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利用关联数据的随机生存森林法测量癌症诊断前后个体的疾病负担:SEER-CAHPS疾病负担指数的开发与内部验证

Random survival forests using linked data to measure illness burden among individuals before or after a cancer diagnosis: Development and internal validation of the SEER-CAHPS illness burden index.

作者信息

Lines Lisa M, Cohen Julia, Kirschner Justin, Halpern Michael T, Kent Erin E, Mollica Michelle A, Smith Ashley Wilder

机构信息

Center for Advanced Methods Development, RTI International, Research Triangle Park, NC, United States; Population and Quantitative Health Sciences, University of Massachusetts Medical School, 55 Lake Ave. North, United States.

Center for Advanced Methods Development, RTI International, Research Triangle Park, NC, United States.

出版信息

Int J Med Inform. 2021 Jan;145:104305. doi: 10.1016/j.ijmedinf.2020.104305. Epub 2020 Oct 21.

Abstract

PURPOSE

To develop and internally validate an illness burden index among Medicare beneficiaries before or after a cancer diagnosis.

METHODS

Data source: SEER-CAHPS, linking Surveillance, Epidemiology, and End Results (SEER) cancer registry, Medicare enrollment and claims, and Medicare Consumer Assessment of Healthcare Providers and Systems (Medicare CAHPS) survey data providing self-reported sociodemographic, health, and functional status information. To generate a score for everyone in the dataset, we tabulated 4 groups within each annual subsample (2007-2013): 1) Medicare Advantage (MA) beneficiaries or 2) Medicare fee-for-service (FFS) beneficiaries, surveyed before cancer diagnosis; 3) MA beneficiaries or 4) Medicare FFS beneficiaries surveyed after diagnosis. Random survival forests (RSFs) predicted 12-month all-cause mortality and drew predictor variables (mean per subsample = 44) from 8 domains: sociodemographic, cancer-specific, health status, chronic conditions, healthcare utilization, activity limitations, proxy, and location-based factors. Roughly two-thirds of the sample was held out for algorithm training. Error rates based on the validation ("out-of-bag," OOB) samples reflected the correctly classified percentage. Illness burden scores represented predicted cumulative mortality hazard.

RESULTS

The sample included 116,735 Medicare beneficiaries with cancer, of whom 73 % were surveyed after their cancer diagnosis; overall mean mortality rate in the 12 months after survey response was 6%. SEER-CAHPS Illness Burden Index (SCIBI) scores were positively skewed (median range: 0.29 [MA, pre-diagnosis] to 2.85 [FFS, post-diagnosis]; mean range: 2.08 [MA, pre-diagnosis] to 4.88 [MA, post-diagnosis]). The highest decile of the distribution had a 51 % mortality rate (range: 29-71 %); the bottom decile had a 1% mortality rate (range: 0-2 %). The error rate was 20 % overall (range: 9% [among FFS enrollees surveyed after diagnosis] to 36 % [MA enrollees surveyed before diagnosis]).

CONCLUSIONS

This new morbidity measure for Medicare beneficiaries with cancer may be useful to future SEER-CAHPS users who wish to adjust for comorbidity.

摘要

目的

制定并在内部验证一项针对医疗保险受益人的癌症诊断前后疾病负担指数。

方法

数据来源:SEER-CAHPS,它将监测、流行病学和最终结果(SEER)癌症登记、医疗保险参保和理赔数据,以及医疗保险医疗服务提供者和系统消费者评估(医疗保险CAHPS)调查数据相链接,后者提供自我报告的社会人口统计学、健康和功能状态信息。为数据集中的每个人生成一个分数,我们在每个年度子样本(2007 - 2013年)内将人群分为4组:1)医疗保险优势(MA)受益人或2)医疗保险按服务收费(FFS)受益人,在癌症诊断前接受调查;3)MA受益人或4)医疗保险FFS受益人,在诊断后接受调查。随机生存森林(RSF)预测12个月全因死亡率,并从8个领域提取预测变量(每个子样本平均 = 44个):社会人口统计学、癌症特异性、健康状况、慢性病、医疗保健利用、活动受限、代理人以及基于地点的因素。大约三分之二的样本用于算法训练。基于验证(“袋外”,OOB)样本的错误率反映正确分类的百分比。疾病负担分数代表预测的累积死亡风险。

结果

样本包括116,735名患有癌症的医疗保险受益人,其中73%在癌症诊断后接受了调查;调查回复后12个月内的总体平均死亡率为6%。SEER-CAHPS疾病负担指数(SCIBI)分数呈正偏态(中位数范围:0.29 [MA,诊断前]至2.85 [FFS,诊断后];平均范围:2.

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