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Record W4391361553 · doi:10.1002/ijc.34852

Psychosocial factors, health behaviors and risk of cancer incidence: Testing interaction and effect modification in an individual participant data meta‐analysis

2024· article· en· W4391361553 on OpenAlexafffund
Maartje Basten, Kuan‐Yu Pan, Lonneke A. van Tuijl, Alexander de Graeff, Joost Dekker, Adriaan W. Hoogendoorn, Femke Lamers, Adelita V. Ranchor, Roel Vermeulen, Lützen Portengen, Adri C. Voogd, Jessica Abell, Philip Awadalla, Aartjan T.F. Beekman, Ottar Bjerkeset, Andy Boyd, Yunsong Cui, Philipp Frank, Henrike Galenkamp, Bert Garssen, Sean Hellingman, Martijn Huisman, Anke Huss, Melanie R. Keats, Almar A. L. Kok, Steinar Krokstad, Flora E. van Leeuwen, Annemarie I. Luik, Nolwenn Noisel, Yves Payette, Brenda W.J.H. Penninx, Ina Rissanen, Annelieke M. Roest, Judith G.M. Rosmalen, Rikje Ruiter, Robert A. Schoevers, David Soave, Mandy Spaan, Andrew Steptoe, Karien Stronks, Erik R. Sund, Ellen Sweeney, Emma L. Twait, Alison Teyhan, W. M. Monique Verschuren, Kimberly D. van der Willik, Mirjam I. Geerlings

Bibliographic record

VenueInternational Journal of Cancer · 2024
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineWilfrid Laurier UniversityUniversity of TorontoDalhousie UniversityPublic Health OntarioOntario Institute for Cancer Research
FundersNational Institute on AgingNorwegian Institute of Public HealthFaculty of Medicine and Health, University of SydneyEconomic and Social Research CouncilHealth CanadaMedical Research CouncilHelse Midt-NorgePartenariat Canadien Contre Le CancerNorges Teknisk-Naturvitenskapelige UniversitetZonMwKWF KankerbestrijdingNational Institute for Health and Care ResearchAmsterdam University Medical CentersWellcome Trust
KeywordsPsychosocialAnxietyMedicineClinical psychologyNeuroticismPsychologyPsychiatryPersonality

Abstract

fetched live from OpenAlex

Depression, anxiety and other psychosocial factors are hypothesized to be involved in cancer development. We examined whether psychosocial factors interact with or modify the effects of health behaviors, such as smoking and alcohol use, in relation to cancer incidence. Two-stage individual participant data meta-analyses were performed based on 22 cohorts of the PSYchosocial factors and CAncer (PSY-CA) study. We examined nine psychosocial factors (depression diagnosis, depression symptoms, anxiety diagnosis, anxiety symptoms, perceived social support, loss events, general distress, neuroticism, relationship status), seven health behaviors/behavior-related factors (smoking, alcohol use, physical activity, body mass index, sedentary behavior, sleep quality, sleep duration) and seven cancer outcomes (overall cancer, smoking-related, alcohol-related, breast, lung, prostate, colorectal). Effects of the psychosocial factor, health behavior and their product term on cancer incidence were estimated using Cox regression. We pooled cohort-specific estimates using multivariate random-effects meta-analyses. Additive and multiplicative interaction/effect modification was examined. This study involved 437,827 participants, 36,961 incident cancer diagnoses, and 4,749,481 person years of follow-up. Out of 744 combinations of psychosocial factors, health behaviors, and cancer outcomes, we found no evidence of interaction. Effect modification was found for some combinations, but there were no clear patterns for any particular factors or outcomes involved. In this first large study to systematically examine potential interaction and effect modification, we found no evidence for psychosocial factors to interact with or modify health behaviors in relation to cancer incidence. The behavioral risk profile for cancer incidence is similar in people with and without psychosocial stress.

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)0.058
metaresearch head score (Gemma)0.076
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.058
Threshold uncertainty score0.305

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0580.076
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0190.097
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0040.003
Research integrity0.0040.004
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 head0.228
GPT teacher head0.506
Teacher spread0.278 · 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 designMeta-analysis
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

Citations22
Published2024
Admission routes2
Has abstractyes

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