Magnitude and Determinants of First-Time and Repeat Testing Among Individuals With Newly Diagnosed HIV Infection Between 2000 and 2001 in Alberta, Canada
Bibliographic record
Abstract
The purpose of the study was to determine the magnitude and predictors of first-time and repeat testing for HIV infection among newly diagnosed cases in Alberta, Canada, and to determine the extent of co-infection with hepatitis C (HCV) and hepatitis B (HBV). Using the Provincial Laboratory for Public Health (PLPH) database, all newly diagnosed HIV cases in Alberta between 2000 and 2001 were identified and the testing history for HIV, HCV, and HBV among these cases since 1992 was reviewed. Significant differences in the characteristics of first-time and repeat testers were identified using the chi test, and where appropriate, the Fisher exact test. The independent variables examined included age, gender, risk factors, area and population of residence, testing agency, and co-infection with HCV and HBV. Logistical regression analyses were conducted to further explore independent factors associated with first-time vs. repeat testing for HIV infection. Of the 398 cases, 278 (69.8%) were newly diagnosed at their first test for HIV infection, 73.1% during 2000 and 67.3% during 2001 (P = 0.81). Among repeat testers, the mean number of previous negative tests was 3.4 (range = 2-11 tests). The median interval between the last negative and first positive test was 648 days (range = 53-2678 days). Repeat testers were 1.9 times more likely to be injecting drug users and 1.8 times more likely to reside in Northern Alberta. Among those with a laboratory test result in the PLPH database, 53.7% were positive for HCV, 47.7 and 64.5% of first-time and repeat testers, respectively; and 19.1% were positive for HBV, 22 and 13.6% of first-time and repeat testers, respectively. A high proportion of HIV cases newly diagnosed between 2000 and 2001 in Alberta had no previous testing history for HIV infection. Even among repeat testers, HIV testing was sought infrequently. There are significant regional differences within Alberta in the characteristics of the HIV epidemic and associated test-seeking behaviors. These data reinforce the need to make the most of each test-seeking event with proper counseling and other relevant support services. Given the high prevalence of co-infection with HCV, these results clearly support the need for testing and counseling strategies to take into account additional risks associated with HCV infections.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".