Update on Existing Care Models for Chronic Kidney Disease in Low- and Middle-Income Countries: A Systematic Review
Notice bibliographique
Résumé
BACKGROUND: Approximately 78% of chronic kidney disease (CKD) cases reside in low- and middle-income countries (LMICs). However, little is known about the care models for CKD in LMICs. OBJECTIVE: Our objective was to update a prior systematic review on CKD care models in LMICs and summarize information on multidisciplinary care and management of CKD complications. DESIGN: We searched MEDLINE, EMBASE, and Global Health databases in September 2020, for papers published between January 1, 2017, and September 14, 2020. We used a combination of search terms, which were different iterations of CKD, care models, and LMICs. The World Bank definition (2019) was used to identify LMICs. SETTING: Our review included studies published in LMICs across 4 continents: Africa, Asia, North America (Mexico), and Europe (Ukraine). The study settings included tertiary hospitals (n = 6), multidisciplinary clinics (n = 1), primary health centers (n = 2), referral centers (n = 2), district hospitals (n = 1), teaching hospitals (n = 1), regional hospital (n = 1), and an urban medical center (n = 1). PATIENTS: . MEASUREMENTS: We retrieved the following details about CKD care: funding, urban or rural location, types of health care staff, and type of care provided, as defined by Kidney Disease Improving Global Outcomes (KDIGO) guidelines for CKD care. METHODS: We included studies which met the following criteria: (1) population was largely adults, defined as age 18 years and older; (2) most of the study population had CKD, and not end-stage kidney disease (ESKD); (3) population resided in an LMIC as defined by the World Bank; (4) manuscript described in some detail a clinical care model for CKD; (5) manuscript was in either English or French. Animal studies, case reports, comments, and editorials were excluded. RESULTS: Eighteen studies (24 care models with 4665 patients) met inclusion criteria. Out of 24 care models, 20 involved interdisciplinary health care teams. Twenty models incorporated international guidelines for CKD management. However, conservative kidney management (management of kidney failure without dialysis or renal transplant) was in a minority of models (11 of 24). Although there were similarities between all the clinical care models, there was variation in services provided and in funding arrangement; the latter ranged from comprehensive government funding (eg, Sri Lanka, Thailand), to out-of-pocket payments (eg, Benin, Togo). LIMITATIONS: These include (1) lack of detail on CKD care in many of the studies, (2) small number of included studies, (3) using a different definition of care model from the original Stanifer et al paper, and (4) using the KDIGO Guidelines as the standard for defining a CKD care model. CONCLUSIONS: Most of the CKD models of care include the key elements of CKD care. However, access to such care depends on the funding mechanism available. In addition, few models included conservative kidney management, which should be a priority for future investment. TRIAL REGISTRATION: Not applicable.
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 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 | 0,018 | 0,072 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,009 | 0,012 |
| Bibliométrie | 0,023 | 0,017 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».