Methodological approaches and data

The overall approach of this study is interdisciplinary, involving methodologies from different scientific fields. Methodologies such as statistical epidemiological modelling (Chapter 2) and mathematical epidemiological modelling (Chapter 5) from the field of epidemiology, health behavior modelling (Chapter 4) from the field of social psychology and cost-benefit analysis (Chapter 6) from the field of economics were used to address the different specific objectives of the study. In this section the different modelling approaches and data issues are discussed for their strengths and weaknesses with respect to their application in this study.

The epidemiological study in Chapter 2 models the risk factors for the incidence of outbreaks of FMD at national level. Consequently, the outbreak unit was used as the sampling unit for data collection. Defining an outbreak is often difficult in traditional extensive farming systems. The world organization for animal health (OIE) defines

outbreak as occurrence of one or more cases in an epidemiological unit, and epidemiological unit as a group of animals with a defined epidemiological relationship that share

approximately the same likelihood of exposure to a pathogen (OIE, 2015a). Defining these epidemiological units in traditional extensive systems is, problematic because of the continuous contiguity in the whole livestock population. As a result of this difficulty people use different levels of aggregation as epidemiological unit e.g., village (Cleland et al., 1996; Picado et al., 2011), sub district (Ayebazibwe et al., 2010) and district to describe an outbreak. The inconsistency of this usage was also observed in the animal disease reporting system of Ethiopia, where outbreaks are reported at district level or kebele level. This can cause distorted animal health information. In this study, district was used as epidemiological unit as more outbreak data was available at district level

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than at kebele level. The use of district as epidemiological unit gave the study a low geographic resolution. This lower resolution level was, however, sufficient for the performed analysis, which was aimed for an epidemiological evaluation at national level. In the risk factor model, a mixed effects logistic regression model was used, by including a random effect for district to control for the clustering effect that arose due to repeated measures of outbreaks within a district in the different years (Dohoo et al., 2003). The use of districts as a sampling unit also raised a question on the possibility of spatial autocorrelation between districts (nearby districts may tend to have similar disease status). As a formal spatial analysis was not possible due to a lack of spatial data, a variable named ‘outbreak in the neighbor district’ was included in the model to account for the effect of a prior outbreak in a neighboring district (see table 2.1). While this could control the spatial correlation effect in the adjacent districts (districts sharing borders), it would not capture the whole spatial effect that is related to the distance between districts.

In the study of factors that affect motivation of farmers to implement FMD control measures (Chapter 4), a health behavior theoretical framework (model) was used. Behavioral models are often used in public health intervention studies (Glanz et al., 2008). There is evidence that theory-based behavioral interventions are more effective than those lacking theoretical bases (Glanz and Bishop, 2010). Recently, a number of papers use behavioral models to study the behavior of farmers with regard to animal health (e.g. Bruijnis et al., 2013; Ellis-iversen et al., 2010; Gunn et al., 2008; Jones et al., 2015). In this study, the Health Belief Model (HBM), an individual health behavioral model commonly used in public health, was applied. The core principle of the HBM is that individual health behavior is determined by personal beliefs or perceptions about the disease and its prevention or treatment measures (Champion and Skinner, 2008). As such, this principle can also be applied to asses farmers’ behaviors in relation to the health of their animals. Application of this model framework in this study helped to structurally evaluate the effect of farmers’ behavioral factors on FMD control.

The HBM contains perception constructs related to the disease and its prevention/ control measures that directly affect health behavior. These perceptions are in turn affected by socio-demographic factors called modifying factors (see section 4.2.1).

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way the results could only be precisely interpreted at the level of individual items instead of at the level of constructs as specified in the model framework.

The HBM outlines a sequence of causal paths from modifying factors through perception constructs to the intended behavior. Ideally this causal sequence has to be analyzed by multivariate techniques like path analysis or structural equation modelling to analyze the relationships along the path of the causal sequence in the whole model (Tanner-Smith and Brown, 2010). In the present study, the data was not suitable for such an analysis. Rather, the sequential cause relationship was analyzed using two separate consecutive analyses. First, the effect of the perceptions on behavior (intention in the context of this study) was analyzed using a binary logistic regression. Then, the effect of modifying factors on perceptions that were found relevant in the first analyses was analyzed using ordinal logistic regression. This approach, while it was enough to test the hypothesized relationship, made assessing the fitness of the whole model as specified in the theory/framework difficult..

Cost-benefit analysis of alternative disease control strategies as performed in Chapter 6 is demanding from modelling and data perspective. It needs a good underlying epidemiological model that reliably predicts the occurrence of the disease under a variety of control strategies, empirical data on the production parameters and the effect of the disease on these production parameters. This is very challenging especially in the Ethiopian situation, where data is scarce in many respects. Ideally, the epidemiological information of FMD and its control should be generated by a simulation model based on the epidemiologic principles of disease transmission and herd immunity. This type of model enables to carry out what-if scenario analyses (Randolph et al., 2000; Willeberg et al., 2011). Spatially explicit herd based mathematical models are commonly used for simulating hypothetical or real disease outbreak incursion in disease free countries or regions (Bates et al., 2003; Durand and Mahul, 2000; Keeling et al., 2001; Martínez- López et al., 2010). This is because disease control measures in these countries are applied at farm level. Examples of such measures include stamping out infected farms or emergency ring vaccination around outbreak farms. These types of models are not directly relevant to the endemic situation where farm/herd level measures like stamping out are not economically feasible nor epidemiologically essential. Developing a spatial model for the endemic situation in this study was also not feasible because of lack of data (e.g. data on spatial location, farm structure, and animal movement were not available). An attempt was made to develop a mathematical non-spatial individual animal based state transition model (Chapter 5). Because of the assumptions that were needed to be made during the estimation of the model parameters from the limited available data, its predictive performance was found unrealistic (see Chapter 5 for detail discussion the model’s performance and its limitations). Moreover, this non-spatial model could not

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simulate the impact of the control measures as considered in the cost benefit analysis of Chapter 6. Because of these limitations, a simple epidemiological simulation model with straightforward relationships between incidence and control, parametrized by expert elicitation, was used in the cost-benefit analysis. Stochastic simulation and an accompanying sensitivity analysis were employed to mitigate data scarcity and quality problems.

National disease control programs have potential effects on market prices which could affect the valuation of costs and benefits in the cost-benefit analysis. Such market effects could be, for example, increase in market supply of products when the disease burden is reduced. Increase in market supply can be caused by lower production losses in the existing production systems or by the increased use of high yielding, disease susceptible animals because the fear of the disease is minimized. These market effects of disease control can be analyzed using a partial equilibrium framework (e.g. surplus analysis) at the sector level or by computable general equilibrium analysis at the level of national economy (Rich et al., 2005). Required data to conduct these kinds of analyses is currently lacking in Ethiopia, and therefore market effects were not considered in the cost-benefit analysis of this study. However, ignoring the market effects will not be expected to affect the performed analysis significantly, due to the dominantly subsistence nature of the livestock production in Ethiopia.