Evidence map›Paper›PMID 33188949›Full record

ArticleInternational journal of medical informatics2021

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.

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

Open access · greenAbstract read
In one paragraph

Article in International journal of medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 5 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 1 country.

Lisa M LinesCenter 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. Electronic address: llines@rti.org.
Julia CohenCenter for Advanced Methods Development, RTI International, Research Triangle Park, NC, United States.
Justin KirschnerCenter for Advanced Methods Development, RTI International, Research Triangle Park, NC, United States.
Michael T HalpernDivision of Cancer Control and Population Sciences, Healthcare Delivery Research Program, National Cancer Institute, Bethesda, MD, United States.
Erin E KentGillings School of Public Health, University of North Carolina, Chapel Hill, NC, United States.
Michelle A MollicaDivision of Cancer Control and Population Sciences, Healthcare Delivery Research Program, National Cancer Institute, Bethesda, MD, United States.
Ashley Wilder SmithDivision of Cancer Control and Population Sciences, Healthcare Delivery Research Program, National Cancer Institute, Bethesda, MD, United States.
National Cancer Institute · USRTI International · USUniversity of Massachusetts Chan Medical School · USUniversity of North Carolina at Chapel Hill · US

Funding

NCI NIH HHS HHSN261201500132U
6 · The paper itself

Abstract

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

methodsData 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.

resultsThe 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]).

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

Indexed as

Medicare Part CNeoplasmsAgedCost of IllnessFee-for-Service PlansHumansSemantic WebUnited StatesCancer registry dataClaims dataMorbidityMortalityRandom survival forestsSurvey data

Identifiers

PMID33188949
PMCPMC7736519
OpenAlexW3093825175

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.