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. (January 2021)

• Record Type: Journal Article
• Authors: Lines, Lisa M.; Cohen, Julia; Kirschner, Justin; Halpern, Michael T.; Kent, Erin E.; Mollica, Michelle A.; Smith, Ashley Wilder
• Is Part Of: International journal of medical informatics. Volume 145(2021)
• Publication Date: 2021-01
• Subjects: Random survival forests -- Morbidity -- Mortality -- Cancer registry data -- Claims data -- Survey data; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2020.104305
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: Morbidity indices help explain how illness burdens affect outcomes. A new data resource needed an index that could work for all insurees included. Covariates came from survey, payor claims and enrollment, and limited clinical data. A random survival forests algorithm predicts deaths within a year post-survey. The measure had an overall error rate of 20 %. 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.