A Flexible Bootstrap Approach to Measurement Error Correction in Outdoor Environmental Epidemiology

Thomas M. Menino Convention & Exhibition Center 

In environmental epidemiology, individual-level exposure measurements are rarely observed and are commonly assigned from spatiotemporal prediction models. This paradigm introduces measurement error due to spatial misalignment between exposure predictions and health outcome locations, potentially leading to biased inference and undercoverage. Existing correction methods typically rely on access to monitoring data and have limited capacity to accommodate complex survey designs or settings where only gridded exposure predictions are available. We propose a flexible bootstrap-based measurement error correction framework for two-stage environmental health analyses that operates entirely on grid-based exposure predictions and does not require direct access to monitoring measurements. The approach embeds a scalable spatial model within a resampling scheme that reconstructs exposure uncertainty through pseudo-monitor sampling, exposure regeneration, and reassignment to individual locations.

Spatial statistics

Environmental Epidemiology

Measurement Error 

Section on Statistics in Epidemiology