COVID-19 y medidas de protección adoptadas en comunidades rurales amazónicas durante los primeros meses de la pandemia
Notice bibliographique
Résumé
BACKGROUND: Motivation for the study. To document the evolution of COVID-19 in rural Amazonian populations, which are still little known. BACKGROUND: Main findings. COVID-19 spread rapidly through rural communities, initially spreading to mestizo hamlets and later affecting indigenous communities. Rural mortality varied by region and ethnicity. Social distancing was difficult, and travel to receive government vouchers contributed to contagion. BACKGROUND: Implications. Identifying the factors that contributed to contagion and the barriers to the adoption of protective measures in rural Amazonian populations will help to face future pandemics. OBJECTIVES.: To analyze the evolution of COVID-19 in rural populations of Loreto and Ucayali in the early stage of the pandemic. MATERIALS AND METHODS.: A community-level longitudinal observational study was conducted and based on two rounds of telephone surveys with local authorities of more than 400 indigenous and non-indigenous rural communities in Loreto and Ucayali, in July and August 2020. We collected information on cases and deaths by COVID-19 in their communities, protective measures adopted and if state assistance was received in the early stage of the pandemic. Descriptive statistics allowed us to evaluate the evolution of the pandemic after the initial outbreak and compare the trends of the two regions, as well as between indigenous and non-indigenous populations. RESULTS.: In July 2020, COVID-19 had reached 91.5% of the communities, although deaths from COVID-19 were reported in 13.0% of the communities, with rural mortality being higher in Ucayali (0.111%) than in Loreto (0.047%) and in non-indigenous communities. By August, prevalence decreased from 44.0% to 32.0% of communities, but became more frequent in indigenous communities, and those in Ucayali. Traveling to the city to receive state bonuses and difficulties maintaining social distancing contributed to the spread. CONCLUSIONS.: Our findings show the evolution of COVID-19 in rural communities and point to important areas of attention in future public policies, for the adoption of protective measures and reconsidering strategies for the distribution of assistance in the face of future pandemics. BACKGROUND: Motivation for the study. To document the evolution of COVID-19 in rural Amazonian populations, which are still little known. BACKGROUND: Main findings. COVID-19 spread rapidly through rural communities, initially spreading to mestizo hamlets and later affecting indigenous communities. Rural mortality varied by region and ethnicity. Social distancing was difficult, and travel to receive government vouchers contributed to contagion. BACKGROUND: Implications. Identifying the factors that contributed to contagion and the barriers to the adoption of protective measures in rural Amazonian populations will help to face future pandemics.
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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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».