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

Naiyana Praditsitthikorn,a,b Surachai Kotirum,a Adun Mohara,a Kuntika Dumrongprat,a Roman Perez Velasco,c Yot Teerawattananona

aHealth Intervention and Technology Assessment Program (HITAP), Ministry of Public Health, Nonthaburi, Thailand. bDepartment of Disease
Control, Ministry of Public Health, Nonthaburi, Thailand. cPharmaceutical Consultant, Bangkok, Thailand.
Correspondence: Yot Teerawattananon. Health Intervention and Technology Assessment Program (HITAP), 6th floor, 6th building, Department of
Health, Ministry of Public Health, Tiwanon Road, Muang, Nonthaburi, 11000, Thailand. E-mail: yot.t@hitap.net

Background In 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.

Objectives Because 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.

Methods An analysis of estimated values of key parameters for economic modeling was performed against their different data sources, following the standard hierarchy of evidence.

Results A 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 lowerquality sources.

Conclusion Hierarchies 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 goodquality 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.

Full Text: http://www.ncbi.nlm.nih.gov/pubmed/24034486