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Immediate and delayed effects of environmental temperature on schizophrenia admissions in Liuzhou, China, 2013–2020: a time series analysis

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Abstract

This study aimed to investigate the associations between environmental temperature and schizophrenia admissions in Liuzhou, China. A Poisson generalized linear model combined with a distributed lag nonlinear model was used to analyze the effects of daily mean temperature on schizophrenia admissions from 2013 to 2020 in Liuzhou. Additionally, subgroup analyses were conducted to investigate possible modifications stratified by gender, marital status, and age. In this study, 10,420 schizophrenia admissions were included. The relative risks of schizophrenia admissions increased as the temperature rose, and the lag effects of high temperature on schizophrenia admissions were observed when the daily mean temperature reached 21.65°C. The largest single effect was observed at lag0, while the largest cumulative effect was observed at lag6. The single effects of high temperatures on schizophrenia admissions were statistically significant in both males and females, but the cumulative effects were statistically significant only in males, with the greatest effect at lag0–7. The single effect of high temperatures on admissions for unmarried schizophrenics was greatest at lag5, while the maximum cumulative effect for unmarried schizophrenia was observed at lag0–7. The single effects of high temperatures on schizophrenia admissions were observed in those aged 0–20, 21–40, and 41–60. The cumulative effects for schizophrenics aged 21–40 were observed from lag0–3 to lag0–7, with the maximum effect at lag0–7. In conclusion, the risk of schizophrenia admissions increased as the environmental temperature increased. The schizophrenics who were unmarried appeared to be more vulnerable to the single and cumulative effects of high temperature.

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Correspondence to Yuhua Wei.

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Zhou, Q., Huang, X., Su, L. et al. Immediate and delayed effects of environmental temperature on schizophrenia admissions in Liuzhou, China, 2013–2020: a time series analysis. Int J Biometeorol (2024). https://doi.org/10.1007/s00484-024-02629-1

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  • DOI: https://doi.org/10.1007/s00484-024-02629-1

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