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The Results of Heatwaves – The Actuary Journal


A take a look at heat-related mortality in Europe and South Korea
Marius Hermanns, Sarah Hogekamp and Yaryna Kolomiytseva


Photograph: Shutterstock/Marc Bruxelle

In line with the European Atmosphere Company, Europe is the fastest-warming continent on the planet, and warmth stress is among the most extreme local weather dangers.1 In 2023, Europe recorded one in every of its warmest summers in historical past. Globally, yearly common temperatures proceed to rise as effectively. Many nations are experiencing a rise of their common temperature and temperature volatility, resulting in excessive warmth or chilly. Thus, persons are being uncovered to extra heatwaves, which have a major affect on human well being, as demonstrated by the excessive variety of extra deaths through the European heatwaves in 2003, 2018 and 2022.2,3,4

To investigate the mortality affect of heatwaves, one ought to first outline what a heatwave is, however resulting from regional variations in temperature, there isn’t any commonplace definition. Usually, heatwaves are outlined by the variety of days (period) with the utmost temperature exceeding a sure threshold. The brink may be both a hard and fast worth or a percentile of the native temperature distribution.5 It is usually necessary to contemplate the potential for delayed results of utmost warmth on mortality.6 For instance, warmth stress or dehydration might worsen power circumstances, resulting in increased mortality within the weeks following a heatwave.

On this article, we analyze the affect of heatwaves on mortality in chosen European nations from 2000-2019 and in South Korea from 2010-2019. We estimate quasi-Poisson regression fashions7 with precise over anticipated (A/E) deaths because the goal variable and embody the variety of days in every week with most temperatures at or above a hard and fast threshold to seize the consequences of utmost warmth period. We additionally embody one- and two-week lags of utmost warmth period to discover its delayed results on the imply A/E.

Information Sources

We thought-about the next teams of nations:

  • The DACH nations (Germany, Austria and Switzerland)
  • The Benelux nations (Belgium, Netherlands and Luxembourg)
  • The Baltic nations (Estonia, Latvia and Lithuania)
  • South Korea

Because the land space of Germany is bigger than the areas of the opposite chosen nations, the evaluation on a nationwide degree for Germany would have been much less informative and was carried out on the state degree as an alternative.

We took the precise weekly deaths from the Short-term Mortality Fluctuations (STMF) knowledge sequence of the Human Mortality Database (HMD) for all nations besides Germany,8 for which we used weekly state-level deaths from the German Federal Statistical Workplace.9 Thereby, we obtained extra knowledge factors for Germany and accounted for state variations in temperature. For the European nations, we included weekly mortality knowledge over the interval 2000-2019, previous the COVID-19 pandemic. For South Korea, the STMF weekly mortality knowledge was obtainable just for 2010-2019.

For all nations besides Germany, we used the HMD inhabitants knowledge initially of a yr.10 For Germany, inhabitants dimension on the finish of a yr for every state was taken from the German Federal Statistical Workplace,11 and we assumed that inhabitants dimension on the finish of a yr was equal to inhabitants dimension initially of the following yr. Additional, we used life tables from the HMD for all nations, together with Germany, to compute anticipated deaths for every nation or state in annually with the inhabitants dimension initially of the yr and life desk of the earlier yr, which was the latest life desk at the moment. We divided the anticipated yearly deaths by 52, eradicating week 53 the place relevant for consistency functions. Weeks in these knowledge sources are outlined in accordance with the ISO 8601 date and time commonplace.

We used CPC Global Unified Temperature knowledge,12 WorldPop population counts13 and nation14 (or German state15) polygons to compute the weighted imply of most temperature for every nation (or German state) for every day over the interval 2000-2019.16 Inhabitants counts had been used as weights since excessive temperatures are anticipated to have a stronger impact on mortality in densely populated areas, as extra persons are in danger there.

See also  Exploring the Role of Actuarial Science in the Public Sector

Modeling Strategy

We estimated quasi-Poisson regression fashions to analyze the affect of utmost warmth period on the imply worth of precise deaths whereas accounting for anticipated deaths in several nations or teams of nations. To this finish, the A/E ratio was taken because the goal variable and anticipated deaths as the load variable. The variety of days with most temperatures at or above a sure threshold in a given week, one week prior and two weeks prior had been included as categorical predictor variables alongside nation (for Europe), yr, age group and gender. We computed the 95% confidence intervals for parameter estimates utilizing the delta method.17

Since excessive warmth occasions usually happen through the summer season months, we restricted the info we used for modeling to weeks 18-40. Larger ranges of the intense warmth period had been mixed into one class (3+, 4+ or 5+) resulting from a low incidence of these ranges, and we selected the extent of 0 days as the bottom degree in all fashions.

Excessive Warmth in Europe

Determine 1 presents modeling outcomes for the DACH, Benelux and Baltic nations. The utmost temperature threshold used to outline excessive warmth was set to 30°C, just like research analyzing heat-related mortality in Germany and Austria.18,19 The leads to the remainder of the article are based mostly on the state-level knowledge for Germany and country-level knowledge for the opposite nations.

Determine 1: Results of Excessive Warmth Period within the DACH, Benelux and Baltic Nations

Figure 1 The DACH countries (Germany, Austria and Switzerland)
Figure 1 The Benelux countries (Belgium, Netherlands and Luxembourg)
Figure 1 The Baltic countries

Supply: Creator visualization of modeling outcomes based mostly on numerous knowledge sources7,8,9,10,11,12,13,14

Determine 1 reveals the estimates of the proportion change within the imply A/E for various ranges of utmost warmth period in a given week relative to the bottom degree of 0 days alongside 95% confidence intervals. The consequences of utmost warmth period within the three teams of nations present an analogous sample: growing the period of utmost warmth will increase the imply A/E relative to the no-extreme-heat situation in a given week, with the biggest impact happening when excessive warmth lasts 4 or 5 or extra days. Particularly, within the DACH and Benelux nations, the imply A/E is estimated to be 17% and 19% increased, respectively, when excessive warmth lasts 5 or extra days in a given week relative to the no-extreme-heat situation. For the Baltic nations, the estimated impact of utmost warmth lasting 5 or extra days in a given week is of comparable magnitude however has excessive uncertainty related to it since there was just one week through the interval 2000-2019 (week 31 in 2014 in Lithuania) when excessive warmth lasted 5 or extra days.

These outcomes are based mostly on an assumption that the consequences of putting up with excessive warmth are the identical no matter which age group we think about. Nonetheless, research present that older or very younger age, sure power circumstances, being pregnant and social deprivation are among the many threat elements for heat-related adversarial well being outcomes.20

As insurers, we’re notably fascinated about whether or not the consequences of utmost warmth differ by age group. To this finish, we mixed knowledge for the DACH, Benelux and Baltic nations, as the consequences of utmost warmth period gave the impression to be related throughout these nations, and we included the interactions of utmost warmth period and its lags and age group into the mannequin. Age teams had been outlined as follows:

  • 0 to 64
  • 65 to 74
  • 75 to 84
  • 85 and older

Determine 2 reveals the estimates of share change within the imply A/E for various ranges of utmost warmth period in a given week relative to the bottom degree of 0 days for various age teams. For the age teams 0 to 64, 65 to 74, 75 to 84, and 85 and older, the imply A/E is estimated to be 11%, 12%, 18% and 22% increased, respectively, when excessive warmth lasts 5 or extra days relative to the no-extreme-heat situation in a given week. General, the consequences of utmost warmth period seem like most outstanding for the oldest age group and least outstanding however nonetheless important for the youthful age teams of 0 to 64 and 65 to 74 years. These findings are in line with different analysis exploring the affiliation between temperature and mortality.21

We explored the delayed results of utmost warmth by including the interactions of age group and one- and two-week lags of the intense warmth period predictor. Will increase within the imply A/E in a given week had been detected just for the intense warmth period of 5 or extra days one week prior (3% to 9% relying on the age group), whereas small decreases had been detected for the intense warmth period of 1 to 5 or extra days two weeks prior. An der Heiden et al. (2020) defined related findings by the displacement of the time of dying by two or three weeks.22

Excessive Warmth in South Korea

Determine 3 presents modeling outcomes for South Korea, overlaying the interval from 2010-2019. We used the utmost temperature threshold of 35°C to outline excessive warmth in South Korea since it’s in line with a threshold the Korea Meteorological Administration makes use of to situation a heatwave warning.23 We might detect weaker or no results of utmost warmth period once we used decrease temperature thresholds.

Determine 3: Results of Excessive Warmth Period in South Korea, 2010-2019

Figure 3 South Korea

Supply: Creator visualization of modeling outcomes based mostly on numerous knowledge sources7,8,9,10,11,12,13,14

Info in Determine 3 reveals that the imply A/E is estimated to be 6% increased when excessive warmth lasts three or extra days relative to the no-extreme-heat situation in a given week in South Korea. Over the interval 2010-2019, excessive warmth period of three or extra days was noticed on the nation degree for under two weeks (weeks 31 and 33 in 2018), resulting in increased uncertainty related to the proportion change estimate.

Needing to make use of totally different temperature thresholds to outline excessive warmth for Europe and South Korea may be defined by the violin-box plots in Determine 4. The plots visualize the each day most temperature distribution in weeks 18-40 over the interval 2010-2019, exhibiting that a lot of the most temperature values for South Korea are concentrated in a better temperature vary and the median most temperature is 7°C to eight°C levels increased than within the chosen European nations.

The estimated results of utmost warmth period appear to be barely weaker in South Korea than in Europe when 30°C and 35°C temperature thresholds are used to outline excessive warmth in Europe and South Korea, respectively. Nonetheless, extra knowledge shall be required to scale back the uncertainty related to these estimates.

Abstract and Outlook

The evaluation of the consequences of utmost warmth period in chosen European nations and South Korea signifies warmth that’s excessive relative to an area temperature distribution and that lasts sufficiently lengthy in a given week or one week prior might have adversarial results on mortality throughout all age teams, with the oldest age teams most affected. Further deaths attributable to excessive warmth and heatwaves might lead to a possible burden for all times insurance coverage corporations. Nonetheless, preventive methods might mitigate the adversarial warmth results.24 A natural extension of this analysis could possibly be to additionally examine the affect of utmost chilly on mortality within the winter months, controlling for the flu results and accounting for the potential for longer lags between chilly publicity and noticed mortality.25

Dr. Marius Hermanns is a pricing actuary at Gen Re within the Life International Underwriting and Analysis and Growth unit. He’s based mostly in Cologne, Germany.
Sarah Hogekamp is a senior pricing actuary at Gen Re within the Life International Underwriting and Analysis and Growth unit. She is predicated in Cologne, Germany.
Yaryna Kolomiytseva is an information scientist at Gen Re within the Life International Underwriting and Analysis and Growth unit. She is predicated in Cologne, Germany.

Statements of truth and opinions expressed herein are these of the person authors and will not be essentially these of the Society of Actuaries or the respective authors’ employers.

Copyright © 2024 by the Society of Actuaries, Chicago, Illinois.



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