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Use of Climate Information in the Assessment of Impact of Malaria Interventions
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Use of Climate Information in the Assessment of Impact of Malaria Interventions

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Presented by Madeleine Thomson, International Research Institute for Climate and Society and The Earth Institute at Columbia University, as part of a symposium organized by MEASURE Evaluation and ...

Presented by Madeleine Thomson, International Research Institute for Climate and Society and The Earth Institute at Columbia University, as part of a symposium organized by MEASURE Evaluation and MEASURE DHS at the 6th MIM Pan-African Malaria Conference.

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  • The following characteristics of climate make it potentially ideal as an additional layer of information for the health sector for application in malaria vulnerability assessments, surveillance and forecasting
  • Climate model projections summarized in the IPCC Fourth Assessment report are in general agreement that eastern Africa will become wetter than the current climate by the end of this century (Figure 1). Yet in recent decades, observed changes in rainfall, particularly for the long rain season (March to June) have instead shown a substantial decline. Recurrent droughts in the Greater Horn of Africa have affected millions of people, adversely impacting pastoralists, agriculture, and water resources. Since 2009, USAID alone has spent over $1.4 billion in food aid to Kenya, Ethiopia and Somalia. Whether the recent droughts are associated with decadal climate variability, anthropogenic climate change (or both) is currently not clear. http://www.usaid.gov/our_work/humanitarian_assistance/ffp/wherewework.html
  •  Figure 1c displays time series of MAM precipitation anomalies averaged across land areas of East Africa (10°S to 12°N, 30°E-52°E; red box in Figure 1b) taken from three datasets: “GPCC” [Rudolf and Rubel, 2005], “GPCP” [Huffman et al., 2009] and again, CAMS_OPI). While precipitation in East Africa shows a high degree of spatial variability given the region’s complex terrain [Hession and Moore, 2011], the area-average emphasizes the bulk behavior of the long rains. A clear decline in precipitation is evident in the time series since the 1980s. However, the decline is seen to be associated with an abrupt GPCC Global Preciptiation Climatology Centre.Brief Description:GPCC Global Preciptiation Climatology Centre monthly precipitation dataset from 1951-present is calculated from global station data. More Details...Temporal Coverage:Monthly values 1950/01 through present.Spatial Coverage:1.0 degree latitude x 1.0 degree longitude global grid (360x180)0.5 degree latitude x 0.5 degree longitude global grid (720x180)90.0N - 90.0S, 0.0E - 360.0Edecrease in precipitation after 1999. Indeed, for GPCP the mean MAM precipitation for the period 1999-2009 is more than 15% less than that for 1979-1998, with the difference in means statistically significant (p < 0.01) based on a two-tailed t-test. The largest monthly departures (not shown) were observed to occur during April and May. Overall, the persistence of the current SST anomalies we identify in the tropical Pacific suggests a continuation of poor long rain performance in East Africa (with implications for other regions as well) and also lends a measure of predictability. During La Niña years in particular, when the likelihood of drought during the short rains is enhanced, the likelihood of multi-season drought since 1999 has also been increased given the general lackluster behavior of the long rains since that time.
  • The log-linear relationship identified cut-offs of 60% parasite prevalence in children <5 years of age to be approximately concordant with the low, medium and high transmission intensity using EIR [25].

Use of Climate Information in the Assessment of Impact of Malaria Interventions Use of Climate Information in the Assessment of Impact of Malaria Interventions Presentation Transcript

  • : Use of climate information in the assessment of impact of malaria interventions. Dr Madeleine Thomson Co-authors: Frank Zadravecz, Derek Willis, Tufa Dinku, Bradfield Lyon, Remi Cousin, Gilma Mantilla and Pietro Ceccato,. Acknowledgements: Ethiopian National Meteorological Agency, Tanzanian Meteorological Agency PMI-CDC USAID Sixth MIM Pan-African Malaria Conference October 6-11, 2013, Durban, South Africa Symposium 38: October 9th, 2013 Analytic challenges in measuring impact of malaria control programs: Methodological approaches, confounders and lessons learned from the multi-agency malaria control impact evaluations PAHO/WHO Collaborating Centre on early warning systems for malaria and other climate sensitive diseases
  • Climate as a confounder for the measurement of the impact of malaria interventions  The RBM / Monitoring and Evaluation Reference Group recommendation ….follow trends in the coverage of malaria control interventions, all factors influencing childhood mortality, malaria-associated morbidity.  A minimal list of potential confounders, determined a priori, should be available for all countries.  The methodology indicates the need to explain contextual (confounding) factors that affect the epidemiology of malaria, like climate.
  • Why is climate unique?  its climatology  seasonality  climate is routinely measured  diurnal rhythm and  potential predictability at multiple time scales  weather  seasonal  decadal (not available)  climate change  and modeled  by others – all over the world
  • Rainfall in Africa …why East Africa is unique Rainfall amount Rainfall invariability Rourke (2011)
  • Predicted and actual trends in East African rainfall Left: Projected climate change 2080-2099) IPCC 4th Assessment, Right: Observed decline in MAM rainfall 19792009) Funk (2011)
  • Recent and abrupt decline in the East African long rains (Mar-Apr-May) – linked to decadal sea- surface temperatures Lyon, B., DeWitt, D., (2012) Geophysical Research Letters
  • In the absence of control climate drives malaria transmission across the continent Gething, P.W., Patil, A.P.*, Smith, D.L., Gu erra, C.A., Elyazar, I.R.F., Johnston , G.L., Tatem, A.J. and Hay, S.I. (2011). A new world malaria map: Plasmodium falciparum endemicity in 2010. Malaria Journal, 10: 378. *indicates equal authorship.
  • Climate impacts on malaria at multiple scales …..from national…. Anomalies in malaria incidence in Botswana is strongly related to rainfall variability during the peak rainfall season December – February. (1982-2003) using CMAP
  • ..to the local level (kericho tea estate)
  • But problems exist for measuring and disseminating rainfall and temperature information across scales  So users:  Do local analysis only  Use satellite - derived estimates of insufficient accuracy  Use gridded data of poor resolution  Use proxies for climate such as NDVI  Ignore climate all together
  • Malaria decline in Eritrea relative to 1999 baseline NDVI Vegetation Index (a proxy for rainfall) also decreased 0 120 80 60 Mean 0 NDVI Malaria incidence Mean malaria incidence 100 0 40 0 ADDS NDVI (Dek mean) 20 ADDS NDVI (MVC) 0 0 CF NDVI (MVC) 1996 1996 1997 1998 1999 2000 Y EAR 2001 2002 2003 1998 1997 2000 1999 2002 2001 2003 Y EAR Graves, P.M. et al., (2008) Effectiveness of malaria control in Eritrea, 1998 to 2003. Tropical Medicine and International Health 13, (2) 218-228
  • Possible outcome if climate is not taken into account……… Changes in observed malaria following intervention (relative to baseline) Changes in climate suitability for malaria transmission following intervention (relative to baseline) increase decrease no change or increase may underestimate impact of intervention may obscure impact of intervention no change (average) decrease no effect may overestimate may obscure extent impact of of failure of intervention intervention
  • Ethiopia Confirmed Malaria Incidence / 1000 / year in relation to climate Combined sources from Ethiopia FMOH; Climate data from IRI SST anomaly LST anomaly Incidence 2.5 10 2 8 1.5 6 1 4 0.5 2 0 0 SST or LST anomaly Malaria conf cases per 1000 persons 12 -0.5 SST v Malaria Incidence y = 0.0268x - 0.157 R² = 0.18107 Graves et al., 2012
  • Enhanced National Climate Services (ENACTS) Ethiopia New ENACTS products combine locally calibrated satellite rainfall and temperature (min and max) estimates and all available quality controlled ground-based meteorological station gauge data (>300 for temperature and >600 for rainfall) available in Ethiopia for the period (1983-2010).
  • Climate suitability for malaria transmission tool Grover-Kopec, E., et al., An online operational rainfall-monitoring resource for epidemic malaria early warning systems in Africa. Malaria Journal, 2005. 4(6).
  • Climate suitability for malaria transmission tool
  • Climate Analysis Tool Belg (Feb-May) Kiremt (Jun-Sep) Rainfall Maximum Temperature Minimum Temperature
  • WASP (Weighted Average Standardised Precipitation ) TOOL
  • Rainfall analysis at the Zone level –East Shoa
  • Malaria control and elimination and economic development in conflict in East Shoa Woreda and sub-Woreda analysis
  • Building capacity to use the information in Ethiopia
  • Enhanced National Climate Services (ENACTS) Tanzania Weighted Average Standardised Precipitation at National level for Tanzania using a 1995-1999 baseline
  • Ethiopia Changes in observed malaria following intervention (relative to baseline) Changes in climate suitability for malaria transmission following intervention (relative to baseline) increase decrease no change or increase may underestimate impact of intervention may obscure impact of intervention no change (average) decrease no effect may overestimate may obscure extent impact of of failure of intervention intervention
  • Tanzania Changes in observed malaria following intervention (relative to baseline) Changes in climate suitability for malaria transmission following intervention (relative to baseline) increase decrease no change or increase may underestimate impact of intervention may obscure impact of intervention no change (average) decrease no effect may overestimate may obscure extent impact of of failure of intervention intervention
  • Conclusions  The new ENACTS product for Ethiopia and Tanzania are suitable for incorporation into national malaria impact assessments  There is significant warming (approx. 0.2-3oC per decade) in many (but not all) regions of Ethiopia and Tanzania over the last three decades.  Ethiopian intervention period (2006-2010) was warmer and wetter than baseline (2000-2005)  Tanzanian intervention period (2000-2010) was warmer and substantially drier than baseline period (1995-1999)  Careful choice of baseline year(s) is key to reduce the impact of climate as a confounder on malaria assessments  Incorporation of climate data into statistical and mathematical models of malaria at multiple scales is now feasible – but malaria data remains weak
  • : Thank you mthomson@iri.columbia.edu PAHO/WHO Collaborating Centre on early warning systems for malaria and other climate sensitive diseases