Journal of Pediatric Psychology, 41(8), 2016, 835–848 doi: 10.1093/jpepsy/jsv083 Advance Access Publication Date: 10 September 2015 Original Research Article

Targeting Health Behaviors to Reduce Health Care Costs in Pediatric Psychology: Descriptive Review and Recommendations Meghan E. McGrady, PHD, and Kevin A. Hommel, PHD Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center All correspondence concerning this article should be addressed to Meghan E. McGrady, PHD, Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, MLC 7039, 3333 Burnet Avenue, Cincinnati, OH 45229-3026, USA. E-mail: [email protected] Received April 13, 2015; revisions received August 11, 2015; accepted August 11, 2015

Abstract Objective Recent efforts to enhance the quality of health care in the United States while reducing costs have resulted in an increased emphasis on cost containment and the introduction of new payment plans. The purpose of this review is to summarize the impact of pediatric health behavior change interventions on health care costs. Methods A review of PubMed, PsycINFO, and PEDE databases identified 15 articles describing the economic outcomes of pediatric health behavior change interventions. Data describing the intervention, health outcome, and economic outcome were extracted. Results All interventions targeting cigarette smoking (n ¼ 3) or the prevention of a chronic medical condition (n ¼ 5) were predicted to avert hundreds of dollars in health care costs per patient. Five of the seven interventions targeting self-management were associated with reductions in health care costs. Conclusions Pediatric health behavior change interventions may be a valuable component of efforts to improve population health while reducing health care costs.

Key words: health behavior; health care services; health policy.

Following the expansion of health care coverage resulting from the Affordable Care Act (U.S. Department of Health and Human Services, 2011), the U.S. Department of Health and Human Services (H.H.S.) is prioritizing efforts to provide high-value, or high-quality and low-cost, health care (Burwell, 2015). To incentivize high-value care, the H.H.S. plans to move away from fee-for-service payments to alternative payment models such as bundled-payment plans (i.e., lump sum payments for an episode of care [Centers for Medicare and Medicaid Services, 2015]). In January 2015, the H.H.S. announced its goal of using alternative payment models for 50% of all Medicare payments by 2018 (U.S. Department of Health and Human Services, 2015). This system encourages the development of integrated teams (i.e., Accountable Care Organizations) that can provide quality services within a fixed budget. Research

demonstrating how behavioral health services can not only improve patient health outcomes, but also result in reduced health care costs could support the role of behavioral health providers (i.e., pediatric psychologists) in these teams (Drotar, 2012; Rozensky & Janicke, 2012; Tovian, 2004). There is now moderate quality evidence (per GRADE guidelines, Balshem et al., 2011) that a number of pediatric behavioral interventions improve health outcomes (Palermo, 2014) and a growing body of literature suggesting that these interventions may also reduce health care utilization (Bandstra, Crist, Napier-Phillips, & Flowerdew, 2011; Finney, Riley, & Cataldo, 1991). Specifically, behavioral interventions that improve medication adherence or target asthma self-management also reduce emergency department (ED) visits and hospitalizations (Boyd et al., 2009; McGrady et al., 2015). Among adults, similar

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improvements translate into reduced health care costs (Blount et al., 2007; Chiles, Lambert, & Hatch, 1999). It remains unknown, however, as to whether similar economic benefits exist for pediatric health behavior change interventions. Consistent with the growing demand to illustrate the potential of psychological services to reduce overall costs (Goodheart, 2010; Rozensky & Janicke, 2012), the purpose of this review is to summarize findings documenting the impact of pediatric health behavior change interventions on health care costs. This information is highly relevant in the context of the changing health care system and has the potential to support clinicians as they advocate for their services and shape the research agenda of pediatric psychology. As barriers to the access and analysis of economic data may have hindered efforts to include these outcomes, recommendations relevant to future research design are briefly discussed.

contents of the Journal of Pediatric Psychology were also searched. The PEDE database is systematically updated each year by researchers at The Hospital for Sick Children and is a comprehensive collection of 2,600 manuscripts detailing pediatric economic evaluations (The Hospital for Sick Children, 2014). While no restrictions were placed on publication date, the PEDE database only includes articles published after 1979. The initial search resulted in 1,042 original manuscripts (Figure 1). The abstracts of all 1,042 manuscripts were screened, and 924 manuscripts that did not include an economic/cost analysis or were not an original research article (e.g., book review) were excluded. Full text versions of the remaining 118 articles were reviewed for eligibility. Articles were excluded if they were not originally published in English (n ¼ 1) or did not include an intervention (n ¼ 12) targeting a pediatric (M age < 18 years, n ¼ 5) health behavior (n ¼ 52). Articles not differentiating between reductions in health care costs and other costs (e.g., productivity) were also excluded (n ¼ 33).

Assessing the Current State of the Literature Methods PubMed and PsycINFO databases were searched in January 2015 using medical subject headings (MesH) terms to identify manuscripts including an economic (i.e., “cost-identification analysis,” “cost-effectiveness analysis,” or “cost-benefit analysis”; Eisenberg, 1989) or cost analysis (i.e., comparing pre- and postintervention costs) of pediatric health behavior interventions (i.e., “behavioral,” “chronic disease”) (Supplementary Table I). To increase the likelihood that all relevant manuscripts were captured, the Paediatric Economic Database Evaluation (PEDE) database and the Records identified through database searching (n=461)

Definition of Variables Health Behaviors For the purposes of this article, health behaviors were defined as “behavior patterns, actions, and habits that relate to health maintenance, to health restoration, and to health improvement” (Gochman, 1997, p. 3). According to this definition, health behaviors include the use of preventative medical services (e.g., vaccinations), adherence to medical regimens required for disease management (e.g., prescribed medications), and self-directed health behaviors (e.g., smoking, exercise). Records identified through Journal of Pediatric Psychology (n=780)

Records after duplicates removed (n=1042)

Records screened (n=1042) Inclusion Criteria: 1. Original research article 2. Economic outcome

Full-text articles assessed for eligibility (n=118)

Studies included in review (n=15)

Figure 1. Study selection flow diagram.

Records excluded (n=924)

Articles excluded (n=103) • Not in English (n=1) • Not an intervention study (n=12) • Not a behavioral intervention (n=52) • Costs include domains other than healthcare (n=33) • M age of sample > 18 (n=5)

Pathways to Cost

Behavioral interventions were eligible for inclusion, thus, if they targeted any of the aforementioned variables. Economic Outcomes The downstream economic impacts of health behaviors can be far-reaching and may include, among others, costs associated with medical care (e.g., hospitalizations, medications), transportation to medical visits, patient time spent at medical visits or home owing to illness or complications (e.g., lost patient wages), and caregiver time spent caring for patient (e.g., lost caregiver wages) (Meunnig, 2002, 2008). As the purpose of this review was to detail the impact of pediatric health behavior change interventions on health care costs, only studies reporting costs related to medical visits (i.e., outpatient visits, hospitalizations, ED visits), medical procedures (i.e., testing), and/or medical treatments (i.e., medications) were included. Health care costs can either be observed or modeled based on estimates from national databases, previously published studies, or expert opinion. While observed values may provide a more accurate estimate of changes in health care costs, modeled values enable researchers to predict the reductions in health care costs resulting from a given intervention across a long time period (e.g., reductions in health care costs over a child’s lifetime) or a large population (e.g., all schoolage children in the United States). Given the distinct advantages offered by both techniques, articles including either observed or modeled health care costs were included. Multiple analytic strategies (i.e., cost-benefit, costidentification, cost-minimization, cost-effectiveness) can be used to quantify the changes in health care costs resulting from a pediatric health behavior change intervention (Eisenberg, 1989; Meunnig, 2002, 2008). Each analytic strategy enables the researcher to answer a different question and thus results in a different economic outcome (see Eisenberg, 1989 for a review). For example, the economic outcome in a cost-identification analysis may be mean differences between groups. Alternatively, cost–benefit analyses report economic outcomes as a benefit–cost ratio describing the potential cost savings per dollars invested. Cost-effectiveness and cost-utility analyses report an incremental cost-effectiveness ratio (ICER) describing the net cost of an intervention (expenditures minus savings) per additional health benefit obtained (Eisenberg, 1989). Articles quantifying the impact of a pediatric health behavior change intervention on health care costs using any of the aforementioned economic outcomes were included. Data Extraction and Analysis Manuscript descriptions, participant characteristics, intervention descriptions, behavioral targets, health

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outcomes, and economic outcomes were extracted using a data collection form. Descriptive statistics were used to summarize manuscript characteristics and outcomes. To facilitate comparisons across studies, when the year and currency for cost data were provided, outcomes were converted to 2015 $US using the medical care component of the Consumer Price Index (U.S. Bureau of Labor Statistics, 2015). Results A total of 15 manuscripts met all inclusion criteria and were included in this review (Table I). Studies included participants between 0 and 18 years of age. Eight studies (53%) evaluated the economic impact of a health behavior change intervention targeting a self-directed health behavior among healthy youth (Table II). The remaining studies (n ¼ 7) evaluated the economic impact of a behavioral intervention targeting disease management among youth with asthma (n ¼ 3), type 1 diabetes (n ¼ 1), or atopic dermatitis (n ¼ 1) or youth who received a liver transplant (n ¼ 2). Additional details regarding intervention content and delivery are included in Table I. Four primary outcomes were used to assess the economic impact of the intervention: costs averted as a result of the intervention (n ¼ 7), costs averted per costs invested (benefit–cost ratio, n ¼ 4), M(SD) costs (n ¼ 6), and/or ICER (costs per additional health benefit obtained, n ¼ 5) (Table III). Self-Directed Health Behaviors The eight studies targeting a self-directed health behavior among healthy youth aimed to prevent youth from initiating a behavior (smoking, n ¼ 3) or developing a medical condition (sexually transmitted infection, n ¼ 2; HIV, n ¼ 1; overweight, n ¼ 1; cancer, n ¼ 1). Thus, for each of these studies, the primary health outcome was the number of behaviors (e.g., number of cigarette smokers) or medical conditions (e.g., cases of sexually transmitted infections) avoided or the resulting number of quality-adjusted life-years (QALY) gained (Table II). A QALY is an outcome measure that accounts for the quantity and quality of life gained by an intervention (Weinstein, Torrance, & McGuire, 2009). This measure assumes that living 1 year at perfect health equates to 1 QALY and living a year at less than perfect health equates to 30 kg/m2) Kyle et al. (2008) QALYs gained through Model predicted based on changes in preventing skin cancer sun protection behaviors (i.e., wearing sunscreen) postintervention Interventions targeting medication adherence Ellis et al. (2005) Metabolic control Shemesh et al. Medication adherence (2008)

Glycosylated hemoglobin (GHb) Tacrolimus trough blood levels

Interventions targeting multiple components of self-management Gilmet et al. (2000) Ratio of anti-inflammaBlue Care Network of Southeastern tory to b-agonist preMichigan claims database scriptions filled, Referral rate Lara et al. (2013) Controller and rescue Caregiver report medication use, access to asthma providers Runge et al. (2006)

Medication use, environmental modifications

Caregiver report

Stabb et al. (2002)

Medication use, diet, environmental modifications

Caregiver report

Interventions targeting pain management Sharek et al. (2006) Pain score Note. QALY ¼ quality-adjusted life-years. a Outcome modeled not observed.

Patient rating scales (i.e., FLACC, Wong Baker Faces)

41 QALYs gained by reducing the number of smokers by 32a 36.6 QALYs gained by preventing 34.9 students from becoming established smokersa

0.35 QALYs gained by avoiding 2.1 cases of sexually transmitted infectionsa 97.75 cases of sexually transmitted infections averteda 0.08–0.11 QALYs saved by averting 0.6–0.8% of a case of HIV/AIDSa

8.55 QALYs gained by avoiding 14.93 cases of adult obesitya 159 QALYs gained by avoiding 10,960 cases of cancera

NR Odds of patients being classified as adherent increased from pre- to postintervention (OR ¼ 2.35) Ratio of anti-inflammatory to b-agonist prescriptions (Baseline: .50, Post: .68) and referral rates (Baseline: 11%, Post: 27%) increased Controller medication use increased, rescue medication use decreased, and access to asthma providers increased (p < .001) Daily use of rescue medication decreased by 77% in the intervention group (p < .05) Medication use (OR ¼ 2.25–3.94, p < .001–.049), dietary restrictions (OR ¼ 3.09, p ¼ .001), and removal of pets (OR ¼ 3.23, p ¼ .03) increased Pain score (intervention: 2.12, control: 2.84, p < .05)

Time horizon

Health care costs

Lifetime

Wang et al. (2001)

Medical treatment for smoking-related diseases Medical treatment for smoking-related diseases Modeled

Modeled

Modeled

ED visits and hospitalizations (hospital direct costs)

Observed

Interventions targeting medication adherence Ellis et al. 9 months ED visits and hospitaliza(2005) tions (hospital charges) Observed

Modeled

Modeled

Modeled

Modeled

12 months

Pinkerton et al. (2000) Brown et al. (2007) Kyle et al. (2008)

Medical treatment for chlamydia, gonorrhea, HIV, human papilloma virus, and herpes simplex virus 2

Medical treatment of HIV/AIDS Ages 40–64 Medical treatment for years obesity-related diseases 1999–2100 Medical treatment for basal cell carcinoma, squamous cell carcinoma, and cutaneous malignant melanoma

3 months

Dealy et al. (2013)

Hospital financial database

Hospital financial database

National databases (i.e., Planned Parenthood, US Bureau of Labor Statistics, New Mexico Human Services Department) Previously published studies Previously published studies Previously published studies using data from the Medicare Current Beneficiary Study

Previously published studies

Previously published studies

Previously published studies, national databases (i.e., National Survey on Drug Use and Health) Chronic Disease Model

Data type Data source

Interventions targeting the prevention of a medical condition Cooper et al. 1 year Medical treatment for Modeled (2012) chlamydia, gonorrhea, genital warts, and HIV

100 years

Vijgen et al. (2008)

Interventions targeting cigarette smoking Kuklinski Lifetime Medical treatment for et al. (2012) smoking-related diseases

Author (year)

Table III. Economic Outcomes of Included Studies

Pre: $3,455 (7,454) Post: $1,902 (5,742)a d ¼ 0.64, p  .05 Pre: $990 (2,056) Post: $382 (1,114)a d ¼ 0.63, p  .05

M(SD)

$56,153 averted

$2,778 averted

$2.39 averted per youth

$1,151 averted per youth

$2,165 averted per youth

$897 averted per youth

Total averted

Economic outcome (2015 US$)

$1.95–$4.02 in medical and productivity costs saved for every $1.00 invested

$2.08–$2.68 in medical costs averted per every $1.00 invested

$5.30 in productivity losses and health care costs averted per every $1.00 invested

Benefit–cost ratio

(continued)

$52,629–$107, 139/QALY $1,444–$1, 452/QALY

Teacher led: $33,189/ QALY Peer Led: $132,573/QALY

$22,974/QALY

$27,269/QALY

ICER

Pathways to Cost 841

12 months

Shemesh et al. (2008) Observed

Observed

ED visits and hospitalizations (insurance company costs) Outpatient visits attributable to nonadherent patients

12 months

12 months

Runge et al. (2006)

Staab et al. (2002)

NR

Caregiver self-report

Intervention: $255,204 Control: $239,203 d ¼ 0.11, p ¼ .78

Intervention Change: $119 Control Change: $65a d ¼ 0.29, p ¼ .04

Pre: $13,133 (1,810) Post: $7,574 (1,236)

Pre: $2,343 (244) Post: $960 (115)

Pre: $720 Post: $178a

Pre: $173 Post: $42a

Pre: $3,334 (7,426) Post: $923 (2,503)a d ¼ 0.62, p ¼ .07 Pre: $13,585 Post: $7,440a

M(SD)

Benefit–cost ratio

$716.10 averted $.79–$1.42 in medper patient ical costs averted per every $1.00 invested

Total averted

Economic outcome (2015 US$)

Note. ICER ¼ incremental cost-effectiveness ratio; NR ¼ not reported; ED ¼ emergency department; QALY ¼ quality-adjusted life-years. a Costs not converted to 2015 $US.

Observed

Observed

Modeled

Modeled

Asthma-related hospitalizations

Outpatient medical visits, ED visits, hospitalizations, emergency calls, ambulance transport, medication Medical consultation and prescription medication

Modeled

Observed

Asthma-related ED visits

Interventions targeting pain management Sharek et al. NR Postoperative medical (2006) treatment

12 months

Lara et al. (2013)

Asthma-related hospitalizations

Blue Care Network of Southeastern Michigan claims database Blue Care Network of Southeastern Michigan claims database Healthcare Cost and Utilization Project Kid’s inpatient and Medical Expenditure Panel Survey databases Healthcare Cost and Utilization Project Kid’s inpatient and Medical Expenditure Panel Survey databases National Catalogue of Fees for Services; Bavarian Red Cross; and expert opinion

Hospital billing record

Hospital financial database

Data type Data source

Health care costs

Interventions targeting multiple components of self-management Gilmet et al. 12 months Asthma-related ED visits Observed (2000)

Time horizon

Author (year)

Table III. Continued

ICER

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Pathways to Cost

Results of each mathematical model evaluating an intervention targeting cigarette smoking indicated that all three interventions were predicted to prevent a portion of youth from becoming regular smokers and as a result, prevent these youth from incurring the costs ($897–$2,165/youth) associated with medical treatment for smoking-related diseases (Kuklinski, Briney, Hawkins, & Catalano, 2012; Vijgen et al., 2008; Wang, Crossett, Lowry, Sussman, & Dent, 2001; Table III). Kuklinski et al. (2012) estimated that each dollar invested in the “Communities that Care” program would result in a return of $5.30 owing to medical cost savings and productivity loses averted. Results of cost-utility analyses conducted by Vijgen et al. (2008) and Wang et al. (2001) indicated that the interventions would require an investment of $27,269 and $22,974, respectively, per QALY gained. Similarly, results of mathematical models evaluating interventions targeting sexual behaviors indicated that all three interventions were predicted to prevent cases of sexually transmitted infections and HIV and as a result, were predicted to avert medical costs that would be required to treat those illnesses (Cooper et al., 2012; Dealy, Horn, Callahan, & Bryan, 2013; Pinkerton, Holtgrave, & Jemmott., 2000). Dealy et al. (2013) predicted $2.08–$2.68 in medical cost savings per every $1.00 invested. Cost-utility analyses suggest that the programs require an investment of $33,189– $52,629 per QALY gained (Cooper et al., 2012; Pinkerton et al., 2000). In both cost-utility analyses, results were highly sensitive to intervention design characteristics. Cooper et al. (2012) found that the costs required for training peer educators made a peerled program less cost-effective ($132,573/QALY) than a similar intervention delivered by teachers ($33,189/ QALY). Pinkerton et al. (2000) found that restricting participants to include those who were already sexually active increased the benefit of the intervention from $107,139/QALY to $52,629/QALY. Interventions designed to encourage youth to engage in healthy behaviors for sun protection and weight management were also expected to result in health care savings. An economic evaluation of the SunWise educational program targeting sun protection behaviors (i.e., wearing sunscreen) suggested that every $1.00 invested in the program would save $1.95–$4.02 in medical and productivity costs (Kyle et al., 2008). Brown et al. (2007) found that a school-based intervention targeting physical activity and diet would require an investment of $1,444–$1,452 per QALY gained and ultimately save tens of thousands of dollars associated with medical treatment for obesity-related diseases. Self-Management Primary behavioral outcomes for studies targeting self-management included medication adherence (n ¼ 2), multiple self-management behaviors (e.g.,

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appropriate medication use, environmental modifications) (n ¼ 4), or pain management (n ¼ 1). Five of the seven interventions targeting self-management collected observed health care cost data while two studies modeled the economic outcomes of the intervention. One of the two studies targeting medication adherence reported statistically significant reductions in health care costs following intervention. In a sample of adolescents with poorly controlled type 1 diabetes, those receiving multisystemic therapy targeting medication adherence evidenced significantly greater reductions in costs associated with ED visits and hospitalizations (d ¼ 0.62–0.64) than those receiving standard medical care (Ellis et al., 2005). Conversely, a clinic-based intervention including adherence monitoring and education did not significantly change outpatient costs among youth who have received a liver transplant (Shemesh et al., 2008). All four studies targeting multiple self-management behaviors demonstrated reductions in health care costs. One multicomponent intervention targeting appropriate medication use among youth with asthma (Lara et al., 2013) resulted in significantly lower costs associated with ED visits and hospitalizations in the 12 months following intervention completion than in the 12 months before implementation. Similarly, youth with asthma participating in an educational intervention evidenced lower ED- and hospitalizationrelated expenditures in the 12 months postintervention as compared with the 12 months preintervention (significance not reported) (Gilmet, Zeitz, & Lewandowski, 2000). An analysis of an Internet-based asthma education program was estimated to avert $0.79 in medical costs per every $1.00 invested (Runge, Lecheler, Horn, Tews, & Schaefer, 2006). However, model estimates indicated that if the program were delivered just to patients with moderate to severe asthma, averted medical costs increased to $1.07–$1.42 per $1.00 invested. Reductions in health care costs experienced by patients with asthma participating in behavioral interventions targeting multiple components of disease self-management may also be realized by patients with other medical conditions participating in similar interventions. Stabb et al. (2002) illustrated that improving self-management is associated with reductions in costs for medical consultations and prescription medications by children with atopic dermatitis (d ¼ 0.29). One intervention targeted pain management among youth who received a liver transplant but failed to demonstrate differences in total postoperative medical costs (Sharek et al., 2006). Conclusions Consistent with previous reviews of interventions targeting adults (Blount et al., 2007; Chiles et al., 1999;

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Friedman, Sobel, Myers, Caudill, & Benson, 1995), the studies included in this review suggest that upfront investments in pediatric health behavior change interventions may prevent medical tests and treatments, outpatient clinic visits, hospitalizations, and/or ED visits and ultimately result in reduced health care costs. The rationale for investing in pediatric health behavior change interventions is further supported by findings illustrating that reductions in health care costs may outweigh the additional costs required for health behavior change interventions (i.e., personnel time, facilities). Specifically, all four studies calculating the benefit–cost ratio of an intervention predicted that the investment required for intervention implementation would ultimately result in overall reduced health care costs. In addition, the five studies examining the costs per additional health benefit obtained (cost-effectiveness or cost-utility analysis) predicted that the intervention would be “likely” or “highly” cost-effective. To use these findings to support advocacy efforts, interested readers are encouraged to refer to resources provided by the American Psychological Association (2014). Despite the promising findings of this review, several limitations and related directions for future research remain. First, a relatively small number of studies met the inclusion criteria and it is possible that the selection of alternative search strategies would have produced additional articles not included in this review. Second, included studies used a variety of economic analytic techniques, and four studies did not provide the information required to adjust costs to 2015 $US, precluding conclusions regarding the overall magnitude of potential economic benefits. Future research is needed to examine the generalizability of the findings reported in this review and provide accurate estimates of potential economic benefits. Recommendations To address the remaining gaps in the literature, researchers developing and evaluating new pediatric health behavior change interventions are encouraged to collect the clinical endpoints and economic outcomes necessary to examine the potential impact of a given intervention on health care costs (Rozensky & Janicke, 2012). Consistent with guidelines provided by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR), a clinical endpoint should be: (1) relevant to the target population; (2) hypothesized to change as a result of the intervention; (3) hypothesized to lead to changes in economic outcomes; and (4) directly observed during the study period. For example, consider an ongoing randomized clinical trial (RCT) of a telehealth self-management intervention for children and adolescents with inflammatory bowel disease (IBD) where it is hypothesized

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that participants in the telehealth behavioral treatment arm will demonstrate significantly greater improvements in oral medication adherence, leading to improvements in disease severity and, consequently, reductions in health care utilization in the 12 months following treatment, compared with patients in the education-only arm (Hommel et al., 2015). In this RCT, disease severity in the 12 months following treatment is: (1) a relevant measure of disease status; (2) hypothesized to improve as a result of improvements in adherence; (3) hypothesized to be associated with health care costs (i.e., hospitalizations); and (4) measured at the final study assessment time point, and thus meets the four aforementioned criteria. In addition to clinical endpoint data, data should be collected for all potentially relevant costs (Ramsey et al., 2005). One framework for identifying potentially relevant costs includes the conceptualization of all costs required for the intervention (e.g., measures, facilities, personnel required for the intervention), all costs associated with medical care for the patient’s diagnosis (e.g., medications, clinic visits, hospitalizations, and ED visits), and all costs associated with the patient and/or caregiver’s time (e.g., lost wages) (Luce, Manning, Siegel, & Lipscomb, 1996; Ramsey et al., 2005). Of note, these costs include both those that are expected to increase as a result of the intervention and those expected to decrease as a result of improved health outcomes postintervention (Figure 2). While the aforementioned exercise is useful in generating all potentially relevant cost variables, determining which of these variables are required to answer the research question depends on the party of interest, or the perspective from which the economic analysis is being conducted. The party of interest may be society as a whole or a more specific entity (e.g., insurance company, government). For example, Brown et al. (2007) assumed a societal perspective, including intervention costs (i.e., personnel salary, training), costs relevant to the health care system (i.e., medical costs for the treatment of obesity-related diseases), and costs relevant to society (i.e., lost labor productivity resulting from obesity), to determine the societal impact of investing in a school-based physical education and nutrition program. To illustrate which cost data a researcher may wish to collect as part of a randomized controlled clinical trial of a behavioral intervention, consider the ongoing RCT of a telehealth self-management intervention for children and adolescents with IBD (Hommel et al., 2015). Potentially relevant costs related to the intervention can be identified by considering the materials required to deliver the intervention (i.e., webcams, electronic adherence monitoring devices, behavioral assessments) as well as the costs to the patient and his or her family (e.g., child care, travel costs, meals) (Figure 2). Notably, providing the intervention to the

Pathways to Cost

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Healthcare Resources TM

1. Webcams (vendor price) 2. Electronic adherence monitoring devices (vendor price) 3. Behavioral assessment battery (vendor price) 4. Interventionist salary (wage rates)

1. Medications (Red Book ) 2. ED visits (EMR billing data) 3. Clinic visits (EMR billing data) 4. Procedures (EMR billing data) 5. Hospitalizations (EMR billing data) 6. Additional out-of-pocket co-payments (self-report)

Non- Healthcare Resources

Costs associated with intervention development, delivery, and implementation

1. Rewards for patient achievement of behavioral goals set in intervention (self-report)

1. Transportation for medical care (Internal Revenue Service mileage reimbursement rate*selfreport miles traveled) 2. Child care for siblings while patient is ill or receiving medical care (self-report)

Costs associated with the patient’s health status

Caregiver and Patient Time

1. Loss of workday hours for patient and/or caregiver (self-reported time spent in TBT or implementing TBT procedures*ageadjusted national average wage)

1. Loss of workday hours for patient (self-reported time lost due to illness*age-adjusted national average wage) 2. Loss of workday hours for caregiver (self-reported time lost caring for patient while ill*ageadjusted national average wage)

Figure 2. Potential costs and assessment methods (represented in bold italics) of a telehealth behavioral treatment (TBT) for youth with inflammatory bowel disease. Note. EMR ¼ electronic medical record. Source: Adapted from Luce et al. (1996).

family in their home via telehealth minimizes many of the costs to the patient and his or her family by removing the need for transportation to the intervention session and child care for siblings not receiving the intervention. Potentially relevant medical costs can be identified by considering that patients in remission are likely to require fewer medical procedures, less frequent outpatient visits for medical management, and less expensive maintenance therapies (e.g., oral medication rather than biological infusion therapies) than patients with active disease. To standardize economic analyses and facilitate comparisons across studies, experts recommend considering society as a whole as the party of interest (Weinstein, Siegel, Gold, Kamlet, & Russell, 1996). Consistent with this guideline, the exemplar list of relevant costs and related data sources included in

Figure 2 assumes a societal perspective. Figure 2, thus, details variables that may be used to capture the impact of pediatric health behavior change interventions on health care costs (as described by the studies included in this review) as well as the impact of these interventions on the family and society. In addition modeling techniques can be used to estimate health care cost outcomes of a given pediatric health behavior change intervention (see Petrou & Gray, 2011 for a review of methods). Modeling techniques offer the advantage of estimating health care costs when observed cost data are not available and may also be used when the full economic benefits of a behavioral intervention may not occur during the study period. For example, the full economic benefits of a self-management intervention in a life-long condition like IBD are not likely to be realized within a

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12-month period, but rather demonstrated over the course of many years as children enter adulthood and as they age. A full discussion of these methods is beyond the scope of this manuscript, but readers interested in these techniques are encouraged to refer to previously published guidelines (Caro, Briggs, Siebert, & Kuntz, 2012). As the literature base in this field grows, meta-analytic procedures can be used to synthesize the potential economic benefits of a given intervention and explore intervention attributes (e.g., intervention effect size) that may moderate economic benefits (Saint, Veenstra, & Sullivan, 1999). Finally, to support the preparation of high quality and consistent manuscripts, authors are encouraged to review and abide by reporting guidelines developed by an ISPOR task force (Husereau et al., 2013). In sum, interventions developed and/or delivered by pediatric psychologists have the potential to improve the health of children and families (Palermo, 2014). Future research should examine whether, as suggested by the results of this review, these interventions also result in economic benefits. Supplementary Data Supplementary data can be found at: http://www.jpepsy.oxfordjournals.org/.

Acknowledgments Gabriella Brown is gratefully acknowledged for her assistance with manuscript preparation.

Funding Funding for the study described in this article was provided by the National Institutes of Health/Eunice Kennedy Shriver National Institute of Child Health and Human Development (to K.A.H.; R01 HD067174). Conflicts of interest: None declared.

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