Devising an Appropriate Method of Allocating Resources in the Health Sector of Bangladesh
Bibliographic record
Abstract
Rationale: The health sector of Bangladesh currently uses incremental budgeting for distributing recurrent resources among the district and sub-district levels. However, the allocation to different geographic areas is not linked to the relative needs (medical, social or economic) for resources of the population, and does not ensure equity of access to services for people at equal needs. As a result, many people with higher need get lower amount of health care - the case of 'inverse-care law' of Gwatkin. It is, therefore, necessary to modify the current method towards a needs based system to make the allocations more responsive to the health care needs of the population. In this context, a study was carried out to assess the potential for devising a needs-based resource allocation method in Bangladesh. Objectives: The specific objectives of the study were to analyse the existing health care resource allocation mechanism of MOHFW, Bangladesh, to assess the extent to which the current health care resource allocation in Bangladesh is equitable and effective for different regions, and to propose a needs-based resource allocation formula for Bangladesh. Methodology: The study reviewed the available literature on different resource allocation methods used in the health sector in UK, Sweden, Canada, New-Zealand, Australia, and Zambia, as these countries consider 'equity' as a guiding principle for allocating health care resources geographically, and trying to adopt the needs-based formulae. To collected information on the current resource allocation and budgeting mechanism of the health sector of Bangladesh, and to find out the appropriate indicators of need and their relative weights, the study interviewed key health sector professionals, which included policy makers, development partners and advisors/consultants who were involved in the resource allocation process. To assess the extent to which the current allocation is related to health care need, a cross-sectional study was carried out in 4 upazillas (sub-district), which included both high and low performing ones. Information on, inter alia, population size, disease profile, remoteness of the area and poverty level, and the current allocation were collected from the study areas. Results: The output of the research was a formula for allocating health care resources among geographic areas in Bangladesh, which was based on the relative needs for health care at the sub-district level. The findings showed that the variables to be included in the capitation formula, in addition to population size, are: disease profile, remoteness of the area, poverty and unmet need for health care. The formula which assigned higher weights on the disease profile and remoteness seems to work better and more realistic. Conclusion: The study suggests that the proposed formula will increase health sector service utilization and will help in improving the equity of resource use in Bangladesh. However, it should be noted that proper allocation of resources is the necessary but not the sufficient condition for improving performances of the sector. Alongside use of an appropriate allocation method, the sector must adopt measures to efficiently utilize the resources allocated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".