A systematic analysis of worldwide disasters, epidemics and pandemics associated mortality of 210 countries for 15 years (2001-2015).

In recent years, the unprecedented death tolls resulting from epidemics and natural disasters made everyone interested, from the general public to country heads, to know about the mortality rates. The coronavirus issue is the most recent example all over the media, and everyone is talking about corona-induced mortality. The study aimed to estimate the disaster-induced mortality rates at the global level for two hundred and ten countries for fifteen years (2001-2015). Using a retrospective study design, we extracted datasets from two data sources, EM-DAT and UNFPA, in October 2019. The cut-off time for the data download was midnight Central European Time, October 17, 2019. The most noticeable finding in this study is that, against the common prevailing notion, both developed and developing countries equally carry the brunt of disaster-induced mortality. This study proposes empirical confirmation of the direction and magnitude of any year-over-year correlation of disaster and mortality rates. Furthermore, the analysis of the trend in mortality rate over the past fifteen years concludes it is not linear. However, there are huge variations across the years and the countries. The study is of paramount importance to initiate a debate amongst the concerned policymakers and stakeholders to regularly monitor the disaster-induced mortality rates. So that effective interventions can be devised to decrease the mortality rates.

Background

The histories of disasters, pandemics, and outbreaks are as old as human history itself [1]. Throughout recorded history, humankind experienced several disasters, epidemics, pandemics, and outbreaks, most reported in different religious scriptures and limited historical documents [2]. At the time, people did not exactly know why disasters happen, how diseases spread, and how to prevent or survive pandemics [1]. As science was not studied in the early days of humanity, people often viewed disasters as the acts of gods [3]. This, along with other contributing factors, cemented human's reliance on religion, a state of the mind where hope forever outlives fear and outwears helplessness caused disasters [[4], [5], [6], [7]]. However, with the advancement of science and technology, people now know that such a phenomenon results from different hazards that exist in nature [8]. Although we cannot prevent disasters in the case of natural hazards, we can mitigate the impact by considering various aspects of disasters, i.e., risk, hazard, and vulnerabilities [8,9]. With this realization, the domain of disaster management emerged in the nineteenth century [10].

Disasters are being classified by different organizations and institutions [11], depending on the nature of onset [12], characteristics, and cause of initiation [13]. While disasters are often mainly grouped into natural and human-made disasters, a wide range of classifications available, ranging from technological, human-made, economic, and hybrid disasters [11]. Definition, classification/types of disaster, are crucial for researchers and aid organizations/agencies to better understand, manage and record disasters, in the absence of which there will be inaccuracy in the data and recording of disaster events [14].

The classification made by Wassenhove [12] is in the context of Supply Chain Management; and have categorized various event with four main attributes based on the mechanism of onset and causes of initiation, i.e., natural or anthropogenic cause. Currently, the definitions, concepts, and terminologies are evolving as the knowledge of disaster management is expanding and entering into other areas.

Generally, the mortality rate is considered a daily routine, and the term is not valued much [19]. However, in recent years, especially during the Coronavirus pandemic, the term mortality rate seems everyone's business from public to presidents and prime ministers of the countries [15]. Hurricane Maria and the Coronavirus pandemic highlight the mortality rate's importance. Every concerned person is assessing the progress and needs for intervention by linking it to the magnitude of the mortality rate. The mortality indicates is critical for many reasons [[16], [17], [18]]. The mortality rate signposts the seriousness/magnitude of the issues. It is an infection like Corona, a global pandemic, or disasters like Hurricane Maria, which swept through Puerto Rico in August 2017. Besides, the high mortality rate may ground public outrage and point to government officials' inabilities to handle the matter appropriately aggressive preventive and management interventions [15]. Furthermore, mortality rates can be used as a benchmark to monitor and evaluate the progress on objectives set by institutions, organizations, countries, regions or even the world [19]. For example, several indicators in the Sustainable Development Goals are linked to the mortality rates [20], which are as follow;

By 2030, significantly reduce the number of deaths and the number of people affected and substantially decrease the direct economic losses relative to the global gross domestic product caused by disasters, including water-related disasters, with a focus on protecting the poor and people in vulnerable situations;

By 2030, reduce the global maternal mortality ratio to less than 70 per 100,000 live births;

By 2030, reduce by one-third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-being;

By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water, soil pollution, and contamination.

The number of disasters worldwide has increased roughly four-fold in the past couple of decades, while the number of persons affected by disasters has increased approximately three-fold over the same period [21,22]. Though several public health and disaster-related studies have been published in recent years, until today, a systematic analysis of disaster-induced mortality is missing. There are multiple factors as a result of which global disaster-induced mortality estimates are missing [22], one of which centres on the lack of a benchmark for assessing disaster-induced mortality, which could make it difficult for scholars to sysrtematically compare and contrast studies (e.g., via systematic review studies). In this regard, this study attempts to create a benchmark by estimating disaster-induced global mortality rates for two hundred and ten countries across the globe.

Methodology

The estimations of mortality rates resulting from natural hazards are not well studied and understood. Reliable data in the context of disaster-induced mortality rarely existed [22,23]; therefore, disaster-induced mortality rates estimation is difficult to accomplish. Unlike in developing countries, the population census data are often available, accessible, and well-maintained in developed countries. However, disaster-induced deaths records are rarely available even in developed countries. In this regard, establishing a benchmark for disaster-induced mortality and initiating a debate amongst policymakers and global health practitioners is essential. Overall, this estimate the disaster-induced mortality rates at the global level for the two hundred and ten countries across the world. In this regard, a retrospective study was undertaken with the following study protocols:

Defining Disaster-Induced Death

To calculate the disaster-induced mortality rate, the first thing that needs to be determined is the definition of disaster-induced deaths. Till today, there is no universally agreed definition of disaster-induced death as there are many medico-legal aspects that vary from country to country, and even within the same country from organization to organization, it difficult to set a commonly accepted definition. This is a global study aiming to estimate disaster-induced mortality for all countries worldwide. Furthermore, this study is not based on individual countries' databases but rather on the datasets compiled and maintained by the EM-DAT. So, the definition set to conduct this study is the same as defined by the EM-DAT, i.e., “Number of people who lost their lives because the event happened” [21].

EM-DAT defines the event of a disaster if it fulfils the following criteria:

For a disaster to be entered into the EM-DAT database, at least one of the following criteria must be fulfilled [21]:

Ten (10) or more people reported killed

Hundred (100) or more people reported affected

Declaration of a state of emergency

Call for international assistance

Study design

This study adopts a retrospective study design.

Datasets used

For disaster-induced mortality, the EM-DAT database is used [24].

For population estimates, the UNFPA database is used [25].

Study area

The objective of this study is to estimate the disaster-induced mortality rates for all the countries in the world. However, the final analysis includes two hundred and ten countries. To calculate the mortality rate, we need to know two variables: the number of deaths and the population.

Study scope

This study focused on disasters that occurred in the fifteen years between January 2001 and December 2015.

Data collection & data analysis timeline

Data analyses were carried out from October 17, 2019, to November 30, 2019. The cut-off time for the data download was midnight Central European Time, October 17, 2019. This time was purposely chosen to complete the downloading of data by assuming that both databases are maintained in Europe, so at this specific time, no more data updating will be in progress.

Case-Scenario for the analysis

The study adopted a five-year time frame scenario. The scenario includes data compilation for time-interval of first from 2001 to 2005, second from 2006 to 2010, and third from 2011–2015. whereas

Process undertaken

In the first step, the population data were yearly compiled from 2001 to 2015 using the UNFPA database.

In the second step, we categorized the yearly data into three groups of five years each, i.e., 2001–2005, 2006–2010, and 2011–2015. The exact process was applied to the dataset on disaster-induced deaths; the only difference is that the data was extracted from EM-DAT.

The third step calculated the mortality rate using the following formula.

DIM = Disaster-Induced Mortality

TND = Total number of deaths of five years

TNP = Cumulative total mid-year population of five years

After calculating the disaster-inducted mortality rate for each country per five-year compilation, data visualization of the findings at a global level was carried out.

Ethical approval

The study was approved by the IRB Office of the Asian Institute of Technology, Thailand (Ref# 02/2017).

Results and discussion

The findings of this study show that from 2001 to 2015, a total of 1,356,831 (1.35 million) people died due to disasters. The number of deaths varies from country to country and continent to continent. The number of fatalities is limited to the absolute number, but the same variation exists if the mortality rate is normalized to per million population. The study results revealed interesting findings, which are discussed systematically in the following sections.

No country is immune to disaster risk

This study created four scenarios to capture an accurate picture of disaster-induced mortality. First, an annual mortality rate was calculated for each country for a time window of fifteen years (2001–2015). However, it is a well-established fact that the nature of the onset of different types of disasters varies; like some disasters are low probability events but high consequences in term injuries and deaths such as earthquakes, while others are high probability events but profound consequences in term injuries and deaths such floods [26,27]. So it is evident that low-probability and high consequences events that happens once may not happen in the next couple of years. Stakeholders, especially policymakers, may assume that the earthquake is not a problem. It does not contribute to mortality by justifying that the region did not have any earthquake-induced deaths in the last nine years. To explain and make the stakeholders realize the existence and magnitude of the problem, this study proposes a step ahead and calculated mortality rates for five-year window time as 2001–2005, 2006–2010, and 2011–2015. The time-window-based analysis in this study concluded exciting findings, visualized in Fig. 1 , Fig. 2 , and Fig. 3 . It is pertinent to mention that almost every country globally has its share of disaster-induced mortality, whether developed or developing (see Fig. 4 ).

Fig. 1

Disaster-induced global mortality rates visualization 2001–2005 (The lighter the colour, the lower the mortality rate; the darker the colour, the higher the mortality rate.). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2

Disaster-induced global mortality rate visualization 2006–2010 (The lighter the colour, the lower the mortality rate; the darker the colour, the higher the mortality rate.). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3

Disaster-induced global mortality rate visualization 2011–2015 (The lighter the colour, the lower the mortality rate; the darker the colour, the higher the mortality rate.). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4

Heatmap visualizing annual mortality rates globally for each country (2001–2015) (The lighter the colour, the lower the mortality rate; the darker the colour, the higher the mortality). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

The high disaster-induced mortality rate is not confined to a specific geography

The figures for disaster-induced mortality rates are not the same for all the countries. This study finds out that during 2001–2005 the highest mortality rates were recorded in Sri Lanka, 371.9 per million, Indonesia 153.5 per million, Haiti 127.9 per million, Niue 113.7 per million, and Pakistan 101.9 per million respectively. While during the next five years, 2006–2010, Haiti's highest mortality rates recorded 4749.10 per million, Myanmar 560.1 per million, Samoa 155.8 per million, American Samoa 119.1 per million, and Russia 78.2millions respectively. Similarly, during 2011–2015, the top five countries with higher mortality are Liberia 224.5 per million, Sierra Leone 123.8 per million, Dominica 82.8 per million, Nepal 72.3 per million, and Guinea 40.3 per million, respectively. The top five countries with the highest mortality rates in all three-time windows shows that only one country, Haiti, appears twice. In contrast, a different set of countries suffered the highest mortality rate every other time. The details can be seen in Table 1 .

Table 1

Country, population, disaster-induced death, and disaster-induced mortality rate.

Country	Population			Deaths			Mortality rate per 1,000,000
	2001–2005	2006–2010	2011–2015	2001–2005	2006–2010	2011–2015	2001–2005	2006–2010	2011–2015
Afghanistan	115,201,014	136,611,771	158,631,759	5305	2463	1637	46	18	10.3
Albania	15,529,368	14,973,049	14,609,803	15	0	6	1	0	0.4
Algeria	162,110,246	174,522,103	191,708,808	3394	238	97	20.9	1.4	0.5
American Samoa	294,405	285,447	276,831	6	34	0	20.4	119.1	0
Angola	91,177,542	108,938,184	130,092,826	534	3615	598	5.9	33.2	4.6
Anguilla	60,062	66,747	71,449	0	0	0	0	0	0
Antigua and Barbuda	436,126	462,402	489,118	0	0	0	0	0	0
Argentina	191,544,442	201,934,799	212,692,823	106	90	127	0.6	0.4	0.6
Armenia	15,084,229	14,565,671	14,474,182	1	0	0	0.1	0	0
Australia	98,628,028	106,721,867	115,726,601	40	594	163	0.4	5.6	1.4
Austria	40,878,467	41,714,958	42,867,071	361	12	25	8.8	0.3	0.6
Azerbaijan	41,823,141	44,133,271	46,917,844	0	3	5	0	0.1	0.1
Bahamas	1,579,813	1,742,522	1,884,854	13	1	34	8.2	0.6	18
Bahrain	3,930,424	5,534,786	6,602,149	0	0	0	0	0	0
Bangladesh	694,465,418	743,916,819	787,816,426	3076	6398	734	0	0	0
Barbados	1,361,521	1,386,547	1,412,297	1	0	0	0.7	0	0
Belarus	48,718,592	47,578,548	47,389,755	0	5	6	0	0.1	0.1
Belgium	52,129,939	53,895,604	55,755,246	1184	947	418	22.7	17.6	7.5
Belize	1,345,686	1,530,856	1,721,056	33	8	1	24.5	5.2	0.6
Benin	37,624,911	43,512,568	50,057,077	435	139	11	11.6	3.2	0.2
Bermuda	323,998	322,839	313,916	4	0	0	12.3	0	0
Bhutan	3,116,354	3,502,235	3,822,272	0	23	1	0	6.6	0.3
Bolivia	44,053,290	48,001,623	52,004,475	289	227	338	6.6	4.7	6.5
Bosnia-Hercegovina	18,889,155	18,785,712	18,044,027	4	6	30	0.2	0.3	1.7
Botswana	9,024,409	9,739,751	10,646,931	0	472	12	0	48.5	1.1
Brazil	912,039,659	964,710,343	1,011,831,544	602	1014	1168	0.7	1.1	1.2
Virgin Island (UK)	110,323	127,911	145,167	0	0	0	0	0	0
Brunei Darussalam	1,765,054	1,896,800	2,028,723	0	0	0	0	0	0
Bulgaria	39,027,540	37,562,859	36,332,278	42	48	64	1.1	1.3	1.8
Burkina Faso	63,344,807	73,517,240	85,422,444	4572	4137	28	72.2	56.3	0.3
Burundi	34,856,251	41,083,136	48,054,464	216	45	204	6.2	1.1	4.2
Cape Verde Island	2,298,200	2,458,303	2,601,502	0	9	0	0	3.7	0
Cambodia	64,223,904	69,430,639	75,125,869	108	216	623	1.7	3.1	8.3
Cameroon	82,650,511	94,604,995	108,332,971	171	662	779	2.1	7	7.2
Canada	158,210,824	167,028,462	176,249,259	68	9	22	0.4	0.1	0.1
Netherlands Antilles	81,053,818	82,916,580	84,310,636	969	1007	3	12	12.1	0
Cayman Islands	230,003	264,226	291,608	2	0	0	8.7	0	0
Central African Rep	19,904,426	21,675,699	22,527,714	380	8	18	19.1	0.4	0.8
Chad	46,794,954	55,721,154	65,706,226	483	494	665	10.3	8.9	10.1
Chile	78,988,988	83,296,217	87,302,564	89	607	231	1.1	7.3	2.6
China P Rep	6,643,059,322	6,836,854,498	7,029,299,208	6049	100,225	5057	0.9	14.7	0.7
Hong Kong (China)	33,894,726	34,707,589	35,761,049	303	3	0	8.9	0.1	0
Macau	2,301,136	2,573,773	2,877,534	0	0	0	0	0	0
Taiwan (China)	6,643,059,322	6,836,854,498	7,029,299,208	6049	100,225	5057	0.9	14.7	0.7
Colombia	210,723,348	224,446,116	236,651,710	621	1169	677	2.9	5.2	2.9
Comoros	2,917,433	3,288,218	3,708,746	1	31	4	0.3	9.4	1.1
Congo	17,549,187	20,574,451	23,764,235	193	301	374	11	14.6	15.7
Cook Island	95,404	95,832	89,057	0	0	0	0	0	0
Costa Rica	20,620,852	22,141,314	23,526,424	29	88	15	1.4	4	0.6
Cote D'ivoire	88,420,065	97,621,059	109,920,048	313	167	62	3.5	1.7	0.6
Croatia	21,954,822	21,754,630	21,381,252	795	12	10	36.2	0.6	0.5
Cuba	56,200,619	56,561,327	57,050,163	32	10	19	0.6	0.2	0.3
Cyprus	4,968,879	5,401,467	5,717,087	0	4	0	0	0.7	0
Korea Dem P Rep	358,829,876	367,763,474	375,767,955	833	1003	525	2.3	2.7	1.4
Denmark	26,942,294	27,493,346	28,183,959	5	0	2	0.2	0	0.1
Djibouti	3,791,553	4,116,530	4,483,388	51	6	0	13.5	1.5	0
Dominica	350,593	355,500	362,102	3	2	30	8.6	5.6	82.8
Dominican Rep	44,837,735	48,177,954	51,397,579	742	357	187	16.5	7.4	3.6
Ecuador	66,459,296	72,246,460	78,306,048	149	83	92	2.2	1.1	1.2
Egypt	369,959,048	405,222,810	449,108,989	31	171	141	0.1	0.4	0.3
El Salvador	29,847,536	30,552,156	31,258,250	1590	332	52	53.3	10.9	1.7
Equatorial guinea	3,482,914	4,350,177	5,421,442	15	0	0	4.3	0	0
Eritrea	18,677,658	21,153,790	23,279,686	0	0	0	0	0	0
Estonia	6,866,583	6,701,013	6,608,623	0	3	1	0	0.4	0.2
Ethiopia	362,886,081	416,154,913	474,618,470	872	1559	110	2.4	3.7	0.2
Fiji	4,086,708	4,217,480	4,398,352	34	34	17	8.3	8.1	3.9
Finland	26,142,067	26,603,914	27,180,521	0	0	0	0	0	0
France	302,806,932	311,597,662	319,551,206	19,581	1502	3339	64.7	4.8	10.4
French Guiana	937,024	1,112,225	1,272,934	0	0	0	0	0	0
French Polynesia	1,241,439	1,315,006	1,368,248	0	1	0	0	0.8	0
Gabon	6,652,145	7,697,412	9,077,077	51	0	1	7.7	0	0.1
Gambia the	6,778,815	7,950,418	9,303,254	6	14	2	0.9	1.8	0.2
Georgia	22,896,000	21,689,302	20,268,755	7	0	52	0.3	0	2.6
Germany	408,221,104	405,876,151	406,462,796	9431	74	29	23.1	0.2	0.1
Ghana	102,321,454	116,528,212	131,746,480	68	186	579	0.7	1.6	4.4
Greece	56,216,444	57,029,147	56,604,888	13	95	12	0.2	1.7	0.2
Grenada	511,929	519,748	529,648	40	0	0	78.1	0	0
Guadeloupe	2,170,207	2,234,563	2,255,689	1	0	0	0.5	0	0
Guam	787,791	795,213	802,790	2	0	0	2.5	0	0
Guatemala	62,527,225	70,050,285	77,992,177	1731	354	494	27.7	5.1	6.3
Guinea	53,201,726	59,064,787	66,162,354	645	452	2665	12.1	7.7	40.3
Guinea Bissau	53,201,726	59,064,787	66,162,354	645	452	2665	12.1	7.7	40.3
Guyana	3,758,602	3,736,033	3,792,179	34	0	0	9	0	0
Haiti	44,886,434	48,523,862	52,149,567	5699	230,447	418	127	4749.10	8
Honduras	35,167,622	39,351,835	43,285,076	111	303	47	3.2	7.7	1.1
Hungary	50,705,379	49,957,767	49,206,845	140	523	16	2.8	10.5	0.3
Iceland	1,442,271	1,552,226	1,634,675	0	0	0	0	0	0
India	5,539,567,266	5,984,056,687	6,391,777,362	49,021	9118	13,228	8.8	1.5	2.1
Indonesia	1,102,887,154	1,180,851,220	1,259,916,235	169,340	11,323	733	153.5	9.6	0.6
Iran Islam Rep	343,931,368	364,359,601	387,152,119	28,393	168	454	82.6	0.5	1.2
Iraq	128,143,609	145,857,115	169,508,498	0	63	79	0	0.4	0.5
Ireland	20,276,854	22,386,024	23,409,117	0	0	4	0	0	0.2
Israel	31,790,131	35,469,160	39,094,860	0	46	4	0	1.3	0.1
Italy	290,758,354	297,267,425	298,251,643	20,142	369	144	69.3	1.2	0.5
Jamaica	13,558,418	13,947,160	14,256,313	36	37	2	2.7	2.7	0.1
Japan	640,243,323	642,607,285	641,382,534	573	419	20,920	0.9	0.7	32.6
Jordan	27,127,450	32,620,751	41,949,588	5	0	0	0.2	0	0
Kazakhstan	76,305,314	80,116,531	86,013,243	54	45	9	0.7	0.6	0.1
Kenya	170,789,562	195,873,731	224,220,826	434	1114	444	2.5	5.7	2
Kiribati	444,963	492,231	542,669	0	0	0	0	0	0
Kuwait	10,906,868	13,350,030	17,903,236	0	0	0	0	0	0
Kyrgyzstan	25,078,855	26,341,862	28,424,484	90	78	16	3.6	3	0.6
Lao P Dem Rep	27,910,128	30,249,643	32,483,577	2	34	152	0.1	1.1	4.7
Latvia	11,527,782	10,859,779	10,208,439	36	40	10	3.1	3.7	1
Lebanon	18,434,279	20,775,160	26,235,632	0	1	5	0	0	0.2
Lesotho	9,589,635	10,007,639	10,591,885	1	0	33	0.1	0	3.1
Liberia	15,607,872	18,311,416	21,428,379	29	4	4810	1.9	0.2	224.5
Libyan Arab Jamah	28,081,480	30,195,068	31,026,792	0	0	16	0	0	0.5
Lithuania	17,058,093	16,070,964	15,008,233	20	9	30	1.2	0.6	2
Luxembourg	2,240,907	2,429,549	2,720,106	170	0	0	75.9	0	0
Madagascar	86,444,911	100,031,021	114,875,557	1301	367	449	15.1	3.7	3.9
Malawi	61,762,010	71,423,162	82,944,515	1939	209	305	31.4	2.9	3.7
Malaysia	123,419,923	135,598,152	148,463,480	143	108	64	1.2	0.8	0.4
Maldives	1,512,836	1,727,507	1,985,381	102	0	4	67.4	0	2
Mali	60,127,984	70,722,568	82,456,228	186	63	98	3.1	0.9	1.2
Marshall is	260,586	261,178	263,890	0	0	0	0	0	0
Martinique	1,970,037	1,983,363	1,946,469	0	15	1	0	7.6	0.5
Mauritania	14,794,617	17,056,690	19,740,342	89	11	8	6	0.6	0.4
Mauritius	6,042,403	6,193,281	6,276,359	3	2	11	0.5	0.3	1.8
Mexico	528,530,940	568,414,702	612,566,842	359	349	433	0.7	0.6	0.7
Micronesia Fed States	533,737	522,816	519,121	48	0	5	89.9	0	9.6
Mongolia	12,355,999	13,158,752	14,345,622	34	83	0	2.8	6.3	0
Montenegro	3,072,187	3,105,658	3,134,798	0	0	1	0	0	0.3
Montserrat	22,774	24,510	25,315	0	0	0	0	0	0
Morocco	149,238,492	158,091,618	169,138,785	759	127	60	5.1	0.8	0.4
Mozambique	98,680,789	114,328,076	132,273,056	348	658	398	3.5	5.8	3
Myanmar	237,949,429	247,454,398	257,315,592	396	138,597	501	1.7	560.1	1.9
Namibia	9,923,702	10,552,234	11,592,628	136	180	125	13.7	17.1	10.8
Nepal	124,628,478	132,395,564	139,941,905	1257	1168	10,119	10.1	8.8	72.3
Netherlands	81,053,818	82,916,580	84,310,636	969	1007	3	12	12.1	0
New Caledonia	1,124,723	1,218,800	1,309,409	2	0	0	1.8	0	0
New Zealand	20,109,001	21,390,937	22,584,867	6	0	186	0.3	0	8.2
Nicaragua	26,202,394	27,971,127	29,726,707	54	323	60	2.1	11.5	2
Niger	63,380,309	76,268,418	92,267,826	1297	533	518	20.5	7	5.6
Nigeria	660,435,771	752,358,950	859,645,909	2120	2790	2214	3.2	3.7	2.6
Niue	8796	8218	8121	1	0	0	113.7	0	0
Northern Mariana is	334,980	288,730	270,824	3	0	0	9	0	0
Norway	22,869,848	23,877,185	25,376,850	0	0	4	0	0	0.2
Oman	11,970,347	13,929,169	18,574,128	37	92	14	3.1	6.6	0.8
Pakistan	738,648,948	818,681,332	908,735,163	75,234	3961	4214	101.9	4.8	4.6
Palau	98,388	101,168	104,659	0	0	0	0	0	0
Panama	16,048,129	17,584,587	19,192,417	26	56	15	1.6	3.2	0.8
Papua New Guinea	30,065,418	33,941,067	37,968,659	198	409	89	6.6	12.1	2.3
Paraguay	28,022,419	30,233,786	32,330,445	0	57	63	0	1.9	1.9
Peru	134,684,172	143,259,801	152,834,696	735	1804	1061	5.5	12.6	6.9
Philippines	415,002,059	453,802,276	492,444,222	3291	6492	13,318	7.9	14.3	27
Poland	192,206,035	191,644,582	191,506,088	682	629	409	3.5	3.3	2.1
Portugal	52,424,084	53,195,107	52,624,466	2734	89	12	52.2	1.7	0.2
Puerto Rico	18,924,961	18,679,183	18,447,726	6	1	2	0.3	0.1	0.1
Korea rep	358,829,876	367,763,474	375,767,955	833	1003	525	2.3	2.7	1.4
Moldova rep	20,857,803	20,565,340	20,358,954	1	17	10	0	0.8	0.5
Reunion	3,853,762	4,079,669	4,251,841	0	2	1	0	0.5	0.2
Romania	108,589,912	104,159,419	100,381,780	183	373	113	1.7	3.6	1.1
Russia	723,257,233	715,802,896	717,931,197	1499	55,975	457	2.1	78.2	0.6
Rwanda	43,356,130	48,586,439	55,344,985	213	146	21	4.9	3	0.4
St Helena	23,033	20,885	20,438	0	0	0	0	0	0
St Kitts and Nevis	236,519	251,656	265,793	0	0	0	0	0	0
St Lucia	805,165	845,517	877,954	0	11	6	0	13	6.8
St Vincent and the Grenadines	541,787	545,687	546,801	4	0	13	7.4	0	23.8
Samoa	888,520	917,937	953,665	10	143	12	11.3	155.8	12.6
Sao tome et Principe	742,482	834,931	935,553	0	0	0	0	0	0
Saudi Arabia	112,900,869	129,858,808	149,602,719	134	185	92	1.2	1.4	0.6
Senegal	53,409,558	61,101,423	70,647,848	365	33	27	6.8	0.5	0.4
Serbia	46,542,321	45,502,527	44,606,937	0	9	81	0	0.2	1.8
Seychelles	428,908	453,065	463,880	3	0	0	7	0	0
Sierra Leone	25,993,986	30,798,461	34,616,061	108	289	4287	4.2	0	0
Singapore	21,260,364	24,235,308	26,791,416	33	0	0	1.6	0	0
Slovakia	26,998,428	27,000,736	27,124,206	5	128	6	0.2	4.7	0.2
Slovenia	9,953,289	10,122,460	10,325,578	290	6	4	29.1	0.6	0.4
Solomon Island	2,234,093	2,522,708	2,817,644	0	75	69	0	29.7	24.5
Somalia	49,217,540	56,887,402	65,722,104	452	1428	200	9.2	25.1	3
South Africa	238,127,887	252,219,373	268,859,921	185	248	95	0.8	1	0.4
South Sudan	37,609,892	46,325,947	55,857,712	0	0	342	0	0	6.1
Spain	213,425,329	229,381,898	233,383,586	15,162	56	36	71	0.2	0.2
Sri Lanka	96,084,757	99,700,211	102,604,409	35,736	499	770	371.9	5	7.5
Palestine (West Bank)	17,208,809	19,288,352	22,088,788	0	0	10	0	0	0.5
Sudan	147,158,769	164,881,668	184,393,140	885	1991	377	6	12.1	2
Suriname	2,441,692	2,575,882	2,712,342	0	5	0	0	1.9	0
Swaziland	5,442,175	5,801,363	6,358,980	0	3	11	0	0.5	1.7
Sweden	44,799,171	46,200,043	48,074,987	8	0	7	0.2	0	0.1
Switzerland	36,496,047	38,257,010	40,644,163	1050	2	13	28.8	0.1	0.3
Syrian Arab Rep	87,371,315	100,716,953	99,031,912	112	6	13	1.3	0.1	0.1
Tajikistan	32,918,707	36,577,119	40,900,216	67	191	35	2	5.2	0.9
Macedonia fry	10,262,515	10,335,813	10,378,961	33	1	2	3.2	0.1	0.2
Thailand	322,599,139	332,656,214	340,591,546	8905	647	1103	27.6	1.9	3.2
Timor-Leste	4,800,390	5,393,316	5,926,440	26	1	2	5.4	0.2	0.3
Togo	26,972,509	30,830,397	35,227,429	370	135	31	13.7	4.4	0.9
Tokelau	6770	5700	5976	0	0	0	0	0	0
Tonga	499,031	514,792	527,002	0	9	1	0	17.5	1.9
Trinidad and Tobago	6,421,738	6,577,494	6,739,209	3	0	0	0.5	0	0
Tunisia	49,709,788	52,063,324	55,080,262	12	33	0	0.2	0.6	0
Turkey	330,331,592	352,466,817	379,068,755	452	230	692	1.4	0.7	1.8
Turkmenistan	23,287,862	24,711,164	26,839,702	0	0	0	0	0	0
Turks and Caicos Island	116,813	147,002	165,348	0	4	0	0	27.2	0
Tuvalu	48,835	51,692	54,081	0	0	0	0	0	0
Uganda	133,310,136	158,492,073	187,932,378	206	901	265	1.5	5.7	1.4
Ukraine	238,071,165	230,975,699	225,582,555	9	879	197	0	3.8	0.9
United Kingdom	298,249,400	310,323,132	323,116,088	338	43	788	1.1	0.1	2.4
Tanzania Uni Rep	186,032,428	216,591,629	253,405,320	82	312	719	0.4	1.4	2.8
United States	1,450,055,687	1,516,514,351	1,577,571,413	2998	1245	1863	2.1	0.8	1.2
Virgin Islands (US)	541,478	534,029	526,798	0	0	0	0	0	0
Uruguay	16,630,221	16,758,778	17,041,504	9	18	0	0.5	1.1	0
Uzbekistan	129,203,572	138,660,063	150,104,416	0	0	13	0	0	0.1
Vanuatu	995,723	1,127,007	1,265,951	4	0	23	4	0	18.2
Venezuela	129,337,085	140,688,190	151,567,731	142	57	17	1.1	0.4	0.1
Vietnam	413,680,598	433,729,927	457,502,732	1242	1920	500	3	4.4	1.1
Wallis and Futuna	73,047	69,781	62,867	0	0	0	0	0	0
Yemen	97,371,395	111,850,238	127,901,031	218	252	69	2.2	2.3	0.5
Zambia	57,150,420	65,498,387	75,839,464	222	328	2	3.9	5	0
Zimbabwe	63,218,130	67,909,561	75,341,107	154	4433	194	2.4	65.3	2.6

The analysis of this study normalizes the mortality rate per million of the country's population. However, the difference is still very high. When viewed through absolute numbers, the situation is bleak and worrisome. When a disaster hits an area, the existing infrastructure in that area can be a deciding factor in whether or not the situation will lead to mass casualties or not. Roads, for example, can provide access to quickly supply relief aid such as medicine and/or medical human resoucres to the affected communities; but if roads are destroyed, entire regions can be cut off not only from the support of relief but also evacuation of injured and vulnerable population such as pregnant women, newborn and elderly people. The findings of this study confirm this assumption. In all three time periods, the top-five highest mortality rates included only the developing countries. However, if the list of countries with the highest disaster-induced mortality rate extended to the top 20, some developed countries like Spain, Japan, and Luxembourg would be included. Besides developed and developing country factors, the disaster-induced mortality is determined by many other factors, such as population density of the affected country, existing capacity to respond immediately to disasters, underlying vulnerabilities such as poverty, and people living in vulnerable areas, etc.

The trend of mortality is not linear in space and time

The finding of this study reveals that in the fifteen years (2001–2015), a total of 1,356,831 (1.35 million) people died due to natural disasters. The number of deaths variestemporally and spatially. From 2001 to 2005, the disaster-induced mortality rate for the world was 16.02 per million, which increased to 43.20 per million between 2006 and 2010, and 6.58 per million during 2011–2015. It is worth mentioning that aftermath of the Indian-Ocean Tsunami in 2004, globally almost every country and related international bodies initiated disaster risk reduction strategies such as the United Nations International Strategies for Disaster Risk (UNISDR) Hyogo Framework of Actions and Sendai Framework for Disaster Risk Reduction, with the objectives to reduced disaster-induced mortality and economic losses.

In January 2005, one hundred sixty-eight countries' governments adopted a 10-year plan to make the world safer from natural hazards at the World Conference on Disaster Reduction, held in Kobe, Hyogo, and Japan. The Hyogo Framework for Action (HFA) was the global blueprint for disaster risk reduction efforts between 2005 and 2015. Its goal was to reduce disaster losses by 2015 substantially - in lives and the social, economic, and environmental assets of communities and countries. However, soon after HFA came into action, the world was struck by major disasters such as China's Sichuan Earthquake of 2008, Pakistan's Earthquake of 2008 (85,000 deaths), The 2010 Haiti Earthquake (250,000 deaths), Cyclone Nargis Myanmar (138,371 deaths), and Cyclone Sidr Bangladesh and India (15,000 deaths), etc. This disaster revealed to the world that instead of reducing the disaster-induced losses under various frameworks, the mortality increased by almost 250%, from 16.02 death per million people to 43.20 deaths per million people. The same findings concluded with time-series analysis based on the annual mortality rate. More extreme events of this increased number of deaths can be attributed to more climate-induced events, expected to rise if carbon emissions are not controlled.

The trend in mortality rate over the past 15 years is not linear, but there are huge variations across the years and the countries. The disaster-induced mortality can be best estimated if observed in five or ten-year scenarios instead of the annual mortality rate. Most disasters, epidemics, and pandemics are low-probability and high magnitude events. This means that even if the policymakers are presented with the annual disaster-induced mortality rates, they might not be convinced to allocate the required budget for disaster risk reduction. This study proposes that the five-year cumulative disaster-induced mortality rate be used instead of annual mortality rates. This may help policymakers, decision-makers, politicians, and related stakeholders realize the magnitude of the disaster impact in an absolute sense.

Conclusion

This study presented the disaster-induced mortality rates for two hundred and ten countries across the globe with the best possible data that exist at the moment. The study concluded that almost every country globally has its fair share disaster-induced mortality, though the magnitude varies from country to country. However, the existing data sets have several limitations, such as a lack of standardization in collecting the data, missing data, and inconsistent reporting and compilation. Disaster-induced mortality needs a more holistic understanding. To calculate precisely, accurate disaster-induced mortality rates and population data is needed with maximum disaggregated data such as gender, age, and disaster cause-specific like floods, heatwaves, earthquakes, epidemics, etc. All such data should be reported on a universally agreed reporting system and collected and officially endorsed by the respective country governments.

Ethics approval and consent to participate

The study is based on secondary data and was exempted from ethical approval by IRB.

Consent for publication

Not applicable

Availability of data and materials

All data generated or analyzed during this study are included in this manuscript and its supplementary information files.

Funding

Any individual or organization did not fund this study.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.