Knowing mean age at diagnosis of breast cancer (BC) in a country is important to set up an efficient BC screening program. The aim of this study was to develop and validate a model to predict mean age at diagnosis of BC at the level of a country. To develop the model, we used the CI5plus database from IARC, which contains incidence data for 122 selected populations for a minimum of 15 consecutive years 1993–2012. The standard model was fitted with a generalized linear model with age of population as fixed effect and continent as random effect. The model was validated on registries of the Cancer Incidence in Five Continents that are not included in the CI5plus database. The intercept of the model was 44.5 (41.8–47.2) and the regression coefficient for age population was 0.35 (95% CI: 0.33–0.37, p < 0.001). The marginal R2 and conditional R2 were respectively 0.159 and 0.69, suggesting that 69% percent of the variance of the mean age at diagnosis of BC was explained by the variance of continent-based population ageing through their linear relationship. The model was highly exportable as the correlation between predicted from the model and observed mean age at diagnosis of BC in registries not included in the CI5plus database was 0.87. We developed a robust model based on population age and continent to predict mean age at diagnosis of BC in populations. This tool could be used to implement BC screening in countries without prevention program.
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