Climate variations and Salmonella infection in Australian subtropical and tropical regions

• Published in: The Science of the total environment Vol. 408; no. 3; pp. 524 - 530
• Main Authors: Zhang, Ying, Bi, Peng, Hiller, Janet E.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier B.V 2010; [Amsterdam; New York]: Elsevier Science; Elsevier
• Subjects: air temperature; Australia - epidemiology; Bacterial diseases; Bacterial diseases of the digestive system and abdomen; bacterial infections; Biological and medical sciences; Climate; climatic factors; Correlation; epidemiological studies; Epidemiology; Epidemiology. Vaccinations; General aspects; Geography; Human bacterial diseases; Humans; Infectious diseases; Mathematical models; Medical sciences; Population Surveillance; Rain; Rainfall; Regression; Regression Analysis; Risk Assessment; Salmonella; Salmonella Infections - epidemiology; Salmonella Infections - transmission; Seasons; subtropics; Temperature; temporal variation; Threshold; Thresholds; Time Factors; time series analysis; Time-series; Tropical Climate; Tropical regions; tropics; weather; Queensland; Australia; Oceania; Australia, Queensland, Townsville; Australia, Queensland, Brisbane; Salmonella; Time-series; Temperature; Epidemiology; Threshold; Human; Subtropical zone; Tropical zone; Time series; Dynamical climatology; Infection; Climate change; Bacteriosis; Bacteria; Salmonellosis; Public health; Enterobacteriaceae
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2009.10.068

This study aims to quantify the relationship between climate variations and cases of Salmonella infection in subtropical and tropical areas in Australia. Brisbane in a subtropical area and Townsville in a tropical area of Queensland were selected as the study regions. Local meteorological variables and notified cases of Salmonella infection from January 1990 to July 2005 were provided by local authorities. Spearman correlation and time-series adjusted Poisson regression were applied controlling for autoregression, lag effects, seasonal variation and long-term trend. Natural cubic spline and Hockey Stick model were used to estimate a potential threshold temperature. Spearman correlation indicated that maximum and minimum temperatures, relative humidity at 9 am and 3 pm, and rainfall were all positively correlated with the number of cases in both Brisbane and Townsville, with the lag values of the effects up to 2 weeks in Brisbane and 2 months in Townsville. Only temperature and rainfall were significantly included in the regression models in both regions. The models suggested that a potential 1 °C rise in maximum or minimum temperature may cause a very similar increase in the number of cases in the two regions. No threshold for the effect of maximum or minimum temperature on Salmonella infection was detected in either region. The association between climate variations and Salmonella infection could be very similar in subtropical and tropical regions in Australia. Temperature and rainfall may be used as key meteorological predictors for the number of cases in both regions.