Princeton University Library Catalog

Age-dependent Patterns of Asthma Seasonality and Environmental Drivers in Mexico

Awh, Katherine [Browse]
Senior thesis
Grenfell, Bryan T. [Browse]
Princeton University. Department of Ecology and Evolutionary Biology [Browse]
Class year:
Summary note:
Asthma is one of the most common respiratory diseases, afflicting 300 million people worldwide each year. Despite being a frequent source of hospitalizations in Mexico, little is known about its seasonality. In this thesis, I explore the seasonality of asthma nationwide in Mexico, paying close attention to age-related differences. In addition, I investigate potential relationships between asthma, climatic variables, and respiratory syncytial virus (RSV).My main findings are that asthma seasonality is age-dependent, evolving from a double-peaked curve to U-shaped with increasing age. The strongest geographic patterns are found for the youngest age group, where the two predominant regions fall manly along the coast and inland in areas of high elevation. We find that RSV may contribute most significantly to asthma in the purple regions of the youngest age groups, but not to the older age groups or the inland regions of the younger age groups. Humidity appears to be the most influential climatic variable for the two youngest age groups, conceivably indirectly triggering asthma by increasing dust mite populations or other aeroallergens. Relative humidity is the strongest predictor of asthma for age 0-4 and specific humidity is the strongest predictor for age 5-14. Meanwhile, the largest predictor of asthma for the oldest age groups could be minimum temperature, with asthma peaks occurring just after the coldest week in the year for all regions above age 45. This thesis provides a novel description of asthma seasonality and age-related differences nationwide in Mexico. In it, I employ a novel approach of using EPIPOI data visualization software to form “seasonal regions” rather than geographic regions to crystallize similar patterns could be a useful qualitative strategy for future epidemiologists interested in reducing noise in the preliminary steps of research.