Older age, chronic medical conditions and polypharmacy in Himalayan trekkers in Nepal: an epidemiologic survey and case series
Bibliographic record
Abstract
BACKGROUND: The number of tourists in Nepal doubled between 2003 and 2013 is nearly 800 000. With the increased popularity of trekking, the number of those with pre-existing medical conditions requiring access to healthcare is likely to increase. We therefore sought to characterize the demographics and health status of trekkers on the Everest Base Camp route in the Solukhumbu Valley. In addition, we report cases that illustrate the potential complications of an ageing and medicated population of trekkers with underlying diseases. METHODS: Trekkers over 18 years were enrolled in a larger observational cohort study on blood pressure at high altitude at 2860 m. They answered a questionnaire regarding demographics, medical history and current medications. Acute medical problems relating to medication use that were brought to the attention of investigators were documented and are presented as case reports. RESULTS: We enrolled 670 trekkers, 394 (59%) male, with a mean age of 48 years (range 18-76). Pre-existing medical conditions were reported by 223 participants (33%). The most frequent conditions included hypertension, hypercholesterolemia, migraines and thyroid dysfunction. A total of 276 participants (41%) reported taking one or more medications. The most common medications were acetazolamide (79, 12%), antihypertensives (50, 8%) and NSAIDs (47, 7%), with 30 classes of drugs represented. Excluding acetazolamide, older trekkers (age >50 years) were more likely than younger ones to take medications (OR = 2.17; 95% CI 1.57-3.00; P <0.05). Acetazolamide use was not related to age. CONCLUSIONS: Our findings illustrate a wide variety of medical conditions present in trekkers in Nepal with wide-ranging potential complications that could pose difficulties in areas where medical care is scarce and evacuation difficult. Our cases illustrate the potential problems polypharmacy poses in trekkers, and the need for local and expedition healthcare workers to be aware of, and prepared for the common medical conditions present.
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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 | 0.001 |
| 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".