Invited Perspective: Oil and Gas Development and Adverse Birth Outcomes: What More Do We Need to Know?

The difference in difference (DID) design is a quasi-experimental research design that researchers often use to study causal relationships in public health settings where randomized controlled trials (RCTs) are infeasible or unethical. However, causal inference poses many challenges in DID designs. In this article, we review key features of DID designs with an emphasis on public health policy research. Contemporary researchers should take an active approach to the design of DID studies, seeking to construct comparison groups, sensitivity analyses, and robustness checks that help validate the method's assumptions. We explain the key assumptions of the design and discuss analytic tactics, supplementary analysis, and approaches to statistical inference that are often important in applied research. The DID design is not a perfect substitute for randomized experiments, but it often represents a feasible way to learn about casual relationships. We conclude by noting that combining elements from multiple quasi-experimental techniques may be important in the next wave of innovations to the DID approach.

The rapid increase in unconventional natural gas (UNG) development in the United States during the past decade has brought wells and related infrastructure closer to population centers. This review evaluates risks to public health from chemical and nonchemical stressors associated with UNG, describes likely exposure pathways and potential health effects, and identifies major uncertainties to address with future research. The most important occupational stressors include mortality, exposure to hazardous materials and increased risk of industrial accidents. For communities near development and production sites the major stressors are air pollutants, ground and surface water contamination, truck traffic and noise pollution, accidents and malfunctions, and psychosocial stress associated with community change. Despite broad public concern, no comprehensive population-based studies of the public health effects of UNG operations exist. Major uncertainties are the unknown frequency and duration of human exposure, future extent of development, potential emission control and mitigation strategies, and a paucity of baseline data to enable substantive before and after comparisons for affected populations and environmental media. Overall, the current literature suggests that research needs to address these uncertainties before we can reasonably quantify the likelihood of occurrence or magnitude of adverse health effects associated with UNG production in workers and communities.

In studies of reproductive outcomes, maternal residence at delivery is often the only information available to characterise environmental exposures during pregnancy. The goal of this investigation was to describe residential mobility during pregnancy and to assess the extent to which change of residence may result in exposure misclassification when exposure is based on the address at delivery. Maternal residential mobility was compared between neural tube defect cases and unaffected controls from Texas participants in the National Birth Defects Prevention Study (NBDPS). Maternal residential information was obtained from the NBDPS interview. Data from the U.S. EPA National Air Toxics Assessment [Assessment System for Population Exposure Nationwide (ASPEN)], modelled at the census tract level, were used to estimate benzene exposure based on address at conception and address at delivery. Quartiles of exposure were assigned based on these estimates and the quartile assignments based on address at conception and address at delivery were compared using traditional methods (kappa statistics) and a novel application of mixed-effects ordinal logistic regression. Overall, 30% of case mothers and 24% of control mothers moved during pregnancy. Differences in maternal residential mobility were not significant between cases and controls, other than case mothers who moved did so earlier during pregnancy than control mothers (P = 0.01). There was good agreement between quartiles of estimated benzene exposure at both addresses (kappa = 0.78, P < 0.0001). Based on the mixed-effects regression model, address at delivery was not significantly different from using address at conception when assigning quartile of benzene exposure based on estimates from ASPEN (odds ratio 1.03, 95% confidence interval 0.85, 1.25). Our results indicate that, in this Texas population, maternal residential movement is generally within short distances, is typically not different between cases and controls, and does not significantly influence benzene exposure assessment.