Integrated HIV-Care Into Primary Health Care Clinics and the Influence on Diabetes and Hypertension Care: An Interrupted Time Series Analysis in Free State, South Africa Over 4 Years
Bibliographic record
Abstract
BACKGROUND: Noncommunicable diseases (NCDs), specifically diabetes and hypertension, are rising in high HIV-burdened countries such as South Africa. How integrated HIV care into primary health care (PHC) influences NCD care is unknown. We aimed to understand whether differences existed in NCD care (pre- versus post-integration) and how changes may relate to HIV patient numbers. SETTING: Public sector PHC clinics in Free State, South Africa. METHODS: Using a quasiexperimental design, we analyzed monthly administrative data on 4 indicators for diabetes and hypertension (clinic and population levels) during 4 years as HIV integration was implemented in PHC. Data represented 131 PHC clinics with a catchment population of 1.5 million. We used interrupted time series analysis at ±18 and ±30 months from HIV integration in each clinic to identify changes in trends postintegration compared with those in preintegration. We used linear mixed-effect models to study relationships between HIV and NCD indicators. RESULTS: Patients receiving antiretroviral therapy in the 131 PHC clinics studied increased from 1614 (April 2009) to 57, 958 (April 2013). Trends in new diabetes patients on treatment remained unchanged. However, population-level new hypertensives on treatment decreased at ±30 months from integration by 6/100, 000 (SE = 3, P < 0.02) and was associated with the number of new patients with HIV on treatment at the clinics. CONCLUSIONS: Our findings suggest that during the implementation of integrated HIV care into PHC clinics, care for hypertensive patients could be compromised. Further research is needed to understand determinants of NCD care in South Africa and other high HIV-burdened settings to ensure patient-centered PHC.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".