The relation between extreme heat and ENSO and characterization of health risks in relation to varying levels of UTCI

Abstract: As a result of climate change, temperatures have been steadily increasing in the last half century and extreme weather events such as storms, extreme heat, extreme precipitation and cold spells have been observed to occur more frequently. This comes with potential consequences for lives and health. There are several factors affecting the climate in a region. A phenomenon that has a large impact in the pacific region is the El niño southern oscillation (ENSO) which dictates variables such as temperature, humidity and precipitation. In this study an ENSO index called ONI was used to represent ENSO and the relationship between high temperature and ONI was investigated. Additionally, five different health outcomes were analyzed to see how they were affected by different levels of the Universal Thermal Climate Index (UTCI) which is a climate index that is based on several atmospheric variables. This thesis is part of the Belmont collaborative AWARD-APR project and the health data was collected through contacts within the project. The data used is daily mortality count for the ICD10 codes X30, X32, I50.0, I50.1 and I50.9 for the period 2008 to 2019. The UTCI data was acquired through an open source database from Copernicus, European Union’s Earth Observation Programme. The data was processed into daily average and maximum UTCI for the period 2008 to 2019. To see if there was a relation between extreme heat and ONI in Taiwan, linear regression models were applied. To investigate the relation between the health outcomes and UTCI, a Poisson regression model was used to describe the incidence. The results indicate that there is a positive relation between 95th percentile average daily temperature during June, July and August in the southern and mid-western parts of Taiwan. Further there is a rather weak indication of a positive relation between average monthly temperature and ONI in the winter and spring months. The results of the health analysis indicate that there is an increased risk of mortality due to these health outcomes during cold conditions. The main reason for the health outcomes being sensitive to low UTCI intervals(<5 ◦C) is that the cardiovascular health outcomes that were included exhibit this trend. The two health outcomes strictly related to heat seem to exhibit the opposite relation although their incidence is rather low to begin with, making the model uncertain. The results show that there is a possibility to use ONI to predict when there will be extreme heat in some regions. Thereby, ONI could indirectly be used to predict when the incidence for certain health outcomes is more prone to increase. For two regions there seemed to be an increase for all health outcomes with increasing UTCI. Thereby the relation between extreme heat and ONI might be useful for predicting and preventing mortality in these regions. For the majority of the regions, cold conditions implicate an increase of the cardiovascular health outcomes. As there is an indication of a positive relation between average monthly temperature and ONI in the winter, this could potentially be investigated further and then be used to predict when the incidence of cardiovascular health outcomes will increase. If the predictions are accurate, preventative measures can be applied. Future studies might want to investigate more closely how cold temperatures are correlated with ONI. Also it could be of interest to investigate the relation between minimum temperature or UTCI in relation to health outcomes in the sense that high minimum temperatures could have an adverse effect on individuals that need to cool down during night time.