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Jauvin Christian

Description

Many studies evaluate the timeliness and accuracy of outbreak detection algorithms used in syndromic surveillance. Of greater interest, however, is defining the outcome associated with improved detection. In case of a waterborne cryptosporidiosis outbreak, public health interventions are aimed exclusively at preventing new infections, and not at medical treatment of infected individuals. The effectiveness of these interventions in reducing morbidity and mortality will depend on their timeliness, the level of compliance, and the duration of exposure to pathogen. In this work, we use simulation modeling to examine several scenarios of issuing a boil-water advisory (BWA) as a response to outbreak detection through syndromic surveillance, and quantify the possible benefits of earlier interventions.

Objective

To quantitatively assess the benefit of issuing a boil-water advisory for preventing morbidity and mortality from a waterborne outbreak of cryptosporidiosis.

Submitted by uysz on
Description

Mandatory notification to public health of priority communicable diseases (CDs) is a cornerstone of disease prevention and control programs. Increasingly, the addresses of CD cases are used for spatial monitoring and cluster detection and public health may direct interventions based on the results of routine spatial surveillance. There has been little assessment of the quality of addresses in surveillance data and the impact of address errors on public health practice.

We launched a pilot study at the Montreal Public Health Department, wherein our objective was to determine the prevalence of address errors in the CD surveillance data. We identified address errors in 25% of all reported cases of communicable diseases from 1995 to 2008. We also demonstrated that address errors could bias routine public health analyses by inappropriately flagging regions as having a high or low disease incidence, with the potential of triggering misguided outbreak investigations or interventions. The final step in our analysis was to determine the impact of address errors on the spatial associations of campylobacter cases in a simulated point source outbreak.

 

Objective

To examine, via a simulation study, the potential impact of residential address errors on the identification of a point source outbreak of campylobacter.

Submitted by hparton on
Description

In Montreal, notifiable diseases are reported to the Public Health Department (PHD). Of 44, 250 disease notifications received in 2009, up to 25% had potential address errors. These can be introduced during transcription, handwriting interpretation and typing at various stages of the process, from patients, labs and/or physicians, and at the PHD. Reports received by the PHD are entered manually (initial entry) into a database. The archive personnel attempts to correct omissions by calling reporting laboratories or physicians. Investigators verify real addresses with patients or physicians for investigated episodes (40–60%). 

The Dracones qualite (DQ) address verification algorithm compares the number, street and postal code against the 2009 Canada Post database. If the reported address is not consistent with a valid address in the Canada Post database, DQ suggests a valid alternative address.

 

Objective

To (1) validate DQ developed to improve data quality for public health mapping and (2) identify the origin of address errors.

Submitted by hparton on
Description

A new TB case can be classified as: 1) a source case for transmission leading to other, secondary active TB cases; 2) a secondary case, resulting from recent transmission; or 3) an isolated case, uninvolved in recent transmission (i.e. neither source nor recipient). Source and secondary cases require more intense intervention due to their involvement in a chain of transmission; thus, accurate and rapid classification of new patients should help public health personnel to effectively prioritize control activities. However, currently accepted method for the classification, DNA fingerprint analysis, takes many weeks to produce the results; therefore, public health personnel often solely rely on their intuition to identify the case who is most likely to be involved in transmission. Various clinical and socio-demographic features are known to be associated with TB transmission. By using these readily available data at the time of diagnosis, it is possible to rapidly estimate the probabilities of the case being source, secondary, and isolated.

Objective

To develop and validate a prediction model which estimates the probability of a newly diagnosed tuberculosis (TB) case being involved in ongoing chain of transmission, based on the case's clinical and socio-demographic attributes available at the time of diagnosis.

Submitted by elamb on