Understanding implementation costs of a pediatric weight management intervention: an economic evaluation protocol

Assessing the market viability of a packaged intensive health behavior and lifestyle treatment

In the USA, more than 14 million children are impacted by obesity. Despite intensive health behavior and lifestyle treatments being found effective, gaps exist in moving these interventions into widespread use. Focusing on market viability could improve the dissemination and sustainment of interventions. The purpose of this paper is to outline the process and results of our market viability assessment for the Healthy Weight Clinic (HWC), a Centers for Disease Control and Prevention-recognized Family Health Weight Program. We conducted a market viability assessment using the Speeding Research-test INTerventions (SPRINT) program to gain insights into the commercialization and marketplace for the HWC. Through the process of customer discovery, we interviewed 50 stakeholders to test our hypotheses pertaining to our business model. Key takeaways were the need for packaged interventions that offer support and training for providers, and interventions that are multidisciplinary and located within the medical home. We also learned that (i) the intervention goals must align with the healthcare organization's performance metrics; (ii) services need to be reimbursable; and (iii) the importance of understanding different customer segments (i.e. program users vs. organization decision-makers) and their unique needs. The market viability assessment is a critical step to transforming the HWC into a viable commercial product. The process we have outlined is replicable by others and by encouraging other teams to design for dissemination we can increase the number of evidence-based, packaged IHBLTs available to children with obesity.

Costs to Implement a Pediatric Weight Management Program Across 3 Distinct Contexts

BACKGROUND

The Connect for Health program is an evidence-based program that aligns with national recommendations for pediatric weight management and includes clinical decision support, educational handouts, and community resources. As implementation costs are a major driver of program adoption and maintenance decisions, we assessed the costs to implement the Connect for Health program across 3 health systems that primarily serve low-income communities with a high prevalence of childhood obesity.

METHODS

We used time-driven activity-based costing methods. Each health system (site) developed a process map and a detailed report of all implementation actions taken, aligned with major implementation requirements (eg, electronic health record integration) or strategies (eg, providing clinician training). For each action, sites identified the personnel involved and estimated the time they spent, allowing us to estimate the total costs of implementation and breakdown costs by major implementation activities.

RESULTS

Process maps indicated that the program integrated easily into well-child visits. Overall implementation costs ranged from $77,103 (Prisma Health) to $84,954 (Denver Health) to $142,721 (Massachusetts General Hospital). Across implementation activities, setting up the technological aspects of the program was a major driver of costs. Other cost drivers included training, engaging stakeholders, and audit and feedback activities, though there was variability across systems based on organizational context and implementation choices.

CONCLUSIONS

Our work highlights the major cost drivers of implementing the Connect for Health program. Accounting for context-specific considerations when assessing the costs of implementation is crucial, especially to facilitate accurate projections of implementation costs in future settings.

A budget impact analysis of cost to implement a whole child health focused, family-based intervention in primary care for children with elevated BMI

BACKGROUND

Although the cost of implementing evidence-based interventions (EBIs) is a key determinant of adoption, lack of cost information is widespread. We previously evaluated the cost of preparing to implement Family Check-Up 4 Health (FCU4Health), an individually tailored, evidence-based parenting program that takes a whole child approach, with effects on both behavioral health and health behavior outcomes, in primary care settings. This study estimates the cost of implementation, including preparation.

METHODS

We assessed the cost of FCU4Health across the preparation and implementation phases spanning 32 months and 1 week (October 1, 2016-June 13, 2019) in a type 2 hybrid effectiveness-implementation study. This family-level randomized controlled trial took place in Arizona with n = 113 predominantly low-income, Latino families with children ages > 5.5 to < 13 years. Using electronic cost capture and time-based activity-driven methods, budget impact analysis from the perspective of a future FCU4Health adopting entity-namely, ambulatory pediatric care clinicians-was used to estimate the cost of implementation. Labor costs were based on 2021 Bureau of Labor Statistics Occupational Employment Statistics, NIH-directed salary cap levels or known salaries, plus fringe benefits at a standard rate of 30%. Non-labor costs were based on actual amounts spent from receipts and invoices.

RESULTS

The cost of FCU4Health implementation to 113 families was $268,886 ($2380 per family). Actual per family cost varied widely, as individual tailoring resulted in families receiving a range of 1-15 sessions. The estimated cost of replicating implementation for future sites ranged from $37,636-$72,372 ($333-$641 per family). Using our previously reported preparation costs (i.e., $174,489; $1544 per family), with estimated replication costs of $18,524-$21,836 ($164-$193 per family), the total cost of delivering FCU4Health was $443,375 ($3924 per family), with total estimated replication costs of $56,160-$94,208 ($497-$834 per family).

CONCLUSIONS

This study provides a baseline for costs associated with implementation of an individually tailored parenting program. Results provide critical information for decision makers and a model for future economic analysis and can be used to inform optimization thresholds for implementation and, when necessary, benchmarks for program adaptation to promote scale-up.

TRIAL REGISTRATION

This trial was prospectively registered on January 6, 2017, at ClinicalTrials.gov (NCT03013309).

Using decision analysis to support implementation planning in research and practice

BACKGROUND

The process of implementing evidence-based interventions, programs, and policies is difficult and complex. Planning for implementation is critical and likely plays a key role in the long-term impact and sustainability of interventions in practice. However, implementation planning is also difficult. Implementors must choose what to implement and how best to implement it, and each choice has costs and consequences to consider. As a step towards supporting structured and organized implementation planning, we advocate for increased use of decision analysis.

MAIN TEXT

When applied to implementation planning, decision analysis guides users to explicitly define the problem of interest, outline different plans (e.g., interventions/actions, implementation strategies, timelines), and assess the potential outcomes under each alternative in their context. We ground our discussion of decision analysis in the PROACTIVE framework, which guides teams through key steps in decision analyses. This framework includes three phases: (1) definition of the decision problems and overall objectives with purposeful stakeholder engagement, (2) identification and comparison of different alternatives, and (3) synthesis of information on each alternative, incorporating uncertainty. We present three examples to illustrate the breadth of relevant decision analysis approaches to implementation planning.

CONCLUSION

To further the use of decision analysis for implementation planning, we suggest areas for future research and practice: embrace model thinking; build the business case for decision analysis; identify when, how, and for whom decision analysis is more or less useful; improve reporting and transparency of cost data; and increase collaborative opportunities and training.