S Tong1, P Bi, K Donald, A J McMichael. 1. Centre for Public Health Research, Queensland University of Technology, Australia. s.tong@qut.edu.au
Abstract
OBJECTIVES: (1) To examine the feasibility to link climate data with monthly incidence of Ross River virus (RRv). (2) To assess the impact of climate variability on the RRv transmission. DESIGN: An ecological time series analysis was performed on the data collected between 1985 to 1996 in Queensland, Australia. METHODS: Information on the notified RRv cases was obtained from the Queensland Department of Health. Climate and population data were supplied by the Australian Bureau of Meteorology and the Australian Bureau of Statistics, respectively. Spearman's rank correlation analyses were performed to examine the relation between climate variability and the monthly incidence of notified RRv infections. The autoregressive integrated moving average (ARIMA) model was used to perform a time series analysis. As maximum and minimum temperatures were highly correlated with each other (r(s)=0.75), two separate models were developed. RESULTS: For the eight major cities in Queensland, the climate-RRv correlation coefficients were in the range of 0.12 to 0.52 for maximum and minimum temperatures, -0.10 to 0.46 for rainfall, and 0.11 to 0.52 for relative humidity and high tide. For the whole State, rainfall (partial regression coefficient: 0.017 (95% confidence intervals 0.009 to 0.025) in Model I and 0.018 (0.010 to 0.026) in Model II), and high tidal level (0.030 (0.006 to 0.054) in Model I and 0.029 (0.005 to 0.053) in Model II) seemed to have played significant parts in the transmission of RRv in Queensland. Maximum temperature was also marginally significantly associated with the incidence of RRv infection. CONCLUSION: Rainfall, temperature, and tidal levels may be important environmental determinants in the transmission cycles of RRv disease.
OBJECTIVES: (1) To examine the feasibility to link climate data with monthly incidence of Ross River virus (RRv). (2) To assess the impact of climate variability on the RRv transmission. DESIGN: An ecological time series analysis was performed on the data collected between 1985 to 1996 in Queensland, Australia. METHODS: Information on the notified RRv cases was obtained from the Queensland Department of Health. Climate and population data were supplied by the Australian Bureau of Meteorology and the Australian Bureau of Statistics, respectively. Spearman's rank correlation analyses were performed to examine the relation between climate variability and the monthly incidence of notified RRv infections. The autoregressive integrated moving average (ARIMA) model was used to perform a time series analysis. As maximum and minimum temperatures were highly correlated with each other (r(s)=0.75), two separate models were developed. RESULTS: For the eight major cities in Queensland, the climate-RRv correlation coefficients were in the range of 0.12 to 0.52 for maximum and minimum temperatures, -0.10 to 0.46 for rainfall, and 0.11 to 0.52 for relative humidity and high tide. For the whole State, rainfall (partial regression coefficient: 0.017 (95% confidence intervals 0.009 to 0.025) in Model I and 0.018 (0.010 to 0.026) in Model II), and high tidal level (0.030 (0.006 to 0.054) in Model I and 0.029 (0.005 to 0.053) in Model II) seemed to have played significant parts in the transmission of RRv in Queensland. Maximum temperature was also marginally significantly associated with the incidence of RRv infection. CONCLUSION: Rainfall, temperature, and tidal levels may be important environmental determinants in the transmission cycles of RRv disease.
Authors: J S Mackenzie; A K Broom; R A Hall; C A Johansen; M D Lindsay; D A Phillips; S A Ritchie; R C Russell; D W Smith Journal: Commun Dis Intell Date: 1998-06-11
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Authors: Shilu Tong; Pat Dale; Neville Nicholls; John S Mackenzie; Rodney Wolff; Anthony J McMichael Journal: Environ Health Perspect Date: 2008-07-24 Impact factor: 9.031