Dataset Information

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

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 (??=? 0.130, SE?=? 0.060, p <0.01) and inversely related to the Southern Oscillation Index (SOI) two months previously (??=? -0.008, SE?=? 0.004, p <0.05). By contrast, salmonellosis was positively associated with temperature (? ?=?0.110, SE?=?0.020, p<0.001) of the current month and SOI of the current (? ?=?0.005, SE?=?0.002, p<0.050) and previous month (? ?=?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.

SUBMITTER: Lal A

PROVIDER: S-EPMC3871872 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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

Lal Aparna A Ikeda Takayoshi T French Nigel N Baker Michael G MG Hales Simon S

PloS one 20131223 12

<h4>Background</h4>Evaluating the influence of climate variability on enteric disease incidence may improve our ability to predict how climate change may affect these diseases.<h4>Objectives</h4>To examine the associations between regional climate variability and enteric disease incidence in New Zealand.<h4>Methods</h4>Associations between monthly climate and enteric diseases (campylobacteriosis, salmonellosis, cryptosporidiosis, giardiasis) were investigated using Seasonal Auto Regressive Integ ...[more]

PMID: 24376707

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Project description:BackgroundThe past decade witnessed an increment in the incidence of hand foot mouth disease (HFMD) in the Pacific Asian region; specifically, in Guangzhou China. This emphasized the requirement of an early warning system designed to allow the medical community to better prepare for outbreaks and thus minimize the number of fatalities.MethodsSamples from 1,556 inpatients (hospitalized) and 11,004 outpatients (non-admitted) diagnosed with HFMD were collected in this study from January 2009 to October 2013. Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied to establish high predictive model for inpatients and outpatient as well as three viral serotypes (EV71, Pan-EV and CA16). To integrate climate variables in the data analyses, data from eight climate variables were simultaneously obtained during this period. Significant climate variable identified by correlation analyses was executed to improve time series modeling as external repressors.ResultsAmong inpatients with HFMD, 248 (15.9%) were affected by EV71, 137 (8.8%) were affected by Pan-EV+, and 436 (28.0%) were affected by CA16. Optimal Univariate SARIMA model was identified: (2,0,3)(1,0,0)52 for inpatients, (0,1,0)(0,0,2)52 for outpatients as well as three serotypes (EV71, (1,0,1)(0,0,1)52; CA16, (1,0,1)(0,0,0)52; Pan-EV, (1,0,1)(0,0,0)52). Using climate as our independent variable, precipitation (PP) was first identified to be associated with inpatients (r = 0.211, P = 0.001), CA16-serotype (r = 0.171, P = 0.007) and outpatients (r = 0.214, P = 0.01) in partial correlation analyses, and was then shown a significant lag in cross-autocorrelation analyses. However, inclusion of PP [lag -3 week] as external repressor showed a moderate impact on the predictive performance of the SARIMA model described here-in.ConclusionClimate patterns and HFMD incidences have been shown to be strongly correlated. The SARIMA model developed here can be a helpful tool in developing an early warning system for HFMD.

Project description:BackgroundDengue fever is the most common arboviral disease in humans, with an estimated 50-100 million annual infections worldwide. Dengue fever cases have increased substantially in the past four decades, driven largely by anthropogenic factors including climate change. More than half the population of Peru is at risk of dengue infection and due to its geography, Peru is also particularly sensitive to the effects of El Niño Southern Oscillation (ENSO). Determining the effect of ENSO on the risk for dengue outbreaks is of particular public health relevance and may also be applicable to other Aedes-vectored viruses.MethodsWe conducted a time-series analysis at the level of the district-month, using surveillance data collected from January 2000 to September 2018 from all districts with a mean elevation suitable to survival of the mosquito vector (<2,500m), and ENSO and weather data from publicly-available datasets maintained by national and international agencies. We took a Bayesian hierarchical modeling approach to address correlation in space, and B-splines with four knots per year to address correlation in time. We furthermore conducted subgroup analyses by season and natural region.ResultsWe detected a positive and significant effect of temperature (°C, RR 1.14, 95% CI 1.13, 1.15, adjusted for precipitation) and ENSO (ICEN index: RR 1.17, 95% CI 1.15, 1.20; ONI index: RR 1.04, 95% CI 1.02, 1.07) on outbreak risk, but no evidence of a strong effect for precipitation after adjustment for temperature. Both natural region and season were found to be significant effect modifiers of the ENSO-dengue effect, with the effect of ENSO being stronger in the summer and the Selva Alta and Costa regions, compared with winter and Selva Baja and Sierra regions.ConclusionsOur results provide strong evidence that temperature and ENSO have significant effects on dengue outbreaks in Peru, however these results interact with region and season, and are stronger for local ENSO impacts than remote ENSO impacts. These findings support optimization of a dengue early warning system based on local weather and climate monitoring, including where and when to deploy such a system and parameterization of ENSO events, and provide high-precision effect estimates for future climate and dengue modeling efforts.

Dataset Information

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

Publications

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

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