Medical Disease and Alcohol Use Among Veterans With Human Immunodeficiency Infection
Notice bibliographique
Résumé
BACKGROUND: Many people with human immunodeficiency (HIV) infection drink alcohol. We asked whether level of exposure to alcohol is associated with medical disease in a linear or nonlinear manner, whether the association depends upon the proximity of alcohol use, and whether it varies by source used to measure disease (chart review vs. ICD-9 Diagnostic Codes). METHODS: The Veterans Aging 3 Site Cohort Study (VACS 3) enrolled 881 veterans, 86% of all HIV-positive patients seen, at 3 VA sites from June 23, 1999, to July 28, 2000. To maximize the sensitivity for alcohol exposure, alcohol use was measured combining data from patient self-report, chart review, and ICD-9 codes. We assigned the greatest exposure level reported from any source. Alcohol use within the past 12 months was considered current. Data on comorbid and AIDS-defining medical diseases were collected via chart review and ICD-9 diagnostic codes. The association of alcohol use (level and timing) and disease was modeled only for diseases demonstrating > or =10% prevalence. Linearity was compared with nonlinearity of association using nested multivariate models and the likelihood ratio test. All multivariate models were adjusted for age, CD4 cell count, viral load, intravenous drug use, exercise, and smoking. RESULTS: Of 881 subjects enrolled, 866 (98%) had sufficient data for multivariate analyses, and 876 (99%) had sufficient data for comparison of chart review with ICD-9 Diagnostic Codes. Of the 866, 42 (5%) were lifetime abstainers; 247 (29%) were past drinkers; and 577 (67%) were current users. Among the 824 reporting past or current alcohol use, 341 (41%) drank in moderation, 192 (23%) drank hazardously, and 291 (35%) carried a diagnosis of abuse or dependence. ICD-9 codes showed limited sensitivity, but overall agreement with chart review was good for 15 of 20 diseases (kappa > 0.4). The following diseases demonstrated a > or =10% prevalence with both measures (hepatitis C, hypertension, diabetes, obstructive lung disease, candidiasis, and bacterial pneumonia). All of these were associated with alcohol use (P < 0.05). Hepatitis C, hypertension, obstructive lung disease, candidiasis, and bacterial pneumonia demonstrated linear associations with level of alcohol use (P < 0.03). Past alcohol use increased the risk of hepatitis C and diabetes after adjustment for level of exposure (P < 0.01). With the exception of candidiasis, the associations between level and timing of alcohol use were similar when measured by ICD-9 codes or by chart review. CONCLUSIONS: Past and current use of alcohol is common among those with HIV infection. Estimates of disease risk associated with alcohol use based upon ICD-9 Diagnostic Codes appear similar to those based upon chart review. After adjustment for level of alcohol exposure, past use is associated with similar (or higher) prevalence of disease as among current drinkers. Finally, level of alcohol use is linearly associated with medical disease. We find no evidence of a "safe" level of consumption among those with HIV infection.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».