Multiple Sclerosis incidence rate in southern Iran: A Bayesian epidemiological study
Bibliographic record
Abstract
Abstract Background: Multiple Sclerosis (MS) remains to be a public health challenge, due to its unknown biological mechanism and clinical impact on young people. The prevalence of this disease in Iran is reported to be 5.3 to 74.28 per 100000 cases. Due to high prevalence of this disease in Fars province, this study aimed to assess the distribution of MS in this region in southern Iran by evaluating its covariates.Method: Data from 5,468 patients diagnosed with MS were collected, according to the McDonald’s criteria, which was reported by the MS Society of Fars from 1991 until 2016. Bayesian spatio-temporal models was also used to describe MS incidence in Fars province. We also investigated the association between overall MS incidence rate and the overall percentage of vitamin D intake, smokers in the population as well as the overall percentage of people with normal BMI as well as alcohol consumption in a population from 1991 until 2016 by Besag, York and Mollie's (BYM) model.Results: County-level crude incidence rates ranged from 0.22 to 11.31 cases per 100,000 population. The highest relative risk was estimated at 1.8 in the city of Shiraz, the capital of Fars province while the lowest relative risk was estimated at 0.11 in Zarindasht County in southern Fars. The percentages of vitamin D3 intake was significantly associated with the incidence of MS. Although 1% increase in Vitamin D3 intake is associated with 2% decrease in the risk of MS, 1% increase in smoking is associated with 16% increase in the risk of MS, respectively.Conclusion: Spatial analysis of MS showed low incidence rate of this disease in the south and south east of Fars province, which is due to the effect of different covariates. As suggested by previous studies, vitamin D and smoking among all covaiates might be associated with high incidence of MS.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".