The risk of thyroid cancer in relation to residential proximity to nuclear power plants: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Ionizing radiation is a human carcinogen, and there is a public concern but limited evidence that it increases the incidence of cancer among those who live near nuclear power plants (NPPs). Previous analyses of thyroid cancer in these populations have been inconsistent, and the last synthesis was published nearly a decade ago. To address these gaps, we undertook a systematic review and meta-analysis. METHODS: A search strategy was developed and applied to PubMed, Scopus, and Web of Science databases. A total of 2006 publications were identified, with 11 studies of thyroid cancer incidence that met the inclusion criteria. Study quality was assessed using the Office of Health Assessment and Translation (OHAT) tool. Summary risk estimates relating residential proximity to the NPPs and thyroid cancer were generated using a random effects model. Heterogeneity in the risk estimates was assessed for study features that included: distance to the NPP, study quality, and biological sex. RESULTS: The 11 studies were categorized as either highly (n = 8) or plausibly (n = 3) prone to bias, primarily due to the reliance on ecological study designs. The meta-analysis summary relative risk of thyroid cancer among those who live close to NPPs (defined by ≤ 25 km distance or jurisdictional areas (e.g., community, county) relative to those who lived further away was 1.09 (95% CI: 0.93-1.29). The risk estimates were higher for studies that modelled more proximal residential distances (≤ 5 km) to NPPs than larger distances (≤ 25 km and jurisdictional areas). We found that the summary risk (RR=1.29, 95% CI: 0.77-2.16) was stronger among those studies less prone to bias. A non-significant increased risk was found among both men and women, but there was no evidence of sex differences in risk. CONCLUSION: Overall, the findings suggest that living near a nuclear power plant increases the risk of thyroid cancer. The small number of studies on this topic, and the finding of higher risks in studies less prone to bias highlights the need for better-designed studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.042 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".