Evidence map›Paper›PMID 39373617›Full record

ArticleCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology2025

Assessing 1-Year Comorbidity Prevalence and Its Survival Implications in Medicare Beneficiaries Diagnosed with Cancer: Insights from a New SEER-Medicare Resource.

Anne-Michelle Noone, Angela B Mariotto, Yoon Duk Hong, Lindsey Enewold

Abstract read
In one paragraph

Article in Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

  1. Trial
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  4. Machine learning approaches to predict early cardiac immune-related adverse events in patients receiving immune checkpoint inhibitors.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
    Article
  5. Article
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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

4 authors.

Anne-Michelle NooneDivision of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland.ORCID 0000-0001-6997-4004
Angela B MariottoDivision of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland.ORCID 0000-0003-2423-1834
Yoon Duk HongDivision of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland.ORCID 0000-0002-9548-5770
Lindsey EnewoldDivision of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland.ORCID 0000-0003-0452-5771

Funding

Intramural NIH HHS Z99 CA999999National Cancer Institute (NCI)
6 · The paper itself

Abstract

backgroundAlmost half of Medicare beneficiaries diagnosed with cancer from 1992 to 2005 had at least one comorbid condition. Conditions affect a range of domains from clinical decision-making to quality of life, which are important to consider when conducting cancer research. We introduce a new Surveillance, Epidemiology, and End Results (SEER)-Medicare resource to facilitate using claims data for patients with cancer.

methodsWe use the SEER-Medicare resource to estimate prevalence of comorbidities, 5-year survival rate by cancer site, stage, age and comorbidity severity, and prevalence of surgery by comorbidity for breast, prostate, colorectal and lung cancers.

resultsOverall, the most prevalent comorbidities in the year prior to cancer diagnosis were diabetes (27%), chronic obstructive pulmonary disease (22%), peripheral vascular disease (14%), and congestive heart failure (12%). Comorbidity severity had a greater impact on the probability of dying from noncancer causes than dying from cancer. Severity of comorbidity and age consistently increased the probability of noncancer death. The percentage of persons receiving surgery tended to be lower among those with severe comorbidity.

conclusionsThis study demonstrates the utility of new SEER*Stat databases that contain Medicare beneficiaries and claims-based measures of comorbidity. Our results demonstrate that comorbidity is common among older persons diagnosed with cancer and the impact of comorbidity on the probability of dying from cancer varies by cancer site, stage at diagnosis, and age. IMPACT: Comorbidity is common among persons with cancer and affects survival. Future research on the impact of comorbidity among cancer survivors is facilitated by new databases.

Indexed as

ComorbidityMedicareNeoplasmsSEER ProgramAgedAged, 80 and overFemaleHumansMalePrevalenceSurvival RateUnited States

Identifiers

PMID39373617
PMCPMC11717627

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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.