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BackgroundObesity and other adverse health outcomes are influenced by individual- and neighbourhood-scale risk factors, including the food environment. At the small-area scale, past research has analysed spatial patterns of food environments for one time period, overlooking how food environments change over time. Further, past research has infrequently analysed relative healthy food access (RHFA), a measure that is more representative of food purchasing and consumption behaviours than absolute outlet density.MethodsThis research applies a Bayesian hierarchical model to analyse the spatio-temporal patterns of RHFA in the Region of Waterloo, Canada, from 2011 to 2014 at the small-area level. RHFA is calculated as the proportion of healthy food outlets (healthy outlets/healthy + unhealthy outlets) within 4-km from each small-area. This model measures spatial autocorrelation of RHFA, temporal trend of RHFA for the study region, and spatio-temporal trends of RHFA for small-areas.ResultsFor the study region, a significant decreasing trend in RHFA is observed (-0.024), suggesting that food swamps have become more prevalent during the study period. For small-areas, significant decreasing temporal trends in RHFA were observed for all small-areas. Specific small-areas located in south Waterloo, north Kitchener, and southeast Cambridge exhibited the steepest decreasing spatio-temporal trends and are classified as spatio-temporal food swamps.ConclusionsThis research demonstrates a Bayesian spatio-temporal modelling approach to analyse RHFA at the small-area scale. Results suggest that food swamps are more prevalent than food deserts in the Region of Waterloo. Analysing spatio-temporal trends of RHFA improves understanding of local food environment, highlighting specific small-areas where policies should be targeted to increase RHFA and reduce risk factors of adverse health outcomes such as obesity.

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Background The echinococcosis is prevalent in 10 provinces /autonomous region in western and northern China. Epidemiological survey of echinococcosis in China in 2012 showed the average prevalence of four counties in Tibet Autonomous Region (TAR) is 4.23%, much higher than the average prevalence in China (0.24%). It is important to understand the transmission risks and the prevalence of echinococcosis in human and animals in TAR. Methods A stratified and proportionate sampling method was used to select samples in TAR. The selected residents were examined by B-ultrasonography diagnostic, and the faeces of dogs were tested for the canine coproantigen against Echinococcus spp . using enzyme-linked immunosorbent assay. The internal organs of slaughtered domestic animals were examined by visual examination and palpation. The awareness of the prevention and control of echinococcosis among of residents and students was investigated using questionnaire. All data were inputted using double entry in the Epi Info database, with error correction by double-entry comparison, the statistical analysis of all data was processed using SPSS 21.0, and the map was mapped using ArcGIS 10.1, the data was tested by Chi-square test and Cochran-Armitage trend test. Results A total of 80 384 people, 7564 faeces of dogs, and 2103 internal organs of slaughtered domestic animals were examined. The prevalence of echinococcosis in humans in TAR was 1.66%, the positive rate in females (1.92%) was significantly higher than that in males (1.41%), ( χ 2 = 30.31, P < 0.01), the positive rate of echinococcosis was positively associated with age ( χ 2 trend = 423.95, P < 0.01), and the occupational populations with high positive rates of echinococcosis were herdsmen (3.66%) and monks (3.48%). The average positive rate of Echinococcus coproantigen in TAR was 7.30%. The positive rate of echinococcosis in livestock for the whole region was 11.84%. The average awareness rate of echinococcosis across the region was 33.39%. Conclusions A high prevalence of echinococcosis is found across the TAR, representing a very serious concern to human health. Efforts should be made to develop an action plan for echinococcosis prevention and control as soon as possible, so as to control the endemic of echinococcosis and reduce the medical burden on the population. Electronic supplementary material The online version of this article (10.1186/s40249-019-0537-5) contains supplementary material, which is available to authorized users.

Conventional methods used to identify crime hotspots at the small‐area scale are frequentist and employ data for one time period. Methodologically, these approaches are limited by an inability to overcome the small number problem, which occurs in spatiotemporal analysis at the small‐area level when crime and population counts for areas are low. The small number problem may lead to unstable risk estimates and unreliable results. Also, conventional approaches use only one data observation per area, providing limited information about the temporal processes influencing hotspots and how law enforcement resources should be allocated to manage crime change. Examining violent crime in the Regional Municipality of York, Ontario, for 2006 and 2007, this research illustrates a Bayesian spatiotemporal modeling approach that analyzes crime trend and identifies hotspots while addressing the small number problem and overcoming limitations of conventional frequentist methods. Specifically, this research tests for an overall trend of violent crime for the study region, determines area‐specific violent crime trends for small‐area units, and identifies hotspots based on crime trend from 2006 to 2007. Overall violent crime trend was found to be insignificant despite increasing area‐specific trends in the north and decreasing area‐specific trends in the southeast. Posterior probabilities of area‐specific trends greater than zero were mapped to identify hotspots, highlighting hotspots in the north of the study region. We discuss the conceptual differences between this Bayesian spatiotemporal method and conventional frequentist approaches as well as the effectiveness of this Bayesian spatiotemporal approach for identifying hotspots from a law enforcement perspective.