The rapid growth of unconventional natural gas development (UNGD), also known as hydraulic fracturing, has raised concerns of potential exposures to hazardous chemicals. Few studies have examined the risk of childhood cancer from exposure to UNGD. A case–control study included 498 children diagnosed with leukemia, lymphoma, central nervous system neoplasms, and malignant bone tumors during the period 2010–2019 identified through the Pennsylvania Cancer Registry. Cases were matched to controls using Pennsylvania birth records. For each subject, a new overall UNGD exposure metric was calculated which incorporates both spatial (proximity) and temporal (duration) aspects of well activity. Conditional logistic regression models were used to estimate the risk of combined and individual cancers by overall UNGD exposure, and well proximity. Children with a higher overall UNGD exposure (3rd/4th quartiles) had an increased risk for the four malignancies combined [OR] 1.69 (95% CI 1.01, 2.82) and 1.79 (95% CI 1.00, 3.19) compared to non-exposed children. Overall, individuals living within 0.5 miles of a UNGD site were 3.94 times (95% CI 1.66, 9.30) more likely to develop a malignancy compared to non-exposed children and the risk of lymphoma within 0.5 miles and 0.5–1 miles was also elevated [ORs of 5.05 (95% CI 1.09, 23.39) and 7.71 (95% CI 1.01, 59.00), respectively] compared to non-exposed. Our study found that overall UNGD cumulative activity as well as a proximity to UNGD wells were associated with an increased risk of childhood lymphoma and overall childhood cancers combined.

Objectives: Adverse birth outcomes such as low birth weight (LBW), small for gestational age (SGA) and preterm birth (PTB), are major indicators of perinatal health, yet evidence on the effects of occupational noise during pregnancy remains limited.Methods: This multi-country register-based study (n=590,454) examined associations between occupational noise exposure estimated using a job-exposure matrix with daily 8-hour equivalent sound levels (<70, ≥70–<75, ≥75–<80, ≥80 dBA) and adverse birth outcomes in metropolitan areas of Denmark, Finland, Norway, and Sweden.Results: Results across the four countries were inconsistent. Indications of higher odds of LBW, SGA, and PTB, particularly in Norway and Sweden were seen, though these associations attenuated after full adjustment. Meta-analyses of fully adjusted models indicated no pooled associations between occupational noise exposure (≥80 dBA category) and birth weight (estimate = –0.35 g, 95% CI [–14.58, 13.88]), LBW (OR = 1.03, 95% CI [0.88, 1.20]), SGA (OR = 1.07, 95% CI [0.88, 1.29]), or PTB (OR = 1.01, 95% CI [0.83, 1.23]). However, sensitivity analyses based on the Swedish data suggested that working hours (full-time, part-time) and work absence may modify the association (birth weight: estimate = -37.66 g, 95% CI [-61.11, -14.22], LBW: OR = 1.49, 95% CI [1.22, 1.81], PTB: OR = 1.44, 95% CI [1.23, 1.70]).Conclusions: Overall, the findings do not support strong associations between occupational noise exposure and adverse birth outcomes. Based on sensitivity analysis, additional data on working hours and work absence may modify the relationship in future studies.

Attention deficit hyperactivity disorder (ADHD) is one of the most diagnosed neurodevelopmental conditions. While its aetiology is primarily attributed to genetic factors, the complex interplay with environmental exposures remains poorly understood. We investigated the relationship between prenatal environmental exposures (excluding biomarkers) and ADHD, utilizing data from seven cohorts (n = 156,563 children) across Europe. A supervised machine learning model (Random Forest) was run at each cohort site to identify the environmental exposures most informative for classifying ADHD. The results were pooled by averaging variable importance ranks across cohorts and highest-ranking variables were then used in logistic regression to obtain interpretable associations. Finally, a random-effects meta-analysis with restricted maximum likelihood estimation was used to infer the associations across cohorts. Based on Random Forest, built and natural urban characteristics, air pollution, and maternal socioeconomic factors were selected as variables with the greatest predictive contribution to ADHD classification. Associations with built and natural urban characteristics and air pollution were not robust across cohorts and largely attenuated in fully adjusted models. Meta-analysis across cohorts showed increased odds of ADHD for maternal smoking during pregnancy (OR = 1.67, 95% CI: 1.31, 2.14, I² = 65.5%) and low maternal education (OR = 1.62, 95% CI: 1.27, 2.07, I² = 51.3%), with substantial heterogeneity. Our findings indicated that maternal smoking during pregnancy and low maternal education were associated with increased odds of ADHD diagnoses in children, suggesting the potential role of socioeconomic disparities. However, gene-environment correlation cannot be ruled out.