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PLoS One. 2013 Dec 23;8(12):e83484. doi: 10.1371/journal.pone.0083484. eCollection 2013.

Climate variability, weather and enteric disease incidence in New Zealand: time series analysis.

Author information

  • 1Department of Public Health, University of Otago, Wellington, New Zealand.
  • 2Dean's Department, University of Otago, Wellington, New Zealand.
  • 3Molecular Epidemiology and Public Health laboratory, Hopkirk Research Institute, Massey University, Palmerston North, New Zealand.

Abstract

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.

PMID:
24376707
[PubMed - in process]
PMCID:
PMC3871872
Free PMC Article

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