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Record W2795476651 · doi:10.1093/cid/cix998

Multimorbidity Among Persons Living with Human Immunodeficiency Virus in the United States

2017· article· en· W2795476651 on OpenAlexafffund
Cherise Wong, Stephen J. Gange, Richard D. Moore, Amy C. Justice, Kate Buchacz, Alison G. Abraham, Peter F. Rebeiro, John R. Koethe, Jeffrey N. Martin, Michael A. Horberg, Cynthia M. Boyd, Mari M. Kitahata, Heidi M. Crane, Kelly A. Gebo, M. John Gill, Michael J. Silverberg, Frank J. Palella, Pragna Patel, Hasina Samji, Jennifer E. Thorne, Charles S. Rabkin, Ángel M. Mayor, Keri N. Althoff, Aimee Freeman, Angela Cescon, Anita Rachlis, Ben Rogers, Benigno Rodríguez, Chris Grasso, Constance A. Benson, Daniel R. Drozd, David A. Fiellin, David W. Haas, Gregory D. Kirk, James H. Willig, Jason Globerman, John T. Brooks, Joseph J. Eron, Joan Montaner, Karyn Gabler, Kathryn Anastos, Kenneth H. Mayer, Lisa P. Jacobson, Madison Kopansky-Giles, Marina B. Klein, Megan Turner, Michael J. Mugavero, Michael S. Saag, P. Richard Harrigan, Robert Dubrow, Robert F. Hunter-Mellado, Robert S. Hogg, Ronald J. Bosch, Rosemary G. McKaig, Sally Bebawy, Sean B. Rourke, Sonia Napravnik, Stephen Boswell, Timothy R. Sterling

Bibliographic record

VenueClinical Infectious Diseases · 2017
Typearticle
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsBC Centre for Disease ControlSimon Fraser UniversityUniversity of Calgary
FundersNational Institute on Minority Health and Health DisparitiesNational Center for Research ResourcesNational Institute of Allergy and Infectious DiseasesNational Institute on AgingNational Eye InstituteNational Institute on Drug AbuseCanadian Institutes of Health ResearchOntario Ministry of Health and Long-Term CareNational Center for Advancing Translational SciencesHealth Resources and Services AdministrationNational Institute on Alcohol Abuse and AlcoholismNational Cancer InstituteEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentAgency for Healthcare Research and Quality
KeywordsMedicineHuman immunodeficiency virus (HIV)GerontologyDemographyEnvironmental healthFamily medicine

Abstract

fetched live from OpenAlex

Background: Age-associated conditions are increasingly common among persons living with human immunodeficiency virus (HIV) (PLWH). A longitudinal investigation of their accrual is needed given their implications on clinical care complexity. We examined trends in the co-occurrence of age-associated conditions among PLWH receiving clinical care, and differences in their prevalence by demographic subgroup. Methods: This cohort study was nested within the North American AIDS Cohort Collaboration on Research and Design. Participants from HIV outpatient clinics were antiretroviral therapy-exposed PLWH receiving clinical care (ie, ≥1 CD4 count) in the United States during 2000-2009. Multimorbidity was irreversible, defined as having ≥2: hypertension, diabetes mellitus, chronic kidney disease, hypercholesterolemia, end-stage liver disease, or non-AIDS-related cancer. Adjusted prevalence ratios (aPR) and 95% confidence intervals (CIs) comparing demographic subgroups were obtained by Poisson regression with robust error variance, using generalized estimating equations for repeated measures. Results: Among 22969 adults, 79% were male, 36% were black, and the median baseline age was 40 years (interquartile range, 34-46 years). Between 2000 and 2009, multimorbidity prevalence increased from 8.2% to 22.4% (Ptrend < .001). Adjusting for age, this trend was still significant (P < .001). There was no difference by sex, but blacks were less likely than whites to have multimorbidity (aPR, 0.87; 95% CI, .77-.99). Multimorbidity was the highest among heterosexuals, relative to men who have sex with men (aPR, 1.16; 95% CI, 1.01-1.34). Hypertension and hypercholesterolemia most commonly co-occurred. Conclusions: Multimorbidity prevalence has increased among PLWH. Comorbidity prevention and multisubspecialty management of increasingly complex healthcare needs will be vital to ensuring that they receive needed care.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.004
Threshold uncertainty score0.899

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.072
GPT teacher head0.421
Teacher spread0.349 · 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 teacher head, 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

Citations180
Published2017
Admission routes2
Has abstractyes

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