To analyse published evidence on the economic evaluation of risk‐based screening (RBS), a full systematic literature review was conducted. After a quality appraisal, we compared the cost‐effectiveness of risk‐based strategies (low‐risk, medium‐risk and high‐risk) with no screening and age‐based screening. Studies were also analysed for modelling, risk stratification methods, input parameters, data sources and harms and benefits. The 10 modelling papers analysed were based on screening performance of film‐based mammography (FBM) (three); digital mammography (DM) and FBM (two); DM alone (three); DM, ultrasound (US) and magnetic resonance imaging (one) and DM and US (one). Seven studies did not include the cost of risk‐stratification, and one did not consider the cost of diagnosis. Disutility was incorporated in only six studies (one for screening and five for diagnosis). None of the studies reported disutility of risk‐stratification (being considered as high‐risk). Risk‐stratification methods varied from only breast density (BD) to the combination of familial risk, genetic susceptibility, lifestyle, previous biopsies, Jewish ancestry and reproductive history. Less or no screening in low‐risk women and more frequent mammography screening in high‐risk women was more cost‐effective compared to no screening and age‐based screening. High‐risk women screened annually yielded a higher mortality rate reduction and more quality‐adjusted life years at the expense of higher cost and false positives. RBS can be cost effective compared to the alternatives. However, heterogeneity among risk‐stratification methods, input parameters, and weaknesses in the methodologies hinder the derivation of robust conclusions. Therefore, further studies are warranted to assess newer technologies and innovative risk‐stratification methods.
Background Health state utility values (HSUVs) are an essential input parameter to cost-utility analysis (CUA). Systematic literature reviews (SLRs) provide summarized information for selecting utility values from an increasing number of primary studies eliciting HSUVs. Quality appraisal (QA) of such SLRs is an important process towards the credibility of HSUVs estimates; yet, authors often overlook this crucial process. A scientifically developed and widely accepted QA tool for this purpose is lacking and warranted. Objectives To comprehensively describe the nature of QA in published SRLs of studies eliciting HSUVs and generate a list of commonly used items. Methods A comprehensive literature search was conducted in PubMed and Embase from 01.01.2015 to 15.05.2021. SLRs of empirical studies eliciting HSUVs that were published in English were included. We extracted descriptive data, which included QA tools checklists or good practice recommendations used or cited, items used, and the methods of incorporating QA results into study findings. Descriptive statistics (frequencies of use and occurrences of items, acceptance and counterfactual acceptance rates) were computed and a comprehensive list of QA items was generated. Results A total of 73 SLRs were included, comprising 93 items and 35 QA tools and good recommendation practices. The prevalence of QA was 55% (40/73). Recommendations by NICE and ISPOR guidelines appeared in 42% (16/40) of the SLRs that appraised quality. The most commonly used QA items in SLRs were response rates (27/40), statistical analysis (22/40), sample size (21/40) and loss of follow up (21/40). Yet, the most commonly featured items in QA tools and GPRs were statistical analysis (23/35), confounding or baseline equivalency (20/35), and blinding (14/35). Only 5% of the SLRS used QA to inform the data analysis, with acceptance rates of 100% (in two studies) 67%, 53% and 33%. The mean counterfactual acceptance rate was 55% (median 53% and IQR 56%). Conclusions There is a considerably low prevalence of QA in the SLRs of HSUVs. Also, there is a wide variation in the QA dimensions and items included in both SLRs and extracted tools. This underscores the need for a scientifically developed QA tool for multi-variable primary studies of HSUVs.
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