Published in:
01-01-2016 | Original Scientific Report
Vital Statistics: Estimating Injury Mortality in Kigali, Rwanda
Authors:
Woon Cho Kim, Jean Claude Byiringiro, Georges Ntakiyiruta, Patrick Kyamanywa, Jean Jacques Irakiza, Jean Paul Mvukiyehe, Zeta Mutabazi, Jean Paul Vizir, Jean de la Croix Allen Ingabire, Steven Nshuti, Robert Riviello, Selwyn O. Rogers Jr., Sudha P. Jayaraman
Published in:
World Journal of Surgery
|
Issue 1/2016
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Abstract
Background
Globally, injury deaths largely occur in low- and middle-income countries. No estimates of injury-associated mortality exist in Rwanda. This study aimed to describe the patterns of injury-related deaths in Kigali, Rwanda using existing data sources.
Methods
We created a database of all deaths reported by the main institutions providing emergency care in Kigali—four major hospitals, two divisions of the Rwanda National Police, and the National Emergency Medical Service—during 12 months (Jan–Dec 2012) and analyzed it for demographics, diagnoses, mechanism and type of injury, causes of death, and all-cause and cause-specific mortality rates.
Results
There were 2682 deaths, 57 % in men, 67 % in adults >18 year, and 16 % in children <5 year. All-cause mortality rate was 236/100,000; 35 % (927) were due to probable surgical causes. Injury-related deaths occurred in 22 % (593/2682). The most common injury mechanism was road traffic crash (cause-specific mortality rate of 20/100,000). Nearly half of all injury deaths occurred in the prehospital setting (47 %, n = 276) and 49 % of injury deaths at the university hospital occurred within 24 h of arrival. Being injured increased the odds of dying in the prehospital setting by 2.7 times (p < 0.0001).
Conclusions
Injuries account for 22 % of deaths in Kigali with road traffic crashes being the most common cause. Injury deaths occurred largely in the prehospital setting and within the first 24 h of hospital arrival suggesting the need for investment in emergency infrastructure. Accurate documentation of the cause of death would help policy-makers make data-driven resource allocation decisions.