Literature DB >> 31203028

Estimation of heat-related morbidity from weather data: A computational study in three prefectures of Japan over 2013-2018.

Sachiko Kodera1, Taku Nishimura1, Essam A Rashed2, Kazuma Hasegawa1, Ichiro Takeuchi3, Ryusuke Egawa4, Akimasa Hirata5.   

Abstract

In recent years, the rates of heat-related morbidity and mortality have begun to increase with the increase in global warming; in this context, it is noteworthy that the number of patients transported by ambulance in heat-related cases in Japan reached 95,137 in 2018. The estimation of heat-related morbidity forms a key factor in proposing and implementing suitable intervention strategies and ambulance availability and arrangements. Heat-related morbidity is known to be fairly correlated to metrics related to ambient conditions, thus necessitating the exploration of new metrics to more accurately estimate morbidity. In this study, we use an integrated computational technique relating to thermodynamics and thermoregulation to estimate daily peak core temperature elevation and daily water loss, which are linked to heat-related illnesses, from weather data of three different prefectures in Japan (Tokyo, Osaka, and Aichi). The correlations of the computed core temperature elevation and water loss as well as conventional ambient conditions are investigated in terms of number of patients suffering from heat-related illnesses transported by ambulance from 2013 to 2018. The estimated water loss per the proposed computation yields better correlation with the number of patients transported by ambulance. In particular, the weight-sum daily water loss for two to three successive days is found to be an important metric for predicting the number of patients transported by ambulance. For the same ambient conditions, morbidity is found to decrease to 0.4 owing to heat adaption at the end of summer (60 days) as compared with that at the end of the rainy season. Thus, the weighted sum of water loss and daily average ambient temperature for successive days can be used as better metrics than conventional weather data for the application of intervention strategies and planning of ambulance arrangements for heat-related morbidity.
Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  Ambulance dispatches; Computational physical modeling; Heat adaptation; Heat-related illness

Mesh:

Year:  2019        PMID: 31203028     DOI: 10.1016/j.envint.2019.104907

Source DB:  PubMed          Journal:  Environ Int        ISSN: 0160-4120            Impact factor:   9.621


  7 in total

1.  Clinical characteristics, prognostic factors, and outcomes of heat-related illness (Heatstroke Study 2017-2018).

Authors:  Junya Shimazaki; Toru Hifumi; Keiki Shimizu; Yasutaka Oda; Jun Kanda; Yutaka Kondo; Shinichiro Shiraishi; Shuhei Takauji; Kei Hayashida; Takashi Moriya; Masaharu Yagi; Junko Yamaguchi; Hiroyuki Yokota; Shoji Yokobori; Masahiro Wakasugi; Arino Yaguchi; Yasufumi Miyake
Journal:  Acute Med Surg       Date:  2020-06-16

2.  Estimation of Time-Course Core Temperature and Water Loss in Realistic Adult and Child Models with Urban Micrometeorology Prediction.

Authors:  Toshiki Kamiya; Ryo Onishi; Sachiko Kodera; Akimasa Hirata
Journal:  Int J Environ Res Public Health       Date:  2019-12-13       Impact factor: 3.390

3.  Influence of Absolute Humidity, Temperature and Population Density on COVID-19 Spread and Decay Durations: Multi-Prefecture Study in Japan.

Authors:  Essam A Rashed; Sachiko Kodera; Jose Gomez-Tames; Akimasa Hirata
Journal:  Int J Environ Res Public Health       Date:  2020-07-24       Impact factor: 3.390

4.  The Effect of Minimum and Maximum Air Temperatures in the Summer on Heat Stroke in Japan: A Time-Stratified Case-Crossover Study.

Authors:  Shinji Otani; Satomi Funaki Ishizu; Toshio Masumoto; Hiroki Amano; Youichi Kurozawa
Journal:  Int J Environ Res Public Health       Date:  2021-02-09       Impact factor: 3.390

5.  Baseline scenarios of heat-related ambulance transportations under climate change in Tokyo, Japan.

Authors:  Marie Fujimoto; Hiroshi Nishiura
Journal:  PeerJ       Date:  2022-07-29       Impact factor: 3.061

6.  Heat health risk assessment analysing heatstroke patients in Fukuoka City, Japan.

Authors:  Nishat Tasnim Toosty; Aya Hagishima; Ken-Ichi Tanaka
Journal:  PLoS One       Date:  2021-06-21       Impact factor: 3.240

7.  Heatstroke predictions by machine learning, weather information, and an all-population registry for 12-hour heatstroke alerts.

Authors:  Soshiro Ogata; Misa Takegami; Taira Ozaki; Takahiro Nakashima; Daisuke Onozuka; Shunsuke Murata; Yuriko Nakaoku; Koyu Suzuki; Akihito Hagihara; Teruo Noguchi; Koji Iihara; Keiichi Kitazume; Tohru Morioka; Shin Yamazaki; Takahiro Yoshida; Yoshiki Yamagata; Kunihiro Nishimura
Journal:  Nat Commun       Date:  2021-07-28       Impact factor: 14.919

  7 in total

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