Simon Brooker,1* Narcis B Kabatereine,2 Fiona Fleming3 and Nancy Devlin4
Accepted 13 August 2007
Estimates of cost and cost-effectiveness are typically based on a limited number of small-scale studies with no investigation of the existence of economies to scale or intra-country variation in cost and cost-effectiveness. This information gap hinders the efficient allocation of health care resources and the ability to generalize estimates to other settings. The current study investigates the intracountry variation in the cost and cost-effectiveness of nationwide school-based treatment of helminth (worm) infection in Uganda. Programme cost data were collected through semi-structured interviews with district officials and from accounting records in six of the 23 intervention districts. Both financial and economic costs were assessed. Costs were estimated on the basis of cost in US$ per schoolchild treated, and an incremental cost-eff本文来自优.文~论^文·网原文请找腾讯32491.14 ectiveness ratio (cost in US$ per case of anaemia averted) was used to evaluate programme cost-effectiveness. Sensitivity analysis was performed to assess the effect of discount rate and drug price. The overall economic cost per child treated in the six districts was US$0.54 and the cost-effectiveness was US$3.19 per case of anaemia averted. Analysis indicated that estimates of both cost and cost-effectiveness differ markedly with the total number of children who received treatment, indicating economies of scale. There was also substantial variation between districts in the cost per individual treated (US$0.41–0.91) and cost per anaemia case averted 煤矿1.5 Mt/a新井初步设计论文+CAD图纸
(US$1.70–9.51). Independent variables were shown to be statistically associated with both sets of estimates. This study highlights the potential bias in
transferring data across settings without understanding the nature of observed
variations.
Keywords Cost analysis, cost-effectiveness, economic evaluation, variation, scaling up,
helminth control, Uganda
* Corresponding author. Department of Infectious and Tropical Disease,
London School of Hygiene and Tropical Medicine, Keppel Street, London
WC1E 7HT, UK. Tel: t44 (0) 207 927 2614. Fax: t44 (0) 207 927 2918.
E-mail: 1 Department of Infectious and Tropical Diseases, London School of Hygiene
and Tropical Medicine, London, UK.
2 Vector Control Division, Ministry of Health, Kampala, Uganda.
3 Schistosomiasis Control Initiative, Department of Infectious Disease
Epidemiology, Imperial College, London, UK.
4 Department of Economics, City University, London, UK.
Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine
_ The Author 2007; all rights reserved. Advance Access publication 17 November 2007
Health Policy and Planning 2008;23:24–35
doi:10.1093/heapol/czm041
Introduction
Cost-effectiveness analysis has become a principal tool to evaluate health interventions, guiding health policy in both developed (McDaid et al. 2003) and developing countries (World Bank 1993; Jamison et al. 2006). Estimates of costeffectiveness are typically taken from a single study or a few small-scale studies in different countries (Walker and Fox-Rushby 2000), with no attempt to review the possible variation in estimates. However, because both intervention costs and effectiveness differ among locations, a single estimate of costeffectiveness is unlikely to be universally applicable (Musgrove and Fox-Rushby 2006). More probable is that costs and cost-effectiveness will vary, even within a single country. For instance, intra-country variation in costs has been demonstrated in the delivery of routine immunization in Peru (Walker et al. 2004), antenatal care in Cuba and Thailand (Hutton et al. 2004), a bednet distribution programme in Malawi (Stevens et al. 2005) and a lymphatic filariasis elimination programme in Egypt (Ramzy et al. 2005). Variations in average costs may arise in the short run from differences in the relative costs of inputs, differences in technical efficiency, or, in the long run, from factors associated with economies of scale (Folland et al. 2004). Differences may also reflect variation in respect to the demography and epidemiology of disease, availability of health care resources and system of health care delivery (Drummond and Pang 2001). Understanding how and why,2442
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