Ambient temperature and risk of preterm birth in three Nepalese regions from March 2019 to May 2020: a retrospective secondary data analysis using distributed lag non-linear models

Abstract: Background: Preterm birth is a global health problem which causes significant short- and long-term morbidity and mortality. Pathophysiology is not entirely clear and due to climate change environmental risk factors, such as extreme temperatures, should be considered as emerging and modifiable risk factors. The evidence from low- and middle-income countries on their role is limited.  Objective: The goal of this study was to assess the relationship of daily mean temperature and number of preterm births in the period from March 2019 to May 2020 in Nepal.  Methods: Perinatal data from nine Nepalese hospitals, which took part in two recent quality improvement studies, was matched with climate data from 15 temperature stations. A time- series analysis using conditional Poisson regression and distributed lag non-linear models was done for the three stations with the largest study populations.  Results: Across the three analyzed regions in Nepal heterogenous results were found. Only in one area an overall increase in preterm birth risk for high temperatures within the last 2 weeks before delivery was found. One region showed a protective effect for heat, but increased risk for low temperatures. In the third region there was no overall association of ambient temperature and risk of preterm birth.  Discussion: Potential explanations for the heterogenous results are different sociodemographic and geographical background of the participants. Limitations concerning the selected study population as well as quality of climate data should be mentioned. Further studies are needed for more detailed investigation.