Evolución de la mortalidad por cáncer de mama y diseminación de la mamografía de cribado en Cataluña: un análisis por regiones sanitarias

Lacasta, Maria José Pérez; Gomis, Aleix Gregori; Lavila, Misericordia Carles; Magarolas, Rosa Gispert; Alonso, Montserrat Martínez; Terré, Ester Vilaprinyo; Farnós, Roger Pla; Monné, Montserrat Rué

doi:10.1590/s1135-57272010000600002

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Although there were no differences observed in incidence rates between Girona and Tarragona, two areas of Catalonia that are geographically separated, it may be that the study area had a lower incidence of BC. Nevertheless, in a previous study, no differences were observed in BC mortality between a geographical region that included the study population, and the provinces of Girona and Tarragona [39]. …”

Section: Discussionmentioning

confidence: 93%

An assessment of existing models for individualized breast cancer risk estimation in a screening program in Spain

et al. 2013

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BackgroundThe aim of this study was to evaluate the calibration and discriminatory power of three predictive models of breast cancer risk.MethodsWe included 13,760 women who were first-time participants in the Sabadell-Cerdanyola Breast Cancer Screening Program, in Catalonia, Spain. Projections of risk were obtained at three and five years for invasive cancer using the Gail, Chen and Barlow models. Incidence and mortality data were obtained from the Catalan registries. The calibration and discrimination of the models were assessed using the Hosmer-Lemeshow C statistic, the area under the receiver operating characteristic curve (AUC) and the Harrell’s C statistic.ResultsThe Gail and Chen models showed good calibration while the Barlow model overestimated the number of cases: the ratio between estimated and observed values at 5 years ranged from 0.86 to 1.55 for the first two models and from 1.82 to 3.44 for the Barlow model. The 5-year projection for the Chen and Barlow models had the highest discrimination, with an AUC around 0.58. The Harrell’s C statistic showed very similar values in the 5-year projection for each of the models. Although they passed the calibration test, the Gail and Chen models overestimated the number of cases in some breast density categories.ConclusionsThese models cannot be used as a measure of individual risk in early detection programs to customize screening strategies. The inclusion of longitudinal measures of breast density or other risk factors in joint models of survival and longitudinal data may be a step towards personalized early detection of BC.

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Section: Discussionmentioning

confidence: 93%

An assessment of existing models for individualized breast cancer risk estimation in a screening program in Spain

et al. 2013

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“…In any case, we would expect to find at least one joinpoint on the breast cancer mortality trend studied. Moreover, changes in breast cancer mortality trends have already been described for the first half of the 1990s for other Spanish regions[Ascunce et al (2007);Salmerón et al (2009);Pérez Lacasta et al (2010)]. Regarding Bayesian criteria, if we focus on the mode of the posterior distribution of the number of joinpoints, in Figure2it can be seen that both methods point to the existence of exactly one joinpoint, indeed, Bayes1 yields a 51.0% probability of one joinpoint, while Bayes2 estimates that probability at 73.6%.…”

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confidence: 81%

A Bayesian Joinpoint regression model with an unknown number of break-points

Martínez-Beneito¹,

García-Donato²,

Salmerón³

2011

Ann. Appl. Stat.

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Joinpoint regression is used to determine the number of segments needed to adequately explain the relationship between two variables. This methodology can be widely applied to real problems, but we focus on epidemiological data, the main goal being to uncover changes in the mortality time trend of a specific disease under study. Traditionally, Joinpoint regression problems have paid little or no attention to the quantification of uncertainty in the estimation of the number of change-points. In this context, we found a satisfactory way to handle the problem in the Bayesian methodology. Nevertheless, this novel approach involves significant difficulties (both theoretical and practical) since it implicitly entails a model selection (or testing) problem. In this study we face these challenges through (i) a novel reparameterization of the model, (ii) a conscientious definition of the prior distributions used and (iii) an encompassing approach which allows the use of MCMC simulation-based techniques to derive the results. The resulting methodology is flexible enough to make it possible to consider mortality counts (for epidemiological applications) as Poisson variables. The methodology is applied to the study of annual breast cancer mortality during the period 1980-2007 in Castellón, a province in Spain.

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“…Data on mammography use was obtained, for the Girona and Tarragona provinces, from three health surveys performed in the years 1994, 2002 and 2006 [29], [30]. Section F in Appendix S1 explains in detail the analysis conducted to estimate the excess of DCIS attributable to sceening.…”

Section: Methodsmentioning

confidence: 99%

Cost-Effectiveness and Harm-Benefit Analyses of Risk-Based Screening Strategies for Breast Cancer

et al. 2014

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The one-size-fits-all paradigm in organized screening of breast cancer is shifting towards a personalized approach. The present study has two objectives: 1) To perform an economic evaluation and to assess the harm-benefit ratios of screening strategies that vary in their intensity and interval ages based on breast cancer risk; and 2) To estimate the gain in terms of cost and harm reductions using risk-based screening with respect to the usual practice. We used a probabilistic model and input data from Spanish population registries and screening programs, as well as from clinical studies, to estimate the benefit, harm, and costs over time of 2,624 screening strategies, uniform or risk-based. We defined four risk groups, low, moderate-low, moderate-high and high, based on breast density, family history of breast cancer and personal history of breast biopsy. The risk-based strategies were obtained combining the exam periodicity (annual, biennial, triennial and quinquennial), the starting ages (40, 45 and 50 years) and the ending ages (69 and 74 years) in the four risk groups. Incremental cost-effectiveness and harm-benefit ratios were used to select the optimal strategies. Compared to risk-based strategies, the uniform ones result in a much lower benefit for a specific cost. Reductions close to 10% in costs and higher than 20% in false-positive results and overdiagnosed cases were obtained for risk-based strategies. Optimal screening is characterized by quinquennial or triennial periodicities for the low or moderate risk-groups and annual periodicity for the high-risk group. Risk-based strategies can reduce harm and costs. It is necessary to develop accurate measures of individual risk and to work on how to implement risk-based screening strategies.

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Evolución de la mortalidad por cáncer de mama y diseminación de la mamografía de cribado en Cataluña: un análisis por regiones sanitarias

Cited by 4 publications

References 17 publications

An assessment of existing models for individualized breast cancer risk estimation in a screening program in Spain

An assessment of existing models for individualized breast cancer risk estimation in a screening program in Spain

A Bayesian Joinpoint regression model with an unknown number of break-points

Cost-Effectiveness and Harm-Benefit Analyses of Risk-Based Screening Strategies for Breast Cancer

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