Prevalence of uncontrolled blood pressure in Meknes, Morocco, and its associated risk factors in 2017
Bibliographic record
Abstract
BACKGROUND: Uncontrolled high blood pressure (UBP) can lead to various cardiovascular complications causing an estimated nine million deaths per year worldwide. In Meknes, epidemiologic data on UBP are scarce, depriving programs from evidence-based information that would allow a better management of hypertension. Hence, we aimed to assess UBP prevalence in hypertensive patients treated in Meknes and identify UBP-associated risk factors. METHODS: Between November and December 2017, we conducted a cross-sectional study enrolling 922 hypertensive patients managed at Meknes's primary health care facilities using the multistage sampling method. We interviewed patients face to face to collect their socio-demographic-characteristics, lifestyle behaviours, clinical parameters and the triad care system-patient-physician. Another questionnaire was self-administered by physicians to characterize therapeutic inertia. A multivariate logistic regression analysis highlighted the risk factors associated with UBP. RESULTS: UBP prevalence was 73% with a mean age of 61±11 years (mean±standard deviation) and a male/female ratio of 1/3. Risk factors associated with UBP were: therapeutic inertia (adjusted odds ratio to other variables (AOR) = 18.2, 95% CI [8.35-39.84]), drug non-adherence (AOR = 1.8, 95% CI [1.07-3.04]), obesity/overweight (AOR = 1.6, 95% CI [1.03-2.58]), unemployment (AOR = 1.9, 95% CI [1.09-3.01]), low income (AOR = 2.6, 95% CI [1.01-6.86]), family history of hypertension (AOR = 1.5, 95% CI [1.07-2.08]) and male sex (AOR = 1.6, 95% CI [1.04-2.58]). CONCLUSION: UBP prevalence is high in Meknes. Prevention should firstly focus on raised awareness of hypertensive patients' self-care management. Secondly, health professionals should better comply to the guidelines of anti-hypertensive treatments. Lastly, health professionals should frequently be reminded to reach therapeutic goals to overcome therapeutic inertia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".