Worldwide disaster loss and damage databases: A systematic review

Abstract

Nowadays, disaster databases have become a valuable tool for disaster risk management and health promotion and serve various purposes. The purpose of this study is to provide a systematic review of disaster databases in the world and to identify the objectives, information sources, criteria, and variables of disaster data registration in the world's reputable databases. To conduct review, all English-language articles published without a time limit until the end of September 2020 were extracted from the databases of Web of Science, PubMed, Scopus, Cochrane Library, Science Direct, Google Scholar, and Embase. Necessary information in the papers including study time, type of disasters, related databases, dimensions and indicators of global and regional databases were extracted by using a researcher-made questionnaire. A total of 22 studies have been reviewed to identify the dimensions and indicators of disaster databases worldwide. The main focus was on global and regional databases, mostly used at the level of scientific societies and disaster experts. After explanation, researchers highlighted each of the disaster databases, along with the main differences available among the existing databases. Some databases have well-defined data collection methods. Their knowledge is high quality and they can be used to create and improve a disaster database at other levels. Disaster database limitations include risk bias, time bias, accounting bias, threshold bias, and geographical bias. To support the right decisions to reduce disaster risk, it is necessary to complement existing global, regional, and national databases. Countries need to take action to set up national databases.

Full text

© 2021 Journal of Education and Health Promotion | Published by Wolters Kluwer - Medknow 1
Worldwide disaster loss and damage
databases: A systematic review
Sadegh Ahmadi Mazhin1,2, Mehrdad Farrokhi1, Mehdi Noroozi3, Juliet Roudini1,
Seyed Ali Hosseini4, Mohammad Esmaeil Motlagh5, Pirhossein Kolivand6,
Hamidreza Khankeh1,7
Abstract:
Nowadays, disaster databases have become a valuable tool for disaster risk management and
health promotion and serve various purposes. The purpose of this study is to provide a systematic
review of disaster databases in the world and to identify the objectives, information sources, criteria,
and variables of disaster data registration in the world’s reputable databases. To conduct review,
all English‑language articles published without a time limit until the end of September 2020 were
extracted from the databases of Web of Science, PubMed, Scopus, Cochrane Library, Science
Direct, Google Scholar, and Embase. Necessary information in the papers including study time, type
of disasters, related databases, dimensions and indicators of global and regional databases were
extracted by using a researcher‑made questionnaire. A total of 22 studies have been reviewed to
identify the dimensions and indicators of disaster databases worldwide. The main focus was on global
and regional databases, mostly used at the level of scientic societies and disaster experts. After
explanation, researchers highlighted each of the disaster databases, along with the main differences
available among the existing databases. Some databases have well‑dened data collection methods.
Their knowledge is high quality and they can be used to create and improve a disaster database at
other levels. Disaster database limitations include risk bias, time bias, accounting bias, threshold
bias, and geographical bias. To support the right decisions to reduce disaster risk, it is necessary
to complement existing global, regional, and national databases. Countries need to take action to
set up national databases.
Keywords:
Database, disasters, emergencies, natural disasters
Introduction
The third world conference on
disaster risk reduction was held in
March 2015 in Sendai. At that time, a
new disaster risk reduction framework
called the Sendai Framework for Disaster
Risk Reduction (SFDRR) was adopted by
187 countries and included seven global
targets. This new framework will apply
between 2015 and 2030. In addition to
the worldwide document, the post‑2015
Sustainable Development Goals (SDGs)
were adopted in September 2015 with 17
global goals and 169 goals. These goals
include reducing mortality, reducing the
number of people affected, and reducing
the direct economic damage caused by
disasters. Providing accurate information on
human impacts and disaster‑related damage
is critical to measuring and monitoring these
objectives.[1,2] Member states are required to
monitor and report disaster damage. Many
developing countries face a lack of capacity
and institutional frameworks to record
disaster damage and have not enough
historical data in this regard.
Disaster damage data are hugely signicant
to support disaster risk reduction decisions
and public health promotion. Ideally, data
should be standardized and recorded using
Address for
correspondence:
Prof. Hamidreza Khankeh,
Health in Emergency and
Disaster Research Center,
University of Social
Welfare and Rehabilitation
Sciences, Tehran, Iran.
E-mail: hamid.khankeh@
ki.se
Received: 24-11-2020
Accepted: 23-12-2020
Published: 30-09-2021
1Health in Emergency and
Disaster Research Center,
University of Social
Welfare and Rehabilitation
Sciences, Tehran, Iran,
2Department of Nursing
and Emergency, Dezful
University of Medical
Sciences, Dezful, Iran,
3Social Determinants
of Health Research
Center, University of
Social Welfare and
Rehabilitation Sciences,
Tehran, Iran, 4Department
of Occupational Therapy,
Social Determinants of
Health Research Centre,
University of Social
Welfare and Rehabilitation
Sciences, Tehran, Iran,
5Department of Pediatrics,
Ahvaz Jundishapur
University of Medical
Sciences, Ahvaz, Iran,
6Shefa Neuroscience
Research Center,
Khatamol Anbia Hospital.
Tehran, Iran, 7Department
of Clinical Science and
Education, Karolinska
Institute, Stockholm,
Sweden
Systematic Review
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DOI:
10.4103/jehp.jehp_1525_20
How to cite this article: Mazhin SA,
Farrokhi M, Noroozi M, Roudini J, Hosseini SA,
Motlagh ME, et al. Worldwide disaster loss and
damage databases: A systematic reviews. J Edu
Health Promot 2021;10:329.
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Mazhin, et al.: Disaster loss and damage databases
2 Journal of Education and Health Promotion | Volume 10 | September 2021
a standard global method.[3,4] According to international
documents, the ideal database in the eld of human
impact and disaster damage is a database that provides
information in a stable, continuous, reliable, and
accessible form and can be used for decision‑making
and policy‑making in the eld of disaster risk reduction.
In general, the purposes of creating and using disaster
damage databases are as follows:
1. Conducting disaster relief, recovery, and
reconstruction programs (physical damage and its
economic equivalent provide a basis for identifying
the nancial needs of recovery and reconstruction)
2. Assessing the risks of future disasters. Due to climate
change and the growing trend of social hazards and
changing vulnerability patterns, past damage is not
a complete indicator for estimating future damage,
but primary data on the past disasters for validation,
calibration, and creating vulnerability curves in the
future damage assessments and estimations are
essential
3. Estimating the economic viability of investments
made to reduce losses
4. Following up, monitoring, and evaluating the
patterns and trends of human impacts and disasters
to achieve the international goals set in disaster risk
reduction (international policy frameworks in the
eld of disaster reduction and climate change such as
SFDRR and United Nations Framework Convention
on Climate Change [UNFCCC])
5. Performing thematic analysis (e.g., gender differences
in mortality rates and damage assessment in specic
sectors).[5‑7]
The details and dimensions of disaster data must
be combined into a set of descriptive terms and
gures called metadata; this combination will enable
us to record the data in a database and display its
various trends and aspects. Disaster databases have
become a valuable tool and serve various purposes,
from risk assessment in the insurance business and
socioeconomic analysis to provide the basis for
decision‑making to reduce disaster risk and public
health promotion. Various scientific institutes use
these banks, researchers, national and international
governmental and nongovernmental organizations,
the media, and of course, the nancial and insurance
sectors.[8]
According to the global lines drawn on disaster
management, each country should have its national
database on natural disasters. However, such databases
still exist in only a few countries. On the other hand,
some sources did mention that one of the reasons for the
high vulnerability of developing countries in the face of
disasters is the low quality of databases in the eld of
disasters.[9,10]
To minimize uncertainty and increase the quality of
disaster statistics and information, global and national
database providers must use common standards and
denitions. Fortunately, fundamental steps have already
been taken in this direction: The consensus, classication,
and terminology dened through natural hazard by
global data banks and related organizations such as
United Nations Development Programme (UNDP) and
Asian Disaster Reduction Center (ADRC). Initiatives
are currently underway to develop the guidelines for
geocoding and to dene human casualty indicators. The
next steps in improving the quality of disaster‑related
damage documentation should focus on accurately
determining the categories of losses such as economic
losses, indirect losses, and subsequent losses. Although
the complexity of economic impact indicators is
undoubtedly a challenge, joint efforts should be made
to engage database operators and data providers,
economists, and organizations involved in disaster loss
assessment to enhance disaster loss data.[11]
A disaster database review paves the way for
recommendations for the development and coordination
of disaster databases by identifying gaps and analyzing
the methods used by existing information systems.[12]
The purpose of this study is to provide an overview
of the disaster damage database in the world and to
determine the objectives, criteria, and variables of
disaster data registration in the world’s reputable and
scientic databases.
Martial and Methods
The present study is a systematic review of the dimensions
and indicators of disaster databases worldwide, which
was performed according to the PRISMA guidelines
for systematic review and meta‑analysis studies.[13] All
stages of research, including search, selection of studies,
quality assessment of articles, and data extraction, were
performed by two researchers independently (The
criterion for researchers’ agreement is the quality of
articles based on the tool used. If the article does not get
the required score in separate reviews, it will be reviewed
by a third researcher).
Data sources
To conduct this review study, both bibliographic and
citation databases were considered as the primary
sources of our data. In this regard, in the initial search,
all English‑language articles published without a time
limit until the end of September 2020 were extracted
during searches in the databases of Web of Science,
PubMed, Scopus, Cochrane Library, Science Direct,
Google Scholar, and Embase. Moreover, books,
academic websites, documents, and credible reports
of international organizations involved in disaster
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Mazhin, et al.: Disaster loss and damage databases
Journal of Education and Health Promotion | Volume 10 | September 2021 3
management were reviewed and searched based on the
purpose of the study.
Search strategy
Researchers searched all articles with Medical Subject
Headings (Mesh); the following keywords and terms
were searched accordingly.
In a database that had operators (AND, OR, NOT), they
were selected from the relevant location on the site.
Moreover, operators were not embedded with keywords.
TS = ((disaster* OR hazard* OR catastrophe* OR
earthquake* OR volcano OR mass movement* OR storm*
OR ood* OR extreme temperature* OR drought* OR
wildre* OR wildre* OR rockfall* OR landslide* OR
avalanche* OR subsidence OR storm surge* OR heatwave*
OR heatwave* OR cold wave* OR cold wave* OR extreme
winter condition* OR inundation* OR windstorm* OR
man‑made* OR Mass casualty incident* OR bioterrorism*
OR outbreak* OR Accidents OR Event* OR Emergency*)
AND (catalog* OR collection OR database* OR inventor*
OR compilation*) AND (impact* OR loss* OR dead* OR
death* OR killed OR affected OR injured* OR homeless
OR displaced OR relocated OR victim* OR fatality* OR
casualty* OR mental health OR morbidity OR mortality).
Timespan = All Years.
Search language = English.
Inclusion criteria
All English‑language articles in the eld of disaster
databases, which were published in the world’s
academic journals within the specified time frame,
mentioned the dimensions and indicators of the
databases and were of good quality (according to the
Strengthening the Reporting of Observational Studies in
Epidemiology [STROBE] checklist, articles with a higher
score have a higher quality), were included in the study.
Narrative studies that spoke about the dimensions and
indicators of these databases following the research
question were also included in the study.
Exclusion criteria
Exclusion criteria included articles that did not meet
the desired quality. Beyond, review studies, narratives,
meta‑analyses, case reports, or series of cases that did
not examine the dimensions and indicators of databases
were also excluded from the study. Articles published
in non‑English languages were excluded from our
research, too.
Quality assessment of articles
The quality of the articles was assessed using the STROBE
checklist.[14] This checklist has 22 parts, which are scored
based on each section; the lowest score of this checklist is
15, and the maximum is 33. In this study, an acceptable
score of 20 was considered.[15,16] Checklist items include
title and abstract, introduction/background/rationale,
objectives, methods/study design, setting, participants,
variables, data sources/measurement, bias, study
size, quantitative variables, statistical methods,
results/participants, descriptive data, outcome data,
main results, other analyses, discussion/key results,
limitations, interpretation, generalizability, and other
information/funding.[14]
Extracting the data
First, by considering the inclusion and exclusion
criteria, the title and abstract of the articles were
reviewed by two researchers independently. Then, full
text of the articles was reviewed, and if both researchers
opted to reject the articles, the reason was mentioned.
In case of disagreement between them, the article
was judged by another reviewer. Data extraction was
performed using a preprepared checklist that includes
study time, type of disasters and related databases,
dimensions, and indicators of the database at global
and regional levels.
Selection of studies
A search of databases yielded 325 studies. Initially, the
articles were entered into Mendeley software( Mendeley
is a free online reference manager at https://www.
mendeley.com. Mendeley is a subsidiary of Elsevier),
and after the initial review, 27 articles were removed
from the study due to duplication. Then, by reviewing
the titles and abstracts of articles, 77 articles were deleted
due to irrelevance. After reviewing the full text of articles,
199 articles were deleted due to lack of components
or indicators of disaster databases and 22 articles had
inclusion criteria that entered the process of a systematic
review [Figure 1].
Results
Researchers extracted articles from this study and
categorized in [Table 1] based on the author’s name, title,
year of publication, and a brief description of the article.
The number of global, regional, and national publicly
available databases has increased significantly over
the past decade, reecting the need and importance of
tracking and monitoring disaster impacts at the local
level. In this study, 25 global and regional disaster loss
databases were identied [Table 2].
Due to the diversity of databases (especially at the
national‑level or specic risk databases) in line with the
purpose of this study, the main focus is on global and
regional databases with an all‑hazards approach, which
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Mazhin, et al.: Disaster loss and damage databases
4 Journal of Education and Health Promotion | Volume 10 | September 2021
is mostly used in the scientic community and by disaster
experts. We examine these databases below.
• Global, regional, and national accident and disaster
registration databases
• Worldwide accident and disaster registration
databases.
• Natural catastrophe services (NatCatSERVICE)
• EM‑DAT
• SIGMA (Swiss Re)
• GLobal unique disaster IDEntier (GLIDE).
Natural catastrophe services
NatCatSERVICE (Munich Re) is a global database of
natural disaster data (“natcat”), founded in 1974 in
Munich, Germany. The database began with the historic
eruption of Mount Vesuvius in 79 AD, and relevant data
are available for systematic and analytical evaluation on
a global scale from the 1980s onward. Currently, about
1200 events are added to this archive each year.
This unique archive provides comprehensive, reliable,
and professional data on insured, economic, and human
damages caused by natural hazards. This database forms
the basis of a wide range of tools and services used in risk
assessment and risk management and is not limited to the
insurance and nance industries. It also includes research
communities and members of the public interest.
NatCat disaster interpretation tools can be congured to
focus only on events in one country or to analyze events
that affect multiple countries, so‑called regional events.
The NatCatSERVICE database contains information
about events in each country and, in the event of regional
events, combines country information with data about
regional events. This concept allows for country‑wide
or regional (such as continental) analyses.
This database only covers disasters caused by natural
hazards. The data are divided into seven categories
based on the severity of the economic and human
damage caused by the incident. Class 0 disasters include
natural events without nancial loss or are included in
the database due to human casualties but are not used
for economic assessments.
Figure 1: Results of PRISMA ow of the systematic literature search
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Mazhin, et al.: Disaster loss and damage databases
Journal of Education and Health Promotion | Volume 10 | September 2021 5
Table 1: General characteristics of the studied articles that were eligible for a systematic review
Author Title Year Summary
El Hadri et al.[17] Natural disasters and countries’
exports: new insights from a
new (and an old) database
2019 This paper examines the effects of disasters on exports from 1979‑2000.
Two different datasets have been used to increase the power and accuracy
of data collection: The EM‑DAT and GeoMet, which are a new set of data
based on geophysical and meteorological data
Ries et al.[18] Disasters in Germany and
France: An analysis of the
emergency events database
from a pediatric perspective
2019 This study aimed to conduct a comprehensive analysis of the disaster
pattern for Germany and France from the children’s perspective. EM‑DAT
analysis shows that children’s data are not explicitly recorded in EM‑DAT
Napolitano E et al.[19] LAND‑deFeND‑An innovative
database structure for
landslides and oods and their
consequences
2018 In this study, the national LAND slides and Floods
database (LAND‑deFeND) is presented, a new database structure that can
organize and manage spatial information collected from different sources
with varying accuracy
Koç et al.[20] The relevance of ood hazards
and impacts in Turkey: What
can be learned from different
disaster loss databases?
2018 In line with the primary purpose of the study in terms of data quality and
accuracy, the TABB database was discussed. The TABB database was
analyzed by comparing the emergency database (EM‑DAT), the global
active archive of major ood disasters ‑ the Dartmouth ood observatory
database, the news archive, and the scientic literature focusing on disaster
lists
Moriyama et al.[21] Comparison of global
databases for disaster loss and
damage data
2018 This article aims to investigate the traits and differentiate existing databases
in three aspects of the threshold, spatial separation, and data quality
control. Restrictions on existing databases are also considered
Brown et al.[22] Volcanic fatalities database:
Analysis of volcanic threat
with distance and victim
classication
2017 In this study, a volcanic mortality database has been updated to include all
data from 1500 AD to 2017. The database contains 635 records of 278,368
killed individuals. Each record includes information on the number of dead
people, the cause of death, the date of the accident, and the place of death
in terms of distance from the volcano
Stahl et al.[23] Impacts of European drought
events: Insights from an
international database of
text‑based reports
2016 This study examines the diversity of drought impact across Europe based
on the drought impact report in Europe (EDII). It presents a unique research
database that has collected nearly 5000 drought impact reports from 33
European countries
Soto[24] Deriving information on
disasters caused by natural
hazards from limited data:
A Guatemalan case study
2015 This work proposes a way to overcome the data constraints needed
when analyzing disasters on a local scale in disaster‑prone areas. In this
proposed method, data are collected using two databases: the SISMICEDE
and the DesInventar databases. SISMICEDE has a short period and high
spatial resolution, while DesInventar has a longer duration but low spatial
resolution
Gall[25] The suitability of disaster loss
databases to measure loss and
damage from climate change
2015 This article examines the appropriateness of disaster databases for
recording the effects of climate change, especially those related to severe
weather conditions and slow‑moving events
Huggel et al.[26] How useful and reliable are
disaster databases in the
context of climate and global
change? A comparative case
study analysis in Peru
2015 The study analyzed three different disaster databases in developing
countries such as Peru: The global database (EM‑DAT), the Latin American
multinational database (DesInventar), and the Peru national database (Peru
SINPAD national information system). The analysis is performed in three
dimensions (1) spatial scale, (2) periods, and (3) the classication and
criteria of disasters
Zêzere et al.[27] DISASTER: A GIS Database on
hydro‑geomorphologic disasters
in Portugal
2014 The DISASTER project provides a compatible hydrogeomorphological
database for Portugal by creating and operating a GIS‑based database
on oods and landslides for 1865‑2010. Data collection is based on the
concept of disasters used in the DISASTER project. Therefore, each
hydrogeomorphological case is stored in the database
Santos et al.[28] Risk analysis for local
management from
hydro‑geomorphologic disaster
databases
2014 This paper describes the applications of a hydrogeomorphological disaster
database that allows for proper local risk management. Two disaster
damage databases have been created with different criteria for events in
Central Portugal using national and regional newspapers: one containing all
disaster events regardless of the amount of damage reported, and the other
one having reported major disasters with casualties
Wirtz et al.[11] The need for data: Natural
disasters and the challenges of
database management
2014 This article describes the criteria and denitions for how global multi‑risk
databases work and the efforts to ensure consistent and international data
management standards. Besides, the basic concept and methodology of
the NatCatSERVICE database are presented, and many of the challenges
associated with data acquisition and data management are described
Contd...
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Mazhin, et al.: Disaster loss and damage databases
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Classes 5 and 6 include major and devastating natural
disasters and play a unique role in the system. These data
provide the most critical and consistent statistics when
identifying damage trends around the world. Category 6
includes all disasters that cause economic losses equal to
5% of GDP/per capita of the country where the disaster
occurred.
Accurate and regular resources and data mining are the
main principles of this database. The Munich Reinsurance
Company relies on several reputable sources, including
news agencies (Factiva/Dow Jones, Associated Press),
which rank them based on the agency’s track record over
time. A rating of 25 equals the most valid, and a rating
of 6 equals the lowest source credit rating.[7,29] To ensure
the quality of the information obtained, conflicting
information from various sources is provided to internal
experts for re‑evaluation and classied into six classes. In
this category, each datum is assigned to a quality level on
a scale from 1 (very good) to 6 (insufcient). Data records
for quality levels 4, 5, or 6 do not comply with database
quality standards and are not used for analysis.[11]
Finally, the consolidated information is stored in
the NatCatSERVICE database. Nine other sources of
information include national insurance associations,
Table 1: Contd...
Author Title Year Summary
Grasso and Dilley[7] A comparative review of
country‑level and regional
disaster loss and damage
databases
2013 This study focuses on the implementation of damage and injury databases
at the national and regional levels. The UNDP conducted the review
Vos[12] Working paper work package
3 review of disaster databases
collecting human impact data in
Europe
2012 This paper focuses on disaster data in Europe as part of the data needed
to measure resilience, focusing on national databases. The primary data
collection strategies included internet search and literature review to identify
disaster databases across Europe, national levels, and global databases
Kron et al.[8] How to deal appropriately
with a natural catastrophe
database ‑ Analysis of ood
losses
2012 In addition to the EM‑DAT and Sigma databases, Munich Re’s NatCat
service is now one of three global databases of its kind with over 30,000
datasets. In this study, using the example of oods and ood losses, the
problems that exist when analyzing trends are discussed
Mohleji[29] Gaining from losses: Using
disaster loss data as a tool
for appraising natural disaster
policy
2011 This study evaluates natural disaster policies through data on disaster
damage. This work is a collection of three separate studies. Through the
data of economic damages caused by natural disasters, it focuses on
analyzing the trend of disaster intensity and answering essential questions
about disaster policy
López‑Peláez and
Pigeon[30]
Co‑evolution between structural
mitigation measures and
urbanization in France and
Colombia: A comparative
analysis of disaster risk
management policies based on
disaster databases
2011 This paper examines the signicant differences between the EM‑DAT and
DesInventar international disaster databases, which are often used as a
basis for designing risk mitigation programs
Marulanda et al.[31] Revealing the socioeconomic
impact of small disasters in
Colombia using the DesInventar
database
2010 This paper presents the results of the evaluation of the DesInventar
database, created in 1994 by the disaster prevention social studies network
in Latin American. Besides, a new version of the local disaster list was
developed in 2005 as part of the US disaster risk index and management
program, with support from the Inter‑American development bank
United Nations
Development
Programme[32]
Risk knowledge fundamentals:
Guidelines and lessons
for establishing and
institutionalizing disaster loss
database
2009 This study documents the experiences of the UNDP regional program
on capacity building for sustainable recovery and risk reduction in the
implementation of disaster loss databases using the DesInventar method
Witham[33] Volcanic disasters and
incidents: A new database
2005 A new database on volcanic eruption, mortality, and urban evacuation has
been proposed. This study aims to quantify the social effects of volcanic
phenomena during the 20th century. The data include the number of dead,
injured, evacuated, and homeless individuals and the nature of the related
volcanic phenomena
Sapir[34] The development of a database
on disasters
1992 In this study, CRED examines the possible designs and feasibility of
database systems for disaster management and response globally. An
EMIS has been proposed to provide fast and accurate end‑user information
by the WHO and other agencies involved in disaster preparedness and
response. This article also presents the technical aspects of the rst
EM‑DAT disaster database
NatCatSERVICE=Natural catastrophe SERVICE, EM‑DAT=Emergency events database, EMIS=Emergency Management Information System, UNDP=United
Nations Development Programme, EDII=European Drought Impact Report Inventory, CRED=Centre for Research on the Epidemiology of Disasters
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Mazhin, et al.: Disaster loss and damage databases
Journal of Education and Health Promotion | Volume 10 | September 2021 7
commercial press, and insurance industry information
services (Lloyd’s List, World Insurance Report, Property
Claims Service); press and media report, international
government institutions (United Nations [UN], European
Union, and WHO), humanitarian institutions (Red Cross);
scientic institutions (National Storm Center, Tsunami
Warning Center, Meteo France, Deutscher Wetterdienst,
Japan Meteorological Agency, World Meteorological
Organization); and academic resources.[7,11,29]
The Munich Reinsurance Company collects the amount
of economic damage at the time of the disaster and then,
at the end of each month, adjusts the monthly amount
of damage caused by the disaster to the current market
rate. It also collects the principal amount of damages in
the currency of the country where the disaster occurred,
because the company is located in Germany, converts
the losses into Euros for commercial purposes, and
nally converts the number of damages from Euros to
US Dollars. The NatCatSERVICE database is reviewed
every 3–6 months. The review process includes checking
the quality of the data by mentioning the data source
ranking and evaluating the amounts of damages by
comparing them with insurance claims payments. The
Munich Reinsurance Company reviews all amounts
of damages and checks suspicious amounts with local
sources if needed.[29]
EM‑DAT
The EM‑DAT is a global disaster database launched
by the CRED Natural Disaster Epidemiology Research
Center at the Université Catholique de Louvain in 1988
in Belgium. EM‑DAT was created with the initial support
of the WHO and the Belgian government.
The primary purpose of this database is to serve
humanitarian goals at the national and international
levels. This database is an active and well‑known
global database for disaster damage assessment. Its
threshold for recording data is specied and the data
Table 2: List of global and regional databases identied in this study
Database Type Ownership
EM‑DAT Global CRED
NatCatSERVICE Global MunichRe
SIGMA Global SwissRe
GLIDE Global ADRC
GFDRR Global World Bank
BD CATNAT Global Global Ubyrisk Consultants
Signicant earthquake database Global disaster‑specic USGS
Global Active Archive of Large Flood Events Global disaster‑specic DFO
CAT‑DAT Damaging Earthquakes Database Global disaster‑specic SOS Earthquakes
Landslide fatality database Global disaster‑specic Durham University International Landslide Centre
Signicant earthquake database Global disaster‑specic NOAA National Geophysical Data Centre
Signicant Volcanic Eruption Database Global disaster‑specic NOAA National Geophysical Data Centre
Global historical tsunami database Global disaster‑specic NOAA (NGDC/WDC)
Cambridge earthquake impact database Global disaster‑specic Cambridge architectural research Ltd
Landslides‑recent events worldwide Global disaster‑specic Geological Survey Canada
GAPHAZ Global disaster‑specic University of Oslo (IACS/IPA)
EFFIS Regional/Europe‑wide
disaster‑specic
EC JRC
GeoMet Geophysicists or
meteorologists
GeoMet‑Data
DesInventar Regional LA RED
Andean Information System for Disaster Prevention
and Relief
Regional Andean information system for disaster prevention and relief
Dartmouth Flood Observatory Database Global disaster‑specic University of Colorado
GVP Global disaster‑specic Smithsonian institution’s
EDII Regional
disaster‑specic
European Drought Center
USGS database Global disaster‑specic The USGS earthquake hazards program
Tropical Cyclones Global disaster‑specic The Earth Observation Research Center of the Japan
Aerospace Exploration agency
GVP=Global volcanism program, NatCatSERVICE=Natural catastrophe services, GLIDE=GLobal unique disaster identier, EM‑DAT=Emergency events
database, EDII=European drought impact report inventory, GeoMet=Geophysical and meteorological database, EFFIS=European forest re information
system, GAPHAZ=Glacier and permafrost hazards in mountains, JRC=Joint research centre, NGDC/WDC=National geophysical data center/world data center,
ADRC=Asia disaster reduction center, IACS/IPA=International Association of Cryospheric Sciences and the International Permafrost Association , SOS=Science
on a Sphere, DFO=Dartmouth ood observatory, BD CATNAT=Base de données catastroph natural, GFDRR=The Global Facility for Disaster Reduction and
Recovery, USGS=The United States Geological Survey, LA RED=The Network of Social Studies on Disaster Prevention in Latin America, CAT‑DAT=Catastrophe
data, The CAT‑DAT damaging earthquakes database, NOAA=National Oceanic and Atmospheric Administration’s
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Mazhin, et al.: Disaster loss and damage databases
8 Journal of Education and Health Promotion | Volume 10 | September 2021
are stored uniformly. These features allow users to
compare disaster damage trends internationally. This
database contains necessary information about the
occurrence and effects of more than 22,000 signicant
disasters worldwide from 1900 to the present day, and
about 300 events are added to this archive every year.
In addition to metadata, data archives primarily include
humanitarian data, such as those killed and missing,
injured, homeless, or evacuated. Damage data (total
damages and insured damages) are mainly based on
information from UN agencies, government ofces,
the International Federation of Red Cross and Red
Crescent Societies, research organizations, insurance
publications (Lloyd’s list), and reinsurance publications.
This database distinguishes between two general
categories of disasters (natural and technological),
followed by several subgroups: geophysical,
meteorological, hydrological, climatic, and biological.
Each of these subgroups is again subdivided into several
types of disasters (e.g., oods, landslides, potholes).
For each reported disaster, the damage is estimated at
the dollar exchange rate. In the EM‑DAT protocol, if
at least one of the following criteria exists, a disaster
must be reported: (1) the death number of 10 or more;
(2) the number of 100 or more affected persons; (3) the
declaration of a state of emergency; or (4) the request for
international assistance by the government concerned.[ 11]
SIGMA (Swiss Re)
SIGMA is a global natural and articial disaster damage
database founded by Swiss insurer Swiss Re and has
published a statistical analysis in annual journals since
1970. The Swiss Re database includes both natural
and articial disasters. Data entry dates back to 1970,
and almost every year, 300–350 new events are added
to the database. In addition to metadata such as risk,
date, and place of disaster (which includes general and
insured damages), information about victims is also
recorded (casualties and missing, injured, or homeless
individuals). Government agencies, nongovernmental
agencies, insurance groups, scientic research institutes,
international agencies such as the UN or the European
Commission, insurance introductory journals, internal
reports, online databases, and daily newspapers are the
data sources used by SIGMA. SIGMA, such as EM‑DAT
and NatCatSERVICE, provides data by country. The
annual list of all events is published in Sigma – natural
catastrophes and artificial disasters – and can be
downloaded from the Swiss Re website, but there is no
other public access to the database.
In some cases, raw data are provided for a few
projects. SIGMA is very strict about inclusion criteria.
These measures include economic losses based on
adjusted ination for the year (86.5 million USD for
2010 and 99 million USD nancial losses or insured losses
including 19.9 million USD for maritime disasters, 39.8
million USD for aviation, and 49.5 million USD for other
damages for 2016) and/or 20 killed/missing, 50 injured,
or 2000 homeless.[11,26]
GLobal unique disaster IDEntier
Another publicly available global database is the GLIDE.
This database is a coordinating role between CRED,
ISDR, UNDP, La Red/DesInventar, and other databases.
The mission of this database is not to provide accurate
information and documentation related to disaster
damage. Still, it serves to establish communication
between disaster damage databases by creating a disaster
event identier. The ADRC, which manages the GLIDE
database, starts a unique identier for each disaster
event to link disaster damage information. The GLIDE
database, such as the EM‑DAT database, imposes a
threshold for event registration. Therefore, it does not
include repetitive events with high frequency and low
intensity.[25]
Accident and disaster registration databases at
the regional level
• DesInventar.
DesInventar
A common conceptual and methodological framework
in Latin America was started in 1994 by a group
of researchers, academics, and institutional actors
associated with social studies network on disaster
prevention. The DesInventar project was started by
LA RED, the Network of Social Studies on Disaster
Prevention in Latin America. LA RED is a nonprot
organization operating mainly in Latin America, the
Caribbean, and currently Asia and Africa.
LA RED established a system for collecting, advising,
and displaying information on small‑, medium‑,
and large‑scale disasters based on the available data,
newspaper sources, and institutional reports in nine
Latin American countries. This project was developed
to complete a conceptual, methodological, and software
tool called Disaster Inventory System. Expansion of
the DesInventar was in the sense that it facilitates
dialog to manage risk between actors, institutions,
departments, state, and national governments if
local‑scale disasters (city or equivalent) are addressed
objectively.
DesInventar is a conceptual and methodological tool
for building databases based on damage, casualties, or
the effects of emergencies or natural disasters with the
support of UNISDR, UNDP, and LA RED. It should
be pointed that methodology (definitions and data
management assistance), a database with a flexible
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Mazhin, et al.: Disaster loss and damage databases
Journal of Education and Health Promotion | Volume 10 | September 2021 9
structure, software for entering the database, and
software for consulting and data analysis (which also
includes options for selection for search criteria) are the
components of DesInventar.
The crisis information management system (DesInventar
methodology) consists of a software product with two
main components. The rst component is the executive
management and the second component is the data
entry module. The latter is a database with a specic
structure and relationships completed by filling in
predened elds (spatial and temporal data, types of
events and reasons, and sources) and direct and indirect
effects (mortality, destroyed homes, infrastructure,
and economic sectors). The analysis module allows
access to database information that includes variables,
relationships between various variables, effects, types of
events, causes, sites, dates, and more. This module makes
it possible to display at the same time the requested
information with tables, graphics, etc.[30,31]
In the following, the registration threshold, data quality
control, resolution/spatial accuracy, time coverage,
information sources, contacts, owners/administrators,
and the advantages of the databases mentioned above
are presented [Table 3].[21,25]
Generally, depending on creating a database, cases
such as the number of people killed, injured, homeless,
missing, and economic damages are registered in the
world’s reputable databases. These cases are listed in
Table 4 as a comparison between databases.[25]
Discussion
In this study, systematic disaster database search
strategies identied 26 global, regional, and international
databases and 22 relevant articles. The search was limited
to English‑language databases that provided searchable
disaster statistics. Therefore, items from databases that
provided information in other languages were not
entered.
Global and regional disaster databases have consistently
and comprehensively covered a wide range of natural
hazard data for many years. The general classication
of databases expressed in various sources is as follows:
• Geological/geophysical event databases
• Meteorological and hydrological accident databases
• Climate event databases
• Other event databases (e.g., weather, biological
epidemic disease).
As mentioned earlier, disaster registration databases
deal with all hazards and, in some cases, with a specic
hazard. Among these databases, distinctions can be
made, such as differences in categories, the scope of
work, and the type of events recorded. Some databases,
with years of experience in disaster data collection,
have well‑established data collection methods. Their
knowledge is high quality and very valuable and can be
used to create and improve a disaster database at other
levels. Many global databases have a national breakdown
level and allow for international comparisons between
countries. It is not appropriate to use these databases
to assess the effects of disasters at lower resolution
geographical levels, such as on a national scale. Thus,
national disaster databases that collect data at lower
resolution levels are valuable for supplementing data
on smaller‑scale events.
A comparison of different disaster databases shows a lot
of inconsistency between global and national databases.
Current international and national databases suffer
from many limitations in controlling the damage caused
by national hazards, leading to misinterpretation of
disaster data. Given the focus on national geography,
disaster‑related databases at the national level can
provide comprehensive and detailed information on
human, social, cultural, environmental, and economic
impacts.[12,20,35]
This study describes the main differences between
existing databases in threshold registration, data quality
control, spatial resolution, time coverage, data sources,
contacts, stakeholders, and database advantages. In its
disaster denitions, EM‑DAT denes a disaster as a
situation or event that affects local response capacity,
necessitating a request for external assistance from a
national or international level.[11]
EM‑DAT criteria for recording disaster data are more
evident than DesInventar and other databases. This
database is the primary source of epidemiological
information about disasters. However, the disaster
threshold ignores the effects of high‑frequency and
low‑intensity disasters. However, cumulative losses
from recurrent, smaller, and larger hazards are more
signicant than large, severe, and unlikely hazards.
Extensive hazards are dened as recurrent or persistent
hazards with low or moderate severity, often of local
nature, leading to catastrophic cumulative effects.
Criteria used by DesInventar validate the term “small
disaster.” However, this denition is comparable to
the CRED denition of disasters as high intensity but
relatively rare events due to differences in the purpose
of these databases in recording disaster data. In Europe,
most research and risk management focus on CRED‑type
disasters.[30]
For DesInventar, on the other hand, the criterion for
recording disasters is creating one or more units of
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Mazhin, et al.: Disaster loss and damage databases
10 Journal of Education and Health Promotion | Volume 10 | September 2021
human or economic damage. In Sigma, the annual
threshold is set and adjusted based on annual ination
and the dollar exchange rate.[26] In general, the Sigma
database uses strict points as entry criteria. The
disaster registration threshold in the NatCatSERVICE
database is lower and occurs as soon as human injury
(loss of life, injury, and homelessness) or property
damage in a data set occurs. These events are classied
into six classes of disasters (categories 1–6), depending
on the severity of the nancial or human impact: from a
natural disaster with minimal economic impact (category
1) to “a major natural disaster” (category 6).[ 11]
The main limitations of disaster databases are over‑ or
under‑reporting of certain types of risk (risk bias),
a gap in historical records (time bias), reliance on
direct or indirect financial losses (accounting bias),
focus on high‑intensity events (threshold bias), and
over‑focus on densely populated or more accessible areas
(geographic bias).[20]
Table 3: Characteristics of global and regional disaster databases
Spatial
coverage
Global Regional
NatCatSERVICE EM‑DAT Sigma GLIDE DesInventar
Threshold
to record
The occurrence of
human injury (loss
of life, injury,
homelessness) or
property damage
One of the following
criteria must be fullled: (1)
10 or more human
deaths, (2) 100 or more
people affected/injured/
homeless, (3) declaration
by the country of a state
of emergency and/or an
appeal for international
assistance
For the 2016 reporting
year ‑ insured losses: 19.9
million USD for maritime
disasters, 39.8 million USD
for aviation, 49.5 million
USD for other losses, or
economic losses: 99 million
USD or Casualties: 20 dead
or missing, 50 injured, 2000
homeless
≥10 fatalities,
≥100 affected,
declaration of
the state of
emergency, or call
for international
assistance
All disasters (one or
more human losses or
one or more dollars of
economic losses)
Data quality
control
Database owner Database owner Database owner Database owner Varies by country
(governments, NGOs,
or research institutes)
Spatial
resolution
Country Country Country Country The minimum level of
geographic resolution
Temporal
coverage
79 AD‑present 1900‑present 1970‑present 1930‑present Varies by country
Data
sources
Property claims
service, insurance
clients, UN agencies,
World Bank, press,
academia, etc.
UN agencies, IFRC, World
Bank, reinsurers, press,
news agencies, etc.
Property claims service,
insurance clients, UN
agencies, World Bank,
press, academia, etc.
UN agencies,
IFRC, World Bank,
reinsurers, press,
news agencies,
etc.
UN agencies, weather
services, geological
services, press, etc.
Audience The general public,
the insurance
industry
Humanitarian community,
academia
The general public, the
insurance industry
Loss database
operators
Emergency
management, hazard
mitigation planning,
academia
Owner Munich Re, Germany Centre for Research on the
Epidemiology of Disasters,
Université Catholique de
Louvain, Belgium
Swiss Re, Austria Asian Disaster
Reduction
Center, Japan
Varies by country
Advantage Reliable information
on insured losses
Graphics can be
obtained based on
the statistical data by
clicking
Actively and continuously
maintained
Human losses are
disaggregated into deaths,
injured, affected, homeless
Data are to be stored in a
uniform format
The threshold to record is
clear
Users can download the
dataset itself
Reliable information on
insured losses
Graphics can be obtained
based on the statistical data
by clicking
This database
collaborates
between CRED,
ISDR, UNDP, La
Red/DesInventar,
and others
The GLIDE
database
generates a
unique identier
for each disaster
event to link loss
information and to
advance event and
data comparability
between databases
Widely used
tool ‑ Human losses
are disaggregated
into deaths, injured,
affected, homeless
Data are to be stored
by each country in
a uniform format
developed to record
disaggregated data.
UNISDR encourages
countries to use
DesInventar in
implementing the
SFDRR
Users can download
the dataset itself
NatCatSERVICE=Natural catastrophe services, GLIDE=Global unique disaster identier, EM‑DAT=Emergency events database, IFRC=International Federation of
Red Cross and Red Crescent, USD=US Dollar, CRED=Center for research on the epidemiology of disasters, ISDR=International Strategy for Disaster Reduction,
UNDP=United nations development programme
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Mazhin, et al.: Disaster loss and damage databases
Journal of Education and Health Promotion | Volume 10 | September 2021 11
Various sources can be used for the input data of a
disaster database: ofcial reports and announcements,
information collected during internet searches,
reports of humanitarian actions of non‑governmental
organizations, data collected by academic institutions,
media reports, etc. In the meantime, the arguments
are in favor of including newspaper reports as one
of the primary sources of information in the disaster
database because: (a) newspapers cover events on a
local scale more than any other source; (b) a similar
event is often reported in different newspapers, so
it is permissible to compare and sift through facts;
(c) newspapers are usually better at maintaining and
accessing their archives; and (d) newspaper information
covers a broader time than other media sources such as
television and the Internet.[28]
A small number of disaster databases allow free access
to disaster information. However, access to the data may
be done after registration or through special agreements
with the responsible institution. Despite the application
of standard denitions of the type of disasters and human
impacts in each database, there is a wide heterogeneity
between databases in terms of the kind of data collected,
the volume of data, and accessibility, depending on the
focus and methods of collecting each database. As a
result, comparing datasets between databases is very
challenging.[12]
The EM‑DAT database may not be appropriate if you
need to use disaster‑related data. The reason given is that
the severity of disasters, which is usually measured by
EM‑DAT based on the amount of damage or the number
Table 4: Comparison of cases registered in global and regional disaster databases
cases registered NatCatSERVICE EM‑DAT Sigma GLIDE DesInventar
Killed x x x x
Injured x x x x
Missing x x x x
Homeless x x
Affected x x x
Evacuated x x x x
Relocated x
Displaced x x
Property loss x
Environmental loss x
Insured loss x x
Aggregate economic loss x x x x
Infrastructure damage x x x x
Economic sector damage x x x x
Geophysical x x x x x
Hydrological x x x x x
Meteorological x x x x x
Climatological x x x x x
Technological x x x
Climate change
NatCatSERVICE=Natural catastrophe services, GLIDE=Global unique disaster identier, EM‑DAT=Emergency events database
of victims, is itself related to the level of development. To
report a disaster in the EM‑DAT database, there must be
at least one of the following criteria: ten or more killed,
100 injured or more, a state of emergency declared, or a
formal request for international assistance.[21]
It should be pointed out that data on armed conict and
terrorism are not included in the EM‑DAT, which may be
biased as the effects of wartime disasters may be much
more severe. The mortality data reported in EM‑DAT and
other global disaster databases for adults and children
are not available separately and categorized. To create
an approach based on vulnerable groups, age‑classied
data are needed.[18,36]
EM‑DAT, Sigma, and NatCatSERVICE have their
specialists evaluate data set quality control, while
DesInventar data quality is government controlled. In
terms of spatial resolution, only DesInventar provides
the location data of a disaster event at the level of city
divisions. Other limitations in databases include lack of
segregated data, limits of spatial coverage and spatial
segregation, incompleteness and reliability of data, and
specic recording of total damage (including indirect
damage).[11,17]
Financial loss is an essential parameter in disaster
databases. There are ambiguities in distinguishing between
direct and indirect damages. NatCatSERVICE denes
direct damage as follows: direct damage is observable and
measurable immediately after a disaster (destruction of
homes, property, schools, vehicles, machinery, livestock,
etc.). In the other class, damages are divided into two
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Mazhin, et al.: Disaster loss and damage databases
12 Journal of Education and Health Promotion | Volume 10 | September 2021
categories: insured damages and economic damages.
Insured claim gures are very reliable since they reect
claims paid by insurance companies. Some studies have
reported that EM‑DAT and NatCatSERVICE do not report
the damage to infrastructure and the agricultural sector
sufciently and accurately. This could call into question
the achievement of these banks’ goals in calculating
economic losses.[11]
Finally, no systematic review of disaster databases
was found in the literature review. in this study, a
comprehensive review of the most authoritative disaster
databases has been conducted. this work paves the
way for a better understanding of the components and
criteria of disaster databases in the eld of creating and
developing disaster databases at all levels.
Limitations
In this study, disaster databases at the national level
and specic risk databases have not been examined.
One of the reasons for this is many of these banks and
the modeling of these databases from reputable global
databases.
In this study, only English‑language studies were
reviewed.
Conclusion
In the context of the unequal and discontinuous increase
in the risk of disasters and their effects, the need to collect
and share disaster impact data is crucial to protect people,
improve public health promotion, and reduce economic
damage. A systematic set of information and standard
data on the occurrence and effects of natural disasters
is an essential tool for scientific and policy‑making
purposes and disaster response and recovery activities.
Many national and regional databases are currently
running with international support. United Nations
Office for Disaster Risk Reduction (UNISDR) has
supported many countries in building and updating
disaster databases in partnership with UNDP. UNISDR
support has been through financial support or
technical assistance (updating, training, advocacy, data
dissemination, and institutional support). In particular,
UNISDR has provided technical assistance to all
countries that use DesInventar software to develop and
enhance their software or applications. Other specialized
UN agencies, such as the WHO and the Food and
Agriculture Organization of the United Nations support
countries to record data in their respective sectors.
To support sound disaster risk reduction decisions and
public health promotion, it is essential to complement
existing global, international, and national databases.
Acknowledgment
This article is part of the PhD thesis that has been
approved by the University of Social Welfare and
Rehabilitation Sciences (Ethical code: IR.USWR.
REC.1399.074). The authors would like to thank all
researchers who have already done research in this eld
and whose results were used in this study.
Financial support and sponsorship
Nil.
Conicts of interest
There are no conicts of interest.
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