Citation

Lokys HL, Junk J, Krein A. Int. J. Biometeorol. 2018; 62(1): 17-27.

Affiliation

ERIN-Environmental Research and Innovation Department, Luxembourg Institute of Science and Technology (LIST), 5, avenue des Hauts-Fourneaux, Esch/Alzette, 4362, Luxembourg.

Copyright

(Copyright © 2018, International Society of Biometeorology, Publisher Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s00484-017-1326-0

PMID

28243726

Abstract

Air quality and thermal stress lead to increased morbidity and mortality. Studies on morbidity and the combined impact of air pollution and thermal stress are still rare. To analyse the correlations between air quality, thermal stress and morbidity, we used a two-stage meta-analysis approach, consisting of a Poisson regression model combined with distributed lag non-linear models (DLNMs) and a meta-analysis investigating whether latitude or the number of inhabitants significantly influence the correlations. We used air pollution, meteorological and hospital admission data from 28 administrative districts along a north-south gradient in western Germany from 2001 to 2011. We compared the performance of the single measure particulate matter (PM10) and air temperature to air quality indices (MPI and CAQI) and the biometeorological index UTCI. Based on the Akaike information criterion (AIC), it can be shown that using air quality indices instead of single measures increases the model strength. However, using the UTCI in the model does not give additional information compared to mean air temperature. Interaction between the 3-day average of air quality (max PM10, max CAQI and max MPI) and meteorology (mean air temperature and mean UTCI) did not improve the models. Using the mean air temperature, we found immediate effects of heat stress (RR 1.0013, 95% CI: 0.9983-1.0043) and by 3 days delayed effects of cold stress (RR: 1.0184, 95% CI: 1.0117-1.0252). The results for air quality differ between both air quality indices and PM10. CAQI and MPI show a delayed impact on morbidity with a maximum RR after 2 days (MPI 1.0058, 95% CI: 1.0013-1.0102; CAQI 1.0068, 95% CI: 1.0030-1.0107). Latitude was identified as a significant meta-variable, whereas the number of inhabitants was not significant in the model.

Language: en

Keywords

Air quality index; Biometeorological index; Distributed lag non-linear model; Hospital admission; Morbidity; Multivariate meta-analysis