Presented by H. Hannah, D. Grace, T. Randolph, W. De Glanville and E. Fevre at the 13th conference of the International Society for Veterinary Epidemiology and Economics, Maastricht, the Netherlands, 20-24 August 2012.
Participatory prevalence estimation: A pilot survey in Kenya
1. Participatory prevalence estimation:
A pilot survey in Kenya
Hannah H1, Grace D 1, Randolph T 1 ,
13th conference of the International
De Glanville W 2 and Fèvre E2
1International Society for Veterinary Epidemiology1
Livestock Research Institute, Kenya
2University of Edinburgh and Economics, 20-24 August 2012
2. Background
Need: locally and globally relevant surveillance tools
Increasing applications of participatory methods
• Participatory epidemiology (PE)
• Participatory disease surveillance (PDS)
Traditional veterinary knowledge role contested
• Build base of evidence
3. Objectives
I. Determine the sensitivity & specificity of individual
farmers to diagnose sick cattle
II. Determine the agreement between prevalence
estimates from PE surveys and concurrent laboratory
analysis for selected health conditions
• Anaemia
• Fascioliasis
• Helminthosis
• Trypanosomiasis
• Theileriosis (East Coast fever – ECF)
4. Methods I
Individual farmer → individual animal health status
• Sensitivity & specificity, PPV & NPV
• Farmer: Is animal ill? If yes, name of health condition
• Gold standard: physical exam & lab analysis
6. Methods II
Community → community herd health status
• Difference of proportions
• Herd prevalence estimates from PE (100 counters)
• “How many animals are sick with [worms] today?”
• Herd census & systematic selection n=80/community
• Physical exam & lab analysis
7. Case definitions
1. Intestinal helminths
> 50 & >800 eggs/gram
2. Fascioloiasis
Any
3. Anaemia
PCV<24
4. Trypanosomes
PCR + AND anaemia
5. Theileriosis (ECF)
PCR+ AND ONE OF fever, lymph nodes, nasal discharge
11. Results II
Education: 15% no primary, <30% completed secondary
Mixed income: sugar cane, crop farming, livestock, small
business, casual employment, others
Economic importance of cattle (rank): 4 (rank range 2 - 8)
Mixed livestock: cattle, sheep/goats, poultry, pigs, turkeys, ducks
Time spent keeping cattle: >200 years/ 5 generations
Community herd size mean: 127 (range=80 - 231)
12. Results II
Performance of communities to estimate prevalence
Community Lab N Difference Difference
(mean) (mean) villages p-value 95% CI
Helminthosis
84.1 54.2 10 <0.001 20.5 41.8
(EPG>50)
Fascioliasis 68.1 21.1 8 <0.001 35.8 59.2
Anaemia (PCV<24) 52.3 15.6 3 <0.001 25.8 47.8
Trypanosomiasis 40.0 7.2 2 <0.001 28.4 51.4
Theileriosis (ECF) 20.0 2.5 2 <0.001 37.0 50.0
13. Discussion
I. Individual farmers
• Under-estimates (70%)
• Implications for treatment
II. Communities
• 5 health conditions of interest
• Over-estimates (30%)
Implications for interpretation of participatory data
Limitations
• Small sample size
• Non-pastoralists: cattle not first livelihood priority
• Incomplete analysis (clustering, lab)
14. Acknowledgements
ILRI, Nairobi EDRSAIA Field Team
Eric Fevre Maseno Cleophas
Delia Grace John Wando
Tom Randolph Peter Omemo
Phil Toye John Ohato
Evalyne Njiri Gabriel Turasha
Steve Kemp VETAID Kenya Field Team
Jane Poole
PAZ Team, Busia
Will De Glanville
Lazarus Omoto
James Akoko
Participating farmers in Western Province, Kenya
15. International Livestock Research Institute
Better lives through livestock
Animal agriculture to reduce poverty, hunger and
environmental degradation in developing countries
ILRI
www.ilri.org
Thank you