Protective effects of low temperature on the risk of hospitalization for mental and behavioral disorders: a time series study

ABSTRACT

Background

The high incidence of mental illness worldwide is a substantial economic burden on society. Temperature and the occurrence of mental and behavioral disorders (MDs) seem to have a relationship, but this has not been widely researched. Therefore, this study aimed to investigate the effect of temperature on the hospitalization rate of patients with MDs in a temperate monsoon climate region of northern China.

Methods

Patients with MDs hospitalized in the Mental Health Center of Hebei Medical University and Psychology Department in The third Hospital of Shijiazhuang, China, from January 2014 to December 2019 were included in the study. The delayed exposure – response relationship between temperature and the number of hospitalized MDs patients was analyzed using a quasi-Poisson generalized additive model combined with a distributed lag nonlinear model.

Results

With the reference of 15.3°C (temperature with the minimum hospitalizations), the single lag effect of low temperature (−2°C, 10th percentile) increased the hospitalization of patients with MDs, while the cumulative effect decreased the risk of hospitalization for some MD subtypes; it was an acute protective effect. The single and cumulative lag effect of high temperature statistically increased the hospitalization rates of patients with MDs. The optimum temperature for each subtype was different, and the males and the younger age group were more susceptible to high temperatures.

Conclusions

Low temperature has an acute protective effect on the hospitalization of patients with MDs. In comparison, high temperature increases hospitalization rates. Men and younger people are more affected by temperature, which provides early warning and health intervention measures for patients with corresponding temperature changes.

KEYWORDS:

• Mental and behavioral disorders
• high temperature
• low temperature
• hospitalization rates
• distributed lag nonlinear model

Declaration of financial/other relationships

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. Peer reviewers on this manuscript have no relevant financial or other relationships to disclose

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/00325481.2023.2201148

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This paper was funded by the National Science Foundation of Hebei Province（No. H2022206544）; Hebei Medical University “14th Five-Year” Clinical Medicine Innovation Research Team Support Program (No.2022LCTD-A1); the government funded clinical medicine excellent talents training project of Hebei Province (No. LS202009) and Science and Technology Program of Hebei Province (SG2021189)