Neonicotinoids are a class of systemic insecticides widely used on food crops globally. These pesticides may be found in “off-target” food items and persist in the environment. Despite the potential for extensive human exposure, there are limited studies regarding the prevalence of neonicotinoid residues in foods sold and consumed in the United States. Residue data for seven neonicotinoid pesticides collected between 1999 and 2015 by the US Department of Agriculture’s Pesticide Data Program (PDP) were collated and summarized by year across various food commodities, including fruit, vegetable, meat, dairy, grain, honey, and baby food, as well as water to qualitatively describe and examine trends in contamination frequency and residue concentrations. The highest detection frequencies (DFs) for neonicotinoids by year on all commodities were generally below 20%. Average DFs over the entire study period, 1999–2015, for domestic and imported commodities were similar at 4.5%. For all the samples (both domestic and imported) imidacloprid was the neonicotinoid with the highest overall detection frequency at 12.0%. However, higher DFs were observed for specific food commodity-neonicotinoid combinations such as: cherries (45.9%), apples (29.5%), pears (24.1%) and strawberries (21.3%) for acetamiprid; and cauliflower (57.5%), celery (20.9%), cherries (26.3%), cilantro (30.6%), grapes (28.9%), collard greens (24.9%), kale (31.4%), lettuce (45.6%), potatoes (31.2%) and spinach (38.7%) for imidacloprid. Neonicotinoids were also detected in organic commodities, (DF < 6%). Individual commodities with at least 5% of samples testing positive for two or more neonicotinoids included apples, celery, and cherries. Generally, neonicotinoid residues on food commodities did not exceed US Environmental Protection Agency tolerance levels. Increases in detection trends for both finished and untreated water samples for imidacloprid were observed from 2004 to 2011. Analysis of PDP data indicates that low levels of neonicotinoids are present in commonly-consumed fruits and vegetables sold in the US. Trends in detection frequencies suggest an increase in use of acetamiprid, clothianidin and thiamethoxam as replacements for imidacloprid. Given these findings, more extensive surveillance of the food and water supply is warranted, as well as biomonitoring studies and assessment of cumulative daily intake in high risk groups, including pregnant women and infants.

There is a growing recognition of social media data as being useful for understanding local area patterns. In this study, we sought to utilize geotagged tweets—specifically, the frequency and type of food mentions—to understand the neighborhood food environment and the social modeling of food behavior. Additionally, we examined associations between aggregated food-related tweet characteristics and prevalent chronic health outcomes at the census tract level. We used a Twitter streaming application programming interface (API) to continuously collect ~1% random sample of public tweets in the United States. A total of 4,785,104 geotagged food tweets from 71,844 census tracts were collected from April 2015 to May 2018. We obtained census tract chronic disease outcomes from the CDC 500 Cities Project. We investigated associations between Twitter-derived food variables and chronic outcomes (obesity, diabetes and high blood pressure) using the median regression. Census tracts with higher average calories per tweet, less frequent healthy food mentions, and a higher percentage of food tweets about fast food had higher obesity and hypertension prevalence. Twitter-derived food variables were not predictive of diabetes prevalence. Food-related tweets can be leveraged to help characterize the neighborhood social and food environment, which in turn are linked with community levels of obesity and hypertension.

Background: Atmospheric particulate matter (PM) has been associated with endothelial dysfunction, an early marker of cardiovascular risk. Our aim was to extend this research to a genetically homogenous, geographically stable rural population using location-specific moving-average air pollution exposure estimates indexed to the date of endothelial function measurement. Methods: We measured endothelial function using brachial artery flow-mediated dilation (FMD) in 615 community-dwelling healthy Amish participants. Exposures to PM < 2.5 μm (PM_2.5) and PM < 10 μm (PM₁₀) were estimated at participants’ residential addresses using previously developed geographic information system-based spatio-temporal models and normalized. Associations between PM exposures and FMD were evaluated using linear mixed-effects regression models, and polynomial distributed lag (PDL) models followed by Bayesian model averaging (BMA) were used to assess response to delayed effects occurring across multiple months. Results: Exposure to PM₁₀ was consistently inversely associated with FMD, with the strongest (most negative) association for a 12-month moving average (− 0.09; 95% CI: − 0.15, − 0.03). Associations with PM_2.5 were also strongest for a 12-month moving average but were weaker than for PM₁₀ (− 0.07; 95% CI: − 0.13, − 0.09). Associations of PM_2.5 and PM₁₀ with FMD were somewhat stronger in men than in women, particularly for PM₁₀. Conclusions: Using location-specific moving-average air pollution exposure estimates, we have shown that 12-month moving-average estimates of PM_2.5 and PM₁₀ exposure are associated with impaired endothelial function in a rural population.