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. Author manuscript; available in PMC: 2021 Jul 1.
Published in final edited form as: Environ Res. 2020 Apr 13;186:109516. doi: 10.1016/j.envres.2020.109516

Dioxin exposure and breast cancer risk in a prospective cohort study

Trang VoPham 1,2,3, Kimberly A Bertrand 4, Rena R Jones 5, Nicole C Deziel 6, Natalie C DuPré 2,3,7, Peter James 8, Ying Liu 9,10, Verónica M Vieira 11, Rulla M Tamimi 3,12, Jaime E Hart 2,13, Mary H Ward 5, Francine Laden 2,3,13
PMCID: PMC7363533  NIHMSID: NIHMS1585664  PMID: 32305677

Abstract

Background:

Dioxins are persistent organic pollutants generated from industrial combustion processes such as waste incineration. To date, results from epidemiologic studies of dioxin exposure and breast cancer risk have been mixed.

Objectives:

To prospectively examine the association between ambient dioxin exposure using a nationwide spatial database of industrial dioxin-emitting facilities and invasive breast cancer risk in the Nurses’ Health Study II (NHSII).

Methods:

NHSII includes female registered nurses in the US who have completed self-administered biennial questionnaires since 1989. Incident invasive breast cancer diagnoses were self-reported and confirmed by medical record review. Dioxin exposure was estimated based on residential proximity, duration of residence, and emissions from facilities located within 3, 5, and 10 km around geocoded residential addresses updated throughout follow-up. Cox regression models adjusted for breast cancer risk factors were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs).

Results:

From 1989 to 2013, 3,840 invasive breast cancer cases occurred among 112,397 participants. There was no association between residential proximity to any dioxin facilities (all facilities combined) and breast cancer risk overall. However, women who resided within 10 km of any municipal solid waste incinerator (MSWI) compared to none had increased breast cancer risk (adjusted HR = 1.15, 95% CI: 1.03, 1.28), with stronger associations noted for women who lived within 5 km (adjusted HR = 1.25, 95% CI: 1.04, 1.52). Positive associations were also observed for longer duration of residence and higher dioxin emissions from MSWIs within 3, 5, and 10 km. There were no clear differences in patterns of association for ER+ vs. ER- breast cancer or by menopausal status.

Discussion:

Results from this study support positive associations between dioxin exposure from MSWIs and invasive breast cancer risk.

Keywords: dioxin, environmental exposure, breast cancer, epidemiology

1. Introduction

Dioxins, a class of structurally and chemically related compounds that includes polychlorinated dibenzo- p-dioxins (PCDD or dioxins) and polychlorinated dibenzofurans (PCDF or furans) (hereafter referred to as dioxins) (Schecter et al. 2006; Srogi 2008), are persistent organic pollutants primarily generated during industrial combustion processes such as waste incineration, production of chlorophenols and chlorophenoxy herbicides, processing of metals, bleaching of paper pulp with free chlorine, and production and use of dioxin-like polychlorinated biphenyls (PCBs) (International Agency for Research on Cancer 2012). As dioxins are highly lipophilic and environmentally persistent, they accumulate in adipose tissues (International Agency for Research on Cancer 2012). Human exposure mainly occurs through dietary ingestion of meat, milk, eggs, and fish as well as through inhalation and dermal absorption (International Agency for Research on Cancer 2012). Humans can also be exposed through occupation, accidental releases, and from ambient air emissions as a result of dispersion, transport, and deposition from industrial point sources (e.g., municipal solid waste incinerators, MSWIs) into the surrounding environment (Jones et al. 2018; Srogi 2008). Populations residing near industrial sources may experience higher ambient exposures as elevated PCDD/F have been detected in soil samples near MSWIs and in residential dust concentrations sampled from homes near industrial facilities (<3-12 km) (Deziel et al. 2012; Deziel et al. 2017; Floret et al. 2006).

The International Agency for Research on Cancer (IARC) classified 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), the most biologically active congener, as a Group 1 human carcinogen primarily based on evidence from all cancers combined in occupationally exposed populations (International Agency for Research on Cancer 1997). Further, 2,3,4,7,8-pentachlorodibenzofuran and 3,3’,4,4’,5-pentachlorobiphenyl were also classified as Group 1 human carcinogens (International Agency for Research on Cancer 2012). Experimental evidence has shown that dioxins may impact breast carcinogenesis through mechanisms related to binding and activation of the aryl hydrocarbon receptor (AhR), which is associated with cell growth, proliferation, and apoptosis (Birnbaum and Fenton 2003). Mixed results from epidemiologic studies of dioxin exposure and breast cancer risk, which have shown positive, inverse, and null associations, may reflect limitations associated with small sample sizes, differences in outcome definitions (e.g., incidence vs. mortality), occupational vs. non-occupational study populations, residual confounding, and differences in exposure assessment (Consonni et al. 2008; Dai and Oyana 2008; Danjou et al. 2015; Danjou et al. 2019; Hardell et al. 1996; Kogevinas et al. 1997; Manuwald et al. 2012; Pesatori et al. 2009; Revich et al. 2001; Reynolds et al. 2005; Viel et al. 2008; Warner et al. 2002; Warner et al. 2011). Further, although emissions may be important in non-occupational settings (Jones et al. 2018), to our knowledge, only two studies have investigated dioxin exposures that are not related to occupational exposures or industrial accidents and breast cancer risk, showing null and inverse associations (Danjou et al. 2019; Viel et al. 2008). However, these studies were limited by the small number of cases living close to dioxin-emitting sources, lack in exposure variability, as well as a relatively small total number of breast cancer cases (Danjou et al. 2019; Viel et al. 2008), making it difficult to interpret the results. The objective of the present analysis was to prospectively examine the association between ambient exposure to dioxins from industrial sources using a comprehensive nationwide spatial database of dioxin-emitting facilities and invasive breast cancer risk.

2. Materials and Methods

2.1. Study population

The Nurses’ Health Study II (NHSII) is an ongoing US nationwide prospective cohort study of 116,429 female registered nurses aged 25-42 years at baseline in 1989 (Bao et al. 2016). Participants (originally residents of California, Connecticut, Indiana, Iowa, Kentucky, Massachusetts, Michigan, Missouri, New York, North Carolina, Ohio, Pennsylvania, South Carolina, and Texas) have lived in all 50 states and Washington, D.C. as of the mid-1990’s. Participants completed self-administered questionnaires every two years to ascertain information regarding incident disease, medical history, diet, lifestyle, and health behaviors. Response rates for each questionnaire cycle exceed 90% (Bao et al. 2016). We excluded women who were missing exposure information (e.g., missing addresses throughout follow-up) or with prior diagnoses of other cancers (except non-melanoma skin cancer). The study protocol was approved by the institutional review board of the Brigham and Women’s Hospital and the human subjects committee of the Harvard T.H. Chan School of Public Health, and those of participating registries as required. Participants provided implied consent through returning questionnaires and written informed consent for release of medical records and collection of tissue specimens.

2.2. Outcome assessment

Invasive breast cancer cases were primarily identified through self-report on biennial questionnaires. Deaths were ascertained from family members, US Postal Service, or National Death Index. A medical record review confirmed breast cancer cases and provided information on tumor characteristics. As 99% of reported breast cancer cases were confirmed through medical record review, self-reported cases not confirmed by medical records were also included in the analysis. We examined breast cancer subtypes defined by hormone receptor status based on tissue microarrays (TMAs) constructed at the Dana-Farber/Harvard Cancer Center Tissue Microarray Core Facility. Three 0.6 mm diameter cores from tumor tissue samples were inserted into TMA blocks. Immunohistochemical staining for markers (e.g., estrogen receptor [ER]) was performed on 5 μm paraffin sections cut from TMA blocks. Immunostained TMA sections were reviewed under a microscope and visually scored for ER positivity as determined by any nuclear staining (≥1%) (Sisti et al. 2016; Tamimi et al. 2008). If TMA information was unavailable, hormone receptor status was determined using the medical record or pathology report.

2.3. Exposure assessment

Biennially updated residential address histories for each participant beginning in 1989 were geocoded to the street or ZIP Code level (Figure 1a). To estimate ambient airborne dioxin exposure, we used a nationwide database of industrial facilities emitting PCDD/F (Figure 1b) from the US Environmental Protection Agency (USEPA) (Jones et al. 2018; US Environmental Protection Agency 2006). This database included information on the facility name, latitude and longitude of the smokestack location, city, county, state, and estimated air emissions of PCDD/F (ng toxic equivalency quotient [TEQ]/year) in 1987 for two facility types MSWIs and secondary copper smelters and in 1995 for ten facility types: cement kilns (hazardous), cement kilns (non-hazardous), coal-fired power plants, hazardous waste incinerators, industrial boilers, iron ore sintering plants, medical waste incinerators, MSWIs, secondary copper smelters, and sewage sludge incinerators (Table S1, Supplementary data). These ten types of facilities accounted for more than 85% of PCDD/F air emissions in the 1990s in the US (US Environmental Protection Agency 2006). In addition, we included information from an ESRI (Redlands, CA) database of hospital locations presumed to have medical waste incinerators; smokestack locations were identified using multiple spatial data sources (Jones et al. 2018) and emissions were estimated based on methods detailed below.A re-review, conducted by Jones et al. (2018), of randomly selected facilities was conducted to determine how consistently facility locations could be identified compared to the original review that created the dioxin database (Jones et al. 2018). A total of n = 240 facilities were examined, including 200 locations that were verified and 40 that were not verified in the original review. Agreement in verification (location confirmed or not) and distances between verified locations (verification error) were estimated. Overall agreement in verification was high (84.2%) and verification errors were small (median = 84 meters) (Jones et al. 2018).

Figure 1.

Figure 1.

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(a) Geocoded residential addresses for NHSII participants (1989-2011) and (b) industrial dioxin-emitting facilities across the US. The scale bar is associated with the continental US.

Using a geographic information system (GIS), we created three time-varying dioxin exposure metrics considering facilities located within 3, 5, and 10 km around each participant’s geocoded residential address updated every two years over the study time period: (1) residential proximity, (2) duration of residence, and (3) emissions. We assumed all facilities were operational during the study time period. The selected distances were based on prior evidence suggesting that they are relevant to human exposures (Deziel et al. 2012; Deziel et al. 2017) and health outcomes (Danjou et al. 2019; Pronk et al. 2013). For example, an emission index (calculated using methods applied in this analysis) that incorporated residential proximity to and emissions from industrial dioxin facilities within 5 km was associated with higher carpet dust concentrations of PCDD/F congeners (e.g., TCDD) in 100 homes in the US (Deziel et al. 2017). Another study of 40 homes in the US showed that residential proximity within 3 and 5 km of cement kilns was associated with higher PCDD/F carpet dust concentrations such as for TCDD (Deziel et al. 2012).

(1) Residential proximity (yes, no) was defined as living within the specified distance (i.e., 3, 5, or 10 km) of any (≥1) dioxin-emitting facilities. (2) Duration of residence (years) was defined as the number of years living within the specified distance of any (≥1) dioxin-emitting facilities. (3) The emission index was calculated using estimated air emissions (ng TEQ/year) for each facility in the database. Emissions expressed in units of ng TEQ/year incorporate the potency of each PCDD/F compound relative to TCDD; higher emission values indicate higher toxicity (International Agency for Research on Cancer 1997). If a facility was missing air emissions data, the average of the estimated air emissions for the facility type in 1995 was used for imputation (Pronk et al. 2013). To estimate changes in air emissions for each facility and each year over the study time period, we used the average emission levels for each facility type in 1987, 1995, 2000, and 2012 obtained from a USEPA national survey of dioxin-emitting facilities and an updated dioxin inventory (Dwyer and Themelis 2015; US Environmental Protection Agency 2006). The linear rate of change in emissions between 1987 and 1995, 1995 and 2000, and 2000 and 2012 was estimated by facility type (Pronk et al. 2013). For each year, we calculated each facility’s estimated emissions by multiplying the rate of change by the facility’s emission levels separately in 1995 and 2000. An inverse distance weighting calculation was applied to emissions from each facility within 3, 5, or 10 km of a residence (i.e., facilities located closer to a residence were weighted more); weighted emissions were summed across all facilities near each residence for each year (Pronk et al. 2013). A time-varying cumulative average emission index was calculated, where the emission index from previous years was averaged and updated every two years over the course of follow-up.

For each specified distance, the duration of residence and emission metrics were defined as three-level categorical variables with categories for no exposure, exposure greater than zero and less than or equal to the median value among participants who ever resided near any dioxin-emitting facility over follow-up, and exposure greater than the median value (Pronk et al. 2013). Overall (referred to as all facilities combined) and facility-specific metrics for residential proximity and duration of residence were examined. We a priori examined emission metrics for all facilities combined and for any facility type showing statistically significant positive associations with breast cancer risk in multivariable models using the residential proximity and duration of residence metrics. Only the MSWI facility type satisfied this criterion. All spatial analyses were conducted using ArcMap 10.5.1 (Esri, Redlands, CA).

2.4. Statistical analysis

Cox proportional hazards regression models were fit to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between each of the time-varying dioxin exposure metrics (residential proximity, duration of residence, and emission index) and invasive breast cancer risk. All models were stratified by age and questionnaire period. Person-time accrued from June 1989 until the end of follow-up in May 2013, incidence of invasive breast cancer, in situ breast cancer, or other cancer (excluding non-melanoma skin cancer), death, or loss to follow-up, whichever occurred first. We a priori determined that the following established and suspected breast cancer risk factors would be included in multivariable models, which were collected from biennial questionnaires (or every other questionnaire for diet and physical activity) or ascertained by linking each participant’s locational information with US Census Bureau data: age (years), race (white, non-white), family history of breast cancer (yes, no), personal history of biopsy-confirmed benign breast disease (BBD) (yes, no), age at menarche (years), parity and age at first birth (nulliparous, 1-2 children and <25 years, 1-2 children and ≥25-30, 1-2 children and ≥30, 3+ children and <25, 3+ children and ≥25-30, 3+ children and ≥30), lactation (never, ever), menopausal status and hormone use (among postmenopausal women only) (never, past, current), height (in), body mass index (BMI) at age 18 (kg/m2), change in BMI since age 18 (kg/m2), physical activity (MET hours/week), smoking status (never, past, current), adult alcohol consumption (g/day), individual-level socioeconomic status (SES) (i.e., personal income [>$100,000, ≤$100,000], marital status [married, not married], and living arrangements [living alone, not living alone]), area-level SES (i.e., Census tract median home value [$10,000] and Census tract median income [$]), and population density (population/mi2). We present results from a basic model adjusted for age and race and a fully adjusted model adjusting for the other aforementioned covariates. We also considered a model that additionally adjusted for oral contraceptive (OC) use (never, past, current), screening mammography (yes, no), animal fat intake (g/day), total fat intake (g/day), alcohol consumption at age 15 and 18 (g/day), Alternate Healthy Eating Index (AHEI) diet score (continuous) (Chiuve et al. 2012), fine particulate matter (PM) air pollution <2.5 μm in diameter (PM2.5, μg/m3), and coarse PM air pollution between 2.5 and 10 μm in diameter (PM2.5-10, μg/m3) (Yanosky et al. 2014). Population density and PM were proxies for high-traffic areas as other sources of airborne dioxin exposure include combustion of gasoline- and diesel-fueled engines (Dopico and Gomez 2015). However, as these variables did not change the effect estimate for dioxin exposure (≥10% change in HR), they were not included in the final model.

We examined potential effect modification by race, menopausal status, BMI, physical activity, animal fat intake, total fat intake, PM2.5, PM2.5-10, and US Census Bureau region of residence (i.e., Northeast, Midwest, West, and South). Differential associations with either dioxin or population subgroups with higher risk or susceptibility for breast cancer have been observed according to these variables (Breivik et al. 2004; DeSantis et al. 2014; International Agency for Research on Cancer 2012; Kaupp et al. 1994; Stavraky and Emmons 1974). Tests for interaction were performed by adding interaction terms to the model and using likelihood ratio tests to determine statistical significance. We also conducted sensitivity analyses according to residential mobility (movers, non-movers) to assess the potential impact of exposure measurement error. The missing indicator method was used to account for any missing covariates. All statistical analyses were conducted using SAS 9.4 (SAS Institute, Cary, NC).

3. Results

Population characteristics for the 112,397 participants included in the analysis overall and by residential proximity to any dioxin-emitting facilities are shown in Table 1. Over follow-up, participants were on average aged 44.9 ± 8.2 years, mostly white (96%), premenopausal (70%), married (93%), never-smokers (65%), with an average BMI of 25.4 ± 4.9 kg/m2, average animal fat intake of 33.9 ± 8 g/day, and living in areas with an average Census tract median income of $63,929 ± 23,634. Participants living near any dioxin-emitting facilities (3, 5, or 10 km) were more likely to be nulliparous, less likely to be married, and more likely to live in more densely populated areas as well as in the Northeast and Midwest regions of the US.

Table 1.

Age-adjusted characteristics of 112,397 NHSII women over follow-up from 1989-2013 by residential proximity to dioxin-emitting facilities

Residential proximity to any (≥1) dioxin-emitting facilities
3 km 5 km 10 km
Variable Overall No Yes No Yes No Yes
Person-years (n) 2,302,566 1,995,939 306,627 1,693,295 609,271 1,055,170 1,247,396
Age (years) (mean ± SD) 44.9 ± 8.2 45.1 ± 8.2 44.1 ± 8.4 45.2 ± 8.2 44.2 ± 8.4 45.4 ± 8.2 44.5 ± 8.3
White (%) 96 96 95 96 95 96 96
Family history of breast cancer (%) 11 11 11 11 11 11 11
Personal history of biopsy-confirmed benign breast disease (BBD) (%) 17 17 17 17 17 17 17
Menopausal status and hormone use (%)
   Premenopausal 70 70 70 70 70 70 70
   Never users 5 5 6 5 6 5 6
   Past users 10 10 10 10 10 10 10
   Current users 8 8 8 8 8 9 8
   Missing 6 6 7 6 7 6 7
Age at menarche (years) (mean ± SD) 12.4 ± 1.4 12.4 ± 1.4 12.4 ± 1.5 12.4 ± 1.4 12.4 ± 1.4 12.4 ± 1.4 12.4 ± 1.4
Parity and age at first birth (%)
   Nulliparous 18 18 23 17 21 16 20
   1-2 children <25 15 15 14 15 14 16 14
   1-2 children ≥25-30 20 21 19 21 19 21 20
   1-2 children ≥30 14 14 13 13 14 13 14
   3+ children <25 12 12 11 12 10 13 11
   3+ children ≥25-30 10 10 9 10 10 10 10
   3+ children ≥30 2 2 2 2 3 2 3
   Missing 9 9 9 9 9 9 9
Breastfeeding (%)
   Never 12 13 13 13 13 13 14
   Ever 57 58 52 58 53 59 55
   Missing 9 9 9 9 9 9 9
Oral contraceptive (OC) use (%)
   Never 12 12 14 12 14 11 13
   Past 75 76 73 76 74 77 74
   Current 7 7 7 7 7 7 7
   Missing 5 5 6 5 6 5 6
Height (in) (mean ± SD) 64.9 ± 2.6 64.9 ± 2.6 64.8 ± 2.6 64.9 ± 2.6 64.8 ± 2.6 64.9 ± 2.6 64.8 ± 2.6
Body mass index (BMI) (kg/m2) at age 18 (mean ± SD) 21.2 ± 3.1 21.2 ± 3.1 21.4 ± 3.3 21.2 ± 3.1 21.3 ± 3.2 21.2 ± 3.1 21.2 ± 3.2
Current BMI (kg/m2) (mean ± SD) 25.4 ± 4.9 25.4 ± 4.9 25.4 ± 5.0 25.4 ± 4.9 25.4 ± 4.9 25.4 ± 4.9 25.4 ± 4.9
Smoking status (%)
   Never 65 66 63 66 63 67 64
   Past 25 25 26 24 26 24 26
   Current 9 9 11 9 10 9 10
Alternate Healthy Eating Index (AHEI) (mean ± SD) 53.4 ± 11.2 53.4 ± 11.2 53.6 ± 11.2 53.4 ± 11.2 53.5 ± 11.1 53.3 ± 11.3 53.5 ± 11.1
Cumulative average total fat intake (g/day) 62.5 ± 9.6 62.5 ± 9.6 62.2 ± 9.7 62.6 ± 9.6 62.2 ± 9.6 62.8 ± 9.6 62.3 ± 9.5
Cumulative average animal fat intake (g/day) 33.9 ± 8.0 34.0 ± 8.0 33.7 ± 8.1 34.0 ± 8.0 33.7 ± 8.0 34.1 ± 8.1 33.8 ± 7.9
Adult alcohol consumption (g/day) (mean ± SD) 3.2 ± 5.3 3.2 ± 5.3 3.4 ± 5.5 3.2 ± 5.3 3.3 ± 5.4 3.2 ± 5.4 3.3 ± 5.3
Physical activity (MET hours/week) (mean ± SD) 20.1 ± 28.0 20.0 ± 27.8 20.7 ± 28.9 20.0 ± 27.8 20.4 ± 28.4 19.9 ± 27.8 20.2 ± 28.1
Census tract median home value ($10,000) (mean ± SD) 16.4 ± 12.2 16.5 ± 11.6 15.9 ± 15.1 16.4 ± 11.4 16.3 ± 14.0 16.2 ± 11.6 16.6 ± 12.6
Census tract median income ($) (mean ± SD) 63,929 ± 23,634 64,367 ± 23,231 61,133 ± 25,926 64,133 ± 22,972 63,421 ± 25,387 61,761 ± 22,388 65,789 ± 24,503
Individual-level income >$100,000 (%) 23 23 22 23 23 22 24
Married (%) 93 93 89 94 90 94 91
Living alone (%) 8 7 11 7 10 6 9
Region of residence (%)
   Northeast 33 33 39 31 39 26 40
   Midwest 33 31 42 30 40 27 37
   West 15 17 6 19 6 25 6
   South 19 20 13 20 15 21 17
Cumulative average PM2.5 (10 μg/m3) (mean ± SD) 1.5 ± 0.3 1.5 ± 0.3 1.6 ± 0.3 1.5 ± 0.3 1.6 ± 0.3 1.5 ± 0.3 1.6 ± 0.3
Cumulative average PM2.5-10 (10 μg/m3) (mean ± SD) 1.1 ± 0.5 1.1 ± 0.5 1.0 ± 0.4 1.1 ± 0.5 1.0 ± 0.4 1.1 ± 0.6 1.0 ± 0.4
Population density (population/mi2) (mean ± SD) 3,788 ± 10,916 3,001 ± 7,338 8,850 ± 22,511 2,616 ± 5,870 7,008 ± 18,349 1,905 ± 2,957 5,359 ± 14,332
Ever-moved Census tract (%) 78 78 79 78 78 78 79
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From 1989 to 2013 (2,302,566 person-years of follow-up), 3,840 invasive breast cancers occurred. Of these cases, there were 2,332 ER+ cases and 585 ER− cases. Residential proximity to any dioxin-emitting facilities (all facilities combined) within 3 km (adjusted HR = 1.03, 95% CI: 0.93, 1.13), 5 km (adjusted HR = 1.04, 95% CI: 0.97, 1.12), and 10 km (adjusted HR = 1.07, 95% CI: 1.00, 1.14) compared to none was not associated with invasive breast cancer risk (Table 2). Similar null associations were observed for ER+ and ER− breast cancers (Table 2) and among premenopausal (Table S2, Supplementary data) and postmenopausal women (Table S3, Supplementary data). Results from the basic model adjusted for age and race did not substantially differ after adjustment for breast cancer risk factors, individual- and area-level SES, and population density in fully adjusted models.

Table 2.

Associations between dioxin exposure and invasive breast cancer risk in NHSII (1989-2013) (n=112,397)

Invasive breast cancer ER+ ER−
Exposure metric Cases/
person-years		 Basica
HR (95% CI)		 Fully adjustedb
HR (95% CI)		 Cases/
person-years		 Basica
HR (95% CI)		 Fully adjustedb
HR (95% CI)		 Cases/
person-years		 Basica
HR (95% CI)		 Fully adjustedb
HR (95% CI)
1. Residential proximity
 All facilities
  3 km
    No 3349/1995939 Referent Referent 2026/1997202 Referent Referent 506/1998553 Referent Referent
    Yes 491/306627 1.01 (0.92, 1.11) 1.03 (0.93, 1.13) 306/306804 1.04 (0.92, 1.17) 1.05 (0.93, 1.19) 69/307025 0.90 (0.70, 1.16) 0.92 (0.71, 1.19)
  5 km
    No 2834/1693295 Referent Referent 1713/1694352 Referent Referent 429/1695479 Referent Referent
    Yes 1006/609271 1.04 (0.97, 1.12) 1.04 (0.97, 1.12) 619/609654 1.06 (0.97, 1.16) 1.05 (0.96, 1.16) 146/610099 0.97 (0.80, 1.17) 0.98 (0.81, 1.19)
  10 km
    No 1729/1055170 Referent Referent 1052/1055809 Referent Referent 269/1056502 Referent Referent
    Yes 2111/1247396 1.09 (1.02, 1.16) 1.07 (1.00, 1.14) 1280/1248197 1.09 (1.00, 1.18) 1.06 (0.98, 1.16) 306/1249076 0.98 (0.83, 1.15) 0.98 (0.83, 1.16)
 Municipal solid waste incinerators (MSWIs)
  3 km
    No 3804/2283160 Referent Referent 2312/2284582 Referent Referent 570/2286142 Referent Referent
    Yes 36/19406 1.20 (0.86, 1.67) 1.20 (0.86, 1.68) 20/19424 1.09 (0.70, 1.69) 1.08 (0.69, 1.68) 5/19436 1.06 (0.44, 2.56) 1.11 (0.46, 2.72)
  5 km
    No 3720/2238551 Referent Referent 2261/2239945 Referent Referent 556/2241472 Referent Referent
    Yes 120/64015 1.23 (1.02, 1.48) 1.25 (1.04, 1.52) 71/64061 1.19 (0.94, 1.50) 1.19 (0.93, 1.52) 19/64106 1.26 (0.79, 1.99) 1.35 (0.84, 2.19)
  10 km
    No 3428/2069456 Referent Referent 2086/2070747 Referent Referent 523/2072141 Referent Referent
    Yes 412/233110 1.14 (1.03, 1.26) 1.15 (1.03, 1.28) 246/233259 1.12 (0.98, 1.28) 1.11 (0.96, 1.27) 52/233436 0.91 (0.69, 1.22) 0.94 (0.70, 1.27)
2. Duration of residence (years)c
 All facilities
  3 km
    0 3000/1803070 Referent Referent 1823/1804184 Referent Referent 453/1805402 Referent Referent
    1-6 387/286292 0.98 (0.88, 1.09) 0.96 (0.87, 1.07) 228/286447 0.97 (0.85, 1.12) 0.95 (0.82, 1.09) 68/286593 1.06 (0.82, 1.38) 1.08 (0.84, 1.40)
    >6 453/213204 1.01 (0.91, 1.11) 1.02 (0.92, 1.12) 281/213375 1.04 (0.92, 1.18) 1.05 (0.92, 1.19) 54/213583 0.89 (0.67, 1.18) 0.92 (0.69, 1.22)
  5 km
    0 2365/1427586 Referent Referent 1424/1428469 Referent Referent 362/1429408 Referent Referent
    1-6 569/438551 1.01 (0.92, 1.11) 0.98 (0.89, 1.08) 353/438754 1.06 (0.94, 1.20) 1.02 (0.91, 1.15) 91/438991 0.95 (0.75, 1.20) 0.95 (0.75, 1.21)
    >6 906/436428 1.00 (0.92, 1.08) 0.99 (0.91, 1.07) 555/436783 1.04 (0.94, 1.15) 1.02 (0.92, 1.13) 122/437178 0.99 (0.80, 1.22) 1.00 (0.81, 1.24)
  10 km
    0 1291/807759 Referent Referent 785/808239 Referent Referent 208/808745 Referent Referent
    1-6 620/584944 0.99 (0.89, 1.09) 0.95 (0.86, 1.05) 386/585157 1.04 (0.91, 1.18) 0.99 (0.87, 1.13) 112/585397 0.91 (0.71, 1.16) 0.90 (0.70, 1.15)
    >6 1929/909863 1.08 (1.01, 1.16) 1.05 (0.97, 1.13) 1161/910610 1.09 (0.99, 1.20) 1.05 (0.96, 1.16) 255/911436 0.99 (0.81, 1.20) 0.98 (0.81, 1.20)
 MSWIs
  3 km
    0 3767/2266905 Referent Referent 2293/2268310 Referent Referent 564/2269856 Referent Referent
    1-6 36/23027 1.10 (0.79, 1.53) 1.07 (0.77, 1.49) 18/23043 0.94 (0.59, 1.49) 0.90 (0.56, 1.43) 7/23055 1.37 (0.65, 2.90) 1.39 (0.65, 2.98)
    >6 37/12634 1.39 (1.01, 1.93) 1.39 (1.00, 1.93) 21/12653 1.30 (0.84, 2.00) 1.28 (0.83, 1.98) 4/12667 1.12 (0.42, 3.00) 1.20 (0.44, 3.23)
  5 km
    0 3636/2196373 Referent Referent 2214/2197728 Referent Referent 546/2199227 Referent Referent
    1-6 91/62258 1.09 (0.88, 1.34) 1.05 (0.85, 1.29) 51/62296 1.02 (0.77, 1.35) 0.96 (0.73, 1.27) 14/62326 1.06 (0.62, 1.81) 1.11 (0.65, 1.90)
    >6 113/43934 1.26 (1.05, 1.52) 1.27 (1.05, 1.54) 67/43982 1.23 (0.97, 1.57) 1.22 (0.95, 1.57) 15/44025 1.26 (0.75, 2.12) 1.34 (0.79, 2.29)
  10 km
    0 3259/1977088 Referent Referent 1985/1978302 Referent Referent 497/1979638 Referent Referent
    1-6 207/159640 1.06 (0.91, 1.22) 1.01 (0.88, 1.17) 118/159733 1.01 (0.84, 1.22) 0.96 (0.80, 1.16) 39/159796 1.17 (0.84, 1.62) 1.19 (0.85, 1.66)
    >6 374/165838 1.12 (1.01, 1.25) 1.11 (0.99, 1.24) 229/165971 1.14 (0.99, 1.31) 1.12 (0.97, 1.29) 39/166143 0.85 (0.61, 1.18) 0.87 (0.62, 1.23)
3. Emission index (ng TEQ/year per km2)c
 All facilities
  3 km
    0 3007/1809819 Referent Referent 1827/1810937 Referent Referent 455/1812156 Referent Referent
    0-0.09 418/246917 1.01 (0.91, 1.12) 0.99 (0.90, 1.10) 251/247085 1.02 (0.89, 1.16) 0.99 (0.87, 1.13) 64/247244 1.05 (0.81, 1.37) 1.07 (0.83, 1.40)
    >0.09 415/245830 0.99 (0.89, 1.10) 1.00 (0.90, 1.11) 254/245984 1.02 (0.89, 1.16) 1.02 (0.89, 1.17) 56/246179 0.90 (0.68, 1.19) 0.93 (0.70, 1.23)
  5 km
    0 2386/1437286 Referent Referent 1434/1438182 Referent Referent 366/1439122 Referent Referent
    0-0.05 702/433082 0.96 (0.88, 1.05) 0.94 (0.86, 1.02) 446/433335 1.04 (0.94, 1.16) 1.01 (0.90, 1.12) 96/433659 0.88 (0.70, 1.10) 0.88 (0.70, 1.10)
    >0.05 752/432198 1.03 (0.95, 1.12) 1.03 (0.94, 1.12) 452/432489 1.05 (0.94, 1.17) 1.04 (0.93, 1.15) 113/432797 1.04 (0.84, 1.29) 1.06 (0.86, 1.32)
  10 km
    0 1305/816691 Referent Referent 796/817173 Referent Referent 209/817689 Referent Referent
    0-0.03 1284/744208 1.06 (0.98, 1.14) 1.02 (0.94, 1.10) 786/744681 1.08 (0.98, 1.20) 1.04 (0.94, 1.15) 177/745230 0.93 (0.76, 1.13) 0.91 (0.74, 1.12)
    >0.03 1251/741667 1.06 (0.98, 1.14) 1.02 (0.94, 1.11) 750/742153 1.06 (0.96, 1.17) 1.02 (0.92, 1.13) 189/742658 1.01 (0.83, 1.24) 1.02 (0.83, 1.25)
 MSWIs
  3 km
    0 3768/2267786 Referent Referent 2294/2269191 Referent Referent 564/2270737 Referent Referent
    0-4.70 30/17418 1.06 (0.74, 1.52) 1.04 (0.72, 1.49) 18/17430 1.07 (0.67, 1.70) 1.04 (0.65, 1.65) 2/17444 0.49 (0.12, 1.98) 0.49 (0.12, 1.98)
    >4.70 42/17362 1.41 (1.04, 1.92) 1.40 (1.03, 1.92) 20/17385 1.13 (0.72, 1.75) 1.09 (0.69, 1.70) 9/17396 2.03 (1.05, 3.95) 2.24 (1.13, 4.47)
  5 km
    0 3645/2198722 Referent Referent 2220/2200080 Referent Referent 546/2201582 Referent Referent
    0-1.95 92/52164 1.09 (0.89, 1.34) 1.06 (0.86, 1.30) 50/52203 0.99 (0.74, 1.31) 0.94 (0.71, 1.25) 14/52230 1.16 (0.68, 1.97) 1.17 (0.68, 1.99)
    >1.95 103/51680 1.20 (0.99, 1.46) 1.21 (0.98, 1.48) 62/51723 1.21 (0.94, 1.55) 1.18 (0.90, 1.54) 15/51766 1.21 (0.72, 2.02) 1.33 (0.77, 2.30)
  10 km
    0 3264/1981883 Referent Referent 1987/1983100 Referent Referent 497/1984440 Referent Referent
    0-0.48 275/161251 1.05 (0.93, 1.19) 1.01 (0.89, 1.15) 160/161362 1.02 (0.87, 1.20) 0.98 (0.83, 1.15) 39/161472 1.01 (0.73, 1.40) 1.01 (0.72, 1.40)
    >0.48 301/159431 1.16 (1.03, 1.31) 1.16 (1.03, 1.32) 185/159544 1.19 (1.02, 1.38) 1.18 (1.00, 1.38) 39/159666 1.00 (0.72, 1.38) 1.06 (0.75, 1.50)
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a

Adjusted for age, race.

b

Additionally adjusted for family history of breast cancer, personal history of biopsy-confirmed BBD, age at menarche, parity, age at first birth, lactation, menopausal status and hormone use (among postmenopausal women only), height, BMI at age 18, change in BMI since age 18, physical activity, smoking status, adult alcohol consumption, Census tract median home value, Census tract median income, marital status, living arrangements, individual-level income, population density.

c

For each specified distance of 3, 5, and 10 km, the duration of residence and emission metrics were defined as three-level categorical variables with categories for no exposure, exposure greater than zero and less than or equal to the median value among participants who ever resided near any dioxin-emitting facility over follow-up, and exposure greater than the median value.

However, there were statistically significant positive associations between dioxin emissions from MSWIs and invasive breast cancer risk (Table 2). Residential proximity to MSWIs within 3 km (adjusted HR = 1.20, 95% CI: 0.86, 1.68), 5 km (adjusted HR = 1.25, 95% CI: 1.04, 1.52), and 10 km (adjusted HR = 1.15, 95% CI: 1.03, 1.28) was associated with invasive breast cancer risk. Longer duration of residence (>6 years compared to 0 years) near MSWIs within 3 km (adjusted HR = 1.39, 95% CI: 1.00, 1.93), 5 km (adjusted HR = 1.27, 95% CI: 1.05, 1.54), and 10 km (adjusted HR = 1.11, 95% CI: 0.99, 1.24) was associated with invasive breast cancer risk. Higher levels of emissions from MSWIs within 3 km (>4.70 ng TEQ/year per km2 compared to 0 ng TEQ/year per km2: adjusted HR = 1.40, 95% CI: 1.03, 1.92), 5 km (>1.95 ng TEQ/year per km2 compared to 0 ng TEQ/year per km2: adjusted HR = 1.21, 95% CI: 0.98, 1.48), and 10 km (>0.48 ng TEQ/year per km2 compared to 0 ng TEQ/year per km2: adjusted HR = 1.16, 95% CI: 1.03, 1.32) were associated with invasive breast cancer risk. In general, positive associations were observed for both ER+ and ER− breast cancer, although effect estimates were less precise due to small numbers in stratified analyses and results were not consistent by exposure metric (Table 2). Positive associations were observed among premenopausal (Table S2, Supplementary data) and postmenopausal women (Table S3, Supplementary data). Dioxin exposure from other facility types was not associated with breast cancer risk (data not shown).

There were no statistically significant interactions between dioxin exposure and race, menopausal status, BMI, physical activity, animal fat intake, total fat intake, PM2.5, PM2.5-10, and region of residence (data not shown). In sensitivity analyses, similar results were observed among participants who did not move (data not shown).

4. Discussion

We did not observe an association between proximity-, duration-, and emissions-based metrics for exposures to any dioxin-emitting facilities (all facilities combined) and risk of invasive breast cancer in the NHSII prospective cohort. However, we observed statistically significant positive associations between dioxin emissions from MSWIs located within 3, 5, and 10 km of residences and invasive breast cancer risk. There were no clear differences in patterns of associations by ER status or menopausal status. To our knowledge, this is the first epidemiologic study to prospectively examine the association between ambient dioxin exposure from industrial sources and breast cancer risk in a non-occupationally exposed US population.

Given that regulation of particular dioxin-emitting facilities (e.g., MSWIs, medical waste incinerators, and hazardous waste incinerators) was not promulgated in the US until the late 1990s (Institute of Medicine 2003) and the estimated half-life for TCDD is 7 to 11 years among humans (Pirkle et al. 1989), ambient exposures from residential proximity to industrial sources of PCDD/F may represent an important source for individuals living nearby via direct releases or migration of dioxins in the air or through contact with polluted soil (Jones et al. 2018). Higher levels of particular PCDD/F congeners were observed in carpet dust from homes near industrial dioxin facilities in the US (Deziel et al. 2012; Deziel et al. 2017). A doubling of the emission index (on the arithmetic scale) incorporating facilities within 5 km of homes was associated with a 4.4 (for 1,2,3,4,6,7,8-heptachlorodibenzodioxin) to 7.9% (for TCDD) increase in PCDD/F carpet dust concentrations (Deziel et al. 2017). Higher MSWI- and medical waste incinerator-specific emission indices (≥median compared to 0) were associated with increased carpet dust concentrations for PCDD/F congeners (e.g., TCDD and octachlorodibenzodioxin) (Deziel et al. 2017). Further, average emissions from MSWI were among the highest of any facility type in the US (87.42 ng TEQ/year in 1987 and 19.88 ng TEQ/year in 1995) (Pronk et al. 2013). Several studies have shown that living near industrial facilities is associated with higher levels of dioxins measured in blood among non-occupationally exposed populations. For example, individuals living within 5 km compared to >5 km of a chemical plant in Russia producing chlorinated compounds had higher serum dioxin levels (Akhmedkhanov et al. 2002; Burns et al. 2009). Other studies in the Czech Republic, Spain, and Taiwan have shown similar associations (Cerna et al. 2007; Lee et al. 2006; Zubero et al. 2009). However, some studies have shown no association between residential proximity to incinerators and dioxins measured in blood or breast milk, although results may have been influenced by small sample sizes, not accounting for time spent outdoors, operational status of the facilities, and/or differences in stack emissions by facility type (De Felip et al. 2008; Deml et al. 1996; Gonzalez et al. 2000; Schuhmacher et al. 1999; Tajimi et al. 2005; Zubero et al. 2017).

PCDD/F are endocrine disrupting compounds, showing high affinity towards binding and activation of the aryl hydrocarbon receptor, an important mediator of cell growth, proliferation, and anti-apoptosis pathways (Birnbaum and Fenton 2003; Gray et al. 2017; International Agency for Research on Cancer 2012). TCDD has been shown to delay proliferation and differentiation of the mammary gland in rats by increasing the number of terminal end buds, lengthening the window of sensitivity to potential carcinogens (Birnbaum and Fenton 2003; Brown et al. 1998; Lewis et al. 2001). In M13SV1 human breast epithelial cells, TCDD exposure induced alterations in the expression of genes involving ERK2 and AKT signal transduction pathways associated with cell proliferation and survival (Ahn et al. 2005). TCDD may also promote breast carcinogenesis through inhibiting p53 tumor suppressor mechanisms and by inducing estrogenic signaling (Seifert et al. 2009). Thus, the observed associations between dioxin exposure and breast cancer risk are biologically plausible.

Results from previous epidémiologic studies examining dioxin exposure and breast cancer risk have been mixed. A prospective study in the Etude Epidemiologique auprès de femmes de la Mutuelle Générale de l'Education Nationale (E3N) cohort in France showed no association between dietary dioxin exposure (assessed by combining a food frequency questionnaire with food dioxin contamination data) and breast cancer risk (n = 3,465 cases, adjusted HR = 0.96, 95% CI: 0.85, 1.09) (Danjou et al. 2015). An ecological study in the US showed that ZIP Code-level soil dioxin levels were associated with elevated odds for breast cancer in several ZIP Codes in Michigan (n = 4,604 cases) (Dai and Oyana 2008). In two hospital-based retrospective case-control studies in Sweden (n = 22 cases) and the US (n= 79 cases), PCDD/F measured in breast adipose tissue at diagnosis was not associated with breast cancer risk (Hardell et al. 1996; Reynolds et al. 2005). The most recent epidemiologic studies following the Seveso accident in Italy, where an industrial explosion at a chemical plant producing trichlorophenol in 1976 released TCDD into the environment, showed an increase in breast cancer incidence but not mortality after 20-25 years of follow-up (Consonni et al. 2008; Pesatori et al. 2009). There was an increased risk of breast cancer in females living in the most contaminated area in the 15 years following the accident (adjusted RR = 2.57, 95% CI: 1.07, 6.20), although these results were based on only 5 cases (Pesatori et al. 2009). In the retrospective Seveso Women’s Health Study (SWHS), serum TCDD levels (most were measured from 1976-1977) were associated with an increased risk for breast cancer (n = 15 cases; HR = 2.1, 95% CI: 1.0, 4.6) (Warner et al. 2002). However, results from an analysis of the SWHS with longer follow-up through 2008 showed that serum TCDD levels were no longer associated with breast cancer risk (n = 33 cases; adjusted HR = 1.44, 95% CI: 0.89, 2.33) (Warner et al. 2011).

Several occupational studies have shown inconsistent results. There was an increased risk for breast cancer mortality among residents of Chapaevsk, Russia, the location of a chemical plant that produced lindane and other chemicals, compared to residents of the Samara Region (n = 58 cases; SMR = 2.1, 95% CI: 1.6, 2.7) (Revich et al. 2001). In the IARC international cohort of individuals occupationally exposed to phenoxy herbicides, there was a suggestive positive association with breast cancer mortality (n = 9 cases; SMR = 2.16, 95% CI: 0.99, 4.10) (Kogevinas et al. 1997). Breast cancer cases for this IARC study were from a Hamburg, Germany cohort of workers employed at a chemical plant that produced herbicides (e.g., lindane) contaminated with TCDD (Manz et al. 1991). In a subsequent study of the Hamburg cohort with 23 years of follow-up after plant closure, the elevation in breast cancer mortality became statistically significant (n = 19 cases; SMR = 1.86, 95% CI: 1.12, 2.91) (Manuwald et al. 2012).

To date, two non-occupational epidemiologic studies (unrelated to industrial accidents) have examined the association between individual-level dioxin exposure from ambient air and breast cancer risk (Danjou et al. 2019; Viel et al. 2008). In a prospective case-control study nested in the E3N cohort, dioxin exposure was estimated using geocoded residential address histories (1990–2008) and dioxin air emissions predicted for facilities located within 10 km using information including stack height and wind direction (Danjou et al. 2019). There was no association between dioxin exposure and breast cancer risk (n = 439 cases; adjusted OR = 1.12, 95% CI: 0.69, 1.82); similar null results were observed for breast cancer subtypes defined by hormone receptor status and menopausal status (Danjou et al. 2019). In a retrospective case-control study in France, there were null and inverse associations between emissions from a MSWI (estimated using a validated dispersion model linked to residential blocks at diagnosis) and breast cancer risk (n = 434 cases) (Viel et al. 2008). Mixed results may be due to small sample sizes, examining breast cancer incidence compared to mortality, and/or investigating occupational exposures that are higher compared to the general population and in which studies generally did not adjust for potential confounders. Further, there were differences in the exposure assessment, for example, using food frequency questionnaires, biomonitoring, and area-level geographic variables at diagnosis.

Our findings of positive associations between ambient dioxin exposures from MSWIs with breast cancer risk are consistent with MSWIs as historically the largest contributor of dioxin emissions in the US (Thomas and Spiro 1995; US Environmental Protection Agency 2006). Municipal solid waste is comprised of the nonhazardous solid portion of waste generated by households, commercial establishments, public and private institutions, and government agencies (National Research Council 2000). Approximately 36 million tons of municipal solid waste is combusted each year (National Research Council 2000). Average emissions from MSWIs were between 1.5 and 2,913 times higher compared to other facilities in 1987 and 1995 (i.e., cement kilns, coal-fired power plants, hazardous waste incinerators, industrial boilers, iron ore sintering plants, medical waste incinerators, and sewage sludge incinerators with emissions ≤8.10 ng TEQ/year in 1987 and 1995) (Pronk et al. 2013). Further, higher MSWI-specific emission indices were associated with increased carpet dust concentrations for PCDD/F congeners including TCDD, octachlorodibenzodioxin, 2,3,7,8-tetrachlorodibenzofuran, 1,2,3,7,8-pentachlorodibenzofuran, 1,2,3,4,7,8-hexachlorodibenzofuran, and 1,2,3,4,6,7,8-heptachlorodibenzofuran (Deziel et al. 2017). In particular, we observed positive associations when examining dioxin exposure according to residential proximity, residential duration, and emissions, although results using the emission metrics showed relatively stronger associations. Further, the strength of the associations was stronger at distances closer to residential locations (3 and 5 km), although positive associations persisted up to 10 km. The emission index may be a relatively better indicator of the burden of airborne dioxin exposure to which an individual is exposed compared to considering general proximity. Results using the 5 and 10 km vs. 3 km exposure metrics were consistently positive and generally statistically significant, reflecting the relatively larger sample size of exposed.

This study had several potential limitations. The generalizability of our findings may be limited as participants of the NHSII were mostly white, although there is no evidence to suggest that the association would differ by race. However, as industrial facilities are more commonly found near environmental justice communities and our population of registered nurses is less likely to live in these areas, we may have been limited in capturing the highest levels of exposure (Mohai et al. 2009). We were not able to directly assess dioxin exposure using biological samples. The locations of the dioxin-emitting facilities are subject to errors from multiple reviewers and data sources. However, a re-review of randomly selected facilities showed good agreement and small verification errors (Jones et al. 2018). The dioxin exposure measures for residential proximity, duration of residence, and emissions are surrogates for airborne exposure that do not account for complex dispersion patterns or environmental fate and transport. There may be exposure misclassification due to imputed emissions data as emissions were available only in 1995 for all facilities except for MSWIs and secondary copper smelters, which were available in 1987 and 1995. We did not have information regarding each facility’s years of operation. We examined dioxin exposure during adulthood; early-life exposures during critical windows of susceptibility to carcinogens may be warranted (Gray et al. 2017). Although we cannot rule out that other compounds associated with the industrial facilities may have adversely impacted breast cancer risk, the positive association between dioxin exposure and breast cancer risk is supported by strong biological plausibility demonstrated in experimental evidence (Birnbaum and Fenton 2003; Gray et al. 2017; International Agency for Research on Cancer 1997, 2012). Further, adjustment for proxies for high-traffic areas (e.g., population density, PM2.5) did not change results. As MSWIs emit other carcinogenic chemicals (Goria et al. 2009), it is possible that other pollutants may have contributed to the positive associations.

This study had several strengths. Our exposure assessment included geocoded residential address histories updated biennially since 1989 as well as a nationwide spatial database of dioxin-emitting facilities, representing a comprehensive assessment of PCDD/F air emissions sources appropriate for assessing general population exposure (Jones et al. 2018). We were able to account for residential mobility over time; thus, women who developed breast cancer before living near a facility were categorized as unexposed as we used their address prior to diagnosis to assign exposure. We used GIS to derive objective measures of ambient dioxin exposure used in previous epidemiologic studies (Danjou et al. 2019; Pronk et al. 2013). We were able to examine exposures from specific industrial facility types, including MSWIs, characterized by different emissions profiles. We were also able to examine a large number of breast cancer cases and breast cancer subtypes defined by ER status. Using information collected from questionnaires and from geocoded addresses linked with databases such as the US Census Bureau, we were able to evaluate potential confounding and effect modification using time-varying information on an extensive number of breast cancer risk factors.

5. Conclusions

In summary, results from this large nationwide prospective non-occupationally exposed cohort study of US women showed a positive association between ambient dioxin exposures from MSWIs and invasive breast cancer risk, which was similar by ER status and menopausal status. Future research assessing dioxin exposure using biological samples to confirm these results and evaluating exposures relevant to contemporary dioxin emission levels (given changes in regulations over time) may be warranted.

Supplementary Material

MMC1

Highlights.

  • Dioxins are persistent organic pollutants from industrial combustion processes

  • Epidemiologic studies of dioxin and breast cancer risk have been mixed

  • No associations were shown for any dioxin facilities (all facilities combined)

  • Positive associations were observed for municipal solid waste incinerators

Acknowledgments:

This work was supported by the National Institutes of Health (NIH) National Cancer Institute (NCI) Training Program in Cancer Epidemiology (T32 CA009001), R00 CA201542, UM1 CA176726, P30 ES000002, and R01 ES017017, and Susan G. Komen for the Cure® (IIR13264020). K.A.B. was supported in part by the Dahod Breast Cancer Research Program at the Boston University School of Medicine. We would like to thank the participants and staff of the Nurses’ Health Study II for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for analyses and interpretation of these data.

Footnotes

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Competing financial interests: The authors declare they have no financial interests.

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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