Multimorbidity, control and treatment of non- communicable diseases among primary healthcare attenders in the Western Cape, South Africa
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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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".