Serum concentrations of per- and polyfluorinated substances and risk of B-cell non-Hodgkin lymphoma.

Per- and polyfluoroalkyl substances (PFAS) are persistent organic pollutants that are detectable in the serum of most U.S. adults. Some studies of highly-exposed individuals have suggested positive associations between PFAS and B-cell non-Hodgkin lymphoma (B-NHL). To investigate whether associations exist at lower exposure levels, we conducted a nested case-control study investigating serum PFAS concentrations and B-NHL within the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. We measured pre-diagnostic serum concentrations of five PFAS among 706 cases (age at diagnosis = 55-93 years, median 73 years) and 706 controls individually matched on age at blood draw, sex, self-reported race and ethnicity, study center, and year of blood collection (the median follow-up years = 10). We estimated odds ratios (ORs) and 95 % confidence intervals (CIs) for PFAS concentrations in relation to B-NHL, both overall and for selected histologic subtypes [diffuse large B cell lymphoma (DLBCL), follicular lymphoma (FL), and marginal zone lymphoma (MZL)] using conditional logistic regression. We found no evidence of a positive association with B-NHL for any of the five PFAS. In analyses of histologic subtypes, perfluorohexane sulfonate (PFHxS) was significantly associated with DLBCL in a model adjusting for all other PFAS (OR for highest vs. lowest quintile = 2.19, 95 % CI = 1.21, 3.95; Ptrend = 0.02), but not in a model without mutual adjustment (OR = 1.37, 95 % CI = 0.82, 2.29; Ptrend = 0.26). We also observed an inverse association between perfluorononanoate and DLBCL (mutually-adjusted OR = 0.83, 95 % CI = 0.69, 0.99 per doubling in concentration), although the association was null among participants with blood drawn prior to 1997 (OR<1997 = 1.00, 95 % CI = 0.82, 1.21; OR≥1997 = 0.65, 95 % CI = 0.53, 0.79; Pinteraction = 0.0003). In conclusion, our findings from a prospective cohort study with PFAS serum concentrations comparable to that of the general population do not support an association with increased risk of B-NHL overall. The suggestive evidence of a positive association between PFHxS and DLBCL warrants further investigation.

Mixed contaminant exposure in tapwater and the potential implications for human-health in disadvantaged communities in California.

Water is an increasingly precious resource in California as years of drought, climate change, pollution, as well as an expanding population have all stressed the state's drinking water supplies. Currently, there are increasing concerns about whether regulated and unregulated contaminants in drinking water are linked to a variety of human-health outcomes particularly in socially disadvantaged communities with a history of health risks. To begin to address this data gap by broadly assessing contaminant mixture exposures, the current study was designed to collect tapwater samples from communities in Gold Country, the San Francisco Bay Area, two regions of the Central Valley (Merced/Fresno and Kern counties), and southeast Los Angeles for 251 organic chemicals and 32 inorganic constituents. Sampling prioritized low-income areas with suspected water quality challenges and elevated breast cancer rates. Results indicated that mixtures of regulated and unregulated contaminants were observed frequently in tapwater throughout the areas studied and the types and concentrations of detected contaminants varied by region, drinking-water source, and size of the public water system. Multiple exceedances of enforceable maximum contaminant level(s) (MCL), non-enforceable MCL goal(s) (MCLG), and other health advisories combined with frequent exceedances of benchmark-based hazard indices were also observed in samples collected in all five of the study regions. Given the current focus on improving water quality in socially disadvantaged communities, our study highlights the importance of assessing mixed-contaminant exposures in drinking water at the point of consumption to adequately address human-health concerns (e.g., breast cancer risk). Data from this pilot study provide a foundation for future studies across a greater number of communities in California to assess potential linkages between breast cancer rates and tapwater contaminants.

Carcinogenic industrial air pollution and postmenopausal breast cancer risk in the National Institutes of Health AARP Diet and Health Study.

BACKGROUND: Chemicals emitted from industrial facilities include known or suspected mammary carcinogens and endocrine disruptors, but epidemiologic studies are limited. We evaluated associations between air emissions of multiple carcinogenic chemicals and postmenopausal breast cancer risk in a large prospective U.S. METHODS: We used the U.S. Environmental Protection Agency's Toxics Release Inventory to estimate historical airborne emissions (1987-1995) of 19 known and probable carcinogens for participants enrolled (1995-1996) in the NIH-AARP Diet and Health Study. Among 170,402 women, 15,124 breast cancers were diagnosed through 2018. We constructed inverse distance- and wind-weighted average emissions metrics within 1, 2, 5, and 10 km of the enrollment address for each chemical. We estimated multivariable adjusted HRs and 95 % CIs for categories (quartiles, tertiles, medians) of each chemical in association with breast cancer overall and separately by type (invasive, ductal carcinoma in situ) and estrogen receptor (ER) status. RESULTS: We observed an association between benzene emissions and breast cancer risk that was strongest at 1 km (HRQ4 vs. non-exposed = 2.06, 95 %CI: 1.34-3.17; p-trend = 0.001). The magnitude of the association weakened with increasing distance (2 km HRQ4 vs. non-exposed = 1.17, 95 %CI=0.92-1.49; p-trend = 0.19; 5 km HRQ4 vs. non-exposed = 1.05, 95 %CI=0.94-1.16; p-trend = 0.37; 10 km HRQ4 vs. non-exposed = 0.95, 95 %CI=0.89-1.02; p-trend = 0.19) and appeared to be most relevant for invasive rather than intraductal disease. Overall risk was also elevated for vinyl chloride at 5 km (HR≥median vs. non-exposed = 1.20, 95 %CI=1.01-1.43; p-trend = 0.04), but not 2 km or 10 km. We observed suggestive associations for asbestos, trichloroethylene, and styrene in different subgroup analyses, but risk patterns were not clear across distances. Associations with other chemicals were generally null, with limited evidence of heterogeneity by disease type or ER status. CONCLUSIONS: An increased risk of breast cancer associated with relatively high levels of industrial benzene emissions warrants additional study, particularly among participants with diverse sociodemographic characteristics that live in areas with higher density of industrial facilities.

Residential proximity to agricultural herbicide and fungicide applications and dust levels in homes of California children.

BACKGROUND: Few studies of the relationship between residential proximity to agricultural pesticide applications and pesticide levels in the home have incorporated crop location or wind direction. We evaluated the relationship between agricultural pesticide applications using the California Pesticide Use Reporting (CPUR) database and pesticide concentrations in carpet dust accounting for land use and wind direction. METHODS: We measured concentrations (ng/g) of seven herbicides and two fungicides in carpet dust samples from 578 California homes (2001-2007). We created three metrics by computing the density (kg/km2) of use of each pesticide reported in CPUR within 0.5-, 1-, 2-, and 4-km buffers around homes 180- and 365-days before sampling (CPUR metric). We apportioned applications to the crop area within the buffers (CROP-A metric) and weighted CPUR applications by the proportion of days that the home was within ±45° of the downwind direction (W-CPUR metric). We modeled natural-log concentrations (Tobit regression) and dust detections (logistic regression) adjusting for season/year, occupation, and home/garden use. RESULTS: Detections were >90 % for glyphosate, 2,4-D, and simazine. Detection rates and dust concentrations increased with increasing CPUR densities for all herbicides and one fungicide. Compared to homes without applications within 4 km, the highest tertile of 365-day glyphosate use was associated with ∼100 % higher concentrations (CPURT3>9.2kg/km2 %change = 110, 95 %CI = 55, 183; CROP-AT3>13.4kg/km2 %change = 144, 95 %CI = 81, 229; and W-CPURT3>2.1kg/km2 %change = 102, 95 %CI = 50, 171). The highest density tertiles of 2,4-D, simazine, and trifluralin were associated with 2- to 6-times higher concentrations, respectively; that was similar across metrics. Across all metrics, agricultural use of dacthal, dicamba, and iprodione were associated with 5- to 10-times higher odds of dust detections. Associations were unclear for 2-methyl-4-chlorophenoxyacetic acid and null for chlorothalonil. CONCLUSIONS: Agricultural herbicide and fungicide use was an important determinant of indoor contamination within 4 km of homes. Accounting for crops and wind direction did not substantially change these relationships.

A Comparison of Statistical Methods for Studying Interactions of Chemical Mixtures.

Properly assessing the effects of environmental chemical exposures on disease risk remains a challenging problem in environmental epidemiology. Various analytic approaches have been proposed, but there are few papers that have compared the performance of different statistical methods on a single dataset. In this paper, we compare different regression-based approaches for estimating interactions between chemical mixture components using data from a case-control study on non-Hodgkin's lymphoma. An analytic challenge is the high percentage of exposures that are below the limit of detection (LOD). Using imputation for LOD, we compare different Bayesian shrinkage prior approaches including an approach that incorporates the hierarchical principle where interactions are only included when main effects exist. Further, we develop an approach where main and interactive effects are represented by a series of distinct latent functions. We also fit the Bayesian kernel machine regression to these data. All of these approaches show little evidence of an interaction among the chemical mixtures when measurements below the LOD were imputed. The imputation approach makes very strong assumptions about the relationship between exposure and disease risk for measurements below the LOD. As an alternative, we show the results of an analysis where we model the exposure relationship with two parameters per mixture component; one characterizing the effect of being below the LOD and the other being a linear effect above the LOD. In this later analysis, we identify numerous strong interactions that were not identified in the analyses with imputation. This case study demonstrated the importance of developing new approaches for mixtures when the proportions of exposure measurements below the LOD are high.

Modeling Historic Arsenic Exposures and Spatial Risk for Bladder Cancer.

Arsenic is a bladder carcinogen though less is known regarding the specific temporal relationship between exposure and bladder cancer diagnosis. In this study, we modeled time-varying mixtures of arsenic exposures at many historic temporal windows to evaluate their association with bladder cancer risk in the New England Bladder Cancer Study. We used arsenic exposure estimates up to 60 years prior to study entry and compared the goodness of fit of models using these mixtures to those using summary measures of arsenic exposures. We used the Bayesian index low rank kriging multiple membership model (LRK-MMM) to estimate the associations of these mixtures with bladder cancer and estimate cumulative spatial risk for bladder cancer using participants' residential histories. We found consistent evidence that modeling arsenic exposures as a time-varying mixture provided better fit to the data than using a single arsenic exposure summary measure. We estimated several positive though not significant associations of the time-varying arsenic mixtures with bladder cancer having odds ratios (ORs) of 1.03-1.14 and identified many significant and positive associations for an interaction among those who consumed water from a private dug well (ORs 1.28-1.60). Arsenic exposures 40-50 years before study entry received elevated importance weights in these mixtures. Additionally, we found two small areas of elevated cumulative spatial risk for bladder cancer in southern New Hampshire and in south central Maine. These results emphasize the importance of considering time-varying mixtures of exposures for diseases with long latencies such as bladder cancer.

Drinking water source and exposure to regulated water contaminants in the California Teachers Study cohort.

BACKGROUND: Pollutants including metals/metalloids, nitrate, disinfection byproducts, and volatile organic compounds contaminate federally regulated community water systems (CWS) and unregulated domestic wells across the United States. Exposures and associated health effects, particularly at levels below regulatory limits, are understudied. OBJECTIVE: We described drinking water sources and exposures for the California Teachers Study (CTS), a prospective cohort of female California teachers and administrators. METHODS: Participants' geocoded addresses at enrollment (1995-1996) were linked to CWS service area boundaries and monitoring data (N = 115,206, 92%); we computed average (1990-2015) concentrations of arsenic, uranium, nitrate, gross alpha (GA), five haloacetic acids (HAA5), total trihalomethanes (TTHM), trichloroethylene (TCE), and tetrachloroethylene (PCE). We used generalized linear regression to estimate geometric mean ratios of CWS exposures across demographic subgroups and neighborhood characteristics. Self-reported drinking water source and consumption at follow-up (2017-2019) were also described. RESULTS: Medians (interquartile ranges) of average concentrations of all contaminants were below regulatory limits: arsenic: 1.03 (0.54,1.71) µg/L, uranium: 3.48 (1.01,6.18) µg/L, GA: 2.21 (1.32,3.67) pCi/L, nitrate: 0.54 (0.20,1.97) mg/L, HAA5: 8.67 (2.98,14.70) µg/L, and TTHM: 12.86 (4.58,21.95) µg/L. Among those who lived within a CWS boundary and self-reported drinking water information (2017-2019), approximately 74% self-reported their water source as municipal, 15% bottled, 2% private well, 4% other, and 5% did not know/missing. Spatially linked water source was largely consistent with self-reported source at follow-up (2017-2019). Relative to non-Hispanic white participants, average arsenic, uranium, GA, and nitrate concentrations were higher for Black, Hispanic and Native American participants. Relative to participants living in census block groups in the lowest socioeconomic status (SES) quartile, participants in higher SES quartiles had lower arsenic/uranium/GA/nitrate, and higher HAA5/TTHM. Non-metropolitan participants had higher arsenic/uranium/nitrate, and metropolitan participants had higher HAA5/TTHM. IMPACT: Though average water contaminant levels were mostly below regulatory limits in this large cohort of California women, we observed heterogeneity in exposures across sociodemographic subgroups and neighborhood characteristics. These data will be used to support future assessments of drinking water exposures and disease risk.

Assessing and attenuating the impact of selection bias on spatial cluster detection studies.

Spatial cluster analyses are commonly used in epidemiologic studies of case-control data to detect whether certain areas in a study region have an excess of disease risk. Case-control studies are susceptible to potential biases including selection bias, which can result from non-participation of eligible subjects in the study. However, there has been no systematic evaluation of the effects of non-participation on the findings of spatial cluster analyses. In this paper, we perform a simulation study assessing the effect of non-participation on spatial cluster analysis using the local spatial scan statistic under a variety of scenarios that vary the location and rates of study non-participation and the presence and intensity of a zone of elevated risk for disease for simulated case-control studies. We find that geographic areas of lower participation among controls than cases can greatly inflate false-positive rates for identification of artificial spatial clusters. Additionally, we find that even modest non-participation outside of a true zone of elevated risk can decrease spatial power to identify the true zone. We propose a spatial algorithm to correct for potentially spatially structured non-participation that compares the spatial distributions of the observed sample and underlying population. We demonstrate its ability to markedly decrease false positive rates in the absence of elevated risk and resist decreasing spatial sensitivity to detect true zones of elevated risk. We apply our method to a case-control study of non-Hodgkin lymphoma. Our findings suggest that greater attention should be paid to the potential effects of non-participation in spatial cluster studies.

Residential proximity to dioxin-emitting facilities and risk of non-Hodgkin lymphoma in the NIH-AARP Diet and Health Study.

BACKGROUND: Polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) are persistent organic pollutants emitted from industrial sources. Residential proximity to these emissions has been associated with risk of non-Hodgkin lymphoma (NHL) in a limited number of studies. METHODS: We evaluated associations between residential proximity to PCDD/F-emitting facilities and NHL in the NIH-AARP Diet and Health Study (N = 451,410), a prospective cohort enrolled in 1995-1996 in 6 states and 2 U.S. cities. We linked enrollment addresses with a U.S. Environmental Protection Agency database of 4,478 historical PCDD/F sources with estimated toxic equivalency quotient (TEQ) emissions. We evaluated associations between NHL and exposures during a historical period prior to enrollment (1980-1995) using an average emissions index, weighted by toxicity, distance, and wind direction (AEI-W [g TEQ/km2]) within 3-, 5- and 10 km of residences. We also evaluated proximity-only metrics indicating the presence/absence of one or more facilities within each distance, and metrics calculated separately for each facility type. We used Cox regression to estimate associations (hazard ratio, HR; 95 % confidence interval, 95 %CI) with NHL and major subtypes, adjusting for demographic, lifestyle, and dietary factors. RESULTS: A total of 6,467 incident cases of NHL were diagnosed through 2011. Participants with an AEI-W ≥ 95th percentile had elevated risk of NHL compared to those unexposed at 3 km (HR = 1.16; 95 %CI = 0.89-1.52; p-trend = 0.24), 5 km (HR = 1.20;95 %CI = 0.99-1.46;p-trend = 0.05) and 10 km (HR = 1.15; 95 %CI = 0.99-1.34; p-trend = 0.04). We found a positive association at 5 km with follicular lymphoma (HR≥95vs.0 = 1.62; 95 %CI = 0.98-2.67; p-trend = 0.05) and a suggestive association for diffuse large B-cell lymphoma (HR≥95vs.0 = 1.40; 95 %CI = 0.91-2.14; p-trend = 0.11). NHL risk was also associated with high emissions from coal-fired power plants within 10 km (HR≥95vs.0 = 1.42; 95 %CI = 1.09-1.84; p-trend = 0.05). CONCLUSIONS: Residential proximity to relatively high dioxin emissions from industrial sources may increase the risk of NHL and specific subtypes.

Association of common maternal infections with birth outcomes: a multinational cohort study.

PURPOSE: It is unclear whether common maternal infections during pregnancy are risk factors for adverse birth outcomes. We assessed the association between self-reported infections during pregnancy with preterm birth and small-for-gestational-age (SGA) in an international cohort consortium. METHODS: Data on 120,507 pregnant women were obtained from six population-based birth cohorts in Australia, Denmark, Israel, Norway, the UK and the USA. Self-reported common infections during pregnancy included influenza-like illness, common cold, any respiratory tract infection, vaginal thrush, vaginal infections, cystitis, urinary tract infection, and the symptoms fever and diarrhoea. Birth outcomes included preterm birth, low birth weight and SGA. Associations between maternal infections and birth outcomes were first assessed using Poisson regression in each cohort and then pooled using random-effect meta-analysis. Risk ratios (RR) and 95% confidence intervals (CI) were calculated, adjusted for potential confounders. RESULTS: Vaginal infections (pooled RR, 1.10; 95% CI, 1.02-1.20) and urinary tract infections (pooled RR, 1.17; 95% CI, 1.09-1.26) during pregnancy were associated with higher risk of preterm birth. Similar associations with low birth weight were also observed for these two infections. Fever during pregnancy was associated with higher risk of SGA (pooled RR, 1.07; 95% CI, 1.02-1.12). No other significant associations were observed between maternal infections/symptoms and birth outcomes. CONCLUSION: Vaginal infections and urinary infections during pregnancy were associated with a small increased risk of preterm birth and low birth weight, whereas fever was associated with SGA. These findings require confirmation in future studies with laboratory-confirmed infection diagnosis.

The protective effect of dietary folate intake on gastric cancer is modified by alcohol consumption: A pooled analysis of the StoP Consortium.

Dietary folate intake has been identified as a potentially modifiable factor of gastric cancer (GC) risk, although the evidence is still inconsistent. We evaluate the association between dietary folate intake and the risk of GC as well as the potential modification effect of alcohol consumption. We pooled data for 2829 histologically confirmed GC cases and 8141 controls from 11 case-control studies from the international Stomach Cancer Pooling Consortium. Dietary folate intake was estimated using food frequency questionnaires. We used linear mixed models with random intercepts for each study to calculate adjusted odds ratios (OR) and 95% confidence interval (CI). Higher folate intake was associated with a lower risk of GC, although this association was not observed among participants who consumed >2.0 alcoholic drinks/day. The OR for the highest quartile of folate intake, compared with the lowest quartile, was 0.78 (95% CI, 0.67-0.90, P-trend = 0.0002). The OR per each quartile increment was 0.92 (95% CI, 0.87-0.96) and, per every 100 µg/day of folate intake, was 0.89 (95% CI, 0.84-0.95). There was a significant interaction between folate intake and alcohol consumption (P-interaction = 0.02). The lower risk of GC associated with higher folate intake was not observed in participants who consumed >2.0 drinks per day, ORQ4v Q1 = 1.15 (95% CI, 0.85-1.56), and the OR100 µg/day = 1.02 (95% CI, 0.92-1.15). Our study supports a beneficial effect of folate intake on GC risk, although the consumption of >2.0 alcoholic drinks/day counteracts this beneficial effect.

The association between dietary fiber intake and gastric cancer: a pooled analysis of 11 case-control studies.

PURPOSE: Gastric cancer (GC) is among the leading causes of cancer mortality worldwide. The objective of this study was to investigate the association between dietary fiber intake and GC. METHODS: We pooled data from 11 population or hospital-based case-control studies included in the Stomach Cancer Pooling (StoP) Project, for a total of 4865 histologically confirmed cases and 10,626 controls. Intake of dietary fibers and other dietary factors was collected using food frequency questionnaires. We calculated the odds ratios (OR) and 95% confidence intervals (CI) of the association between dietary fiber intake and GC by using a multivariable logistic regression model adjusted for study site, sex, age, caloric intake, smoking, fruit and vegetable intake, and socioeconomic status. We conducted stratified analyses by these factors, as well as GC anatomical site and histological type. RESULTS: The OR of GC for an increase of one quartile of fiber intake was 0.91 (95% CI: 0.85, 0.97), that for the highest compared to the lowest quartile of dietary fiber intake was 0.72 (95% CI: 0.59, 0.88). Results were similar irrespective of anatomical site and histological type. CONCLUSION: Our analysis supports the hypothesis that dietary fiber intake may exert a protective effect on GC.

Dietary intake of copper and gastric cancer: a pooled analysis within the Stomach cancer Pooling (StoP) Project.

BACKGROUND: Evidence on the potential association between dietary copper intake and gastric cancer (GC) is lacking. Thus, we aimed to evaluate this association within the Stomach cancer Pooling (StoP) Project-an international consortium of epidemiological studies on GC. METHODS: Data from five case-control studies within the StoP Project were included (2448 cases, 4350 controls). We estimated adjusted odds ratios (ORs) and 95% CIs for the association between dietary copper intake and GC using multivariable mixed-effects logistic regression models. We also modelled the dose-response relationship between copper intake and GC using a logistic mixed-effects model with fractional polynomial. RESULTS: The OR for the highest quartile of copper intake compared with the lowest one was 0.78 (95% CI: 0.63-0.95; P for trend = 0.013). Results were similar for non-cardia-type (OR: 0.72; 95% CI: 0.57-0.91), intestinal-type (OR: 0.75; 95% CI: 0.56-0.99) and other histological-type GC (OR: 0.65; 95% CI: 0.44-0.96). The dose-response analysis showed a steep decrease in ORs for modest intakes (<1 mg/day), which were subsequently steady for ≤3 mg/day (OR: 0.09; 95% CI: 0.02-0.41) and slowly increased for higher intakes. CONCLUSIONS: The findings of our large study suggest that copper intake might be inversely associated with GC, although their confirmation by prospective studies is required.

Urinary biomonitoring of glyphosate exposure among male farmers and nonfarmers in the Biomarkers of Exposure and Effect in Agriculture (BEEA) study.

Glyphosate is the most widely applied herbicide worldwide. Glyphosate biomonitoring data are limited for agricultural settings. We measured urinary glyphosate concentrations and assessed exposure determinants in the Biomarkers of Exposure and Effect in Agriculture (BEEA) study. We selected four groups of BEEA participants based on self-reported pesticide exposure: recently exposed farmers with occupational glyphosate use in the last 7 days (n = 98), farmers with high lifetime glyphosate use (>80th percentile) but no use in the last 7 days (n = 70), farming controls with minimal lifetime use (n = 100), and nonfarming controls with no occupational pesticide exposures and no recent home/garden glyphosate use (n = 100). Glyphosate was quantified in first morning void urine using ion chromatography isotope-dilution tandem mass spectrometry. We estimated associations between urinary glyphosate concentrations and potential determinants using multivariable linear regression. Glyphosate was detected (≥0.2 µg/L) in urine of most farmers with recent (91 %) and high lifetime (93 %) use, as well as farming (88 %) and nonfarming (81 %) controls; geometric mean concentrations were 0.89, 0.59, 0.46, and 0.39 µg/L (0.79, 0.51, 0.42, and 0.37 µg/g creatinine), respectively. Compared with both control groups, urinary glyphosate concentrations were significantly elevated among recently exposed farmers (P < 0.0001), particularly those who used glyphosate in the previous day [vs. nonfarming controls; geometric mean ratio (GMR) = 5.46; 95 % confidence interval (CI): 3.75, 7.93]. Concentrations among high lifetime exposed farmers were also elevated (P < 0.01 vs. nonfarming controls). Among recently exposed farmers, glyphosate concentrations were higher among those not wearing gloves when applying glyphosate (GMR = 1.91; 95 % CI: 1.17, 3.11), not wearing long-sleeved shirts when mixing/loading glyphosate (GMR = 2.00; 95 % CI: 1.04, 3.86), applying glyphosate exclusively using broadcast/boom sprayers (vs. hand sprayer only; GMR = 1.70; 95 % CI: 1.00, 2.92), and applying glyphosate to crops (vs. non-crop; GMR = 1.72; 95 % CI: 1.04, 2.84). Both farmers and nonfarmers are exposed to glyphosate, with recency of occupational glyphosate use being the strongest determinant of urinary glyphosate concentrations. Continued biomonitoring of glyphosate in various settings is warranted.

Construction of residential histories to estimate long-term environmental exposures in the California Teachers Study cohort.

Environmental epidemiologic studies using geospatial data often estimate exposure at a participant's residence upon enrollment, but mobility during the exposure period can lead to misclassification. We aimed to mitigate this issue by constructing residential histories for participants in the California Teachers Study through follow-up (1995-2018). Address records have been collected from the US Postal Service, LexisNexis, Experian, and California Cancer Registry. We identified records of the same address based on geo-coordinate distance (≤250 m) and street name similarity. We consolidated addresses, prioritizing those confirmed by participants during follow-up questionnaires, and estimating the duration lived at each address using dates associated with records (e.g., date-first-seen). During 23 years of follow-up, about half of participants moved (48%, including 14% out-of-state). We observed greater mobility among younger women, Hispanic/Latino women, and those in metropolitan and lower socioeconomic status areas. The cumulative proportion of in-state movers remaining eligible for analysis was 21%, 32%, and 41% at 5, 10, and 20 years post enrollment, respectively. Using self-reported information collected 10 years after enrollment, we correctly identified 94% of movers and 95% of non-movers as having moved or not moved from their enrollment address. This dataset provides a foundation for estimating long-term environmental exposures in diverse epidemiologic studies in this cohort. IMPACT: Our efforts in constructing residential histories for California Teachers Study participants through follow-up (1995-2018) benefit future environmental epidemiologic studies. Address availability during the exposure period can mitigate misclassification due to residential changes, especially when evaluating long-term exposures and chronic health outcomes. This can reduce differential misclassification among more mobile subgroups, including younger women and those from lower socioeconomic and urban areas. Our approach to consolidating addresses from multiple sources showed high accuracy in comparison to self-reported residential information. The residential dataset produced from this analysis provides a valuable tool for future studies, ultimately enhancing our understanding of environmental health impacts.

Organochlorine pesticides and risk of papillary thyroid cancer in U.S. military personnel: a nested case-control study.

BACKGROUND: The effects of organochlorine pesticide (OCP) exposure on the development of human papillary thyroid cancer (PTC) are not well understood. A nested case-control study was conducted with data from the U.S. Department of Defense Serum Repository (DoDSR) cohort between 2000 and 2013 to assess associations of individual OCPs serum concentrations with PTC risk. METHODS: This study included 742 histologically confirmed PTC cases (341 females, 401 males) and 742 individually-matched controls with pre-diagnostic serum samples selected from the DoDSR. Associations between categories of lipid-corrected serum concentrations of seven OCPs and PTC risk were evaluated for classical PTC and follicular PTC using conditional logistic regression, adjusted for body mass index category and military branch to compute odds ratios (OR) and 95% confidence intervals (CIs). Effect modification by sex, birth cohort, and race was examined. RESULTS: There was no evidence of associations between most of the OCPs and PTC, overall or stratified by histological subtype. Overall, there was no evidence of an association between hexachlorobenzene (HCB) and PTC, but stratified by histological subtype HCB was associated with significantly increased risk of classical PTC (third tertile above the limit of detection (LOD) vs.

Endocrine disrupting chemical mixture exposure and risk of papillary thyroid cancer in U.S. military personnel: A nested case-control study.

Single-pollutant methods to evaluate associations between endocrine disrupting chemicals (EDCs) and thyroid cancer risk may not reflect realistic human exposures. Therefore, we evaluated associations between exposure to a mixture of 18 EDCs, including polychlorinated biphenyls (PCBs), brominated flame retardants, and organochlorine pesticides, and risk of papillary thyroid cancer (PTC), the most common thyroid cancer histological subtype. We conducted a nested case-control study among U.S. military servicemembers of 652 histologically-confirmed PTC cases diagnosed between 2000 and 2013 and 652 controls, matched on birth year, sex, race/ethnicity, military component (active duty/reserve), and serum sample timing. We estimated mixture odds ratios (OR), 95% confidence intervals (95% CI), and standard errors (SE) for associations between pre-diagnostic serum EDC mixture concentrations, overall PTC risk, and risk of histological subtypes of PTC (classical, follicular), adjusted for body mass index and military branch, using quantile g-computation. Additionally, we identified relative contributions of individual mixture components to PTC risk, represented by positive and negative weights (w). A one-quartile increase in the serum mixture concentration was associated with a non-statistically significant increase in overall PTC risk (OR = 1.19; 95% CI = 0.91, 1.56; SE = 0.14). Stratified by histological subtype and race (White, Black), a one-quartile increase in the mixture was associated with increased classical PTC risk among those of White race (OR = 1.59; 95% CI = 1.06, 2.40; SE = 0.21), but not of Black race (OR = 0.95; 95% CI = 0.34, 2.68; SE = 0.53). PCBs 180, 199, and 118 had the greatest positive weights driving this association among those of White race (w = 0.312, 0.255, and 0.119, respectively). Findings suggest that exposure to an EDC mixture may be associated with increased classical PTC risk. These findings warrant further investigation in other study populations to better understand PTC risk by histological subtype and race.

Dietary intake of vitamin C and gastric cancer: a pooled analysis within the Stomach cancer Pooling (StoP) Project.

BACKGROUND: Previous studies suggest that dietary vitamin C is inversely associated with gastric cancer (GC), but most of them did not consider intake of fruit and vegetables. Thus, we aimed to evaluate this association within the Stomach cancer Pooling (StoP) Project, a consortium of epidemiological studies on GC. METHODS: Fourteen case-control studies were included in the analysis (5362 cases, 11,497 controls). We estimated odds ratios (ORs) and corresponding 95% confidence intervals (CIs) for the association between dietary intake of vitamin C and GC, adjusted for relevant confounders and for intake of fruit and vegetables. The dose-response relationship was evaluated using mixed-effects logistic models with second-order fractional polynomials. RESULTS: Individuals in the highest quartile of dietary vitamin C intake had reduced odds of GC compared with those in the lowest quartile (OR: 0.64; 95% CI: 0.58, 0.72). Additional adjustment for fruit and vegetables intake led to an OR of 0.85 (95% CI: 0.73, 0.98). A significant inverse association was observed for noncardia GC, as well as for both intestinal and diffuse types of the disease. The results of the dose-response analysis showed decreasing ORs of GC up to 150-200 mg/day of vitamin C (OR: 0.54; 95% CI: 0.41, 0.71), whereas ORs for higher intakes were close to 1.0. CONCLUSIONS: The findings of our pooled study suggest that vitamin C is inversely associated with GC, with a potentially beneficial effect also for intakes above the currently recommended daily intake (90 mg for men and 75 mg for women).

Nitrate exposure from drinking water and dietary sources among Iowa farmers using private wells.

Nitrate levels are increasing in water resources across the United States and nitrate ingestion from drinking water has been associated with adverse health risks in epidemiologic studies at levels below the maximum contaminant level (MCL). In contrast, dietary nitrate ingestion has generally been associated with beneficial health effects. Few studies have characterized the contribution of both drinking water and dietary sources to nitrate exposure. The Agricultural Health Study is a prospective cohort of farmers and their spouses in Iowa and North Carolina. In 2018-2019, we assessed nitrate exposure for 47 farmers who used private wells for their drinking water and lived in 8 eastern Iowa counties where groundwater is vulnerable to nitrate contamination. Drinking water and dietary intakes were estimated using the National Cancer Institute Automated Self-Administered 24-Hour Dietary Assessment tool. We measured nitrate in tap water and estimated dietary nitrate from a database of food concentrations. Urinary nitrate was measured in first morning void samples in 2018-19 and in archived samples from 2010 to 2017 (minimum time between samples: 2 years; median: 7 years). We used linear regression to evaluate urinary nitrate concentrations in relation to total nitrate, and drinking water and dietary intakes separately. Overall, dietary nitrate contributed the most to total intake (median: 97 %; interquartile range [IQR]: 57-99 %). Among 15 participants (32 %) whose drinking water nitrate concentrations were at/above the U.S. Environmental Protection Agency MCL (10 mg/L NO3-N), median intake from water was 44 % (IQR: 26-72 %). Total nitrate intake was the strongest predictor of urinary nitrate concentrations (R2 = 0.53). Drinking water explained a similar proportion of the variation in nitrate excretion (R2 = 0.52) as diet (R2 = 0.47). Our findings demonstrate the importance of both dietary and drinking water intakes as determinants of nitrate excretion.

Modeling variation in mixture effects over space with a Bayesian spatially varying mixture model.

Mixture analysis is an emerging statistical tool in epidemiological research that seeks to estimate the health effects associated with mixtures of several exposures. This approach acknowledges that individuals experience many simultaneous exposures and it can estimate the relative importance of components in the mixture. Health effects due to mixtures may vary over space driven by to political, demographic, environmental, or other differences. In such cases, estimating a global mixture effect without accounting for spatial variation would induce bias in effect estimates and potentially lower statistical power. To date, no methods have been developed to estimate spatially varying chemical mixture effects. We developed a Bayesian spatially varying mixture model that estimates spatially varying mixture effects and the importance weights of components in the mixture, while adjusting for covariates. We demonstrate the efficacy of the model through a simulation study that varies the number of mixtures (one and two) and spatial pattern (global, one-dimensional, radial) and magnitude of mixture effects, showing that the model is able to accurately reproduce the spatial pattern of mixture effects across a diverse set of scenarios. Finally, we apply our model to a multi-center case-control study of non-Hodgkin lymphoma (NHL) in Detroit, Iowa, Los Angeles, and Seattle. We identify significant spatially varying positive and inverse associations with NHL for two mixtures of pesticides in Iowa and do not find strong spatial effects at the other three centers. In conclusion, the Bayesian spatially varying mixture model represents a novel method for modeling spatial variation in mixture effects.