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癌症诊断时分期的公共报告中的数据缺失与随机变异:基于英格兰人群数据的横断面分析

Missing data and chance variation in public reporting of cancer stage at diagnosis: Cross-sectional analysis of population-based data in England.

作者信息

Barclay Matthew E, Lyratzopoulos Georgios, Greenberg David C, Abel Gary A

机构信息

Cambridge Centre for Health Services Research, Department of Public Health and Primary Care, Forvie Site, Robinson Way, Cambridge, CB2 0SR, United Kingdom; National Cancer Registration and Analysis Service, Public Health England, Victoria House, Capital Park, Fulbourn, Cambridge, CB21 5XA, United Kingdom.

Cambridge Centre for Health Services Research, Department of Public Health and Primary Care, Forvie Site, Robinson Way, Cambridge, CB2 0SR, United Kingdom; National Cancer Registration and Analysis Service, Public Health England, Victoria House, Capital Park, Fulbourn, Cambridge, CB21 5XA, United Kingdom; Epidemiology of Cancer Healthcare and Outcomes (ECHO) Research Group, Department of Behavioural Science and Health, University College London, WC1E 7HB, United Kingdom.

出版信息

Cancer Epidemiol. 2018 Feb;52:28-42. doi: 10.1016/j.canep.2017.11.005. Epub 2017 Nov 23.

DOI:10.1016/j.canep.2017.11.005
PMID:29175263
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5786666/
Abstract

BACKGROUND

The percentage of cancer patients diagnosed at an early stage is reported publicly for geographically-defined populations corresponding to healthcare commissioning organisations in England, and linked to pay-for-performance targets. Given that stage is incompletely recorded, we investigated the extent to which this indicator reflects underlying organisational differences rather than differences in stage completeness and chance variation.

METHODS

We used population-based data on patients diagnosed with one of ten cancer sites in 2013 (bladder, breast, colorectal, endometrial, lung, ovarian, prostate, renal, NHL, and melanoma). We assessed the degree of bias in CCG (Clinical Commissioning Group) indicators introduced by missing-is-late and complete-case specifications compared with an imputed 'gold standard'. We estimated the Spearman-Brown (organisation-level) reliability of the complete-case specification. We assessed probable misclassification rates against current pay-for-performance targets.

RESULTS

Under the missing-is-late approach, bias in estimated CCG percentage of tumours diagnosed at an early stage ranged from -2 to -30 percentage points, while bias under the complete-case approach ranged from -2 to +7 percentage points. Using an annual reporting period, indicators based on the least biased complete-case approach would have poor reliability, misclassifying 27/209 (13%) CCGs against a pay-for-performance target in current use; only half (53%) of CCGs apparently exceeding the target would be correctly classified in terms of their underlying performance.

CONCLUSIONS

Current public reporting schemes for cancer stage at diagnosis in England should use a complete-case specification (i.e. the number of staged cases forming the denominator) and be based on three-year reporting periods. Early stage indicators for the studied geographies should not be used in pay-for-performance schemes.

摘要

背景

对于英格兰医疗保健委托组织所对应的地理区域人群,早期诊断出的癌症患者百分比会公开报告,并与绩效薪酬目标挂钩。鉴于分期记录不完整,我们调查了该指标在多大程度上反映了潜在的组织差异,而非分期完整性和随机变异方面的差异。

方法

我们使用了基于人群的数据,这些数据来自于2013年被诊断患有十种癌症之一(膀胱癌、乳腺癌、结直肠癌、子宫内膜癌、肺癌、卵巢癌、前列腺癌、肾癌、非霍奇金淋巴瘤和黑色素瘤)的患者。我们评估了与估算的“金标准”相比,缺失即晚期和完整病例规范引入的临床委托组(CCG)指标中的偏差程度。我们估计了完整病例规范的斯皮尔曼 - 布朗(组织层面)可靠性。我们根据当前的绩效薪酬目标评估了可能的错误分类率。

结果

在缺失即晚期方法下,早期诊断出的肿瘤的估计CCG百分比偏差范围为 -2至 -30个百分点,而在完整病例方法下偏差范围为 -2至 +7个百分点。使用年度报告期,基于偏差最小的完整病例方法的指标可靠性较差,与当前使用的绩效薪酬目标相比,会将27/209(13%)的CCG错误分类;就其潜在表现而言,明显超过目标的CCG中只有一半(53%)会被正确分类。

结论

英格兰目前关于癌症诊断分期的公共报告计划应采用完整病例规范(即构成分母的分期病例数),并基于三年报告期。所研究地区的早期阶段指标不应用于绩效薪酬计划。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5748/5786666/ad2833a7555d/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5748/5786666/69da95e8fac7/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5748/5786666/ad2833a7555d/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5748/5786666/69da95e8fac7/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5748/5786666/ad2833a7555d/gr5.jpg

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