Risk Stratification in Cost-Effectiveness Analyses of Cancer Screening: Intervention Eligibility, Strategy Choice, and Optimality

Screening for liver fibrosis: lessons from colorectal and lung cancer screening

Many countries have incorporated population screening programmes for cancer, such as colorectal and lung cancer, into their health-care systems. Cirrhosis is more prevalent than colorectal cancer and has a comparable age-standardized mortality rate to lung cancer. Despite this fact, there are no screening programmes in place for early detection of liver fibrosis, the precursor of cirrhosis. In this Perspective, we use insights from colorectal and lung cancer screening to explore the benefits, challenges, implementation strategies and pathways for future liver fibrosis screening initiatives. Several non-invasive methods and referral pathways for early identification of liver fibrosis exist, but in addition to accurate detection, screening programmes must also be cost-effective and demonstrate benefit through a reduction in liver-related mortality. Randomized controlled trials are needed to confirm this. Future randomized screening trials should evaluate not only the screening tests, but also interventions used to halt disease progression in individuals identified through screening.

A Simple Cost-Effectiveness Model of Screening: An Open-Source Teaching and Research Tool Coded in R

Applied cost-effectiveness analysis models are an important tool for assessing health and economic effects of healthcare interventions but are not best suited for illustrating methods. Our objective is to provide a simple, open-source model for the simulation of disease-screening cost-effectiveness for teaching and research purposes. We introduce our model and provide an initial application to examine changes to the efficiency frontier as input parameters vary and to demonstrate face validity. We described a vectorised, discrete-event simulation of screening in R with an Excel interface to define parameters and inspect principal results. An R Shiny app permits dynamic interpretation of simulation outputs. An example with 8161 screening strategies illustrates the cost and effectiveness of varying the disease sojourn time, treatment effectiveness, and test performance characteristics and costs on screening policies. Many of our findings are intuitive and straightforward, such as a reduction in screening costs leading to decreased overall costs and improved cost-effectiveness. Others are less obvious and depend on whether we consider gross outcomes or those net to no screening. For instance, enhanced treatment of symptomatic disease increases gross effectiveness, but reduces the net effectiveness and cost-effectiveness of screening. A lengthening of the preclinical sojourn time has ambiguous effects relative to no screening, as cost-effectiveness improves for some strategies but deteriorates for others. Our simple model offers an accessible platform for methods research and teaching. We hope it will serve as a public good and promote an intuitive understanding of the cost-effectiveness of screening.

Cost-Effectiveness Analysis of Prostate Cancer Screening in the UK: A Decision Model Analysis Based on the CAP Trial

BACKGROUND AND OBJECTIVE

Most guidelines in the UK, Europe and North America do not recommend organised population-wide screening for prostate cancer. Prostate-specific antigen-based screening can reduce prostate cancer-specific mortality, but there are concerns about overdiagnosis, overtreatment and economic value. The aim was therefore to assess the cost effectiveness of eight potential screening strategies in the UK.

METHODS

We used a cost-utility analysis with an individual-based simulation model. The model was calibrated to data from the 10-year follow-up of the Cluster Randomised Trial of PSA Testing for Prostate Cancer (CAP). Treatment effects were modelled using data from the Prostate Testing for Cancer and Treatment (ProtecT) trial. The participants were a hypothetical population of 10 million men in the UK followed from age 30 years to death. The strategies were: no screening; five age-based screening strategies; adaptive screening, where men with an initial prostate-specific antigen level of < 1.5 ng/mL are screened every 6 years and those above this level are screened every 4 years; and two polygenic risk-stratified screening strategies. We assumed the use of pre-biopsy multi-parametric magnetic resonance imaging for men with prostate-specific antigen ≥ 3 ng/mL and combined transrectal ultrasound-guided and targeted biopsies. The main outcome measures were projected lifetime costs and quality-adjusted life-years from a National Health Service perspective.

RESULTS

All screening strategies increased costs compared with no screening, with the majority also increasing quality-adjusted life-years. At willingness-to-pay thresholds of £20,000 or £30,000 per quality-adjusted life-year gained, a once-off screening at age 50 years was optimal, although this was sensitive to the utility estimates used. Although the polygenic risk-stratified screening strategies were not on the cost-effectiveness frontier, there was evidence to suggest that they were less cost ineffective than the alternative age-based strategies.

CONCLUSIONS

Of the prostate-specific antigen-based strategies compared, only a once-off screening at age 50 years was potentially cost effective at current UK willingness-to-pay thresholds. An additional follow-up of CAP to 15 years may reduce uncertainty about the cost effectiveness of the screening strategies.

Cost-Effectiveness Analyses of Lung Cancer Screening Using Low-Dose Computed Tomography: A Systematic Review Assessing Strategy Comparison and Risk Stratification

OBJECTIVES

Our first study objective was to assess the range of lung cancer screening intervals compared within cost-effectiveness analyses (CEAs) of low-dose computed tomography (LDCT) and to examine the implications for the strategies identified as optimally cost effective; the second objective was to examine if and how risk subgroup-specific policies were considered.

METHODS

PubMed, Embase and Web of Science were searched for model-based CEAs of LDCT lung screening. The retrieved studies were assessed to examine if the analyses considered sufficient strategy variation to permit incremental estimation of cost effectiveness. Regarding risk selection, we examined if analyses considered alternative risk strata in separate analyses or as alternative risk-based eligibility criteria for screening.

RESULTS

The search identified 33 eligible CEAs, 23 of which only considered one screening frequency. Of the 10 analyses considering multiple screening intervals, only 4 included intervals longer than 2 years. Within the 10 studies considering multiple intervals, the optimal policy choice would differ in 5 if biennial intervals or longer had not been considered. Nineteen studies conducted risk subgroup analyses, 12 of which assumed that subgroup-specific policies were possible and 7 of which assumed that a common screening policy applies to all those screened.

CONCLUSIONS

The comparison of multiple strategies is recognised as good practice in CEA when seeking optimal policies. Studies that do include multiple intervals indicate that screening intervals longer than 1 year can be relevant. The omission of intervals of 2 years or longer from CEAs of LDCT screening could lead to the adoption of sub-optimal policies. There also is scope for greater consideration of risk-stratified policies which tailor screening intensity to estimated disease risk. Policy makers should take care when interpreting current evidence before implementing lung screening.