Multiple sclerosis in Canada 2011 to 2031: results of a microsimulation modelling study of epidemiological and economic impacts
Bibliographic record
Abstract
INTRODUCTION: The objective of our study was to present model-based estimates and projections on current and future health and economic impacts of multiple sclerosis (MS) in Canada over a 20-year time horizon (2011-2031). METHODS: Using Statistics Canada's Population Health Microsimulation Model (POHEM) framework, specifically the population-based longitudinal, microsimulation model named POHEM-Neurological, we identified people with MS from health administrative data sources and derived incidence and mortality rate parameters from a British Columbia population-based cohort for future MS incidence and mortality projections. We also included a utility-based measure (Health Utilities Index Mark 3) reflecting states of functional health to allow projections of health-related quality of life. Finally, we estimated caregiving parameters and health care costs from Canadian national surveys and health administrative data and included them as model parameters to assess the health and economic impact of the neurological conditions. RESULTS: The number of incident MS cases is expected to rise slightly from 4051 cases in 2011 to 4794 cases per 100 000 population in 2031, and the number of Canadians affected by MS will increase from 98 385 in 2011 to 133 635 in 2031. The total per capita health care cost (excluding out-of-pocket expenses) for adults aged 20 and older in 2011 was about $16 800 for individuals with MS, and approximately $2500 for individuals without a neurological condition. Thus, after accounting for additional expenditures due to MS (excluding out-of-pocket expenses), total annual health sector costs for MS are expected to reach $2.0 billion by 2031. As well, the average out-of-pocket expenditure for people with MS was around $1300 annually throughout the projection period. CONCLUSION: MS is associated with a significant economic burden on society, since it usually affects young adults during prime career- and family-building years. Canada has a particularly high prevalence of MS, so research such as the present study is essential to provide a better understanding of the current and future negative impacts of MS on the Canadian population, so that health care system policymakers can best plan how to meet the needs of patients who are affected by MS. These findings also suggest that identifying strategies to prevent MS and more effectively treat the disease are needed to mitigate these future impacts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".