Health Policy and Planning Advance Access originally published online on September 9, 2005
Health Policy and Planning 2005 20(6):337-346; doi:10.1093/heapol/czi043
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Original article |
Fever and its treatment among the more and less poor in sub-Saharan Africa
Development Research Group, The World Bank, Washington, DC, USA
Correspondence: Deon Filmer, Development Research Group, The World Bank, 1818 H Street NW, Washington, DC 20433, USA. Tel: +1–202–473–1303; E-mail: dfilmer{at}worldbank.org
This paper uses individual and household level data to explore empirically the associations between household wealth and the incidence and treatment of fever, as an indicator of malaria, among children in sub-Saharan Africa. The data used are from Demographic and Health Surveys collected in the 1990s from 22 countries where malaria is prevalent. The results suggest that the incidence of fever and its treatment are related to poverty in sub-Saharan Africa. Incidence is typically lower at the very top of the wealth distribution. The relationship, however, is not strong, especially after controlling for potentially confounding factors. Treatment patterns are strongly related to poverty as wealthier households are more likely to seek care or advice. While it is perhaps unsurprising that treatment from private sources increases with household wealth, government services – despite their public nature – are typically also used more by wealthier households. While general results hold for many of the countries, there is sufficient variation across countries that any policy seeking to reform the health sector in order to better cater to the poor needs to be informed by country-specific work.
Key Words: fever, malaria, inequality, child health
1Sampling weights are used to adjust for over- and under-sampling within countries.
2In Madagascar, Niger, Tanzania and Zambia, data on treatment refer to a survey from a different year than that used in the incidence analysis.
3A similar asset index approach has also been used by others to analyze health outcomes in DHS data; for example, child mortality in Bonilla-Chacin and Hammer (1999), child survival in Uganda in Stecklov et al. (1999), child anthropometric outcomes in Wagstaff and Watanabe (1999), and to document inequalities in a variety of health outcomes and behaviours in Gwatkin et al. (2000). Sahn and Stifel (2000) use a similar approach to analyze poverty directly.
4In order to define quintiles, individuals are sorted by the wealth index within each country, and cutoff values for the quintiles of the population are derived. Households are then assigned to each of these groups on the basis of their value of the asset index. The interpretation is, therefore, that the poorest quintile is the group in which the poorest 20% of the population live. Note that the use of the term ‘poor’ here differs from the usual notion derived from being below a poverty line. In this analysis, it refers to the population that lives in households with low values of the wealth index.
5That is, despite the fact that smaller countries will have larger sample sizes relative to their populations, the weights will adjust for this, so the results for the aggregated data can be interpreted as population weighted averages.
6Clusters are the lowest level from which a sample of households is drawn, i.e. these are typically the primary sampling unit in the data with about 20 households in a cluster.
7Unlike the incidence analysis, the estimated MNL model does not include the interaction of the sub-national and month of interview dummy variables. This is because the model becomes hard to identify as the number of variables increases. Since the interaction terms were included mostly to adjust for potential seasonality in incidence, and the MNL is conditional on a child having a fever, their omission in this model is not too problematic.
8In this part of the analysis, care from traditional healers is grouped with no treatment because the MNL model cannot be implemented when only few cases choose one particular choice.
9The countries of sub-Saharan Africa under study here are virtually all in areas which are suited to stable malaria (MARA 1998). Stable malaria describes areas with year-round transmission, which may be low or high intensity. Northern regions of Chad, Mali and Niger are not suited to stable malaria but Northern Mali is excluded by virtue of DHS sample design, and dummy variables for sub-national region will account for differences in northern Chad and Niger.
10For examples of studies addressing this issue see Butler et al. (1987) for an example from the United States, and Sindelar and Thomas (1991), Strauss and Thomas (1996) and Deolalikar (1998) for discussions relating to poor countries.
11Brinkmann and Brinkmann (1991) concluded that between 8 and 25% of persons with malaria visit health services, with self-treatment being more common in urban than in rural areas. McCombie (1996) found a substantial variation across countries. On average, close to 50% of cases relied exclusively on self-treatment, usually with antimalarials. Most episodes involved self-treatment using purchased drugs. More recent studies, with relatively consistent results, are Mwenesi et al. (1995), Ruebush et al. (1995) and (Ahorlu et al. 1997). McCombie (2002) summarizes that the rate of self-treatment is high in a number of studies, with perceived severity as a major determinant of self-treatment.
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