Prevalence of Hypertension among Patients Attending Mobile Medical Clinics in the Philippines after Typhoon Haiyan
Bibliographic record
Abstract
INTRODUCTION: On November 8, 2013, Super Typhoon Haiyan struck the Philippines, causing a humanitarian emergency. According to the World Health Organization, non-communicable diseases (NCDs), also known as chronic diseases, are the leading cause of death and disability around the world. NCDs kill 38 million people each year. Sixteen million NCD deaths occur before the age of 70; 82% of which occurred in low- and middle-income countries. NCDs are further exacerbated during a crisis, and addressing them should be a concern of any medical disaster response. METHODS: We conducted a retrospective observational study to determine the prevalence of hypertension among patients seeking medical care at mobile medical clinics after Typhoon Haiyan in the Philippines. RESULTS: A total of 3,730 adults were evaluated at the mobile medical clinics. Analysis of the medical records revealed that the overall prevalence of hypertension among adult patients was 47%. Approximately 24% of adult females and 27% of adult males were classified with stage 2 Hypertension. CONCLUSIONS: Evidence-based guidelines on the management of hypertension and other NCDs (diabetes mellitus, cardiovascular disease, chronic lung disease and mental health) during humanitarian emergencies are limited. Clinical care of victims of humanitarian emergencies suffering with NCDs should be a critical part of disaster relief and recovery efforts. We therefore recommend the development of best practices and evidence based management guidelines of hypertension and other NCDs in post-disaster settings.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".