Assessing key model parameters for economic evaluation of pandemic influenza interventions: the data source matters

Abstract

BackgroundIn our previous systematic review of economic evaluations of pandemic influenza interventions, five model parameters, namely probability of pandemic, duration of pandemic, severity, attack rate, and intervention efficacy, were not only consistently used in all studies but also considered important by authors.ObjectivesBecause these parameters originated from sources of varying quality ranging from experimental studies to expert opinion, this study aims to analyze the variation in values used according to sources of information across studies.MethodsAn analysis of estimated values of key parameters for economic modeling was performed against their different data sources, following the standard hierarchy of evidence.ResultsA lack of good‐quality evidence to estimate pandemic duration, pandemic probability, and mortality reduction from antiviral treatment results in a large variation of values used in economic evaluations. Although there are variations in quality of evidence used for attack rate, basic reproduction number, and reduction in hospitalizations from antiviral treatment, the estimated values do not vary significantly. The use of higher‐quality evidence results in better precision of estimated values compared to lower‐quality sources.ConclusionHierarchies of evidence are a necessary tool to identify appropriate model parameters to populate economic evaluations and should be included in methodological guidelines. Knowledge gaps in some key parameters should be addressed, because if good‐quality evidence is available, future economic evaluations will be more reliable. Some gaps may not be fulfilled by research but consensus among experts to ensure consistency in the use of these assumptions.

Value for Money in H1N1 Influenza: A Systematic Review of the Cost-Effectiveness of Pandemic Interventions

“…One must also notice that the cost-effectiveness found for an intervention is highly dependent on the modeling assumptions [10,11] and on the costs and benefits accounted for in the studies. To illustrate this last point, we may consider vaccination (see Table 3).…”

“…On the positive side, severity and fatality parameters play a central role in the benefit estimations [10,45]. If they are taken into consideration, studies' results generally end up within the same range, allowing conclusions to be drawn.…”

“…Such research could propose a larger range of scenarios for extreme events [53,54] and innovative models for preparedness. Our review also shows the need for a unified framework [10,12] to serve as a basis for cost-effectiveness assessment, as well as the need for tools enabling a quick value-for-money evaluation of the strategic response to a pandemic crisis, thus expanding the guidance provided by World Health Organization CHOICE (CHOosing Interventions that are Cost-Effective) [52] to these situations. This need for additional guidelines on the economic evaluation of influenza measures was also raised in the previous systematic review [5].…”

“…In addition, the unquestionable medical effectiveness of a targeted vaccine, once available, draws the attention of researchers, leaving little room for such attention to be given to nonmedical measures that could potentially be costeffective: "Japan's low incidence/death cases (during H1N1) may be due to individuals' prevention behavior" [9]. Indeed, the sensitivity analysis of the studies' parameters shows that among those parameters, infectivity and severity play a large role [10][11][12]. In our graphical representation, the infectivity parameter of a study is represented in terms of the reproductive number (Ro) and fatality in terms of the CFR.…”

“…Figure1presents in a graphical way the cost-utility results, or CCUM, of several pandemic interventions grouped by certain categories; infectivity is seen on the X-axis and severity (fatality) on the Y-axis. The size of the circle represents the CCUM value (i.e., the larger the circle, the more costly the intervention is in dollars per DALY/QALY).Indeed, the sensitivity analysis of the studies' parameters shows that among those parameters, infectivity and severity play a large role[10][11][12]. In our graphical representation, the infectivity parameter of a study is represented in terms of the reproductive number (Ro) and fatality in terms of the CFR.…”