Evidence map›Paper›PMID 42162233›Full record

ArticleJournal of exposure science & environmental epidemiology2026

A national analysis of the impact of proximity to nuclear power plants on lung, breast and colon cancer mortalities in the U.S., 2000-2020.

Yazan Alwadi, Joel Schwartz, David C Christiani, Marco Kaltofen, Brent A Coull, John S Evans, Yaguang Wei, Petros Koutrakis

Abstract read
In one paragraph

Article in Journal of exposure science & environmental epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. 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

8 authors.

Yazan AlwadiDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA. yazan_alwadi@fas.harvard.edu.
Joel SchwartzDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
David C ChristianiDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Marco KaltofenBoston Chemical Data Corp., Natick, MA, USA.
Brent A CoullDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
John S EvansDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Yaguang WeiDepartment of Environmental Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Petros KoutrakisDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Funding

The Boston Lung Cancer Survival CohortU01CA209414 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI David C Christiani · 2017 to 2026
$12.2M
Ambient Particle Radioactivity and Lung Cancer SurvivalR21ES037885 · NIEHS · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI CHRISTIANI, DAVID C · 2025 to 2025
$433k
NCI NIH HHS U01 CA209414NIEHS NIH HHS R21 ES037885
6 · The paper itself

Abstract

backgroundNuclear power plants emit low levels of ionizing radiation, an established risk factor for breast, colon, and lung cancers, yet the long-term effects of chronic environmental exposure in U.S. populations remain unclear.

objectiveTo evaluate sex- and age-specific associations between proximity to nuclear power plants and mortality from the three most common cancers in the U.S.: breast, colon, and lung cancer.

methodsWe quantified county-level proximity to nuclear power plants using the sum of inverse distances from each residence county's population-weighted center to all plants within 200 km, updated annually from 2000 to 2020. Cancer-specific mortality data (breast, colon, and lung) from the CDC were analyzed by sex and five age groups (45-54, 55-64, 65-74, 75-84, 85 + ). Relative risks (RRs) were estimated using generalized estimating equations with a Poisson link. Models were adjusted for sociodemographic factors, urbanicity, region, and temporal trends.

resultsProximity to nuclear power plants was associated with elevated mortality from breast, colon, and lung cancers. From 2000 to 2020, an estimated 39,767 female deaths (95% CI: 9312-69,381), representing 2.01% (95% CI: 0.47-3.50%), and 38,124 male deaths (95% CI: 16,106-59,600), representing 2.33% (95% CI: 0.98-3.64%), were attributable to this proximity. Lung cancer accounted for the largest burden in both sexes, followed by breast and colon cancer in females and colon cancer in males. Mortality risks declined with increasing distance, becoming negligible beyond 50 km. SIGNIFICANCE: This national-scale analysis provides new evidence that proximity to nuclear power plants is associated with increased mortality from major cancers in the U.S. The magnitude and consistency of the findings highlight the importance of updated risk assessments, sustained surveillance, and strengthened public health planning for communities living near nuclear facilities. IMPACT: This study provides the first national assessment of sex- and age-specific mortality from breast, colon, and lung cancers in relation to proximity to U.S. nuclear power plants, revealing consistent patterns not previously demonstrated. These findings fill a major gap in environmental epidemiology and underscore the need for cohort studies, refined exposure assessments, and pathway-specific analyses to strengthen causal interpretation. As nuclear power gains momentum in national energy planning, establishing clearer evidence on potential health impacts is increasingly essential for guiding research priorities and public health preparedness.

Indexed as

AttributableBreastCancerColon cancerEnvironmentalEpidemiologyLung cancerMortalityNuclearPlantsPower

Identifiers

PMID42162233
PMCPMC13656289

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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.