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Record W3083285041 · doi:10.1177/0706743720954059

Association of Urbanicity with Schizophrenia and Related Mortality in China: Association de l’urbanicité avec la schizophrénie et la mortalité qui y est reliée en Chine

2020· article· en· W3083285041 on OpenAlexvenueno aff
Yanan Luo, Lihua Pang, Chao Guo, Lei Zhang, Xiaoying Zheng

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2020
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
FundersChangjiang Scholar Program of Chinese Ministry of EducationChina Postdoctoral Science Foundation
KeywordsSchizophrenia (object-oriented programming)PopulationDemographyChinaProportional hazards modelResidenceGeographyPsychologyMedicineEnvironmental healthPsychiatry

Abstract

fetched live from OpenAlex

Objective: Although higher prevalence of schizophrenia in Chinese urban areas was observed, studies focused on the association between schizophrenia and urbanicity were less in China. Using a national representative population-based data set, this study aimed to investigate the relationship between urbanicity and schizophrenia and its related mortality among adults aged 18 years old and above in China. Methods: Data were obtained from the Second China National Sample Survey on Disability in 2006 and follow-up studies from 2007 to 2010 each year. We restricted our analysis to 1,909,205 participants aged 18 years or older and the 2,071 schizophrenia patients with information of survival and all-caused mortality of the follow-up surveys from 2007 to 2010.Schizophrenia was ascertained according to the International Statistical Classification of Diseases, 10th Revision. The degree of urbanicity and the region of residence were used to be the proxies of urbanicity. Of these, the degree of urbanicity measured by the ratio of nonagricultural population to total population and the region of residence measured by six categorical variables (first-tier cities, first-tier city suburbs, second-tier cities, second-tier city suburbs, other city areas, and rural areas). Logistics regression models and restricted polynomial splines were used to examine the linear/nonlinear relationship between urbanicity and the risk of schizophrenia. Cox proportional hazards regression models were used to test the role of urbanicity on mortality risk of schizophrenia patients. Results: 10% increase in the degree of urbanicity was associated with increased risk of schizophrenia ( OR = 1.44; 95% CI, 1.32 to 1.57). The nonlinear model further confirmed the association between the degree of urbanicity and the risk of schizophrenia. This association existed sex difference, as the level of urbanicity increased, schizophrenia risk of males grew faster than the risk of females. The hazard ratio (HR) of mortality in schizophrenia patients decreased with the elevated of urbanicity level, with a HR of 0.42 (95% CI, 0.21 to 0.84). Conclusions: This research suggested that incremental changes in the degree of urbanicity linked to higher risk of schizophrenia, and as the degree of urbanicity elevated, the risk of schizophrenia increased more for men than for women. Additionally, we found that schizophrenia patients in higher degree of urbanicity areas had lower risk of mortality. These findings contributed to the literature on schizophrenia in developing nations under a non-Western context and indicates that strategies to improve mental health conditions are needed in the progress of urbanicity.

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)0.001
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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.259
Teacher spread0.251 · 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

Citations22
Published2020
Admission routes1
Has abstractyes

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