BackgroundBreast cancer is the most common cancer in women worldwide, with a great diversity in outcomes among individual patients. The ability to accurately predict a breast cancer outcome is important to patients, physicians, researchers, and policy makers. Many models have been developed and tested in different settings. We systematically reviewed the prognostic models developed and/or validated for patients with breast cancer.MethodsWe conducted a systematic search in four electronic databases and some oncology websites, and a manual search in the bibliographies of the included studies. We identified original studies that were published prior to 1st January 2017, and presented the development and/or validation of models based mainly on clinico-pathological factors to predict mortality and/or recurrence in female breast cancer patients.ResultsFrom the 96 articles selected from 4095 citations found, we identified 58 models, which predicted mortality (n = 28), recurrence (n = 23), or both (n = 7). The most frequently used predictors were nodal status (n = 49), tumour size (n = 42), tumour grade (n = 29), age at diagnosis (n = 24), and oestrogen receptor status (n = 21). Models were developed in Europe (n = 25), Asia (n = 13), North America (n = 12), and Australia (n = 1) between 1982 and 2016. Models were validated in the development cohorts (n = 43) and/or independent populations (n = 17), by comparing the predicted outcomes with the observed outcomes (n = 55) and/or with the outcomes estimated by other models (n = 32), or the outcomes estimated by individual prognostic factors (n = 8). The most commonly used methods were: Cox proportional hazards regression for model development (n = 32); the absolute differences between the predicted and observed outcomes (n = 30) for calibration; and C-index/AUC (n = 44) for discrimination.Overall, the models performed well in the development cohorts but less accurately in some independent populations, particularly in patients with high risk and young and elderly patients. An exception is the Nottingham Prognostic Index, which retains its predicting ability in most independent populations.ConclusionsMany prognostic models have been developed for breast cancer, but only a few have been validated widely in different settings. Importantly, their performance was suboptimal in independent populations, particularly in patients with high risk and in young and elderly patients.Electronic supplementary materialThe online version of this article (10.1186/s12885-019-5442-6) contains supplementary material, which is available to authorized users.

No abstract

BackgroundNew Zealand has major ethnic disparities in breast cancer survival with Māori (indigenous people) and Pacific women (immigrants or descended from immigrants from Pacific Islands) faring much worse than other ethnic groups. This paper identified underlying factors and assessed their relative contribution to this risk differential.MethodsThis study involved all women who were diagnosed with primary invasive breast cancer in two health regions, covering about 40% of the national population, between January 2000 and June 2014. Māori and Pacific patients were compared with other ethnic groups in terms of demographics, mode of diagnosis, disease factors and treatment factors. Cox regression modelling was performed with stepwise adjustments, and hazards of excess mortality from breast cancer for Māori and Pacific patients were assessed.ResultsOf the 13,657 patients who were included in this analysis, 1281 (9.4%) were Māori, and 897 (6.6%) were Pacific women. Compared to other ethnic groups, they were younger, more likely to reside in deprived neighbourhoods and to have co-morbidities, and less likely to be diagnosed through screening and with early stage cancer, to be treated in a private care facility, to receive timely cancer treatment, and to receive breast conserving surgery. They had a higher risk of excess mortality from breast cancer (age and year of diagnosis adjusted hazard ratio: 1.76; 95% CI: 1.51–2.04 for Māori and 1.97; 95% CI: 1.67–2.32 for Pacific women), of which 75% and 99% respectively were explained by baseline differences. The most important contributor was late stage at diagnosis. Other contributors included neighbourhood deprivation, mode of diagnosis, type of health care facility where primary cancer treatment was undertaken and type of loco-regional therapy.ConclusionsLate diagnosis, deprivation and differential access to and quality of cancer care services were the key contributors to ethnic disparities in breast cancer survival in New Zealand. Our findings underscore the need for a greater equity focus along the breast cancer care pathway, with an emphasis on improving access to early diagnosis for Māori and Pacific women.Electronic supplementary materialThe online version of this article (10.1186/s12885-017-3797-0) contains supplementary material, which is available to authorized users.

No abstract

Population food and nutrition monitoring plays a critical role in understanding suboptimal nutrition at the population level, yet current monitoring methods such as national surveys are not practical to undertake on a continuous basis. Supermarket sales data potentially address this gap by providing detailed, timely, and inexpensive monitoring data for informing policies and anticipating trends. This paper reviews 22 studies that used supermarket sales data to examine food purchasing patterns. Despite some methodological limitations, feasibility studies showed promising results. The potential and limitations of using supermarket sales data to supplement food and nutrition monitoring methods are discussed.