Background: Influenza seasonality in the tropics is poorly understood and not as well documented as in temperate regions. In addition, low-income populations are considered highly vulnerable to such acute respiratory disease, owing to limited resources and overcrowding. Nonetheless, little is known about their actual disease burden for lack of data. We therefore investigated associations between tropical influenza incidence and weather variability among children under five in a poor urban area of Dhaka, Bangladesh. Design: Acute respiratory illness data were obtained from a population-based respiratory and febrile illness surveillance dataset of Kamalapur, a low-income urban area in southeast Dhaka. Analyzed data were from January 2005 through December 2008. Nasopharyngeal wash specimens were collected from every fifth eligible surveillance participant during clinic visits to identify influenza virus infection with viral culture and reverse transcriptase-polymerase chain reaction. Time series analysis was conducted to determine associations between the number of influenza cases per week and weather factors. Zero-inflated Poisson and generalized linear Poisson models were used in the analysis for influenza A and B, respectively. Results: Influenza A had associations with minimum temperature, relative humidity (RH), sunlight duration, and rainfall, whereas only RH was associated with influenza B. Although associations of the other weather factors varied between the two subtypes, RH shared a similar positive association when humidity was approximately 50-70%. Conclusions: Our findings of a positive RH association is consistent with prior studies, and may suggest the viral response in the tropics. The characteristics of settlement areas, population demographics, and typical overcrowding of urban poverty may also contribute to different impacts of rainfall from higher economic population. Further investigations of associations between tropical influenza and weather variability for urban low-income populations are required for better understanding.
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- Time series