MALARIA INCIDENCE AND DETERMINANT OF WELFARE LOSS AMONG FARMING HOUSEHOLDS IN KABBA/BUNNU AREA OF KOGI STATE, NIGERIA

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1 MALARIA INCIDENCE AND DETERMINANT OF WELFARE LOSS AMONG FARMING HOUSEHOLDS IN KABBA/BUNNU AREA OF KOGI STATE, NIGERIA ABSTRACT Mohammed 1, A. B., Adewumi 2, M. O., and Omokolu 3, O. A. 1 Department of Agricultural Economics and Rural Sociology, Ahmadu Bello University, Zaria. 2 Department of Agricultural Economics and Farm Management, University of Ilorin, Nigeria. 3 Department of Paediatrics and Child Health, College of Health Sciences, University of Ilorin, Nigeria. bashraj25@yahoo.co.uk; The study assessed the incidence of malaria, welfare loss, as well as the determinants of welfare loss among farming households in Kabba/Bunnu Local Government Area of Kogi State, Nigeria. The data collection was based on cohort random sampling of 72 household monitored for a period of 8 months during the 2012 farming season. Simple descriptive statistics and ordinary least square regression analysis were used to determine the incidence of malaria as well as the determinants of welfare loss respectively. The incidence of malaria among the households was found to be high. Households spent an average of N8,515.28, N1, and N2, on treatment, care giving and prevention of malaria per annum respectively. On the average, 38 man-days in the farming year were lost as a result of malaria incidence in the study area. The income realized by household was estimated as 175,046 per ha. Welfare loss in the study area was estimated as 69, (US $411.36) per farming season. Household size and malaria Incidence were the major determinant of welfare loss in the study area. It could be concluded that the incidence of malaria impact negatively on welfare loss thus certain necessary measures has to be adequately taken to ensure that the existing malaria problems are dealt with and further occurrence is minimized, if not forestalled all together. It is recommended that awareness should be created on the use of mosquito nets. The area should be targeted for free net distribution and training on utilization. INTRODUCTION Nigerian agriculture is dominated by the small scale farmers who produce the bulk of the food in the country. Despite their unique and pivotal position, the small holder farmers belong to the poorest segment of the population and therefore, cannot invest much on their farms. The vicious circle of poverty among these farmers has led to the unimpressive performance of the agricultural sector. The kind of agriculture practiced in Nigeria is mainly rain fed, labor intensive and with little external input. The small holders in this system are responsible for a large share of the total agricultural output and cultivated land. Several efforts by government such as deploying innovative financing mechanism to facilitate the disbursement of agricultural loans by commercial banks and Bank of Agriculture, provision of incentives for access of farmers to weather index insurance, launching of database for farmers in the country for the efficient and effective distribution of subsidized seeds and fertilizers through mobile phones in 2012 and the rapid expansion of irrigation facilities and revamping of existing ones have been undertaken to raise production and productivity of these farmers so as to achieve food security despite this efforts The problems undermining crop production still persist thus indicating that some other factors still need to be explored and addressed for proper planning and well-targeted policy. Due to over dependency on labor, their health is critical in improving and sustaining agricultural production. Health problem has been found to be a major influence on agricultural productivity in Nigeria and malaria has been a contributor to ill health in Africa (Chima et al., 2003; Fink and Masiye, 2015; Shayo et al., 2015). Eradication of the scourge of malaria and other life threatening diseases are some of the major concerns of the government. The importance of reducing malaria is also epitomized in MDGs programme. Malaria is the number one public health problem in Nigeria and accounts for the major cause of hospitalization, morbidity and mortality (Shankar, 2000; Hay et al., 2010; Snow, 2015). Malaria illness imposes great burden on the society as it has adverse effects on the physical, mental and social well-being of the people as well as on the economic development of the nation. Thus malaria still poses a huge challenge to people s health in the country and is the leading cause of ill health (Shankar, 2000). According to Roll Back Malaria (RBM) estimate, in 2007, there were 2,969,950 reported cases and 10,289 reported mortalities due to malaria in Nigeria (Malaria Consortium). While the majority of the global burden of malaria lies within 30 countries of sub-saharan Africa, only four countries including Nigeria account for 50% of all malaria-related mortality. (RBM,2008) in Nigeria, malaria cases account for about 66% of all visits to health care clinics and 30% of hospitalizations. (UNICEF, 2009) These estimates render malaria the preeminent tropical parasitic disease and one of the top three killers among communicable diseases (Sachs and Malaney, 2002). The high rates of morbidity and mortality due to malaria that currently afflict Nigeria are not only matters of human health, but are issues detrimental to our economic growth and overall productivity as a nation, as well. Currently, Nigeria spends $906 million (USD) annually on the treatment, prevention, and opportunity costs associated with malaria, such as years of productive life lost to morbidity and NJAFE VOL. 12 No. 4,

2 mortality. Looking at the situation in a nation that already suffers from diminishing economic growth and widespread financial corruption, cannot afford such an immense financial loss. Those suffering from the debilitating symptoms of malaria are less productive and therefore less likely to pull themselves out of crippling poverty, as this implies more days of work missed and more expenses incurred due to treatment costs. Thus it is necessary to estimate the welfare loss of household in the study area, welfare loss for this study is computed as the cost of medical expenses (cost of treatment, prevention and care-giving) and the imputed cost of day loss. Agriculture being a labour intensive sector, the role of labour resource to guarantee highest productivity and growth of the agricultural sector cannot be over emphasized. The quality of labour therefore becomes an important determinant of productivity. On this premise, it would be right to say that any factor that will have an influence on the level of availability of family and hired labours will drastically impact on production output. Most studies relating malaria and agricultural productivity have been largely inferential and establishing a link between malaria and welfare loss in a direct manner has been elusive in the literature. Thus, the question is does malaria really impact negatively on welfare loss or not? Against this background, the need to examine the direct effect of malaria incidence on welfare loss among farming households in the study area becomes imperative. The specific objectives of this study were to examine the incidence of malaria in the study area, examine the input and output characteristics of households, determine the farming household and identify the determinant of welfare loss in the study area. METHODOLOGY Kabba Bunu is a Local Government in Kogi State and it falls within the guinea savannah zone. The local government area is located in the western part of the State which falls between latitude 7 and 31 N of the equator and longitude 5 41' and 6 15 ' E with an estimated population of 145, 446 (NPC, 2006). The study area is known to have a tropical savannah climate with distinct wet and dry season. The wet season range from the Month of April to October while the dry season is between November and March. The annual temperature varies between 27 C and 37 C with relative humidity between 30% and 40% in January and rises between 70% and 80% in July to August. The soil in the study area is predominantly sandy loam in texture. The indigenes are farmers engaging in crop production, rearing of livestock and fish rearing. Kabba-Bunnu is blessed with suitable ecological and climatic conditions which make it possible to produce various agricultural products such as yam, cassava, cocoyam, maize, millet, rice guinea corn, palm produce, cowpea and others. Primary data were used for this study. Data were collected through the administration of well structure questionnaire. The primary data were obtained between the months of May to December The sampling is based on a cohort longitudinal study. A two stage sampling technique was employed in the selection of sample for this study. In the first stage 12 villages out of 44 villages were randomly selected from the local government area, and then a random selection of 10% sample size to acquire data from the list of farming households figure in the sampled villages. Thus a total of 72 households were used for the study. The cohort house hold was followed up for a period of eight months from May to December 2012 in order to document malaria incidences and farming activities of the households within the farming cycle. During this period data were collected on febrile episode i.e. fever episode in index subject (household head), dependent first degree relative (wives and children) and other relatives within the households (second degree relative). Results of malaria test carried out, cost of treatment, man hour loss, and Agricultural productivity (grain equivalent) were also documented. All the selected household in the cohort were followed up for a period of eight months during the period of study. A total of 432 household members were followed up during the study period. Out of the estimated sample of 432 household members 258(59.7%) were children, 75(17.36%) were males while 99(22.9%) were females Malaria testing was carried out and documented. Other information such as socio-economic characteristics of household heads, data on agricultural production, malaria occurrence in the households, and number of days absent from farm due to malaria episodes, cost of treatment, prevention and care giving were collected. In other to document the incidence of malaria in the study area a base line malaria test was carried out for all the selected farmers upon enrolment into the study group. A weekly visit was made to the farmers household by the health workers. Activities during the visit included an audit of family members and their wellbeing; identifying the occurrence of fever within the preceding week and conducting a malaria parasite test on any member of the family with fever or history of fever within the preceding week. Following results of the malaria test, the number of days that farmers could not attend to their farms activities as a result of malaria was documented. Test for malaria were carried out on the members of household using a Rapid Diagnostic Test (RDT) based upon the detection of Plasmodium falciparum histidine rich protein ii antigen. This is a very simple and easy to deploy test that is currently recommended by the World Health Organization for the community level testing for malaria parasite (WHO, 2006). Rapid diagnostic tests have considerable potential as tool to improve the diagnosis of malaria its use in malaria diagnosis is increasing in many countries as the result of the ease of use with minimal training. Health extension workers were recruited and trained on the use of RDT by competent health professionals, each NJAFE VOL. 12 No. 4,

3 of the twelve health workers were assigned a post and were responsible for conducting the malaria parasite test on house hold members with history of fever within the last one week. In this regard each health worker was involved in the screening of episodes of fever in each cohort of 6 farming families. RDTs test whether a person with malaria-like symptoms actually has malaria by testing the blood of the patient for chemical substances produced by malaria parasites. Malaria parasites produce proteins called antigens. RDTs detect malaria antigens, so if they are present, the person will test positive. If malaria antigens are not present, the person will test negative. The analytical tools employed for this study are simple descriptive statistics and ordinary least square (OLS) regression analysis Ordinary least square regression analysis The study adapted the analytical technique of Ordinary Least Squares to determine the determinant of welfare loss among farming households in the study area. In analysing the determinant of welfare loss from farming households in the study area, it was believed that the welfare loss was influenced by some households specific variables. Conveniently the Ordinary Least Square (OLS) regression analysis was used to analyse the data the implicit OLS model is specified as follows: Y= f (Z 1, Z 2, Z 3, Z 4 ) + e Where, Y = welfare loss (cost of medical expenses + cost of imputed day lost in naira) Z 1 = adjusted house hold size (num) Z 2 = level of education (years) Z 3 = use of mosquito net (number of times slept under mosquito net) Z 4 = incidence of malaria (proportion of malaria in the household) e i = error term RESULTS AND DISCUSSION Incidence of malaria in farming households All the households in the study area had episodes of malaria during the eight month period covered by this study. The result on Table 1 revealed that households had average of seven episodes of fever in the study area. The maximum number of fever episodes recorded was 24 per the period covered for the study (May to December). During the farming season under consideration more than half of the farming households (58.33%) had between 1 and 5 episodes of malaria. Households had an average of 5 episodes of malaria occurrence in a year. This is in line with previous studies, (Attanayake et al., 2000). About 18% of the households were admitted in the hospital due to malaria fever with a maximum of two persons per household. A typical farming household spent an average of 8, on malaria treatment per annum and 1, per malaria episode. Averagely, households in the study area spent 1, on transportation to and from hospital during the period of admission in the hospital. A mean sum of 2, was spent on protective device to eliminate or reduce malaria incidence during the period under consideration. An average of was spent on prevention per month. The study also revealed that 66.7% of the farming household did not make use of mosquito net. A typical household in the study area slept under mosquito net twenty-three times with an average of six times in the periods under survey. This may explain the high incidence of malaria during the eight-month period of the study. Due to malaria incidence, farming households were unable to go to their farms for 3 days out of the 99days of family labour in the area. The findings of this study are similar to those of Ajani and Ashagidigbi (2008) and Alaba and Olumuyiwa (2006). Ajani and Ashagidigbi (2008) reported that the increase in malaria incidence had a significant effect on the health and income of the farmers through an average of 22 days of incapacitation Also Alaba and Olumuyiwa (2006) recorded an average number of work day lost per malaria episodes by productive adults in farming households of 16 days. This also collaborated finding of Badiane and Ulimwengu (2013) on farming household s loss of an average of 30 days of work within a year in Uganda Table 1: Description of households malaria incidences and implications Variables Mean Minimum Maximum SD Number with fever Number with Malaria Number of respondent admitted due to malaria Day Loss due to Malaria in Manday Number of times slept on net Amount spent on treatment( ) 8, , Amount spent on care giving( ) 1, Amount spent on prevention( ) 2, , Source: Field survey, USD is equivalent of 150 NJAFE VOL. 12 No. 4,

4 Input characteristics of farming household Farming household in the study area had a mean farm size of 1.83 hectare. The entire households studied had between hectares of land while more than half of the households (55.9%), cultivated between 1.1 to 2 hectares. (Table 2) The coefficient of variation of reveals a high disparity in the size of farms of the households in the study area The average family labour used by the households during the eight months (May to December) study period was man-days with an average hired labour of man-days. The average rate of fertilizer and chemical applied per hectare for all the crops considered in the study area were kg and 1.24 litres respectively. High variability was recorded in the levels of all variables used by the households except for seed. This finding suggests that majority of the farming households made use of planting materials from previous harvests Table 2: Description of farm input used in the study area Inputs Average Minimum Maximum SD CV Land (ha) Family labour (mandays) Hired labour (mandays) Seed (grain equivalent) Chemical (litres) Fertilizer (kg) Source: Field survey, SD=Standard Deviation, CV=Coefficient of Variation. Output characteristics of malaria households The average output realized in the study area was 13, grains equivalent for the major crops cultivated as shown in Table 3. Results of the analysis revealed that an average yield of kg ha -1 of cassava, kgha -1 of yam,457.15kg ha -1 of maize, 97.37kg ha -1 of pepper and 40.60kg ha -1 of sorghum was realized. This showed that output in the study area was highest for cassava followed by yam and least for sorghum. Table 3: Average crop yield per farming household in kg ha 1 Crops Sum Mean Minimum Maximum SD Cassava ( ) Yam ( ) Maize (457.15) Pepper (97.37) Sorghum 5, (40.60) Total* 951, , , , Source: Field survey, Figures in parenthesis are values in kgha 1, Figures in* are grain Equivalent Weight Values. Welfare loss Table 4 presents welfare loss and farm income of farming households in the study area. Welfare loss for this study is defined as the amount spent on medical expenses and imputed cost of day loss. The welfare loss estimated in the study area was 69, per farming season. The total farm output realized for the study was GEW ha -1 out of which GEW (48.34%) was sold, GEW (43.75%) were consumed while GEW (7.97%) were given out as gift. The estimated farm income for the study is 175,046ha -1. Results of analysis revealed that households lost about 40% of their income to malaria. Indicating that incidence of malaria negatively affects their income level as such preventive measure against malaria needs to be addressed in the study area. Determinant of welfare loss of farming households Table 5 presents the results of ordinary least squares regression of the determinant of welfare loss of farming households in the study area. Results revealed that all the variables fitted are statistically significant at 1% level. Adjusted household size and malaria incidence has positive coefficient which indicated a direct relationship with welfare loss. This suggested that if household size increases there is the tendency that the number of malaria cases increases which would increase the intensity of welfare loss also increase in the incidence of malaria translates into spending money on treatment and care giving thus increasing welfare loss. The coefficient of level of education has negative sign corresponding to a priori expectation implying increase in educational level creates much awareness on malaria, its prevention and early treatments as such household would take adequate precaution against malaria thus reducing its incidence and by extension welfare loss. The use of mosquito net by the household has negative coefficient, the negative signs implies increase in the use of mosquito net decreases welfare loss. Household size and malaria incidence are found to be the major determinants of welfare loss in the study area. NJAFE VOL. 12 No. 4,

5 Table 4: Welfare Loss and Farm Income of Farming Households Imputed cost of day lost ( ) 57, Cost of medical expenses ( ) 12, Welfare loss ( ) 69, Total output GEW per ha Less output consumed Less output as gift Equal output sold Income per ha(( ) 175,046 Welfare loss (%) Source: field survey, 2012, average wage rate per day = 1500, price of maize /kg = 50 Table 5: Parameter estimates for the ordinary least square regression model Variables Coefficient t-value p-value Constant Household size *** 0.00 Level of education *** 0.00 Malaria incidence *** 0.00 Use of mosquito net *** R F Field survey, *** Statistically significant at probability (<0.01) CONCLUSION AND RECOMMENDATIONS Households had an average of 8 episodes of malaria occurrence in a year and lost 40% of their income to malaria incidence. Household size and malaria incidence are the major determinant of welfare loss in the study area. It is therefore recommended that the use of malaria control strategies such as insecticide-treated mosquito nets and environmental sanitation should be strengthened also considering the number of day loss due to malaria incidence and cost of medical expenses, efforts should be aimed at facilitating early detection of malaria through the use of Rapid Diagnostic Test (RDT) and this is to be complimented with prompt administration of effective treatment. This would not only lower the cost of treatment but also reduce the number of work day lost by farming households and by extension welfare loss. REFERENCES Ajani, O. L.Y. and Ashagidigbi, W. M Effect of malaria on rural household s farm income in Oyo state. African Journal of Biomedical Research, 11(3): Alaba, A. and Olumuyiwa, A Malaria in rural Nigeria: Implications for the millennium development goals: African economic research consortium (AERC) Cornell conference on bottom-up interventions and economic growth in sub-saharan Africa. May 31-June 1, 2007, Nairobi, Kenya. Asante, F. A The Links between Malaria and Agriculture, Reducing Malaria Prevalence and increasing Agricultural Production in Endemic Countries presented at a Conference on Improving Vector Control Measures for the Integrated Fight against Malaria: From Research to Implementation. Paris, France Attanayake, N., Fox-Rushby, J. and Mills, A Household Costs of Malaria Morbidity: A Study in Matale District, Sri Lanka. Tropical Medical Integrated Health. 5(9), Badiane, O. and Ulimwengu, J Malaria Incidence and Agricultural Efficiency in Uganda. Journal of Agricultural Economics. 44: Breman, J., Mills, A., Snow, R., Steketee, R., White, N. and Mendis, K Conquering Malaria. In Disease Control Priorities in Developing Countries.. 2 nd Ed. DT Jamison, JG. Chima, R. I., Goodman, C. A. and Mills, A The economic impact of malaria in Africa: a critical review of the evidence. Health Policy, 63(1), Fink, G. and Masiye, F Health and agricultural productivity: Evidence from Zambia. Journal of Health Economics, 42, Hay, S. I., Okiro, E. A., Gething, P. W., Patil, A. P., Tatem, A. J., Guerra, C. A. and Snow, R. W Estimating the global clinical burden of Plasmodium falciparum malaria in PLoS Med, 7(6), e doi: /journal.pmed NPC (National Population Commission National Population and Housing Census National Population Commission Abuja, Nigeria Shankar, A.H Nutritional Modulation of Malaria Morbidity and Mortality. J. Infect.Dis. 182(Suppl 1): S37 S53. Shayo, E. H., Rumisha, S. F., Mlozi, M. R. S., Bwana, V. M., Mayala, B. K., Malima, R. C. and Mboera, L. E. G Social determinants of malaria and health care-seeking patterns among rice farming and pastoral communities in Kilosa District in central Tanzania. Acta Tropica, 144, Snow, R. W Global malaria eradication and the importance of Plasmodium falciparum epidemiology in Africa. BMC Med, 13, 23. doi: /s WHO, The Role of Laboratory Diagnosis to Support Malaria Disease Management focus on the use of Rapid Diagnostic Test (RDT) in areas of High Transmission Geneva, World Health Organization. NJAFE VOL. 12 No. 4,