Estimating long-term average household air pollution concentrations from repeated short-term measurements in the presence of seasonal trends and crossover.

ABSTRACT: Estimating long-term exposure to household air pollution is essential for quantifying health effects of chronic exposure and the benefits of intervention strategies. However, typically only a small number of short-term measurements are made. We compare different statistical models for combining these short-term measurements into predictions of a long-term average, with emphasis on the impact of temporal trends in concentrations and crossover in study design. We demonstrate that a linear mixed model that includes time adjustment provides the best predictions of long-term average, which have lower error than using household averages or mixed models without time, for a variety of different study designs and underlying temporal trends. In a case study of a cookstove intervention study in Hondu

SUBMITTER: Keller JP 

PROVIDER: S-EPMC8835562 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature