Geographical Variation In Medication and Health Resource Use In Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Understanding disease-modifying therapy (DMT) use and healthcare resource utilization by different geographical areas among people living with multiple sclerosis (pwMS) may identify care gaps that can be used to inform policies and practice to ensure equitable care. METHODS: Administrative data was used to identify pwMS on April 1, 2017 (index date) in Alberta. DMT use and healthcare resource utilization were compared between those who resided in various geographical areas over a 2-year post-index period; simple logistic regression was applied. RESULTS: = 12,338), a higher proportion of pwMS who resided in urban areas (versus rural) received ≥ 1 DMT dispensation (32.3% versus 27.4%), had a neurologist (67.7% versus 63.9%), non-neurologist specialist (88.3% versus 82.9%), ambulatory care visit (87.4% versus 85.3%), and MS tertiary clinic visit (59.2% versus 51.7%), and a lower proportion had an emergency department (ED) visit (46.3% versus 62.4%), and hospitalization (20.4% versus 23.0%). Across the provincial health zones, there were variations in DMT selection, and a higher proportion of pwMS who resided in the Calgary health zone, where care is managed by MS tertiary clinic neurologists, had an outpatient visit to a neurologist or MS tertiary clinic versus those who resided in other zones where delivery of MS-related care is more varied. CONCLUSIONS: Urban/rural inequalities in DMT use and healthcare resource utilization appear to exist among pwMS in Alberta. Findings suggest the exploration of barriers with consequent strategies to increase access to DMTs and provide timely outpatient MS care management, particularly for those pwMS residing in rural areas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".