Behavior Management in Young Children Exposed to Trauma: A Case Study of Three Evidence-Based Treatments.

Young children are particularly vulnerable to traumatic events and the development of posttraumatic stress symptoms, including comorbid disruptive behaviors. Fortunately, several evidence-based interventions have been shown to be effective at decreasing both posttraumatic stress symptoms and disruptive behaviors in young children. This paper provides an overview of three such interventions-Child-Parent Psychotherapy (CPP), Parent-Child Interaction Therapy (PCIT), and Trauma-Focused Cognitive Behavioral Therapy (TF-CBT). An illustrative case study is used to compare how each intervention addresses disruptive behaviors, with a focus on theoretical underpinnings, model similarities, and model differences. The models each have empirical evidence for the treatment of disruptive behavior in young children, and therefore, may be appropriate for treating children with a history of trauma exposure and comorbid disruptive behaviors. Child, caregiver, and environmental factors are essential to consider when identifying an evidence-based intervention for this population.

Eating Disorder Risk and Pathogenic Behaviors Among Collegiate Student-Athletes.

CONTEXT: Eating disorders (EDs) are a cluster of behavioral conditions characterized by uneasy thoughts and behaviors that grow into severe or persistent eating disturbances. The demands on student-athletes may create mental and physical stressors that increase the likelihood of EDs and disordered eating. OBJECTIVE: To examine the ED risk through eating attitudes and behaviors in male and female student-athletes and across various sport types (endurance, aesthetic, power, ball or team, or technical sports). DESIGN: Cross-sectional study. SETTING: Collegiate athletics. PATIENTS OR OTHER PARTICIPANTS: National Collegiate Athletic Association Division I and II student-athletes (n = 2054; males = 631; females = 1423) from 40 institutions. MAIN OUTCOME MEASURE(S): Participants completed a web-based demographic survey and the Eating Attitudes Test-26 (EAT-26). Multiple χ2 analyses examined participants classified as at risk for EDs. Independent-samples t tests and a 1-way analyses of variance compared sex and sport type across EAT-26 totals and subscale (Dieting, Bulimia, and Oral Control) scores. RESULTS: Overall, 25.3% (n = 520/2054) of student-athletes were classified as at risk for EDs. Differences were found between sex and ED risk (χ21,2054 = 32.9, P ≤ .01; 17.3% [n = 109/631] males, 28.9% [n = 411/1423] females) and across ED risk and sport type (χ24,2054 = 13.4, P = .01). When examining females only, we observed differences across ED risk and sport type (χ24,1423 = 13.4, P ≤ .01). No differences were evident across ED risk and sport type for males. Differences were seen between sex and binge eating (χ21,2054 = 6.8, P = .009), sex and diet pill use (χ21,2054 = 19.6, P ≤ .01), and sport type and diet pill use (χ24,2054= 12.2, P = .016), excessive exercise (χ24,2054 = 32.1, P ≤ .01), and losing more than 20 lb (9 kg) in the last 6 months (χ24,2054 = 10.2, P ≤ .037). CONCLUSIONS: Student-athletes in the collegiate setting are at risk for EDs. Medical professionals, such as athletic trainers, need to be educated on the potential risk factors that may lead to EDs. Protocols for prevention, screening and recognition, and referral should be developed for student-athletes at risk for EDs.

Energy Availability, Mental Health, and Sleep Patterns of Athletic Trainers.

CONTEXT: Engaging in exercise and appropriate nutritional intake improves mental health by reducing anxiety, depression, and sleep disturbances. However, few researchers have examined energy availability (EA), mental health, and sleep patterns in athletic trainers (ATs). OBJECTIVE: To examine ATs' EA, mental health risk (ie, depression, anxiety), and sleep disturbances by sex (male, female), job status (part time [PT AT], full time [FT AT]), and occupational setting (college or university, high school, or nontraditional). DESIGN: Cross-sectional study. SETTING: Free living in occupational settings. PATIENTS OR OTHER PARTICIPANTS: A total of 47 ATs (male PT ATs = 12, male FT ATs = 12; female PT ATs = 11, female FT ATs = 12) in the southeastern United States. MAIN OUTCOME MEASURE(S): Anthropometric measurements consisted of age, height, weight, and body composition. Energy availability was measured through energy intake and exercise energy expenditure. We used surveys to assess the depression risk, anxiety (state or trait) risk, and sleep quality. RESULTS: Thirty-nine ATs engaged in exercise, and 8 did not exercise. Overall, 61.5% (n = 24/39) reported low EA (LEA); 14.9% (n = 7/47) displayed a risk for depression; 25.5% (n = 12/47) indicated a high risk for state anxiety; 25.5% (n = 12/47) were at high risk for trait anxiety, and 89.4% (n = 42/47) described sleep disturbances. No differences were found by sex and job status for LEA, depression risk, state or trait anxiety, or sleep disturbances. Those ATs not engaged in exercise had a greater risk for depression (risk ratio [RR] = 1.950), state anxiety (RR = 2.438), trait anxiety (RR = 1.625), and sleep disturbances (RR = 1.147), whereas ATs with LEA had an RR of 0.156 for depression, 0.375 for state anxiety, 0.500 for trait anxiety, and 1.146 for sleep disturbances. CONCLUSIONS: Although most ATs engaged in exercise, their dietary intake was inadequate, they were at increased risk for depression and anxiety, and they experienced sleep disturbances. Those who did not exercise were at an increased risk for depression and anxiety. Energy availability, mental health, and sleep affect overall quality of life and can affect ATs' ability to provide optimal health care.

Investigation of Eating Disorder Risk and Body Image Dissatisfaction among Female Competitive Cheerleaders.

Social agents associated with cheerleading environments are increasingly linked to body image dissatisfaction (BID) and eating disorders (ED). This study examined ED risk across team type, squad type, and position. An additional purpose determined BID in clothing type (daily clothing, midriff uniform, and full uniform), and meta-perceptions from the perspective of peers (MP peers), parents (MP parents), and coaches (MP coaches). Female cheerleaders (n = 268) completed an online survey which included demographics, the Eating Attitudes Test-26, and pathogenic behavior questions. Body image perceptions were assessed by using the Sex-Specific Figural Stimuli Silhouettes. Overall, 34.4% of cheerleaders (n = 268; mean age: 17.9 ± 2.7 years) exhibited an ED risk. Compared to All-Star cheerleaders, college cheerleaders demonstrated significant higher ED risk (p = 0.021), dieting subscale scores (p = 0.045), and laxative, diet pill, and diuretic use (p = 0.008). Co-ed teams compared to all-girl teams revealed higher means for the total EAT-26 (p = 0.018) and oral control subscale (p = 0.002). The BID in clothing type revealed that cheerleaders wanted to be the smallest in the midriff option (p < 0.0001, η2 = 0.332). The BID from meta-perception revealed that cheerleaders felt that their coaches wanted them to be the smallest (p < 0.001, η2 = 0.106). Cheerleaders are at risk for EDs and BID at any level. Regarding the midriff uniform, MP from the perspective of coaches showed the greatest difference between perceived and desired body image.

Examination of the Prevalence of Female Athlete Triad Components among Competitive Cheerleaders.

The purpose of this study was to examine individual and combined Female Athlete Triad components within collegiate cheerleaders, an at-risk group. Cheerleaders (n = 19; age: 20.3 ± 1.2 years) completed anthropometric measurements, health history questionnaires, resting metabolic rate, the eating disorder inventory-3 and symptom checklist, blood sample, and DXA scan. Participants completed dietary and exercise logs for 7 days and used heart rate monitors to track daily and exercise energy expenditure. Proportions were calculated for low energy availability (LEA) risk, disordered eating risk, and pathogenic behaviors. Chi-square analysis was used to determine the difference between cheerleaders who experience low EA with or without disordered eating risk. All cheerleaders demonstrated LEA for the days they participated in cheerleading practice, 52.6% demonstrated LEA with eating disorder risk and 47.4% demonstrated LEA without eating disorder risk, 52.6% self-reported menstrual dysfunction, 14% experienced menstrual dysfunction via hormonal assessment, and 0% demonstrated low bone mineral density. Overall, 47.7% presented with one Triad component, 52.6% demonstrated two Triad components using self-reported menstrual data, and 10.5% demonstrated two Triad components using hormonal assessments. All cheerleaders displayed LEA. These findings support the need for increased education on the individual components of the Triad and their potential consequences by qualified personal.

Work and Life in the Sport Industry: A Review of Work-Life Interface Experiences Among Athletic Employees.

OBJECTIVE: To review the literature on work-life interfaces in the sport industry, focusing on athletic trainers, coaches, athletes, and other sport personnel. DATA SOURCES: Studies were identified using SPORTDiscus, PsychINFO, and Google Scholar. Search terms included work-family balance, work-life balance, work-family conflict, work-life conflict, work-family enrichment, and work-life enrichment. These search terms were used in different combinations and configurations in the search process. STUDY SELECTION: Included studies were peer-reviewed journal articles, with primary data collection, and published in English. In the articles, researchers also examined some aspect of the work-family interface in relation to the sport industry. DATA EXTRACTION: The initial searches returned 110 articles. Of these, 21 articles were removed for a lack of focus on the sport industry, for being a duplicate, or for focusing outside the work-life interface in sport. A total of 89 articles remained for a full analysis. An additional 20 articles were then removed because the authors either did not collect primary data or focused outside our study purpose. Therefore, 69 articles were included in the review. DATA SYNTHESIS: The theoretical framework, study population, population region, methods, article focus, and findings from the articles were recorded. Articles were then grouped based on the study population focus (eg, athletic trainer, coach, or other). CONCLUSIONS: The results suggested that investigators' primary interests were athletic trainers and coaches, primarily with respect to work-life balance and work-life conflict. Less attention was paid to international participants, athletes, and topics related to work-life enrichment. The field will continue to progress as more populations and perspectives are studied. Furthermore, an additional emphasis on positive organizational behaviors, such as work-life enrichment and life-work enrichment, will move the literature forward and answer useful questions with both theoretical and practical outcomes.

Examination of eating disorder risk among university marching band artists.

BACKGROUND: Marching band artists are a physically active population, composed of approximately 27,000 people in the United States. University marching band artists face many of the same physically active demands and mental stressors as student athletes, potentially predisposing them to injury, illness, and risk for eating disorders (EDs). The purpose of this study was to examine ED risk across sex in university marching band artists, and to determine the type of risk based on the Eating Disorder Inventory-3 (EDI-3) and Eating Disorder Inventory-3 Symptom Check List (EDI-3 SC). A secondary aim examined marching band artists and pathogenic weight control behavior use across sex. METHODS: This was a cross-sectional study. A total of 150 marching band artists (female: n = 84, male: n = 66, age = 19.9 ± 1.1 years) from three National Collegiate Athletic Association Division I university marching bands participated in the study. We screened for ED risk using the EDI-3, and the EDI-3 SC. RESULTS: Overall, marching band artists were at risk for EDs, using only the EDI-3, 45.3% (n = 68) were at risk, with females at significant higher risk than males [χ2 = 5.228, p = .022]; using only the EDI-3 SC, 54% (n = 81) were at risk and no significant differences were found across sex. Overall, 48% of all participants reported dieting and 20.7% engaged in excessive exercise to control weight. Significant differences were found between sex and purging to control weight [χ2 = 3.94, p = .047] and laxative use [χ2 = 4.064, p = .044], with females engaging in behavior more than males. CONCLUSIONS: Eating disorder risk was prevalent for both female and male marching band artists, with females displaying higher risk for EDs than males. Furthermore, marching band artists are engaging in pathogenic behaviors to control their weight. Healthcare providers (e.g., physicians, athletic trainers, physical therapist, dietitians, etc.) working in this setting should be aware of the risk factors displayed in marching band artists, and be able to provide education, prevention, and clinical interventions to this population. Additionally, marching band administrators should be aware of all medical risk factors and the benefit of having a healthcare provider (e.g., athletic trainer) to oversee the healthcare and wellness of marching band artists.

Energy Balance, Eating Disorder Risk, and Pathogenic Behaviors Among Athletic Trainers.

CONTEXT: Research exists on energy balances (EBs) and eating disorder (ED) risks in physically active populations and occupations by settings, but the EB and ED risk in athletic trainers (ATs) have not been investigated. OBJECTIVE: To assess ATs' energy needs, including the macronutrient profile, and examine ED risk and pathogenic behavioral differences between sexes (men, women) and job statuses (part time or full time) and among settings (college or university, high school, nontraditional). DESIGN: Cross-sectional study. SETTING: Free-living in job settings. PATIENTS OR OTHER PARTICIPANTS: Athletic trainers (n = 46; male part-time graduate assistant ATs = 12, male full-time ATs = 11, female part-time graduate assistant ATs = 11, female full-time ATs = 12) in the southeastern United States. MAIN OUTCOME MEASURE(S): Anthropometric measures (sex, age, height, weight, body composition), demographic characteristics (job status [full- or part-time AT], job setting [college/university, high school, nontraditional], years of AT experience, exercise background, alcohol use), resting metabolic rate, energy intake (EI), total daily energy expenditure (TDEE), exercise energy expenditure, EB, macronutrients (carbohydrates, protein, fats), the Eating Disorder Inventory-3, and the Eating Disorder Inventory-3 Symptom Checklist. RESULTS: The majority of participants (84.8%, n = 39) had an ED risk, with 26.1% (n = 12) engaging in at least 1 pathogenic behavior, 50% (n = 23) in 2 pathogenic behaviors, and 10.8% (n = 5) in >2 pathogenic behaviors. Also, 82.6% of ATs (n = 38) presented in negative EB (EI < TDEE). Differences were found in resting metabolic rate for sex and job status (F1,45 = 16.48, P = .001), EI (F1,45 = 12.01, P = .001), TDEE (F1,45 = 40.36, P < .001), and exercise energy expenditure (F1,38 = 5.353, P = .026). No differences were present in EB for sex and job status (F1,45 = 1.751, P = .193); χ2 analysis revealed no significant relationship between ATs' sex and EB ({\rm{\chi }}_{1,46}^2= 0.0, P = 1.00) and job status and EB ({\rm{\chi }}_{1,46}^2 = 2.42, P = .120). No significant relationship existed between Daily Reference Intakes recommendations for all macronutrients and sex or job status. CONCLUSIONS: These athletic trainers experienced negative EB, similar to other professionals in high-demand occupations. Regardless of sex or job status, ATs had a high ED risk and participated in unhealthy pathogenic behaviors. The physical and mental concerns associated with these findings indicate a need for interventions targeted at ATs' health behaviors.

Core Body Temperatures in Collegiate Marching Band Artists During Rehearsals and Performances.

CONTEXT: To our knowledge, no researchers have investigated thermoregulatory responses and exertional heat illness (EHI) risk factors in marching band (MB) artists performing physical activity in high environmental temperatures. OBJECTIVE: To examine core temperature (Tc) and EHI risk factors in MB artists. DESIGN: Descriptive epidemiology study. SETTING: Three rehearsals and 2 football games for 2 National Collegiate Athletic Association Division I institution's MBs. PATIENTS OR OTHER PARTICIPANTS: Nineteen volunteers (females = 13, males = 6; age = 20.5 ± 0.9 years, height = 165.1 ± 7.1 cm, mass = 75.0 ± 19.1 kg) completed the study. MAIN OUTCOME MEASURE(S): We measured Tc, wet bulb globe temperature, and relative humidity preactivity, during activity, and postactivity. Other variables were activity time and intensity, body surface area, hydration characteristics (fluid volume, sweat rate, urine specific gravity, percentage of body mass loss), and medical history (eg, previous EHI, medications). The statistical analysis consisted of descriptive information (mean ± standard deviation), comparative analyses that determined differences within days, and correlations that identified variables significantly associated with Tc. RESULTS: The mean time for rehearsals was 102.8 ± 19.8 minutes and for games was 260.5 ± 47.7 minutes. Mean maximum Tc was 39.1 ± 1.1°C for games and 38.4 ± 0.7°C for rehearsals; the highest Tc (41.2°C) occurred during a game. Fluid consumption did not match sweat rates (P < .001). Participants reported to games in a hypohydrated state 63.6% of the time. The maximum Tc correlated with the maximum wet bulb globe temperature (r = 0.618, P < .001) and was higher in individuals using mental health medications (rpb = -0.254, P = .022) and females (rpb = 0.330, P = .002). Body surface area (r = -0.449, P < .001) and instrument mass (r = -0.479, P < .001) were negatively correlated with Tc. CONCLUSIONS: Marching band artists experienced high Tc during activity and should have access to athletic trainers who can implement EHI-prevention and -management strategies.

Energy Availability With or Without Eating Disorder Risk in Collegiate Female Athletes and Performing Artists.

CONTEXT: Female athletes and performing artists can present with low energy availability (LEA) from either unintentional (eg, inadvertent undereating) or intentional (eg, eating disorder [ED]) methods. Whereas LEA and ED risk have been examined independently, few researchers have examined them simultaneously. Awareness of LEA with or without ED risk may provide clinicians with innovative prevention and intervention strategies. OBJECTIVE: To examine LEA with or without ED risk (eg, eating attitudes, pathogenic behaviors) in female collegiate athletes and performing artists and compare sport type and LEA with the overall ED risk. DESIGN: Cross-sectional study. SETTING: Free living in sport-specific settings. PATIENTS OR OTHER PARTICIPANTS: A total of 121 collegiate female athletes and performing artists (age = 19.8 ± 2.0 years, height = 168.9 ± 7.7 cm, mass = 63.6 ± 9.3 kg) participating in equestrian (n = 28), soccer (n = 20), beach volleyball (n = 18), softball (n = 17), volleyball (n = 12), and ballet (n = 26). MAIN OUTCOME MEASURE(S): Anthropometric measurements (height, mass, body composition), resting metabolic rate, energy intake, total daily energy expenditure, exercise energy expenditure, Eating Disorder Inventory-3 (EDI-3), and EDI-3 Symptom Checklist were assessed. Chi-square analysis was used to examine differences between LEA and sport type, LEA and ED risk, ED risk and sport type, and pathogenic behaviors and sport type. RESULTS: Most (81%, n = 98) female athletes and performing artists displayed LEA and differences between LEA and sport type (\(\def\upalpha{\unicode[Times]{x3B1}}\)\(\def\upbeta{\unicode[Times]{x3B2}}\)\(\def\upgamma{\unicode[Times]{x3B3}}\)\(\def\updelta{\unicode[Times]{x3B4}}\)\(\def\upvarepsilon{\unicode[Times]{x3B5}}\)\(\def\upzeta{\unicode[Times]{x3B6}}\)\(\def\upeta{\unicode[Times]{x3B7}}\)\(\def\uptheta{\unicode[Times]{x3B8}}\)\(\def\upiota{\unicode[Times]{x3B9}}\)\(\def\upkappa{\unicode[Times]{x3BA}}\)\(\def\uplambda{\unicode[Times]{x3BB}}\)\(\def\upmu{\unicode[Times]{x3BC}}\)\(\def\upnu{\unicode[Times]{x3BD}}\)\(\def\upxi{\unicode[Times]{x3BE}}\)\(\def\upomicron{\unicode[Times]{x3BF}}\)\(\def\uppi{\unicode[Times]{x3C0}}\)\(\def\uprho{\unicode[Times]{x3C1}}\)\(\def\upsigma{\unicode[Times]{x3C3}}\)\(\def\uptau{\unicode[Times]{x3C4}}\)\(\def\upupsilon{\unicode[Times]{x3C5}}\)\(\def\upphi{\unicode[Times]{x3C6}}\)\(\def\upchi{\unicode[Times]{x3C7}}\)\(\def\uppsy{\unicode[Times]{x3C8}}\)\(\def\upomega{\unicode[Times]{x3C9}}\)\(\def\bialpha{\boldsymbol{\alpha}}\)\(\def\bibeta{\boldsymbol{\beta}}\)\(\def\bigamma{\boldsymbol{\gamma}}\)\(\def\bidelta{\boldsymbol{\delta}}\)\(\def\bivarepsilon{\boldsymbol{\varepsilon}}\)\(\def\bizeta{\boldsymbol{\zeta}}\)\(\def\bieta{\boldsymbol{\eta}}\)\(\def\bitheta{\boldsymbol{\theta}}\)\(\def\biiota{\boldsymbol{\iota}}\)\(\def\bikappa{\boldsymbol{\kappa}}\)\(\def\bilambda{\boldsymbol{\lambda}}\)\(\def\bimu{\boldsymbol{\mu}}\)\(\def\binu{\boldsymbol{\nu}}\)\(\def\bixi{\boldsymbol{\xi}}\)\(\def\biomicron{\boldsymbol{\micron}}\)\(\def\bipi{\boldsymbol{\pi}}\)\(\def\birho{\boldsymbol{\rho}}\)\(\def\bisigma{\boldsymbol{\sigma}}\)\(\def\bitau{\boldsymbol{\tau}}\)\(\def\biupsilon{\boldsymbol{\upsilon}}\)\(\def\biphi{\boldsymbol{\phi}}\)\(\def\bichi{\boldsymbol{\chi}}\)\(\def\bipsy{\boldsymbol{\psy}}\)\(\def\biomega{\boldsymbol{\omega}}\)\(\def\bupalpha{\bf{\alpha}}\)\(\def\bupbeta{\bf{\beta}}\)\(\def\bupgamma{\bf{\gamma}}\)\(\def\bupdelta{\bf{\delta}}\)\(\def\bupvarepsilon{\bf{\varepsilon}}\)\(\def\bupzeta{\bf{\zeta}}\)\(\def\bupeta{\bf{\eta}}\)\(\def\buptheta{\bf{\theta}}\)\(\def\bupiota{\bf{\iota}}\)\(\def\bupkappa{\bf{\kappa}}\)\(\def\buplambda{\bf{\lambda}}\)\(\def\bupmu{\bf{\mu}}\)\(\def\bupnu{\bf{\nu}}\)\(\def\bupxi{\bf{\xi}}\)\(\def\bupomicron{\bf{\micron}}\)\(\def\buppi{\bf{\pi}}\)\(\def\buprho{\bf{\rho}}\)\(\def\bupsigma{\bf{\sigma}}\)\(\def\buptau{\bf{\tau}}\)\(\def\bupupsilon{\bf{\upsilon}}\)\(\def\bupphi{\bf{\phi}}\)\(\def\bupchi{\bf{\chi}}\)\(\def\buppsy{\bf{\psy}}\)\(\def\bupomega{\bf{\omega}}\)\(\def\bGamma{\bf{\Gamma}}\)\(\def\bDelta{\bf{\Delta}}\)\(\def\bTheta{\bf{\Theta}}\)\(\def\bLambda{\bf{\Lambda}}\)\(\def\bXi{\bf{\Xi}}\)\(\def\bPi{\bf{\Pi}}\)\(\def\bSigma{\bf{\Sigma}}\)\(\def\bPhi{\bf{\Phi}}\)\(\def\bPsi{\bf{\Psi}}\)\(\def\bOmega{\bf{\Omega}}\)\({\rm{\chi }}_5^2\) = 43.8, P < .001). The majority (76.0%, n = 92) presented with an ED risk, but the ED risk did not differ by sport type (P = .94). The EDI-3 Symptom Checklist revealed that 61.2% (n = 74) engaged in pathogenic behaviors, with dieting being the most common (51.2%, n = 62). Most (76.0%, n = 92) displayed LEA with an ED risk. No differences were found in LEA by ED risk and sport type. Softball players reported the most LEA with an ED risk (82.4%, n = 14), followed by ballet dancers (76%, n = 19). CONCLUSIONS: Our results suggested that a large proportion of collegiate female athletes and performing artists were at risk for LEA with an ED risk, thus warranting education, identification, prevention, and intervention strategies relative to fueling for performance.

Examination of Energy Balance, Eating Disorder Risk, and Pathogenic Behaviors among Athletic Trainers.

CONTEXT: Research exists on energy balance (EB) and eating disorder (ED) risk in physically active populations and occupations by settings, but EB and ED in athletic trainers (ATs) has not been investigated. OBJECTIVE: To assess ATs' energy needs, including macronutrient profile, and to examine ED risk and pathogenic behavior between sex (males, females), job status (part-time=PT-AT; full-time=FT-AT) and setting (college/university, high school, non-traditional). DESIGN: Cross-sectional and descriptive. SETTING: Free-living in job settings. PARTICIPANT: ATs (n=46; males PT-AT n=12, males FT-AT n=11; females PT-AT n=11, female FT-AT n=12) in Southeastern United States. MAIN OUTCOME MEASURES: Anthropometric measurements (age, height, weight, body composition), resting metabolic rate (RMR), energy intake (EI), total daily energy expenditure (TDEE), exercise energy expenditure (EEE), EB, macronutrients (carbohydrates, protein, fats), Eating Disorder Inventory-3, and the Eating Disorder Inventory-3 Symptom Checklist. RESULTS: Majority (84.8%, n=39) had ED risk, with 26.1% (n=12) engaging in at least 1 pathogenic behavior, 50% (n=23) in 2 pathogenic behaviors, and 10.8% (n=5) in more than 2 pathogenic behaviors. 82.6% of ATs (n=38) presented in negative EB (EI

A scoping review of strategies for financing the implementation of evidence-based practices in behavioral health systems: State of the literature and future directions.

Background: Increased availability of evidence-based practices (EBPs) is essential to alleviating the negative public health and societal effects of behavioral health problems. A major challenge to implementing and sustaining EBPs broadly is the limited and fragmented nature of available funding. Method: We conducted a scoping review that assessed the current state of evidence on EBP financing strategies for behavioral health based on recent literature (i.e., post-Affordable Care Act). We defined financing strategies as techniques that secure and direct financial resources to support EBP implementation. This article introduces a conceptualization of financing strategies and then presents a compilation of identified strategies, following established reporting guidelines for the implementation strategies. We also describe the reported level of use for each financing strategy in the research literature. Results: Of 23 financing strategies, 13 were reported as being used within behavioral health services, 4 had potential for use, 5 had conceptual use only, and 1 was potentially contraindicated. Examples of strategies reported being used include increased fee-for-service reimbursement, grants, cost sharing, and pay-for-success contracts. No strategies had been evaluated in ways that allowed for strong conclusions about their impact on EBP implementation outcomes. Conclusion: The existing literature on EBP financing strategies in behavioral health raises far more questions than answers. Therefore, we propose a research agenda that will help better understand these financing strategies. We also discuss the implications of our findings for behavioral health professionals, system leaders, and policymakers who want to develop robust, sustainable financing for EBP implementation in behavioral health systems. Plain language abstract: Organizations that treat behavioral health problems (mental health and substance use) often seek to adopt and use evidence-based practices (EBPs). A challenge to adopting EBPs broadly is the limited funding available, often from various sources that are poorly coordinated with one another. To help organizations plan effectively to adopt EBPs, we conducted a review of recent evidence (i.e., since the passage of the 2010 Affordable Care Act) on strategies for financing EBP adoption in behavioral health systems. We present definitions of 23 identified strategies and describe each strategy's reported (in the research literature) level of use to fund EBP adoption in behavioral health services. Of the 23 financing strategies, 13 strategies had evidence of use, 4 had potential for use, 5 had conceptual use only, and 1 was potentially contraindicated. Examples of strategies with evidence of use include increased fee-for-service reimbursement, grants, cost sharing, and pay-for-success contracts. This comprehensive list of EBP financing strategies may help guide decision-making by behavioral health professionals, system leaders, and policymakers. The article also presents a research agenda for building on the current research literature by (1) advancing methods to evaluate financing strategies' effects, (2) partnering with stakeholders and decision-makers to examine promising financing strategies, (3) focusing on strategies and service systems with the greatest needs, (4) improving methods to guide the selection of financing strategies, and (5) paying greater attention to sustainable long-term financing of EBPs.

Economic Impact of the Statewide Implementation of an Evidence-Based Treatment: Multisystemic Therapy in New Mexico.

Several states have made considerable investments into large-scale implementation of evidence-based treatments (EBTs), yet little is known about key success indicators for these implementation efforts such as cost and sustainability. To that end, the present study examined the economic impact of statewide implementation of multisystemic therapy (MST; Henggeler, Schoenwald, Borduin, Rowland, & Cuningham, 2009), a family- and community-based behavioral EBT for serious juvenile offenders in New Mexico. Participants were 1,869 youth who received MST across 23 teams during the study period. We conducted a cost-benefit analysis using metrics from state data sources that compared the cost of MST to its benefits (i.e., avoided expenses from pre- to posttreatment) in two domains: (a) behavioral health services (i.e., Medicaid claims) and (b) juvenile crime (i.e., taxpayer expenses, tangible and intangible expenses to crime victims). MST costs were based on Medicaid claims, which were reimbursed at an enhanced billing rate that was intended to cover expenses for both clinical and implementation (e.g., training, quality assurance) activities. Results suggest that implementation of MST in New Mexico over the 7-year study period may have produced net benefits, through 2 years posttreatment, of more than $4,643 per youth in avoided behavioral health claims and $15,019 per youth through reductions in juvenile crime. Stated differently, every dollar that New Mexico spent on MST appeared to have returned $3.34 for a total benefit of $64.2 million over the course of the study. We discuss implications of these findings for policymakers, administrators, and researchers who are interested in increasing the sustainability of complex EBTs in community settings.