Evidence-magnitude-controllability integrated assessment of acute mountain sickness risk factors: Practical implications for prioritizing prevention and control strategies

BACKGROUND: Acute mountain sickness (AMS) prevention requires prioritizing multiple interrelated determinants in pre-travel counseling. Conventional syntheses often focus on statistical significance of single factors and rarely integrate evidence strength (E∗), effect magnitude (M∗), and controllability (C∗) into actionable priorities. METHODS: We mapped factors into an E-M-C space and computed a Priority Index (PI = E∗ × M∗ × C∗ × kweight), with down-weighting for sparsely studied factors (kweight = 0.5 for k < 5; 0.33 for k < 3). Pooled effects were synthesized using conservative random-effects inference with prespecified robustness checks (heterogeneity assessment and influence/leave-one-out analyses). RESULTS: Across 81 variable-level syntheses, heterogeneity was common but the principal signals remained stable under conservative inference and influence checks. High-priority risk signals were dominated by the ascent process (ascent speed, ascent mode-especially direct air travel-and attained/sleeping altitude), while acetazolamide prophylaxis remained the leading protective option. We identified an action-ready set (i.e., directly modifiable levers suitable for routine counseling) for immediate implementation. A small set of factors with greater uncertainty were classified as confirm-before-adopt (i.e., plausible but currently insufficient to drive routine advice), warranting standardized exposure contexts and multi-centre prospective validation. CONCLUSIONS: The E-M-C framework and PI provide a quantitative, reproducible basis for prioritizing AMS prevention actions in pre-travel counseling. Despite common heterogeneity, the principal signals support high-yield, actionable counseling priorities. Managing the ascent process and considering acetazolamide when indicated remain the most immediate, controllable risk-reduction options; importantly, the framework adds a structured way to rank competing determinants and communicate uncertainty to guide future evidence-building and iterative updates.