Mortality-Associated Characteristics of Patients with Traumatic Brain Injury at the University Teaching Hospital of Kigali, Rwanda. 

Abstract

OBJECTIVE:

Traumatic brain injury (TBI) is a leading cause of death and disability. Patients with TBI in low and middle-income countries have worse outcomes than patients in high-income countries. We evaluated important clinical indicators associated with mortality for patients with TBI at University Teaching Hospital of Kigali, Kigali, Rwanda.

METHODS:

A prospective consecutive sampling of patients with TBI presenting to University Teaching Hospital of Kigali Accident and Emergency Department was screened for inclusion criteria: reported head trauma, alteration in consciousness, headache, and visible head trauma. Exclusion criteria were age <10 years, >48 hours after injury, and repeat visit. Data were assessed for association with death using logistic regression. Significant variables were included in a multivariate logistic regression model and refined via backward elimination.

RESULTS:

Between October 7, 2013, and April 6, 2014, 684 patients were enrolled; 14 (2%) were excluded because of incomplete data. Of patients, 81% were male with mean age of 31 years (range, 10-89 years; SD 11.8). Most patients (80%) had mild TBI (Glasgow Coma Scale [GCS] score 13-15); 10% had moderate (GCS score 9-12) and 10% had severe (GCS score 3-8) TBI. Multivariate logistic regression determined that GCS score <13, hypoxia, bradycardia, tachycardia, and age >50 years were significantly associated with death.

CONCLUSIONS:

GCS score <13, hypoxia, bradycardia, tachycardia, and age >50 years were associated with mortality. These findings inform future research that may guide clinicians in prioritizing care for patients at highest risk of mortality.

Road traffic injury in sub-Saharan African countries: A systematic review and summary of observational studies. 

Abstract

OBJECTIVE:

The aim of this study is to evaluate, through a systematic review of hospital-based studies, the proportion of road traffic injuries and fatalities in sub-Saharan Africa (SSA).

METHODS:

In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Meta-analysis of Observational Studies in Epidemiology guidelines, we searched the following electronic databases: PubMed, Embase, Africa-Wide Information, Global Health, and Web of Science. Articles were eligible if they measured proportion of road traffic injuries (RTIs) in SSA by using hospital-based studies. In addition, a reference and citation analysis was conducted as well as a data quality assessment.

RESULTS:

Up to 2015, there were a total of 83 hospital-based epidemiologic studies, including 310,660 trauma patients and 99,751 RTI cases, in 13 SSA countries. The median proportion of RTIs among trauma patients was 32% (4 to 91%), of which the median proportion of death for the included articles was 5% (0.3 to 41%).

CONCLUSION:

The number of studies evaluating RTI proportions and fatalities in SSA countries is increasing but without the exponential rise expected from World Health Organization calls for research during the Decade of Action for Road Traffic Injuries. Further research infrastructure including standardization of taxonomy, definitions, and data reporting measures, as well as funding, would allow for improved cross-country comparisons.

High road utilizers surveys compared to police data for road traffic crash hotspot localization in Rwanda and Sri Lanka. 

Abstract

Backgrond

Road traffic crashes (RTCs) are a leading cause of death. In low and middle income countries (LMIC) data to conduct hotspot analyses and safety audits are usually incomplete, poor quality, and not computerized. Police data are often limited, but there are no alternative gold standards. This project evaluates high road utilizer surveys as an alternative to police data to identify RTC hotspots.

Methods

Retrospective police RTC data was compared to prospective data from high road utilizer surveys regarding dangerous road locations. Spatial analysis using geographic information systems was used to map dangerous locations and identify RTC hotspots. We assessed agreement (Cohen’s Kappa), sensitivity/specificity, and cost differences.

Results

In Rwanda police data identified 1866 RTC locations from 2589 records while surveys identified 1264 locations from 602 surveys. In Sri Lanka, police data identified 721 RTC locations from 752 records while survey data found 3000 locations from 300 surveys. There was high agreement (97 %, 83 %) and kappa (0.60, 0.60) for Rwanda and Sri Lanka respectively. Sensitivity and specificity are 92 % and 95 % for Rwanda and 74 % and 93 % for Sri Lanka. The cost per crash location identified was $2.88 for police and $2.75 for survey data in Rwanda and $2.75 for police and $1.21 for survey data in Sri Lanka.

Conclusion

Surveys to locate RTC hotspots have high sensitivity and specificity compared to police data. Therefore, surveys can be a viable, inexpensive, and rapid alternative to the use of police data in LMIC.

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