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

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

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)

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

‘Gold Standard’
Blood Stool Clinical
PCV McMaster Temperature
Total protein Kato-Katz MM colour
Buffy coat Baermann Skin elasticity
Thin blood smear Sedimentation Discharges
Thick blood smear Lymph nodes
Hemoglobin Ectoparasites
Hair coat
Anaemia PCV<24 Flukes
Haemoparasites: Lungworms
Helminths: Validation:
Trypanosomes Trypanosomes
Strongyles
Theileiria spp. ECF
Strongoloides
Rickettsia Coccidia
Anaplasma Monezia
Babesia Nematodirus
Trichuris

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

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

Results I
Lab / clinical diagnosis Farmer
N 123 123
Sick 79 29
Anaemia (PCV<24) Cough
Fever Diarrhoea
Fascioliasis Mastitis
Helminths >800 Mavumba*
Lungworm Skin
Microfilariasis Worms
Diagnosis/ Mastitis Infertility
Signs Signs: enlarged lymph nodes Staring coat/ lacrimation,
(LN) weight loss &/or LN
Signs: Ocular discharge
Signs: Staring coat
Theileirosis (ECF)
Trypanosomiasis
*Generalized enlarged LN

Results I
“Is this animal sick today?” Individual farmer estimate
Mean 95% CI
Gold Standard Prevalence 62 55.1 72.7
Farmer Sensitivity 24 15.1 35.0
Farmer Specificity 77 62.2 88.5
Positive Predictive Value 65 45.7 82.1
Negative Predictive Value 36 26.5 46.7

Farmer
Lab/Clinical Diagnosis + Total
Sick Not Sick
ECF 9 12 21
ECF & Trypanosomes 0 3 3
ECF & Fascioliasis 1 6 7
Trypanosomes 0 1 1
Trypanosomes & Fascioliasis 0 1 1
Fascioliasis 2 17 19
Fever 1 1 2
Anemia (PCV<24) 0 2 2
Intestinal helminths >800 EPG 2 9 11
Signs: Staring coat* 3 6 9
Signs: Ocular discharge 1 1 2
Healthy 4 13 17
Intestinal helminths >50 EPG 6 22 28
Total 29 94 123

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)

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

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)

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

International Livestock Research Institute
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ILRI
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