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Assessing infections at multiple levels of aggregation
M Kadohira1, J J McDermott, M M Shoukri
1Department of Population Medicine, University of Guelph, ONT, Canada.
Preventive Veterinary Medicine
|January 1, 1997
Summary
Multi-level analysis revealed varying disease clustering patterns in Kenyan cattle. Identifying these patterns, like brucellosis and trypanosomiasis clustering, aids targeted disease control and transmission research.
Area of Science:
- Veterinary Epidemiology
- Statistical Modeling
- Disease Ecology
Background:
- Sero-prevalence of cattle diseases varies geographically and by management.
- Understanding disease clustering is crucial for effective control strategies.
Purpose of the Study:
- To investigate sero-prevalence patterns of four infectious diseases in Kenyan cattle.
- To compare statistical methods for analyzing clustered disease data at farm, area, and district levels.
Main Methods:
- Two-stage cluster sampling of cattle in three contrasting Kenyan districts (Samburu, Kiambu, Kilifi).
- Application and comparison of Schall's algorithm (generalized linear mixed model) with OLR, GEE, Jackknife, and SAS VARCOMP.
- Multi-level analysis incorporating farm, area, and district random effects.
Main Results:
- Schall's algorithm provided comparable estimates to GEE and Jackknife for farm-level clustering.
- Infectious bovine rhinotracheitis showed high farm-level clustering; brucellosis showed moderate clustering across levels.
- Trypanosomiasis prevalence varied significantly by district, area, and farm.
Conclusions:
- Multi-level modeling effectively identifies disease clustering patterns in cattle populations.
- Understanding disease clustering at different organizational levels is essential for targeted disease control interventions.
- Identified patterns can guide research into disease transmission dynamics and risk factors.