Primary care for the urban poor in India during the pandemic: Uninterrupted management of non-communicable diseases and home-based care of patients with COVID-19 infection
Notice bibliographique
Résumé
Problem The two waves of COVID-19 severely affected the healthcare system in India. The government responded to the first wave with a strict nationwide lockdown which disrupted primary care, including the management of non-communicable diseases (NCDs). The second wave overwhelmed healthcare facilities leading to inadequate access to hospital services. Collectively, these issues required urgent responses, including the adaptation of primary care. Approach The Low-Cost Effective Care Unit (LCECU) of Christian Medical College, Vellore (CMC) has a network of community volunteers, community health workers, an outreach nurse, social workers and doctors who operate clinics in six poorer areas of Vellore. The network adapted quickly, responding to the lockdown during the first wave and ensuring ongoing primary care for patients with non-communicable diseases. During the second wave, the team developed a system in collaboration with other CMC departments to provide home-based care for patients with COVID-19. Local setting The LCECU is a 48-bed unit of the Department of Family Medicine, part of the 3,000-bed CMC. It originated in 1982, aiming to care for the poor populations of Vellore town. It has been actively working among urban communities since 2002, with a focus on delivering Community Oriented Primary Care (COPC), for six poor urban communities since 2016. Relevant changes During the first wave of COVID the LCECU team ensured patients with NCDs had uninterrupted primary care and medications by visiting them in their homes. The team also addressed food insecurity by organizing a daily lunch service for 600 people for over 2 months. In the second wave, the team responded to community needs by organizing and delivering home-based care to monitor patients affected by COVID-19. Lessons learned The COVID-19 pandemic raises many questions about the preparedness of health systems for disasters that disproportionately affect marginalized populations globally. COVID-19 is only one of the many potential disasters, including non-communicable diseases, mental health problems, pollution, climate change, and lifestyle illness. There is an urgent need to study models of care that support vulnerable communities in an accessible, cost-effective, and patient-oriented way, particularly in low- and middle-income countries. This paper outlines lessons on how the LCECU team addressed disaster management: 1. The COVID-19 pandemic has highlighted the importance of primary care-based rapid response interventions in disaster management. 2. The LCECU model demonstrated the effectiveness of a primary care intervention based on pre-existing networks and familiarity between primary care teams and the community. 3. Establishing community-based health care via interdisciplinary teams, including community health workers, community volunteers, outreach nurses, and doctors, is key. 4. Addressing other social determinants of health, such as food insecurity, is an important component of care delivery.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 | 0,000 |
| 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 ».