[Defining substance related disorders in administrative health databanks].
Notice bibliographique
Résumé
Introduction Epidemiogical surveys in the general population can provide relevant information on substance use and substance-related disorders (SRD). However, because of time and resource constraints, this data is limited in its scope. Health administrative databanks consist of routinely collected data covering a large sample size, often representative of the general population. They allow for further longitudinal analyses of comorbidities patterns and health services utilization over decades in individuals with SRD. Developing algorithms to identify these individuals is crucial before being able to tap into these databanks. Objective To present and to reflect on the methodological process leading to the creation of SRD case definitions in administrative health databanks. Methods The Quebec Integrated Chronic Disease Surveillance System (QICDSS) contains five linked administrative health databanks that are updated annually and covers over 98% of the general population. Codes from the 9th and 10th revisions of the International Classification of Diseases (ICD-9 and ICD-10) were used to define individuals who have a SRD, according to diagnoses made by a physician. First, all ICD codes that could potentially define a SRD were identified through a literature review. Second, relevant codes were selected. Third, case definition algorithms were created by grouping codes that describe a similar concept. These three steps were performed by comparing our codes with previous propositions from other teams, and through group discussions with a committee of experts (one psychiatrist, two general practitioners, one emergency doctor, and two researchers). Results Relevant ICD codes were found in specific chapters on SRD, but also in different sections concerning physical diseases that are induced by substance use or concerning poisoning and intoxication. In total, 89 ICD-9 codes and 197 ICD-10 codes were identified. From this list, codes that were almost never used in the QICDSS, codes that were almost never reported by other research teams, codes that were not specific to substance use, and codes related to tobacco use were all excluded. Codes were first categorized if they were related to alcohol or to another substance. No distinction could be made according to a specific substance, mainly because of imprecision surrounding ICD-9 coding. From this retained list, six case definitions were created: 1) alcohol use disorders (i.e. abuse or dependence); 2) drug use disorders; 3) alcohol induced disorders (i.e. withdrawal, induced psychotic disorders and other mental disorders, physical diseases 100% attributable to alcohol); 4) drug induced disorders; 5) alcohol intoxication; 6) drug intoxication. Discussion and conclusion Although unanimous consensus by the expert committee had to be obtained during code selection and grouping to create these case definitions for SRD, further validation needs to be conducted to determine if these algorithms identify appropriately individuals with SRD. Once tested in other databanks using the ICD system, these case definitions can be used to perform analyses concerning prevalence and incidence, comorbidities patterns and health services utilization to obtain a more complete picture of SRD.
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 | 0,015 | 0,059 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,026 | 0,045 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,008 |
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 ».