Search

Highlights: Electronic health records are not a single system but a series of overlapping and legacy systems that require time and expertise to use efficiently.Commonly measured patient characteristics such as weight and body mass index are relatively easy to locate for most trial enrollees but less common characteristics, like ejection fraction, are not.Acquiring essential supplementary data-in this trial, state data on hospital admission-can be a lengthy and difficult process.

Behavioral interventions involving electronic devices, financial incentives, gamification, and specially trained staff to encourage healthy behaviors are becoming increasingly prevalent and important in health innovation and improvement efforts. Although considerations of cost are key to their wider adoption, cost information is lacking because the resources required cannot be costed using standard administrative billing data. Pragmatic clinical trials that test behavioral interventions are potentially the best and often only source of cost information but rarely incorporate costing studies. This article provides a guide for researchers to help them collect and analyze, during the trial and with little additional effort, the information needed to inform potential adopters of the costs of adopting a behavioral intervention. A key challenge in using trial data is the separation of implementation costs, the costs an adopter would incur, from research costs. Based on experience with 3 randomized clinical trials of behavioral interventions, this article explains how to frame the costing problem, including how to think about costs associated with the control group, and describes methods for collecting data on individual costs: specifications for costing a technology platform that supports the specialized functions required, how to set up a time log to collect data on the time staff spend on implementation, and issues in getting data on device, overhead, and financial incentive costs.

Objectives: In 2016, the Second Panel on Cost-effectiveness in Health and Medicine updated the seminal work of the original panel from 2 decades earlier. The Second Panel had an opportunity to reflect on the evolution of cost-effectiveness analysis (CEA) and to provide guidance for the next generation of practitioners and consumers. In this article, we present key topics for future research and policy., Methods: During the course of its deliberations, the Second Panel discussed numerous topics for advancing methods and for improving the use of CEA in decision making. We identify and consider 7 areas for which the panel believes that future research would be particularly fruitful. In each of these areas, we highlight outstanding research needs. The list is not intended as an exhaustive inventory but rather a set of key items that surfaced repeatedly in the panel's discussions. In the online Appendix , we also list and expound briefly on 8 other important topics., Results: We highlight 7 key areas: CEA and perspectives (determining, valuing, and summarizing elements for the analysis), modeling (comparative modeling and model transparency), health outcomes (valuing temporary health and path states, as well as health effects on caregivers), costing (a cost catalogue, valuing household production, and productivity effects), evidence synthesis (developing theory on learning across studies and combining data from clinical trials and observational studies), estimating and using cost-effectiveness thresholds (empirically representing 2 broad concepts: opportunity costs and public willingness to pay), and reporting and communicating CEAs (written protocols and a quality scoring system)., Conclusions: Cost-effectiveness analysis remains a flourishing and evolving field with many opportunities for research. More work is needed on many fronts to understand how best to incorporate CEA into policy and practice.

Objective: To explore the use of cluster analysis to define groups of similar countries for the purpose of evaluating the cost-effectiveness of a public health intervention-maternal immunization-within the constraints of a project budget originally meant for an overall regional analysis., Methods: We used the most common cluster analysis algorithm, K-means, and the most common measure of distance, Euclidean distance, to group 37 low-income, sub-Saharan African countries on the basis of 24 measures of economic development, general health resources, and past success in public health programs. The groups were tested for robustness and reviewed by regional disease experts., Results: We explored 2-, 3- and 4-group clustering. Public health performance was consistently important in determining the groups. For the 2-group clustering, for example, infant mortality in Group 1 was 81 per 1,000 live births compared with 51 per 1,000 in Group 2, and 67% of children in Group 1 received DPT immunization compared with 87% in Group 2. The experts preferred four groups to fewer, on the ground that national decision makers would more readily recognize their country among four groups., Conclusions: Clusters defined by K-means clustering made sense to subject experts and allowed a more detailed evaluation of the cost-effectiveness of maternal immunization within the constraint of the project budget. The method may be useful for other evaluations that, without having the resources to conduct separate analyses for each unit, seek to inform decision makers in numerous countries or subdivisions within countries, such as states or counties.

The appropriate development of a model begins with understanding the problem that is being represented. The aim of this article is to provide a series of consensus-based best practices regarding the process of model conceptualization. For the purpose of this series of papers, the authors consider the development of models whose purpose is to inform medical decisions and health-related resource allocation questions. They specifically divide the conceptualization process into two distinct components: the conceptualization of the problem, which converts knowledge of the health care process or decision into a representation of the problem, followed by the conceptualization of the model itself, which matches the attributes and characteristics of a particular modeling type to the needs of the problem being represented. Recommendations are made regarding the structure of the modeling team, agreement on the statement of the problem, the structure, perspective and target population of the model, and the interventions and outcomes represented. Best practices relating to the specific characteristics of model structure, and which characteristics of the problem might be most easily represented in a specific modeling method, are presented. Each section contains a number of recommendations that were iterated among the authors, as well as the wider modeling taskforce, jointly set up by the International Society for Pharmacoeconomics and Outcomes Research and the Society for Medical Decision Making.

The expected utility framework underlies much research in medical decision making. Because the framework requires decisions to be decomposed into probabilities of states and the values of those states, researchers have investigated the two components separately from each other and from patients' actual decisions. The authors propose that it would be productive to focus more research on the relationships among risk perceptions, outcome valuations, and choices in the same decision makers. They outline exploratory analyses based on two existing national surveys, the Medical Expenditure Panel Survey and the Joint Canada/United States Survey of Health.

Purpose: Previous cost-effectiveness analyses of colorectal cancer screening have not considered the value of patient time despite consensus recommendations to do so. The authors sought to estimate the amount and value of patient time required for screening colonoscopy., Methods: Patients who were scheduled to undergo screening colonoscopy were recruited from a university endoscopy center. Participants completed a time diary for the screening colonoscopy process, including time spent in preparation, travel, waiting, colonoscopy, and recovery. The authors defined several time intervals and estimated their value. The primary time interval of interest, called occupied time, included preparation, travel, waiting, the colonoscopy procedure, and on-site recovery. Time was valued at the 2005 average wage rate. The authors performed sensitivity analyses to test other time intervals and wage rates. They then incorporated patient time costs into a previously published cost-effectiveness analysis of colorectal cancer screening to examine their impact., Results: One hundred ten subjects completed the study. The sample was 57% female, 85% Caucasian, and 90% insured (40% Medicare, 4% Medicaid). The mean occupied time was 23.2 hours, worth $432 at the average wage rate. The authors estimate that including patient time costs in cost-effectiveness analysis would increase the cost per life-year saved with screening colonoscopy by 68%, from $13,100 to $22,000. Sensitivity analyses showed that the increase could range from 17% to 224% depending on the time interval valued., Conclusions: Patient time constitutes an important cost in colonoscopy screening and should be included in cost-effectiveness analyses.