Evidence map›Paper›PMID 33880445›Full record

ArticleAnnals of cancer epidemiology2021

Geospatial analysis of multiple cancers in individuals in the US, 2004-2014.

Lia C Scott, Tzy-Mey Kuo, Dora Il'yasova, Lee R Mobley

Open access · diamondAbstract read
In one paragraph

Article in Annals of cancer epidemiology, 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
0.8field-weighted citation impact, top 29% 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, 7 citations in OpenAlex.

  1. Article
  2. Where Should the Cancer Control Interventions Target: A Geospatial Hotspot Analysis for Major Cancer Mortality 2018 to 2022 in the United States.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2025
    Article
  3. 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

4 authors at 2 institutions in 1 country.

Lia C ScottSchool of Public Health, Georgia State University, Atlanta, GA, USA.
Tzy-Mey KuoLineberger Cancer Center, University of North Carolina, Chapel Hill, NC, USA.
Dora Il'yasovaSchool of Public Health, Georgia State University, Atlanta, GA, USA.
Lee R MobleyMobley Consulting, LLC, Atlanta, GA, USA.
Georgia State University · USUNC Lineberger Comprehensive Cancer Center

Funding

Geospatial Factors & Impacts: Measurement and UseR01CA126858 · NCI · RESEARCH TRIANGLE INSTITUTE · PI ANSELIN, LUC, MOBLEY, LEE R · 2007 to 2015
$3.4M
Evaluating Racial & Geospatial Disparities and Factors in Triple-Negative Breast CancerF31MD012752 · NIMHD · GEORGIA STATE UNIVERSITY · PI SCOTT, LIA · 2018 to 2018
$26k
NCI NIH HHS R01 CA126858NIMHD NIH HHS F31 MD012752
6 · The paper itself

Abstract

backgroundThere is a projected rapid increase in cancer survivors in the US population, from 15.5 million in 2016 to 26.1 million by 2040. Improvements in treatment and detection have led to increased survival, however, there is now a risk of developing new cancers as a result of environment toxins, behavioral risk factors, genetic predisposition, and late-term effects of radiation and chemotherapeutic treatments. This study takes a geospatial approach to examining the place of occurrence of multiple cancers originating in the population of four screenable cancers-female breast, colorectal, prostate, and cervical cancers-among the US population.

methodsDuring 2004-2014, 6,523,532 primary cancer patients with one of these four screenable cancers were examined, and subsequent primary cancers (multiple cancers of any type) were noted. Individual level analyses estimated the odds of diagnosis with multiple cancers controlling for age, sex, and race-ethnicity. Change in effects on odds of multiple cancer diagnoses with age, sex, and race-ethnicity were evaluated controlling separately for late-stage diagnosis of the primary cancer or each primary cancer diagnosis type. County-level spatial cluster analysis was employed to identify and visualize higher than average multiple cancer rates.

resultsOver half of the study population were female and almost 30% of the study population were diagnosed at late-stage for their first cancer. Multiple occurrences of all cancers increased during the time period for patients with initial breast or colorectal cancers. Among BC primary cancer cases, subsequent multiple cancers were mostly new breast cancers. By contrast, for CRC primary cancer cases, subsequent multiple cancers were about equally likely to be new CRC cases or other cancer types. Sex, age and race-ethnicity were all significantly associated with multiple cancers. In the model controlling for CRC as the primary type, the age and race-ethnicity effects were somewhat different than for all the other models. Thus, there was something distinctly different about the multiple cancer incidence among patients with CRC as their primary cancer as compared to patients with BC, CVC, or PC primaries. In subsequent analyses by county, there were distinct geospatial patterns in multiple cancer rates with most high-rate clusters occurring in the north- and mid-west US.

conclusionsThere were distinct individual level and geospatial disparities in multiple cancer diagnoses for the study population of all primary breast, colorectal, cervical, or prostate cancer patients during the decade studied. It is importance to emphasize continued screening for cancer survivors and research on personal and environmental drivers of multiple primary cancers.

Indexed as

cancermultiple primary neoplasmsNational Program of Cancer Registries (NPCR)Spatial analysis

Identifiers

PMID33880445
PMCPMC8055046
OpenAlexW3139598527

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.