Socio-economic status and malaria-related outcomes in Mvomero District, Tanzania

Abstract

While policies often target malaria prevention and treatment – proximal causes of malaria and related health outcomes – too little attention has been given to the role of household- and individual-level socio-economic status (SES) as a fundamental cause of disease risk in developing countries. This paper presents a conceptual model outlining ways in which SES may influence malaria-related outcomes. Building on this conceptual model, we use household data from rural Mvomero, Tanzania, to examine empirical relationships among multiple measures of household and individual SES and demographics, on the one hand, and malaria prevention, illness, and diagnosis and treatment behaviours, on the other. We find that access to prevention and treatment is significantly associated with indicators of households’ wealth; education-based disparities do not emerge in this context. Meanwhile, reported malaria illness shows a stronger association with demographic variables than with SES (controlling for prevention). Greater understanding of the mechanisms through which SES and malaria policies interact to influence disease risk can help to reduce health disparities and reduce the malaria burden in an equitable manner.

Keywords:

• Malaria
• socio-economic status
• fundamental causes
• insecticide-treated nets
• malaria diagnosis

Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant No. 0720981. Additional support was provided through several Duke University funding sources: the Graduate Award for Research and Training in Global Health; the Faculty Award for Research in Global Health; the Provost's Common Fund; the Aleane Webb Dissertation Research Award; the Graduate School Dissertation Travel Award; the Student International Discussion Group Travel Grant; and the Environmental Internship Fund. Dickinson also acknowledges support from the Robert Wood Johnson Foundation's Health and Society Scholars Program. We are grateful to Leonard Mboera for his collaboration in the design and implementation of this project, and to Pauline Bernard, Gibson Kagaruki, Michael Ligola, Stanley Lucas, Chacha Manga and Rogers Rindeni for their dedicated and invaluable research assistance. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or other supporting institutions.

Notes

1. Other variables that have been shown to be significantly correlated with wealth in similar contexts include drinking water and latrine access (Njau et al. 2006, Vyas and Kumaranayake 2006). Regrettably, these data were not collected in our survey.

2. Condition of nets is also an important factor to consider. In practice, most (93%) of the nets that were observed by interviewers were either ‘intact’ or had only ‘small holes’. Because we did not observe a lot of variation in nets’ condition, and because nets with small holes can still be effective if they are treated with insecticides, we believe ITNs per capita is a more useful measure of prevention access in this context.

3. It is also possible that the relationship between SES and malaria is heterogeneous within villages. For example, education may have larger effects for very poor households. Our sample size precludes a more thorough examination of these interactions and the effects we identify should be viewed as average effects within villages.

4. Bivariate relationships were also examined. Results available from the authors upon request.

5. As an alternative health-seeking indicator, we also looked at whether or not each case was diagnosed at a health facility, regardless of whether a blood test was used. Housing size and quality and individual's age were associated with health care access using this measure.