Le Système intégré de surveillance des maladies chroniques du Québec (SISMACQ), une approche novatrice

Blais, Claudia; Jean, Sylvie; Sirois, Caroline; Rochette, Louis; Plante, Céline; Larocque, Isabelle; Doucet, Montserrat; Ruel, Guillaume; Simard, M.; Gamache, Philippe; Hamel, Denis; St-Laurent, Danielle; Émond, Valérie

doi:10.24095/hpcdp.34.4.06f

Cited by 9 publications

(7 citation statements)

References 9 publications

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“…The most common health topics were related to chronic conditions including cardiovascular, renal, and respiratory diseases, cancer, and diabetes. The high number of research publications focused on these topics may be explained by their high prevalence in the population and heavy burden on the health care system, as well as the availability of surveillance data (Blais et al, 2014;Ke et al, 2022). These findings also correspond to the most frequent medications mentioned in the articles, such as metformin, heparin, and simvastatin.…”

Section: Discussionmentioning

confidence: 83%

“…The fact that this type of data is regularly and continuously collected through a consistent way without requiring extra resources is a significant advantage for research and allows for more advanced research questions (Timofte et al, 2018). Furthermore, health care administrative databases have been successfully used for disease surveillance (Blais et al, 2014). As those databases get larger and technology improves, new analysis methods are needed to exploit them correctly.…”

Section: Introductionmentioning

confidence: 99%

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Major areas of interest of artificial intelligence research applied to health care administrative data: a scoping review

et al. 2022

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Introduction: The ongoing collection of large medical data has created conditions for application of artificial intelligence (AI) in research. This scoping review aimed to identify major areas of interest of AI applied to health care administrative data.Methods: The search was performed in seven databases: Medline, Embase, CINAHL, Web of science, IEEE, ICM digital library, and Compendex. We included articles published between January 2001 and March 2021, that described research with AI applied to medical diagnostics, pharmacotherapy, and health outcomes data. We screened the full text content and used natural language processing to automatically extract health areas of interest, principal AI methods, and names of medications.Results: Out of 14,864 articles, 343 were included. We determined ten areas of interest, the most common being health diagnostic or treatment outcome prediction (32%); representation of medical data, clinical pathways, and data temporality (i.e., transformation of raw medical data into compact and analysis-friendly format) (22%); and adverse drug effects, drug-drug interactions, and medication cascades (15%). Less attention has been devoted to areas such as health effects of polypharmacy (1%); and reinforcement learning (1%). The most common AI methods were decision trees, cluster analysis, random forests, and support vector machines. Most frequently mentioned medications included insulin, metformin, vitamins, acetaminophen, and heparin.Conclusions: The scoping review revealed the potential of AI application to health-related studies. However, several areas of interest in pharmacoepidemiology are sparsely reported, and the lack of details in studies related to pharmacotherapy suggests that AI could be used more optimally in pharmacoepidemiologic research.

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Section: Discussionmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Major areas of interest of artificial intelligence research applied to health care administrative data: a scoping review

et al. 2022

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“…We used provincial administrative health data from the province of Quebec (population of approximately 8.2 million in 2013) to create this population-based cohort [ 7 ]. In Canada, residents of each province are recipients of provincial universal health care plans, covering physician encounters (including all clinic, inpatient, and emergency room encounters) and hospitalizations.…”

Section: Methodsmentioning

confidence: 99%

Long-term exposure to fine particulate matter and ozone and the onset of systemic autoimmune rheumatic diseases: an open cohort study in Quebec, Canada

et al. 2022

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Objectives To estimate associations between fine particulate matter (PM2.5) and ozone and the onset of systemic autoimmune rheumatic diseases (SARDs). Methods An open cohort of over 6 million adults was constructed from provincial physician billing and hospitalization records between 2000 and 2013. We defined incident SARD cases (SLE, Sjogren’s syndrome, scleroderma, polymyositis, dermatomyositis, polyarteritis nodosa and related conditions, polymyalgia rheumatic, other necrotizing vasculopathies, and undifferentiated connective tissue disease) based on at least two relevant billing diagnostic codes (within 2 years, with at least 1 billing from a rheumatologist), or at least one relevant hospitalization diagnostic code. Estimated PM2.5 and ozone concentrations (derived from remote sensing and/or chemical transport models) were assigned to subjects based on residential postal codes, updated throughout follow-up. Cox proportional hazards models with annual exposure levels were used to calculate hazard ratios (HRs) for SARDs incidence, adjusting for sex, age, urban-versus-rural residence, and socioeconomic status. Results The adjusted HR for SARDS related to one interquartile range increase in PM2.5 (3.97 µg/m3) was 1.12 (95% confidence interval 1.08–1.15), but there was no clear association with ozone. Indirectly controlling for smoking did not alter the findings. Conclusions We found associations between SARDs incidence and PM2.5, but no relationships with ozone. Additional studies are needed to better understand interplays between the many constituents of air pollution and rheumatic diseases.

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“…18 This database comprises health services records provided by the provincial health insurance board (Régie de l'assurance maladie du Québec), which provides universal health insurance coverage to all residents (8 000 000 inhabitants) of the Province of Quebec (Canada). 18 The covered services include those provided in ED, hospitals, outpatient and primary care clinics, home care services, and accommodation in public long-term care centers for eligible people. 19 The Quebec Integrated Chronic Disease Surveillance System notably gives access to these datasets: (i) patient demographic information (e.g.…”

Section: Design and Data Sourcesmentioning

confidence: 99%

Profiles of healthcare use of persons living with dementia: A population‐based cohort study

Dufour,

Margo‐Dermer,

Hudon

et al. 2024

Geriatrics Gerontology Int

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AimPersons living with dementia are a heterogeneous population with complex needs whose healthcare use varies widely. This study aimed to identify the healthcare use profiles in a cohort of persons with incident dementia, and to describe their characteristics.MethodsThis is a retrospective cohort study of health administrative data in Quebec (Canada). The study population included persons who: (i) had an incident dementia diagnosis between 1 April 2015 and 31 March 2016; (ii) were aged ≥65 years and living in the community at the time of diagnosis. We carried out a latent class analysis to identify subgroups of healthcare users. The final number of groups was chosen based on clinical interpretation and statistical indicators.ResultsThe study cohort consisted of 15 584 individuals with incident dementia. Four profiles of healthcare users were identified: (i) Low Users (36.4%), composed of individuals with minimal healthcare use and fewer comorbidities; (ii) Ambulatory Care‐Centric Users (27.5%), mainly composed of men with the highest probability of visiting cognition specialists; (iii) High Acute Hospital Users (23.6%), comprised of individuals mainly diagnosed during hospitalization, with higher comorbidities and mortality rate; and (iv) Long‐Term Care Destined Users (12.5%), who showed the highest proportion of antipsychotics prescriptions and delayed hospitalization discharge.ConclusionsWe identified four distinct subgroups of healthcare users within a population of persons living with dementia, providing a valuable context for the development of interventions tailored to specific needs within this diverse population. Geriatr Gerontol Int 2024; ••: ••–••.

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Le Système intégré de surveillance des maladies chroniques du Québec (SISMACQ), une approche novatrice

Cited by 9 publications

References 9 publications

Major areas of interest of artificial intelligence research applied to health care administrative data: a scoping review

Major areas of interest of artificial intelligence research applied to health care administrative data: a scoping review

Long-term exposure to fine particulate matter and ozone and the onset of systemic autoimmune rheumatic diseases: an open cohort study in Quebec, Canada

Profiles of healthcare use of persons living with dementia: A population‐based cohort study

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