ESTIMATING THE HEALTH AND ECONOMIC BURDEN OF SOCIOECONOMIC INEQUALITIES IN BREAST CANCER SCREENING: AN INDIVIDUAL-LEVEL MICROSIMULATION STUDY
Author(s)
Romain Demeulemeester1, Emmanuel Chamorey, PhD2, Christian Pradier, MD3, Laurent Bailly, MD4, Laurent Molinier, MD-PhD5, Pierre Dellamonica, MD-PhD6, Nadège COSTA, PhD7.
1Biostatistician, University Hospital of Toulouse, Toulouse, France, 2Centre Antoine Lacassagne, NICE Cedex 2, France, 3University Hospital of Nice, Nice, France, 4Unité de recherche Clinique Côte d’Azur, Nice, France, 5University Hospital of Toulouse, Toulouse, France, 6Unité de recherche clinique Côte d'Azur, Nice, France, 7University hospital of Toulouse, Toulouse, France.
1Biostatistician, University Hospital of Toulouse, Toulouse, France, 2Centre Antoine Lacassagne, NICE Cedex 2, France, 3University Hospital of Nice, Nice, France, 4Unité de recherche Clinique Côte d’Azur, Nice, France, 5University Hospital of Toulouse, Toulouse, France, 6Unité de recherche clinique Côte d'Azur, Nice, France, 7University hospital of Toulouse, Toulouse, France.
OBJECTIVES: To quantify the health and economic burden attributable to socioeconomic inequalities in organised breast cancer screening participation and to estimate the potential value of transport support in reducing this burden.
METHODS: An individual-level discrete-event microsimulation model was developed to simulate breast cancer screening, diagnosis and management. The model combines (i) a natural history model parameterised from the published literature, (ii) a behavioural model predicting participation in organised screening according to individual socioeconomic and territorial characteristics, and (iii) a care pathway model reconstructed from French National Health Data System (SNDS) trajectories using, transition probabilities and parametric time-to-event models. Costs were estimated from healthcare reimbursements, and outcomes included life-years gained (LYG) and quality-adjusted life-years (QALYs). A counterfactual scenario eliminating deprivation-related differences in screening participation was compared with current practice to estimate the burden of inequalities. A transport support intervention was subsequently evaluated as a strategy to reduce this burden. Parameter uncertainty was explored using probabilistic Monte Carlo simulation.
RESULTS: Preliminary simulations suggest that socioeconomic inequalities in screening participation result in later-stage diagnosis, reducing survival by approximately 1.5 life-years and 0.68 QALYs per woman eligible for screening compared with a counterfactual scenario without deprivation-related disparities. The associated reduction in lifetime healthcare costs (approximately €18,600 per woman) primarily reflects shorter survival and therefore a shorter period over which healthcare costs accumulate, rather than more efficient care. A transport support intervention partially offsets this burden, increasing screening participation and yielding an average gain of approximately 2.9 life-years and 1.33 QALYs compared with current practice, at an incremental cost of approximately €33,000. These estimates remain preliminary pending final model calibration and validation.
CONCLUSIONS: This microsimulation framework quantifies the health and economic burden of socioeconomic inequalities in breast cancer screening. It provides a decision-support tool for evaluating policies aimed at improving equitable access to early diagnosis and optimising resource allocation.
METHODS: An individual-level discrete-event microsimulation model was developed to simulate breast cancer screening, diagnosis and management. The model combines (i) a natural history model parameterised from the published literature, (ii) a behavioural model predicting participation in organised screening according to individual socioeconomic and territorial characteristics, and (iii) a care pathway model reconstructed from French National Health Data System (SNDS) trajectories using, transition probabilities and parametric time-to-event models. Costs were estimated from healthcare reimbursements, and outcomes included life-years gained (LYG) and quality-adjusted life-years (QALYs). A counterfactual scenario eliminating deprivation-related differences in screening participation was compared with current practice to estimate the burden of inequalities. A transport support intervention was subsequently evaluated as a strategy to reduce this burden. Parameter uncertainty was explored using probabilistic Monte Carlo simulation.
RESULTS: Preliminary simulations suggest that socioeconomic inequalities in screening participation result in later-stage diagnosis, reducing survival by approximately 1.5 life-years and 0.68 QALYs per woman eligible for screening compared with a counterfactual scenario without deprivation-related disparities. The associated reduction in lifetime healthcare costs (approximately €18,600 per woman) primarily reflects shorter survival and therefore a shorter period over which healthcare costs accumulate, rather than more efficient care. A transport support intervention partially offsets this burden, increasing screening participation and yielding an average gain of approximately 2.9 life-years and 1.33 QALYs compared with current practice, at an incremental cost of approximately €33,000. These estimates remain preliminary pending final model calibration and validation.
CONCLUSIONS: This microsimulation framework quantifies the health and economic burden of socioeconomic inequalities in breast cancer screening. It provides a decision-support tool for evaluating policies aimed at improving equitable access to early diagnosis and optimising resource allocation.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
EE542
Topic
Economic Evaluation, Health Policy & Regulatory, Patient-Centered Research
Disease
Oncology