Citation

Szyszkowicz M. Int. J. Environ. Res. Public Health 2020; 17(1): e17010202.

Affiliation

Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON K1A 0K9, Canada.

Copyright

(Copyright © 2020, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/ijerph17010202

PMID

31892178

Abstract

Numerous epidemiological studies have shown associations between short-term ambient air pollution exposure and various health problems. The time-stratified case-crossover design is a popular technique for estimating these associations. In the standard approach, the case-crossover model is realized by using a conditional logistic regression on data that are interpreted as a set of cases (i.e., individual health events) and controls. In statistical calculations, for each case record, three or four corresponding control records are considered. Here, the case-crossover model is realized as a conditional Poisson regression on counts with stratum indicators. Such an approach enables the reduction of the number of data records that are used in the numerical calculations. In this presentation, the method used analyzes daily counts on the shortest possible time-window, which is composed of two consecutive days. The proposed technique is positively tested on four challenging simulated datasets, for which classical time-series methods fail. The methodology presented here also suggests that the length of exposure (i.e., size of the time-window) may be associated with the severity of health conditions.

Language: en

Keywords

air pollution; case-crossover; cluster; concentration; counts; time-series