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Enregistrement W2806540911 · doi:10.3310/hta22290

What works to increase attendance for diabetic retinopathy screening? An evidence synthesis and economic analysis

2018· review· en· W2806540911 sur OpenAlexafffund
John G Lawrenson, Ella Graham-Rowe, Fabiana Lorencatto, Stephen Rice, Catey Bunce, Jill Francis, Jennifer Burr, Patricia Aluko, Luke Vale, Tünde Pető, Justin Presseau, Noah Ivers, Jeremy Grimshaw

Notice bibliographique

RevueHealth Technology Assessment · 2018
Typereview
Langueen
DomaineMedicine
ThématiqueRetinal Diseases and Treatments
Établissements canadiensWomen's College HospitalUniversity of TorontoOttawa HospitalUniversity of Ottawa
Organismes subventionnairesHealth Technology Assessment ProgrammeCanadian Institutes of Health ResearchDepartment of Health and Social CareNational Institute for Health and Care Research
Mots-clésMedicinePsychological interventionAttendancePsycINFOMEDLINECochrane LibraryMeta-analysisRandomized controlled trialSystematic reviewConfidence intervalFamily medicinePhysical therapyNursingInternal medicine

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Diabetic retinopathy screening (DRS) is effective but uptake is suboptimal. OBJECTIVES: To determine the effectiveness of quality improvement (QI) interventions for DRS attendance; describe the interventions in terms of QI components and behaviour change techniques (BCTs); identify theoretical determinants of attendance; investigate coherence between BCTs identified in interventions and determinants of attendance; and determine the cost-effectiveness of QI components and BCTs for improving DRS. DATA SOURCES AND REVIEW METHODS: Phase 1 - systematic review of randomised controlled trials (RCTs) evaluating interventions to increase DRS attendance (The Cochrane Library, MEDLINE, EMBASE and trials registers to February 2017) and coding intervention content to classify QI components and BCTs. Phase 2 - review of studies reporting factors influencing attendance, coded to theoretical domains (MEDLINE, EMBASE, PsycINFO and sources of grey literature to March 2016). Phase 3 - mapping BCTs (phase 1) to theoretical domains (phase 2) and an economic evaluation to determine the cost-effectiveness of BCTs or QI components. RESULTS: Phase 1 - 7277 studies were screened, of which 66 RCTs were included in the review. Interventions were multifaceted and targeted patients, health-care professionals (HCPs) or health-care systems. Overall, interventions increased DRS attendance by 12% [risk difference (RD) 0.12, 95% confidence interval (CI) 0.10 to 0.14] compared with usual care, with substantial heterogeneity in effect size. Both DRS-targeted and general QI interventions were effective, particularly when baseline attendance levels were low. All commonly used QI components and BCTs were associated with significant improvements, particularly in those with poor attendance. Higher effect estimates were observed in subgroup analyses for the BCTs of 'goal setting (outcome, i.e. consequences)' (RD 0.26, 95% CI 0.16 to 0.36) and 'feedback on outcomes (consequences) of behaviour' (RD 0.22, 95% CI 0.15 to 0.29) in interventions targeting patients and of '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 HCPs. Phase 2 - 3457 studies were screened, of which 65 non-randomised studies were included in the review. The following theoretical domains were likely to influence attendance: 'environmental context and resources', 'social influences', 'knowledge', 'memory, attention and decision processes', 'beliefs about consequences' and 'emotions'. Phase 3 - mapping identified that interventions included BCTs targeting important barriers to/enablers of DRS attendance. However, BCTs targeting emotional factors around DRS were under-represented. QI components were unlikely to be cost-effective whereas BCTs with a high probability (≥ 0.975) of being cost-effective at a societal willingness-to-pay threshold of £20,000 per QALY included 'goal-setting (outcome)', 'feedback on outcomes of behaviour', 'social support' and 'information about health consequences'. Cost-effectiveness increased when DRS attendance was lower and with longer screening intervals. LIMITATIONS: Quality improvement/BCT coding was dependent on descriptions of intervention content in primary sources; methods for the identification of coherence of BCTs require improvement. CONCLUSIONS: Randomised controlled trial evidence indicates that QI interventions incorporating specific BCT components are associated with meaningful improvements in DRS attendance compared with usual care. Interventions generally used appropriate BCTs that target important barriers to screening attendance, with a high probability of being cost-effective. Research is needed to optimise BCTs or BCT combinations that seek to improve DRS attendance at an acceptable cost. BCTs targeting emotional factors represent a missed opportunity to improve attendance and should be tested in future studies. STUDY REGISTRATION: This study is registered as PROSPERO CRD42016044157 and PROSPERO CRD42016032990. FUNDING: The National Institute for Health Research Health Technology Assessment programme.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,030
score de la tête « metaresearch » (Gemma)0,101
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
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 enseignants0,030
Score d'incertitude au seuil0,159

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0300,101
Méta-épidémiologie (sens strict)0,0030,001
Méta-épidémiologie (sens large)0,0150,023
Bibliométrie0,0120,007
Études des sciences et des technologies0,0010,001
Communication savante0,0050,003
Science ouverte0,0020,002
Intégrité de la recherche0,0040,004
Charge utile insuffisante (le modèle a refusé de juger)0,0100,001

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 Opus0,065
Tête enseignante GPT0,454
Écart entre enseignants0,389 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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

Citations60
Publié2018
Routes d'admission2
Résumé présentoui

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