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J Ment Health Policy Econ. Author manuscript; available in PMC 2016 February 23. Published in final edited form as: J Ment Health Policy Econ. 2015 September ; 18(3): 115–124.
Thirty Day Hospital Readmission for Medicaid Enrollees with Schizophrenia: The Role of Local Health Care Systems Alisa B. Busch, MD, MS1,2,3 [Assistant Professor, Chair, Director], Psychiatry & Health Care Policy, Harvard Medical School; Health Services Research Division, Partners Psychiatry and Mental Health; Integration of Clinical Measurement & Health Services Research, McLean Hospital
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Arnold M. Epstein, MD, MA4 [Chair, Professor], John H. Foster Professor of Health Policy and Management, Harvard School of Public Health; Department of Health Policy and Management, Harvard School of Public Health; Medicine and Health Care Policy, Harvard Medical School Thomas G. McGuire, PhD2,6 [Professor], Health Economics, Harvard Medical School Sharon-Lise T. Normand, PhD2,5 [Professor], and Health Care Policy (Biostatistics), Harvard Medical School; Biostatistics, Harvard School of Public Health
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Richard G. Frank, PhD2,6 [Professor] Margaret T. Morris Professor of Health Economics, Harvard Medical School
Abstract Background—Examining health care system characteristics possibly associated with 30-day readmission may reveal opportunities to improve healthcare quality as well as reduce costs. Aims of the Study—Examine the relationship between 30-day mental health readmission for persons with schizophrenia and county-level community treatment characteristics.
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Methods—Observational study of 18 state Medicaid programs (N=274 counties, representing 103,967 enrollees with schizophrenia--28,083 of whom received ≥1 mental health hospitalization) using Medicaid administrative and United States Area Health Resource File data from 2005. Medicaid is a federal-state program and major health insurance provider for low income and disabled individuals, and the predominant provider of insurance for individuals with schizophrenia. The Area Health Resource File provides county-level estimates of providers. We first fit a regression model examining the relationship between 30-day mental health readmission
Corresponding Author and Address for Reprints: Alisa Busch, McLean Hospital, Mailstop 226, 115 Mill St., Belmont, MA 02478, Phone: (617) 855-2989 / Fax: (617) 855-2174 / [email protected]. 1McLean Hospital, Belmont, MA 2Department of Health Care Policy, Harvard Medical School, 180 Longwood Ave., Boston, MA 3Health Services Research Division, Partners Psychiatry & Mental Health, Boston, MA 4Department of Health Policy and Management, Harvard School of Public Health, 677 Huntington Ave., Boston, MA 02115; Division of General Medicine, Brigham and Women's Hospital, Boston, MA 5Department of Biostatistics, Harvard School of Public Health, Boston, MA 6National Bureau of Economic Research, Cambridge, MA
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and enrollee characteristics (e.g., demographics, substance use disorder [SUD], and general medical comorbidity) from which we created a county-level demographic and comorbidity casemix adjuster. The case-mix adjuster was included in a second regression model examining the relationship between 30-day readmission and county-level factors: 1) quality (antipsychotic/visit continuity, post-hospital follow-up); 2) mental health hospitalization (length of stay, admission rates); and 3) treatment capacity (e.g., population-based estimates of outpatient providers/clinics). We calculated predicted probabilities of readmission for significant patient and county-level variables.
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Results—Higher county rates of mental health visits within 7-days post-hospitalization were associated with lower readmission probabilities (e.g., county rates of 7-day follow up of 55% versus 85%, readmission predicted probability(PP)[95%CI]=16.1%[15.8%-16.4%] versus 13.3%[12.9%-13.6%]). In contrast, higher county rates of mental health hospitalization were associated with higher readmission probabilities (e.g., country admission rates 10% versus 30%, readmission predicted probability=11.3%[11.0%-11.6%] versus 16.7%[16.4%-17.0%]). Although not our primary focus, enrollee comorbidity was associated with higher predicted probability of 30-day mental health readmission: PP[95%CI] for enrollees with SUD=23.9%[21.5%-26.3%] versus 14.7%[13.9%-15.4%] for those without; PP[95%CI] for those with ≥three chronic medical conditions = 25.1%[22.1%-28.2%] versus none=17.7%[16.3%-19.1].
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Discussion—County rates of hospitalization and 7-day follow-up post hospital discharge were associated with readmission, along with patient SUD and general medical comorbidity. This observational design limits causal inference and utilization patterns may have changed since 2005. However, overall funding for U.S. Medicaid programs remained constant since 2005, reducing the likelihood significant changes. Last, our inability to identify community capacity variables associated with readmission may reflect imprecision of some variables as measured in the Area Health Resource File. Implications for Health Care Provision and Use & for Health Policies—Healthcare policy and programming to reduce 30-day mental health readmissions should focus on countylevel factors that contribute to hospitalization in general and improving transitions to community care, as well as patient comorbidity. Implications for Further Research—Given the likely importance of local care systems, to reduce readmission future research is needed to refine community-level capacity variables that are associated with reduced readmissions; and to evaluate models of care coordination in this population.
Introduction Author Manuscript
Hospital readmissions are burdensome to patients and families. They are an increasing focus of healthcare policy because they can reflect substandard care and result in high costs. In 2012 the United States' Centers for Medicare and Medicaid Services (CMS) began reducing all Medicare payments to hospitals with excessive readmissions for enrollees initially hospitalized with congestive heart failure, pneumonia, and acute myocardial infarction.(1) In 2015 the program expanded to include additional medical conditions and procedures, and a higher penalty (up to 3% of Medicare payments). This CMS policy is likely to further expand to other clinical areas. Persons hospitalized for psychiatric illnesses, particularly J Ment Health Policy Econ. Author manuscript; available in PMC 2016 February 23.
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schizophrenia, have among the highest readmission rates.(2-6). Medicaid, a program jointly funded by the federal government and states and major provider of health insurance to the non-elderly poor and disabled, is the predominant insurance program for individuals with schizophrenia(7). The prevalence of schizophrenia in Medicaid programs, coupled with high readmission rates for these individuals, makes reducing readmissions in this population a high priority(8, 9). At least one state Medicaid program, Texas, began penalizing hospitals in 2012 for potentially preventable readmissions, including for mental health/substance use disorders(9). Others, such as Illinois, are actively considering such legislation(10).
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Patient characteristics may contribute to readmission, but so can care available in the community. Most care for individuals with severe mental illness in Medicaid is from local service systems but we know little about them and their role in readmissions. For example, how important are comorbidity, poor quality of care, the general propensity of providers to hospitalize in a local care system, and outpatient provider capacity? The primary objective of this study is to examine the relationship between 30-day readmission and county-level characteristics such as quality of care measures, hospital utilization, and community treatment capacity across 274 counties in 18 state Medicaid programs. We examine mental health readmissions specifically so we may examine how the mental health system affects readmission. We hypothesize that, after controlling for patient demographic and clinical characteristics, community care characteristics such as elements of community quality of care, overall propensity to use hospital service, and treatment capacity will be associated with mental health readmissions.
Methods Author Manuscript
Data sources
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We drew our study cohort from a multi-state dataset of the 2005 Medicaid Analytic eXtract (MAX) administrative data. Due to the complexity in compiling data from multiple Medicaid programs, multi-state MAX datasets typically take several years to be complete and available for research analysis(11). MAX data include the inpatient, outpatient, pharmacy claims, and their associated costs for state Medicaid programs. We excluded states in the MAX data system that did not report complete general medical and mental health claims or encounter data for Medicaid recipients with schizophrenia. We defined county-level schizophrenia treatment utilization and quality using the 2005 MAX data. For local treatment capacity we use county-level data from the 2005 Area Health Resource File. The Area Health Resource File is a county-specific database that includes annual information about county-level healthcare resources such as health facilities and health professionals. Assessment of state's Medicaid claims completeness Many state Medicaid programs have contracted with managed behavioral healthcare organizations (MBHCOs) to either provide or administer their Medicaid program's behavioral healthcare. MBHCOs often track utilization via encounter data (not claims) and states that contract with MHBCOs do not universally include the behavioral health
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encounters when submitting to the MAX data system. After consulting with the MAX data distributer and state Medicaid authorities regarding the completeness of their MAX behavioral health data, we excluded 22 states from the analysis due to known incomplete behavioral health utilization data in those states. Using MAX data for the remaining 28 states, we assembled a schizophrenia cohort from which we could examine the data to verify completeness. The schizophrenia cohort was defined as enrollees who were non-elderly adults (ages 18-64) with either two outpatient claims on different service dates where schizophrenia (ICD9 code 295) was in the first or second diagnostic field or at least 1 inpatient hospitalization for schizophrenia. We conducted a series of state by state analyses to examine the number of enrollees diagnosed with schizophrenia who were in fee-forservice, MBHCO or capitated programs (capitated programs would not have encounters noted in the administrative data). We also examined utilization within each of these three categories, including inpatient, ambulatory, general medical and behavioral health claims, as well as total and mental health spending. When the 28 state analyses indicated that we did not have complete encounter data for the enrollee population in a given state, we contacted representatives from those Medicaid programs to inquire if the data were in fact submitted to CMS. After this process, 19 Medicaid programs for which we had complete data remained (AK, AR, CA, GA, IL, IN, KS, KY, MN, MS, MT, NC, NH, NJ, NY, OK, SC, SD, WV). Study population
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In addition to the process described above for determining which states had complete utilization data available, we developed minimum enrollment criteria at the enrollee level to ensure that we could adequately track utilization. For example, many states contract with HMOs to authorize services and pay providers. HMOs typically pay providers on a per member/per month basis and do not report encounter data. Few Medicaid enrollees with schizophrenia are in in HMO plans (∼2% in our data). However, since the ability to observe service utilization was important for this study, we further required a minimum of 10 of 12 months of non-HMO Medicaid enrollment. We also excluded individuals who were dually eligible for Medicare insurance (the U.S. federally funded insurance for the elderly and disabled), since the Medicaid utilization claims for these individuals would have been incomplete. Since some of our key measures of local mental health delivery system quality are at the aggregate county-level, we reviewed the distribution of schizophrenia enrollees within counties. In counties with few schizophrenia enrollees, low sample sizes could produce unreliable county estimates of readmission. Therefore, we excluded counties with 30 days after the index prescription). Another variable involved the proportion of individuals with schizophrenia in the county who received “continuous contact” in their outpatient mental health care (defined as a mental health visit at least once every 2 months). A third county-level quality measure was the proportion of county mental health discharges with a follow-up community appointment within 7 days. All visit claims were required to have a primary or secondary mental health diagnosis, and were selected based on Healthcare Procedure Common Procedure Coding (Common Procedural Terminology [CPT] or state-specific mental health procedure codes). Mental health follow-up within 7 days of hospital discharge included residential/partial hospital/intensive outpatient treatment, outpatient initial evaluations/mental status evaluations (including medication management [CPT 90862]), or psychotherapy (individual, group, family). Mental health “continuous contact” included the above visit types, as well as outpatient case management, psychosocial rehabilitation, or addiction detoxification. Two variables measured county-level hospital utilization for schizophrenia enrollees—the mental health hospitalization rate and the average hospital length of stay. We included county rates of mental health hospitalization because in the general medical literature, county-level hospitalization rate is associated with readmission(26), suggesting that drivers of readmission and admission may be similar (or have some overlap). We included average length of stay because prior mental health literature is inconsistent regarding whether it is associated with readmission(5, 27-31).
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County-level treatment capacity factors were calculated from the Area Health Resource File and included the number of clinicians per 100,000 census population (psychiatrists, primary care clinicians), inpatient mental health specific bed capacity (free standing psychiatric and alcohol and chemical dependency beds), and outpatient treatment centers (community mental health centers and federally qualified health centers). Although we planned to include non-physician specialty care providers (i.e., social workers, psychologists) in the analysis, the Area Health Resource File did not include complete data on these groups. Data Analytic Procedures
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We first calculated descriptive statistics for the schizophrenia enrollees with ≥1 mental health hospitalization, and for the county capacity and quality measures (mean/standard deviation for continuous variables, percent for categorical variables). We assessed the pairwise correlations of the county measures to determine if any should be excluded from the model due to high correlation. To adjust for county-level case-mix, we first estimated a patient-level logistic regression model with the index hospitalization as the unit of observation and the outcome as occurrence of a 30-day mental health readmission for enrollees who had at least one mental health hospitalization in 2005. Explanatory variables included patient demographic and
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clinical characteristics, and accounted for repeated observations on the same person for those having >1 index hospitalization through a dichotomous indicator variable. This model produced a predicted probability of mental health readmission for each person which we then averaged over all persons within each county.
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Our main analysis relied upon a county-level linear regression model in which the unit of observation was the county and readmission rate served as the dependent variable. Explanatory variables included the above described county measures of quality, hospitalization, and capacity, and the county-level case-mix adjuster derived from the patient characteristics index hospitalization model. To adjust for county schizophrenia enrollee population size, we weighted the linear regression model by the number of schizophrenia-diagnosed enrollees with at least one mental health hospitalization in each county. We excluded from this model the county rate of primary care clinicians because our correlation analyses found it highly correlated with that of psychiatrists (correlation coefficient=0.95). For both models we accounted for clustering using the Generalized Estimating Equations (GEE) estimator(32), clustering on county in the personhospitalization level model, and on state in the county-level model.
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We determined the predicted probability and 95% confidence intervals of readmission for key significant enrollee and county-level variables. Predicted probabilities provide an easily interpretable magnitude of the associations. For dichotomous variables, we used predicted probabilities to contrast readmission rates for those with and without the dichotomous characteristic. For continuous variables (e.g., county-level variables), we selected a discrete set of county means to serve as contrasts. Specifically, we calculated predicted probabilities of readmission for changes representing approximately one-half to one standard deviation above and below the mean value for the county-level factor to provide realistic contrasts of predicted probability rates. All analyses were conducted using Stata version 11.2.
Results
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Comorbidity was common among this hospitalized population (N=28,083, Table 1). Fiftyone percent had a comorbid substance use disorder diagnosis. More than half had a cardiovascular, diabetes, hypertension or chronic pulmonary disorder, and nearly 20% at least two of those conditions; 12% had comorbid neurological conditions that can affect cognition. Nearly 25% of the hospitalized sample experienced a readmission within 30-days of discharge. Sixty-four percent of the study sample contributed one index hospitalization during the year; an additional 24% contributed two, and 9% contributed three (data not shown). County-level measures showed low rates of care quality--particularly for continuous antipsychotic medication days supply (mean= .57[sd=.10]) and ≥1 mental health visit per 2 months (.50[.12]). Rates of post-hospital discharge follow-up (.70[.24]) appeared to be better. In the patient-level logistic regression model (Table 2), comorbid substance use disorders, general medical conditions, and neurological conditions were significantly associated with
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an increase in an individual's risk of readmission, however, the association was more pronounced for comorbid substance use and general medical conditions. Comorbid substance use disorders were associated with a readmission predicted probability of 23.9% [95%CI=21.5%, 26.3%], whereas those without a comorbid substance use disorder had a readmission predicted probability of 14.6% [95%CI=13.9%, 15.4%]. Individuals diagnosed with three or four of the general medical conditions examined here had a readmission predicted probability of 25.1%[22.1%, 28.2%], whereas those with none of the comorbid medical conditions had a 17.7%[16.3%, 19.1%] predicted probability of readmission.
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In the county-level linear regression (Table 3), county demographic and comorbidity characteristics (i.e., the variable we constructed to be the case-mix adjuster) was significantly associated with an increased probability of readmission. Lower county rates of 7 day post-discharge follow-up and higher county readmission rates were associated with higher risks of readmission. For example, if the county rate of 7 day follow-up post hospitalization were 85%, the predicted probability of readmission was 13.3% [95%CI 12.9%, 13.6%], whereas it increased to 16.1%[15.8%, 16.4%] if the county rate of 7-day post-discharge follow-up were 55%. For a 30% county hospitalization rate, the predicted probability of a hospital readmission was 16.7%[95%CI 16.4%, 17.0%]. Whereas if the county hospitalization rate was lower at 10%, the predicted probability of readmission would be 11.3%[11.0%, 11.6%].
Conclusions
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Three important county-level findings emerged from our study. First, independent of county case-mix, we found quality of transitional care as measured by the county-wide probability of a community visit within 7 days of discharge was associated with a decreased probability of readmission for enrollees. Second, overall county rates of mental health hospitalization were strongly associated with an increased probability of readmission for enrollees who had at least one index hospitalization. Third, none of the county treatment capacity variables were associated with readmission rates. Among our measures of community treatment quality, only county rate of post-discharge follow up was associated with readmissions. Community visit within 7-days of hospital discharge is a quality measure is endorsed by the National Quality Forum(19), but our findings represent the first study actually to find it associated with lower readmission rates. One could also argue that hospitals are capable of having a large impact on this measure although the availability of community providers is obviously very important.
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We also found that the county mental health hospitalization rate was associated with higher readmission rates. Our results are consistent with recent general medical research(26) suggesting that overall propensity of a community/geographical area to hospitalize may be an important predictor of readmissions as well. Perhaps there are inadequate resources in a geographical location to support individuals in the community who are having an acute exacerbation, or that local culture of the community-based care has a lower threshold for hospitalizing individuals in some geographical regions compared to others.
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Surprisingly, few of our measures of county-level treatment quality or capacity were associated with readmission. For example, a county's rate of achieving antipsychotic medication continuity for all schizophrenia-diagnosed Medicaid enrollees was not associated with readmission. This is in contrast to prior research showing that medication continuity is associated with reduced mental health admissions in persons with schizophrenia(22, 23). Possibly, the prior observed association between higher medication continuity rates and lower hospitalization may be due to selection bias (i.e., less severely ill patients may be more adherent patients and [independently] less likely hospitalized in a given year). Given that our medication continuity variable is a reflection of county-level adherence for all individuals with schizophrenia (not just those hospitalized), it is less influenced by this possible bias.
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Finally, while not focus of our study, we found that comorbidity (substance use disorder and general medical) was a key determinant of 30-day readmission. Comorbid substance use disorders are destabilizing for persons with psychiatric illness, including schizophrenia(33, 34), so our result was not surprising. However, the effect size was larger than previously appreciated—increasing 30-day rehospitalization risk by almost two-thirds. Addressing this comorbidity could be important in improving stability and reducing rehospitalization rates for this population. These findings can provide important guidance for public health insurance programs, which may have restrictive policies that impede adequate substance use disorder care. For example, in the U.S., state Medicaid substance use benefits can be very restrictive(35) and more restrictive Medicaid substance use treatment benefits have been associated with lower provider acceptance of Medicaid(36)—further exacerbating problems with treatment access. While it is unclear the extent that additional supports and treatment to individuals with comorbid substance use disorders will reduce readmissions, the substantial impact of substance use disorders in mental health readmissions highlights an opportunity for public insurance programs such as Medicaid, to influence improved access to treatment through healthcare policy. Medical comorbidity is recognized as a risk factor for 30-day general medical readmission(4, 37-39). More recent evidence finds that general medical conditions are associated with mental health readmissions in a mixed psychiatric population(5). However, no prior literature has documented the “dose response” relationship of general medical comorbidity on mental health readmissions. This finding underscores the importance of coordinated care addressing both the mental health and general medical needs of this vulnerable population. Targeted efforts for both may reduce both mental health and general medical readmissions (and admissions).
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There are several limitations worth noting. Our data are observational and therefore we cannot infer causality in our associations. In our county-level model we have attempted to control for some of the threats to causal inference by, when possible, using measures where the denominator reflected all schizophrenia diagnoses enrollees in a county, not just those hospitalized (e.g., hospitalization rates, medication and visit continuity rates). Our data reflect utilization that occurred in 2005, prior to the 2007-2009 U.S. recession when many states reduced their Medicaid funding – this fact could impact our results. Unfortunately, government release of multi-state MAX data for analysis typically lags several years due to
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data availability and completeness(11). However, despite the U.S. recession, the overall rate of federal and state Medicaid spending has remained stable(40). Given the overall stability of public spending, we do not expect that the relationship between readmission and patient/ county characteristics would have changed over time, so results from this study will remain meaningful. It is unclear whether the county rate of hospitalization reflects only regional differences in providers' propensity to hospitalize or also community treatment capacity, the latter feature incompletely captured using claims and Area Health Resource File data.
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We selected county-level measures that face validity would suggest are a reasonable place to start to examine community treatment capacity. However, they may be imprecise and reflect a limitation of these variables as measured in the Area Health Resource File. For example, county rates of psychiatrists working in Community Mental Health Centers, or Community Mental Health Center size may be more relevant than overall county rates of psychiatrists or Community Mental Health Centers. Similarly, the rate of free-standing psychiatric beds may not reflect the total inpatient psychiatry bed capacity of a community. Additionally, Area Health Resource File limitations precluded us from including estimates of non-physician mental health specialists (e.g., psychologists, social workers) and non-inpatient substance use treatment availability, important components of the mental health workforce and service system. Other, more nuanced community capacity or treatment characteristics that we were unable to measure (e.g., state supported housing, assertive community treatment) could be important areas for future study. Given it is unlikely that local systems factors are unrelated to readmissions, an important area of future research will be to either refine these variables, or develop and empirically test new ones.
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Despite these limitations, this study provides new information about factors associated with of 30 day readmission for enrollees with schizophrenia served in the U.S. public system. Knowing the magnitude of increased readmission probability associated with these comorbidities (e.g., a 63.6% increase for substance use disorders and up to 41.8% medical conditions) clarifies the large potential impact that targeted interventions for these higher risk groups may have. Lower county level rates of admission and higher rates of postdischarge community follow-up within 7 days also were associated with (lower) readmission probability. These differences in readmission probability are meaningful from a health policy perspective as they represent opportunities for significant cost savings, in addition to improved transitions to care.
Supplementary Material Refer to Web version on PubMed Central for supplementary material.
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Acknowledgments The authors gratefully acknowledge support from the National Institute of Mental Health (NIMH R01 MH081819) and the Health Services Research Division of Partners Psychiatry & Mental Health (AB). We are also grateful for the programming expertise of Christina Fu, Ph.D.
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Table 1
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Descriptive characteristics of the schizophrenia diagnosed enrollees with ≥1 behavioral health hospitalization in 2005 (N=28,083 enrollees) and county characteristics (N=274). Patient Characteristic
N
%
13,400
47.7
White
9,222
32.8
Black
11,954
42.6
Hispanic
3,525
12.6
Other
3,382
12.0
Supplemental Security Income months > other Medicaid eligibility months
25,465
90.7
Substance use disorder
14,324
51.0
Neurological disordera
3,451
12.3
0 conditions
14,795
52.7
1 condition
7,978
28.4
2 conditions
3,746
13.3
3 conditions
1,268
4.5
4 conditions
296
1.05
6,976
24.8%
Female Race/Ethnicity
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Medical illness burden (non-neurological)b
At least one 30-day readmission
Age County characteristics
Mean
SD
40.1
11.2
Mean
SD
Min
Max
Continuous antipsychotic medication days supplyc
0.57
.10
.03
.93
At least one behavioral health visit per 2 months
0.50
.12
.05
.88
Behavioral health community follow-up within 7 days post behavioral health hospitalization discharge
0.70
.24
0
1.0
Behavioral health admission rate
0.22
.09
.01
.46
Average behavioral health hospital length of stay
9.0
4.0
1.3
42.2
Psychiatrists
8.8
10.1
0
94.6
Primary care physicianse
63.1
33.9
6.1
344.0
Free-standing psychiatric care beds
18.2
27.5
0
339.5
Free-standing alcohol/chemical dependency beds
2.8
8.4
0
69.2
Community mental health centers
0.26
.54
0
3.6
Federally qualified health centers
1.8
3.3
0
31.9
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Quality of Care for Enrollees Diagnosed with Schizophrenia
Utilization variables that may affect behavioral health readmission
Community Treatment Capacityd
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a
Diagnoses include: dementia, Parkinson's disease, anoxic brain damage.
b
Categories include: cardiovascular, diabetes, chronic pulmonary, hypertension.
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No gap in days supply > 30days in year.
d
Rates per 100,000 census population in county.
e
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Internal medicine, primary care, and family practice physicians.
Patient characteristics derived from claims data, county characteristics derived from claims data and Area Health Resource File.
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Table 2
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Multivariate logistic regression results of patient characteristics associated with 30-day behavioral health readmission for schizophrenia diagnosed Medicaid enrollees (N=28,083 enrollees). Pseudo R2=1.8%
Coefficient
Standard Error
Age
-.005*
.002
Female
-.07*
.04
Race/Ethnicity (white = reference) Black
.04
.07
.09**
.04
.07
.05
SSI months> other Medicaid eligibility months
-.08
.12
Prior index hospitalization
.11*
.05
Substance use disorder
.60***
.05
Neurological disorder+
.11***
.02
1 condition
.22***
.05
2 conditions
.31***
.07
3-4 conditions
.45***
.05
Hispanic Other
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Medical illness burden (non-neurological, reference = 0) ++
p. value: * ≤.05 ** ≤.01 *** ≤.001 Predicted Probability of 30 Day Behavioral Health Readmission for Statistically Significant Case Mix Variables for an Individual Predicted Probability (%)
95% CI
White
19.4
18.1-20.6
Hispanic
20.8
19.7-21.9
No
14.6
13.9-15.4
Yes
23.9
21.5-26.3
No
19.7
18.0-21.4
Yes
21.5
19.5-23.5
None
17.7
16.3-19.1
One
21.0
18.9-23.2
Two
22.6
20.2-25.1
Three-Four
25.1
22.1-28.2
No
18.9
17.7-20.0
Yes
20.6
18.5-22.7
Race
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Comorbid substance use disorder
Comorbid neurological+
Comorbid general medical++
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Prior hospitalization (>30 days earlier)
Presence of ICD9 code for:
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+
dementia, Parkinson's Disease, anoxic brain damage.
++ cardiovascular condition, hypertension, diabetes, or COPD.
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Table 3
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Multivariate linear regression results of county characteristics associated with 30-day behavioral health readmission rate for Medicaid enrollees diagnosed with schizophrenia (N=274 counties, 18 states). R2=46.3%
Coefficient
Standard Error
.72***
.16
Continuous antipsychotic medication days supply+
.004
.02
Visit continuity (no break > 60 days)
.011
.03
-.09***
.01
Behavioral health hospitalization admission rate+
.27***
.07
Average behavioral health hospitalization length of stay ++
.0002
.0007
Psychiatrists
.0001
.0002
Free-standing psychiatric care beds
.00005
.0001
Free-standing alcohol/chemical dependency beds
County demographic/comorbidity index (case mix adjuster) Schizophrenia quality of care
Behavioral health community follow-up within 7 days post hospital discharge++ Utilization variables that may affect behavioral health readmission
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Community Treatment Capacity—per 100,000 U.S. Census Population
-.0005
.0004
Community mental health centers
-.01ˆ
.007
Federally qualified health centers
-.0004
.002
Predicted Probability (%)
95% CI
85%
13.3
12.9-13.6
55%
16.1
15.8-16.4
30%
16.7
16.4-17.0
10%
11.3
11.0-11.6
p. value: * ≤.05 ** ≤.01 *** ≤001 ˆp. value ≤1 Predicted Probability of 30 Day Behavioral Health Readmission for Statistically Significant County-Level Rates Measures (adjusted for case mix) for a County Behavioral health community follow-up within 7 days post hospital discharge++
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Rate of behavioral health hospitalization admissions in county*
+
Denominator = all schizophrenia diagnosed enrollees.
++
Denominator = all schizophrenia diagnosed enrollees with at least one MH/SUD hospitalization.
Results weighted based on the number of schizophrenia-diagnosed enrollees with ≥1 behavioral health hospitalization in each county.
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J Ment Health Policy Econ. Author manuscript; available in PMC 2016 February 23. Published in final edited form as: J Ment Health Policy Econ. 2015 September ; 18(3): 115–124.
Thirty Day Hospital Readmission for Medicaid Enrollees with Schizophrenia: The Role of Local Health Care Systems Alisa B. Busch, MD, MS1,2,3 [Assistant Professor, Chair, Director], Psychiatry & Health Care Policy, Harvard Medical School; Health Services Research Division, Partners Psychiatry and Mental Health; Integration of Clinical Measurement & Health Services Research, McLean Hospital
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Arnold M. Epstein, MD, MA4 [Chair, Professor], John H. Foster Professor of Health Policy and Management, Harvard School of Public Health; Department of Health Policy and Management, Harvard School of Public Health; Medicine and Health Care Policy, Harvard Medical School Thomas G. McGuire, PhD2,6 [Professor], Health Economics, Harvard Medical School Sharon-Lise T. Normand, PhD2,5 [Professor], and Health Care Policy (Biostatistics), Harvard Medical School; Biostatistics, Harvard School of Public Health
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Richard G. Frank, PhD2,6 [Professor] Margaret T. Morris Professor of Health Economics, Harvard Medical School
Abstract Background—Examining health care system characteristics possibly associated with 30-day readmission may reveal opportunities to improve healthcare quality as well as reduce costs. Aims of the Study—Examine the relationship between 30-day mental health readmission for persons with schizophrenia and county-level community treatment characteristics.
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Methods—Observational study of 18 state Medicaid programs (N=274 counties, representing 103,967 enrollees with schizophrenia--28,083 of whom received ≥1 mental health hospitalization) using Medicaid administrative and United States Area Health Resource File data from 2005. Medicaid is a federal-state program and major health insurance provider for low income and disabled individuals, and the predominant provider of insurance for individuals with schizophrenia. The Area Health Resource File provides county-level estimates of providers. We first fit a regression model examining the relationship between 30-day mental health readmission
Corresponding Author and Address for Reprints: Alisa Busch, McLean Hospital, Mailstop 226, 115 Mill St., Belmont, MA 02478, Phone: (617) 855-2989 / Fax: (617) 855-2174 / [email protected]. 1McLean Hospital, Belmont, MA 2Department of Health Care Policy, Harvard Medical School, 180 Longwood Ave., Boston, MA 3Health Services Research Division, Partners Psychiatry & Mental Health, Boston, MA 4Department of Health Policy and Management, Harvard School of Public Health, 677 Huntington Ave., Boston, MA 02115; Division of General Medicine, Brigham and Women's Hospital, Boston, MA 5Department of Biostatistics, Harvard School of Public Health, Boston, MA 6National Bureau of Economic Research, Cambridge, MA
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and enrollee characteristics (e.g., demographics, substance use disorder [SUD], and general medical comorbidity) from which we created a county-level demographic and comorbidity casemix adjuster. The case-mix adjuster was included in a second regression model examining the relationship between 30-day readmission and county-level factors: 1) quality (antipsychotic/visit continuity, post-hospital follow-up); 2) mental health hospitalization (length of stay, admission rates); and 3) treatment capacity (e.g., population-based estimates of outpatient providers/clinics). We calculated predicted probabilities of readmission for significant patient and county-level variables.
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Results—Higher county rates of mental health visits within 7-days post-hospitalization were associated with lower readmission probabilities (e.g., county rates of 7-day follow up of 55% versus 85%, readmission predicted probability(PP)[95%CI]=16.1%[15.8%-16.4%] versus 13.3%[12.9%-13.6%]). In contrast, higher county rates of mental health hospitalization were associated with higher readmission probabilities (e.g., country admission rates 10% versus 30%, readmission predicted probability=11.3%[11.0%-11.6%] versus 16.7%[16.4%-17.0%]). Although not our primary focus, enrollee comorbidity was associated with higher predicted probability of 30-day mental health readmission: PP[95%CI] for enrollees with SUD=23.9%[21.5%-26.3%] versus 14.7%[13.9%-15.4%] for those without; PP[95%CI] for those with ≥three chronic medical conditions = 25.1%[22.1%-28.2%] versus none=17.7%[16.3%-19.1].
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Discussion—County rates of hospitalization and 7-day follow-up post hospital discharge were associated with readmission, along with patient SUD and general medical comorbidity. This observational design limits causal inference and utilization patterns may have changed since 2005. However, overall funding for U.S. Medicaid programs remained constant since 2005, reducing the likelihood significant changes. Last, our inability to identify community capacity variables associated with readmission may reflect imprecision of some variables as measured in the Area Health Resource File. Implications for Health Care Provision and Use & for Health Policies—Healthcare policy and programming to reduce 30-day mental health readmissions should focus on countylevel factors that contribute to hospitalization in general and improving transitions to community care, as well as patient comorbidity. Implications for Further Research—Given the likely importance of local care systems, to reduce readmission future research is needed to refine community-level capacity variables that are associated with reduced readmissions; and to evaluate models of care coordination in this population.
Introduction Author Manuscript
Hospital readmissions are burdensome to patients and families. They are an increasing focus of healthcare policy because they can reflect substandard care and result in high costs. In 2012 the United States' Centers for Medicare and Medicaid Services (CMS) began reducing all Medicare payments to hospitals with excessive readmissions for enrollees initially hospitalized with congestive heart failure, pneumonia, and acute myocardial infarction.(1) In 2015 the program expanded to include additional medical conditions and procedures, and a higher penalty (up to 3% of Medicare payments). This CMS policy is likely to further expand to other clinical areas. Persons hospitalized for psychiatric illnesses, particularly J Ment Health Policy Econ. Author manuscript; available in PMC 2016 February 23.
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schizophrenia, have among the highest readmission rates.(2-6). Medicaid, a program jointly funded by the federal government and states and major provider of health insurance to the non-elderly poor and disabled, is the predominant insurance program for individuals with schizophrenia(7). The prevalence of schizophrenia in Medicaid programs, coupled with high readmission rates for these individuals, makes reducing readmissions in this population a high priority(8, 9). At least one state Medicaid program, Texas, began penalizing hospitals in 2012 for potentially preventable readmissions, including for mental health/substance use disorders(9). Others, such as Illinois, are actively considering such legislation(10).
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Patient characteristics may contribute to readmission, but so can care available in the community. Most care for individuals with severe mental illness in Medicaid is from local service systems but we know little about them and their role in readmissions. For example, how important are comorbidity, poor quality of care, the general propensity of providers to hospitalize in a local care system, and outpatient provider capacity? The primary objective of this study is to examine the relationship between 30-day readmission and county-level characteristics such as quality of care measures, hospital utilization, and community treatment capacity across 274 counties in 18 state Medicaid programs. We examine mental health readmissions specifically so we may examine how the mental health system affects readmission. We hypothesize that, after controlling for patient demographic and clinical characteristics, community care characteristics such as elements of community quality of care, overall propensity to use hospital service, and treatment capacity will be associated with mental health readmissions.
Methods Author Manuscript
Data sources
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We drew our study cohort from a multi-state dataset of the 2005 Medicaid Analytic eXtract (MAX) administrative data. Due to the complexity in compiling data from multiple Medicaid programs, multi-state MAX datasets typically take several years to be complete and available for research analysis(11). MAX data include the inpatient, outpatient, pharmacy claims, and their associated costs for state Medicaid programs. We excluded states in the MAX data system that did not report complete general medical and mental health claims or encounter data for Medicaid recipients with schizophrenia. We defined county-level schizophrenia treatment utilization and quality using the 2005 MAX data. For local treatment capacity we use county-level data from the 2005 Area Health Resource File. The Area Health Resource File is a county-specific database that includes annual information about county-level healthcare resources such as health facilities and health professionals. Assessment of state's Medicaid claims completeness Many state Medicaid programs have contracted with managed behavioral healthcare organizations (MBHCOs) to either provide or administer their Medicaid program's behavioral healthcare. MBHCOs often track utilization via encounter data (not claims) and states that contract with MHBCOs do not universally include the behavioral health
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encounters when submitting to the MAX data system. After consulting with the MAX data distributer and state Medicaid authorities regarding the completeness of their MAX behavioral health data, we excluded 22 states from the analysis due to known incomplete behavioral health utilization data in those states. Using MAX data for the remaining 28 states, we assembled a schizophrenia cohort from which we could examine the data to verify completeness. The schizophrenia cohort was defined as enrollees who were non-elderly adults (ages 18-64) with either two outpatient claims on different service dates where schizophrenia (ICD9 code 295) was in the first or second diagnostic field or at least 1 inpatient hospitalization for schizophrenia. We conducted a series of state by state analyses to examine the number of enrollees diagnosed with schizophrenia who were in fee-forservice, MBHCO or capitated programs (capitated programs would not have encounters noted in the administrative data). We also examined utilization within each of these three categories, including inpatient, ambulatory, general medical and behavioral health claims, as well as total and mental health spending. When the 28 state analyses indicated that we did not have complete encounter data for the enrollee population in a given state, we contacted representatives from those Medicaid programs to inquire if the data were in fact submitted to CMS. After this process, 19 Medicaid programs for which we had complete data remained (AK, AR, CA, GA, IL, IN, KS, KY, MN, MS, MT, NC, NH, NJ, NY, OK, SC, SD, WV). Study population
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In addition to the process described above for determining which states had complete utilization data available, we developed minimum enrollment criteria at the enrollee level to ensure that we could adequately track utilization. For example, many states contract with HMOs to authorize services and pay providers. HMOs typically pay providers on a per member/per month basis and do not report encounter data. Few Medicaid enrollees with schizophrenia are in in HMO plans (∼2% in our data). However, since the ability to observe service utilization was important for this study, we further required a minimum of 10 of 12 months of non-HMO Medicaid enrollment. We also excluded individuals who were dually eligible for Medicare insurance (the U.S. federally funded insurance for the elderly and disabled), since the Medicaid utilization claims for these individuals would have been incomplete. Since some of our key measures of local mental health delivery system quality are at the aggregate county-level, we reviewed the distribution of schizophrenia enrollees within counties. In counties with few schizophrenia enrollees, low sample sizes could produce unreliable county estimates of readmission. Therefore, we excluded counties with 30 days after the index prescription). Another variable involved the proportion of individuals with schizophrenia in the county who received “continuous contact” in their outpatient mental health care (defined as a mental health visit at least once every 2 months). A third county-level quality measure was the proportion of county mental health discharges with a follow-up community appointment within 7 days. All visit claims were required to have a primary or secondary mental health diagnosis, and were selected based on Healthcare Procedure Common Procedure Coding (Common Procedural Terminology [CPT] or state-specific mental health procedure codes). Mental health follow-up within 7 days of hospital discharge included residential/partial hospital/intensive outpatient treatment, outpatient initial evaluations/mental status evaluations (including medication management [CPT 90862]), or psychotherapy (individual, group, family). Mental health “continuous contact” included the above visit types, as well as outpatient case management, psychosocial rehabilitation, or addiction detoxification. Two variables measured county-level hospital utilization for schizophrenia enrollees—the mental health hospitalization rate and the average hospital length of stay. We included county rates of mental health hospitalization because in the general medical literature, county-level hospitalization rate is associated with readmission(26), suggesting that drivers of readmission and admission may be similar (or have some overlap). We included average length of stay because prior mental health literature is inconsistent regarding whether it is associated with readmission(5, 27-31).
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County-level treatment capacity factors were calculated from the Area Health Resource File and included the number of clinicians per 100,000 census population (psychiatrists, primary care clinicians), inpatient mental health specific bed capacity (free standing psychiatric and alcohol and chemical dependency beds), and outpatient treatment centers (community mental health centers and federally qualified health centers). Although we planned to include non-physician specialty care providers (i.e., social workers, psychologists) in the analysis, the Area Health Resource File did not include complete data on these groups. Data Analytic Procedures
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We first calculated descriptive statistics for the schizophrenia enrollees with ≥1 mental health hospitalization, and for the county capacity and quality measures (mean/standard deviation for continuous variables, percent for categorical variables). We assessed the pairwise correlations of the county measures to determine if any should be excluded from the model due to high correlation. To adjust for county-level case-mix, we first estimated a patient-level logistic regression model with the index hospitalization as the unit of observation and the outcome as occurrence of a 30-day mental health readmission for enrollees who had at least one mental health hospitalization in 2005. Explanatory variables included patient demographic and
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clinical characteristics, and accounted for repeated observations on the same person for those having >1 index hospitalization through a dichotomous indicator variable. This model produced a predicted probability of mental health readmission for each person which we then averaged over all persons within each county.
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Our main analysis relied upon a county-level linear regression model in which the unit of observation was the county and readmission rate served as the dependent variable. Explanatory variables included the above described county measures of quality, hospitalization, and capacity, and the county-level case-mix adjuster derived from the patient characteristics index hospitalization model. To adjust for county schizophrenia enrollee population size, we weighted the linear regression model by the number of schizophrenia-diagnosed enrollees with at least one mental health hospitalization in each county. We excluded from this model the county rate of primary care clinicians because our correlation analyses found it highly correlated with that of psychiatrists (correlation coefficient=0.95). For both models we accounted for clustering using the Generalized Estimating Equations (GEE) estimator(32), clustering on county in the personhospitalization level model, and on state in the county-level model.
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We determined the predicted probability and 95% confidence intervals of readmission for key significant enrollee and county-level variables. Predicted probabilities provide an easily interpretable magnitude of the associations. For dichotomous variables, we used predicted probabilities to contrast readmission rates for those with and without the dichotomous characteristic. For continuous variables (e.g., county-level variables), we selected a discrete set of county means to serve as contrasts. Specifically, we calculated predicted probabilities of readmission for changes representing approximately one-half to one standard deviation above and below the mean value for the county-level factor to provide realistic contrasts of predicted probability rates. All analyses were conducted using Stata version 11.2.
Results
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Comorbidity was common among this hospitalized population (N=28,083, Table 1). Fiftyone percent had a comorbid substance use disorder diagnosis. More than half had a cardiovascular, diabetes, hypertension or chronic pulmonary disorder, and nearly 20% at least two of those conditions; 12% had comorbid neurological conditions that can affect cognition. Nearly 25% of the hospitalized sample experienced a readmission within 30-days of discharge. Sixty-four percent of the study sample contributed one index hospitalization during the year; an additional 24% contributed two, and 9% contributed three (data not shown). County-level measures showed low rates of care quality--particularly for continuous antipsychotic medication days supply (mean= .57[sd=.10]) and ≥1 mental health visit per 2 months (.50[.12]). Rates of post-hospital discharge follow-up (.70[.24]) appeared to be better. In the patient-level logistic regression model (Table 2), comorbid substance use disorders, general medical conditions, and neurological conditions were significantly associated with
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an increase in an individual's risk of readmission, however, the association was more pronounced for comorbid substance use and general medical conditions. Comorbid substance use disorders were associated with a readmission predicted probability of 23.9% [95%CI=21.5%, 26.3%], whereas those without a comorbid substance use disorder had a readmission predicted probability of 14.6% [95%CI=13.9%, 15.4%]. Individuals diagnosed with three or four of the general medical conditions examined here had a readmission predicted probability of 25.1%[22.1%, 28.2%], whereas those with none of the comorbid medical conditions had a 17.7%[16.3%, 19.1%] predicted probability of readmission.
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In the county-level linear regression (Table 3), county demographic and comorbidity characteristics (i.e., the variable we constructed to be the case-mix adjuster) was significantly associated with an increased probability of readmission. Lower county rates of 7 day post-discharge follow-up and higher county readmission rates were associated with higher risks of readmission. For example, if the county rate of 7 day follow-up post hospitalization were 85%, the predicted probability of readmission was 13.3% [95%CI 12.9%, 13.6%], whereas it increased to 16.1%[15.8%, 16.4%] if the county rate of 7-day post-discharge follow-up were 55%. For a 30% county hospitalization rate, the predicted probability of a hospital readmission was 16.7%[95%CI 16.4%, 17.0%]. Whereas if the county hospitalization rate was lower at 10%, the predicted probability of readmission would be 11.3%[11.0%, 11.6%].
Conclusions
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Three important county-level findings emerged from our study. First, independent of county case-mix, we found quality of transitional care as measured by the county-wide probability of a community visit within 7 days of discharge was associated with a decreased probability of readmission for enrollees. Second, overall county rates of mental health hospitalization were strongly associated with an increased probability of readmission for enrollees who had at least one index hospitalization. Third, none of the county treatment capacity variables were associated with readmission rates. Among our measures of community treatment quality, only county rate of post-discharge follow up was associated with readmissions. Community visit within 7-days of hospital discharge is a quality measure is endorsed by the National Quality Forum(19), but our findings represent the first study actually to find it associated with lower readmission rates. One could also argue that hospitals are capable of having a large impact on this measure although the availability of community providers is obviously very important.
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We also found that the county mental health hospitalization rate was associated with higher readmission rates. Our results are consistent with recent general medical research(26) suggesting that overall propensity of a community/geographical area to hospitalize may be an important predictor of readmissions as well. Perhaps there are inadequate resources in a geographical location to support individuals in the community who are having an acute exacerbation, or that local culture of the community-based care has a lower threshold for hospitalizing individuals in some geographical regions compared to others.
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Surprisingly, few of our measures of county-level treatment quality or capacity were associated with readmission. For example, a county's rate of achieving antipsychotic medication continuity for all schizophrenia-diagnosed Medicaid enrollees was not associated with readmission. This is in contrast to prior research showing that medication continuity is associated with reduced mental health admissions in persons with schizophrenia(22, 23). Possibly, the prior observed association between higher medication continuity rates and lower hospitalization may be due to selection bias (i.e., less severely ill patients may be more adherent patients and [independently] less likely hospitalized in a given year). Given that our medication continuity variable is a reflection of county-level adherence for all individuals with schizophrenia (not just those hospitalized), it is less influenced by this possible bias.
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Finally, while not focus of our study, we found that comorbidity (substance use disorder and general medical) was a key determinant of 30-day readmission. Comorbid substance use disorders are destabilizing for persons with psychiatric illness, including schizophrenia(33, 34), so our result was not surprising. However, the effect size was larger than previously appreciated—increasing 30-day rehospitalization risk by almost two-thirds. Addressing this comorbidity could be important in improving stability and reducing rehospitalization rates for this population. These findings can provide important guidance for public health insurance programs, which may have restrictive policies that impede adequate substance use disorder care. For example, in the U.S., state Medicaid substance use benefits can be very restrictive(35) and more restrictive Medicaid substance use treatment benefits have been associated with lower provider acceptance of Medicaid(36)—further exacerbating problems with treatment access. While it is unclear the extent that additional supports and treatment to individuals with comorbid substance use disorders will reduce readmissions, the substantial impact of substance use disorders in mental health readmissions highlights an opportunity for public insurance programs such as Medicaid, to influence improved access to treatment through healthcare policy. Medical comorbidity is recognized as a risk factor for 30-day general medical readmission(4, 37-39). More recent evidence finds that general medical conditions are associated with mental health readmissions in a mixed psychiatric population(5). However, no prior literature has documented the “dose response” relationship of general medical comorbidity on mental health readmissions. This finding underscores the importance of coordinated care addressing both the mental health and general medical needs of this vulnerable population. Targeted efforts for both may reduce both mental health and general medical readmissions (and admissions).
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There are several limitations worth noting. Our data are observational and therefore we cannot infer causality in our associations. In our county-level model we have attempted to control for some of the threats to causal inference by, when possible, using measures where the denominator reflected all schizophrenia diagnoses enrollees in a county, not just those hospitalized (e.g., hospitalization rates, medication and visit continuity rates). Our data reflect utilization that occurred in 2005, prior to the 2007-2009 U.S. recession when many states reduced their Medicaid funding – this fact could impact our results. Unfortunately, government release of multi-state MAX data for analysis typically lags several years due to
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data availability and completeness(11). However, despite the U.S. recession, the overall rate of federal and state Medicaid spending has remained stable(40). Given the overall stability of public spending, we do not expect that the relationship between readmission and patient/ county characteristics would have changed over time, so results from this study will remain meaningful. It is unclear whether the county rate of hospitalization reflects only regional differences in providers' propensity to hospitalize or also community treatment capacity, the latter feature incompletely captured using claims and Area Health Resource File data.
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We selected county-level measures that face validity would suggest are a reasonable place to start to examine community treatment capacity. However, they may be imprecise and reflect a limitation of these variables as measured in the Area Health Resource File. For example, county rates of psychiatrists working in Community Mental Health Centers, or Community Mental Health Center size may be more relevant than overall county rates of psychiatrists or Community Mental Health Centers. Similarly, the rate of free-standing psychiatric beds may not reflect the total inpatient psychiatry bed capacity of a community. Additionally, Area Health Resource File limitations precluded us from including estimates of non-physician mental health specialists (e.g., psychologists, social workers) and non-inpatient substance use treatment availability, important components of the mental health workforce and service system. Other, more nuanced community capacity or treatment characteristics that we were unable to measure (e.g., state supported housing, assertive community treatment) could be important areas for future study. Given it is unlikely that local systems factors are unrelated to readmissions, an important area of future research will be to either refine these variables, or develop and empirically test new ones.
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Despite these limitations, this study provides new information about factors associated with of 30 day readmission for enrollees with schizophrenia served in the U.S. public system. Knowing the magnitude of increased readmission probability associated with these comorbidities (e.g., a 63.6% increase for substance use disorders and up to 41.8% medical conditions) clarifies the large potential impact that targeted interventions for these higher risk groups may have. Lower county level rates of admission and higher rates of postdischarge community follow-up within 7 days also were associated with (lower) readmission probability. These differences in readmission probability are meaningful from a health policy perspective as they represent opportunities for significant cost savings, in addition to improved transitions to care.
Supplementary Material Refer to Web version on PubMed Central for supplementary material.
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Acknowledgments The authors gratefully acknowledge support from the National Institute of Mental Health (NIMH R01 MH081819) and the Health Services Research Division of Partners Psychiatry & Mental Health (AB). We are also grateful for the programming expertise of Christina Fu, Ph.D.
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Table 1
Author Manuscript
Descriptive characteristics of the schizophrenia diagnosed enrollees with ≥1 behavioral health hospitalization in 2005 (N=28,083 enrollees) and county characteristics (N=274). Patient Characteristic
N
%
13,400
47.7
White
9,222
32.8
Black
11,954
42.6
Hispanic
3,525
12.6
Other
3,382
12.0
Supplemental Security Income months > other Medicaid eligibility months
25,465
90.7
Substance use disorder
14,324
51.0
Neurological disordera
3,451
12.3
0 conditions
14,795
52.7
1 condition
7,978
28.4
2 conditions
3,746
13.3
3 conditions
1,268
4.5
4 conditions
296
1.05
6,976
24.8%
Female Race/Ethnicity
Author Manuscript
Medical illness burden (non-neurological)b
At least one 30-day readmission
Age County characteristics
Mean
SD
40.1
11.2
Mean
SD
Min
Max
Continuous antipsychotic medication days supplyc
0.57
.10
.03
.93
At least one behavioral health visit per 2 months
0.50
.12
.05
.88
Behavioral health community follow-up within 7 days post behavioral health hospitalization discharge
0.70
.24
0
1.0
Behavioral health admission rate
0.22
.09
.01
.46
Average behavioral health hospital length of stay
9.0
4.0
1.3
42.2
Psychiatrists
8.8
10.1
0
94.6
Primary care physicianse
63.1
33.9
6.1
344.0
Free-standing psychiatric care beds
18.2
27.5
0
339.5
Free-standing alcohol/chemical dependency beds
2.8
8.4
0
69.2
Community mental health centers
0.26
.54
0
3.6
Federally qualified health centers
1.8
3.3
0
31.9
Author Manuscript
Quality of Care for Enrollees Diagnosed with Schizophrenia
Utilization variables that may affect behavioral health readmission
Community Treatment Capacityd
Author Manuscript
a
Diagnoses include: dementia, Parkinson's disease, anoxic brain damage.
b
Categories include: cardiovascular, diabetes, chronic pulmonary, hypertension.
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No gap in days supply > 30days in year.
d
Rates per 100,000 census population in county.
e
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Internal medicine, primary care, and family practice physicians.
Patient characteristics derived from claims data, county characteristics derived from claims data and Area Health Resource File.
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Table 2
Author Manuscript
Multivariate logistic regression results of patient characteristics associated with 30-day behavioral health readmission for schizophrenia diagnosed Medicaid enrollees (N=28,083 enrollees). Pseudo R2=1.8%
Coefficient
Standard Error
Age
-.005*
.002
Female
-.07*
.04
Race/Ethnicity (white = reference) Black
.04
.07
.09**
.04
.07
.05
SSI months> other Medicaid eligibility months
-.08
.12
Prior index hospitalization
.11*
.05
Substance use disorder
.60***
.05
Neurological disorder+
.11***
.02
1 condition
.22***
.05
2 conditions
.31***
.07
3-4 conditions
.45***
.05
Hispanic Other
Author Manuscript
Medical illness burden (non-neurological, reference = 0) ++
p. value: * ≤.05 ** ≤.01 *** ≤.001 Predicted Probability of 30 Day Behavioral Health Readmission for Statistically Significant Case Mix Variables for an Individual Predicted Probability (%)
95% CI
White
19.4
18.1-20.6
Hispanic
20.8
19.7-21.9
No
14.6
13.9-15.4
Yes
23.9
21.5-26.3
No
19.7
18.0-21.4
Yes
21.5
19.5-23.5
None
17.7
16.3-19.1
One
21.0
18.9-23.2
Two
22.6
20.2-25.1
Three-Four
25.1
22.1-28.2
No
18.9
17.7-20.0
Yes
20.6
18.5-22.7
Race
Author Manuscript
Comorbid substance use disorder
Comorbid neurological+
Comorbid general medical++
Author Manuscript
Prior hospitalization (>30 days earlier)
Presence of ICD9 code for:
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+
dementia, Parkinson's Disease, anoxic brain damage.
++ cardiovascular condition, hypertension, diabetes, or COPD.
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Table 3
Author Manuscript
Multivariate linear regression results of county characteristics associated with 30-day behavioral health readmission rate for Medicaid enrollees diagnosed with schizophrenia (N=274 counties, 18 states). R2=46.3%
Coefficient
Standard Error
.72***
.16
Continuous antipsychotic medication days supply+
.004
.02
Visit continuity (no break > 60 days)
.011
.03
-.09***
.01
Behavioral health hospitalization admission rate+
.27***
.07
Average behavioral health hospitalization length of stay ++
.0002
.0007
Psychiatrists
.0001
.0002
Free-standing psychiatric care beds
.00005
.0001
Free-standing alcohol/chemical dependency beds
County demographic/comorbidity index (case mix adjuster) Schizophrenia quality of care
Behavioral health community follow-up within 7 days post hospital discharge++ Utilization variables that may affect behavioral health readmission
Author Manuscript
Community Treatment Capacity—per 100,000 U.S. Census Population
-.0005
.0004
Community mental health centers
-.01ˆ
.007
Federally qualified health centers
-.0004
.002
Predicted Probability (%)
95% CI
85%
13.3
12.9-13.6
55%
16.1
15.8-16.4
30%
16.7
16.4-17.0
10%
11.3
11.0-11.6
p. value: * ≤.05 ** ≤.01 *** ≤001 ˆp. value ≤1 Predicted Probability of 30 Day Behavioral Health Readmission for Statistically Significant County-Level Rates Measures (adjusted for case mix) for a County Behavioral health community follow-up within 7 days post hospital discharge++
Author Manuscript
Rate of behavioral health hospitalization admissions in county*
+
Denominator = all schizophrenia diagnosed enrollees.
++
Denominator = all schizophrenia diagnosed enrollees with at least one MH/SUD hospitalization.
Results weighted based on the number of schizophrenia-diagnosed enrollees with ≥1 behavioral health hospitalization in each county.
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