Multimorbidity, control and treatment of non- communicable diseases among primary healthcare attenders in the Western Cape, South Africa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: South Africa (SA) is facing a heavy burden of non-communicable diseases (NCDs). Few studies address multimorbidity, control and treatment of NCDs in patients attending primary healthcare (PHC) clinics. OBJECTIVES: To describe multimorbidity, related risk factors, disease severity and treatment status of patients with four important NCDs attending public sector PHC clinics in two districts in SA. METHODS: A cross-sectional sample of patients completed baseline data collection for a randomised controlled trial of a health systems intervention. The study population comprised adults attending PHC clinics in the Eden and Overberg districts of the Western Cape in 2011. Four subgroups of patients were identified: hypertension, diabetes, chronic respiratory disease and depression. A total of 4 393 participants enrolled from 38 clinics completed a baseline structured questionnaire and had measurements taken. Prescription data were recorded. RESULTS: Of participants with hypertension, diabetes, respiratory disease and depression, 80%, 92%, 88% and 80%, respectively, had at least one of the other three conditions. There were low levels of control and treatment: 59% of participants with hypertension had a blood pressure ≥140/90 mmHg, the mean haemoglobin A1c (HbA1c) value in participants with diabetes was 9%, 12% of participants in the depression group were prescribed an antidepressant at a therapeutic dose, and 48% of respiratory participants were prescribed a b2-agonist and 34% an inhaled corticosteroid. CONCLUSION: Considerable multimorbidity and unmet treatment needs exist among patients with NCDs attending public sector PHC clinics. Improved strategies are required for diagnosing and managing NCDs in this sector.
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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 | 0.000 |
| 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 | 0.001 |
| 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 it