Health Systems Readiness to Manage the Hypertension Epidemic in Primary Health Care Facilities in the Western Cape, South Africa: A Study Protocol
Bibliographic record
Abstract
BACKGROUND: Developing countries are undergoing a process of epidemiological transition from infectious to noncommunicable diseases, described by the United Nations Secretary General Ban Ki-Moon as ''a public health emergency in slow motion." One of the most prevalent in sub-Saharan Africa is hypertension, which is a complex chronic condition often referred to as a "silent killer" and key contributor to the development of cardiovascular and cerebrovascular diseases. Hypertensive patients in this setting are estimated to increase from 74.7 million in 2008 to 125.5 million in 2025, a 68% increase. However, there is an important gap between emerging high-level policies and recommendations, and the near-absence of practical guidance and experience delivering long-term medical care for noncommunicable diseases within resource-limited health systems. OBJECTIVE: To address this gap, our study will consist of field investigations to determine the minimum health systems requirements to ensure successful delivery of antihypertensive medications when scaling-up interventions to control the hypertension epidemic. METHODS: A cross-sectional analytic study will be conducted in the Western Cape using a mixed-method approach with two semistructured interview guides. The first will be for health professionals involved in the care of hypertensive patients within at least 6 community health centers (3 urban and 3 rural) to understand the challenges associated with their care. The second will be to map and assess the current supply chain management system of antihypertensive medications by interviewing key informants at different levels of the processes. Finally, modeling and simulation tools will be used to understand how to estimate minimum numbers of health workers required at each supply chain interval to ensure successful delivery of medications when scaling-up interventions. RESULTS: Funding for the study was secured through a Doctoral Research Award in October 2014 from the International Development Research Centre (IDRC). The study is currently in the data analysis phase and results are expected during the first half of 2016. CONCLUSIONS: This investigation will highlight the detailed processes in place for the care of hypertensive patients in primary health care facilities, and thus also identify the challenges. It will also describe the drug supply chain management systems in place and identify their strengths and weaknesses. The findings, along with the estimates from modeling and simulation, will inform the health system minimum requirements to scale-up interventions to manage and control the hypertension epidemic in the Western Cape province of South Africa.
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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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".