Estimating causal effects of air quality regulations using principal stratification for spatially correlated multivariate intermediate outcomes

Abstract

Methods for causal inference regarding health effects of air quality regulations are met with unique challenges because (1) changes in air quality are intermediates on the causal pathway between regulation and health, (2) regulations typically affect multiple pollutants on the causal pathway towards health, and (3) regulating a given location can affect pollution at other locations, that is, there is interference between observations. We propose a principal stratification method designed to examine causal effects of a regulation on health that are and are not associated with causal effects of the regulation on air quality. A novel feature of our approach is the accommodation of a continuously scaled multivariate intermediate response vector representing multiple pollutants. Furthermore, we use a spatial hierarchical model for potential pollution concentrations and ultimately use estimates from this model to assess validity of assumptions regarding interference. We apply our method to estimate causal effects of the 1990 Clean Air Act Amendments among approximately 7 million Medicare enrollees living within 6 miles of a pollution monitor.

The importance of the exposure metric in air pollution epidemiology studies: When does it matter, and why?

“…Selecting an appropriate exposure estimation approach for a particular study may not be straightforward, or may require additional expertise not readily available, since the variety of alternative exposure metrics currently being used makes it hard to compare results across studies in a meaningful way, even for the same pollutant and health outcome. The latter also poses a challenge to the U.S. Environmental Protection Agency (EPA) when interpreting and synthesizing published literature during the periodic regulatory review of the science upon which National Ambient Air Quality Standards (NAAQS) are set, as well as in the emerging literature on accountability research which examines the causal relationship between air quality regulations and ensuring changes in health outcomes (Moore et al 2008(Moore et al , 2010Zigler et al 2012).…”

Particulate Matter Matters

“…Some of the most well-known examples are the ban of coal sales in Dublin (19); the differential reduction in TSPs across the United States as a consequence of the 1981–82 recession (20); the air pollution reduction interventions before, during, and after the Beijing Olympic games (21); a steel plant strike (22); features of the U.S. Clean Air Act (6, 23); and the Chinese policy that provided free coal for heating in cities north of the Huai River (24) (see sidebar). [See also (25–28) for detailed reviews.…”

“…It would be useful to know whether previous efforts to reduce particulates air pollution actually produced the projected improvements in human health [e.g., (6, 14, 29)]. Second, estimates of the health effects of PM will play a central role in numerous upcoming regulatory decisions.…”

“…There is a growing consensus in economics, political science, statistics, and other fields that the associational or regression approach to inferring causal relations—on the basis of adjustment with observable confounders—is unreliable in many settings (4–6). We discuss how quasi-experimental (QE) techniques provide an opportunity to improve understanding of the relation between human health and regulation of air pollution from particulates.…”

Stronger Instruments by Design

“…Thus by not matching adjacent counties we hope to reduce the likelihood that rainfall recorded on election day in one location is less likely to travel to a location farther away. See Zigler, Dominici and Wang (2012) for another example where spatial correlation is characterized as a possible SUTVA violation. 2 As we proposed above, we perform a grid search over combinations of Λ and e. In this case, we used a grid search for values of Λ from 0 to 1.10 and sinks between 0 and 1435.…”