Cost-effectiveness analyses: making a pseudoscience legitimate

Factors that Influence Medical Reserve Corps Recruitment

The Medical Reserve Corps (MRC) is a key strategy used in the United States to assure an adequate surge capacity healthcare workforce for response to disasters. A survey of Hawaiian healthcare providers (n = 1,057) was conducted to identify factors that influence interest, ability, and willingness to join the MRC; 468 (44.3%) healthcare providers responded. Overall, females were more likely to demonstrate an interest in joining the MRC, while physicians and dentists reported lower levels of ability and willingness, in addition to a lower level of interest in joining the MRC than the other professional groups. The most important motivating factor in joining the MRC was altruism and the ability to help one's own community. Respondents reported a number of factors that would influence their decision to join or remain a MRC member. These included: (1) time commitment required; (2) MRC organization and management; (3) provision of MRC-sponsored training or education sessions and continuing education credits; (4) concerns regarding the safety of family members during a disaster; (5) professional liability protection for work performed during MRC operations; and (6) competing personal obligations. Strategies targeting these factors probably will be most effective in recruitment and retention of MRC volunteers as well as members of other public health surge capacity volunteer groups.

Markov Influence Diagrams

Markov influence diagrams (MIDs) are a new type of probabilistic graphical model that extends influence diagrams in the same way that Markov decision trees extend decision trees. They have been designed to build state-transition models, mainly in medicine, and perform cost-effectiveness analyses. Using a causal graph that may contain several variables per cycle, MIDs can model various patient characteristics without multiplying the number of states; in particular, they can represent the history of the patient without using tunnel states. OpenMarkov, an open-source tool, allows the decision analyst to build and evaluate MIDs—including cost-effectiveness analysis and several types of deterministic and probabilistic sensitivity analysis—with a graphical user interface, without writing any code. This way, MIDs can be used to easily build and evaluate complex models whose implementation as spreadsheets or decision trees would be cumbersome or unfeasible in practice. Furthermore, many problems that previously required discrete event simulation can be solved with MIDs; i.e., within the paradigm of state-transition models, in which many health economists feel more comfortable.

The missing link in preconceptional care: the role of comparative effectiveness research

This paper discusses an important element that is missing from the existing algorithm of preconception care, namely, comparative effectiveness research (CER). To our knowledge, there has been limited assessment of the comparative effectiveness of diverse interventions that promote preconception health, conditions under which these are most effective, for which particular populations, and their comparative costs. CER can improve the decision making process for the funding, development, implementation, and evaluation of comprehensive preconception care programs, specifically by identifying the most effective interventions with acceptable costs to society. This paper will examine the framework behind preconception care and how the inclusion of comparative effectiveness research and evaluation into the existing algorithm of preconception care could foster improvement in maternal and child health. We discuss challenges and opportunities regarding the utilization of CER in the decision making process in preconception health, and finally, we provide recommendations for future directions.

Evidence-Based Decision-Making (Part II): Applications in Disaster Relief Operations

Recognized limitations to data in disaster management have led to dozens of initiatives to strengthen data gathering and decision-making during disasters. These initiatives are complicated by fundamental problems of definitions of terms, ambiguity of concepts, lack of standardization in methods of data collection, and inadequate attempts to strengthen the analytic capability of field organizations. Cross-cutting issues in needs assessment, coordination, and evaluation illustrate additional recurring challenges in dealing with evidence in humanitarian assistance. These challenges include lack of agency expertise, dyscoordination at the field level, inappropriate reliance on indicators that measure process rather than outcome, flawed scientific inference, and erosion of the concept of minimum standards.

Decision-making in disaster management currently places a premium on expert or eminence-based decisions. By contrast, scientific advances in disaster medicine call for evidence-based decisions whose strength of evidence is established by the methods of data acquisition. At present, disaster relief operations may be data driven, but that does not mean that they are soundly evidence-based.

Options for strengthening evidence-based activities include rigorously adhering to evidenced-based interventions, using evidence-based tools to identify new approaches to problems of concern, studying model programs as well as failed ones to identify approaches that deserve replication, and improving standards for evidence of effectiveness in disaster science and services.