Extreme temperature events (ETEs) are important climatological natural disasters whose consequences have been largely underappreciated due to current challenges in defining them and measuring their impacts. Taking an exploratory approach, this study examines the historical records of 422 ETE occurrences across 71 countries in the period 1900–2011 from the International Disaster Database (EM-DAT) with more detailed analyses for records from 1971 to 2011. The various limitations associated with ETE data and the EM-DAT database are discussed and followed by analyses for heat and cold events. Globally, after adjusting for bias due to increased reporting, it was found that there may be genuine increases in ETE occurrences. Trends for mortality are much more uncertain, with possibly a higher increase for heat than for cold events if the high death counts of the 2003 and 2010 heat waves are included. If excluded, only mortality for cold seems to have increased over the years. Comparisons with other mortality databases suggest that EM-DAT’s global coverage may not be entirely complete. Furthermore, it may have underestimated numbers of death counts, especially for small-scale heat events and cold events in general. Further analyses by Human Development Index (HDI) categories also suggest two additional and opposing biases: an increased reporting bias for more developed nations and an underreporting bias for less developed nations. Country-level analyses based on both absolute and adjusted data suggest that a handful of countries have been most severely impacted by ETE. These mainly comprise developed nations but also include five medium- and low-HDI countries in Asia.
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Because the index is only available for 1980, 1990, 2000, 2005–2012, values for missing years were substituted with the values from the next available year.
“Extreme weather event” would also be confusing as this term also includes other disasters such as floods and storms.
Only earthquake events were used (as opposed to all geophysical events) because there exist many more records for earthquake than for volcanoes and dry mass movements in EM-DAT; furthermore, the earthquake hazard is well understood, making the assumption that it remains constant more robust.
Biological disasters include epidemics, insect infestations, and animal stampedes. In the past half century (1962–2011), there were almost a quarter million (>227,000) deaths due to epidemics, but none due to insect infestations. Both sub-types of disasters can be exacerbated by other disasters such as flood, storms, and extreme temperatures. But as any mortality thus resulted would clearly be considered indirect (and difficult to quantify), biological disasters are not considered in this study.
Droughts were excluded because mortality from droughts is often the indirect result of famine or civil unrest (Peduzzi et al. 2010).
The EM-DAT data originally contains 76 distinct country and territory names but some names in fact refer to the same geographical location due to changes in government over time. In this study, event entries listed under prior official names were re-associated with the current country names for the geographical location of occurrence. For example, entries listed under Czechoslovakia were counted as entries under Czech Republic. Even though the former Czechoslovakia was composed of both Czech Republic and Slovakia prior to the 1993 split, no sub-national information was available to attribute the entries to one country or another for certain. Events from former Serbia and Montenegro were counted as entries under Serbia for the same reason. One event from the former Yugoslavia was noted to occur in Belgrade; therefore, it was counted as an entry under Serbia. One event from Hong Kong was counted as an entry under China.
Modeled using Poisson regression of event counts with one predictor variable (year) and robust standard errors for the parameter estimates. Robust standard errors were obtained using the R package “sandwich”, for technical details see Zeileis (2004, 2006). Parameter estimates for the variable “year” are provided in Table 13 in the “Appendix”. Goodness-of-fit of the model was done by examining the residual deviance. This entailed comparing the deviance of the fitted model against the maximum deviance of the ideal model (where predicted values equal the observed) using a chi-square test; a non-significant result would suggest that the model fits the data (UCLA 2013). It was found that the model does not fit any set of data well (except for all ETEs for the period 1991–2011). Nevertheless, almost all estimates for the variable “year” are significant. As this statistical analysis serves only as a first approximation for reporting bias, the estimates together with their 95 % CIs are deemed sufficient for this study. The parameter estimates then were used to calculate incidence rates, see Table 4.
As this part of the analysis only deals with the number of events and not mortality, no adjustment for population increase over time was made.
Crude death rate data fitted by Poisson regression were done with robust standard errors for the parameter estimates obtained using the R package “sandwich” (Zeileis 2004, 2006); crude rate = annual death count/contemporaneous population × 100,000. As the statistical analyses done here are more for exploratory purposes rather than to formulate specific explanatory or predictive models, especially given the limitations of the data as discussed in the text, therefore only one predictor variable (year) is used. Goodness-of-fit of the overall model was examined as described in footnote 7. The parameter estimates for “year” are listed in Table 14 in the “Appendix”. It should be pointed out that potentially models with both “year” and “number of events” as predictor variables can be fitted. However, chi-square tests comparing models with only “year” as predictor against models with both “year” and “number of events” show that there are no statistical difference between the two. This may be due to the fact that event magnitude has not been factored in.
Log-transformed (natural logged after adding a constant 0.0001) crude rate data fitted by robust linear regression were done using the R package “robust” (Wang et al. 2013).
Section 2.1.1 explains the codes chosen to query the mortality data. Heat- and cold-related symptoms that obviously did not originate from the ambient environment (e.g. from man-made sources, falling into water, etc.) were left out. But even if the two heat- and cold-related symptoms chosen (T67 and T68) incurred some death counts that are not due to the ambient environment to be included, the percentage is relatively small. Out of all deaths that have either X30 or any of the T67 codes listed as one of the 20 multiple causes of death (with any underlying cause of death), only 9 % have just T67 listed; for deaths that have either X31 or T68 listed as one of the 20 multiple causes of death (with any underlying cause of death), only 15 % have just T68 listed.
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The author thanks Ms. Régina Below of CRED (Center for Research on the Epidemiology of Disasters) for providing the extreme temperature events data in the EM-DAT database. The author also thanks The Gates Cambridge Trust for their support during this analysis and the preparation of this article.
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Lee, W.V. Historical global analysis of occurrences and human casualty of extreme temperature events (ETEs). Nat Hazards 70, 1453–1505 (2014). https://doi.org/10.1007/s11069-013-0884-7