Public Health Insurance and Health Care Utilization for Children in Immigrant Families

Objectives To estimate the impacts of public health insurance coverage on health care utilization and unmet health care needs for children in immigrant families. Methods We use survey data from National Health Interview Survey (NHIS) (2001–2005) linked to data from Medical Expenditures Panel Survey (MEPS) (2003–2007) for children with siblings in families headed by at least one immigrant parent. We use logit models with family fixed effects. Results Compared to their siblings with public insurance, uninsured children in immigrant families have higher odds of having no usual source of care, having no health care visits in a 2 year period, having high Emergency Department reliance, and having unmet health care needs. We find no statistically significant difference in the odds of having annual well-child visits. Conclusions for practice Previous research may have underestimated the impact of public health insurance for children in immigrant families. Children in immigrant families would likely benefit considerably from expansions of public health insurance eligibility to cover all children, including children without citizenship. Immigrant families that include both insured and uninsured children may benefit from additional referral and outreach efforts from health care providers to ensure that uninsured children have the same access to health care as their publicly-insured siblings.     

Comparing oral health care utilization estimates in the United States across three nationally representative surveys

OBJECTIVE: To compare estimates of dental visits among adults using three national surveys. DATA SOURCES/STUDY DESIGN: Cross-sectional data from the National Health Interview Survey (NHIS), National Health and Nutrition Examination Survey (NHANES), and National Health Expenditure surveys (NMCES, NMES, MEPS). STUDY DESIGN: This secondary data analysis assessed whether overall estimates and stratum-specific trends are different across surveys. DATA COLLECTION: Dental visit data are age standardized via the direct method to the 1990 population of the United States. Point estimates, standard errors, and test statistics are generated using SUDAAN. PRINCIPAL FINDINGS: Sociodemographic, stratum-specific trends are generally consistent across surveys; however, overall estimates differ (NHANES III [364-day estimate] versus 1993 NHIS: -17.5 percent difference, Z = 7.27, p value < 0.001; NHANES III [365-day estimate] vs. 1993 NHIS: 5.4 percent difference, Z = -2.50, p value = 0.006; MEPS vs. 1993 NHIS: -29.8 percent difference, Z = 16.71, p value < 0.001). MEPS is the least susceptible to intrusion, telescoping, and social desirability. CONCLUSIONS: Possible explanations for discrepancies include different reference periods, lead-in statements, question format, and social desirability of responses. Choice of survey should depend on the hypothesis. If trends are necessary, choice of survey should not matter however, if health status or expenditure associations are necessary, then surveys that contain these variables should be used, and if accurate overall estimates are necessary, then MEPS should be used. A validation study should be conducted to establish "true" utilization estimates.     

Impact of health insurance status on vaccination coverage in children 19-35 months old, United States, 1993-1996

OBJECTIVES: To show how health insurance (privately and publicly insured, insured and uninsured) relates to vaccination coverage in children 19-35 months old, and how this can be used to better target public health interventions. METHODS: The National Health Interview Survey (NHIS) gathers information on the health and health care of the U.S. non-institutionalized population through household interviews. The authors combined immunization and health insurance supplements from the 1993 through 1996 NHIS, and classified children 19-35 months old by their immunization and insurance status. Results were compared using both bivariate and multivariate analyses, and the backwards stepwise selection method was used to build multivariate logistic regression models. RESULTS: Uninsured children tended to have lower vaccination coverage than those who had insurance, either private or public. Among those with insurance, publicly insured children had lower vaccination coverage than privately insured children. Backwards stepwise regression retained insurance status, metropolitan statistical area, and education of responsible adult family member as major predictors of immunization. Factors considered but not retained in the final model included child race/ethnicity, family poverty index, and region of country. CONCLUSIONS: Insurance status was a critical predictor of vaccination coverage for children ages 19-35 months. After controlling for confounders, the uninsured were about 24% less likely to receive all recommended shots than the insured and, among the insured, those with public insurance were about 24% less likely to receive all recommended vaccines than those with private insurance.     

HEALTH SERVICE USE AMONG THE PREVIOUSLY UNINSURED: IS SUBSIDIZED HEALTH INSURANCE ENOUGH?

Although it has been shown that gaining Medicare coverage at age 65 years increases health service use among the uninsured, difficulty in changing habits or differences in the characteristics of previously uninsured compared with insured individuals may mean that the previously uninsured continue to use the healthcare system differently from others. This study uses Medicare claims data linked to two different surveys—the National Health Interview Survey and the Health and Retirement Study—to describe the relationship between insurance status before age 65 years and the use of Medicare‐covered services beginning at age 65 years. Although we do not find statistically significant differences in Medicare expenditures or in the number of hospitalizations by previous insurance status, we do find that individuals who were uninsured before age 65 years continue to use the healthcare system differently from those who were privately insured. Specifically, they have 16% fewer visits to office‐based physicians but make 18% and 43% more visits to hospital emergency and outpatient departments, respectively. A key question for the future may be why the previously uninsured seem to continue to use the healthcare system differently from the previously insured. This question may be important to consider as health coverage expansions are implemented. Copyright © 2011 John Wiley & Sons, Ltd.     

The impact of survey design modifications on health insurance coverage estimates in a National Longitudinal Health Care Survey

National health insurance coverage estimates for the overall population and specific population subgroups are critical to policymakers and others concerned with access to medical care and the cost and sources of payment for that care. The Medical Expenditure Panel Survey (MEPS) is one of the core health care surveys in the United States that serves as a primary source for these essential national health insurance coverage estimates. The survey is designed to provide annual national estimates of the health care use, medical expenditures, sources of payment and insurance coverage for the U.S. civilian non-institutionalized population. In 2007, the survey experienced two dominant survey design modifications: (1) a new sample design attributable to the sample redesign of the National Health Interview Survey, and (2) an upgrade to the CAPI platform for the survey instrument, moving from a DOS to a Windows based environment. This study examines the impact of these survey design modifications on the national estimates of insurance coverage. The overlapping panel design of the MEPS survey and its longitudinal features are particularly well suited to assess the impact of survey redesign modifications on estimates. Since two independent nationally representative samples are pooled to produce calendar year estimates, one has the capacity to compare estimates based on the “original survey design” in contrast to those derived from the “survey redesign.” This paper examines the correlates of nonresponse incorporated in the estimation techniques and adjustment methods employed in the survey, and the measures utilized for post-stratification overall and by panel. Particular attention is given to assessing the level of convergence in coverage estimates based on the alternative designs as well as the alignment of model based analyses that discern which factors are associated with health insurance classifications. The paper concludes with a discussion of strategies under consideration that may yield additional improvements in the accuracy for these critical policy relevant survey estimates.     

Variation in preventive service use among the insured and uninsured: does length of time without coverage matter?

Lacking health insurance has consequences for the ways in which individuals seek care. In this research, the authors use data from the first panel (1996) of the Medical Expenditure Panel Survey to assess the relationship between preventive services and the length of time with insurance during a 12-month period. Regression analyses show that individuals with continuous coverage during the entire period have dramatically higher rates of preventive service use than individuals who lack coverage for all 12 months. For most services, the authors also find modest differences in preventive service use between the continually insured and those individuals with coverage for 1 to 6 months. Rates of preventive service use for individuals with 7 to 11 months of coverage are statistically indistinguishable from the continually insured. The authors' findings highlight the importance of considering the length of time without coverage when evaluating preventive service use of the uninsured population.     

Meeting the Need for State-Level Estimates of Health Insurance Coverage: Use of State and Federal Survey Data

Objective. Critically review estimates of health insurance coverage available from different sources, including the federal government, state survey initiatives, and foundation-sponsored surveys for use in state policy research.
Study Setting and Design. We review the surveys in an attempt to flesh out the current weaknesses of survey data for state policy uses. The main data sources assessed in this analysis are federal government surveys (such as the Current Population Survey's Annual Social and Economic Supplement, and the National Health Interview Survey), foundation-supported surveys (National Survey of America's Families, and the Community Tracking Survey), and state-sponsored surveys.
Principal Findings. Despite information on estimates of health insurance coverage from six federal surveys, states find the data lacking for state policy purposes. We document the need for state representative data on the uninsured and the recent history of state data collection efforts spurred in part by the Health Resources Services Administration State Planning Grant program. We assess the state estimates of uninsurance from the Current Population Survey and make recommendations for a new consolidated federal survey with better state representative data.
Conclusions. We think there are several options to consider for coordinating a federal and state data collection strategy to inform state and national policy on coverage and access.     

Recent Health Insurance Trends for US Families: Children Gain While Parents Lose

In the past decade, political and economic changes in the United States (US) have affected health insurance coverage for children and their parents. Most likely these policies have differentially affected coverage patterns for children (versus parents) and for low-income (versus high-income) families. We aimed to examine—qualitatively and quantitatively—the impact of changing health insurance coverage on US families. Primary data from interviews with Oregon families (2008–2010) were analyzed using an iterative process. Qualitative findings guided quantitative analyses of secondary data from the nationally-representative Medical Expenditure Panel Survey (MEPS) (1998–2009); we used Joinpoint Regression to assess average annual percent changes (AAPC) in health insurance trends, examining child and parent status and type of coverage stratified by income. Interviewees reported that although children gained coverage, parents lost coverage. MEPS analyses confirmed this trend; the percentage of children uninsured all year decreased from 9.6 % in 1998 to 6.1 % in 2009; AAPC = ?3.1 % (95 % confidence interval [CI] from ?5.1 to ?1.0), while the percentage of parents uninsured all year rose from 13.6 % in 1998 to 17.1 % in 2009, AAPC = 2.7 % (95 % CI 1.8–3.7). Low-income families experienced the most significant changes in coverage. Between 1998 and 2009, as US children gained health insurance, their parents lost coverage. Children’s health is adversely affected when parents are uninsured. Investigation beyond children’s coverage rates is needed to understand how health insurance policies and changing health insurance coverage trends are impacting children’s health.     

The impact of survey attrition on health insurance coverage estimates in a National Longitudinal Health Care Survey

Timely, accurate and reliable estimates of the population’s health insurance status are essential inputs to policymakers to inform assessments of the population’s access to medical care and analyses of associated health care expenditures. Alternative criteria that have been used to produce annual estimates of the uninsured include the following specifications: those uninsured for a full-year, those ever uninsured during a year, and those uninsured at a specific point in time. The Medical Expenditure Panel Survey (MEPS), one of the core health care surveys in the United States, supports all three types of estimates. In this paper, a summary is provided of the survey operations, informational materials, the interviewer training and experience of the field force, and the refusal conversion techniques employed in the MEPS to maintain respondent cooperation for five rounds of interviewing, to help minimize sample attrition. The impact of nonresponse attributable to survey attrition is also assessed with respect to the national health insurance coverage estimates derived from the MEPS. The study includes an examination of the quality of the nonresponse adjustments employed to adjust for potential nonresponse bias attributable to survey attrition. The overlapping panel design of the MEPS survey is particularly well suited to inform these studies. The presentation concludes with a discussion of strategies under consideration that may yield additional improvements in the accuracy for these critical policy relevant survey estimates.     

Affordability of the Health Expenditures of Insured Americans Before the Affordable Care Act

Central to the Affordable Care Act is the notion of affordability and the role of health insurance in making otherwise unaffordable health care affordable. We used data from the 1996 to 2008 versions of the Medical Expenditure Panel Survey to estimate the portion of overall health care expenditures by insured respondents that would otherwise have been beyond their disposable incomes and assets. We found that about one third of insured expenditures would have been unaffordable, with a much higher percentage among publicly insured individuals. This result suggests that one of the main functions of insurance is to cover expenses that insured individuals would not otherwise be able to afford.Central to the Affordable Care Act (ACA; Pub L No. 111–148) is the notion of affordability and the role of health insurance in making otherwise unaffordable health care affordable. Yet, to our knowledge, no reports in the health policy literature have estimated the extent to which insurance accomplishes this function. We used data from the 1996 to 2008 versions of the Medical Expenditure Panel Survey (MEPS) to estimate the portion of total health care expenditures by insured respondents that would have been beyond their disposable income and assets if they had been uninsured. We focused on the pre-ACA period because that period represents the political context in which the act was passed.The MEPS data include information on demographic characteristics, medical care expenditures, health insurance coverage, incomes, and assets among a representative sample of US households. The information used in our analysis was derived from the household component of the MEPS, which is limited to members of the civilian, noninstitutionalized population who were present in the household during the entire survey period. We employed the restricted-use version of the MEPS to gain access to information on respondents’ assets.     

On modelling response propensity for dwelling unit (DU) level non-response adjustment in the Medical Expenditure Panel Survey (MEPS)

Non-response is a common problem in household sample surveys. The Medical Expenditure Panel Survey (MEPS), sponsored by the Agency for Healthcare Research and Quality (AHRQ), is a complex national probability sample survey. The survey is designed to produce annual national and regional estimates of health-care use, expenditures, sources of payment, and insurance coverage for the U.S. civilian non-institutionalized population. The MEPS sample is a sub-sample of respondents to the prior year's National Health Interview Survey (NHIS) conducted by the National Center for Health Statistics (NCHS). The MEPS, like most sample surveys, experiences unit, or total, non-response despite intensive efforts to maximize response rates. This paper summarizes research on comparing alternative approaches for modelling response propensity to compensate for dwelling unit (DU), i.e. household level non-response in the MEPS.Non-response in sample surveys is usually compensated for by some form of weighting adjustment to reduce the bias in survey estimates. To compensate for potential bias in survey estimates in the MEPS, two separate non-response adjustments are carried out. The first is an adjustment for DU level non-response at the round one interview to account for non-response among those households subsampled from NHIS for the MEPS. The second non-response adjustment is a person level adjustment to compensate for attrition across the five rounds of data collection. This paper deals only with the DU level non-response adjustment. Currently, the categorical search tree algorithm method, the chi-squared automatic interaction detector (CHAID), is used to model the response probability at the DU level and to create the non-response adjustment cells. In this study, we investigate an alternative approach, i.e. logistic regression to model the response probability. Main effects models and models with interaction terms are both evaluated. We further examine inclusion of the base weights as a covariate in the logistic models. We compare variability of weights of the two alternative response propensity approaches as well as direct use of propensity scores.The logistic regression approaches produce results similar to CHAID; however, using propensity scores from logistic models with interaction terms to form five classification groups for weight adjustment appears to perform best in terms of limiting variability and bias. Published in 2007 by John Wiley & Sons, Ltd.     

Health Insurance Coverage and Mortality Revisited

Objective. To improve understanding of the relationship between lack of insurance and risk of subsequent mortality. Data Sources. Adults who reported being uninsured or privately insured in the National Health Interview Survey from 1986 to 2000 were followed prospectively for mortality from initial interview through 2002. Baseline information was obtained on 672,526 respondents, age 18–64 at the time of the interview. Follow‐up information on vital status was obtained for 643,001 (96 percent) of these respondents, with approximately 5.4 million person‐years of follow‐up. Study Design. Relationships between insurance status and subsequent mortality are examined using Cox proportional hazard survival analysis. Principal Findings. Adjusted for demographic, health status, and health behavior characteristics, the risk of subsequent mortality is no different for uninsured respondents than for those covered by employer‐sponsored group insurance at baseline (hazard ratio 1.03, 95 percent confidence interval [CI], 0.95–1.12). Omitting health status as a control variable increases the estimated hazard ratio to 1.10 (95 percent CI, 1.03–1.19). Also omitting smoking status and body mass index increases the hazard ratio to 1.20 (95 percent CI, 1.15–1.24). The estimated association between lack of insurance and mortality is not larger among disadvantaged subgroups; when the analysis is restricted to amenable causes of death; when the follow‐up period is shortened (to increase the likelihood of comparing the continuously insured and continuously uninsured); and does not change after people turn 65 and gain Medicare coverage. Conclusions. The Institute of Medicine's estimate that lack of insurance leads to 18,000 excess deaths each year is almost certainly incorrect. It is not possible to draw firm causal inferences from the results of observational analyses, but there is little evidence to suggest that extending insurance coverage to all adults would have a large effect on the number of deaths in the United States.     

Children’s Receipt of Health Care Services and Family Health Insurance Patterns

PURPOSE Insured children in the United States have better access to health care services; less is known about how parental coverage affects children’s access to care. We examined the association between parent-child health insurance coverage patterns and children’s access to health care and preventive counseling services.METHODS We conducted secondary analyses of nationally representative, cross-sectional, pooled 2002–2006 data from children (n = 43,509), aged 2 to 17 years, in households responding to the Medical Expenditure Panel Survey (MEPS). We assessed 9 outcome measures pertaining to children’s unmet health care and preventive counseling needs.RESULTS Cross-sectionally, among US children (aged 2 to 17 years) living with at least 1 parent, 73.6% were insured with insured parents, 8.0% were uninsured with uninsured parents, and the remaining 18.4% had discordant family insurance coverage patterns. In multivariable analyses, insured children with uninsured parents had higher odds of an insurance coverage gap (odds ratio [OR] = 2.45; 95% confidence interval [CI], 2.02–2.97), no usual source of care (OR = 1.31; 95% CI, 1.10–1.56), unmet health care needs (OR = 1.11; 95% CI, 1.01–1.22), and having never received at least 1 preventive counseling service (OR = 1.20; 95% CI, 1.04–1.39) when compared with insured children with insured parents. Insured children with mixed parental insurance coverage had similar vulnerabilities.CONCLUSIONS Uninsured children had the highest rates of unmet needs overall, with fewer differences based on parental insurance status. For insured children, having uninsured parents was associated with higher odds of going without necessary services when compared with having insured parents.     

Do people shift their use of health services over time to take advantage of insurance?

This paper provides a test of the hypothesis that people shift their consumption of health services to time periods when they have more generous insurance coverage, in order to take advantage of third-party payment. We use data from the Survey of Income and Program Participation to compare utilization rates for people in transition between being insured and being uninsured to those of people who are continuously insured and continuously uninsured. We find little support for the hypothesis that people anticipate changes in their insurance status and arrange their health care consumption accordingly.     

American Indian/Alaska Native Uninsurance Disparities: A Comparison of 3 Surveys

Objectives. We examined whether 3 nationally representative data sources produce consistent estimates of disparities and rates of uninsurance among the American Indian/Alaska Native (AIAN) population and to demonstrate how choice of data source impacts study conclusions.Methods. We estimated all-year and point-in-time uninsurance rates for AIANs and non-Hispanic Whites younger than 65 years using 3 surveys: Current Population Survey (CPS), National Health Interview Survey (NHIS), and Medical Expenditure Panel Survey (MEPS).Results. Sociodemographic differences across surveys suggest that national samples produce differing estimates of the AIAN population. AIAN all-year uninsurance rates varied across surveys (3%–23% for children and 18%–35% for adults). Measures of disparity also differed by survey. For all-year uninsurance, the unadjusted rate for AIAN children was 2.9 times higher than the rate for White children with the CPS, but there were no significant disparities with the NHIS or MEPS. Compared with White adults, AIAN adults had unadjusted rate ratios of 2.5 with the CPS and 2.2 with the NHIS or MEPS.Conclusions. Different data sources produce substantially different estimates for the same population. Consequently, conclusions about health care disparities may be influenced by the data source used.Access to quality health care is a priority for the nation. Access to such care is designated in Healthy People 2010 as one of the 10 Leading Health Indicators, marking it as a priority area for improving the health of the nation¹ and reducing health disparities.² American Indians/Alaska Natives (AIANs) are one group that continues to have substantial health disparities compared with other racial groups.^3–8 However, disparities in health care coverage and access for AIANs have received only intermittent attention,^9–13 leaving a marked gap in our understanding. Previously documented issues for research on AIAN health care disparities include gaps in data availability for AIANs^14,15 as well as problems with national-level estimates masking the differences across geographic areas.^13,16 However, it is also possible that there are differences in the magnitude of estimates or the conclusions drawn, depending on which data source is used to examine health care disparities.Because no single data source contains all possible measures of health and health care, different data sources are often used to answer complementary but different questions. In the case of national surveillance and annual snapshot reports, information from numerous data sources are used to present a more complete picture of health for the US population. Healthy People 2010 uses National Health Interview Survey (NHIS) data to monitor insurance coverage and access to a usual source of care and uses National Vital Statistics System data to monitor access to prenatal care.¹ In the chapter on access to care, the National Healthcare Disparities Report also uses NHIS data to examine uninsurance and access to a usual source of care but uses the Medical Expenditure Panel Survey (MEPS) to examine all-year uninsurance and access to a primary care provider.¹⁷ A few recent studies that examined health care access for AIANs used other data sources, such as the National Survey of America''s Families¹² or the Behavioral Risk Factor Surveillance Survey.¹³We use 3 general population surveys commonly used for health care coverage and access research to examine the implications of using different data sources for estimating health care disparities specific to AIANs. We use uninsurance disparities as an example but acknowledge at the outset that different data sources measure insurance coverage in different ways. Our purpose is not to critically review measures of uninsurance or to critique the surveys that collect these data. Rather, we aim to demonstrate that choice of data source matters for disparities research, often for a variety of reasons. Our intent is 2-fold: (1) to examine whether 3 nationally representative data sources produce trustworthy and consistent estimates of the AIAN population in the United States and (2) to highlight the impact that choice of data source can have on conclusions about uninsurance disparities.     

Health service access, use, and insurance coverage among American Indians/Alaska Natives and Whites: what role does the Indian Health Service play?

OBJECTIVES: We compared access and utilization of health services among American Indians/Alaska Natives (AIANs) with that among non-Hispanic Whites. METHODS: We used data from the 1997 and 1999 National Survey of America's Families to estimate odds ratios for several measures of access and utilization and the effects of Indian Health Service (IHS) coverage. RESULTS: AIANs had less insurance coverage and worse access and utilization than Whites. Over half of low-income uninsured AIANs did not have access to the IHS. However, among the low-income population, AIANs with only IHS access fared better than uninsured AIANs and as well as insured Whites for key measures but received less preventive care. CONCLUSIONS: The IHS partially offsets lack of insurance for some uninsured AIANs, but important needs were potentially unmet.     

Uninsured rural families.

CONTEXT: Although research shows higher uninsured rates among rural versus urban individuals, prior studies are limited because they do not examine coverage across entire rural families. PURPOSE: This study uses the Medical Expenditure Panel Survey (MEPS) to compare rural and urban insurance coverage within families, to inform the design of coverage expansions that build on the current rural health insurance system. METHODS: We pooled the 2001 and 2002 MEPS Household Component survey, aggregated to the family level (excluding households with all members 65 and older). We examined (1) differences in urban, rural-adjacent, and rural nonadjacent family insurance coverage, and (2) the characteristics of rural families related to their patterns of coverage. FINDINGS: One out of 3 rural families has at least 1 uninsured member, a rate higher than for urban families-particularly in nonadjacent counties. Yet, three fourths of uninsured rural families have an insured member. For 42% of rural nonadjacent families, this is someone with public coverage (Medicaid/SCHIP or Medicare); urban families are more likely to have private health insurance or a private/public mix. CONCLUSIONS: Strategies to expand family coverage through employers may be less effective among rural nonadjacent than urban families. Instead, expansions of public coverage or tax credits enabling entire families to purchase an individual/self-employment plan would better ensure that rural nonadjacent families achieve full coverage. Subsidies or incentives would need to be generous enough to make coverage affordable for the 52% of uninsured rural nonadjacent families living below 200% of the federal poverty level.     

A comparison of national estimates from the National Health Interview Survey and the Behavioral Risk Factor Surveillance System

OBJECTIVES: The purpose of this study was to compare national estimates from the National Health Interview Survey (NHIS) and the Behavioral Risk Factor Surveillance System (BRFSS). METHODS: The authors compared data from the 2 surveys on smoking, height, weight, body mass index, diabetes, hypertension, immunization, lack of insurance coverage, cost as a barrier to medical care, and health status. RESULTS: Overall national estimates were similar for 13 of the 14 measures examined. Small differences according to demographic characteristics were found for height and body mass index, with larger differences for health status. CONCLUSIONS: Although estimates differed within subgroups, the BRFSS provided national estimates comparable to those of the NHIS. BRFSS national data could provide rapidly available information to guide national policy and program decisions.     

Does Health Insurance Continuity Among Low-income Adults Impact Their Children’s Insurance Coverage?

Parent’s insurance coverage is associated with children’s insurance status, but little is known about whether a parent’s coverage continuity affects a child’s coverage. This study assesses the association between an adult’s insurance continuity and the coverage status of their children. We used data from a subgroup of participants in the Oregon Health Care Survey, a three-wave, 30-month prospective cohort study (n = 559). We examined the relationship between the length of time an adult had health insurance coverage and whether or not all children in the same household were insured at the end of the study. We used a series of univariate and multivariate logistic regression models to identify significant associations and the rho correlation coefficient to assess collinearity. A dose response relationship was observed between continuity of adult coverage and the odds that all children in the household were insured. Among adults with continuous coverage, 91.4% reported that all children were insured at the end of the study period, compared to 83.7% of adults insured for 19–27 months, 74.3% of adults insured for 10–18 months, and 70.8% of adults insured for fewer than 9 months. This stepwise pattern persisted in logistic regression models: adults with the fewest months of coverage, as compared to those continuously insured, reported the highest odds of having uninsured children (adjusted odds ratio 7.26, 95% confidence interval 2.75, 19.17). Parental health insurance continuity is integral to maintaining children’s insurance coverage. Policies to promote continuous coverage for adults will indirectly benefit children.     

How Well Does Medicaid Work in Improving Access to Care?

OBJECTIVE: To provide an assessment of how well the Medicaid program is working at improving access to and use of health care for low-income mothers. DATA SOURCE/STUDY SETTING: The 1997 and 1999 National Survey of America's Families, with state and county information drawn from the Area Resource File and other sources. STUDY DESIGN: Estimate the effects of Medicaid on access and use relative to private coverage and being uninsured, using instrumental variables methods to control for selection into insurance status. DATA COLLECTION/EXTRACTION METHOD: This study combines data from 1997 and 1999 for mothers in families with incomes below 200 percent of the federal poverty level. PRINCIPAL FINDINGS: We find that Medicaid beneficiaries' access and use are significantly better than those obtained by the uninsured. Analysis that controls for insurance selection shows that the benefits of having Medicaid coverage versus being uninsured are substantially larger than what is estimated when selection is not accounted for. Our results also indicate that Medicaid beneficiaries' access and use are comparable to that of the low-income privately insured. Once insurance selection is controlled for, access and use under Medicaid is not significantly different from access and use under private insurance. Without controls for insurance selection, access and use for Medicaid beneficiaries is found to be significantly worse than for the low-income privately insured. CONCLUSIONS: Our results show that the Medicaid program improved access to care relative to uninsurance for low-income mothers, achieving access and use levels comparable to those of the privately insured. Our results also indicate that prior research, which generally has not controlled for selection into insurance coverage, has likely understated the gains of Medicaid relative to uninsurance and overstated the gains of private coverage relative to Medicaid.