Factors Contributing to Reduced Caregiver Strain in a Publicly Funded Child Mental Health System

Introduction

Many caregivers experience significant stress and burden as a result of caring for their children with mental health problems, with 6% to 11% of caregivers in community samples reporting clinically elevated levels of strain (Angold et al., 1998). Caregivers of children with disruptive behavior problems experience particularly high levels of caregiver strain given the extra demands of caring for children with behavioral problems (Tsai, Yeh, & Slymen, 2013). Caregiver strain includes both observable negative life occurrences or objective strain (e.g., financial strain, interruptions at work, spending less time with other family members/friends) and negative emotions or subjective impacts that are both internalized (e.g., sadness, worry, guilt) and externalized (e.g., anger, resentment, embarrassment; Brannan, Heflinger, & Bickman, 1997).

A growing body of evidence suggests that caregiver strain influences receipt of child mental health services beyond what can be explained by child symptom severity and functioning (Brannan & Heflinger, 2006). Although greater caregiver strain is associated with greater initial use of child mental health services (Brannan & Heflinger, 2006; Bussing, Mason, Leon, & Sinha, 2003; Cook et al., 2004; Garland, Aarons, Hawley, & Hough, 2003; Shin & Brown, 2009), it is also associated with long gaps in treatment (Brannan, Heflinger, & Foster, 2003), poor service coordination (Yatchmenoff, Koren, Friesen, Gordon, & Kinney, 1998), and premature treatment dropout in community mental health settings (Pellerin, Costa, Weems, & Dalton, 2010). In addition, higher levels of caregiver strain predict use of more costly services, including psychiatric hospitalization (Bickman, Foster, & Lambert, 1996), residential or inpatient care (Brannan et al., 2003; Cook et al., 2004), and medication use (Cook et al., 2004). Subjective internalized strain in particular appears to be associated with receiving a higher level of care and more inconsistent care (Brannan et al., 2003). Given that higher caregiver strain has been associated with inefficiencies in care and higher overall mental health service costs, it is a relevant factor in efforts to improve the quality and effectiveness of community-based care for children with behavior problems.

Results of efficacy studies indicate that parent training (independent of child involvement in treatment) is associated with reductions in caregiver strain (Anastopoulos, Shelton, DuPaul, & Guevremont, 1993; Feldman & Werner, 2002; Moretti & Obsuth, 2009). In addition, there is some evidence to suggest that evidence-based treatments for children with behavioral problems are more effective when perceived caregiver stress is reduced (Kazdin & Whitley, 2003). The reduction of caregiver strain may be a key factor in maintaining child outcomes over time. Indeed, psychiatrically hospitalized children whose caregivers reported high strain initially but low strain after discharge showed the largest and most sustained decreases in externalizing symptoms (Blader, 2006). However, the impact of usual care psychotherapy (i.e., routine psychotherapy provided in community-based, non-research settings) on caregiver strain is unknown. Furthermore, the impact of caregiver strain on child mental health outcomes in usual outpatient care is not well understood.

Previous research has demonstrated that a number of factors contribute to strain, including child’s severity of emotional and behavioral symptoms (Brannan & Heflinger, 2001; Bussing et al., 2003; Sales, Greeno, Shear, & Anderson, 2004), service system characteristics (Brannan & Heflinger, 2006), and caregiver characteristics, such as educational level, family income, and race/ethnicity (Kang, Brannan, & Heflinger, 2005; McDonald, Gregoire, Poertner, & Early, 1997; Shin & Brown, 2009). However, little is known about the extent to which this association may be bidirectional. In addition, little is known about which factors (child, caregiver, and service system) may predict the greatest change in strain associated with treatment. Finally, caregiver strain in usual care is particularly relevant to recent calls to reform mental health care for children, with greater emphasis on addressing caregivers’ needs in treatment (see Hoagwood & Burns, 2014). Within this reformed family-centered model of care, efforts are made for caregivers to be better informed and empowered to take an active role in their child’s treatment, thereby helping to sustain their child’s treatment gains. By improving our understanding of caregiver strain in usual care, family support services could be tailored more effectively.

This study examines caregiver strain within a community sample of families involved in publicly funded outpatient treatment for their child with a disruptive behavior problem. The first purpose of this study is to describe the patterns of change in caregiver strain following initiation of usual care child mental health services. Second, this study seeks to identify predictors of change in caregiver strain from the following areas: child, caregiver, system characteristics, and service use. Finally, the direct relation between child symptom severity and caregiver strain is examined over time, with the hypothesis that it is bidirectional (i.e., higher child symptom severity increases caregiver strain, and higher strain can exacerbate child symptoms).

Method

Data for this study were collected as part of the Practice and Research: Advancing Collaboration (PRAC) study—an observational study that comprehensively examined usual care for children with disruptive behavior problems served in community-based outpatient clinics from 2004 to 2007. The main aims of the original study were to describe the type of care provided to children who presented to usual care and to examine the impact of care on child and family outcomes. Participants were followed for 16 months following service entry. Please see Garland et al. (2010) for a detailed description of the practice settings, client recruitment, and therapist characteristics.

Participants

Participating clinics

The six participating clinics represent the largest contractors for publicly funded, clinic-based outpatient mental health care for children in one of the largest counties in the United States and serve ethnically and diagnostically diverse children and their families. No intervention to influence service delivery took place during the study period.

Therapist participants

All therapists from participating clinics were assigned random numbers and recruited sequentially until a sample was gathered that reflected the county’s therapist distribution by discipline, training, and licensure status. In the 3 years following initial recruitment, all new therapists working at least half-time were recruited. Most recruited therapists (n = 131, 80%) agreed to participate, but only 96 had a patient enrolled in the study who consented and subsequently participated in the study. These 96 therapists were primarily female (n = 81, 84%) and Caucasian (n = 64, 67%) with an average age of 32.4 years (SD = 9.1) and an average of 2.9 (SD = 3.6, range = 0–25) years of therapy experience.

Child and parent participants

A total of 218 children (84% of eligible participants) were included in the current study. Inclusion criteria for child participants included the following: (a) Presenting problems included a disruptive behavior problem (aggression, defiance, delinquency, oppositional behavior by parent report), (b) age 4 to 13 years, (c) primary language for child and parent was English or Spanish, and (d) child was entering a new episode of psychotherapy (defined as no therapy for the previous 3 months) with a participating therapist.

Characteristics of the 218 participating children and their caregivers are provided in Table 1. The average child age was 9 years, the majority of participants were male (68%), and fewer than half were non-Hispanic Caucasian. Although all children presented with disruptive behavior problems, their clinician-assigned primary diagnoses varied, with attention deficit hyperactivity disorder (ADHD) being the most common (n = 82, 38%). Caregivers (herein referred to as parents) of these children were primarily female (n = 206, 94.5%) mothers (n = 178, 81.7%) with an average age of 39.7 years (SD = 10.1). Approximately two thirds had at least a high school diploma (n = 142, 67.0%); the other third was split between having less than a high school education (n = 39, 18.4%) and having at least a college degree (n = 31, 14.6%). Average annual household income was $30,739 (SD = 34.454). Sixteen percent (n = 35) spoke Spanish as a primary language.

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Table 1.

Sample Descriptives for Study Participants.

Variable	n	M (SD) or %	Range
Caregiver Strain Questionnaire
Objective	217	2.4 (1.0)	[1, 5]
Subjective Externalizing	217	2.3 (0.8)	[1, 5]
Subjective Internalizing	217	3.6 (1.0)	[1, 5]
Demographic characteristics
Child age	218	9.0 (2.7)	[4, 14]
Child gender (male)	148	67.9%
Child race/ethnicity
Non-Hispanic Caucasian	103	47.2%
Latino/Hispanic	65	29.8%
African American	22	10.1%
Other/mixed	28	12.8%
Child clinical factors at entry
Primary diagnosis
Disruptive behavior disorder	47	21.8%
ADHD	82	38.0%
Mood disorder	49	22.7%
Anxiety disorder	19	8.8%
Autism spectrum disorder/other	19	8.8%
Diagnostic comorbidity (>1 diagnosis)	93	43.1%
Eyberg Child Behavior Inventory	218	147.3 (36.9)	[59, 237]
Parent/family clinical factors
Brief Symptom Inventory	215	56.8 (11.4)	[33, 80]
Family Relationship Inventory	217	9.1 (4.7)	[−5, 17]
Life Events Questionnaire	217	3.0 (1.7)	[0, 8]
Treatment characteristics
Psychotherapy services
Number of visits	215	20.6 (15.9)	[0, 70]
Frequency of visits	215	0.6 (0.2)	[0.0, 1.2]
Medication use
Antipsychotics	201	11.0%
Antidepressants	201	8.3%
ADHD medication	201	30.3%
Mood stabilizers/antiepileptics	201	7.3%
Other	201	4.6%
Primary referral source
Parent	113	53.8%
School staff	48	22.9%
Other	49	23.3%

Note. ADHD = attention deficit hyperactivity disorder.

Analysis Plan

Results

Across all caregivers, average total caregiver strain decreased approximately one half point across 8 months, representing a 12.5% change on the 5-point scale (see Figure 1). Moderate decreases (d = .54) were observed in objective caregiver strain from baseline (M = 2.39, SD = 0.99) to 8 months (M = 1.92, SD = 0.81), as well as subjective internalizing caregiver strain (d = .74) from baseline (M = 3.56, SD = 0.98) to 8 months (M = 2.81, SD = 1.07). Change in subjective externalizing caregiver strain from baseline (M = 2.26, SD = 0.83) to 8 months (M = 1.96, SD = 0.81) was small (d = .37).

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Figure 1.

Change in caregiver strain across time.

ICCs were calculated for all three outcomes to assess the percent of variability in each outcome that is attributable to differences in therapists and parents. For objective caregiver strain, a negligible amount of variance was accounted for at the therapist level (ICC = .021). However, parent level factors accounted for approximately 50% (ICC = .529) of the variance in objective strain, confirming the need to include this level in multivariable analyses. For subjective externalized caregiver strain, approximately 8% of the variance in this outcome was accounted for at the therapist level (ICC = .083) and 50% was accounted for at the parent level (ICC = .506). For subjective internalized strain, therapist-level factors accounted for approximately 8% of the variance in this outcome (ICC = .083) whereas parent-level factors accounted for approximately 35% of the variance (ICC = .358). Therefore, both levels were included in subsequent analyses for both externalized and internalized strain.

Aim 1: Identify Predictors of Change in Caregiver Strain

Bivariate associations between predictor variables and each outcome variable were first examined (see Table 2). Predictors for each outcome that were marginal/significant were then entered into an initial multivariable model. The final multivariable model (see Table 3) included all predictors that were marginal/significant in this initial multivariable model. The following variables were not significant bivariate predictors of any of the three subtypes of caregiver strain: child gender, parent level of education, diagnostic comorbidity, quality of family relationships, family empowerment, treatment expectations, treatment satisfaction, number of psychotherapy visits, psychotherapy service intensity, use of antipsychotics and/or antidepressants, and funding source (Medi-Cal vs. school-based).

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Table 2.

Predictors of Change in Caregiver Strain: Bivariate Model Predictors.

Bivariate model predictors	Objective			Subjective Externalized			Subjective Internalized
	B	SE	p value	B	SE	p value	B	SE	p value
Child demographic characteristics
Child age	0.01	0.01	.63	<0.01	0.01	.81	0.02	0.01	.079
Child gender	0.04	0.07	.52	−0.01	0.10	.88	−0.07	0.08	.42
Child race/ethnicity
Caucasian a vs. Latino/Hispanic	−0.07	0.08	.35	−0.05	0.08	.51	−0.07	0.09	.42
Caucasian vs. African American	−0.06	0.11	.55	0.14	0.11	.21	0.06	0.13	.63
Caucasian vs. other/mixed	−0.17	0.10	.083	−0.18	0.11	.093	−0.05	0.11	.64
Child clinical factors
Primary diagnosis
DBD vs. ADHD	0.19	0.08	.021	0.09	0.08	.25	0.05	0.10	.62
DBD vs. mood	0.26	0.09	.006	0.02	0.09	.83	0.05	0.11	.64
DBD vs. anxiety	0.04	0.12	.76	−0.06	0.12	.61	−0.21	0.15	.15
DBD vs. ASD/other	0.29	0.12	.017	−0.02	0.12	.89	0.07	0.15	.62
Diagnostic comorbidity	0.02	0.06	.75	−0.05	0.06	.46	0.01	0.08	.90
Eyberg Child Behavior Inventory	<−0.01	<0.01	<0.001	<−0.01	< 0.01	.005	<−0.01	<0.01	.035
Caregiver/family clinical factors
Brief Symptom Inventory	−0.01	<0.01	.017	<−0.01	< 0.01	.18	< −0.01	< 0.01	.71
Family Relationship Inventory	0.01	0.01	.20	0.01	0.01	.33	< −0.01	0.01	.84
Life Events Questionnaire b	−0.08	0.06	.15	−0.12	0.05	.018	0.02	0.07	.72
Treatment characteristics
Psychotherapy services
Number of visits	<−0.01	<0.01	.97	<−0.01	< 0.01	.40	< −0.01	< 0.01	.45
Frequency of visits	−0.19	0.16	.24	0.05	0.14	.71	−0.07	0.17	.70
Medication use
Antipsychotics	−0.06	0.19	.75	−0.08	0.19	.66	−0.08	0.24	.74
Antidepressants	−0.14	0.24	.55	<0.01	0.21	.98	−0.24	0.28	.39
ADHD medication	−0.25	0.13	.055	0.05	0.13	.72	−0.05	0.17	.76
Mood stabilizers/antiepileptics	−0.48	0.23	.032	−0.28	0.22	.20	−0.20	0.29	.49
Primary referral source
Self vs. school staff	0.07	0.09	.40	0.08	0.08	.31	0.18	0.09	.054
Self vs. other	0.10	0.08	.23	−0.03	0.08	.65	0.16	0.09	.095

Note. Results of marginal and significant predictors are italicized. All categorical variables with three groups or more were dummy coded, with the following reference groups: non-Hispanic Caucasian (race/ethnicity), some high school or less (parent education), DBD (primary diagnosis), and self-referral (referral source). For the two binary variables, male gender and Medi-Cal funding were coded 0. DBD = disruptive behavior disorder; ADHD = attention deficit hyperactivity disorder; ASD = autism spectrum disorder.

a

Non-Hispanic Caucasian (race/ethnicity). ^b Square-root transformation.

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Table 3.

Predictors of Change in Caregiver Strain: Multivariable Models.

	Objective			Subjective Externalized			Subjective Internalized
Multivariable models	B	SE	p value	B	SE	p value	B	SE	p value
Child symptom severity	−0.51	0.29	<.00001	<−0.01	<−0.01	.005	<−0.01	< 0.01	.099
Mood stabilizer use	−0.80	0.22	.0002		—		—
Self vs. school referral source		—			—		0.17	0.07	.076
Final overall model	−0.33	0.07	<.00001	−0.15	0.03	<.00001	−0.45	0.05	<.00001

Objective caregiver strain

In the final multivariable model predicting objective caregiver strain, child symptom severity and mood stabilizer/antiepileptic use were the two significant predictors. This final overall model predicted a decrease of 0.33 points in objective caregiver strain per time point (SE = 0.07, p < .00001) for parents with children who had average symptom severity at entry and were taking a mood stabilizer/antiepileptic by 4 months into the study period. Child symptom severity was significantly associated with steeper slope, such that higher child symptom severity at entry into services was associated with a steeper decrease in objective caregiver strain over time (B = −0.51, SE = 0.29, p < .00001). In addition, caregivers of children who were using mood stabilizers/antiepileptic reported a faster rate of decline in objective caregiver strain (B = −0.80, SE = 0.22, p = .0002). Race, diagnosis, parent psychopathology, and child stimulant were no longer significant in the initial multivariable model and were therefore excluded from the final model.

Subjective externalized caregiver strain

Only child symptom severity significantly predicted subjective externalized strain in the final multivariable model, with an overall decrease of 0.15 points per time point (SE = 0.03, p < .00001) for caregivers of children with average symptom severity at baseline. Higher initial child symptom severity predicted greater albeit minimal decreases in externalized strain than low symptom severity (B < −0.01, SE < 0.01, p = .005). Race and ongoing negative life events did not remain significant predictors in the initial multivariable model and were therefore excluded.

Subjective internalized caregiver strain

When entered into the initial multivariable model, child symptom severity and school referral were marginally significant. When entered into the final multivariable model, the overall model was significant, predicting a 0.45 point decrease in subjective internalized caregiver strain per time point (SE = 0.05, p < .00001) for caregivers of children with average symptom severity using stimulant medication who were self-referred. Higher initial child symptom severity predicted greater but small decreases in strain than low symptom severity (B < −0.01, SE < .01, p = .017). School referral for services marginally predicted less steep decreases in strain (B = 0.17, SE = 0.07, p = .076). Child age and stimulant use were no longer significant predictors of subjective internalized strain in the initial multivariable model and were therefore excluded.

Aim 2: Examine the Direct Relationship Between Change in Child Symptom Severity and Change in Caregiver Strain Over Time to Determine the Nature and Direction of the Association

Objective caregiver strain

Simultaneous correlations between caregiver strain and child symptom severity were moderate at each time point and ranged from .45 to .53 (ps < .001). In the autoregressive model (see Figure 2), greater objective caregiver strain at 4 months (accounting for both baseline caregiver strain and symptom severity) predicted higher child symptom severity at 8 months (accounting for earlier measurements of symptom severity; β = .18, SE < 0.01, p < .05). No other paths between child symptom severity and objective caregiver strain were statistically significant.

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Figure 2.

Objective caregiver strain autoregressive model.

Subjective externalized caregiver strain

Simultaneous correlations between caregiver strain and child symptom severity were moderate at each time point and ranged from .31 to .39 (ps < .001). In the autoregressive model (see Figure 3), greater baseline externalized caregiver strain predicted higher child symptom severity at 4 months (controlling for baseline symptom severity; β = .12, SE = 2.54, p < .05). In turn, higher child symptom severity at 4 months predicted greater externalized caregiver strain at 8 months (accounting for earlier measurements of caregiver strain; β = .13, SE < 0.01, p < .05). No other paths between child symptom severity and externalized caregiver strain were statistically significant.

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Figure 3.

Subjective externalized caregiver strain autoregressive model.

Subjective internalized caregiver strain

Simultaneous correlations between caregiver strain and child symptom severity were moderate at each time point and ranged from .41 to .51 (ps < .001). In the autoregressive model (see Figure 4), similar to externalized strain, higher child symptom severity at 4 months predicted greater internalized caregiver strain at 8 months (β = .27, SE < 0.01, p < .001). No other paths between child symptom severity and internalized caregiver strain were statistically significant.

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Figure 4.

Subjective internalized caregiver strain autoregressive model.

Discussion

Overall, there were small to medium decreases in caregiver strain over the 8-month study period. Caregivers reported less objective and subjective strain 8 months after initiating mental health services, with the greatest change occurring in the first 4 months. However, subjective internalized caregiver strain remained in a moderate range 8 months after initiating treatment, although much larger reductions in caregiver strain have been found in other usual care settings with time-limited therapy (i.e., 10 sessions; Moretti & Obsuth, 2009). Despite examining a range of demographic and clinical factors, few robust associations were found. Therefore, it is possible that decreases in strain may largely be the result of initiating mental health services rather than actual receipt of services. Indeed, the number of psychotherapy visits and service intensity were not associated with reductions in caregiver strain. Nevertheless, the results suggest that targeting child disruptive behavior problems may affect reductions in both externalized and internalized subjective strain, whereas targeting objective caregiver strain may reduce severity of disruptive behavior problems.

Across all three types of strain, initial disruptive behavior severity was the most robust predictor of reduced strain. It is possible that this effect reflects greater “relief” on the part of caregivers with more symptomatic children on entering treatment. However, this effect was only clinically significant for objective strain, as reductions in subjective strain associated with symptom severity were minimal, suggesting that it may require greater therapist efforts (or greater child symptom reduction) to change caregivers’ subjective experience of strain compared with change in actual objective events associated with strain. While more severe initial disruptive behaviors predicted a steeper decline in caregiver strain, greater disruptive behavior severity sustained at 4 months (accounting for initial symptoms) predicted less improvement in subjective externalized and internalized strain at 8 months. In other words, caregiver strain is greater at 8 months for caregivers of children whose symptom severity remains high at 4 months.

Autoregressive models also revealed the reverse—greater caregiver strain affected child disruptive behavior severity. After accounting for initial objective caregiver strain, greater objective caregiver strain at 4 months predicted less improvement in child behavior at 8 months. Similarly, greater initial subjective externalized caregiver strain predicted less improvement in child behavior at 4 months. These results reinforce a possible causal relation between disruptive behavior problems and caregiver strain. Furthermore, they suggest there is a true bidirectional relationship between caregiver strain and disruptive behavior problems, such that reducing behavior problems may enhance positive changes in subjective strain, at the same time as reducing objective strain may lead to reductions in behavior problems.

These findings suggest the equal importance of addressing child symptom improvement and caregiver strain reduction in treatment as changes in one affect the other. Indeed, caregivers who receive education and support services report reduced caregiver strain improved emotional functioning in their children (Kutash et al., 2013). Furthermore, among parents who report initial high caregiver strain, peer support was associated with decreased strain and increased treatment efficacy (Kutash, Duchnowski, Green, & Ferron, 2011). There is also evidence to suggest that treatment for children with behavioral problems is more effective when caregiver strain is reduced (Kazdin & Whitley, 2003). Therapists might intervene directly to reduce caregiver strain through a variety of techniques, including teaching skills, reframing child problems, increasing caregiver social support, and providing families with additional resources, support, and empathy. Some of these techniques are included as part of parent training, which has been associated with reductions in caregiver strain (Anastopoulos et al., 1993; Feldman & Werner, 2002; Moretti & Obsuth, 2009), but research suggests that adjunct services provided specifically to caregivers may be needed for improved outcomes, particularly among caregivers with high initial strain (Kutash et al., 2011; Kutash et al., 2013). Indirectly, caregiver strain might also be reduced by improving child behavior, child coping skills, and child–caregiver interactions.

Use of certain psychotropic medications was also associated with steeper decreases in caregiver strain (objective), even after accounting for initial severity of disruptive behavior problems. Caregivers of children taking a mood stabilizer/antiepileptic reported greater decreases in objective strain than caregivers whose children were not using these medications, regardless of symptom severity at entry into services. Again, it may be that this reduction reflects an alleviation of strain on receiving services (in this case, medication management) for their child. Alternatively, decreases in child problems as a direct result of mood stabilizer/antiepileptic use may be responsible for decreases in caregiver strain. Finally, school referral (as compared with self-referral) was associated with a less steep decline in subjective internalized strain. In other words, caregivers referred to child services by school staff did not experience as great of a decrease in strain as those who were self-referred, suggesting that caregiver “relief” is greater for those who self-initiate entry into treatment. It is possible that treatment of these children may have focused more on behaviors in the school context compared with the home context, or caregivers of these children may have been less active in treatment than when they self-referred.

The role of caregiver strain in influencing mental health service utilization is well established. However, it is less commonly studied as an indicator of treatment outcome, despite its potential impact on future child symptom severity and reentry into mental health services. This study was comprehensive in its examination of predictors of caregiver strain, with longitudinal data on children, families, and the services provided to them. The sample of children with disruptive behavior problems who presented for usual care services provides a unique context in which to examine caregiver strain. The multifaceted data allowed for exploration of a variety of predictors consistent with the literature, and the analytic approach was appropriate in dealing with multilevel data and participant attrition. However, these findings may not generalize to other clinical populations (i.e., presenting problems other than disruptive behavior) and/or other geographic areas. In addition, many but not all factors related to caregiver strain were available; for example, social support may buffer against strain (McCabe, Yeh, Lau, Garland, & Hough, 2003) but was not measured in this study. Furthermore, only caregiver report was obtained for disruptive behavior problem severity and caregiver strain, such that caregivers who perceived greater themselves to have greater strain may have also reported more severe child behavior problems. Finally, it is not possible to know the extent to which caregiver strain would have decreased naturally as a result of time.

Despite some limitations, this study adds to current evidence suggesting that caregiver strain plays an important role in predicting child symptomatology; as such, it is an important outcome of child treatment. Therapists providing mental health services to families are challenged with the task of attending to multiple treatment goals. Striking a balance between intervention at the child functioning (e.g., symptom severity) and caregiver functioning levels (e.g., caregiver distress and strain as a result of the child’s problems) is important. Indeed, high caregiver strain is associated with less effective service utilization (Brannan et al., 2003; Pellerin et al., 2010; Yatchmenoff et al., 1998), which may directly affect child outcomes. Furthermore, child symptomatology and caregiver strain reciprocally affect each other, highlighting the importance of addressing both as treatment targets.

Due to the naturalistic nature of this study, it is not possible to know how strain would have been affected for caregivers whose children were not receiving care or were receiving different types of treatment. However, the lack of association of reduction in caregiver strain with number of service visits and service intensity is informative and suggests that that usual care services may not be optimally effective in reducing caregiver strain. This highlights a potential need for specific parent-focused intervention strategies to mediate the interaction between caregiver strain and child symptoms to better serve the mental health needs of the entire family. Future research might also better disentangle the most indicated sequence of treatment—whether to focus initially on child symptoms, caregiver strain, or both simultaneously. Ultimately, this understanding may help to improve effective utilization of mental health services and lead to better child and family outcomes.