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Lawrence C McCandless

Publications and source records attributed to Lawrence C McCandless.

2 recordsLinked to original sources

Prenatal exposure to indoor PM2.5 and children's cognitive performance at 4 years of age: an observational analysis from the UGAAR randomized controlled trial.

Outdoor fine particulate matter (PM2.5) concentrations during pregnancy are linked to reduced cognitive performance in children. We previously reported that portable HEPA filter air cleaners use during pregnancy improved children's mean full-scale IQ (FSIQ), but no previous studies have evaluated the relationship between indoor PM2.5 during pregnancy and FSIQ in childhood. We conducted an observational analysis using data from the Ulaanbaatar Gestation and Air Pollution Research (UGAAR) randomized controlled trial. Using a previously developed model of weekly indoor PM2.5 concentrations, we estimated the average concentrations in participants' homes over the full pregnancy and in each trimester. When the children were four years old, we measured FSIQ using the Wechsler Preschool and Primary Scale of Intelligence (WPPSI-IV). We used multiple linear regression to assess the adjusted relationships between interquartile range (IQR) contrasts in indoor PM2.5 during pregnancy and FSIQ among 475 mother-child dyads. An 8.8 μg/m3 increase in indoor PM2.5 concentration over the full pregnancy was associated with a reduction of 1.4 points (95% CI: -3.4, 0.6) in mean FSIQ. The strongest association between PM2.5 concentrations and FSIQ was in the first trimester, when a 19.1 μg/m3 contrast was associated with a 2.8-point reduction (95% CI: -5.7, 0.2) in mean FSIQ. Indoor PM2.5, particularly during early pregnancy, may impair brain development, leading to lower mean FSIQ scores in four-year old children. These results, combined with our previous analysis of HEPA filter air cleaners, indicate that reducing PM2.5 exposure during pregnancy has beneficial effects on children's cognitive performance.

Humans↗

Bayesian sensitivity analysis for unmeasured confounding in observational studies.

We consider Bayesian sensitivity analysis for unmeasured confounding in observational studies where the association between a binary exposure, binary response, measured confounders and a single binary unmeasured confounder can be formulated using logistic regression models. A model for unmeasured confounding is presented along with a family of prior distributions that model beliefs about a possible unknown unmeasured confounder. Simulation from the posterior distribution is accomplished using Markov chain Monte Carlo. Because the model for unmeasured confounding is not identifiable, standard large-sample theory for Bayesian analysis is not applicable. Consequently, the impact of different choices of prior distributions on the coverage probability of credible intervals is unknown. Using simulations, we investigate the coverage probability when averaged with respect to various distributions over the parameter space. The results indicate that credible intervals will have approximately nominal coverage probability, on average, when the prior distribution used for sensitivity analysis approximates the sampling distribution of model parameters in a hypothetical sequence of observational studies. We motivate the method in a study of the effectiveness of beta blocker therapy for treatment of heart failure.

Adrenergic beta-Antagonists↗