Combining Entomological, Epidemiological, and Space Mapping data for Malaria Risk-mapping in...
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Transcript of Combining Entomological, Epidemiological, and Space Mapping data for Malaria Risk-mapping in...
Combining Entomological, Epidemiological, and Space Mapping data for Malaria Risk-mapping in Northern Uganda
Findings and Implications
Ranjith de Alwis, Abt Associates
November 15, 2012
Abt Associates | pg 2
Contents
Malaria and malaria control in Uganda
Indoor residual spraying (IRS) in Uganda
Impact of IRS on malaria prevalence
Entomological monitoring activities and findings
Risk mapping
Lessons learned
Recommendations
Abt Associates | pg 3
Malaria and Malaria Control
Malaria transmission highly endemic and perennial 90% of population at risk 99% Plasmodium falciparum
Major vectors Anopheles gambiae Anopheles funestus
Interventions IRS ITNs/LLINs IPT Improved diagnosis/case
management
Abt Associates | pg 4
Indoor Residual Spraying (IRS)
IRS—most effective malaria vector control method
Currently, the primary factor for deciding where to use IRS is malaria incidence, which results in expensive blanket coverage
Stratification based on risk—more effective strategy but requires reliable and representative data over time
Abt Associates | pg 5
Indoor Residual Spraying (IRS)
Data needed for planning IRS Vector bionomics (species and behaviour) Vector susceptibility to insecticides Suitability of structures and population compliance Malaria prevalence patterns to determine time to spray
On-going monitoring needs for decision making Vector bionomics Vector susceptibility Residual efficacy of insecticide
Data needed for decisions on phase-out or scale-up of IRS Malaria epidemiological data over the time Meteorological information Feasibility of carrying out of other interventions
Abt Associates | pg 6
Indoor Residual spraying (IRS)
Started in 2006 in South Western districts
Moved to Northern districts in 2007
7-8 rounds have completed
Started with Lambda-Cyhalothrin Then moved to Alpha-Cypermethrin DDT was used in 2 districts for one
round Since 2010 Bendiocarb
Target Population – 2.8 million Approx. 900,000 structures
Abt Associates | pg 7
Marked reduction in malaria cases, especially after Bendiocarb
Impact of IRS on Malaria Prevalence
Abt Associates | pg 8
Impact of IRS on Malaria Prevalence
Location based data not available in health institutions
Difficulties in combining epidemiological data with other information
Abt Associates | pg 9
Pre- and post-spraying PSCs
Post-spraying wall bioassays
Monthly wall bioassays National Susceptibility
Study (2011) Vector bionomics ****
Entomological Monitoring Activities
Abt Associates | pg 10
Pyrethroid Spray Collections (2009-12)
Abt Associates | pg 11
Monthly Wall Bioassays (2009-12)
Abt Associates | pg 12
National Susceptibility Study
Abt Associates | pg 13
Risk Mapping
2005 risk map based on malaria endemicity.
2012 risk map detailed at district level to facilitate development of national vector control policy. Planned to used a spatial model based on district-level
information: Malaria prevalence data Entomological data Intervention data Meteorological data Demographic, physical and geographical data
Data challenges Malaria data is not representative or reliable No recent entomological data
Low, predictive power of the risk map model – Need to improve.
Abt Associates | pg 14
Malaria Risk Maps
Abt Associates | pg 15
Lessons Learned
IRS effectiveness Combining all these data help us to
– Use correct insecticide– Manage resistance– Understand residual efficacy
Indoor resting behavior Reduction of malaria prevalence / When to phase out IRS
Strengthen other control methods. Importance of location based data at lower administration
levels Risk mapping in project area Will allow scale up of malaria control activities nationally
while phasing out/reducing IRS in on-going areas
Abt Associates | pg 16
Recommendations
To scale up vector control nationally while reducing IRS in on-going areas, we will need:
Location based data Confirmed malaria cases Establishment of indicators institutions Spatial analysis of population distribution Spraying time and frequency Vector bionomics Resistance status