Objectives: During pregnancy, toxic heavy metals are encountered as mixtures in real-world environments; however, evidence regarding the effects of such mixed exposures on neonatal and early infant growth remains limited. This study aimed to evaluate the associations between prenatal heavy metal mixture exposure and child growth outcomes from birth to 24 months.Methods: We analyzed 4,978 mother–infant pairs from the Korean Children’s Environmental Health Study (Ko-CHENS). Prenatal blood concentrations of lead (Pb), mercury (Hg), and cadmium (Cd) were measured. Weight was assessed at birth and at 6, 12, and 24 months, whereas head circumference was assessed at 6, 12, and 24 months. Mixture effects were evaluated using weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR).Results: Higher levels of prenatal heavy metal mixture exposure were associated with decreased birth weight, an effect primarily driven by cadmium. Head circumference showed consistent reductions at 6, 12, and 24 months, predominantly attributable to lead. Nonlinear patterns were observed in some exposure–response relationships. Even when the levels of other metals were held constant, cadmium consistently exhibited the strongest negative association with birth weight, while lead showed the greatest negative effect on head circumference. No significant interactions among the three metals were observed.Conclusions: Prenatal exposure to a mixture of heavy metals was associated with lower birth weight and smaller head circumference during infancy. These findings underscore the value of considering co-occurring metal exposures and multiple growth indicators across early-life time points when evaluating potential health implications of prenatal environmental exposures. Given the observational design, the findings should be interpreted as associations, and head circumference as an anthropometric growth indicator rather than a direct measure of brain growth or neurodevelopment.
Yea Joon Kim, Jiwoong Yu, Woojoo Lee· Journal of Health Informatic...· 0 citations
BACKGROUND
Overlap weighting (OW) is increasingly used to estimate treatment effects in observational cancer studies. OW has attractive features: it targets the clinical equipoise population and mitigates the influence of extreme propensity score (PS) weights. Additionally, under regularity conditions, when the PS model is fitted using a standard logistic regression model (LRM) with all observed covariates included, OW achieves exact covariate balance between treated and control groups, meaning that the standardized mean differences for the included covariates are zero. However, in oncology data, imbalanced treatment patterns, small subgroups, and limited PS overlap frequently cause separation and non-convergence, undermining stable estimation in logistic regression.
METHODS
We evaluated five methods for constructing PSs for estimating the average treatment effect in the overlap population (ATO): standard logistic regression, Firth's penalized logistic regression, a double-penalized logistic regression method that combines Firth's correction with ridge regularization, and two variants designed to preserve exact covariate balance. Performance was assessed through Monte Carlo simulations under varying overlap, data complexity, and model misspecification. We also applied these methods to a retrospective cohort of 5348 patients with early-stage breast adenocarcinoma and Charlson Comorbidity Index ≥2 from the multi-institutionally linked nationwide data to estimate the effect of definitive surgery on 3-year all-cause mortality.
RESULTS
In simulations, standard LRMs showed unstable estimation or non-convergence in finite-sample settings characterized by low treatment prevalence and limited effective overlap. Penalized methods improved numerical stability, reduced extreme PS values, and generally showed better finite-sample performance, particularly when the LRM is not converged. In the breast cancer study, only 2.1% of patients did not undergo surgery, indicating marked treatment imbalance. Overall estimates were similar across methods, but in patients aged <40 years, a LRM yielded an extreme ATO estimate, whereas Firth's and double-penalized methods produced more stable and consistent results.
CONCLUSIONS
In oncology subgroups where low treatment prevalence and limited effective overlap lead to unstable or non-convergent LRMs, penalized regression approaches provide a practical strategy for improving ATO estimation.
Sangwon Lee, H. Chae, Dong-woo Choi et al.· Cancer Epidemiology· 0 citations
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