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Anthropogenic and Ecological Determinants of HPAI in Southeast Asia
Kapan et al. (2006) hypothesized that the on-going process in Southeast Asia of replacing traditional farming methods such as multi-species livestock husbandry with industrial, mass-production-oriented operations poses significant environmental health risks (e.g., Mallin and Cahoon 2003) due to increases in livestock pools and thus opportunities for disease transmission. Simultaneously, rapid urban and peri-urban development in these countries has often been accompanied by more refuse, standing water, and animals in and around homes that have been correlated with environmental health risks (e.g., Graham et al. 2004). With respect to HPAI, expansion of the urban fringe has placed a larger proportion of the human population in contact with formerly dispersed farm environments that include potentially infected poultry and swine populations. Such urban–rural interfaces have been hotspots of other infectious diseases such as leishmaniasis (Oliveira et al. 2004).
An array of anthropogenic and ecological studies of the determinants of HPAI in Southeast Asia has supported these hypotheses. Gilbert et al. (2006, 2007) showed
that the interaction of poultry and particularly domestic duck populations within the rice paddy production system was as an important factor for the maintenance and spread of HPAI virus in Thailand. Pfeiffer et al. (2007) showed that rice paddy production intensity and density of domestic chickens and water birds were also associated with a higher risk of HPAI outbreaks in Vietnam, lending support to the rice-duck-chicken hypothesis. The same study showed that increased distance from high density human population areas consistently decreased HPAI risk (Pfeiffer et al. 2007). The study finds support for the hypothesis of “the presence of a fairly widespread infection reservoir in Vietnam …, possibly in domestic and wild water birds” (Pfeiffer et al. 2007). Gilbert et al. (2008b) demonstrated that a few key factors such as human population density, rice cropping intensity, and to some extent poultry density, managed to explain a large proportion of the spatial variation in HPAI disease risk; the same study also notes that considerable variation remained unexplained, and suggests that other factors such as poultry production and marketing systems, agricultural seasonality, the potential for contacts between domestic and wild birds, and climatic and other conditions affecting the persistence of the virus in the environment should be considered. Fang et al. (2008) found the minimal distance to the nearest national highway, annual precipitation, and the interaction between minimal distance to the nearest lake and wetland, were important predictive environmental variables for the risk of HPAI in China. A study of postvaccination outbreaks in southern Vietnam found poultry flock density, fraction of houses with electricity, rescaled Normalized Difference Vegetation Index, buffalo density and sweet potato yield to be significant risk factors (Henning et al. 2009).
Of particular interest to this study is the claim by Gilbert et al. (2014) that the highest risks of HPAI impact in Southeast Asia are to be expected where extensive and intensive systems of poultry production co-exist. The extensive systems allow virus circulation and persistence; the intensive systems promote disease evolution. A study in Thailand found differences in avian influenza risk rates across scale of operations (Otte et al. 2006), which was attributed to bio-security (waste management) features.
Spencer (2013) sought to establish whether bird deaths followed a Kuznets curve as settlement infrastructure patterns evolved. Vietnam's 1999 Census of Population and Housing provides counts of households by housing construction materials (traditional/temporary or modern), water supply (stream, rain, well, piped), and sanitation infrastructure (none, pit, composting, flush). Spencer converted each of these 4-category, ranked urbanization measures into four distinct measures of settlement “coherence”. For each coherence measure, greater mixing (i.e. incoherence) of the four categories was set to center on a value of zero, with more “traditional” settlement a mixture dominated by the least sophisticated (e.g. no toilet) of each response category valued at (−1), and the most “modern” settlements a mixture dominated by the most sophisticated (e.g. running water) of each response category valued at (+1). Working at the district level, Spencer plotted these three coherence indices, as well as a composite index combining the three, against the probability that a district in any of Vietnam's provinces (including cities) had an outbreak of HPAI in 2004 or 2005. After accounting for a minimal threshold effect of development, Spencer (2013) found a distinct Kuznets relationship exists between settlement coherence and HPAI. In particular, the sanitation coherence index explained over one third of variance in outbreaks (R-square = 0.37, bivariate), and the water supply coherence index explained over half (R-square = 0.56, bivariate). Overall, the findings suggest that the urban infrastructure transition is associated with HPAI outbreaks in poultry and may be used as a general predictor of emerging infectious disease risk.
These initial findings illustrate the potential theoretical contributions of a transitional approach to the study of HPAI. This suggests that for the urbanization measure, at least those measures centered on water supply and sanitation, the basic function may be a Kuznets curve rather than a linear or a more complex curve. Our current project is conducting similar exercises for agricultural change and habitat alteration. We are developing transition indices for agricultural change and habitat alteration, plotting them against the probability of HPAI outbreak, and choosing the curve that best fits the data. A twice-changing slope as the best fit would suggest a more complex fluctuation of risk between traditional and modern landscapes, and a u-shaped curve would suggest that transitional landscapes are associated with reduced risk.
Lastly, we are examining how perceptions of HPAI risk vary with urbanization, agricultural change, and habitat alteration. Of the few studies that have examined determinants of HPAI risk perceptions, all have focused on perceived risk of HPAI to humans (rather than perceived risk to the health of poultry). Three studies conducted in Asian countries (de Zwart et al. 2007; Fielding et al. 2005; Figuié and Fournier 2008) showed perceived human risk was correlated with demographics (women and older people perceived more risk) and efficacy (perceived availability of protective actions and ability to engage in those actions led to lower perceived risk). In Laos, Barennes et al. (2007) reported that protective behavior was more likely with higher levels of education, urban living, knowledge of HPAI, and owning poultry. Only one of the above studies (Figuié and Fournier 2008) was conducted in Vietnam. No studies have examined the relative importance of socio-ecological variables (urbanization, agricultural change, habitat alteration) versus socio-psychological variables (efficacy, knowledge, affective response, risk avoidance, demographics) in determining perceptions of risk to the health of poultry. Moreover, no studies have examined whether risk perceptions and protective behaviors vary across traditional, transitional, and modern settings or with observed risk (poultry deaths).
The framework we are proposing is based on an assumption that risk management policies need to be derived from a broad-based understanding of how decision makers perceive, explain, and prioritize risk. An analysis of EID risk that focuses only on socio-ecological variables will not reveal the socio-psychological differentiation of individuals who are more or less successful in responding to and managing EID outbreaks. Currently, however, there exists a gap in knowledge about the underlying mechanisms that explain variation in perceptions of the risk of EID and how these perceptions vary with social-ecological transition. Furthermore, most research on perceived risk has been done in democratic, Western countries, not in a context where there is tight state control of key institutions that interpret and disseminate disease risk information. To further understand how EID risk signals are processed by individuals in the context of CNH systems in Vietnam, we need to examine in depth the processes through which people collect and integrate data from natural and human systems. This work will advance basic knowledge about the complex linkages among ecological, social, and psychological variables that amplify or attenuate the intensity and frequency of EID.
In sum, literature suggests an array of important elements characterize CNH systems. Using unidimensional measures, Spencer (2013) showed that the relationship between transition and disease can be examined empirically. However, more research is necessary to understand the complex interactions among natural and human elements at diverse spatial, temporal, and organizational scales, and how they relate to EID outbreaks.
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