1. 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

2. 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

3. 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

4. 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

5. 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

6. 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

7. Abt Associates | pg 7  Marked reduction in malaria cases, especially after Bendiocarb Impact of IRS on Malaria Prevalence

8. 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

9. 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

10. Abt Associates | pg 10 Pyrethroid Spray Collections (2009-12)

11. Abt Associates | pg 11 Monthly Wall Bioassays (2009- 12)

12. Abt Associates | pg 12 National Susceptibility Study

13. 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.

14. Abt Associates | pg 14 Malaria Risk Maps

15. 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

16. 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