Outils pratiques en santé publique pour l adaptation au changement climatique

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1 Bienvenue au Forum Virtuel de l Agence de la santé publique du Canada Outils pratiques en santé publique pour l adaptation au changement climatique Le 21 octobre, 2011, 11 h (HNE) Réunion d expertsd Dr. Lea Berrang Ford, Université McGill Chen Chih Chen, Université de la Saskatchewan Dr. Huaiping Zhu, Université York works.ca Un projet de: RRASP/PHIRN, Réseau de recherche sur l amélioration de la santé des populations Population Health Improvement Research Network University of Ottawa l'université d'ottawa

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4 Predicting the effects of climate & climate change on the occurrence of mosquitoborne diseases in Canada Lea BERRANG FORD Department of Geography McGill University Co authors: Valerie Hongoh, Rose Eckhardt, Roland Ngom 1

5 Outline: 1. Background 2. Aim & objectives 3. Key results 4. Discussion 2

6 Hongoh et al. (2009) Biodiversity 3

7 AIM: To develop and test a general approach to predicting the effects of climate and climate change on the occurrence of mosquito borne diseases in Canada ( ) OBJECTIVES: 1. Review: Conduct a review of current and potential vector borne disease risks in Canada, as well as modeling approaches for assessing vector borne disease risk. 2. Model development: Develop national maps of the distributions of mosquitoes of public health significance in Canada. 3. Model predictions: Predict the distribution of mosquito vectors and case study pathogens of public health importance in Canada. 4. Case study: Develop and test a conceptual modeling approach to assess the impact of climate change on a vector borne disease in Canada. 4

8 Objective 1: Review: Conduct a review of current and potential vector borne disease risks in Canada, as well as modeling approaches for assessing vector borne disease risk. Key results: 1. Climatic and environmental determinants of vector habitat and abundance are not generalizable. 2. The impact of climate changes will differ by species, and possibly by region. 3. This may necessitate unique prediction models for each species, potentially stratified by region. Hongoh et al. (2009) Biodiversity 5

9 Objective 1: Review: Conduct a review of current and potential vector borne disease risks in Canada, as well as modeling approaches for assessing vector borne disease risk. Evaluation of spatial and statistical modeling approaches for vector mapping: 1.Logistic regression (stepwise automatic) 2.Logistic regression (manually, biological) 3.MARS (Multivariate Adaptive Regression Splines) 4.Fuzzy logic modeling (presence only data) 5.MaxEnt (Maximum Entropy) habitat modeling (presence only data) 6

10 Culex pipiens Model A (backward stepwise) Model B (biological, manual) Model C (MARS) V. Hongoh 7

11 Objective 1: Review: Conduct a review of current and potential vector borne disease risks in Canada, as well as modeling approaches for assessing vector borne disease risk. Key results: 1. Standard logistic models are sufficient for species that have strong and simple temperature or environmental dependence (e.g. Culex pipiens, Culex tarsalis). 2. For species with more complicated distributions, presence only modeling approaches are more reliable (Maxent or Fuzzy Logic). 3. The main constraint to mosquito modeling is the high probabilities of false absence observations in vector surveillance data (rendering absence data obstructive to modeling). 4. Off the shelf, automated habitat modeling programs such as MaxEnt may provide easy and efficient (though not necessarily rigorous or highly accurate) vector distributions. Manuscript in press: Ngom, R., L. Berrang Ford, and V. Hongoh (In Press) Fuzzy Logic vs. logistic regression to predict habitat suitability of relevant vector species in Canada. International Journal of Health Geographics. 8

12 Objective 2: Model development: Develop national maps of the distributions of mosquitoes of public health significance in Canada. Collection of national mosquito surveillance data (surveillance discontinued) Presence / absence observations * All provinces except for BC, NFL Table 1. Sources of mosquito data and years of surveillance. Source Years Alberta Environment Manitoba Public Health Division New Brunswick Museum Nova Scotia Department of Natural Resources Ontario Ministry of Health and Long Term Care PEI Dept. Of Health and Social Services Ministère de la Santé et des Services Sociaux (Québec) Saskatchewan Ministry of Health Collection of national climate normals NRCAN, Temperature, precipitation, degree days Vegetation/ land cover V. Hongoh 9

13 Objective 2: Model development: Develop national maps of the distributions of mosquitoes of public health significance in Canada. Objective 3: Model predictions: Predict the distribution of mosquito vectors and case study pathogens of public health importance in Canada. 10

14 Culex pipiens Hongoh et al. (2011) Applied Geography 11

15 Culex pipiens Current distribution Based on existing literature (Darsie & Ward 2005) Hongoh et al. (2011) Applied Geography 12

16 Culex pipiens Future projections Scenario A2 (Business as usual) Hongoh et al. (2011) Applied Geography 13

17 Culex pipiens Future projections Scenario B1 (Environmental action, global integration) Hongoh et al. (2011) Applied Geography 14

18 Culex tarsalis Future projections Scenario A2 (Business as usual) R. Ngom 15

19 Culiseta melanura Future projections Scenario A2 (Business as usual) R. Ngom 16

20 Objective 4: Case study: Develop and test a conceptual modeling approach to assess the impact of climate change on a vector borne disease in Canada. Malaria re emergence in Canada Berrang Ford et al

21 Objective 4: Case study: Develop and test a conceptual modeling approach to assess the impact of climate change on a vector borne disease in Canada. Malaria re emergence in Canada Key results: 1. The distribution of A. quadrimaculatus extends farther north than previously available maps. 2. Competent vectors and imported parasites coincide in southern Ontario during summer months. 3. Despite this, vector are predominantly found in the outer suburban areas of the GTA, while parasite importation is focused in Toronto s downtown core. 4. There is low likelihood of malaria transmission in Ontario, even under increased immigration and climate change, due to limited spatial coincidence of vectors and imported parasites at the local scale. 5. The impact of climate change on vectors will depend on trends in other determinants of transmission Manuscript in preparation: Eckhardt, R., L. Berrang Ford, N. Ross, and D. Buckeridge (In preparation) An assessment of the potential for autochthonous malaria transmission in Ontario, Canada. 18

22 Objective 4: Case study: Develop and test a conceptual modeling approach to assess the impact of climate change on a vector borne disease in Canada. Malaria re emergence in Canada Key results: 1. The importation of malaria cases is strongly associated with low income, high immigrant neighbourhoods (Africa, Asia) in Toronto suburbs, and in proximity to Pearson airport. 2. Location (neighbourhoods) of high risk for importation of new infectious diseases may be predictable. 3. Importation risk is highest during summer months, coinciding with peak vector densities. Manuscript in review: Eckhardt, R., L. Berrang Ford, N. Ross, D. Buckeridge, and D. Pillai (In review) A spatial case control analysis of imported malaria cases in Ontario, Canada. Emerging Infectious Diseases. 19

23 Constraints, limitations and bias Lack of data from BC and the United States constrains accurate prediction, particularly in the west and for invasive vector species. We cannot map vectors reliably in the west with current data. 2.Limited pathogen data constrain focus on disease transmission (vs. vector mapping) 3. Mosquito species are ecologically and regionally unique, requiring individual models by species and regionally within Canada (i.e. We need separate models for the east and west, and possibly central Canada). 4.Poor (and discontinued) mosquito surveillance data (particularly absence observations) constrain the accuracy, validity and reliability of current and future maps. 5. The future is uncertain 20

24 Key results and the good news: 1. Given current data sources, simple software approaches may be feasible for vector scenario mapping 2.New pathogen importation into Canada may be spatially predictable, with implications for hotspot surveillance. 3. Determinants of transmission other than climate may mitigate new disease risk (or visa versa). Consideration of climate impacts needs to be combined with understanding of other trends in transmission determinants. 4.Introduction of new vector borne pathogens is probably a larger concern for potential public health burden in Canada than increased incidence of current VBD, highlighting the importance of modeling importation risk (e.g. Biodispora). 21

25 THANK YOU & QUESTIONS Relevant publications Hongoh, V. et al. (2009) A review of environmental determinants and risk factors for avian associated mosquito arboviruses in Canada. Biodiversity 10: Hongoh, V. et al. (2011, In press) Expanding geographical distribution of the mosquito, Culex pipiens, in Canada under climate change. Applied Geography. Ngom, R. et al. (Accepted) Fuzzy Logic vs. logistic regression and maximum entropy to predict habitat suitability of relevant vector species in Canada. International Journal of Health Geographics. Eckhardt, R. et al. (In review) Imported malaria cases in Ontario, Canada are strongly patterned by neighbourhood economic and immigration levels in the Toronto area. Emerging Infectious Diseases. Eckhardt, R. et al. (In final preparation) An assessment of the potential for autochthonous malaria transmission in Ontario, Canada. Ngom, R. et al. (In final preparation) The impact of climate change on distributions of mosquito vectors in Canada. 22

26 EXTRA SLIDES 23

27 Culex pipiens Based on existing literature (Darsie & Ward 2005) Hongoh et al. (2011) Applied Geography 24

28 Gaussian membership function 25

29 Table 2: Threshold dependent sensitivity of the vectors models on the test dataset Threshold outcome Positive threshold outcome= 0.1 Positive threshold outcome= 0.5 Species Sensitivity Specificity Sensitivity Specificity Cx. tarsalis 91% 60% 80% 71% Cs. melanura 99% 9% 99% 16% Oc. canadensis 96% 2% 89% 6% An. Punctipennis 95% 7% 91% 14% Ae. Sollicitans 100% 14% 100% 48% Oc. triseriatus 96% 8% 93% 14% Ae. japonicus 99% 18% 95% 27% Cq. perturbans 97% 5% 92% 36% Ae. vexans 90% 11% 87% 19% Cx. restuans 87% 17% 67% 20% R. Ngom 26

30 Tab le 4: Progress rates of the species presence in Canada Species name Progress rate in the 2020s Progress rate in the 2050s Progress rate in the 2080s Aedes Sollicitans 49,97 74,10 83,52 Ochlerotatus canadensis 57,48 67,60 72,50 Ochlerotatus triseriatus 68,23 77,47 79,98 Aedes vexans 66,42 74,89 80,68 Culex restuans 61,13 76,40 61,13 Coquilletidia peturbans 50,00 66,38 73,64 Culiseta melanura 42,55 59,02 67,09 Ochlerotatus japonicus 68,81 82,69 87,62 Anophele Punctipennis 13,97 74,17 81,79 Culex tarsalis 31,17 35,62 41,48 R. Ngom 27

31 Model predictions (Culex pipiens) CGCM3 CCSM3.0 A2 B1 A2 B1 2020s 145% 127% 151% 164% Predictions poor in western Canada, but strong in Eastern Canada 2050s 255% 181% 279% 199% 2080s 518% 211% 555% 235% Model prediction capacity evaluations applied on the extended and reduced samples Samples Extended sample Models Logistic models Positive outcome threshold = 0.5 Fuzzy Logic models Positive outcome threshold = 0.5 Species sensitivity specificity sensitivity specificity Cx. tarsalis 49% 95% 90% 84% Ae. japonicus 0% 100% 95% 39% Cq. Perturbans 0% 100% 70% 20% Hongoh et al. (2009) Biodiversity 28

32 Hongoh et al. (2009) Biodiversity 29

33 Hongoh et al. (2009) Biodiversity 30

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