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Quantitative Assessment of the Contribution of Meteorological Variables to the Prediction of the Number of Heat Stroke Patients for Tokyo

Sato, Takuto 日下, 博幸 Hino, Hideitsu 筑波大学

2022.08.02

概要

This study reveals the best combination of meteorological variables for the prediction of the number of emergency transport due to heat stroke over 64 years old in Tokyo metropolis based on a generalized linear model using 2008−2016 data. Temperature, relative humidity, wind speed, and solar radiation were used as candidates of the explanatory variables. The variable selection with Akaike’s information criterion (AIC) showed that all the four meteorological elements were selected for the prediction model. Additional analysis showed that the combination of daily mean temperature, maximum relative humidity, maximum wind speed, and total solar radiation as explanatory variables gives the best prediction, with approximately 19% less error than the conventional single-variable model which only uses the daily mean temperature. Finally, we quantitatively estimated the relative contribution of each variable to the prediction of the daily number of heat stroke patients using standardized partial regression coefficients. The result reveals that temperature is the largest contributor. Solar radiation is second, with approximately 20% of the temperature effect. Relative humidity and wind speed make relatively small contributions, each contributing approximately 10% and 9% of the temperature, respectively. This result provides helpful information to propose more sophisticated thermal indices to predict heat stroke risk.
(Citation: Sato, T., H. Kusaka, and H. Hino, 2020: Quanti- tative assessment of the contribution of meteorological variables to the prediction of the number of heat stroke patients for Tokyo. SOLA, 16, 104−108, doi:10.2151/sola.2020-018.)

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参考文献

Akatsuka, S., T. Uno, and M. Horiuchi, 2016: The relationship between the heat disorder incidence rate and heat stress indices at Yamanashi prefecture in Japan. Adv. Meteorol., 2016, 1−11.

Bobb, J. F., F. Dominici, and R. D. Peng, 2011: A bayesian model averaging approach for estimating the relative risk of mortality associated with heat waves in 105 U.S. cities. Biometrics, 67, 1605−1616.

Di Napoli, C., F. Pappenberger, and H. L. Cloke, 2018: Assessing heat-related health risk in Europe via the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol., 62, 1155−1165. Dobson, A. J., and A. G. Barnett, 2008: An Introduction to Gener-

alized Linear Models. Third edition, CRC press, pp. 307 Fujibe, F., J. Matsumoto, and H. Suzuki, 2018a: Spatial and

temporal features of heat stroke mortality in Japan and their relation to temperature variations, 1999−2014. Geographi- cal Review of Japan Series B, 91, 17−27.

Fujibe, F., J. Matsumoto, and H. Suzuki, 2018b: Regional features of the relationship between daily heat-stroke mortality and temperature in different climate zones in Japan. SOLA, 14, 144−147.

Fuse, A., S. Saka, R. Fuse, T. Araki, S. Kin, M. Miyauchi, and H. Yokota, 2014: Weather data can predict the number of heat stroke patient. Journal of Japanese Association for Acute Medicine, 25, 757−765 (in Japanese with English abstract).

Honda, Y., M. Kabuto, M. Ono, and I. Uchiyama, 2007: Deter-mination of optimum daily maximum temperature using climate data. Environ. Health Prev. Med., 12, 209−216.

Ishigaki, H., T. Matsubara, S. Gonda, and T. Horikoshi, 2001: Ex- perimental trial on the seasonal differences of the combined effect of air temperature and humidity on the human physi- ological and psychological responses. J. Archit. Plann. En- viron, Eng., 543, 49−56 (in Japanese with English abstract).

Lambert, D., 1992: Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34, 1−14.

Miyatake, N., N. Sakano, and S. Murakami, 2012: The relation between ambulance transports stratified by heat stroke and air temperature in all 47 prefectures of Japan in August, 2009: Ecological study. Envion. Health Prev. Med., 17, 77−80.

Nakai, S., T. Itoh, and T. Morimoto, 1999: Deaths from heat-stroke in Japan: 1968−1994. Int. J. Biometeorol., 43, 124−127.

Nairn, J. R., and R. J. B. Fawcett, 2015: The excess heat factor: A metric for heatwave intensity and its use in classifying heatwave severity. Int. J. Environ. Res. Public Health, 12, 227−253.

Ohashi, Y., Y. Kikegawa, T. Ihara, and N. Sugiyama, 2014: Nu- merical simulations of outdoor heat stress index and heat disorder risk in the 23 wards of Tokyo. J. Appl. Meteor. Climatol, 53, 583−597

Ono, M., 2013: Heat stroke and the thermal environment. Japan Medical Assoc. J., 56, 199−205.

Piver, W. T., M. Ando, F. Ye, and C. J. Portier, 1999: Temperature and air pollution as risk factors for heat stroke in Tokyo, July and August 1980−1995. Environ. Health Perspect., 107, 911−916.

Semanza, J. C., C. H. Rubin, K. H. Falter, J. D. Selanikio, W. D. Flanders, H. L. Howe, and J. L. Wilhelm, 1996: Heat-related deaths during the July 1995 heat wave in Chicago. The New England J. Medicine, 335, 84−90.

Suzuki-Parker, A., and H. Kusaka, 2016: Future projections of labor hours based on WBGT for Tokyo and Osaka, Japan, using multi-period ensemble dynamical downscale simula- tions. Int. J. Biometeorol., 60, 307−310.

Zhang, K., Y. Li, J. D. Schwartz, and M. S. O’Neill, 2014: What weather variables are important in predicting heat-related mortality? A new application of statistical learning methods. Environ. Res., 132, 350−359.

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