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Year-to-year variation in weather-epistaxis correlations: a pilot study for multi-year research design
Badriya Yussuf1, Hemang Brahmbhatt1, Remo Accorona2
1University Hospitals of Northamptonshire NHS Foundation Trust, Northampton, United Kingdom.
Acta Oto-Laryngologica
|February 12, 2026
Summary
Weather correlations with nosebleeds (epistaxis) vary yearly, masking true patterns. Multi-year studies are crucial to understand environmental health relationships and avoid inconsistent findings in epistaxis research.
Area of Science:
- Environmental Health
- Medical Research
- Statistical Analysis
Background:
- Conflicting correlations between weather and epistaxis (nosebleeds) exist in prior studies.
- Traditional combined analyses may obscure temporal patterns, leading to inconsistent research findings.
- Year-to-year variations, not geographic differences, may explain these discrepancies.
Purpose of the Study:
- To assess the annual stability of weather-epistaxis correlations.
- To establish a methodology for multi-year studies that accounts for temporal variations.
- To improve understanding of environmental health relationships in epistaxis.
Main Methods:
- A pilot study analyzed 2,201 epistaxis cases from two UK hospitals (2023-2024).
- Weather parameters were analyzed separately for each year to compare year-specific and combined approaches.
- Correlation stability and temporal variation effects were examined.
Main Results:
- Notable year-to-year differences in weather-epistaxis correlations were observed, though not statistically significant.
- Humidity correlations reversed direction (positive in 2023 to negative in 2024).
- Temperature and pressure correlations showed increased negative magnitudes over the study period, masked by combined analysis.
Conclusions:
- Year-to-year variations in weather-epistaxis correlations exist and can be masked by combined analyses.
- Extended observation periods (5-10 years) are recommended for future studies.
- Temporal variation is a significant factor in epistaxis research, necessitating robust methodologies.
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