low-income children (those in families with incomes below 200 percent of the federal poverty level). In low-uninsurance communities, 76 percent of low-income children were eligible for Medicaid prior to SCHIP, compared with about half of low-income children in high-uninsurance communities.

Eligibility levels for public programs increased across all areas after the passage and implementation of SCHIP, but the increase was especially dramatic in areas with high uninsurance rates. In such communities, the percentage of children eligible for Medicaid/ SCHIP rose from 28 percent in 1996-97 to 48 percent in 1998-99, while that of low-income children eligible for Medicaid/SCHIP rose from 52 percent to 91 percent. The result is that 92 percent of all children, and 98 percent of low-income children, are now eligible for employer-sponsored or public coverage. Moreover, differences in eligibility for coverage between high- and low-uninsurance communities have been virtually eliminated, despite the fact that there continue to be large differences in children's eligibility for employer coverage.

It is particularly noteworthy that expansions in eligibility for public coverage appear to have compensated for structural differences in local economies that result in generally lower eligibility for employer coverage in high-uninsurance communities. While there are no differences between low- and high-uninsurance areas in the percentage of children who have at least one full-time employed parent (Exhibit 3), differences in the types of jobs parents have are clearly related to lower eligibility for employer coverage. Key characteristics that are known to be related to eligibility for employer coverage include the size of the firm (small firms are less likely to offer coverage), industry, and union membership among the workforce.¹²

Take-up rates. Given the dramatic expansions in eligibility for public coverage for children in high-uninsurance communities, why was there not an even larger increase in the proportion of children with public coverage in these areas? The reason is due entirely to the fact that take-up rates of public or private programs are lower in high-uninsurance communities, and these take-up rates did not increase over the two-year study period.

Prior to SCHIP, 90 percent of children in low-uninsurance communities who were eligible for employer or public coverage had actually taken up this coverage, compared with only 79 percent of children in high-uninsurance communities (Exhibit 4). Among low-income children, take-up rates were 83 percent in low-uninsurance communities and 68 percent in high-uninsurance communities. Take-up rates in high-uninsurance communities are lower for both employer and public coverage, although differences in the take-up of public coverage were not statistically significant.

These differences in take-up rates between high- and low-uninsurance communities have persisted since SCHIP and may have even become larger. In high-uninsurance communities overall take-up rates continue to lag well behind those in low-uninsurance communities. Take-up rates of employer coverage among low-income children declined in high-uninsurance communities, although this decline was not statistically significant. Take-up rates for public programs have also decreased slightly in all areas since SCHIP. The decrease in take-up among those eligible for Medicaid/SCHIP may reflect a large increase in the number of children newly eligible for public coverage, and the 1998-99 CTS survey may reflect the early stages of SCHIP implementation before many newly eligible persons became aware of the program.

Reasons for lower take-up rates. Lower take-up rates in high-uninsurance communities reflect a combination of higher costs for employer-sponsored coverage, lower incomes among families with children, and noneconomic factors.

When we adjusted for differences across communities in the cost of living, we found average monthly premiums for family coverage to be higher in high-uninsurance areas than in low-uninsurance areas ($405 versus $374) (Exhibit 5), although this difference is statistically significant only at the .10 level.¹³ In addition, the employee share of the premium for family coverage is higher in high-uninsurance communities, especially when differences in the cost of living are adjusted for. This indicates that employers in these communities pay a somewhat smaller part of the premium, on average.

In addition, fewer families with children in high-uninsurance communities may be able to afford employer coverage. Average incomes for families with children were much lower in high uninsurance communities ($44,900) than in low-uninsurance communities ($56,700). Also, half of children in high-uninsurance communities are in families with incomes below 200 percent of the federal poverty level, compared with 37 percent of children in low-uninsurance communities. Lower incomes in high-uninsurance communities also may contribute to lower enrollment rates in public programs, since states have a smaller revenue base with which to fund outreach activities to increase enrollment.

Nevertheless, one might expect that take-up of public programs would be higher in high-uninsurance communities given that employer coverage is less affordable. This suggests that noneconomic factors are important in accounting for low take-up rates as well. These might include greater stigma attached to public programs in high-uninsurance communities, as well as lower preferences for health coverage among the population. One key characteristic of high-uninsurance communities is the relatively large percentage of children who are Hispanic (29 percent) compared with communities with low uninsurance rates (10 percent). Hispanics typically have lower take-up rates for health insurance programs for which they are eligible.¹⁴ This could be attributable to immigration concerns, language barriers, lack of awareness of public programs, or not understanding the role that insurance coverage plays in the United States in securing access to high-quality health care.¹⁵

Policymakers have understood from the beginning that the key to the success of SCHIP is in getting eligible children to enroll. To this end, both federal and state governments are devoting considerable energy to outreach activities and are streamlining enrollment procedures. The results of this study suggest that outreach activities and other efforts to stimulate enrollment need to be especially focused in high-uninsurance areas, both because they include a large concentration of the nation's uninsured children and because take-up rates of public and private coverage have historically been lower in these areas. While our findings reflect the early stages of SCHIP and therefore do not take into account enrollment increases since the data were collected, lower take-up rates of public and private coverage in high-uninsurance areas prior to SCHIP suggest that barriers to enrollment are potentially greater in these areas.

It is possible that areas with low take-up of public programs are in states that have greater barriers to enrollment, although it is not immediately apparent what these barriers may be. Almost all states engage in various types of outreach activities, such as using advertisements, Web sites, and toll-free hotlines, and have involved schools, health care providers, employers, and the business community in efforts to publicize the program and identify potential enrollees.¹⁶ Also, most states have attempted to remove administrative barriers, such as by having relatively short application forms and not requiring face-to-face interviews or asset tests.¹⁷ Somewhat fewer states allow joint applications for Medicaid and SCHIP and twelve-month continuous eligibility, although there do not appear to be any systematic differences between states with high-uninsurance communities and states with low-uninsurance communities in the number that have adopted these procedures.

Despite these efforts, barriers to enrollment persist, and it is likely that states and communities vary in the extent to which these barriers inhibit enrollment. These include the continuing stigma of enrollment in public programs among some potential beneficiaries, remaining complexities in the application process, confusion or apathy about eligibility among some potential beneficiaries, and cost-sharing provisions in some states.¹⁸ Also, only a handful of states allow presumptive eligibility (that is, allowing children to temporarily enroll before the formal application process is completed), and none of the states with high-uninsurance communities do so.

Efforts by most states to reduce barriers and increase enrollment also may vary in the intensity of activity and the amount of resources that states are willing and able to devote to outreach and enrollment activities. In the past, states with high uninsurance rates have had more limited fiscal capacity as well as a lower level of fiscal effort with respect to Medicaid spending than have states with lower uninsurance rates.¹⁹ While federal matching rates for SCHIP are more generous than for Medicaid, concerns have been raised about the large amount of funds allocated to states in the early years of SCHIP that have not been spent. In particular, states with high-uninsurance communities appeared to have a considerably higher percentage of unspent SCHIP funds in 1999 compared with states with low-uninsurance communities.²⁰ Whether this reflects the state's level of effort in getting eligible children enrolled is unclear, but it nevertheless is troubling since these states have the farthest to go in getting eligible children enrolled.

While low enrollment rates in public programs may reflect less effort by these states to enroll children, it is also possible that neither higher costs for private insurance nor limitations in state programs fully account for persistently high uninsurance rates in these areas. In addition to the particular difficulties in enrolling uninsured Hispanic children noted earlier, most of the high-uninsurance communities are concentrated in the southern and western states, while virtually all of the low-uninsurance communities are in the Northeast and upper-Midwest. These more general regional differences may have evolved along with the development of the economies of these regions: Large-scale manufacturing industries and heavily unionized workforces in the Northeast and Midwest have contributed greatly to the extensive private insurance coverage of the population in these areas.²¹ This may have resulted in a greater predisposition toward health coverage in these regions, as reflected in the decisions of employers, individuals, and state policymakers. To the extent that "cultural" differences across communities do play a role in influencing participation in public and private health insurance coverage, policymakers should be mindful that changing these patterns may take considerably more time and effort than they had anticipated. This time and effort are essential if SCHIP is to succeed in reducing the number of uninsured children.

The Center for Studying Health System Change is supported in full by the Robert Wood Johnson Foundation. The author thanks Jack Hadley, Paul Ginsburg, and Ann Greiner for providing helpful comments on an earlier draft. Beny Wu of Social and Scientific Systems provided excellent programming assistance.

NOTES

1. M.H. Park and P.J. Cunningham, Some Communities Make Progress in Reducing Children's Uninsurance, Data Bulletin no. 19 (Washington: Center for Studying Health System Change, October 2000).
2. P.J. Cunningham and P.B. Ginsburg, "What Accounts for Differences in Uninsurance Rates across Communities?" Inquiry (Spring 2001): 6-21.
3. A more detailed description of the design of the Community Tracking Study can be found in P. Kemper et al., "The Design of the Community Tracking Study: A Longitudinal Study of Health System Change and Its Effect on People," Inquiry (Summer 1996): 195-206; and C.E. Metcalf et al., Site Definition and Sample Design for the Community Tracking Study, Technical Pub. no. 1 (Washington: HSC, 1996).
4. R. Strouse et al., Report on Survey Methods for the Community Tracking Study's 1996-1997 Household Survey, Technical Pub. no. 15 (Washington: HSC, November 1998).
5. Estimates are weighted to reflect all US children below age eighteen and to account for survey nonresponse. Standard errors used in tests of statistical significance take into account the complex survey design, including the clustering of most of the sample into sixty sites; the national supplement; mixed sample frames (telephone and in-person interviews); and the selection of multiple families within a household.
6. We use the 1996-97 survey (pre-SCHIP) to classify the communities, to observe how changes in eligibility and enrollment in public and private insurance coverage changed across the three groups of communities since the passage and implementation of SCHIP.
7. Thresholds for the "low" and "high" groups identify communities that are significantly below and above the national average for children's uninsurance rates (12 percent based on the CTS data), while thresholds for the "moderate" group identify communities that are closer to the national average for children's uninsurance. Three groups were selected, to ensure adequate representation of both communities and individuals within each group of sites. Using slightly different thresholds does not materially affect either the results or the conclusions.
8. J. Currie and J. Gruber, "Health Insurance Eligibility, Utilization of Medical Care, and Child Health," Quarterly Journal of Economics (May 1998): 431-466; and T.M. Selden, J.S. Banthin, and J.W. Cohen, "Medicaid's Problem Children: Eligible but Not Enrolled," Health Affairs (May/June 1998): 192-200.
9. Thirty-five states and the District of Columbia are included in the sixty-site sample. For the 1996-97 survey (pre-SCHIP), eligibility for public programs was restricted to Medicaid and selected state programs. Since other state coverage programs varied considerably in terms of program design, comprehensiveness of benefits, and whether or not enrollment was capped, we include only those for which program penetration exceeded 5 percent of uninsured children in the state. These include California's Access for Infants and Mothers, Massachusetts's Medical Security Plan, Minnesota Care, New York's Child Health Plus, Pennsylvania's Children's Health Insurance Program, and Washington State's Basic Health Plan.
10. Some states only began implementation of SCHIP during the field period for the 1998-99 survey. Thus, eligibility would have differed for persons in these states depending on whether they were interviewed near the beginning or end of the field period.
11. As with other survey-based estimates of Medicaid enrollment, CTS estimates of Medicaid enrollees are much lower than those based on Medicaid administrative data. However, CTS estimates of the percentage enrolled in Medicaid or other state coverage for 1998-99 (16 percent) are also lower than Current Population Survey (CPS) estimates of children enrolled in public coverage (23 percent). CTS estimates are lower mostly because some categories of public coverage (Medicare, Indian Health Service) are included in the CPS estimates but not the CTS estimates. Also, Medicaid beneficiaries who report dual coverage (private insurance as well as public) are assigned to these other coverage categories in this study, whereas they are reported in both categories in most published estimates based on the CPS. See, for example, P. Fronstin, Sources of Health Insurance and Characteristics of the Uninsured: Analysis of the March 2000 Current Population Survey, EBRI Issue Brief no. 217 (Washington: Employee Benefit Research Institute, January 2000).
12. P.F. Cooper and B.S. Schone, "More Offers, Fewer Takers for Employment-Based Health Insurance: 1987 and 1996," Health Affairs (Nov/Dec 1997): 142-149; S.H. Long and M.S. Marquis, "Gaps in Employer Coverage: Lack of Supply or Lack of Demand?" Health Affairs (Supplement 1993): 282-293; and K. Swartz, The Medically Uninsured: Special Focus on Workers (Washington: Urban Institute, 1989).
13. These adjustments were carried out by deflating the monthly premium averages for each site by the ACCRA cost-of-living index for each specific site.
14. J.P. Stuber et al., Beyond Stigma: What Barriers Actually Affect the Decisions of Low-Income Families to Enroll in Medicaid?, Issue Brief (Washington: George Washington University Center for Health Services Research and Policy, July 2000); and P.J. Cunningham, "Choosing to Be Uninsured: Determinants and Consequences of the Decision to Decline Employer-Sponsored Coverage," HSC Working Paper (Washington: HSC, October 1999).
15. Stuber et al., Beyond Stigma; and M. Perry, S. Kannel, and E. Castillo, Barriers to Health Coverage for Hispanic Workers: Focus Group Findings (New York: Commonwealth Fund, December 2000).
16. M. Rosenbach et al., Implementation of the State Children's Health Insurance Program: Momentum Is Increasing after a Modest Start (Washington: Mathematica Policy Research, January 2001); and M. Edmunds, M. Teitelbaum, and C. Gleason, All Over the Map: A Progress Report on the State Children's Health Insurance Program (Washington: Children's Defense Fund, July 2000).
17. Rosenbach et al., Implementation; Edmunds et al., All Over the Map; and L. Cox and D.C. Ross, Medicaid for Children and CHIP Income Eligibility Guidelines and Enrollment Procedures: Findings from a 50 State Survey (Washington: Kaiser Commission on Medicaid and the Uninsured, April 2000).
18. Rosenbach et al., Implementation.
19. C. Trenholm and S. King, Disparities in State Health Coverage: A Matter of Policy or Fortune (Washington: Academy for Health Services Research and Health Policy, December 2000).
20. These comparisons are based on state-level estimates of unspent SCHIP funds in 1999 compiled by G.M. Kenney, F.C. Ullman, and A. Weil, Three Years into SCHIP: What States Are and Are Not Spending, New Federalism: Issues and Options for States, Series A, no. A-44 (Washington: Urban Institute, September 2000).
21. This hypothesis is discussed more extensively in R. Cunningham, "Explaining Local Variations in Private Coverage Rates: It's the Labor Market," Medicine and Health: Perspectives (28 February 2000). Also, in analyzing factors that accounted for variations in uninsurance rates across the sixty CTS communities, Peter Cunningham and Paul Ginsburg found large regional differences in uninsurance rates even after exhaustively accounting for differences in population characteristics, labor-market factors, state health policy, health care costs, and health system characteristics. Cunningham and Ginsburg, "What Accounts for Differences?"

Peter Cunningham, pcunningham{at}hschange.org, is a senior health researcher at the Center for Studying Health System change in Washington D.C. He has engaged in extensive research on access to care, the uninsured, and the health care safety net.

©2001 Project HOPE–The People-to-People Health Foundation, Inc.