SCHIP at 10: A Synthesis of the Evidence on Substitution of SCHIP For Other Coverage

Transcription

1 Contract No.: (03) MPR Reference No.: SCHIP at 10: A Synthesis of the Evidence on Substitution of SCHIP For Other Coverage Final Report September 2007 So Limpa-Amara Angela Merrill Margo Rosenbach Submitted to: Centers for Medicare & Medicaid Services Office of Research, Development, and Information 7500 Security Boulevard Baltimore, MD Project Officer: Susan Radke Submitted by: Mathematica Policy Research, Inc. 955 Massachusetts Ave., Suite 801 Cambridge, MA Telephone: (617) Facsimile: (617) Project Director: Margo Rosenbach

2 This report was prepared for the Centers for Medicare & Medicaid Services (CMS), U.S. Department of Health and Human Services (DHHS), under contract number (03). The contents of this publication do not necessarily reflect the views or policies of CMS or DHHS, nor does the mention of trade names, commercial products, or organizations imply endorsement by CMS, DHHS, or Mathematica Policy Research, Inc. (MPR). The authors are solely responsible for the contents of this publication.

3 A CKNOWLEDGEMENTS T he authors would like to recognize the valuable contributions of Sylvia Kuo, a former researcher at MPR, who worked on an earlier draft of this paper. We also thank Judith Wooldridge for reviewing and commenting on an earlier draft. Finally, we appreciate the assistance of Margaret Hallisey in providing production support. In addition, we thank CMS staff for their review of the paper.

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5 C ONTENTS Page EXECUTIVE SUMMARY... xi EARLY FINDINGS ON SUBSTITUTION INFLUENCED THE DESIGN OF SCHIP... 3 OVERVIEW OF STATE ANTI-SUBSTITUTION STRATEGIES... 4 HOW ANTI-SUBSTITUTION STRATEGIES AFFECT THE SCHIP APPLICATION AND ELIGIBILITY DETERMINATION PROCESS... 6 ESTIMATES OF SUBSTITUTION IN SCHIP VARY WIDELY... 8 Population-Based Studies... 8 Enrollee-Based Studies Applicant-Based Studies DISCUSSION CONCLUSION REFERENCES... 23

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7 T ABLES Table Page 1 SCHIP PROGRAM FEATURES DESIGNED TO PREVENT SUBSTITUTION OF PRIVATE INSURANCE COVERAGE, BY STATE: FFY SUMMARY OF SCHIP SUBSTITUTION ESTIMATES, BY TYPE OF STUDY SUMMARY OF POPULATION-BASED STUDIES ON THE EXTENT OF SUBSTITUTION UNDER SCHIP SUMMARY OF ENROLLEE-BASED STUDIES ON THE EXTENT OF SUBSTITUTION UNDER SCHIP COMPARISON OF METHODS FOR MEASURING SUBSTITUTION... 20

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9 F IGURES Figure Page 1 EFFECT OF ANTI-SUBSTITUTION STRATEGIES ON THE SCHIP ENROLLMENT PROCESS... 7

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11 E XECUTIVE S UMMARY Purpose. As the State Children s Health Insurance Program (SCHIP) faces reauthorization in 2007, it is timely to review evidence from the past decade on the extent of substitution of SCHIP for private coverage. This report synthesizes and assesses evidence from published and unpublished literature and state SCHIP annual reports on the magnitude of substitution in SCHIP. Wide-ranging estimates across studies are explained as a function of differences in purposes, methods, and analytic perspectives. Background. When SCHIP was enacted in 1997 under Title XXI of the Social Security Act, policymakers sought to safeguard against the substitution of SCHIP for other insurance coverage. The phenomenon of substitution is frequently referred to as crowd out. Recognizing the potential for substitution in SCHIP, states have used strategies to reduce its likelihood. These strategies include (1) designing SCHIP benefits to resemble private benefit packages (including premiums), (2) requiring a waiting period of uninsurance before enrolling in SCHIP, and (3) providing premium assistance to help families purchase employer-sponsored coverage if it is more cost-effective than SCHIP. Many states, however, allow children who have lost coverage involuntarily (such as due to divorce, death of a parent, or job loss) to enroll in SCHIP. Approach. Because there is no direct way to assess how many children would have taken up (or retained) private insurance in the absence of SCHIP, it is difficult to measure the level of substitution in SCHIP. This review examines three types of evidence: (1) population-based studies, (2) enrollee-based studies, and (3) applicant-based studies. Population-based studies reflect the level of substitution estimated to occur within the population of low-income children who were eligible for SCHIP, based on surveys of nationally representative samples of children. Enrollee-based studies estimate substitution among children who recently enrolled in SCHIP based on enrollee surveys. Applicant-based studies estimate substitution among those who applied for SCHIP based on state administrative data, including the extent to which substitution was averted because of denials during the eligibility determination process.

12 xii Findings. The evidence suggests that substitution of SCHIP for private coverage (crowd out) does occur, with the magnitude ranging from less than 10 percent to 56 percent, depending on how substitution is defined and measured. Population-based studies estimate that substitution of SCHIP for private coverage ranges from 10 to 56 percent. Most of these studies estimate substitution among children who were simulated to be eligible for SCHIP and who were below 300 percent of the FPL. These studies do not estimate substitution that would occur in higher income groups. These studies define substitution as any decline in private coverage within the population of lowincome children who were eligible for SCHIP (regardless of the reason for loss of coverage). These studies use multivariate methods to estimate substitution by simulating eligibility for SCHIP and comparing changes in private coverage among SCHIP-eligible children versus a comparison group. The methodology is designed to capture foregone opportunities for taking up private coverage after a child is enrolled in SCHIP. However, study limitations, as acknowledged by the authors, include the instability of estimates based on the choice of comparison group or multivariate methodology, error in self-reported insurance status, issues with imputing SCHIP eligibility, and limited ability to account for state-specific anti-substitution rules. Enrollee-based studies estimate that substitution is between 0.7 and 15 percent, based on descriptive analysis of pre-schip insurance status and access to employer coverage among children who recently enrolled in SCHIP. These studies take into account reasons for loss of coverage, and do not count involuntary loss of coverage as substitution (such as job loss, divorce, death of a parent). However, these studies may underestimate the extent of substitution because they generally do not account for the likelihood that families had access to private coverage before or after their children enrolled in SCHIP (also known as foregone opportunities ). Estimates from applicant-based studies are typically below 10 percent. These studies estimate substitution among those who applied for SCHIP based on state administrative data. These studies apply state-specific anti-substitution rules to their estimates of substitution (including waiting periods and reasons for dropping coverage). Like the enrollee-based studies, these studies focus on children s availability of private insurance coverage at the time of SCHIP application or enrollment, and do not account for foregone opportunities for taking up private coverage after a child is enrolled in SCHIP. Implications. This study suggests that some amount of substitution is unavoidable, regardless of how substitution is defined and measured. The salient policy questions include how much and what kind of substitution is acceptable. On one hand, the populationbased studies consider any reason for declines in private coverage as substitution, whereas the enrollee- and applicant-based studies take into account state-specific reasons for loss of private coverage (such as job loss, divorce, death of a parent, or in some cases, SCHIP at 10: Synthesis of the Evidence on Substitution

13 unaffordability of private coverage). Thus, conclusions about the extent of substitution will depend not only on how substitution is defined and measured, but also on perspectives on the circumstances under which substitution may be acceptable. xiii SCHIP at 10: A Synthesis of the Evidence on Substitution

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15 SCHIP AT 10: A SYNTHESIS OF T HE E VIDENCE ON S UBSTITUTION OF SCHIP FOR O THER C OVERAGE T he State Children s Health Insurance Program (SCHIP) plays a special role as a safety net that bridges the gap between private insurance and Medicaid coverage for uninsured, low-income children under age 19 in the United States. When SCHIP was enacted in 1997 under Title XXI of the Social Security Act, policymakers sought to safeguard against the substitution of SCHIP for other insurance coverage. The phenomenon of substitution is frequently referred to as crowd out. Substitution occurs when people who are covered by, or have access to, private insurance (such as employersponsored coverage) enroll in public programs (such as SCHIP), thereby shifting the cost of coverage onto public programs. Substitution may also occur when employers drop, or decide not to offer, dependent coverage because SCHIP is available. Substitution can occur at the time of enrollment that is, when children drop or forgo private coverage when they apply for or enroll in SCHIP. It can also occur when families forgo private coverage while their children are enrolled in SCHIP; in the absence of SCHIP, they may have gained access to and taken up employer-sponsored coverage. Recognizing the potential for substitution of SCHIP for other coverage, states have used strategies to reduce its likelihood. These strategies include (1) designing SCHIP benefits to resemble private benefit packages (including premiums), (2) requiring a waiting period of uninsurance before enrolling in SCHIP, and (3) providing premium assistance to help families purchase employer-sponsored coverage if it is more cost-effective than SCHIP. State anti-substitution strategies are designed to promote the goals of efficiency and equity. As applied to coverage expansions for children, efficiency refers to the targeting of SCHIP funds to currently uninsured children, while equity refers to the provision of similar benefits to children in similar circumstances (Blumberg 2003). To the extent that families voluntarily drop private coverage and shift to public coverage, public spending increases, but the uninsurance rate does not decline. Strategies to prevent substitution promote the efficiency of the SCHIP program by targeting SCHIP to uninsured low-income

16 2 children. 1 State strategies may also take into account concerns about equity, by allowing certain families to enroll in SCHIP for good cause, even though they had private coverage at the time of application. Good cause exceptions vary from state to state but may include loss of coverage due to employment changes, death of a parent, divorce, or excessive cost relative to an affordability criterion (such as 5 to 10 percent of family income). Measuring the level of substitution and the effect of anti-substitution provisions in SCHIP is difficult because there is no direct way to assess how many children would have taken up (or retained) private insurance in the absence of SCHIP. 2 Although researchers and policymakers may observe certain trends in public or private coverage, it is difficult to attribute these trends to substitution versus other factors. For example, employers may choose not to provide (or may drop) dependent coverage to lower their costs. It is hard to determine if these decisions are due to the availability of SCHIP, and, hence, represent a form of substitution, or whether they are a function of a poor economy or other factors. Similarly, families eligible for SCHIP may drop employer-sponsored coverage for their children and enroll them in public coverage. Again, it cannot be easily determined whether they switch coverage voluntarily (for example, because the SCHIP benefits are more generous or lower in cost) or involuntarily (for example, because of changes in employment or divorce). As SCHIP faces reauthorization in 2007, it is timely to review evidence from the past decade on the extent to which substitution occurred under SCHIP. The focus is on evidence related to substitution by individuals, as there is limited evidence on employer response to the availability of SCHIP. 3 This report synthesizes and assesses evidence from published and unpublished literature and state SCHIP annual reports on the magnitude of substitution in SCHIP. Three types of evidence are reviewed: (1) population-based studies, (2) enrolleebased studies, and (3) applicant-based studies. 4 The next section reviews how evidence on 1 Efficiency in SCHIP refers to maximizing the use of SCHIP funds to reduce the rate of uninsured, lowincome children. To the extent that SCHIP funds are allocated to covering children who were previously insured (and who voluntarily dropped their coverage), efficiency is reduced because the level of uninsurance does not decline as a result of such expenditures. 2 See Davidson et al. (2004) for a detailed discussion of the challenges in measuring substitution. 3 To our knowledge, only one study has examined health insurance decisions by employers following the implementation of SCHIP. Buchmueller et al. (2005) found that the implementation of SCHIP was not associated with employer decisions whether or not to offer any health insurance coverage or to offer family coverage. However, they found that an increase in SCHIP income eligibility thresholds was associated with an increase in the marginal cost of family coverage. Moreover, they found that firms with higher rates of SCHIP eligibility among employees dependents had lower take-up of employer coverage overall, as well as lower takeup of family coverage. 4 These terms indicate the unit of analysis and inference. Population-based studies reflect the level of substitution estimated to occur within the population of low-income children who were eligible for SCHIP, based on surveys of nationally representative samples of children. Enrollee-based studies estimate substitution among children who recently enrolled in SCHIP. Applicant-based studies estimate substitution among those who applied for SCHIP, including the extent to which substitution was averted because of denials during the eligibility determination process. SCHIP at 10: Synthesis of the Evidence on Substitution

17 the level of substitution resulting from the Medicaid expansions influenced the design of SCHIP, including the strategies states have implemented to prevent substitution. We then present a conceptual framework for assessing and interpreting SCHIP substitution estimates. The following section presents evidence on substitution from the three types of studies, and the final section discusses how different estimation methods and analytic perspectives may lead to wide-ranging results. In summary, we found that estimates of substitution following the implementation of SCHIP ranged from less than 10 percent to more than 50 percent, varying by the definition, estimation methodology, and data source. Population-based studies produced the highest estimates, and applicant-based studies the lowest. Whereas population-based studies defined substitution among SCHIP-eligible children who dropped coverage for any reason (regardless of the circumstance), enrollee-based and applicant-based studies focused on SCHIP enrollees and applicants who dropped coverage for reasons other than good cause, as defined by each state. These findings not only demonstrate the sensitivity of the results to how substitution is defined and measured, but also raise important policy questions about how much and what kind of substitution is acceptable. 3 EARLY FINDINGS ON SUBSTITUTION INFLUENCED THE DESIGN OF SCHIP Research on substitution associated with the Medicaid expansions in the late 1980s and early 1990s suggested that a substantial proportion of children who enrolled in Medicaid would have had employer-based private coverage in the absence of the expansions (Blumberg et al. 2000; Card and Shore-Sheppard 2004; Cutler and Gruber 1996; Dubay and Kenney 1996; Ham and Shore-Sheppard 2003; Shore-Sheppard 2000; Thorpe and Florence 1998; Yazici and Kaestner 2000). The extent of substitution resulting from past Medicaid expansions ranged from 2 to 57 percent, depending on study specifications (Ham and Shore- Sheppard 2003). This literature also suggested that the extent of substitution would be higher for children in higher-income families, as these families would be more likely to have employed parents and, thus, have more offers of employer-based coverage than lowerincome families. Such concerns about substitution influenced the design of SCHIP. Eligibility for SCHIP was restricted to children who were uninsured, not eligible for traditional Medicaid, under age 19, and below 200 percent of the federal poverty level (FPL). 5 Although Title XXI allowed states flexibility in their program design, the final rules specified certain minimum requirements with regard to substitution (Federal Register 2001). All states are required to monitor the extent of substitution in their SCHIP programs. Additional 5 States were required to maintain the Medicaid income thresholds they had in place on June 1, In other words, SCHIP eligibility picks up where Medicaid eligibility leaves off. For states that had expanded Medicaid eligibility above 150 percent of the FPL, the SCHIP income eligibility threshold was 50 percentage points above the Medicaid threshold. In addition, several states effectively raised the income threshold above 200 percent of the FPL by using income disregards. For additional information about Medicaid and SCHIP income thresholds, see Rosenbach et al. (2003). SCHIP at 10: A Synthesis of the Evidence on Substitution

18 4 requirements apply to states that offer SCHIP coverage to children whose families earn more than 200 percent of the FPL. These states must (1) study the extent to which substitution occurs, (2) identify specific strategies to limit substitution if monitoring efforts show unacceptable levels of substitution, and (3) specify a trigger point at which substitution prevention strategies would be instituted. States providing coverage for children in families with income more than 250 percent of the FPL must have a substitution prevention strategy. 6 OVERVIEW OF STATE ANTI-SUBSTITUTION STRATEGIES In response to concerns about the potential for substitution, states have implemented strategies to deter families from substituting SCHIP for private coverage. 7 Under SCHIP, states must determine whether a child applying for the program currently has other insurance coverage, because Title XXI prevents states from enrolling such children in SCHIP. 8 Depending on the state, however, children may be allowed to enroll in SCHIP if (1) the coverage was obtained through nongroup sources, (2) the loss of private coverage was involuntary (for example, because of a parent s employment change or divorce), or (3) the child has special health care needs (Rosenbach et al. 2003). In addition, Kaye et al. (2006) reported that nine states included an affordability criterion, allowing families to drop private coverage if the cost exceeded a certain threshold as a percent of family income (usually 5 or 10 percent of family income). 9 As Table 1 shows, nearly all states have implemented one or more anti-substitution strategies. To prevent families from voluntarily dropping coverage when they apply for SCHIP, 33 states have implemented waiting periods without health insurance, ranging in length from 1 to 12 months. Of the 39 states with separate child health programs, all but 9 had a waiting period. In most states with combination programs, the waiting period applies only to the separate component and not to the Medicaid expansion SCHIP component. Notably, 10 of the 13 states with income thresholds above 200 percent of the FPL have waiting periods In addition, Title XXI requires states that offer premium assistance programs for employer-sponsored coverage to impose a six-month waiting period of uninsurance (unless transitioning from Medicaid coverage), although reasonable exceptions are allowed (Federal Register 2001). 7 See Lutzky and Hill (2001) for an assessment of states early experiences with anti-substitution policies under SCHIP. 8 States with a SCHIP-funded Medicaid expansion program may enroll children with other coverage in traditional Medicaid (Title XIX) and claim the regular Medicaid match rather than the enhanced SCHIP match. 9 The nine states that reported an affordability criterion are Colorado, Connecticut, Georgia, Maine, Nevada, South Dakota, Texas, Virginia, and West Virginia. 10 The three exceptions are Minnesota, New York, and Rhode Island. Minnesota uses SCHIP funding to cover a small number of infants, as well as uninsured pregnant women who are not eligible for Medicaid. MinnesotaCare, authorized under a Section 1115 Medicaid waiver, is the state s primary public insurance vehicle, which does impose a six-month waiting period of uninsurance. New York and Rhode Island have vigorous monitoring of previous private insurance coverage and use premiums as a deterrent to substitution. SCHIP at 10: Synthesis of the Evidence on Substitution

19 5 Table 1. SCHIP Program Features Designed to Prevent Substitution of Private Insurance Coverage, by State: FFY 2004 State Program Type Maximum Eligibility Threshold (% of FPL) Waiting Period Without Insurance Coverage (Months) Coordination with Private Insurance Coverage Verification of Existence of Private Insurance Premium Assistance Program Cost Sharing Benefit Design Benefit Limits Number of States Alabama S-SCHIP b - b - Alaska M-SCHIP b - b - Arizona S-SCHIP b - b - Arkansas COMBO b - b - California COMBO a b - b - Colorado S-SCHIP b - b b Connecticut S-SCHIP b - b b Delaware COMBO a b - b - District of Columbia M-SCHIP b Florida COMBO b - b - Georgia S-SCHIP b - b - Hawaii M-SCHIP Idaho COMBO a b b b - Illinois COMBO b b b - Indiana COMBO a b - b - Iowa COMBO b - b - Kansas S-SCHIP b - b - Kentucky COMBO a b - b - Louisiana M-SCHIP b b - - Maine COMBO a b - b - Maryland COMBO b - b - Massachusetts COMBO b b b b Michigan COMBO a b - b - Minnesota COMBO Mississippi S-SCHIP b - b - Missouri M-SCHIP b - b b Montana S-SCHIP b - b - Nebraska M-SCHIP b Nevada S-SCHIP b - b - New Hampshire COMBO a b - b - New Jersey COMBO a b b b b New Mexico M-SCHIP b - b - New York COMBO b - b - North Carolina S-SCHIP b - North Dakota COMBO a b - b - Ohio M-SCHIP b Oklahoma M-SCHIP Oregon S-SCHIP b Pennsylvania S-SCHIP b Rhode Island COMBO b b b - South Carolina M-SCHIP b South Dakota COMBO a b Texas S-SCHIP b - b - Utah S-SCHIP b - b b Vermont S-SCHIP b - Virginia COMBO a b b b - Washington S-SCHIP b - b - West Virginia S-SCHIP b - b - Wisconsin M-SCHIP b b b - Wyoming S-SCHIP b - b - Source: SCHIP annual reports from 49 states and the District of Columbia, federal fiscal year Note: Tennessee does not have a SCHIP program. a For states with a combination of M-SCHIP and S-SCHIP programs, the waiting period applies to the S-SCHIP program only. M-SCHIP = Medicaid Expansion SCHIP Program; S-SCHIP = Separate Child Health Program; COMBO = Combination of M-SCHIP and S-SCHIP Programs. SCHIP at 10: A Synthesis of the Evidence on Substitution

20 6 Nearly all states reported some form of coordination with private insurance during the eligibility determination process. Forty-five states reported that they actively verified whether a child had private insurance coverage, through such mechanisms as record matches with insurance databases or verification with employers. Eight states offered a premium assistance component that subsidizes the cost of employer-sponsored coverage when it is cost-effective compared to SCHIP coverage. Benefit design features, such as cost-sharing (39 states) and benefit limits (6 states), were intended to make SCHIP coverage similar in scope to that offered by private insurers, thus providing a disincentive for families to voluntarily drop employer-sponsored coverage for their children. 11 HOW ANTI-SUBSTITUTION STRATEGIES AFFECT THE SCHIP APPLICATION AND ELIGIBILITY DETERMINATION PROCESS Anti-substitution strategies make the pathways to eligibility and enrollment more complicated for SCHIP than for traditional Medicaid. Traditional Medicaid requires that children meet eligibility criteria related to age and income. 12 SCHIP has, at a minimum, the additional requirement that a child have no other creditable insurance coverage at the time of application. 13 The framework presented in Figure 1 demonstrates the pathways created by anti-substitution strategies for children who meet age and income eligibility criteria. For example, some families may choose not to apply for SCHIP coverage due to antisubstitution strategies, while others may apply but must meet requirements on current or past insurance status. In states that have a waiting period, children will be denied SCHIP if they recently had other coverage, unless they qualify under state-specific exceptions, such as the involuntary loss of employer-sponsored coverage. 14 In the eight states that offer premium assistance to subsidize the cost of employer-sponsored coverage, states will consider whether such coverage meets specific criteria related to employer cost-sharing and benchmark benefits and is cost-effective to the state compared to SCHIP coverage. Figure 1 depicts the effect of anti-substitution strategies on SCHIP denial rates as depicted in boxes A, B, and C, including denials because children had insurance when they applied (B/A), or because they voluntarily dropped coverage within the waiting period (C/A). This framework provides a context for understanding the complexity of monitoring and measuring substitution in SCHIP, because of the differing anti-substitution strategies among states and over time. 11 These states indicated in their SCHIP annual reports that cost-sharing and benefit limits were explicit components of their anti-substitution policies. Other states may have adopted benefit limits but not have reported that this was an explicit anti-substitution mechanism. 12 As of July 1, 2006, states also require Medicaid applicants to provide proof of citizenship. 13 Creditable health coverage refers to health benefits for basic preventive and catastrophic services offered through a group health plan or health insurance coverage. 14 States with a waiting period deny (or, in some cases, delay) coverage to children who have had coverage for a specified period before applying. SCHIP at 10: Synthesis of the Evidence on Substitution

21 7 Figure 1. Effect of Anti-Substitution Strategies on the SCHIP Enrollment Process Eligible Based on Age-Income Anti-Substitution Strategies Apply for SCHIP A Not Apply for SCHIP Yes Currently have ESI? No Denied B Waiting Period? No Enrolled Yes Yes Had ESI in the Past X Months? No No State-specific exceptions: Involuntary loss of ESI? Nongroup coverage? ESI not affordable? Child has special health care needs? Yes Enrolled Denied C Can reapply and enroll after waiting period Enrolled ID, MA, NJ, VA, WI IL, LA, MA State Policy Options Eligibility Screens ESI = Employer-sponsored insurance. If access to ESI and Employer meets minimum cost-sharing ESI meets benchmark benefits Cost-effective for the state Then enroll in premium assistance program SCHIP at 10: A Synthesis of the Evidence on Substitution

22 8 ESTIMATES OF SUBSTITUTION IN SCHIP VARY WIDELY Table 2 summarizes the three types of studies that estimate the magnitude of substitution in SCHIP. In population-based studies, substitution is typically defined as the percent of children who were eligible for SCHIP who would have had private insurance in the absence of the program. 15 This definition captures not only children whose families dropped private coverage at the time of application or enrollment, but also children whose families did not take up dependent coverage while enrolled. Enrollee-based studies typically define substitution as the percent of children enrolled in SCHIP who had other coverage before they enrolled. Finally, applicant-based studies typically present SCHIP administrative data on the percent of SCHIP applicants who had other insurance coverage at the time of application or the percent who dropped other coverage to apply for or enroll in SCHIP. As Table 2 shows, the population-based studies tend to exhibit the highest rates of substitution, followed by the enrollee-based studies, with applicant-based studies producing the lowest rates. The next three sections discuss the findings and methods from each type of study and the following section attempts to reconcile the findings across the three types of studies. Table 2. Summary of SCHIP Substitution Estimates, by Type of Study Type of Study (Number of Studies) Population-based studies (7) Enrollee-based studies (4) Applicant-based studies (34)* Description of Substitution Estimate Percent of children who were age- and income-eligible for SCHIP who would have had private insurance in the absence of SCHIP Percent of children who enrolled in SCHIP who had other coverage at the time of application or in the six months before applying Percent of applicants who had other coverage at the time of application Percent of applicants who dropped coverage Range of Estimates 10 to 56 percent 0.7 to 15 percent 0 to 17 percent 0 to 15 percent * Refers to the number of states reporting one or more estimates in their FFY 2004 state SCHIP annual report. Population-Based Studies The specific aim of population-based studies is to account for trends in public and private coverage that would have occurred in the absence of SCHIP, by comparing the coverage outcomes of eligible and ineligible children before and after the expansions took 15 Population-based studies simulate SCHIP eligibility based on age and income data available in the survey. SCHIP at 10: Synthesis of the Evidence on Substitution

23 place. These studies estimate the effect of expanding SCHIP eligibility on selection of private versus public insurance coverage, controlling for other factors that would be likely to affect insurance choice. A fundamental measurement challenge, however, is that SCHIP eligibility cannot be observed in the data and must be imputed, based on such variables as age, family structure, and income. Population-based studies use two methodological approaches: (1) difference-indifferences, and (2) instrumental variables. The difference-in-differences method attempts to replicate experimental methods, using a comparison group to subtract out any changes in insurance coverage due to economic trends. Researchers most commonly choose comparison groups consisting of children with similar characteristics who are not eligible for SCHIP (such as near-eligible, higher-income children) or other populations that may have insurance trends that are similar to the target population (such as married, childless women with incomes below 500 percent of the FPL). 16 (As will be shown, however, the results can be sensitive to the selection of a comparison group.) Substitution rates are often calculated using the following illustrative equation: 9 Decline in private coverage among those eligible for SCHIP Increase in public coverage among those eligible for SCHIP Decline in private coverage among those in the comparison group Increase in public coverage among those in the comparison group Other studies use an instrumental variables approach to measure the effect of SCHIP eligibility on public versus private coverage. Unlike the difference-in-differences studies, these studies do not rely on a comparison group to measure the effects of eligibility expansions. Rather, this method involves devising an instrument to capture eligibility changes. The instrument is measured at the group level, rather than the individual level to avoid the endogeneity of predicting individual coverage outcomes based on changes in individual eligibility status. For example, Hudson et al. (2005) computed the mean probability of eligibility within cells defined by various characteristics, such as age, race, nativity, predicted poverty, state, and year. These variables are highly correlated with changes in SCHIP eligibility and identify people the expansions were most likely to affect. Table 3 summarizes the methods and findings for seven population-based studies of SCHIP substitution, including two studies that relied on pre-schip data to predict the extent of substitution that might occur in SCHIP. Prediction of Substitution Using Pre-SCHIP Data. During the early stages of SCHIP implementation, two studies predicted the likely magnitude of substitution, based on the Medicaid expansion experience (Selden et al. 1999; Holahan et al. 2000). As Table 3 16 Married, childless women with incomes below 500 percent of the FPL were selected as a comparison group because they have similar prevalence of dependent coverage as SCHIP-eligible children (Hudson et al. 2005). For example, these women often receive dependent coverage through their spouse. SCHIP at 10: A Synthesis of the Evidence on Substitution

24 10 shows, these studies estimated substitution rates ranging from 21 to 43 percent of SCHIP enrollment, depending on various assumptions about program design. As discussed below, however, these estimates should be interpreted with caution because these studies used behavior among the Medicaid-eligible population to approximate SCHIP enrollment behavior. Selden et al. (1999) used the 1996 Medical Expenditure Panel Survey (MEPS) to model the insurance decisions of Medicaid-eligible children. They projected SCHIP eligibility based on family income criteria. Assuming that SCHIP-eligible children would behave just as those eligible for Medicaid would, Selden et al. projected that, in 1999, 21 percent of SCHIP enrollees would have had private insurance in the absence of the program. The authors noted that their analysis was simplistic and did not take into account anti-substitution strategies proposed by states, which could reduce the level of substitution. On the other hand, they also noted that substitution could be higher as a result of application simplification efforts. Holahan et al. (2000) used the Current Population Survey (CPS) to simulate SCHIP participation under various income and cost-sharing scenarios. The authors applied assumptions derived primarily from the Medicaid experience on participation rates. They projected that SCHIP substitution would range from 23 to 43 percent, depending on the scenario. If SCHIP simply expanded Medicaid coverage to 150 percent of the FPL, they projected that 23 percent of SCHIP-enrolled children would have had private coverage in the absence of SCHIP. Alternatively, they estimated that if SCHIP extended eligibility to 300 percent of the FPL as a separate child health program with low premiums, the rate of substitution would be 43 percent. For a separate child health program with high premiums, they calculated that the substitution rate would be 36 percent. 17 These early simulations predicted which children would be eligible for SCHIP and modeled their subsequent insurance choices using the experience of Medicaid eligibles and various assumptions about program participation rates. Researchers recognized the limitations of these early studies in that families of children made eligible for SCHIP may behave differently than families of Medicaid-eligible children. For example, parents of SCHIP-eligible children may be more likely to have offers of employer-sponsored coverage. The authors acknowledged that several factors may offset the potential for substitution, including that SCHIP-eligible families may be more sensitive to the stigma of enrolling in a public program and that they may be exposed to anti-substitution strategies during the application and enrollment process. 17 Holahan et al. (2000) considered low- and high-premium scenarios under a separate child health program, because premiums were not allowed under Medicaid expansion programs without a waiver. The lowpremium scenario assumed no premiums for children in families with income below 150 percent of the FPL; premiums were assumed to be $250 per year at 200 percent of the FPL and $375 per year at 300 percent of the FPL, regardless of the number of children in the family. The high-premium scenario also assumed no premiums for children in families with income below 150 percent of the FPL. Above this level, however, the model assumed premiums of one percent of income for families with one child and two percent of income for families with more than one child. SCHIP at 10: Synthesis of the Evidence on Substitution

25 Table 3. Summary of Population-Based Studies on the Extent of Substitution Under SCHIP SCHIP at 10: Synthesis of the Evidence on Substitution Study Authors Selden, Banthin, and Cohen (1999) Holahan, Uccello, Kim, and Feder (2000) Cunningham, Hadley, and Rechovsky (2002a, 2002b, 2002c) Davidoff, Kenney, and Dubay (2005) LoSasso and Buchmueller (2004) Hudson, Selden, and Banthin (2005) Bansak and Raphael (2006) Data MEPS, 1996 CPS, 1995; HIAA employer survey, 1991 CTS, , , NHIS, 1997, 2000, 2001 CPS, MEPS, CPS, 1998 and 2002 Definition of Substitution Methods Estimates of Substitution Prediction of SCHIP Substitution Using Pre-SCHIP Data Proportion of simulated SCHIP enrollees who would have had private coverage Proportion of simulated SCHIP enrollees who would have had private coverage Simulated SCHIP and private coverage participation with multivariate model. Used Medicaid participation and crowdout rates to simulate behavior under SCHIP. Varied assumptions related to income levels, premiums (none/low/high), and barriers to enrollment (assumed M-SCHIP programs have high barriers to enrollment and S-SCHIP programs have low barriers). Estimation of Substitution Using Post-SCHIP Data Percent of total increase in public insurance attributed to declines in private insurance, due to coverage expansions Proportion of increase in public insurance offset by decline in private insurance, among children with chronic conditions made eligible for SCHIP Percent of enrollees in SCHIP plans who would have had private coverage Proportion of increase in public insurance due to SCHIP eligibility expansions offset by decrease in private insurance due to SCHIP Proportion of public coverage take-up offset by declines in private coverage, among children determined to be SCHIP eligible Simulated state-level eligibility for Medicaid and SCHIP using a standardized population; used variant of difference-in-differences approach, with comparison group of children in states with smaller increases in eligibility. Simulated eligibility using federal and state-specific Medicaid and SCHIP eligibility rules; used difference-indifferences approach. Performed multivariate analysis of insurance choice using simulated public coverage eligibility as instrumental variable. Simulated SCHIP eligibility in a sample of children between 100 and 300% FPL using state-by-state rules for SCHIP; compared results from difference-intrends approach and instrumental variables approach on the effect of eligibility on insurance choice. Conducted multivariate analysis of insurance status among children meeting eligibility rules for SCHIP; included interactions of policy variables and year capture differential effects of policy adoption. 21% using state rules for eligibility 24% using federal eligibility rules 23% among children <150% FPL, M-SCHIP 26% among children <150% FPL, S-SCHIP 36% among children <300% FPL, high premium S-SCHIP 43% among children <300% FPL, low premium S-SCHIP 23% among children <200% FPL 39% among children 100 to 200% FPL 29% using control group of children with chronic conditions at incomes between 25% and 125% above state-specific income thresholds 10% among children <300% FPL 47% among children < 300% FPL if parent report of nongroup coverage is considered reporting error and recoded to public insurance 56% using control group of children 300 to 500% FPL 46% using control group of married childless women <500% FPL 42% using IV approach and excluding transition years (1998 and 1999) 39% using IV approach with a subset of children 100 to 300% FPL 21% controlling for child, parent, family characteristics and dummy variables for state policy design features CPS = Current Population Survey; CTS = Community Tracking Study; FPL = Federal poverty level; HIAA = Health Insurance Association of America MEPS = Medical Expenditure Panel Survey; M-SCHIP = Medicaid expansion SCHIP program; NHIS = National Health Interview Survey; S-SCHIP = Separate Child Health program 11

26 12 Estimation of Substitution Using Post-SCHIP Data. Five studies provide rigorous multivariate estimates of the extent of substitution occurring in SCHIP. Like the simulation studies discussed above, this work draws on methods established in the Medicaid substitution literature. These studies use pooled cross-sectional data from national surveys to estimate the proportion of the increase in public insurance due to SCHIP eligibility expansions that were offset by declines in private coverage due to the SCHIP expansions. Because most national surveys do not distinguish SCHIP coverage from Medicaid coverage, this literature typically focuses on the SCHIP target group namely, children whose family income lies below 200 or 300 percent of the FPL. Estimates of substitution range from 10 to 56 percent, depending on the assumptions and methods used (Bansak and Raphael 2006; Davidoff et al. 2005; Hudson et al. 2005; LoSasso and Buchmueller 2004; Cunningham et al. 2002a, 2002b, 2002c). Cunningham et al. (2002b, 2002c) performed a multinomial logistic regression analysis of predictors of health insurance coverage, using data from the Community Tracking Study between Round 1 ( ) and Round 3 ( ). 18 They compared changes in coverage for children in states with more generous eligibility expansions to those in states with narrower expansions. This study is unique because it directly controlled for the change in the price of employer-sponsored coverage and community-level premiums over time. Cunningham et al. (2002b, 2002c) estimated SCHIP substitution to be 39 percent within the SCHIP target population of near-poor children (100 to 200 percent of the FPL) and 23 percent among the low-income population below 200 percent of the FPL. Although the uninsured rate declined by four points during the study period, they found little evidence that the eligibility expansions were associated with the reduction in uninsurance. Instead, they suggest that other program features, such as outreach and enrollment simplifications, may have contributed to the decrease in uninsurance rates. Davidoff et al. (2005) estimated SCHIP substitution among children with chronic conditions using the National Health Interview Survey (NHIS) from 1997, 2000, and 2001, and a comparison group of near-eligible children with incomes between 25 and 125 percentage points above the state-specific SCHIP income eligibility threshold. They obtained a substitution estimate of 29 percent, which reflects a net increase in public coverage of 9.8 percentage points and a net decrease in private coverage of 2.9 percentage points. They concluded that the estimated decline in private coverage was relatively small due to the presence of waiting periods. 18 We discuss the most recent work by Cunningham et al. (2002b), which reported the substitution estimates. Cunningham et al. (2002c) is the companion data appendix that derived the estimates. Earlier work by Cunningham et al. (2002a) described the methods for both analyses and found that 38 percent of children whose income was between 100 and 200 percent of the FPL substituted public for private coverage between Round 1 ( ) and Round 2 ( ), although this estimate was not statistically significant. They suggest that the lack of statistical significance may be the result of slow SCHIP implementation. By Round 2, many states had not fully implemented their programs and were only just beginning to enroll children into SCHIP. SCHIP at 10: Synthesis of the Evidence on Substitution

27 LoSasso and Buchmueller (2004) modeled substitution in a sample of children below 300 percent of the FPL, based on CPS data from 1997 to They attributed 10 percent of the estimated increase in public enrollment among children made eligible for SCHIP to substitution of public for private coverage. However, they obtained a much higher estimate of 47 percent when they considered reports of nongroup coverage in the CPS to be misreported public coverage. 19 The authors also controlled for the presence of a waiting period and estimated a substitution rate of 50 percent with no waiting period, 31 percent with a three-month waiting period, and 0 percent with a six-month waiting period. As would be expected, waiting periods appeared to significantly reduce the estimated SCHIP take-up rate among eligible children. Hudson et al. (2005) used MEPS data from 1996 to 2002, and compared alternative estimation strategies to quantify the effect of expanding SCHIP eligibility on public and private insurance coverage. They found that the results were not only sensitive to the estimation strategy but also had wide confidence intervals. For example, the estimated rate of SCHIP substitution ranged from 39 percent using the instrumental variables approach with children in the SCHIP target group (between 100 and 300 percent of the FPL) to 56 percent using the difference-in-trends approach with a comparison group of children between 300 and 500 percent of the FPL. Even though the study concluded that the substitution estimates were imprecise, regardless of the specification, it consistently found statistically significant decreases in uninsurance associated with the SCHIP expansions. The most recent study, conducted by Bansak and Raphael (2006), used CPS data to estimate the effect of being eligible for SCHIP on public and private insurance coverage in 1997 and They estimated substitution among SCHIP-eligible children to be 21 percent, adjusting for child and family characteristics and state policy design features. They estimated the net effect of SCHIP was to increase insurance coverage by nearly seven percentage points. Estimated SCHIP take-up rates were higher in states that eliminated asset tests, extended continuous coverage, used joint applications during renewal, and extended benefits to parents. Estimated SCHIP take-up rates were lower in states that implemented separate child health programs (compared to Medicaid expansion programs) and in states that had a waiting period without insurance coverage. Caveats of Population-Based Studies. The strengths of population-based studies are that they provide national estimates of the extent of substitution under SCHIP, and they are designed to account for secular trends in private coverage. However, this work has several limitations, including the instability of estimates, error in self-reported insurance status, issues with imputing SCHIP eligibility, and limited ability to account for state-specific antisubstitution rules The authors found a positive correlation between SCHIP eligibility and reports of nongroup coverage, but not with group coverage and SCHIP, supporting their hypothesis that families may be unaware they are enrolled in a public insurance program. SCHIP at 10: A Synthesis of the Evidence on Substitution

28 14 First, the lack of methodological consistency and consensus in the literature has led to imprecise estimates, as shown in Table 2. Hudson et al. (2005) explored two methods difference-in-trends using two different comparison groups and instrumental variables using simulated eligibility to predict insurance status and found their substitution estimates were highly sensitive to specification, ranging from more than 50 percent to negative (data not shown). The authors, therefore, expressed lack of confidence in their estimates. They cited the imprecision and lack of robustness of crowd-out measures as a barrier to quantifying the precise extent of substitution in SCHIP. Second, there is considerable concern about whether families correctly identify SCHIP participation through self-reported survey data. To address this concern, LoSasso and Buchmueller (2004) conducted sensitivity analyses in which they interpreted nongroup private insurance coverage as public coverage; based on the high correlation between SCHIP eligibility and nongroup coverage, they speculated that many families confused SCHIP with individual coverage. Their resulting estimates of substitution varied widely, ranging from 10 percent using the self-reported insurance status to 47 percent when nongroup coverage was considered as misreported public coverage. Third, the imputation of SCHIP eligibility is subject to error using the available data sources. Depending on their methods, population-based studies impute SCHIP eligibility to identify the sample for the analysis, assign the treatment group, or create an instrumental variable. Researchers impute SCHIP eligibility by applying the complex, state-specific age, income, and asset rules to the survey data. However, survey data may be missing key variables (such as assets or income disregards), or certain variables (particularly income) may contain reporting error. Thus, some studies find that children they predicted would not be eligible for SCHIP reported they had public coverage, indicating that the imputation of eligibility using survey data contains measurement error or there is recall error in the selfreported data. Fourth, the population-based studies do not take into account the circumstances under which transitions in insurance coverage occur. Most states allow children to enroll under certain circumstances in which involuntary loss of coverage occurs. Unfortunately, involuntary loss of coverage due to such factors as death of a spouse, divorce, or job loss cannot be distinguished from voluntary reasons for taking up SCHIP, such as lower cost or more generous benefits. Thus, substitution estimates from population-based studies may be overstated, to the extent that involuntary loss of coverage among SCHIP-eligible children is systematically higher than that in the comparison group. Taken together, these limitations may account for the wide variation in substitution estimates between and within studies (Table 2), and they should be considered when interpreting substitution results from population-based studies. Enrollee-Based Studies Another approach to measuring substitution is through enrollee-based studies, which provide direct estimates of the number of children who dropped private coverage to enroll SCHIP at 10: Synthesis of the Evidence on Substitution