ABSTRACT
The China Infectious Disease Automated-alert and Response System (CIDARS) was successfully implemented and became operational nationwide in 2008.The CIDARS plays an important role in and has been integrated into the routine outbreak monitoring efforts of the Center for Disease Control (CDC) at all levels in China.In the CIDARS,thresholds are determined using the'Mean+2SD'in the early stage which have limitations.This study compared the performance of optimized thresholds defined using the'Mean +2SD'method to the performance of 5 novel algorithms to select optimal 'Outbreak Gold Standard (OGS)'and corresponding thresholds for outbreak detection.Data for infectious disease were organized by calendar week and year.The'Mean+2SD',C1,C2,moving average (MA),seasonal model (SM),and cumulative sum (CUSUM) algorithms were applied.Outbreak signals for the predicted value (Px) were calculated using a percentile-based moving window.When the outbreak signals generated by an algorithm were in line with a Px generated outbreak signal for each week,this Px was then defined as the optimized threshold for that algorithm.In this study,six infectious diseases were selected and classified into TYPE A (chickenpox and mumps),TYPE B (influenza and rubella) and TYPE C [hand foot and mouth disease (HFMD) and scarlet fever].Optimized thresholds for chickenpox (P55),mumps (P50),influenza (P40,P55,and P75),rubella (P45 and P75),HFMD (P65 and P70),and scarlet fever (P75 and Ps0) were identified.The C1,C2,CUSUM,SM,and MA algorithms were appropriate for TYPE A.All 6 algorithms were appropriate for TYPE B.C1 and CUSUM algorithms were appropriate for TYPE C.It is critical to incorporate more flexible algorithms as OGS into the CIDRAS and to identify the proper OGS and corresponding recommended optimized threshold by different infectious disease types.
ABSTRACT
Background：In April 2013, a hospital in Suzhou City notified authorities of a patient with nitrite poisoning with two other family members who had similar toxic symptoms five days prior. We investigated the event to identify the cause, source and possible route of contamination.Methods：A case was defined as any person living in the Yang Shan Hua Yuan community who had been diagnosed with cyanoderma and food poisoning symptoms from 15 to 25 April 2013. Active case finding was conducted by interviewing community residents and reviewing medical records from local clinics; information was then retrospectively collected on the patient’s food history, cooking procedures and food sources.Results：We identified three nitrite poisoning cases, one male and two females, from the same family. The time between dinner and onset of illness was less than an a hour. A retrospective survey showed that a substance presumed to be sugar mixed with asparagus on 17 April and with stir-fried asparagus on 21 April was the suspected contaminant. The presumed sugar came from a clean-up of a neighbouring rental house. Nitrite was detected in a vomitus sample, the sugar substance and two leftover food samples.Conclusion：This family cluster of nitrite poisoning resulted from the mistaken use of nitrite as sugar to cook dishes. We recommend that sodium nitrite be dyed a bright colour to prevent such a mistake and that health departments strengthen food hygiene education to alert people about the danger of eating unidentified food from an unknown source.