Evidence map›Paper›PMID 42189260›Full record

ArticleCancer causes & control : CCC2026

Geospatial heterogeneity of hotspots for incidence and late-stage diagnosis of breast, colorectal, and lung cancer.

Andrew P Loehrer, Heather A Carlos, Julia E Weiss, Qianfei Wang, Chelsea V Leversedge, Julia E Katter, Joseph D Phillips, Dana Ferrari-Light, Tracy Onega, Xun Shi

Abstract read
In one paragraph

Article in Cancer causes & control : CCC, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Andrew P LoehrerDartmouth-Hitchcock Medical Center, Lebanon, NH, USA. andrew.p.loehrer@hitchcock.org.ORCID http://orcid.org/0000-0001-7747-224X
Heather A CarlosGeisel School of Medicine at Dartmouth, Hanover, NH, USA.
Julia E WeissGeisel School of Medicine at Dartmouth, Hanover, NH, USA.
Qianfei WangGeisel School of Medicine at Dartmouth, Hanover, NH, USA.
Chelsea V LeversedgeGeisel School of Medicine at Dartmouth, Hanover, NH, USA.
Julia E KatterGeisel School of Medicine at Dartmouth, Hanover, NH, USA.
Joseph D PhillipsDartmouth-Hitchcock Medical Center, Lebanon, NH, USA.
Dana Ferrari-LightDartmouth-Hitchcock Medical Center, Lebanon, NH, USA.
Tracy OnegaUniversity of Utah, Huntsman Cancer Institute, Salt Lake City, UT, USA.
Xun ShiDepartment of Geography, Dartmouth College, Hanover, NH, USA.

Funding

Translational Engineering in Cancer (TEC)P30CA023108 · NCI · DARTMOUTH COLLEGE · PI Steven D Leach · 1985 to 2026
$91.3M
Geospatial drivers of cancer care disparities and their susceptibility to health policy changesK08CA263546 · NCI · DARTMOUTH-HITCHCOCK CLINIC · PI Andrew Phillip Loehrer · 2022 to 2026
$869k
NCI NIH HHS 5P30CA023108NCI NIH HHS K08 CA263546NCI NIH HHS K08CA263546NCI NIH HHS P30 CA023108
6 · The paper itself

Abstract

purposeCancer control relies on the identification of populations at risk (hotspots) of new or late-stage cancer diagnoses. However, the extent to which hotspots differ between cancer sites or between outcome measures has been poorly characterized. We sought to determine the geospatial heterogeneity of hotspots of breast, colorectal, and lung cancer incidence and late-stage diagnoses.

methodsWe identified adult patients diagnosed with female breast, colorectal, and lung cancer between 2010 and 2019 in Indiana. To identify hotspots for incidence and late-stage diagnoses, we disaggregated the patient residential location information from the Census block group level to the approximated individual point level. Statistically significant hotspots were identified with kernel ratio estimation. The total areas of hotspots and the overlap between hotspots were compared.

results133,773 patients diagnosed with breast (n = 54,903), colorectal (n = 28,594), and lung (n = 50,276) cancer were included. Geospatial visualization demonstrated marked spatial deviation, with little overlapping area between incidence and late-stage hotspots for all three cancer sites (1-266 km

conclusionsOur results demonstrate considerable geospatial heterogeneity of hotspots between different outcome measures and different sites of cancer. The use of disaggregated spatial data enables a more granular, precise comparison of cancer hotspots. The greatest overlap was seen between incidence hotspots of breast and colorectal cancer, which suggests similar spatial drivers and the potential for coordinated cancer control strategies.

Indexed as

Breast NeoplasmsColorectal NeoplasmsLung NeoplasmsAdultAgedFemaleHumansIncidenceIndianaMaleMiddle AgedNeoplasm StagingAccess to careCancer controlCancer incidenceHotspotPrevention

Identifiers

PMID42189260
PMCPMC13212680

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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.