Spatio-temporal quasi-experimental methods for rare disease outcomes: The impact of reformulated gasoline on childhood hematologic cancer

TL;DR

This paper introduces Bayesian spatio-temporal matrix completion methods to estimate the causal impact of traffic-related air pollution from reformulated gasoline on childhood hematologic cancers, addressing challenges of rare outcomes.

Contribution

It develops novel Bayesian methods for causal inference in quasi-experiments with rare, unstable outcomes, improving estimation stability and uncertainty quantification.

Findings

Reduced TRAP associated with lower childhood leukemia and lymphoma incidence.

Bayesian methods outperform traditional approaches in stability and accuracy.

Application demonstrates causal link between pollution reduction and cancer risk.

Abstract

Although some pollutants emitted in vehicle exhaust, such as benzene, are known to cause leukemia in adults with high exposure levels, less is known about the relationship between traffic-related air pollution (TRAP) and childhood hematologic cancer. In the 1990s, the US EPA enacted the reformulated gasoline program in select areas of the US, which drastically reduced ambient TRAP in affected areas. This created an ideal quasi-experiment to study the effects of TRAP on childhood hematologic cancers. However, existing methods for quasi-experimental analyses can perform poorly when outcomes are rare and unstable, as with childhood cancer incidence. We develop Bayesian spatio-temporal matrix completion methods to conduct causal inference in quasi-experimental settings with rare outcomes. Selective information sharing across space and time enables stable estimation, and the Bayesian approach facilitates uncertainty quantification. We evaluate the methods through simulations and apply them to estimate the causal effects of TRAP on childhood leukemia and lymphoma.