Skip to main content
Download PDF
Download ePub

The risk of thyroid cancer in relation to residential proximity to nuclear power plants: a systematic review and meta-analysis

Environmental Health volume 23, Article number: 106 (2024) Cite this article

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.

Peer Review reports

Introduction

Over the last several decades, there has been a substantial increase in thyroid cancer worldwide, namely, the age-standardized incidence rate (ASIR) increased from 2.11 per 100,000 person-years in 1990 to 3.15 in 2017 [1]. This is predominately due to the increased capability of modern medical imaging being able to identify more cases of papillary carcinoma as well as increased surveillance [2, 3] and to a lesser extent due to other speculative factors such as increases in endocrine disrupting pollutants (eg: pesticides, phthalates compounds of flame retardants, and polyhalogenated aromatic hydrocarbons) [4] and increases in exposure to radiation from environmental (nuclear energy, industrial activity, etc.) [5] and medical sources [6, 7].

Increased exposure to ionizing radiation from a population-based perspective also occurs among individuals who live near nuclear power plants (NPPs). These plants release several gaseous and liquid radioactive effluents during routine operations [8]. Although NPPs tend to be located outside metropolitan areas, over time communities near these plants often experience increased population growth and urbanization due to employment, infrastructure, and urban sprawl [9]. It follows that a greater number of individuals are living near these plants, with the potential to be exposed to prolonged low doses of ionizing radiation, despite increased radiation protection measures implemented since the 1980s [10]. Even though exposure levels are low and not expected to exceed prescribed limits (1 millisievert (mSv) per calendar year – effective dose) [11], individuals living around NPPs are uneasy about the possible health risks, especially cancer, due to exposure to radiation [12, 13]. This public concern is due to positive findings from a series of epidemiological studies that attracted widespread media attention [14]. One of the most prominent studies was the health-district level ecological study that found an increased risk of childhood leukemia near a large nuclear fuel reprocessing site in Sellafield, UK in the 1980s [15]. Subsequent epidemiological studies of populations living near NPPs showed mixed results for childhood leukemia [16,17,18,19,20,21,22,23], and among adults, for other cancer sites such as thyroid [24, 25] and breast [24,25,26,27]. As a whole, these findings have not alleviated the concerns of residents [28, 29].

Individuals can be exposed to ionizing radiation either i) externally through high energy radiation (e.g., gamma radiation) that penetrates the human body or ii) internally from inhalation or ingestion of radionuclides (e.g., iodine-131,cesium-134, beryllium-7, potassium-40 etc.) that emit radiation [30]. While this exposure to ionizing radiation is low, the International Agency for Research on Cancer has classified this exposure as a human carcinogen [31] that increases the risk of developing several cancers including those of the thyroid [26, 32], breast [24, 25], bladder [33], lung [24], and kidney [26]. At this time, there is support for a linear no-threshold model implying that low levels of exposure may increase cancer risk [34].

Thyroid cancer is particularly relevant to ionizing radiation, as the thyroid gland is a highly radiosensitive organ [31, 35]. Radioisotopes of iodine are of significant concern for thyroid cancer. The primary biological mechanism underlying this sensitivity relates to the thyroid gland’s need for iodine from the bloodstream to produce hormones that regulate energy and metabolism. However, the gland is unable to distinguish between stable and radioactive iodine during this process [36]. A key iodine isotope of interest for thyroid cancer (although of only a physical half-life of 8 days) that is released from an NPP is Iodine-131 – this is a volatile radionuclide, that can be inhaled or ingested and can accumulate in the thyroid [37].

Residents who live near NPPs are exposed to ionizing radiation primarily from discharged radionuclides (internal exposure) such as elemental tritium (HT), tritium oxide (HTO), carbon-14 (C-14), iodine-131 etc., [38, 39] and the effective doses are estimated to be in the range of 0.0004 mSv/year [38] to 0.052 mSv/year [39]. Comparatively, these doses are much lower than dose estimates from higher-exposure populations such as the International Nuclear Workers Study (INWORKS) (17.4 mSv mean cumulative colon and lung dose) [40] and Japanese atomic bomb survivors (mean dose ~ 200 mSv) [41].

Epidemiologic studies support an excess risk of cancer in relation to prolonged exposure to low-dose ionizing radiation [42]. However, assessing carcinogenicity for prolonged low-dose exposure to ionizing radiation is extremely challenging in observational studies as it requires long follow-up periods to account for etiologically relevant exposure windows and to identify sufficient cancer cases [8, 43]. Consequently, studies of thyroid cancer risk among populations living near NPPs have mostly been ecological in nature with no individual-level data. Findings from these studies have been inconsistent with some studies reporting increased risks [26, 32, 44] while others not [45, 46]. There are several possible explanations for the heterogeneity in the risk estimates across studies, including exposure characterization, study quality (heterogeneity in methodology, analysis etc.), and biological sex differences in susceptibility.

Sex differences in thyroid cancer risk have been reported in populations living near NPPs, with some studies showing higher thyroid cancer risks in women than men [25, 47] whereas other studies have shown the opposite [33]. The biological mechanisms that could explain these differences are not established, but it has been suggested that sex differences in susceptibility are due to the role of gene variation in DNA damage/ repair [48], polymorphism in estrogen receptors [49], sex-chromosomal features [50], and hormonal regulation [48]. Women have higher background thyroid cancer rates suggestive that they are more susceptible than men [51]. Perhaps most compelling is the evidence of a higher thyroid cancer risk per same unit dose increase in women relative to men [52]. Understanding sex differences in risk is important for not only strengthening conclusions for characterizing risks for subgroups, but also for providing insights into underlying biological mechanisms that contribute to differential susceptibility.

Findings from past studies of thyroid cancer in relation to residential proximity to NPP have undoubtedly been influenced by exposure measurement error due to methodological challenges (low population and cases) and the reliance on ecological designs. Past studies tended to classify exposed populations as those living within relatively large buffers from NPPs (e.g., ≤ 20 km [33] or ≤ 25 km [39]), or jurisdictional areas (e.g.: community, county, municipality etc.) [26, 44, 46]. This spatial resolution may be inadequate as highlighted by findings from exposure studies that used advanced air dispersion models that incorporate meteorological parameters such as wind speed and direction that have shown radiation exposures are substantially higher for residences within 5 km of an NPP [38, 39]. It follows that there is an important need to evaluate the heterogeneity in risk estimates by residential distance to the NPP to best identify those at risk.

To date, there has been one systematic review of this topic [53]. This study by Kim et al. considered publications on residential proximity to NPPs and thyroid cancer up to March 2015, and conducted a meta-analysis on 13 studies (10 incidence and 4 mortality). Overall, this review found no increased risk with a standardized incidence ratio (SIR) of 0.98 (95% CI: 0.87 – 1.11), however, a statistically significant increased risk was observed among studies that restricted to risk estimates derived among populations living within 20 km of an NPP (OR = 1.75; 95% CI: 1.17 – 2.64). Additionally, this review reported no increased risks for subgroup analyses, specifically considering biological sex and types of reference populations [53].

The previous meta-analysis was published in 2016 and several papers with updated follow-up periods, or detailed breakdown of risk estimates have since been published [33, 47, 54]. This paper sought to provide an updated systematic review and meta-analysis focusing on thyroid cancer incidence while assessing sources of heterogeneity, specifically by subgroups of exposure definition, biological sex, and study quality.

Methodology

This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [55]. The protocol of this review was registered on the International Prospective Register of Systematic Reviews (PROSPERO) in October 2022 (registration number: CRD42022364057) [56].

Eligibility criteria

To formally identify the exclusion and inclusion criteria, a search strategy was formulated using the Population, Exposure, Comparator, and Outcomes (PECO) framework [57]. Population: Humans of all ages and sexes were included. Exposure: The exposure of interest was residential proximity to NPPs and this was defined according to several measures including: distance buffers, residency in administrative units (county, town, municipalities etc.), or exposure estimations (such as doses estimates from air dispersion models) for individuals living near these plants during routine operations. Comparator: Three types of comparisons were of interest including i) comparisons based on proximity to the NPP (usually defined by distance buffers) ii) comparisons based on jurisdictional areas near a NPP and further away defined by areas, towns, municipalities, counties, etc., and iii) comparisons based on dispersion model exposures. Outcomes: Thyroid cancer incidence among all ages was the outcome of interest. However, all cancers among all ages were considered relevant during the search process, as some studies report all cancers in the title and abstract but provide risk estimates by cancer site in the manuscript. Thyroid cancer mortality studies were excluded because unlike cancer sites with poor prognosis such as lung cancer where incidence can estimate mortality [58], 80—85% of all thyroid cancer cases are papillary thyroid cases [59], which are slow-growing cancers with a high survival rate [60]. Consequently, mortality studies of cancers with high survival rates such as thyroid cancer may be prone to bias.

All article types other than reviews, including letters, commentaries, and short articles were considered eligible if they contained relevant estimates of relative risk. Lastly, we included only studies published in English.

Search strategy and databases

The search strategy for three indexed databases PubMed, Scopus, and Web of Science was developed in consultation with a health librarian (H.M.) at Carleton University. A shortlist of 10 articles that met the eligibility criteria was tested for validity using the search strategy that was developed. Databases and corresponding search terms used for the final search strategy—are provided in Supplementary Table 1. The search strategy was tested on PubMed using Medical Subject Headings (MeSH) and free-text terms and then replicated on two other databases (i.e., Scopus, and Web of Sciences (WOS)). Lastly, we included papers published from 1983 till November 2nd, 2023.

Screening

Studies identified from these databases were imported into Covidence software [61]. Duplicates were removed by Covidence. Two reviewers (S.A.C. and G.G.) screened the titles and abstracts, and conflicts were resolved by an expert in environmental epidemiology (P.V.). Subsequently, identified articles went through full-text screening and conflict resolution by the same reviewers. Additionally, a reviewer (S.A.C.) screened the reference list of retained articles following full-text review to identify any possible additional missing articles. Figure 1 presents the PRISMA diagram detailing the screening process, and the number of articles at each stage of the review.

Fig. 1
figure 1

PRISMA flowchart depicting study selection process

Full size image

Data extraction

A tailor-made data extraction template was created using Covidence. For each article, a reviewer (S.A.C.) extracted relevant data while the accuracy and missingness of these data were checked by one of two reviewers (G.G. or C.W.). The data extracted included: 1) article title, 2) lead author, 3) year published, 4) country/region of the study population, 5) study design, 6) type of nuclear facility and number of facilities, 7) study period, 8) type of exposure measurement (distance, dose estimate etc.), 9) exposure measurement and comparison 10) study population 11) type of regression model and covariate adjustment and finally 12) risk measures and endpoints by cancer site, male, female and age were extracted separately when reported.

For cohort and ecological studies, we extracted RRs, HRs, or SIRs as effect estimates. There were several instances where we combined risk estimates within a single study to obtain an overall summary risk estimate. For example, when risk estimates were provided only for i) nested buffers (non-overlapping and concentric zones eg: ≤ 5 km, > 5 – 10 km, etc.) ii) individual nuclear sites and iii) sex-specific. For all the above instances, data across categories were collapsed using a random effects model (DerSimonian and Laird) [62]. Three studies had female only estimates. Two studies [46, 63] did not provide risk estimates for males and for one study [64] we were unable to combine risk estimates across 5 km equidistant buffers for males to obtain an overall risk. This is because there were zero cases and subsequently no confidence intervals for four of the five buffers for the male sub-group.

When data/populations used in multiple publications overlapped, we selected the study with the longest follow-up period, most cases, or those that provided more detailed risk estimates (e.g., male/female estimate separately rather than overall). Key characteristics of the included studies are given in Table 1.

Table 1 Characteristics of included studies in the meta-analysis for thyroid cancer incidence risk among populations residing in proximity to nuclear power plants
Full size table

Study quality

To assess the quality of the identified studies, we used the Office of Health Assessment and Translation (OHAT) tool developed by the National Toxicology Program, National Institute of Environmental Health Sciences (NIEHS) [67]. This risk of bias tool is widely used for assessing the quality of environmental health studies [68,69,70]. Briefly, the tool categorizes biases (domains) from each study into one of four risk of bias (ROB) groups - definitely low ROB, probably low ROB, probably high ROB, definitely high ROB. Each study is then classified into one of three tiers: tier 1 indicates low risk of bias, tier 2 plausible risk, and tier 3 high risk of bias. We considered exposure characterization and confounding bias as the key domains whereas selection bias, attrition/exclusion bias, outcome assessment, selective reporting, and appropriate statistical methods were considered as the other domains. Tier 1 studies were those where all key and most other domains scored definitely low ROB or probably low ROB. Tier 3 studies were those where all key domains and some other domains scored probably high ROB, or definitely high ROB. Tier 2 studies are those that fell into neither tiers 1 or 2 [67]. Two reviewers (S.A.C. and L.R.) scored the papers independently and deliberated to resolve conflicts and reach a consensus risk of bias score where there was disagreement. Table 2 provides results of the bias analysis and Supplementary Table 2 provides the criteria that were used for scoring the observational studies.

Table 2 Assessment of bias using the Office of Health Assessment and Translation (OHAT) tool
Full size table

Statistical analysis

An overall summary risk estimate and the corresponding 95% confidence interval along with a forest plot were generated using the random-effects model (DerSimonian and Laird) [71]. A random effects model was chosen as the studies varied on several aspects including exposure characterization, outcome measurement, population characteristics, and adjustment for confounders [72]. For the overall summary effect estimate, we used the most commonly reported distance buffer across studies, this was either jurisdictional area level comparisons or large buffers such as ≤ 25 km, ≤ 30 km etc. The I-square statistic and its corresponding p-values were used to characterize the heterogeneity in the measures of association across the studies [71]. Egger’s test and funnel plot were used to assess potential publication bias.

Next, we categorized studies into three groups based on the most proximal exposure characterization (smallest reported buffer) reported in each study. The three groups were i) studies that defined the exposed population as within 5 km of an NPP, ii) studies that used residential buffers within 25 km of an NPP, and iii) those that relied on jurisdictional measures of proximity (e.g., town, county, etc.). No studies using dispersion models of exposure were identified. For each of these three exposure metrics, the weighted summary risk estimates and the corresponding 95% confidence interval along with a forest plot were generated using the random effects model. Based on these results, the smallest buffer reported was used to assess heterogeneity among subgroups of biological sex and study quality in an attempt to capture the most etiologically relevant risk estimates reported in each study. For all the subgroup analyses, the Cochran’s Q statistic [73] was used to assess heterogeneity in the summary measure of association between the groups. This test provides a probability based on the chi-square distribution indicating the likelihood of variation across studies within each subgroup [73].

All analyses were performed using Stata version 18 (StataCorp LLC, College Station, TX, USA).

Results

This systematic review identified 2,006 research articles, of which 24 reported risk estimates between residential proximity to an NPP and thyroid cancer (incidence or mortality) After removing overlapping study populations and mortality studies, 11 thyroid cancer incidence papers were retained for analysis (Fig. 1). Of the included papers, nine were ecological studies, one was a cohort study, and one a birth cohort study. The studies represented 10 countries (Belgium, Canada, France, Italy, South Korea, Slovenia, Taiwan, UK, Ukraine, and USA), 27 nuclear power plants, and 1 nuclear waste repository site (Table 1).

All papers were categorized as either highly (n = 8) or plausibly (n = 3) prone to biases, primarily due to exposure characterization or confounding biases. Exposure characterization varied among the studies as four used jurisdictional area comparisons (community, county, municipality, and region) [26, 44, 46, 63] while the others used variable buffers from ≤ 5 km [54, 64], ≤ 14.2 km [66], ≤ 20 km [33], ≤ 25 km [39]. The cohort study collected the length of residence (exposure duration) for living near an NPP (using 5 km, 5 – 30 km etc.) prospectively [47]. The birth cohort study retrospectively collected residential mobility related information for the identified cohort members in the counties of interest [65]. With respect to confounding, only one cohort study [47] could control for individual-level confounders (apart from age and sex). The OHAT risk of bias assessment summaries are shown in Table 2.

The overall weighted summary estimate effect for the 11 incident thyroid cancer studies for those who live near an NPP was RR = 1.09 (95% CI: 0.93 -1.29) (Fig. 2). This measure of association across these studies exhibited a high degree of heterogeneity (I-square = 82.5%).

Fig. 2
figure 2

Forest plot for total incident thyroid cancer among residents in proximity to a nuclear power plants

Full size image
Fig. 3
figure 3

Funnel plot of incident thyroid cancer studies among residents in proximity to a nuclear power plant

Full size image

The Egger’s test suggested the presence of publication bias (p-value =0.04), however, the trim and fill imputed no new studies - the corresponding funnel plot is shown in Fig. 3. We also conducted an analysis of influence, which showed that no single study exerted an undue influence on the summary measure (Supplementary Figure 1).

In the analysis of studies grouped by the smallest buffer reported in each study, we found that the sub-group of studies (n = 3) that were based on ≤ 5 km proximity to the NPP reported a stronger effect (RR = 1.55; 95% CI: 0.47 -5.13), when compared to findings derived with a buffer distance of ≤ 25 km (n = 3) (RR = 1.04; 95% CI: 0.87 -1.24) or by modeling jurisdictional areas (n = 5) (RR = 1.07; 95%CI: 0.83 -1.37) (Fig. 4). Even though these risk estimates were notably different across these three exposure definitions, the confidence intervals overlapped and Cochran’s Q statistic for heterogeneity between the subgroups was not statistically significant (p-value = 0.80; I-square = 70.2%).

Fig. 4
figure 4

Forest plot for incident thyroid cancer among residents in proximity to a nuclear power plant using the most precise buffer reported

Full size image

Next in the heterogeneity analysis by study quality, we found a more pronounced effect for studies plausibly prone to bias compared to studies highly prone to bias, however, the difference in these summary risk estimates was not statistically significant. The weighted summary estimate for studies that were plausibly prone to bias (Tier 2) was RR = 1.29 (95% CI = 0.77—2.16) and for studies highly prone to bias (Tier 3) was RR = 1.03 (95% CI = 0.87 – 1.23) (Fig. 5). The Cochran’s Q statistic for heterogeneity between the subgroups was not statistically significant (p-value = 0.43; I-square = 70.2%). There were no Tier 1 studies based on our OHAT assessment.

Fig. 5
figure 5

Forest plot for incident thyroid cancer among residents in proximity to a nuclear power plants by quality of studies

Full size image

Lastly, we found a non-statistically significant increase in risk for men and women, but no statistically significant difference in risk estimates was observed between the sexes (p-value = 0.66 and I-square = 89.9%). The weighted summary risk for men was RR = 1.18 (95% CI: 0.90 – 1.55) and for women was RR = 1.09 (95% CI: 0.88 – 1.34) (Fig. 6).

Fig. 6
figure 6

Forest plot for incident thyroid cancer among residents in proximity to a nuclear power plants by biological sex

Full size image

Discussion

In this meta-analysis of 11 studies of incident outcomes, we found a non-statistically significant elevated risk of thyroid cancer risk for those who live near NPPs when compared to those who do not. The summary risk was stronger for the sub-group of studies whose risk estimates were calculated using smaller distance buffers. We also found that the summary risk estimates were stronger when restricted to studies less prone to bias. Lastly, we found no risk difference between men and women.

Our findings of a slightly increased risk of thyroid cancer for those who live near NPPs (RR = 1.09; 95% CI: 0.93 -1.29) differ somewhat from the previous meta-analysis where the summary risk estimate was essentially null (SIR = 0.98; 95% CI: 0.87 – 1.11) [53]. The difference in our summary estimates is due to the inclusion of three additional studies. This includes two studies published after the previous systematic review [63, 66] as well as one other study that was published before but excluded from their review [39]. The identification of these additional studies is likely due to our decision to apply our search across three databases, whereas the Kim et al. (2016) study [53] relied on Embase and Medline. Additionally, the present meta-analysis includes risk estimates derived from updated three cohorts that resulted in a higher number of incidence cancers [33, 47, 54].

The meta-analysis found substantial heterogeneity (I-square value > 80%) across the study-specific measures of association. This pattern can be explained by many reasons, including variability in the characterization of exposure, study quality (variability in methodology, type of models, adjustment factors in models), as well as differences in characteristics of the study population (e.g., age and sex distribution). Below, we discuss the results of heterogeneity analysis for three factors.

Firstly, our subgroup analyses suggest that some of the observed heterogeneity is due to study differences in exposure characterization by distance. Specifically, we observed an attenuation of risk estimates with increasing residential distances to the NPP. The risk estimate based on smaller distance buffers (≤ 5 km) were higher, however, the confidence interval for this summary measure was quite wide owing to the small number of such studies and the small number of identified cancers in these studies. These findings of a stronger risk estimate with shorter distances are in line with exposure modelling work which reports that radiation exposures drop off quickly with increasing distances to the NPP [74,75,76]. For instance, a recent French study reported distances to be inversely correlated to estimated dose for concentric circles of 5 km zones. Although within these equidistant zones, there was considerable variability due to meteorological and topographical factors, the effective dose for ≤ 5 km was 1.2 mSv per year compared to 0.04 mSv per year for the 15–20 km [75]. Additionally, in line with our findings, a review [77] that looked at a rare cancer (childhood leukemia) for those living near NPPs reported that the risk of living within a 25 km buffer was RR = 1.00 (95%CI: 0.95 -1.05) but the risk within 5 km was RR = 1.45 (95%CI: 0.74 -2.86) for case–control studies and RR = 1.33 (95%CI: 1.05 -1.68) for ecological/cohort [77].

Secondly, we hypothesized that some heterogeneity is due to differences in study quality (apart from spatial resolution in characterizing exposure). We found stronger risk estimates for studies less prone to biases compared to those highly prone to biases. Two key biases that impacted the quality of studies in our meta-analysis were non-differential misclassification of the exposure and confounding bias. Non-differential misclassification of the exposure may have affected most studies in our review. Summary estimates can be susceptible to yielding false positive estimates if poor quality studies are biased toward overestimating or false negative estimates if poor quality studies are biased toward underestimating [78]. In our meta-analysis, studies that were highly prone to bias were mostly ecological and, there was considerable misclassification of exposure (larger exposure assessment units). In these studies, individuals with lower exposure were grouped with higher exposure individuals, which likely biased most estimates to dilution and led to an underestimation of the adverse effect. This also correlates with the stronger effect we found in studies plausibly prone to biases and the diluted effect in those highly prone to biases. Our findings by study quality were similar to the previous meta-analysis that found a statistically significant association among higher quality studies that classified exposure as living less than 20 km from an NPP [53]. In relation to confounding bias, most studies in our analysis used aggregate levels of data, which in extension meant little to no ability to adjust for variables that could influence the association between thyroid cancer and living near a nuclear plant. Although this likely led to some residual confounding and biased estimates, it is important to note that there is little evidence for any risk factors that could confound the association between living near a nuclear power plant and thyroid cancer. Other than radiation [79], thyroid cancer related risk factors in the literature that could be relevant include being female [49], family history [80], higher body mass index [81], and iodine in the diet [82], however, evidence for these factors are contradictory [79].

Lastly, apriori we hypothesized that some heterogeneity in the risk estimates is due to differences in the sex distribution across study populations. Studies of populations with variable exposure levels have reported differential susceptibility by biological sex in the development of thyroid cancer when exposed to ionizing radiation [83]. However, our meta-analysis found no statistically significant difference in the summary measure of effects between the two groups. Our findings are likely due to three factors i) the ecological nature of most studies which extends to the inability to assign exposures at the right etiological window, ii) the lack of individual-level data, and iii) uncontrolled confounding for thyroid cancer related risk factors, although supported by mixed evidence for factors such as diet [82], family medical history [79, 80], and lifestyle factors [79]. These factors could be important in detecting accurate point estimates and differences, if present. Additionally, populations that provide considerably strong evidence for differential susceptibility among men and women are those that are exposed to higher doses (such as nuclear accident survivors [84, 85] and chronic and acute occupational exposures [86, 87]).

There are several limitations to be acknowledged. Firstly, to assess variability due to exposure characterization we used overlapping buffers ≤ 5 km, ≤ 25 km, and jurisdictional area level comparisons. Ideally, if we had information by nested buffers: ≤ 5 km, > 5—≤ 10 km, > 10—≤ 15 km, and so on, we would have been able to assess if there was a dose–response pattern. However, only two papers [54, 64] included in the meta-analysis provided estimates for 5 km equidistant nested buffer zones, which was insufficient for a meaningful meta-analysis.

We were unable to explore variations in residential proximity to NPPs and thyroid cancer risk by age. Most papers in our analysis provided risk estimates for all ages [39, 54, 63, 65], several did not specify age groups [26, 44, 46, 64, 66] and two papers provided estimates for only adults [33, 47]. Age is an important modifying factor in radiation-induced thyroid cancer as previous analyses of the Chernobyl and Fukushima accident survivors have shown higher sensitivity related to developing thyroid cancer for exposures received at an early age than later in adulthood [88]. A similar pattern was also evident in a pooled analysis of nine cohorts of children exposed to low doses (diagnostically and therapeutically) [89]. The higher susceptibility during childhood may be due to developing cells and faster metabolism [90]. The limited breakdown of risk by age in the identified papers did not allow us to assess variation in risk by age – a key biological risk factor.

Our study has several key strengths including comprehensive literature searching and screening of three indexed databases, reference list search of included papers, and grey literature search. We also conducted subgroup analyses based on key environmental epidemiological principles and current literature to assess heterogeneity in results.

In summary, the results of our meta-analysis suggest a possible modest increase of thyroid cancer incidence for those living near NPPs. Additionally, we also found a stronger effect among studies plausibly prone to biases vs those highly prone to biases and a stronger effect among studies that used smaller buffers vs those that used larger buffers. Our analysis highlights the scarcity of high-quality studies in this research area and the need for future well-designed cohort studies with individual-level data, longer follow-up periods, and accurate exposure characterization.

Data availability

Data used for the meta-analysis is provided within the manuscript.

Abbreviations

NPP:

Nuclear power plant

TWh:

Terawatt-hours

mSv:

Millisievert

SIR:

Standardized incidence ratio

ASIR:

Age-standardized incidence rate

RR:

Relative risk

OR:

Odds ratio

HR:

Hazard risk

CIs:

Confidence intervals

PRISMA:

Preferred Reporting Items for Systematic Reviews and Meta-analysis

OHAT:

Office of Health Assessment and Translation

PROSPERO:

The International Prospective Register of Systematic Reviews

PECO:

Population, Exposure, Comparator, and Outcomes

NIEHS:

National Institute of Environmental Health Sciences

References

  1. Deng Y, Li H, Wang M, Li N, Tian T, Wu Y, et al. Global Burden of Thyroid Cancer From 1990 to 2017. JAMA Netw Open. 2020;3(6):e208759.

    Article  Google Scholar 

  2. Kitahara CM, Sosa JA. The changing incidence of thyroid cancer. Nat Rev Endocrinol. 2016;12(11):646–53.

    Article  Google Scholar 

  3. Topstad D, Dickinson JA. Thyroid cancer incidence in Canada: a national cancer registry analysis. CMAJ Open. 2017;5(3):E612–6.

    Article  Google Scholar 

  4. Alsen M, Sinclair C, Cooke P, Ziadkhanpour K, Genden E, van Gerwen M. Endocrine Disrupting Chemicals and Thyroid Cancer: An Overview. Toxics. 2021;9(1):14.

    Article  CAS  Google Scholar 

  5. Saenko V, Mitsutake N. Radiation-Related Thyroid Cancer. Endocr Rev. 2024;45(1):1–29.

    Article  Google Scholar 

  6. van Gerwen M, Cerutti JM, Sinclair CF. Editorial: Environmental exposures and thyroid health. Front Endocrinol (Lausanne). 2023;14:1154547.

    Article  Google Scholar 

  7. van Gerwen M, Alsen M, Genden E. It May Not All Be Overdiagnosis: The Potential Role of Environmental Exposures in the Thyroid Cancer Incidence Increase. Epidemiology. 2022;33(5):607–10.

    Article  Google Scholar 

  8. United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR). Sources, Effects and Risks of Ionizing Radiation: UNSCEAR 2012, Report to the General Assembly with Scientific Annexes. 2012. Available from: https://www.unscear.org/unscear/en/publications/2012.html.

  9. Greenberg MR, Krueckeberg DA, Kaltman M, Metz W, Wilhelm C. Local planning v. national policy: Urban growth near nuclear power stations in the United States. Town Plan Rev. 1986;57(3):225–37.

    Article  Google Scholar 

  10. World Nuclear Association. Safety of Nuclear Power Reactors. Available from: https://world-nuclear.org/information-library/safety-and-security/safety-of-plants/safety-of-nuclear-power-reactors.aspx.

  11. Minister of Justice. Radiation Protection Regulations (SOR/2000–203) 2021. Available from: https://laws-lois.justice.gc.ca/PDF/SOR-2000-203.pdf.

  12. CBC News. Canada to watch cancer rates near nuclear plants 1999. Available from: https://www.cbc.ca/news/science/canada-to-watch-cancer-rates-near-nuclear-plants-1.189844.

  13. BBC. Nuclear power: Are we too anxious about the risks of radiation? 2020. Available from: https://www.bbc.com/news/science-environment-54211450.

  14. Janiak MK. Epidemiological evidence of childhood leukaemia around nuclear power plants. Dose-Response. 2014;12(3):349–64.

    Article  Google Scholar 

  15. Black D. Investigation of the Possible Increased Incidence of Cancer in West Cumbria. London: HMSO; 1984. Available from: https://pdf.library.soton.ac.uk/BOPCRIS/22690/pdf/22690_1.pdf

  16. Sermage-Faure C, Laurier D, Goujon-Bellec S, Chartier M, Guyot-Goubin A, Rudant J, et al. Childhood leukemia around French nuclear power plants–the Geocap study, 2002–2007. Int J Cancer. 2012;131(5):E769–80.

    Article  CAS  Google Scholar 

  17. McLaughlin JR, Clarke AE, Nishri D, Anderson WT. Childhood Lukemia in the vicinity of Canadian Nuclear facilities. Cancer Causes Control. 1992;4:51–8.

    Article  Google Scholar 

  18. Michaelis J, Keller B, Haaf G, Kaatsch P. Incidence of childhood malignancies in the vicinity of West German nuclear power plants. Cancer Cases Control. 1992;3(3):255–6.

    Article  CAS  Google Scholar 

  19. Spycher BD, Feller M, Zwahlen M, Röösli M, von der Weid NX, Hengartner H, et al. Childhood cancer and nuclear power plants in Switzerland: A census-based cohort study. Int J Epidemiol. 2011;40(5):1247–60.

    Article  Google Scholar 

  20. Heinavaara S, Toikkanen S, Pasanen K, Verkasalo PK, Kurttio P, Auvinen A. Cancer incidence in the vicinity of Finnish nuclear power plants: an emphasis on childhood leukemia. Cancer Causes Control. 2010;21(4):587–95.

    Article  Google Scholar 

  21. Bithell JF, Dutton SJ, Draper G, Neary NM. Distribution ofchildhood leukaemias and non-Hodgkin’s lymphomas near nuclear installations in England and Wales. BMJ. 1994;309(6953):501–5.

    Article  CAS  Google Scholar 

  22. COMARE. Committee on Medical Aspects of Radiation in the Environment (COMARE); SEVENTEENTH REPORT: Further consideration of the incidence of cancers around the nuclear installations at Sellafield and Dounreay 2015. Available from: https://assets.publishing.service.gov.uk/media/5a7d93f6e5274a6b89a50e61/COMARE11thReport.pdf.

  23. COMARE. Committee on Medical Aspects of Radiation in the Environment (COMARE): FOURTEENTH REPORT: Further consideration of the incidence of childhood leukaemia around nuclear power plants in Great Britain. 2011. Available from: https://assets.publishing.service.gov.uk/media/5a7deb97ed915d74e33eee15/COMARE14threport.pdf.

  24. Wanigaratne S, Holowaty E, Jiang H, Norwood TA, Pietrusiak MA, Brown P. Estimating cancer risk in relation to tritium exposure from routine operation of a nuclear-generating station in Pickering, Ontario. Chron Dis Injuries Canada. 2013;33(4):247–56.

    CAS  Google Scholar 

  25. Ahn YO, Li ZM, KREEC Study Group. Cancer risk in adult residents near nuclear power plants in Korea - a cohort study of 1992-2010. J Korean Med Sci. 2010;27(9):999–1008.

  26. Bazyka DA, Prysyazhnyuk AY, Romanenko AY, Fedorenko ZP, Gudzenko NA, Fuzik MM, et al. Cancer incidence and nuclear facilities in Ukraine: A community-based study. Exp Oncol. 2012;34(2):116–20.

    CAS  Google Scholar 

  27. López-Abente G, Vidal-Ocabo E, Tello-Anchuela O, Aragonés N, García-Pérez J, Pastor-Barriuso R, et al. Exposure to ionising radiations arising from the operation of nuclear installations and cancer mortality. Int J Environ Sci Technol. 2014;11(1):97–110.

    Article  Google Scholar 

  28. Friends of the Earth Canada. New Poll Finds Canadians Do Not Trust Nuclear Energy and Reactors 2020. Available from: https://foecanada.org/2020/01/new-poll-finds-canadians-do-not-trust-nuclear-energy-and-reactors/.

  29. American Nuclear Society. Survey reveals support for, but misconceptions about, nuclear energy 2023. Available from: https://www.ans.org/news/article-5591/survey-reveals-support-for-but-misconceptions-about-nuclear-energy/.

  30. Committee on the Analysis of Cancer Risks in Populations near Nuclear Facilities-Phase I, Nuclear and Radiation Studies Board, Division on Earth and Life Studies, & National Research Council. Analysis of Cancer Risks in Populations Near Nuclear Facilities: Phase I. National Academies Press (US). 2012. Available from: https://nap.nationalacademies.org/catalog/13388/analysis-of-cancer-risks-in-populations-near-nuclear-facilities-phase.

  31. World Health Organization (WHO) & International Agency for Research on Cancer (IARC). IARC monographs on the evaluation of carcinogenic risks to humans; Volume 75 ionizing radiation, part 1: x- and gamma (γ)-radiation, and neutrons. Lyon: 2000. Available from: https://publications.iarc.fr/Book-And-Report-Series/Iarc-Monographs-On-The-Identification-Of-Carcinogenic-Hazards-To-Humans/Ionizing-Radiation-Part-1-X--And-Gamma-%CE%B3--Radiation-And-Neutrons-2000.

  32. Rekacewicz C, de Vathaire F, Delise MJ. Differentiated thyroid carcinoma incidence around the French nuclear power plant in Chooz. Lancet. 1993;341(8843):493.

    Article  CAS  Google Scholar 

  33. Desbiolles A, Roudier C, Goria S, Stempfelet M, Kairo C, Quintin C, et al. Cancer incidence in adults living in the vicinity of nuclear power plants in France, based on data from the French Network of Cancer Registries. Int J Cancer. 2018;142(5):899–909.

    Article  CAS  Google Scholar 

  34. Shore RE, Beck HL, Boice JD, Caffrey EA, Davis S, Grogan HA, Mettler FA, Preston RJ, Till JE, Wakeford R, Walsh L, Dauer LT. Implications of recent epidemiologic studies for the linear nonthreshold model and radiation protection. Journal of radiological protection: official journal of the Society for Radiological Protection. 2018;38(3):1217–33. https://doiorg.publicaciones.saludcastillayleon.es/10.1088/1361-6498/aad348.

  35. World Health Organization (WHO), IARC (The International Agency for Research on Cancer) Scientific Publication No. 165. Tumour site concordance and mechanisms of carcinogenesis (Chapter 18: Ionizing Radiation). 2019. Available from: https://publications.iarc.fr/Book-And-Report-Series/Iarc-Scientific-Publications/Tumour-Site-Concordance-And-Mechanisms-Of-Carcinogenesis-2019.

  36. American Thyroid Association. Nuclear Radiation and the Thyroid. Available from: https://www.thyroid.org/nuclear-radiation-thyroid/.

  37. Washington State Department of Health: Division of Environmental Health Office of Radiation Protection. Iodine-131 (I-131): Fact Sheet 320–085 2003. Available from: https://doh.wa.gov/sites/default/files/legacy/Documents/Pubs//320-085_i131_fs.pdf.

  38. Lee SC, Pan CY, Lai PC, Lee TA, Wu WT, Chang SL, et al. Assessment of resident doses near nuclear power plants in Taiwan for epidemiology study. J Environ Radioact. 2020;225: 106443.

    Article  CAS  Google Scholar 

  39. Lane R, Dagher E, Burtt J, Thompson PA. Radiation Exposure and Cancer Incidence (1990 to 2008) around Nuclear Power Plants in Ontario. Canada J Environ Protect. 2013;04(09):888–913.

    Article  Google Scholar 

  40. Thierry-Chef I, Richardson DB, Daniels RD, Gillies M, Hamra GB, Haylock R, et al. Dose Estimation for a Study of Nuclear Workers in France, the United Kingdom and the United States of America: Methods for the International Nuclear Workers Study (INWORKS). Radiat Res. 2015;183(6):632–42.

    Article  CAS  Google Scholar 

  41. Shore RE, Dauer LT, Beck HL, Caffrey EA, Davis S, Grogan HA, et al. National Council on Radiation Protection and Measurements (NCRP). Implications of Recent Epidemiologic Studies for the Linear-Nonthreshold Model and Radiation Protection. Bethesda: National Council on Radiation Protection and Measurements; 2018. Available from: https://ncrponline.org/shop/commentaries/commentary-no-27-implications-of-recent-epidemiologic-studies-for-the-linear-nonthreshold-model-and-radiation-protection-2018/.

  42. Hauptmann M, Daniels RD, Cardis E, Cullings HM, Kendall G, Laurier D, et al. Epidemiological Studies of Low-Dose Ionizing Radiation and Cancer: Summary Bias Assessment and Meta-Analysis. J Natl Cancer Inst Monogr. 2020;2020(56):188–200.

    Article  Google Scholar 

  43. Rothman K, Greenland S, Lash T. Modern Epidemiology. Philadelphia: Lippincott Williams & Wilkins; 2008.

    Google Scholar 

  44. Boice JD, Bigbee WL, Mumma MT, Tarone RE, Blot WJ. County Mortality and Cancer Incidence in Relation to Living near Two Former Nuclear Materials Processing Facilities in Pennsylvania—An Update. Health Phys. 2009;96(2):128–37.

    Article  CAS  Google Scholar 

  45. Bollaerts K, Fierens S, Van Bladel L, Simons K, Sonck M, Poffijn A, et al. Thyroid cancer incidence in the vicinity of nuclear sites in Belgium, 2000–2008. Thyroid. 2014;24(5):906–17.

    Article  CAS  Google Scholar 

  46. Zadnik V, Zagar T, Drobne S, Zakelj MP. Estimation of cancer burden in Brezice municipality, a community neighboring Krsko nuclear power plant in Slovenia. Croat Med J. 2008;49(2):257–66.

    Article  Google Scholar 

  47. Kim JM, Kim MH, Ju YS, Hwang SS, Ha M, Kim BK, et al. Reanalysis of Epidemiological Investigation of Cancer Risk among People Residing near Nuclear Power Plants in South Korea. Int J Environ Res Public Health. 2018;15(3):481.

    Article  Google Scholar 

  48. Cardano M, Buscemi G, Zannini L. Sex disparities in DNA damage response pathways: Novel determinants in cancer formation and therapy. iScience. 2022;25(3):103875.

  49. Rahbari R, Zhang L, Kebebew E. Thyroid cancer gender disparity. Future Oncol. 2010;6(11):1771–9.

    Article  CAS  Google Scholar 

  50. Tran Q-L, Davies L. Thyroid cancer incidence differences between men and women. Curr Opin Endocr Metab Res. 2023;(31):1-7.

  51. Shank JB, Are C, Wenos CD. Thyroid Cancer: Global Burden and Trends. Indian J Surg Oncol. 2022;13(1):40–5.

    Article  Google Scholar 

  52. National Research Council. Health Risks from Exposure to Low Levels of Ionizing Radiation: BEIR VII. Washington, DC: The National Academies Press; 2006.

    Google Scholar 

  53. Kim J, Bang Y, Lee WJ. Living near nuclear power plants and thyroid cancer risk: A systematic review and meta-analysis. Environ Int. 2016;87:42–8.

    Article  Google Scholar 

  54. Demoury C, De Schutter H, Faes C, Carbonnelle S, Fierens S, Molenberghs G, et al. Thyroid cancer incidence near nuclear sites in Belgium: An ecological study at small geographical level. Int J Cancer. 2020;146(11):3034–43.

    Article  CAS  Google Scholar 

  55. PRISMA: Transparent reporting of systematic reviews and meta-analyses. PRISMA 2020 Checklist 2020. Available from: https://prisma-statement.org/documents/PRISMA_2020_checklist.pdf.

  56. Cottagiri SA, King WD, Villeneuve P. Meta-analysis on exposure to low-dose ionizing radiation and cancer risk for those living in the vicinity of nuclear power plants. PROSPERO 2022 CRD42022364057 2022. Available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022364057.

  57. Morgan RL, Whaley P, Thayer KA, Schunemann HJ. Identifying the PECO: A framework for formulating good questions to explore the association of environmental and other exposures with health outcomes. Environ Int. 2018;121(Pt 1):1027–31.

    Article  Google Scholar 

  58. Ellis L, Woods LM, Esteve J, Eloranta S, Coleman MP, Rachet B. Cancer incidence, survival and mortality: explaining the concepts. Int J Cancer. 2014;135(8):1774–82.

    Article  CAS  Google Scholar 

  59. Limaiem F, Rehman A, Anastasopoulou C, Mazzoni T. Papillary Thyroid Carcinoma: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023. Available from: https://www.ncbi.nlm.nih.gov/books/NBK536943/.

  60. Sawka A, Brierley J, Ezzat S, Goldstein D. Managing newly diagnosed thyroid cancer. CMAJ. 2014;186(4):269–75.

    Article  Google Scholar 

  61. Covidence systematic review software, Veritas Health Innovation, Melbourne, Australia 2023. Available from: www.covidence.org.

  62. Deeks J, Higgins J, Altman De. Chapter 10: Analysing data and undertaking meta-analyses. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane, 2023. Available from www.training.cochrane.org/handbook.

  63. Salerno C, Marciani P, Vanhaecht K, Palin LA, Panella M. Incidence of oncological pathologies 2002–2010 in the southwestern Piedmont area, province of Vercelli, neighbouring municipalities of former nuclear sites. Ann Ig. 2016;28(3):208–17.

    Google Scholar 

  64. Gulis G, Fitz O. Cancer incidence around the nuclear power plant Jaslovske Bohunice. Cent Eur J Public Health. 1998;6(3):183–7.

    CAS  Google Scholar 

  65. Bunch KJ, Vincent TJ, Black RJ, Pearce MS, McNally RJ, McKinney PA, et al. Updated investigations of cancer excesses in individuals born or resident in the vicinity of Sellafield and Dounreay. Br J Cancer. 2014;111(9):1814–23.

    Article  CAS  Google Scholar 

  66. Wang SI, Yaung CL, Lee LT, Chiou SJ. Cancer incidence in the vicinity of nuclear power plants in Taiwan: a population-based study. Environ Sci Pollut Res. 2016;23(1):571–80.

    Article  CAS  Google Scholar 

  67. National Institute of Environmental Health Sciences (NIEHS). Division of the National Toxicology Program. Handbook for Conducting a Literature-Based Health Assessment Using OHAT Approach for Systematic Review and Evidence Integration. Available from: https://ntp.niehs.nih.gov/sites/default/files/ntp/ohat/pubs/handbookmarch2019_508.pdf.

  68. Boogaard H, Patton AP, Atkinson RW, Brook JR, Chang HH, Crouse DL, et al. Long-term exposure to traffic-related air pollution and selected health outcomes: A systematic review and meta-analysis. Environ Int. 2022;164: 107262.

    Article  CAS  Google Scholar 

  69. Peters CE, Quinn EK, Rodriguez-Villamizar LA, MacDonald H, Villeneuve PJ. Exposure to low-dose radiation in occupational settings and ischaemic heart disease: a systematic review and meta-analysis. Occup Environ Med. 2023;80(12):706–14.

    Article  Google Scholar 

  70. Thompson R, Lawrance EL, Roberts LF, Grailey K, Ashrafian H, Maheswaran H, et al. Ambient temperature and mental health: a systematic review and meta-analysis. Lancet Planet Health. 2023;7(7):e580–9.

    Article  Google Scholar 

  71. Higgins J, Green S. Cochrane Handbook for Systematic Reviews of Interventions. 2008.

  72. Borenstein M, Hedges L, Higgins J, Rothstein H. Chapter 13: Fixed-Effect Versus Random-Effects Models. In: John Wiley & Sons L, editor. Introduction to Meta-Analysis. 2009. p. 71-79

  73. West SL, Gartlehner G, Mansfield AJ, Poole C, Tant E, Lenfestey N, Lux LJ, Amoozegar J, Morton SC, Carey TC, Viswanathan M, Lohr KN. Comparative Effectiveness Review Methods: Clinical Heterogeneity. Agency for Healthcare Research and Quality; September 2010. Methods Research Paper. AHRQ Publication No. 10-EHC070-EF. Available at http://effectivehealthcare.ahrq.gov/.

  74. Shen BM, Ji YQ, Tian Q, Shao XZ, Yin LL, Su X. Determination of total tritium in urine from residents living in the vicinity of nuclear power plants in Qinshan, China. Int J Environ Res Public Health. 2015;12(1):888–94.

    Article  Google Scholar 

  75. Evrard AS, Hemon D, Morin A, Laurier D, Tirmarche M, Backe JC, et al. Childhood leukaemia incidence around French nuclear installations using geographic zoning based on gaseous discharge dose estimates. Br J Cancer. 2006;94(9):1342–7.

    Article  Google Scholar 

  76. Committee on the Analysis of Cancer Risks in Populations near Nuclear Facilities-Phase I, Nuclear and Radiation Studies Board, Division on Earth and Life Studies, & National Research Council. Analysis of Cancer Risks in Populations Near Nuclear Facilities: Phase I. National Academies Press (US); 2012. Available at: https://nap.nationalacademies.org/catalog/13388/analysis-of-cancer-risks-in-populations-near-nuclear-facilities-phase.

  77. Mueller W, Gilham C. Childhood leukemia and proximity to nuclear power plants: A systematic review and meta-analysis. J Cancer Policy. 2015;6:44–56.

    Article  Google Scholar 

  78. Higgins JPT, Altman DG, Sterne JAC (editors). Chapter 8: Assessing risk of bias in included studies. In: Higgins JPT, Churchill R, Chandler J, Cumpston MS (editors), Cochrane Handbook for Systematic Reviews of Interventions version 5.2.0 (updated June 2017), Cochrane, 2017. Available from www.training.cochrane.org/handbook.

  79. Liu Y, Su L, Xiao H. Review of Factors Related to the Thyroid Cancer Epidemic. Int J Endocrinol. 2017;2017:5308635.

    Article  Google Scholar 

  80. Byun SH, Min C, Choi HG, Hong SJ. Association between Family Histories of Thyroid Cancer and Thyroid Cancer Incidence: A Cross-Sectional Study Using the Korean Genome and Epidemiology Study Data. Genes (Basel). 2020;11(9):1039.

    Article  CAS  Google Scholar 

  81. Kitahara CM, Platz EA, Freeman LE, Hsing AW, Linet MS, Park Y, et al. Obesity and thyroid cancer risk among U.S. men and women: a pooled analysis of five prospective studies. Cancer Epidemiol Biomarkers Prev. 2011;20(3):464–72.

    Article  Google Scholar 

  82. Clero E, Doyon F, Chungue V, Rachedi F, Boissin JL, Sebbag J, et al. Dietary iodine and thyroid cancer risk in French Polynesia: a case-control study. Thyroid. 2012;22(4):422–9.

    Article  CAS  Google Scholar 

  83. Narendran N, Luzhna L, Kovalchuk O. Sex Difference of Radiation Response in Occupational and Accidental Exposure. Front Genet. 2019;10:260.

    Article  CAS  Google Scholar 

  84. Prysyazhnyuk A, Gristchenko V, Fedorenko Z, Gulak L, Fuzik M, Slipenyuk K, et al. Twenty years after the Chernobyl accident: solid cancer incidence in various groups of the Ukrainian population. Radiat Environ Biophys. 2007;46(1):43–51.

    Article  CAS  Google Scholar 

  85. Furukawa K, Preston D, Funamoto S, Yonehara S, Ito M, Tokuoka S, et al. Long-term trend of thyroid cancer risk among Japanese atomic-bomb survivors: 60 years after exposure. Int J Cancer. 2013;132(5):1222–6.

    Article  CAS  Google Scholar 

  86. Lee WJ, Ko S, Bang YJ, Choe SA, Choi Y, Preston DL. Occupational radiation exposure and cancer incidence in a cohort of diagnostic medical radiation workers in South Korea. Occup Environ Med. 2021;78(12):876–83.

    Article  Google Scholar 

  87. Adliene D, Griciene B, Skovorodko K, Laurikaitiene J, Puiso J. Occupational radiation exposure of health professionals and cancer risk assessment for Lithuanian nuclear medicine workers. Environ Res. 2020;183: 109144.

    Article  CAS  Google Scholar 

  88. Pacini F, Vorontsova T, Demidchik EP, Molinaro E, Agate L, Romei C, et al. Post-Chernobyl thyroid carcinoma in Belarus children and adolescents: comparison with naturally occurring thyroid carcinoma in Italy and France. J Clin Endocrinol Metab. 1997;82(11):3563–9.

    CAS  Google Scholar 

  89. Lubin JH, Adams MJ, Shore R, Holmberg E, Schneider AB, Hawkins MM, et al. Thyroid Cancer Following Childhood Low-Dose Radiation Exposure: A Pooled Analysis of Nine Cohorts. J Clin Endocrinol Metab. 2017;102(7):2575–83.

    Article  Google Scholar 

  90. Tong J, Hei TK. Aging and age-related health effects of ionizing radiation. Radiat Med Protect. 2020;1(1):15–23.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge researchers Gagan Gill (G.G.) and Claudia Waddingham (C.W.) for their invaluable assistance in screening articles and assessing the accuracy of data extracted for the meta-analysis. Additionally, we would also like to thank Heather MacDonald (H.M.), health librarian at Carleton University who assisted in completion of search strategies.

Funding

Funding for this project was provided by the Canadian Institutes of Health Research (CIHR) under grant reference number 438480.

Author information

Authors and Affiliations

  1. Department of Public Health Sciences, School of Medicine, Queens University, 99 University Ave, Kingston, ON, K7L 3N6, Canada

    Susanna Abraham Cottagiri, Will King & Paul J. Villeneuve

  2. Faculty of Health, Industrial University of Santander, Cra. 32, Santander, Bucaramanga, #29-31, Colombia

    Laura Rodriguez-Villamizar

  3. Department of Neuroscience, Health Sciences Building, Carleton University, 1125 Colonel By Drive, Ottawa, ON, K1S 5B6, Canada

    Paul J. Villeneuve

Authors
  1. Susanna Abraham Cottagiri
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Will King
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Laura Rodriguez-Villamizar
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Paul J. Villeneuve
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

S.A.C. contributed to data extraction and validation, formal analysis, interpretation of data, writing original draft, reviewing and editing draft, W.K. contributed to supervision, conceptualization, interpretation of data, and reviewing and editing draft, L.R. contributed to formal analysis and reviewing & editing draft, P.V. contributed to supervision, conceptualization, interpretation of data, reviewing and editing draft, and funding. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Susanna Abraham Cottagiri.

Ethics declarations

Ethics approval and consent to participate

Not applicable (use of secondary data).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cottagiri, S.A., King, W., Rodriguez-Villamizar, L. et al. The risk of thyroid cancer in relation to residential proximity to nuclear power plants: a systematic review and meta-analysis. Environ Health 23, 106 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12940-024-01143-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12940-024-01143-6

Keywords

Environmental Health

ISSN: 1476-069X