Prevalence and factors associated with infectious intestinal diseases in Ras Al Khaimah, United Arab Emirates, 2017: A population-based cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The United Arab Emirates (UAE) is a rapidly developing high-income country that has experienced significant population growth, urbanization, and improvements in the standard of living since its formation in 1971. Published estimates on the prevalence of infectious intestinal diseases (IID) in the UAE are scarce and exclusively based on hospital data. The aim of this study was to provide the first prevalence estimates of IID in the UAE. METHODS: A population-based cross-sectional study design using a telephone-based questionnaire was used to estimate the IID prevalence in the previous 4 weeks in a representative sample of the Ras Al Khaimah (RAK) population from January to September 2017. RESULTS: Data were collected from 1254 participants (57.3% male; 25.2% <18 years). The prevalence of IID was 4.2% in the 4 weeks prior to the interview. Multivariate logistic regression analysis identified that being female (odds ratio (OR) 2.4, 95% confidence interval (CI) 1.2-5.1) and having a middle-range monthly household income (approx. USD 4080-<6800: OR 5.42, 95% CI 1.15-25.48; approx. USD 6800-<9530: OR 7.13, 95% CI 1.47-34.57) were positively associated with IID. Age ≥6 years was negatively associated with IID (OR 0.95, 95% CI 0.90-0.99). Forty-nine percent of participants with an IID sought medical care and 20.8% took over-the-counter medication. CONCLUSIONS: This study provides the first population-based prevalence estimates of IID in the UAE, which are similar to those reported in China (4%), but lower than those reported in Canada (10%), the Netherlands (7%), and the USA (6%).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".