When I moved to South Sudan, the world’s newest country, it didn’t help me to know that there were 25 aid worker victims of serious violence in 2012. I wanted to know what the risk/rate of violence was (ideally by location). I wanted to know the rate of malaria amongst aid workers and I really wanted to know the fatality rate of malaria amongst aid workers. Because I enjoy going up hills and black mambas are pretty scary stuff, I was also interested in finding out the risk of getting bitten by one…
In the last two blogs, I wrote about security and other risks faced by aid workers. In this blog I’ll put this risks in context by looking at what all the numbers mean for aid workers in South Sudan.
There are 17,000 aid workers in South Sudan, making it one of the largest aid operations in the world. In 2012 there were 25 major attacks on aid workers, which puts the rate of major attacks on aid workers in South Sudan at 147 per 100,000 (that is a risk of 0.1% per year). This includes aid workers which were killed, wounded or kidnapped. The breakdown by type of attack is shown in the chart. .
With 9 murders of aid workers, that puts the aid worker murder rate in South Sudan at 53 per 100,000. How does this compare to the murder rates of other places?
The murder rate of aid workers in South Sudan is 44 times higher than that of London. Or about the same as that of Detroit.
While violence is a great concern for aid workers, diseases and accidents account for a much larger proportion of events which lead to medical evacuation, hospitalization or death. For expatriate aid workers, violence is responsible for only 9% of deaths, medical evacuations and hospitalizations. Accidents account for 13% of cases, whereas illnesses are responsible for 78% of all cases.
From the studies discussed in the previous blog, one can guesstimate around 10% of aid workers in South Sudan will contract malaria. While I don’t have data on other diseases, it would be reasonable to assume they would add up to a considerable risk. Over a third of aid workers in South Sudan will suffer deteriorating health. I don’t have a figure for the risk of aid workers to traffic accidents in South Sudan, but this will be high, somewhere between the risk of a major attack and the risk of contracting malaria. 40% of aid workers will find their deployment more stressful than they expect. 10% will suffer from anxiety and 20% from depression. Once deployments are finished, there will be a risk of longer term effects, both physical and mental, but there is little data to quantify this risk.
Finally, I have no idea of my risk of dying to a black mamba. And while I try to accept that in a long enough time scale my survival probability will drop to zero, I very much hope a black mamba isn’t involved. In fact, as Woody Allen put it, ‘I don’t want to achieve immortality through my work. I want to achieve it through not dying.’
This is the final blog in a three part series on aid workers and risk. Part 1 is available here and Part 2 is available here.
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Sources & further reading:
Here is the statement from Toby Lanzer, South Sudan’s Humanitarian Co-ordinator, where he mentions the number of aid workers in South Sudan.
Data on events which lead to medical evacuation, hospitalization or death come from this study by Rowley et Al. Recommended if you are looking for academic works which analyse rates.
Data on numbers of aid workers killed or seriously injured comes from Humanitarian Outcomes (2013), Aid Worker Security Database, https://aidworkersecurity.org/. Note the 2012 data has not yet been verified.
Murder rate data: the data on murder rates from London was obtained here. Data from other cities is from a yearly report by the ‘Citizen Council for Public Security, Justice and Peace’ think tank.
Good stuff. I’ve been a soldier(7 years) and an aid worker (now for 30 years). Aid work in such places is MUCH more dangerous than being an infantry soldier.
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But what’s the murder rate for *international aid worker types* in Acapulco, New Orleans, and Detroit? (Both a silly and a serious question) (When considering career options, the riskiness surely goes: Oxfam admin assistant in New Orleans < Oxfam admin assistant in South Sudan < Gang member in a poor neighbourhood of New Orleans <= Gang member in poor neighbourhood of South Sudan).
Is this paragraph referring to South Sudan data, or global data? "For expatriate aid workers, violence is responsible for only 9% of deaths, medical evacuations and hospitalizations. Accidents account for 13% of cases, whereas illnesses are responsible for 78% of all cases."
Great article though, thank you!
Hi Sally,
Thanks for your kind words and excellent questions.
You are of course absolutely right in your first paragraph analysis. Unfortunately, I’ve never seen any data to quantify the comparative risk of aid workers at home vs in deployment. The closest I’ve found is this study [http://www.bmj.com/content/311/7016/1343], which found aid workers are approximately twice as likely to die than non aid workers. Quite a few caveats though: the first is that it only looked at Dutch people (so if you want to know the data for your country of origin, I can’t help you. Unless you happen to be Dutch!). The second is that it compared mortality rates of Dutch aid workers with the general population, as opposed to aid workers who live in The Neatherlands (or, more feasible, civil servants). Another big caveat is that the study only looked at aid workers who died during deployment, and not those who died post-deployment as a result of their deployments.
As for your question, the data from the following paragraph is global data (from this study [http://www.ncbi.nlm.nih.gov/pubmed/18450278]) “For expatriate aid workers, violence is responsible for only 9% of deaths, medical evacuations and hospitalizations. Accidents account for 13% of cases, whereas illnesses are responsible for 78% of all cases.”
There is very little data available for South Sudan. When data for South Sudan was not available, I have used data for sub-saharan Africa if possible. If that wasn’t available, data for Africa. And failing that, global data.
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