Combining Entomological, Epidemiological, and Space Mapping data for Malaria Risk-mapping in Northern Uganda Findings and Implications Ranjith de Alwis,

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

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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

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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

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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

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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

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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

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Marked reduction in malaria cases, especially after Bendiocarb

Impact of IRS on Malaria Prevalence

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Impact of IRS on Malaria Prevalence

Location based data not available in health institutions

Difficulties in combining epidemiological data with other information

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Pre- and post-spraying PSCs

Post-spraying wall bioassays

Monthly wall bioassays National Susceptibility

Study (2011) Vector bionomics ****

Entomological Monitoring Activities

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Pyrethroid Spray Collections (2009-12)

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Monthly Wall Bioassays (2009-12)

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National Susceptibility Study

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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.

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Malaria Risk Maps

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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

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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