| 孙凤,陶庆梅,陶秋山,杨兴华,曹纯铿,詹思延.台湾地区30～59岁健康体检人群肥胖5年发病风险预测模型[J].Chinese journal of Epidemiology,2012,33(9):921-925 |
| 台湾地区30～59岁健康体检人群肥胖5年发病风险预测模型 |
| Estimation ou the risk of 5-years obesity development among adults aged 30-59, based on the Taiwan MJ Health-checkup Database |
| Received:April 19, 2012 |
| DOI：10.3760/cma.j.issn.0254-6450.2012.09.010 |
| KeyWord: 肥胖 风险预测模型 纵向数据 |
| English Key Word: esity Risk predictive model Longitudinal data |
| FundProject: |
| Author Name | Affiliation | E-mail | | Sun Feng | Department of Epidemiology and Biostatistics, School of Public Health
Department of Pharmacy Administration and Clinical Pharmacy, School for Pharmaceutical, Peking University, Beijing l00191, China
Department of Preventive Medicine, shihe University School of Medieine | | | Tao Qing-mei | Department of Epidemiology and Biostatistics, School of Public Health | | | Tao Qiu-shan | Department of Epidemiology and Biostatistics, School of Public Health | | | Yang Xing-hua | School of Public Health and Famly Medieine, Capital Medical University | | | Cao Chun-jian | MJ Health Management Organization, Taiwan | | | Zhan Si-yan | Department of Epidemiology and Biostatistics, School of Public Health | siyan-zhan@bjmu.edu.cn |
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| Abstract: |
| 目的 构建台湾地区30～59岁健康体检人群肥胖5年发病风险(个体化)预测模型。方法 选择1998-2006年首次参加台湾美兆健康体检的30-59岁人群15 085人，剔除基线患肥胖者918人共计14 167人，将4个体检中心分为建模队列(台北中心，n=8104)和验证队列(另3个中心，n=6063)。按建模队列5年后是否发生肥胖为因变量、基线指标为自变量进行单变量分析，并建立多元逐步logisticM归模型，以ROC曲线下面积(AUC)为判定预测模型拟合优度的主要指标，用验证队列对模型的外部效度进行评估。建模后再将人群预测风险概率正态化转为可实际麻用操作的4个风险等级。结果 4个体检中心基线时正常体重、超重和肥胖人群的比例分别为50.00%-60.00%、26.47%～31.l1%和5 .76%一7.24%。剔除基线患者后，全部受检者5年肥胖发病率为2 73%(386/14167)，4个中心肥胖发病率为2.66%-2.91%。多变量logistic回归构建的预测模型包括年龄、性别、糖尿病家族史、近3个月体重变化≥4 kg和腰围5个指标。建模队列建立的预测模型AUC约为0．898(95%C1：0.884—0.912)，验证队列外部效度验证结果为AUC=0.88l(95%C1：0.862～0900)。将建模队列划分为4个风险等级后，显示中危(16.0%)和高危(2.9%)的个体5年内发生肥胖的危险分别比一般人群高7.8倍和16.6倍。结论 利用台湾美兆健康体检纵向数据资料建立的肥胖5年个体风险预测模型，其效度和信度均较高，且评价标准简单实用，无论对个体自身肥胖风险评价还是对社区人群肥胖监测均具有应用价值。 |
| English Abstract: |
| Objective This study aimed to provide an epidemiological modeling in evaluating the risk of developing obesity within 5years in Taiwan population aged 30-59 years. Methods After excluding 918 individuals who were obesitive at baseline, a cohort of 14 167 non-obesity subjects aged 30-59 years in the initia lyear during 1998-2006, was formed to derive a Risk Score which could predict the lncident obesity(IO)．Multivariate logistic regression was used to derive the risk functions, using the check-up center (Taipei training cohort，n=8104) of the overall cohort. Rules based on these risk functions were evaluated in the left three centers (testing cohort．n=6063)．Risk functions were produced to detect the IO on a training sample using the multivariate logistic regression models. Starting with variables that could predict the IO through univariate models, we constructed multivariable logistic regression models in a stepwise manner which eventually could include all the variables. We evaluated the predictability of the model by the area under the receiver-operating characteristic (ROC )curve (AUC) and to testify its diagnostic property on the testing sample. Once the final model was defined．the next step was to establish rules to characterize 4 diffbrent degrees of risk based on the cut points of these probabilities after transforming into normal distribution by log-transformation．Results At baseline, the range of the proportion of normal weight, overweight and obesity were 50.00%-60.00%, 26.47%-3l.1l% and 5.76%-72.4% respectively in four check-up centers of Taiwan. After excluding 918 obesity individuals at baseline. We aseertained 386 (2.73%, 386/14167)cases having IO and 2.66%-2.91%of them having centered obesitym the four check-up centers respectivcly．Final multivariable logistic regression model would inelude five risk factors: sex, age, history of diabetes, weight deduction ≥4 kg within 3 months and waist circumference. The area under the ROC curve(AUC) was 0.898, (95%Cl:0.884-0.912) that could predict the development of obesity within 5 years. The curve also had adequate performance in testing the sample [AUC=0.881(95%Cl:0.862-0.900)]. After labeling the four risk degrees, 16.0% and 2.9% of the total subjects were in the mediate and high risk populations respectively and were 7.8 and 16.6 time shigher, when comparing with the population at risk in general. Conclusion The predictability and reliability of our obesity risk score model, derived based on Taiwan MJ Longitudinal Health-checkup-based Population Database, were relatively satisfactory, with its simple and practicable predictiv evariables and the risk degree form. This model could help individuals to self assess the situation of risk on obesity and could also guide the community caretakers to monitor the trend of obesity development. |
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