Time biases result from unequal reporting quality and coverage over time1. The figure below shows the occurrence of disasters in EM-DAT. The figure shows a significant increase that starts in the 1960s. This increase coincides with the creation of OFDA. In 1973, OFDA started compiling disaster data, and the CRED was created2. In 1988, the CRED took over the disaster database and created EM-DAT. In the meantime, communication technologies have improved, with the first personal computers and satellites appearing in the 1970s and the advent of the World Wide Web in the 1990s (see also History of EM-DAT)
Technologies and initiatives can be considered responsible for the dominant trend observed. Therefore, it is challenging to infer insight into the actual drivers of disasters such as climate change, population growth, or disaster risk management. Accordingly, excluding pre-2000 data from trend analyses based on EM-DAT is strongly recommended. From September 2023 onward, the CRED refers to pre-2000 data as
Historic data in the EM-DAT Public Table.
Hazard-related biases result from unequal reporting quality and coverage for different hazard types1. For example, in EM-DAT, data related to biological hazards (e.g., epidemics) and extreme temperature hazards (e.g., heat waves) are less covered and the cover of lower quality. Some hazard-related biases are illustrated in the Accounting Biases and Geographic Biases sections.
Threshold biases result from unequal reporting quality and coverage for disasters of different magnitudes1. High-impact disasters attract more attention, resulting in better media coverage and reporting. This could lead to threshold biases in EM-DAT. The EM-DAT entry criteria introduce a kind of threshold bias, as shown in the figure below regarding disaster mortality, while some studies have shown locally that small disasters may have a high cumulative impact, e.g.3. Regarding disasters that fit EM-DAT’s entry criteria, it is fair to assume that disasters close to the entry criteria are more likely to be missing than high-impact disasters. However, as shown in the figure below, the cumulative mortality associated with low-mortality events exceeds the cumulative impact of higher-mortality events.
Accounting biases result from unequal reporting quality and coverage for different impact variables1. For instance, in EM-DAT, the economic losses are, on average, less frequently reported than the human impact variables, which may also depend on the hazard type (see the figure below). Furthermore, insured damages are naturally more reported than uninsured damages, which creates a geographic bias where there is a lack of insurance coverage, as in Africa. For droughts, EM-DAT fails to capture the associated mortality because it is overlooked as an indirect impact, as evidenced by the UNDRR 2021 Global Assessment Report on Droughts.
Besides, it cannot be assumed that because an impact is reported in EM-DAT, there is no accounting bias. In general, direct impacts are often reported by EM-DAT sources, while indirect impact estimates are less available. For instance, indirect deaths for a flood event correspond to the number of fatalities occurring during the event, while indirect deaths result from disease outbreaks due to deteriorated sanitary conditions. Yet, indirect mortality is sometimes more important than direct mortality, for examples.4
Geographic biases result from unequal reporting quality and coverage across space1. In general, EM-DAT has a relatively worse coverage for Sub-Saharan Africa regarding the occurrence and the accounting of impact variables5. Any disaster type may be subject to geographic biases in EM-DAT as there may be discrepancies between reporting systems from one country to another (see General Issues).
This issue is particularly pronounced regarding heat waves6, as shown in the figure below. Heat waves are often overlooked in Sub-Saharan Africa7. About 52% of heatwave events in EM-DAT occurred in nine countries: Japan, India, Pakistan, the USA, followed by Western European countries (France, Belgium, United Kingdom, Spain, and Germany).
Gall, M., Kevin A. B., and Susan L. C. “When Do Losses Count?: Six Fallacies of Natural Hazards Loss Data.” Bulletin of the American Meteorological Society 90, No. 6,799810 (2009). https://doi.org/10.1175/2008BAMS2721.1. ↩︎ ↩︎ ↩︎ ↩︎ ↩︎
CRED “Happy Birthday, CRED: Celebrating 50 Years of Disaster Epidemiological Research, Data Collection, and International Cooperation”, Centre for Research on the Epidemiology of Disasters (CRED), Brussels, Belgium, CRED Crunch No. 71 (2023), https://www.cred.be/sites/default/files/CredCrunch71.pdf. ↩︎
Marulanda, M. C., Cardona, O. D., and Barbat, A. H. “Revealing the socioeconomic impact of small disasters in Colombia using the DesInventar database”, Disasters, 34, 552–570 (2010), https://doi.org/10.1111/j.1467-7717.2009.01143.x ↩︎
Osuteye, E., Johnson, C., and Brown, D. “The data gap: An analysis of data availability on disaster losses in sub-Saharan African cities”, International Journal of Disaster Risk Reduction, 26, 24–33 (2017), https://doi.org/10.1016/j.ijdrr.2017.09.026. ↩︎
Brimicombe, C., Di Napoli, C., Cornforth, R., Pappenberger, F., Petty, C., and Cloke, H. L. “Borderless Heat Hazards With Bordered Impacts”, Earth’s Future, 9, e2021EF002064 (2021), https://doi.org/10.1029/2021EF002064. ↩︎