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[Current situation on the China infectious disease automated-alert and response system in Guangdong Province, China].

[Article in Chinese]

Author information

  • 1Public Health Emergency Preparedness and Response Division, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China. fy95@163.com
  • 2Public Health Emergency Preparedness and Response Division, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou 511430, China.

Abstract

OBJECTIVE:

To analyze the current situation on China Infectious Disease Automated-alert and Response System (CIDARS) in Guangdong province, China.

METHODS:

Early-warning signals and response time were analyzed by using three approaches of CIDARS. Positive rates of early-warning signals and error early-warning rates prior and after the adjustment,were compared.

RESULTS:

Totally, 114 585 early-warning signals appeared, with an average response time of 1.35 hours from April 21, 2008 to December 31, 2012. There were 12,394 early-warning signals in terms of fixed threshold method with a positive rate of 7.96%. 85 727 early-warning signals appeared under the mobile percentile method with a positive rate of 0.85%. There were 16,464 early-warning signals by using accumulation and control chart methods,with a positive rate of 1.82%. Results showed there was a positive correlation between the number of reported cases and the number of early-warning signals (r = 0.924, P < 0.01). The overall positive rate in Guangdong province increased from 1.48% to 2.14%, after the adjustment done by the Chinese Center for Disease Control and Prevention in 2010. The error early-warning rates regarding eight infectious diseases including hepatitis A, bacillary or amebic dysentery, both typhoid and paratyphoid fevers had reduced.

CONCLUSION:

The early-warning signal response appeared timely in CIDARS with good operation. However, despite the improvement on the efficacy of CIDARS, some functions and parameters of the systems still need to be adjusted.

PMID:
24423767
[PubMed - indexed for MEDLINE]
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