Kaitlyn E Watson1,2, Kyle M Gardiner2, Judith A Singleton1,2. 1. School of Pharmacy, University of Queensland, Brisbane 4102, Australia. 2. School of Clinical Sciences, Queensland University of Technology, Brisbane 4000, Australia.
Abstract
BACKGROUND: Extreme heat (EH) events are increasing in frequency and duration and cause more deaths in Australia than any other extreme weather event. Consequently, EH events lead to an increase in the number of patient presentations to hospitals. METHODS: Climatic observations for Hobart's region and Royal Hobart Hospital (RHH) emergency department admissions data were collected retrospectively for the study period of 2003-2010. A distributed lag non-linear model (DLNM) was fitted using a generalized linear model with quasi-Poisson family to obtain adjusted estimates for the relationship between temperature and the relative risk of being admitted to the RHH. RESULTS: The model demonstrated that relative to the annual mean temperature of 14°C, the relative risk of being admitted to the RHH for the years 2003-2010 was significantly higher for all temperatures above 27°C (P < 0.05 in all cases). The peak effect upon admission was noted on the same day as the EH event, however, the model suggests that a lag effect exists, increasing the likelihood of admission to the RHH for a further 14 days. CONCLUSIONS: To relieve the added burden on emergency departments during these events, adaptation strategies adopted by public health organizations could include preventative health initiatives.
BACKGROUND: Extreme heat (EH) events are increasing in frequency and duration and cause more deaths in Australia than any other extreme weather event. Consequently, EH events lead to an increase in the number of patient presentations to hospitals. METHODS: Climatic observations for Hobart's region and Royal Hobart Hospital (RHH) emergency department admissions data were collected retrospectively for the study period of 2003-2010. A distributed lag non-linear model (DLNM) was fitted using a generalized linear model with quasi-Poisson family to obtain adjusted estimates for the relationship between temperature and the relative risk of being admitted to the RHH. RESULTS: The model demonstrated that relative to the annual mean temperature of 14°C, the relative risk of being admitted to the RHH for the years 2003-2010 was significantly higher for all temperatures above 27°C (P < 0.05 in all cases). The peak effect upon admission was noted on the same day as the EH event, however, the model suggests that a lag effect exists, increasing the likelihood of admission to the RHH for a further 14 days. CONCLUSIONS: To relieve the added burden on emergency departments during these events, adaptation strategies adopted by public health organizations could include preventative health initiatives.
Authors: Jakub Lickiewicz; Katarzyna Piotrowicz; Patricia Paulsen Hughes; Marta Makara-Studzińska Journal: Int J Environ Res Public Health Date: 2020-12-07 Impact factor: 3.390