{"paper_id":"33e856a2-ed15-49f4-b2a9-922d8b2b50e5","body_text":"Life expectancy loss and recovery by age and sex following catastrophic events in Europe during the 19th and 20th centuries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Life expectancy loss and recovery by age and sex following catastrophic events in Europe during the 19th and 20th centuries Eliud Silva, José Manuel Aburto This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5313297/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Aug, 2025 Read the published version in Canadian Studies in Population → Version 1 posted 9 You are reading this latest preprint version Abstract Following catastrophic events, such as pandemics or wars, a systematic loss in life expectancy at birth ( \\(\\:{e}_{0}\\) ) can be observed. We aimed to estimate the time required for \\(\\:{e}_{0}\\) to recover after mortality crises and identify which age groups either contribute to the decline or assist in restoring pre-crisis levels. We focused exclusively on analyzing the largest European pandemics and wars of the 19th and 20th centuries, using data from the Human Mortality Database (HMD). To achieve this, we employed Arriaga's decomposition to examine two specific \\(\\:{e}_{0}{\\prime\\:}\\) s: one just before the most substantial decline during the mortality crisis, marking the deepest drop, and another at the point where recovery is observed. The events were categorized into pandemics and non-pandemics and further stratified by sex. Various statistical tests were conducted to enable valid comparisons. Our findings reveal that World Wars caused the most significant declines in \\(\\:{e}_{0}\\) . Statistical analyses indicate no significant disparities based on the type of event or sex. Notably, youth and children emerge as the primary age group contributing to the decline and recovery of \\(\\:{e}_{0}\\) following both catastrophic events. However, not all of them fully recover to the mortality levels observed before the crisis. Life expectancy catastrophic events pandemics wars mortality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction A primary concern for institutions, such as population and health ministries, insurance companies, and others, is to understand the extent of the effects of the COVID-19 pandemic on mortality. Historically, mortality crises such as those brought about by the First (WWI) and Second (WWII) World Wars caused a sharp increase in mortality sharply for a relatively short period (Vigezzi et al., 2022). Meanwhile, other types of crises, such as the dissolution of the Soviet Union (RC), induced a protracted stagnation in mortality improvements (Cockerham, 1997; Becker and Bloom, 1998; Aburto and Van Raalte, 2018). Four years following the start of the COVID-19 pandemic, many affected countries are still uncertain as to when mortality will return to pre-COVID-19 levels. One possibility for measuring the effects on mortality caused by catastrophic events is a summary index, such as life expectancy at birth ( . Life expectancy is defined by average number of years a group of newborns can expect to live, assuming they experience the mortality rates of a specific year consistently throughout their lives. It is comparable over time and across populations as it is unaffected by population size or age structure (Swanson and Siegel, 2004). Previous research on the impacts of the COVID-19 pandemic documented significant losses in in many countries during its first years (Aburto et al., 2022; Schöley et al., 2022; Heuveline, 2022), in many cases comparable in magnitude to those observed in previous mortality crises, such as epidemics and wars. We aim to contribute to the existing literature by analyzing how changed during past crises and how long it took to recover from the observed trends before the crises happened. Specifically, our objectives are: a) to estimate the recovery time of in selected European countries following catastrophic events, and b) to identify the primary age groups that experience losses and contribute to the recovery of after a given crisis, during both the 19th and 20th centuries. We use data from the Human Mortality Database (HMD) (University of California at Berkeley and the Max Planck Institute for Demographic Research, 2023) and research papers on the timing of pandemics and wars. The paper is organized as follows. Section 2 presents previous research into the impact of catastrophic events on . Section 3 describes our strategy for the selected mortality crises: our demographic approach for estimating , and how to identify losses and recoveries for different age groups. Section 4 presents decompositions from selected countries and displays the main results by sex and type of event, following several statistical tests. Finally, a discussion section is presented. 2 Background Schöley et al. ( 2022 ) highlighted the global impact of \\(\\:{e}_{0}\\) losses in 2020 due to the COVID-19 pandemic, examining the evolution of \\(\\:{e}_{0}\\) , focusing mainly on various European countries. The study revealed a distinct divergence in the mortality impact of the COVID-19 pandemic in 2021. Their findings underscored \\(\\:{e}_{0}\\) deficits among individuals aged 60 and above, with COVID-19 mortality being the primary contributor to these losses in 2021. In the context of Latin America, Lima et al. ( 2021 ) explored a similar theme and observed significant excess mortality in a number of these countries. They also emphasized that as the pandemic progressed, increased mortality rates became more evident in areas characterized by lower socioeconomic and sanitary conditions. Consequently, this led to declines in \\(\\:{e}_{0}\\) in these areas, ranging from 2 to 10 years. Research into past pandemic events has focused on describing historical catastrophic epidemics, such as the Black Death, Smallpox, Spanish Flu, Cholera, and the HIV/AIDS crises [see for example, Samal ( 2014 ), Huremović ( 2019 ), Kaur et al. ( 2020 ), Morabia ( 2020 ), and Piret and Boivin ( 2021 )], and their impact on mortality. There are also noteworthy findings regarding domestic cases that analyze the specific impact of pandemics. For instance, studies have examined the effects of the 1630 plague in Italy (Alfani et al., 2024 ), the 1918-19 influenza pandemic in Bangladesh (Chandra, 2013 ), and more recently black-white disparities in the US, 1980–2000 (Aburto et al., 2021 ). Since the 17th century and until the 1960s, there have been total of seven Cholera pandemics (Samal, 2014 ). Notably, the 6th pandemic brought the highest number of deaths, exceeding 500,000 in Russia. Previous Cholera pandemics also carried a high death toll and affected various countries, including China, Russia, and the UK, as well as several African and Latin American countries. Morabia ( 2020 ) notes that the first of these Cholera pandemics originated in India and spread to France, the Netherlands, the US, and Colombia. The second affected Europe, North Africa, and North America, and the third reached as far as Central and South America. According to Khan et al. ( 2020 ), the origin of all these pandemics can be traced back to India, excluding the 7th pandemic, which originated in Indonesia. From 1918 to 1920, the Spanish Flu, caused by the Influenza A subtype H1N1, led to an estimated 17–50 million deaths (Khan et al., 2020 ). Despite its significant impact, the origin of the Spanish Flu remains elusive, as it quietly traversed the globe in at least three distinct waves (Piret and Boivin, 2021 ). According to Johnson and Mueller ( 2002 ), close to 500 million people were infected during this pandemic. Huremović ( 2019 ) contends that the Spanish Flu marked the dawn of the modern medicine and remains the last pandemic to carry with it catastrophic human cost, with a deathtoll even exceeding that of WWI (Tsoucalas et al., 2016 ). Acknowledging the uncertainty of its origin, he suggests coutries such as the US, China, and Spain as the potential ground zero for the virus (Huremović, 2019 ). In terms of non-pandemic events, we considered the following, according to their occurrence across time: World War I (WWI), Great Depression (GD), Spanish Civil War (SCW), World War II (WWII), and the collapse of the Soviet Union (CSU). WWI profoundly impacted \\(\\:{e}_{0}\\) and the long-term health of its survivors. In Europe, the war caused an estimated 9 to 11 million military deaths and 6 to 13 million civilian deaths (Prost, 2014 ). This situation was immediately exacerbated by the Spanish flu outbreak, which further weakened the public health system in the aftermath of the war (Johnson & Mueller, 2002 ). In general, economic crises have been strongly associated with increases in mortality rates (Doerr & Hofmann, 2022 ). The GD of 1929 serves as a notable example of this trend. Although determining the exact number of deaths in Europe caused by the Depression is challenging, substantial evidence suggests that \\(\\:{e}_{0}\\) was adversely affected by severe socioeconomic challenges, including unemployment, poverty, and deteriorating public health conditions (Tapia Granados & Diez Roux, 2009 ). Demographically speaking, both the SCW (1936–1939) and WWII (1939–1945) had a severe impact on \\(\\:{e}_{0}\\) in both Spain and the rest of Europe. According to some estimates, the SCW caused at least 150,000 deaths, and long-lasting consequences on the health and longevity of the Spanish people (Renshaw, 2010 ). Estimations regarding WWII suggest approximately 39 million deaths in Europe alone (Kesternich et al, 2014 ). Finally, the CSU illustrates the intensity with which adverse political and economic circumstances triggered mortality reversal (for details, see Shkolnikov et al., 2004 ). Indeed, it represented approximately 1.6 million excess deaths from 1990 to 1995 (Bloom & Malaney, 1998 ). Two significant pandemics not addressed here are Yellow Fever and HIV/AIDS. Yellow Fever predominantly impacted Asia (Wasserman et al., 2016 ), Africa, and the Americas (Chippaux & Chippaux, 2018 ), making it less relevant to this discussion focused on Europe. HIV/AIDS, as analyzed by Govender et al. ( 2021 ), presents a distinct case due to its unique origin, spread, duration, and mortality impact. Unlike the acute and devastating outbreaks of Cholera and the Spanish Flu pandemics, which were eventually controlled through immediate public health interventions, the HIV/AIDS pandemic has developed into a prolonged global crisis. This crisis has been characterized by a slower, yet persistently high, mortality rate, demanding sustained and long-term management strategies. 3 Material and Methods Selecting countries and catastrophic events The HMD provides valuable mortality data for several high- and middle-income countries, with Sweden offering the most extensive historical data, spanning from 1751 to 2023. During the time available, some catastrophic events can be recognized, with their timing varied according to the approach of the authors. To identify the events, we rely on Piret and Boivin ( 2021 ) (Table 1 ). As mentioned, we also considered WWI, GD, SCW, WWII, and RC. Table 1 shows the dates chosen for the analysis. Table 1 Pandemic periods, according to different authors. Source: Own elaboration. Pandemic Samal ( 2014 ) Kaur et al. ( 2020 ) Morabia ( 2020 ) Huremović ( 2019 ) Khan et al. ( 2020 ) Piret and Boivin ( 2021 ) 1st Cholera (1CP) 1816–1826 1817–1824 1817–1824 2nd Cholera (2CP) 1829–1851 1826–1837 1826–1837 1827–1835 3rd Cholera (3CP) 1852–1860 1852–1860 1841–1859 1846–1860 1839–1856 4th Cholera (4CP) 1863–1875 1863–1875 5th Cholera (5CP) 1881–1896 1881–1896 1881–1886 6th Cholera (6CP) 1899–1923 1899–1923 1899–1923 7th Cholera (7CP) 1962–1966 1961-Present 1961-Present Spanish Flu (SF) 1918–1919 1918–1920 1918–1920 1918–1920 1918–1919 The blank space due to the event is not mentioned. The events were analyzed separately for males and females. The first, sixth, and seventh cholera pandemics (1CP, 6CP, 7CP) have been excluded from the analysis. The first pandemic lacked clear data in the available datasets. For the sixth and seventh pandemics, simultaneous catastrophic events either obscured their independent effects or resulted in a lack of available information. Specifically, during the sixth pandemic, the occurrence of the Spanish Flu (SF) and WWI complicated the analysis. Regarding the seventh pandemic, the most significant impacts were observed in Indonesia, Bangladesh, India, Russia, and other developing countries where data were unavailable. Consequently, the analysis focused on 36 catastrophic events per sex, as detailed in Table 2 . Table 2 Selected events by country and sex. Source: Own elaboration. Event Years DNK FIN FRA ITA NLD NOR RUS SPN SWE EW ♂ ♁ ♂ ♂ ♂ ♁ ♁ ♁ ♂ ♁ ♂ ♁ ♂ ♁ ♂ ♁ ♂ ♁ ♂ ♁ 2nd Cholera (2CP) 1827–1835 † † † † † † 3rd Cholera (3CP) 1839–1856 † † † † † † † † 4th Cholera (4CP) 1863–1875 † † † † † † † † 5th Cholera (5CP) 1881–1886 † † † † † † World War I (WWI) 1914–1918 † † † † † † † † † † † † † † † † Spanish Flu (SF) 1918–1919 † † Great Depression (GD) 1929 † † † † Spanish Civil War (SCW) 1936–1939 † † World War II (WWII) 1939–1945 † † † † † † † † † † † † † † † † Russian collapse (RC) 1989–1991 † † DNK-Denmark, FIN-Finland, FRA-France, ITA-Italy, NLD-Netherlands, NOR-Norway, RUS-Russia, SPN-Spain, SWE-Sweden, EW-England, and Wales, ♂-men, and ♁-women. Data description We identified the largest declines in \\(\\:{e}_{0}\\) by country for the aforementioned events. Then, the data were divided into sex and type of event: pandemic (2CP, 3CP, 4CP, 5CP, and SF) and non-pandemic (WWI, GD, SCW, WWII, RC), respectively. Several non-parametric tests were carried out to analyze their possible normality (Shapiro-Wilk test), their potential similar distributions (Kolmogorov-Smirnov for two samples test), and whether there are significant differences by type of event and sex regarding medians (Kruscal-Wallis test), and variances (Fligner-Killeen test). For making the estimates R version 4.3.3 was employed (R Core Team, 2024 ). We propose that recovery occurs when, given a fall(s) in \\(\\:{e}_{0}\\) due to a catastrophic event, the one considering the previous figures before the shock, is equal to or greater than the one before that event has happened (in some cases, they are slightly smaller, and \\(\\:{e}_{0}\\) keeps in that level at least two years). In other words, when \\(\\:{e}_{0}\\) in a population rebounds to previous levels after experiencing a decline during a catastrophic event, it has stability for at least a biennium. We assumed that heterogeneity is an underlying part of these events, and applying some smoothing techniques could distort the corresponding behavior's recovery. Hence, we do not smooth the series what represent \\(\\:{e}_{0}\\) across time. Identifying age groups that suffer loss and support recovery of \\(\\:{\\varvec{e}}_{0}\\) To identify ages that have had the most significant impacts on changes in \\(\\:{e}_{0}\\) , we use Arriaga’s ( 1984 ) decomposition $$\\:{{}_{n}{}^{}\\varDelta\\:}_{x}=\\frac{{l}_{x}^{1}}{{l}_{0}^{1}}\\left(\\frac{{nL}_{x}^{2}}{{l}_{x}^{2}}-\\frac{{nL}_{x}^{1}}{{l}_{x}^{1}}\\right)+\\frac{{T}_{x+n}^{2}}{{l}_{0}^{1}}\\left(\\frac{{l}_{x}^{1}}{{l}_{x}^{2}}-\\frac{{l}_{x+n}^{1}}{{l}_{x+n}^{2}}\\right)$$ 1 where the first part corresponds to the direct effect and the second to the indirect and interaction effects; thereby, the immediate impact measures the change in mortality rates between ages \\(\\:x\\) and \\(\\:x+n\\) . That is the effect of change in the number of lived years between \\(\\:x\\) and \\(\\:x+n\\) on \\(\\:{e}_{0}\\) . The second part measures the contribution resulting from the persons-years to be added because additional survivors at age \\(\\:x+n\\) are exposed to new mortality conditions. The total change is given by \\(\\:{\\sum\\:}_{0}^{\\omega\\:}{{}_{n}{}^{}\\varDelta\\:}_{x}\\) . The superindices of the variables from (1) refer to the respective mortality table employed. For instance, in the recovery case: those variables with number 1, from the year when the deeper fall was identified, and those with number 2, from when recovery was achieved. We approach the decomposition in two stages. Initially, we break down the difference between the pre-shock \\(\\:{e}_{0}\\) and the lowest observed \\(\\:{e}_{0}\\) following the shock. This helps identify which age groups were primarily responsible for the decline. Subsequently, we decompose the difference between the post-shock lowest \\(\\:{e}_{0}\\) and the subsequent recovery \\(\\:{e}_{0}\\) . This second step reveals which age groups contribute to the recovery. Additionally, we calculate the combined contributions to both decline and recovery to discern which age groups achieved full recovery and which continued to experience higher mortality post-shock during population-level recovery (see Table 5 ). 4 Results Data description According to the available datasets (see Table 3 ) and applying first-order differences in \\(\\:{e}_{0}\\) time series of the selected countries, it is possible to identify the worst falls, by country, sex, and year. The deadliest pandemic was 4CP, mainly affecting Denmark, France, and the Netherlands. Meanwhile, WWI caused more dramatic falls as a non-pandemic event; it affected all countries analyzed (except Russia, Spain, and Denmark). Overall, the most significant shocks have been recorded in male populations. Table 3 Worst falls of \\(\\:{e}_{0}{\\prime\\:}s\\) by event and country across time. Source: Own elaboration. Event DNK FIN FRA ITA NLD NOR RUS SPN SWE EW Total ♂ ♁ ♂ ♁ ♂ ♁ ♂ ♁ ♂ ♁ ♂ ♁ ♂ ♁ ♂ ♁ ♂ ♁ ♂ 2CP -7.8 -5.4 -13.2 1828 -5.4 -5.4 1832 -4.0 -4.0 1834 -3.8 -3.8 3CP -9.1 -11.0 -10.9 -10.7 -4.1 -4.2 -3.0 -5.1 -2.6 -4.9 -65.5 1839 -1.7 -1.7 1846 -3.0 -3.0 -2.6 -2.6 -11.1 1847 -3.0 -3.6 -6.6 1849 -1.8 -4.3 -4.4 -2.3 -12.8 1852 -1.8 -1.6 -3.3 1853 -4.4 -4.6 -8.9 1854 -6.5 -6.2 -12.8 1855 -4.1 -4.2 -8.3 4CP -9.7 -9.0 -12.8 -9.5 -10.3 -13.1 -4.0 -3.9 -2.1 -74.5 1863 -2.1 -2.1 1864 -7.6 -7.1 -14.7 1866 -3.0 -2.6 -5.5 1867 -2.1 -1.9 -4.1 1870 -5.4 -3.7 -2.9 -2.9 -14.8 1871 -7.4 -5.9 -4.5 -4.6 -22.4 1874 -2.1 -1.9 -1.9 -5.9 1875 -2.0 -3.1 -5.0 5CP -2.2 -1.8 -1.7 -2.8 -3.4 -11.7 1881 -2.2 -2.2 1882 -2.8 -3.4 -6.1 1886 -1.8 -1.7 -3.5 GD -2.4 -2.3 -2.4 -2.6 -9.7 1929 -2.4 -2.3 -2.4 -2.6 -9.7 RC -0.4 -0.4 1990 -0.4 -0.4 SCW -7.8 -3.3 -11.1 1936 -3.8 -3.8 1937 -4.0 -3.3 -7.3 SF -14.5 -15.7 -30.3 1917 -1.3 -1.3 -2.6 1918 -11.7 -12.8 -24.5 1920 -1.5 -1.7 -3.1 WWI -18.4 -4.7 -27.2 -8.9 -25.9 -22.3 -8.0 -8.1 -7.5 -7.3 -9.4 -8.7 -18.4 -7.0 -181.7 1914 -20.0 -3.3 -23.3 1915 -10.7 -3.3 -5.6 -19.6 1916 -4.7 -2.4 -7.1 1917 -3.0 -3.2 -6.3 1918 -18.4 -4.7 -7.1 -8.9 -7.5 -19.1 -8.0 -8.1 -7.5 -7.3 -9.4 -8.7 -3.8 -7.0 -125.5 WWII -27.5 -2.7 -26.1 -8.0 -7.8 -1.3 -15.2 -5.0 -2.4 -4.5 -2.3 -102.9 1939 -4.6 -4.6 1940 -11.3 -14.2 -3.6 -3.3 -2.4 -4.5 -2.3 -41.6 1942 -3.6 -3.6 1943 -4.7 -4.2 -1.3 -10.3 1944 -11.6 -2.7 -7.2 -4.5 -3.6 -2.6 -32.1 1945 -8.3 -2.4 -10.7 Total -18.8 -20.0 -48.2 -11.9 -76.9 -44.9 -35.5 -25.3 -37.7 -30.4 -19.7 -19.6 -0.4 -22.3 -19.1 -14.8 -8.7 -27.8 -18.8 -501.0 DNK-Denmark, FIN-Finland, FRA-France, ITA-Italy, NLD-Netherlands, NOR-Norway, RUS-Russia, SPN-Spain, SWE-Sweden, EW-England, and Wales, ♂-men, and ♁-women. Data Source: Human Mortality Database. To apply Arriaga's approach, we have defined first year ( \\(\\:FY\\) ), shock year ( \\(\\:SY\\) ), and last year ( \\(\\:LY\\) ) of the fitting period, chosen on a case-by-case basis. The \\(\\:FY\\) refers to the year before the shock of interest, during which the trend of \\(\\:{e}_{0}\\) was upward. The \\(\\:SY\\) represents the year of the most significant shock to \\(\\:{e}_{0}\\) ​ caused by the event during its period. It should be remembered that there is not always a single shock, in such cases, the largest one is considered. The \\(\\:LY\\) is the year when recovery has been identified, and it remains at least two years greater than those from \\(\\:FY\\) . So, the recovery time is given by \\(\\:R=FY-LY\\) , for every case, respectively. Figure 1 illustrates examples of both pandemic and non-pandemic events across the three specified years. The period during which each event occurred is marked between the blue vertical lines. In the top-left panel, it took 7 years to recover the male \\(\\:{e}_{0}\\) level from 1867 after the 4CP. In the top-right panel, following the most significant decline in \\(\\:{e}_{0}\\) for the female Spanish population, 6 years were required to return to the 1916 \\(\\:{e}_{0}\\) level. In the bottom-left panel, the male population of England & Wales also took 7 years to recover after WWII. Finally, in the bottom-right panel, the Norwegian female population also needed 7 years to regain their pre-WWI \\(\\:{e}_{0}\\) level. Figure 1 Estimate \\(\\:R{\\prime\\:}s\\) given catastrophic events: selected cases. FRA-France, SPN-Spain, EW-England and Wales, NOR-Norway. Source: Own elaboration. Data Source: Human Mortality Database. Recoveries, \\(\\:R{\\prime\\:}s\\) , were analyzed for the total population, then divided by sex and by pandemic vs. non-pandemic events. Histograms (see Fig. 2 ) consistently show right skewness across all groups, attributed to the effect of long periods required to recover \\(\\:{e}_{0}\\) following some events. For the total dataset, the mean recovery time in \\(\\:{e}_{0}\\) was 7.264 years, the median 7.000, and standard deviation of 3.719 years. Additionally, Kolmogorov-Smirnov tests for two samples (KS2) were conducted for all possible group combinations, and no significant differences were found among the distributions at α = 1%. As indicated by the histograms, given the asymmetry, only the pandemic group shows normality as verified by the Shapiro-Wilk (SW) test at α = 1%. For the group with all data, the statistic was W = 0.781 and p-value = 0.000 (see details in Table 4 ). Although there are numerical differences in the descriptive statistics among the groups, these differences are not statistically significant in terms of medians and variance recoveries at α = 1%. In other words, for the selected countries and events, the time required to recover the median and variance of \\(\\:{e}_{0}\\) is the same for both pandemic and non-pandemic events and for males and females. Table 4 Several statistics of \\(\\:R{\\prime\\:}s\\) by groups. Source: Own elaboration. Statistic/group Pandemic Non-pandemic Test Pandemic vs Non-pandemic Statistic, p-value Mean 7.656 6.950 Kruskal-Wallis 2.814, 0.0934 Median 7.500 7.000 Standard deviation 2.935 4.254 Fligner-Killeen 1.333, 0.248 Range 12.000 24.000 SW(Statistic, p-value) 0.939, 0.072 0.644, 0.000 Statistics/group ♂ ♁ Test ♂ vs ♁ Statistic, p-value Mean 7.556 6.972 Kruskal-Wallis 0.639, 0.424 Median 7.000 7.000 Standard deviation 3.960 3.493 Fligner-Killeen 0.597, 0.440 Range 24.000 18.000 SW(Statistic, p-value) 0.701, 0.000 0.864, 0.000 Identifying age groups that impact the \\(\\:{\\varvec{e}}_{0}\\varvec{{\\prime\\:}}\\varvec{s}\\) A key focus of this paper is identifying the age groups that contribute to the losses and recoveries of \\(\\:{e}_{0}\\) ​ following catastrophic events. To address this, the following table illustrates pandemics and non-pandemic events by country and sex, concentrating on three data points: \\(\\:FY,\\:SY\\) and \\(\\:LY\\) . Additionally, heatmaps with numerical data are used to visualize the impacts for each case by age, with events grouped chronologically. It is worth noting that differences between sexes are minimal across these years. Table 5 Years for comparing \\(\\:{e}_{0}\\) losses and recoveries by event and country. Source: Own elaboration. Group Event Years ( \\(\\:FY,\\:SY,\\:LY\\) ) ♂ ♁ ♂ ♁ ♂ ♁ DNK FIN FRA Pandemic 2CP 3CP 4CP 5CP SF 1844, 1853, 1854 1863, 1865, 1872 1844, 1853, 1854 1863, 1865, 1872 1887, 1892, 1895 1887, 1892, 1895 1831, 1834, 1835 1845, 1849, 1850 1867, 1871, 1874 1831, 1834, 1835 1845, 1849, 1850 1867, 1871, 1874 Non-pandemic WWI GD SCW WWII RC 1913, 1918, 1921 1943, 1945, 1947 1913, 1918, 1921 1943, 1945, 1947 1914, 1918, 1921 1926, 1929, 1930 1938, 1941, 1946 1914, 1918, 1921 1926, 1929, 1930 1939, 1940, 1941 1913, 1915, 1920 1939, 1944, 1946 1912, 1918, 1921 1939, 1944, 1946 ITA NLD NOR Pandemic 2CP 3CP 4CP 5CP SF 1885, 1886, 1888 1885, 1886, 1888 1862, 1871, 1873 1863, 1871, 1873 1870, 1876, 1878 1880, 1882, 1886 1870, 1876, 1878 1880, 1882, 1886 Non-pandemic WWI GD SCW WWII RC 1914, 1918, 1922 1939, 1943, 1946 1914, 1918, 1922 1939, 1943, 1946 1913, 1918, 1920 1939, 1945, 1947 1913, 1918, 1920 1939, 1945, 1946 1913, 1918, 1920 1939, 1942, 1945 1913, 1918, 1920 1939, 1941, 1945 RUS SPA SWE Pandemic 2CP 3CP 4CP 5CP SF 1916, 1918, 1922 1916, 1918, 1922 1827, 1829, 1835 1854, 1857, 1859 1825, 1829, 1835 1854, 1857, 1859 Non-pandemic WWI GD SCW WWII RC 1987, 1994, 2013 1989, 1994, 2009 1935, 1939, 1943 1936, 1941, 1942 1913, 1918, 1920 1942, 1944, 1946 1913, 1918, 1920 1942, 1944, 1946 EW Pandemic 2CP 3CP 4CP 5CP SF 1845, 1849, 1860 1862, 1864, 1872 1881, 1882, 1888 1845, 1849, 1860 1862, 1864, 1872 1881, 1882, 1888 Non-pandemic WWI GD SCW WWII RC 1912, 1918, 1920 1928, 1929, 1930 1939, 1945, 1946 1917, 1918, 1920 1928, 1929, 1930 1939, 1940, 1942 First year ( \\(\\:FY\\) ), shock year ( \\(\\:SY\\) ), and last year ( \\(\\:LY\\) ) \\(\\:\\:\\) during catastrophic events. DNK-Denmark, FIN-Finland, FRA-France, ITA-Italy, NLD-Netherlands, NOR-Norway, RUS-Russia, SPN-Spain, SWE-Sweden, EW-England, and Wales, ♂-men, and ♁-women. The following heatmaps illustrate the changes in \\(\\:{e}_{0}\\) across various age groups (x-axis) and catastrophic events (y-axis). Declines are depicted in red, and recoveries are in green, with numerical values quantifying the magnitude of change. Each Figure is a panel which is divided into three sections: the top displays losses, the middle shows recoveries, and the bottom indicates the difference between the two. A white color in the last suggests a full recovery to pre-crisis levels; red indicates incomplete recovery, and green signifies an overcompensation in recovery. These visualizations aim to capture trends in \\(\\:{e}_{0}\\) changes across different age groups and selected events. Pandemics males The male population in the 0–4 and 5–9 age groups were the most affected (see Fig. 3 ), with a significant decline seen in the 0–4 age group during the 3CP-SWE event, where \\(\\:{e}_{0}\\) dropped by 4.3 years. However, this same age group also experienced the largest recovery, gaining 5.9 years during the 2CP-SWE event. Notably, significant declines were rare among older age groups, with exceptions like the 20–24 age group during 4CP-FRA and the 25–34 age group during SF-SPN. While some age groups, such as the 0–4 group during 4CP-FRA, showed robust recoveries, not all fully returned to pre-pandemic levels. For instance, the 5–9 age group post-5CP-NOR and young adults aged 25–49 post-4CP-FRA did not completely regain their pre-crisis \\(\\:{e}_{0}\\) . Patterns of decline and recovery varied across age groups and events, with some showing strong recoveries while others remained below pre-pandemic levels. Non-pandemics males Significant declines in \\(\\:{e}_{0}\\) occurred among the 20–29 age groups (see Fig. 4 ). The most severe was observed in the 20–24 age group during WWII in Finland (5.9 years). Conversely, the most substantial recovery happened in the same age group following WWI in France, with an increase of 10 years. While children under 5 were not the most severely impacted overall, they still experienced significant reductions, such as a 4.8-year decline in \\(\\:{e}_{0}\\) during WWI in Italy. The 20–24 age group again showed the most notable improvement during the recovery phases. Certain age groups, did not fully return to pre-crisis mortality levels after WWI and WWII in France and Finland. Likewise, \\(\\:{e}_{0}\\) declines were particularly severe in countries heavily affected by WWI and WWII, such as France. While some countries, like Italy and Finland, showed strong recoveries post-WWII, others, such as France, exhibited uneven recovery patterns. In contrast, Sweden and Denmark displayed overcompensation in recovery for certain age groups, with \\(\\:{e}_{0}\\) even surpassing pre-crisis levels, underscoring the varied impact of these historical events on different populations. Pandemics females Similar to males (see Fig. 5 ), the most affected age groups for females during pandemics were 0–4 years, followed by 5–9 years. The 3CP-SWE event caused the largest decline in \\(\\:{e}_{0}\\) for females, with a drop of 4.2 years for those under 5, and a 3.8-year decline due to SF-SPN. However, the most significant recovery for this group occurred after the 2CP-SWE event, with an increase of 5.8 years. Substantial declines were also seen in older age groups during the 4CP-FRA and SF-SPN events, reflecting patterns observed in males. While notable recoveries were recorded for children under 5, women across age groups from 5–34 years gained more than a year of \\(\\:{e}_{0}\\) after SF-SPN, except for those aged 10–14. Not all age groups fully returned to pre-crisis levels, with children under 5 post-3CP-EW and those aged 5–9 post-4CP-NLD failing to fully recover. Significant declines were particularly pronounced among younger age groups, with the most impactful events being major conflicts and severe crises in countries like France and Sweden. Recovery varied across age groups and countries, with some populations, particularly younger ones in Sweden and Denmark, showing strong rebounds, while other age groups in different countries exhibited slower or incomplete recoveries. Non-Pandemics females For non-pandemic events affecting females (see Fig. 6 ), significant \\(\\:{e}_{0}\\) declines were concentrated among those aged 5–49 years during WWI, with the most pronounced decline of 6.2 years observed in the under-5 population in Italy. Despite a strong recovery for this age group (9.7 years), not all age groups returned to pre-crisis mortality levels. For instance, the under-5 age group in Sweden saw only partial recovery following WWII (2.8 years) and WWI (1.4 years). The analysis highlights significant declines, particularly in younger age groups, during events like WWI and WWII, with countries such as France and Finland experiencing notable reductions in \\(\\:{e}_{0}\\) . These findings underscore the vulnerability of younger women during these crises. In terms of recovery, the outcomes were varied, with some age groups, especially younger women, demonstrating strong rebounds, as seen in France and Sweden. However, older age groups and those in countries like Finland and Italy exhibited slower recoveries, indicating the long-term effects of these events. 5 Discussion Our estimates confirm that life expectancy recovery typically spans several years, regardless of the type of catastrophic event. Nowadays, the recovery period could be shortened through assertive and effective health policies focused on enhancing the well-being of affected groups. However, our ability to measure potential changes is constrained by the available data from past events and countries. Whether the catastrophic event is a pandemic or not, the required time for life expectancy recovery remains consistent. From a statistical standpoint, we can infer that the recovery duration is not significantly different between sexes. Additionally, we observed considerable variability in the dynamics of declines. In some instances, partial recoveries may be followed by subsequent falls before a more profound decline triggers recovery, occurring throughout a few or many years. The age groups that consistently and systematically contribute to the recovery of the mentioned mortality index are primarily children and, to a lesser extent, young males in the male population, especially in non-pandemic events. Therefore, if a demographic policy is formulated and implemented to improve life expectancy, it should target these specific population segments. Lastly, analyzing patterns following famines or natural disasters could be an intriguing avenue for future research papers, as it complements our understanding of how life expectancy recovers in various catastrophic events. The findings emphasize the long-lasting impact of catastrophic events on female \\(\\:{e}_{0}\\) , particularly for older populations, who may be less resilient in recovering from such disruptions. Recovery in life expectancy following catastrophic events can be attributed to various factors that warrant exploration. Meanwhile, it is well-established that during stable periods, life expectancy experiences improvements due to advancements in education levels (Kaplan et al., 2014 ), social development (Jiang et al., 2018 ), and medicine (Kalache et al., 2019 ), among other factors. Understanding the precise reasons for life expectancy recovery is challenging, and investigating the relationship between explanatory variables and recovery extends beyond the scope of our study. This paper aims to identify some statistical characteristics of selected recoveries across time rather than delving into the specific causes. Some limitations are recognized. Firstly, obtaining comprehensive information on all global pandemics and wars is practically unattainable. For instance, the Black Death from 1346 to 1353 (Huremović, 2019 ) and the Smallpox from the 16th to 19th centuries (Samal, 2014 ). Unfortunately, given their timeframe, there is no data, and also, the estimates have considerable uncertainty regarding the recovery in life expectancy. Additionally, certain catastrophic events, such as famines, slavery processes, and natural disasters (earthquakes, hurricanes, tornadoes, etc.), were excluded due to insufficient available information, resulting in a descriptive study of selected disastrous events. According to Zarulli et al. ( 2018 ), who explore mortality in extreme conditions, including epidemics, famines, and slavery, it has been demonstrated that women tend to have longer lifespans than men under such challenging circumstances. Overall patterns indicate that women exhibit lower mortality rates across various age groups and generally enjoy more extended life expectancy than men. The authors argue that during periods of crisis, the well-established trend of higher mortality rates for men compared to women due to accidents or violence could contribute to the gender disparity. However, reaching conclusive assessments is challenging due to the limited availability of data on causes of death during moments of crisis. Through our findings, we are online with the notable advantage for women over men in recovering life expectancy, both pandemic and non-pandemic events. Most forecasting methods of life expectancy do not consider mortality crises. The Torri and Vaupel model (2012) is an appropriate tool to estimate life expectancy when it is desired to forecast the index for the total population. Meanwhile, when the objective is predicting life expectancy jointly by sex and considering its gap, the method proposed by Pascariu et al. ( 2018 ) should be employed. As others mentioned in those papers, these methods have been established and examined when non-catastrophic events are presented in the corresponding population. For this reason, these approaches are unsuitable for predicting life expectancy, its declines and recoveries, under our framework, that is, when a crisis occurs. Given the circumstances during catastrophic events, we consider that our proposal helps us easily measure the time recovery and the main groups that contribute to reestablish the life expectancy level. Given the availability of age- and sex-specific mortality rates in the HMD for the selected countries and events, employing well-established mortality forecasting techniques, such as the Lee-Carter model or its variations, might seem like the initial and obvious choice for estimating underlying rates, \\(\\:{e}_{0}\\) , and their recovery times. However, several limitations are evident across all scenarios. The linear assumptions of the Lee-Carter model are problematic due to the complex nature of mortality trends observed around catastrophic events in the selected cases. Additionally, assuming a static age pattern across all frameworks is inappropriate, as it implies uniform mortality improvement rates across all age groups over time, that how it has been shown is something unreal. Furthermore, for comparative purposes, there is uncertainty regarding the consistent data quality and sample sizes for all cases over time. There are some notable similarities and differences between our work and the recent study by Goldstein & Lee ( 2024 ). Like the authors, we identify reversals—referred to here as 'declines or falls'—but we also consider recoveries following each selected catastrophic event. They observe that these falls do not significantly disrupt the long-term trend of increasing life expectancy, whereas our focus is on recovery time, without engaging in predictive analysis. The authors emphasize that after significant events, such as the COVID-19 pandemic, life expectancy trends generally return to their pre-event trajectories. This finding is consistent with our analysis of earlier events, where we pinpoint the specific year in which life expectancy recovered to pre-crisis levels. However, while their study suggests that incorporating life expectancy reversals into models could improve forecasting accuracy, we take a different approach. We conduct \\(\\:{e}_{0}\\) decompositions that provide detailed insights into the uneven recovery across different age groups. ​We acknowledge that more recent catastrophic events may require less time for recovery due to factors such as medical advancements, improved civil regulations, greater public adherence to controls, and sufficient and homogeneous economic support for the entire population. However, this may not be universally applicable across all countries and regions. Standardizing conditions among countries or territories at different times for comparing recovery times seems impossible, leading us to focus solely on descriptive terms. When a catastrophic event occurs and impacts demographic variables, particularly mortality, the question of when life expectancy will return to its previous level usually arises. This uncertainty is particularly challenging to address in the aftermath of events like the COVID-19 pandemic, wars and/or adverse weather conditions. Recovery timelines vary due to each country's unique circumstances and policies implemented before, during, and after the pandemic. Historically, the effects on life expectancy recovery have proven to be enduring. Consequently, we suggest that, following the COVID-19 pandemic, it may be reasonable to anticipate a recovery period of several years for some countries or regions. Declarations Ethics Statement This research was conducted in full compliance with ethical standards recognized in the scientific community. No vulnerable populations were involved, and all statistical procedures were carried out with the highest accuracy and integrity. Additionally, no conflicts of interest were present during the study. Funding This study did not receive any funding. Author Contribution E. 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Cite Share Download PDF Status: Published Journal Publication published 22 Aug, 2025 Read the published version in Canadian Studies in Population → Version 1 posted Editorial decision: Revision requested 12 Dec, 2024 Reviews received at journal 08 Dec, 2024 Reviews received at journal 03 Dec, 2024 Reviewers agreed at journal 15 Nov, 2024 Reviewers agreed at journal 13 Nov, 2024 Reviewers invited by journal 12 Nov, 2024 Editor assigned by journal 25 Oct, 2024 Submission checks completed at journal 25 Oct, 2024 First submitted to journal 22 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-5313297\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":370248311,\"identity\":\"24994823-4e5f-4d47-9a54-fc96ebf74766\",\"order_by\":0,\"name\":\"Eliud Silva\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACxgYwJSHDwMB8AMg4QLwWHgYGtgTitMAAUAuPAXFamNvbHz5g+GPBY3D8zMfPBTV3EvvbD7Bu5sHnsJ4zxgaMbRI8BmdyN0vPOPYsccaZBLabM/BpmZHDJsHYIMEj2ZC7QZqH7XBiww0Gthsf8GpJf/6D4Q9QS/+bx795/h1OnA/SkoBXS4IZMLAkePglctikedsOJ24gaAvQLxKJbSAtz8ysefsOG288k9iG1y+GwBD78OFPnRwbf/Lj2zzfDsvOO3742G18IWbYACTQXA6NXlxAHq/sKBgFo2AUjAIQAACsaFAyMcVisgAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Universidad Anáhuac\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Eliud\",\"middleName\":\"\",\"lastName\":\"Silva\",\"suffix\":\"\"},{\"id\":370248312,\"identity\":\"d9654d0c-59b2-4eeb-bb74-093388a00277\",\"order_by\":1,\"name\":\"José Manuel Aburto\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"London School of Hygiene \\u0026 Tropical Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"José\",\"middleName\":\"Manuel\",\"lastName\":\"Aburto\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-10-22 16:08:29\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-5313297/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-5313297/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1007/s42650-025-00094-8\",\"type\":\"published\",\"date\":\"2025-08-22T16:29:26+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":69238848,\"identity\":\"bb8d9bf5-65c4-4d69-98a7-b07401c24288\",\"added_by\":\"auto\",\"created_at\":\"2024-11-18 10:03:13\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":97686,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eEstimate R's given catastrophic events: selected cases. 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Data Source: Human Mortality Database.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5313297/v1/e30086d996c761ab4ae76f6c.png\"},{\"id\":69238853,\"identity\":\"8653a6ca-8cf5-4f59-bf9f-0a8cc6418264\",\"added_by\":\"auto\",\"created_at\":\"2024-11-18 10:03:13\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":159375,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePandemics females: losses (top), recoveries (middle), and differences (bottom) by age group\\u003c/p\\u003e\\n\\u003cp\\u003eNote: Minor discrepancies may occur at the lowest level due to rounding. DNK-Denmark, FIN-Finland, FRA-France, ITA-Italy, NLD-Netherlands, NOR-Norway, RUS-Russia, SPN-Spain, SWE-Sweden, EW-England and Wales. Source: Own elaboration. Data Source: Human Mortality Database.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5313297/v1/ce692e129cf4d14e26413fa5.png\"},{\"id\":69240239,\"identity\":\"bc17d791-963e-4f9a-97f7-56c729c41488\",\"added_by\":\"auto\",\"created_at\":\"2024-11-18 10:19:13\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":191956,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eNon-pandemics females: losses (top), recoveries (middle), and differences (bottom) by age group\\u003c/p\\u003e\\n\\u003cp\\u003eNote: Minor discrepancies may occur at the lowest level due to rounding. DNK-Denmark, FIN-Finland, FRA-France, ITA-Italy, NLD-Netherlands, NOR-Norway, RUS-Russia, SPN-Spain, SWE-Sweden, EW-England and Wales. Source: Own elaboration. Data Source: Human Mortality Database.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5313297/v1/880889331a4495c026e83288.png\"},{\"id\":89847282,\"identity\":\"e77decb5-3596-45ee-9f86-f4b6b8507032\",\"added_by\":\"auto\",\"created_at\":\"2025-08-25 16:42:54\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2155978,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5313297/v1/8284919e-5aef-412e-8e6a-3f52c15ef2d7.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Life expectancy loss and recovery by age and sex following catastrophic events in Europe during the 19th and 20th centuries\",\"fulltext\":[{\"header\":\"1 Introduction\",\"content\":\"\\u003cp\\u003eA primary concern for institutions, such as population and health ministries, insurance companies, and others, is to understand the extent of the effects of the COVID-19 pandemic on mortality. Historically, mortality crises such as those brought about by the First (WWI) and Second (WWII) World Wars caused a sharp increase in mortality sharply for a relatively short period (Vigezzi et al., 2022). Meanwhile, other types of crises, such as the dissolution of the Soviet Union (RC), induced a protracted stagnation in mortality improvements (Cockerham, 1997; Becker and Bloom, 1998; Aburto and Van Raalte, 2018). Four years following the start of the COVID-19 pandemic, many affected countries are still uncertain as to when mortality will return to pre-COVID-19 levels.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eOne possibility for measuring the effects on mortality caused by catastrophic events is a summary index, such as life expectancy at birth (\\u0026nbsp;. Life expectancy is defined by average number of years a group of newborns can expect to live, assuming they experience the mortality rates of a specific year consistently throughout their lives. It is comparable over time and across populations as it is unaffected by population size or age structure (Swanson and Siegel, 2004). Previous research on the impacts of the COVID-19 pandemic documented significant losses in\\u0026nbsp;\\u0026nbsp;\\u0026nbsp;in many countries during its first years (Aburto et al., 2022; Sch\\u0026ouml;ley et al., 2022; Heuveline, 2022), in many cases comparable in magnitude to those observed in previous mortality crises, such as epidemics and wars.\\u003c/p\\u003e\\n\\u003cp\\u003eWe aim to contribute to the existing literature by analyzing how\\u0026nbsp;\\u0026nbsp;\\u0026nbsp;changed during past crises and how long it took to recover from the observed trends before the crises happened. Specifically, our objectives are: a) to estimate the recovery time of\\u0026nbsp;\\u0026nbsp;\\u0026nbsp;in selected European countries following catastrophic events, and b) to identify the primary age groups that experience losses and contribute to the recovery of\\u0026nbsp;\\u0026nbsp;\\u0026nbsp;after a given crisis, during both the 19th and 20th centuries. We use data from the Human Mortality Database (HMD) (University of California at Berkeley and the Max Planck Institute for Demographic Research, 2023) and research papers on the timing of pandemics and wars.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe paper is organized as follows. Section 2 presents previous research into the impact of catastrophic events on \\u0026nbsp;. Section 3 describes our strategy for the selected mortality crises: our demographic approach for estimating \\u0026nbsp;, and how to identify losses and recoveries for different age groups. Section 4 presents decompositions from selected countries and displays the main results by sex and type of event, following several statistical tests. Finally, a discussion section is presented.\\u003c/p\\u003e\"},{\"header\":\"2 Background\",\"content\":\"\\u003cp\\u003eSch\\u0026ouml;ley et al. (\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) highlighted the global impact of \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e losses in 2020 due to the COVID-19 pandemic, examining the evolution of \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, focusing mainly on various European countries. The study revealed a distinct divergence in the mortality impact of the COVID-19 pandemic in 2021. Their findings underscored \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e deficits among individuals aged 60 and above, with COVID-19 mortality being the primary contributor to these losses in 2021. In the context of Latin America, Lima et al. (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) explored a similar theme and observed significant excess mortality in a number of these countries. They also emphasized that as the pandemic progressed, increased mortality rates became more evident in areas characterized by lower socioeconomic and sanitary conditions. Consequently, this led to declines in \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e in these areas, ranging from 2 to 10 years.\\u003c/p\\u003e \\u003cp\\u003eResearch into past pandemic events has focused on describing historical catastrophic epidemics, such as the Black Death, Smallpox, Spanish Flu, Cholera, and the HIV/AIDS crises [see for example, Samal (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e), Huremović (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), Kaur et al. (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), Morabia (\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), and Piret and Boivin (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e)], and their impact on mortality. There are also noteworthy findings regarding domestic cases that analyze the specific impact of pandemics. For instance, studies have examined the effects of the 1630 plague in Italy (Alfani et al., \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), the 1918-19 influenza pandemic in Bangladesh (Chandra, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e), and more recently black-white disparities in the US, 1980\\u0026ndash;2000 (Aburto et al., \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eSince the 17th century and until the 1960s, there have been total of seven Cholera pandemics (Samal, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Notably, the 6th pandemic brought the highest number of deaths, exceeding 500,000 in Russia. Previous Cholera pandemics also carried a high death toll and affected various countries, including China, Russia, and the UK, as well as several African and Latin American countries. Morabia (\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) notes that the first of these Cholera pandemics originated in India and spread to France, the Netherlands, the US, and Colombia. The second affected Europe, North Africa, and North America, and the third reached as far as Central and South America. According to Khan et al. (\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), the origin of all these pandemics can be traced back to India, excluding the 7th pandemic, which originated in Indonesia.\\u003c/p\\u003e \\u003cp\\u003eFrom 1918 to 1920, the Spanish Flu, caused by the Influenza A subtype H1N1, led to an estimated 17\\u0026ndash;50\\u0026nbsp;million deaths (Khan et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Despite its significant impact, the origin of the Spanish Flu remains elusive, as it quietly traversed the globe in at least three distinct waves (Piret and Boivin, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). According to Johnson and Mueller (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e), close to 500\\u0026nbsp;million people were infected during this pandemic. Huremović (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e) contends that the Spanish Flu marked the dawn of the modern medicine and remains the last pandemic to carry with it catastrophic human cost, with a deathtoll even exceeding that of WWI (Tsoucalas et al., \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). Acknowledging the uncertainty of its origin, he suggests coutries such as the US, China, and Spain as the potential ground zero for the virus (Huremović, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn terms of non-pandemic events, we considered the following, according to their occurrence across time: World War I (WWI), Great Depression (GD), Spanish Civil War (SCW), World War II (WWII), and the collapse of the Soviet Union (CSU). WWI profoundly impacted \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e and the long-term health of its survivors. In Europe, the war caused an estimated 9 to 11\\u0026nbsp;million military deaths and 6 to 13\\u0026nbsp;million civilian deaths (Prost, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). This situation was immediately exacerbated by the Spanish flu outbreak, which further weakened the public health system in the aftermath of the war (Johnson \\u0026amp; Mueller, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn general, economic crises have been strongly associated with increases in mortality rates (Doerr \\u0026amp; Hofmann, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). The GD of 1929 serves as a notable example of this trend. Although determining the exact number of deaths in Europe caused by the Depression is challenging, substantial evidence suggests that \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e was adversely affected by severe socioeconomic challenges, including unemployment, poverty, and deteriorating public health conditions (Tapia Granados \\u0026amp; Diez Roux, \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eDemographically speaking, both the SCW (1936\\u0026ndash;1939) and WWII (1939\\u0026ndash;1945) had a severe impact on \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e in both Spain and the rest of Europe. According to some estimates, the SCW caused at least 150,000 deaths, and long-lasting consequences on the health and longevity of the Spanish people (Renshaw, \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). Estimations regarding WWII suggest approximately 39\\u0026nbsp;million deaths in Europe alone (Kesternich et al, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Finally, the CSU illustrates the intensity with which adverse political and economic circumstances triggered mortality reversal (for details, see Shkolnikov et al., \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e). Indeed, it represented approximately 1.6\\u0026nbsp;million excess deaths from 1990 to 1995 (Bloom \\u0026amp; Malaney, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e1998\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eTwo significant pandemics not addressed here are Yellow Fever and HIV/AIDS. Yellow Fever predominantly impacted Asia (Wasserman et al., \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), Africa, and the Americas (Chippaux \\u0026amp; Chippaux, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), making it less relevant to this discussion focused on Europe. HIV/AIDS, as analyzed by Govender et al. (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), presents a distinct case due to its unique origin, spread, duration, and mortality impact. Unlike the acute and devastating outbreaks of Cholera and the Spanish Flu pandemics, which were eventually controlled through immediate public health interventions, the HIV/AIDS pandemic has developed into a prolonged global crisis. This crisis has been characterized by a slower, yet persistently high, mortality rate, demanding sustained and long-term management strategies.\\u003c/p\\u003e\"},{\"header\":\"3 Material and Methods\",\"content\":\"\\u003cp\\u003e \\u003cb\\u003eSelecting countries and catastrophic events\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe HMD provides valuable mortality data for several high- and middle-income countries, with Sweden offering the most extensive historical data, spanning from 1751 to 2023. During the time available, some catastrophic events can be recognized, with their timing varied according to the approach of the authors. To identify the events, we rely on Piret and Boivin (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). As mentioned, we also considered WWI, GD, SCW, WWII, and RC. Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e shows the dates chosen for the analysis.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ePandemic periods, according to different authors.\\u003c/p\\u003e \\u003cdiv class=\\\"Credit\\\"\\u003e\\u003cp\\u003eSource: Own elaboration.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePandemic\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSamal (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eKaur et al. (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMorabia (\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eHuremović (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eKhan et al. (\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003ePiret and Boivin (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1st Cholera (1CP)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1816\\u0026ndash;1826\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1817\\u0026ndash;1824\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1817\\u0026ndash;1824\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2nd Cholera (2CP)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1829\\u0026ndash;1851\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1826\\u0026ndash;1837\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1826\\u0026ndash;1837\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1827\\u0026ndash;1835\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3rd Cholera (3CP)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1852\\u0026ndash;1860\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1852\\u0026ndash;1860\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1841\\u0026ndash;1859\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1846\\u0026ndash;1860\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1839\\u0026ndash;1856\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4th Cholera (4CP)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1863\\u0026ndash;1875\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1863\\u0026ndash;1875\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e5th Cholera (5CP)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1881\\u0026ndash;1896\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1881\\u0026ndash;1896\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1881\\u0026ndash;1886\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e6th Cholera (6CP)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1899\\u0026ndash;1923\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1899\\u0026ndash;1923\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1899\\u0026ndash;1923\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e7th Cholera (7CP)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1962\\u0026ndash;1966\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1961-Present\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1961-Present\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSpanish Flu (SF)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1918\\u0026ndash;1919\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1918\\u0026ndash;1920\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1918\\u0026ndash;1920\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1918\\u0026ndash;1920\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1918\\u0026ndash;1919\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe blank space due to the event is not mentioned.\\u003c/p\\u003e \\u003cp\\u003eThe events were analyzed separately for males and females. The first, sixth, and seventh cholera pandemics (1CP, 6CP, 7CP) have been excluded from the analysis. The first pandemic lacked clear data in the available datasets. For the sixth and seventh pandemics, simultaneous catastrophic events either obscured their independent effects or resulted in a lack of available information. Specifically, during the sixth pandemic, the occurrence of the Spanish Flu (SF) and WWI complicated the analysis. Regarding the seventh pandemic, the most significant impacts were observed in Indonesia, Bangladesh, India, Russia, and other developing countries where data were unavailable. Consequently, the analysis focused on 36 catastrophic events per sex, as detailed in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eSelected events by country and sex.\\u003c/p\\u003e \\u003cdiv class=\\\"Credit\\\"\\u003e\\u003cp\\u003eSource: Own elaboration.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"22\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c12\\\" colnum=\\\"12\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c13\\\" colnum=\\\"13\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c14\\\" colnum=\\\"14\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c15\\\" colnum=\\\"15\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c16\\\" colnum=\\\"16\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c17\\\" colnum=\\\"17\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c18\\\" colnum=\\\"18\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c19\\\" colnum=\\\"19\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c20\\\" colnum=\\\"20\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c21\\\" colnum=\\\"21\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c22\\\" colnum=\\\"22\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eEvent\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eYears\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eDNK\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003eFIN\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003eFRA\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c10\\\" namest=\\\"c9\\\"\\u003e \\u003cp\\u003eITA\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c12\\\" namest=\\\"c11\\\"\\u003e \\u003cp\\u003eNLD\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c14\\\" namest=\\\"c13\\\"\\u003e \\u003cp\\u003eNOR\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c16\\\" namest=\\\"c15\\\"\\u003e \\u003cp\\u003eRUS\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c18\\\" namest=\\\"c17\\\"\\u003e \\u003cp\\u003eSPN\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c20\\\" namest=\\\"c19\\\"\\u003e \\u003cp\\u003eSWE\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c22\\\" namest=\\\"c21\\\"\\u003e \\u003cp\\u003eEW\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e♂\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e♁\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e♂\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e♂\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e♂\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e♁\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e♁\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e♁\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e♂\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e♁\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e♂\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e♁\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e♂\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e♁\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c17\\\"\\u003e \\u003cp\\u003e♂\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c18\\\"\\u003e \\u003cp\\u003e♁\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e♂\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c20\\\"\\u003e \\u003cp\\u003e♁\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c21\\\"\\u003e \\u003cp\\u003e♂\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c22\\\"\\u003e \\u003cp\\u003e♁\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2nd Cholera (2CP)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1827\\u0026ndash;1835\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c21\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c22\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3rd Cholera (3CP)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1839\\u0026ndash;1856\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c21\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c22\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4th Cholera (4CP)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1863\\u0026ndash;1875\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c21\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c22\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e5th Cholera (5CP)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1881\\u0026ndash;1886\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c21\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c22\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWorld War I (WWI)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1914\\u0026ndash;1918\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c21\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c22\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSpanish Flu (SF)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1918\\u0026ndash;1919\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c21\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c22\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGreat Depression (GD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1929\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c21\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c22\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSpanish Civil War (SCW)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1936\\u0026ndash;1939\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c21\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c22\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWorld War II (WWII)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1939\\u0026ndash;1945\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c21\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c22\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRussian collapse (RC)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1989\\u0026ndash;1991\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c21\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c22\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eDNK-Denmark, FIN-Finland, FRA-France, ITA-Italy, NLD-Netherlands, NOR-Norway, RUS-Russia, SPN-Spain, SWE-Sweden, EW-England, and Wales, ♂-men, and ♁-women.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eData description\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eWe identified the largest declines in \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e by country for the aforementioned events. Then, the data were divided into sex and type of event: pandemic (2CP, 3CP, 4CP, 5CP, and SF) and non-pandemic (WWI, GD, SCW, WWII, RC), respectively. Several non-parametric tests were carried out to analyze their possible normality (Shapiro-Wilk test), their potential similar distributions (Kolmogorov-Smirnov for two samples test), and whether there are significant differences by type of event and sex regarding medians (Kruscal-Wallis test), and variances (Fligner-Killeen test).\\u003c/p\\u003e \\u003cp\\u003eFor making the estimates R version 4.3.3 was employed (R Core Team, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). We propose that recovery occurs when, given a fall(s) in \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e due to a catastrophic event, the one considering the previous figures before the shock, is equal to or greater than the one before that event has happened (in some cases, they are slightly smaller, and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e keeps in that level at least two years). In other words, when \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e in a population rebounds to previous levels after experiencing a decline during a catastrophic event, it has stability for at least a biennium. We assumed that heterogeneity is an underlying part of these events, and applying some smoothing techniques could distort the corresponding behavior's recovery. Hence, we do not smooth the series what represent \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e across time.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eIdentifying age groups that suffer loss and support recovery of\\u003c/b\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\varvec{e}}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003cp\\u003eTo identify ages that have had the most significant impacts on changes in \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, we use Arriaga\\u0026rsquo;s (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e1984\\u003c/span\\u003e) decomposition\\u003cdiv id=\\\"Equ1\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ1\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:{{}_{n}{}^{}\\\\varDelta\\\\:}_{x}=\\\\frac{{l}_{x}^{1}}{{l}_{0}^{1}}\\\\left(\\\\frac{{nL}_{x}^{2}}{{l}_{x}^{2}}-\\\\frac{{nL}_{x}^{1}}{{l}_{x}^{1}}\\\\right)+\\\\frac{{T}_{x+n}^{2}}{{l}_{0}^{1}}\\\\left(\\\\frac{{l}_{x}^{1}}{{l}_{x}^{2}}-\\\\frac{{l}_{x+n}^{1}}{{l}_{x+n}^{2}}\\\\right)$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e1\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003ewhere the first part corresponds to the direct effect and the second to the indirect and interaction effects; thereby, the immediate impact measures the change in mortality rates between ages \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:x\\\\)\\u003c/span\\u003e\\u003c/span\\u003e and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:x+n\\\\)\\u003c/span\\u003e\\u003c/span\\u003e. That is the effect of change in the number of lived years between \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:x\\\\)\\u003c/span\\u003e\\u003c/span\\u003e and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:x+n\\\\)\\u003c/span\\u003e\\u003c/span\\u003e on \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e. The second part measures the contribution resulting from the persons-years to be added because additional survivors at age \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:x+n\\\\)\\u003c/span\\u003e\\u003c/span\\u003e are exposed to new mortality conditions. The total change is given by \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\sum\\\\:}_{0}^{\\\\omega\\\\:}{{}_{n}{}^{}\\\\varDelta\\\\:}_{x}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e. The superindices of the variables from (1) refer to the respective mortality table employed. For instance, in the recovery case: those variables with number 1, from the year when the deeper fall was identified, and those with number 2, from when recovery was achieved.\\u003c/p\\u003e \\u003cp\\u003eWe approach the decomposition in two stages. Initially, we break down the difference between the pre-shock \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e and the lowest observed \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e following the shock. This helps identify which age groups were primarily responsible for the decline. Subsequently, we decompose the difference between the post-shock lowest \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e and the subsequent recovery \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e. This second step reveals which age groups contribute to the recovery. Additionally, we calculate the combined contributions to both decline and recovery to discern which age groups achieved full recovery and which continued to experience higher mortality post-shock during population-level recovery (see Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e).\\u003c/p\\u003e\"},{\"header\":\"4 Results\",\"content\":\"\\u003cp\\u003e \\u003cb\\u003eData description\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eAccording to the available datasets (see Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e) and applying first-order differences in \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e time series of the selected countries, it is possible to identify the worst falls, by country, sex, and year. The deadliest pandemic was 4CP, mainly affecting Denmark, France, and the Netherlands. Meanwhile, WWI caused more dramatic falls as a non-pandemic event; it affected all countries analyzed (except Russia, Spain, and Denmark). Overall, the most significant shocks have been recorded in male populations.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eWorst falls of \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}{\\\\prime\\\\:}s\\\\)\\u003c/span\\u003e\\u003c/span\\u003e by event and country across time.\\u003c/p\\u003e \\u003cdiv class=\\\"Credit\\\"\\u003e\\u003cp\\u003eSource: Own elaboration.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"21\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c12\\\" colnum=\\\"12\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c13\\\" colnum=\\\"13\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c14\\\" colnum=\\\"14\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c15\\\" colnum=\\\"15\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c16\\\" colnum=\\\"16\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c17\\\" colnum=\\\"17\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c18\\\" colnum=\\\"18\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c19\\\" colnum=\\\"19\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c20\\\" colnum=\\\"20\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c21\\\" colnum=\\\"21\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eEvent\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eDNK\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003eFIN\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003eFRA\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003eITA\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c11\\\" namest=\\\"c10\\\"\\u003e \\u003cp\\u003eNLD\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c13\\\" namest=\\\"c12\\\"\\u003e \\u003cp\\u003eNOR\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003eRUS\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c16\\\" namest=\\\"c15\\\"\\u003e \\u003cp\\u003eSPN\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c18\\\" namest=\\\"c17\\\"\\u003e 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colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c21\\\"\\u003e \\u003cp\\u003e-6.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1849\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-1.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-4.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-4.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c20\\\"\\u003e \\u003cp\\u003e-2.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c21\\\"\\u003e \\u003cp\\u003e-12.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1852\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-1.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e-1.6\\u003c/p\\u003e \\u003c/td\\u003e 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align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-4.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c21\\\"\\u003e \\u003cp\\u003e-8.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1854\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-6.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-6.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c21\\\"\\u003e \\u003cp\\u003e-12.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1855\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e-4.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e-4.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd 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align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-9.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-12.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-9.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e-10.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e-13.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" 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align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c21\\\"\\u003e \\u003cp\\u003e-14.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e 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align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c21\\\"\\u003e \\u003cp\\u003e-5.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e 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colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e-2.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e-3.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c21\\\"\\u003e \\u003cp\\u003e-11.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1881\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-2.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c21\\\"\\u003e 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colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e-2.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e-3.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c21\\\"\\u003e 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colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c21\\\"\\u003e 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colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e-2.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c20\\\"\\u003e \\u003cp\\u003e-2.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c21\\\"\\u003e \\u003cp\\u003e-9.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1929\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-2.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-2.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" 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align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-20.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e-3.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c21\\\"\\u003e \\u003cp\\u003e-23.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1915\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e-10.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-3.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e-5.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c21\\\"\\u003e \\u003cp\\u003e-19.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1916\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e-4.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e-2.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c21\\\"\\u003e \\u003cp\\u003e-7.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1917\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e-3.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" 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colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-7.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-4.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e-3.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e-2.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c21\\\"\\u003e \\u003cp\\u003e-32.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1945\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e-8.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e-2.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c13\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c14\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c15\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c16\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c17\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c18\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c19\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c20\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c21\\\"\\u003e \\u003cp\\u003e-10.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-18.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-20.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-48.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-11.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-76.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-44.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e-35.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e-25.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e-37.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e-30.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e-19.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e-19.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e-0.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e-22.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e-19.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c17\\\"\\u003e \\u003cp\\u003e-14.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c18\\\"\\u003e \\u003cp\\u003e-8.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e-27.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c20\\\"\\u003e \\u003cp\\u003e-18.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c21\\\"\\u003e \\u003cp\\u003e-501.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eDNK-Denmark, FIN-Finland, FRA-France, ITA-Italy, NLD-Netherlands, NOR-Norway, RUS-Russia, SPN-Spain, SWE-Sweden, EW-England, and Wales, ♂-men, and ♁-women. Data Source: Human Mortality Database.\\u003c/p\\u003e \\u003cp\\u003eTo apply Arriaga's approach, we have defined first year (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:FY\\\\)\\u003c/span\\u003e\\u003c/span\\u003e), shock year (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:SY\\\\)\\u003c/span\\u003e\\u003c/span\\u003e), and last year (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:LY\\\\)\\u003c/span\\u003e\\u003c/span\\u003e) of the fitting period, chosen on a case-by-case basis. The \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:FY\\\\)\\u003c/span\\u003e\\u003c/span\\u003e refers to the year before the shock of interest, during which the trend of \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e was upward. The \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:SY\\\\)\\u003c/span\\u003e\\u003c/span\\u003e represents the year of the most significant shock to \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e​ caused by the event during its period. It should be remembered that there is not always a single shock, in such cases, the largest one is considered. The \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:LY\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is the year when recovery has been identified, and it remains at least two years greater than those from \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:FY\\\\)\\u003c/span\\u003e\\u003c/span\\u003e. So, the recovery time is given by \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:R=FY-LY\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, for every case, respectively.\\u003c/p\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e illustrates examples of both pandemic and non-pandemic events across the three specified years. The period during which each event occurred is marked between the blue vertical lines. In the top-left panel, it took 7 years to recover the male \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e level from 1867 after the 4CP. In the top-right panel, following the most significant decline in \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e for the female Spanish population, 6 years were required to return to the 1916 \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e level. In the bottom-left panel, the male population of England \\u0026amp; Wales also took 7 years to recover after WWII. Finally, in the bottom-right panel, the Norwegian female population also needed 7 years to regain their pre-WWI \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e level.\\u003c/p\\u003e \\u003cp\\u003eFigure\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e Estimate \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:R{\\\\prime\\\\:}s\\\\)\\u003c/span\\u003e\\u003c/span\\u003e given catastrophic events: selected cases. FRA-France, SPN-Spain, EW-England and Wales, NOR-Norway. Source: Own elaboration. Data Source: Human Mortality Database.\\u003c/p\\u003e \\u003cp\\u003eRecoveries, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:R{\\\\prime\\\\:}s\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, were analyzed for the total population, then divided by sex and by pandemic vs. non-pandemic events. Histograms (see Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e) consistently show right skewness across all groups, attributed to the effect of long periods required to recover \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e following some events. For the total dataset, the mean recovery time in \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e was 7.264 years, the median 7.000, and standard deviation of 3.719 years. Additionally, Kolmogorov-Smirnov tests for two samples (KS2) were conducted for all possible group combinations, and no significant differences were found among the distributions at α\\u0026thinsp;=\\u0026thinsp;1%.\\u003c/p\\u003e \\u003cp\\u003eAs indicated by the histograms, given the asymmetry, only the pandemic group shows normality as verified by the Shapiro-Wilk (SW) test at α\\u0026thinsp;=\\u0026thinsp;1%. For the group with all data, the statistic was W\\u0026thinsp;=\\u0026thinsp;0.781 and p-value\\u0026thinsp;=\\u0026thinsp;0.000 (see details in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). Although there are numerical differences in the descriptive statistics among the groups, these differences are not statistically significant in terms of medians and variance recoveries at α\\u0026thinsp;=\\u0026thinsp;1%. In other words, for the selected countries and events, the time required to recover the median and variance of \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is the same for both pandemic and non-pandemic events and for males and females.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eSeveral statistics of \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:R{\\\\prime\\\\:}s\\\\)\\u003c/span\\u003e\\u003c/span\\u003e by groups.\\u003c/p\\u003e \\u003cdiv class=\\\"Credit\\\"\\u003e\\u003cp\\u003eSource: Own elaboration.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStatistic/group\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePandemic\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNon-pandemic\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTest Pandemic vs Non-pandemic\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eStatistic, p-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7.656\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.950\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eKruskal-Wallis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e2.814, 0.0934\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMedian\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7.500\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStandard deviation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.935\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.254\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eFligner-Killeen\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e1.333, 0.248\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRange\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSW(Statistic, p-value)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.939, 0.072\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.644, 0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStatistics/group\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e♂\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e♁\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTest ♂ vs ♁\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eStatistic, p-value\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7.556\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.972\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eKruskal-Wallis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.639, 0.424\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMedian\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStandard deviation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.960\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.493\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eFligner-Killeen\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e0.597, 0.440\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRange\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e24.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSW(Statistic, p-value)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.701, 0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.864, 0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eIdentifying age groups that impact the\\u003c/b\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\varvec{e}}_{0}\\\\varvec{{\\\\prime\\\\:}}\\\\varvec{s}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003cp\\u003eA key focus of this paper is identifying the age groups that contribute to the losses and recoveries of \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e​ following catastrophic events. To address this, the following table illustrates pandemics and non-pandemic events by country and sex, concentrating on three data points: \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:FY,\\\\:SY\\\\)\\u003c/span\\u003e\\u003c/span\\u003e and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:LY\\\\)\\u003c/span\\u003e\\u003c/span\\u003e. Additionally, heatmaps with numerical data are used to visualize the impacts for each case by age, with events grouped chronologically. It is worth noting that differences between sexes are minimal across these years.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eYears for comparing \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e losses and recoveries by event and country.\\u003c/p\\u003e \\u003cdiv class=\\\"Credit\\\"\\u003e\\u003cp\\u003eSource: Own elaboration.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"12\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c12\\\" colnum=\\\"12\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eGroup\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" morerows=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eEvent\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"8\\\" nameend=\\\"c12\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003eYears (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:FY,\\\\:SY,\\\\:LY\\\\)\\u003c/span\\u003e\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003e♂\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003e♁\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e♂\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e♁\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e♂\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e♁\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c8\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003eDNK\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c10\\\" namest=\\\"c9\\\"\\u003e \\u003cp\\u003eFIN\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c12\\\" namest=\\\"c11\\\"\\u003e \\u003cp\\u003eFRA\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePandemic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e2CP\\u003c/p\\u003e \\u003cp\\u003e3CP\\u003c/p\\u003e \\u003cp\\u003e4CP\\u003c/p\\u003e \\u003cp\\u003e5CP\\u003c/p\\u003e \\u003cp\\u003eSF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e1844, 1853, 1854 1863, 1865, 1872\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e1844, 1853, 1854\\u003c/p\\u003e \\u003cp\\u003e1863, 1865, 1872\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003e1887, 1892, 1895\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e1887, 1892, 1895\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e1831, 1834, 1835\\u003c/p\\u003e \\u003cp\\u003e1845, 1849, 1850\\u003c/p\\u003e \\u003cp\\u003e1867, 1871, 1874\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e1831, 1834, 1835\\u003c/p\\u003e \\u003cp\\u003e1845, 1849, 1850\\u003c/p\\u003e \\u003cp\\u003e1867, 1871, 1874\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNon-pandemic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eWWI\\u003c/p\\u003e \\u003cp\\u003eGD\\u003c/p\\u003e \\u003cp\\u003eSCW\\u003c/p\\u003e \\u003cp\\u003eWWII\\u003c/p\\u003e \\u003cp\\u003eRC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e1913, 1918, 1921\\u003c/p\\u003e \\u003cp\\u003e1943, 1945, 1947\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e1913, 1918, 1921\\u003c/p\\u003e \\u003cp\\u003e1943, 1945, 1947\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003e1914, 1918, 1921\\u003c/p\\u003e \\u003cp\\u003e1926, 1929, 1930\\u003c/p\\u003e \\u003cp\\u003e1938, 1941, 1946\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e1914, 1918, 1921\\u003c/p\\u003e \\u003cp\\u003e1926, 1929, 1930\\u003c/p\\u003e \\u003cp\\u003e1939, 1940, 1941\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e1913, 1915, 1920\\u003c/p\\u003e \\u003cp\\u003e1939, 1944, 1946\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e1912, 1918, 1921\\u003c/p\\u003e \\u003cp\\u003e1939, 1944, 1946\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c8\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eITA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c10\\\" namest=\\\"c9\\\"\\u003e \\u003cp\\u003eNLD\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c12\\\" namest=\\\"c11\\\"\\u003e \\u003cp\\u003eNOR\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePandemic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2CP\\u003c/p\\u003e \\u003cp\\u003e3CP\\u003c/p\\u003e \\u003cp\\u003e4CP\\u003c/p\\u003e \\u003cp\\u003e5CP\\u003c/p\\u003e \\u003cp\\u003eSF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c6\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e1885, 1886, 1888\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003e1885, 1886, 1888\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e1862, 1871, 1873\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e1863, 1871, 1873\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e1870, 1876, 1878\\u003c/p\\u003e \\u003cp\\u003e1880, 1882, 1886\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e1870, 1876, 1878\\u003c/p\\u003e \\u003cp\\u003e1880, 1882, 1886\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNon-pandemic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWWI\\u003c/p\\u003e \\u003cp\\u003eGD\\u003c/p\\u003e \\u003cp\\u003eSCW\\u003c/p\\u003e \\u003cp\\u003eWWII\\u003c/p\\u003e \\u003cp\\u003eRC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c6\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e1914, 1918, 1922\\u003c/p\\u003e \\u003cp\\u003e1939, 1943, 1946\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003e1914, 1918, 1922\\u003c/p\\u003e \\u003cp\\u003e1939, 1943, 1946\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e1913, 1918, 1920\\u003c/p\\u003e \\u003cp\\u003e1939, 1945, 1947\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e1913, 1918, 1920\\u003c/p\\u003e \\u003cp\\u003e1939, 1945, 1946\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e1913, 1918, 1920\\u003c/p\\u003e \\u003cp\\u003e1939, 1942, 1945\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e1913, 1918, 1920\\u003c/p\\u003e \\u003cp\\u003e1939, 1941, 1945\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c8\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eRUS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c10\\\" namest=\\\"c9\\\"\\u003e \\u003cp\\u003eSPA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c12\\\" namest=\\\"c11\\\"\\u003e \\u003cp\\u003eSWE\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePandemic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2CP\\u003c/p\\u003e \\u003cp\\u003e3CP\\u003c/p\\u003e \\u003cp\\u003e4CP\\u003c/p\\u003e \\u003cp\\u003e5CP\\u003c/p\\u003e \\u003cp\\u003eSF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c6\\\" namest=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e1916, 1918, 1922\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e1916, 1918, 1922\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e1827, 1829, 1835\\u003c/p\\u003e \\u003cp\\u003e1854, 1857, 1859\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e1825, 1829, 1835\\u003c/p\\u003e \\u003cp\\u003e1854, 1857, 1859\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNon-pandemic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWWI\\u003c/p\\u003e \\u003cp\\u003eGD\\u003c/p\\u003e \\u003cp\\u003eSCW\\u003c/p\\u003e \\u003cp\\u003eWWII\\u003c/p\\u003e \\u003cp\\u003eRC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c6\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e1987, 1994, 2013\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003e1989, 1994, 2009\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e1935, 1939, 1943\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e1936, 1941, 1942\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e1913, 1918, 1920\\u003c/p\\u003e \\u003cp\\u003e1942, 1944, 1946\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e1913, 1918, 1920\\u003c/p\\u003e \\u003cp\\u003e1942, 1944, 1946\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c8\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eEW\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePandemic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2CP\\u003c/p\\u003e \\u003cp\\u003e3CP\\u003c/p\\u003e \\u003cp\\u003e4CP\\u003c/p\\u003e \\u003cp\\u003e5CP\\u003c/p\\u003e \\u003cp\\u003eSF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c6\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e1845, 1849, 1860\\u003c/p\\u003e \\u003cp\\u003e1862, 1864, 1872\\u003c/p\\u003e \\u003cp\\u003e1881, 1882, 1888\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003e1845, 1849, 1860\\u003c/p\\u003e \\u003cp\\u003e1862, 1864, 1872\\u003c/p\\u003e \\u003cp\\u003e1881, 1882, 1888\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNon-pandemic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWWI\\u003c/p\\u003e \\u003cp\\u003eGD\\u003c/p\\u003e \\u003cp\\u003eSCW\\u003c/p\\u003e \\u003cp\\u003eWWII\\u003c/p\\u003e \\u003cp\\u003eRC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c6\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e1912, 1918, 1920\\u003c/p\\u003e \\u003cp\\u003e1928, 1929, 1930\\u003c/p\\u003e \\u003cp\\u003e1939, 1945, 1946\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c8\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003e1917, 1918, 1920\\u003c/p\\u003e \\u003cp\\u003e1928, 1929, 1930\\u003c/p\\u003e \\u003cp\\u003e1939, 1940, 1942\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c12\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eFirst year (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:FY\\\\)\\u003c/span\\u003e\\u003c/span\\u003e), shock year (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:SY\\\\)\\u003c/span\\u003e\\u003c/span\\u003e), and last year (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:LY\\\\)\\u003c/span\\u003e\\u003c/span\\u003e)\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003eduring catastrophic events. DNK-Denmark, FIN-Finland, FRA-France, ITA-Italy, NLD-Netherlands, NOR-Norway, RUS-Russia, SPN-Spain, SWE-Sweden, EW-England, and Wales, ♂-men, and ♁-women.\\u003c/p\\u003e \\u003cp\\u003eThe following heatmaps illustrate the changes in \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e across various age groups (x-axis) and catastrophic events (y-axis). Declines are depicted in red, and recoveries are in green, with numerical values quantifying the magnitude of change. Each Figure is a panel which is divided into three sections: the top displays losses, the middle shows recoveries, and the bottom indicates the difference between the two. A white color in the last suggests a full recovery to pre-crisis levels; red indicates incomplete recovery, and green signifies an overcompensation in recovery. These visualizations aim to capture trends in \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e changes across different age groups and selected events.\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003ePandemics males\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe male population in the 0\\u0026ndash;4 and 5\\u0026ndash;9 age groups were the most affected (see Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e), with a significant decline seen in the 0\\u0026ndash;4 age group during the 3CP-SWE event, where \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e dropped by 4.3 years. However, this same age group also experienced the largest recovery, gaining 5.9 years during the 2CP-SWE event. Notably, significant declines were rare among older age groups, with exceptions like the 20\\u0026ndash;24 age group during 4CP-FRA and the 25\\u0026ndash;34 age group during SF-SPN. While some age groups, such as the 0\\u0026ndash;4 group during 4CP-FRA, showed robust recoveries, not all fully returned to pre-pandemic levels. For instance, the 5\\u0026ndash;9 age group post-5CP-NOR and young adults aged 25\\u0026ndash;49 post-4CP-FRA did not completely regain their pre-crisis \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e. Patterns of decline and recovery varied across age groups and events, with some showing strong recoveries while others remained below pre-pandemic levels.\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eNon-pandemics males\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eSignificant declines in \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e occurred among the 20\\u0026ndash;29 age groups (see Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). The most severe was observed in the 20\\u0026ndash;24 age group during WWII in Finland (5.9 years). Conversely, the most substantial recovery happened in the same age group following WWI in France, with an increase of 10 years. While children under 5 were not the most severely impacted overall, they still experienced significant reductions, such as a 4.8-year decline in \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e during WWI in Italy. The 20\\u0026ndash;24 age group again showed the most notable improvement during the recovery phases. Certain age groups, did not fully return to pre-crisis mortality levels after WWI and WWII in France and Finland. Likewise, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e declines were particularly severe in countries heavily affected by WWI and WWII, such as France. While some countries, like Italy and Finland, showed strong recoveries post-WWII, others, such as France, exhibited uneven recovery patterns. In contrast, Sweden and Denmark displayed overcompensation in recovery for certain age groups, with \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e even surpassing pre-crisis levels, underscoring the varied impact of these historical events on different populations.\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003ePandemics females\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eSimilar to males (see Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e), the most affected age groups for females during pandemics were 0\\u0026ndash;4 years, followed by 5\\u0026ndash;9 years. The 3CP-SWE event caused the largest decline in \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e for females, with a drop of 4.2 years for those under 5, and a 3.8-year decline due to SF-SPN. However, the most significant recovery for this group occurred after the 2CP-SWE event, with an increase of 5.8 years. Substantial declines were also seen in older age groups during the 4CP-FRA and SF-SPN events, reflecting patterns observed in males. While notable recoveries were recorded for children under 5, women across age groups from 5\\u0026ndash;34 years gained more than a year of \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e after SF-SPN, except for those aged 10\\u0026ndash;14. Not all age groups fully returned to pre-crisis levels, with children under 5 post-3CP-EW and those aged 5\\u0026ndash;9 post-4CP-NLD failing to fully recover. Significant declines were particularly pronounced among younger age groups, with the most impactful events being major conflicts and severe crises in countries like France and Sweden. Recovery varied across age groups and countries, with some populations, particularly younger ones in Sweden and Denmark, showing strong rebounds, while other age groups in different countries exhibited slower or incomplete recoveries.\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eNon-Pandemics females\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eFor non-pandemic events affecting females (see Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e), significant \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e declines were concentrated among those aged 5\\u0026ndash;49 years during WWI, with the most pronounced decline of 6.2 years observed in the under-5 population in Italy. Despite a strong recovery for this age group (9.7 years), not all age groups returned to pre-crisis mortality levels. For instance, the under-5 age group in Sweden saw only partial recovery following WWII (2.8 years) and WWI (1.4 years). The analysis highlights significant declines, particularly in younger age groups, during events like WWI and WWII, with countries such as France and Finland experiencing notable reductions in \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e. These findings underscore the vulnerability of younger women during these crises. In terms of recovery, the outcomes were varied, with some age groups, especially younger women, demonstrating strong rebounds, as seen in France and Sweden. However, older age groups and those in countries like Finland and Italy exhibited slower recoveries, indicating the long-term effects of these events.\\u003c/p\\u003e \"},{\"header\":\"5 Discussion\",\"content\":\"\\u003cp\\u003eOur estimates confirm that life expectancy recovery typically spans several years, regardless of the type of catastrophic event. Nowadays, the recovery period could be shortened through assertive and effective health policies focused on enhancing the well-being of affected groups. However, our ability to measure potential changes is constrained by the available data from past events and countries. Whether the catastrophic event is a pandemic or not, the required time for life expectancy recovery remains consistent. From a statistical standpoint, we can infer that the recovery duration is not significantly different between sexes. Additionally, we observed considerable variability in the dynamics of declines. In some instances, partial recoveries may be followed by subsequent falls before a more profound decline triggers recovery, occurring throughout a few or many years.\\u003c/p\\u003e \\u003cp\\u003eThe age groups that consistently and systematically contribute to the recovery of the mentioned mortality index are primarily children and, to a lesser extent, young males in the male population, especially in non-pandemic events. Therefore, if a demographic policy is formulated and implemented to improve life expectancy, it should target these specific population segments. Lastly, analyzing patterns following famines or natural disasters could be an intriguing avenue for future research papers, as it complements our understanding of how life expectancy recovers in various catastrophic events. The findings emphasize the long-lasting impact of catastrophic events on female \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, particularly for older populations, who may be less resilient in recovering from such disruptions.\\u003c/p\\u003e \\u003cp\\u003eRecovery in life expectancy following catastrophic events can be attributed to various factors that warrant exploration. Meanwhile, it is well-established that during stable periods, life expectancy experiences improvements due to advancements in education levels (Kaplan et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e), social development (Jiang et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), and medicine (Kalache et al., \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), among other factors. Understanding the precise reasons for life expectancy recovery is challenging, and investigating the relationship between explanatory variables and recovery extends beyond the scope of our study. This paper aims to identify some statistical characteristics of selected recoveries across time rather than delving into the specific causes.\\u003c/p\\u003e \\u003cp\\u003eSome limitations are recognized. Firstly, obtaining comprehensive information on all global pandemics and wars is practically unattainable. For instance, the Black Death from 1346 to 1353 (Huremović, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e) and the Smallpox from the 16th to 19th centuries (Samal, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Unfortunately, given their timeframe, there is no data, and also, the estimates have considerable uncertainty regarding the recovery in life expectancy. Additionally, certain catastrophic events, such as famines, slavery processes, and natural disasters (earthquakes, hurricanes, tornadoes, etc.), were excluded due to insufficient available information, resulting in a descriptive study of selected disastrous events.\\u003c/p\\u003e \\u003cp\\u003eAccording to Zarulli et al. (\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), who explore mortality in extreme conditions, including epidemics, famines, and slavery, it has been demonstrated that women tend to have longer lifespans than men under such challenging circumstances. Overall patterns indicate that women exhibit lower mortality rates across various age groups and generally enjoy more extended life expectancy than men. The authors argue that during periods of crisis, the well-established trend of higher mortality rates for men compared to women due to accidents or violence could contribute to the gender disparity. However, reaching conclusive assessments is challenging due to the limited availability of data on causes of death during moments of crisis. Through our findings, we are online with the notable advantage for women over men in recovering life expectancy, both pandemic and non-pandemic events.\\u003c/p\\u003e \\u003cp\\u003eMost forecasting methods of life expectancy do not consider mortality crises. The Torri and Vaupel model (2012) is an appropriate tool to estimate life expectancy when it is desired to forecast the index for the total population. Meanwhile, when the objective is predicting life expectancy jointly by sex and considering its gap, the method proposed by Pascariu et al. (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e) should be employed. As others mentioned in those papers, these methods have been established and examined when non-catastrophic events are presented in the corresponding population. For this reason, these approaches are unsuitable for predicting life expectancy, its declines and recoveries, under our framework, that is, when a crisis occurs. Given the circumstances during catastrophic events, we consider that our proposal helps us easily measure the time recovery and the main groups that contribute to reestablish the life expectancy level.\\u003c/p\\u003e \\u003cp\\u003eGiven the availability of age- and sex-specific mortality rates in the HMD for the selected countries and events, employing well-established mortality forecasting techniques, such as the Lee-Carter model or its variations, might seem like the initial and obvious choice for estimating underlying rates, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, and their recovery times. However, several limitations are evident across all scenarios. The linear assumptions of the Lee-Carter model are problematic due to the complex nature of mortality trends observed around catastrophic events in the selected cases. Additionally, assuming a static age pattern across all frameworks is inappropriate, as it implies uniform mortality improvement rates across all age groups over time, that how it has been shown is something unreal. Furthermore, for comparative purposes, there is uncertainty regarding the consistent data quality and sample sizes for all cases over time.\\u003c/p\\u003e \\u003cp\\u003eThere are some notable similarities and differences between our work and the recent study by Goldstein \\u0026amp; Lee (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Like the authors, we identify reversals\\u0026mdash;referred to here as 'declines or falls'\\u0026mdash;but we also consider recoveries following each selected catastrophic event. They observe that these falls do not significantly disrupt the long-term trend of increasing life expectancy, whereas our focus is on recovery time, without engaging in predictive analysis. The authors emphasize that after significant events, such as the COVID-19 pandemic, life expectancy trends generally return to their pre-event trajectories. This finding is consistent with our analysis of earlier events, where we pinpoint the specific year in which life expectancy recovered to pre-crisis levels. However, while their study suggests that incorporating life expectancy reversals into models could improve forecasting accuracy, we take a different approach. We conduct \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e decompositions that provide detailed insights into the uneven recovery across different age groups.\\u003c/p\\u003e \\u003cp\\u003e​We acknowledge that more recent catastrophic events may require less time for recovery due to factors such as medical advancements, improved civil regulations, greater public adherence to controls, and sufficient and homogeneous economic support for the entire population. However, this may not be universally applicable across all countries and regions. Standardizing conditions among countries or territories at different times for comparing recovery times seems impossible, leading us to focus solely on descriptive terms.\\u003c/p\\u003e \\u003cp\\u003eWhen a catastrophic event occurs and impacts demographic variables, particularly mortality, the question of when life expectancy will return to its previous level usually arises. This uncertainty is particularly challenging to address in the aftermath of events like the COVID-19 pandemic, wars and/or adverse weather conditions. Recovery timelines vary due to each country's unique circumstances and policies implemented before, during, and after the pandemic. Historically, the effects on life expectancy recovery have proven to be enduring. Consequently, we suggest that, following the COVID-19 pandemic, it may be reasonable to anticipate a recovery period of several years for some countries or regions.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eEthics Statement \\u003c/h2\\u003e\\n\\u003cp\\u003eThis research was conducted in full compliance with ethical standards recognized in the scientific community. No vulnerable populations were involved, and all statistical procedures were carried out with the highest accuracy and integrity. Additionally, no conflicts of interest were present during the study.\\u003c/p\\u003e\\u003ch2\\u003eFunding\\u003c/h2\\u003e \\u003cp\\u003eThis study did not receive any funding.\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eE. S. and J.M.A. wrote the main manuscript text, E.S. prepared all Figures and Tables. All authors reviewed the manuscript.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eSequence of mortality data supporting the findings were obtained from the Human Mortality Database (https://www.mortality.org/), both ex`s and mortality tables. Subsequently they were processed to generate the estimates.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAburto, J. M., Kristensen, F. F., \\u0026amp; Sharp, P. (2021). 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Divergent trends in lifespan variation during mortality crises. \\u003cem\\u003eDemographic Research\\u003c/em\\u003e, \\u003cem\\u003e46\\u003c/em\\u003e, 291\\u0026ndash;336. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.4054/DemRes.2022.46.11\\u003c/span\\u003e\\u003cspan address=\\\"10.4054/DemRes.2022.46.11\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eUniversity of California Berkeley and Max Planck Institute for Demographic Research (2023). Human mortality database (HMD). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e\\u003c/span\\u003e\\u003cspan address=\\\"http://www.mortality.org;www.humanmortality.de\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e, data downloaded on 1/07/23.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWasserman, S., Tambyah, P. A., \\u0026amp; Lim, P. L. (2016). 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Women live longer than men, even during severe famines and epidemics. \\u003cem\\u003eProceedings of the National Academy of Sciences\\u003c/em\\u003e, \\u003cem\\u003e115\\u003c/em\\u003e(4), E832-E840. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1073/pnas.1701535115\\u003c/span\\u003e\\u003cspan address=\\\"10.1073/pnas.1701535115\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"canadian-studies-in-population\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"csip\",\"sideBox\":\"Learn more about [Canadian Studies in Population](https://link.springer.com/journal/42650)\",\"snPcode\":\"42650\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/42650/3\",\"title\":\"Canadian Studies in Population\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Life expectancy, catastrophic events, pandemics, wars, mortality\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5313297/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5313297/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eFollowing catastrophic events, such as pandemics or wars, a systematic loss in life expectancy at birth (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e) can be observed. We aimed to estimate the time required for \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e to recover after mortality crises and identify which age groups either contribute to the decline or assist in restoring pre-crisis levels. We focused exclusively on analyzing the largest European pandemics and wars of the 19th and 20th centuries, using data from the Human Mortality Database (HMD). To achieve this, we employed Arriaga's decomposition to examine two specific \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}{\\\\prime\\\\:}\\\\)\\u003c/span\\u003e\\u003c/span\\u003es: one just before the most substantial decline during the mortality crisis, marking the deepest drop, and another at the point where recovery is observed. The events were categorized into pandemics and non-pandemics and further stratified by sex. Various statistical tests were conducted to enable valid comparisons. Our findings reveal that World Wars caused the most significant declines in \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e. Statistical analyses indicate no significant disparities based on the type of event or sex. Notably, youth and children emerge as the primary age group contributing to the decline and recovery of \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{e}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e following both catastrophic events. However, not all of them fully recover to the mortality levels observed before the crisis.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Life expectancy loss and recovery by age and sex following catastrophic events in Europe during the 19th and 20th centuries\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-11-18 10:03:08\",\"doi\":\"10.21203/rs.3.rs-5313297/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2024-12-12T06:18:34+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-12-08T21:11:57+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-12-03T14:57:05+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"278490217347560900401903085400952623695\",\"date\":\"2024-11-15T22:08:58+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"251032273717035945737171488824261512329\",\"date\":\"2024-11-13T13:35:13+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-11-13T04:55:40+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-10-25T06:24:03+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-10-25T06:23:42+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Canadian Studies in Population\",\"date\":\"2024-10-22T16:00:50+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"canadian-studies-in-population\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"csip\",\"sideBox\":\"Learn more about [Canadian Studies in Population](https://link.springer.com/journal/42650)\",\"snPcode\":\"42650\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/42650/3\",\"title\":\"Canadian Studies in Population\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"9a473d61-2fff-4bb4-9bd3-063310409477\",\"owner\":[],\"postedDate\":\"November 18th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-08-25T16:35:24+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-5313297\",\"link\":\"https://doi.org/10.1007/s42650-025-00094-8\",\"journal\":{\"identity\":\"canadian-studies-in-population\",\"isVorOnly\":false,\"title\":\"Canadian Studies in Population\"},\"publishedOn\":\"2025-08-22 16:29:26\",\"publishedOnDateReadable\":\"August 22nd, 2025\"},\"versionCreatedAt\":\"2024-11-18 10:03:08\",\"video\":\"\",\"vorDoi\":\"10.1007/s42650-025-00094-8\",\"vorDoiUrl\":\"https://doi.org/10.1007/s42650-025-00094-8\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5313297\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5313297\",\"identity\":\"rs-5313297\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}