ORCID Profile
0000-0003-2517-1087
Current Organisations
University of Tokyo
,
Seoul National University
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Publisher: Springer Science and Business Media LLC
Date: 25-03-2022
DOI: 10.1038/S41598-022-09049-4
Abstract: Epidemiological analyses of health risks associated with non-optimal temperature are traditionally based on ground observations from weather stations that offer limited spatial and temporal coverage. Climate reanalysis represents an alternative option that provide complete spatio-temporal exposure coverage, and yet are to be systematically explored for their suitability in assessing temperature-related health risks at a global scale. Here we provide the first comprehensive analysis over multiple regions to assess the suitability of the most recent generation of reanalysis datasets for health impact assessments and evaluate their comparative performance against traditional station-based data. Our findings show that reanalysis temperature from the last ERA5 products generally compare well to station observations, with similar non-optimal temperature-related risk estimates. However, the analysis offers some indication of lower performance in tropical regions, with a likely underestimation of heat-related excess mortality. Reanalysis data represent a valid alternative source of exposure variables in epidemiological analyses of temperature-related risk.
Publisher: Environmental Health Perspectives
Date: 2016
DOI: 10.1289/EHP.1409392
Publisher: Springer Science and Business Media LLC
Date: 24-09-2021
DOI: 10.1186/S12199-021-00992-8
Abstract: Ambient temperature may contribute to seasonality of mortality in particular, a warming climate is likely to influence the seasonality of mortality. However, few studies have investigated seasonality of mortality under a warming climate. Daily mean temperature, daily counts for all-cause, circulatory, and respiratory mortality, and annual data on prefecture-specific characteristics were collected for 47 prefectures in Japan between 1972 and 2015. A quasi-Poisson regression model was used to assess the seasonal variation of mortality with a focus on its litude, which was quantified as the ratio of mortality estimates between the peak and trough days (peak-to-trough ratio (PTR)). We quantified the contribution of temperature to seasonality by comparing PTR before and after temperature adjustment. Associations between annual mean temperature and annual estimates of the temperature-unadjusted PTR were examined using multilevel multivariate meta-regression models controlling for prefecture-specific characteristics. The temperature-unadjusted PTRs for all-cause, circulatory, and respiratory mortality were 1.28 (95% confidence interval (CI): 1.27–1.30), 1.53 (95% CI: 1.50–1.55), and 1.46 (95% CI: 1.44–1.48), respectively adjusting for temperature reduced these PTRs to 1.08 (95% CI: 1.08–1.10), 1.10 (95% CI: 1.08–1.11), and 1.35 (95% CI: 1.32–1.39), respectively. During the period of rising temperature (1.3 °C on average), decreases in the temperature-unadjusted PTRs were observed for all mortality causes except circulatory mortality. For each 1 °C increase in annual mean temperature, the temperature-unadjusted PTR for all-cause, circulatory, and respiratory mortality decreased by 0.98% (95% CI: 0.54–1.42), 1.39% (95% CI: 0.82–1.97), and 0.13% (95% CI: − 1.24 to 1.48), respectively. Seasonality of mortality is driven partly by temperature, and its litude may be decreasing under a warming climate.
Publisher: Environmental Health Perspectives
Date: 11-2019
DOI: 10.1289/EHP4898
Publisher: Environmental Health Perspectives
Date: 16-03-2018
DOI: 10.1289/EHP2223
Publisher: Springer Science and Business Media LLC
Date: 29-11-2019
DOI: 10.1038/S41598-019-53838-3
Abstract: Although there have been enormous demands and efforts to develop an early warning system for malaria, no sustainable system has remained. Well-organized malaria surveillance and high-quality climate forecasts are required to sustain a malaria early warning system in conjunction with an effective malaria prediction model. We aimed to develop a weather-based malaria prediction model using a weekly time-series data including temperature, precipitation, and malaria cases from 1998 to 2015 in Vhembe, Limpopo, South Africa and apply it to seasonal climate forecasts. The malaria prediction model performed well for short-term predictions (correlation coefficient, r 0.8 for 1- and 2-week ahead forecasts). The prediction accuracy decreased as the lead time increased but retained fairly good performance (r 0.7) up to the 16-week ahead prediction. The demonstration of the malaria prediction process based on the seasonal climate forecasts showed the short-term predictions coincided closely with the observed malaria cases. The weather-based malaria prediction model we developed could be applicable in practice together with skillful seasonal climate forecasts and existing malaria surveillance data. Establishing an automated operating system based on real-time data inputs will be beneficial for the malaria early warning system, and can be an instructive ex le for other malaria-endemic areas.
Publisher: Springer Science and Business Media LLC
Date: 04-08-2016
Publisher: Elsevier BV
Date: 09-2018
Publisher: Environmental Health Perspectives
Date: 06-2020
DOI: 10.1289/EHP5312
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 24-09-2021
Publisher: Elsevier BV
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 13-05-2022
Publisher: Elsevier BV
Date: 11-2020
No related grants have been discovered for Yoonhee Kim.