Buckeridge David

Research has shown that Canadian First Nation (FN) populations were disproportionately affected by the 2009 H1N1 influenza pan- demic. However, the mechanisms for the disproportionate outcomes are not well understood. Possibilities such as healthcare access, in- frastructure and housing issues, and pre-existing comorbidities have been suggested. We estimated the odds of hospitalization and inten- sive care unit admission for cases of H1N1 influenza among FN liv- ing in Manitoba, Canada, to determine the effect of location of residency and other factors on disease outcomes during the 2009 H1N1 pandemic.

Objective

We sought to measure from surveillance data the effect of prox- imity to an urban centre (rurality) and other risk factors, (e.g., age, residency on a FN reservation, and pandemic wave) on hospitaliza- tion and intensive care unit admission for severe influenza.

The choice of outbreak detection algorithm and its configuration can result in important variations in the performance of public health surveillance systems. Our work aims to characterize the performance of detectors based on outbreak types. We are using Bayesian networks (BN) to model the relationships between determinants of outbreak detection and the detection performance based on a significant study on simulated data.

Objective

To predict the performance of outbreak detection algorithms under different circumstances which will guide the method selection and algorithm configuration in surveillance systems, to characterize the dependence of the performance of detection algorithms on the type and severity of outbreak, to develop quantitative evidence about determinants of detection performance.

The international Society for Disease Surveillance has successfully brought together practitioners and researchers to share tools, ideas, and strategies to strengthen health surveillance systems. The Society has evolved from an initial focus on syndromic surveillance to a broader consideration of innovation in health surveillance. More recently, ISDS has also worked to support surveillance research and practice in International resource-constrained settings. Individuals who work in surveillance in developed countries outside the USA, however, have received little direct attention from ISDS. The policy and practice contexts in these countries are often quite different than the USA, so there is a need to support surveillance innovation in these countries in a manner that fits the context. Canadian surveillance practitioners and researchers comprise the largest International group of ISDS members, and these members have expressed an interest in working with ISDS to translate surveillance innovations into practice in Canada, where a national surveillance network and forum is lacking. This Round Table will consider how ISDS can help to support members in countries like Canada and will identify next steps for promoting the science and practice of disease surveillance in the Canadian context.

Objective

1) To explore how ISDS can better support researchers and public health practitioners working in the field of disease surveillance outside the United States;

and

2) To identify current surveillance issues in the Canadian public health system where ISDS can support dialogue and action.

 

Outbreaks of waterborne gastrointestinal disease occur routinely in North America, resulting in considerable morbidity, mortality, and cost (Hrudey, Payment et al. 2003). Outbreak detection methods generally attempt to identify anomalies in time, but do not identify the type or source of an outbreak. We seek to develop a framework for both detection and classification of outbreaks using information in both space and time. Outbreak detection can be improved by using simulated outbreak data to build, validate, and evaluate models that aim to improve accuracy and timeliness of outbreak detection.

Objective

To develop a methodological framework for detecting and classifying outbreaks of gastrointestinal disease on the island of Montreal, with the goal of improving early outbreak detection using simulated surveillance data.

Effective use of data for disease surveillance depends critically on the ability to trust and quantify the quality of source data. The Scalable Data Integration for Disease Surveillance project is developing tools to integrate and present surveillance data from multiple sources, with an initial focus on malaria. Consideration of data quality is particularly important when integrating data from diverse clinical, population-based, and other sources. Several global initiatives to reduce the burden of malaria (Presidents Malaria Initiative, Roll Back Malaria Initiative and The Global Fund to Fight AIDS, Tuberculosis and Malaria) have published lists of recommended indicators. Values for these indicators can be obtained from different data sources, with each source having different data quality properties as a consequence of the type of data collected and the method used to collect the data. Our goal is to develop a framework for organizing the data quality (DQ) properties of indicators used for disease surveillance in this setting.

There is growing recognition that an inability to access timely health indicators can hamper both the design and the effective implementation of infectious diseases control interventions. In malaria control, the global use of standard interventions has driven down the burden of disease in many regions. Further gains in high transmission areas and elimination in lower transmission settings, however, will require an enhanced understanding of malaria epidemiology, population characteristics, and efficacy of clinical and public health programs at the local level. Currently, there is a dearth of information available to fine-tune malaria control interventions at the local level. A key obstacle is the fragmentation of data into silos, as existing data cannot be brought together to estimate accurate and timely health metrics.

Objective

Driven by the need to bring malaria surveillance data from different sources together to support evidence-based decision making, we are conducting the “Scalable Data Integration for Disease Surveillance” (SDIDS) project. This project aims to foster the integration of existing surveillance data to support evidence-based decision-making in malaria control and demonstrate a model applicable to other diseases. Central to this initiative is collaboration between academia, governmental and NGO sectors.

Real-time monitoring and analysis of vaccine concerns over time and location could help immunisation programmes to tailor more effective and timely strategies to address specific public health concerns. In recent years attempts [1, 2] are being made to develop a more systematic monitoring of broader public vaccine concerns resulting in vaccine refusals and potential disease outbreaks. Automated sentiment analysis software applications are being developed to detect and track the emergence and spread, geographically and temporally, of online social media reports on vaccines by developing a new application for opinion mining and sentiment analysis. Although many of the current approaches for automated sentiment analysis provide a timely method to assess the sentiment of a population towards vaccination, they do not assess beliefs, perceptions and behaviours. Incorporating semantic approach by using ontologies captures the domain knowledge and supports automated extraction and analysis of text in blog posts related to vaccination.

Objective

This paper presents our approach on design and development of an integrated semantic platform to capture the domain knowledge on vaccine sentiments, beliefs, and behaviours using ontologies. The vaccine sentiment ontology (VASON) provides more structure around the vast amount of unstructured data scattered over blog posts to facilitate blog content analysis, and discovering patterns of words or phrases in blogs text (e.g. specifying topics, themes, sentiment, beliefs and so on). It also assists in revealing opinionated claims and assertions in blogs and specifying the authors, forms, functions, geographical locations, audiences of blogs, as well as bloggers’ motives.

The catchment area of a health-care facility is used to assess health service utilization and calculate population-based rates of disease. Current approaches for catchment definition have significant limitations such as being based solely on distance from the facility or using an arbitrary threshold for inclusion.

Objective

We propose a simple statistical method, the cumulative case ratio, for defining a catchment area using surveillance data.

In Canada, the economic impact of unhealthy eating is estimated at $6.3 billion annually and in the US the estimated cost is $87 billion. Despite the critical need to identify effective diet-related interventions through empirical evaluation, public health practitioners and researchers lack timely access to representative data sources collected at a fine spatial and temporal resolution. Food surveys, for example, are costly, infrequent, delayed, and subject to biases.

The Nielsen Corporation collects data on food purchasing directly from scanners in grocery and convenience stores around the world. These data hold great potential for public health practice. We were interested in using these data to analyze purchases of regular (sugary) soda and water, before and after two interventions aimed at reducing sugary drink consumption. The first intervention, ‘Gobes-tu ça’, was a counter-advertising campaign targeting the age group with the highest consumption of soda, 12-17 year olds. The second intervention, ‘Sois-futé, bois santé’, targeted elementary school students. Both began in the Fall of 2011 and ramped up over time.

Objective

To demonstrate the utility of automatically captured store-level (i.e. point-of-sale) food purchasing data for the surveillance of dietary patterns before and after interventions. We assessed the effects of two interventions in Montreal, Canada that were intended to reduce the consumption of sugary drinks.

Obesity and related chronic diseases cost Canadians several billion dollars annually. Dietary intake, and in particular consumption of carbonated sweetened drinks (soda), has a strong effect on the incidence of obesity and other illness. Marketing research suggests that in-store promotion, and more specifically price discounting, has a strong effect on the purchase of energy-dense products such as soda. Attempts by public health authorities to monitor price discounts are currently limited by a lack of data and methods. Although rarely used in public health surveillance, electronic retail sales data collected around the world by marketing companies such as the Nielsen Corporation have an immense potential to measure dietary choices at high geographical resolution. These scanned sales data are recorded in real-time and they include a detailed product description, price, purchased quantity, store location, and product-specific advertising activities.

Objective

To assess the influence of in-store price discounts on soda purchasing by neighborhood socio-economic status in Montreal, Canada using digital grocery store-level sales data.