Evidence map›Paper›PMID 25417241›Full record

ArticleJournal of the National Cancer Institute. Monographs2014

Health-care utilization by prognosis profile in a managed care setting: using the Surveillance, Epidemiology and End Results Cancer Survival Calculator SEER*CSC.

Borsika A Rabin, Jennifer L Ellis, John F Steiner, Larissa Nekhlyudov, Eric J Feuer, Benjamin F Hankey, Laurie Cynkin, Elizabeth Bayliss

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the National Cancer Institute. Monographs, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Cancer survival: an overview of measures, uses, and interpretation.Journal of the National Cancer Institute. Monographs · 2014
    Article
  4. Article
  5. Article
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

8 authors.

Borsika A RabinDepartment of Family Medicine and Colorado Health Outcomes Program, School of Medicine, University of Colorado, Denver, CO (BAR); Cancer Research Network Cancer Communication Research Center (BAR), Institute for Health Research (JLE, JFS, EB), Kaiser Permanente Colorado, Denver, CO; Department of Population Medicine, Harvard Medical School, Boston, MA, Department of Medicine, Harvard Vanguard Medical Associates, Boston, MA (JN); Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD (EJF, BFH, LC). borsika.rabin@ucdenver.edu.
Jennifer L EllisDepartment of Family Medicine and Colorado Health Outcomes Program, School of Medicine, University of Colorado, Denver, CO (BAR); Cancer Research Network Cancer Communication Research Center (BAR), Institute for Health Research (JLE, JFS, EB), Kaiser Permanente Colorado, Denver, CO; Department of Population Medicine, Harvard Medical School, Boston, MA, Department of Medicine, Harvard Vanguard Medical Associates, Boston, MA (JN); Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD (EJF, BFH, LC).
John F SteinerDepartment of Family Medicine and Colorado Health Outcomes Program, School of Medicine, University of Colorado, Denver, CO (BAR); Cancer Research Network Cancer Communication Research Center (BAR), Institute for Health Research (JLE, JFS, EB), Kaiser Permanente Colorado, Denver, CO; Department of Population Medicine, Harvard Medical School, Boston, MA, Department of Medicine, Harvard Vanguard Medical Associates, Boston, MA (JN); Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD (EJF, BFH, LC).
Larissa NekhlyudovDepartment of Family Medicine and Colorado Health Outcomes Program, School of Medicine, University of Colorado, Denver, CO (BAR); Cancer Research Network Cancer Communication Research Center (BAR), Institute for Health Research (JLE, JFS, EB), Kaiser Permanente Colorado, Denver, CO; Department of Population Medicine, Harvard Medical School, Boston, MA, Department of Medicine, Harvard Vanguard Medical Associates, Boston, MA (JN); Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD (EJF, BFH, LC).
Eric J FeuerDepartment of Family Medicine and Colorado Health Outcomes Program, School of Medicine, University of Colorado, Denver, CO (BAR); Cancer Research Network Cancer Communication Research Center (BAR), Institute for Health Research (JLE, JFS, EB), Kaiser Permanente Colorado, Denver, CO; Department of Population Medicine, Harvard Medical School, Boston, MA, Department of Medicine, Harvard Vanguard Medical Associates, Boston, MA (JN); Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD (EJF, BFH, LC).
Benjamin F HankeyDepartment of Family Medicine and Colorado Health Outcomes Program, School of Medicine, University of Colorado, Denver, CO (BAR); Cancer Research Network Cancer Communication Research Center (BAR), Institute for Health Research (JLE, JFS, EB), Kaiser Permanente Colorado, Denver, CO; Department of Population Medicine, Harvard Medical School, Boston, MA, Department of Medicine, Harvard Vanguard Medical Associates, Boston, MA (JN); Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD (EJF, BFH, LC).
Laurie CynkinDepartment of Family Medicine and Colorado Health Outcomes Program, School of Medicine, University of Colorado, Denver, CO (BAR); Cancer Research Network Cancer Communication Research Center (BAR), Institute for Health Research (JLE, JFS, EB), Kaiser Permanente Colorado, Denver, CO; Department of Population Medicine, Harvard Medical School, Boston, MA, Department of Medicine, Harvard Vanguard Medical Associates, Boston, MA (JN); Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD (EJF, BFH, LC).
Elizabeth BaylissDepartment of Family Medicine and Colorado Health Outcomes Program, School of Medicine, University of Colorado, Denver, CO (BAR); Cancer Research Network Cancer Communication Research Center (BAR), Institute for Health Research (JLE, JFS, EB), Kaiser Permanente Colorado, Denver, CO; Department of Population Medicine, Harvard Medical School, Boston, MA, Department of Medicine, Harvard Vanguard Medical Associates, Boston, MA (JN); Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, MD (EJF, BFH, LC).

Funding

Training and DevelopmentP20CA137219 · NCI · KAISER FOUNDATION RESEARCH INSTITUTE · PI DEARING, JAMES WILLIAM · 2008 to 2012
$7.8M
Determining Processes of Cardiovascular Care Relevant to Complex PatientsR21HS019520 · AHRQ · KAISER FOUNDATION RESEARCH INSTITUTE · PI BAYLISS, ELIZABETH A · 2010 to 2010
$462k
AHRQ HHS R21 HS19520-01NCI NIH HHS P20 CA137219PHS HHS HHSN261201200414P
6 · The paper itself

Abstract

backgroundAccurate estimation of the probability of dying of cancer versus other causes is needed to inform goals of care for cancer patients. Further, prognosis may also influence health-care utilization. This paper describes health service utilization patterns of subgroups of prostate cancer and colorectal cancer (CRC) patients with different relative probabilities of dying of their cancer or other conditions.

methodsA retrospective cohort of cancer patients from Kaiser Permanente Colorado were divided into three groups using the predicted probabilities of dying of cancer and other causes calculated by the nomograms in the National Cancer Institute Surveillance, Epidemiology and End Results Cancer Survival Calculator. Demographic, disease-related characteristics, and health service utilization patterns were described across subgroups.

resultsThe cohort consisted of 2092 patients (1102 prostate cancer and 990 CRC). A new diagnosis of cancer increased utilization of cancer-related services with rates as high as 9.1/1000 person-days for prostate cancer and 36.2/1000 person-days for CRC. Little change was observed in the number of primary and other specialty care visits from prediagnosis to 1 and 2 years postdiagnosis.

conclusionsWe found that although a new diagnosis of cancer increased utilization of cancer-related services for an extended time period, the timing of cancer diagnosis did not appear to affect other types of utilization. Future research should assess the reason for the lack of impact of cancer and unrelated comorbid conditions on utilization and whether desired outcomes of care were achieved.

Indexed as

Colorectal NeoplasmsDelivery of Health CareHealth Services Needs and DemandHumansMaleManaged Care ProgramsNomogramsPatient Acceptance of Health CarePrognosisProstatic NeoplasmsRetrospective StudiesSEER ProgramSurvival RateUnited States

Identifiers

PMID25417241
PMCPMC4841171

What OpenQuestion holds

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Registered trials

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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.