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Record W3158857818

Effects of User Fees and Improvements in Quality of Health Services on Demand for Health Care: Findings from Rural Areas of Bangladesh

2007· article· en· W3158857818 on OpenAlexaff
Nahid Akhter Jahan, Barkat-e Khuda

Bibliographic record

VenueSSRN Electronic Journal · 2007
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsInstitute of Health Economics
Fundersnot available
KeywordsSubsidyBusinessHealth careQuality (philosophy)User feeCost sharingMultinomial logistic regressionIntervention (counseling)Public economicsActuarial scienceEconomicsEconomic growthMedicineNursing
DOInot available

Abstract

fetched live from OpenAlex

Tightened budgets have forced many developing countries to make difficult choices regarding the financing and provision of health care services. Some governments are beginning to reevaluate policy of providing heavy subsidies and are considering whether to introduce various cost recovery schemes. In Bangladesh, user fees were introduced for a range of services in the Thana Health Complexes (THCs) under the Thana Functional Improvement Pilot Project (TFIPP), which has been developing innovative approaches to increase utilization of health services by improving their quality, effectiveness and efficiency. However, the success of any cost sharing scheme aimed at mobilizing resources from private individuals is dependent on price responsiveness of demand for health care. The main objective of this study was to examine the possible trade-offs between cost recovery and utilization of health services for different income groups. The study also analyzed the impact of user fees on health seeking behavior and perceived quality of care.The study used the data from facility based patient-exit interviews and community based household interviews carried out in February 1999 under the TFIPP. Information was collected from 2400 respondents in intervention and control areas. The latter, for the user fees intervention, are areas within the TFIPP geographical coverage where user fees were not introduced. The study used multinomial logit approach that not only focused on one decision (whether health care was sought) but also on the type of health care that was demanded.The results of the study showed that the major determinants of demand for health care were total expenditure on health care, including charged price, income, perceived quality of care, asset income, education and health program campaign. The study revealed that user fees had a very negligible effect on utilization, and price elasticities were very low. Household's income positively influenced the demand for health care and the other two supplier specific variables-- distance to facility and travel time-- had significant adverse effect on utilization. The findings showed that price elasticities decreased as the level of household income increased.Improving basic services such as vaccinations, child care and availability of drugs was likely to have a significant effect on demand for health care. The estimated effects of availability of health workers and behavior of provider were positive, but have lower elasticities. Perceived quality of care played an important role in demand for health care and it was considerably higher in intervention areas than in control areas. The reason might be that the user fees in these facilities were set according to a community participatory process and retained as well as used to improve quality of services at the local level. The design of policy reforms in the public health care sector requires reliable estimates of the effects of user fees on utilization and quality of health services. The findings of this study revealed that some amount of user fees can be introduced without adversely affecting the utilization, if the perceived quality of services is improved simultaneously.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.014
GPT teacher head0.410
Teacher spread0.396 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2007
Admission routes1
Has abstractyes

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