TY - JOUR
T1 - Climate variability, weather and enteric disease incidence in New Zealand
T2 - Time series analysis
AU - Lal, Aparna
AU - Ikeda, Takayoshi
AU - French, Nigel
AU - Baker, Michael G.
AU - Hales, Simon
PY - 2013/12/23
Y1 - 2013/12/23
N2 - Background: Evaluating the influence of climate variability on enteric disease incidence may improve our ability to predict how climate change may affect these diseases. Objectives: To examine the associations between regional climate variability and enteric disease incidence in New Zealand. Methods: Associations between monthly climate and enteric diseases (campylobacteriosis, salmonellosis, cryptosporidiosis, giardiasis) were investigated using Seasonal Auto Regressive Integrated Moving Average (SARIMA) models. Results: No climatic factors were significantly associated with campylobacteriosis and giardiasis, with similar predictive power for univariate and multivariate models. Cryptosporidiosis was positively associated with average temperature of the previous month (β= 0.130, SE = 0.060, p<0.01) and inversely related to the Southern Oscillation Index (SOI) two months previously (β = -0.008, SE = 0.004, p<0.05). By contrast, salmonellosis was positively associated with temperature (β = 0.110, SE = 0.020, p,0.001) of the current month and SOI of the current (β = 0.005, SE = 0.002, p<0.050) and previous month (β = 0.005, SE = 0.002, p<0.05). Forecasting accuracy of the multivariate models for cryptosporidiosis and salmonellosis were significantly higher. Conclusions: Although spatial heterogeneity in the observed patterns could not be assessed, these results suggest that temporally lagged relationships between climate variables and national communicable disease incidence data can contribute to disease prediction models and early warning systems.
AB - Background: Evaluating the influence of climate variability on enteric disease incidence may improve our ability to predict how climate change may affect these diseases. Objectives: To examine the associations between regional climate variability and enteric disease incidence in New Zealand. Methods: Associations between monthly climate and enteric diseases (campylobacteriosis, salmonellosis, cryptosporidiosis, giardiasis) were investigated using Seasonal Auto Regressive Integrated Moving Average (SARIMA) models. Results: No climatic factors were significantly associated with campylobacteriosis and giardiasis, with similar predictive power for univariate and multivariate models. Cryptosporidiosis was positively associated with average temperature of the previous month (β= 0.130, SE = 0.060, p<0.01) and inversely related to the Southern Oscillation Index (SOI) two months previously (β = -0.008, SE = 0.004, p<0.05). By contrast, salmonellosis was positively associated with temperature (β = 0.110, SE = 0.020, p,0.001) of the current month and SOI of the current (β = 0.005, SE = 0.002, p<0.050) and previous month (β = 0.005, SE = 0.002, p<0.05). Forecasting accuracy of the multivariate models for cryptosporidiosis and salmonellosis were significantly higher. Conclusions: Although spatial heterogeneity in the observed patterns could not be assessed, these results suggest that temporally lagged relationships between climate variables and national communicable disease incidence data can contribute to disease prediction models and early warning systems.
UR - http://www.scopus.com/inward/record.url?scp=84893480785&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0083484
DO - 10.1371/journal.pone.0083484
M3 - Article
SN - 1932-6203
VL - 8
JO - PLoS ONE
JF - PLoS ONE
IS - 12
M1 - e83484
ER -