Social support and cognitive function in middle- and older-aged adults: descriptive analysis of CLSA tracking data.

BACKGROUND: Cognitive function is important for healthy aging. Social support availability (SSA) may modify cognitive function. We descriptively examined the association between SSA and cognitive function in a population-level sample of middle- and older-aged adults. METHODS: We analyzed the tracking dataset of the Canadian Longitudinal Study on Aging. Participants aged between 45 and 85 years answered questions about SSA and performed three cognitive tests (Rey Auditory Verbal Learning Test, Animal Fluency Test and Mental Alternation Test) via telephone. We divided global SSA and global cognitive function scores into tertiles and generated contingency tables for comparisons across strata defined by sex, age group, region of residence, urban vs. rural residence and education. RESULTS: The proportion of participants with low global cognitive function was often greater among persons who reported low global SSA. The proportion of persons with high cognitive function was greater in participants with high SSA. The findings were most pronounced for females, 45- to 54-year olds, all regions (especially Québec) except Atlantic Canada, urban dwellers and persons with less than high school education. CONCLUSIONS: Our results can help public health officials focus on providing social supports to subgroups of the population who would benefit the most from policy interventions.

Geographic Clustering of Admissions to Inpatient Psychiatry among Adults with Cognitive Disorders in Ontario, Canada: Does Distance to Hospital Matter?

OBJECTIVE: This study examined relationships among hospital accessibility, socio-economic context, and geographic clustering of inpatient psychiatry admissions for adults with cognitive disorders in Ontario, Canada. METHOD: A retrospective cross-sectional analysis was conducted using admissions data from 71 hospitals with inpatient psychiatry beds in Ontario, Canada between 2011 and 2014. Data included 7,637 unique admissions for 4,550 adults with a DSM-IV diagnosis of Delirium, Dementia, Amnestic and other Cognitive Disorders. Bayesian spatial Poisson regression was employed to examine the relationship between accessibility of general hospitals with psychiatric beds and psychiatric hospitals, area-level marginalization, and hospitalization rate with the risk of admission to inpatient psychiatry among adults with cognitive disorders across 516 Forward Sortation Areas (FSA) in Ontario. RESULTS: Residential instability and the overall hospitalization rate were significantly associated with an increase in the relative risk of admissions to inpatient psychiatry. Accessibility to general hospitals and psychiatric hospitals were marginally insignificant at the 95% credible interval in the final model. Significant geographic clustering of admissions was identified where individuals residing in FSA's with the highest relative risk were 2.0 to 7.1 times more likely to be admitted to inpatient psychiatry compared to the average. CONCLUSIONS: Geographic clustering of inpatient psychiatry admissions for adults with cognitive disorders exists across the Province of Ontario, Canada. At the geographic level, the risk of admission was positively associated with residential instability and the overall hospitalization rate, but not distance to the closest general or psychiatric hospital.

Do marginalized neighbourhoods have less healthy retail food environments? An analysis using Bayesian spatial latent factor and hurdle models.

BACKGROUND: Findings of whether marginalized neighbourhoods have less healthy retail food environments (RFE) are mixed across countries, in part because inconsistent approaches have been used to characterize RFE 'healthfulness' and marginalization, and researchers have used non-spatial statistical methods to respond to this ultimately spatial issue. METHODS: This study uses in-store features to categorize healthy and less healthy food outlets. Bayesian spatial hierarchical models are applied to explore the association between marginalization dimensions and RFE healthfulness (i.e., relative healthy food access that modelled via a probability distribution) at various geographical scales. Marginalization dimensions are derived from a spatial latent factor model. Zero-inflation occurring at the walkable-distance scale is accounted for with a spatial hurdle model. RESULTS: Neighbourhoods with higher residential instability, material deprivation, and population density are more likely to have access to healthy food outlets within a walkable distance from a binary 'have' or 'not have' access perspective. At the walkable distance scale however, materially deprived neighbourhoods are found to have less healthy RFE (lower relative healthy food access). CONCLUSION: Food intervention programs should be developed for striking the balance between healthy and less healthy food access in the study region as well as improving opportunities for residents to buy and consume foods consistent with dietary recommendations.

Identifying food deserts and swamps based on relative healthy food access: a spatio-temporal Bayesian approach.

BACKGROUND: Obesity 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. METHODS: This 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. RESULTS: For 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. CONCLUSIONS: This 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.

An Offenders-Offenses Shared Component Spatial Model for Identifying Shared and Specific Hotspots of Offenders and Offenses: A Case Study of Juvenile Delinquents and Violent Crimes in the Greater Toronto Area.

Objectives: We attempted to apply the Bayesian shared component spatial modeling (SCSM) for the identification of hotspots from two (offenders and offenses) instead of one (offenders or offenses) variables and developed three risk surfaces for (1) common or shared by both offenders and offenses; (2) specific to offenders, and (3) specific to offenses. Methods: We applied SCSM to examine the joint spatial distributions of juvenile delinquents (offenders) and violent crime (offenses) in the York Region of the Greater Toronto Area at the dissemination area level. The spatial autocorrelation, overdispersion, and latent covariates were adjusted by spatially structured and unstructured random effect terms in the model. We mapped the posterior means of the estimated shared and specific risks for identifying the three risk surfaces and types of hotspots. Results: Results suggest that about 50% and 25% of the relative risks of juvenile delinquents and violent crimes, respectively, could be explained by the shared component of offenders and offenses. The spatially structured terms attributed to 48% and 24% of total variations of the delinquents and violent crimes, respectively. Contrastingly, the unstructured random covariates influenced 3% of total variations of the juvenile delinquents and 51% for violent crimes. Conclusions: The Bayesian SCSM presented in this study identifies shared and specific hotspots of juvenile delinquents and violent crime. The method can be applied to other kinds of offenders and offenses and provide new insights into the clusters of high risks that are due to both offenders and offenses or due to offenders or offenses only.

Modelling the spatiotemporal spread of COVID-19 outbreaks and prioritization of the risk areas in Toronto, Canada.

Modelling the spatiotemporal spread of a highly transmissible disease is challenging. We developed a novel spatiotemporal spread model, and the neighbourhood-level data of COVID-19 in Toronto was fitted into the model to visualize the spread of the disease in the study area within two weeks of the onset of first outbreaks from index neighbourhood to its first-order neighbourhoods (called dispersed neighbourhoods). We also model the data to classify hotspots based on the overall incidence rate and persistence of the cases during the study period. The spatiotemporal spread model shows that the disease spread to 1-4 neighbourhoods bordering the index neighbourhood within two weeks. Some dispersed neighbourhoods became index neighbourhoods and further spread the disease to their nearby neighbourhoods. Most of the sources of infection in the dispersed neighbourhood were households and communities (49%), and after excluding the healthcare institutions (40%), it becomes 82%, suggesting the expansion of transmission was from close contacts. The classification of hotspots informs high-priority areas concentrated in the northwestern and northeastern parts of Toronto. The spatiotemporal spread model along with the hotspot classification approach, could be useful for a deeper understanding of spatiotemporal dynamics of infectious diseases and planning for an effective mitigation strategy where local-level spatially enabled data are available.

Bayesian spatial methods for small-area injury analysis: a study of geographical variation of falls in older people in the Wellington-Dufferin-Guelph health region of Ontario, Canada.

OBJECTIVES: To examine falls in older people in the Wellington-Dufferin-Guelph (WDG) health region of Ontario, Canada, and to identify areas with excess RR and associated risk factors, particularly those related to private dwellings. METHODS: Cases of hospitalisation following falls among older people in the WDG health region between 2002 and 2006 were geocoded to the dissemination area level and used in the spatial analysis. The falls data and covariates from the 2006 Canadian census were analysed using Poisson log-linear models with (spatial and non-spatial) random effects at the dissemination area level. A Bayesian approach with Markov chain Monte Carlo simulation allowed the spatial random effects models to be fitted. Map decomposition was used to visualise the results. RESULTS: The percentage of occupied private dwellings requiring repairs and median income were significantly associated with falls in older people in the WDG health region. Twenty-six dissemination areas with high RR of falls in older people in the WDG health region were identified. Map decomposition revealed that RR were also driven by unknown factors that have spatial patterns. CONCLUSIONS: This research identified an association between falls in older people and housing conditions; the higher the percentage of dwellings requiring repairs in an area, the higher its risk of falls in older people. Bayesian spatial modelling accounts for measurement errors and unobserved or unknown risk factors that have spatial patterns. The findings have the potential to contribute to future research in reducing falls in older people and generate more interest in using Bayesian spatial modelling approaches in injury and public health research.

Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling.

Spatiotemporal patterns and trends of COVID-19 at a local spatial scale using Bayesian approaches are hardly observed in literature. Also, studies rarely use satellite-derived long time-series data on the environment to predict COVID-19 risk at a spatial scale. In this study, we modelled the COVID-19 pandemic risk using a Bayesian hierarchical spatiotemporal model that incorporates satellite-derived remote sensing data on land surface temperature (LST) from January 2020 to October 2021 (89 weeks) and several socioeconomic covariates of the 140 neighbourhoods in Toronto. The spatial patterns of risk were heterogeneous in space with multiple high-risk neighbourhoods in Western and Southern Toronto. Higher risk was observed during Spring 2021. The spatiotemporal risk patterns identified 60% of neighbourhoods had a stable, 37% had an increasing, and 2% had a decreasing trend over the study period. LST was positively, and higher education was negatively associated with the COVID-19 incidence. We believe the use of Bayesian spatial modelling and the remote sensing technologies in this study provided a strong versatility and strengthened our analysis in identifying the spatial risk of COVID-19. The findings would help in prevention planning, and the framework of this study may be replicated in other highly transmissible infectious diseases.

Spatiotemporal clusters and the socioeconomic determinants of COVID-19 in Toronto neighbourhoods, Canada.

The aim of this study is to identify spatiotemporal clusters and the socioeconomic drivers of COVID-19 in Toronto. Geographical, epidemiological, and socioeconomic data from the 140 neighbourhoods in Toronto were used in this study. We used local and global Moran's I, and space-time scan statistic to identify spatial and spatiotemporal clusters of COVID-19. We also used global (spatial regression models), and local geographically weighted regression (GWR) and Multiscale Geographically weighted regression (MGWR) models to identify the globally and locally varying socioeconomic drivers of COVID-19. The global regression model identified a lower percentage of educated people and a higher percentage of immigrants in the neighbourhoods as significant predictors of COVID-19. MGWR shows the best fit model to explain the variables affecting COVID-19. The findings imply that a single intervention package for the entire area would not be an effective strategy for controlling COVID-19; a locally adaptable intervention package would be beneficial.

Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review.

The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.

Age- and Sex-Specific Association Between Vegetation Cover and Mental Health Disorders: Bayesian Spatial Study.

BACKGROUND: Despite growing evidence that reduced vegetation cover could be a putative risk factor for mental health disorders, the age- and the sex-specific association between vegetation and mental health disorder cases in urban areas is poorly understood. However, with rapid urbanization across the globe, there is an urgent need to study this association and understand the potential impact of vegetation loss on the mental well-being of urban residents. OBJECTIVE: This study aims to analyze the spatial association between vegetation cover and the age- and sex-stratified mental health disorder cases in the neighborhoods of Toronto, Canada. METHODS: We used remote sensing to detect urban vegetation and Bayesian spatial hierarchical modeling to analyze the relationship between vegetation cover and mental health disorder cases. Specifically, an Enhanced Vegetation Index was used to detect urban vegetation, and Bayesian Poisson lognormal models were implemented to study the association between vegetation and mental health disorder cases of males and females in the 0-19, 20-44, 45-64, and ≥65 years age groups, after controlling for marginalization and unmeasured (latent) spatial and nonspatial covariates at the neighborhood level. RESULTS: The results suggest that even after adjusting for marginalization, there were significant age- and sex-specific effects of vegetation on the prevalence of mental health disorders in Toronto. Mental health disorders were negatively associated with the vegetation cover for males aged 0-19 years (-7.009; 95% CI -13.130 to -0.980) and for both males (-4.544; 95% CI -8.224 to -0.895) and females (-3.513; 95% CI -6.289 to -0.681) aged 20-44 years. However, for older adults in the 45-64 and ≥65 years age groups, only the marginalization covariates were significantly associated with mental health disorder cases. In addition, a substantial influence of the unmeasured (latent) and spatially structured covariates was detected in each model (relative contributions>0.7), suggesting that the variations in area-specific relative risk were mainly spatial in nature. CONCLUSIONS: As significant and negative associations between vegetation and mental health disorder cases were found for young males and females, investments in urban greenery can help reduce the future burden of mental health disorders in Canada. The findings highlight the urgent need to understand the age-sex dynamics of the interaction between surrounding vegetation and urban dwellers and its subsequent impact on mental well-being.

Urban-rural variation in the association between social support availability and cognitive function in middle-aged and older adults: Results from the baseline Tracking Cohort of the Canadian Longitudinal Study on Aging.

The purpose of this study was to investigate if and how the associations between social support availability (SSA) and cognitive function varied across urban, rural, and geographical regions in Canada. Data from a population-level sample of community-dwelling adults aged 45-85 years were obtained from the baseline Tracking Cohort of the Canadian Longitudinal Study on Aging. The associations between SSA and two domains of cognitive function, memory and executive function, were analyzed using multilevel regression models. SSA was positively and significantly associated with both executive function and memory. We found SSA had stronger positive associations with executive function among participants living in rural areas compared to urban areas in all geographical regions; however, geographical variation in the associations between SSA and memory were not supported by model results. Understanding how the associations between cognitive function and modifiable risk factors, including SSA, vary across geographical contexts is important for developing policies and programs to support healthy aging.

High-resolution ultrasonography of gingival biomarkers for periodontal diagnosis in healthy and diseased subjects.

OBJECTIVE: To determine the capacity of ultrasonographic image-based measurements of gingival height and alveolar bone level for monitoring periodontal health and disease. METHODS: Sixteen subjects were recruited from patients scheduled to receive dental care and classified as periodontally healthy (n = 10) or diseased (n = 6) according to clinical guidelines. A 40-MHz ultrasound system was used to measure gingival recession, gingival height, alveolar bone level, and gingival thickness from 66 teeth for comparison to probing measurements of pocket depth and clinical attachment level. Interexaminer variability and comparison between ultrasound measurements and probing measurements was performed via Bland-Altman analysis. RESULTS: Gingival recession and its risk in non-recessed patients could be determined via measurement of the supra- and subgingival cementoenamel junction relative to the gingival margin. Interexaminer bias for ultrasound image analysis was negligible (<0.10 mm) for imaged gingival height (iGH) and 0.45 mm for imaged alveolar bone level (iABL). Diseased subjects had significantly higher imaging measurements (iGH, iABL) and clinical measurements (probing pocket depth, clinical attachment level) than healthy subjects (p < 0.05). Subtraction of the average biologic width from iGH resulted in 83% agreement (≤1 mm difference) between iGH and probing pocket depth measurements. CONCLUSIONS: Ultrasonography has an equivalent diagnostic capacity as gold-standard physical probing for periodontal metrics while offering more detailed anatomical information.

Understanding the Differential Impact of Vegetation Measures on Modeling the Association between Vegetation and Psychotic and Non-Psychotic Disorders in Toronto, Canada.

Considerable debate exists on whether exposure to vegetation cover is associated with better mental health outcomes. Past studies could not accurately capture people's exposure to surrounding vegetation and heavily relied on non-spatial models, where the spatial autocorrelation and latent covariates could not be adjusted. Therefore, a suite of five different vegetation measures was used to separately analyze the association between vegetation cover and the number of psychotic and non-psychotic disorder cases in the neighborhoods of Toronto, Canada. Three satellite-based and two area-based vegetation measures were used to analyze these associations using Poisson lognormal models under a Bayesian framework. Healthy vegetation cover was found to be negatively associated with both psychotic and non-psychotic disorders. Results suggest that the satellite-based indices, which can measure both the density and health of vegetation cover and are also adjusted for urban and environmental perturbations, could be better alternatives to simple ratio- and area-based measures for understanding the effect of vegetation on mental health. A strong dominance of spatially structured latent covariates was found in the models, highlighting the importance of adopting a spatial approach. This study can provide critical guidelines for selecting appropriate vegetation measures and developing spatial models for future population-based epidemiological research.

Small-scale health-related indicator acquisition using secondary data spatial interpolation.

BACKGROUND: Due to the lack of small-scale neighbourhood-level health related indicators, the analysis of social and spatial determinants of health often encounter difficulties in assessing the interrelations of neighbourhood and health. Although secondary data sources are now becoming increasingly available, they usually cannot be directly utilized for analysis in other than the designed study due to sampling issues. This paper aims to develop data handling and spatial interpolation procedures to obtain small area level variables using the Canadian Community Health Surveys (CCHS) data so that meaningful small-scale neighbourhood level health-related indicators can be obtained for community health research and health geographical analysis. RESULTS: Through the analysis of spatial autocorrelation, cross validation comparison, and modeled effect comparison with census data, kriging is identified as the most appropriate spatial interpolation method for obtaining predicted values of CCHS variables at unknown locations. Based on the spatial structures of CCHS data, kriging parameters are suggested and potential small-area-level health-related indicators are derived. An empirical study is conducted to demonstrate the effective use of derived neighbourhood variables in spatial statistical modeling. Suggestions are also given on the accuracy, reliability and usage of the obtained small area level indicators, as well as further improvements of the interpolation procedures. CONCLUSIONS: CCHS variables are moderately spatially autocorrelated, making kriging a valid method for predicting values at unsampled locations. The derived variables are reliable but somewhat smoother, with smaller variations than the real values. As potential neighbourhood exposures in spatial statistical modeling, these variables are more suitable to be used for exploring potential associations than for testing the significance of these associations, especially for associations that are barely significant. Given the spatial dependency of current health-related risks, the developed procedures are expected to be useful for other similar health surveys to obtain small area level indicators.

Association between functional social support and cognitive function in middle-aged and older adults: a protocol for a systematic review.

INTRODUCTION: Maintenance of cognitive function into old age is important for ageing populations. Researchers seek to identify modifiable risk and protective factors for cognitive function. One such modifiable factor is functional social support, that is, one's perception of whether their social network can provide resources such as material help, companionship, information and emotional contact, if needed. While the literature generally reports positive associations between functional social support and cognitive function, results vary according to study methods such as the tool used to measure functional social support or the specific cognitive domain under investigation. Our review will summarise the association between functional social support and cognitive function in middle-aged and older-aged adults who reside in any setting (eg, community dwelling, long-term care facilities). We will also identify sources of discrepant findings between studies. METHODS AND ANALYSIS: This protocol was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols guideline. PubMed, PsycINFO, Sociological Abstracts, Cumulative Index of Nursing and Allied Health Literature (CINAHL) and Scopus will be searched from inception to the present using a search strategy developed with a medical librarian's help. We will supplement the database searches with a grey literature search. English-language or French-language studies with a comparison group will be subject to inclusion, regardless of the measures used to assess functional social support or cognitive function. We will assess risk of bias with the Cochrane Risk of Bias Tool-Version 2 or the Newcastle-Ottawa Scale, narratively synthesise the extracted data and conduct a meta-analysis of studies with similar characteristics (eg, sample age and sex, cognitive function outcomes). Two independent raters will screen articles and assess risk of bias. ETHICS AND DISSEMINATION: This review is timely given the push toward early diagnosis and treatment of dementia/major neurocognitive disorder and other types of cognitive impairment. This protocol does not require a formal ethics review. We will publish our findings in a peer-reviewed journal.

Social support availability is positively associated with memory in persons aged 45-85 years: A cross-sectional analysis of the Canadian Longitudinal Study on Aging.

OBJECTIVES: This study examines the association between a modifiable psychosocial factor, social support availability (SSA), and the memory domain of cognitive function in persons aged 45-85 years. METHODS: We used baseline data from the Canadian Longitudinal Study on Aging (CLSA) (n = 21,241) to conduct multiple linear regression analyses of the association between SSA (overall and four subscales) and memory. The CLSA assessed immediate and delayed recall memory using the Rey Auditory Verbal Learning Test (RAVLT). RESULTS: Higher levels of each type of SSA were positively associated with better performance on both immediate and delayed recall memory. The largest associations (ß coefficients [95% confidence intervals]) for z-score differences on the RAVLT were observed for overall SSA (immediate: 0.07 [0.04-0.10]; delayed recall: 0.06 [0.02-0.09]) and the emotional/informational subscale (immediate: 0.06 [0.03-0.09]; delayed recall: 0.05 [0.02-0.08]). CONCLUSION: SSA is modifiable and positively associated with memory. Public health initiatives to provide support resources such as material aid, emotional support, or companionship may entail positive benefits for memory. Promotion of SSA is also important for policies encouraging early diagnosis and intervention in dementia.

Exploring and visualizing the small-area-level socioeconomic factors, alcohol availability and built environment influences of alcohol expenditure for the City of Toronto: a spatial analysis approach. / Explorer et visualiser à échelle fine les facteurs socioéconomiques, l'accès aux boissons alcoolisées et l'incidence de l'environnement bâti sur les dépenses en alcool dans la ville de Toronto : démarche d'analyse spatiale.

INTRODUCTION: Many Canadians continue to drink alcohol in excess of the recommended low-risk guidelines. In this study, we visualized the geographic variation of licensed premises alcohol expenditures in Toronto and examined the effects of area-level socioeconomic characteristics, alcohol availability and built environment influences on alcohol expenditures at the Dissemination Area (DA) level. METHODS: Dissemination Area average total household expenditures on alcohol from licensed premises, from the 2010 Survey of Household Spending, was the main outcome variable. Moran's I and Local Moran's I were used to quantify geographic variation and determine hot spots and cold spots of expenditure. We used DA-level socioeconomic characteristics from the 2006 Census of Canada, and the density of licensed premises and other built environment characteristics from the 2008 DMTI Spatial and 2010 CanMap datasets to predict alcohol expenditures in multivariate spatial regression models. RESULTS: The results indicated that the most significant area-level predictors of alcohol expenditure were the percentage of individuals in management or finance occupations and the percentage with postsecondary education (one-unit increases associated with 78.6% and 35.0% increases in expenditures respectively). Presence of subway lines in the immediate and neighbouring areas was also significant (one-unit increases resulted in 5% and 28% increases respectively). Alcohol outlet density was also positively associated with alcohol expenditures. CONCLUSION: The associations identified between licensed premises alcohol expenditures and small-area-level characteristics highlight the potential importance of small-area-level factors in understanding alcohol use. Understanding the small-area-level characteristics of expenditures and geographic variation of alcohol expenditures may provide avenues for alcohol use reduction initiatives and policies.

INTRODUCTION: De nombreux Canadiens continuent de boire beaucoup plus d'alcool que ce que préconisent les Directives de consommation d'alcool à faible risque. Dans cette étude, nous avons illustré les variations spatiales associées aux dépenses engagées dans des établissements licenciés à Toronto et nous avons examiné les effets des facteurs socioéconomiques locaux, de l'accès aux boissons alcoolisées et de l'incidence de l'environnement bâti sur les dépenses en alcool à l'échelle des aires de diffusion (AD). MÉTHODOLOGIE: D'après l'Enquête sur les dépenses des ménages de 2010, ce sont les dépenses totales moyennes des ménages à l'échelle des AD en boissons alcoolisées achetées dans des établissements licenciés qui sont la variable ayant la plus grande incidence sur les résultats. L'indice de Moran et l'indice local de Moran ont été utilisés pour quantifier les variations spatiales et pour identifier les points chauds et les points froids en termes de dépenses. Nous avons utilisé, dans divers modèles multidimensionnels de régression spatiale, les caractéristiques socioéconomiques à l'échelle des AD tirées du Recensement de 2006 ainsi que des ensembles de données de 2008 de DMTI Spatial et de 2010 de CanMap concernant la densité des établissements licenciés et les autres caractéristiques socioéconomiques de l'environnement bâti afin de prédire les dépenses en alcool. RÉSULTATS: Les principaux indicateurs à échelle fine associés aux dépenses en alcool étaient le pourcentage de personnes occupant des postes dans les domaines de la gestion ou des finances ainsi que le pourcentage de personnes ayant une éducation post-secondaire (une augmentation d'une unité étant associée à des augmentations respectives de l'ordre de 78,6 % et de 35,0 % des dépenses). La présence de lignes de métro dans l'environnement immédiat et dans le quartier constituait aussi un facteur important (une augmentation d'une unité entraînant des hausses de l'ordre de respectivement 5 % et 28 % des dépenses). La densité des points de vente d'alcool constituait également un facteur positif sur les dépenses en alcool. CONCLUSION: Les liens entre les dépenses en alcool engagées dans des établissements licenciés et les caractéristiques des zones à échelle fine mettent en lumière l'importance que pourraient jouer les facteurs à cette échelle dans notre compréhension de la consommation d'alcool. Bien comprendre les caractéristiques des unités géographiques à échelle fine et les variations spatiales en matière de dépenses en alcool pourrait offrir des outils pour les politiques et initiatives de réduction de la consommation d'alcool.

Exploring Geographic Variation of Mental Health Risk and Service Utilization of Doctors and Hospitals in Toronto: A Shared Component Spatial Modeling Approach.

Mental Health has been known to vary geographically. Different rates of utilization of mental health services in local areas reflect geographic variation of mental health and complexity of health care. Variations and inequalities in how the health care system addresses risks are two critical issues for addressing population mental health. This study examines these issues by analyzing the utilization of mental health services in Toronto at the neighbourhood level. We adopted a shared component spatial modeling approach that allows simultaneous analysis of two main health service utilizations: doctor visits and hospitalizations related to mental health conditions. Our results reflect a geographic variation of both types of mental health service utilization across neighbourhoods in Toronto. We identified hot and cold spots of mental health risks that are common to both or specific to only one type of health service utilization. Based on the evidence found, we discuss intervention strategies, focusing on the hotspots and provision of health services about doctors and hospitals, to improve mental health for the neighbourhoods. Limitations of the study and further research directions are also discussed.

Diving into the consumer nutrition environment: A Bayesian spatial factor analysis of neighborhood restaurant environment.

Neighborhood restaurant environment (NRE) plays a vital role in shaping residents' eating behaviors. While NRE 'healthfulness' is a multi-facet concept, most studies evaluate it based only on restaurant type, thus largely ignoring variations of in-restaurant features. In the few studies that do account for such features, healthfulness scores are simply averaged over accessible restaurants, thereby concealing any uncertainty that attributed to neighborhoods' size or spatial correlation. To address these limitations, this paper presents a Bayesian Spatial Factor Analysis for assessing NRE healthfulness in the city of Kitchener, Canada. Several in-restaurant characteristics are included. By treating NRE healthfulness as a spatially correlated latent variable, the adopted modeling approach can: (i) identify specific indicators most relevant to NRE healthfulness, (ii) provide healthfulness estimates for neighborhoods without accessible restaurants, and (iii) readily quantify uncertainties in the healthfulness index. Implications of the analysis for intervention program development and community food planning are discussed.