Predicting the stage shift as a result of breast cancer screening in low- and middle-income countries: a proof of concept

Abstract

Objective: To provide proof of concept for a simple model to estimate the stage shift as a result of breast cancer screening in low-and middle-income countries (LMICs). Stage shift is an essential early detection indicator and an important proxy for the performance and possible further impact of screening programmes. Our model could help LIMCs to choose appropriate control strategies. Methods: We assessed our model concept in three steps. First, we calculated the proportional performance rates (i.e. index number Z) based on 16 screening rounds of the Nijmegen Screening Program (384,884 screened women). Second, we used linear regression to assess the association between Z and the amount of stage shift observed in the programme. Third, we hypothesized how Z could be used to estimate the stage shift as a result of breast cancer screening in LMICs. Results: Stage shifts can be estimated by the proportional performance rates (Zs) using linear regression. Zs calculated for each screening round are highly associated with the observed stage shifts in the Nijmegen Screening Program (Pearson's R: 0.798, R square: 0.637). Conclusions: Our model can predict the stage shifts in the Nijmegen Screening Program, and could be applied to settings with different characteristics, although it should not be straightforwardly used to estimate the impact on mortality. Further research should investigate the extrapolation of our model to other settings. As stage shift is an essential screening performance indicator, our model could provide important information on the performance of breast cancer screening programmes that LMICs consider implementing.

Early detection and treatment strategies for breast cancer in low-income and upper middle-income countries: a modelling study

“…For early detection, the anticipated shifts in disease stage associated with candidate screening strategies depend on the completeness and quality of screening implementation in the target population, which might have logistical determinants that are dependent on location. Zelle and colleagues 42 used a mathematical model to predict stage shift as a result of screening in LMICs, but this model requires specification of other parameters pertaining to disease natural history, which are often unknown. We modelled expected stage shifts using data from the control group (clinical breast examination only) of the Canadian breast screening trial.…”

Normal Risk Ovarian Screening Study: 21-Year Update

“…An association has been observed between stage shift and mortality in screening trials for non-small-cell lung and breast cancers. [16][17][18][19] A 20% or greater magnitude reduction in late stage has been associated with a significant decrease in mortality in randomized trials of mammography, 18 and stage shift is an integral component of models that predicts the mortality advantage of mammographic screening. 19 While the UKCTOCS had a <20% stage shift, it remains to be determined whether a higher stage shift, as occurred in NROSS, would significantly reduce mortality.…”

Cancer screening simulation models: a state of the art review

“…Most studies conducted for low-income countries' populations address cervical cancer vaccination and do not deal with screening of any other type of cancer. This likely reflects the limited feasibility of screening in the setting of low-income countries due to a lack of adequate cancer registries, health care organizations, and financial constraints [ 34 ].…”