Département des maladies chroniques et des traumatismes, Institut de veille sanitaire, St-Maurice, France.
Cancer Epidemiol. 2011 Jun;35(3):235-42. doi: 10.1016/j.canep.2010.10.009. Epub 2010 Dec 14.
This study aimed at modelling the effect of organized breast cancer screening on mortality in France. It combined results from a Markov model for breast cancer progression, to predict number of cases by node status, and from relative survival analyses, to predict deaths. The method estimated the relative risk of mortality at 8 years, in women aged 50-69, between a population screened every two years and a reference population.
Analyses concerned cases diagnosed between 1990 and 1996, with a follow-up up to 2004 for the vital status. Markov models analysed data from 3 screening programs (300,000 mammographies) and took into account opportunistic screening among participants to avoid bias in parameter's estimates. We used survival data from cancers in the general population (n=918, 7 cancer registries) and from screened cancers (n=565, 3 cancer registries), after excluding a subgroup of screened cases with a particularly high survival. Sensitivity analyses were performed.
Markov model main analysis lacked of fit in two out of three districts. Fit was improved in stratified analyses by age or district, though some lack of fit persisted in two districts. Assuming 10% or 20% overdiagnosed screened cancers, mortality reduction was estimated as 23% (95% CI: 4, 38%) and 19% (CI: -3, 35%) respectively. Results were highly sensitive to the exclusion in the screened cancers survival analysis. Conversely, RR estimates varied moderately according to the Markov model parameters used (stratified by age or district).
The study aimed at estimating the effect of screening in a screened population compared to an unscreened control group. Such a control group does not exist in France, and we used a general population contaminated by opportunistic screening to provide a conservative estimate. Conservative choices were systematically adopted to avoid favourable estimates. A selection bias might however affect the estimates, though it should be moderate because extreme social classes are under-represented among participants. This modelling provided broad estimates for the effect of organized biennial screening in France in the early nineteen-nineties. Results will be strengthened with longer follow-up.
本研究旨在构建模型，以评估法国组织性乳腺癌筛查对死亡率的影响。该研究将乳腺癌进展的马尔可夫模型的结果（用于预测基于淋巴结状态的病例数量）与相对生存分析相结合，以预测死亡人数。该方法估计了在 8 年内，年龄在 50-69 岁之间的人群中，每两年筛查一次的人群与参考人群之间的死亡率相对风险。
分析对象为 1990 年至 1996 年间诊断出的病例，并对其生存状态进行了截至 2004 年的随访。马尔可夫模型分析了来自 3 个筛查项目（30 万次乳房 X 光检查）的数据，并考虑了参与者中的机会性筛查，以避免参数估计的偏差。我们使用了一般人群（n=918，来自 7 个癌症登记处）和筛查人群（n=565，来自 3 个癌症登记处）的癌症生存数据，同时排除了一组生存特别高的筛查病例。还进行了敏感性分析。
马尔可夫模型的主要分析在三个地区中有两个地区不拟合。通过按年龄或地区进行分层分析，可以改善拟合情况，尽管在两个地区仍然存在一些拟合不足。假设 10%或 20%的筛查癌症过度诊断，死亡率降低的估计值分别为 23%（95%CI：4，38%）和 19%（CI：-3，35%）。这些结果对筛查人群的生存分析中排除的病例非常敏感。相反，RR 估计值根据使用的马尔可夫模型参数（按年龄或地区分层）而有所不同。
本研究旨在评估在筛查人群中与未筛查对照组相比，筛查的效果。法国不存在这样的对照组，我们使用了一个被机会性筛查污染的一般人群来提供一个保守的估计值。为了避免有利的估计值，我们系统地采用了保守的选择。然而，选择偏倚可能会影响估计值，尽管由于极端社会阶层在参与者中代表性不足，这种偏倚应该是适度的。这种建模为 20 世纪 90 年代初法国组织性两年一次筛查的效果提供了广泛的估计值。随着随访时间的延长，结果将得到加强。
