Descriptive Epidemiology of Affective Disorders in Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Affective disorders present an important clinical challenge in multiple sclerosis (MS). Due to prohibitive sample size requirements, population-based studies have not yet provided an adequate description of the underlying epidemiology of this association. OBJECTIVE: To describe the epidemiology of affective disorders in MS in a general population sample. METHODS: The study presented here accessed administrative data from a universal healthcare insurance plan in the Canadian province of Alberta. Physician billing data recorded in the Alberta Health Care Insurance Plan was used to identify members of the population >15 years of age with and without MS. Crude and stratified estimates of the association between affective disorders and MS were made. Logistic regression analysis was used to evaluate statistical interactions and to provide adjusted estimates of the association. RESULTS: The estimated prevalence of MS in the population within the targeted age range (2.3 million individuals) was 386/100,000 and that of affective disorders was 7.7%. As expected, an association between MS and affective disorders was identified (crude relative prevalence: 2.2). The association varied in strength over age-sex categories. Although the prevalence of affective disorder was higher in women with MS than men with MS, the association of MS with affective disorders was stronger in men. The strength of association declined with age in both men and women. Affective disorder prevalence in people with MS becomes similar to that of the general population in older age groups. CONCLUSION: Affective disorders occur with an increased frequency in MS. This is true in men and women and across all relevant age groups, although the association gets weaker with advancing age. Higher frequencies of affective disorder occur in women with MS than in men with MS. The frequency of affective disorder in people with MS is highest in the 25-44 age group, and declines in older age categories.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".