Households in Tanzania face foot-and-mouth disease (FMD) challenges as 50% own livestock, grazing is open, and 4 of 7 FMD serotypes are present. Vaccination could help but vaccines are not matched to serotypes. The study surveyed 432 households to determine what drives FMD vaccination decisions and how proximity of risk influences those decisions. Results showed higher income levels and experiencing an outbreak near a neighbor increased willingness to pay for emergency vaccination, while using government veterinary services decreased it. Improving vaccine effectiveness and matching serotypes could increase routine vaccination uptake.

1. Household perceptions of risk as drivers
for FMD vaccination
Ashley Railey
Washington State University
Tiziana Lembo—Boyd Orr Centre for Population and Ecosystem Health; Institute of Biodiversity,
Animal Health and Comparative Medicine; University of Glasgow, Scotland
Guy Palmer—Paul G Allen School for Global Animal Health, Washington State University, USA
Gabriel Shirima—Nelson Mandela African Institute of Science and Technolology, Tanzania
Tom Marsh—Paul G Allen School for Global Animal Health, Washington State University, USA

2. FMD in Tanzania
50% of households own livestock*
Open-grazing
FMD is endemic
4 of 7 serotypes in Tanzania
Up to 3 outbreaks a year (Casey and Reeve et al., 2018)
Vaccines NOT matched to serotypes
Targeted vaccination (Casey and Reeve et al., 2018)
What drives household FMD vaccination decisions?
*2015 Tanzanian Livestock Modernization Initiative
2 of 10

3. Problem
Uncertainty in decision-making
Human health decisions*
Immediacy and proximity of risk (Brook & McLachlan 2006)
Negative past experiences with FMD vaccines in Tanzania
Vaccination for FMD may improve household wellbeing
Household income and wealth (Marsh et al., 2016)
Production losses (Casey and Reeve et al., 2018)
*see seasonal influenza literature (Bloom et al 2017; Galvani, Reluga & Chapman 2007)
3 of 10

4. Ngorongoro
Serengeti
TANZANIA
KENYA
National parks
Study Districts
Study Sites
Survey
April 2016-July 2016
Multi-stage random sampling
432 households
4 of 10

5. Descriptive Statistics
Household characteristics
80% male head of household
85% have some formal education
46% off-farm income
38% crop income
63% sold cattle (past year)
Livestock characteristics
42 cows (median 20)
91% produce milk
Disease Factors
70% reported FMD in past year
19% vaccinated livestock 5 of 10

6. Methodology
1) What drives FMD vaccination decisions?
Stated preference willingness to pay-
Derived from individual preferences, not the market
Hypothetical
Double-Bounded Contingent Valuation
(Hanemann, Loomis, & Kanninen, 1991)
One cow, 6 mo duration Q1 Initial Price
Q2 Higher price
Q2 Lower Price
If yes
If no
6 of 10

7. Methodology
Routine vaccination Emergency vaccination
• Biannual, planned application
• Population-level protection
• Lower perceived risk of infection
Vaccine efficacy: 50 or 100 percent
Outbreak distance: village or neighbor
• Spatial and temporal immediacy
• Individual-focused protection
• Higher perceived risk of infection
2) How are vaccination decisions influenced by proximity of
perceived risk?
7 of 10

8. Amount in Tanzanian Shillings (2100Tsh~USD 1)
Predicted WTP Distributions
8 of 10

9. AF Railey, T Lembo, GH Palmer, G Shirima, and TL Marsh (2018). Spatial and temporal risk as drivers for adoption of foot and mouth disease
vaccination. Vaccine 36, 5077-5083.
Table 3 Vaccination Determinants
Variable
Routine Marginal
Effects
(CI 95%) P value
Emergency
Marginal Effects
(CI 95%) P value
Education (0=Formal; 1=No Formal) 681 (-7,1356) 0.096 655 (-369,1679) 0.295
Income
Off-Farm (≤25,000 Tsh) Base Case
Off-Farm (25-100,000) 589 (-34,1213) 0.119 1962 (835,3090) 0.004
Off-Farm (>100,000) 1022 (360,1685) 0.010 1763 (672,2854) 0.007
Crops (≤100,000 Tsh) Base Case
Crops (100-500,000) 1635 (806,2465) 0.001 2294 (1034,3554) 0.003
Crops (>500,000) -445 (-1067,176) 0.237 -403 (-1513,3554) 0.552
Herd Size† 26 (-192,243) 0.846 42 (-348,432) 0.859
Expected Milk Loss (in liters per cow) 306 (-94,707) 0.207 423 (-205,1051) 0.270
Cattle sold in past year 36 (-.33,71) 0.096 11 (-48,71) 0.753
FMD experience in past year (0=No;
1=Yes)
-241 (751,270) 0.439 -283 (-1156,590) 0.595
Vaccinated for any cattle disease in
past year (0=No; 1=Yes)
-247 (-795,299) 0.457 216 (-754,1186) 0.715
Use of government vet (0=No;1=Yes) -663 (-1113,-214) 0.014 -1817 (-2626,-1008) 0.001
Vaccine efficacy (0=100%; 1=50%) 1573 (370,2778) 0.031 2318 (107,4529) 0.085
Gender (0=Female; 1=Male) 1031 (321,1740) 0.016 857 (-478,2192) 0.292
Gender*efficacy (0=100%; 1=50%) -1458 (-2740,-174) 0.060 -2737 (-5066,406) 0.053
District (0=Ngorongoro; 1=Serengeti) -270 (-751,212) 0.358 94 (-779,967) 0.860
Outbreak (0=Village; 1=@Neighbor) -476 (-1244,293) 0.314
Log Likelihood -415 -498
Chi-2 Statistic 39.09 41.87
† Log of variable
USD 1.00=2100 Tanzanian shillings

10. How do we increase vaccination
uptake?
Demonstrate field effectiveness
Better quality vaccines and supply chains
Improve surveillance and household participation
Predict outbreaks in space and time
Contributions to community control
Market assessment
?
10 of 10

11. Acknowledgements
UNIVERSITY SUPPORT:
Dr. Thomas L. Marsh (Washington State University)
Dr. Timothy Baszler (Washington State University)
Dr. Thomas Rotolo (Washington State University)
Dr. Felix Lankester (Washington State University)
Dr. Tiziana Lembo (University of Glasgow)
Dr. Gabriel Shirima (Nelson Mandela African
Institute of Science and Technology)
FUNDING:
Bill and Melinda Gates Foundation—
Program for Enhancing the Health and Productivity
of Livestock (PEHPL)
MSD Animal Health—Tanzania Research Platform
Special Thanks:
Administrative and grant assistance:
Rose Mosha
Sara Pepper and Allen School staff
Dr. Guy Palmer
FIELD TEAM:
Emmanuel Sindoya, Victor Sianga, Isaya
Ole Seki, Jackson Dudui, Loserian Ole
Maoi, Timas Yohana, Sunguro Matonge,
Ryoba Makorere, Mayaya Masunga,
Ahmed, and Zilpah. Linus Otieno