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Millions of US children lack health insurance coverage. Efforts to expand children’s insurance often focus on extending public coverage to uninsured parents. Less is known about the uninsured children whose parents already have coverage.
To identify predictors of uninsurance among US children with insured parents.
Cross-sectional and full-year analyses of pooled 2002-2005 data from the nationally-representative Medical Expenditure Panel Survey (MEPS).
All children under age 19 in four yearly MEPS files with positive full-year weights who had at least one parent residing in the same household (unweighted total number = 39,710).
Cross-sectional and full-year uninsurance among children with at least one insured parent.
Cross-sectionally, over 3.3 percent of US children were uninsured with at least one insured parent (unweighted total number = 1,380, weighted average yearly population of approximately 2.3 million children). In multivariable analyses, children experiencing this discordant pattern of family coverage were more likely Hispanic (odds ratio [OR] 1.58; 95% confidence interval [CI], 1.23 to 2.03) compared to white, non-Hispanic; low and middle income (OR 2.02; 95% CI, 1.42 to 2.88; and OR 1.48; 95% CI, 1.09 to 2.03, respectively) compared to high income; from single-parent homes (OR 1.99; 95% CI 1.59 to 2.49) compared to children living with two married parents; and living with parents who had less than a high school education (OR 1.44; 95% CI 1.10 to 1.89) compared to those with at least one parent who had completed high school. Children whose parents had public coverage were less likely to be uninsured (OR 0.64; 95% CI 0.43 to 0.96) compared to those whose parents reported private health insurance. These predictors remained significant in full-year analyses. Similar patterns of vulnerability were also found among a subset of uninsured children with privately-covered parents.
Predictors of uninsurance among children with at least one insured parent included: having low and middle household incomes, being of Hispanic origin, having parents who did not complete high school, and living in a single-parent household. Having a parent covered by only public insurance was associated with better children’s coverage rates.
Over 9 million children in the US have no health insurance coverage.1, 2 When including children with a coverage gap at some point during the year, the number of uninsured children doubles.3-6 It is estimated that almost three-quarters of these uninsured children qualify for public insurance coverage.7-9 The importance of covering these children is well established.10 Children with stable health insurance coverage have more consistent access to healthcare services, which contribute to better health outcomes.10-14 Discontinuities in children’s health insurance coverage, even for only a few months, are associated with significant unmet healthcare needs.15, 16
States seeking to maximize rates of continuous coverage for eligible children have sought federal waivers to expand Medicaid coverage to low-income parents. When public health insurance is extended to parents, children eligible for public insurance have more stable coverage.17-22 Conversely, when state Medicaid programs have scaled back parental benefits, coverage for children in low-income families has been adversely affected.16
The association between coverage for children and parents has been widely reported.20, 25, 26 In a recent Oregon study, children eligible for public insurance who had uninsured parents were fourteen times more likely to be uninsured, compared to their counterparts with insured parents.16 When entire families have access to health insurance, children not only benefit from more consistent insurance coverage but also have improved access to a regular source of care and higher rates of preventive services.7, 19, 22-24 If entire families cannot gain consistent coverage, it is most often the children who have insurance and the parents who go uncovered, especially since the creation of the State Children’s Health Insurance Program.27, 28 Less is known, however, about how commonly this pattern is reversed. How many children in families with covered parents are uninsured? And, what individual and family characteristics predispose children to being left behind in these families who have managed to gain some coverage?
During the recent debates to reauthorize the State Children’s Health Insurance Program (SCHIP), much of the spotlight has focused on the subset of families with child-only coverage who would benefit from expanding health insurance to low-income parents.29-31 The primary objective of this paper is to examine the other subset of families experiencing a breakdown in the parent/child health insurance link—those with uninsured children who have at least one insured parent. Using nationally-representative pooled data from four years of the Medical Expenditure Panel Survey (2002-2005), our study aimed to identify the demographic and socioeconomic characteristics of children more likely to be in families with parent-only coverage.
This study was a secondary analysis of data obtained from the Medical Expenditure Panel Survey-Household Component (MEPS-HC) files, sponsored and made available to the public by the Agency for Health Care Research and Quality (AHRQ).32 The MEPS-HC survey collects data from a subsample of the National Health Interview Survey and utilizes a stratified and clustered random sample with weights that produce nationally representative estimates for insurance coverage and a wide range of health-related demographic and socioeconomic characteristics for the civilian, non-institutionalized US population.33-35 The MEPS-HC collects data from the same households interviewed five times over a two-year period, and certain groups (e.g., low income, racial minorities) are over-sampled. While MEPS-HC respondents are interviewed at various times, they are asked specific questions about insurance status information month-by-month and as of December 31st of each year.
We combined four years of data from the MEPS-HC (2002-2005). These four years were chosen because 2005 is the most current data available, and the four years spanning 2002-2005 all have a common variance structure, making it easier to ensure compatibility and comparability of our specific variables of interest within the complex sample design of the MEPS. We also found no significant difference between the prevalence of uninsured children with insured parents when comparing each of the four years. While MEPS data are reported separately in yearly files, the overlapping panel design of the MEPS facilitates the combination of data from two overlapping panels for each year (e.g. data for 2002 combines the overlapping panels of 2001-2002 and 2002-2003). Our analysis included all children under the age of 19 in each of the yearly files (2002, 2003, 2004, 2005) with positive full-year weights who had at least one parent residing in the same household (total unweighted number=39,710; weighted average yearly population of over 72 million). Although MEPS defines a child as being under 18, we included 18 year-olds because the standard definitions of child eligibility in Medicaid and SCHIP include children aged 18 and younger.
Overall, we found no statistically significant difference between the insurance status of children living with one parent as compared to those living with two parents—the children’s point-in-time uninsurance rate was approximately 12% for both. So, we included all children in one group and moved forward in determining parental insurance status and linking each child to one or both parents. We constructed parent insurance variables using the MOPID##31X (mother identifier) and/or the DAPID##31X (father identifier) variables from MEPS, which include biological, adopted, and step parents. MEPS does not include variables for linking foster parents or non-parent guardians in this way. While family composition included many different combinations, there were three main categories, including: 2 married parents (n=26,001); single-parent with insurance information for a second linked parent (n=8,278); single-parent without insurance information about the second parent (5,431). We excluded 3,012 children with no linked parent identifier [the child was born after the data collection point (n=1490) and/or because the child was not living with either parent (n=1522)]. Of note, this excluded group had a significantly higher uninsurance rate overall; however, there was no way to link them to reliable parent information or partial/full-year insurance data. This group was also disproportionately poor and non-White, non-Hispanic.
Among the 34,279 children who could be linked to 2 parents, 72.2% had both parents insured all year, 23.9% had both parents not insured all year, and 3.9% had one parent insured and the other uninsured. Noting less than a 4% discrepancy between the insurance status of 2 linked parents, we created a variable representing “at least one parent insured” for the most consistency overall. Children were selected for inclusion into cross-sectional (point-in-time) and full year models based on having at least one insured parent. The point-in-time parental insurance status was based on whether at least one linked parent was insured on December 31 of the given year; full-year insured parental insurance status was based on whether at least one linked parent was insured during every month of the given year. We followed detailed programming instructions in the on-line MEPS user handbook and consulted with experts at AHRQ to construct variables and link files.36
Among children with at least one insured parent, the primary outcome was health insurance status. In cross-sectional analyses, we selected only children with at least one parent covered on December 31 of the given year and then used the same point-in-time variable to determine whether each child was uninsured or insured on December 31. For the full-year analyses, we selected children with at least one parent covered for the full year of interest and then we assessed whether or not each child had a health insurance coverage gap sometime during the given year. We created four child coverage gap variables using MEPS monthly insurance reports: children with full-year coverage (or full coverage during all available months), children without insurance for anywhere between 1 and 12 months of the year, children without insurance for greater than 6 months, and children without insurance coverage all year. Of note, these groups were not mutually exclusive, and coverage gaps were not necessarily continuous from one month to the next.
The conceptual model for predicting access to health care designed by Aday and Andersen was adapted to identify thirteen variables in the MEPS-HC dataset that might influence children’s access to health insurance coverage.37 We used two-tailed, chi-square bivariate analyses to test for significant association between potential predictors and the outcomes. Ten independent variables were significantly associated with at least one of the outcomes (p<0.10): household income, child’s age, child’s race/ethnicity, language spoken at home, parental education, family composition, region of residence, child’s health status, child’s chronic limitations, and parental employment. We included eight of these variables in the final bivariate and multivariable models (we found strong correlations between language at home and race/ethnicity as well as health status and chronic limitations).
Child’s race/ethnicity was self determined by parent respondents based on standard options provided by MEPS interviewers. We used one combined child race/ethnicity variable. The household income groups were based on the MEPS-HC constructed variable that divides families into five income groups based on earnings as a percentage of the federal poverty level (FPL), which takes into account income as well as household size and composition. The five groups included: poor (<100% FPL); near poor (100% to <125% FPL); low income (125% to <200% FPL); middle income (200% to <400% FPL); and high income (≥400% FPL). Noting similarities between children with a single parent, we created two family composition groups: children with a single parent (those who could be linked to one or both parents) and children with two married parents. In addition to the significantly associated covariates, we also kept type of parental coverage in the model because this variable has a strong conceptual relationship to family insurance patterns. We then conducted a series of multiple logistic regression analyses to assess the adjusted associations between demographic and socioeconomic characteristics and children’s uninsurance among all children with at least one insured parent. After noting the vulnerability of children with privately-covered parents, we conducted a similar post-hoc analysis on the subset of children with parents who had only private coverage.
We used SUDAAN Version 9.0.1 (Research Triangle Institute, Research Triangle Park, NC) for all statistical analyses to account for the complex sampling design of the MEPS; α level was set at .05 for all multivariable analyses. This study protocol was reviewed by the Oregon Health and Science University Institutional Review Board and deemed exempt.
Cross-sectionally, 77.4% of children in the United States were insured with at least one insured parent (unweighted n=27,528) and 8.5% were uninsured with uninsured parents (unweighted n=4,236). Insured children with uninsured parents represented approximately 10.8% of the population (unweighted n=6,444), and 3.3% of children had the discordant pattern of interest in this study: uninsured child with at least one insured parent (unweighted n=1,380, representing, when weighted, an average annual population of over 2.3 million children). In full-year estimates, 4.1% of children with a parent insured all year had a health insurance coverage gap (unweighted n=1,716, average weighted annual population of nearly 3 million). An estimated 1.6% of these children were uninsured for the entire year (unweighted n=653, average weighted annual population of over one million) (Table 1). The weighted cross-sectional estimates suggest that at any point-in-time, over a quarter of uninsured children in the US between 2002 and 2005 had at least one parent with health insurance coverage.
Among children with an insured parent, the distribution of certain demographic and socioeconomic characteristics was different when comparing the insured versus the uninsured children (Table 2). At one point in time, a higher percentage of uninsured children (21.9%) were from low-income families, compared to those with insurance coverage (13.2%). In contrast, a lower percentage of uninsured children (21.7%) were in high-income families as compared to the percentage insured (33.9%). Nearly a quarter of uninsured children were of Hispanic origin (24.7%), compared to only 14.2% of the insured children. The percentage of uninsured children living in single-parent households (39.9%) was also significantly higher than the percentage of insured children (25.3%). Higher percentages of uninsured children lived in the South (39.5%) or the Western United States (29.9%) as compared with insured children in the South (32.6%) or the West (23.4%). There was a higher percentage of insured children with excellent health status (49.8%), compared with uninsured (44.7%), and 88.1% of insured children had at least one parent who had completed high school as compared with only 80.3% of uninsured children. These patterns persisted when comparing insured versus uninsured children in the full-year analyses.
Table 3 shows the cross-sectional and full year multivariable comparisons revealing several factors that were consistently associated with a higher likelihood that the child was uninsured, despite having at least one parent in the household with coverage. Cross-sectionally, children experiencing this discordant pattern of family coverage were more likely Hispanic (odds ratio [OR] 1.58; 95% confidence interval [CI], 1.23 to 2.03) compared to white, non-Hispanics; low and middle income (OR 2.02; 95% CI, 1.42 to 2.88 and OR 1.48; 95% CI, 1.09 to 2.03, respectively) compared to high income; from single-parent homes (OR 1.99; 95% CI 1.59 to 2.49) compared to children living with two married parents; and living with parents who had less than a high school education (OR 1.44; 95% CI 1.10 to 1.89) compared to those with at least one parent who had completed high school. Children whose parents had public coverage were less likely to be uninsured (OR 0.64; 95% CI 0.43 to 0.96) compared to those whose parents reported private health insurance. These patterns persisted throughout full-year models.
While the disparities in insurance coverage associated with ethnicity and parental educational attainment were not surprising, it was more interesting to note that low and middle income children were more vulnerable than the poorest and the richest subgroups in full-year analyses. For example, compared to high income children (reference group = 1.00), low income children were more likely to lack health insurance coverage for greater than 6 months (OR 1.73; 95% CI 1.18 to 2.55). Children from middle income families were also more likely than those from high income families to have long coverage gaps (OR 1.56; 95% CI 1.11 to 2.19). Middle income children were the most likely to have gone all year without coverage (OR 1.48; 95% CI 1.00 to 2.19). Although not evident at a point-in-time or all year, children from the two poorest groups were more likely than high income children to experience a coverage gap (OR 1.69; 95 % CI 1.11 to 2.59 for poor families; OR 2.15; 95% CI 1.33 to 3.49 for the near poor).
Parental type of coverage was also associated with different rates of children’s coverage. Compared to children whose parents had any private insurance (reference group = 1.00), those whose parents had only public insurance were less likely to be uninsured at a point-in-time (OR 0.64; 95% CI 0.43 to 0.96), to have any length coverage gap (OR 0.54; 95% CI0.37 to 0.78), and to have a coverage gap greater than six months of the year (OR 0.59; 95% CI 0.35 to 0.98). In the post-hoc analyses focusing on children whose parents reported only private coverage for the full 12 months of a given year, the predictors of children’s uninsurance were similar (Table 4).
While the largest group of uninsured children in the US is still those with uninsured parents, over 2 million uninsured children have a parent with health insurance. Some of the children in this study who lacked insurance coverage at one point-in-time or for less than 6 months might have been transitioning between plans or waiting to qualify for coverage. However, a child lacking coverage for greater than 6 months of the year suggests a chronic problem. This study found similar patterns of demographic and socioeconomic characteristics that predispose children to getting left behind in these families with covered parents, whether examining point-in-time, coverage gaps or a full year without coverage. Low and middle household incomes, low parental educational attainment, Hispanic ethnicity, single-parent households, geographic residence in the South or West, and having a parent with private insurance coverage were consistently associated with a higher likelihood that a child was uninsured.
While the primary approach to insuring families in the United States is still based on an employer-sponsored model complemented by public programs for the poorest families (e.g. Medicaid, SCHIP), the disparities that persist in children’s health insurance coverage associated with household income illustrate weaknesses within this current model. The vulnerability of low and middle income children with insured parents reflects the income gap between public and private coverage. While some of the low income, uninsured children likely qualify for public coverage, many of the families earning between 125 and 400 percent of the FPL fall into this gap—earning too much to qualify for public insurance, but not enough to afford private insurance for the entire family.27, 38 In 2005, the average single employee health insurance premium was $3,991 with workers paying approximately 18% of the cost ($723 yearly). The average family premium was $11,381 with workers required to pay over 25% of the cost ($2,890). In firms with over 50% low wage workers, employees paid even more to cover their families ($3,089 yearly).39 The working adults in these families may be able to afford private insurance for themselves but cannot afford to pay the premiums to cover their entire family. As one parent recently reported, uninsured children in this family income gap are the most vulnerable: “I was actually relieved when my husband lost his job because it made my son eligible for (public) coverage again. There is no feeling in the world worse than trying to figure out if you should really take an injured child to the doctor, or not, because of lack of money.”40 Further reliance on the current employer-sponsored private health insurance system may not be realistic, especially when the cost of a family health insurance premium is projected to exceed the average US household income by 2025.41
Among the group of uninsured children who qualify for public insurance, their parents may not be aware of their children’s eligibility or may be attempting to move away from receiving public benefits.7-9, 42 They may also have yearly income fluctuations that disqualify their children from continuous enrollment.7, 8, 43 And, each state has different income eligibility requirements, so children in a family earning 200 percent FPL would qualify for SCHIP in one state but not another.6, 9, 44 Another explanation unique to single parents stems from some state practices that aggressively track absent parents to recoup the costs of public insurance. Single parents who fear retaliation from an ex-partner whose wages are involuntarily garnered by the state may not choose to enroll their children. Therefore, some of the best short-term solutions to address the children’s uninsurance problem are certainly to increase public outreach and retention efforts to keep eligible children enrolled in public insurance benefits, to ease prohibitive barriers, and to expand the State Children’s Health Insurance Program, which has been successful at improving children’s insurance rates.9, 45
It has been difficult to implement these types of basic policy measures because of concerns about parents dropping their children from private insurance plans and migrating to public coverage unnecessarily (“crowd-out”). However, the large number of low and middle income children uninsured all year, despite having at least one fully insured parent, is evidence that children are being “crowded-out” by unaffordable private insurance and not by the allure of public coverage. If crowd-out was simply due to the availability of SCHIP, children in this study who qualified for SCHIP would have been insured publicly and those who did not qualify would have maintained private coverage. Even children from the poorest families in this study—those most likely to qualify for continuous public insurance—were more likely than high income children to be experiencing coverage gaps. While they were not going uncovered for long periods of time as were the low and middle income children, there is still work to be done to reduce high churning rates among the poor and near poor children in public insurance programs.6, 46
As longer term policies are developed to cover children being left behind by the current private health insurance market, the underlying philosophy inherent in a system that covers everyone in the family under the same plan may need further consideration before it gets left behind. Evidence suggests that when family members are covered separately under different plans or when certain individuals have coverage and others do not, children’s health suffers.21, 29, 47 Furthermore, if we abandon assumptions that insurance coverage is a “household good” and move further towards defining it as an “individual good,” we add layers of complexity for vulnerable families who must simultaneously learn different systems for enrollment and utilization of multiple insurance plans.48 Discordant patterns of family health insurance will become the norm rather than the exception; the current trend is certainly moving in that direction.49 While a good short-term fix, it is unclear whether expansions in child-only public insurance programs that largely exclude parents will serve as the best longer term solution. Among all children with an insured parent in this study, those whose parents had only public coverage were less likely to be uninsured (Table 3) confirming previous evidence that covering both parent and child in the same public program may lead to more stable children’s coverage.16, 19, 20, 23, 25, 26 This approach may also be the most economical.50
If we accept the premise that families are better off covered under one plan but reject a public health insurance program for all members of the family, we must again revisit whether the employer-based model is sustainable. To guage how well the private system provides full coverage for entire families, table 4 shows predictors of uninsurance among children in this study whose parents had only private coverage. Among this subgroup, not only the low and middle income children but all four groups below 400 percent FPL were more likely to be uninsured for greater than 6 months when compared to children in the highest income families. Interestingly, while these uninsured children are casualties of the current employer-sponsored system, some of them qualify for public coverage but are not consistently enrolled for reasons that are only partially understood.6, 23, 46, 49 These families may benefit from being able to purchase public coverage on a sliding-scale that would allow for fluidity of coverage with frequent fluctuations in family income and circumstances. Another possible policy intervention would be the expansion of partial assistance programs that help make private coverage more affordable for families who prefer coverage for everyone under one plan.23, 49 This approach, however, relies heavily on the current private insurance market and enables a flawed system to continue. The bolder alternative requires that we replace the current insurance paradigm with a new model, one which provides the most hope for achieving long-term sustainability and the best chance to keep both parents and children healthy.
Our results should be considered in the context of several limitations. First, secondary analyses rely on the methods used to gather information about households and the insurance status of each family member. For example, MEPS collects data on whether employers offer insurance without specific information about whether dependents are included in this offering, so we were unable to ascertain if the uninsured children were being offered employer-sponsored insurance. We also could not determine if the child had been denied individual coverage due to a pre-existing condition. In addition, we were unable to determine the insurance status of the second parent for 5,431 children in single-parent households, and we had no parental information on children living separately from both parents. While our findings show a significant problem even without including this data, our estimates do not account for some of the most vulnerable children, thus underestimating the problem. Second, as with all observational studies that rely on self-report, response bias remains a possibility. Third, although the MEPS-HC is representative of the civilian, non-institutionalized US population, the format of our analyses limits causal inferences.
Further reliance on the private health insurance market to provide all children with stable and affordable health insurance coverage may not be a realistic option. Incremental expansions in public insurance programs for children will continue to improve children’s insurance rates in the short term. However, the longer term solutions to keeping all children insured are likely to be more complicated. Unless health insurance coverage models are designed to keep entire families covered, some children will continue to get left behind. It is time to think beyond health insurance models to achieve a sustainable healthcare system and the best possible health outcomes for all families.
Jennifer DeVoe and Carrie Tillotson have had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Funding Sources: Dr. DeVoe’s time on this project was supported by grant number 1 K08 HS16181 from the Agency for Healthcare Research and Quality (AHRQ). Ms. Tillotson is employed by the Biostatistics Shared Resource of Oregon Health and Science University and by the Oregon Clinical and Translational Research Institute (OCTRI), grant number UL1 RR024140 01 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. These funding agencies had no involvement in the design and conduct of the study; analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. AHRQ collects and manages the Medical Expenditure Panel Survey.
Drs. Julie Hudson and Jessica Vistnes from the Agency for Healthcare Research and Quality were extremely helpful in guiding our statistical programming. We are grateful to Drs. Eun Sul Lee and Ed Fryer for sharing their biostatistical expertise. We also wish to acknowledge several anonymous reviewers who took time to share their valuable insights in order to further improve this manuscript.
Disclosures: We have no financial conflicts of interest to disclose.
Jennifer E. DeVoe, Department of Family Medicine, Oregon Health and Science University, 3181 Sam Jackson Park Rd, mailcode: FM, Portland, OR 97239, Phone 503-494-8936, Fax 503-494-2746, devoej/at/ohsu.edu.
Carrie Tillotson, Oregon Health and Science University, 3181 Sam Jackson Park Rd, Portland, OR 97239, Email: tillotso/at/ohsu.edu.
Lorraine S. Wallace, University of Tennessee Graduate School of Medicine, Department of Family Medicine, 1924 Alcoa Highway, U-67, Knoxville, TN 37920, Email: lwallace/at/mc.utmck.edu.