1

Participatory prevalence estimation:

A pilot survey in Kenya

Hannah H1, Grace D 1, Randolph T 1 ,

De Glanville W 2 and Fèvre E2 1International Livestock Research Institute, Kenya 2University of Edinburgh

13th conference of the International

Society for Veterinary Epidemiology

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

Haemoparasites:

Trypanosomes

Theileiria spp.

Rickettsia

Anaplasma

Babesia

Flukes

Lungworms

Helminths:

Strongyles

Strongoloides

Coccidia

Monezia

Nematodirus

Trichuris

Validation:

Trypanosomes

ECF

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

Diagnosis/

Signs

Anaemia (PCV<24) Cough

Fever Diarrhoea

Fascioliasis Mastitis

Helminths >800 Mavumba*

Lungworm Skin

Microfilariasis Worms

Mastitis Infertility

Signs: enlarged lymph nodes

(LN)

Staring coat/ lacrimation,

weight loss &/or LN

Signs: Ocular discharge

Signs: Staring coat

Theileirosis (ECF)

Trypanosomiasis

*Generalized enlarged LN

Results I

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

“Is this animal sick today?” Individual farmer estimate

Lab/Clinical Diagnosis + Farmer

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

Community

(mean)

Lab

(mean)

N

villages

Difference

p-value

Difference

95% CI

Helminthosis

(EPG>50) 84.1 54.2 10 <0.001 20.5 41.8

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

Performance of communities to estimate prevalence

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

Eric Fevre

Delia Grace

Tom Randolph

Phil Toye

Evalyne Njiri

Steve Kemp

Jane Poole

PAZ Team, Busia

Will De Glanville

Lazarus Omoto

James Akoko

Participating farmers in Western Province, Kenya

EDRSAIA Field Team

Maseno Cleophas

John Wando

Peter Omemo

John Ohato

Gabriel Turasha

VETAID Kenya Field Team

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