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. 2017 Sep;97(3_Suppl):32-45.
doi: 10.4269/ajtmh.16-0696.

Using Rainfall and Temperature Data in the Evaluation of National Malaria Control Programs in Africa

Madeleine C Thomson  1   2 , Israel Ukawuba  1 , Christine L Hershey  3 , Adam Bennett  4 , Pietro Ceccato  1 , Bradfield Lyon  1 , Tufa Dinku  1
Affiliations

Using Rainfall and Temperature Data in the Evaluation of National Malaria Control Programs in Africa

Madeleine C Thomson et al. Am J Trop Med Hyg. 2017 Sep.

Abstract

Since 2010, the Roll Back Malaria (RBM) Partnership, including National Malaria Control Programs, donor agencies (e.g., President's Malaria Initiative and Global Fund), and other stakeholders have been evaluating the impact of scaling up malaria control interventions on all-cause under-five mortality in several countries in sub-Saharan Africa. The evaluation framework assesses whether the deployed interventions have had an impact on malaria morbidity and mortality and requires consideration of potential nonintervention influencers of transmission, such as drought/floods or higher temperatures. Herein, we assess the likely effect of climate on the assessment of the impact malaria interventions in 10 priority countries/regions in eastern, western, and southern Africa for the President's Malaria Initiative. We used newly available quality controlled Enhanced National Climate Services rainfall and temperature products as well as global climate products to investigate likely impacts of climate on malaria evaluations and test the assumption that changing the baseline period can significantly impact on the influence of climate in the assessment of interventions. Based on current baseline periods used in national malaria impact assessments, we identify three countries/regions where current evaluations may overestimate the impact of interventions (Tanzania, Zanzibar, Uganda) and three countries where current malaria evaluations may underestimate the impact of interventions (Mali, Senegal and Ethiopia). In four countries (Rwanda, Malawi, Mozambique, and Angola) there was no strong difference in climate suitability for malaria in the pre- and post-intervention period. In part, this may be due to data quality and analysis issues.

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Figures

Figure 1.
Figure 1.
Observed rainfall gauge data made available by the Ethiopian National Meteorological Agency to (A) the Global Telecommunications System of the World Weather Watch, (B) Enhanced National Climate Services products (ENACTS) monitoring products, and (C) ENACTS historical products.
Figure 2.
Figure 2.
Outputs of National Meteorological Agency Enhanced National Climate Services products (ENACTS) Maproom Climate Analysis tool used to examine recent climate trends for the two rainy seasons-the Belg (left column) and Meher/Kiremt (right column) for (A) Oromia region Ethiopia; (B) trend in rainfall (top) max T (middle) and min T (bottom) for the Belg rainy season (February to May); (C) trend in rainfall (top) max T (middle) and min T (bottom) for the Kiremt rainy season (June to September). Similar information can be created using the ENACTS tool at region, zone, or woreda level.
Figure 3.
Figure 3.
Tanzania ENACTS Weighted Anomaly Standardized Precipitation (WASP) Index using Enhanced National Climate Services products blended station and satellite data for Tanzania using a baseline period January 1995 to December 1999. Brown indicates time where rainfall was below the baseline average, whereas green indicates rainfall was above the baseline average. The WASP analysis is overlaid with the timing of interventions described in detail in Smithson and others (2015).
Figure 4.
Figure 4.
Enhanced National Climate Services products (ENACTS) Weighted Anomaly Standardized Precipitation rainfall analysis using varied baselines: (A) Tanzania 1995–1999 baseline, (B) Zanzibar Central 1995–1999 baseline, (C) Rwanda ENACTS 1996–2000 baseline, (D) Ethiopia ENACTS 2000–2005 baseline, and (E) Mali 1990–2005 baseline.
Figure 5.
Figure 5.
Climate Hazards Group InfraRed Precipitation with Station Data Weighted Anomaly Standardized Precipitation rainfall analysis using varied baselines: (A) Senegal 2005–2010 baseline, (B) Angola 2003–2005 baseline, (C) Malawi 1990–1999 baseline, (D) Uganda 1990–1999 baseline, and (E) Mozambique 1993–2002 baseline.
Figure 6.
Figure 6.
Possible outcomes if climate is not factored into malaria impact assessment—including country results.
Figure 7.
Figure 7.
Likely impact of El Nino rainfall in Africa. In addition, general atmospheric warming occurs across the tropics during an El Nino event. Local temperature will be influenced by rainfall (https://iridl.ldeo.columbia.edu/maproom/IFRC/FIC/ElNinoandRainfall.pdf).
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