3.Analysis of effect on infectious diseases outbreak detection performance by classifying provinces for moving percentile method.
Honglong ZHANG ; Qiao SUN ; Shengjie LAI ; Xiang REN ; Dinglun ZHOU ; Xianfei YE ; Lingjia ZENG ; Jianxing YU ; Liping WANG ; Hongjie YU ; Zhongjie LI ; Wei LYU ; Yajia LAN ; Weizhong YANG
Chinese Journal of Preventive Medicine 2014;48(4):265-269
OBJECTIVEProviding evidences for further modification of China Infectious Diseases Automated-alert and Response System (CIDARS) via analyzing the outbreak detection performance of Moving Percentile Method (MPM) by optimizing thresholds in different provinces.
METHODSWe collected the amount of MPM signals, response results of signals in CIDARS, cases data in nationwide Notifiable Infectious Diseases Reporting Information System, and outbreaks data in Public Health Emergency Reporting System of 16 infectious diseases in 31 provinces in Chinese mainland from January 2011 to October 2013. The threshold with the optimal sensitivity, the shortest time to detect outbreak and the least number of signals was considered as the best threshold of each disease in Chinese mainland and in each province.
RESULTSAmong all the 16 diseases, the optimal thresholds of 10 diseases, including dysentery, dengue, hepatitis A, typhoid and paratyphoid, meningococcal meningitis, Japanese encephalitis, scarlet fever, leptospirosis, hepatitis, typhus in country level were the 90(th) percentile (P90), which was the same as provincial level for those diseases.For the other 6 diseases, including other infectious diarrhea, influenza, acute hemorrhagic conjunctivitis, mumps, rubella and epidemic hemorrhagic fever, the nationwide optimal thresholds were the 80th percentile (P80), which was different from that by provinces for each disease. For these 6 diseases, the number of signals generated by MPM with the optimal threshold for each province was decreased by 23.71% (45 557), 15.59% (6 124), 14.07% (1 870), 9.44% (13 881), 8.65% (1 294) and 6.03% (313) respectively, comparing to the national optimal threshold, while the sensitivity and time to detection of CIDARS were still the same.
CONCLUSIONOptimizing the threshold by different diseases and provinces for MPM in CIDARS could reduce the number of signals while maintaining the same sensitivity and time to detection.
China ; Communicable Diseases ; Disease Notification ; Disease Outbreaks ; prevention & control ; Humans ; Population Surveillance ; methods
4.The implement performance of China Infectious Diseases Automated-alert and Response System in 2011-2013.
Zhongjie LI ; Jiaqi MA ; Shengjie LAI ; Honglong ZHANG ; Xiang REN ; Lingjia ZENG ; Jianxing YU ; Liping WANG ; Lianmei JIN ; Hongjie YU ; Jinfeng WANG ; Yajia LAN ; Weizhong YANG
Chinese Journal of Preventive Medicine 2014;48(4):252-258
OBJECTIVETo analyze the implement performance of China Infectious Diseases Automated-alert and Response System (CIDARS) of 31 provinces in mainland China, and to provide the evidences for further promoting the application and improvement of this system.
METHODSThe amount of signals, response situation and verification outcome of signals related to 32 infectious diseases of 31 provinces in mainland China in CIDARS were investigated from 2011 to 2013, the changes by year on the proportion of responded signals and timeliness of signal response were descriptively analyzed.
RESULTSA total of 960 831 signals were generated nationwide on 32 kinds of infectious diseases in the system, with 98.87% signals (949 936) being responded, and the median (the 25(th) percentile to the 75(th) percentile (P25-P75) ) of time to response was 1.0 (0.4-3.3) h. Among all the signals, 242 355 signals were generated by the fixed-value detection method, the proportion of responded signals was 96.37% (62 349/64 703), 98.75% (68 413/69 282) and 99.37% (107 690/108 370), respectively, and the median (P25-P75) of time to response was 1.3 (0.3-9.7), 0.8(0.2-4.9) and 0.7 (0.2-4.2) h, respectively. After the preliminary data verification, field investigation and laboratory test by local public health staffs, 100 232 cases (41.36%) were finally confirmed.In addition, 718 476 signals were generated by the temporal aberration detection methods, and the average amount of signal per county per week throughout the country were 1.53, and 8 155 signals (1.14%) were verified as suspected outbreaks. During these 3 years, the proportion of signal response was 98.89% (231 149/233 746), 98.90% (254 182/257 015) and 99.31% (226 153/227 715), respectively, and the median (P25-P75) of time to response was 1.1 (0.5-3.3), 1.0 (0.5-2.9) and 1.0 (0.5-2.6) h, respectively.
CONCLUSIONFrom 2011 to 2013, the proportion of responded signals and response timeliness of CIDARS maintained a rather high level, and further presented an increasing trend year by year. But the proportion of signals related to suspected outbreaks should be improved.
China ; Communicable Diseases ; Disease Notification ; Disease Outbreaks ; prevention & control ; Humans ; Population Surveillance ; methods
6.Mobile device-based reporting system for Sichuan earthquake-affected areas infectious disease reporting in China.
Biomedical and Environmental Sciences 2012;25(6):724-729
OBJECTIVETo describe the experience of the China CDC in rebuilding reporting capacity and response to the Sichuan earthquake through use of mobile phones.
METHODSSoftware engineering and business modeling are used to design and develop a cell phone-based reporting system. The PDA-based system used by the Field Adapted Survey Toolkit (FAST) was deployed
RESULTSApproximately one week after deployment of the mobile phone-based reporting system, the cumulative reporting rate reached the same level (81%) as the same period in 2007. In the Sichuan provincial pilot investigation for infectious disease, 1339 records were collected using PDAs developed and deployed by FAST.
CONCLUSIONThe mobile-based system is recognized as a quick and effective response solution to this public health challenge. Our experience suggests that mobile-based data collection tools provide faster, cleaner, standardized, and shareable data for critical decision making. This system could be adapted as complementary to national infectious disease reporting systems after natural disaster occurrences.
Cell Phone ; China ; Communicable Diseases ; epidemiology ; Disease Notification ; methods ; Earthquakes ; Emergencies ; Humans ; Population Surveillance ; methods
7.Study on early warning threshold values for 7 common communicable diseases in Gansu province, 2016.
Y CHENG ; X F LIU ; L MENG ; X T YANG ; D P LIU ; K F WEI ; X J JIANG ; H X LIU ; Y H ZHENG
Chinese Journal of Epidemiology 2018;39(3):352-356
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 P(90), P(80), P(95), P(90), P(80) and P(90) 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.
China
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Communicable Disease Control/methods*
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Communicable Diseases/epidemiology*
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Disease Notification
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Disease Outbreaks/prevention & control*
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Humans
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Models, Theoretical
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Population Surveillance/methods*
9.Study on early warning method for influenza epidemic in Gansu province.
Xiaoting YANG ; Xinfeng LIU ; Lei MENG ; Dongpeng LIU ; Deshan YU ; Hongyu LI ; Zhongyi JIANG ; Hui ZHANG
Chinese Journal of Epidemiology 2016;37(3):430-433
OBJECTIVETo explore the appropriate early warning method for influenza epidemic in Gansu province.
METHODSBy using simple control chart, moving percentile method, exponential smoothing method and cumulative sum control chart method, the annual incidence data of influenza-like illness in Gansu province during 2014-2015 were analyzed, and the sensitivities, specificities, positive predictive values, Jorden indexes and Kappa values of the 4 methods were evaluated and compared.
RESULTSThe 2014-2015 seasonal influenza epidemic occurred in the fiftieth week of 2014 in Gansu, and the epidemic peak lasted for 6 weeks. Cumulative sum control chart method had the best early warning effect with the sensitivity of 66.67% and specificity of 93.48%.
CONCLUSIONIt is feasible to use cumulative sum control chart method to give early warning of influenza epidemic in Gansu.
China ; epidemiology ; Disease Notification ; methods ; Epidemics ; Feasibility Studies ; Humans ; Influenza, Human ; epidemiology ; Seasons