[Study on risk factors of XXXX among urban @entity1 in China].

@entity1 @entity308 @entity0 @entity5 @entity1834 @entity1558
OBJECTIVE: @entity0 is the most common @entity5 in @entity1 all over the world. It is necessary to establish monitoring methods and to identify risk factors. METHODS: Six cities including Beijing, Shanghai, Tianjin, Chongqing, Wuhan and Guangzhou were chosen as the sample areas. 2 100 cases with 1:1 matched controls were interviewed with a uniformed questionnaire. Conditional logistic regression and cluster analysis were used for multivariate analysis. RESULTS: The common risk factors for the combined data of the six cities were high educational level (OR: 1.17), high body mass index (OR: 1.51), high intake of milk products (OR: 1.49), high intake of eggs (OR: 1.14), @entity308 (OR: 2.78), history of @entity1558 (OR: 2.24), old age at marriage (OR: 1.42), @entity1834 (OR: 1.22), short duration of lactation (OR: 1.17). Among these factors, different cities had different ORs, as high educational level (OR: 1.20 - 1.61), high body mass index (OR: 1.40 - 22.36), high intake of milk products (OR: 1.64 - 3.39), @entity308 (OR: 1.60 - 10.50), history of @entity1558 (OR: 1.73 - 19.44). Some statistically significant correlations existed in different cities such as early age at menarche, old age at marriage, long menstrual periods, high intake of meat, eggs, sea food and passive smoking were risk factors. Some risk factors varied between premenopausal and postmenopausal @entity1 . Beijing, Wuhan and Chongqing were clustered as the same group in cluster analysis, so did Shanghai and Tianjin, but Guangzhou could not be grouped with other cities. CONCLUSION: Some common risk factors for @entity0 did exist in the six cities, but there were varieties in different areas which showed different places would have their own characteristics. Thus, careful study of risk factors, especially in the selection of variables and data analysis need to be carried on.
