#229 Health inequalities and outcomes following acute kidney injury: a systematic review & meta-analyses of observational studies
Notice bibliographique
Résumé
Abstract Background and Aims Inequalities in health describe the uneven distribution of health outcomes that result from genetic or environmental factors. The social determinants of health (SDOH) represent the socioeconomic context within which people are born, grow, work, live and age. Inequalities have been described in relation to AKI incidence and aetiology, including by sex/gender and race/ethnicity, but the extent to which inequalities and the SDOH impact on AKI outcomes is uncertain. The aim of this systematic review and meta-analysis was to determine the impact of health inequalities on AKI outcomes. Method This review has been registered on PROSPERO (CRD42023422307). We included observational studies of adults who experienced at least one episode of AKI that reported outcomes with stratification or subgroup comparison by sex/gender, race/ethnicity, socioeconomic status, income, education, employment, housing, smoking, mental health conditions, geography or insurance status. The primary outcome was all-cause mortality at any time post-AKI. Secondary outcomes were: progression to acute kidney disease; incident chronic kidney disease (CKD); progressive CKD; AKI recovery; cardiovascular events; hospitalisations; intensive care unit admission and hospital length of stay. The search was conducted in MEDLINE, Embase and Web of Science from inception to 10th January 2024. Study selection, extraction and risk of bias via the Newcastle-Ottawa scale were performed independently and studies meta-analysed where possible. Results 7,312 titles/abstracts were screened, and 36 studies included (n = 2,038,441 patients with AKI). Most studies were from high-income countries (n = 31) based on the World Bank classification. No studies contained data from low-income settings and few included data from lower-middle income countries (n = 3) (Fig. 1). Evidence predominantly related to sex/gender (n = 25), race/ethnicity (n = 14) and socioeconomic status (n = 11). Table 1 summarises the results for the primary outcome. In random-effects meta-analyses of relevant studies, no sex/gender differences in all-cause mortality (OR 1.02 [95% CI 0.83–1.25], I2 99%, n = 14) or AKI recovery (OR 0.88 [95% CI 0.69–1.11], I2 67%, n = 7) were seen. Of twelve studies reporting mortality by race/ethnicity, six found no variation by racial/ethnic group. Nine studies reported mortality by socioeconomic status, six of which showed an increase in all-cause mortality among the most deprived sub-populations compared to the most affluent with relative effect sizes varying from minimal (i.e. HR 0.999 least vs most deprived) to modest (i.e. HR 1.20 most vs least deprived). Few studies assessed the impact of mental health (n = 3), insurance (n = 1), housing (n = 2), geography (n = 1) or smoking status (n = 3) and no reports quantified the impact of income, education, employment or substance use. Heterogeneity and level of evidence were not formally assessed. Conclusion This systematic review highlights a paucity of evidence related to health inequalities and AKI, specifically from low-income settings and pertaining to the impact of mental health conditions, substance use, income, education, employment, insurance access, housing and geography. No sex/gender differences in AKI mortality or recovery were identified. Our results support the need for increased resource allocation for patients with AKI who live in socioeconomic deprivation due to increased mortality. There is a need for further evidence to inform policy and target interventions to achieve equitable kidney care.
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 | 0,019 | 0,067 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,015 | 0,029 |
| Bibliométrie | 0,009 | 0,012 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 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 ».