Health Policy and Planning Advance Access originally published online on November 21, 2005
Health Policy and Planning 2006 21(1):10-16; doi:10.1093/heapol/czj004
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Original article |
Do malaria preventive interventions reach the poor? Socioeconomic inequities in expenditure on and use of mosquito control tools in Sudan
1 Gates Malaria Partnership, London School of Hygiene and Tropical Medicine, London, UK, 2 Health Policy Research Unit, Department of Pharmacology and Therapeutics, College of Medicine, University of Nigeria, Enugu Campus, Nigeria, 3 National Malaria Administration, Khartoum, Sudan, 4 Ministry of Health, Khartoum, Sudan and 5 World Health Organization, Eastern Mediterranean Regional Office (EMRO), Cairo, Egypt
Correspondence: Obinna Onwujekwe, Health Policy Research Unit, Department of Pharmacology and Therapeutics, College of Medicine, University of Nigeria, Enugu Campus, Enugu, Nigeria. E-mail: onwujekwe{at}yahoo.co.uk
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Abstract |
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Objectives: To determine levels of socioeconomic inequities in the prevention of malaria, and to examine the implications of the findings for improving the equitable control of malaria in the Sudan.
Methods: A cross-sectional survey using a pre-tested interviewer-administered questionnaire was administered to 720 randomly selected householders from six localities in Gezira and Khartoum States. A socioeconomic status (SES) index, which was developed using principal components analysis, was used to examine socioeconomic inequity in the prevention of malaria.
Findings: Socioeconomic status was positively related to expenditures and use of vector control tools. The poorest households spent the least amounts of money to prevent malaria and were the least likely to own mosquito nets.
Conclusion: The inequity in the prevention of malaria in the study areas has to be redressed before malaria can be effectively controlled in Sudan. Malaria control managers should continually determine the extent to which malaria preventive tools reach the poorest socioeconomic groups, and fashion strategies that will ensure that equity is always maintained.
Key Words: equity, socioeconomic status, SES, malaria, vector control, Sudan
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Introduction |
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Malaria is the major health problem in Sudan, accounting for about 21% of all diseases seen at outpatient departments and 32% of inpatient admissions in health facilities (NMA 2002
). The prevention of the disease is undertaken using a wide range of vector control interventions, which include annual indoor residual house spraying (IRHS), larviciding with chemicals, fogging (space spraying), environmental management, and recently, insecticide-treated nets (ITNs). However, there is a lack of information about how the malaria preventive tools reach various socioeconomic groups. This information could be used to improve the equity in access and use of the malaria control interventions in order to achieve a significant reduction in the disease burden and its elimination as a public health problem in Sudan.
The gap in knowledge about the levels of inequity in access to and use of the vector control tools is applicable not only in Sudan but also in most malaria-endemic countries. Studies have already shown the cost-effectiveness of some of these tools (Goodman et al. 2001; Kamolratanakul et al. 2001; Guyatt et al. 2002a). However, if the poorest households have limited access to and use of the tools, their burden of malaria may be disproportionately higher. Other studies have shown that some people may be too poor to pay for vector control tools (Guyatt et al. 2002b), thereby leading to greater exposure to the disease. It has also been argued that ‘health inequality leads to poverty and poverty promotes development of diseases’ (Guyatt et al. 2002c). In Cambodia, it was found that even relatively modest out-of-pocket health expenditure frequently causes indebtedness and can lead to poverty (Van Damme et al. 2004).
The examination of the level of equity in healthcare is becoming increasingly topical and important. Equity is defined by the International Society for Equity in Health (ISEqH) as the ‘absence of potentially remediable, systematic differences in one or more aspects of health’ (ISEqH 2003). The level of inequity in access to and use of malaria control tools should be tackled and reduced if there is to be a considerable reduction in the burden of malaria. Therefore, it is important that programme managers and policymakers become aware about the extent to which health care services, such as malaria preventive interventions, are reaching different socioeconomic segments of the society.
It has been shown that the poorest households are usually less likely to purchase and access health care services, such as malaria preventive tools (Carme et al. 1994). Inequitable access to and use of malaria preventive tools could lead to increased exposure to malaria for the poorest households and could further lead to a depletion of their household resources. These could lead to a decrease in household production (Asenso-Okyere et al. 1994; Nur 1993; Onwujekwe et al. 2000) and lack of money to meet other basic household needs such as food and education.
This paper examines the level of socioeconomic equity in access to, expenditure on and use of malaria vector control tools. This information would be useful to programme managers and policymakers to plan and implement interventions that could be used to remedy inequity that might exist. Such interventions could specifically ensure that distribution and payment strategies that are adopted for vector control interventions are geared towards reducing the burden of malaria and equitably reach all socioeconomic groups in the country.
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Methods |
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Study areas
Three localities from Gezira and Khartoum states were the study sites. Gezira State is the site for the Gezira Agricultural scheme, while the capital of Sudan is within Khartoum State. The localities were an urban, a peri-urban and a rural area, so that the households will be representative of the general population. The localities in Gezira State were Wad Medani (urban), Umra-Qura (peri-urban) and Medina Arab (rural). Similarly, Omdurman (urban), Bahry (peri-urban) and Albuga (rural) were selected in Khartoum State.
The vector control tools studied
These were IRHS, ITNs, fogging and larviciding with chemicals (LWC). They were selected after a preliminary inventory of the vector control activities revealed that they were the main approaches used in the country. Environmental management, though also prominently used to prevent malaria, was not included in the study. Theoretically, IRHS, LWC and fogging were supposed to be provided free of charge, but in reality people who wanted the services contributed money to buy the chemicals and pay some of the spray men. ITNs were introduced into the national control programme in 2002 and are distributed on a user fee basis.
Mode of operation of the vector control interventions1
At the national level, the government funds 60% of the cost of IRHS and is committed to continue this funding as long as malaria is a problem in Sudan. The World Health Organization (WHO) funds about 20% of IRHS costs and has a renewable 1 year funding commitment. The rest of the funding for IRHS at the national level comes from private companies, public boards and the Gambiae control project in Northern Sudan, which is financed by the Egyptian government. The Sudanese government has not started funding ITN activities. There was a provision of US$400 000 in the current budget for ITN activities, but the money had not been released at the time of the study. WHO and the United Nation's Children's Fund (UNICEF) donated some ITNs to the National Malaria Administration (NMA), which it in turn supplies at a subsidized rate to the State Ministry of Health/State Malaria Administration and also sells directly to consumers at the same subsidized rate. The actual market price of the nets is US$4.00 each, but they are sold at US$2.00 each to the consumers. The NMA stocks in equal share both temporarily and permanently treated nets. The NMA also buys nets from the market to supplement the donated stock from WHO and UNICEF, because while WHO donated 50 000 nets and UNICEF donated 120 000 nets, the current requirement in the country is 5 million nets. The funding commitment from consumers is potentially everlasting as long as the ITNs continue to be effective. However, both UNICEF and WHO have a funding commitment of 2 years. In the case of LWC, government funds 90%, while WHO funds 10%, and fogging is funded only by the government. The government is committed to funding these two activities in the long-term while WHO has a 2 year commitment.
At the state level in Gezira State, the government funds 75% of IRHS costs and WHO funds the rest, which the State Malaria Administration (SMA) believes are long-term funding commitments. The households fund 75% of the costs of ITNs by paying US$2.78 per net; the money is deposited in an ITN revolving fund which is managed by the SMA and used to continuously replenish the stock of the nets. The rest of the funding for ITN activities comes from WHO and UNICEF. However, there is a bank that imports and sells nets through the Ministry of Health. This bank (Savings bank) fixed the price of their nets at US$5.00, more than the SMA price, resulting in low purchase by consumers. At present, 100 000 nets of the Savings bank lie unsold at the SMA store. The funding ratio for LWC is 70% government and 30% UNICEF, and for fogging it is 50% government and 50% WHO, according to the SMA director, who believes that these are long-term funding commitments. In Khartoum state, the SMA is not involved in IRHS. In the case of ITNs, the people contribute 50% of the funding and WHO funds the remaining 50%. An Islamic organization called Zakat buys the nets at US$2.5 per net from the SMA and gives them free to poor people. According to the director of SMA, the funding for LWC is 75% government and 25% WHO, and for fogging it is 85% government and 15% WHO. While the funding commitment from the government is long-lasting, that from WHO is a renewable 1 year commitment.
At the level of the localities, the government is mostly responsible for financing IRHS, LWC and fogging activities to the tune of almost 100%, certainly for LWC and fogging. The funds from the Ministry of Health and locality authorities often come late, leading to delay in or cancellation of some activities. In Medina Arab, the government completely finances all vector control activities. However, a few localities contribute money to assist in the operating costs of IRHS but the proportion of their contributions to the direct costs of IRHS varies.
Consumers fund IRHS activities by paying some money to cover the cost of some of the activities. However, the contributions do not usually exceed US$1.48 per household or the amount decided upon by the localities to ensure that their areas are sprayed. At the time of the study, in Umra-Qura locality, the consumers contributed about 12.5% of the costs of IRHS to cover some operating costs and the State Ministry of Health and local authority contributed the rest. However, while the funds from the State Ministry of Health were dependable, those from the locality authority were not, and shortfalls usually led to the skipping of some IRHS activities, like reducing the number of rooms sprayed. Conversely, the consumers completely financed the purchase of their ITNs in all localities. However, the price of the nets differed from district to district. In the Khartoum State localities of Bahry and Omdurman, the nets were sold at US$2.41 each, while they were sold at US$2.22 each in Albugaa. However, in the Gezira State localities, the nets were sold at US$2.78 per net. In Wad Medani, the Red Crescent buys nets from the locality, which it distributes free of charge to poor people.
The survey
A pre-tested interviewer-administered questionnaire was used to collect data from simple randomly selected householders. The questionnaire was administered to 120 households in each of the six localities by the trained interviewers, giving a total of 720 respondents. The respondents in each household were either the household head or the spouse. However, in the event that these two people were not around, information was collected from an available adult household member. Where there was no available adult household member, that particular household was excluded from the study and another household randomly selected to replace it.
Data were collected on household demographic and socioeconomic characteristics, their level of use of different vector control tools and levels of expenditure on various mosquito control tools. A 1-year recall period was used after pre-testing showed that 1-month recall was too short to capture infrequent expenditures to prevent malaria. In addition, people more readily remembered the small expenditure that they made to prevent malaria in the past year. Whilst it was straightforward to collect data on ownership and/or use of ITNs, IRHS and untreated nets because they occurred at household level, we used the fact that one's immediate neighbourhood was sprayed as the proxy of consumption of larviciding and fogging because they occurred at the community level. Information on wealth and income (using the cost of food as proxy) were collected, to help determine the relationship between uptake of preventive measures and socioeconomic status (SES). The spraying of a consumer's immediate residential area was used as a proxy for the use of LWC and fogging, whilst for IRHS and ITNs it was, respectively, whether the person's home was sprayed and whether the person actually bought and slept under an insecticide-treated net.
Data analysis
Information on ownership of assets and weekly per capita value of food were used to develop a socioeconomic status (SES) index that was used to determine the effects of socioeconomic status on the outcome variables of interest. The assets were ownership of a refrigerator, television set, satellite dish, car and radio. Weekly per capita value of food was also included as one of the variables as a proxy of household income because food consumes more than 50% of household income.
Principal components analysis (PCA) was used to generate the SES index (Filmer and Pritchett 2001; Onwujekwe et al. 2004). The SES index was used to divide the households into quartiles and chi-squared analysis was used to determine the statistical significance of the differentiation of the dependent variables into SES quartiles. The first principal component was used to derive weights for the SES index. The highest weight was given to ownership of a fridge (0.53), followed by TV (0.48), satellite dish (0.43), car (0.41), radio (0.37) and per capita value of food (0.01). Households were divided into quartiles on the basis of the value of the SES index. The measure of inequity was the ratio of the mean of the poorest SES group (1st quartile) over that of the least poor SES group (4th quartile) (inter-quartile ratio) (Onwujekwe 2005).
Logistic regression analyses were estimated so as to examine the demographic and socioeconomic factors that could further explain the reasons for use and ownership of the different tools. A full-to-reduced modelling approach was used in order to arrive at the best models. The independent variables with the smallest t-statistic, and whose removal did not adversely affect the other coefficients or the prediction of the models, were removed sequentially. The F-test for the hypothesis that the coefficient of that variable was zero was used to decide whether the variable would be finally dropped or re-entered into the regression. The variables were finally dropped if the probability associated with the F-test was more than 0.10.
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Results |
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As stated, a total of 120 households were interviewed in each of the six localities, bringing the overall total to 720 (Table 1). Most of the respondents were either the head of the household or the spouse, female, married and had some education. Television sets and radio sets were the most common household assets, while satellite dishes and cars were the least common household assets. The mean per capita weekly value of food was 1731 Sudanese Dinars (US$6.41).
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Use of malaria preventive tools and household expenditures on the different vector control tools
Table 2 shows that the least number of households used an ITN, while a majority had their houses sprayed with insecticides (IRHS) in the previous year. Just 40.4% claimed to have been exposed to fogging and only 17.2% used untreated mosquito nets. However, a sizeable proportion (18.6%) used neither ITNs nor IRHS. Table 2 also shows that the highest average amount of money was spent by households to purchase ITNs, while expenditures on space spraying represented the lowest household expenditure on the vector control services. A total of 37 people, 5 people, 1 person, 5 people and 3 people did not know whether or not they consumed larviciding, space spraying, untreated nets, ITNs and IRHS, respectively.
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Socioeconomic inequity
In Table 3 it is shown that the richer households spent more money than the poorest ones to prevent malaria (p<0.05). However, there was no statistically significant relationship between socioeconomic status and contracting malaria, as the incidence of presumptive malaria amongst the SES quartiles was 106, 111, 99 and 95 for Q1, Q2, Q3 and Q4, respectively, with a chi-square for trend of 1.85 (p = 0.17). The inequity in expenditures to prevent malaria was very prominent and was also statistically significant using the aggregate expenditure (total of all vector control tools). The expenditures on ITNs showed the highest level of inequity. The difference between SES quartiles in expenditures on untreated nets was very small and the trend was statistically insignificant. The inter-quartile ratios (IQR) confirm that apart from expenditure on untreated nets, all expenditures on other vector control tools were tilted in favour of the rich households compared with the poorest ones.
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Table 4 shows that apart from ownership of untreated nets and use of IRHS, there were varying levels of statistically significant inequity trends in the consumption of the other three vector control tools. For instance, in the case of larviciding, 38 people in the 1st quartile claimed that they consumed the service, while 75 people in the 4th quartile made a similar claim. The results also show that while only 9 people in the 1st quartile used ITNs, the number was 36 people in the 4th quartile.
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Table 5 shows reduced models of the logistic regression analyses results on determinants of use and ownership of the various tools. The results show that socioeconomic status was positively and statistically significantly related to use of larviciding, space spraying and ITNs (p<0.05). Education was also positively related to use of all vector control tools with the exception of ITNs; the results show that people who had attended school were more likely to have used four of the vector control tools. The other variables that explained use of different vector control tools were respondents being household heads (status in household) in the case of space spraying, and increased number of household residents for larviciding and ITNs, although the signs on the coefficients were different for these two tools. All the regression analyses were statistically significant. The least number of correct predictions were found with respect to use of IRHS and the highest predictions found with respect to use of ITNs.
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Discussion |
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This study provides evidence on the inequity in expenditure on and consumption of malaria vector control interventions in Sudan. In general, it was seen that richer households had greater access to, use of and overall expenditures on malaria preventive tools. The positive effect of socioeconomic status on expenditures and use of vector control tools implies that the poorest SES groups were the least capable of taking precautions against malaria through the use of malaria preventive tools. However, it has been argued that ‘health interventions initially reach those of higher socioeconomic status and only later affect the poor’ (Victora et al. 2000
). This may be the case for ITNs, which are still relatively new in the country, but may not be the case for other vector control tools.
The inequitable expenditure is arguably a reflection of the severe budget constraints faced by the poorest households. It is also possible that the small expenditures that the poorest households are currently incurring could be depleting a greater proportion of their household income when compared with the better-off households. However, as was shown in this study and also by Armstrong-Schellenberg et al. (2003) and de Savigny et al. (2004), the risk to acquire malaria is fairly well distributed across SES groups, but with the better-off households having the better capacity to prevent and deal with the disease. However, it has been argued elsewhere that the poorest SES groups have increased exposure to the disease (Litsios 1990). In Vietnam, it was found that reductions in malaria incidence through government-financed malaria control programmes can contribute to a higher household income for all households in endemic areas (Laxminarayan 2004).
ITNs were the least utilized, while IRHS was the most utilized, out of all the major vector control tools that were evaluated. Surprisingly, many respondents did not feel that larviciding and space spraying was for their benefit, accounting for the fact that they stated they did not use these vector control tools. Maybe this is because of the extreme public good nature of those tools, where there is no private and exclusive consumption. The very high average expenditure on ITNs relative to other vector control tools reflects the fact that the few people who had ITNs actually paid for them, while most of the people who used the other services did not pay for them because the government paid. Nonetheless, it is paradoxical that while few people used ITNs, the average amount of money spent on them was the highest out of all the malaria preventive services.
The results show that whilst both expenditures on and use of ITNs, which were mostly distributed through the public health care system, were inequitable, both indicators were equitable for untreated nets, which were sold through the commercial sector. Hence, it could be argued that the commercial sector provided equal opportunities to all SES groups to access and purchase nets, while the public health care system is restricted to people who could access it to know about the ITNs and buy them. Therefore, it is advocated that the commercial sector in Sudan should be developed to complement the public health care system in the sale and distribution of ITNs.
The logistic regression analyses confirmed the findings from the tabulations that socioeconomic status was a statistically significantly positive explanatory factor for the use of ITNs, larviciding and space spraying. This demonstrates that added impetus should be given to finding strategies to improve the apparently redeemable inequity in the use of vector control tools. The finding that education improved the use of most of the vector control tools should be explored so as to increase the overall coverage of all tools and the appreciation of the people that they are consuming them (especially those that are pure public goods such as larviciding and space spraying). Many studies in Africa have established the positive relationship of education and improved use of malaria control tools (Tarimo et al. 2000; Njama et al. 2003).
A limitation of the study is the year recall period, which some people may argue could lead to invalid responses because people could have forgotten the expenditures that they made in such a long period. However, a counter argument is that since expenditures on preventive goods and services are made infrequently, unlike those for acute curative services, people are more likely to remember expenditures made on prevention even after 1 year. Also, coverage of the malaria vector control tools was small, especially for ITNs, but as was shown in Tanzania, the immediate goal may be to increase coverage in the general population and this would then lead to improvements in equity (Nathan et al. 2004). Another limitation of the study was the difficulty and measurement problems in determining the use of public goods and services such as IRHS, larviciding and fogging.
In conclusion, the inequity in the prevention of malaria in the study areas has to be redressed as part of concerted efforts to ensure that the disease ceases to be a major public health problem in Sudan. Malaria vector control distribution and financing interventions that could be used to improve equity should be developed and the aim of these efforts should be ‘to work towards keeping inequity to a minimum while health problems are tackled through initiatives’ (Armstrong Schellenberg et al. 2003). Malaria control managers should re-evaluate interventions such as larviciding and IRHS. They should ensure that all SES groups benefit equally from the services; that their provision is not limited to mostly affluent neighbourhoods where the people can and usually do contribute more money to buy insecticides and financially motivate the spray-men. In the case of ITNs, pro-poor payment mechanisms such as vouchers (Marchant et al. 2002) could be used to increase their purchase by the poorest SES group. Malaria control managers should continually determine the extent to which malaria preventive tools reach the poorest SES groups and fashion strategies that will ensure that equity is maintained in vector control activities.
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Biographies |
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Obinna Onwujekwe, a health economist, is currently a post-doctoral fellow with the Gates Malaria Partnership, London School of Hygiene and Tropical Medicine, in addition to being the head of the Department of Health Administration and Management, College of Medicine, University of Nigeria Enugu-Campus.
El-Fatih Malik, a physician, is currently the Deputy Director of the National Malaria Administration in Khartoum, Sudan.
Sara Hassan Mustafa is a medical officer with the International Health Division, Ministry of Health, Khartoum.
Abraham Mnzavaa is an entomologist with the Eastern Mediterranean Regional Office of the World Health Organization, Alexandria, Egypt.
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Acknowledgments |
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We would like to thank the following persons and organizations for their help: the staff at EMRO; staff of the WHO office in Khartoum, Sudan, especially Dr Mahmoud Wais, Dr Hashim, Dr Sabatinelli, Malik, Marwa and Muna; staff of NMA and State MA in Gezira and Khartoum States, especially Dr Abbas and Mubarak; staff of Blue Nile Research Institute in Wad Medani, especially Samia Seif, Heba, Prof Khalaffala and Mr Tarig; our research assistants. We are very grateful to Kara Hanson and Nkem Dike for reviewing earlier drafts of the paper.
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Endnotes |
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1Sourced from Onwujekwe (2002)
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