Urinary nitrate and sodium in a high-risk area for upper gastrointestinal cancers: Golestan Cohort Study☆.

BACKGROUND: The epidemiological evidence regarding the carcinogenicity of nitrate and sodium in drinking water is limited, partly because measuring the exposure at the individual level is complex. Most studies have used nitrate in water supplies as a proxy for individual exposure, but dietary intakes and other factors may contribute to the exposure. The present study investigates the factors associated with urinary nitrate and sodium in a high-risk area for esophageal and gastric cancers. METHODS: For this cross-sectional study, we used data and samples collected in 2004-2008 during the enrollment phase of the Golestan Cohort Study from a random sample of 349 participants (300 individuals from 24 rural villages and 49 from the city of Gonbad), stratified by average water nitrate in their district, the source of drinking water, and the usual dietary intake of nitrate and sodium. Nitrate, sodium, and creatinine were measured in a spot urine sample collected at the time of interview. We used the provincial cancer registry data to calculate the cumulative incidence rates of esophageal and gastric cancers for each location through June 1, 2020, and used weighted partial Pearson correlation to compare the incidence rates with median urinary nitrate and sodium in each village or the city. RESULTS: Among 349 participants (mean age±SD: 50.7 ± 8.6 years), about half (n = 170) used groundwater for drinking, and the use of groundwater was significantly more common in high-elevation locations (75.8%). The geometric mean of the creatinine-corrected urinary nitrate concentration was 68.3 mg/g cr (95%CI: 64.6,72.3), and the corresponding geometric mean for urinary sodium was 150.0 mmoL/g cr (95%CI: 139.6,161.1). After adjusting for confounders, urinary nitrate was associated with being a woman, drinking groundwater, and living in high-elevation locations, but not with estimated dietary intake. Urinary sodium concentration was significantly associated with monthly precipitation at the time of sampling but not with elevation or drinking water source. There were significant positive correlations between both median urinary nitrate and sodium in each location and esophageal cancer incidence rates adjusted for sex and age (r = 0.65 and r = 0.58, respectively, p < 0.01), but not with gastric cancer incidence. CONCLUSION: In a rural population at high risk for esophageal and gastric cancers, nitrate excretion was associated with living at a higher elevation and using groundwater for drinking. The associations between nitrate and sodium excretion with esophageal cancer incidence warrant future investigation.

Evaluation of a commercial database to estimate residence histories in the los angeles ultrafines study.

BACKGROUND: Commercial databases can be used to identify participant addresses over time, but their quality and impact on environmental exposure assessment is uncertain. OBJECTIVE: To evaluate the performance of a commercial database to find residences and estimate environmental exposures for study participants. METHODS: We searched LexisNexis® for participant addresses in the Los Angeles Ultrafines Study, a prospective cohort of men and women aged 50-71 years. At enrollment (1995-1996) and follow-up (2004-2005), we evaluated attainment (address found for the corresponding time period) and match rates to survey addresses by participant characteristics. We compared geographically-referenced predictors and estimates of ultrafine particulate matter (UFP) exposure from a land use regression model using LexisNexis and survey addresses at enrollment. RESULTS: LexisNexis identified an address for 69% of participants at enrollment (N = 50,320) and 95% of participants at follow-up (N = 24,432). Attainment rate at enrollment modestly differed (≥5%) by age, smoking status, education, and residential mobility between surveys. The match rate at both survey periods was high (82-86%) and similar across characteristics. When using LexisNexis versus survey addresses, correlations were high for continuous values of UFP exposure and its predictors (rho = 0.86-0.92). SIGNIFICANCE: Time period and population characteristics influenced the attainment of addresses from a commercial database, but accuracy and subsequent estimation of specific air pollution exposures were high in our older study population.

Polycyclic aromatic hydrocarbons: determinants of residential carpet dust levels and risk of non-Hodgkin lymphoma.

PURPOSE: To investigate the risk of non-Hodgkin lymphoma (NHL) associated with residential carpet dust measurements of polycyclic aromatic hydrocarbons (PAHs). METHODS: We evaluated the relationship between residential carpet dust PAH concentrations (benz(a)anthracene, benzo(a)pyrene, benzo(b)fluoranthene, benzo(k)fluoranthene, chrysene, dibenz(a,h)anthracene, and indeno(1,2,3-c,d)pyrene, and their sum) and risk of NHL (676 cases, 511 controls) in the National Cancer Institute Surveillance Epidemiology and End Results multicenter case-control study. As a secondary aim, we investigated determinants of dust PAH concentrations. We computed odds ratios (OR) and 95 % confidence interval (CI) for associations between NHL and concentrations of individual and summed PAHs using unconditional logistic regression, adjusting for age, gender, and study center. Determinants of natural log-transformed PAHs were investigated using multivariate least-squares regression. RESULTS: We observed some elevated risks for NHL overall and B cell lymphoma subtypes in association with quartiles or tertiles of PAH concentrations, but without a monotonic trend, and there was no association comparing the highest quartile or tertile to the lowest. In contrast, risk of T cell lymphoma was significantly increased among participants with the highest tertile of summed PAHs (OR = 3.04; 95 % CI, 1.09-8.47) and benzo(k)fluoranthene (OR = 3.20; 95 % CI, 1.13-9.11) compared with the lowest tertile. Predictors of PAH dust concentrations in homes included ambient air PAH concentrations and the proportion of developed land within 2 km of a residence. Older age, more years of education, and white race were also predictive of higher levels in homes. CONCLUSION: Our results suggest a potential link between PAH exposure and risk of T cell lymphoma and demonstrate the importance of analyzing risk by NHL histologic type.

Accuracy of residential geocoding in the Agricultural Health Study.

BACKGROUND: Environmental exposure assessments often require a study participant's residential location, but the positional accuracy of geocoding varies by method and the rural status of an address. We evaluated geocoding error in the Agricultural Health Study (AHS), a cohort of pesticide applicators and their spouses in Iowa and North Carolina, U.S.A. METHODS: For 5,064 AHS addresses in Iowa, we compared rooftop coordinates as a gold standard to two alternate locations: 1) E911 locations (intersection of the private and public road), and 2) geocodes generated by matching addresses to a commercial street database (NAVTEQ) or placed manually. Positional error (distance in meters (m) from the rooftop) was assessed overall and separately for addresses inside (non-rural) or outside town boundaries (rural). We estimated the sensitivity and specificity of proximity-based exposures (crops, animal feeding operations (AFOs)) and the attenuation in odds ratios (ORs) for a hypothetical nested case-control study. We also evaluated geocoding errors within two AHS subcohorts in Iowa and North Carolina by comparing them to GPS points taken at residences. RESULTS: Nearly two-thirds of the addresses represented rural locations. Compared to the rooftop gold standard, E911 locations were more accurate overall than address-matched geocodes (median error 39 and 90 m, respectively). Rural addresses generally had greater error than non-rural addresses, although errors were smaller for E911 locations. For highly prevalent crops within 500 m (>97% of homes), sensitivity was >95% using both data sources; however, lower specificities with address-matched geocodes (more common for rural addresses) led to substantial attenuation of ORs (e.g., corn <500 m ORobs = 1.47 vs. ORtrue = 2.0). Error in the address-matched geocodes resulted in even greater ORobs attenuation for AFO exposures. Errors for North Carolina addresses were generally smaller than those in Iowa. CONCLUSIONS: Geocoding error can be minimized when known coordinates are available to test alternative data and methods. Our assessment suggests that where E911 locations are available, they offer an improvement upon address-matched geocodes for rural addresses. Exposure misclassification resulting from positional error is dependent on the geographic database, geocoding method, and the prevalence of exposure.

Environmental determinants of polychlorinated biphenyl concentrations in residential carpet dust.

Polychlorinated biphenyls (PCBs), banned in the United Sates in the late 1970s, are still found in indoor and outdoor environments. Little is known about the determinants of PCB levels in homes. We measured concentrations of five PCB congeners (105, 138, 153, 170, and 180) in carpet dust collected between 1998 and 2000 from 1187 homes in four sites: Detroit, Iowa, Los Angeles, and Seattle. Home characteristics, occupational history, and demographic information were obtained by interview. We used a geographic information system to geocode addresses and determine distances to the nearest major road, freight route, and railroad; percentage of developed land; number of industrial facilities within 2 km of residences; and population density. Ordinal logistic regression was used to estimate the associations between the covariates of interest and the odds of PCB detection in each site separately. Total PCB levels [all congeners < maximum practical quantitation limit (MPQL) vs at least one congener ≥ MPQL to < median concentration vs at least one congener > median concentration] were positively associated with either percentage of developed land [odds ratio (OR) range 1.01-1.04 for each percentage increase] or population density (OR 1.08 for every 1000/mi(2)) in each site. The number of industrial facilities within 2 km of a home was associated with PCB concentrations; however, facility type and direction of the association varied by site. Our findings suggest that outdoor sources of PCBs may be significant determinants of indoor concentrations.

Spatial Heterogeneity in Positional Errors: A Comparison of Two Residential Geocoding Efforts in the Agricultural Health Study.

Geocoding is a powerful tool for environmental exposure assessments that rely on spatial databases. Geocoding processes, locators, and reference datasets have improved over time; however, improvements have not been well-characterized. Enrollment addresses for the Agricultural Health Study, a cohort of pesticide applicators and their spouses in Iowa (IA) and North Carolina (NC), were geocoded in 2012-2016 and then again in 2019. We calculated distances between geocodes in the two periods. For a subset, we computed positional errors using "gold standard" rooftop coordinates (IA; N = 3566) or Global Positioning Systems (GPS) (IA and NC; N = 1258) and compared errors between periods. We used linear regression to model the change in positional error between time periods (improvement) by rural status and population density, and we used spatial relative risk functions to identify areas with significant improvement. Median improvement between time periods in IA was 41 m (interquartile range, IQR: -2 to 168) and 9 m (IQR: -80 to 133) based on rooftop coordinates and GPS, respectively. Median improvement in NC was 42 m (IQR: -1 to 109 m) based on GPS. Positional error was greater in rural and low-density areas compared to in towns and more densely populated areas. Areas of significant improvement in accuracy were identified and mapped across both states. Our findings underscore the importance of evaluating determinants and spatial distributions of errors in geocodes used in environmental epidemiology studies.

Modeling groundwater nitrate exposure in private wells of North Carolina for the Agricultural Health Study.

Unregulated private wells in the United States are susceptible to many groundwater contaminants. Ingestion of nitrate, the most common anthropogenic private well contaminant in the United States, can lead to the endogenous formation of N-nitroso-compounds, which are known human carcinogens. In this study, we expand upon previous efforts to model private well groundwater nitrate concentration in North Carolina by developing multiple machine learning models and testing against out-of-sample prediction. Our purpose was to develop exposure estimates in unmonitored areas for use in the Agricultural Health Study (AHS) cohort. Using approximately 22,000 private well nitrate measurements in North Carolina, we trained and tested continuous models including a censored maximum likelihood-based linear model, random forest, gradient boosted machine, support vector machine, neural networks, and kriging. Continuous nitrate models had low predictive performance (R2 < 0.33), so multiple random forest classification models were also trained and tested. The final classification approach predicted <1 mg/L, 1-5 mg/L, and ≥5 mg/L using a random forest model with 58 variables and maximizing the Cohen's kappa statistic. The final model had an overall accuracy of 0.75 and high specificity for the higher two categories and high sensitivity for the lowest category. The results will be used for the categorical prediction of private well nitrate for AHS cohort participants that reside in North Carolina.

Modeling groundwater nitrate concentrations in private wells in Iowa.

Contamination of drinking water by nitrate is a growing problem in many agricultural areas of the country. Ingested nitrate can lead to the endogenous formation of N-nitroso compounds, potent carcinogens. We developed a predictive model for nitrate concentrations in private wells in Iowa. Using 34,084 measurements of nitrate in private wells, we trained and tested random forest models to predict log nitrate levels by systematically assessing the predictive performance of 179 variables in 36 thematic groups (well depth, distance to sinkholes, location, land use, soil characteristics, nitrogen inputs, meteorology, and other factors). The final model contained 66 variables in 17 groups. Some of the most important variables were well depth, slope length within 1 km of the well, year of sample, and distance to nearest animal feeding operation. The correlation between observed and estimated nitrate concentrations was excellent in the training set (r-square=0.77) and was acceptable in the testing set (r-square=0.38). The random forest model had substantially better predictive performance than a traditional linear regression model or a regression tree. Our model will be used to investigate the association between nitrate levels in drinking water and cancer risk in the Iowa participants of the Agricultural Health Study cohort.