Subcategorizing the Expected Value of Perfect Implementation to Identify When and Where to Invest in Implementation Initiatives

The Value-of-Information and Value-of-Implementation from Clinical Trials of Diagnostic Tests for HIV-Associated Tuberculosis: A Modeling Analysis

Objectives. Conventional value-of-information (VOI) analysis assumes complete uptake of an optimal decision. We employed an extended framework that includes value-of-implementation (VOM)-the benefit of encouraging adoption of an optimal strategy-and estimated how future trials of diagnostic tests for HIV-associated tuberculosis could improve public health decision making and clinical and economic outcomes. Methods. We evaluated the clinical outcomes and costs, given current information, of 3 tuberculosis screening strategies among hospitalized people with HIV in South Africa: sputum Xpert (Xpert), sputum Xpert plus urine AlereLAM (Xpert+AlereLAM), and sputum Xpert plus the newer, more sensitive, and costlier urine FujiLAM (Xpert+FujiLAM). We projected the incremental net monetary benefit (INMB) of decision making based on results of a trial comparing mortality with each strategy, rather than decision making based solely on current knowledge of FujiLAM's improved diagnostic performance. We used a validated microsimulation to estimate VOI (the INMB of reducing parameter uncertainty before decision making) and VOM (the INMB of encouraging adoption of an optimal strategy). Results. With current information, adopting Xpert+FujiLAM yields 0.4 additional life-years/person compared with current practices (assumed 50% Xpert and 50% Xpert+AlereLAM). While the decision to adopt this optimal strategy is unaffected by information from the clinical trial (VOI = $ 0 at $3,000/year-of-life saved willingness-to-pay threshold), there is value in scaling up implementation of Xpert+FujiLAM, which results in an INMB (representing VOM) of $650 million over 5 y. Conclusions. Conventional VOI methods account for the value of switching to a new optimal strategy based on trial data but fail to account for the persuasive value of trials in increasing uptake of the optimal strategy. Evaluation of trials should include a focus on their value in reducing barriers to implementation.

Highlights

In conventional VOI analysis, it is assumed that the optimal decision will always be adopted even without a trial. This can potentially lead to an underestimation of the value of trials when adoption requires new clinical trial evidence. To capture the influence that a trial may have on decision makers' willingness to adopt the optimal decision, we also consider value-of-implementation (VOM), a metric quantifying the benefit of new study information in promoting wider adoption of the optimal strategy. The overall value-of-a-trial (VOT) includes both VOI and VOM.Our model-based analysis suggests that the information obtained from a trial of screening strategies for HIV-associated tuberculosis in South Africa would have no value, when measured using traditional methods of VOI assessment. A novel strategy, which includes the urine FujiLAM test, is optimal from a health economic standpoint but is underutilized. A trial would reduce uncertainties around downstream health outcomes but likely would not change the optimal decision. The high VOT (nearly $700 million over 5 y) lies solely in promoting uptake of FujiLAM, represented as VOM.Our results highlight the importance of employing a more comprehensive approach for evaluating prospective trials, as conventional VOI methods can vastly underestimate their value. Trialists and funders can and should assess the VOT metric instead when considering trial designs and costs. If VOI is low, the VOM and cost of a trial can be compared with the benefits and costs of other outreach programs to determine the most cost-effective way to improve uptake.

Implementation of Ticagrelor Reduced Mortality in Routine Clinical Care: Evidence From a Natural Experiment Including 109 995 Patients With Myocardial Infarction in Sweden

Background Effectiveness estimates from observational studies on ticagrelor use in routine clinical care are conflicting, with some contrary to the results of the pivotal randomized controlled trial of ticagrelor in acute coronary syndrome. The aim of this study was to estimate the effect of implementing and using ticagrelor in routine clinical care in patients with myocardial infarction using a natural experimental approach. Methods and Results This is a retrospective cohort study including patients hospitalized for myocardial infarction in Sweden between 2009 and 2015. The study exploited differences in the timing and speed of ticagrelor implementation between treatment centers as a source of random treatment assignment. The effect of implementing and using ticagrelor was estimated based on the admitting center's likelihood of treating patients with ticagrelor, measured as the proportion of patients treated in the 90 days before patient admission. The main outcome was 12-month mortality. The study included 109 955 patients, of whom 30 773 were treated with ticagrelor. Being admitted to a treatment center with higher past ticagrelor use was associated with a reduction in 12-month mortality (2.5 percentage points for 100% versus 0% past use [95% CI, 0.2-4.8]). The results are in line with the findings from the ticagrelor pivotal trial. Conclusions Using a natural experiment, this study finds that the implementation and use of ticagrelor in routine clinical care has reduced 12-month mortality in patients admitted to the hospital with myocardial infarction in Sweden and supports the external validity of randomized evidence on ticagrelor effectiveness.

Regional variation in prescription drug spending: Evidence from regional migrants in Sweden

There is substantial variation in drug spending across regions in Sweden, which can be justified if caused by differences in health need, but an indication of inefficiencies if primarily caused by differences in place-specific supply-side factors. This paper aims to estimate the relative effect of individual demand-side factors and place-specific supply-side factors as drivers of geographical variation in drug spending in Sweden. We use individual-level register data on purchases of prescription drugs matched with demographic and socioeconomic data of a random sample of about 900,000 individuals over 2007-2016. The primary empirical approach is a two-way fixed effect model and an event study where we identify demand- and supply-side effects based on how regional and local migrants change drug spending when moving across regional and municipal borders. As an alternative approach in robustness checks, we also use a decomposition analysis. The results show that the place-specific supply-side effect accounts for only about 5%-10% of variation in drug spending and remaining variation is due to individual demand-side effects. These results imply that health policies to reduce regional variation in drug spending would have limited impact if targeted at place-specific characteristics.

Cost-Effectiveness Analysis in Implementation Science: a Research Agenda and Call for Wider Application

Purpose of Review

Cost-effectiveness analysis (CEA) can help identify the trade-offs decision makers face when confronted with alternative courses of action for the implementation of public health strategies. Application of CEA alongside implementation scientific studies remains limited. We aimed to identify areas for future development in order to enhance the uptake and impact of model-based CEA in implementation scientific research.

Recent Findings

Important questions remain about how to broadly implement evidence-based public health interventions in routine practice. Establishing population-level implementation strategy components and distinct implementation phases, including planning for implementation, the time required to scale-up programs, and sustainment efforts required to maintain them, can help determine the data needed to quantify each of these elements. Model-based CEA can use these data to determine the added value associated with each of these elements across systems, settings, population subgroups, and levels of implementation to provide tailored guidance for evidence-based public health action. There is a need to integrate implementation science explicitly into CEA to adequately capture diverse real-world delivery contexts and make detailed, informed recommendations on the aspects of the implementation process that provide good value.

Summary

We describe examples of how model-based CEA can integrate implementation scientific concepts and evidence to help tailor evaluations to local context. We also propose six distinct domains for methodological advancement in order to enhance the uptake and impact of model-based cost-effectiveness analysis in implementation scientific research.