Corrigendum to "Parents' attitudes regarding their children's play during COVID-19: Impact of socioeconomic status and urbanicity". [SSM - Popul. Health. (24) (2023) 101-549].

[This corrects the article DOI: 10.1016/j.ssmph.2023.101549.].

Corrigendum: Prioritizing determinants of cognitive function in healthy middle-aged and older adults: insights from a machine learning regression approach in the Canadian longitudinal study on aging.

[This corrects the article DOI: 10.3389/fpubh.2023.1290064.].

The effect of non-medical cannabis retailer proximity on use of mental health services for psychotic disorders in Ontario, Canada.

BACKGROUND: Cannabis is associated with the onset and persistence of psychotic disorders. Evidence suggests that accessibility of substances is associated with an increased risk of use-related harms. We sought to examine the effect of residing in proximity to non-medical cannabis retailers on the prevalence of health service use for psychosis. METHODS: We conducted a cross-sectional study using linked health administrative data, and used geospatial analyses to determine whether people in Ontario, Canada (aged 14-60 years) resided within walking (1.6 km) or driving (5.0 km) distance of non-medical cannabis retailers (open as of February-2020). We identified outpatient visits, emergency department (ED) visits, and hospitalizations for psychotic disorders between 01-April-2019 and 17-March-2020. We used zero-inflated Poisson regression models and gamma generalized linear models to estimate the association between cannabis retailer proximity and indicators of health service use. RESULTS: Non-medical cannabis retailers were differentially located in areas with high levels of marginalization and pre-existing health service use for psychosis. People residing within walking or driving distance of a cannabis retailer had a higher rate of psychosis-related outpatient visits, ED visits, and hospitalizations, compared to people living outside these areas. This effect was stronger among those with no prior service use for psychosis. CONCLUSIONS: Proximity to a non-medical cannabis retailer was associated with higher health service use for psychosis, even after adjustment for prior health service use. These findings suggest that opening of non-medical cannabis retailers could worsen the burden of psychosis on mental health services in areas with high-risk populations.

Parents' attitudes regarding their children's play during COVID-19: Impact of socioeconomic status and urbanicity.

Objectives: The COVID-19 pandemic has had a profound impact on the daily routines of parents and children. This study explored the influence of socioeconomic status (SES) and urbanicity on parents' attitudes toward their children's active play opportunities 6 months and 1.5 years into COVID-19. Methods: A sample of 239 Ontario parents of children aged 12 and younger completed two online surveys (August-December 2020; 2021) to assess parents' intentions, beliefs, and comforts concerning their child's eventual return to play, in addition to various sociodemographic and physical activity variables. Descriptive analyses were run as well as an exploratory factor analysis (EFA) was conducted to group the 14 attitude items into subscales for analysis, to ensure reliability and validity of attitude measures. Results: In general, parents in communities with more urban features (e.g., densely populated areas), single-parents, full-time employed parents, and parents with lower-incomes were more hesitant to return their children to active play during the pandemic. Conclusion: Findings from this work highlight SES and urbanicity disparities that continue to exist during COVID-19.

Prevalence of dementia, heart disease and stroke in community-dwelling adults in Canada, 2016-2021: opportunities for joint prevention.

INTRODUCTION: This aim of this study is to provide updated estimates on the prevalence of dementia, heart disease, and stroke in Canadian communities. Targeting all three conditions together, at the community level, may be key to disease prevention and health aging in the Canadian population. METHODS: Using nationwide health survey data, we calculated the age-standardized prevalence of self-reported dementia, heart disease and stroke in adults aged 18 years and over residing in Canadian communities from 2016 to 2021. Poisson regression models were used to detect statistically significant changes in the prevalence of all three conditions from 2016 to 2021. RESULTS: Less than 1% (~ 175,000 individuals) of adults residing in Canadian communities reported dementia, 5% (~ 1.5 million individuals) reported heart disease, and more than 1% (~ 370,000 individuals) reported stroke annually from 2016 to 2021. Overall, the age-standardized prevalence for stroke decreased minimally from 2016 to 2021 (p = 0.0004). Although the age-standardized prevalence of heart disease and dementia decreased from 2016 to 2018, subsequent increases in prevalence from 2018 to 2021 led to a lack of overall statistically significant changes from 2016 to 2021 (p = 0.10 for heart disease and p = 0.37 for dementia). CONCLUSION: Recent increases in the prevalence of dementia, heart disease and stroke in Canadian communities threaten to reverse any gains in vascular disease prevention over the past six years. Findings reveal the urgent need for intensified prevention efforts that are community-based with a focus on joint reduction in the shared risk factors contributing to all three diseases.

Who Gets the Flu? Individualized Validation of Influenza-like Illness in Urban Spaces.

Urban dwellers are exposed to communicable diseases, such as influenza, in various urban spaces. Current disease models are able to predict health outcomes at the individual scale but are mostly validated at coarse scales due to the lack of fine-scaled ground truth data. Further, a large number of transmission-driving factors have been considered in these models. Because of the lack of individual-scaled validations, the effectiveness of factors at their intended scale is not substantiated. These gaps significantly undermine the efficacy of the models in assessing the vulnerability of individuals, communities, and urban society. The objectives of this study are twofold. First, we aim to model and, most importantly, validate influenza-like illness (ILI) symptoms at the individual scale based on four sets of transmission-driving factors pertinent to home-work space, service space, ambient environment, and demographics. The effort is supported by an ensemble approach. For the second objective, we investigate the effectiveness of the factor sets through an impact analysis. The validation accuracy reaches 73.2-95.1%. The validation substantiates the effectiveness of factors pertinent to urban spaces and unveils the underlying mechanism that connects urban spaces and population health. With more fine-scaled health data becoming available, the findings of this study may see increasing value in informing policies that improve population health and urban livability.

How regularly do people visit service places?

It is often believed that regularities are embedded in mobile behaviors. Highly regular mobile behaviors, such as daily commutes between home and workplace, have been actively investigated in the context of health risks. Less regular mobile behaviors, such as visits to service places (e.g., supermarkets and healthcare facilities), have not received much attention. This study explores the regularity in service place visits using a deep learning method and the effect of place type on the stability of recurring visits using an entropy assessment. Results reveal both periodic and bursty visit behaviors to service places. The periodic visits are prominent on the weekly and bi-weekly scales, and the bursty visits dominate the multi-day scales. Service place type indeed affects the stability of recurring visits, and certain place types have the strongest effect. The research findings substantially expand the knowledge of mobile behaviors and are valuable in informing both visitor-based and place-based health risks.

Prioritizing determinants of cognitive function in healthy middle-aged and older adults: insights from a machine learning regression approach in the Canadian longitudinal study on aging.

Introduction: The preservation of healthy cognitive function is a crucial step toward reducing the growing burden of cognitive decline and impairment. Our study aims to identify the characteristics of an individual that play the greatest roles in determining healthy cognitive function in mid to late life. Methods: Data on the characteristics of an individual that influence their health, also known as determinants of health, were extracted from the baseline cohort of the Canadian Longitudinal Study of Aging (2015). Cognitive function was a normalized latent construct score summarizing eight cognitive tests administered as a neuropsychological battery by CLSA staff. A higher cognitive function score indicated better functioning. A penalized regression model was used to select and order determinants based on their strength of association with cognitive function. Forty determinants (40) were entered into the model including demographic and socioeconomic factors, lifestyle and health behaviors, clinical measures, chronic diseases, mental health status, social support and the living environment. Results: The study sample consisted mainly of White, married, men and women aged 45-64 years residing in urban Canada. Mean overall cognitive function score for the study sample was 99.5, with scores ranging from 36.6 to 169.2 (lowest to highest cognitive function). Thirty-five (35) determinants were retained in the final model as significantly associated with healthy cognitive functioning. The determinants demonstrating the strongest associations with healthy cognitive function, were race, immigrant status, nutritional risk, community belongingness, and satisfaction with life. The determinants demonstrating the weakest associations with healthy cognitive function, were physical activity, greenness and neighborhood deprivation. Conclusion: Greater prioritization and integration of demographic and socioeconomic factors and lifestyle and health behaviors, such greater access to healthy foods and enhancing aid programs for low-income and immigrant families, into future health interventions and policies can produce the greatest gains in preserving healthy cognitive function in mid to late life.

Network effects in influenza spread: The impact of mobility and socio-economic factors.

This paper introduces new methods of modeling and analyzing social networks that emerge in the context of disease spread. Four methods of constructing informative networks are presented, two of which use. static data and two use temporal data, namely individual citizen mobility observations taken over an extensive period of time. We show how the built networks can be analyzed, and how the numerical results can be interpreted, using network permutation-based surprise analysis. In doing so, we explain the relationship of surprise analysis with conventional network hypothesis testing and Quadratic Assignment Procedure regression. Surprise analysis is more comprehensive, and can be without limitation performed with any form(s) of network subgraphs, including those with multiple nodal attributes, weighted links, and temporal features. To illustrate our methodological work in application, we put them to use for interpreting networks constructed from the data collected over one year in an observational study in Buffalo and Erie counties in New York state during the 2016-2017 influenza season. Even with the limitations in the data size, our methods are able to reveal the global (city- and season-wide) patterns in the spread of influenza, taking into account population mobility and socio-economic factors.

What drives disease flows between locations?

Communicable diseases 'flow' between locations. These flows dictate where and when certain communities will be affected. While the prediction of disease flows is essential for the timely intervention of epidemics, few studies have addressed this critical issue. This study predicts disease flows during an epidemic by considering the epidemiological, network, and temporal contextual factors using a deep learning approach. A series of scenario analyses helps identify the effects of these contextual factors on disease flows. Results show that the extended spatial-temporal effect of the epidemiological factors stimulates disease flows. The compound effects of the network factors enhance the transmission efficiency of these flows. Lastly, the temporal effect accelerates the combined effects of epidemiological and network factors on the flows. Findings of this study reveal the intricate nature of disease flows and lay a solid foundation for real-time surveillance of epidemics and pandemics to inform timely interventions for a broad range of communicable diseases.

A location-centric network approach to analyzing epidemic dynamics.

Recent health threats, such as the SARS, H1N1, and Ebola pandemics, have stimulated great interest in network models to study the transmission of communicable diseases through human interaction and mobility. Most current network models have focused on an individual-centric perspective where individuals are represented as nodes, and the interactions among them as edges. Few of these models are concerned with the discovery of the spatial patterns and dynamics of epidemics. We propose a location-centric, transmission network approach, in which nodes denote locations and edges denote possible disease transmissions between locations. We then identify the dynamics of transmission flows, the dynamics of critical locations, and the spatial-temporal extent of transmission pathways to assess the impact of these spatial dynamics on the evolution of an epidemic. Results show that transmission flows shift from elementary schools to middle schools and finally universities and professional schools at different phases of an epidemic. Critical locations, identified using network analysis, are responsible for the upsurge in transmission flows during the peaks of the epidemic. The length of transmission pathways shows a power law distribution and their spatial extent is rather small. Insights gained from this study will help devise spatially sensitive strategies to control communicable diseases.