Short-term of Historical data to Construct Influenza Surveillance Model in Taiwan, 2005-2008

• Main Authors: Yu-Cheng Chang, 張育誠
• Other Authors: Tzu-Chi Lee
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/22655551308694779699

碩士 === 高雄醫學大學 === 公共衛生學研究所 === 98 === Background: According to WHO statistics, seasonal influenza has a great impact on mortality, with 90% of frail elderly people. Several influenza pandemic, causing considerable impacts, such as the Spanish flu in 1918, Asian flu in 1957 and Hong Kong flu in 1968. To construct the accurate and efficient monitoring system is very important. By using the short-term history data to construct influenza surveillance model in Taiwan, if its timeliness, sensitivity and specificity are well provide. We could the suggestions on the construction monitoring system of influenza in Taiwan. In addition, many studies have pointed out that environmental factors will affect influenza, in the study also included such issues to control risk factors. Methods: In order to control risk factors associated with influenza, we use auto-regression model to identify risk factors, and use principal components analysis to combine risk factors and sentinel surveillance data as one observed indicator. In this study, the influenza monitoring model as simple linear regression, the regression predicted values and the gold standard for ROC analysis to evaluate the monitoring system by the sensitivity and specificity. Results: Controling of other risk factors and the autocorrelation, the standardized mortality of pneumonia and influenza is related to temperature and influenza virus activity. Controling of other risk factors and the autocorrelation, the influenza virus with the laboratory detection rate is related to temperature, ozone and communications rate of influenza sentinel physicians. To use pneumonia and influenza mortality data as the gold standard, principal component data obtained as a result of the monitoring model is better than influenza sentinel surveillance data obtained as a result of the monitoring model. But to use the influenza virus detection laboratory data as the gold standard, the results is different to pneumonia and influenza mortality data as the gold standard. Influenza sentinel surveillance data obtained as a result of the monitoring model is better than principal component data obtained as a result of the monitoring model. Conclusion: The influenza surveillance system by using sentinel surveillance data, ozone, flu virus activity and temperature with simple linear regression model has good performance by the assessing of sensitivity, specificity and timeliness.