This article addresses the Core Competency of Systems-Based Practice


Serving Persons With Severe Mental Illness in Primary Care–Based Medical Homes Marisa E. Domino, Ph.D., Rebecca Wells, Ph.D., M.H.S.A., Joseph P. Morrissey, Ph.D.

Objective: Primary care–based medical homes are rapidly disseminating through populations with chronic illnesses. Little is known about how these models affect the patterns of care for persons with severe mental illness who typically receive much of their care from mental health specialists. This study examined whether enrollment in a primary care medical home alters the patterns of care for Medicaid enrollees with severe mental illness. Methods: The authors conducted a retrospective secondary data analysis of medication adherence, outpatient and emergency department visits, and screening services used by adult Medicaid enrollees with diagnoses of schizophrenia (N=7,228), bipolar disorder (N=13,406), or major depression (N=45,000) as recorded in North Carolina Medicaid claims from 2004–2007. Participants not enrolled in a medical home (control group) were matched by propensity score to medical home participants on the basis of demographic

Health homes that provide and coordinate care for individuals with chronic mental disorders and other chronic illnesses are a fundamental reform implemented through the Affordable Care Act (ACA). For Medicaid enrollees, health homes may be in either specialty or primary care settings (1–3). Health homes are expected to improve the quality of care and result in greater efficiency through increased use of evidence-based practices, better utilization of health information technology, and better coordination of care (4–7). It remains to be seen whether the implementation of health homes and patient-centered medical homes, a related model not specific to Medicaid and generally limited to primary care settings, will improve care for people with mental illness (3,8,9). Approximately 7% of U.S. adults have a diagnosis of major depressive disorder in a given year, 3% have bipolar disorder (10), and .5% have schizophrenia (11). Disproportionate numbers of people with mental illness have general medical comorbidities, such as cardiovascular or pulmonary disease, diabetes, or arthritis (12,13), as well as high rates of smoking, drinking and other drug use, poor nutrition, sedentary lifestyles (14), unstable living situations, incarceration, poverty, and victimization, all of which undermine health. Mental health problems have also Psychiatric Services 66:5, May 2015

characteristics and comorbidities. Those dually enrolled in Medicare were excluded. Results: Results indicate that medical home enrollees had greater use of both primary and specialty mental health care, better medication adherence, and reduced use of the emergency department. Better rates of preventive lipid and cancer screening were found only for persons with major depression. Conclusions: Enrollment in a medical home was associated with substantial changes in patterns of care among persons with severe mental illness. These changes were associated with only a modest set of incentives, suggesting that medical homes can have large multiplier effects in primary care of persons with severe mental illness. Psychiatric Services 2015; 66:477–483; doi: 10.1176/

been shown to exacerbate the disability associated with general medical disorders, and patients with such comorbidities use high levels of medical services (15–17). The overall result is a life span estimated to be 25 or more years shorter than that for persons without mental illness (18). Despite high levels of specialty mental health care use (1,12,13) and accordingly high costs (15,16), many individuals with severe mental illness have inadequate access to primary care, and their general medical comorbidities go untreated (19,20). Medical homes for people with severe mental illness may have greater success in behavioral rather than primary care settings (21). However, specialty mental health providers are often in short supply (22), a gap that is likely to increase with the influx of newly insured people as the ACA is implemented (8). This raises the question of whether and how primary care medical homes improve both general medical and mental health care use among individuals with severe mental illness. In this study, we compared the patterns of care for persons with severe mental illness treated through North Carolina’s Medicaid medical home program and patterns for comparable individuals not enrolled in medical homes. North Carolina’s



more than 15-year history with the Medicaid-based medical homes model may provide an indication of what is to come as other states phase similar models into their Medicaid programs after the ACA. BACKGROUND ON HEALTH SERVICES USE The disproportionate burden of general medical comorbidities among people with severe mental illness suggests the need for more disease prevention and management. For instance, cardiovascular, pulmonary, and infectious diseases have been estimated to cause over half the premature deaths among people with schizophrenia (23). Primary care offers a potential intervention point for modifying health behaviors, such as counseling about smoking cessation or dietary changes. Specialty mental health care visits are important for diagnosis and treatment adjustments. In addition, psychosocial therapies are almost exclusively delivered by specialty mental health care providers or in specialty treatment settings. However, primary care may substitute in part for some specialty mental health services, such as medication management and care coordination among mental health providers and services. Medication adherence is notably low in the population with severe mental illness (24,25). Low medication adherence has been identified as a primary reason for hospitalization among individuals with schizophrenia (24,26). Preventive health care use may also reduce mortality through timely identification of treatable diseases. For instance, lipid profiles can determine the risk of heart disease, one of the leading causes of death among persons with severe mental illness (27). Despite clinical consensus on the need for metabolic screening (28,29), screening rates for people taking antipsychotics remain low (30). Rates of cancer and resultant mortality have also been shown to be disproportionately high among people with mental illness (31,32). Screening for cancers has been shown to be lower in this population despite the greater risk (33,34). PATIENT-CENTERED PRIMARY CARE–BASED MEDICAL HOMES Patient-centered medical homes have been gaining momentum as a model of primary care (35,36) and, beginning in 2008, have been recognized by the National Committee for Quality Assurance (NCQA). The medical homes approach is based on greater coordination of care among patients and providers, greater use of high-quality and safe medical technologies, a shifting of focus toward the full set of personal circumstances that may affect health status, greater access to care, and greater communication between patients and their provider team (37). Patient-centered medical homes may require transformation of Medicaid financing to incorporate the principles that define medical homes, which largely occur outside the fee-for-service structure (38). Payment system reforms targeting persons with mental illness in insurance programs often focus on controlling 478

mental health expenditures rather than on extracting efficiencies from general medical care. Reforms such as mental health carve-outs or prior authorization for psychotropic medications have demonstrated substantial reductions in expenditures on mental health treatments, often improving the efficiency of service provision. Mental health carve-outs, for example, have a demonstrated track record of reducing mental health expenditures by up to 40% without reducing the quality of care received (39). These efficiency gains have generally been achieved through creating greater access to appropriate outpatient mental health care and offsetting reductions in psychiatric hospital utilization. Improvements in primary care for persons with mental illness have typically taken the form of integrated models of care, where a specialty behavioral health provider is colocated in primary care settings (40). This approach has been useful for less severe mental illnesses (1), but the evidence base for persons with severe mental illness has not been established. Given the disproportionately high level of chronic medical conditions experienced by persons with severe mental illness, it is likely that further efficiency gains can be made from transforming the delivery of medical care, although the degree to which this is true is unknown. NORTH CAROLINA’S MEDICAL HOMES PROGRAM North Carolina’s Medicaid program has been a leader nationally in developing medical homes for patients with complex health problems (41–45). The process of phasing in the Medicaid medical homes model began in 1996 (41). Physicians organized themselves into nonprofit networks to support an enhanced version of primary care case management. Networks help practices coordinate care and implement disease management, case management, and other special emphasis programs for patients with chronic conditions (46). During our study period, both practices and networks received a $2.50 per-member per-month (PMPM) fee for serving as a medical home; rates have since increased. Providers that serve as medical homes receive enhanced training in team approaches in detecting and managing a number of chronic diseases, including diabetes and asthma. The medical homes program in North Carolina predates NCQA recognition, but North Carolina currently has one of the highest such patient-centered medical home recognition rates, with over 340 recognized practices statewide as of September 2012 (47). We are unaware of any previous empirical tests of the effects of medical homes on care utilization for people with severe mental illness. However, previous research on other populations has found that medical homes improve use of preventive care (44) and increase quality (48). We therefore sought to answer two questions. First, would adult Medicaid enrollees with severe mental illness enrolled in medical homes use more primary care, use less care in emergency departments, and have greater medication adherence? Second, would changes in care from participation in a medical home be uniform across the three severe mental illnesses Psychiatric Services 66:5, May 2015


examined (schizophrenia, bipolar disorder, and major depressive disorder)? METHODS We compared outcomes related to the use of health care services by persons with severe mental illness who were ever enrolled in medical homes with outcomes of similar persons with severe mental illness who were never enrolled in medical homes. During our study period (2004–2007), medical home enrollment was largely voluntary, raising the risk that selection effects might drive any observed differences in outcomes. To control for measurable factors that might confound focal estimates, we used 1:1 propensity score matching. Specifically, we ran logit models of ever enrolled or never enrolled in a medical home at the person level for each diagnostic group by using demographic characteristics and health condition indicators, including alcohol and drug use disorders (all ICD-9 codes indicating use, abuse, or dependence), other psychiatric and medical conditions, and a count of the total number of conditions (49). We selected propensity score–matched observations within one-quarter of a standard deviation caliper. Covariate balance in the matched samples was attained for all variables, as determined by a difference in covariates of less than one-half of a standard deviation (SD) (50). For most variables considered in this study, the difference was less than .2 SD. We used generalized estimating equation (GEE) models of person-month–level outcomes from the matched sample. In its parameter estimates, GEE specifically accounts for the correlation of repeated observations of individuals. The gamma family was used for the adherence models, the Poisson for visit counts, and the binomial for binary variables on any service use and preventive care. Models with count and continuous outcomes used a log link to account for the rightskewed distribution of variables. Preventive care measures were run on person-level data by using generalized linear models with a binomial link. Because of concerns about selection bias on unobservable differences by medical home status, we also ran our analyses using person-level fixed effects, which control for timeinvariant differences between the two groups on measurable as well as unmeasurable factors through the inclusion of individual intercepts (51). Results were very similar to those reported here, indicating that unmeasured selection of timeinvariant variables was not confounding these results. We obtained Medicaid claims and enrollment data from the North Carolina Division of Medical Assistance through the Carolina Cost and Quality Initiative (www.shepscenter. for 2004–2007, a period when there was growing enrollment in medical homes by Medicaid enrollees with severe mental illness. This period is relevant to other states only now ramping up their medical homes programs. Medicaid claims include all services and medications paid for by the Medicaid program. Adults age 18 or older with at least two outpatient visits or one inpatient stay with a diagnosis of Psychiatric Services 66:5, May 2015

bipolar disorder, schizophrenia, or major depression were identified in the claims data (N=272,149). Persons meeting criteria for bipolar disorder or schizophrenia were excluded from the sample with major depression. Persons dually enrolled in Medicare or who had Medicaid claims indicating nursing home use were also excluded because of the lack of data on pharmacy use after the implementation of Medicare Part D (N=89,110). Months with partial Medicaid coverage or with less than 90% of days enrolled were also excluded (N=3,601). Data were aggregated to the person-month level. The final matched sample size was 1,713,357 observations for 64,617 participants. The study was reviewed by the University of North Carolina Institutional Review Board. Time-varying monthly outcomes included an indicator of any visit and the number of visits to primary care providers; any visit and the number of visits to specialty mental health providers; medication adherence for each target medication class; and emergency department visits. Medication adherence was calculated separately for antidepressants and antipsychotics according to the proportion of days covered (PDC), reflecting the proportion of days in a month for which the study participant had a filled prescription of any medication in each target class (52,53). Months without any dispensed medication were assigned an adherence value of 0 under the assumption of recommended continuous treatment. Because persons with bipolar disorder use multiple classes of medications depending on current symptoms, we omitted adherence results for that sample. Primary care visits were identified by using provider specialty codes of family and internal medicine in the claims data files, and specialty mental health visits were identified as claims for psychiatrists, psychologists, social workers, or counselors. We examined HEDIS indicators for preventive general medical care using procedure codes in the claims data files, including the receipt of cholesterol screening and cancer screening for age- and gender-appropriate populations according to the American Cancer Association guidelines—namely colorectal cancer screening for enrollees age 50 and older, breast cancer screening for women age 40 and older, and cervical cancer screening for women ages 21–65. Because of the infrequency of screening services, we modeled the receipt of each service at any time over the four-year study period, controlling for months of Medicaid enrollment and yielding one observation per person. Medical homes enrollment was determined on a monthly basis regardless of the number of months enrolled. RESULTS In the matched samples (Table 1), we included 7,228 adult Medicaid enrollees with schizophrenia, 13,406 with bipolar disorder, and 45,000 with major depressive disorder. The average ages of persons in medical homes were similar across conditions, as were the number of comorbidities and the percentage who were Latino and of nonwhite, non–AfricanAmerican race. The percentage of African Americans and the



TABLE 1. Demographic characteristics of Medicaid enrollees, by diagnostic group and medical home statusa Schizophrenia In a medical home (N=3,614) Characteristic Age (M6SD) N of comorbidities (M6SD) Male Latino African American Other race a



40.4612.4 2.462.5 1,840 75 1,772 230

Bipolar disorder

Never in a medical home (N=3,614) N


40.7612.7 1.962.3 50.9 2.1 49.0 6.4

1,989 86 1,821 220

In a medical home (N=6,703) N


36.3611.3 2.762.5 55.0 2.4 50.4 6.1

2,010 124 1,301 361

Major depressive disorder

Never in a medical home (N=6,703) N


37.4611.9 2.162.3 30.0 1.8 19.4 5.4

2,012 120 1,105 321

In a medical home (N=22,500) N


40.3612.5 3.362.7 30.0 1.8 16.5 4.8

6,077 788 5,908 1,938

Never in a medical home (N=22,500) N


40.4613.4 2.362.5 27.0 3.5 26.3 8.6

5,519 702 5,088 1,686

24.5 3.1 22.6 7.5

Propensity score–matched control group. Balance attained on all covariates as determined by standardized differences between medical home enrollees and those in the control group (#.4). Balance was attained at ,.25 standardized difference for all variables, except for number of comorbidities in the group with major depressive disorder. Covariates included those listed in the table and a series of 25 indicators for mental and general medical health modified from the Chronic Illness and Disability Payment System.

percentage of males both varied widely across the three conditions. After controlling for baseline covariates, we found that persons with severe mental illness in medical homes had greater rates of use of both primary and specialty mental health care, compared with propensity matched controls (Table 2, column 3). The association between being enrolled in a medical home and

having any primary care visits was similar across diagnostic groups, ranging from a 24 to 26 percentage point increase in the probability of one or more primary care visits per month (p,.01)—that is, a 57%285% proportionate increase in the rates of primary care use compared with rates for persons not in medical homes. The effects of being enrolled in a medical home on the number of primary care visits were also similar across groups, amounting to about half (.54 to .61) a TABLE 2. Differences in monthly service use between Medicaid medical home enrollees and primary care visit per month. a matched control group, by diagnostic groupa Enrollees in Medical homes In a medical Never in a had slightly increased use of home medical home Average specialty mental health care, (unadjusted (unadjusted marginal representing less than a 1 perOutcome 95% CI mean) mean) effectb centage point increase, or a 1% Schizophrenia (N=7,188; 22% relative effect (p,.01). observations=213,649) Any primary care use (%) 52.6 31.1 26.4** 26.1 to 26.8 The increase in the number of N of primary care visits 1.1 .67 .58** .56 to .60 specialty mental health visits Any specialty mental health visits (%) 40.7 41.4 .96** .53 to 1.40 ranged from about a fifth of a N of specialty mental health visits 2.7 2.6 .62** .58 to .66 c visit per month (.20) for people Adherence .45 .42 .11** .10 to .12 with major depression to more Any emergency department use (%) 12.8 11.8 2.64** –.92 to –.37 than half a visit per month (.62) Bipolar disorder (N=13,351; observations =354,420) for people with schizophrenia. Any primary care use (%) 54.4 39.1 25.7** 25.4 to 25.9 Medication adherence, as N of primary care visits 1.1 .84 .61** .60 to .63 measured by the PDC, was Any specialty mental health visits (%) 30.6 32.1 .62** .30 to .94 relatively low. In the control N of specialty mental health visits 1.7 1.7 .50** .48 to .52 group, patients with schizoAny emergency department use (%) 18.3 17.2 –.85** –1.10 to –.59 phrenia had medication for Major depressive disorder (N=44,560; an average of 42% of days in observations=1,177,561) Any primary care use (%) 56.2 41.5 23.5** 23.4 to 23.7 a month over the study period, N of primary care visits 1.1 .79 .54** .53 to .55 and those with depression had Any specialty mental health visits (%) 12.7 14.3 .13* ,.01 to .25 medication for 30% of days. N of specialty mental health visits .60 .64 .20** .19 to .20 c Adherence among persons with Adherence .30 .30 .13** .12 to .13 schizophrenia in medical homes Any emergency department use (%) 16.5 13.7 –.60** –.73 to .47 a was 11 percentage points greater Source: North Carolina Medicaid Claims data. Results are from generalized estimating equation models. b Difference from the control group after adjustment for baseline covariates. For variables measured in mean perthan adherence in the matched centages, differences are expressed in percentage points. control group in propensityc Adherence was measured according to the proportion of days covered; 0, no medication in the target class disadjusted results (p,.01), and pensed in a calendar month, 1.0, participant had dispensed medication each day in the month. *p,.05, **p,.01 adherence was 13 percentage 480

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points higher for those in TABLE 3. Differences in use of preventive care over four years between Medicaid medical home a medical homes with major enrollees and a matched control group, by diagnostic group Never in a medical Average depressive disorder (p,.01). In a medical home home (unadjusted marginal These increases amount to Service (unadjusted mean %) mean %) effectb 95% CI a 26%243% relative increase over adherence rates in the Schizophrenia Lipid panel 38.3 26.5 1.6 –.36 to 3.6 sample of patients not in Cancer screening medical homes. We also found Colorectal (age .50) 34.1 21.8 1.2 –3.5 to 6.0 lower rates of emergency deBreast (women age $40) 21.8 14.7 –.7 –4.2 to 2.8 Cervical (women age 21–65) 28.2 20.1 1.8 –1.2 to 4.7 partment use in propensityBipolar disorder adjusted models for all three Lipid panel 35.0 26.8 –.2 –1.7 to 1.2 diagnostic groups enrolled in Cancer screening medical homes (p,.01), alColorectal (age .50) 31.0 23.0 –1.4 –5.9 to 3.2 though the effects were modBreast (women age $40) 22.1 13.4 1.1 –1.6 to 3.8 est, amounting to less than 1 Cervical (women age 21–65) 32.8 25.1 .5 –1.3 to 2.4 percentage point decrease in Major depressive disorder use, or a 5% proportional efLipid panel 37.7 26.4 1.7** .9 to 2.5 Cancer screening fect (p,.01). Colorectal (age .50) 36.6 24.5 2.3* .5 to 4.1 Even though medical home Breast (women age $40) 20.8 12.9 1.5* .2 to 2.8 enrollees had greater use of Cervical (women age 21–65) 31.1 23.5 1.2* .2 to 2.3 outpatient services, medical a Source: North Carolina Medicaid Claims data. Results are from generalized estimating equation models. home enrollment was asso- b Difference from the control group after adjustment for baseline covariates. Differences are expressed in percentage ciated with significantly more points. use of preventive services only *p,.05, **p,.01 for people with major depressive disorder (Table 3). For this group, enrollment in a medical depressive disorder in medical homes. This difference across home was associated with an almost 2 percentage point greater groups might reflect the greater integration of depression probability of having a lipid panel (p,.01), a 2.3 percentage treatment in primary care and greater physician understanding point increase in the probability of being screened for colorectal and comfort in managing depression as opposed to psychotic cancer (p,.05), a 1.5 percentage point increase in the probaconditions. This interpretation is also consistent with the bility of being screened for breast cancer (p,.05), and a 1.2 finding of a proportionately greater number of specialty percentage point increase in the probability of being screened mental health visits for persons with schizophrenia and for cervical cancer (p,.05). Although modest in absolute terms, bipolar disorder compared with those with major depressive given the low rates of screening in this population, these difdisorder. ferences represent a 5%212% relative increase, the highest A number of limitations should be considered in interbeing for breast cancer screening. pretation of these results. Medicaid claims data reflect only services paid through the Medicaid program and may not reflect all services received. Measures of severity of the DISCUSSION target psychiatric conditions were not available in the claims In this study, Medicaid enrollees with severe mental illness data. Measures of medication adherence are based on filled in a medical home had considerably greater use of primary claims only and may not reflect actual pill taking. If selection care and higher medication adherence and modestly greater into the medical homes programs was not fully accounted use of specialty mental health services than Medicaid enrollees for by the baseline demographic and medical comorbidity who were not in a medical home, even after the analysis balvariables, then some degree of selection bias could have anced the matched samples on an array of health comorbiddriven the results. However, results from fixed-effects ities and sociodemographic characteristics. Evidence suggests models that controlled for time-invariant differences bethat such enhanced health care use will decrease symptomtween those in medical homes and those not in medical atology (54,55) and the need for emergent care (56). It is homes yielded virtually identical results, indicating that somewhat surprising that the modest incentives provided by unmeasured time-invariant selection was not likely to bias the Community Care of North Carolina program during our the reported results; time-varying differences could still study period, amounting to $5 PMPM split equally between pose a problem. Finally, North Carolina has been undergoing practices and networks, were associated with the relatively a series of mental health service reforms over the past delarge effects on primary care use and medication adherence cade (57). We do not believe these reforms disproportionreported here. ately affected medical home enrollees, but if they did, then Of interest, these access gains translated into increased they could confound the results here attributed to medical use of preventive services only for persons with a major homes. Psychiatric Services 66:5, May 2015



CONCLUSIONS Findings provide new evidence not previously available about how primary care patient–centered medical homes affect use of general medical care by persons with severe mental illness. Greater knowledge about the overall and conditional effects of medical homes on the quantity and quality of care will assist policy makers in their efforts to implement the ACA and to improve the efficiency and effectiveness of care for persons with mental illness in primary care settings. AUTHOR AND ARTICLE INFORMATION Dr. Domino and Dr. Morrissey are with the Department of Health Policy and Management and the Cecil G. Sheps Center for Health Services Research, University of North Carolina, Chapel Hill (e-mail: domino@ Dr. Wells is with the Department of Management, Policy, and Community Health, University of Texas, Houston. Portions of this work were presented at the National Institutes of Mental Health economics meeting, Minneapolis, Minnesota, June 11–13, 2012. Funding for data set creation was provided by NARSAD and the North Carolina Translational and Clinical Sciences Institute. Excellent programming support was provided by Shirley Richards. Earlier versions of the analysis benefited from thoughtful comments by Richard Frank, Ph.D., and by participants at the National Institutes of Mental Health economics meeting, Minneapolis, Minnesota, June 11–13, 2012. The authors report no financial relationships with commercial interests. Received December 9, 2013; revision received September 5, 2014; accepted October 9, 2014; published online February 17, 2015.

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Serving persons with severe mental illness in primary care-based medical homes.

Primary care-based medical homes are rapidly disseminating through populations with chronic illnesses. Little is known about how these models affect t...
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