Estimating the lagged effect of price discounting: a time-series study on sugar sweetened beverage purchasing in a supermarket.

BACKGROUND: Price discount is an unregulated obesogenic environmental risk factor for the purchasing of unhealthy food, including Sugar Sweetened Beverages (SSB). Sales of price discounted food items are known to increase during the period of discounting. However, the presence and extent of the lagged effect of discounting, a sustained level of sales after discounting ends, is previously unaccounted for. We investigated the presence of the lagged effect of discounting on the sales of five SSB categories, which are soda, fruit juice, sport and energy drink, sugar-sweetened coffee and tea, and sugar-sweetened drinkable yogurt. METHODS: We fitted distributed lag models to weekly volume-standardized sales and percent discounting generated by a supermarket in Montreal, Canada between January 2008 and December 2013, inclusive (n = 311 weeks). RESULTS: While the sales of SSB increased during the period of discounting, there was no evidence of a prominent lagged effect of discounting in four of the five SSB; the exception was sports and energy drinks, where a posterior mean of 28,459 servings (95% credible interval: 2661 to 67,253) of excess sales can be attributed to the lagged effect in the target store during the 6 years study period. CONCLUSION: Our results indicate that studies that do not account for the lagged effect of promotions may not fully capture the effect of price discounting for some food categories.

Generating community measures of food purchasing activities using store-level electronic grocery transaction records: an ecological study in Montreal, Canada.

OBJECTIVE: Geographic measurement of diets is generally not available at areas smaller than a national or provincial (state) scale, as existing nutrition surveys cannot achieve sample sizes needed for an acceptable statistical precision for small geographic units such as city subdivisions. DESIGN: Using geocoded Nielsen grocery transaction data collected from supermarket, supercentre and pharmacy chains combined with a gravity model that transforms store-level sales into area-level purchasing, we developed small-area public health indicators of food purchasing for neighbourhood districts. We generated the area-level indicators measuring per-resident purchasing quantity for soda, diet soda, flavoured (sugar-added) yogurt and plain yogurt purchasing. We then provided an illustrative public health application of these indicators as covariates for an ecological spatial regression model to estimate spatially correlated small-area risk of type 2 diabetes mellitus (T2D) obtained from the public health administrative data. SETTING: Greater Montreal, Canada in 2012. PARTICIPANTS: Neighbourhood districts (n 193). RESULTS: The indicator of flavoured yogurt had a positive association with neighbourhood-level risk of T2D (1·08, 95 % credible interval (CI) 1·02, 1·14), while that of plain yogurt had a negative association (0·93, 95 % CI 0·89, 0·96). The indicator of soda had an inconclusive association, and that of diet soda was excluded due to collinearity with soda. The addition of the indicators also improved model fit of the T2D spatial regression (Watanabe-Akaike information criterion = 1765 with the indicators, 1772 without). CONCLUSION: Store-level grocery sales data can be used to reveal micro-scale geographic disparities and trends of food selections that would be masked by traditional survey-based estimation.

Price discounting as a hidden risk factor of energy drink consumption.

OBJECTIVES: Global consumption of caffeinated energy drinks (CED) has been increasing dramatically despite increasing evidence of their adverse health effects. Temporary price discounting is a rarely investigated but potentially powerful food marketing tactic influencing purchasing of CED. Using grocery transaction records generated by food stores in Montreal, we investigated the association between price discounting and purchasing of CED across socio-economic status operationalized by education and income levels in store neighbourhood. METHODS: The outcome, log-transformed weekly store-level sales of CED, was modelled as a function of store-level percent price discounting, store- and neighbourhood-level confounders, and an interaction term between discounting and each of tertile education and income in store neighbourhood. The model was separately fit to transactions from supermarkets, pharmacies, supercentres, and convenience stores. RESULTS: There were 18,743, 12,437, 3965, and 49,533 weeks of CED sales from supermarkets, pharmacies, supercentres, and convenience stores, respectively. Percent price discounting was positively associated with log sales of CED for all store types, and the interaction between education and discounting was prominent in supercentres: -0.039 [95% confidence interval (CI): -0.051, -0.028] and -0.039 [95% CI: -0.057, -0.021], for middle- and high-education neighbourhoods relative to low-education neighbourhoods, respectively. Relative to low-income areas, the associations of discounting and log CED sales in supercentres for neighbourhoods with middle- and high-income tertile were 0.022 [95% CI: 0.010, 0.033] and 0.015 (95% CI: -0.001, 0.031), respectively. CONCLUSION: Price discounting is an important driver of CED consumption and has a varying impact across community education and income.

RéSUMé: OBJECTIFS: La consommation de boissons énergisantes contenant de la caféine (BEC) a augmenté malgré leurs effets néfastes sur la santé. L'escompte sur les prix fait rarement l'objet d'une étude, mais serait potentiellement une puissante tactique de marketing encourageant l'achat de BEC. À l'aide des registres de transactions d'épiceries montréalaises, nous avons étudié la corrélation entre les rabais et les ventes de BEC selon la situation socio-économique − scolarité et revenu − du quartier. MéTHODES: Les ventes hebdomadaires de BEC en magasins ont régressé en fonction du pourcentage de remise, des facteurs de confusion et d'une interaction entre la remise et le niveau de scolarité et revenu d'un quartier, séparément des transactions des grandes surfaces, pharmacies, supermarchés et dépanneurs. RéSULTATS: Il y a eu 18 743, 12 437, 3 965 et 49 533 semaines de vente BEC dans les grandes surfaces, pharmacies, supermarchés et dépanneurs. Le pourcentage d'actualisation était positivement associé aux ventes de BEC. L'interaction avec l'éducation au tercile était importante dans les supercentres : −0,039 [intervalle de confiance (IC) à 95% : −0,051, −0,028] et −0,039 [IC à 95% : −0,057, −0,021], pour les quartiers au niveau d'éducation moyenne et élevée par rapport aux quartiers au niveau d'éducation faible, respectivement. L'interaction avec le revenu dans les supercentres était de 0,022 [IC à 95% : 0,010, 0,033] et de 0,015 (IC à 95% : −0,001, 0,031) pour le quartier avec tertile à revenu moyen et élevé, respectivement. CONCLUSION: L'actualisation augmente les achats de BEC avec un impact variable selon l'éducation et le revenu d'une communauté.

Application of Machine Learning and Grocery Transaction Data to Forecast Effectiveness of Beverage Taxation.

Sugar Sweetened Beverages (SSB) are the primary source of artificially added sugar and have a casual association with chronic diseases. Taxation of SSB has been proposed, but limited evidence exists to guide this public health policy. Grocery transaction data, with price, discounting and other information for beverage products, present an opportunity to evaluate the likely effects of taxation policy. Sales are often non-linearly associated with price and are affected by the prices of multiple competing brands. We evaluated the predictive performance of Boosted Decision Tree Regression (B-DTR) and Deep Neural Networks (DNN) that account for the non-linearity and competition across brands, and compared their performance to a benchmark regression, the Least Absolute Shrinkage and Selection Operator (LASSO). B-DTR and DNN showed a lower Mean Squared Error (MSE) of prediction in the sales of most major SSB brands in comparison to LASSO, indicating a superior accuracy in predicting the effectiveness of SSB taxation. We demonstrated the application of machine learning methods and large transactional data from grocery stores to forecast the effectiveness food taxation.

An Area-Level Indicator of Latent Soda Demand: Spatial Statistical Modeling of Grocery Store Transaction Data to Characterize the Nutritional Landscape in Montreal, Canada.

Measurement of neighborhood dietary patterns at high spatial resolution allows public health agencies to identify and monitor communities with an elevated risk of nutrition-related chronic diseases. Currently, data on diet are obtained primarily through nutrition surveys, which produce measurements at low spatial resolutions. The availability of store-level grocery transaction data provides an opportunity to refine the measurement of neighborhood dietary patterns. We used these data to develop an indicator of area-level latent demand for soda in the Census Metropolitan Area of Montreal in 2012 by applying a hierarchical Bayesian spatial model to data on soda sales from 1,097 chain retail food outlets. The utility of the indicator of latent soda demand was evaluated by assessing its association with the neighborhood relative risk of prevalent type 2 diabetes mellitus. The indicator improved the fit of the disease-mapping model (deviance information criterion: 2,140 with the indicator and 2,148 without) and enables a novel approach to nutrition surveillance.

Susceptibility to price discounting of soda by neighbourhood educational status: an ecological analysis of disparities in soda consumption using point-of-purchase transaction data in Montreal, Canada.

Introduction: Price discounting is a marketing tactic used frequently by food industries and retailers, but the extent to which education modifies the effect of discounting on the purchasing of unhealthy foods has received little attention. We investigated whether there was a differential association of price discounting of soda with store-level soda purchasing records between 2008 and 2013 by store-neighbourhood education in Montreal, Canada. Methods: Using data on grocery purchase transactions from a sample of supermarkets, pharmacies, supercentres and convenience stores, we performed an ecological time-series analysis, modelling weekly store-level sales of soda as a function of store-level price discounting, store- and neighbourhood-level confounders and an interaction term between discounting and categorical education in the neighbourhood of each store. Results: Analysis by store type (n = 18 743, 12 437, 3965 and 49 533 store-weeks for superstores, pharmacies, supercentres and convenience stores, respectively) revealed that the effect measure modification of discounting by neighbourhood education on soda purchasing was lower in stores in the more educated neighbourhoods, most notably in pharmacies: -0.020 [95% confidence interval (CI): -0.028, -0.012] and -0.038 (95% CI: -0.051, -0.025), for middle- and high-education categories, respectively). Weaker effect modification was observed in convenience stores. There was no evidence of effect modification in supercentres or superstores. Conclusions: Price discounting is an important environmental risk factor for soda purchasing and can widen education inequalities in excess sugar intake across levels of education. Interventions to regulate price discounting warrant further investigation as a public health strategy to improve population nutrition, particularly in lower-education neighbourhoods.

Montreal Accord on Patient-Reported Outcomes (PROs) use series - Paper 5: patient-reported outcomes can be linked to epidemiologic measures to monitor populations and inform public health decisions.

BACKGROUND: A comprehensive picture of population health status can be captured by the incorporation of patient-reported outcome measures into population health monitoring. OBJECTIVE: The aim of the study was to identify the current state, opportunities, and future research related to the utilization of patient-reported outcome measures for population health monitoring in Canada. DISCUSSIONS: Patient-reported outcome measures can help to identify community health needs in public health topics including injury, chronic disease, and communicable disease. They are currently collected in major national surveys in Canada, while electronic medical records, personal health records, and online patient communities could facilitate rapid and cost-effective collection of population-based data. Their effective use requires well-validated measurements and consideration to the factors affecting validity and reproducibility and the modes of administering questionnaires. Methodologically, relevant public health application of patient-reported outcome measures includes analysis assessing measurement comparability across sociodemographically and clinically diverse population and the influence of physical and social environment. Research is needed to develop and apply analytical methods, harmonize patient-reported outcome measures across data sources and health jurisdictions, and link these measures to traditional epidemiologic indicators. Achieving these goals will benefit from collaboration among governmental agencies and expert groups.

A novel application of point-of-sales grocery transaction data to enhance community nutrition monitoring.

Unhealthy eating is the most important preventable cause of global death and disability. Effective development and evaluation of preventive initiatives and the identification of disparities in dietary patterns require surveillance of nutrition at a community level. However, nutrition monitoring currently relies on dietary surveys, which cannot efficiently assess food selection at high spatial resolution. However, marketing companies continuously collect and centralize digital grocery transaction data from a geographically representative sample of chain retail food outlets through scanner technologies. We used these data to develop a model to predict store-level sales of carbonated soft drinks, which was applied to all chain food outlets in Montreal, Canada. The resulting map of purchase patterns provides a foundation for developing novel, high-resolution nutrition indicators that reflect dietary preferences at a community level. These detailed nutrition portraits will allow health agencies to tailor healthy eating interventions and promotion programs precisely to meet specific community needs.

Food insecurity and low CD4 count among HIV-infected people: a systematic review and meta-analysis.

Food insecurity is defined as a limited or uncertain ability to acquire acceptable foods in socially acceptable ways, or limited or uncertain availability of nutritionally adequate and safe foods. While effective antiretroviral treatment can significantly increase CD4 counts in the majority of patients, there are certain populations who remain at relatively low CD4 count levels. Factors possibly associated with poor CD4 recovery have been extensively studied, but the association between food insecurity and low CD4 count is inconsistent in the literature. The objective is to systematically review published literature to determine the association between food insecurity and CD4 count among HIV-infected people. PubMed, Web of Science, ProQuest ABI/INFORM Complete, Ovid Medline and EMBASE Classic, plus bibliographies of relevant studies were systematically searched up to May 2015, where the earliest database coverage started from 1900. Studies that quantitatively assessed the association between food insecurity and CD4 count among HIV-infected people were eligible for inclusion. Study results were summarized using random effects model. A total of 2093 articles were identified through electronic database search and manual bibliographic search, of which 8 studies included in this meta-analysis. Food insecure people had 1.32 times greater odds of having lower CD4 counts compared to food secure people (OR = 1.32, 95% CI: 1.15-1.53) and food insecure people had on average 91 fewer CD4 cells/µl compared to their food secure counterparts (mean difference = -91.09, 95% CI: -156.16, -26.02). Food insecurity could be a potential barrier to immune recovery as measured by CD4 counts among HIV-infected people.

From Cues to Nudge: A Knowledge-Based Framework for Surveillance of Healthcare-Associated Infections.

We propose an integrated semantic web framework consisting of formal ontologies, web services, a reasoner and a rule engine that together recommend appropriate level of patient-care based on the defined semantic rules and guidelines. The classification of healthcare-associated infections within the HAIKU (Hospital Acquired Infections - Knowledge in Use) framework enables hospitals to consistently follow the standards along with their routine clinical practice and diagnosis coding to improve quality of care and patient safety. The HAI ontology (HAIO) groups over thousands of codes into a consistent hierarchy of concepts, along with relationships and axioms to capture knowledge on hospital-associated infections and complications with focus on the big four types, surgical site infections (SSIs), catheter-associated urinary tract infection (CAUTI); hospital-acquired pneumonia, and blood stream infection. By employing statistical inferencing in our study we use a set of heuristics to define the rule axioms to improve the SSI case detection. We also demonstrate how the occurrence of an SSI is identified using semantic e-triggers. The e-triggers will be used to improve our risk assessment of post-operative surgical site infections (SSIs) for patients undergoing certain type of surgeries (e.g., coronary artery bypass graft surgery (CABG)).

Towards probabilistic decision support in public health practice: predicting recent transmission of tuberculosis from patient attributes.

OBJECTIVE: Investigating the contacts of a newly diagnosed tuberculosis (TB) case to prevent TB transmission is a core public health activity. In the context of limited resources, it is often necessary to prioritize investigation when multiple cases are reported. Public health personnel currently prioritize contact investigation intuitively based on past experience. Decision-support software using patient attributes to predict the probability of a TB case being involved in recent transmission could aid in this prioritization, but a prediction model is needed to drive such software. METHODS: We developed a logistic regression model using the clinical and demographic information of TB cases reported to Montreal Public Health between 1997 and 2007. The reference standard for transmission was DNA fingerprint analysis. We measured the predictive performance, in terms of sensitivity, specificity, negative predictive value, positive predictive value, the Receiver Operating Characteristic (ROC) curve and the Area Under the ROC (AUC). RESULTS: Among 1552 TB cases enrolled in the study, 314 (20.2%) were involved in recent transmission. The AUC of the model was 0.65 (95% confidence interval: 0.61-0.68), which is significantly better than random prediction. The maximized values of sensitivity and specificity on the ROC were 0.53 and 0.67, respectively. CONCLUSIONS: The characteristics of a TB patient reported to public health can be used to predict whether the newly diagnosed case is associated with recent transmission as opposed to reactivation of latent infection.

Analytical performance of norovirus real-time RT-PCR detection protocols in Canadian laboratories.

BACKGROUND: Noroviruses (NoVs) are the leading cause of infectious gastroenteritis worldwide. Real-time reverse transcription PCR (real-time RT-PCR) is the preferred method of NoV detection for the majority of testing laboratories. Although the accepted target region for molecular detection assays is the conserved ORF1/ORF2 junction, multiple variations have been published with differences in primers, probes, reagents, multiplexing, etc. OBJECTIVES: We assessed the detection limit for GII.4 NoV real-time RT-PCR assays as well as the ability to detect the non-GII.4 NoV genotypes in each participating laboratory. STUDY DESIGN: A panel of 25 RNA samples was circulated to 18 testing laboratories for comparison of their real-time RT-PCR procedures for NoV detection. RESULTS: Multiple protocols with slight differences in reagents or conditions successfully detected 10 genome equivalents or fewer of NoV per reaction. Multiplex procedures were significantly associated (p=0.04) with false negative results, particularly for a GI.2 strain. Sensitive detection was associated with false positive results (p=0.03). CONCLUSIONS: Overall, the data indicate that comparable results are produced under slightly different assay conditions.