Human Immunodeficiency Virus Infection Newly Diagnosed at Autopsy in New York City, 2008–2012
Bibliographic record
Abstract
Background. Studying the most extreme example of late diagnosis, new HIV diagnoses after death, may be instructive to HIV testing efforts. Using the results of routine HIV testing of autopsies performed by the Office of Chief Medical Examiner (OCME), we identified new HIV diagnoses after death in New York City (NYC) from 2008 to 2012. Methods. Population-based registries for HIV and deaths were linked to identify decedents not known to be HIV-infected before death. Multivariable logistic regression models were constructed to determine correlates of a new HIV diagnosis after death among all persons newly diagnosed with HIV and among all HIV-infected decedents receiving an OCME autopsy. Results. Of 264 893 deaths, 24 426 (9.2%) were autopsied by the NYC OCME. Of these, 1623 (6.6%) were infected with HIV, including 142 (8.8%) with a new HIV diagnosis at autopsy. This represents 0.8% (142 of 18 542) of all new HIV diagnoses during the 5-year period. Decedents newly diagnosed with HIV at OCME autopsy were predominantly male (73.9%), aged 13-64 years (85.9%), non-white (85.2%), unmarried (81.7%), less than college educated (83.8%), and residents of an impoverished neighborhood (62.0%). Of all HIV-infected OCME decedents aged ≥65 years (n = 71), 22.0% were diagnosed at autopsy. The strongest independent correlate of new HIV diagnosis at autopsy in both multivariable models was age ≥65 years. Conclusions. Human immunodeficiency virus diagnoses first made after death are rare, but, when observed, these diagnoses are more commonly found among persons ≥65 years, suggesting that despite highly visible efforts to promote HIV testing community-wide, timely diagnosis among older adults living in impoverished, high-prevalence neighborhoods may require additional strategies.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".