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Enregistrement W2256528525 · doi:10.1002/14651858.cd012054.pub2

Interventions to increase attendance for diabetic retinopathy screening

2018· review· en· W2256528525 sur OpenAlexaff
John G Lawrenson, Ella Graham-Rowe, Fabiana Lorencatto, Jennifer Burr, Catey Bunce, Jill Francis, Patricia Aluko, Stephen Rice, Luke Vale, Tünde Pető, Justin Presseau, Noah Ivers, Jeremy Grimshaw

Notice bibliographique

RevueCochrane Database of Systematic Reviews · 2018
Typereview
Langueen
DomaineMedicine
ThématiqueRetinal Diseases and Treatments
Établissements canadiensUniversity of OttawaWomen's College HospitalOttawa Hospital
Organismes subventionnairesMedical Research CouncilNational Institute for Health and Care Research
Mots-clésMedicinePsychological interventionAttendancePsycINFOMEDLINERandomized controlled trialCochrane LibraryIntervention (counseling)Family medicineNursingInternal medicine

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Despite evidence supporting the effectiveness of diabetic retinopathy screening (DRS) in reducing the risk of sight loss, attendance for screening is consistently below recommended levels. OBJECTIVES: The primary objective of the review was to assess the effectiveness of quality improvement (QI) interventions that seek to increase attendance for DRS in people with type 1 and type 2 diabetes.Secondary objectives were:To use validated taxonomies of QI intervention strategies and behaviour change techniques (BCTs) to code the description of interventions in the included studies and determine whether interventions that include particular QI strategies or component BCTs are more effective in increasing screening attendance;To explore heterogeneity in effect size within and between studies to identify potential explanatory factors for variability in effect size;To explore differential effects in subgroups to provide information on how equity of screening attendance could be improved;To critically appraise and summarise current evidence on the resource use, costs and cost effectiveness. SEARCH METHODS: We searched the Cochrane Library, MEDLINE, Embase, PsycINFO, Web of Science, ProQuest Family Health, OpenGrey, the ISRCTN, ClinicalTrials.gov, and the WHO ICTRP to identify randomised controlled trials (RCTs) that were designed to improve attendance for DRS or were evaluating general quality improvement (QI) strategies for diabetes care and reported the effect of the intervention on DRS attendance. We searched the resources on 13 February 2017. We did not use any date or language restrictions in the searches. SELECTION CRITERIA: We included RCTs that compared any QI intervention to usual care or a more intensive (stepped) intervention versus a less intensive intervention. DATA COLLECTION AND ANALYSIS: We coded the QI strategy using a modification of the taxonomy developed by Cochrane Effective Practice and Organisation of Care (EPOC) and BCTs using the BCT Taxonomy version 1 (BCTTv1). We used Place of residence, Race/ethnicity/culture/language, Occupation, Gender/sex, Religion, Education, Socioeconomic status, and Social capital (PROGRESS) elements to describe the characteristics of participants in the included studies that could have an impact on equity of access to health services.Two review authors independently extracted data. One review author entered the data into Review Manager 5 and a second review author checked them. Two review authors independently assessed risks of bias in the included studies and extracted data. We rated certainty of evidence using GRADE. MAIN RESULTS: We included 66 RCTs conducted predominantly (62%) in the USA. Overall we judged the trials to be at low or unclear risk of bias. QI strategies were multifaceted and targeted patients, healthcare professionals or healthcare systems. Fifty-six studies (329,164 participants) compared intervention versus usual care (median duration of follow-up 12 months). Overall, DRS attendance increased by 12% (risk difference (RD) 0.12, 95% confidence interval (CI) 0.10 to 0.14; low-certainty evidence) compared with usual care, with substantial heterogeneity in effect size. Both DRS-targeted (RD 0.17, 95% CI 0.11 to 0.22) and general QI interventions (RD 0.12, 95% CI 0.09 to 0.15) were effective, particularly where baseline DRS attendance was low. All BCT combinations were associated with significant improvements, particularly in those with poor attendance. We found higher effect estimates in subgroup analyses for the BCTs 'goal setting (outcome)' (RD 0.26, 95% CI 0.16 to 0.36) and 'feedback on outcomes of behaviour' (RD 0.22, 95% CI 0.15 to 0.29) in interventions targeting patients, and 'restructuring the social environment' (RD 0.19, 95% CI 0.12 to 0.26) and 'credible source' (RD 0.16, 95% CI 0.08 to 0.24) in interventions targeting healthcare professionals.Ten studies (23,715 participants) compared a more intensive (stepped) intervention versus a less intensive intervention. In these studies DRS attendance increased by 5% (RD 0.05, 95% CI 0.02 to 0.09; moderate-certainty evidence).Fourteen studies reporting any QI intervention compared to usual care included economic outcomes. However, only five of these were full economic evaluations. Overall, we found that there is insufficient evidence to draw robust conclusions about the relative cost effectiveness of the interventions compared to each other or against usual care.With the exception of gender and ethnicity, the characteristics of participants were poorly described in terms of PROGRESS elements. Seventeen studies (25.8%) were conducted in disadvantaged populations. No studies were carried out in low- or middle-income countries. AUTHORS' CONCLUSIONS: The results of this review provide evidence that QI interventions targeting patients, healthcare professionals or the healthcare system are associated with meaningful improvements in DRS attendance compared to usual care. There was no statistically significant difference between interventions specifically aimed at DRS and those which were part of a general QI strategy for improving diabetes care. This is a significant finding, due to the additional benefits of general QI interventions in terms of improving glycaemic control, vascular risk management and screening for other microvascular complications. It is likely that further (but smaller) improvements in DRS attendance can also be achieved by increasing the intensity of a particular QI component or adding further components.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,007
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,118
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,007
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0090,004
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

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.

Tête enseignante Opus0,182
Tête enseignante GPT0,445
Écart entre enseignants0,263 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeRevue systématique
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations93
Publié2018
Routes d'admission1
Résumé présentoui

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