Cost data in implementation science: categories and approaches to costing

Gold, Heather T.; McDermott, Cara L.; Hoomans, Ties

doi:10.1186/s13012-021-01172-6

Cited by 55 publications

(52 citation statements)

References 55 publications

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“…Thoughtfully considering how prior literature can inform future decision-making in different contexts is thus core to any decision analysis, and all inputs used throughout a decision analysis should be interrogated and justified [73]. One major area of current focus with implementation science that is often missing detail is the costs of implementation [74][75][76][77]. Recent publications by the "Economics and Cost" action group of the Consortium for Cancer Implementation Science have set out definitions, guidance, methods, and best practices for understanding the costs of implementation and conducting economic evaluations in implementation science [36,74,[77][78][79][80] and complement ongoing work in the field [6,8,12,14,61,75,76,[81][82][83][84].…”

Section: Improve Reporting and Transparency Of Cost Datamentioning

confidence: 99%

Using decision analysis to support implementation planning in research and practice

Smith

Knocke

Lich

2022

Implement Sci Commun

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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.

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Section: Improve Reporting and Transparency Of Cost Datamentioning

confidence: 99%

Using decision analysis to support implementation planning in research and practice

Smith

Knocke

Lich

2022

Implement Sci Commun

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“…Although these results highlight the value of considering long-term health outcomes when investing in implementation of smoking cessation interventions, they also underscore challenging issues in implementation of effective cessation strategies. For instance, the costs of delivering telephone counseling will be borne by health systems, clinics, and quitlines, with a potential negative impact on their budgets, whereas the benefits and cost offset occur downstream in other sectors of the health care system ( 68 ). New policies and reimbursement strategies will likely be needed to support dissemination and implementation efforts for smoking cessation and other similar prevention efforts.…”

Section: Discussionmentioning

confidence: 99%

Cost-Effectiveness of a Telephone-Based Smoking Cessation Randomized Trial in the Lung Cancer Screening Setting

Cao

Smith

Mandelblatt

et al. 2022

JNCI Cancer Spectrum

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Background There are limited data on the cost-effectiveness of smoking cessation interventions in lung cancer screening settings. We conducted an economic analysis embedded in a national randomized trial of two telephone counseling cessation interventions. Methods We used a societal perspective to compare the short-term cost per 6-month bio-verified quit and long-term cost-effectiveness of the interventions. Trial data were used to micro-cost intervention delivery, and the data were extended to a lifetime horizon using an established Cancer Intervention Surveillance and Modeling Network lung cancer model. We modeled the impact of screening accompanied by 8-weeks vs. 3-weeks of telephone counseling (plus nicotine replacement) vs. screening alone based on 2021 screening eligibility. Lifetime downstream costs (2021 dollars) and effects (life-years gained, quality-adjusted life-years saved [QALYs]) were discounted at 3%. Sensitivity analyses tested the effects of varying quit rates and costs; all analyses assumed non-relapse after quitting. Results The costs for delivery of the 8-week vs. 3-week protocol were $380.23 vs. $144.93 per person and quit rates were 7.14% vs. 5.96%, respectively. The least costly strategy was a 3-week counseling approach. An 8-week (vs. 3-week) counseling approach increased costs but gained QALYs for an incremental cost-effectiveness ratio of $4,029 per QALY. Screening alone cost more and saved fewer QALYs than either counseling strategy. Conclusions were robust in sensitivity analyses. Conclusions Telephone-based cessation interventions with nicotine replacement are considered cost-effective in the lung screening setting. Integrating smoking cessation interventions with lung screening programs has the potential to maximize long-term health benefits at reasonable costs.

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“…We will use a micro-costing method [ 84 , 85 ] to determine the costs to deliver LEAP and AD. The LEAP coaches and academic detailers developed a list of the activities they will perform for each participating site.…”

Section: Methodsmentioning

confidence: 99%

Maintaining Implementation through Dynamic Adaptations (MIDAS): protocol for a cluster-randomized trial of implementation strategies to optimize and sustain use of evidence-based practices in Veteran Health Administration (VHA) patients

Damschroder

Sussman

Pfeiffer

et al. 2022

Implement Sci Commun

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Background The adoption and sustainment of evidence-based practices (EBPs) is a challenge within many healthcare systems, especially in settings that have already strived but failed to achieve longer-term goals. The Veterans Affairs (VA) Maintaining Implementation through Dynamic Adaptations (MIDAS) Quality Enhancement Research Initiative (QUERI) program was funded as a series of trials to test multi-component implementation strategies to sustain optimal use of three EBPs: (1) a deprescribing approach intended to reduce potentially inappropriate polypharmacy; (2) appropriate dosing and drug selection of direct oral anticoagulants (DOACs); and (3) use of cognitive behavioral therapy as first-line treatment for insomnia before pharmacologic treatment. We describe the design and methods for a harmonized series of cluster-randomized control trials comparing two implementation strategies. Methods For each trial, we will recruit 8–12 clinics (24–36 total). All will have access to relevant clinical data to identify patients who may benefit from the target EBP at that clinic and provider. For each trial, clinics will be randomized to one of two implementation strategies to improve the use of the EBPs: (1) individual-level academic detailing (AD) or (2) AD plus the team-based Learn. Engage. Act. Process. (LEAP) quality improvement (QI) learning program. The primary outcomes will be operationalized across the three trials as a patient-level dichotomous response (yes/no) indicating patients with potentially inappropriate medications (PIMs) among those who may benefit from the EBP. This outcome will be computed using month-by-month administrative data. Primary comparison between the two implementation strategies will be analyzed using generalized estimating equations (GEE) with clinic-level monthly (13 to 36 months) percent of PIMs as the dependent variable. Primary comparative endpoint will be at 18 months post-baseline. Each trial will also be analyzed independently. Discussion MIDAS QUERI trials will focus on fostering sustained use of EBPs that previously had targeted but incomplete implementation. Our implementation approaches are designed to engage frontline clinicians in a dynamic optimization process that integrates the use of actional clinical data and making incremental changes, designed to be feasible within busy clinical settings. Trial registration ClinicalTrials.gov: NCT05065502. Registered October 4, 2021—retrospectively registered.

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Cost data in implementation science: categories and approaches to costing

Cited by 55 publications

References 55 publications

Using decision analysis to support implementation planning in research and practice

Using decision analysis to support implementation planning in research and practice

Cost-Effectiveness of a Telephone-Based Smoking Cessation Randomized Trial in the Lung Cancer Screening Setting

Maintaining Implementation through Dynamic Adaptations (MIDAS): protocol for a cluster-randomized trial of implementation strategies to optimize and sustain use of evidence-based practices in Veteran Health Administration (VHA) patients

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