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Am. J. Trop. Med. Hyg., 76(5), 2007, pp. 830-836
Copyright © 2007 by The American Society of Tropical Medicine and Hygiene

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DETERMINANTS OF BED NET USE IN THE GAMBIA: IMPLICATIONS FOR MALARIA CONTROL

VIRGINIA WISEMAN, ANTHONY SCOTT, BRENDAN MCELROY, LESONG CONTEH*, AND WARREN STEVENS
Gates Malaria Partnership and Health Policy Unit, London School of Hygiene and Tropical Medicine, London, United Kingdom; Melbourne Institute of Applied Economic and Social Research, The University of Melbourne, Melbourne, Victoria, Australia; Department of Economics, University College, Cork, Ireland; Medical Research Council, Fajara, The Gambia

Malaria is still one of the biggest health threats in the developing world, with an estimated 300 million episodes per year and one million deaths, most of which are in sub-Saharan Africa. Although the efficacy and cost-effectiveness of treated bed nets has been widely reported, little is known about the range, strength, or interaction between different factors that influence their demand at the household level. This study modeled the determinants of bed net ownership as well as the factors that influence the number of bed nets purchased. Data was collected from 1,700 randomly selected households in the Farafenni region of The Gambia. Interviews were also held with 129 community spokespersons to explore the extent to which community level factors such as the quality of roads and access to market centers also influence demand for bed nets. The results of each model of demand and their policy implications are discussed.

Received June 13, 2006. Accepted for publication November 27, 2006.

Acknowledgments: We thank the people of Farafenni for allowing us to interview them and for sharing their experiences, our field team in The Gambia led by Fafanding Kinteh for their tireless efforts, Drs. Paul Snell and David Jeffries for support in designing the database, and Dr. Amy Ratcliffe for advice on survey design.

Financial support: This study was supported by the Gates Malaria Partnership at the London School of Hygiene and Tropical Medicine and by the Medical Research Council in The Gambia.

* Address correspondence to Lesong Conteh, Health Policy Unit and Gates Malaria Partnership, London School of Hygiene and Tropical Medicine, 50 Bedford Square, London WCIB 3DP, United Kingdom. E-mail: lesong.conteh{at}lshtm.ac.uk

Authors’ addresses: Virginia Wiseman, Gates Malaria Partnership, London School of Hygiene and Tropical Medicine, 50 Bedford Square, London WCIB 3DP, United Kingdom, Telephone: 20-7299-4716, Fax: 20-7299-4720, E-mail: virginia.wiseman{at}lshtm.ac.uk. Anthony Scott, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne, Melboune, Victoria 3010, Australia, Telephone: 61-3-8344-2115, Fax: 61-3-8344-2111, E-mail: a.scott{at}unimelb.edu.au. Brendan McElroy, Department of Economics, University College, Cork, Ireland, Telephone: 35-32-1490-2632, E-mail: b.mcelroy{at}ucc.ie. Lesong Conteh, Health Policy Unit and Gates Malaria Partnership, London School of Hygiene and Tropical Medicine, 50 Bedford Square, London WCIB 3DP, United Kingdom, Telephone: 20-7927-2939, Fax: 20-7299-4720, E-mail: lesong.conteh{at}lshtm.ac.uk. Warren Stevens, Medical Research Council, Fajara, The Gambia, E-mail: wstevens{at}mrc.gm.

Note: Appendix Table 1, Determinants of demand for treated and untreated bed nets (full sample), appears online at www.ajtmh.org.

{ddagger} Principal components analysis assumes that household long-run wealth, or access to material resources, explains the maximum variance in the asset variables. According to McKenzie (2003: 5), given an asset vector x = (x1, x2, . . . , xp)' , the first principal component of the observations, y, is the linear combination


Formula

whereby sample variance is maximized, subject to the restriction that a'a = 1, where a is the vector of coefficients, and xk and sk are the mean and standard deviation of variable xk. The wealth index of household i with assets xi is yi = a'xi where xi is the vector of standardized variables above. The wealth index has zero mean and variance {lambda}, where {lambda} is the largest eigenvalue of the correlation matrix of the asset vector x.

§ Some respondents entered positive values in the ‘none’ category, which suggests they interpreted it as ‘other’. On that basis we include it here.

Overdispersion occurs when the variance is greater than the mean.




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