Pedestrian injuries in school-attending children: a comparison of injury data sources in a low-income setting

Inj Prev. 2009 Apr;15(2):100-4. doi: 10.1136/ip.2008.018689.

Abstract

Objective: To estimate and compare the rate of pedestrian injuries in primary school-attending children of urban Uganda using different data sources.

Design: Data collection from a hospital-based trauma registry, police data, teacher reports, and a cross-sectional community-based survey.

Setting: Kawempe, the largest urban district in the capital Kampala, Uganda. Patients or

Subjects: Primary school-attending children aged 4-12 from 39 randomly selected schools were included in the trauma registry, police data, and teacher reports. 1828 households randomly selected from the 39 schools were interviewed for the community survey.

Main outcome measure: A pedestrian injury. For the trauma registry-defined as a pedestrian injury resulting in a visit to the hospital. For the police data-defined as a pedestrian injury reported to the police. For the teacher reports and survey-defined as a pedestrian injury resulting in at least a day off school.

Results: The estimated pedestrian injury rates per 100 000 person-years were 54.0 (95% CI 25.3 to 117.4), <53.97 (95% CI 23.8 to 125.9), 1878.8 (95% CI 1513.1 to 2322.4), and 764.0 (95% CI 523.3 to 1117.2) from the trauma registry, police data, teacher reports, and community survey, respectively.

Conclusions: Pedestrian injury rates differed significantly between different data sources. Users must be aware of the different target populations, definitions, and limitations of the data sources before direct comparisons are made. Injury reports by volunteer teachers may be a feasible source of injury data in other low/middle-income countries.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Accidents, Traffic / statistics & numerical data*
  • Child
  • Child, Preschool
  • Cross-Sectional Studies
  • Data Collection / methods*
  • Female
  • Humans
  • Male
  • Police / statistics & numerical data
  • Poverty
  • Risk Factors
  • Schools / statistics & numerical data
  • Students / statistics & numerical data*
  • Uganda / epidemiology
  • Urban Health
  • Wounds and Injuries / epidemiology*