AN INVESTIGATION OF SOCIAL AND CLINICAL FACTORS INFLUENCING TRENDS IN CD4 COUNT AMONG HIV-INFECTED PATIENTS IN SASKATOON, CANADA
Notice bibliographique
Résumé
Introduction The province of Saskatchewan has the highest incidence of HIV in Canada, and high AIDS-related morbidity and mortality. The HIV infected population in Saskatchewan is unique in Canada because the majority of cases are among individuals of First Nations and Métis ethnicity, the proportion of women who are infected is significantly greater than in other areas of the country, and the most commonly reported exposure is injection drug user (IDU). The highest proportion of cases occur in Saskatoon. Objectives The objectives of this study were (1) to identify factors associated with trends in CD4 count and (2) to identify characteristics of individuals exhibiting faster and slower rates of CD4 cell decline. Methods This is a retrospective longitudinal study from a medical chart review at the Positive Living Program and the Westside Community Clinic in Saskatoon. Inclusion criteria was HIV diagnosis between 1 January 2003 and 30 November 2011, and 18 years of age at time of diagnosis. Results Mean follow-up time for the 457 eligible patients was 46.3 (SD±26.8) months. 254 (53.6%) were male, average age at diagnosis was 35.6 (SD±10.14) years, and 279 (61.1%) were First Nations or Métis. Average baseline log viral load and CD4 count were 4.4 (SD±0.96) and 377.7 cells/mm3(SD±232.9), respectively. 340 (74.4%) were Hepatitis C virus (HCV) coinfected, 333 (72.9%) had a history of IDU and 143 (31.1%) were infected with a sexually transmitted infection (STI). 279 (61.1%) patients were recipients of antiretroviral therapy (ARV) during follow-up. 197 (43.1%) were diagnosed with AIDS, either clinical or immunological. 33 (7.2%) were deceased from any cause. Due to high colinearity between First Nations or Métis ethnicity, HCV-coinfection and IDU, three separate multivariate mixed effects models were built. In the first model, First Nations or Métis ethnicity (p=0.028), receipt of ARV (p<0.0001), time (in months) (p=0.0045), receipt of social assistance (0.0108) and increasing age at diagnosis (p=0.0011) were significantly associated with lower CD4 counts. Receipt of ARV over time was significantly associated with a rise in CD4 count (p=0.0089). In the second model, HCV coinfection (p=0.0048), receipt of ARV (p<0.0001), time (in months) (p=0.0003), and increasing age at diagnosis (p=0.0386) were significantly associated with lower CD4 counts. Receipt of ARV over time (p=0.0004) was again associated with an increase in CD4 count. Finally, in the third model, history of IDU (p=0.0470), receipt of ARV (p<0.0001), time (in months) (p=0.0003), and increasing age at diagnosis (p=0.0181) were significantly associated with lower CD4 counts. Receipt of ARV over time (p=0.0010) was associated with an increase in CD4 count. A history of IDU, HCV coinfection and receipt of ARV were all characteristics of individuals more likely to be represented among the 25% steepest slopes of CD4 decline, where CD4 decline was defined by both linear regression and mixed-effects models. Conclusions First Nations or Métis ethnicity, HCV coinfection, history of IDU, receipt of social assistance and ARV were identified as factors associated with a more rapid CD4 decline. Individuals exhibiting such factors might benefit from more frequent follow-up by clinicians and earlier initiation of ARV.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,004 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».