Sex-specific-evaluation of metabolic syndrome prevalence in Algeria: insights from the 2016–2017 non-communicable diseases risk factors survey
Bibliographic record
Abstract
Metabolic syndrome (MetS) is a core driver of cardiovascular diseases (CVD); however, to date, gender differences in MetS prevalence and its components have not been assessed in the Algerian adult general population. This study aimed to determine the gender differences in MetS prevalence and its components, in the general population of Algeria. Secondary analysis was performed on data from the Algerian 2016-2017 non-communicable disease risk factor survey. MetS was determined according to the harmonized Joint Interim Statement criteria. A Poisson regression model based on Generalised Estimating Equations was used to estimate the adjusted prevalence ratios (aPR) for the sex-specific factors associated with MetS. Overall, the prevalence of MetS was 34.0% (95% CI 32.4-35.6). MetS prevalence in women and men was 39.1% (95% CI 37.0-41.3) and 29.1% (95% CI 27.2-31.2), respectively. The most frequent triad was the clustering of abdominal obesity with low HDL-cholesterol and high blood pressure among women (8.9%; 95% CI [8.0-10.0]) and low HDL-cholesterol with high blood pressure and hyperglycaemia among men (5.2%; 95% CI [4.3-6.3]). Increasing age (aPR 3.21 [2.35-4.39] in men and aPR 3.47 [2.86-4.22] in women), cohabitation (aPR 1.14 [1.05-1.24]), women residing in urban areas (aPR 1.13 [1.01-1.26]), men with higher educational levels (aPR 1.39 [1.14-1.70]), and men with insufficient physical activity (aPR 1.16 [1.05-1.30]) were associated with higher risk of MetS. In this population-based study, one in three Algerian adults had MetS, and key components including abdominal obesity, low HDL-cholesterol, and high blood pressure, are very common, especially in women. Reinforcing interventions for weight management targeting married women living in urban areas and improving sufficient physical activity in men with higher socioeconomic status could provide maximal health gains and stem the CVD epidemic in Algeria.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".