Medicaid Lapses and Low-Income Young Adults’ Receipt of Outpatient Mental Health Care After an Inpatient Stay
Abstract
Objective
This study examined low-income young adults’ use of outpatient mental health services after an inpatient mental health stay, with a focus on Medicaid enrollment lapses and public mental health safety-net coverage.
Methods
The sample included 1,174 young adults ages 18 to 26 who had been discharged from inpatient psychiatric care in a mid-Atlantic state. All were enrolled in Medicaid at the time of discharge, and all were eligible for continued outpatient public mental health services regardless of Medicaid enrollment. Administrative claims data were used to examine outpatient mental health clinic use, psychotropic medication possession, inpatient readmission, and emergency department admission during the 365-day period after the index discharge. The main independent variable was a lapse in Medicaid enrollment. An instrumental-variables regression model was used to minimize estimation bias resulting from unmeasured confounding between lapses and service use.
Results
Nearly a third (30%) of the young adults had an enrollment lapse. In instrumental-variables analysis, those whose coverage lapsed were less likely than those who had continuous Medicaid coverage to have at least two clinic visits (38% versus 80%); they also had a lower average psychotropic medication possession ratio (25% versus 55%).
Conclusions
Age-related Medicaid enrollment lapses were common in this sample of young adults and were associated with receipt of less clinical care postdischarge despite continued eligibility for public services. States should examine opportunities to assist young adults with serious mental health problems who are aging out of Medicaid enrollment categories for children.
Some observers have expressed concern that many public mental health systems are not adequately addressing lapses in Medicaid insurance coverage among low-income young adults with serious mental health problems (1,2). Such lapses often occur when enrollees in Medicaid or the State Children’s Health Insurance Program (SCHIP) “age out” of eligibility categories for children and do not immediately transition into an adult Medicaid category (3,4). Medicaid lapses can result from loss of eligibility resulting from changes in income or assets, failure to meet the adult Medicaid standard of disability, or other factors. Many Medicaid enrollees fail to reapply at the time of reenrollment, obtain health insurance from another source and consequently drop Medicaid, or have their applications rejected for other administrative reasons (5,6). Although Medicaid enrollment lapses would be expected to impede low-income young adults’ access to public mental health care providers, evidence substantiating this concern is scarce.
The transition from child Medicaid eligibility to narrower adult eligibility categories may result in loss of eligibility for many young adults over the age of 18 (3). The perception of an especially high rate of disenrollment in early adulthood is supported by limited evidence. Using data from all states, Czajka (5) found that although 53% of Medicaid enrollees ages 19–64 had at least one enrollment lapse over a three-year period, 85% of 18-year-old Medicaid enrollees had a lapse. Pullmann and colleagues (7), using Medicaid data from one state, examined Medicaid enrollment over a seven-year period for a cohort of Medicaid-enrolled 16-year-olds with mental health conditions and found that enrollment decreased sharply at ages 18 and 19.
A key concern with Medicaid lapses in this demographic group is that many low-income young adults become uninsured or transition to private health plans or other public plans that often include substantial consumer cost sharing for mental health care (8,9). According to available estimates, 64%−76% of all young adults who lose Medicaid become uninsured (3,10). Cost-sharing provisions among the minority of young adults who obtain insurance coverage may deter their use of needed mental health services.
However, state and local safety-net financing for public mental health care could in many cases offset the impacts of losing Medicaid coverage (11,12). Many uninsured and even some privately insured low-income young adults who meet state or local need criteria can qualify for receipt of public mental health services at minimal out-of-pocket expense. On the other hand, underuse of safety-net coverage after a lapse in Medicaid enrollment could be common given young adults’ variable attachment to services and providers (13–15). Research suggests that few states have the organizational infrastructure needed to help young adults with serious mental health problems navigate age-related transitions in their coverage and care (13).
This study examined whether Medicaid enrollment lapses have an impact on young adults’ receipt of outpatient public mental health care after discharge from an inpatient mental health stay. The focus on a sample discharged from inpatient care was chosen because inpatient admission indicates a generally high level of need for outpatient mental health care and because many public mental health systems recognize recent psychiatric hospitalization as a criterion for priority access to public mental health services and medications. The study sample was drawn from Maryland’s public mental health system, which is financed mostly by Medicaid, with additional state and federal financing provided to county “core service agencies” for persons who are either uninsured or underinsured (16). Maryland covers outpatient mental health care after an inpatient psychiatric discharge for all public mental health system clients regardless of their Medicaid status (17). As a result, this study examined whether Medicaid enrollment lapses had an impact on young adults’ receipt of mental health care even though the young adults were eligible for safety-net mental health coverage.
Methods
Data and sample
The study sample included 1,177 persons ages 18 to 26 who had completed at least one episode of inpatient mental health care between October 1, 2005, and September 30, 2006, at either a general or a psychiatric hospital in Maryland and who had been enrolled in Medicaid as of the discharge date. Administrative claims data for these young adults’ use of mental health services were obtained from the State of Maryland, Mental Hygiene Administration. These data were merged with administrative data from the Department of Health and Mental Hygiene (that is, from the state Medicaid agency) on use of non–mental health services, medication prescriptions, and Medicaid enrollment. The merged data set encompassed health care use financed by Medicaid plus any state-financed care provided by mental health care providers participating in the public system. Any privately financed service events, such as encounters resulting in claims to private health insurance plans or paid out of pocket, and any “free” care provided in federally qualified health centers were not captured. One person who was dually enrolled in Medicare and two others who had no qualifying psychiatric diagnosis (ICD-9 codes 290 to 319, except 299 for autism spectrum) were excluded. These exclusions left 1,174 in the sample. The study was declared exempt from institutional review board review by the Maryland Department of Health and Mental Hygiene and the University of Maryland School of Medicine.
Empirical model
The study period included the 180 days before the individual’s index hospital discharge date and 365 days afterward. The primary dependent variable was outpatient clinic use during the 365-day postdischarge period. Outpatient clinics are staffed by licensed mental health clinicians and generally provide medication management, counseling, and individual and group psychotherapy. The number of outpatient clinic visits was divided into three categories representing degrees of engagement and participation: no or one visit, two to nine visits, and ten or more visits. Completion of at least two visits was interpreted as a measure of engagement, and completion of ten visits was considered a measure of sustained treatment participation. Results from empirical “dose-response” studies suggest that approximately ten encounters are needed to achieve a clinically significant response among at least half of all clients (18). It has also been found that 70% of all premature treatment dropout occurs after the first or second outpatient clinic visit (19). Additional dependent measures of mental health care use during the 365-day postdischarge period were a psychotropic medication possession ratio (MPR), an indicator of any inpatient psychiatric rehospitalization, and an indicator of any psychiatric emergency department visits. The psychotropic MPR was calculated as the number of days of antipsychotics, antidepressants, mood stabilizers, and stimulants received divided by 365 days minus the number of days spent in inpatient hospital care. Ratio values exceeding 1.0 were assigned a value of 1.
The primary independent variable in the analysis was whether the young adult’s Medicaid coverage lapsed (a transition from Medicaid enrolled to not enrolled) during the 365 days after discharge, operationalized as a period of ≥14 days not enrolled. A ≥14-day lapse was considered meaningful because mental health clinic appointments after inpatient discharge are normally scheduled at least biweekly. A sensitivity analysis was conducted with a more stringent definition of ≥30 days not enrolled.
Covariates
Covariates were chosen to represent predisposing, enabling, and need factors (20–22), both for continued Medicaid enrollment and for mental health services. Predisposing characteristics included patient age, sex, and race-ethnicity (23–26). Enabling factors included residence in an urban area (operationalized as Rural-Urban Commuting Area code 1 [27]) and receipt of prenatal care (women only) during the180-day period before the index discharge date. Receipt of prenatal care was based on state vital statistics data.
In relation to need for continued Medicaid and mental health services, ICD-9 diagnoses for mental health conditions during the 180-day preindex period were used to code schizophrenia (295.X), bipolar disorder (ICD-9 codes 296.0 and 296.4–296.9), psychotic disorder not otherwise specified (NOS) (297.1 and 298.9), depression or dysthymia (296.2, 296.3, and 300.4), mood disorder NOS (296.9 and 311), and all other codes for mental health conditions (290.X–319.X, except 299).
Medicaid claims data were used to identify for the 180-day preindex period the five most frequent primary diagnoses in general medical illness categories. These diagnoses were assigned to major condition categories by using the Clinical Classifications Software (CCS) (28), which maps ICD-9 codes into 231 separate condition categories. An index, range from 0 to 5, was then created for the total number of unique CCS conditions for each person. A separate indicator was created for any alcohol or illicit drug use disorder diagnosis (ICD-9 codes 304.X and 305.X, excluding 305.1).
Measures of mental health service use during the 180-day preindex period were number of inpatient mental health bed days, number of outpatient mental health clinic days (based on CPT codes 908XX), and receipt of a psychotropic medication prescription (that is, any antipsychotic, mood stabilizer, antidepressant, or stimulant). Greater mental health service utilization before the index inpatient discharge might indicate greater need for Medicaid and mental health care postdischarge.
The number of days a patient was enrolled in Medicaid during the preindex period and the category of Medicaid enrollment as of the index discharge date were used as indicators of attachment to Medicaid. For the enrollment variable, Medicaid coverage groups were collapsed into three categories on the basis of how a person qualified for Medicaid: disabled or foster care (included Supplemental Security Income, institutional care, and foster care enrollment categories), medically needy (included “spend down” and related enrollment categories for persons with chronic health care needs who did not meet income and asset tests for Medicaid disability categories), and low-income families (included Temporary Assistance for Needy Families, Medicaid expansion categories for low-income children and pregnant women, SCHIP, and other Medicaid state plan categories for low-income persons).
Estimation approach
Outpatient clinic service use (no or one visit, two to nine visits, and ten or more visits) was estimated with two separate probability models. The first model was used to estimate the probability of two or more visits versus no or one visit. The second model was used to estimate the conditional probability of ten or more visits conditional on having had two or more visits. The probit model form was used because dependent variables were binary-valued. Predictive margins were estimated for the adjusted probabilities of service use within each category when the Medicaid enrollment lapse indicator was either 0 (no lapse) or 1 (lapse), holding the values of other covariates constant (29). “Marginal effects,” defined as the difference in these predictive margins (30), are also reported.
An instrumental-variables regression approach (31–34) was used to protect against estimation bias resulting from unmeasured confounding between the likelihood of an enrollment lapse and mental health service use postdischarge. The key instrumental variable was a binary indicator of whether the young adult was either 18 or 20 years old as of the index discharge date. In Maryland, 19th and 21st birthdays are two important child-adult Medicaid transition dates. Young adults in households receiving income through the Temporary Assistance for Needy Families program, qualifying young adults with chronic health care needs who live with a low-income parent, and children enrolled in foster care are generally covered by Medicaid until their 19th birthday or until their 21st birthday if their family incomes remain below statutory limits. Children who are disabled and receiving Supplemental Security Income generally are covered until two months past their 19th birthday, at which time some transition to adult Medicaid.
Results
A total of 356 (30%) of the 1,174 persons in the sample had an enrollment lapse during the 365 days after the index inpatient discharge. The mean±SD number of days from discharge to the initial enrollment lapse was 183±102 days, and the mean number of days without Medicaid was 177±102 (median=185 days; interquartile range=83 to 256 days). Among the 356 persons whose enrollment lapsed, 81 (23%) re-enrolled in Medicaid during the 365-day postdischarge period. In this subgroup, the mean number of days not enrolled was 97.
As shown in Table 1, compared with other young adults, those with an enrollment lapse were slightly younger (21.3±2.3 versus 21.7±2.3 years, p=.008), were less likely to have received a schizophrenia diagnosis (21% versus 31%, p<.001), were more likely to have received a depression diagnosis (26% versus 20%, p=.026) or a mood disorder NOS diagnosis (15% versus 9%, p=.015), and had fewer diagnoses of general medical illnesses (2.0±1.9 versus 2.5±2.0, p<.001). Those with an enrollment lapse also used less mental health care across all categories, had fewer Medicaid enrollment days before discharge (122.0±71.8 days versus 151.3±56.7 days, p<.001), and were less likely to be enrolled at discharge in a Medicaid category for persons with disabilities or foster care (12% versus 54%, p<.001).
| Characteristic | Overall (N=1,174) | No lapse (N=818) | Lapse (N=356) | pa | |||
|---|---|---|---|---|---|---|---|
| N | % | N | % | N | % | ||
| Male | 577 | 49 | 398 | 49 | 171 | 50 | .954 |
| Age (M±SD) | 21.6±2.3 | 21.7±2.3 | 21.3±2.3 | .008 | |||
| Race-ethnicity | |||||||
| Black, non-Hispanic | 546 | 47 | 391 | 48 | 155 | 43 | .203 |
| White, non-Hispanic | 539 | 46 | 372 | 45 | 167 | 47 | .705 |
| Other | 89 | 8 | 55 | 7 | 34 | 10 | .115 |
| Urban residence | 972 | 83 | 689 | 84 | 283 | 79 | .058 |
| Psychiatric diagnosis | |||||||
| Schizophrenia | 326 | 28 | 251 | 31 | 75 | 21 | <.001 |
| Bipolar disorder | 380 | 32 | 273 | 33 | 107 | 30 | .431 |
| Psychosis NOS | 44 | 4 | 26 | 3 | 18 | 5 | .154 |
| Depression or dysthymia | 251 | 21 | 160 | 20 | 91 | 26 | .026 |
| Mood disorder NOS | 129 | 11 | 77 | 9 | 52 | 15 | .015 |
| Other | 44 | 4 | 31 | 4 | 13 | 4 | .913 |
| Substance use disorder diagnosis | 116 | 10 | 83 | 10 | 33 | 9 | .590 |
| General medical illness diagnosis (M±SD) | 2.3±2.0 | 2.5±2.0 | 2.0±1.9 | <.001 | |||
| Any prenatal service use before index hospitalization | 135 | 12 | 107 | 13 | 28 | 8 | .005 |
| Mental health care use before index hospitalization | |||||||
| Inpatient (acute) bed days (M±SD) | .2±1.9 | .2±2.1 | .2±1.3 | <.001 | |||
| Outpatient clinic days (M±SD) | 4.9±8.1 | 5.7±8.7 | 3.1±6.3 | <.001 | |||
| ≥1 psychotropic prescriptions | 523 | 45 | 437 | 53 | 86 | 24 | <.001 |
| Medicaid enrollment days before index hospitalization (M±SD) | 142.1±63.0 | 151.3±56.7 | 122.0±71.8 | <.001 | |||
| Medicaid enrollment category at discharge | |||||||
| Disabled or foster care | 485 | 41 | 444 | 54 | 41 | 12 | <.001 |
| Low-income family | 261 | 22 | 128 | 16 | 133 | 37 | <.001 |
| Medically needy low income | 428 | 36 | 246 | 30 | 182 | 51 | <.001 |
a
Proportional and mean differences between lapse and no lapse groups were tested with an F test (df=1 and 1,173).
Table 2 presents data for the study outcome variables over the 365-day postdischarge period, by enrollment lapse status. Young adults with an enrollment lapse were more likely than those with continuous Medicaid coverage to have had either no clinic visits or only one visit (49% versus 24%, p<.001), and they were less likely to have completed ten or more visits (21% versus 45%, p<.001). They also had a lower average MPR (.2±.3 versus .6±.4, p<.001) and were less likely to have been admitted to inpatient mental health care (13% versus 31%, p<.001) and to have been seen in a psychiatric emergency department (15% versus 32%, p<.001).
| Dependent variable | Overall (N=1,174) | No lapse (N=818) | Lapse (N=356) | Fa | p | |||
|---|---|---|---|---|---|---|---|---|
| N | % | N | % | N | % | |||
| Outpatient clinic visits | ||||||||
| 0 or 1 | 372 | 32 | 196 | 24 | 176 | 49 | 70.0 | <.001 |
| 2 to 9 | 359 | 31 | 254 | 31 | 105 | 29 | .3 | .593 |
| ≥10 | 443 | 38 | 368 | 45 | 75 | 21 | 74.2 | <.001 |
| Medication possession ratio (M±SD) | .5±.4 | .6±.4 | .2±.3 | 240.3 | <.001 | |||
| Inpatient admission | 296 | 25 | 250 | 31 | 46 | 13 | 54.0 | <.001 |
| Emergency department visit | 313 | 27 | 260 | 32 | 53 | 15 | 45.9 | <.001 |
a
df=1 and 1,173
Table 3 shows the regression estimates. Being age 18 or 20 at discharge was positively related to the likelihood of an enrollment lapse (p<.001). In instrumental-variables analyses, enrollment lapses were associated with a lower probability of completing at least two outpatient clinic visits (p<.001) and with a lower average MPR (p=.014). Enrollment lapses were not significantly associated with completing at least ten outpatient clinic visits when the person had completed at least two, nor were they associated with inpatient admission or receipt of care in an emergency department. By contrast, standard probit estimates indicated significant negative associations between enrollment lapses and service use for all outcomes. A sensitivity analysis, in which a lapse was defined as not enrolled for ≥30 days yielded results similar to the main results.
| Model and dependent variable | β | z | p | 95% CI | Predictive margin (%) | ||
|---|---|---|---|---|---|---|---|
| No lapse | Lapse | MEb | |||||
| Enrollment lapse ≥14 days | |||||||
| Instrumental-variables probitc | |||||||
| ≥2 outpatient clinic visits (reference: 0 or 1) | –1.3 | –5.02 | <.001 | –1.9 to –.8 | 80 | 38 | –42 |
| ≥10 outpatient clinic visits (reference: 2 to 9)d | .4 | .65 | .516 | –.9 to .6 | 48 | 64 | 16 |
| Medication possession ratio | –.3 | –2.45 | .014 | –.5 to –.1 | 55 | 25 | –30 |
| Inpatient admission | –.8 | –1.55 | .121 | –1.8 to –.2 | 32 | 12 | –20 |
| Emergency department visit | –.3 | –.45 | .652 | –1.5 to .9 | 29 | 21 | –8 |
| Standard probite | |||||||
| ≥2 outpatient clinic visits (reference: 0 or 1) | –.6 | –6.05 | <.001 | –.8 to –.4 | 74 | 56 | –18 |
| ≥10 outpatient clinic visits (reference: 2 to 9) | –.4 | –3.29 | .001 | –.6 to –.2 | 55 | 51 | –4 |
| Medication possession ratio | –.2 | –10.61 | <.001 | –.3 to –.2 | 53 | 30 | –23 |
| Inpatient admission | –.4 | –3.63 | <.001 | –.6 to –.2 | 28 | 17 | –11 |
| Emergency department visit | –.4 | –3.34 | .001 | –.6 to –.1 | 29 | 19 | –10 |
| Enrollment lapse ≥30 days | |||||||
| Instrumental-variables probitc | |||||||
| ≥2 outpatient clinic visits (reference: 0 or 1) | –1.4 | –5.55 | <.001 | –1.9 to –.9 | 80 | 35 | –45 |
| ≥10 outpatient clinic visits (reference: 2 to 9) | .5 | .68 | .497 | –.9 to 1.9 | 47 | 64 | 17 |
| Medication possession ratio | –.3 | –2.43 | .015 | –.5 to –.1 | 54 | 25 | –29 |
| Inpatient admission | –.8 | –1.61 | .108 | –1.8 to –.2 | 32 | 11 | –21 |
| Emergency department visit | –.1 | –.23 | .817 | –1.4 to –1.1 | 28 | 23 | –5 |
| Standard probite | |||||||
| ≥2 outpatient clinic visits (reference: 0 or 1) | –.6 | –6.18 | <.001 | –.8 to –.4 | 74 | 55 | –19 |
| ≥10 outpatient clinic visits (reference: 2 to 9) | –.5 | –4.59 | <.001 | –.7 to –.3 | 69 | 53 | –16 |
| Medication possession ratio | –.2 | –10.03 | <.001 | –.3 to –.2 | 52 | 30 | –22 |
| Inpatient admission | –.4 | –3.35 | .001 | –.6 to –.2 | 28 | 18 | –10 |
| Emergency department visit | –.3 | –3.03 | .002 | –.5 to –.1 | 29 | 20 | –9 |
a
Analyses were also adjusted for all covariates: gender, age, race-ethnicity, psychiatric diagnoses, substance use disorder diagnosis, general medical illness diagnoses, prenatal care use, urban residence, prior inpatient mental health days, prior outpatient mental health clinic days, prior receipt of psychotropic medications, prior Medicaid enrollment days, and Medicaid enrollment category at discharge.
b
ME (marginal effect) is equal to the difference in regression-adjusted predicted mean percentages for the two groups (no lapse and lapse), holding the values of all other covariates constant at the sampled values.
c
Instrumental variable was age 18 or 20 (F=26.2, df=1 and 1,173, p<.001, for test of no effect on enrollment lapse).
d
Includes 802 persons who had ≥2 outpatient clinic visits
e
No instrumental variables
The predictive margins listed in Table 3 are useful for interpreting the regression coefficients. Persons with a Medicaid lapse had a predicted 38% chance of completing at least two outpatient mental health clinic visits, compared with an 80% chance for persons with no lapse, a difference of 42 percentage points. Similarly, the predicted MPR was 25% of days postdischarge for persons with a Medicaid lapse versus 55% of days postdischarge for persons with no lapse, a 30 percentage point lower rate of possession.
Discussion
In this study, 30% of low-income young adults who had been hospitalized for a mental health condition experienced a lapse in Medicaid enrollment within a year of being discharged from the hospital. Having a Medicaid enrollment lapse was associated with a lower probability of completing at least two outpatient mental health clinic visits (38% versus 80%) and with a lower rate of psychotropic medication possession (25% of days versus 55% of days) during the first 365 days after discharge from psychiatric inpatient care, compared with having continuous Medicaid enrollment.
Although the findings that loss of Medicaid coverage was related to less use of outpatient mental health clinic services and a lower psychotropic MPR may not be surprising, this sample was unusual in that all of these young adults were eligible to receive outpatient public mental health services and psychotropic medication after their discharge regardless of their eligibility for Medicaid. This suggests that inability to pay was not the predominant reason that the young adults who left Medicaid received less care.
Most of these young adults—all of whom had had a psychiatric hospitalization—presumably needed outpatient mental health care during the year after discharge. Young adults with Medicaid enrollment lapses might have on average differed from other young adults in relation to their propensity to engage with the mental health treatment system. Previous research has indicated that failure to engage in outpatient mental health care is not consistently related to lower service need (15,35) but is consistently associated with having substance use problems, psychiatric comorbidity, and difficulties forming a treatment alliance with a provider (15,18,35). Moreover, data from national epidemiological surveys indicate that young adults with serious mental illness are, in general, less likely to use services than adults in other age groups (36) and commonly do not participate in any treatment for months or years after illness symptoms begin (37).
Various incidental factors could also have influenced decisions not to use mental health services among the young adults whose Medicaid enrollment lapsed. Some might not have known that they were eligible for public mental health services. The loss of child Medicaid benefits could also have coincided with other life transitions (38), which may have further complicated continuation of outpatient care. Evidence from qualitative research also suggests that even the modest requirement to contact a provider and complete an application for public coverage could have deterred some young adults from seeking care (39).
Mental health service use that occurred outside the public mental health system, which was not measured, could also partially or fully account for the negative association found between enrollment lapses and mental health service use. Some young adults could have obtained mental health care for free or on a sliding scale from federally qualified health centers or other public clinics, some may have obtained private insurance coverage, and some may have moved to another state.
However, we think that the bias introduced by measurement error is unlikely to threaten the validity of our findings. Former Medicaid enrollees and young adults are among the least likely groups to obtain private health insurance coverage (3,8,40). Moreover, among low-income young adults, research indicates that having private health insurance coverage may not improve receipt of specialty mental health care over being uninsured (14), perhaps because private insurance usually covers only part of mental health care costs (9,10).
Young adults with a Medicaid enrollment lapse had a lower rate of inpatient readmission, used fewer outpatient clinic services, and received fewer medications in the first year after the index hospital discharge, compared with young adults with no enrollment lapse (Table 2). These differences do not by themselves indicate quality-of-care differences in the services provided to the two groups. Moreover, an examination of mental health service use over time intervals less than or greater than the one-year interval used in this study may have yielded results with different implications for service use and quality of care. Rather, the differences in service use suggest that the group that had a Medicaid enrollment lapse is distinct and consequently may have distinct needs for posthospitalization guidance and support services.
This study used an instrumental-variables regression approach to minimize self-selection bias. In contrast to standard regression, the instrumental-variables approach sets up a contrast of persons with higher versus lower probabilities of a Medicaid enrollment lapse, where the probability of a lapse is proportional to the young adult’s age at inpatient discharge. Young adults whose age at discharge was 18 or 20 years were more likely than persons whose age at discharge was 19 or 21–26 to have an enrollment lapse during the subsequent 365 days, but the former group may have been similar to other young adults in relation to other determinants of service use. Differences between the instrumental-variables estimates and the standard regression estimates (Table 3) suggest that standard regression estimates were sensitive to selection bias and may have resulted in either overestimation or underestimation of the impact of continuous Medicaid enrollment, depending on the study outcome variable.
Conclusions
Medicaid coverage commonly lapses as young adults with serious mental health problems cross age thresholds associated with transitions from child to adult Medicaid eligibility. Discontinuities in Medicaid coverage may impede these young adults’ engagement in outpatient mental health programs and receipt of psychotropic medications, even among young adults who have recently been discharged from a hospital and are eligible for continued public mental health safety-net services. This raises the additional prospect of logistical challenges for health care planners once mandatory insurance coverage provisions of the 2010 Affordable Care Act are implemented. Seriously ill young adults who transition into and out of Medicaid and health insurance exchange plans may experience service disruptions and require formal supports to help them negotiate such transitions. The results of this study suggest that states should examine opportunities to bolster care coordination supports for acutely ill young adults during periods of heightened Medicaid and service transition.
Acknowledgments and disclosures
This research was funded by the National Institute of Mental Health under grant number R34-MH081303. The authors thank Jack Clark, M.A., for expert database programming.
Dr. Slade has served as a paid consultant to H. Lundbeck A/S. Mr. Abrams has served as a paid consultant to Maryland's Medicaid authority. The other authors report no competing interests.
References
1.
Davis M, Green M, Hoffman C: The service system obstacle course for transition-age youth and young adults; in Transition of Youth and Young Adults With Emotional or Behavioral Difficulties: An Evidence-Based Handbook. Edited by Clark HB, Unruh D. Baltimore, Brookes, 2009
2.
Davis M, Koroloff N: The great divide: how public mental health policy fails young adults; in Research on Community-Based Mental Health Services for Children and Adolescents. Edited by Fisher WH. Bingley, United Kingdom, Emerald Group Publishing, 2006
3.
Schwartz K, Damico A: Aging Out of Medicaid: What Is the Risk of Becoming Uninsured? Washington, DC, Kaiser Commission on Medicaid and the Uninsured, 2010
4.
Fishman E: Aging out of coverage: young adults with special health needs. Health Affairs 20(6):254–266, 2001
5.
Czajka JL: Medicaid Enrollment Gaps, 2005 to 2007. Washington, DC, Mathematica Policy Research, 2012
6.
Hill I: Is There a Hole in the Bucket? Understanding SCHIP Retention. Occasional paper 67, Washington, DC, Urban Institute, 2003
7.
Pullmann MD, Heflinger CA, Satterwhite Mayberry L: Patterns of Medicaid disenrollment for youth with mental health problems. Medical Care Research and Review 67:657–675, 2010
8.
Goldman HH, Frank RG, Burnam MA, et al.: Behavioral health insurance parity for federal employees. New England Journal of Medicine 354:1378–1386, 2006
9.
Zuvekas SH: Trends in mental health services use and spending, 1987-1996. Health Affairs 20(2):214–224, 2001
10.
Sommers BD: Loss of health insurance among non-elderly adults in Medicaid. Journal of General Internal Medicine 24:1–7, 2009
11.
Hogan MF: The public sector and mental health parity: time for inclusion. Journal of Mental Health Policy and Economics 1:189–198, 1998
12.
Goldman HH, Grob GN: Defining “mental illness” in mental health policy. Health Affairs 25:737–749, 2006
13.
Davis M, Geller JL, Hunt B: Within-state availability of transition-to-adulthood services for youths with serious mental health conditions. Psychiatric Services 57:1594–1599, 2006
14.
Ringeisen H, Casanueva CE, Urato M, et al.: Mental health service use during the transition to adulthood for adolescents reported to the child welfare system. Psychiatric Services 60:1084–1091, 2009
15.
O’Brien A, Fahmy R, Singh SP: Disengagement from mental health services: a literature review. Social Psychiatry and Psychiatric Epidemiology 44:558–568, 2009
16.
Hepburn B: Update on Maryland's Public Mental Health System. Maryland Psychiatrist 29:12–14, 2003
17.
Maryland's Public Mental Health System Provider Manual. Baltimore, Value Options Maryland. Available at maryland.valueoptions.com. Accessed July 12, 2012
18.
Hansen NB, Lambert MJ, Forman EM: The psychotherapy dose-response effect and its implications for treatment delivery services. Clinical Psychology: Science and Practice 9:329–343, 2002
19.
Olfson M, Mojtabai R, Sampson NA, et al.: Dropout from outpatient mental health care in the United States. Psychiatric Services 60:898–907, 2009
20.
Aday LA, Andersen R: A framework for the study of access to medical care. Health Services Research 9:208–220, 1974
21.
Medical Expenditure Panel Survey Household Component Data, 2003. Rockville, Md, Agency for Healthcare Research and Quality, 2006
22.
Gelberg L, Andersen RM, Leake BD: The behavioral model for vulnerable populations: application to medical care use and outcomes for homeless people. Health Services Research 34:1273–1302, 2000
23.
Bartels SJ, Clark RE, Peacock WJ, et al.: Medicare and Medicaid costs for schizophrenia patients by age cohort compared with costs for depression, dementia, and medically ill patients. American Journal of Geriatric Psychiatry 11:648–657, 2003
24.
Dickey B, Azeni H: Persons with dual diagnoses of substance abuse and major mental illness: their excess costs of psychiatric care. American Journal of Public Health 86:973–977, 1996
25.
Goldstein JM, Tsuang MT: Gender and schizophrenia. Schizophrenia Bulletin 16:179–344, 1990
26.
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
27.
Rural-urban commuting area codes. Seattle, WWAMI Rural Health Research Center. Available at depts.washington.edu/uwruca/index.php. Accessed Nov 1, 2011
28.
Elixhauser A, McCarthy EM: Clinical Classifications for Health Policy Research, Version 2: Hospital Inpatient Statistics, 1995. Healthcare Cost and Utilization Project (HCUP 3) Research Note 1, AHCPR pub no 96-0017. Rockville, Md, Agency for Health Care Policy and Research, 1996
29.
Graubard BI, Korn EL: Predictive margins with survey data. Biometrics 55:652–659, 1999
30.
Christofides LN, Stengos T, Swidinsky R: On the calculation of marginal effects in the bivariate probit model. Economics Letters 54:203–208, 1997
31.
Angrist J, Imbens G, Rubin D: Identification of causal effects using instrumental variables. Journal of the American Statistical Association 91:444–455, 1996
32.
Hogan JW, Lancaster T: Instrumental variables and inverse probability weighting for causal inference from longitudinal observational studies. Statistical Methods in Medical Research 13:17–48, 2004
33.
Angrist JD, Krueger AB: Instrumental variables and the search for identification: from supply and demand to natural experiments. Journal of Economic Perspectives 15:69–85, 2001
34.
Heckman JJ: Randomization as an instrumental variable. Review of Economics and Statistics 78:336–341, 1996
35.
Kreyenbuhl J, Nossel IR, Dixon LB: Disengagement from mental health treatment among individuals with schizophrenia and strategies for facilitating connections to care: a review of the literature. Schizophrenia Bulletin 35:696–703, 2009
36.
Wang PS, Demler O, Kessler RC: Adequacy of treatment for serious mental illness in the United States. American Journal of Public Health 92:92–98, 2002
37.
Wang PS, Berglund P, Olfson M, et al.: Failure and delay in initial treatment contact after first onset of mental disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry 62:603–613, 2005
38.
Davis M, Vander Stoep A: The transition to adulthood for youth who have serious emotional disturbance: developmental transition and young adult outcomes. Journal of Mental Health Administration 24:400–427, 1997
39.
Mitchell AJ, Selmes T: Why don’t patients attend their appointments? Maintaining engagement with psychiatric services. Advances in Psychiatric Treatment 13:423–434, 2007
40.
Cohen RA, Martinez ME: Health Insurance Coverage: Early Release of Estimates From the National Health Interview Survey, January–March, 2011. Atlanta, National Center for Health Statistics, 2011. Available at www.cdc.gov/nchs/nhis/releases.htm. Accessed May 17, 2012
Information & Authors
Information
Published In
Cover: Marooned, by Howard Pyle, 1909. Oil on canvas. Delaware Art Museum, Museum Purchase, 1912.
History
Published in print: April 2014
Published online: 15 October 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.).