An analysis of how health systems integrated priority-setting in the pandemic planning in a sample of Latin America and the Caribbean countries
Notice bibliographique
Résumé
BACKGROUND: Latin America and the Caribbean (LAC) are among those regions most affected by the COVID-19 pandemic worldwide. The COVID-19 pandemic has strained health systems in the region. In this context of severe healthcare resource constraints, there is a need for systematic priority-setting to support decision-making which ensures the best use of resources while considering the needs of the most vulnerable groups. The aim of this paper was to provide a critical description and analysis of how health systems considered priority-setting in the COVID-19 response and preparedness plans of a sample of 14 LAC countries; and to identify the associated research gaps. METHODS: A documentary analysis of COVID-19 preparedness and response plans was performed in a sample of 14 countries in the LAC region. We assessed the degree to which the documented priority-setting processes adhered to established quality indicators of effective priority-setting included in the Kapiriri and Martin framework. We conducted a descriptive analysis of the degree to which the reports addressed the quality parameters for each individual country, as well as a cross-country comparison to explore whether parameters varied according to independent variables. RESULTS: While all plans were led and supported by the national governments, most included only a limited number of quality indicators for effective priority-setting. There was no systematic pattern between the number of quality indicators and the country's health system and political contexts; however, the countries that had the least number of quality indicators tended to be economically disadvantaged. CONCLUSION: This study adds to the literature by providing the first descriptive analysis of the inclusion of priority-setting during a pandemic, using the case of COVID-19 response and preparedness plans in the LAC region. The analysis found that despite the strong evidence of political will and stakeholder participation, none of the plans presented a clear priority-setting process, or used a formal priority-setting framework, to define interventions, populations, geographical regions, healthcare setting or resources prioritized. There is need for case studies that analyse how priority-setting actually occurred during the COVID-19 pandemic and the degree to which the implementation reflected the plans and the parameters of effective priority-setting, as well as the impact of the prioritization processes on population health, with a focus on the most vulnerable groups.
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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 | 0,128 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».