High Mortality Among Human Immunodeficiency Virus (HIV)-Infected Individuals Before Accessing or Linking to HIV Care: A Missing Outcome in the Cascade of Care?
Bibliographic record
Abstract
BACKGROUND: The "cascade of care" displays the proportion of individuals who are infected with human immunodeficiency virus (HIV), diagnosed, linked, retained, on antiretroviral treatment, and HIV suppressed. We examined the implications of including death in the use of this cascade for program and public health performance metrics. METHODS: Individuals newly diagnosed with HIV and living in Calgary between 2006 and 2013 were included. Through linkage with Public Health and death registries, the deaths (ie, all-cause mortality) and their distribution within the cascade were determined. Mortality rates are reported per 100 person-years. RESULTS: Estimated new HIV infections were 680 (543 confirmed and 137 unknown cases). Forty-three individuals, after diagnosis, were never referred for HIV care. Despite referral(s), 88 individuals (18%) never attended the clinic for HIV care. Of individuals retained in care, 87% received antiretroviral therapy and 76% achieved viral suppression. Thirty-six deaths were reported (mortality rate, 1.50/100 person-years). One diagnosis was made posthumously. Deaths (20 of 35; 57%) occurred for individuals linked but not retained in care (6.93/100 person-years), and 70% were HIV-related. Mortality rate for patients in care was 0.79/100 person-years. Retained patients with detectable viremia had a death rate of 2.49/100, which contrasted with 0.28/100 person-years in those with suppressed viremia. Eight of these 15 deaths (53%) were HIV-related. CONCLUSIONS: Over half of deaths occurred in those referred but not effectively linked or retained in HIV care, and these cases may be easily overlooked in standard HIV mortality studies. Inclusion of deaths into the cascade may further enhance its value as a public health metric.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".