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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Psychiatr Serv. Author manuscript; available in PMC 2018 January 1.
Published in final edited form as:
PMCID: PMC5205553
NIHMSID: NIHMS795695

The impact of Massachusetts behavioral health child screening policy on service utilization

Abstract

Objective

In 2008, Massachusetts Medicaid implemented a pediatric BH screening mandate. This study conducted a population level, longitudinal policy analysis to determine the impact of the policy on ambulatory, emergency, and inpatient BH care in comparison to California where no similar policy existed.

Methods

Using Medicaid Analytic Extract (MAX) data, an interrupted time series with control series design was performed to assess changes in service utilization in the 18 months (January 2008-June 2009) following a BH screening policy compared with California. Outcomes included population rates of: BH screening; BH-related outpatient visits; BH-related emergency department visits; BH-related hospitalizations; and psychotropic drug use. Medicaid eligible children from January 1, 2006 to December 31, 2009 with at least 10 months of Medicaid eligibility who were aged greater than 4.5 years to less than 18 years were included.

Results

Compared to California, Massachusetts rates of BH screening and BH-related outpatient visits rose significantly following the screening policy. BH screening rose about 13 per 1000 youth per month during the first 9 months and BH-related outpatient visits rose about 4.5 per 1000 youth per month (p<0.001). While BH-related emergency department visits, hospitalization and psychotropic drug use increased, there was no difference in rate of increase in Massachusetts compared to California.

Conclusions

The goal of BH screening is to identify previously unidentified children with BH issues and provide earlier treatment options. The short-term outcomes of the Massachusetts policy suggest that screening at preventive care visits led to more BH-related outpatient visits among vulnerable children.

INTRODUCTION

Child behavioral health (BH) conditions frequently present in pediatric primary care. In fact, primary care providers are children's default source of mental health care (1). As a consequence, BH screening is recommended by national organizations, and increasingly covered by public and private insurers, as a way of increasing identification of BH health issues and enhancing families’ engagement in needed services (2, 3). However, evidence of the impact of screening on subsequent BH care is lacking. In particular, there are no studies comparing states with policies requiring screening to states without similar mandates. A legal settlement in Massachusetts, mandating screening of children covered by the state's Medicaid program, provides a unique opportunity to study the policy impact of screening mandates on rates of subsequent use of BH services at the population level.

The Massachusetts mandate resulted from a class action lawsuit, Rosie D. versus Patrick,(4). In 2006, a US District Court found Massachusetts out of compliance with federal Medicaid law (5). The resulting remedial plan required Massachusetts to provide screening and follow-up to Medicaid-eligible children in what has become one of the largest child mental health system changes in the nation (6). As the first phase of the plan, primary care providers were required to screen for BH at well-child visits for all children and youth ≤ 21 starting in December of 2007. It was not until July 2009 that additional mental health services were implemented as part of the plan, thus providing an 18 month period in which to study the impact of mandated BH screening as a stand-alone intervention (7).

Prior studies of child BH screening find that screening leads to an increase in outpatient BH visits, though outcomes have been mixed and vary across health systems and diagnoses.(8, 9) Several studies of the Massachusetts screening mandate in particular found that claims for behavioral health screening and evaluations rose after BH screening began (10, 11), and that identification of a new BH problem at a screening visit, or being in foster care at the time of screening, predicted subsequent receipt of a BH visit (12). However, these studies did not control for the potential impact of regional or national trends in the use of children's BH services (increasing nation-wide) (13, 14), or for ongoing efforts to improve children's access to BH services (9, 15, 16). They also did not compare trends in Massachusetts to any other states. In this paper, we examine the impact of the screening mandated under Rosie D using an interrupted time series design – a strong quasi-experimental approach for assessing changes in service utilization – along with comparison longitudinal data from another state. Our goal was to determine whether a policy of mandated BH screening increased outpatient, emergency, and inpatient BH care utilization rates by Medicaid children in Massachusetts at a population level.

METHODS

This study used Medicaid Analytic Extract (MAX) claims data from Massachusetts and California to compare utilization of BH services (screening, outpatient, emergency department, inpatient, and medication) after initiation of a BH screening mandate in children. California was chosen as a comparison state since it had a large diverse and stable Medicaid population and no competing intervention. We initially considered New York , a more proximate state, but during analysis it was discovered that a similar BH policy was initiated at the same time as the Massachusetts policy. Data were obtained under data use agreement number 21668. An interrupted time series (ITS) with control series design was utilized.

BH Screening Policy in Massachusetts under Rosie D

The Massachusetts policy requires primary care providers to conduct BH screening at well child visits. Providers must select screens from a menu of validated tools (17) and are compensated if they submit claims using a specific billing code. The State monitors screening rates for Medicaid enrollees and feeds them back to pediatric practices.

Data

Data was obtained for all Medicaid eligible children for the period January 1, 2006 to December 31, 2009, in two states, Massachusetts and California. The data included demographic and enrollment data (Personal summary file), hospital inpatient claims (Inpatient file), outpatient claims (Other file), and pharmacy claims (Rx file). We used the encrypted Medicaid Statistical Information System (MSIS) identification number to identify individuals. The Massachusetts BH screening policy went into effect January 1, 2008, providing 24 months of data prior to the policy implementation and 18 months of post-policy data prior to the implementation of additional behavioral health program components in the fall of 2009.

This study was approved by the Institutional Review Board at Cambridge Health Alliance and Group Health Cooperative.

Population

The population of interest in this study is children of “screenable” school age. We limited our sample to children aged greater than 4.5 years to less than 18 years at the time of screening since the majority of providers used the same BH screen (18),the Pediatric Symptom Checklist and the youth Pediatric Symptom Checklist (19), which covered this age range. We included all BH claims for youth up to age 20 in order to capture utilization that occurred after a screening encounter at 17.99 years of age (i.e., to capture BH utilization in the “runout’ period).

Inclusion Criteria

For inclusion, all individuals required at least 10 months of Medicaid eligibility in each calendar year to insure that the majority of utilization was included. However, the cohort of youth included was “rolling” in the sense that individuals could enter and exit the study cohort in a given year.

Population Standardization

Because we used a rolling cohort of youth, change in the composition of the patient population is a threat to the validity of the interrupted time series design (20). For example, changes in the distribution of sex or age over study years could confound any change in BH utilization rates. We examined the demographic and enrollment characteristics of the population in each year and found differences in: managed care enrollment, eligibility status (e.g., foster care, poverty), race, and Hispanic ethnicity. Thus, we standardized the population of youth on these characteristics to the distribution in January 2008 – the date that the screening policy was implemented. It is also important to note that there were no sudden changes in level or slope of demographic characteristics that could confound observed effects. Since the analysis of trends is within populations, there was no need to standardize populations between states.

Outcome variables

Definitions of utilization outcomes have been used in prior research (12, 21) and were met if a minimum of one visit per utilization type (categories 1-5) per day occurred. Outcomes included; BH screening as defined by any outpatient visit with a 96110 Current Procedural Terminology (CPT) code (developmental testing); BH-related Outpatient Visits: defined as psychiatric services such as diagnostic interviews, psychopharmacology management and psychotherapy, health behavioral assessment and intervention services, visits to other mental health professionals, the Massachusetts Behavioral Health Partnership's (MBHP) additional codes introduced to track services not otherwise identified with existing codes (crisis intervention, family counseling and case management); and well-child visits or ambulatory visits with an associated BH-related International Classification of Diseases, 9th Revision (ICD9) code (290 to 319); BH-related Emergency Department (ED) Visits: ED visits were defined by the place of service code on records in the OT file (“Other” file in MAX data); those related to mental health were identified by an associated BH related ICD9 code; BH-related Hospitalizations: defined as inpatient stays with a BH related ICD9 code; and Psychotropic medication use as defined by National Drug Codes included on the Mental Health Research Network (MHRN) list (22). The medication categories included ADD-Other (non-stimulant medications), antidepressants, anti-anxiety-other (non-benzodiazepines), anticonvulsants, antipsychotic-1st generation, antipsychotic-2nd generation, benzodiazepines, COMBO (all combination psychotropic medications), hypnotic-other (e.g., zolpidem), lithium, and stimulants (a full list of study medications and NDC codes is available upon request). Drugs with possible dual use were excluded, including: antidepressants used primarily for migraines and enuresis in children (imipramine, amitryptiline), antidepressants used for sleep (doxepin, trazadone) when no other psychiatric medication was being used and there were no BH ICD9 code, and anticonvulsants unless accompanied by any BH ICD9 code. For example, if a patient had a bipolar diagnosis on any prior visit and also used an anticonvulsant, they were included as using psychopharmacology.

Calculation of Utilization Rates

We calculated monthly population utilization rates adjusted for managed care enrollment, eligibility status (e.g., foster care, poverty), race, and Hispanic ethnicity as described above. The numerator for rates is the occurrence of a claim for service in the calendar month for one of the diagnostic or procedure codes listed above. The denominator for rates is all youth eligible to receive services (i.e., currently insured by Medicaid) and of screenable age in the same calendar month. That is, we did not remove youth from the denominator in subsequent study months when they received BH screening in prior study months.

We further adjusted the population utilization rates for seasonality using the Census Bureau algorithm (proc X11 in SAS) (23, 24).

Interrupted Time Series (ITS) Analysis

We fit segmented regression models (25-27) for each of the BH utilization rates in the 24 months prior to mandatory screening and 18 months post mandate. The segmented regression models included terms for the change in intercept (immediate level change), secular trend (overall slope), and change in trend (increase/decrease in slope post policy). We then constructed difference-in-differences models comparing Massachusetts to California to determine significant changes in all outcomes related to the policy.

RESULTS

The demographics of the Massachusetts and California populations are shown in Table 1. The two states are fairly similar with regard to age and gender distributions but differ substantially by race and eligibility criteria. However, this did not affect the almost identical baseline trends in outcome measures.

Table 1
Demographic Characteristics for Eligible Massachusetts and California Youth

After reviewing the time series analyses, we observed that the overall monthly rate of screening stabilized at 9 months post mandate in our data. Preliminary data from MassHealth also indicated that it took 9 months to achieve a 50% BH screening rate at well-child visits. Thus, we censored the first 8 months of observations in the time series analyses in order to evaluate the impact of the policy when it achieved at least “half strength”. Such censoring has been applied in several previous studies where the analysis excluded the implementation or “phase-in” period (28-31).

Figure 1 shows the rate of BH screening among all eligible youth (including those without ambulatory visits). Use of the screening code rose to about 13 per 1000 youth per month during the first 9 months following mandatory screening. The rate of BH screening remained relatively stable over the next 10 months, averaging about 11 per 1000 youth per month. During the same time frame, there was no concomitant rise in screening seen in California data.

Figure 1
Rates of Behavioral Health Screening (CPT 96110) in MA and CA

Figure 2 shows the monthly rate of any BH-related outpatient service utilization. The adjusted rate was about 35 per 1000 youth per month in the two years prior to the mandate with a slightly increasing secular trend (.8/1000, p=.03). Following the phase in period screening, rates of outpatient use increased dramatically in the fall of 2008 to about 50 per 1000 youth per month. It actually began to rise during the phase in period and then remained stable thereafter. The trend in utilization increased at about 4.5/1000 per month (p<.001). No increase was seen in California. See Table 2 for regression coefficients corresponding to the estimates for policy (immediate change), time (secular trend), and time-after (post-mandate trend).

Figure 2
Rates of Any Outpatient Behavioral Health Utilization in MA and CA
Table 2
Segmented Regression Model Results by Utilization Type

Figure 3 shows the rate of BH-related ED visits over the study period. BH-related ED use averaged about 17/1000 youth per month in the pre-mandate period. Following the screening implementation period, rates of ED use increased at a rate of 4.8 per thousand per month (p<.001). However, the difference-in-differences analysis revealed that a similar increase occurred in California during the same time period.

Figure 3
Rates of Behavioral Health Related Emergency Department Utilization in MA and CA

BH-related hospitalizations averaged about .3 per1000 youth per month in the pre-mandate period. Beginning in the fall of 2008, inpatient stays with a BH diagnosis began to increase at about .1/1000 youth per month with an expected rate of about .4/1000 by June of 2009. The difference-in-differences model did not show any statistically significant change in inpatient stays when compared to California. The monthly rate of any psychotropic medication use was about 126/1000 in the pre-mandate period with a decreasing secular trend of 2.2 per1000 per month (p=.02). Following the implementation period, the trend reversed with the rate increasing at about 9.7/1000 per month compared to control (p=.01). Again, the difference-indifferences ITS analysis did not show statistically significant change for psychotropic medication use.

DISCUSSION

The Massachusetts policy mandate to screen all children and youth for BH problems is the first of its kind in the nation (21). In examining the policy's impact on service utilization rates compared to our comparison state of California, we found that screening (use of the 96110 code) significantly increased in Massachusetts and was associated with a concomitant increase in rates of BH-related outpatient services. However, when assessed in comparison to trends in California, BH-hospitalizations, BH-related ED utilization and psychotropic medication use in Massachusetts showed no evidence of being influenced by the new policy.

It is important to put our findings into context. During the study period BH-related ED visits for children rose (32, 33). Evidence also suggests that psychotropic medication use and outpatient mental health treatment for children increased between 1996-1998 and 2010-2012 (34). Inpatient hospitalizations related to BH conditions for children also rose between 2006 and 2011 (33) Other changes also occurred in the delivery of child and adolescent mental health, including the black-box warning on the use of antidepressants (31) rising rates of specific disorders (specifically, bipolar disorder) (35), and heightened interest in identifying and treating behavioral health issues in pediatric offices (36). However, none were concurrent with the changes we noted post-policy in Massachusetts.

The increase in screening rates following the mandate has been documented elsewhere (10, 11), but they did not control for external factors nor did they compare trends to other states. It is possible that we missed screening conducted without the recommended code; however, a pre-policy Massachusetts Medicaid chart review found that formal screening tools were only used in 4% of well child visits (37). In the comparison state, California, screening may have occurred as part of Early and Periodic Screening, Diagnosis and Treatment (EPSDT) visits without specific reimbursement for identified screening codes but trends did not change over time. Our analysis provides evidence that mandates – coupled with reimbursement - can rapidly increase BH screening rates in pediatric practice and increase the utilization rates of outpatient BH services. Several questions remain unanswered. First, does increased BH utilization translate to better or more appropriate care? Further investigation is needed to ascertain treatment quality and outcomes. Second, does early entry into treatment ultimately mitigate the need for higher cost care such as emergency department visits and hospitalizations? While we did not see a significant change in these outcomes, our time frame for evaluation is limited since Massachusetts added additional programs after 18 months which would confound any longer term analysis of outcomes related strictly to the screening policy. Further work to determine the relationship between screening, treatment and inpatient utilization is needed.

Several other findings merit discussion. For example, despite an increase in outpatient care rates, we saw no concomitant increase in psychotropic medication utilization rates. This finding is consistent with prior studies of screening programs.(21, 38) It is important to note that BH-related outpatient services include visits delivered by both BH professionals as well as primary care clinicians. It is possible that clinicians initially used first line recommended “talk therapy options (2, 39-42), rather than psychopharmacologic interventions or that visits were likely to be BH issues encountered in primary care (adjustment reactions, depression, etc.) rather than more severe mental illness. Prior research indicates that newly identified children were more likely to have internalizing rather than externalizing symptoms (8). This finding may also reflect recent concern regarding over-medication of children and FDA “black box” warnings among pediatricians (31). Further research would be needed to examine specific diagnoses and whether psychotropic medication would have been indicated.

In addition, while both states noted an increase in BH-related ED visit rates, we did not see a significant difference between the states. This is contrary to a recent study of a single system in Massachusetts where a combination of screening and co-located care led to an increase in BH-related ED visit rates (43). However, this study did not use a comparison state and it was focused on the impact of screening and co-located care which may have contributed to increased ED use for reasons cited in the paper.

Limitations

The current study has a number of limitations. First, the study lacked information on BH need and clinical outcomes at the population level. The use of an interrupted time series approach assumes that no co-interventions occurred simultaneously with the policy of interest. However, our discussions with Massachusetts program staff and investigation of relevant documents did not identify any other policy changes at the same time. In addition, it is important to note that this study does not examine the impact of the policy on individual children identified as having BH issues, or on children who actually received screening. It is intended to examine the overarching population impact of the screening policy on utilization rates. Our ITS design explicitly controls for secular trends in two states and is thus a strong quasi-experimental design for examining the impact of policy on rates of service utilization (44).

CONCLUSION

The goal of BH screening in primary care is to identify previously unidentified children with BH issues and provide earlier treatment options. As in other screening programs, the hope is to ultimately deter longer term negative outcomes. While we could not measure the long term outcomes of the Massachusetts policy (e.g., recovery rates), it is evident that the policy increased screening at preventive care visits. The increase in screening, in turn, was associated with an increase in use of outpatient BH health care in the Medicaid-insured population of youth in Massachusetts.

ACKNOWLEDGEMENTS

We would like to acknowledge Mr. Chester J. Pabiniak, MS at GroupHealth Research Institute, Seattle, WA for his analytic contribution.

Grant Support: All phases of this study were supported by grant R21MH094942 and grant U19MH092201 from the National Institute of Mental Health

Footnotes

Disclosures: The authors report no financial relationships with commercial interests

Contributor Information

Karen Hacker, Allegheny County Health Department, 542 Fourth Avenue, Pittsburgh, Pennsylvania 15219. University of Pittsburgh - Graduate School of Public Health, Pittsburgh, Pennsylvania 15261, ten.dhca@rekcaHK..

Robert Penfold, Group Health Research Institute - Department of Health Services Research, Seattle, Washington.

Lisa Arsenault, Institute for CommunityHealth, Cambridge, Massachusetts.

Fang Zhang, Harvard Pilgrim HealthCare Institute, Boston, Massachusetts.

Stephen B. Soumerai, Harvard Medical School - Department of Population Medicine, 133 Brookline Ave 6th Floor 4, Boston, Massachusetts 02215.

Lawrence S. Wissow, Johns Hopkins Bloomberg School of Public Health - Department of Health Policy and Management.

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