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Background: Infection by antibiotic resistant bacteria (ARB) is a global health crisis and asymptomatic colonization increases risk of infection. Nonhuman studies have linked heavy metal exposure to the selection of ARB; however, few epidemiologic studies have examined this relationship. This study analyzes the association between urinary lead level and colonization by ARB in a nonclinical human population., Methods: Data came from the Survey of the Health of Wisconsin 2016-2017, and its ancillary Wisconsin Microbiome Study. Urinary lead levels, adjusted for creatinine, were used to assess exposure. ARB included methicillin resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci (VRE), fluoroquinolone resistant Gram-negative bacilli (RGNB), and Clostridium difficile ( C. diff ), from skin, nose, and mouth swabs, and saliva and stool samples. Logistic regression, adjusted for covariates, was used to evaluate associations between Pb and ARB. Secondary analysis investigated Pb resistance from ARB isolates., Results: Among 695 participants, 239 (34%) tested positive for ARB. Geometric mean urinary Pb (unadjusted) was 0.286 µg/L (95% confidence intervals [CI] = 0.263, 0.312) for ARB negative participants and 0.323 µg/L (95% CI = 0.287, 0.363) for ARB positive participants. Models adjusted for demographics, diet, and antibiotic use showed elevated odds of positive colonization for those in the 95th percentile (vs. below) of Pb exposure (odds ratio [OR] = 2.05, 95% CI = 0.95, 4.44), and associations were highest in urban residents (OR = 2.85, 95% CI = 1.07, 7.59). RGNB isolates were most resistant to Pb., Discussion: These novel results suggest that Pb exposure is associated with increased colonization by ARB, and that RGNB are particularly resistant to Pb., Competing Interests: The authors declare that they have no conflicts of interest with regard to the content of this report., (Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The Environmental Epidemiology. All rights reserved.)

Introduction: Bicycles are a source of transportation, recreation, and exercise throughout the world. Bicycling is associated with both health and environmental benefits but also poses a risk of injury. The use of bicycle helmets has been shown to reduce morbidity and mortality associated with cycling. It is unknown if helmet use differs across Wisconsin geographic areas and sociodemographic groups., Methods: Data were obtained from the Survey of the Health of Wisconsin (SHOW). Bicycle use and helmet use frequency were determined from a self-administered questionnaire that contained questions specific to preventative health behaviors. Descriptive statistics summarized overall bicycle ridership. Chi-square and Student t tests were performed to assess relationships between bicycle and helmet use across geographic categories and sociodemographic groups., Results: Differences between sex, race or ethnicity, and education level were found to be associated with bicycle ridership and the frequency of helmet use. Men were significantly more likely to report riding a bicycle and never wearing a helmet. Individuals from urban communities reported always wearing a helmet more often than rural communities. Higher education levels were associated with higher levels of bicycle and helmet use. Race or ethnicity was not associated with bicycle ridership but was associated with differences in helmet use frequency., Conclusion: Nearly half of those who ride bicycles in Wisconsin report never wearing a helmet. Since bicycle ridership and helmet use were found to be associated with a number of sociodemographic characteristics, any solution should consider the role of equity when attempting to increase ridership or helmet use., (Copyright© Board of Regents of the University of Wisconsin System and The Medical College of Wisconsin, Inc.)

Conditional means regression, including ordinary least squares (OLS), provides an incomplete picture of exposure-response relationships particularly if the primary interest resides in the tail ends of the distribution of the outcome. Quantile regression (QR) offers an alternative methodological approach in which the influence of independent covariates on the outcome can be specified at any location along the distribution of the outcome. We implemented QR to examine heterogeneity in the influence of early childhood lead exposure on reading and math standardized fourth grade tests. In children from two urban school districts (n=1,076), lead exposure was associated with an 18.00 point decrease (95% CI: -48.72, -3.32) at the 10th quantile of reading scores, and a 7.50 point decrease (95% CI: -15.58, 2.07) at the 90th quantile. Wald tests indicated significant heterogeneity of the coefficients across the distribution of quantiles. Math scores did not show heterogeneity of coefficients, but there was a significant difference in the lead effect at the 10th (β=-17.00, 95% CI: -32.13, -3.27) versus 90th (β=-4.50, 95% CI: -10.55, 4.50) quantiles. Our results indicate that lead exposure has a greater effect for children in the lower tail of exam scores, a result that is masked by conditional means approaches., (Copyright © 2014 Elsevier Inc. All rights reserved.)