Effectiveness of contact tracing in the control of infectious diseases: a systematic review
Notice bibliographique
Résumé
BACKGROUND: Contact tracing is used for multiple infectious diseases, most recently for COVID-19, but data regarding its effectiveness in disease control are scarce. To address this knowledge gap and inform public health decision making for COVID-19, we systematically reviewed the existing literature to determine the effectiveness of contact tracing in the control of communicable illness. METHODS: We searched PubMed, Embase, and the Cochrane Library from database inception up to Nov 22, 2021, for published studies evaluating associations between provider-initiated contact tracing for transmissible infectious diseases and one of three outcomes of interest: case detection rates among contacts or at the community level, overall forward transmission, or overall disease incidence. Clinical trials and observational studies were eligible, with no language or date restrictions. Reference lists of reviews were searched for additional studies. We excluded studies without a control group, using only mathematical modelling, not reporting a primary outcome of interest, or solely examining patient-initiated contact tracing. One reviewer applied eligibility criteria to each screened abstract and full-text article, and two reviewers independently extracted summary effect estimates and additional data from eligible studies. Only data reported in published manuscripts or supplemental material was extracted. Risk of bias for each included study was assessed with the Cochrane Risk of Bias 2 tool (randomised studies) or the Newcastle-Ottawa Scale (non-randomised studies). FINDINGS: We identified 9050 unique citations, of which 47 studies met the inclusion criteria: six were focused on COVID-19, 20 on tuberculosis, eight on HIV, 12 on curable sexually transmitted infections (STIs), and one on measles. More than 2 million index patients were included across a variety of settings (both urban and rural areas and low-resource and high-resource settings). Of the 47 studies, 29 (61·7%) used observational designs, including all studies on COVID-19, and 18 (38·3%) were randomised controlled trials. 40 studies compared provider-initiated contact tracing with other interventions or evaluated expansions of provider-initiated contact tracing, and seven compared programmatic adaptations within provider-initiated contact tracing. 29 (72·5%) of the 40 studies evaluating the effect of provider-initiated contact tracing, including four (66·7%) of six COVID-19 studies, found contact tracing interventions were associated with improvements in at least one outcome of interest. 23 (48·9%) studies had low risk of bias, 22 (46·8%) studies had some risk of bias, and two (4·3%) studies (both randomised controlled trials on curable STIs) had high risk of bias. INTERPRETATION: Provider-initiated contact tracing can be an effective public health tool. However, the ability of authorities to make informed choices about its deployment might be limited by heterogenous approaches to contact tracing in studies, a scarcity of quantitative evidence on its effectiveness, and absence of specificity of tracing parameters most important for disease control. FUNDING: The Sullivan Family Foundation, Massachusetts General Hospital Executive Committee on Research, and US National Institutes of Health.
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 | 0,024 | 0,119 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,014 | 0,013 |
| Bibliométrie | 0,009 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 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 ».