A Virtual Community for Disability Advocacy: Development of a Searchable Artificial Intelligence-Supported Platform.

BACKGROUND: The lack of availability of disability data has been identified as a major challenge hindering continuous disability equity monitoring. It is important to develop a platform that enables searching for disability data to expose systemic discrimination and social exclusion, which increase vulnerability to inequitable social conditions. OBJECTIVE: Our project aims to create an accessible and multilingual pilot disability website that structures and integrates data about people with disabilities and provides data for national and international disability advocacy communities. The platform will be endowed with a document upload function with hybrid (automated and manual) paragraph tagging, while the querying function will involve an intelligent natural language search in the supported languages. METHODS: We have designed and implemented a virtual community platform using Wikibase, Semantic Web, machine learning, and web programming tools to enable disability communities to upload and search for disability documents. The platform data model is based on an ontology we have designed following the United Nations Convention on the Rights of Persons with Disabilities (CRPD). The virtual community facilitates the uploading and sharing of validated information, and supports disability rights advocacy by enabling dissemination of knowledge. RESULTS: Using health informatics and artificial intelligence techniques (namely Semantic Web, machine learning, and natural language processing techniques), we were able to develop a pilot virtual community that supports disability rights advocacy by facilitating uploading, sharing, and accessing disability data. The system consists of a website on top of a Wikibase (a Semantic Web-based datastore). The virtual community accepts 4 types of users: information producers, information consumers, validators, and administrators. The virtual community enables the uploading of documents, semiautomatic tagging of their paragraphs with meaningful keywords, and validation of the process before uploading the data to the disability Wikibase. Once uploaded, public users (information consumers) can perform a semantic search using an intelligent and multilingual search engine (QAnswer). Further enhancements of the platform are planned. CONCLUSIONS: The platform ontology is flexible and can accommodate advocacy reports and disability policy and legislation from specific jurisdictions, which can be accessed in relation to the CRPD articles. The platform ontology can be expanded to fit international contexts. The virtual community supports information upload and search. Semiautomatic tagging and intelligent multilingual semantic search using natural language are enabled using artificial intelligence techniques, namely Semantic Web, machine learning, and natural language processing.

The Potential of an Artificial Intelligence for Disability Advocacy: The WikiDisability Project.

Human rights monitoring for people with disabilities is in urgent need for disability data that is shared and available for local and international disability stakeholders (e.g., advocacy groups). Our aim is to use a Wikibase for editing, integrating, storing structured disability related data and to develop a Natural Language Processing (NLP) enabled multilingual search engine to tap into the wikibase data. In this paper, we explain the project first phase.

Preconsult interactive computer-assisted client assessment survey for common mental disorders in a community health centre: a randomized controlled trial.

BACKGROUND: Access disparities for mental health care exist for vulnerable ethnocultural and immigrant groups. Community health centres that serve these groups could be supported further by interactive, computer-based, self-assessments. METHODS: An interactive computer-assisted client assessment survey (iCCAS) tool was developed for preconsult assessment of common mental disorders (using the Patient Health Questionnaire [PHQ-9], Generalized Anxiety Disorder 7-item [GAD-7] scale, Primary Care Post-traumatic Stress Disorder [PTSD-PC] screen and CAGE [concern/cut-down, anger, guilt and eye-opener] questionnaire), with point-of-care reports. The pilot randomized controlled trial recruited adult patients, fluent in English or Spanish, who were seeing a physician or nurse practitioner at the partnering community health centre in Toronto. Randomization into iCCAS or usual care was computer generated, and allocation was concealed in sequentially numbered, opaque envelopes that were opened after consent. The objectives were to examine the interventions' efficacy in improving mental health discussion (primary) and symptom detection (secondary). Data were collected by exit survey and chart review. RESULTS: Of the 1248 patients assessed, 190 were eligible for participation. Of these, 148 were randomly assigned (response rate 78%). The iCCAS (n = 75) and usual care (n = 72) groups were similar in sociodemographics; 98% were immigrants, and 68% were women. Mental health discussion occurred for 58.7% of patients in the iCCAS group and 40.3% in the usual care group (p ≤ 0.05). The effect remained significant while controlling for potential covariates (language, sex, education, employment) in generalized linear mixed model (GLMM; adjusted odds ratio [OR] 2.2; 95% confidence interval [CI] 1.1-4.5). Mental health symptom detection occurred for 38.7% of patients in the iCCAS group and 27.8% in the usual care group (p > 0.05). The effect was not significant beyond potential covariates in GLMM (adjusted OR 1.9; 95% CI 0.9-4.1). INTERPRETATION: The studied intervention holds potential for community health centres to improve mental health discussion. Further research with larger samples should examine the impact on detection and enhance generalizability. Trial registration: ClinicalTrials.gov, no: NCT02023957, registered on Dec. 12, 2013.

The dynamics of the relationship between diabetes incidence and low income: longitudinal results from Canada's National Population Health Survey.

UNLABELLED: This paper sheds light on the dynamic relationship between people's experiences of low income and the development of type 2 diabetes (T2DM) by moving beyond the static perspective provided by cross-sectional studies to a long-term approach informed by longitudinal analyses. METHODS: We analyzed data from the Canadian National Population Health Survey (NPHS) conducted by Statistics Canada from 1994 to 2007. The longitudinal sample is composed of 17,276 respondents (8046 males, 9230 females) 12 years of age or older. We further developed an algorithm to distinguish T2DM from other types of diabetes. Proportional hazard models with time-varying predictors were used to explore the dynamics of the relationship between low income and T2DM. RESULTS: The results suggest that living in low income and experiencing persistent low income are significant precursors of developing T2DM. Being in low income in the previous cycle of T2DM onset was associated with 77% higher risk of T2DM (hazard ratio 1.77; 95% CI: 1.48-2.12). The association between low income and diabetes incidence remains significant after adjusting for age, sex, health behaviors, and psychological distress (hazard ratio 1.24; 95% CI: 1.02-1.52). CONCLUSION: This study contributes to the under-developed research examining longitudinally the relationship between socioeconomic status and diabetes incidence. Employing this long-term approach, this study calls attention to the primary effect of socioeconomic position on diabetes incidence that cannot be explained entirely by behavioral factors. Findings draw attention to the need to address the role played in T2DM by the inequitable distribution of the social determinants of health.

Diabetes prevalence and income: Results of the Canadian Community Health Survey.

UNLABELLED: This paper contributes to a growing body of literature indicating the importance of income as a key socioeconomic status marker in accounting for the increased prevalence of type 2 diabetes (T2DM). METHODS: We analyzed data from the Canadian Community Health Survey cycle 3.1 conducted by Statistics Canada. Descriptive statistics on the prevalence of self-reported diabetes were computed. Multiple logistic regression was used to examine the association between income and prevalence of T2DM. RESULTS: In 2005 an estimated 1.3 million Canadians (4.9%) reported having diabetes. The prevalence of T2DM in the lowest income group is 4.14 times higher than in the highest income group. Prevalence of diabetes decreases steadily as income goes up. The likelihood of diabetes was significantly higher for low-income groups even after adjusting for socio-demographic status, housing, BMI and physical activity. There is a graded association between income and diabetes with odds ratios almost double for men (OR 1.94, 95% CI 1.57-2.39) and almost triple for women (OR 2.75 95% CI 2.24-3.37) in the lowest income compared to those in highest income. CONCLUSION: These findings suggest that strategies for diabetes prevention should combine person-centered approaches generally recommended in the diabetes literature research with public policy approaches that acknowledge the role of socioeconomic position in shaping T2DM prevalence/incidence.

Type 2 diabetes in vulnerable populations: community healthcare providers' perspectives on health service needs and policy implications.

OBJECTIVES: To identify a) ways of enhancing health services for vulnerable populations with type 2 diabetes, taking into account the social determinants of health; and b) health and social policy approaches to reducing the incidence of type 2 diabetes and improving its management. METHODS: Focus groups were held with 18 community healthcare providers at 3 community health centres in Toronto, Ontario. RESULTS: Community healthcare providers' perspectives were organized under 3 themes: a) the compounding effects of social factors on the health of people with diabetes; b) the need for responsive support at multiple levels; and c) barriers to change. Participants showed a good understanding of the impact of social determinants of health on patients' lives, and they had many ideas about prevention/ health promotion and strategies to enhance health services. They seemed less aware of the important role that political advocacy can play. CONCLUSION: Assessment of the policy environment and political advocacy through coalition-building with communities and other health and social sector service providers should become part of healthcare professionals' education and responsibility. Adequate income and access to proper resources would help with the prevention and optimal management of diabetes.

Simulation of the cumulative effects of chemical spills using a spatial-temporal dynamics analysis algorithm.

Accidents in urban areas involving chemical spills demands development of not only feasible emergency strategies, but also a consistent framework to protect the environment and prevent accidents. This can be possible only by a sound understanding of the environmental impact of spills and their potential long-term effects. Furthermore, the impact assessment of chemical spills can not be done disregarding the spatial-temporal pattern of previous exposures reciprocally influenced by both chemical and environmental properties. In this context, this paper presents an analysis framework to quantify the cumulative effects of chemical spills at any given point of a certain area based on a "present" history of exposure coupled with chemical and environmental properties to predict possible scenarios of future exposure and estimate in advance potential alarming levels of pollution. In the present circumstances when increasing knowledge is required for an accurate prediction of spill migration through unsaturated soil, this paper proposes an algorithm capable of incorporating models of increasing complexities to simulate the single-spill events once new advancements in the field are taken. The algorithm developed is illustrated using a simple model with homogenous and steady-state conditions to simulate the single-spill events. A hypothetical case study was constructed to illustrate the analysis steps and the benefits of the algorithm.