COVID-19 Resilience and Risk Reduction Intervention in Rural Populations of Western India: Retrospective Evaluation
Notice bibliographique
Résumé
BACKGROUND: Globally, especially in the low- and middle-income countries (LMICs), rural populations were more susceptible to the negative impact of the COVID-19 pandemic due to lower levels of community awareness, poor hygiene, and health literacy accompanying pre-existing weak public health systems. Consequently, various community-based interventions were engineered in rural regions worldwide to mitigate the COVID-19 pandemic by empowering people to mount both individual and collective public health responses against the pandemic. However, to date, there is paucity of information on the effectiveness of any large-scale community intervention in controlling and mitigating the effects of COVID-19, especially from the perspective of LMICs. OBJECTIVE: This retrospective impact evaluation study was conducted to evaluate the effect of a large-scale rural community-based intervention, the COVID-Free Village Program (CFVP), on COVID-19 resilience and control in rural populations in Maharashtra, India. METHODS: The intervention site was the rural areas of the Pune district where CFVP was implemented from August 2021 to February 2022, while the adjoining district, Satara, represented the control district where the COVID-Free Village Scheme was implemented. Data were collected during April-May 2022 from 3500 sample households in villages across intervention and comparison arms by using the 2-stage stratified random sampling through face-to-face interviews followed by developing a matched sample using propensity score matching methods. RESULTS: The participants in Pune had a significantly higher combined COVID-19 awareness index by 0.43 (95% CI 0.29-0.58) points than those in Satara. Furthermore, the adherence to COVID-appropriate behaviors, including handwashing, was 23% (95% CI 3%-45%) and masking was 17% (0%-38%) higher in Pune compared to those in Satara. The probability of perception of COVID as a serious illness in patients with heart disease was 22% (95% CI 1.036-1.439) higher in Pune compared to that in Satara. The awareness index of COVID-19 variants and preventive measures were also higher in Pune by 0.88 (95% CI 0.674-1.089) points. In the subgroup analysis, when the highest household educational level was restricted to middle school, the awareness about the COVID-control program was 0.69 (95% CI 0.36-1.021) points higher in Pune, while the awareness index of COVID-19 variants and preventive measures was higher by 0.45 (95% CI 0.236-0.671) points. We did not observe any significant changes in the overall COVID-19 vaccination coverage due to CFVP implementation. Furthermore, the number of COVID-19 deaths in both the sampled populations were very low. The probability of observing COVID-19-related stigma or discrimination in Pune was 68% (95% CI 0.133-0.191) lower than that in Satara. CONCLUSIONS: CFVP contributed to improved awareness and sustainability of COVID-appropriate behaviors in a large population although there was no evidence of higher COVID-19 vaccination coverage or reduction in mortality, signifying potential applicability in future pandemic preparedness, especially in resource-constrained settings.
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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 | 0,005 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».