Lessons from the COVID-19 pandemic to strengthen NCD care and policy in humanitarian settings: a mixed methods study exploring humanitarian actors’ experiences
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic and response severely impacted people living with non-communicable diseases (PLWNCDs) globally. It exacerbated pre-existing health inequalities, severely disrupted access to care, and worsened clinical outcomes for PLWNCDs, who were at higher risk of morbidity and mortality from the virus. The pandemic's effects were likely magnified in humanitarian settings, where there were pre-existing gaps in continuity of care for non-communicable diseases (NCDs). We sought to explore factors affecting implementation of NCD care in crisis settings during the COVID-19 pandemic and the adaptations made to support implementation. METHODS: Guided by the Consolidated Framework for Implementation Research, we undertook an online survey of 98 humanitarian actors from multiple regions and organization types (March-July 2021), followed by in-depth interviews with 13 purposively selected survey respondents (October-December 2021). Survey data were analysed using descriptive statistics, while interview data were analysed thematically, using both deductive and inductive approaches. RESULTS: Initially, humanitarian actors faced challenges influenced by external actors' priorities, such as de-prioritisation of NCD care by governments, travel restrictions and supply chain interruptions. With each infection wave and lockdown, humanitarian actors were better able to adapt and maintain NCD services. The availability of COVID-19 vaccines was a positive turning point, especially for the risk management of people with NCDs and protection of health workers. Key findings include that, despite pre-existing challenges, humanitarian actors largely continued NCD services during the crisis. Enabling factors that supported continuity of NCD services included the ability to quickly pivot to remote means of communication with PLWNCDs, flexibility in medicine dispensing, and successful advocacy to prioritize NCD management within health systems. Key lessons learned included the importance of partnerships and cooperation with other health actors, and the mobilisation or repurposing of community health workers/volunteer networks. CONCLUSIONS: The COVID-19 experience should prompt national and global health stakeholders to strengthen inclusion of NCDs in emergency preparedness, response, and resilience planning. Key lessons were learned around remote care provision, including adapting to NCD severity, integrating community health workers, providing context-adapted patient information, combating misinformation, and strengthening cross-sectoral partnerships.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,034 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,013 | 0,015 |
| Communication savante | 0,011 | 0,011 |
| Science ouverte | 0,003 | 0,015 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 source (Gemma direct ou Codex distillé), 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 ».