Examining Patterns of Psychotherapy Service Utilization Among Medicaid-Enrolled Adolescents
Abstract
Objective:
Adolescents with behavioral health disorders (i.e., mental health disorders and substance use) often experience frequent recurrence of symptoms, suggesting a need for an ongoing behavioral health intervention, rather than a single course of treatment. However, little is known about mental health care service use among adolescents over longer periods. The authors examined longitudinal patterns of outpatient behavioral health service utilization in a large sample of adolescents.
Methods:
Medicaid claims for 8,197 adolescents (ages 10.0–13.9 years, mean±SD=11.5±1.2; 61% male) from one Indiana county between 2006 and 2017 were examined, with a focus on outpatient psychotherapy visits. Latent class analysis (LCA) was used to detect clusters of longitudinal patterns of outpatient psychotherapy visits across 5 years, beginning with an adolescent’s first behavioral health visit.
Results:
A five-class LCA model emerged with unique classes of service use based on duration and level of engagement (frequency) of monthly outpatient psychotherapy visits. Most adolescents fell in the nonuse class (38.7% of the sample). Additional classes were defined as late-onset low engagement (17.1%), early-onset high engagement (15.5%), early-onset moderate engagement (16.7%), and continuously high engagement (11.9%). Statistically significant differences were found across the classes in average duration and frequency of involvement (p<0.001), as well as in demographic characteristics (race, age, gender, and ethnicity) and behavioral health diagnoses (p<0.001).
Conclusions:
These findings confirm that adolescents with behavioral health diagnoses do not follow a uniform pattern of psychotherapy utilization. The distinct patterns of service use point toward the need to identify appropriate long-term service recommendations for adolescents.
HIGHLIGHTS
•
Five unique patterns of outpatient psychotherapy service use among adolescents varied by duration of treatment involvement and level of monthly engagement.
•
Almost half of the adolescents had minimal or no psychotherapy utilization over a 5-year period after an initial behavioral health visit.
•
The findings suggest that adolescents do not show a uniform pattern of psychotherapy utilization.
Adolescence (i.e., ages 10–19 years) is a peak time for the development of behavioral health issues, including mental health and substance use problems. Indeed, behavioral health treatment accounts for the largest proportion of health care spending for children and adolescents (1). Youths often experience recurrences of behavioral health symptoms, suggesting the need for treatment across the course of adolescence rather than time-limited care. Similarly, extensive evidence indicates that both internalizing (e.g., depression and anxiety) and externalizing (e.g., attention-deficit hyperactivity disorder [ADHD] and behavior disorders) problems that start in childhood can persist through adolescence (2, 3). Recent research has more closely examined patterns of depression and anxiety among youths and adolescents and reported that such problems typically follow an episodic course and that recurrences and relapses are common; for example, among community and clinical samples of youths, 50%–70% of those with depression experienced recurrence within 2–3 years of the index episode (4). Externalizing problems such as ADHD are also likely to persist throughout childhood and adolescence (5). Despite the recurrent and episodic nature of behavioral health problems, much of the literature on behavioral health treatment focuses on single courses of psychotherapy interventions (6); less is understood regarding long-term trajectories of behavioral health care among adolescents. In this study, we examined outpatient psychotherapy utilization to identify population-wide service utilization patterns across a period in which behavioral health symptoms are likely to recur.
In some studies (6–9), researchers examined patterns of behavioral health service utilization among youths in the United States; however, most of these studies are dated, only examined a narrow window of time, and relied on self-report data to determine rates and patterns of service utilization. In one study, Saloner et al. (10) attempted to define episodes of mental health care and to examine patterns of treatment utilization and episodes of care among a large sample of U.S. youths across a 2-year period to better illustrate how youths do not follow a linear trajectory or single episode of care. Consistent with results from previous studies, they found low rates of both treatment utilization and continuity of care over a 2-year period, with an average duration of <6 months and with 40% of youths having only one mental health visit (10). However, the authors used survey data to examine a wide variety of mental health services, and almost 40% of the participants were lost to attrition (10).
In some studies, researchers have used administrative data to more accurately examine patterns of behavioral health care utilization by youths and to identify unique care trajectories. In one study, Reid and colleagues (11) used an administrative data sample of Canadian youths and identified five distinct patterns of service use defined by duration of service involvement, service frequency, and defined episodes of care. Such findings illustrate the heterogeneity in patterns of care among children and adolescents ages 5–13 years; specifically, some in this age group receive multiple episodes of care, whereas others engage in only single episodes of care with varying duration and intensity. Still, these findings are based on a sample of Canadian youths; given the unique complexities of the U.S. health care system as well as the dynamic nature of child and adolescent developmental stages, it is important to determine whether these findings extend to other samples, such as U.S. adolescents.
This study expands on previous research to better understand patterns of outpatient psychotherapy utilization among adolescents in the United States. In this study, we focused on a large sample of adolescents enrolled in Medicaid; 51% of U.S. youths were enrolled in Medicaid and CHIP (Children’s Health Insurance Program) (12). Thus, a better understanding of outpatient psychotherapy utilization among adolescents in this large group is needed. We focused on the course of outpatient psychotherapy utilization because this modality is the most commonly used service compared with higher-intensity behavioral health services, such as inpatient or residential services. Moreover, ongoing outpatient services are used, in part, to prevent more intensive (and higher-cost) services; therefore, it is important that youths with behavioral health needs receive appropriate and consistent services (13, 14).
Methods
Sample
A large administrative data set of adolescents with Medicaid claims between 2006 and 2017 from one Indiana county was evaluated for analyses. The data set included only Medicaid claims for youth services (ages 10–19 years) because eligibility ends at age 19. Only adolescents ages 10.0–13.9 were included because we considered only adolescents who were age eligible to receive services across the 5-year window, which was the focus of the study. Only individuals with a record of at least two outpatient visits and with Medicaid enrollment gaps of <12 months during the 5-year period were included. Study analyses were conducted between March 2020 and June 2022, and all procedures were performed in accordance with the Indiana University Institutional Review Board.
Measures
Age at first behavioral health visit, race, ethnicity, and gender were coded in the administrative records. Outpatient behavioral health visits were operationalized as Medicaid outpatient visit claims with a CPT code for a mental health or substance use diagnosis as the primary reason for the visit. Variables were created to represent the number of gaps in Medicaid enrollment (calculated from month to month as enrolled or not enrolled) and the average duration of Medicaid enrollment gap.
Behavioral health visit type.
Outpatient visits classified as psychotherapy (including initial psychiatric diagnostic evaluation as well as individual, group, and family therapy) were included (a table of CPT codes used for outpatient visits is shown in the online supplement to this article). Inpatient and emergency department (ED) visits associated with a primary mental health or substance use diagnosis were also coded; however, only inpatient and ED visits that had occurred in the first month of an individual’s service window were included. These visits were included given that some youths are not connected to outpatient behavioral health services until they experience an acute event or crisis; we wanted to capture those adolescents who initiated outpatient services after an acute crisis event.
Diagnosis.
We classified diagnoses that were associated with an adolescent’s initial behavioral health visit into the following broad diagnostic categories: internalizing disorders (depression, mood, anxiety, and trauma- and stress-related disorders), externalizing disorders (ADHD and behavior disorders), developmental disorders (autism and tic and learning disorders), substance use disorders, and nonspecific psychiatric disorders (visits that were coded as behavioral health but with an unclear diagnosis). For each diagnostic category, 0 (no) or 1 (yes) values denoted presence of diagnosis at first visit; individuals could have multiple diagnoses.
Service utilization.
The average number of months per year that an individual had at least one outpatient psychotherapy visit was calculated to estimate service utilization.
Statistical Methods
Latent class analysis (LCA) that used PROC LCA, version 1.3.2 (15), was applied to outpatient psychotherapy visit utilization (yes or no) across 5 years (60 months) after an individual’s first behavioral health visit of any type (outpatient, inpatient, or ED behavioral health–related visit). After an adolescent’s first behavioral health visit, data were evaluated monthly over the next 5 years; adolescents were coded as having either at least one outpatient behavioral health visit (1) or no visit (0) for each month of the 5-year period. A monthly time frame was chosen on the basis of previous operationalizations of episodes of care and typical patterns of behavioral health visit frequency (10, 11, 16).
The number of classes was selected by comparing the following fit statistics for LCA models ranging from two to 10 classes: Bayesian information criterion (BIC), adjusted BIC (aBIC), and consistent Akaike information criterion (CAIC). Lower values of BIC, aBIC, and CAIC indicate better fit. We also calculated the percentage reduction of these values among models with a different number of classes (m), representing the percentage of improvement in model fit. Entropy values were also examined, with values approaching 1 denoting clear separation of classes. We considered models with ≥2% improvement in all three fit criteria for m-class models over m-1-class models to determine the optimal number of classes (11). In addition to these model fit criteria, we selected models that included at least 5% of the total sample in each class to ensure clinical utility.
Follow-up chi-square tests and analyses of variance (ANOVAs) were conducted to examine differences in demographic characteristics, diagnoses, and service utilization across classes. ANOVA post hoc pairwise comparisons were conducted, while controlling for familywise type I error at 0.05, with the simulation option in PROC GLM. All statistical analyses were conducted with SAS, version 9.4.
Results
The final sample included 8,197 adolescents; the mean±SD age was 11.5±1.2 years at the time of the first behavioral health visit, and 60.8% were male, 54.3% were White, 44.3% were Black, and 4.9% were Hispanic or Latinx (Table 1).
| Characteristic | N | % |
|---|---|---|
| Age at first service (M±SD years, range) | 11.5±1.2 | 10.0–13.9 |
| Gender | ||
| Female | 3,216 | 39.2 |
| Male | 4,981 | 60.8 |
| Race | ||
| White | 4,447 | 54.3 |
| Black | 3,633 | 44.3 |
| Asian | 98 | 1.2 |
| Other | 19 | .2 |
| Ethnicity | ||
| Hispanic | 403 | 4.9 |
| Non-Hispanic | 7,794 | 95.1 |
| Medicaid enrollment | ||
| N of enrollment gaps (M±SD, range) | .4±.7 | 0–5 |
| Enrollment gap duration (M±SD months, range) | 1.1±2.4 | 0–12 |
| Disorder diagnosisa | ||
| Internalizing | 4,059 | 49.5 |
| Externalizing | 4,863 | 59.3 |
| Developmental | 339 | 4.1 |
| Substance use | 25 | .3 |
| Nonspecific mental health | 75 | .9 |
a
Individuals could have multiple diagnoses.
LCA: Patterns of Service Use
A five-class model was retained because it minimized fit indices (BIC=254,043, aBIC=253,077, CAIC=254,347), avoided overextraction of classes, maximized the confidence of group membership (entropy=0.92), and had classes containing at least 5% of the sample (Table 2). Furthermore, examination of profile plots for models comprising six classes revealed fewer unique distinctions among class trajectories. Each class represented a distinct trajectory for monthly outpatient visit utilization patterns based on adolescents’ probability of receiving a visit and patterns of service use: nonuse (38.7%), defined as a significant drop in service use after the first behavioral health visit; late-onset low engagement (17.1%), defined as a longer period of nonuse after the first visit with a period of low engagement (lower probability of monthly visit) between years 3 and 5; early-onset moderate engagement (16.7%), describing a variable frequency of service engagement (i.e., a varying probability of monthly visit) across years 1–3; early-onset high engagement (15.5%), describing a period of high engagement in services (higher probability of monthly visit) after the first visit and a drop in service engagement after year 2; and continuously high engagement (11.9%), defined as consistently high engagement across the 5-year period (Figure 1, Table 3).
| Class size (m) | BIC | aBIC | CAIC | Model fit improvement for m-class from m-1-class model (%) | Entropy |
|---|---|---|---|---|---|
| 2 | 289,100 | 288,716 | 289,221 | .95 | |
| 3 | 272,757 | 272,178 | 272,939 | 5.7 | .93 |
| 4 | 260,313 | 259,540 | 260,556 | 4.6 | .93 |
| 5 | 254,043 | 253,077 | 254,347 | 2.5 | .92 |
| 6 | 249,580 | 248,420 | 249,945 | 1.8 | .92 |
| 7b | 246,015 | 244,662 | 246,441 | .92 | |
| 8b | 243,532 | 241,985 | 244,019 | .91 | |
| 9b | 241,411 | 239,670 | 241,959 | .91 | |
| 10b | 239,540 | 237,605 | 240,149 | .91 |
a
aBIC, adjusted Bayesian information criterion; BIC, Bayesian information criterion; CAIC, consistent Akaike information criterion.
b
Denotes <5% of the sample represented in every class.
FIGURE 1. Patterns of service use in a 5-year period among 8,197 adolescents whose Medicaid claims were examined between 2006 and 2017a
aThe y-axis shows the probability of an adolescent having at least one outpatient visit in a given month. The x-axis shows months starting with an adolescent’s first recorded behavioral health visit through the end of the 5-year period (month 60). For example, at month 38, an adolescent in class 3 had a 0.70 probability of a visit, meaning that 70% of adolescents in the group were estimated to have a psychotherapy visit in month 38, whereas close to 0% of adolescents in class 4 tended to be seen in that month.
| Class | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Early-onset high engagement (1) | Early-onset moderate engagement (2) | Continuously high engagement (3) | Nonuse (4) | Late-onset low engagement (5) | ||||||
| Variable | N | % | N | % | N | % | N | % | N | % |
| Sample N | 1,274 | 15.5 | 1,370 | 16.7 | 980 | 11.9 | 3,168 | 38.7 | 1,405 | 17.1 |
| Age at start (M±SD years) | 11.5±1.32,3,4,5 | 11.3±1.21,3,4 | 11.1±1.21,2,4,5 | 11.7±1.21,2,3,5 | 11.3±1.21,3,4 | |||||
| Medicaid enrollment | ||||||||||
| N enrollment gaps (month to month) (M±SD) | .4±.72,3,4,5 | .3±.61,3,4 | .1±.41,2,4,5 | .5±.81,2,3,5 | .3±.71,3,4 | |||||
| Enrollment gap (M±SD months) | 1.1±2.42,3,4,5 | .9±2.11,3,4 | .3±1.41,2,4,5 | 1.5±2.81,2,3,5 | .8±2.01,3,4 | |||||
| Gender | ||||||||||
| Female | 503 | 40 | 520 | 38 | 383 | 39 | 1,238 | 39 | 572 | 41 |
| Raceb | ||||||||||
| White | 657 | 523,5 | 749 | 553 | 597 | 611,2,4 | 1,631 | 523,5 | 813 | 581,4 |
| Black | 599 | 473,5 | 600 | 443 | 370 | 381,2,4 | 1,490 | 473,5 | 574 | 411,4 |
| Ethnicity | ||||||||||
| Hispanic | 63 | 5 | 56 | 44 | 33 | 34 | 195 | 62,3,5 | 56 | 44 |
| Disorder diagnosisc | ||||||||||
| Internalizing | 656 | 52 | 679 | 50 | 451 | 46 | 1,603 | 51 | 670 | 48 |
| Externalizing | 769 | 613 | 850 | 623 | 513 | 521,2,4,5 | 1,869 | 593 | 862 | 613 |
| Developmental | 47 | 43 | 56 | 43 | 67 | 71,2,4,5 | 95 | 33 | 74 | 53 |
| Nonspecific mental health | 9 | 1 | 9 | 1 | 4 | <1 | 46 | 2 | 7 | 1 |
| Substance use | 3 | <1 | 4 | <1 | 0 | — | 12 | <1 | 6 | <1 |
| Without Medicaid (M±SD months) | 1.5±3.12,3,4,5 | 1.1±2.81,3,4 | .4±1.41,2,4,5 | 2.2±4.11,2,3,5 | 1.1±2.71,3,4 | |||||
| Months per year with visit (M±SD)d | 2.5±.9 | 4.1±1.3 | 7.3±1.7 | .9±.7 | 3.1±1.3 | |||||
| 1st month ED visit | 17 | 15 | 23 | 2 | 16 | 2 | 76 | 2 | 43 | 31 |
| 1st month inpatient visit | 41 | 35 | 66 | 55 | 53 | 55 | 127 | 45 | 121 | 91,2,3,4 |
a
Superscripts (1, 2, 3, 4, or 5) for each class value denote the classes whose values within a row statistically significantly differed from the value (p<0.05; analysis of variance with post hoc pairwise tests adjusted for multiple comparisons). A value without superscripts indicates no significant difference from those for any of the other classes. ED, emergency department.
b
Ns within this category may not add up to the total because of missing data.
c
Adolescents could have multiple diagnoses.
d
All classes were statistically significantly different from one another. Statistical significance was defined as p<0.05 (two-sided); F statistic for all variables, p<0.001.
Descriptive Analyses Across Patterns of Use
The five classes we identified could be further characterized by comparing them on demographic characteristics and conceptually relevant service use statistics. The classes significantly differed in the frequency and intensity of services, with values ranging from a mean of 0.9±0.7 months per year with a visit among adolescents in the nonuse class to 7.3±1.7 months per year with a visit in the continuously high engagement class (p<0.001) (Table 3). The late-onset low-engagement class had the highest percentage of individuals with behavioral health–related inpatient (9%) and ED (3%) visits in the initial month of service utilization, compared with the other classes (p<0.001).
With respect to Medicaid enrollment patterns, the nonuse class had the longest enrollment gaps (mean of 1.5±2.8 months) and the most months during the 5-year period without Medicaid (mean of 2.2±4.1 total months) (p<0.001) (Table 3). With respect to diagnoses, the continuously high engagement class had the highest percentage of individuals with developmental disorders (7%) and the lowest rates of externalizing disorders (52%) compared with all other classes (p<0.001). Classes did not differ in rates of internalizing disorders or substance use disorders. The consistently high engagement class had the highest percentage of White adolescents (61%), and the nonuse class had the highest percentage of Hispanic or Latinx adolescents (6% vs. <5% in the other classes, p<0.001).
Discussion
In this study, we sought to examine patterns of outpatient psychotherapy utilization among a large sample of adolescents during a 5-year period. In this large sample, five unique patterns of service utilization were identified that could be characterized by differences in duration of involvement and level of engagement as well as by differences in rates of diagnoses and demographic characteristics. The largest class (38.7% of the sample) was characterized by adolescents with minimal service use (classified as nonuse); this class had a psychotherapy visit of on average <1 month per year, had the highest percentage of Hispanic or Latinx adolescents, and had the longest duration of Medicaid enrollment gaps. One class, characterized as late-onset low engagement, had the highest rates of ED and inpatient behavioral health visits in the first month. Two classes were characterized by service utilization immediately after the initial visit for the first 3 years before a drop in utilization: one class of individuals with high engagement followed by a significant drop in engagement after year 2 and another class with variable and moderate engagement during the first 3 years. The smallest class of individuals were those with continuously high engagement throughout the 5-year window; this class had the highest percentage of adolescents with developmental disorders.
It is well understood that different behavioral health problems require specific treatment approaches with varying courses and service intensity (9, 17). In this study, we focused on the use of psychotherapy services in particular, and our results suggest that symptoms are not always “cured” after a single course of manualized treatment; this conclusion is inconsistent with much of the research on adolescent behavioral health treatment that focuses on symptom changes across a time-limited standardized course of manualized treatment (6, 18, 19). Moreover, there is evidence for a subset of “high-cost” outpatient service users with multiple and more complex diagnoses (17). Although the data did not account for problem severity, results indicating different patterns of service use and differences in prevalence of diagnoses across classes do suggest that severity is a subgroup-differentiating feature. For example, the continuously high engagement class had the highest rate of developmental disorder diagnoses, and although we note significant heterogeneity in the severity of symptoms and level of functioning within and across these diagnoses, this general diagnostic class is associated with more intensive and high-cost services (17). Taken together, our findings underscore the need for additional research to consider trajectories of behavioral health problems across this dynamic developmental period to inform best practices for managing such problems as well as informing health care policy so that appropriate access to treatment is available.
Most adolescents in the study fell into the nonuse class; this observation is consistent with findings that the near majority of youths do not engage in or do not seek needed psychotherapy services and that continuity of care and treatment engagement is challenging (7, 10, 11, 20). This study is one of the first to examine long-term trajectories of care among a large sample of Medicaid-insured adolescents. Other studies based on samples of privately insured youths in the United States have also shown high rates of early treatment termination or lack of continuity of care (7, 10). However, in one recent study, lower rates of treatment utilization were found among adolescents with Medicaid compared with those with private insurance (21). Thus, further research is needed to better understand potential differences in service utilization by health insurance type. The nature of the data used in the present study did not allow for exploration of reasons for low utilization; however, previous literature has highlighted family preferences (22), patient-provider relationships (23, 24), geographic access to care (25), and system-level variables (i.e., poor care coordination) as reasons for poor care continuity. Taken together, the findings underscore the need for efforts to increase service utilization through improved service connection, such as through utilization of integrated care models (26), treatment engagement strategies, and treatment retention.
We also noted differences in demographic characteristics across the classes we identified that are consistent with previous findings. For example, the nonuse group had the highest percentage of Hispanic or Latinx adolescents, consistent with previous findings of lower service utilization among minority youths (10, 27–29). These findings highlight the need for health care policy to address such disparities. With respect to psychotherapy in particular, extensive research is available on the need for cultural adaptations to evidence-based interventions (30, 31); although this premise could not be deciphered from the data, results hint at the need to further explore whether implemented treatment models are culturally appropriate, because recent meta-analytic results suggest that culturally adapted interventions are associated with greater symptom reduction and better retention in treatment (31, 32).
We note several limitations of this study. The study data were limited to Medicaid claims, and reasons for nonenrollment were unknown. Furthermore, significant policy changes occurred between 2006 and 2017, such as Medicaid expansion, that could have affected patterns of service use. The data were from Indiana, which did not expand Medicaid until 2015; therefore, in future analyses with more recent data, researchers should examine changes in utilization due to Medicaid expansion. Given that the data were from an urban county in one state, results may not be generalizable to other areas. Heterogeneous and dynamic patterns in Medicaid eligibility and enrollment change that can occur monthly (i.e., “churning”) also make it challenging to delineate large classes of adolescents on the basis of whether drops in service use were due to Medicaid enrollment or another reason. Data reported in the LCA models were only for outpatient psychotherapy visits because this service area was the focus of this article; further research is needed to examine use of inpatient, ED, and residential services. Similarly, the data did not account for medication management services given that psychotherapy is different from medication treatment alone; in future studies, researchers should examine use of medication and psychotherapy (21). Last, because of the aforementioned data limitations, we were unable to account for the severity of adolescents’ behavioral health problems; however, as mentioned above, other studies have identified a link between type and severity of problems and patterns of service utilization (10, 11, 16). In future studies, researchers should further examine differences in utilization on the basis of problem severity.
Conclusions
To our knowledge, this is the first study to use administrative data to examine long-term patterns of outpatient psychotherapy utilization among a large heterogeneous sample of adolescents with varied behavioral health treatment needs. The findings reemphasize that most youths on Medicaid receive minimal outpatient psychotherapy services after their initial visit, despite psychotherapy being the gold standard of treatment for many behavioral health problems. In fact, <15% of the sample appeared to use psychotherapy consistently over time. This analysis serves as a necessary starting point to investigate both drivers and consequences of differences in psychotherapy utilization.
Despite some limitations, our findings do provide meaningful information. The classes identified point to the unique service needs of adolescents and the necessity to consider treatment across this developmental period rather than examining single, time-limited courses of manualized psychotherapy, which is typically presented in the literature. Additionally, although the observed variation in psychotherapy utilization patterns was unsurprising, this analysis quantifies the nature and extent of this variation over a long period. Furthermore, results from this large and diverse sample provide a benchmark for comparison in future studies considering long-term treatment trajectories. Such research may also be beneficial for policy makers and health care administrators to better understand the needs of the specific population they are serving and to allocate resources accordingly, particularly to high-risk groups of youths who may benefit most from continuous services.
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Information & Authors
Information
Published In
History
Received: 23 August 2021
Revision received: 23 June 2022
Accepted: 22 July 2022
Published online: 4 January 2023
Published in print: April 01, 2023
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Competing Interests
Dr. Monahan has 1.5% equity ownership in a technology start-up L.L.C. called MyShyft (formerly RestUp). The other authors report no financial relationships with commercial interests.
Funding Information
This study was supported by Agency for Healthcare Research and Quality grants R01-HS-022681 and R01-HS-023318.
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