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Record W3049006087 · doi:10.1093/ofid/ofaa347

Concurrent Hepatitis C and B Virus and Human Immunodeficiency Virus Infections Are Associated With Higher Mortality Risk Illustrating the Impact of Syndemics on Health Outcomes

2020· article· en· W3049006087 on OpenAlexafffundabout
Zahid A Butt, Stanley Wong, Carmine Rossi, Mawuena Binka, Jason Wong, Amanda Yu, Maryam Darvishian, Maria Alvarez, N. Chapinal, Geoffrey McKee, Mark Gilbert, Mark Tyndall, Mel Krajden, Naveed Z. Janjua

Bibliographic record

VenueOpen Forum Infectious Diseases · 2020
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsBC Cancer AgencyBC Centre for Disease ControlUniversity of WaterlooUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMedicineVirologyHuman immunodeficiency virus (HIV)Hepatitis C virusHepatitis B virusVirusImmunologyIntensive care medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Hepatitis C virus (HCV), hepatitis B virus (HBV), and human immunodeficiency virus (HIV) infections are associated with significant mortality globally and in North America. However, data on impact of concurrent multiple infections on mortality risk are limited. We evaluated the effect of HCV, HBV, and HIV infections and coinfections and associated factors on all-cause mortality in British Columbia (BC), Canada. METHODS: The BC Hepatitis Testers Cohort includes ~1.7 million individuals tested for HCV or HIV, or reported as a case of HCV, HIV, or HBV from 1990 to 2015, linked to administrative databases. We followed people with HCV, HBV, or HIV monoinfection, coinfections, and triple infections from their negative status to date of death or December 31, 2016. Extended Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for factors associated with all-cause mortality. RESULTS: Of 658 704 individuals tested for HCV, HBV, and HIV, there were 33 804 (5.13%) deaths. In multivariable Cox regression analysis, individuals with HCV/HBV/HIV (HR, 8.9; 95% CI, 8.2-9.7) infections had the highest risk of mortality followed by HCV/HIV (HR, 4.8; 95% CI, 4.4-5.1), HBV/HIV (HR, 4.1; 95% CI, 3.5-4.8), HCV/HBV (HR, 3.9; 95% CI, 3.7-4.2), HCV (HR, 2.6; 95% CI, 2.6-2.7), HBV (HR, 2.2; 95% CI, 2.0-2.3), and HIV (HR, 1.6; 95% CI, 1.5-1.7). Additional factors associated with mortality included injection drug use, problematic alcohol use, material deprivation, diabetes, chronic kidney disease, heart failure, and hypertension. CONCLUSIONS: Concurrent multiple infections are associated with high mortality risk. Substance use, comorbidities, and material disadvantage were significantly associated with mortality independent of coinfection. Preventive interventions, including harm reduction combined with coinfection treatments, can significantly reduce mortality.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.057
GPT teacher head0.389
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2020
Admission routes3
Has abstractyes

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