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Record W7135616656

Longitudinal trends in causes of death among adults with HIV in Europe and North America on antiretroviral therapy from 1996 to 2020: a collaboration of cohort studies

2024· article· en· W7135616656 on OpenAlexaff
Adam Trickey, KA McGinnis, M John Gill, Sophie Abgrall, Juan Berenguer Berenguer, Christoph Wyen, Mojgan Hessamfar, Peter Reiss, Katharina Kusejko, Michael J Silverberg, Arkaitz Imaz, Ramón Teira, A. Arminio Monforte, Robert Zangerle, Jodie L. Guest, Vasileios Papastamopoulos, Heidi M Crane, Timothy R Sterling, Sophie Grabar, Suzanne M Ingle, Jonathan A C Sterne

Bibliographic record

VenueBristol Research (University of Bristol) · 2024
Typearticle
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsInstitute of Infection and ImmunityAlberta Hip and Knee ClinicUniversity of Calgary
Fundersnot available
KeywordsMortality rateAntiretroviral therapyCohortCohort studyCause of deathEpidemiologyHuman immunodeficiency virus (HIV)Poisson regressionPopulation
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND Mortality rates among persons with HIV (PWH) have fallen since 1996 following the widespread availability of effective antiretroviral therapy (ART). Patterns of cause-specific mortality are evolving as the population of PWH ages. We aimed to investigate longitudinal trends in cause-specific mortality rates among PWH starting ART in Europe and North America. METHODS We used data on PWH aged ≥16 years old when starting ART between 1996 and 2020 from 17 European and North American HIV cohorts contributing data to the Antiretroviral Therapy Cohort Collaboration. Causes of death were classified by both a clinician and an algorithm if ICD9/10 data were available, or independently by two clinicians. Disagreements were resolved through panel discussion. We used Poisson models to compare cause-specific mortality rates during calendar periods 1996-99, 2000-03, 2004-07, 2008-11, 2012-15 and 2016-20, adjusted for time-updated age, CD4 count, and whether ART-naïve at the start of each period. FINDINGS Among 189,301 PWH, 16,832 (8.9%) died. Causes of death were classified for 13180 (78%) deaths: the most common causes were AIDS (4203 deaths; 25%), non-AIDS non-hepatitis malignancy (2311; 14%) and cardiovascular (1403; 8%). The proportion of deaths due to AIDS declined from 49% during 1996-9 to 16% during 2016-20. Rates of all-cause mortality per 1000 person-years decreased from 16.8 (95%CI: 15.4-18.4) during 1996-99 to 7.9 (7.6-8.2) during 2016-20. Rates of all-cause mortality declined with time: the average adjusted mortality rate ratio [aMRR] per calendar period was 0.86 (95%CI 0.85-0.87). Rates of cause-specific mortality also declined: the most pronounced reduction was for AIDS-related mortality (average aMRR per period 0.82; 95%CI 0.80-0.85). There were also reductions in rates of cardiovascular-related, liver-related, non-AIDS infection-related, non-AIDS-non-hepatocellular carcinoma malignancy-related, and suicide/accident-related mortality (average aMRRs per period 0.83 (0.79-0.87), 0.90 (0.85-0.94), 0.92 (0.88-0.97), 0.95 (0.91-0.98), and 0.89 (0.83-0.95), respectively). Mortality rates among people who acquired HIV through injecting drug use were stable among men and increased among women. INTERPRETATION There have been reductions over time in rates of most major causes of death, particularly AIDS-related deaths, among PWH on ART. However, such reductions were not seen for all subgroups. Interventions targeted at high-risk groups, substance use, and comorbidities may further increase life expectancy in PWH towards that in the general population. FUNDING US National Institute on Alcohol Abuse and Alcoholism.

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)0.010
metaresearch head score (Gemma)0.011
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 score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.011
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.050
GPT teacher head0.350
Teacher spread0.300 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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