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1.
Chinese Journal of Epidemiology ; (12): 352-356, 2018.
Artículo en Chino | WPRIM | ID: wpr-737961

RESUMEN

Objective To optimize the warning threshold values of common communicable diseases in Gansu province,and improve the early warning effect.Method An early warning model was set up for influenza,scarlet fever,other infectious diarrheal diseases,dysentery,typhoid and paratyphoid,viral hepatitis type E and hand foot and mouth disease (HFMD) respectively in Gansu by using the moving percentile method and cumulative sum method.By calculating the sensitivity,specificity,predictive value of positive test,predictive value of negative test,Youden'index and receiver-operating characteristic curve,the optimum early warning threshold values for communicable diseases in Gansu were selected.Results The optimum early warning boundary values of influenza,scarlet fever,other infectious diarrheal diseases,dysentery,typhoid and paratyphoid,and viral hepatitis type E were P90,P80,P95,P90,P80 and P90 respectively.The optimum early warning parameters of HFMD were k =1.2,H=5σ.Under the optimum early warning boundary values/parameters,the early warning sensitivities of influenza,scarlet fever,other infectious diarrheal diseases,dysentery,typhoid and paratyphoid,viral hepatitis type E and HFMD were 86.67%,100.00%,91.67%,100.00%,100.00%,100.00% and 100.00%,the specificities were 86.49%,62.22%,75.00%,100.00%,97.92%,89.13% and 74.47%.The predictive values of positive test were 72.22%,29.17%,52.38%,100.00%,80.00%,54.55% and 29.41%,and the predictive values of negative test were 94.12%,100.00%,96.77%,100.00%,100.00%,100.00% and 100.00%,and the Youden'indexes were 0.73,0.62,0.67,1.00,0.98,0.89 and 0.74.Receiver-operating characteristic curve showed that the values/parameters of this warning boundary were the points closest to the upper left of the coordinate diagram.Conclusion The early warning thresholds of influenza,other infectious diarrheal diseases,dysentery and hepatitis E in Gansu may be raised appropriately and the early warning parameters of HFMD need to be adjusted to improve the effectiveness of early warning.

2.
Chinese Journal of Epidemiology ; (12): 352-356, 2018.
Artículo en Chino | WPRIM | ID: wpr-736493

RESUMEN

Objective To optimize the warning threshold values of common communicable diseases in Gansu province,and improve the early warning effect.Method An early warning model was set up for influenza,scarlet fever,other infectious diarrheal diseases,dysentery,typhoid and paratyphoid,viral hepatitis type E and hand foot and mouth disease (HFMD) respectively in Gansu by using the moving percentile method and cumulative sum method.By calculating the sensitivity,specificity,predictive value of positive test,predictive value of negative test,Youden'index and receiver-operating characteristic curve,the optimum early warning threshold values for communicable diseases in Gansu were selected.Results The optimum early warning boundary values of influenza,scarlet fever,other infectious diarrheal diseases,dysentery,typhoid and paratyphoid,and viral hepatitis type E were P90,P80,P95,P90,P80 and P90 respectively.The optimum early warning parameters of HFMD were k =1.2,H=5σ.Under the optimum early warning boundary values/parameters,the early warning sensitivities of influenza,scarlet fever,other infectious diarrheal diseases,dysentery,typhoid and paratyphoid,viral hepatitis type E and HFMD were 86.67%,100.00%,91.67%,100.00%,100.00%,100.00% and 100.00%,the specificities were 86.49%,62.22%,75.00%,100.00%,97.92%,89.13% and 74.47%.The predictive values of positive test were 72.22%,29.17%,52.38%,100.00%,80.00%,54.55% and 29.41%,and the predictive values of negative test were 94.12%,100.00%,96.77%,100.00%,100.00%,100.00% and 100.00%,and the Youden'indexes were 0.73,0.62,0.67,1.00,0.98,0.89 and 0.74.Receiver-operating characteristic curve showed that the values/parameters of this warning boundary were the points closest to the upper left of the coordinate diagram.Conclusion The early warning thresholds of influenza,other infectious diarrheal diseases,dysentery and hepatitis E in Gansu may be raised appropriately and the early warning parameters of HFMD need to be adjusted to improve the effectiveness of early warning.

3.
Chinese Journal of Epidemiology ; (12): 450-453, 2011.
Artículo en Chino | WPRIM | ID: wpr-273168

RESUMEN

Objective To compare the different thresholds of 'moving percentile method' for outbreak detection in the China Infectious Diseases Automated-alert and Response System (CIDARS). Methods The thresholds of P50, P60, P70, P80 and P90 were respectively adopted as the candidates of early warning thresholds on the moving percentile method. Aberration was detected through the reported cases of 19 notifiable infectious diseases nationwide from July 1,2008 to June 30,2010. Number of outbreaks and time to detection were recorded and the amount of signals acted as the indicators for determining the optimal threshold of moving percentile method in CIDARS. Results The optimal threshold for bacillary and amebic dysentery was P50. For non-cholera infectious diarrhea,dysentery, typhoid and paratyphoid, and epidemic mumps, it was P60. As for hepatitis A, influenza and rubella, the threshold was P70, but for epidemic encephalitis B it was P80. For the following diseses as scarlet fever, typhoid and paratyphoid, hepatitis E, acute hemorrhagic conjunctivitis, malaria, epidemic hemorrhagic fever, meningococcal meningitis, leptospirosis, dengue fever, epidemic endemic typhus,hepatitis C and measles, it was P90. When adopting the adjusted optimal threshold for 19 infectious diseases respectively, 64 840(12.20%)signals had a decrease, comparing to the adoption of the former defaulted threshold(P50)during the 2 years. However, it did not reduce the number of outbreaks being detected as well as the time to detection, in the two year period. Conclusion The optimal thresholds of moving percentile method for different kinds of diseases were different.Adoption of the right optimal threshold for a specific disease could further optimize the performance of outbreak detection for CIDARS.

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