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Association of Childhood Area-Level Ethnic Density and Psychosis Risk Among Ethnoracial Minoritized Individuals in the US

2023· article· en· W4385850531 on OpenAlexaff
Deidre M. Anglin, Adriana Espinosa, Jean Addington, Kristin S. Cadenhead, Tyrone D. Cannon, Barbara A. Cornblatt, Matcheri S. Keshavan, Daniel H. Mathalon, Diana O. Perkins, William S. Stone, Ming T. Tsuang, Scott W. Woods, Elaine F. Walker, Carrie E. Bearden, Benson S. Ku

Bibliographic record

VenueJAMA Psychiatry · 2023
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersNational Center for Advancing Translational SciencesNational Institute of Mental Health
KeywordsProdromePsychosisEthnic groupMediationDemographyMedicineAssociation (psychology)PsychologyClinical psychologyPsychiatry

Abstract

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Importance: The protective ethnic density effect hypothesis, which suggests that minoritized individuals who grow up in neighborhoods with a high proportion of ethnoracial minoritized groups are protected from the effects of perceived discrimination, has not been examined among individuals at clinical high risk of psychosis (CHR-P). This level of examination may help identify intervention targets for preventing psychosis among high-risk individuals. Objective: To examine the association between area-level ethnic density during childhood, perceived discrimination, and psychosis risk outcomes among ethnoracial minoritized individuals with CHR-P. Design, Setting, and Participants: Data were collected as part of the North American Prodrome Longitudinal Study-2 (NAPLS 2) between November 2008 and March 2013. Participants included ethnoracial minoritized youth with CHR-P. Area-level ethnoracial minoritized density pertained to the percent of ethnoracial minoritized individuals within the participant's county during childhood. Generalized mixed-effects models with random intercepts for participants, NAPLS 2 site, and county estimated the associations between area-level ethnic density and the risk of psychosis risk outcomes. Self-reported experience of discrimination was assessed. Mediation analyses computed the indirect association of perceived discrimination in the prospective correlation between ethnic density and psychosis risk outcomes. Analyses took place between December 2021 and June 2023. Main Outcomes and Measures: Psychosis risk outcomes included remission, symptomatic, progression, and conversion to psychosis and were assessed throughout 24-month follow-up. Results: Of 193 individuals, the mean (SD) age was 17.5 (3.4) years and 113 males (58.5%) were included. Participants self-identified as Asian (29 [15.0%]), Black (57 [29.0%]), Hispanic (any race; 87 [45.0%]), or other (First Nations, Middle Eastern, and interracial individuals; 20 [10.4%]). Greater area-level minoritized density was associated with a lower likelihood of remaining symptomatic (relative risk [RR], 0.54 [95% CI, 0.33-0.89]) and having progressively worsening symptoms (RR, 0.52 [95% CI, 0.32-0.86]) compared with being in remission. More perceived discrimination was associated with a higher risk of staying symptomatic (RR, 1.43 [95% CI, 1.09-1.88]) and progressively worsening (RR, 1.34 [95% CI, 1.02-1.78]) compared with being in remission. Perceived discrimination significantly mediated 21.7% (95% CI, 4.1%-67.0%; P = .02) of the association between area-level minoritized density and the likelihood of being in remission. Conclusions and Relevance: This study found that among ethnoracial minority youth with CHR-P, growing up in communities with a greater proportion of ethnically minoritized individuals was associated with remission of psychosis risk symptoms partly through lower levels of perceived discrimination. Understanding how the social environment impacts early psychosis risk may help develop effective interventions to prevent psychosis, especially for vulnerable minoritized youth.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.084
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.028
GPT teacher head0.312
Teacher spread0.284 · 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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Citations27
Published2023
Admission routes1
Has abstractyes

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