All simulation models of breast cancer are wrong but some are useful
Authors: Stefan Michiels, Suzette Delaloge
Journal: The Lancet Global Health (2018)

The worldwide incidence of breast cancer was estimated to be about 1·7 million cases, with a mortality of slightly above half a million deaths, in 2012.1Torre LA Bray F Siegel RL Ferlay J Lortet-Tieulent J Jemal A Global cancer statistics, 2012.CA Cancer J Clin. 2015; 65: 87-108Crossref PubMed Scopus (21383) Google Scholar Its incidence is increasing worldwide in all types of countries. In low-income countries, it is the second most common cause of cancer-related death among women (after cervix uteri) and, in middle-income countries, is the most common. Although breast cancer-related mortality has decreased in many high-income countries over the past 20 years in response to screening policies and therapies, public health systems in low-income and middle-income countries (LMICs) cannot meet all the conditions for the implementation of effective screening and high-quality treatment.2Denny L de Sanjose S Mutebi M et al.Interventions to close the divide for women with breast and cervical cancer between low-income and middle-income countries and high-income countries.Lancet. 2017; 389: 861-870Summary Full Text Full Text PDF PubMed Scopus (99) Google Scholar In their Article3Birnbaum J Duggan C Anderson BO Etzioni R Early detection and treatment strategies for breast cancer in low-income and upper middle-income countries: a modelling study.Lancet Glob Health. 2018; 6: e885-e893Summary Full Text Full Text PDF PubMed Scopus (24) Google Scholar published in The Lancet Global Health, Jeanette Birnbaum and colleagues present an interesting simulation model of breast cancer with the aim of predicting the effect of early detection modalities and systemic adjuvant treatments—chemotherapy and endocrine therapy—on breast cancer-specific mortality. The ultimate goal is to inform policies for the control of breast cancer in LMICs. They use a simplified microsimulation model with two cancer states (early and advanced stage breast cancer) to avoid more complex natural history modelling, in the hope that this model would be easier to apply to countries with incomplete health data. Three different early detection programmes are evaluated in the East African region (low-income) and Colombia (upper middle-income): poor awareness of breast cancer, clinical screening, and mammography screening. Three treatment strategies are proposed based on oestrogen receptor status and bimodal cancer stage. The authors report that, in Uganda, a relative reduction in breast cancer-related mortality of 41% (95% uncertainty interval 32–49%) could be obtained by using clinical breast screening plus chemotherapy or endocrine therapy as compared with current standard of care, and that, in Colombia, a relative reduction in breast cancer-related mortality of 25% (15–32%) through mammography screening plus chemotherapy or endocrine therapy. An important feature of this work is that readers can upload incidence and survival data on the accompanying website to apply the model to a specific context. The model projected breast cancer-related mortality reductions in the absence of more fine-tuned benefit and risk data from randomised clinical trials or observational studies in LMICs. It does not include quality-of-life, cost-effectiveness, and other impact data. Notably, overdiagnosis, overtreatment, false positive findings, and survivorship care are major challenges faced by western countries regarding their breast cancer health policy, which might not directly apply to LMICs. The relative contributions of screening and treatment on the observed decrease in mortality in high-income countries are often debated. A landmark analysis4Plevritis SK Munoz D Kurian AW et al.Association of screening and treatment with breast cancer mortality by molecular subtype in US women, 2000–2012.JAMA. 2018; 319: 154-164Crossref PubMed Scopus (103) Google Scholar of six Cancer Intervention and Surveillance Network simulation models estimated a relative reduction in breast cancer-related mortality of 37% (model range 27–42%) in the USA in 2000, of which 44% (35–60%) was associated with mammography screening and 56% (40–65%) with first-generation chemotherapy or endocrine therapy. The relative weight of screening tended to decrease in 2012 versus treatment.4Plevritis SK Munoz D Kurian AW et al.Association of screening and treatment with breast cancer mortality by molecular subtype in US women, 2000–2012.JAMA. 2018; 319: 154-164Crossref PubMed Scopus (103) Google Scholar Indeed, in high-income countries, mammography screening is used as standard but comes with previously cited harms (such as overdiagnosis, overtreatment, and false positive biopsy results). This situation caused the focus of research to shift towards the introduction of individual risk-based mammography screening, such as in our European MyPeBS randomised trial that should start in late 2018. In LMICs, however, alternative screening strategies based on clinical breast examination and self-examination are thought to be more cost-effective in terms of human and financial resources.2Denny L de Sanjose S Mutebi M et al.Interventions to close the divide for women with breast and cervical cancer between low-income and middle-income countries and high-income countries.Lancet. 2017; 389: 861-870Summary Full Text Full Text PDF PubMed Scopus (99) Google Scholar, 5Corbex M Burton R Sancho-Garnier H Breast cancer early detection methods for low and middle income countries, a review of the evidence.Breast. 2012; 21: 428-434Summary Full Text Full Text PDF PubMed Scopus (71) Google Scholar Chemotherapy and endocrine therapy have been so carefully studied in large meta-analyses by the Early Breast Cancer Trialists' Collaborative Group that there is no doubt about the magnitude of their effect on breast cancer-related mortality. In the simulation by Birnbaum and colleagues, the control population in Colombia is based on estimates of considerable chemotherapy and endocrine therapy administration in the US population in 2000, so it is not surprising that the proposed treatment strategies with chemotherapy only resulted in a small point estimate of relative benefit. The simulation model has made some strong assumptions that need careful verification. The use of the historical tumour stage distribution of Connecticut in the 1930s is debatable. It would have been useful to introduce some uncertainty in the parameters extracted from literature and other data sources. The barriers to effective health infrastructures in LMICs might also be too heterogeneous for a one-size-fits-all simulation model. For example, the model assumes that surgery is a standard of care (programme A); however, the investments needed for training and quality assurance of surgery—the cornerstone of treatment for breast cancer—are already extremely high for low-income countries. The statistician George Box famously said “all models are wrong, but some are useful”;7 as such, this model is a first step in the right direction to make more informed decisions about policies for breast cancer control in LMICs. The model will need to be directly compared against other models proposed in the scientific literature and will ideally include some benchmark data from LMICs before it can be used in real-life settings. We declare no competing interests. Early detection and treatment strategies for breast cancer in low-income and upper middle-income countries: a modelling studyThe best projected outcomes were in settings where access to both early detection and adjuvant therapy is improved. Even in the absence of mammographic screening, improvements in detection can provide substantial benefit in settings where advanced-stage presentation is common. Full-Text PDF Open Access