Non-communicable disease prevention policy process in five African countries authors
Notice bibliographique
Résumé
BACKGROUND: The increasing burden of non-communicable diseases (NCDs) in sub-Saharan Africa is causing further burden to the health care systems that are least equipped to deal with the challenge. Countries are developing policies to address major NCD risk factors including tobacco use, unhealthy diets, harmful alcohol consumption and physical inactivity. This paper describes NCD prevention policy development process in five African countries (Kenya, South Africa, Cameroon, Nigeria, Malawi), including the extent to which WHO "best buy" interventions for NCD prevention have been implemented. METHODS: The study applied a multiple case study design, with each country as a separate case study. Data were collected through document reviews and key informant interviews with national-level decision-makers in various sectors. Data were coded and analyzed thematically, guided by Walt and Gilson policy analysis framework that examines the context, content, processes and actors in policy development. RESULTS: Country-level policy process has been relatively slow and uneven. Policy process for tobacco has moved faster, especially in South Africa but was delayed in others. Alcohol policy process has been slow in Nigeria and Malawi. Existing tobacco and alcohol policies address the WHO "best buy" interventions to some extent. Food-security and nutrition policies exist in almost all the countries, but the "best buy" interventions for unhealthy diet have not received adequate attention in all countries except South Africa. Physical activity policies are not well developed in any study countries. All have recently developed NCD strategic plans consistent with WHO global NCD Action Plan but these policies have not been adequately implemented due to inadequate political commitment, inadequate resources and technical capacity as well as industry influence. CONCLUSION: NCD prevention policy process in many African countries has been influenced both by global and local factors. Countries have the will to develop NCD prevention policies but they face implementation gaps and need enhanced country-level commitment to support policy NCD prevention policy development for all risk factors and establish mechanisms to attain better policy outcomes while considering other local contextual factors that may influence policy implementation such as political support, resource allocation and availability of local data for monitoring impacts.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».