Research on Biomedical Engineering
Research on Biomedical Engineering
Original article

The use of intervention analysis of the mortality rates from breast cancer in assessing the Brazilian screening programme

Alfonso Rosales-López, Letícia Martins Raposo, Flavio Fonseca Nobre, Rosimary Terezinha de Almeida.

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Introduction: There is a need to develop methods to evaluate public health interventions. Therefore, this work proposed an intervention analysis on time series of breast cancer mortality rates to assess the effects of an action of the Brazilian Screening Programme. Methods: The analysed series was the monthly female breast cancer mortality rates from January 1996 to March 2016. The intervention was the establishment of the National Information System on Breast Cancer in June 2009. The Box-Tiao approach was used to build a Global Intervention Model (GIM) composed of a component that fits the series without the intervention, and a component that fits the effect with the intervention. The intervention’s response time was estimated and used to define the length of the residual series to assess the predictive accuracy of the GIM, which was compared to a one-step-ahead forecasting approach. Results: The pre-intervention period was fitted to a SARIMA (0,1,2) (1,1,1)12 model and the intervention’s effect to an ARIMA (1,1,0) model. The intervention led to an increase in the mortality rates, and its response time was 24 months. The forecast error (MAPE) for the GIM was 3.14%, and for the one-step-ahead forecast it was 2.15%. Conclusion: This work goes one step further in relation to the studies carried out to evaluate the Breast Cancer Screening Programme in Brazil, considering that it was possible to quantify the effects and the response time of the intervention, demonstrating the potential of the proposed method to be used to evaluate health interventions.


Interrupted time series analysis, National health programs, Mass screening, Breast neoplasms, Mortality rate.


Azevedo e Silva G, Bustamante-Teixeira MT, Aquino EM, Tomazelli JG, Santos-Silva I. Access to early breast cancer diagnosis in the Brazilian Unified National Health System: an analysis of data from the Health Information System. Cad Saude Publica. 2014; 30(7):1537-50. PMid:25166949.

Barreto AS, Mendes MF, Thuler LS. Evaluation of a strategy adopted to expand adherence to breast cancer screening in Brazilian Northeast. Rev Bras Ginecol Obstet. 2012; 34(2):86-91. PMid:22437768.

Box G, Jenkins G, Reinsel G, Ljung G. Time series analysis: forecasting and control. 5th ed. New Jersey, USA: John Wiley and Sons Inc.; 2015.

Box GE, Tiao G. Intervention analysis with applications to economic and environmental problems. J Am Stat Assoc. 1975; 70(349):70-9.

Brasil. Ministério da Saúde. Secretaria de Atenção à Saúde. Instituto Nacional de Câncer. Coordenação de Prevenção e Vigilância. Controle do Câncer de Mama: documento de consenso. 1st ed. Rio de Janeiro: INCA; 2004.

Brasil. Ministério da Saúde. Secretaria de Atenção à Saúde. Portaria SAS/MS Nº 779 de 31 de dezembro de 2008. Define o Sistema de Informação do Controle do Câncer de Mama (SISMAMA). Diário Oficial da República Federativa do Brasil, Brasília, jan. 2008. [cited 2016 November 8]. Available from:

Brasil. Ministério Saúde. Departamento de Informática do SUS. Sistema de Informação sobre Mortalidade. Informações de Saúde (TABNET). Estatísticas Vitais [internet]. Brasília: DATASUS; 2015. [cited 2016 April 13]. Available from:

Cochrane AL. Archie Cochrane in his own words: Selections arranged from his 1972 introduction to “effectiveness and efficiency: Random reflections on the health services.”. Control Clin Trials. 1989; 10(4):428-33. PMid:2691208.

Draborg E, Gyrd-Hansen D, Bo Poulsen P, Horder M. International comparison of the definition and the practical application of health technology assessment. Int J Technol Assess Health Care. 2005; 21(1):89-95. PMid:15736519.

Felix JD, Castro DS, Amorim MH, Zandonade E. Breast cancer mortality trends among women in the state of Espirito Santo Between 1980 and 2007. Rev Bras Cancerol. 2011; 57:159-66.

Foundation R. The R Project for Statistical Computing. 2015. [cited 2016 April 12]. Available from:

Freitas-Junior R, Gonzaga CM, Freitas NM, Martins E, Dardes RC. Disparities in female breast cancer mortality rates in Brazil between 1980 and 2009. Clinics. 2012; 67(7):731-7. PMid:22892915.

Girianelli VR, Gamarra CJ, Azevedo e Silva G. Disparities in cervical and breast cancer mortality in Brazil. Rev Saude Publica. 2014; 48(3):459-67. PMid:25119941.

Hofvind S, Wang H, Thoresen S. Do the results of the process indicators in the Norwegian Breast Cancer Screening Program predict future mortality reduction from breast cancer? Acta Oncol. 2004; 43(5):467-73. PMid:15360051.

Instituto Brasileiro de Geografia e Estatística. [internet]. Brasília: IBGE; 2015. [cited 2016 April 12]. Available from:

Lavis JN, Wilson MG, Grimshaw JM, Haynes RB, Ouimet M, Raina P, Gruen RL, Graham ID. Supporting the use of health technology assessments in policy making about health systems. Int J Technol Assess Health Care. 2010; 26(4):405-14. PMid:20923592.

Malmgren JA, Parikh J, Atwood MK, Kaplan HG. Impact of mammography detection on the course of breast cancer in women aged 40–49 years. Radiology. 2012; 262(3):797-806. PMid:22357883.

Mathes T, Antoine S-L, Prengel P, Bühn S, Polus S, Pieper D. Health technology assessment of public health interventions: A synthesis of methodological guidance. Int J Technol Assess Health Care. 2017; 33(2):135-46. PMid:28434414.

Paci E, Warwick J, Falini P, Duffy SW. Overdiagnosis in screening: is the increase in breast cancer incidence rates a cause for concern? J Med Screen. 2004; 11(1):23-7. PMid:15006110.

Petticrew M, Chalabi Z, Jones DR. To RCT or not to RCT: deciding when ‘more evidence is needed’ for public health policy and practice. J Epidemiol Community Health. 2012; 66(5):391-6. PMid:21652521.

Ramsay CR, Matowe L, Grilli R, Grimshaw JM, Thomas RE. Interrupted time series designs in health technology assessment: Lessons from two systematic reviews of behavior change strategies. Int J Technol Assess Health Care. 2003; 19(4):613-23. PMid:15095767.

Renck DV, Barros F, Domingues MR, Gonzalez MC, Sclowitz ML, Caputo EL, Gomes LM. Equity in access to breast cancer screening in a mobile mammography program in southern Rio Grande do Sul State, Brazil. Cad Saude Publica. 2014; 30(1):88-96. PMid:24627016.

Rezende MCR, Koch HA, Figueiredo JA, Thuler LCS. Factors leading to delay in obtaining definitive diagnosis of suspicious lesions for breast cancer in a dedicated health unit in Rio de Janeiro. Rev Bras Ginecol Obstet. 2009; 31(2):75-81. PMid:19407912.

Ribeiro RA, Caleffi M, Polanczyk CA. Cost-effectiveness of an organized breast cancer screening program in Southern Brazil. Cad Saude Publica. 2013; 29(1 Suppl):S131-45. PMid:25402242.

Silva RCF, Hortale VA. Breast cancer screening in Brazil: Who, how and why? Rev Bras Cancerol. 2012; 58:67-71.

World Health Organization. World Health Organ Programme Proj Cancer. Breast cancer: Prevention and control [internet]. Geneva: WHO; 2012. [cited 2016 April 12]. Available from:

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