Estimating Causal Effects of Interventions on Early-life Environmental Exposures Using Observational Data

AbstractPurpose of ReviewWe discuss how epidemiologic studies have used observational data to estimate the effects of potential interventions on early-life environmental exposures. We summarize the value of posing questions about interventions, how a group of techniques known as “g-methods” can provide advantages for estimating intervention effects, and how investigators have grappled with the strong assumptions required for causal inference.Recent FindingsWe identified nine studies that estimated health effects of hypothetical interventions on early-life environmental exposures. Of these, six examined air pollution. Interventions evaluated by these studies included setting exposure levels at a specific value, shifting exposure distributions, and limiting exposure levels to less than a threshold value. Only one study linked exposure contrasts to a specific intervention on an exposure source, however.SummaryThere is growing interest in estimating intervention effects of early-life environmental exposures, in part because intervention effects are directly related to possible public health actions. Future studies can build on existing work by linking research questions to specific hypothetical interventions that could reduce exposure levels. We discuss how framing questions around interventions can help overcome some of the barriers to causal inference and how advances related to machine learning may strengthen studies by sidestepping the overly restrictive assumptions of pa...

Source: Current Environmental Health Reports - November 23, 2022 Category: Environmental Health Source Type: research