563173
research-article2014
MCRXXX10.1177/1077558714563173Medical Care Research and ReviewAbramowitz and O’Hara
Data and Trends
The Financial Burden of Medical Spending: Estimates and Implications for Evaluating the Impact of ACA Reforms
Medical Care Research and Review 2015, Vol. 72(2) 187–199 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1077558714563173 mcr.sagepub.com
Joelle Abramowitz1 and Brett O’Hara1
Abstract This article uses the 2013 Current Population Survey Annual Social and Economic Supplement to estimate the financial burden of medical out-of-pocket costs by comparing medical out-of-pocket expenditures to income. This measure is important for evaluating the magnitude of burden, better understanding who bears it, and establishing a baseline to assess the impact of the Patient Protection and Affordable Care Act. We examine the distribution of burden and the incidence of high burden across all families and by individuals’ health insurance status and demographic and socioeconomic characteristics. We look more closely at one group vulnerable to having high burden: those younger than age 65 with incomes between 138% and 200% of the federal poverty line. We find that 18.5% of these individuals have incomes below the threshold of expansion Medicaid eligibility after accounting for non-overthe-counter medical expenses and examine the characteristics associated with being classified below this threshold. Keywords burden, out-of-pocket medical spending, Affordable Care Act
This article, submitted to Medical Care Research and Review on June 30, 2014, was revised and accepted for publication on November 10, 2014. 1U.S.
Census Bureau, Washington, DC, USA
Corresponding Author: Joelle Abramowitz, U.S. Census Bureau, 4600 Silver Hill Road, SEHSD, HQ-7H168-E, Washington, DC 20233, USA. Email: [email protected]
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Introduction Many families face adverse health events and as a result incur large medical expenditures. High out-of-pocket costs are incurred both by families where some or all members are uninsured and by families that, though covered by health insurance, pay an unaffordable share of their family income in health insurance premiums and cost sharing. Further, lingering effects of the recession on unemployment, underemployment, and real household income impair the ability of individuals and families, particularly those with low incomes, to maintain health insurance coverage and manage their medical expenditures. It is important to examine the financial burden imposed by medical costs on individuals and families to evaluate the magnitude of the problem, to better understand who bears those burdens, and to establish a baseline to assess the impact of the Patient Protection and Affordable Care Act (ACA) on family financial burden, particularly for those with large expenses and low incomes. A number of the reforms of the ACA have the potential, over the near and long term, to substantially relieve the burden of high out-of-pocket costs through different channels. Some states have extended Medicaid eligibility to individuals with family income below 138% of the federal poverty line (FPL). For individuals who do not qualify for Medicaid, health insurance exchanges facilitate the purchase of private plans and provide subsidies based on income. For example, for those with family income between 133% and 150% of the FPL, premium contributions are limited to between 3.0% and 4.0% of income, and for those with family income between 150% and 200% of the FPL, premium contributions are limited to between 4.0% and 6.3% of income (Collins Robertson, Garber, & Doty, 2012). In addition, beginning in 2014, the ACA also limits the amount of out-of-pocket cost-sharing for plans purchased through the health insurance marketplace (i.e., on the exchange), and these limits are also based on income: Expenditures are limited to $1,983 for individuals and $3,967 for families earning between 100% and 200% of the FPL, not including premiums (Collins et al., 2012). These provisions, among others, have the potential to ease family financial burden related to medical expenditures, especially among those with low incomes. While a number of studies have investigated the potential effects of state Medicaid expansion (Caswell, Waidmann, & Blumberg, 2013, for example), this analysis considers another vulnerable group: those younger than age 65 with incomes below 200% but above 138% of the FPL, the cutoff for Medicaid eligibility. We consider this particular group because these individuals are ineligible for state Medicaid expansion but are poised to see a substantial impact of other ACA reforms. In this article, we examine how some of the other major provisions of the ACA, such as premium subsidies and maximum limits on out-of-pocket expenditures, might affect this vulnerable group. To measure the financial burden of medical out-of-pocket costs, we compare an individual’s actual family medical out-of-pocket expenditures (MOOP) to his or her family income using the 2013 Current Population Survey Annual Social and Economic Supplement1 (CPS ASEC). We first examine the distribution of MOOP across all families and by the health insurance status and demographic and socioeconomic
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characteristics of individuals in those families and measure the percentage of families who face the highest burdens (those with MOOP greater than 10% of family income). Next, we consider the potential for the new ACA provisions to affect those younger than age 65 with income below 200% but above 138% of the FPL by examining the characteristics of individuals most likely to be facing financial distress from medical expenses. We use the Medicaid benefit eligibility cutoff (138% of the FPL) as a proxy for economic hardship. To consider who in this group is likely to be affected by the implementation of the premium subsidies, we examine the correlates of falling below the Medicaid benefit eligibility cutoff after accounting for premium expenses. To consider who in this group is likely to be affected by the implementation of the limits on MOOP, we examine the correlates of falling below the Medicaid benefit eligibility cutoff after accounting for nonpremium MOOP. To consider the overall impact of these reforms, we examine the correlates of falling below the Medicaid benefit eligibility cutoff after accounting for both premium and nonpremium medical expenditures.
New Contribution This article considers the potential impact of ACA reforms on low-income, high-burden families who remain ineligible for Medicaid, which complements work assessing the potential impact of state Medicaid expansion. In particular, the study examines the factors associated with falling below the threshold of Medicaid eligibility after accounting for different types of medical expenses, which is relevant for considering the characteristics of individuals likely to be affected by the insurance coverage, subsidy options, and out-of-pocket spending limits created by the ACA. This article also builds on previous work in the literature on underinsurance that uses the Medical Expenditure Panel Survey (MEPS) and the Commonwealth Fund Health Insurance Survey (Banthin & Bernard, 2006; Banthin, Cunningham, & Bernard, 2008; Cunningham, 2012; Schoen, Doty, Robertson, & Collins, 2011; Short & Banthin, 1995) by estimating a measure of medical care economic burden using the CPS ASEC and examining the relationship between burden and insurance, demographic, and geographic characteristics. In addition, this article builds on previous work examining medical expenditures and the classification of poverty (Caswell & Short, 2011; O’Hara, 2004) by considering how different types of medical expenditures contribute to economic hardship.
Data and Methods The analysis sample consists of the 2013 CPS ASEC, a nationally representative survey of the civilian noninstitutionalized population living in the United States, covering the 2012 calendar year. We use 2013 data rather 2014 data due to a change in sample size in 2014: In 2013, approximately 80,000 households were interviewed representing approximately 200,000 individuals, whereas in 2014, only approximately 50,000 households were interviewed with uniform income questions. This analysis uses questions introduced in the 2011 CPS ASEC such as amounts paid for health insurance
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premiums, medical out-of-pocket payments, spending on over-the-counter (OTC) health-related products, and total spending on medical care were collected (U.S. Census Bureau and Bureau of Labor Statistics, 2011). The addition of these variables allows for computation of medical care economic burden using the CPS ASEC. There are several benefits to using the CPS ASEC as compared to other data sources. The CPS ASEC is conducted annually and is large enough to be used to produce state-level estimates. This is particularly beneficial for future research considering different characteristics of ACA implementation across states. The smaller sample size of other surveys, such as the MEPS and Commonwealth Fund Health Insurance Survey, prohibits the production of state-level estimates. In addition, the timeliness of estimates from the MEPS and low response rates in the Commonwealth Fund Health Insurance Survey are concerns for future research. Although the MEPS collects more detailed and precise information on MOOP than does the CPS ASEC, previous work has shown the comparability of the CPS ASEC MOOP data to the MEPS (Caswell & O’Hara, 2010) and the comparability of MEPS to other data sources (Foster, 2010). In addition, while the CPS ASEC does not collect information related to the use of specific medical services, self-reported health is collected as a measure of health status. Given all of these considerations, the CPS ASEC is used in this analysis and is poised to provide an important estimate of burden in the future. MOOP include such expenses as the individual’s portion of insurance premiums and payments and copayments for hospital visits, medical providers, dental service, prescription medications, vision aids, medical supplies, and OTC health-related items. In 2012, spending on OTC health-related items made up 18.8% of nonpremium medical spending. We impute out-of-pocket premium expenses for persons who did not report paying for any premium expenses but reported that their employer paid for some or none of their employer-sponsored insurance or reported directly purchasing their insurance on the market (Janicki, O’Hara, & Zawacki, 2013). Out-of-pocket premium expenses were imputed for 5.7% of observations in the final sample. We also impute Medicare Part B premiums and add them to MOOP for individuals for whom these premiums were included in income but not in the initial MOOP amount. Medicare Part B premiums were imputed for 11.1% of observations in the final sample. Income is measured before taxes. Results were also estimated using income after deducting imputed federal and state taxes as a robustness check and were not significantly different from each other in most cases. Both MOOP and income numbers cover the 2012 calendar year. In all analyses, standard errors are calculated using replicate weighting methods. This analysis calculates burden by comparing family MOOP to family income for each individual in the sample. For the purposes of this analysis, the family is defined as the health insurance unit as outlined by the State Health Access Data Assistance Center (State Health Access Data Assistance Center, 2012). MOOP and income are calculated at the level of the health insurance unit rather than the level of the individual because the health insurance unit more accurately reflects the relevant unit of analysis for insurance coverage and determining eligibility for public programs as well the level at which financial burdens are shared and experienced and decisions about spending on medical expenditures are made. The original sample consisted of 202,269
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60.0%
Proporon of Families
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
Burden Percentage
Figure 1. Distribution of Nonzero Family Burden Values. Source: 2013 CPS ASEC.
individuals. Families with self-employed individuals were dropped from the analysis because these individuals could earn negative income (n = 182,582). Families with MOOP of greater than $200,000 were dropped from the analysis because these MOOP values seemed implausibly large as amounts paid out of pocket (n = 182,573). Families with zero or negative income were dropped from the analysis because it was not possible to calculate burden for these families (n = 173,557). Families with incomes of $100 or less were dropped from the analysis because these families’ resulting burden estimates were unrepresentative of the sample (n = 173,138).2 The final sample consists of 173,138 individuals corresponding to 88,246 families.
Results Distribution of MOOP and Burden The distribution of MOOP values is very skewed. Many families report zero MOOP, while a small number of families report extremely large MOOP. Approximately 6.9% of families have MOOP values equal to zero. As with the distribution of MOOP, the distribution of burden is also skewed: many families have no or low burden, but a small number of families have very high burden. Figure 1 presents the distribution of nonzero burden values.
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Descriptive Characteristics of Burden We first examine the mean and 50th, 90th, and 95th percentiles for burden for the full analysis sample and by various characteristics. Table 1 presents results for the full sample and for select characteristics. Each set of characteristics represent mutually exclusive attributes. Mean estimates are presented for comparison purposes, but the percentile estimates provide a more revealing depiction of the burden distribution. The mean burden for the full sample is 12.1%. Fifty percent of individuals have burden of less than 4.7%, and the top 5% have burden of greater than 32.7%. The main factors appearing to be correlated with higher burden include age (being older than 65), having a disability, reporting poor health, having low income (less than 138% of the FPL), and insurance type (direct purchase insurance). Race/ethnicity,3 geography, and marriage/family size factors are correlated with much smaller differences in mean burden between groups. Since burden is calculated at the family level but we examine individual-level characteristics, estimates for individuals by particular characteristics also reflect to some extent the burden of the family members of those individuals. In addition, it is important to note that burden could reflect both disparities in health status as well as disparities in the willingness to seek medical care.
A Descriptive Look at the Percentage With High Burden To obtain a fuller picture of the impact of burden on individuals, we next examine the percentage of individuals in families with high burden, a measure that has been used throughout the literature as a marker of underinsurance (Cunningham, 2012; Schoen et al., 2011). Following the literature, an individual is considered to have high burden if his or her family burden is 10% or more of his or her family income. To compare our data with results in the literature, we use the 2011 CPS ASEC data covering the 2010 calendar year applying the same sample criteria as outlined before to estimate the percentage with high burden including and excluding health insurance premiums and expenses from health-related OTC products. Results are presented in Table 2. We find that the percentage of all individuals having high burden including health insurance premiums is 26.9% including OTC expenses and 24.1% excluding OTC expenses. Examining this estimate by age group, 19.2% of individuals younger than 65 have high burden including premiums and excluding OTC expenses, similar to the comparable 2009 estimate of 18.8% using MEPS (Cunningham, 2012).4 We also find that 8.2% of individuals ages 19–64 have high burden excluding premiums which is lower than the comparable 2010 estimate of 17.6% using Commonwealth Fund Health Insurance Survey data (Schoen et al., 2011).
Medical Expenses and Economic Hardship Next, we examine the characteristics of individuals experiencing economic hardship as a result of different types of medical expenses, providing insight into the characteristics of individuals likely to be affected by different ACA provisions. We use the Medicaid benefit eligibility cutoff (138% of the FPL) as a proxy for economic
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Full sample Health insurance Direct purchase only type Employer only All paid by employer Some paid by employer None paid by employer Combination of government Medicaid only Medicare only Military only VA health care TRICARE/other military Employer and direct Private and government Uninsured Race/ethnicity Asian, not Hispanic Black, not Hispanic Hispanic Other, not Hispanic White, not Hispanic Disability status No disability Disability Nativity status Native Not native Marital status Single (age ≥ 15) Married Age group 0–18 19–25 26–64 65+ HHS poverty < 1.38 of poverty line guidelines ≥ 1.38, < 2 ≥ 2, < 2.5 ≥ 2.5, < 3 ≥ 3, < 4 ≥4 Region Midwest Northeast South West Metropolitan Inside principal city status Outside principal city Not in a CBSA/MSA Family size Two people or more Single unit Self-reported Excellent health status Very good Good Fair Poor
N
Mean
50th Percentile
90th Percentile
173,138 5,067 84,054 7,426 31,160 2,187 4,845 20,142 8,453 2,280 528 1,732 5,811 20,254 22,232 9,531 19,720 31,844 6,585 105,458 158,344 14,794 150,995 22,143 65,054 68,112 50,806 13,324 87,567 21,441 42,080 21,305 15,225 12,822 21,756 59,950 32,704 38,478 55,922 46,034 55,895 85,024 32,219 128,925 44,213 58,009 55,263 40,988 13,562 5,316
12.1% 47.4% 9.9% 5.4% 9.2% 22.8% 10.8% 7.7% 16.5% 7.2% 12.8% 5.4% 14.5% 18.5% 7.7% 12.0% 10.2% 9.6% 12.5% 13.1% 11.4% 18.7% 12.2% 11.2% 13.5% 11.8% 10.0% 14.4% 10.7% 18.8% 25.1% 12.0% 10.2% 9.7% 8.3% 5.4% 12.8% 12.4% 12.0% 11.4% 10.7% 12.8% 12.6% 11.2% 14.2% 9.5% 10.1% 14.6% 17.8% 22.7%
4.7% 11.7% 4.8% 1.6% 5.3% 8.1% 5.8% 1.7% 10.4% 1.3% 1.9% 1.2% 4.5% 10.0% 1.1% 3.5% 3.9% 3.0% 3.9% 5.4% 4.4% 9.5% 4.9% 3.4% 3.9% 5.7% 4.2% 1.6% 4.1% 11.2% 3.8% 6.7% 6.8% 6.7% 6.0% 3.9% 5.3% 4.3% 5.0% 4.1% 3.8% 4.9% 5.9% 5.0% 3.8% 3.7% 4.3% 5.6% 8.7% 10.2%
21.3% 55.3% 16.9% 10.8% 16.2% 33.3% 20.8% 14.7% 29.6% 10.3% 17.9% 8.4% 25.0% 34.3% 12.1% 18.5% 20.0% 17.9% 20.5% 22.6% 19.8% 33.3% 21.5% 19.2% 22.3% 22.2% 17.8% 19.3% 17.9% 33.6% 39.5% 28.9% 24.1% 21.5% 18.0% 11.5% 21.8% 21.5% 21.9% 19.7% 19.7% 21.3% 24.6% 20.6% 23.2% 16.4% 18.7% 23.4% 32.6% 36.4%
95th Percentile 32.7% 100.0% 24.5% 17.5% 22.9% 56.8% 30.0% 25.3% 42.8% 19.9% 23.3% 16.7% 41.7% 49.4% 22.2% 29.0% 32.0% 28.7% 32.1% 33.8% 30.5% 49.4% 33.0% 30.6% 36.1% 32.1% 27.5% 37.6% 27.6% 47.8% 72.0% 40.4% 31.4% 28.7% 23.3% 15.6% 32.6% 33.3% 33.5% 31.1% 31.0% 32.5% 36.3% 31.0% 38.0% 25.2% 28.4% 35.0% 50.3% 55.3%
Note: HHS = Health and Human Services; CBSA = core-based statistical area; MSA = metropolitan statistical area. Source: 2013 CPS ASEC.
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Table 2. Comparison of Percent With High Burden. Percent With High Burden
Sample Full sample Age < 65 Age 19–64
N 174,880 154,785 102,483
Including Including Excluding Excluding Premiums Premiums, Premiums, Premiums Comparison Estimate and OTC Excluding OTC Including OTC and OTC 26.9% 22.0% 21.6%
24.1% 19.2% 19.1%
9.4% 8.3% 8.2%
7.1% 6.2% 6.2%
18.8% 17.6%
Note: 18.8% comparison estimate includes premiums and excludes OTC expenses. 17.6% excludes premiums and includes OTC expenses. Source: 2011 CPS ASEC.
hardship and consider individuals younger than age 65 with family income below 200% but above 138% of the FPL since these individuals are ineligible for Medicaid expansion in any state but are poised to see a sizable impact of other ACA reforms. We exclude individuals age 65 and older from this analysis because they are eligible for Medicaid and should not be affected by the ACA reforms considered in this analysis. In the analysis sample, we find that 18.5% of individuals younger than age 65 with income between 138% and 200% of the FPL have incomes below the Medicaid benefit eligibility cutoff after accounting for medical expenses. We examine the characteristics associated with falling below Medicaid benefit eligibility cutoff after deducting the individual’s portion of premium expenses (estimated for individuals with private insurance only), nonpremium MOOP, and both premium and nonpremium medical expenditures from income for this group. To this end, we perform several regressions estimating the likelihood of being classified above 138% of the FPL before accounting for medical expenses and being classified below this threshold after accounting for these expenses. We control for all characteristics presented previously with the exception of the U.S. Department of Health and Human Services poverty guidelines. In these regression analyses, we exclude from MOOP expenses from health-related OTC products. Table 3 presents results as average marginal effects of logit regressions for the characteristics associated with falling below the Medicaid benefit eligibility cutoff after accounting for medical expenses. Across all regressions, direct purchase coverage is associated with being more likely to be classified below the benefit eligibility cutoff. Following the implementation of the ACA, we expect the effect of direct purchase coverage to decrease as subsidies lower the amounts individuals pay for these plans. As compared to the uninsured, accounting for all medical expenses including premiums, those with any type of insurance coverage are more likely to be classified below 138% of the FPL except for those with Medicaid, who are not significantly different, and those with military coverage, who are less likely. Results from the regression excluding premium expenses are consistent, except that the effect of having a
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Table 3. Correlates of Falling Below the Medicaid Eligibility Cutoff: Regression Estimates. After Premiums— Private Only
In percentage point units
Average Marginal Effect
Deltamethod SE
After MOOP (Excluding Premiums) Average Marginal Effect
Deltamethod SE
After MOOP (Including Premiums) Average Marginal Effect
Deltamethod SE
Health Insurance Type (Omitted [Premiums] = Private and Government; Omitted [MOOP] = Uninsured) Direct purchase only .15*** .0288 .08*** .0159 .35*** Employer only –.03 .0190 .03*** .0091 .18*** Combination of government –.01 .0191 .06** Medicaid only –.02* .0132 .00 Medicare only .00 .0143 .11*** Military only –.12*** .0400 –.23*** Both employer and direct –.04 .0327 .04** .0154 .18*** Both private and .04*** .0127 .18*** government Family Size (Omitted = Single Unit) Two or more people –.03* .0167 –.03*** .0093 –.04*** Race/Ethnicity (Omitted = White, not Hispanic) Asian, not Hispanic .00 .0367 –.04** .0194 –.02 Black, not Hispanic –.03 .0228 –.02* .0099 –.02 Hispanic –.04** .0199 –.02 .0117 –.02 Other, not Hispanic .04 .0344 –.01 .0179 .06** Region (Omitted = West) Midwest –.01 .0226 .00 .0096 .02 Northeast .05** .0240 .01 .0122 .05*** South .05** .0211 .01 .0100 .04*** Metropolitan Status (Omitted = Not in a CBSA/MSA) Inside principal city .04* .0250 .01 .0126 .00 Outside principal city .03 .0221 .02* .0118 .01 Disability (Omitted = Disability) No disability –.04* .0203 –.01 .0095 –.03** Nativity (Omitted = Not Native) Native –.02 .0200 .00 .0086 .00 Marital Status (Omitted = Married) Single –.03* .0177 –.04*** .0109 –.06*** Age Group (Omitted = 26–64) 0–18 .00 .0160 .04*** .0097 .05*** 19–25 –.15*** .0191 .00 .0083 –.08*** Self-Reported Health Status (Omitted = Poor) Excellent –.01 .0390 –.1*** .0148 –.1*** Very good –.03 .0374 –.09*** .0136 –.11*** Good –.01 .0381 –.06*** .0137 –.07*** Fair .01 .0373 –.02* .0133 –.01 Source: 2013 CPS ASEC. *p < .1. **p < .05. ***p < .01.
.0217 .0117 .0272 .0186 .0228 .0633 .0235 .0196
.0125 .0270 .0149 .0154 .0227 .0146 .0173 .0144 .0158 .0153 .0145 .0124 .0147 .0122 .0125 .0222 .0207 .0207 .0193
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combination of government coverage or having Medicare is no longer significant and those with Medicaid are found to be less likely to be classified below the cutoff. Comparing results for premiums and nonpremium MOOP, we see interesting results. As compared to individuals living alone, those living in households with two or more people are less likely to be classified below the Medicaid eligibility cutoff after accounting for both premiums and nonpremium MOOP. Compared with non-Hispanic Whites, Hispanics are less likely to be classified below the cutoff after accounting for premiums, but not nonpremium MOOP, whereas non-Hispanic Blacks and non-Hispanic Asians are less likely to be classified below the cutoff after accounting for nonpremium MOOP, but not premiums. We see differences by region after accounting for premiums, with those in the Northeast and South more likely to be classified below the cutoff as compared with those in the West, but not after accounting for nonpremium MOOP. Compared with individuals not living in a core-based statistical area or metropolitan statistical area, those living in principal cities are more likely to be classified below the cutoff after accounting for premiums, but not nonpremium MOOP, while individuals living outside principal cities are more likely to be classified below the cutoff after accounting for nonpremium MOOP, but not premiums. We see no differences by nativity status. After accounting for both premiums and nonpremium MOOP, single individuals are less likely to be classified below the cutoff as compared with their married counterparts. Nondisabled individuals are less likely to be classified below the cutoff than their disabled counterparts after accounting for premiums, but not nonpremium MOOP. Compared with those aged 26–64 years, those aged 19–25 years are less likely to be classified below the cutoff after accounting for premiums but not nonpremium MOOP, but those aged 0–18 years are more likely to be classified below the cutoff after accounting for nonpremium MOOP, but not premiums. Compared with individuals reporting poor health, all other individuals were less likely to be classified below the cutoff after accounting for nonpremium MOOP, but not premiums. These results provide insight on the characteristics of individuals likely to benefit from new ACA provisions. The results suggest that individuals with direct purchase insurance, individuals living alone, non-Hispanic Whites and Asians and individuals in the “other” race/ethnicity group, those living in the South and Northeast and in principal cities, disabled individuals, married individuals, and those aged 0–18 and 26–64 years are most likely to benefit from the ACA’s premium subsidies for plans purchased on the exchange. The results further suggest that individuals with any type of private coverage, individuals living alone, non-Hispanic Whites, married individuals, children aged 0–18 years, and individuals reporting poor health stand to benefit the most from the ACA’s maximum limit on out-of-pocket cost-sharing.
Discussion and Conclusions As data become available to evaluate the effects of the ACA, we expect to see changes in the severity of burden, the characteristics associated with burden, and medical expense-related Medicaid eligibility. For example, we found that individuals with direct purchase insurance coverage are more likely to be classified below
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the expansion Medicaid eligibility cutoff after accounting for premium expenses as compared with individuals with some mix of private and government coverage. As the ACA aims to expand health insurance coverage for those with low incomes through Medicaid and private coverage, future work can examine whether, following the implementation of the ACA, private coverage is associated with less of an effect on burden and falling below the cutoff for expansion Medicaid eligibility. This article has discussed how the ACA sets maximum limits on how much consumers with health insurance coverage can be required to pay out-of-pocket annually for their medical care beginning in 2014. Future work could evaluate whether these caps are binding and the impact on burden and Medicaid eligibility for individuals and families; to investigate the effects of the ACA, 2015 CPS ASEC data covering the 2014 calendar year will be available in September 2015. Individuals at the margin of Medicaid income eligibility that have high MOOP are a vulnerable population. They are not eligible for state Medicaid expansions, yet they may have low incomes after accounting for medical expenses. Especially in light of the state expansions, these individuals might have incentives for pursuing avenues to become eligible for Medicaid rather than other insurance options. On the other hand, these individuals might opt to take advantage of the new insurance coverage and subsidy options created by the ACA. Subsequent work can assess the impact of these policy reforms on this vulnerable population. Future work can also examine what drives the disparities in burden and medical expense-related Medicaid income eligibility between individuals with different characteristics. For example, descriptively, we found disparities in burden by family size, region, metropolitan status, nativity status, and race/ethnicity, among other characteristics. These disparities could reflect differences in health status, demographic characteristics, cost of living, or the willingness to seek medical care. Even after controlling for demographic factors, we found that compared with non-Hispanic Whites, Hispanics are less likely to be classified below the Medicaid eligibility cutoff after accounting for premiums while Blacks and Asians are less likely to be classified below the Medicaid eligibility cutoff after accounting for nonpremium MOOP. Future work exploring how factors like health status and the willingness to seek medical care contribute to burdenrelated outcomes would be a valuable contribution to this literature. This work has examined the distribution of burden across all families, measured the percentage of families who face the highest burdens, and examined the factors associated with falling below the threshold of Medicaid eligibility after accounting for medical expenses. Our findings provide a basis for future work exploring the effects of the ACA on these measures as well as policies. In addition, our results examine the policy consideration that states could determine income eligibility for Medicaid after taking into account nonpremium MOOP spending. Authors’ Note This article is released to inform interested parties of ongoing research and to encourage discussion of work in progress. Any views expressed on statistical, methodological, technical, or operational issues are those of the authors and not necessarily those of the U.S. Census Bureau.
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Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding The authors received no financial support for the research, authorship, and/or publication of this article.
Notes 1. Data are subject to error arising from a variety of sources. For more information on sampling and nonsampling error, see http://www.census.gov/prod/techdoc/cps/cpsmar13. pdf (accessed June 5, 2014). Data used in this analysis are publicly available through DataFerrett (http://dataferrett.census.gov/). Other research materials are available from the authors by request. 2. Estimates including these families with very low incomes were skewed. Including individuals from these families, the mean burden estimate was considerably higher (by 53.0 percentage points) while the median was lower (by 0.5 percentage points) compared with the analytical sample. 3. Respondents may report more than one race. The results presented throughout this article use the race-alone or single-race concept whereby a particular race/ethnic group is defined as those who reported that particular race/ethnicity and no other. 4. We are unable to perform a formal statistical test of the difference of the means for our estimates and the MEPS estimates because the standard errors associated with the MEPS estimates were not published.
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Foster, A. (2010). Out-of-pocket health care expenditures: A comparison. Monthly Labor Review. Washington, DC: U.S. Bureau of Labor Statistics. Janicki, H., O’Hara, B., & Zawacki, A. (2013). Comparing methods for imputing employer health insurance contributions in the Current Population Survey (SEHSD Working Paper 2013-23). Washington, DC: U.S. Census Bureau. O’Hara, B. (2004). Do medical out-of-pocket expenses thrust families into poverty? Journal of Health Care for the Poor and Underserved, 15, 63-75. Schoen, C., Doty, M., Robertson, R., & Collins, S. (2011). Affordable Care Act reforms could reduce the number underinsured by 70 percent. Health Affairs, 30, 1762-1771. Short, P., & Banthin, J. (1995). New estimates of the underinsured younger than 65 years. Journal of the Americal Medical Association, 274, 1302-1306. State Health Access Data Assistance Center. (2012). Defining “family” for studies of health insurance coverage. (Issue Brief 27). Minneapolis, MN: University of Minnesota. U.S. Census Bureau and Bureau of Labor Statistics. (2011). Current Population Survey: Annual Social and Economic (ASEC) Supplement Survey, 2011 Technical Documentation. Washington, DC: U.S. Department of Commerce and U.S. Department of Labor.
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research-article2014
MCRXXX10.1177/1077558714563173Medical Care Research and ReviewAbramowitz and O’Hara
Data and Trends
The Financial Burden of Medical Spending: Estimates and Implications for Evaluating the Impact of ACA Reforms
Medical Care Research and Review 2015, Vol. 72(2) 187–199 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1077558714563173 mcr.sagepub.com
Joelle Abramowitz1 and Brett O’Hara1
Abstract This article uses the 2013 Current Population Survey Annual Social and Economic Supplement to estimate the financial burden of medical out-of-pocket costs by comparing medical out-of-pocket expenditures to income. This measure is important for evaluating the magnitude of burden, better understanding who bears it, and establishing a baseline to assess the impact of the Patient Protection and Affordable Care Act. We examine the distribution of burden and the incidence of high burden across all families and by individuals’ health insurance status and demographic and socioeconomic characteristics. We look more closely at one group vulnerable to having high burden: those younger than age 65 with incomes between 138% and 200% of the federal poverty line. We find that 18.5% of these individuals have incomes below the threshold of expansion Medicaid eligibility after accounting for non-overthe-counter medical expenses and examine the characteristics associated with being classified below this threshold. Keywords burden, out-of-pocket medical spending, Affordable Care Act
This article, submitted to Medical Care Research and Review on June 30, 2014, was revised and accepted for publication on November 10, 2014. 1U.S.
Census Bureau, Washington, DC, USA
Corresponding Author: Joelle Abramowitz, U.S. Census Bureau, 4600 Silver Hill Road, SEHSD, HQ-7H168-E, Washington, DC 20233, USA. Email: [email protected]
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Introduction Many families face adverse health events and as a result incur large medical expenditures. High out-of-pocket costs are incurred both by families where some or all members are uninsured and by families that, though covered by health insurance, pay an unaffordable share of their family income in health insurance premiums and cost sharing. Further, lingering effects of the recession on unemployment, underemployment, and real household income impair the ability of individuals and families, particularly those with low incomes, to maintain health insurance coverage and manage their medical expenditures. It is important to examine the financial burden imposed by medical costs on individuals and families to evaluate the magnitude of the problem, to better understand who bears those burdens, and to establish a baseline to assess the impact of the Patient Protection and Affordable Care Act (ACA) on family financial burden, particularly for those with large expenses and low incomes. A number of the reforms of the ACA have the potential, over the near and long term, to substantially relieve the burden of high out-of-pocket costs through different channels. Some states have extended Medicaid eligibility to individuals with family income below 138% of the federal poverty line (FPL). For individuals who do not qualify for Medicaid, health insurance exchanges facilitate the purchase of private plans and provide subsidies based on income. For example, for those with family income between 133% and 150% of the FPL, premium contributions are limited to between 3.0% and 4.0% of income, and for those with family income between 150% and 200% of the FPL, premium contributions are limited to between 4.0% and 6.3% of income (Collins Robertson, Garber, & Doty, 2012). In addition, beginning in 2014, the ACA also limits the amount of out-of-pocket cost-sharing for plans purchased through the health insurance marketplace (i.e., on the exchange), and these limits are also based on income: Expenditures are limited to $1,983 for individuals and $3,967 for families earning between 100% and 200% of the FPL, not including premiums (Collins et al., 2012). These provisions, among others, have the potential to ease family financial burden related to medical expenditures, especially among those with low incomes. While a number of studies have investigated the potential effects of state Medicaid expansion (Caswell, Waidmann, & Blumberg, 2013, for example), this analysis considers another vulnerable group: those younger than age 65 with incomes below 200% but above 138% of the FPL, the cutoff for Medicaid eligibility. We consider this particular group because these individuals are ineligible for state Medicaid expansion but are poised to see a substantial impact of other ACA reforms. In this article, we examine how some of the other major provisions of the ACA, such as premium subsidies and maximum limits on out-of-pocket expenditures, might affect this vulnerable group. To measure the financial burden of medical out-of-pocket costs, we compare an individual’s actual family medical out-of-pocket expenditures (MOOP) to his or her family income using the 2013 Current Population Survey Annual Social and Economic Supplement1 (CPS ASEC). We first examine the distribution of MOOP across all families and by the health insurance status and demographic and socioeconomic
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characteristics of individuals in those families and measure the percentage of families who face the highest burdens (those with MOOP greater than 10% of family income). Next, we consider the potential for the new ACA provisions to affect those younger than age 65 with income below 200% but above 138% of the FPL by examining the characteristics of individuals most likely to be facing financial distress from medical expenses. We use the Medicaid benefit eligibility cutoff (138% of the FPL) as a proxy for economic hardship. To consider who in this group is likely to be affected by the implementation of the premium subsidies, we examine the correlates of falling below the Medicaid benefit eligibility cutoff after accounting for premium expenses. To consider who in this group is likely to be affected by the implementation of the limits on MOOP, we examine the correlates of falling below the Medicaid benefit eligibility cutoff after accounting for nonpremium MOOP. To consider the overall impact of these reforms, we examine the correlates of falling below the Medicaid benefit eligibility cutoff after accounting for both premium and nonpremium medical expenditures.
New Contribution This article considers the potential impact of ACA reforms on low-income, high-burden families who remain ineligible for Medicaid, which complements work assessing the potential impact of state Medicaid expansion. In particular, the study examines the factors associated with falling below the threshold of Medicaid eligibility after accounting for different types of medical expenses, which is relevant for considering the characteristics of individuals likely to be affected by the insurance coverage, subsidy options, and out-of-pocket spending limits created by the ACA. This article also builds on previous work in the literature on underinsurance that uses the Medical Expenditure Panel Survey (MEPS) and the Commonwealth Fund Health Insurance Survey (Banthin & Bernard, 2006; Banthin, Cunningham, & Bernard, 2008; Cunningham, 2012; Schoen, Doty, Robertson, & Collins, 2011; Short & Banthin, 1995) by estimating a measure of medical care economic burden using the CPS ASEC and examining the relationship between burden and insurance, demographic, and geographic characteristics. In addition, this article builds on previous work examining medical expenditures and the classification of poverty (Caswell & Short, 2011; O’Hara, 2004) by considering how different types of medical expenditures contribute to economic hardship.
Data and Methods The analysis sample consists of the 2013 CPS ASEC, a nationally representative survey of the civilian noninstitutionalized population living in the United States, covering the 2012 calendar year. We use 2013 data rather 2014 data due to a change in sample size in 2014: In 2013, approximately 80,000 households were interviewed representing approximately 200,000 individuals, whereas in 2014, only approximately 50,000 households were interviewed with uniform income questions. This analysis uses questions introduced in the 2011 CPS ASEC such as amounts paid for health insurance
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premiums, medical out-of-pocket payments, spending on over-the-counter (OTC) health-related products, and total spending on medical care were collected (U.S. Census Bureau and Bureau of Labor Statistics, 2011). The addition of these variables allows for computation of medical care economic burden using the CPS ASEC. There are several benefits to using the CPS ASEC as compared to other data sources. The CPS ASEC is conducted annually and is large enough to be used to produce state-level estimates. This is particularly beneficial for future research considering different characteristics of ACA implementation across states. The smaller sample size of other surveys, such as the MEPS and Commonwealth Fund Health Insurance Survey, prohibits the production of state-level estimates. In addition, the timeliness of estimates from the MEPS and low response rates in the Commonwealth Fund Health Insurance Survey are concerns for future research. Although the MEPS collects more detailed and precise information on MOOP than does the CPS ASEC, previous work has shown the comparability of the CPS ASEC MOOP data to the MEPS (Caswell & O’Hara, 2010) and the comparability of MEPS to other data sources (Foster, 2010). In addition, while the CPS ASEC does not collect information related to the use of specific medical services, self-reported health is collected as a measure of health status. Given all of these considerations, the CPS ASEC is used in this analysis and is poised to provide an important estimate of burden in the future. MOOP include such expenses as the individual’s portion of insurance premiums and payments and copayments for hospital visits, medical providers, dental service, prescription medications, vision aids, medical supplies, and OTC health-related items. In 2012, spending on OTC health-related items made up 18.8% of nonpremium medical spending. We impute out-of-pocket premium expenses for persons who did not report paying for any premium expenses but reported that their employer paid for some or none of their employer-sponsored insurance or reported directly purchasing their insurance on the market (Janicki, O’Hara, & Zawacki, 2013). Out-of-pocket premium expenses were imputed for 5.7% of observations in the final sample. We also impute Medicare Part B premiums and add them to MOOP for individuals for whom these premiums were included in income but not in the initial MOOP amount. Medicare Part B premiums were imputed for 11.1% of observations in the final sample. Income is measured before taxes. Results were also estimated using income after deducting imputed federal and state taxes as a robustness check and were not significantly different from each other in most cases. Both MOOP and income numbers cover the 2012 calendar year. In all analyses, standard errors are calculated using replicate weighting methods. This analysis calculates burden by comparing family MOOP to family income for each individual in the sample. For the purposes of this analysis, the family is defined as the health insurance unit as outlined by the State Health Access Data Assistance Center (State Health Access Data Assistance Center, 2012). MOOP and income are calculated at the level of the health insurance unit rather than the level of the individual because the health insurance unit more accurately reflects the relevant unit of analysis for insurance coverage and determining eligibility for public programs as well the level at which financial burdens are shared and experienced and decisions about spending on medical expenditures are made. The original sample consisted of 202,269
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60.0%
Proporon of Families
50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
Burden Percentage
Figure 1. Distribution of Nonzero Family Burden Values. Source: 2013 CPS ASEC.
individuals. Families with self-employed individuals were dropped from the analysis because these individuals could earn negative income (n = 182,582). Families with MOOP of greater than $200,000 were dropped from the analysis because these MOOP values seemed implausibly large as amounts paid out of pocket (n = 182,573). Families with zero or negative income were dropped from the analysis because it was not possible to calculate burden for these families (n = 173,557). Families with incomes of $100 or less were dropped from the analysis because these families’ resulting burden estimates were unrepresentative of the sample (n = 173,138).2 The final sample consists of 173,138 individuals corresponding to 88,246 families.
Results Distribution of MOOP and Burden The distribution of MOOP values is very skewed. Many families report zero MOOP, while a small number of families report extremely large MOOP. Approximately 6.9% of families have MOOP values equal to zero. As with the distribution of MOOP, the distribution of burden is also skewed: many families have no or low burden, but a small number of families have very high burden. Figure 1 presents the distribution of nonzero burden values.
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Descriptive Characteristics of Burden We first examine the mean and 50th, 90th, and 95th percentiles for burden for the full analysis sample and by various characteristics. Table 1 presents results for the full sample and for select characteristics. Each set of characteristics represent mutually exclusive attributes. Mean estimates are presented for comparison purposes, but the percentile estimates provide a more revealing depiction of the burden distribution. The mean burden for the full sample is 12.1%. Fifty percent of individuals have burden of less than 4.7%, and the top 5% have burden of greater than 32.7%. The main factors appearing to be correlated with higher burden include age (being older than 65), having a disability, reporting poor health, having low income (less than 138% of the FPL), and insurance type (direct purchase insurance). Race/ethnicity,3 geography, and marriage/family size factors are correlated with much smaller differences in mean burden between groups. Since burden is calculated at the family level but we examine individual-level characteristics, estimates for individuals by particular characteristics also reflect to some extent the burden of the family members of those individuals. In addition, it is important to note that burden could reflect both disparities in health status as well as disparities in the willingness to seek medical care.
A Descriptive Look at the Percentage With High Burden To obtain a fuller picture of the impact of burden on individuals, we next examine the percentage of individuals in families with high burden, a measure that has been used throughout the literature as a marker of underinsurance (Cunningham, 2012; Schoen et al., 2011). Following the literature, an individual is considered to have high burden if his or her family burden is 10% or more of his or her family income. To compare our data with results in the literature, we use the 2011 CPS ASEC data covering the 2010 calendar year applying the same sample criteria as outlined before to estimate the percentage with high burden including and excluding health insurance premiums and expenses from health-related OTC products. Results are presented in Table 2. We find that the percentage of all individuals having high burden including health insurance premiums is 26.9% including OTC expenses and 24.1% excluding OTC expenses. Examining this estimate by age group, 19.2% of individuals younger than 65 have high burden including premiums and excluding OTC expenses, similar to the comparable 2009 estimate of 18.8% using MEPS (Cunningham, 2012).4 We also find that 8.2% of individuals ages 19–64 have high burden excluding premiums which is lower than the comparable 2010 estimate of 17.6% using Commonwealth Fund Health Insurance Survey data (Schoen et al., 2011).
Medical Expenses and Economic Hardship Next, we examine the characteristics of individuals experiencing economic hardship as a result of different types of medical expenses, providing insight into the characteristics of individuals likely to be affected by different ACA provisions. We use the Medicaid benefit eligibility cutoff (138% of the FPL) as a proxy for economic
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Full sample Health insurance Direct purchase only type Employer only All paid by employer Some paid by employer None paid by employer Combination of government Medicaid only Medicare only Military only VA health care TRICARE/other military Employer and direct Private and government Uninsured Race/ethnicity Asian, not Hispanic Black, not Hispanic Hispanic Other, not Hispanic White, not Hispanic Disability status No disability Disability Nativity status Native Not native Marital status Single (age ≥ 15) Married Age group 0–18 19–25 26–64 65+ HHS poverty < 1.38 of poverty line guidelines ≥ 1.38, < 2 ≥ 2, < 2.5 ≥ 2.5, < 3 ≥ 3, < 4 ≥4 Region Midwest Northeast South West Metropolitan Inside principal city status Outside principal city Not in a CBSA/MSA Family size Two people or more Single unit Self-reported Excellent health status Very good Good Fair Poor
N
Mean
50th Percentile
90th Percentile
173,138 5,067 84,054 7,426 31,160 2,187 4,845 20,142 8,453 2,280 528 1,732 5,811 20,254 22,232 9,531 19,720 31,844 6,585 105,458 158,344 14,794 150,995 22,143 65,054 68,112 50,806 13,324 87,567 21,441 42,080 21,305 15,225 12,822 21,756 59,950 32,704 38,478 55,922 46,034 55,895 85,024 32,219 128,925 44,213 58,009 55,263 40,988 13,562 5,316
12.1% 47.4% 9.9% 5.4% 9.2% 22.8% 10.8% 7.7% 16.5% 7.2% 12.8% 5.4% 14.5% 18.5% 7.7% 12.0% 10.2% 9.6% 12.5% 13.1% 11.4% 18.7% 12.2% 11.2% 13.5% 11.8% 10.0% 14.4% 10.7% 18.8% 25.1% 12.0% 10.2% 9.7% 8.3% 5.4% 12.8% 12.4% 12.0% 11.4% 10.7% 12.8% 12.6% 11.2% 14.2% 9.5% 10.1% 14.6% 17.8% 22.7%
4.7% 11.7% 4.8% 1.6% 5.3% 8.1% 5.8% 1.7% 10.4% 1.3% 1.9% 1.2% 4.5% 10.0% 1.1% 3.5% 3.9% 3.0% 3.9% 5.4% 4.4% 9.5% 4.9% 3.4% 3.9% 5.7% 4.2% 1.6% 4.1% 11.2% 3.8% 6.7% 6.8% 6.7% 6.0% 3.9% 5.3% 4.3% 5.0% 4.1% 3.8% 4.9% 5.9% 5.0% 3.8% 3.7% 4.3% 5.6% 8.7% 10.2%
21.3% 55.3% 16.9% 10.8% 16.2% 33.3% 20.8% 14.7% 29.6% 10.3% 17.9% 8.4% 25.0% 34.3% 12.1% 18.5% 20.0% 17.9% 20.5% 22.6% 19.8% 33.3% 21.5% 19.2% 22.3% 22.2% 17.8% 19.3% 17.9% 33.6% 39.5% 28.9% 24.1% 21.5% 18.0% 11.5% 21.8% 21.5% 21.9% 19.7% 19.7% 21.3% 24.6% 20.6% 23.2% 16.4% 18.7% 23.4% 32.6% 36.4%
95th Percentile 32.7% 100.0% 24.5% 17.5% 22.9% 56.8% 30.0% 25.3% 42.8% 19.9% 23.3% 16.7% 41.7% 49.4% 22.2% 29.0% 32.0% 28.7% 32.1% 33.8% 30.5% 49.4% 33.0% 30.6% 36.1% 32.1% 27.5% 37.6% 27.6% 47.8% 72.0% 40.4% 31.4% 28.7% 23.3% 15.6% 32.6% 33.3% 33.5% 31.1% 31.0% 32.5% 36.3% 31.0% 38.0% 25.2% 28.4% 35.0% 50.3% 55.3%
Note: HHS = Health and Human Services; CBSA = core-based statistical area; MSA = metropolitan statistical area. Source: 2013 CPS ASEC.
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Table 2. Comparison of Percent With High Burden. Percent With High Burden
Sample Full sample Age < 65 Age 19–64
N 174,880 154,785 102,483
Including Including Excluding Excluding Premiums Premiums, Premiums, Premiums Comparison Estimate and OTC Excluding OTC Including OTC and OTC 26.9% 22.0% 21.6%
24.1% 19.2% 19.1%
9.4% 8.3% 8.2%
7.1% 6.2% 6.2%
18.8% 17.6%
Note: 18.8% comparison estimate includes premiums and excludes OTC expenses. 17.6% excludes premiums and includes OTC expenses. Source: 2011 CPS ASEC.
hardship and consider individuals younger than age 65 with family income below 200% but above 138% of the FPL since these individuals are ineligible for Medicaid expansion in any state but are poised to see a sizable impact of other ACA reforms. We exclude individuals age 65 and older from this analysis because they are eligible for Medicaid and should not be affected by the ACA reforms considered in this analysis. In the analysis sample, we find that 18.5% of individuals younger than age 65 with income between 138% and 200% of the FPL have incomes below the Medicaid benefit eligibility cutoff after accounting for medical expenses. We examine the characteristics associated with falling below Medicaid benefit eligibility cutoff after deducting the individual’s portion of premium expenses (estimated for individuals with private insurance only), nonpremium MOOP, and both premium and nonpremium medical expenditures from income for this group. To this end, we perform several regressions estimating the likelihood of being classified above 138% of the FPL before accounting for medical expenses and being classified below this threshold after accounting for these expenses. We control for all characteristics presented previously with the exception of the U.S. Department of Health and Human Services poverty guidelines. In these regression analyses, we exclude from MOOP expenses from health-related OTC products. Table 3 presents results as average marginal effects of logit regressions for the characteristics associated with falling below the Medicaid benefit eligibility cutoff after accounting for medical expenses. Across all regressions, direct purchase coverage is associated with being more likely to be classified below the benefit eligibility cutoff. Following the implementation of the ACA, we expect the effect of direct purchase coverage to decrease as subsidies lower the amounts individuals pay for these plans. As compared to the uninsured, accounting for all medical expenses including premiums, those with any type of insurance coverage are more likely to be classified below 138% of the FPL except for those with Medicaid, who are not significantly different, and those with military coverage, who are less likely. Results from the regression excluding premium expenses are consistent, except that the effect of having a
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Table 3. Correlates of Falling Below the Medicaid Eligibility Cutoff: Regression Estimates. After Premiums— Private Only
In percentage point units
Average Marginal Effect
Deltamethod SE
After MOOP (Excluding Premiums) Average Marginal Effect
Deltamethod SE
After MOOP (Including Premiums) Average Marginal Effect
Deltamethod SE
Health Insurance Type (Omitted [Premiums] = Private and Government; Omitted [MOOP] = Uninsured) Direct purchase only .15*** .0288 .08*** .0159 .35*** Employer only –.03 .0190 .03*** .0091 .18*** Combination of government –.01 .0191 .06** Medicaid only –.02* .0132 .00 Medicare only .00 .0143 .11*** Military only –.12*** .0400 –.23*** Both employer and direct –.04 .0327 .04** .0154 .18*** Both private and .04*** .0127 .18*** government Family Size (Omitted = Single Unit) Two or more people –.03* .0167 –.03*** .0093 –.04*** Race/Ethnicity (Omitted = White, not Hispanic) Asian, not Hispanic .00 .0367 –.04** .0194 –.02 Black, not Hispanic –.03 .0228 –.02* .0099 –.02 Hispanic –.04** .0199 –.02 .0117 –.02 Other, not Hispanic .04 .0344 –.01 .0179 .06** Region (Omitted = West) Midwest –.01 .0226 .00 .0096 .02 Northeast .05** .0240 .01 .0122 .05*** South .05** .0211 .01 .0100 .04*** Metropolitan Status (Omitted = Not in a CBSA/MSA) Inside principal city .04* .0250 .01 .0126 .00 Outside principal city .03 .0221 .02* .0118 .01 Disability (Omitted = Disability) No disability –.04* .0203 –.01 .0095 –.03** Nativity (Omitted = Not Native) Native –.02 .0200 .00 .0086 .00 Marital Status (Omitted = Married) Single –.03* .0177 –.04*** .0109 –.06*** Age Group (Omitted = 26–64) 0–18 .00 .0160 .04*** .0097 .05*** 19–25 –.15*** .0191 .00 .0083 –.08*** Self-Reported Health Status (Omitted = Poor) Excellent –.01 .0390 –.1*** .0148 –.1*** Very good –.03 .0374 –.09*** .0136 –.11*** Good –.01 .0381 –.06*** .0137 –.07*** Fair .01 .0373 –.02* .0133 –.01 Source: 2013 CPS ASEC. *p < .1. **p < .05. ***p < .01.
.0217 .0117 .0272 .0186 .0228 .0633 .0235 .0196
.0125 .0270 .0149 .0154 .0227 .0146 .0173 .0144 .0158 .0153 .0145 .0124 .0147 .0122 .0125 .0222 .0207 .0207 .0193
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combination of government coverage or having Medicare is no longer significant and those with Medicaid are found to be less likely to be classified below the cutoff. Comparing results for premiums and nonpremium MOOP, we see interesting results. As compared to individuals living alone, those living in households with two or more people are less likely to be classified below the Medicaid eligibility cutoff after accounting for both premiums and nonpremium MOOP. Compared with non-Hispanic Whites, Hispanics are less likely to be classified below the cutoff after accounting for premiums, but not nonpremium MOOP, whereas non-Hispanic Blacks and non-Hispanic Asians are less likely to be classified below the cutoff after accounting for nonpremium MOOP, but not premiums. We see differences by region after accounting for premiums, with those in the Northeast and South more likely to be classified below the cutoff as compared with those in the West, but not after accounting for nonpremium MOOP. Compared with individuals not living in a core-based statistical area or metropolitan statistical area, those living in principal cities are more likely to be classified below the cutoff after accounting for premiums, but not nonpremium MOOP, while individuals living outside principal cities are more likely to be classified below the cutoff after accounting for nonpremium MOOP, but not premiums. We see no differences by nativity status. After accounting for both premiums and nonpremium MOOP, single individuals are less likely to be classified below the cutoff as compared with their married counterparts. Nondisabled individuals are less likely to be classified below the cutoff than their disabled counterparts after accounting for premiums, but not nonpremium MOOP. Compared with those aged 26–64 years, those aged 19–25 years are less likely to be classified below the cutoff after accounting for premiums but not nonpremium MOOP, but those aged 0–18 years are more likely to be classified below the cutoff after accounting for nonpremium MOOP, but not premiums. Compared with individuals reporting poor health, all other individuals were less likely to be classified below the cutoff after accounting for nonpremium MOOP, but not premiums. These results provide insight on the characteristics of individuals likely to benefit from new ACA provisions. The results suggest that individuals with direct purchase insurance, individuals living alone, non-Hispanic Whites and Asians and individuals in the “other” race/ethnicity group, those living in the South and Northeast and in principal cities, disabled individuals, married individuals, and those aged 0–18 and 26–64 years are most likely to benefit from the ACA’s premium subsidies for plans purchased on the exchange. The results further suggest that individuals with any type of private coverage, individuals living alone, non-Hispanic Whites, married individuals, children aged 0–18 years, and individuals reporting poor health stand to benefit the most from the ACA’s maximum limit on out-of-pocket cost-sharing.
Discussion and Conclusions As data become available to evaluate the effects of the ACA, we expect to see changes in the severity of burden, the characteristics associated with burden, and medical expense-related Medicaid eligibility. For example, we found that individuals with direct purchase insurance coverage are more likely to be classified below
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the expansion Medicaid eligibility cutoff after accounting for premium expenses as compared with individuals with some mix of private and government coverage. As the ACA aims to expand health insurance coverage for those with low incomes through Medicaid and private coverage, future work can examine whether, following the implementation of the ACA, private coverage is associated with less of an effect on burden and falling below the cutoff for expansion Medicaid eligibility. This article has discussed how the ACA sets maximum limits on how much consumers with health insurance coverage can be required to pay out-of-pocket annually for their medical care beginning in 2014. Future work could evaluate whether these caps are binding and the impact on burden and Medicaid eligibility for individuals and families; to investigate the effects of the ACA, 2015 CPS ASEC data covering the 2014 calendar year will be available in September 2015. Individuals at the margin of Medicaid income eligibility that have high MOOP are a vulnerable population. They are not eligible for state Medicaid expansions, yet they may have low incomes after accounting for medical expenses. Especially in light of the state expansions, these individuals might have incentives for pursuing avenues to become eligible for Medicaid rather than other insurance options. On the other hand, these individuals might opt to take advantage of the new insurance coverage and subsidy options created by the ACA. Subsequent work can assess the impact of these policy reforms on this vulnerable population. Future work can also examine what drives the disparities in burden and medical expense-related Medicaid income eligibility between individuals with different characteristics. For example, descriptively, we found disparities in burden by family size, region, metropolitan status, nativity status, and race/ethnicity, among other characteristics. These disparities could reflect differences in health status, demographic characteristics, cost of living, or the willingness to seek medical care. Even after controlling for demographic factors, we found that compared with non-Hispanic Whites, Hispanics are less likely to be classified below the Medicaid eligibility cutoff after accounting for premiums while Blacks and Asians are less likely to be classified below the Medicaid eligibility cutoff after accounting for nonpremium MOOP. Future work exploring how factors like health status and the willingness to seek medical care contribute to burdenrelated outcomes would be a valuable contribution to this literature. This work has examined the distribution of burden across all families, measured the percentage of families who face the highest burdens, and examined the factors associated with falling below the threshold of Medicaid eligibility after accounting for medical expenses. Our findings provide a basis for future work exploring the effects of the ACA on these measures as well as policies. In addition, our results examine the policy consideration that states could determine income eligibility for Medicaid after taking into account nonpremium MOOP spending. Authors’ Note This article is released to inform interested parties of ongoing research and to encourage discussion of work in progress. Any views expressed on statistical, methodological, technical, or operational issues are those of the authors and not necessarily those of the U.S. Census Bureau.
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Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding The authors received no financial support for the research, authorship, and/or publication of this article.
Notes 1. Data are subject to error arising from a variety of sources. For more information on sampling and nonsampling error, see http://www.census.gov/prod/techdoc/cps/cpsmar13. pdf (accessed June 5, 2014). Data used in this analysis are publicly available through DataFerrett (http://dataferrett.census.gov/). Other research materials are available from the authors by request. 2. Estimates including these families with very low incomes were skewed. Including individuals from these families, the mean burden estimate was considerably higher (by 53.0 percentage points) while the median was lower (by 0.5 percentage points) compared with the analytical sample. 3. Respondents may report more than one race. The results presented throughout this article use the race-alone or single-race concept whereby a particular race/ethnic group is defined as those who reported that particular race/ethnicity and no other. 4. We are unable to perform a formal statistical test of the difference of the means for our estimates and the MEPS estimates because the standard errors associated with the MEPS estimates were not published.
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