Objective To understand the influencing factors of hypertension control,and to provide a theoretical basis for developing intervention measures.Methods A two-stage cluster random sampling method was performed and a total of 1 377 cases and 749 controls in Yuhang District were selected.Univariate and multivariate logistic regression analysis were used.Results The control rate of hypertension was 64. 77%.Hypertension control was related to BMI,course of disease and models of follow-up by univariate logistic regression analysis(P<0. 05 ).The multivariate logistic regression analysis showed that older age (OR =0. 983,95%CI=0. 974 -0. 993 ),male (OR =1. 272,95%CI=1. 053 -1. 535 ), overweight (OR=0. 709,95%CI=0. 576-0. 872),obesity (OR=0. 297,95%CI=0. 210-0. 421)and model of group follow-up (OR=0. 495,95%CI=0. 375 -0. 654)were the major influencing factors.Conclusion The older age,male, overweigt,obesity and model of group follow-up were the major influencing factors.Comprehensive intervention measures should be strengthened so as to improve the control rate of hypertension in community.

OBJECTIVETo construct a forecasting model of influenza-like illness in Zhejiang Province.

METHODSThe number of influenza-like cases and related pathogens among outpatients and emergency patients were obtained from 11 sentinel hospitals in Zhejiang Province during 2012 to 2013 (total 104 weeks), and corresponding meteorological factors were also collected. The epidemiological characteristics of influenza during the period were then analyzed. Linear correlation and rank correlation analyses were conducted to explore the association between influenza-like illness and related factors. Optimal parameters were selected by cross validation. Support vector machine was used to construct the forecasting model of influenza-like illness in Zhejiang Province and verified by the historical data.

RESULTSCorrelation analysis indicated that 8 factors were associated with influenza-like illness occurred in one week. The results of cross validation showed that the optimal parameters were C=3, ε=0.009 and γ=0.4. The results of influenza-like illness forecasting model after verification revealed that support vector machine had the accuracy of 50.0% for prediction with the same level, while it reached 96.7% for prediction within the range of one level higher or lower.

CONCLUSIONSupport vector machine is suitable for early warning of influenza-like illness.

China ; epidemiology ; Forecasting ; Humans ; Influenza, Human ; epidemiology ; Sentinel Surveillance ; Support Vector Machine