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

J Med Screen. 2015 Mar;22(1):8-19. doi: 10.1177/0969141314559956. Epub 2014 Nov 21.

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.

Keywords: Breast cancer control; CBE screening; breast cancer screening; developing countries; model; stage distribution; validation.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Breast Neoplasms / diagnosis
  • Breast Neoplasms / epidemiology*
  • Breast Neoplasms / pathology
  • Developing Countries
  • Early Detection of Cancer / statistics & numerical data*
  • Female
  • Humans
  • Linear Models
  • Mass Screening
  • Middle Aged
  • Models, Biological
  • Poverty
  • Young Adult