4.5 Review

Seasonality and schizophrenia: a comprehensive overview of the seasonal pattern of hospital admissions and potential drivers

期刊

INTERNATIONAL JOURNAL OF BIOMETEOROLOGY
卷 64, 期 8, 页码 1423-1432

出版社

SPRINGER
DOI: 10.1007/s00484-020-01910-3

关键词

Seasonal pattern; Climatic factor; Socio-demographic attribute; Neuro-chemical imbalance

资金

  1. Queensland University of Technology Higher Degree Research Program
  2. Australian Government Research Training Program scholarship

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Schizophrenia is a severe neuropsychiatric disorder with heterogeneous aetiology mostly affecting younger people and causing immense disability. Seasonal patterns may be observed in schizophrenia hospital admissions with possible association with changing climatic parameters and socio-demographic characteristics. This study critically reviewed studies that have assessed seasonal variations of hospital admissions for schizophrenia and/or explored an association with climate parameters and/or other potential factors. Following PRISMA guidelines, a systematic literature search was conducted using electronic databases (e.g. MEDLINE, Science Direct, PsycINFO, Pub Med) from inception to February 29, 2020. Thirty five papers were identified, of which only six (17.1%) examined evidence for a seasonal pattern or monthly excess of hospital admissions and the remaining twenty nine (82.9%) assessed climatic and socio-demographic attributes relating to the seasonal pattern or increased hospitalisation for schizophrenia. While most studies reported a summer peak in hospital admission rates, other studies reported a winter peak. Most of the evidence indicated that higher temperatures (> 28 degrees C) were positively correlated with schizophrenia admission rates. The individual effects of other climatic parameters (e.g. relative humidity, rainfall, atmospheric pressure, sunlight) were less frequently assessed. Males, people of 21-60 years old, and those married were more vulnerable to climatic variability specifically to higher temperatures. Further studies using large sample sizes, analysis of a wide range of interacting environmental variables and sophisticated statistical approaches are needed to better understand the underlying mechanisms involved. This will also provide more reliable statistical evidence that will help in the prevention and better management of cases.

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