Critical Appraisal of Decision Models Used for the Economic Evaluation of Bladder Cancer Screening and Diagnosis: A Systematic Review

Abstract

Background and objective: Bladder cancer is common among current and former smokers. High bladder cancer mortality may be decreased through early diagnosis and screening. The aim of this study was to appraise decision models used for the economic evaluation of bladder cancer screening and diagnosis, and to summarise the main outcomes of these models.

Methods: MEDLINE via PubMed, Embase, EconLit and Web of Science databases was systematically searched from January 2006 to May 2022 for modelling studies that assessed the cost effectiveness of bladder cancer screening and diagnostic interventions. Articles were appraised according to Patient, Intervention, Comparator and Outcome (PICO) characteristics, modelling methods, model structures and data sources. The quality of the studies was also appraised using the Philips checklist by two independent reviewers.

Results: Searches identified 3082 potentially relevant studies, which resulted in 18 articles that met our inclusion criteria. Four of these articles were on bladder cancer screening, and the remaining 14 were diagnostic or surveillance interventions. Two of the four screening models were individual-level simulations. All screening models (n = 4, with three on a high-risk population and one on a general population) concluded that screening is either cost saving or cost effective with cost-effectiveness ratios lower than $53,000/life-years saved. Disease prevalence was a strong determinant of cost effectiveness. Diagnostic models (n = 14) assessed multiple interventions; white light cystoscopy was the most common intervention and was considered cost effective in all studies (n = 4). Screening models relied largely on published evidence generalised from other countries and did not report the validation of their predictions to external data. Almost all diagnostic models (n = 13 out of 14) had a time horizon of 5 years or less and most of the models (n = 11) did not incorporate health-related utilities. In both screening and diagnostic models, epidemiological inputs were based on expert elicitation, assumptions or international evidence of uncertain generalisability. In modelling disease, seven models did not use a standard classification system to define cancer states, others used risk-based, numerical or a Tumour, Node, Metastasis classification. Despite including certain components of disease onset or progression, no models included a complete and coherent model of the natural history of bladder cancer (i.e. simulating the progression of asymptomatic primary bladder cancer from cancer onset, i.e. in the absence of treatment).

Conclusions: The variation in natural history model structures and the lack of data for model parameterisation suggest that research in bladder cancer early detection and screening is at an early stage of development. Appropriate characterisation and analysis of uncertainty in bladder cancer models should be considered a priority.

Figure 1.. Incremental cost-effectiveness ratio for bladder cancer screening with different prevalence rate for the disease

Squares reflect the outcomes “per cancer detected”, circles reflect the outcomes the life years saved or quality adjusted life years. The ICERs under the axe represent cost-saving outcomes. The grey circle reflects the ICER in the UK study (assumed cost-effectiveness threshold £20,000).

Figure 2.. Critical appraisal of the economic models using the Philips et al. checklist

Dimensions of Quality in the Philips et al checklist: S1 Clear statement of decision problem, defined objectives and decision makers; S2 Clear statement, justification, and consistency of scope and perspective; S3 Rationale for Structure explained and based on evidence; S4 Structural Assumptions justified and reasonable; S5 Strategies/comparators defined with all the options considered; S6 Model Type based on decision problem; S7 Sufficient and justified time horizon; S8 Disease states/pathways reflect biological process; S9. Cycle length justified by the nature of the disease; D1 Data identification is transparent, appropriate, justified, and high quality; D2a Baseline data described and justified; D2b Treatment effect based on recognised meta-synthesis, justified extrapolation and survival, with all assumptions documented and justified; D2c Costs and discounting accord with standard guidelines; D2d Quality of life weights (utilities) appropriate, justified, and referenced; D3 Data incorporation justified and transparent; C. Internal and external consistency is evaluated. The categories used: “Yes”, “No” (No, Partially, or Can’t tell), “NA” (not applicable).