Fighting Corruption in the Health Sector: Methods, Tools and Good Practices
Notice bibliographique
Résumé
Several quantitative and qualitative studies highlight the fact that the burden of corruption in the health sector impacts the poor most heavily, given their limited access to resources. Poor women, for example, may not get critical health care services simply because they are unable to pay informal fees: a recent study by Amnesty International on maternal health in Burkina Faso found that one of the primary causes of the deaths of thousands of pregnant women annually (including during childbirth) is due to corruption by health professionals. Further evidence from the International Monetary Fund (IMF) shows that corruption has a significant, negative effect on health indicators such as infant and child mortality, even after adjusting for income, female education, health spending, and level of urbanization. Corruption lowers the immunization rate of children and discourages the use of public health clinics. In many countries, its pervasiveness impedes improvement in health outcomes and therefore is a serious barrier to the achievement of the Millennium Development Goals (MDGs). This study highlights where and how corruption is a threat in the health sector, and how it can be diagnosed and tackled. Some of the common corrupt practices in the health sector identified include absenteeism, theft of medical supplies, informal payments, fraud, weak regulatory procedures, opaque and improperly designed procurement procedures, diversion of supplies in the distribution system for private gains and embezzlement of health care funds. Each of these practices alone represents a major challenge in many developing countries.Effective interventions addressing such vulnerabilities need to be designed so that health goals are more likely to be achieved. This study provides examples of anti-corruption interventions that can help policy makers and practitioners to determine what may be most appropriate for their situation. For example, the public posting of medical supply prices can help prevent collusion; regular external and internal audits can help ensure budgets are allocated and spent appropriately; and citizen scorecards can help decision makers identify where potential problems lie. Stand-alone anti-corruption interventions cannot eliminate all risks, however. Instead, what is needed is a multi- pronged approach that includes a variety of supporting interventions mainstreamed across sectors. The study concludes with some considerations for UNDP staff and others working on health-related projects. The following 10 key lessons are identified and discussed: Health policy goals should include anti-corruption considerations. There is no ‘one size fits all’ approach to combating corruption in the health sector. More than one anti-corruption intervention should be employed to deal with one risk. Prioritization is essential: based on evidence, governments and others involved in health projects and programming should prioritize areas of the health system that are most susceptible to corruption and implement appropriate interventions. It is important to work with other sectors. Prevention is the best strategy: therefore, it is best not to wait for corruption to happen before beginning to deal with it. Numerous empirical diagnostic tools should be employed. Partners with experience in implementing anti-corruption strategies and tactics should be identified for technical support. Broad participation in health policy and planning helps. Good behaviour should be rewarded, and bad behaviour punished.
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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,326 | 0,211 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,018 | 0,014 |
| Études des sciences et des technologies | 0,011 | 0,043 |
| Communication savante | 0,020 | 0,017 |
| Science ouverte | 0,007 | 0,016 |
| Intégrité de la recherche | 0,005 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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.
score_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 découleClassification
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Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».