The burden of hypertension, diabetes mellitus, and cardiovascular risk factors among adult Malawians in HIV care: consequences for integrated services
Bibliographic record
Abstract
BACKGROUND: Hypertension and diabetes prevalence is high in Africans. Data from HIV infected populations are limited, especially from Malawi. Integrating care for chronic non-communicable co-morbidities in well-established HIV services may provide benefit for patients by preventing multiple hospital visits but will increase the burden of care for busy HIV clinics. METHODS: Cross-sectional study of adults (≥18 years) at an urban and a rural HIV clinic in Zomba district, Malawi, during 2014. Hypertension and diabetes were diagnosed according to stringent criteria. Proteinuria, non-fasting lipids and cardio/cerebro-vascular disease (CVD) risk scores (Framingham and World Health Organization/International Society for Hypertension) were determined. The association of patient characteristics with diagnoses of hypertension and diabetes was studied using multivariable analyses. We explored the additional burden of care for integrated drug treatment of hypertension and diabetes in HIV clinics. We defined that burden as patients with diabetes and/or stage II and III hypertension, but not with stage I hypertension unless they had proteinuria, previous stroke or high Framingham CVD risk. RESULTS: Nine hundred fifty-two patients were enrolled, 71.7% female, median age 43.0 years, 95.9% on antiretroviral therapy (ART), median duration 47.7 months. Rural and urban patients' characteristics differed substantially. Hypertension prevalence was 23.7% (95%-confidence interval 21.1-26.6; rural 21.0% vs. urban 26.5%; p = 0.047), of whom 59.9% had stage I (mild) hypertension. Diabetes prevalence was 4.1% (95%-confidence interval 3.0-5.6) without significant difference between rural and urban settings. Prevalence of proteinuria, elevated total/high-density lipoprotein-cholesterol ratio and high CVD risk score was low. Hypertension diagnosis was associated with increasing age, higher body mass index, presence of proteinuria, being on regimen zidovudine/lamivudine/nevirapine and inversely with World Health Organization clinical stage at ART initiation. Diabetes diagnosis was associated with higher age and being on non-standard first-line or second-line ART regimens. CONCLUSION: Among patients in HIV care 26.6% had hypertension and/or diabetes. Close to two-thirds of hypertension diagnoses was stage I and of those few had an indication for antihypertensive pharmacotherapy. According to our criteria, 13.0% of HIV patients in care required drug treatment for hypertension and/or diabetes.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".