Situational analysis of hypertension management at primary health care level in São Paulo, Brazil: population, healthcare professional and health system perspectives
Bibliographic record
Abstract
BACKGROUND: Government-led, population-wide initiatives are crucial for advancing the management of hypertension - a leading cause of cardiovascular disease (CVD) morbidity and mortality. An urban population health initiative was conducted against this backdrop, focussing on hypertension in the primary health system in São Paulo, Brazil. Within the frame of the initiative and under the supervision and leadership of the municipal health authorities, a situational analysis was conducted on the needs in hypertension management, marking the first phase of a Design Thinking process. This article describes the situational analysis process and presents the identified elements to be strengthened considering hypertension diagnosis, treatment and control. METHODS: First, a mixed-methods approach was used, starting with a literature review of municipal hypertension data followed by meetings (N = 20) with the local public health administration to assess health system level components. To investigate activities on hypertension diagnosis, treatment and control, nine primary healthcare units were selected from two districts of São Paulo city- Itaquera and Penha- which received an online form addressed to managers, participated in conversation circles of staff and patients, and underwent shadowing of community health agents. RESULTS: Data gave rise to two main outputs: (i) a patient care journey map; and (ii) a matrix summarizing the identified needs at patient, healthcare professional and health system level for diagnosis, treatment and control of hypertension. Patient awareness and knowledge of hypertension was found to be insufficient and its management needs to be improved. For health professionals, disease awareness, technical training, more time dedicated to patients, and simplified guidelines and clinical decision-making tools for hypertension management were identified as principal needs. The situational analysis found that the healthcare systems efficiency might be improved by establishing defined treatment and care delivery goals with a focus on outcomes and implemented through action plans. CONCLUSIONS: This situational analysis identified several needs related to hypertension control in São Paulo that are in line with global challenges to improve the control of CVD risk factors. Findings were also confirmed locally in an expansion phase of this situational analysis to additional primary care facilities. As a consequence, solutions were designed, promptly taken up and implemented by the municipal health secretariat.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".