Search

Infectious disease models can be of great use for understanding the underlying mechanisms that influence the spread of diseases and predicting future disease progression. Modeling has been increasingly used to evaluate the potential impact of different control measures and to guide public health policy decisions. In recent years, there has been rapid progress in developing spatio-temporal modeling of infectious diseases and an example of such recent developments is the discrete-time individual-level models (ILMs). These models are well developed and provide a common framework for modeling many disease systems; however, they assume the probability of disease transmission between two individuals depends only on their spatial separation and not on their spatial locations. In cases where spatial location itself is important for understanding the spread of emerging infectious diseases and identifying their causes, it would be beneficial to incorporate the effect of spatial location in the model. In this study, we thus generalize the ILMs to a new class of geographically dependent ILMs, to allow for the evaluation of the effect of spatially varying risk factors (e.g., education, social deprivation, environmental), as well as unobserved spatial structure, upon the transmission of infectious disease. Specifically, we consider a conditional autoregressive (CAR) model to capture the effects of unobserved spatially structured latent covariates or measurement error. This results in flexible infectious disease models that can be used for formulating etiological hypotheses and identifying geographical regions of unusually high risk to formulate preventive action. The reliability of these models is investigated on a combination of simulated epidemic data and Alberta seasonal influenza outbreak data ($2009$). This new class of models is fitted to data within a Bayesian statistical framework using Markov chain Monte Carlo methods. [ABSTRACT FROM AUTHOR]

Objective: Increasing evidence links unhealthy food environments with diet quality and overweight/obesity. Recent evidence has demonstrated that relative food environment measures outperform absolute measures. Few studies have examined the interplay between these two measures. We examined the separate and combined effects of the absolute and relative densities of unhealthy food outlets within 1600 m buffers around elementary schools on children's diet- and weight-related outcomes.Design: This is a cross-sectional study of 812 children from thirty-nine schools. The Youth Healthy Eating Index (Y-HEI) and daily vegetables and fruit servings were derived from the Harvard Food Frequency Questionnaire for Children and Youth. Measured heights and weights determined BMI Z-scores. Food outlets were ranked as healthy, somewhat healthy and unhealthy according to provincial paediatric nutrition guidelines. Multilevel mixed-effects regression models were used to assess the effect of absolute (number) and relative (proportion) densities of unhealthy food outlets within 1600 m around schools on diet quality and weight status.Setting: Two urban centres in the province of Alberta, Canada.Participants: Grade 5 students (10-11 years).Results: For children attending schools with a higher absolute number (36+) of unhealthy food outlets within 1600 m, every 10 % increase in the proportion of unhealthy food outlets was associated with 4·1 lower Y-HEI score and 0·9 fewer daily vegetables and fruit.Conclusions: Children exposed to a higher relative density of unhealthy food outlets around a school had lower diet quality, specifically in areas where the absolute density of unhealthy food outlets was also high. [ABSTRACT FROM AUTHOR]