The present study identified districts in Nepal where the risk of FMD, as estimated by FMD reports from the VDCs, was significantly higher than that for the average district and it identified factors associated with the variation in risk among the districts. As indicated by results of the multivariate Bayesian model, district factors associated with a high risk of FMD were a high human population, a high buffalo population, and a large number of animal health technicians. These factors accounted for most of the observed clustering, as indicated by the higher value of SDu (0.884), compared with the value of the SDs (0.036).
The finding that districts with many buffalo and technicians had higher relative risks of VDC-reported FMD (1.76, PI: 1.24, 2.62; and 1.31, PI: 0.96, 1.84, respectively), compared with districts with fewer buffalo and technicians, suggests possible links between FMD risk and buffaloes and technical support. Risk was found to vary differently depending on the number of technicians and buffaloes in the district, as indicated by the significant interaction between number of buffalo and number of technicians (RR: 0.58, PI: 0.38, 0.83). Alternative explanations could account for the interaction found between number of buffalo and number of technicians and its association with FMD risk. The sensitivity of reporting FMD could have been higher in districts with a large number of technicians or, perhaps, technicians in some way contributed to transmission of the disease (Figs. and ). A high risk associated with a larger number of buffaloes could relate to enhanced informal owner surveillance that might be expected in areas where buffaloes are raised. Buffaloes are considerably more expensive than other cattle in Nepal and other countries in South Asia and, as such, generally would be expected to command greater attention to their health and productivity. Consequently, an observed higher risk of FMD in regions with a large number of buffaloes could represent greater awareness and scrutiny for disease, and thus more rigorous observation and reporting of suspicious FMD cases, which may have biased the results presented here. Alternatively, the findings could be interpreted to indicate that buffalo serve in some biological way to increase risk of FMD, perhaps through a virus-host relationship that would foster transmission. Although water buffaloes (Bubalus bubalis
) can effectively transmit the FMD virus to other susceptible species (Gomes et al. 1997
; Dutta et al. 1983
), and the carrier state has been found in Indian buffaloes (Samara and Pinto 1983
; Barros et al. 2006
), there are no reports suggesting that water buffaloes are more likely to become infected or that they are more likely to shed large amounts of viruses. If buffalo were more susceptible or if they did shed more virus for longer periods, one would expect districts with many buffaloes to have greater risks of FMD, compared with districts with fewer buffaloes.
Depiction of the interaction between the number of technicians and the number of buffaloes per district on the risk ratio (RR) for FMD in Nepal
The finding of a positive association (RR: 1.14, PI: 0.8-1.7) between a district’s human population and the number of VDCs reporting FMD suggests that the size of the human population in a district, or some unmeasured factors associated with the size of the human population, was in some way associated with increasing the risk of FMD. Features of human demographics, such as population density, socio-economic status, and literacy rate, have been discussed elsewhere as possibly having a role in fostering animal diseases, including FMD (Rivas et al. 2006
; Harrington et al. 2005
), where human population size might be considered as a surrogate for the extent of animal movement expected in a region (Rivas et al. 2006
). In the model presented here, human population size was used in the model as a proxy for several factors thought to contribute to FMD spread, including transmission of FMD by people and animal movement, which would be expected to increase in response to higher demands for meat and milk in densely populated areas. Consideration of candidate models, which had very small differences in DIC, was helpful in assessing use of the human population variable as a fixed effect. These candidate models were compared with models that considered road density and number of animals slaughtered, which were highly correlated with size of the human population and could also be considered as a proxy for animal movement. The positive association found between the number of animals slaughtered daily in a district and the FMD risk when human population was removed from the model, although not statistically significant, (Table ) suggested that human population variable could be acting as a proxy for the number of animals slaughtered daily. An important consideration is that, apart from accounting for factors that have a positive effect on FMD, inclusion of a human population variable as a fixed effect also would identify other correlated factors that were negatively associated with FMD risk, or that could have equivocal effects. For example, as reported elsewhere, higher levels of education could be expected to help reduce FMD risk but also could increase sensitivity of reporting (Saini et al. 1992
), thereby presenting as little or no effect either positive or negative. Here, inclusion of the literacy rate variable into the model was associated with an apparent increased risk of FMD attributed to human population size, which might suggest that for districts with a population that is more aware and educated, the sensitivity of reporting FMD would be higher because FMD cases would more likely be reported than in districts with a lesser literacy rate (Table ).
The 25-fold difference in standard deviations of the structured (SDs
0.036) and unstructured (SDu
0.884) random effects suggests that most of the variation in FMD risk that was not explained by factors included in the model were not spatially associated. Further study would be warranted to identify possible reasons for why FMD risk in some districts could not be explained by human population, number of buffalo, or number of technicians (Fig. ).
In summary, results of this study indicate that the FMD risk in Nepal in 2004 was spatially clustered in specific areas of the country, and that the elevated risk in those areas was largely associated with the size of human and buffalo populations and with the number of technicians. Further studies should be directed to clarify the reasons for these associations, in order to increase the knowledge on the epidemiological dynamics of FMD in Nepal and to improve the efficiency of resource allocation and control efforts in the country.