Deriving stage at diagnosis from multiple population-based sources: colorectal and lung cancer in England
Deriving stage at diagnosis from multiple population-based sources: colorectal and lung cancer in England
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DOI：
10.1038/bjc.2016.177
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发表时间：
2016-07-26
影响因子：
8.8
通讯作者：
Rachet, B.
中科院分区：
文献类型：
--
作者：
Benitez-Majano, S.;Fowler, H.;Rachet, B.
背景：诊断时的分期是癌症生存率的一个强有力的预测因素。阶段分布和阶段特异性管理的差异有助于解释癌症结局的地理差异。因此，阶段信息对于改善癌症控制政策至关重要。尽管最近取得了进展，但阶段信息往往不完整。很少报告数据收集方法和阶段类别的定义。这些不一致性可能导致为单个肿瘤分配冲突的阶段，并混淆对国际比较和特定阶段癌症结局的时间趋势的解释。我们提出了一种算法，使用多个常规的，基于人口的数据源，以获得最完整和可靠的阶段informationspossibly.Methods：我们的分层方法得出一个单一的阶段类别，每个肿瘤优先考虑的信息被认为是最好的质量从多个数据集和肿瘤阶段的各个组成部分。它结合了国际癌症控制联盟TNM恶性肿瘤分类的规则。该算法说明了在英国的结肠直肠癌和肺癌。我们将癌症特异性临床审计数据（从临床多学科团队收集）与国家癌症登记数据联系起来。我们优先考虑临床稽查中的阶段变量，并在需要时添加登记研究中的信息。我们比较了阶段分布和阶段特定的净生存使用两套定义的总结阶段与对比水平的假设处理缺失的个人TNM组件。这项工作扩展了以前的算法，我们开发的国际比较阶段特异性survival.Results：在2008年和2012年之间，163 915原发性结直肠癌病例和168 158原发性肺癌病例被诊断为成人在英格兰。使用最严格的概括分期定义（所有单个TNM组分的有效信息），结直肠癌分期完整性为56.6%（从2008年的33.8%到2012年的85.2%）。肺癌分期完整性为76.6%（从2008年的57.3%到2012年的91.4%）。阶段分布不同的战略，以定义总结阶段。特定阶段的生存率与已发表的reports.Conclusions一致：我们提供了一个强大的策略来协调阶段的推导，可以适用于不同国家的其他癌症和数据来源。优先考虑高质量信息、报告个体TNM变量来源以及报告处理缺失数据的假设的一般方法适用于使用分期的任何基于人群的癌症研究。此外，我们的研究强调，需要进一步提高阶段类别的定义和报告的透明度，承认使用现成的阶段变量的局限性和潜在的差异。
Background: Stage at diagnosis is a strong predictor of cancer survival. Differences in stage distributions and stage-specific management help explain geographic differences in cancer outcomes. Stage information is thus essential to improve policies for cancer control. Despite recent progress, stage information is often incomplete. Data collection methods and definition of stage categories are rarely reported. These inconsistencies may result in assigning conflicting stage for single tumours and confound the interpretation of international comparisons and temporal trends of stage-specific cancer outcomes. We propose an algorithm that uses multiple routine, population-based data sources to obtain the most complete and reliable stage information possible.Methods: Our hierarchical approach derives a single stage category per tumour prioritising information deemed of best quality from multiple data sets and various individual components of tumour stage. It incorporates rules from the Union for International Cancer Control TNM classification of malignant tumours. The algorithm is illustrated for colorectal and lung cancer in England. We linked the cancer-specific Clinical Audit data (collected from clinical multi-disciplinary teams) to national cancer registry data. We prioritise stage variables from the Clinical Audit and added information from the registry when needed. We compared stage distribution and stage-specific net survival using two sets of definitions of summary stage with contrasting levels of assumptions for dealing with missing individual TNM components. This exercise extends a previous algorithm we developed for international comparisons of stage-specific survival.Results: Between 2008 and 2012, 163 915 primary colorectal cancer cases and 168 158 primary lung cancer cases were diagnosed in adults in England. Using the most restrictive definition of summary stage (valid information on all individual TNM components), colorectal cancer stage completeness was 56.6% (from 33.8% in 2008 to 85.2% in 2012). Lung cancer stage completeness was 76.6% (from 57.3% in 2008 to 91.4% in 2012). Stage distribution differed between strategies to define summary stage. Stage-specific survival was consistent with published reports.Conclusions: We offer a robust strategy to harmonise the derivation of stage that can be adapted for other cancers and data sources in different countries. The general approach of prioritising good-quality information, reporting sources of individual TNM variables, and reporting of assumptions for dealing with missing data is applicable to any population-based cancer research using stage. Moreover, our research highlights the need for further transparency in the way stage categories are defined and reported, acknowledging the limitations, and potential discrepancies of using readily available stage variables.