Assessing the hidden burden and costs of COVID-19 pandemic in South Asia: Implications for health and well-being of women, children and adolescents
Notice bibliographique
Résumé
The COVID-19 pandemic has disproportionately affected vulnerable populations. With its intensity expected to be cyclical over the foreseeable future, and much of the impact estimates still modeled, it is imperative that we accurately assess the impact to date, to help with the process of targeted rebuilding of services. We collected data from administrative health information systems in six South Asian countries (Afghanistan, Bangladesh, Nepal, India, Pakistan and Sri Lanka), to determine essential health services coverage disruptions between January-December 2020, and January-June 2021, compared to the same calendar months in 2019, and estimated the impact of this disruption on maternal and child mortality using the Lives Saved Tool. We also modelled impact of prolonged school closures on continued enrollment, as well as potential sequelae for the cohort of girls who have likely dropped out. Coverage of key maternal and child health interventions, including antenatal care and immunizations, decreased by up to 60%, with the largest disruptions observed between April and June 2020. This was followed by a period of recovery from July 2020 to March 2021, but a reversal of most of these gains in April/May 2021, likely due to the delta variant-fueled surge in South Asia at the same time. We estimated that disruption of essential health services between January 2020 and June 2021 potentially resulted in an additional 19,000 maternal and 317,000 child deaths, an increase of 19% and 13% respectively, compared to 2019. Extended school closures likely resulted in 9 million adolescents dropping out permanently, with 40% likely being from poorest households, resulting in decreased lifetime earnings. A projected increase in early marriages for girls who dropped out could result in an additional 500,000 adolescent pregnancies, 153,000 low birthweight births, and 27,000 additional children becoming stunted by age two years. To date, the increase in maternal and child mortality due to health services disruption has likely exceeded the overall number of COVID-19 deaths in South Asia. The indirect effects of the pandemic were disproportionately borne by the most vulnerable populations, and effects are likely to be long-lasting, permanent and in some cases inter-generational, unless policies aimed at alleviating these impacts are instituted at scale and targeted to reach the poorest of the poor. There are also implications for future pandemic preparedness.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 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 ».