Unintentional injury mortality and socio-economic development among 15-44-year-olds: in a health transition perspective

Public Health. 2000 Sep;114(5):416-22. doi: 10.1038/sj.ph.1900646.


Injury imposes one of the greatest health risks in terms of mortality and morbidity among 15-44-y-olds. There is evidence that socio-economic development (SED) is related to injury risk, but the findings are inconsistent. We aimed to study the magnitude, pattern and relative importance of unintentional injury mortality (UIM) in relation to SED in this age group. Cross-sectional data on UIM by age-sex specific groups were obtained for 54 countries from the World Health Statistics Annuals 1993-1995. The relationship between UIM and SED (measured in gross national product (GNP) per capita) was studied using two methods: (1) with regression analysis, and (2) by categorizing the data into four income-based country groups and then comparing the differences in their mean values. The results were: (1) UIM rates were inversely correlated with GNP per capita and the relationship became stronger with increasing age (r=-0.22 for both sexes in the 15-24-y-olds, r=-0.65 for males, r=-0.54 for females in the 35-44-y-olds); (2) there was an increase in UIM rates between low-income and lower-middle-income countries (LoMIC), but a decrease between LoMIC and upper middle-income (UpMIC), and finally also a significant decrease between UpMIC and high-income countries in most age-sex groups (ie P<0.005 for males, P<0.05 for females in the 35-44-y-olds). The highest rates of UIM were in LoMIC for all age-sex groups. Male rates were consistently higher than female in all age groups. In conclusion, SED was inversely related to UIM. There was an initial positive relation between GNP per capita and UIM, which became negative with increasing GNP per capita. We also found a health transition that had taken place in all country groups.

Publication types

  • Comparative Study

MeSH terms

  • Accidents / mortality*
  • Adolescent
  • Adult
  • Age Distribution
  • Cause of Death
  • Female
  • Global Health*
  • Health Transition*
  • Humans
  • Income / statistics & numerical data*
  • Male
  • Morbidity
  • Population Surveillance
  • Regression Analysis
  • Risk Factors
  • Sex Distribution
  • Socioeconomic Factors
  • Wounds and Injuries / mortality*