AN INVESTIGATION OF SOCIAL AND CLINICAL FACTORS INFLUENCING TRENDS IN CD4 COUNT AMONG HIV-INFECTED PATIENTS IN SASKATOON, CANADA
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".