Background
An estimated 5 million people worldwide died from injuries in 2000, with a mortality rate of 83.7/100,000 population, accounting for 9% of the world's deaths in 2000 and 12% of the world's burden of disease[1].
In China, injuries accounted for approximately 750,000 deaths and 3.5 million hospitalizations in 1999, acting as the leading cause of death for those aged 1 to 44 years[2]. The estimated annual economic cost of injury is equivalent to 12.5 billion US dollars, almost four times the total public health services budget of China[3]. Injury has become an important public health problem in China[4].
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Shandong Province, located in the eastern part of China, has an area of 156,700 km2 and a population of about 90 million. According to vital statistics, the injury fatality rate was 57.02 per 100,000 in Shandong Province in 2001, accounting for 9.08% of all deaths[5].
We conducted a community-based household survey in the Shandong Province of China in order to describe the pattern of injury occurrence, understand the circumstances and risk factors associated with injury, and illustrate the burden caused by injury.
Methods
Participants were selected using a stratified, multi-stage random sampling method. According to national socioeconomic classification criteria, an urban area was classified as a large, middle size, or small city based on population size. Rural areas were classified as rural area class 1, rural area class 2, and rural area class 3 based on the health and economic situation in the area. Two city districts in each urban area level, three counties in each rural area level, two sub-districts (towns) in each district (county), three resident commissions (villages) in each sub-district (town), and 200 households in each resident commission (village) were selected randomly. All permanent residents (inhabitancy of > 6 months) in selected households were to be interviewed.
The survey was carried out in four cities and six rural counties from April to June in 2004. Interviewers from local disease control institutions or hospitals underwent standard training. The study was approved by the Ethics Committee of the Shandong Centre for Disease Control and Prevention. After the interviewers described the study to each family in the sample, informed verbal consent was sought.
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All members of the chosen households were interviewed. Information was obtained from relatives if an interviewee was unavailable or younger than ten years. The survey focused on injuries that occurred in 2003 and met any of the following three criteria: 1) injuries diagnosed in medical institutions; 2) injuries requiring treatment or care; and 3) injuries that required rest for at least half a day. In the case of multiple injuries, all episodes were included. Injuries were classified according to ICD-10.
Main indexes
The incidence rate of injury by individual was calculated. Average household income in 2003 was surveyed to measure the economic status of respondents. Years of potential life lost (YPLL), working years of potential life lost (WYPLL), valued years of potential life lost(VYPLL)[6], and direct and indirect economic loss (IEL) caused by injury were analyzed (Table 1).
Table 1
Health economic indexes and calculation*
Abbreviation | Name | Formula and calculation |
|---|---|---|
YPLL | Years of potential life lost | |
WYPLL | Working years of potential life lost | |
VYPLL** | Valued years of potential life lost | |
IEL*** | Indirect economic loss |
Data analysis
Epidata 3.0 and Stata 8.0 were used for data input and analysis. Rates and ratios were the main indexes of the study. Bivariate analyses were performed using cross tabulations. A chi-squared test was used to test for homogeneity. Multivariate logistic regression analysis was also used to identify risk factors of injury incidence.
Results
A total of 24,438 participants of all ages completed the survey. No significant differences were found on the age and sex distribution between the sample and population (Table 2). The response rate of the survey was 97.75%. In the survey, 1,655 respondents reported suffering an injury, resulting in an incidence of 67.7 per 1,000 per year. Among the injuries reported, 17 cases were fatal, leading to a fatality rate of 69.56/100,000.
Table 2
Demographic composition of the sample and population
Variables | Population (%) | Sample (%) |
|---|---|---|
Gender | ||
Male | 50.6 | 50.4 |
Female | 49.4 | 49.6 |
X2 = 0.47, P > 0.05 | ||
Age group | ||
0- | 5.1 | 3.9 |
5- | 15.7 | 10.2 |
15- | 50.3 | 50.1 |
45- | 17.3 | 22.0 |
60+ | 11.6 | 13.8 |
X2 = 2.29, P > 0.05 | ||
Distribution of injury incidence
Injury incidence broken down by demographic factors and mechanisms is shown in Table 3. The incidence of total injury was higher for males (81.1 per 1,000) than females (54.1 per 1,000) (X2 = 70.6, P < 0.01). Injuries for males predominated at all ages, except for those over age 60. Males were characterized by a high incidence of traffic injuries (17.5 per 1,000) and females by a high incidence of falls (12.3 per 1,000). The fatality rate for males (105.57/100,000) was also higher than for females (32.99/100,000) (X2 = 72.4, P < 0.01).
Table 3
Demographic and injury mechanism distribution of individuals suffered injury (N/1 000)*
Factors | Age group | |||||
|---|---|---|---|---|---|---|
0- | 5- | 15- | 45- | 60+ | Total | |
Sex | ||||||
Male | 39(69.3) | 140(109.1) | 497(80.9) | 216(81.0) | 107(64.6) | 999(81.1) |
Female | 20(40.7) | 94(78.1) | 237(39.2) | 171(63.2) | 134(79.9) | 656(54.1) |
X2 = 70.6, P < 0.01 | ||||||
Area | ||||||
Urban | 11(28.5) | 45(53.2) | 206(40.5) | 92(45.0) | 66(46.5) | 420(42.9) |
Rural | 48(71.9) | 189(115.2) | 528(74.4) | 295(88.6) | 175(91.4) | 1235(84.3) |
X2 = 158.9, P < 0.01 | ||||||
Mechanism | ||||||
Traffic accident | 3(2.8) | 24(9.7) | 182(14.9) | 72(13.4) | 30(9.0) | 311(12.7) |
Fall | 19(18.0) | 46(18.5) | 82(6.7) | 78(14.5) | 92(27.6) | 317(13.0) |
Collision | 4(3.8) | 47(18.9) | 93(7.6) | 50(9.3) | 17(5.1) | 211(8.6) |
Strain | 4(3.8) | 27(10.9) | 131(10.7) | 45(8.4) | 44(13.2) | 251(10.3) |
Cut | 2(1.9) | 21(8.4) | 83(6.8) | 30(5.6) | 8(2.4) | 144(5.9) |
Squeeze | 1(0.9) | 13(5.2) | 41(3.4) | 25(4.7) | 7(2.1) | 87(3.6) |
Burn | 10(9.5) | 7(2.8) | 38(3.1) | 23(4.3) | 11(3.3) | 89(3.6) |
Poisoning | 1(0.9) | 2(0.8) | 14(1.1) | 19(3.5) | 14(4.2) | 50(2.0) |
Animal bite | 14(13.3) | 44(17.7) | 54(4.4) | 37(6.9) | 16(4.8) | 165(6.8) |
Total | 59(56.0) | 234(94.1) | 734(60.2) | 387(72.0) | 241(72.3) | 1,655(67.7) |
Age-specific incidence of injury was the highest among males aging 5 to 14 and was the lowest among females aged 15 to 44. A high incidence of falls and collisions was observed in the 5 to 14 age group. Injuries in the 15 to 44 age group accounted for 44.3% of all injuries occurred.
The five leading causes of injury were falls (13.0 per 1,000), traffic injuries (12.7 per 1,000), strains (10.3 per 1,000), collisions/wounds (8.6 per 1,000), and bites by insects or animals (6.8 per 1,000).
Injury incidence by social economic factors is shown in Table 4. There are significant differences of injury incidence among different educational levels (X2 = 85.8, P < 0.01), with lower injury incidence among those with higher educational attainment. Injuries were more frequently observed among those who were illiterate (99.4 per 1,000) compared to others.
Table 4
Social economic composition of individuals suffered injury (N/1,000)*
Factors | Sex | ||
|---|---|---|---|
Male | Female | Total | |
Education | |||
Illiterate | 181(101.5) | 61(93.7) | 242(99.4) |
Elementary school | 168(58.4) | 271(98.6) | 439(78.0) |
Junior high school | 170(40.2) | 429(88.0) | 599(65.8) |
Senior high school | 88(42.8) | 148(61.3) | 236(52.8) |
College and above | 17(31.4) | 35(39.9) | 52(36.6) |
X2 = 85.8, P < 0.01 | |||
Occupation | |||
Government employee | 12(47.8) | 17(38.7) | 29(42.0) |
Labourer | 72(40.7) | 209(79.5) | 281(63.9) |
Professional | 24(45.2) | 66(77.1) | 90(64.9) |
Farmer | 217(59.5) | 275(79.0) | 492(69.0) |
Business and service | 32(35.3) | 66(89.6) | 98(59.6) |
Student | 129(70.0) | 217(104.7) | 346(88.4) |
Retired | 49(67.4) | 45(63.3) | 94(65.4) |
Housework | 78(55.5) | 22(101.4) | 100(61.6) |
Unemployed | 8(34.8) | 16(67.8) | 24(51.5) |
Others | 4(22.5) | 13(64.0) | 17(44.6) |
X2 = 43.9, P < 0.01 | |||
Average income (RMB yuan/year) | |||
< 2000 | 304(71.9) | 432(101.7) | 736(86.8) |
2000- | 192(48.1) | 332(81.5) | 524(65.0) |
5000+ | 155(43.0) | 218(59.0) | 373(51.1) |
X2 = 80.8, P < 0.01 | |||
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A difference in injury incidence by occupation was observed (X2 = 43.9, P < 0.01). Students (8.8%), farmers (6.9%), retired individuals (6.5), professionals (6.5%) and labourers (6.4%) show a high incidence of injury, accounting for 82.9% of total injuries. Government employees were most likely to be affected by traffic injuries, followed by labourers and those in the business and service industry, while the retired and preschool children were mainly affected by falls.
Injury incidence also varied (X2 = 80.8, P < 0.01) among different levels of income. Respondents with lower income tended to have a higher incidence of injury. Among those with the lowest income (2,000 yuan or below), incidence of a fall was observed most frequently. Sprain was the leading mechanism among those with the highest income (5,000 yuan or above).
Injury incidence in rural areas (84.3 per 1,000) was higher than in urban areas (42.9 per 1,000) (X2 = 158.9, P < 0.01), with the majority of injuries occurring in rural areas (74.6%).
Logistic regression analysis shows that sex, area, education, income, and occupation are significantly associated with injury incidence (Table 5). Males are 60% more likely to incur an injury compared with females. Rural residents have an injury incidence rate 1.99 times that of urban residents. Analysis by level of education shows a tendency for those with higher education levels to have a lower risk of injury incidence. The odds of injury for the illiterate group are 3.17 times that of the college and above group. Among different income levels, those with the lowest income (< 2,000) show the highest risk of injury incidence, while the middle level (2,000–4,999) show the lowest risk. Analysis of occupation shows that farmers have the lowest risk of injury incidence, while students and professionals face a higher risk.
Table 5
Multivariate logistic regression analysis of injury incidence
Indicators | B | SE | P | OR | 95.0% C.I. for OR | |
|---|---|---|---|---|---|---|
Lower | Upper | |||||
Sex (Male vs Female) | 0.47 | 0.06 | < 0.01 | 1.60 | 1.43 | 1.79 |
Area (Rural vs Urban) | 0.69 | 0.08 | < 0.01 | 1.99 | 1.69 | 2.34 |
Education
| < 0.01 | |||||
Illiterate | 1.15 | 0.18 | < 0.01 | 3.17 | 2.23 | 4.51 |
Elementary school | 0.69 | 0.17 | < 0.01 | 2.00 | 1.44 | 2.77 |
Junior high school | 0.53 | 0.16 | < 0.01 | 1.70 | 1.23 | 2.34 |
Senior high school | 0.38 | 0.17 | < 0.01 | 1.47 | 1.06 | 2.03 |
College and above | 1 | |||||
Average Income
| ||||||
< 2000 | 0.23 | 0.06 | < 0.01 | 1.26 | 1.12 | 1.43 |
2000- | 1.00 | |||||
5000+ | 0.16 | 0.09 | > 0.05 | 1.17 | 0.99 | 1.38 |
Occupation
| ||||||
Farmer | 1 | |||||
Government employee | 0.19 | 0.21 | > 0.05 | 1.21 | 0.81 | 1.82 |
Labourer | 0.40 | 0.09 | < 0.01 | 1.49 | 1.26 | 1.77 |
Professional | 0.55 | 0.13 | < 0.01 | 1.74 | 1.34 | 2.25 |
Business and service | 0.19 | 0.12 | > 0.05 | 1.21 | 0.97 | 1.53 |
Student | 0.61 | 0.08 | < 0.01 | 1.84 | 1.57 | 2.14 |
Retired | 0.42 | 0.13 | < 0.01 | 1.52 | 1.18 | 1.95 |
Housework | 0.13 | 0.12 | > 0.05 | 1.14 | 0.90 | 1.44 |
Unemployed | 0.45 | 0.22 | < 0.05 | 1.57 | 1.02 | 2.43 |
Others | 0.22 | 0.26 | > 0.05 | 1.24 | 0.75 | 2.07 |
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Characteristics of injury occurrence
Injuries occurred mostly at home (31.7%), in a highway/street (31.2%), or at the workplace (22.5%). Injuries at these three places accounted for 85.5% of the total injuries. For males, the injuries were likely to occur in a street or highway, while females were most likely to be injured at home. Injuries in the street/highway generally resulted from traffic injuries (56.3%), while those sustained at home mainly involved falls (26.4%). Injuries at the workplace were diverse, involving collisions (18.3%), strains (17.8%), cuts (16.8%), and falls (16.0%).
Injuries were most likely to happen during work hours or housework. Injuries occurring during work hours mainly involved falls (17.5%), collisions (17.1%), cuts (15.4%), and strains (15.2%). During housework time, incidences of falls (24.7%), strains (19%), and cuts (17.3%) were higher. A high incidence of traffic injuries (57.7%) was observed while shopping and commuting to and from work.
Severity of injury
Of all injuries, 75.0% were minor and did not need inpatient care, 20.3% were moderate and required hospitalization but did not disable the patient, and 4.7% were serious injuries that left the patient disabled, yielding a disability rate of 3.4 per 1,000 (2.6 per 1,000 for male, 0.8 per 1,000 for female). Disabling injuries were mainly caused by traffic injuries (34.9%), falls (21.7%), and strains (13.4%).
On average, injuries led to 13.6 days (14.9 for male, 11.7 for female) of rest, and 5.4 days (6.1 for male, 4.4 for female) of hospitalization. Explosions, traffic injuries, and falls caused longer periods of both hospitalization and rest.
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Life lost caused by injury
In the survey, 17 persons died of injury, and injuries caused 662 YPLL, 534 WYPLL, and 411 VYPLL. For each fatal injury, the average YPLL, WYPLL, and VYPLL were 38.9, 31.4 and 24.2 mean years, respectively. Traffic injuries and falls were the two major causes of YPLL, leading to 251 and 136 mean years of YPLL, and accounting for 37.9% and 20.5% of the total, respectively.
Economic loss caused by injury
In the survey, the 1,772 total injuries caused 2,080,156 and 4,000,251 RMB yuan of direct and indirect economic loss, averaging 1,174 and 2,257 RMB yuan for each injury (Table 6). The contribution of males was more than three times that of females. Injuries in rural and urban areas caused economic losses of 3,275,661 and 2,804,746 RMB yuan, respectively. The economic costs of injury in rural areas was slightly higher than that in urban areas. Traffic injuries led to an economic cost of 2,723,713 RMB yuan, accounting for 44.79% of the total.
Table 6
Economic loss* caused by injury
Mechanisms | Direct loss | Indirect loss 1 | Indirect loss 2 | Total | ||||
|---|---|---|---|---|---|---|---|---|
RMB Yuan | % | RMB Yuan | % | RMB Yuan | % | RMB Yuan | % | |
Traffic injury | 881235 | 42.4 | 146675 | 35.0 | 1695802 | 47.4 | 2723713 | 44.8 |
Fall | 496011 | 23.8 | 108324 | 25.9 | 857164 | 23.9 | 1461499 | 24.0 |
Struck | 179766 | 8.6 | 34833 | 8.3 | 351619 | 9.8 | 566219 | 9.3 |
Cut | 122883 | 5.9 | 26095 | 6.2 | 340112 | 9.5 | 489090 | 8.0 |
Animal bite | 23836 | 1.2 | 5453 | 1.3 | 260249 | 7.3 | 289538 | 4.8 |
Strain | 99517 | 4.8 | 44157 | 10.5 | 143674 | 2.4 | ||
Burn | 83515 | 4.0 | 19006 | 4.5 | 102520 | 1.7 | ||
Squeeze | 71967 | 3.5 | 17055 | 4.1 | 89021 | 1.5 | ||
Explosion | 48501 | 2.3 | 5659 | 1.4 | 54160 | 0.9 | ||
Poisoning | 43264 | 2.1 | 7378 | 1.8 | 50642 | 0.8 | ||
Others | 29661 | 1.4 | 4153 | 1.0 | 76516 | 2.1 | 110330 | 1.8 |
Total | 2080156 | 100 | 418789 | 100 | 3581462 | 100 | 6080407 | 100 |
Discussion
The survey shows an incidence rate of 66.7 per 1,000 and fatality rate of 69.6/100,000 for total injuries in the Shandong Province. The injury incidence in our study was higher than that of Shijiazhuang (4.32%), Shenzhen (6.35%)[7], and 4 rural communities (6.51%)[8], but was lower than that in four communities in the Zhejiang Province (16.11%) in China. It was also higher than that in Tanzania (3.27%)[9] and Pakistan (4.1%)[10], but lower than that in the 12-month cohort study in India (12.7%)[11] and in the state of Colorado in the United States (14.7%)[12].
The study found a higher injury incidence among males compared with females. Traffic injuries show an extraordinarily higher incidence among males than females, but there is a higher incidence of falls among females in the 60 years and older age group.
A higher incidence of injury was observed in rural areas compared with urban areas. Residents in rural areas are estimated to have a risk of injury incidence that is 1.99 times that in urban areas. This is similar to findings from other studies[9, 12, 13], but different from the study in Uganda, in which a high incidence of injury in urban areas was found[14]. In contrast to other studies in developing countries, a higher incidence of cuts in rural studies was not found in our study, as in Ghana[13]and Tanzania[9], nor lower incidence of traffic injuries, like Pakistan[10] and Bangladesh[15]. This may be a reflection of the motorization of agriculture activities in rural areas of the Shandong Province.
For different ages, those in the 5 to 14 year-old group and the 60 years and older group had higher injury incidence rates, and the main cause of injury for these two age groups was falling. This is different from two U.S. studies showing a lower injury occurrence among elders[12, 16].
Socio-economic status has been documented to be an important determinant of injury. In our study, those with both the lowest and highest income were at a higher risk of injury incidence. However, a study in America showed that income and education in the multivariate models were largely unrelated to overall injury morbidity[17].
Home, highway/street, and workplace were found to be the major sites where most injuries happened. This was different from the study in Bangladesh, in which injuries in a highway/street were not common[15].
Based on the results of this study, we estimate that 6,522,955 cases of injury and 62,585 deaths resulting from injury occurred in Shandong in 2003, and the total number of injuries caused 22.3 billion RMB yuan in economic cost, accounting for 1.8% of the GDP in the Shandong Province in the same year. Traffic injuries accounted for 44.79% of the total economic loss.
Limitations of the study
The findings of this study are subject to several limitations. First, the information is based on a self-reported survey requiring respondents to recall injuries occurring within the past year, which is subject to recall bias [18‐21] and might result in an underestimation of injury occurrence. There is also potential for bias due to unreliable memory or embarrassment regarding certain types of injuries, such as assaults or domestic violence. This may also lead to underestimated injury rates.
In the study, clinical injury severity assessment was not available. Disability days were used instead as a measure of severity of injury. This can only result in a crude evaluation of injury severity.
Logistic regression method was employed to evaluate the risks of injury incidence. The Hosmer-Lemeshow test showed poor fitness of the model, which might attribute to the large size of the study sample.
Despite its limitations, this study has generated information that is useful for targeting prevention at the local level.
Conclusion
Our study demonstrates that injury has become a public health problem in the Shandong Province of China as the result of social urbanization and modernization. Injuries have caused large losses of life as well as economic losses. Falls and traffic injuries were the two most common causes of injury.
According to the characteristic of injury incidence, prevention priority should be given to traffic injuries, especially in rural areas. Major causes of traffic injury need to be further studied. Collaborations between police, traffic, and health departments are necessary for traffic injury prevention. Adolescents (5–14 years) suffered a higher incidence of falls, collision, and traffic injury. This was also consistent with the high injury incidence among students. Education in schools on corresponding injury prevention should be an effective measure for protection of this population.
Acknowledgements
We thank Dr. Krishnan from Western Pacific Regional Office of WHO for his support on the preparation of this paper and all coordinators from the local anti-epidemic stations and hospitals for their support on the field work in each district.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
JM designed and conducted the study, performed statistical analysis, and wrote the initial draft and revisions of the manuscript after consultation with other authors. XG and AX participated in the design, study, and revision of the manuscript. JZ and CJ participated in statistical analysis and coordination, as well as in revision of manuscript. All authors have read and approved the final manuscript.