RESUMEN
Background: Time series analysis is suitable for investigations of relatively direct and short-term effects of exposures on outcomes. In environmental epidemiology studies, this method has been one of the standard approaches to assess impacts of environmental factors on acute non-infectious diseases (e.g. cardiovascular deaths), with conventionally generalized linear or additive models (GLM and GAM). However, the same analysis practices are often observed with infectious diseases despite of the substantial differences from non-infectious diseases that may result in analytical challenges. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, systematic review was conducted to elucidate important issues in assessing the associations between environmental factors and infectious diseases using time series analysis with GLM and GAM. Published studies on the associations between weather factors and malaria, cholera, dengue, and influenza were targeted. Findings: Our review raised issues regarding the estimation of susceptible population and exposure lag times, the adequacy of seasonal adjustments, the presence of strong autocorrelations, and the lack of a smaller observation time unit of outcomes (i.e. daily data). These concerns may be attributable to features specific to infectious diseases, such as transmission among individuals and complicated causal mechanisms. Conclusion: The consequence of not taking adequate measures to address these issues is distortion of the appropriate risk quantifications of exposures factors. Future studies should pay careful attention to details and examine alternative models or methods that improve studies using time series regression analysis for environmental determinants of infectious diseases.
RESUMEN
Background: Time series analysis is suitable forinvestigations of relatively direct and short-term effects of exposures on outcomes.In environmental epidemiology studies, this method has been one of the standardapproaches to assess impacts of environmental factors on acute non-infectious diseases(e.g. cardiovascular deaths), with conventionally generalized linear or additivemodels (GLM and GAM). However, the same manner of practices of this method is observedwith infectious diseases despite of the substantial differences fromnon-infectious diseases which may result in analytical challenges. Methods: Following Preferred ReportingItems for Systematic Reviews and Meta-Analyses guideline, systematic review wasconducted to elucidate important issues in assessing the associations betweenenvironmental factors and infectious diseases using time series analysis withGLM or GAM. Published studies in relation to associations between weatherfactors, and malaria, cholera, dengue, or influenza were targeted. Findings: Issues regarding theestimation of susceptible population and exposure lag times, adequacy ofseasonal adjustments, the presence of strong autocorrelations, and a lack of smallerobservation time unit of outcomes (i.e. daily data) were raised from our review.These concerns may be attributed to the features specific to infectious diseases,such as transmissions among individuals and complicated causal mechanisms. Conclusion: The consequence of not takingadequate measures to address these issues is distortion of the appropriate riskquantifications of exposures factors. The future studies are required careful attentionsto details, and recommended to examine alternative models or methods thatimprove studies with time series regression analysis for environmental determinantsof infectious diseases.