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Budgeting based on need: a model to determine sub-national allocation of resources for health services in Indonesia

BACKGROUND: 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...

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Autores principales: Ensor, Tim, Firdaus, Hafidz, Dunlop, David, Manu, Alex, Mukti, Ali Ghufron, ayu Puspandari, Diah, von Roenne, Franz, Indradjaya, Stephanus, Suseno, Untung, Vaughan, Patrick
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3453525/
https://www.ncbi.nlm.nih.gov/pubmed/22931536
http://dx.doi.org/10.1186/1478-7547-10-11
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author Ensor, Tim
Firdaus, Hafidz
Dunlop, David
Manu, Alex
Mukti, Ali Ghufron
ayu Puspandari, Diah
von Roenne, Franz
Indradjaya, Stephanus
Suseno, Untung
Vaughan, Patrick
author_facet Ensor, Tim
Firdaus, Hafidz
Dunlop, David
Manu, Alex
Mukti, Ali Ghufron
ayu Puspandari, Diah
von Roenne, Franz
Indradjaya, Stephanus
Suseno, Untung
Vaughan, Patrick
author_sort Ensor, Tim
collection PubMed
description BACKGROUND: 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. METHODS: A standardised costing methodology was developed that is sensitive to the main expected drivers of local cost variation including demographic structure, epidemiology and location. Essential package costing is often undertaken at a country level. It is less usual to utilise the methods across different parts of a country in a way that takes account of variation in population needs and location. Costing was based on best clinical practice in Indonesia and province specific data on distribution and costs of facilities. The resulting model was used to estimate essential package costs in a representative district in each province of the country. FINDINGS: Substantial differences in the costs of providing basic services ranging from USD 15 in urban Yogyakarta to USD 48 in sparsely populated North Maluku. These costs are driven largely by the structure of the population, particularly numbers of births, infants and children and also key diseases with high cost/prevalence and variation, most notably the level of malnutrition. The approach to resource allocation was implemented using existing data sources and permitted the rapid construction of a needs based formula that is highly specific to the package mandated across the country. Refinement could focus more on resources required to finance demand side costs and expansion of the service package to include priority non-communicable services.
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spelling pubmed-34535252012-09-25 Budgeting based on need: a model to determine sub-national allocation of resources for health services in Indonesia Ensor, Tim Firdaus, Hafidz Dunlop, David Manu, Alex Mukti, Ali Ghufron ayu Puspandari, Diah von Roenne, Franz Indradjaya, Stephanus Suseno, Untung Vaughan, Patrick Cost Eff Resour Alloc Research BACKGROUND: 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. METHODS: A standardised costing methodology was developed that is sensitive to the main expected drivers of local cost variation including demographic structure, epidemiology and location. Essential package costing is often undertaken at a country level. It is less usual to utilise the methods across different parts of a country in a way that takes account of variation in population needs and location. Costing was based on best clinical practice in Indonesia and province specific data on distribution and costs of facilities. The resulting model was used to estimate essential package costs in a representative district in each province of the country. FINDINGS: Substantial differences in the costs of providing basic services ranging from USD 15 in urban Yogyakarta to USD 48 in sparsely populated North Maluku. These costs are driven largely by the structure of the population, particularly numbers of births, infants and children and also key diseases with high cost/prevalence and variation, most notably the level of malnutrition. The approach to resource allocation was implemented using existing data sources and permitted the rapid construction of a needs based formula that is highly specific to the package mandated across the country. Refinement could focus more on resources required to finance demand side costs and expansion of the service package to include priority non-communicable services. BioMed Central 2012-08-29 /pmc/articles/PMC3453525/ /pubmed/22931536 http://dx.doi.org/10.1186/1478-7547-10-11 Text en Copyright ©2012 Ensor et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Ensor, Tim
Firdaus, Hafidz
Dunlop, David
Manu, Alex
Mukti, Ali Ghufron
ayu Puspandari, Diah
von Roenne, Franz
Indradjaya, Stephanus
Suseno, Untung
Vaughan, Patrick
Budgeting based on need: a model to determine sub-national allocation of resources for health services in Indonesia
title Budgeting based on need: a model to determine sub-national allocation of resources for health services in Indonesia
title_full Budgeting based on need: a model to determine sub-national allocation of resources for health services in Indonesia
title_fullStr Budgeting based on need: a model to determine sub-national allocation of resources for health services in Indonesia
title_full_unstemmed Budgeting based on need: a model to determine sub-national allocation of resources for health services in Indonesia
title_short Budgeting based on need: a model to determine sub-national allocation of resources for health services in Indonesia
title_sort budgeting based on need: a model to determine sub-national allocation of resources for health services in indonesia
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3453525/
https://www.ncbi.nlm.nih.gov/pubmed/22931536
http://dx.doi.org/10.1186/1478-7547-10-11
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