scholarly journals A summary of surveillance, morbidity and microbiology of laboratory-confirmed cases of infant botulism in Canada, 1979–2019

2021 ◽  
Vol 47 (78) ◽  
pp. 322-328
Author(s):  
Richard Harris ◽  
Christine Tchao ◽  
Natalie Prystajecky ◽  
Jennifer Cutler ◽  
John W Austin
Keyword(s):  
Public Health ◽  
Disease Surveillance ◽  
Botulinum Neurotoxin ◽  
Age Of Onset ◽  
Public Health Laboratory ◽  
Discharge Abstract Database ◽  
Disease Surveillance System ◽  
Reference Service ◽  
Infant Botulism ◽  
Health Canada

Background: Infant botulism is a rare toxicoinfectious disease caused by colonization of the infant’s intestine with botulinum neurotoxin-producing clostridia (i.e. Clostridium botulinum or neurotoxigenic strains of C. butyricum or C. baratii). Our goal was to examine data from laboratory-confirmed cases of infant botulism reported in Canada to summarize incidence over time, over geographic distribution by province or territory, and by sex, and to compare these parameters with data from the Canadian Notifiable Disease Surveillance System (CNDSS). The average age of onset, serotype of botulinum neurotoxin (BoNT), case outcomes, length of hospitalization and suitability of clinical specimens for laboratory confirmation were also determined. Methods: We examined laboratory records from the Health Canada Botulism Reference Service and the British Columbia Centre for Disease Control (BCCDC) Public Health Laboratory. The Discharge Abstract Database (DAD) and the Hospital Morbidity Database (HMDB) of the Canadian Institute of Health Information (CIHI) were queried for data on hospitalization of infant botulism cases. The CNDSS was queried for data on reported cases of infant botulism. Results: From 1979 to 2019, 63 laboratory-confirmed cases of infant botulism were confirmed by the Health Canada Botulism Reference Service and the BCCDC Public Health Laboratory for an annual rate of 4.30 cases per million live births. From 1983 to 2018, 57 cases of infant botulism were reported to the CNDSS. Of the 63 cases confirmed by the reference laboratories, the median age of onset was 16 weeks with a range of 2 to 52 weeks. The majority of cases were type A (76%) and B (21%), with single cases of type F and type AB. Of the 23 laboratory-confirmed cases with matched hospital records, 13 were transferred to special care and eight needed ventilator support; no deaths were reported. Conclusion: Spores of C. botulinum are present naturally in the environment, thus diagnosis of infant botulism does not require a history of exposure to high-risk foods such as honey. Stool samples are the most useful diagnostic specimen.

scholarly journals Health Canada publishes Learning from SARS report and recommends expansion of communicable disease surveillance in Canada

2003 ◽  
Vol 7 (41) ◽  
Author(s):  
Keyword(s):  
Public Health ◽  
Advisory Committee ◽  
Disease Surveillance ◽  
Communicable Disease ◽  
Health Canada

Health Canada has published the report of the National Advisory Committee on SARS and Public Health, Learning from SARS: Renewal of public health in Canada

scholarly journals Public Health and Epidemiology Informatics: Recent Research Trends Moving toward Public Health Data Science

2020 ◽  
Vol 29 (01) ◽  
pp. 231-234
Author(s):  
Sébastien Cossin ◽  
Rodolphe Thiébaut ◽  
Keyword(s):  
Public Health ◽  
Rural Communities ◽  
Low Income ◽  
Disease Surveillance ◽  
Data Science ◽  
Health Data ◽  
Low Income Countries ◽  
Editorial Committee ◽  
Disease Surveillance System ◽  
Public Health Data

Objectives: To introduce and summarize current research in the field of Public Health and Epidemiology Informatics. Methods: PubMed searches of 2019 literature concerning public health and epidemiology informatics were conducted and the returned references were reviewed by the two section editors to select 14 candidate best papers. These papers were then peer-reviewed by external reviewers to allow the Editorial Committee a curated selection of the best papers. Results: Among the 835 references retrieved from PubMed, two were finally selected as best papers. The first best paper leverages satellite images and deep learning to identify remote rural communities in low-income countries; the second paper describes the development of a worldwide human disease surveillance system based on near real-time news data from the GDELT project. Internet data and electronic health records are still widely used to detect and monitor disease activity. Identifying and targeting specific audiences for public health interventions is a growing subject of interest. Conclusions: The ever-increasing amount of data available offers endless opportunities to develop methods and tools that could assist public health surveillance and intervention belonging to the growing field of public health Data Science. The transition from proofs of concept to real world applications and adoption by health authorities remains a difficult leap to make.

scholarly journals Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal Clustering (Preprint)

2020 ◽  
Author(s):  
Mehnaz Adnan ◽  
Xiaoying Gao ◽  
Xiaohan Bai ◽  
Elizabeth Newbern ◽  
Jill Sherwood ◽  
...  
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Case Reports ◽  
Surveillance Data ◽  
Data Sources ◽  
Google Trends ◽  
School Absenteeism ◽  
Disease Surveillance System ◽  
Spatiotemporal Clustering

BACKGROUND Over one-third of the population of Havelock North, New Zealand, approximately 5500 people, were estimated to have been affected by campylobacteriosis in a large waterborne outbreak. Cases reported through the notifiable disease surveillance system (notified case reports) are inevitably delayed by several days, resulting in slowed outbreak recognition and delayed control measures. Early outbreak detection and magnitude prediction are critical to outbreak control. It is therefore important to consider alternative surveillance data sources and evaluate their potential for recognizing outbreaks at the earliest possible time. OBJECTIVE The first objective of this study is to compare and validate the selection of alternative data sources (general practice consultations, consumer helpline, Google Trends, Twitter microblogs, and school absenteeism) for their temporal predictive strength for Campylobacter cases during the Havelock North outbreak. The second objective is to examine spatiotemporal clustering of data from alternative sources to assess the size and geographic extent of the outbreak and to support efforts to attribute its source. METHODS We combined measures derived from alternative data sources during the 2016 Havelock North campylobacteriosis outbreak with notified case report counts to predict suspected daily Campylobacter case counts up to 5 days before cases reported in the disease surveillance system. Spatiotemporal clustering of the data was analyzed using Local Moran’s I statistics to investigate the extent of the outbreak in both space and time within the affected area. RESULTS Models that combined consumer helpline data with autoregressive notified case counts had the best out-of-sample predictive accuracy for 1 and 2 days ahead of notified case reports. Models using Google Trends and Twitter typically performed the best 3 and 4 days before case notifications. Spatiotemporal clusters showed spikes in school absenteeism and consumer helpline inquiries that preceded the notified cases in the city primarily affected by the outbreak. CONCLUSIONS Alternative data sources can provide earlier indications of a large gastroenteritis outbreak compared with conventional case notifications. Spatiotemporal analysis can assist in refining the geographical focus of an outbreak and can potentially support public health source attribution efforts. Further work is required to assess the location of such surveillance data sources and methods in routine public health practice.

scholarly journals Infectious Disease Reporting and Outbreak Management Improvement Project

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Donald E. Brannen ◽  
Melissa Branum ◽  
Amy Schmitt
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Reporting System ◽  
Median Number ◽  
Clinical Services ◽  
Disease Reporting ◽  
E Coli ◽  
Disease Surveillance System ◽  
The Impact

ObjectiveImprove disease reporting and outbreak mangement.IntroductionSpecific communicable diseases have to be reported by law withina specific time period. In Ohio, prior to 2001, most of these diseasereports were on paper reports that were reported from providers tolocal health departments. In turn the Communicable Disease Nursemailed the hardcopies to the Ohio Department of Health (ODH).In 2001 the Ohio Disease Reporting System (ODRS) was rolled out toall local public health agencies in Ohio.1ODRS is Ohio’s portion ofthe National Electronic Disease Surveillance System. ODRS shouldnot be confused with syndromic surveillance systems that are fordetecting a disease outbreak before the disease itself is detected.2Chronic disease surveillance system data has been evaluated forlong term trends and potential enhancements.3However, the use ofcommunicable disease reports vary greatly.4 However, the exportdata has not routinely been used for quality improvement purposesof the disease reporting process itself. In December 2014, GreeneCounty Public Health (GCPH) begain a project to improve reportingof communicable diseases and the response to disease outbreaks.MethodsInitial efforts were to understand the current disease reportingprocess: Quantitative management techniques including creating alogic model and process map of the existing process, brainstormingand ranking of issues. The diseases selected to study included:Campylobacteriosis, Cryptosporidiosis, E. coli O157:H7 &shiga toxin-producing E. coli, Giardiasis, Influenza-associatedhospitalization, Legionnaires’ disease, Pertussis, Salmonellosis,and Shigellosis. The next steps included creating a data collectionand analysis plan. An updated process map was created and thepre- and post-process maps were compared to identify areas toimprove. The median number of days were compared before andafter improvements were implemented. Modeling of the impact ofthe process improvements on the median number of days reportedwas conducted. Estimation of the impact in healthy number of daysderived from the reduction in days to report (if any) were calculated.ResultsProcess improvements identified: Ensure all disease reportersuse digital reporting methods preferably starting with electroniclaboratory reporting directly to the online disease reporting system,with other methods such as direct web data entry into system, faxinglab reports, orsecure emailing reports, with no or little hard copy mailing;Centralize incoming email and fax reports (eliminating process steps);Standardize backup staffing procedures for disease reporting staff;Formalize incident command procedures under the authorized personin charge for every incident rather than distribute command betweenenvironmental and clinical services; and place communicable diseasereporting under that single authority rather than clinical services. Thedays to report diseases were reduced from a median of 2 to .5 days(p<.001). All the diseases were improved except for crytosporodiumdue to an outlier report two months late. The estimated societalhealthy days saved were valued at $52,779 in the first eight monthsafter implementation of the improvements.ConclusionsImprovements in disease reporting decreased the reporting timefrom over 2 days to less than 1 day on average. Estimated societalhealthy days saved by this project during the first 9 months was$52,779. Management of early command and control for outbreakresponse was improved.

scholarly journals Potential Early Identification of a Large Campylobacter Outbreak Using Alternative Surveillance Data Sources: Autoregressive Modelling and Spatiotemporal Clustering

10.2196/18281 ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. e18281
Author(s):  
Mehnaz Adnan ◽  
Xiaoying Gao ◽  
Xiaohan Bai ◽  
Elizabeth Newbern ◽  
Jill Sherwood ◽  
...  
Keyword(s):  
Public Health ◽  
Surveillance System ◽  
Disease Surveillance ◽  
Case Reports ◽  
Surveillance Data ◽  
Data Sources ◽  
Google Trends ◽  
School Absenteeism ◽  
Disease Surveillance System ◽  
Spatiotemporal Clustering

Background Over one-third of the population of Havelock North, New Zealand, approximately 5500 people, were estimated to have been affected by campylobacteriosis in a large waterborne outbreak. Cases reported through the notifiable disease surveillance system (notified case reports) are inevitably delayed by several days, resulting in slowed outbreak recognition and delayed control measures. Early outbreak detection and magnitude prediction are critical to outbreak control. It is therefore important to consider alternative surveillance data sources and evaluate their potential for recognizing outbreaks at the earliest possible time. Objective The first objective of this study is to compare and validate the selection of alternative data sources (general practice consultations, consumer helpline, Google Trends, Twitter microblogs, and school absenteeism) for their temporal predictive strength for Campylobacter cases during the Havelock North outbreak. The second objective is to examine spatiotemporal clustering of data from alternative sources to assess the size and geographic extent of the outbreak and to support efforts to attribute its source. Methods We combined measures derived from alternative data sources during the 2016 Havelock North campylobacteriosis outbreak with notified case report counts to predict suspected daily Campylobacter case counts up to 5 days before cases reported in the disease surveillance system. Spatiotemporal clustering of the data was analyzed using Local Moran’s I statistics to investigate the extent of the outbreak in both space and time within the affected area. Results Models that combined consumer helpline data with autoregressive notified case counts had the best out-of-sample predictive accuracy for 1 and 2 days ahead of notified case reports. Models using Google Trends and Twitter typically performed the best 3 and 4 days before case notifications. Spatiotemporal clusters showed spikes in school absenteeism and consumer helpline inquiries that preceded the notified cases in the city primarily affected by the outbreak. Conclusions Alternative data sources can provide earlier indications of a large gastroenteritis outbreak compared with conventional case notifications. Spatiotemporal analysis can assist in refining the geographical focus of an outbreak and can potentially support public health source attribution efforts. Further work is required to assess the location of such surveillance data sources and methods in routine public health practice.

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