BACKGROUND: Exposure mixtures frequently occur in data across many domains, particularly in the fields of environmental and nutritional epidemiology. Various strategies have arisen to answer questions about exposure mixtures, including methods such as weighted quantile sum (WQS) regression that estimate a joint effect of the mixture components. OBJECTIVES: We demonstrate a new approach to estimating the joint effects of a mixture: quantile g-computation. This approach combines the inferential simplicity of WQS regression with the flexibility of g-computation, a method of causal effect estimation. We use simulations to examine whether quantile g-computation and WQS regression can accurately and precisely estimate the effects of mixtures in a variety of common scenarios. METHODS: We examine the bias, confidence interval (CI) coverage, and bias-variance tradeoff of quantile g-computation and WQS regression and how these quantities are impacted by the presence of noncausal exposures, exposure correlation, unmeasured confounding, and nonlinearity of exposure effects. RESULTS: Quantile g-computation, unlike WQS regression, allows inference on mixture effects that is unbiased with appropriate CI coverage at sample sizes typically encountered in epidemiologic studies and when the assumptions of WQS regression are not met. Further, WQS regression can magnify bias from unmeasured confounding that might occur if important components of the mixture are omitted from the analysis. DISCUSSION: Unlike inferential approaches that examine the effects of individual exposures while holding other exposures constant, methods like quantile g-computation that can estimate the effect of a mixture are essential for understanding the effects of potential public health actions that act on exposure sources. Our approach may serve to help bridge gaps between epidemiologic analysis and interventions such as regulations on industrial emissions or mining processes, dietary changes, or consumer behavioral changes that act on multiple exposures simultaneously. https://doi.

Background The parametric g-formula can be used to estimate the effect of a policy, intervention, or treatment. Unlike standard regression approaches, the parametric g-formula can be used to adjust for time-varying confounders that are affected by prior exposures. To date, there are few published examples in which the method has been applied. Methods We provide a simple introduction to the parametric g-formula and illustrate its application in analysis of a small cohort study of bone marrow transplant patients in which the effect of treatment on mortality is subject to time-varying confounding. Results Standard regression adjustment yields a biased estimate of the effect of treatment on mortality relative to the estimate obtained by the g-formula. Conclusions The g-formula allows estimation of a relevant parameter for public health officials: the change in the hazard of mortality under a hypothetical intervention, such as reduction of exposure to a harmful agent or introduction of a beneficial new treatment. We present a simple approach to implement the parametric g-formula that is sufficiently general to allow easy adaptation to many settings of public health relevance.

Background Autism spectrum disorders are often idiopathic. Studies have suggested associations between immune response and these disorders. We explored associations between parental autoimmune disorders and children’s diagnosis of autism by linking Swedish registries. Methods Data for each participant were linked across 3 Swedish registries. The study includes 1227 cases and 25 matched controls for each case (30,693 controls with parental linkage). Parental diagnoses comprised 19 autoimmune disorders. We estimated odds ratios (ORs) using multivariable conditional logistic regression. Results Parental autoimmune disorder was weakly associated with autism spectrum disorders in offspring (maternal OR = 1.6 [95% confidence interval = 1.1–2.2]; paternal OR = 1.4 [1.0 –2.0]). Several maternal autoimmune diseases were correlated with autism. For both parents, rheumatic fever was associated with autism spectrum disorders. Conclusions These data support previously reported associations between parental autoimmune disorders and autism spectrum disorders. Parental autoimmune disorders may represent a critical pathway that warrants more detailed investigation.