| Objective:1. To understand the epidemiologic characteristics of influenza in Kunming city from2008-2011.2. To establish an auto-regression integrated moving average (ARIMA) model to predictive incidence trend of influenza-like illness (ILI).3. To estimate the excess mortality attributed to influenza by Serfling regression model in Kunming from2008-2012, so as to provide scientific reference for disease control and prevention.Methods:1. A retrospective epidemiological analysis was conducted on the surveillance data and the detection results of influenza like-illness (ILI)in Kunming from2008to2011.2. ARIMA model was establish by using variation of weekly ILI%(Proportion of influenza like illness cases in total out-patient, ILI%) from Jan,2008to Oct,2011.Data of ILI cases in2012were used to evaluation the predictive capacity of the model.3. Age-specific Serfling regression modela have been established based on the data of pneumonia and influenza (P&I) deaths, respiratory and circulatory(R&C) deaths, all cause (AC) deaths to estimated the excess mortality and death numbers attributed to influenza for three age groups in Kunming:the all-age,<65years old and≥65years old.4. SPSS17.0statistical package be used for analysis, including X2test,Correlation,Normality test,Time-series analysis.To establish the Serfling regression models by SAS9.1(SAS Institute Inc,Cary,NC).Results: 1.The average proportion of ILI cases in total outpatients (ILI%) was3.20%in sentinel hospitals from2008to2011.The ILI cases were mainly from small age group population and the constituent ratio decreased gradually with age increased(x2=246.592, P＜0.0001). The ILI%during the H1N1pandemic (2009.6-2010.8) were significantly higher than that of non pandemic period (X2=688.95, P＜0.0001) and the peak of ILI%from small age group time occur significantly earlier than the elderly age group. Totally6095samples from ILI cases were detected and480strains of influenza viruses were isolated. Virus isolation rate during a pandemic was significantly higher than that of non-epidemic period. Samples and virus from small age group people were more than that from elderly group. The baseline of ILI%was4.24%in Kunming.2. There are two peak of ILI%in Kunming. One peak from Novermber to March and another from July to September. The model of ARIMA(1,0,0)x(1,1,0)52was establish and the residual from model were white-noise series. The relative predictive error were15.26%,11.68%,12.12%,17.57%from2009-2012.3. From2008-2012, the AC-based estimates of the annually averaged excess death and mortality attributed to influenza were1581(95%CI:582-3103) and25.6(95%CI:9.7-50.6) per100000person years respectively. The R&C-based estimates excess death and mortality attributed to influenza were1120(95%CI:303-2215) and18.1(95%CI:5.0-36.0) per100000person years respectively. The P&I-based estimates excess death and mortality attributed to influenza were116(95%CI:52-206) and1.9(95%CI:0.9-3.4) per100000person years respectively.Conclusion:1. The surveillance data from2008-2011has shown the epidemic characteristics of influenza:It usually occurs as a seasonal, local epidemics and cause pandemic when the viral antigens changed. Children, adolescents are at high risk of influenza (HIN1)2. The ARIMA model with considering seasonality can fit the weekly variation of the ILI and can be used to analyze the influenza incidence and make prediction in Kunming. It will provide scientific evidence for prevention and control of influenza epidemic. 3. The Serfling regression model can be used for estimated the excess mortality attributed to influenza in Kunming. The study indicated that the estimated excess death attributed to influenza exceeded1000person each year in Kunming and most of them are senior people(≥65years old).Therefore influenza vaccine inoculation should be extensively provided for them. It can reduce the risk of health damage to these people. |
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