| 谭爱春,田丹平,黄渊秀,高林,邓欣,李黎,何琼,陈田木,胡国清,吴静.致死性道路交通伤害预测模型的构建[J].Chinese journal of Epidemiology,2014,35(2):174-177 |
| 致死性道路交通伤害预测模型的构建 |
| Development of forecasting models for fatal road traffic injurie |
| Received:August 13, 2013 |
| DOI：10.3760/cma.j.issn.0254-6450.2014.02.016 |
| KeyWord: 道路交通伤害 模型 预测 |
| English Key Word: Road traffic injury Models Forecasting |
| FundProject: |
| Author Name | Affiliation | E-mail | | Tan Aichun | Department of Epidemiology and Health Statistics, Public Health School, Central South University, Changsha 410078, China | | | Tian Danping | Department of Epidemiology and Health Statistics, Public Health School, Central South University, Changsha 410078, China | | | Huang Yuanxiu | Department of Epidemiology and Health Statistics, Public Health School, Central South University, Changsha 410078, China | | | Gao Lin | Department of Epidemiology and Health Statistics, Public Health School, Central South University, Changsha 410078, China | | | Deng Xin | Department of Epidemiology and Health Statistics, Public Health School, Central South University, Changsha 410078, China | | | Li Li | Department of Epidemiology and Health Statistics, Public Health School, Central South University, Changsha 410078, China | | | He Qiong | Department of Epidemiology and Health Statistics, Public Health School, Central South University, Changsha 410078, China | | | Chen Tianmu | Department of Epidemiology and Health Statistics, Public Health School, Central South University, Changsha 410078, China | | | Hu Guoqing | Department of Epidemiology and Health Statistics, Public Health School, Central South University, Changsha 410078, China | | | Wu Jing | Division of NCD Control and Community Health, Chinese Center for Disease Control and Prevention | Email:wujingcdc@163.com |
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| Abstract: |
| 目的 构建针对致死性道路交通伤害预测模型，为预测道路交通伤害未来发展趋势提供基础。方法 查询WHO死亡数据库获取不同国家道路交通伤害死亡人数资料，通过世界银行、WHO、联合国人口司等机构网站获取各国不同年份人均GDP、城市化水平、机动化水平及教育水平等资料，构建包含上述4个自变量的男女各年龄组道路交通伤害死亡率对数模型，并与WHO模型拟合优度进行比较。结果 共收集2626份数据（来自153个国家／地区；男女各半；时间1965--2010年）。选用人均GDP、机动化水平、城市化水平和教育水平4个变量构建的道路交通伤害死亡率拟合模型均具有统计学意义（P<0．001），男性0～4、5～14、15～24、25～34、35～44、45～54、55～64、≥65岁组模型的决定系数Ｒ2分别为22．7％、31．1％、51．8％、52．3％、44．9％、41．8％、40．1％、25．5％，女性各年龄组分另为别22．9％、32．6％、51．1％、49．3％、41．3％、35．9％、30．7％、20．1％；WHO模型仅选用人均GDP、教育水平和时间变量构建不同性别、年龄组预测模型，差异均有统计学意义（P<0．001），男性各年龄组模型决定系数砰分别为14．9％、22．0％、31．5％、33．1％、30．7％、28．5％、27．7％、17．8％；女性各年龄组模型分别为14．1％、20．6％、30．4％、31．8％、26．7％、24．3％、17．3％、8．8％。结论 本研究构建的道路交通伤害预测模型优于WHO模型。 |
| English Abstract: |
| Objective To develop the forecasting models for fatal road traffic injuries and to provide evidence for predicting the future trends on road traffic injuries. Methods Data on the mortality of road traffic injury including factors as gender and age in different countries, were obtained from the World Health Organization Mortality Database. Other information on GDP per capita, urbanization, motorization and education were collected from online resources of World Bank, WHO, the United Nations Population Division and other agencies. We fitted logarithmic models of road traffic injury mortality by gender and age group, including predictors of GDP per capita, urbanization, motorization and education. Sex- and age-specific forecasting models developed by WHO that including GDP per capita, education and time etc. were also fitted. Coefficient of determination (RZ) was used to compare the performance between our modes and WHO models. Results 2 626 sets of data were collected from 153 countries/regions for both genders, between 1965 and 2010. The forecasting models of road traffic injury mortality based on GDP per capita, motorization, urbanization and education appeared to be statistically significant (P<0.001)，and the coefficients of determination for males at the age groups of 0-4, 5-14, 15-24, 25-34, 35-44, 45-54, 55-64, 65+were 22.7%， 31.1%，51.8%，52.3 % , 44.9%，41.8%，40.1 %，25.5% , respectively while the coefficients for these age groups in women were 22.9%，32.6%，51.1%，49.3% , 41.3%，35.9%，30.7% , 20.1 % , respectively. The WHO models that were based on the GDP per capita, education and time variables were statistically significant (P<0.001)and the coefficients of determination were 14.9%，22.0%，31.5%，33.1%， 30.7%，28.5%，27.7% and 17.8% for males,but 14.1%，20.6%，30.4%，31.8%，26.7%，24.3%，17.3% and 8.8% for females, respectively. Conclusion The forecasting models that we developed seemed to be better than those developed by WHO. |
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