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Mamiya Hiroshi

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.

Referenced File
Submitted by elamb on

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.

Submitted by teresa.hamby@d… on

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.

Submitted by teresa.hamby@d… on
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