Global investments in TB control: Economic benefits

Health Affairs

Volumn 28, Issue 4, 2009, Pages

  Laxminarayan, Ramanan ^c   Klein, Eili Y ^a   Darley, Sarah ^b   Adeyi, Olusoji ^b  

^a RESOURCES FOR THE FUTURE   (United States)

^b WORLD BANK   (United States)

^c NONE

Author keywords

[No Author keywords available]

Indexed keywords

ANTIRETROVIRUS AGENT; COTRIMOXAZOLE; ISONIAZID;

ARTICLE; CONTROLLED STUDY; COST BENEFIT ANALYSIS; ECONOMIC ASPECT; FINANCIAL MANAGEMENT; FUNDING; HEALTH CARE COST; INVESTMENT; MORTALITY; TUBERCULOSIS;

AFRICA SOUTH OF THE SAHARA; COST-BENEFIT ANALYSIS; DIRECTLY OBSERVED THERAPY; HEALTH CARE COSTS; HUMANS; TUBERCULOSIS;

EID: 67651211502     PISSN: 02782715     EISSN: 15445208     Source Type: Journal    
DOI: 10.1377/hlthaff.28.4.w730     Document Type: Article

References (56)

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4. Only about 20-50 percent of TB cases can be identified by microscopy, although these individuals are more infectious and more likely to die. Estimates of case fatality without treatment are most reliable for patients from natural history studies in the pre-chemotherapy era. See C. Dye et al., "Global Burden of Tuberculosis: Estimated Incidence, Prevalence, and Mortality by Country," Journal of the American Medical Association 282, no. 7 (1999): 677-686;
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10. Ibid.
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22. Ibid.
23. Ibid.;
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26. and Becker et al., "The Quantity and Quality of Life." 16. Environmental Protection Agency, Guidelines for Preparing Economic Analyses (Washington: EPA, 2000).
27. We used mean VSLs because they are insensitive to multiple factors including rates of labor-force participation and risk preferences of the TB-infected population. They also correct for highly skewed income distributions. External factors, such as changes in the risk of multidrug-resistant TB outbreaks or the course of the HIV-1 epidemic are not factored into the VSL calculation. For a fuller discussion of the problems with using VSL, see T.Grüne-Yanoff, "Mismeasuring the Value of Statistical Life," 27 November 2006, http://www.infra.kth.se/~gryne/VLS061120.pdf (accessed 11 June 2008).
28. R. Laxminarayan, Measuring the Economic Impact of Tuberculosis: A Reviewof Approaches (Washington: Resources for the Future, 2006).
29. M. Gold et al., Cost-Effectiveness in Health and Medicine (New York: Oxford University Press, 1996);
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32. Becker et al., "The Quantity and Quality of Life";
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34. See the Disease Control Priorities Project home page, http://www.dcp2.org.
35. Statistics South Africa, "Mortality and Causes of Death in South Africa, 2003 and 2004: Findings from Death Notification," 31 May 2006, http://www.statssa.gov.za/Publications/P03093/P030932003,2004.pdf (accessed 7 July 2007);
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37. D. Morgan and J.A.G Whitworth, "The Natural History of HIV-1 Infection in Africa," Nature Medicine 7, no. 2 (2001): 143-145.
38. GDP baseline levels (U.S. dollar UN estimates at market prices code 19470) retrieved from the UN Statistical Database, http://unstats.un.org/unsd/ databases.htm (accessed 10 June 2008).
39. Growth-rate data retrieved from the International Monetary Fund home page, http://www.imf.org (accessed 12 June 2008). Baseline was 2006 estimate. Growth rates were assumed to be the average of the previous decade.
40. The framework for estimating implementation costs is described in detail by K. Floyd, A. Pantoja, and C. Dye, "Financing Tuberculosis Control: The Role of a Global Financial Monitoring System," Bulletin of the World Health Organization 85, no. 5 (2007): 334-340. The no DOTS scenario assumes that the pre-DOTS level of treatment, case detection, and cure rates for a region remain steady. The cost analysis is limited to financial outlays and does not consider other costs, such as those associated with travel. The per patient cost of DOTS-related treatment (including antiretrovirals where necessary) was calculated at the regional level based on the weighted averages of country-level financial data, most of which were collected through an annual survey conducted by the WHO Stop TB Department's Global Financial Monitoring Project. Treatment costs for high-burden countries for which financial data were not available were extrapolated from available information. The methods for estimating DOTS expansion and improvement costs varied by region, based on the available data and knowledge of solutions. For Africa (high- and low-prevalence regions), these estimates were based on Uganda's DOTS expansion experience; Peru's DOTS-Plus program; and HIV/AIDS pilot programs in Malawi, South Africa, and Zambia.
41. C.L. Ballard, J.B. Shoven, and J. Whalley, "General Equilibrium Computations of the Marginal Welfare Costs of Taxes in the United States," American Economic Review 75, no. 1 (1985): 128-138;
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46. For more information and parameter distributions, see the online supplement, as in Note 10.
47. For specific country estimates, ibid.
48. Blower and Dowlatabadi, "Sensitivity and Uncertainty Analysis."
49. Correlation coefficients are presented in Appendix 3 in the online supplement, as in Note 10.
50. M.W. Borgdorff et al., "Gender and Tuberculosis: A Comparison of Prevalence Surveys with Notification Data to Explore Sex Differences in Case Detection," International Journal of Tuberculosis and Lung Disease 4, no. 2 (2000): 123-132.
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53. R. Rajeswari et al., "Socio-Economic Impact of Tuberculosis on Patients and Family in India," International Journal of Tuberculosis and Lung Disease 3, no. 10 (1999): 869-877.
54. See Dye et al., "Prospects for Worldwide Tuberculosis Control," for a fuller examination of the uncertainty in the epidemiological model.
55. J.C. Knowles and J.R. Behrman, Assessing the Economic Benefits of Investing in Youth in Developing Countries (Washington: World Bank, 2003);
56. and J. Gaag and J. Tan, The Benefits of Early Child Development Programs: An Economic Analysis (Washington: World Bank, 1998).