Title |
Budgeting based on need: a model to determine sub-national allocation of resources for health services in Indonesia
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Published in |
Cost Effectiveness and Resource Allocation, August 2012
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DOI | 10.1186/1478-7547-10-11 |
Pubmed ID | |
Authors |
Tim Ensor, Hafidz Firdaus, David Dunlop, Alex Manu, Ali Ghufron Mukti, Diah ayu Puspandari, Franz von Roenne, Stephanus Indradjaya, Untung Suseno, Patrick Vaughan |
Abstract |
Allocating national resources to regions based on need is a key policy issue in most health systems. Many systems utilise proxy measures of need as the basis for allocation formulae. Increasingly these are underpinned by complex statistical methods to separate need from supplier induced utilisation. Assessment of need is then used to allocate existing global budgets to geographic areas. Many low and middle income countries are beginning to use formula methods for funding however these attempts are often hampered by a lack of information on utilisation, relative needs and whether the budgets allocated bear any relationship to cost. An alternative is to develop bottom-up estimates of the cost of providing for local need. This method is viable where public funding is focused on a relatively small number of targeted services. We describe a bottom-up approach to developing a formula for the allocation of resources. The method is illustrated in the context of the state minimum service package mandated to be provided by the Indonesian public health system. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Indonesia | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Indonesia | 2 | 2% |
Unknown | 118 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 26 | 22% |
Student > Postgraduate | 13 | 11% |
Researcher | 13 | 11% |
Student > Ph. D. Student | 10 | 8% |
Lecturer | 8 | 7% |
Other | 23 | 19% |
Unknown | 27 | 23% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 28 | 23% |
Economics, Econometrics and Finance | 17 | 14% |
Business, Management and Accounting | 13 | 11% |
Nursing and Health Professions | 12 | 10% |
Social Sciences | 6 | 5% |
Other | 14 | 12% |
Unknown | 30 | 25% |