Impact of COVID-19 on mental health and health-related quality of life of humanitarian and healthcare workers in low-income countries. The case of Eastern Africa.
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic has had a direct impact on the health care system, adversely affecting services delivery and continuity, particularly in low-income countries. The overwhelming workload, the shortage of personal protective equipment, and the lack of specific Personal protective equipment (PPE), and drugs are some noted challenges. As a result of this critical situation, healthcare and humanitarian workers who are directly involved in the diagnosis, management, and prevention of COVID-19 are at high risk of contracting COVID-19 disease and developing psychological disorders, distress, and other mental health symptoms. OBJECTIVE: To assess the magnitude of mental health and health-related quality of life (HRQoL) outcomes and associated factors among humanitarian and healthcare workers (HCW) working on prevention and management of COVID-19 in East African Countries (EAC). DESIGN, SETTINGS, AND PARTICIPANTS: This cross-sectional, online-based survey study collected socio-demographic, mental health, and HRQoL data from 739 frontline and second-line workers in healthcare facilities and humanitarian NGOs working on COVID-19 prevention and management in seven Eastern African countries (Burundi, Kenya, Tanzania, South Sudan, Somalia, Ethiopia, and Rwanda). MAIN OUTCOMES AND MEASURES: The degree of symptoms of depression, anxiety, insomnia, and distress, alcohol, and tobacco consumption, HRQoL (SF-6Dv2 and CORE-6D), and fear of COVID-19. ANALYSIS: Multivariable logistic regression analysis, one-way ANOVA, and T-test to identify factors associated with mental health and HRQoL outcomes. RESULTS: A total of 739 contacted individuals in December 2020 completed the survey. The study participants included 62.7% of males and 37.3% of females. Among them, 12.4% were humanitarians and 87.6% were healthcare workers. About 83% were from Burundi and 17% from other Eastern African countries. The HRQoL mean scores measured by the SF-6Dv2 and the CORE-6D were respectively 0.86 and 0.80. Multivariable logistic regression analysis showed that country of origin, chronic disease, being tested positively to COVID-19, being exposed to death due to COVID-19, increased alcohol uptake, having experienced nightmare, insomnia, distress, stress, and fear of COVID-19 were independent predictors of HRQoL of front- and second-line workers. Multivariable Logistic Regression Analysis also found that having a chronic disease, being exposed to patients and death due to COVID-19 cases, depression, insomnia, stress, and fear of COVID-19 were independent predictors of the CORE-6D score. CONCLUSION: This study showed that healthcare and humanitarian workers are affected by mental health disorders such as depression, anxiety, stress, and insomnia, which negatively impacted their Health-related quality of life (HRQoL). The study findings suggested that psychological support to ensure humanitarian and healthcare worker's safety and wellbeing is required during and after this pandemic.
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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 | 0,001 |
| 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 ».