Racial-Ethnic Disparities in Use of Antidepressants in Private Coverage: Implications for the Affordable Care Act
Abstract
Objective
The objective of this study was to examine racial-ethnic disparities in the use of antidepressants among people with private coverage and people with public insurance or no coverage.
Methods
Data were from Medical Expenditure Panel Surveys (2006–2010), and logistic regression was used for the analysis.
Results
Among persons with depression and private coverage, racial-ethnic minority groups were significantly less likely than non-Hispanic whites to use antidepressants (N=4,468; adjusted odds ratio [AOR]=.50, 95% confidence interval [CI]=.33–.66 for non-Hispanic blacks; AOR=.70, CI=.55–.89 for Hispanics). No significant racial-ethnic disparity in the use of antidepressants was found in Medicare (N=1,944), Medicaid (N=2,125), and uninsured populations (N=1,679). For all racial-ethnic groups, persons with no insurance coverage had much lower rates of antidepressant use than their insured counterparts.
Conclusions
A wide racial-ethnic gap in the use of antidepressants existed in private coverage. As the nation continues to implement the Affordable Care Act, which will increase the number of enrollees from racial-ethnic minority groups in private plans, continuing efforts will be needed to reduce racial-ethnic disparities in the use of antidepressants.
Depression is a prevalent mental disorder that is often accompanied by poor general health conditions and thus results in increased health care utilization and expenditures (1). Antidepressants are commonly recommended and are shown to effectively alleviate depression symptoms. As more and better antidepressants have become available over the past decades, the use of antidepressants has significantly increased (2).
Despite the overall growth in antidepressant treatment, studies have reported persistent racial and ethnic disparities in use of antidepressants: among persons with a similar diagnosis of depression, the odds of antidepressant use were lower among blacks and Hispanics than whites by 20%−70% in the 1990s and early 2000s (3,4). Lack of access to health insurance and relatively low income in minority groups are among the main explanations for these disparities (5). Some researchers thus suggest that increasing insurance coverage rates among racial-ethnic minority groups can narrow gaps in health care utilization (6). Although the existence of racial disparities in the use of services in public insurance programs implies that other factors also contribute to the disparities (7–9), the literature generally indicates that increased insurance coverage reduces racial disparities in health care.
The 2010 Affordable Care Act (ACA), which aims to achieve near-universal coverage, offers an important opportunity to mitigate disparities in insurance coverage and thus in health service use. A major mechanism to expand coverage by the ACA is the provision of federal subsidies to low-income families (138%−400% of the federal poverty level [FPL]) to help them purchase private coverage through health insurance exchanges. A report estimates that about 26 million people will purchase insurance through exchanges by 2022, which would increase the number of privately insured people by approximately 15% (10). Among those who would newly obtain coverage through exchanges, racial-ethnic minority groups are expected to account for a large proportion because they constitute about 55% of the currently uninsured population and are at a greater risk than whites of having low incomes (11).
This study examined racial-ethnic disparities in the use of antidepressants among people with private coverage between 2006 and 2010. Private insurance is one of the main channels through which people obtain coverage in the United States. Further, the privately covered population will grow under the ACA, particularly among racial-ethnic minority groups. However, no research, to our knowledge, has analyzed the existence or degree of racial and ethnic disparities in private coverage. Our study thus aimed to provide information that could be used in exploring ways to address disparity issues in antidepressant use under the ACA.
We also examined antidepressant use among people without insurance coverage and those in Medicare and Medicaid during the same period. Analysis of the use of antidepressants among the uninsured may heuristically inform us about whether the ACA would increase the overall use of antidepressants. Analysis of the Medicare and Medicaid groups allowed us to compare the degree of racial-ethnic disparities in private coverage with those in other insurance groups. Although several prior studies reported racial-ethnic disparities in antidepressant use in Medicare and Medicaid (7–9), they used data from the 1990s or early 2000s—a period prior to the introduction of Medicare Part D. Our study therefore provides updated information on disparities in antidepressant use in Medicare and Medicaid populations, which may have been affected by the changes in prescription drug coverage in recent years.
Methods
We used multiyear data (2006–2010) of the Medical Expenditure Panel Survey Household Component (MEPS-HC), which includes five rounds of interviews over two-and-a-half years with a nationally representative sample of noninstitutionalized U.S. populations. The data include information on health care utilization and sociodemographic characteristics of respondents and their families.
We identified individuals with depression using MEPS-HC Medical Conditions files. The survey asked respondents to describe their medical conditions, and professional coders used the ICD-9-CM to record those self-reported conditions. We defined respondents as having depression if they had an ICD-9-CM code of 311 in any round of their interviews during a calendar year. The unit of analysis was person-year.
From MEPS-HC Prescribed Medicines files, we identified the following antidepressants: tricyclic antidepressants amitriptyline, amoxapine, clomipramine, desipramine, doxepin, imipramine, maprotiline, mirtazapine, nortriptyline, and protriptyline; selective serotonin reuptake inhibitors citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, and sertraline; and others (bupropion, duloxetine, nefazodone, selegiline, trazodone, and venlafaxine). We constructed a binary indicator of whether the respondent had at least one of these medications during the same year when he or she reported a diagnosis of depression.
For a respondent’s race and ethnicity, we constructed three categories: non-Hispanic whites (for simplicity, we refer to this group as whites), non-Hispanic blacks (referred to as blacks), and Hispanics. We excluded other racial-ethnic groups, which accounted for 5.1% of the sample.
For insurance coverage, we categorized the sample into four groups: private coverage, Medicare, Medicaid, and the uninsured. The private coverage group excluded people who were eligible for either Medicare or Medicaid. Those who were eligible for both Medicare and Medicaid were classified into Medicare—the payer for prescription drugs during the study period.
Covariates included age, gender, education, family income, employment status, and health risk. Year dummies were also included to capture time-specific effects.
We used logistic regression to analyze variation across racial and ethnic groups in the use of antidepressants, controlling for health risk and sociodemographic factors. Using whites as the reference group, we obtained the adjusted odds ratios (AORs) of antidepressant use for blacks and Hispanics. We conducted the analysis separately for each insurance group.
We performed two additional analyses on private coverage. First, we limited the sample to privately insured people whose family income was between 125% and 399% FPL. This subgroup is likely to have similar characteristics to people who would purchase private coverage through exchanges under the ACA. This analysis thus provides information that is particularly applicable to understanding racial-ethnic disparities in private coverage under the ACA.
Second, we conducted an analysis by adding a cost-sharing index, which is measured by the ratio of a respondent’s out-of-pocket drug spending to his or her total drug spending during the given year. The purpose of including this index was to account for the possibility that minority groups, compared with whites, may enroll in plans with less generous drug benefits for antidepressants. Our index, based on actual out-of-pocket spending on drugs, may imperfectly capture generosity of plan benefits because actual spending is affected by benefit designs. However, this ratio would be an inappropriate measure in our analysis of antidepressant use only if cost-sharing substantially differed between antidepressants and other drugs. Although this differential cost sharing might be the case in some plans, it would lead to biased results only if race and ethnicity were systematically associated with choosing coverage that has such within-plan differential cost sharing across drug classes, which is very unlikely.
Standard errors were clustered by individual in all analyses to account for repeated interviews of the same person. Because the study used publicly available data, the institutional review board waived the need for approval.
Results
Table 1 presents descriptive statistics of all covariates used in the analysis. The total number of person-years was 10,216. During the study period, people from racial-ethnic minority groups made up a smaller share in private coverage (12%) than in other insurance or uninsured groups (19%−36%) after we accounted for the oversampling of racial and ethnic minority groups in the MEPS.
| Private coverage(N=4,468) | Medicare(N=1,944)b | Medicaid(N=2,125) | Uninsured(N=1,679) | |||||
|---|---|---|---|---|---|---|---|---|
| Variable | N | % | N | % | N | % | N | % |
| Age (M±SEc) | 43.5±.3 | 64.1±.4 | 35.6±.6 | 41.6±.6 | ||||
| Gender | ||||||||
| Male | 1,445 | 34.1 | 652 | 33.1 | 588 | 30.1 | 652 | 43.3 |
| Female | 3,023 | 65.9 | 1,292 | 66.9 | 1,537 | 69.9 | 1,027 | 56.7 |
| Race-ethnicity | ||||||||
| Non-Hispanic white | 3,475 | 87.8 | 1,295 | 80.7 | 996 | 64.3 | 916 | 71.0 |
| Non-Hispanic black | 416 | 5.1 | 320 | 9.3 | 482 | 16.7 | 261 | 10.5 |
| Hispanic | 577 | 7.2 | 329 | 10.1 | 647 | 18.9 | 502 | 18.5 |
| Education | ||||||||
| 0–11 years | 634 | 11.2 | 741 | 30.0 | 1,197 | 50.7 | 585 | 28.2 |
| 12 years | 1,233 | 26.0 | 625 | 36.2 | 570 | 30.3 | 597 | 37.7 |
| ≥13 years | 2,578 | 62.9 | 558 | 33.9 | 343 | 19.0 | 483 | 34.1 |
| Family income (% federal poverty level) | ||||||||
| ≤124% | 359 | 5.6 | 873 | 37.5 | 1,428 | 62.5 | 726 | 36.3 |
| 125%–199% | 548 | 9.8 | 399 | 20.4 | 416 | 21.1 | 350 | 20.2 |
| 200%–399% | 1,611 | 34.1 | 450 | 26.3 | 237 | 13.8 | 430 | 28.4 |
| ≥400% | 1,950 | 50.6 | 222 | 15.8 | 44 | 2.6 | 173 | 15.1 |
| Mental health status | ||||||||
| Poor or fair | 955 | 20.8 | 822 | 40.1 | 993 | 47.2 | 601 | 35.4 |
| Good or excellent | 3,496 | 79.3 | 1,099 | 59.9 | 1,097 | 52.9 | 1,040 | 64.6 |
| General health status | ||||||||
| Poor or fair | 927 | 19.7 | 1,126 | 55.5 | 954 | 43.8 | 637 | 35.4 |
| Good or excellent | 3,525 | 80.3 | 797 | 44.5 | 1,135 | 56.2 | 1,005 | 64.6 |
| Marital status | ||||||||
| Never married | 897 | 21.5 | 283 | 14.0 | 779 | 39.0 | 458 | 28.5 |
| Married | 2,473 | 55.0 | 649 | 35.2 | 395 | 17.9 | 642 | 36.8 |
| Separated, divorced, or widowed | 913 | 20.5 | 995 | 50.8 | 629 | 31.8 | 514 | 32.3 |
| Employment status | ||||||||
| Unemployed | 1,132 | 23.3 | 1,805 | 91.5 | 1,665 | 75.7 | 860 | 45.4 |
| Employed | 3,336 | 76.7 | 139 | 8.5 | 460 | 24.3 | 819 | 54.6 |
| Activities of daily living (ADL) status | ||||||||
| No limitation | 4,386 | 98.7 | 1,655 | 88.0 | 1,981 | 95.6 | 1620 | 99.0 |
| Limited | 55 | 1.3 | 268 | 12.1 | 108 | 4.4 | 21 | 1.0 |
| Residence region | ||||||||
| No metropolitan statistical area (MSA) | 659 | 15.3 | 431 | 21.4 | 400 | 20.4 | 340 | 21.2 |
| MSA | 3,794 | 84.7 | 1,502 | 78.6 | 1,692 | 79.6 | 1305 | 78.8 |
| Chronic condition | ||||||||
| High blood pressure | 1,457 | 32.5 | 1,286 | 66.4 | 701 | 32.4 | 595 | 33.6 |
| Heart disease | 582 | 12.9 | 680 | 36.1 | 333 | 16.3 | 233 | 12.7 |
| High cholesterol | 1,614 | 37.7 | 1,146 | 61.6 | 554 | 26.0 | 441 | 25.1 |
| Diabetes | 432 | 9.7 | 573 | 28.1 | 276 | 11.4 | 175 | 8.7 |
| Joint pain | 2,380 | 54.3 | 1,495 | 77.0 | 1,111 | 56.0 | 950 | 58.4 |
| Arthritis | 1,311 | 29.4 | 1,277 | 67.0 | 692 | 33.0 | 477 | 28.3 |
| Asthma | 632 | 14.3 | 419 | 20.9 | 546 | 24.3 | 247 | 15.2 |
| Number of prescription drugs taken (M±SEc) | 3.8±.1 | 6.6±.1 | 4.1±.1 | 2.7±.1 | ||||
a
Population weights from MEPS were applied; Ns are in person-years.
b
Medicare includes persons dually eligible for Medicare and Medicaid.
c
Linearized standard errors after population weights from MEPS were applied
Table 2 reports antidepressant use by race-ethnicity and by insurance type. The table shows large differences in the use of antidepressants across racial-ethnic groups in private coverage: 44% among whites, 26% among blacks, and 29% among Hispanics. This gap was larger than the gaps between racial-ethnic groups with Medicare, Medicaid, or no insurance coverage. Moreover, the disparity in private coverage gradually increased during the study period, while the Medicare and Medicaid groups did not show clear over-year trends (Figure 1).
| Private coverage | Medicarea | Medicaid | Uninsured | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Total | Antidepressant user | Total | Antidepressant user | Total | Antidepressant user | Total | Antidepressant user | |||||
| Race-ethnicity | N | N | % | N | N | % | N | N | % | N | N | % |
| Non-Hispanic white | 3,475 | 1,522 | 43.8 | 1,295 | 729 | 56.3 | 996 | 393 | 39.5 | 916 | 300 | 32.8 |
| Non-Hispanic black | 416 | 106 | 25.5 | 320 | 155 | 48.4 | 482 | 167 | 34.7 | 261 | 61 | 23.4 |
| Hispanic | 577 | 166 | 28.8 | 329 | 154 | 46.8 | 647 | 212 | 32.8 | 502 | 109 | 21.7 |
a
Includes persons dually eligible for Medicare and Medicaid
Figure 1 Proportion (unadjusted) on antidepressant treatment, by racial-ethnic group and insurance coverage
a Includes persons dually eligible for Medicare and Medicaid
Table 2 also indicates that all three racial-ethnic groups without insurance had lower rates of antidepressant use than their insured counterparts. This is consistent with the literature and general expectation that insurance coverage increases the use of services. However, this descriptive information does not inform us how much antidepressant use would increase under the ACA because currently uninsured people may be a self-selected group of relatively low service users compared with those who purchase private coverage.
Table 3 shows the results from the logistic analysis of antidepressant use for each insurance group. In private coverage, blacks and Hispanics were significantly less likely to use antidepressants than whites (adjusted odds ratio [AOR]=.50 for blacks and .70 for Hispanics). For other insurance groups, no significant difference in the use of antidepressants was found across racial-ethnic groups.
| Private coverage(N=4,468) | Medicarea(N=1,944) | Medicaid(N=2,125) | Uninsured(N=1,679) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variable | AOR | 95% CIb | p | AOR | 95% CIb | p | AOR | 95% CIb | p | AOR | 95% CIb | p |
| Race-ethnicity (reference: non-Hispanic white) | ||||||||||||
| Non-Hispanic black | .50 | .33–.66 | <.001 | .78 | .57–1.08 | .135 | .94 | .70–1.28 | .706 | .70 | .47–1.07 | .098 |
| Hispanic | .70 | .55–.89 | .003 | .81 | .58–1.12 | .204 | .92 | .70–1.21 | .560 | .87 | .61–1.23 | .422 |
| Year (reference: 2006) | ||||||||||||
| 2007 | 1.26 | 1.05–1.51 | .011 | 1.03 | .80–1.32 | .830 | 1.11 | .83–1.48 | .475 | 1.07 | .75–1.52 | .709 |
| 2008 | 1.50 | 1.22–1.84 | <.001 | 1.06 | .77–1.46 | .731 | 1.77 | 1.30–2.43 | <.001 | 1.81 | 1.22–2.67 | .003 |
| 2009 | 2.17 | 1.78–2.64 | <.001 | 1.08 | .80–1.45 | .624 | 1.73 | 1.28–2.34 | <.001 | 1.69 | 1.15–2.47 | .007 |
| 2010 | 3.00 | 2.45–3.68 | <.001 | 1.80 | 1.34–2.43 | <.001 | 2.23 | 1.63–3.05 | <.001 | 2.12 | 1.47–3.15 | <.001 |
| Age (years) | 1.01 | 1.00–1.02 | .033 | 1.01 | .99–1.02 | .129 | 1.01 | 1.00–1.03 | .033 | 1.00 | .99–1.02 | .725 |
| Female (reference: male) | 1.06 | .90–1.25 | .506 | 1.02 | .79–1.30 | .898 | 1.17 | .89–1.52 | .259 | .91 | .67–1.22 | .515 |
| Education (reference: 0–11 years) | ||||||||||||
| 12 years | 1.06 | .82–1.38 | .641 | 1.02 | .77–1.32 | .908 | 1.15 | .87–1.51 | .340 | .91 | .64–1.29 | .586 |
| ≥13 years | 1.15 | .90–1.47 | .257 | 1.08 | .80–1.46 | .628 | 1.19 | .87–1.65 | .282 | 1.14 | .79–1.63 | .485 |
| Family income (reference: ≤124% federal poverty level ) | ||||||||||||
| 125%–199% | .98 | .72–1.34 | .905 | 1.01 | .77–1.32 | .951 | 1.03 | .80–1.34 | .802 | 1.12 | .80–1.58 | .503 |
| 200%–399% | 1.11 | .85–1.47 | .438 | 1.08 | .82–1.43 | .565 | 1.10 | .79–1.54 | .559 | 1.13 | .81–1.58 | .484 |
| ≥400% | 1.04 | .78–1.38 | .797 | 1.11 | .76–1.62 | .582 | .77 | .34–1.73 | .520 | 1.36 | .85–2.17 | .205 |
| Good or excellent mental health (reference: poor or fair) | .78 | .65–.93 | .007 | .83 | .66–1.04 | .110 | .74 | .59–.92 | .008 | .67 | .49–.89 | .006 |
| Good or excellent general health (reference: poor or fair) | 1.05 | .86–1.29 | .625 | 1.17 | .92–1.50 | .202 | 1.05 | .80–1.36 | .739 | 1.31 | .94–1.84 | .110 |
| Marital status (reference: never married) | ||||||||||||
| Married | 1.13 | .90–1.42 | .311 | 1.11 | .76–1.64 | .586 | .86 | .62–1.20 | .372 | 1.24 | .84–1.83 | .277 |
| Separated, divorced, or widowed | 1.03 | .78–1.35 | .842 | 1.15 | .79–1.66 | .460 | 1.05 | .76–1.45 | .755 | 1.26 | .83–1.91 | .287 |
| Employed (reference: unemployed) | .98 | .82–1.17 | .835 | 1.50 | .98–2.31 | .062 | .99 | .76–1.29 | .941 | .76 | .57–1.00 | .053 |
| Has limited activities of daily living (reference: no limitation) | .67 | .33–1.33 | .252 | .82 | .59–1.15 | .258 | 1.25 | .75–2.09 | .400 | .91 | .22–3.75 | .900 |
| Metropolitan statistical area (MSA) (reference: no MSA) | 1.00 | .81–1.25 | .970 | .94 | .71–1.26 | .708 | .71 | .53–.95 | .022 | 1.08 | .75–1.54 | .684 |
| Chronic condition | ||||||||||||
| High blood pressure | .99 | .82–1.20 | .945 | 1.02 | .79–1.31 | .904 | .88 | .65–1.19 | .408 | .99 | .67–1.43 | .971 |
| Heart disease | .80 | .63–1.01 | .060 | .67 | .52–.87 | .002 | .71 | .51–1.00 | .049 | .75 | .49–1.14 | .175 |
| High cholesterol | 1.08 | .90–1.28 | .411 | 1.12 | .88–1.43 | .353 | 1.16 | .85–1.58 | .350 | 1.44 | 1.04–2.01 | .030 |
| Diabetes | .76 | .57–1.01 | .061 | .72 | .55–.94 | .017 | .57 | .38–.84 | .004 | .58 | .35–.98 | .040 |
| Joint pain | .92 | .78–1.09 | .317 | 1.04 | .79–1.38 | .762 | 1.10 | .85–1.43 | .464 | .90 | .67–1.21 | .487 |
| Arthritis | 1.01 | .84–1.22 | .904 | .81 | .62–1.06 | .122 | .95 | .72–1.27 | .739 | .83 | .58–1.20 | .327 |
| Asthma | 1.02 | .82–1.26 | .877 | .78 | .58–1.05 | .099 | .94 | .72–1.21 | .612 | .57 | .38–.91 | .018 |
| Number of prescription drugs taken | 1.22 | 1.18–1.27 | <.001 | 1.22 | 1.17–1.28 | <.001 | 1.22 | 1.16–1.29 | <.001 | 1.48 | 1.36–1.60 | <.001 |
a
Includes persons dually eligible for Medicare and Medicaid
b
Adjusted for clustering within an individual
Table 4 presents the findings for selected variables from the additional analyses on private coverage. The subgroup analysis of those with family income between 125% and 399% FPL produced very similar results to the overall analysis (AOR=.54 for blacks and .67 for Hispanics). The results from the analysis that included a cost-sharing index were also similar to the primary analysis (AOR=.56 for blacks and .71 for Hispanics). The effect of the cost-sharing variable was negative, as expected, but insignificant.
| Subgroup analysis of those at 125%–399% FPL(N=2,159)a | With cost-sharing index (overall analysis of private coverage)(N=4,468) | |||||
|---|---|---|---|---|---|---|
| Variable | AOR | 95% CIb | p | AOR | 95% CIb | p |
| Race-ethnicity (reference: non-Hispanic white) | ||||||
| Non-Hispanic black | .54 | .38–.76 | <.001 | .56 | .42–.74 | <.001 |
| Hispanic | .67 | .49–.92 | .013 | .71 | .56–.91 | .006 |
| Cost-sharing index | .82 | .64–1.06 | .126 | |||
a
FPL, federal poverty level
b
Adjusted for clustering within an individual
Discussion
Main findings
We found that racial-ethnic disparities existed in antidepressant use among people with depression in private coverage between 2006 and 2010: the odds of using antidepressants were lower among blacks and Hispanics compared with whites by 50% and 30%, respectively. These are large gaps and somewhat alarming, particularly considering that no significant racial-ethnic disparities were found in other insured groups. Considering that racial-ethnic disparities have been a long-standing issue in health care, it is surprising that there has been no report or research on this issue in private coverage. It may have been assumed that racial-ethnic minority groups would not use health care on significantly different levels compared with whites when minority groups have access to private coverage. However, our analysis shows that is not the case.
Private plans may not have sought strategies to monitor and reduce racial and ethnic gaps in the use of antidepressants (or other health services) given that racial-ethnic minority groups account for a relatively small proportion of their members. This may partly explain our finding. In Medicaid, where people from racial-ethnic minority groups make up a relatively large share compared with other groups, racial-ethnic disparities in health service use have long been recognized. Interventions have been initiated to reduce the disparities that occur with Medicaid; such interventions include one-on-one outreach, education, and translation services (12,13). Some prior work reported few racial-ethnic disparities in the use of antidepressants among people with Medicaid coverage (8), and our analysis also found no significant racial-ethnic disparities in antidepressant use.
Variation in unobserved generosity of benefits across private plans may be an alternative explanation for our finding. Unlike Medicare and Medicaid, which specify standard benefits, individuals in the private sector choose their own plans and benefits. Compared with whites, racial-ethnic minority populations may enroll in private plans with less generous benefits. In our analysis, inclusion of a cost-sharing index did not change the results. However, lacking plan-specific benefit information, we constructed that index based on actual out-of-pocket spending on all drugs. The index may thus have imperfectly captured benefit generosity for antidepressants. Further, some aspects of benefits may remain unobserved, such as utilization management (limiting medication quantity, for example) and breadth of drug formularies. Unmeasured benefit generosity may have influenced antidepressant use disproportionally in racial and ethnic minority groups. A recent study reported that the introduction of Medicare Part D particularly benefited racial-ethnic minority groups, partially supporting this possibility (14). Our analysis focusing on a post–Part D period also found no significant disparities in Medicare.
Implications and future research
Our study has implications for the ACA provision. First, the higher level of antidepressant use among people with private insurance compared with those without insurance suggests that antidepressant use is likely to increase under the ACA, although it is not clear how much it might increase. Insurance coverage increases service utilization by lowering the price of services, which improves access to necessary care and creates financial incentives to use inefficient services. However, this potential increase in service use by coverage expansion will be reduced by the extent to which selection exists in private coverage: privately covered people may have greater need and preference for health services than currently uninsured populations. Thus the ACA may not increase antidepressant use to the level of current enrollees in private coverage.
Second, the wide racial-ethnic gap in antidepressant use among privately covered people (including persons with income levels similar to those who would enroll in private plans through exchanges) indicates that disparities are unlikely to be resolved solely by the expansion of coverage. These findings suggest that efforts to identify and reduce racial-ethnic disparities should be continued under the ACA. They also call for particular attention to private plans, which would experience an increase in racial and ethnic minority enrollees under the ACA.
The ACA stipulates that private plans must offer essential health benefits (similar to typical employer-based plan benefits) and that cost sharing will be subsidized for low-income families to limit their out-of-pocket expenditures; however, no provision specifies how benefits for each service are to be designed. Plans are likely to develop their own benefit schemes as long as their schemes meet the general requirement. Certain specifics of benefit designs could disproportionally affect racial and ethnic minority groups with mental health conditions. Evaluation of how benefit schemes in private coverage evolve and how minority populations’ access to health services improves would be necessary to reduce racial-ethnic disparities under the ACA.
Besides designing financial incentives for consumers, private plans’ initiatives to identify and remove any access barriers that may exist among racial-ethnic minority groups could also help mitigate disparities. The ACA provision requires collecting and reporting data on race-ethnicity, which would be used to analyze trends in disparities. This effort is encouraging, but it is not yet clear what interventions would be developed based on findings from those data. Data collection efforts should be accompanied by development and implementation of interventions to reduce racial-ethnic disparities.
Our study did not evaluate any specific approaches to reduce racial-ethnic disparities in antidepressant use. However, assessing the patterns of racial-ethnic disparities is a first step toward exploring strategies to close the gaps. An important next step would be to investigate reasons for less usage of antidepressants among racial-ethnic minority groups compared with whites (for example, collecting qualitative data from targeted interviews), which would provide useful information to develop specific interventions. Continuous monitoring of the pattern of racial-ethnic disparities and implementation and evaluation of any interventions would be essential to improve minority populations’ access to needed mental health care.
Limitations
Several cautions should be noted in interpreting the results. First, insurance expansion is likely to influence the diagnosis of depression itself, but our analysis did not incorporate that. However, descriptive data from MEPS showed that the prevalence of depression in each racial-ethnic group was similar between people without insurance and those with private coverage, indicating the uninsured are as likely as those with coverage to be aware of their condition. The uninsured may have obtained the diagnosis in formal health care settings (before losing coverage or through uncompensated care), or they may have self-diagnosed, given that depression is a symptomatic condition. In any case, the data suggest that not examining a possible increase in the diagnosis of depression under the ACA is unlikely to affect our results.
Second, we did not analyze whether antidepressants were used for mild symptoms that may not require medications (15,16). Information on the severity of depression was not available in the data. Although potential overuse of antidepressants is a health care quality concern, underuse in depression treatment is a commonly documented issue, and its consequences on health outcomes are greater than those of overuse (17–19).
Third, the low level of antidepressant use among people from racial-ethnic minority groups may partially reflect their preferences for other treatments (such as counseling) instead of medications (20–24). If this is the case, the differences in antidepressant use across racial-ethnic groups would be of less concern, and it would be important to ensure racial-ethnic minority groups’ access to alternative therapies. Exploring reasons for low rates of antidepressant use is an important topic to pursue in future research.
Fourth, we identified patients with depression and antidepressant use on the basis of self-reported information. Thus our data were subject to reporting errors. However, there is no evidence that those errors systematically differed across racial-ethnic groups, and their impact on our findings concerning racial-ethnic differences is unlikely to be significant.
Finally, characteristics of people from racial-ethnic minority groups who would purchase coverage under the ACA may not be the same as those currently enrolled in private coverage. This implies that our finding may not be fully applied to “newly” insured groups under the ACA. However, our subgroup analysis suggests that a similar pattern is likely to be observed among those people.
Conclusions
A wide racial-ethnic gap in the use of antidepressants in private coverage existed between 2006 and 2010. As we implement the ACA, which will increase the number of racial-ethnic minority enrollees in private plans, continuing efforts are needed to reduce racial-ethnic disparities in the use of antidepressants.
Acknowledgments and disclosures
The authors report no competing interests.
References
1.
Simon GE, VonKorff M, Barlow W: Health care costs of primary care patients with recognized depression. Archives of General Psychiatry 52:850–856, 1995
2.
Olfson M, Marcus SC: National patterns in antidepressant medication treatment. Archives of General Psychiatry 66:848–856, 2009
3.
Miranda J, Cooper LA: Disparities in care for depression among primary care patients. Journal of General Internal Medicine 19:120–126, 2004
4.
Harman JS, Mulsant BH, Kelleher KJ, et al.: Narrowing the gap in treatment of depression. International Journal of Psychiatry in Medicine 31:239–253, 2001
5.
Hargraves JL, Hadley J: The contribution of insurance coverage and community resources to reducing racial/ethnic disparities in access to care. Health Services Research 38:809–829, 2003
6.
Hargraves JL: The insurance gap and minority health care, 1997–2001. Tracking Report 2:1–4, 2002
7.
Akincigil A, Olfson M, Siegel M, et al.: Racial and ethnic disparities in depression care in community-dwelling elderly in the United States. American Journal of Public Health 102:319–328, 2012
8.
Melfi CA, Croghan TW, Hanna MP, et al.: Racial variation in antidepressant treatment in a Medicaid population. Journal of Clinical Psychiatry 61:16–21, 2000
9.
Crystal S, Sambamoorthi U, Walkup JT, et al.: Diagnosis and treatment of depression in the elderly Medicare population: predictors, disparities, and trends. Journal of the American Geriatrics Society 51:1718–1728, 2003
10.
The Budget and Economic Outlook: Fiscal Years 2013 to 2023. Washington, DC, Congressional Budget Office, 2013
11.
Health Coverage by Race and Ethnicity: The Potential Impact of the Affordable Care Act. Washington, DC, Kaiser Commission on Medicaid and the Uninsured, 2013
12.
Reducing Racial and Ethnic Disparities: A Quality Improvement Initiative in Medicaid Managed Care. Hamilton, NJ, Center for Health Care Strategies, 2007
13.
Llanos K, Palmer L: Using Data on Race and Ethnicity to Improve Health Care Quality for Medicaid Beneficiaries. Hamilton, NJ, Center for Health Care Strategies, 2006
14.
Chen J, Rizzo JA, Ortega AN: Racial and ethnic differences in drug expenditures and access under Medicare Part D. Journal of Health Care for the Poor and Underserved 22:1059–1074, 2011
15.
Baumeister H: Inappropriate prescriptions of antidepressant drugs in patients with subthreshold to mild depression: time for the evidence to become practice. Journal of Affective Disorders 139:240–243, 2012
16.
Barbui C, Cipriani A, Patel V, et al.: Efficacy of antidepressants and benzodiazepines in minor depression: systematic review and meta-analysis. British Journal of Psychiatry 198:11–16, 2011
17.
Gilberg K, Laouri M, Wade S, et al.: Analysis of medication use patterns: apparent overuse of antibiotics and underuse of prescription drugs for asthma, depression, and CHF. Journal of Managed Care Pharmacy 9:232–237, 2003
18.
Kessler RC, Demler O, Frank RG, et al.: Prevalence and treatment of mental disorders, 1990 to 2003. New England Journal of Medicine 352:2515–2523, 2005
19.
Bridge JA, Iyengar S, Salary CB, et al.: Clinical response and risk for reported suicidal ideation and suicide attempts in pediatric antidepressant treatment: a meta-analysis of randomized controlled trials. JAMA 297:1683–1696, 2007
20.
Dwight-Johnson M, Sherbourne CD, Liao D, et al.: Treatment preferences among depressed primary care patients. Journal of General Internal Medicine 15:527–534, 2000
21.
Cooper LA, Gonzales JJ, Gallo JJ, et al.: The acceptability of treatment for depression among African-American, Hispanic, and white primary care patients. Medical Care 41:479–489, 2003
22.
Cooper-Patrick L, Powe NR, Jenckes MW, et al.: Identification of patient attitudes and preferences regarding treatment of depression. Journal of General Internal Medicine 12:431–438, 1997
23.
Quiñones AR, Thielke SM, Beaver KA, et al.: Racial and ethnic differences in receipt of antidepressants and psychotherapy by veterans with chronic depression. Psychiatric Services 65:193–200, 2014
24.
Ince BU, Riper H, van ‘t Hof E, et al.: The effects of psychotherapy on depression among racial-ethnic minority groups: a metaregression analysis. Psychiatric Services 65:612–617, 2014
Information & Authors
Information
Published In
Cover: Girl on a Swing, by Maxfield Parrish. Drawing, oil on paper. The Metropolitan Museum of Art, bequest of Susan Vanderpoel Clark (67.155.3). Image © Metropolitan Museum of Art, New York. Image source: Art Resource. New York.
History
Published online: 1 September 2014
Published in print: September 2014
Authors
Metrics & Citations
Metrics
Citations
Export Citations
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
For more information or tips please see 'Downloading to a citation manager' in the Help menu.
View Options
View options
PDF/EPUB
View PDF/EPUBLogin options
Already a subscriber? Access your subscription through your login credentials or your institution for full access to this article.
Personal login Institutional Login Open Athens loginNot a subscriber?
PsychiatryOnline subscription options offer access to the DSM-5-TR® library, books, journals, CME, and patient resources. This all-in-one virtual library provides psychiatrists and mental health professionals with key resources for diagnosis, treatment, research, and professional development.
Need more help? PsychiatryOnline Customer Service may be reached by emailing [email protected] or by calling 800-368-5777 (in the U.S.) or 703-907-7322 (outside the U.S.).