[Influence of the climate variability on acute respiratory infections in the city of Bogotá]. / Influencia de la variabilidad climática en las enfermedades respiratorias agudas en Bogotá.

INTRODUCTION: Acute respiratory infection is one of the most significant causes of morbidity in Bogota, and its burden of disease has increased in association with climate variability . OBJECTIVE: The aim of the study was to evaluate weekly trends of acute respiratory infection in relation to meteorological variables (temperature, relative humidity and cumulative rainfall) in Bogota during 2011 and 2012. MATERIALS AND METHODS: Epidemiological and meteorological data from 104 weeks were gathered. Temporal variability was taken into account including previous weeks and spatial variability was considered by studying each zone of the city separately (north, south, east, west). Statistical analysis was performed through Poisson dynamic regression models. RESULTS: The relative humidity had the greater impact on acute respiratory infection and its effects lasted between 8 to 10 weeks. Cumulative rainfall had effects only in the east zone, while the temperature presented mild effects across the four different zones of the city. CONCLUSION: Such results are the first step for the design of health-related early warning systems associated with climate variability.

Influencia de la variabilidad climática en las enfermedades respiratorias agudas en Bogotá / Influence of the climate variability on acute respiratory infections in the city of Bogotá

Introducción. La enfermedad respiratoria es una de las principales causas de morbilidad en Bogotá y los efectos de la variabilidad climática se han reflejado en un aumento del número de casos. Objetivo. En el presente estudio se analizó el comportamiento semanal de la enfermedad respiratoria aguda en Bogotá y se asoció con las variables climatológicas de temperatura, humedad relativa y precipitación, analizando su impacto en la aparición de casos en la ciudad. Materiales y métodos. El análisis se llevó a cabo mediante la estimación de modelos de regresión de Poisson, con datos epidemiológicos de 104 semanas correspondientes a los años 2011 y 2012. La variabilidad climática temporal se analizó considerando los efectos de las variables en semanas anteriores y, para el análisis de la variabilidad espacial, la ciudad se dividió en cuatro zonas: norte, sur, oriente y occidente. Resultados. Los resultados de las correlaciones cruzadas demostraron que en tres de las cuatro zonas la humedad relativa tenía un mayor impacto sobre los casos de enfermedad respiratoria aguda y su efecto persistía hasta por ocho y diez semanas. La precipitación, por el contrario, tuvo impacto únicamente en la zona oriente, mientras que la temperatura tuvo efectos moderados en todas las zonas. Conclusión. Debido al componente dinámico de estos modelos, los resultados son un primer paso para el diseño de un sistema de alerta temprana en salud que tome en cuenta la variabilidad climática.

Introduction: Acute respiratory infection is one of the most significant causes of morbidity in Bogota, and its burden of disease has increased in association with climate variability . Objective: The aim of the study was to evaluate weekly trends of acute respiratory infection in relation to meteorological variables (temperature, relative humidity and cumulative rainfall) in Bogota during 2011 and 2012. Materials and methods: Epidemiological and meteorological data from 104 weeks were gathered. Temporal variability was taken into account including previous weeks and spatial variability was considered by studying each zone of the city separately (north, south, east, west). Statistical analysis was performed through Poisson dynamic regression models. Results: The relative humidity had the greater impact on acute respiratory infection and its effects lasted between 8 to 10 weeks. Cumulative rainfall had effects only in the east zone, while the temperature presented mild effects across the four different zones of the city. Conclusion: Such results are the first step for the design of health-related early warning systems associated with climate variability.