title: Patterns of vulnerability to non-fatal injuries in Sudan: initial evidence from a national cross-sectional survey.
abstract: BACKGROUND
  Successful injury prevention requires identification and targeting of particularly vulnerable groups. Little is known about injury vulnerability patterns in Sudan. This paper aimed to fill this gap using survey data.
  
  
  METHODS
  Data from the Sudan Household Health Survey were used. This was a national cross-sectional interview survey of 83,510 individuals selected by multistage cluster random sampling. Multivariable Poisson regression was used to investigate the association of cause-specific injury that received care by traditional healers, outpatient care and inpatient care, and those that received only inpatient care, with age, gender, area of residence (urban or rural), socioeconomic status and education. Relevant interactions were tested.
  
  
  RESULTS
  Independent of other sociodemographic variables, men were at higher risk of road traffic injury (prevalence ratio (PR): 3.3 95% CI 2.4 to 4.7), falls (PR: 1.5, 95% CI 1.3 to 1.9), assault (PR: 3.0 95% CI 1.8-5) and mechanical injury (PR: 2.0 95% CI 1.2 to 3.1) that received any form of healthcare. Those aged 65 years and over also had the highest risk of those injury causes, while children under 5 years were the most likely to suffer burn injuries. Socioeconomic status was associated with assault (PR for the richest group 0.4 95% CI 0.2 to 0.8). Vulnerability patterns for injury that received inpatient care were fairly similar for some causes.
  
  
  CONCLUSIONS
  In Sudan, existing disease prevention and health promotion programmes should expand to target men, children under 5 years, elderly people and those of low socioeconomic status with injury prevention interventions. Further research is needed to investigate the context-specific proximal risk factors that shape the various vulnerability patterns observed.
authors: Safa
authors: S
authors: Abdalla
mesh_terms: D000059:Accidents / Q000706:statistics & numerical data*; D000293:Adolescent; D000328:Adult; D017677:Age Distribution; D000368:Aged; D002648:Child; D002675:Child, Preschool; D016000:Cluster Analysis; D003430:Cross-Sectional Studies; D004522:Educational Status; D005260:Female; D006306:Health Surveys; D006801:Humans; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D015995:Prevalence; D012307:Risk Factors; D012424:Rural Population / Q000706:statistics & numerical data; D017678:Sex Distribution; D012959:Socioeconomic Factors; D013397:Sudan / Q000453:epidemiology; D014754:Violence / Q000706:statistics & numerical data; D014947:Wounds and Injuries / Q000453:epidemiology / Q000209:etiology; D055815:Young Adult