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Enregistrement 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 sur OpenAlexaff
Nahid Akhter Jahan, Barkat-e Khuda

Notice bibliographique

RevueSSRN Electronic Journal · 2007
Typearticle
Langueen
DomaineHealth Professions
ThématiqueGlobal Health Care Issues
Établissements canadiensInstitute of Health Economics
Organismes subventionnairesnon disponible
Mots-clésSubsidyBusinessHealth careQuality (philosophy)User feeCost sharingMultinomial logistic regressionIntervention (counseling)Public economicsActuarial scienceEconomicsEconomic growthMedicineNursing
DOInon disponible

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,055
Score d'incertitude au seuil0,109

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,007
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,014
Tête enseignante GPT0,410
Écart entre enseignants0,396 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2007
Routes d'admission1
Résumé présentoui

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