Inside the black box
Modelling health care financing reform in data-poor contexts
Bibliographic Data
| ID | 11491647 |
|---|---|
| Authors | D Mcintyre (0000-0003-4892-4491), Di Mcintyre (0000-0001-6319-4111, Ifakara Health Institute), Josephine Borghi (0000-0002-0482-5451, Ifakara Health Institute) |
| Year | 2012 |
| Volume | 27 |
| Issue | suppl 1 |
| Pages | i77-i87 |
| Publication date | 2012-03-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Health Policy and Planning (JOURNAL) |
| Journal identifiers | ISSN: 0268-1080 • E-ISSN: 1460-2237 |
| Publisher | Oxford University Press (PUBLISHER • GB) |
| DOI | 10.1093/heapol/czs006 |
| PMID | 22388503 |
| OpenAlex | W2100957014 |
| Language | EN |
| Citations received | 2 |
| References cited | 7 |
Modelling the likely financial resource requirements and potential sources of revenue for health system reform options is of great potential value to policy-makers. Models provide an indication of the financial feasibility and sustainability of such reforms and highlight the implications of alternative reform paths. There has been increasing use of financial models of health sector reform in recent years, particularly since the development of user-friendly software such as SimIns, which was developed by the World Health Organization (WHO) and Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ). This paper outlines the process of developing country-specific spreadsheet-based models to explore the financial resource requirements of health system reform options in South Africa and Tanzania. Building one's own model, although time consuming, allows for greater flexibility and forces the analysts to give careful consideration to the assumptions underlying the model. The core variables in our models are: population, health service utilization rates and unit costs. The paper outlines the types of disaggregation of these variables, the range of possible data sources, key challenges with securing accurate data for each variable, and relevant evidence on which to base key assumptions, and how we went about addressing these challenges. We also briefly review how to model the revenue-generating potential of alternative sources of health care financing. The intention of the paper is to provide guidance for analysts who wish to develop their own models, and to illustrate, with reference to the South African and Tanzanian modelling experience, how one has to adapt to data constraints and context-specific modelling requirements.
Actuarial science · Business · Economic growth · Economics · Flexibility (engineering) · Health care · Population · Public economics · Resource (disambiguation) · Revenue · Computer Science · Finance · Global Health Care Issues · Global Maternal and Child Health · Healthcare Policy and Management · Medicine
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| Citations per year | 0.14 |
|---|---|
| Citation span | 2012 - 2020 (9) |
| Highly cited | No |