The Effect of Climate Variation on Infectious Diseases in Humans in New Zealand

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1 The Effect of Climate Variation on Infectious Diseases in Humans in New Zealand Aleisha Meade Jackie Benschop Geoff Jones Nigel French Dave Slaney

2 Outline Introduction Data Selection of Diseases Past Association Models Prediction Models Summary

3 Introduction Aims: Association between climate and infectious diseases in humans ( ) Prediction models to estimate burden of disease in 2015, 2040 and 2090 with respect to future climate change scenarios

4 Introduction Project is a joint collaboration between: Environmental Science & Research Ltd (ESR) National Institute of Water & Atmospheric Research (NIWA) EpiCentre, Massey University (Palmerston North) Landcare Research Waikato University University of Queensland University of Adelaide WHO Europe

5 Data Weekly measurements 11,491 grids matched to virtual climate stations Weather Data: Absolute Humidity Rain Days, Rainfall Temperature Wind Speed Disease Data: EpiSurv, ESR.

6 Data Demographic Data: Population count, Gender, Age, Ethnicity Social Deprivation Index District Health Board Environmental Data: Rurality, Animal Density, Land Use Drinking Water Quality Holidays Vaccination Coverage Beef numbers 2006 Sheep numbers 2006

7 Outline Introduction Data Selection of Diseases Past Association Models Prediction Models Summary

8 Selection of Indicator Diseases 52 notifiable diseases ranked 3 study regions consisting of 81 amalgamated grids Canterbury study region Predominantly sheep farming Manawatu study region Beef and dairy farming Waikato study region Dairy farming North to South gradient

9 Selection of Indicator Diseases The seven possible indicator diseases and their exposure routes: Respiratory: influenza hospitalisation, meningococcal disease. Enteric (food and/or water): campylobacteriosis, cryptosporidiosis, giardiasis, salmonellosis, yersiniosis. Four indicator diseases chosen: Campylobacteriosis, cryptosporidiosis, hospitalised influenza and meningococcal disease.

10 Outline Introduction Data Selection of Diseases Past Association Models Prediction Models Summary

11 Past Association Models Screening: Poisson Model Seasonality adjustment: sine and cosine Weather, environmental & demographic variables

12 Past Association Models Screening: Poisson Model Seasonality adjustment: sine and cosine Weather, environmental & demographic variables Knorr-Held Richardson Model

13 Past Association Models Interim Results: Cryptosporidiosis Associated Weather Variables: Average Rain (-ve) Average Temperature (-ve) Associated Demographic and Environmental Confounders: Rural areas (+ve) Female (-ve) Under 4 years (+ve) Over 65 years (+ve) Ethnicity (-ve) with baseline European Social Deprivation Index, District Health Board

14 Outline Introduction Data Selection of Diseases Past Association Models Prediction Models Summary

15 Prediction Models Knorr-Held Richardson Model Predict Incidence Risk of disease for 2015, 2040 and 2090: Spatial adjustment for the area level effect Climate scenarios

16 Projections of future climate

17 Prediction Models Knorr-Held Richardson Model Predict Incidence Risk of disease for 2015, 2040 and 2090: Spatial adjustment for the area level effect Climate scenarios Demographic and environmental kept constant Amalgamate results to DHB, TLA, Iwi

18 Summary Screening: Poisson Model Association and Prediction Models: Modified Knorr-Held Richardson Model Web-based tool to identify communities at risk. Strategies for disease risk reduction.

19 Acknowledgements Jonathan Marshall Simon Spencer Data Sources: Environmental Science & Research Ltd (ESR) National Institute of Water & Atmospheric Research (NIWA) Statistics New Zealand NZ Ministry of Health AgriBase