Abstract
BackgroundEstimated medical costs (“T”) and QALYs (“Q”) associated with smoking are frequently used in cost-utility analyses of tobacco control interventions. The goal of this study was to understand how researchers have addressed the methodological challenges involved in estimating these parameters.MethodsData were collected as part of a systematic review of tobacco modeling studies. We searched five electronic databases on July 1, 2013 with no date restrictions and synthesized studies qualitatively. Studies were eligible for the current analysis if they were U.S.-based, provided an estimate for Q, and used a societal perspective and lifetime analytic horizon to estimate T. We identified common methods and frequently cited sources used to obtain these estimates.ResultsAcross all 18 studies included in this review, 50 % cited a 1992 source to estimate the medical costs associated with smoking and 56 % cited a 1996 study to derive the estimate for QALYs saved by quitting or preventing smoking. Approaches for estimating T varied dramatically among the studies included in this review. T was valued as a positive number, negative number and $0; five studies did not include estimates for T in their analyses. The most commonly cited source for Q based its estimate on the Health Utilities Index (HUI). Several papers also cited sources that based their estimates for Q on the Quality of Well-Being Scale and the EuroQol five dimensions questionnaire (EQ-5D).ConclusionsCurrent estimates of the lifetime medical care costs and the QALYs associated with smoking are dated and do not reflect the latest evidence on the health effects of smoking, nor the current costs and benefits of smoking cessation and prevention. Given these limitations, we recommend that researchers conducting economic evaluations of tobacco control interventions perform extensive sensitivity analyses around these parameter estimates.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-016-3319-z) contains supplementary material, which is available to authorized users.
Highlights
Estimated medical costs (“T”) and qualityadjusted life year (QALY) (“Q”) associated with smoking are frequently used in costutility analyses of tobacco control interventions
The lifetime medical costs associated with smoking (“T”) and the number of quality-adjusted life years associated with smoking prevention or cessation (“Q”) are essential drivers of the cost-effectiveness of a policy option, but methodologically challenging to estimate for two reasons: first, the true values of these parameters can change with evolving evidence on the harms of smoking [7,8,9] and rising medical costs; and second, the costs and benefits of smoking prevention and cessation are distal and do not accrue until years following an intervention
The aim of the current study is to address this gap by providing a detailed investigation into how the parameters T and Q have been estimated in tobacco control literature
Summary
Estimated medical costs (“T”) and QALYs (“Q”) associated with smoking are frequently used in costutility analyses of tobacco control interventions. The lifetime medical costs associated with smoking (“T”) and the number of quality-adjusted life years associated with smoking prevention or cessation (“Q”) are essential drivers of the cost-effectiveness of a policy option, but methodologically challenging to estimate for two reasons: first, the true values of these parameters can change with evolving evidence on the harms of smoking [7,8,9] and rising medical costs; and second, the costs and benefits of smoking prevention and cessation are distal and do not accrue until years following an intervention. The current study builds upon existing reviews of economic evaluations in tobacco control [8, 10] While these previous reviews focused on synthesizing the findings of economic evaluations [8, 10] and on standardizing cost-effectiveness ratios to facilitate comparisons between interventions [8], they do not provide in-depth assessments of the models used to generate findings for individual studies.
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