Health services use in people with chronic diseases during the pandemic: Results from the iCARE study
Notice bibliographique
Résumé
Abstract Background COVID-19 containment measures, such as stay-at-home orders and transportation shutdowns, might have created barriers to healthcare access and impacted service utilization worldwide. This cross-sectional study aims to explore how COVID-19 impacted health service utilization in individuals with chronic health conditions across the globe. Methods We included a convenience sample of the global iCARE study (www.icarestudy.com) from March, 2020 to January, 2021. Logistic regressions were used to test association between difficulties getting non-COVID-19 related care and presence of physical health conditions, individuals' sex and age as well as countries' economy, with appropriate time adjustments. Stratified analysis was conducted for three continents (Europe, Asia and America). Results The study included 28,340 individuals from 33 countries (female (71%), mean age=43.4, low and middle-income country (LMIC) (31%)). Overall, 30% of people with chronic diseases had difficulties getting non-COVID related care, compared to 24% of individuals with no conditions. Individuals with chronic conditions were around 1.6 times more likely to have trouble in healthcare access in Europe, America, and Asia. Older age was associated with fewer difficulties in healthcare access (in Asia and America). Females, compared to males, had around 30% and 50% higher odds of difficulties in access (in America and Europe, respectively). Finally, individuals from LMICs reported higher difficulties in getting care (OR = 1.64; 95% CI = 1.50-1.79). Conclusions We observed decreased service utilization in people with chronic diseases during the pandemic, with certain disparities across continents and country income. Future research should assess indirect health effects of the pandemic (i.e., morbidity and mortality of specific diseases). Public health efforts should be directed towards improving the resilience of health systems, healthcare access and delivery in emergency situations. Key messages Globally, around 1 in 3 individuals with chronic health conditions reported decreased healthcare utilization during the pandemic. COVID-19 impacts on healthcare utilization were more pronounced in low and middle-income countries, compared to high-income countries.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,009 | 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».