Socio-economic risk factors associated with foot and mouth disease, and contagious bovine pleuropneumonia outbreaks in Uganda

Sylvia Angubua Baluka, Eria Hisali *, Francis Wasswa*, Michael Ocaido and Anthony Mugisha

College of Veterinary Medicine, Animal Resources and Biosecurity, Makerere University,
P. O Box 7062, Kampala, Uganda
sbaluka3@gmail.com
* College of Business and Management Studies (CoBAMS), Makerere University.

Abstract

Socio-economic risk factors associated with outbreaks of foot and mouth disease, and contagious bovine pleuropneumonia at the farm or household level in Uganda were assessed. Animal disease outbreaks pose significant threats to livestock sectors throughout the world, both from the point of economic impacts of the disease itself and the measures taken to mitigate the risk of disease introduction. The researchers collected household level data on FMD and CBPP outbreaks on several aspects i.e. how the first case reported probably got infected, number of herds affected and cases in a given outbreak, mortalities, associated economic losses and other social economic variables such as age, sex and level of education in the districts of Nakasongola, Nakaseke, Isingiro and Rakai. Descriptive and logistic regression techniques were used to analyze the data.

Both risk factors; weather and movement were significant for FMD but only weather was significant for CBPP at the conventional statistical test levels. The results indicate that drought and response to drought is the most significant socio-economic risk factor associated with FMD and CBPP outbreaks in the study districts even after adjusting for socio-economic and community characteristics. There is need to refocus livestock (cattle) diseases particularly transboundary animal diseases prevention and control interventions in Uganda towards addressing drought by prioritizing provision of adequate water sources for livestock farmers on a sustainable basis.

Key words: cattle-movements, drought, transboundary animal diseases

Introduction

Animal disease outbreaks pose significant threats to livestock sectors throughout the world, both from the point of economic impacts of the disease itself and the measures taken to mitigate the risk of disease introduction (Rich and Perry 2011). Transboundary animal diseases (TADs) especially foot and mouth disease (FMD), and contagious bovine pleuropneumonia (CBPP) that affect cattle, exert broader impacts on food security, nutrition, health and environment besides the financial costs (Tomo 2009).

TADs have the potential to decrease the productivity of cattle as well as quality and quantity of animal source foods and limit other uses such as draft power. Livestock contribute significantly to food security of the poor (AU-IBAR 2010) and cattle account for about 60% of the value of edible livestock products in form of milk and beef.

Animal movements and informal livestock trade within and between various countries in the region have been cited as major risk factors in the spread of TADs (ADB 2007). In agreement (Mekonen et al 2011) indicated that lack of livestock movement control was a factor for the high frequency of FMD outbreaks in Ethiopia’s pastoral herds of marginal lowland areas. Age and breed of cattle were considered risk factors, and FMD morbidity was particularly higher among calves than adult cattle, and in crosses than locals (Mazengia et al 2010). On the other hand, Jori et al 2009 identified price differential between cattle inside the control zone as opposed to those outside the control zone, as a major risk factor, which creates the economic incentive for illegal cattle movements.

Several studies in Uganda have highlighted the role of wildlife in FMD transmission. For instance (Ocaido et al 2009) highlighted the role of wild animals in dynamics of FMD especially around game parks. Ayabazibwe (2010) showed that the African buffalos played an important role in maintaining FMD virus in the National Parks in Uganda. Batwala (2011) assessed the risk factors in FMD outbreaks at the cattle-wildlife interface and showed that communally grazed cattle had the highest risk while cattle from fenced farms had the lowest risk.

Following TADs outbreaks, the livestock sector is subjected to rigorous controls to protect consumers and other livestock from these diseases that are associated with consequences such as isolation from local, regional and international markets hence constraining trade in live animals and animal products (Kitching et al 2007).

Preventive measures for TADs particularly FMD include biosecuirty, farm premises and individual animal identification, traceability, improved laboratory capacity and capabilities, and surveillance (Swallow 2012). FMD optimal control strategy depends on the circumstances of the outbreak. For instance a rapidly established emergency vaccination strategy in a large zone around the first diagnosed farm was more effective than preventive culling around all infected farms in small circles while combined strategies of preventive culling and emergency vaccination were beneficial in a region with a low farm density (Traulsen et al 2011).

Besides causing the greatest production losses in cattle and pigs, FMD status is an important determinant of a country’s participation in international trade in livestock and livestock products such as beef i.e. existence of FMD is an effective barrier from the markets with the highest prices for these animal products (James and Rushton, 2002). Balinda et al (2009) showed that TADs exert their impact on farmers via the control policies that are enforced to limit or control the extent of FMD spread during outbreaks using short-term measures such as ring vaccination and restrictions of the movement of livestock and livestock products (LLPs) to and from affected areas.

FMD outbreaks affect the agricultural economy in disease free developed countries as well as livelihoods and income generation in developing countries where the disease is endemic (Di Nardo et al (2011). According to the OIE, FMD is the most important animal disease due to its economic, commercial and social impact (Picardo et al 2010, James and Rushton, 2002), and due to the impact from the reaction of veterinary services to the presence of the disease and associated restrictions on trade of animals both locally and internationally.

However, the above literature does not explicitly identify the socio-economic risk factors associated with FMD and or CBPP outbreaks at the household or farm level thus the need for the current study. Understanding the risk factors for FMD and CBPP outbreaks aids decision makers in designing more effective control policies (Wooldridge et al 2006).

Materials and Methods

Study area, design, sampling and data collection

The study was conducted in four districts i.e. Nakasongola, Nakaseke, Isingiro and Rakai (Figure 1). Study districts were selected based on three criteria i.e. high cattle population, importance of cattle in community livelihoods, experience of FMD and CBPP outbreaks in the past 5years and close geographical proximity to international borders or major highways.

Figure 1. Map of Uganda showing the study districts (Source: Author’s Drawing 2012)

A cross-sectional study involving farmers was conducted aided by a combination of qualitative and quantitative designs. The qualitative design employed focus group discussions and key informant interviews using a semi-structured questionnaire to identify the socio-economic risk factors associated with FMD and CBPP outbreaks. The structured questionnaire was employed to collect data to quantify the identified socio-economic risk factors.

Study farms were selected using simple random sampling from the sampling frame constructed for each district with the help of extension Veterinarians and farmers' association leaders. The sampling frame was comprised of 224, 173, 291, and 185 farmers for Nakasongola, Nakaseke, Isingiro and Rakai districts respectively. Veterinary extension staff, local leaders and farmers for the key informant interviews and focus group discussions (FGDs) were selected purposively.

A sample of 110 farmers was selected from each study district giving a total of 440. A final sample of 390 farmers (89%) was interviewed. The non-response rate of 11% was due to the migratory nature of some selected respondents who were not found on the farms with their animals even after the second and third callbacks. Hence they were left out without replacement since their percentage was too small to affect the results significantly.

Dependent variables (FMD and CBPP)

The dependent variables were whether a farmer had experienced FMD and or CBPP on their farms within the period under study. The responses captured were (Yes = 1 or No = 0). The study was conducted between July 2011 and June, 2012.

Independent Variables

The independent variables were the socio-economic characteristics i.e. gender, age, education level and experience in terms of years in livestock farming. Gender might influence disease occurrence in that women lack land ownership rights or own smaller pieces of land and have less decision making power in the community thus their cattle may be forced to move in search of grazing land more than those of men. Older and more experienced cattle farmers tend to recognize FMD and CBPP at the onset more easily than the young farmers and hence are more capable of reporting to animal health workers in time. Farmers with some education tend to recognize the diseases better and are more likely to report to animal health workers or veterinary authorities in time. Potential socio-economic risk factors included uncontrolled cattle movements, drought, close proximity to wildlife conservation areas, use of communal grazing and watering points, and socio-cultural practices such as payment of bride price and exchange of gifts in form of cattle. Other important risk factors included communal cattle dips but these were not identified in this study because they are no longer used in the study districts while livestock markets were considered by the author under a separate study that focused on farmers as well as cattle traders or transporters and processors.

Socio-economic risk factors perceived by farmers as revealed by interviews

Using participatory methods namely FGDs, the researcher identified the major socio-economic risk factors associated with FMD and CBPP outbreaks as perceived by farmers to include uncontrolled cattle movements, communal grazing and watering points, drought and socio-cultural practices such as payment and or transfer of bride price in form of cattle and exchange of cattle gifts. Uncontrolled cattle movements was the most important risk factor in the transmission of FMD and CBPP at the household or farm level as identified by participants in the FGDs.

Statistical Analysis

Statistical analysis was performed using descriptive analysis and data summary, factor analysis and multivariate logistic regression. Logistic regression is a powerful analytical tool for analyzing categorical variables. Factor analysis was used as a data reduction technique and to examine the underlying structure of the risk factor variables to aid in refining the variables and obtaining the composite variables that were used in data analysis. Factor analysis is merited in this study for its data reduction ability.

Factor analysis was used to analyze the socio-economic risk factors associated with FMD and CBPP outbreaks as identified by farmers using Varimax rotation method and compared by their loadings based on the uniqueness and Cronbach’s alpha values. The rotation converged into five iterations. The conventional practice for interpreting factor analysis loadings, is to regard as significant any variable with factor loadings of 0.40 and above as associated with the relevant factor. Accordingly, a factor loading cut-off of 4.00 was used and any factor loading below or less than 4.00 was considered insignificant. The six component groups resulted into six iterations. The factor loading benchmark for socio-cultural practices (bride price) had a factor loading less than 0.40 and was thus considered insignificant. Thus socio-cultural practices (bride price) was eliminated leaving only four risk factors i.e. response to drought, close proximity to wildlife, communal grazing and watering points, and unrestricted cattle movements. Response to drought (uniqueness = 0.592) and close proximity to wildlife (uniqueness = 0.709) combined to define a new factor called weather, communal grazing and watering points (uniqueness = 0.533) and unrestricted cattle movements (uniqueness = 0.420) combined to define a new factor called movement, that were used in the LRM. The unit of analysis was the farm or household.

Logistic Regression

The logistic model formula is as follows:

P = Z = ß₀ + ß₁X₁ + ß₂X₂ = ....ß_kX_{k                       (1)}                                     

The variable  is the measure of the total contribution of risk factors used in the model. Here ß_o is the intercept (constant), and ß1, ß₂ to ß_k  are the regression coefficients of the predictor variables, X₁, X₂ up to X_k. The computed p value or  f(z) is the probability of a particular outcome in the presence of the risk factors with the value range of 0 to 1. If  p is the probability then  p/(1-p) gives the corresponding odds ratio (or the relative risk ratio, RRR).

For any random variable , equation (1) can be written as:

Yi = X_iß + µ_i                                                                                 (2)

where: y_i denotes the dichotomous qualitative variable (e.g. cattle suffered FMD=1 and did not suffer FMD=0), denotes the vector of predictor variables (e.g. sex, age, district), ß denotes a vector of parameters to be estimated and µ denotes residuals (errors).

The model in equation (2) can be estimated via maximum likelihood as follows:  

The predictor variables used in the model include gender, age, education level, years of experience in livestock farming, risk factors (e.g. response to drought) and the district where the farmer is located.

Results and Discussion

Table 1 presents the percentage distribution of respondents according to sex, age, educational level and experience in livestock farming.

Majority of respondents were male i.e. 85.9% in Nakasongola, 92.9% in Nakaseke, 82.5% in Isingiro and 84.5% in Rakai which reflects male dominance that is consistent even in other sectors given that production resources such as land are owned by men and women may only be granted user rights (Table 1).

Majority of respondents were aged 40 years and above i.e. 70.7% in Nakasongola, 65.4% in Nakaseke, 75.3% in Isingiro and 75.8% in Rakai indicating that older people dominate livestock rearing which raises questions of sustainability (Table1 ).

Majority of respondents had attained little or no education i.e. 64.1% in Nakasongola, 74.5% in Nakaseke, 50.3% in Rakai and the lowest proportion of respondents with little or no education was reported in Isingiro (47.4%). This implies that training of farmers in modern farming technologies and their up-take is likely to meet hindrances given the high levels of illiteracy (Table 1).

Factors associated with FMD

Table 2 presents cross tabulations of gender, age, education, experience in years in livestock farming and study districts, as percentages against FMD prevalence. Per demographic group, the shares of the occurrence of FMD are presented as percentages of the total demographic group. Table 2 also presents the 95% confidence intervals and the chi-squared test for group equality of means.

Sex was not significant i.e. 31% of males and females reported that their cattle suffered from FMD in the last 5 years (see Table 2). Education or lack of education was a significant factor that influenced FMD prevalence e.g. only 4.2% of illiterates reported FMD on their farms compared to those with tertiary education (34.1%) as shown in Table 2. Among the study districts, FMD prevalence was highest in Isingiro (52.4%) followed by Rakai (44%), Nakasongola (22.2%) and was lowest in Nakaseke (5.3%) as shown in Table 2.

Multivariate Logistic Regression Model Results for FMD

The Logistic Regression Model (LRM) was used to identify the risk factors which influence the occurrence of FMD and CBPP outbreaks. LRM was chosen for its ability to test several possible risk factors in the same model so as to take care of confounding factors (Cramer 2003, Kleinbaum and Klein 2010, Peng et al 2002).

Each investigated risk factor was univariately explored in logistic regression model. Significant risk factors at the level of 95% of significance were subjected to multivariable modeling to control for possible confounding. Age was treated as a continuous variable and actual individual age of respondents was captured.

Multivariate logistic regression was used to explore associations with adjustments for confounders. With ordered logistic regression, possible confounders were tested and excluded by modeling the associations between the socio-economic characteristics, districts and risk factors.

Table 3 presents logistic regression of background characteristics namely gender, age, education, years of experience in livestock farming, districts and risk factors associated with FMD.

Only districts were significant at the 1% test level (p < 0.01) with Nakaseke at (0.14, 0.08), Isingiro (4.77, 1.73) and Rakai (3.57, 1.29) in identifying farmers who had experienced FMD. Model 2 indicates that both risk factors i.e. weather and movement were significant at the 1% test level (p < 0.01) at (1.687, 0.25) and (1.554, 0.237) respectively. Model 3 was the best model in explaining the occurrence of FMD outbreaks, weather and districts were significant at the 1% test level (p < 0.01) with weather at (1.78, 0.33) and districts at, Nakaseke (0.13, 0.08), Isingiro (3.62, 1.38) and Rakai (3.08, 1.17) while movement was not significant in explaining FMD outbreaks (Table 3).

Factors associated with CBPP

Table 4 presents the cross tabulation of demographic characteristics associated with CBPP and percentage distribution of demographic characteristics associated with the prevalence of CBPP. Table 4 also presents the 95% confidence intervals and the chi-squared test for group equality of means. As was the case in Table 2, for each demographic group in Table 4, the shares of the occurrence of CBPP are presented as percentages of the total demographic group.

Sex was slightly significant i.e. 9.1% of males and 13% of females reported that their cattle suffered from CBPP in the last 5 years. Age was a significant factor in determining FMD prevalence i.e. the young (18-24) age group did not experience CBPP at all while the older age group (30-39) reported the highest prevalence (15.6%) for CBPP. Probably this is due to the fact that CBPP outbreaks last occurred in some study areas with exception of Nakasongola, several years before the study, the younger farmers who must have just joined livestock farming recently have not experienced any CBPP outbreak (Table 4).

Education level had significant influence on CBPP prevalence e.g. respondents with tertiary education showed the lowest prevalence (2.2%). Experience or duration in livestock rearing was significant in determining whether a farmer experienced CBPP on their farms. Respondents with less than 10years’ experience in livestock rearing did not report any CBPP outbreak on their farms while those with 10-20years’ experience reported the highest prevalence (18.8%) for CBPP (Table 4).

Among the study districts, CBPP prevalence was highest in Nakasongola (24.2%) followed by Rakai (9.5%), Nakaseke (6.8%) and was lowest in Isingiro (0.7%). Generally Nakaseke had low prevalence for both FMD and CBPP. Overall, FMD prevalence was higher than CBPP prevalence (Table 4).

Multivariate Logistic Regression Model Results for CBPP

Multivariate regression results for CBPP are presented in Table 5.

Model 1 considered only experience in livestock rearing, age group 10 – 20 years was significant at the 5% test level i.e. (p < 0.05, 2.54, 1.08) in identifying farmers who had experienced CBPP. According to Model 2, only districts were significant, Nakaseke and Isingiro at the 1% test level (p < 0.01) at (0.14, 0.08) and (0.39, 0.04) respectively, while Rakai was significant at the 5% test level p < 0.05) at (0.34, 0.18) in identifying farmers who had experienced CBPP. Model 3 indicates that only weather was significant at the 1% test level (p < 0.01) at (2.31, 0.60). Model 4 contained all the variables and was the best in explaining CBPP outbreaks in the study districts. According to model 4, sex was significant at the 10% test level (p < 0.1) at (2.58, 1.39) and districts were significant, Nakaseke and Isingiro at the 1% test level at (0.09, 0.05) and (0.03, 0.03) respectively while Rakai was significant at the 5% test level (p < 0.05, 0.27, 0.15) see Table 5.

Farmers in Isingiro, Rakai and Nakaseke were less likely to experience CBPP than Nakasongola. Most of the variables included in the multivariate logistic regression i.e. socio-economic characteristics, risk factors and districts did not show a significant association with CBPP occurrence on a farm suggesting that CBPP occurrence is majorly sporadic which makes it a more difficult disease to study or control than FMD (Table 5).

Statistical analysis revealed that weather (drought) was the most significant risk factor for both FMD and CBPP. Drought could probably be the cause of the uncontrolled cattle movements that preceded many FMD and CBPP outbreaks. Uncontrolled movements could involve both domestic and wild animals which are all important in the spread of these diseases. Outbreaks of infectious animal diseases occur regularly as a result of both legal and illegal animal movements (Fevre et al 2006). However, unofficially traded animals are a much greater risk for disease spread because they are not necessarily subjected to veterinary inspection and control.

Strict movement control and in the event of an outbreak, employing test and slaughter of all infected and in-contact cattle with FMD combined with game proof fencing, limited vaccination of cattle in the areas adjacent to FMD endemic areas of the Kruger Park and active surveillance for FMD helped South Africa to achieve FMD control (Moerane 2008). Similarly, Botswana achieved effective FMD prevention by implementing the concept of zoning or regionalization of FMD control by using fences as efficient barriers between high risk zones and disease free zones (Mapitse 2008), besides strict import controls, border security and quarantine measures towards reducing external and internal FMD challenges plus annual vaccination in cattle in FMD high risk areas. Besides the above interventions, public education is an important pillar in the FMD control policy so as to enhance early detection and control (Mapitse 2008). Namibia separated the northern communal areas from the Southern commercial farming areas by a veterinary cordon fence to protect the Southern commercial farming areas from the northern FMD endemic areas (Perry et al 2003) and they were able to control FMD in the Southern commercial area.

Weather remained significant when risk factors were considered alone and even when subjected to multivariate analysis with socio-economic characteristics and districts. Hence the analysis has shown that drought is the most important risk factor associated with FMD and CBPP outbreaks. This is supported by an earlier study (Rufael et al 2008) that indicated that outbreaks of FMD peaked in Borana cattle during the two dry seasons and were attributed to increased cattle movements in search for pastures and water in the dry season grazing areas.

Conclusions

• Weather (drought) was the most important risk factor associated with the occurrence of FMD and CBPP outbreaks. Drought pushes or contributes towards unrestricted cattle movements and probably triggers other factors that contribute towards transmission and the resultant outbreaks of FMD and CBPP. Future investments in disease control particularly for FMD and CBPP in Uganda should address drought. The priority should be provision of adequate water sources to the livestock farmers on a sustainable basis, and the options include rain water harvesting and drilling of water dams on every farm to discourage communal grazing and watering through government-farmer cost sharing arrangements.

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Received 9 May 2013; Accepted 19 November 2013; Published 1 December 2013

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