王文绢,施侣元,董福霞,孙雁玲,董士中,秦祖喧,魏金枝,李英群.矿区人群糖尿病的危险因素分析——以人群为基础的病例对照研究[J].Chinese journal of Epidemiology,1998,19(6):346-349 |
矿区人群糖尿病的危险因素分析——以人群为基础的病例对照研究 |
Analysis on the Risk Factors in Patients with Diabetes Mellitus from Population in Mining Districts—A Population-Based Case-Control Study |
Received:April 20, 1998 Revised:June 11, 1998 |
DOI: |
KeyWord: 矿区人群 糖尿病 危险因素 病例对照研究 |
English Key Word: Diabetes mellitus Population in mining districts Risk factors Case-Control study |
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Abstract: |
在河南平顶山煤矿区人群糖尿病(DM)现况调查基础上,对筛选出的174例DM和3066例糖耐量正常者(NGT)进行以人群为基础的病例对照研究。经非条件logistic回归分析调整了其它因素的影响和控制混杂因子后显示:年龄、母亲有DM史、同胞有DM史、最重时体质指数(BMI)高、腰臀比值(WHR)高、舒张压高、多食高梁和豆类可能是DM患病的独立危险因子,其OR值分别为2.04、6.04、2.24、1.85、2.57、1.51、2.22和1.25,PAR%分别为80.04%、7.19%、3.18%、37.35%、48.80%、8.15%、3.20%和10.63%;职业性体力活动强度高和多食浅色蔬菜可能是DM患病的独立保护因子,其OR值分别为0.89和0.50,PAR%分别为-19.20%和-269.5%。混杂分析表明,年龄对多因素模型中各因素存在不同程度的正、负混杂。 |
English Abstract: |
According to data from prevalence study on population from Pingdingshan coal mining districts in Henan province, we analysed 174 patients with diabetes mellitus(DM) and 3 066 control subjects with normal blood glucose(NGT) by a population-based case-control study. After the adjustment of other factors and controlled on confounding factors, the results of unconditional logistic multivariate regression analysis demonstrated that age, DM history of mother and sib,highest BMI through one's life,higher concurrent WHR,higher systolic blood pressure, frequently eating Chinese sorghum and legume may serve as independent risk factors of DM, their odds ratios(OR)were 2.04, 6.04, 2.24, 1.85, 2.57, 1.51, 2.22, 1.25 and their population attribution rates (PAR%) were 80.04 %, 7.19 %, 3.18 %, 37.35 %, 48.80 %, 8.15 %, 3.20 %, 10.63 % respectively. Higher occupational physical activity and frequently eating vegetables of light colour might serve as independent protective factors of DM, with ORs 0.89 and 0.50 and PAR% of -19.20% and -269.5 % respectively. Confounding analysis showed that age was both a positive and negative confounding factor to other factors in the logistic regression model. |
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