Introduction
Sectoral cost-effectiveness analysis
Methodological inconsistency
Data unavailability
Lack of generalizability
Limited technical or implementation capacity
Generalized cost-effectiveness analysis: a new approach to sectoral CEA
Conceptual foundations
Practical implementation
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Use of a common set of analytical tools in WHO-CHOICE has overcome the problem of synthesizing studies that employ different perspectives and measures [20]. In order to collect, synthesize, analyze and report the costs and effects in a standardized manner, several tools have been developed. A multi-state modelling tool, PopMod [21] allows the analyst to estimate health effects by tracing what would happen to each age and sex cohort of a given population over 100 years, with and without each intervention. In order to collect programme-level costs associated with running the intervention (such as administration, training, and media) and patient-level costs (such as primary-care visits, diagnostics tests and medicines), a standard costing tool, Cost-It [22], has been developed. Finally, a tool has been developed for analysing the uncertainty around point estimates of cost-effectiveness (MCLeague [23]).
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Estimation of a null scenario as the starting point for analysis of the costs and effects of current and new interventions enhances the comparability of results, although it should be emphasized that local analysts may need to modify certain parameters (e.g. demographic structures, epidemiological characteristics, treatment coverage) in order to more accurately reflect a country's specific circumstances.
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WHO-CHOICE results to date have been made available at the level of 14 epidemiological sub-regions of the world (see Table 1). This is a compromise between providing detailed information on all interventions in all 192 member countries of WHO, something that is not possible in the shorter term, and the global approach that has been used in the past [5]. Generation of a single global estimate of the costs and effectiveness of a given intervention has not been attempted since such estimates provide almost no information that decision-makers can use in a country context.
Region* | Mortality stratum** | Countries |
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AFR | D | Algeria, Angola, Benin, Burkina Faso, Cameroon, Cape Verde, Chad, Comoros, Equatorial Guinea, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Madagascar, Mali, Mauritania, Mauritius, Niger, Nigeria, Sao Tome And Principe, Senegal, Seychelles, Sierra Leone, Togo |
AFR | E | Botswana, Burundi, Central African Republic, Congo, Côte d'Ivoire, Democratic Republic Of The Congo, Eritrea, Ethiopia, Kenya, Lesotho, Malawi, Mozambique, Namibia, Rwanda, South Africa, Swaziland, Uganda, United Republic of Tanzania, Zambia, Zimbabwe |
AMR | A | Canada, United States Of America, Cuba |
AMR | B | Antigua And Barbuda, Argentina, Bahamas, Barbados, Belize, Brazil, Chile, Colombia, Costa Rica, Dominica, Dominican Republic, El Salvador, Grenada, Guyana, Honduras, Jamaica, Mexico, Panama, Paraguay, Saint Kitts And Nevis, Saint Lucia, Saint Vincent And The Grenadines, Suriname, Trinidad And Tobago, Uruguay, Venezuela |
AMR | D | Bolivia, Ecuador, Guatemala, Haiti, Nicaragua, Peru |
EMR | B | Bahrain, Cyprus, Iran (Islamic Republic Of), Jordan, Kuwait, Lebanon, Libyan Arab Jamahiriya, Oman, Qatar, Saudi Arabia, Syrian Arab Republic, Tunisia, United Arab Emirates |
EMR | D | Afghanistan, Djibouti, Egypt, Iraq, Morocco, Pakistan, Somalia, Sudan, Yemen |
EUR | A | Andorra, Austria, Belgium, Croatia, Czech Republic, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Luxembourg, Malta, Monaco, Netherlands, Norway, Portugal, San Marino, Slovenia, Spain, Sweden, Switzerland, United Kingdom |
EUR | B | Albania, Armenia, Azerbaijan, Bosnia and Herzegovina, Bulgaria, Georgia, Kyrgyzstan, Poland, Romania, Slovakia, Tajikistan, The Former Yugoslav Republic Of Macedonia, Serbia and Montenego, Turkey, Turkmenistan, Uzbekistan |
EUR | C | Republic of Moldova, Russian Federation, Ukraine |
SEAR | B | Indonesia, Sri Lanka, Thailand |
SEAR | D | Bangladesh, Bhutan, Democratic People's Republic Of Korea, India, Maldives, Myanmar, Nepal |
WPR | A | Australia, Japan, Brunei Darussalam, New Zealand, Singapore |
WPR | B | Cambodia, China, Lao People's Democratic Republic, Malaysia, Mongolia, Philippines, Republic Of Korea, Viet Nam |
Cook Islands, Fiji, Kiribati, Marshall Islands, Micronesia (Federated States Of), Nauru, Niue, Palau, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu |
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The use of an uncertainty framework, in which cost-effectiveness estimates for multiple interventions are presented in terms of their probability of being cost-effective at different budget levels, provides decision-makers with policy-relevant data on the choices to be made under conditions of resource expansion (or reduction) [23, 24].
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Finally, a number of assumptions have been made with regard to the efficiency of implemented interventions. For example, in most settings it is assumed that health care facilities deliver services at 80% capacity utilization (e.g. that health personnel are fully occupied 80% of their time); or that regions have access to the lowest priced generic drugs internationally available. The reason for this is that there is limited value in providing information to decision-makers on the costs and effectiveness of interventions that are undertaken poorly (such assumptions, however, can be changed to reflect local experiences as required).