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.

中文翻译：

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使用关联数据测量癌症诊断前后个体疾病负担的随机生存森林：SEER-CAHPS 疾病负担指数的开发和内部验证

目的

在癌症诊断之前或之后制定并内部验证医疗保险受益人的疾病负担指数。

方法

数据来源：SEER-CAHPS，将监测、流行病学和最终结果 (SEER) 癌症登记、医疗保险登记和索赔以及医疗保险消费者对医疗保健提供者和系统的评估 (医疗保险 CAHPS) 调查数据联系起来，提供自我报告的社会人口统计、健康和健康状况功能状态信息。为了对数据集中的每个人进行评分，我们将每个年度子样本（2007-2013）中的 4 个组制成表格：1) Medicare Advantage (MA) 受益人或 2) Medicare 按服务收费 (FFS) 受益人，在癌症诊断前进行调查; 3) MA 受益人或 4) 诊断后接受调查的 Medicare FFS 受益人。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（每个子样本平均值 = 44）：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、活动限制、代理和基于位置的因素。大约三分之二的样本用于算法训练。基于验证（“袋外”，OOB）样本的错误率反映了正确分类的百分比。疾病负担评分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 Medicare 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) 评分呈正偏态（中位范围：0.29 [MA，诊断前] 至 2.85 [FFS，诊断后]；平均范围：2.08 [MA，诊断前] 至 4.88 [MA ，诊断后]）。分布中最高十分之一的死亡率为 51%（范围：29-71%）；最低十分之一的死亡率为 1%（范围：0-2%）。总体错误率为 20%（范围：9% [诊断后接受调查的 FFS 参与者] 至 36% [诊断前接受调查的 MA 参与者]）。

结论

这项针对癌症 Medicare 受益人的新发病率衡量标准可能对希望调整合并症的未来 SEER-CAHPS 用户有用。