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Inj Prev 14:381-388 doi:10.1136/ip.2008.018598
  • Original Article

Injury and social determinants among in-school adolescents in six African countries

  1. K Peltzer
  1. Human Sciences Research Council and University of the Free State, Pretoria, South Africa
  1. Professor K Peltzer, Social Aspects of HIV/AIDS and Health, Human Sciences Research Council, Private Bag X41, Pretoria 0001, South Africa; KPeltzer{at}hsrc.ac.za
  • Accepted 14 August 2008

Abstract

Background: There is a lack of data on injury and its social correlates among in-school adolescents in Africa.

Objectives: To estimate the prevalence of injury among adolescents in six African countries, and to examine the consistency of associations cross-nationally between sociodemographics, social risk factors, and the occurrence of adolescent injury in Africa.

Design: Cross-sectional national data from the Global School-based Health Survey (GSHS) conducted in six African countries between 2003 and 2004.

Setting: Surveys administered in classrooms.

Subjects: The sample included 20 765 students aged 13–15 years from six African countries (Kenya, Namibia, Swaziland, Uganda, Zambia, Zimbabwe) chosen by a two-stage cluster sample design to represent all students in grades 6, 7, 8, 9, and 10 in each country.

Results: The mean percentage over all the countries of adolescents reporting one or more serious injuries within the past 12 months was 68.2%, ranging from 38.6% in Swaziland to 71.5% in Zambia. In multivariate regression analysis, risk behaviors were associated with annual injury prevalence, with the highest odds for loneliness, followed by hunger, truancy, depression, smoking, and drug use. The observed risk for all injuries, as well as injuries related to sports, motor vehicles, fighting, and burns, increased consistently with increasing number of risk behaviors.

Conclusions: A high annual injury prevalence was found, and risk-taking played a role in the etiology of injury. There is a need to consider an integrated approach to injury etiology in planning injury prevention and safety promotion activities for schoolchildren, paying particular attention to lifestyle factors that have the potential to influence risk of injury.

Globally, 98% of all unintentional childhood injuries occur in low-income and middle-income countries.1 Unintentional injuries are a major cause of death and disability in young children; each year, about 875 000 children under the age of 18 die from injuries, and 10–30 million have their lives affected by injury.2 An analysis of the 1990 Global Burden of Disease Study found the childhood injury rate to be highest in Africa and South Asia, and this burden is expected to grow over the next few decades.3 The annual prevalence of medically treated injuries among youth aged 11, 13 and 15 years in 11 industrialized countries has been found to be 41.3%,4 and among 146 440 adolescents in 35 industrialized countries it was found to be 33–64% for boys and 23–51% for girls.5

Pickett et al6 state that “The etiology of youth injury involves a complex interplay between human and environmental factors.” Adolescent risk-taking behavior can be seen as a focus for etiological studies.5 Various studies have identified risk-taking behaviors among adolescents—such as non-use of seatbelts, bullying, excessive time with friends, alienation at school and from parents, truancy,6 poverty and drunkenness,5 smoking, alcohol and drug consumption, frequent participation in sport activities, involvement in physical fights, long periods spent away from home with friends, experience of bullying, poor self-assessed health, low academic achievement, unhappiness, and feeling unsafe at school—were associated with injury risk.7 Pickett et al8 found that “multiple risk behaviors may play an important role in the social etiology of youth injury”, and also that “the identification of behaviors that place an adolescent or groups of adolescents at risk for poor health has potential significance for the prevention of injuries.” There is, in particular, a lack of data on injury and its social correlates among in-school adolescents in Africa. Following the findings of the studies reviewed above, the aim of this study was to estimate the prevalence of injury among adolescents in six African countries, and to examine associations between sociodemographics, social risk factors, and the occurrence of adolescent injury in Africa.

METHODS

Description of survey and study population

This study involved secondary analysis of existing data from the Global School-based Health Survey (GSHS) from six African countries (Kenya, Namibia, Swaziland, Uganda, Zambia, and Zimbabwe). Details and data of the GSHS can be accessed at http://www.who.int/chp/gshs/methodology/en/index.html. National samples were included from the first five countries, whereas three areas were included from Zimbabwe: Harare, Bulawayo, and Manicaland. The aim of the GSHS is to collect data from students aged 13–15 years. The GSHS is a school-based survey of students in grades 6, 7, 8, 9, and 10. These classes were selected because they contain the majority of 13–15-year-old school-attending adolescents. A two-stage cluster sample design was used to collect data to represent all students in grades 6–10 in the country. At the first stage of sampling, schools were selected with probability proportional to their reported enrolment size. In the second stage, classes in the selected schools were randomly selected, and all students in selected classes were eligible to participate irrespective of their actual age. Students self-completed the questionnaires to record their responses to each question on a computer scanable answer sheet.

Measures

The 10 core questionnaire modules of the GSHS address the leading causes of morbidity and mortality among children and adults worldwide: use of tobacco, alcohol, and other drugs; dietary behaviors; hygiene; mental health; physical activity; sexual behaviors that contribute to HIV infection, other sexually transmitted infections, and unintended pregnancy; unintentional injuries and violence; protective factors and respondent demographics.9

Outcome measures: injury

For the main outcome, study participants were asked, “During the past 12 months, how many times were you seriously injured?” (serious injury was defined as “when it makes you miss at least one full day of usual activities (such as school, sports, or a job) or requires treatment by a doctor or nurse). Eight options were provided, ranging from 1 (0 times) to 8 (12 or more times). A response of 0 indicated not having sustained a serious injury, and a response of 1 or more times was classified as having experienced a serious injury. Additional items on injury included close-ended questions that addressed activity (“During the past 12 months, what were you doing when the most serious injury happened?”), external cause (“During the past 12 months, what was the major cause of the most serious injury that happened to you?”), how it happened (During the past 12 months, how did the most serious injury happen to you?), and type of injury (“During the past 12 months, what was the most serious injury that happened to you?”). (For response options, see table 2.)

Risk factor variables

Hunger

A measure of hunger was derived from a question reporting the frequency that a young person went hungry because there was not enough food at home in the past 30 days. Response options were from 1 (never) to 5 (always) (coded 1 (most of the time or always) and 0 (never, rarely, or sometimes)).

Smoking cigarettes

“During the past 30 days, on how many days did you smoke cigarettes?” Response options were from 1 (0 days) to 7 (all 30 days) (coded 1 (1 or 2 to all 30 days) and 0 (0 days)). Excessive drinking

“During your life, how many times did you drink so much alcohol that you were really drunk?” Response options were from 1 (0 times) to 4 (10 or more times) (coded 1 (1 or 2 to 10 or more times) and 0 (0 times)).

Drugs

“During your life, how many times have you used drugs, such as glue, benzene, marijuana, cocaine, or mandrax?” Response options were from 1 (0 times) to 4 (10 or more times) (coded 1 (1 or 2 to 10 or more times) and 0 (0 times)).

Truancy

“During the past 30 days, on how many days did you miss classes or school without permission?” Response options were from 1 (0 times) to 5 (10 or more times) ((coded 1 (1 or 2 to 10 or more times) and 0 (0 times)).

Non-condom use

“The last time you had sexual intercourse, did you or your partner use a condom?” Response options were 1 (I have never had sexual intercourse), 2 (yes), and 3 (no) (coded 1 (2) and 0 (1 or 3)).

Depression

“During the past 12 months, did you ever feel so sad or hopeless almost every day for two weeks or more in a row that you stopped doing your usual activities?” Response options were 1 (yes) and 2 (no) (coded 1 (1) and 2 (0)).

Worried

“During the past 12 months, how often have you been so worried about something that you could not sleep at night?” Response options were from 1 (never) to 5 (always) (coded 1 (most of the time or always) and 0 (never, rarely, or sometimes)).

Lonely

“During the past 12 months, how often have you felt lonely?” Response options were from 1 (never) to 5 (always) (coded 1 (most of the time or always) and 0 (never, rarely, or sometimes)).

Sociodemographic and confounding variables

Sociodemographic and confounding variables included: country, age (in years), sex, physical activity, was in a fight, and bullied. Physical activity was assessed by asking participants, “During the past 7 days, on how many days were you physically active for a total of at least 60 minutes per day?” and “During a typical or usual week, on how many days are you physically active for a total of at least 60 minutes per day?” “The mean number of days from the past week and a typical week were used as an index of participation”5 (Cronbach α = 76). Being in a fight was measured with the question “During the past 12 months, how many times were you in a physical fight?” Response options were from 1 (0 times) to 8 (12 or more times) (coded 1 (once to 12 or more times) and 0 (0 times)). Bullying was measured with the question “During the past 30 days, on how many days were you bullied?” Response options were from 1 (0 times) to 7 (all 30 days) (coded 1 (1 or 2 days to all 30 days) and 0 (0 days)).

Data analysis

In order to compare study samples across countries, each country sample was restricted to the age group 13–15 years; younger and older participants were excluded from the analyses. Data were analysed using Stata software V10.0. This software has the advantage of directly including robust standard errors that account for the sampling design, ie, cluster sampling due to the sampling of school classes. In further analysis, the injury risk variable was recoded into two categories: not injured (0); injured at least once (1). Associations between potential risk factors and injuries among schoolchildren were evaluated by calculating odds ratios (ORs). Logistic regression was used for evaluation of the impact of explanatory variables on risk of injury (binary-dependent variable). The dependent variable was the injury event, and the independent variables were factors that significantly increased injury risk in the univariate analysis. For the individual risk behavior and additive score analyses, crude and adjusted ORs and associated 95% CIs were calculated for each level of exposure compared with baseline (the reference level: multiple risk behavior score of 0).

A weighting factor was used in the analysis to reflect the likelihood of selection of each student into the sample and to reduce bias by compensating for differing patterns of non-response. The weight used for estimation of prevalence estimates is given by the following formula:

W = W1 × W2 × f1 × f2 × f3 × f4

where W1 is the inverse of the probability of selecting the school, W2 is the inverse of the probability of selecting the classroom within the school, fl is a school-level non-response adjustment factor calculated by school size category (small, medium, large), f2 is a class-level non-response adjustment factor calculated for each school, f3 is a student-level non-response adjustment factor calculated by class, and f4 is a post-stratification adjustment factor calculated by grade.

In the analysis, weighted percentages are reported. The reported sample size refers to the sample that was asked the target question. Two-sided 95% CIs are reported. p⩽0.05 is used to indicate significance. Both the reported 95% CIs and the p value were adjusted for the multistage stratified cluster sample design of the study.

RESULTS

Sample

The sample included 20 765 students aged 13–15 years from six African countries; there were slightly more female (52.3%) than male students (47.7%), and most of the students (81.2%) were attending school grades 6 or 7. Data from the different countries had been selected in 2003 or 2004 (table 1). At the participant level, response rates varied from 75% in Zambia to 99% in Swaziland.

Table 1 Details of participating country samples included in the analyses (students aged 13–15 years only)

Descriptive results

The percentage of adolescents reporting one or more serious injuries within the past 12 months was 68.2% for all countries, ranging from 38.6% in Swaziland to 71.5% in Zambia. Serious injuries occurred slightly more often (but not significantly so) in boys than girls in all countries. Estimates of adolescents reporting a single injury were much less variable, ranging from 22.3% in Swaziland to 31.1% in Zimbabwe, whereas larger differences in prevalence estimates by country were found in the number of adolescents reporting multiple injuries, ranging from 16.2% in Swaziland to 46.4% in Zambia. By major activity, playing or training for a sport (21.3%) was the leading external cause of injury, followed by a “fall” (19.7%), walking or running (16.8%), riding a donkey, horse, bicycle, scooter, or ox cart (11.8%), motor vehicle accident (9.2%), in a fire (5.8%), fighting with someone (5.8%), and attacked, assaulted, or abused by someone (4.4%). A relatively high number of adolescents (14.0%) indicated that they had hurt themselves on purpose. The most common injuries were cut, puncture, stab wound (21.8%), broken bone/dislocated joint (17.9%), concussion/head injury (7.4%), and burns injury (4.9%) (table 2).

Table 2 Annual prevalence of injury events by sex and country

Sociodemographics, potential confounders, and annual injury prevalence

Annual injury prevalence differed significantly by country, with Swaziland and Namibia having significantly lower prevalence rates than Zambia and Kenya (table 3). Boys had higher (but not significantly so) annual injury prevalence rates than girls, and lower age and lower school grade were associated with higher prevalence rates. Having been in a fight in the past 12 months and having been bullied in the past month were both associated with annual injury prevalence, whereas higher physical activity was not associate with injury prevalence (table 4).

Table 3 Sociodemographics and annual injury prevalence
Table 4 Confounding variables and annual injury prevalence

Risk behaviors and injury

The prevalence of various risk behaviors varied from 46.1% for depression in the past year and 38.9% for truancy in the past month to 11.7% for current smoking and 14.8% for having experienced hunger most of the time or always in the past month; the prevalence of non-condom use at last sex was 45.0% (for those who had been sexually active). Because consistent findings for all risk behaviors studied were obtained from the six study countries (not reported here), a combined logistic regression analysis was then performed using the overall sample. In multivariate regression analysis, the risk behaviors, loneliness, hunger, truancy, depression, smoking, and drug use, were associated with the occurrence of injuries. Hunger (an indicator of low economic status) was the highest predictor of motor vehicle and sports injuries, and loneliness was associated with motor vehicle injuries. Truancy was associated with fighting and burn injuries, and depression with sport and burn injuries.

Further, table 5 shows the distribution of injured cases and non-injured adolescents according to the presence/absence of the individual risk factors that went into the multiple-risk behavior score, as well as odds ratios generated from the logistic regression analysis. The observed risk for all injuries, as well as injuries related to sports, motor vehicles, fighting, and burns, increased consistently and strongly with increasing number of risk behaviors, even after adjustment for age, sex, and school grade (table 5).

Table 5 Logistic regression analysis for association between risk behaviors and injury (overall analysis for all injury types) and between the number of risk behaviors and restricted analysis by type/context of injury

DISCUSSION

In this study of in-school adolescents in six African countries using the GSHS of 2003/2004, a high percentage of adolescents (68.2%) reporting one or more serious injuries within the past 12 months was found for all countries, ranging from 38.6% in Swaziland to 71.5% in Zambia. Boys were seriously injured slightly more often (69.6%) than girls (68.2%) in all countries. This annual prevalence of severe injury was higher than that found in other studies: 36% in Canadian youth10; 52% (boys) and 33% (girls) in South African grade 8 students11; 59% (boys) and 40% (girls) in Lithuanian schoolchildren7; 41.3% in youth aged 11, 13, and 15 years over 11 countries4; 33–62% (males) and 19–39% (females) over 35 countries5; in Scottish schoolchildren, 41.9% of all children who were injured and needed medical treatment in the past 12 months.12 In the present study of African schoolchildren, the definition of injury (“when it makes you miss at least one full day of usual activities (such as school, sports, or a job) or requires treatment by a doctor or nurse”) was wider than in most of the above studies, which restricted the injury definition to requiring medical treatment. The wider definition of injury, also including serious injuries not requiring medical treatment, may also have contributed to the higher annual injury prevalence found in this sample compared with other studies. This high prevalence may still be an underestimate considering the decline in the estimates over the 12-month recall period. Mock et al13 found in a Ghanaian setting that longer recall periods resulted in significantly greater underestimation of the injury rate than shorter recall periods. Shorter recall periods (1–3 months) should be used for calculation of the overall non-fatal injury rate. However, longer recall periods (12 months) can be used to obtain information on more severe, but less common, injuries.

The highest annual prevalence rate in this study was found for sport-related injuries (21.3%). Other studies have also reported sports and recreation to be the most common activities associated with injury,14 with even higher rates, eg, 32.2% of all medically attended injuries in Scottish schoolchildren.15 In the present study, 4.9% reported a burns injury. In sub-Saharan Africa, burns injuries are a major cause of prolonged hospital stay, disfigurement, disability, and death. Studies of childhood injuries in Africa consistently rank burns among the top three causes of injury, and often these are associated with a higher death rate than other types of injury.16

Key messages

  • Globally, 98% of all unintentional childhood injuries occur in low-income and middle-income countries.

  • The etiology of injury involves a complex interplay between human and environmental factors.

  • A number of theories offer different explanations for the relationship between development and risk-taking among adolescents.

  • In this study, a high annual injury prevalence was found among adolescents in six African countries.

  • Risk-taking played a role in the etiology of injury among adolescents in six African countries.

  • The observed risk for all injuries, as well as injuries related to sports, motor vehicles, fighting, and burns, increased consistently and strongly with increasing number of risk behaviors: loneliness, hunger, truancy, depression, smoking, drug use, non-condom use, and excessive drinking.

This analysis is one of the first African cross-national examinations of adolescent injury patterns. Large cross-national variations in the prevalence of severe injury were found. It is not clear whether these variations are attributable to underlying differences in risk. The Swaziland sample had the lowest annual injury prevalence and also the highest educational level of the participants; being in a higher school grade was significantly inversely associated with the occurrence of injury. Depending on the country, the GSHS was administered at different times of year. Risks of adolescent injury vary by season, and injuries are more reliably reported within 3 months than within 12 months.17 Variations in the timing of the survey across countries may have affected the injury rates and hence the cross-national comparisons.5

In multivariate regression analysis, the risk behaviors, loneliness, hunger, truancy, depression, smoking, and drug use, were associated with the occurrence of injuries. Similar associations between risk behaviors and the occurrence of injury were found in other studies—for example, low socioeconomic status, substance use (smoking, drinking, drug use), truancy, and unhappiness.578 Variations in the strength and direction of associations were observed for different combinations of social risk factors and types of injury. Hunger (as an indicator of low economic status) and loneliness were the highest predictors of motor vehicle injuries, hunger and depression were the highest predictors of sports injuries, truancy and drugs were the highest predictors of fighting injuries, and truancy and depression were the highest predictors of burn injuries. In a study of 35 western countries,5 hunger was positively and consistently associated with injuries caused by fighting but not with sport-related injuries. Hunger was strongly associated with the occurrence of all injuries, which agrees with other studies.51820 Poorer children may not be afforded protection against physical risks in their social environment.5 Contrary to the finding that low economic status was also associated with sports injuries, Pickett et al14 and Simpson et al21 found that high material wealth was positively associated with medically treated and sports-related injuries. In a univariate analysis, drunkenness was generally associated with the occurrence of injury, as found in other studies.581122 The present study found that the risk for all injuries and those related to sports, motor vehicles, fighting and burns increased consistently and strongly with increasing number of risk behaviors. Pickett et al6 also found, in a study of young people in 12 countries, gradients of injury risk according to the numbers of risk behaviors reported. The finding of gradients of risk of adolescent injury across these six African countries supports the targeting of multiple forms of risk behavior simultaneously in health interventions.6 An integrated approach to planning injury prevention and safety promotion activities in schoolchildren is required, paying particular attention to lifestyle factors that have the potential to influence injury risk. Educational programs that address the underlying causes and determinants of behavior, rather than the individual behaviors themselves, may therefore offer an effective and efficient method of organizing health education for young people.

Limitations of the study

This study has several limitations. Firstly, the GSHS only enrolls adolescents who are in school. School-attending adolescents may not be representative of all adolescents of a country, as the occurrence of injury and injury-related risk behavior may differ between the two groups. As the questionnaire was self-completed, it is possible that some study participants may have miss-answered, either intentionally or inadvertently, some of the questions asked. Intentional miss-reporting was probably minimized by the fact that study participants completed the questionnaires anonymously. Furthermore, this study was based on data collected in a cross-sectional survey. I cannot therefore ascribe causality to any of the associated factors in the study.23 Finally, the analysis was limited to the risk factors included in the GSHS. There are many other potentially important risk and protective factors (eg, over-activity, failure to use seatbelts and bicycle helmets, perpetrating aggressive/bullying behavior, ongoing conflict with parents, urban/rural situation, family, school, or material support81424) that could be associated with the occurrence of injury that were not measured.

Acknowledgments

I am grateful to the World Health Organization (Geneva) for making the data available for analysis. I also thank the Ministries of Education and Health and the study participants for making the Global School Health Survey in the four African countries possible. The governments of the respective study countries and the World Health Organization did not influence the analysis or the decision to publish these findings. Finally, reviewers are thanked for their useful comments.

Footnotes

  • Competing interests: None.

REFERENCES

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