Transcript Malaria Working Groups

Indicators for Malaria
Impact Evaluation
Impact Evaluation Team
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Malaria Working Groups
1.
2.
3.
4.
Biometrics Working Group
Cognitive and Educational Working Group
Socio-Economic Working Group (+ KAP)
Cost Effectiveness
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Malaria Impact Evaluation Team
PROGRAM COORDINATION& HARMONIZATION
METHODOLOGY/ IE QUALITY
Technical Advisory Group
Country 1
Project
Working
Coordinator
Group 1:
Biometrics
Working
Group 2:
Cognitive
Working
Group 3:
SocioEconomic
Working
Group 4:
KAP
Working
Group 5:
Cost
Effectiveness
s
OPERATIONAL RESEARCH
COUNTRY-SPECIFIC TEAM
GOVERNMENT
Associate
Researcher
PROJECT MANAGEMENT
PROGRAM TEAM
MIEP RESEARCH TEAM
CASE COUNTRYI: OR IMPLEMENTATION- FIELD WORK
 Local Research Partner(Government Agency, Academia, NGO)
 Embedded Field Research Coordinator (Liaison)
CLIENT / POLICY LINK & PROJECT IMPLEMENTATION
MALARIA & IE EXPERTS
IE TEAM WORKING WITHCLIENT
Cluster
Coordinating
Team
FIELD OPERATION INCASE COUNTRY
CLIENT DEVELOPED IE PROGRAM
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Developing a Common Approach
to Measuring the Biometric Impact
of Malaria Control Interventions
Biometrics Working Group
Malaria Impact Evaluation
Joseph Keating (Tulane University)
Simon Brooker (LSHTM)
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Malaria Impact Indicators I: Parasitaemia and Disease
Populations:
-Children < 5 years old
-Pregnant women
-Population in malarious areas
– Data source: population based household survey, HMIS – high versus low
transmission seasons; stable versus unstable transmission areas
– Diagnostic method: finger-prick, thick and thin blood smear for microscopy
(Gold Standard) or Rapid Diagnostic Test (RDT) kit
Indicators:
-Prevalence of malaria parasite infection (< 5 years old/all ages)
-All cause mortality in children < 5 years old
-Laboratory confirmed malaria death rate (< 5 years old/all ages)
-Malaria incidence
Costs:
-RDT: USD $1-3 plus cost of training personnel; Microscopy (Gold
Standard): varies as a function of existing equipment, reagents, and
trained personnel
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Malaria Impact Indicators II: Anemia
Populations:
Children 6-59 months
Pregnant women
Schoolchildren
–
Data source: population based household survey, clinic based survey, school survey
–
Diagnostic method: finger-prick blood sample, portable Hemocue machine
Costs: $0.5/sample
Accuracy: 0.1 g/L
Anaemia definition: age specific, e.g. 110g/L (under
5s); 115-120 g/L (school-age children)
Alternative methods: Haemoglobin Colour Scale
finger-prick blood sample, special chromatography
paper ($0.05/sample but accuracy only to 10 g/L
therefore unsuitable for impact evaluation)
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Developing a Common
Approach for Cognitive and
Educational Assessments
Cognitive and Educational Working Group
Malaria Impact Evaluation
Matthew Jukes (Harvard University)
Don Bundy (World Bank)
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Improvement in Cognitive Function (SDs)
Impact of Early Childhood Malaria
Prevention on Global Cognitive Function
0.6
0.5
p=.01
0.4
0.3
p=.08
0.2
0.1
0
3 yrs in program
4 yrs in program
Jukes et al PLOS clinical trials 2006
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Can IPT in schools
reduce parasitaemia
and anaemia
and improve school
performance?
Malaria Infection
in Semi-immune Schoolchildren
Most common
Asymptomatic
Parasitaemia
Less common
Clinical
Attack
IPT
A randomised
controlled trial of IPT
using SP+AQ in 30
primary schools in
western Kenya
Anaemia
Reduced Attention
During Lessons
Absent from
School
Educational Achievement
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Impact of IPT on sustained
attention and education?
Clinical
Attack
Anaemia
Reduced Attention
During Lessons
Absent from
School
Educational Achievement
Outcome
n
Mean
difference
95% CI
p-value
Effect
size
Counting sounds
(max score=20)
481
2.12
(-0.17, 4.42)
0.07
0.65
Code transmission
(max score=40)
469
7.74
(2.83, 10.65)
0.005
1.01
Exam score 6
286
0.55
(-2.26, 3.36)
0.35
0.15
Exam score 7
266
0.69
(-0.93, 2.15)
0.21
0.30
Clarke et al. forthcoming
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Language Differences in Cognitive
Tests Performance
60%
Digit Span % Correct
50%
40%
Digits 1 to 5
Digits 1 to 9
30%
20%
10%
0%
Mandinka
Wollof
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Jukes et al. forthcoming
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Developing a Common Approach to
Measuring the Socio-Economic Impact
of Malaria Control Interventions
Socio-Economic Working Group
Malaria Impact Evaluation
Jed Friedman (World Bank)
Edit V. Velenyi (World Bank)
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From Data ….. To Impact
Monitor Change
in Indicators
and
Forecast
(M&E)
Organize
Integrate
Analyze
(MIS)
Data
Impact
Pathway
Information
Package
(MIS)
for
Implement the Plan
(System)
Evidencebased
Action
Influence the Plan
(Planners)
Planning
Knowledge
Evidence
Package &
Communicate to
Planners & Stakeholders
(MIS)
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Savigni and Binka (2004)
But what data for IE?
• “The data we have are not the data we want.”
• “The data we want are not the data we need.”
• “The data we need are not available.”
• How do we then measure impact?
• What impact do we measure?
• How precise is what we measure?
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Quotes: Savigni and Binka (2004)
Factors Influencing Malaria Burden
Underlying
Health Status
Endemicity
Immunological
Status
Observed Disease Burden
Knowledge Attitude and Practice (KAP)
Socio-Economic
Status
Social
Organization
Cultural
Roles
Cultural
Beliefs
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Jone and Williams (2004)
Health Links to GDP
Macro Economic Impact: Poor health reduces GDP per capita by reducing
both labor productivity and the relative size of the labor force.
Higher Fertility and
Child Mortality
Higher Dependency
Ratio
Labor Force Reduced by
Early Mortality
Child Illness
Lower GDP per
Capita
Adult Illness
& Malnutrition
Child Malnutrition
Reduced Labor
Productivity
Reduced Access to
Resources & Economy
Reduced Schooling &
Impaired Cognitive
Capacity
Reduced Investment in
Physical Capital
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Berman, Alilio, and Mills (2004)
Data Sources
Types and Levels of Data for Health Information Systems
Important for Malaria Control Programs
Type
Level
Cross-Sectional Retrospective
Population Survey
Individual and HH (Census, DHS, MICS)
Health Facility
Modeling
Longitudinal Prospective
Prospective Surveillance
(Vital events and DSS)
Routine Reporting
(HMIS, IDS, DHS)
HF Survey
Risk Mapping (GIS)
Remote Sensing and Early
Warning Systems
DHS = Demographic and health Survey, MICS = Multi-Indicators and Cluster Survey,
DSS = Demographic Surveillance System, HMIS = Health Management Information
System, IDS = Integrated Disease Surveillance, HF = Health Facility, GIS = Geographic
Information System
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Savigni and Binka (2004)
Conceptual Framework
Economic Burden of Illness for HHs
Individual & Household
Health
System
Box 3a: Direct Costs
Box 3b: Indirect Costs
Box 6:
Access,
fees, quality of
care, insurance
Box 2:
Treatment
Behavior
Box 4:
Coping Strategies
(Risky, less risky)
Box 1:
Reported
Illness
Box 5:
Impact on Livelihood
(Assets, income, food
security)
Social
Resources
Box 7:
Social
Networks
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Russell (2004)
Weak / Missing Link …
Biomedical & Socio-Economic
• Asset v. Consumption Module
• Health Care Seeking & Expenditures
• Copying Mechanisms & Poverty
• Labor Market / School Participation
• KAP
– Community Effects
– Social Norms (Gender, Vulnerability)
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Weak / Missing Link … (2)
Biomedical & Socio-Economic
Some Operational / Technical Issues
• Are the questions tailored to capture the
intervention? Is our approach parsimonious?
• Should the sample be expanded?
• What is our knowledge gain, and the
marginal cost of the information?
• Are we gaining predictive power and making
a good Biomedical-SE link?
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