Abstract
Along with climate change, unstable weather patterns are becoming more frequent. However, the temporal trend associated with the effect of temperature variation on schizophrenia (SCZ) is not clear. Daily time-series data on SCZ and meteorological factors for 15-year between January 1, 2005 and December 31, 2019 were collected. And we used the Poisson regression model combined with the time-varying distribution lag nonlinear model (DLNM) to explore the temporal trend of the association between three temperature variation indicators (diurnal temperature range, DTR; temperature variability, TV; temperature change between neighboring days, TCN) and SCZ hospitalizations, respectively. Meanwhile, we also explore the temporal trend of the interaction between temperature and temperature variation. Stratified analyses were performed in different gender, age, and season. Across the whole population, we found a decreasing trend in the risk of SCZ hospitalizations associated with high DTR (from 1.721 to 1.029), TCN (from 1.642 to 1.066), and TV (TV0–1, from 1.034 to 0.994; TV0–2, from 1.041 to 0.994, TV0–3, from 1.044 to 0.992, TV0–4, from 1.049 to 0.992, TV0–5, from 1.055 to 0.993, TV0–6, from 1.059 to 0.991, TV0–7, from 1.059 to 0.990), but an increasing trend in low DTR (from 0.589 to 0.752). Subgroup analysis results further revealed different susceptible groups. Besides, the interactive effect suggests that temperature variation may cause greater harm under low-temperature conditions. There was a synergy between TCN and temperature on the addition and multiplication scales, which were 1.068 (1.007, 1.133) and 0.067 (0.009, 0.122), respectively. Our findings highlight public health interventions to mitigate temperature variation effects needed to focus not only on high temperature variations but also moderately low temperature variations. Future hospitalizations for SCZ associated with temperature variation may be more severely affected by temperature variability from low temperature environments. The temporal trend is associated with the effect of temperature variation on schizophrenia (SCZ).
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Data Availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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This work was supported by the National Natural Science Foundation of China (grant number: 81773518).
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All authors contributed to the final version of the manuscript and have approved the final article. Their contributions to the article were as follows: Rubing Pan: Conceptualization, Software, Writing-original draft. Zhenhai Yao, Yifu Ji: Acquisition of data, Weizhuo Yi, Yangyang He, Chao Tang, Yanhu Ji: Software. Qiannan Wei, Jian Song, Xiangguo Liu, Shasha Son: Validation, Jian Cheng: Writing - review and editing, Methodology. Hong Su: Project administration; Funding acquisition; Writing-review and editing.
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This study was approved by the Ethical Committee of Anhui Medical University (Hefei, Anhui, China).
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Supplementary information
Fig. S1
Exposure-response relationships between diurnal temperature range (DTR) and hospitalizations for schizophrenia by changing DTR lag days from 7 to 21 (PNG 1962 kb)
Fig. S2
Exposure–response relationships between diurnal temperature range (DTR) and hospitalizations for schizophrenia by changing the degree of freedom of time from 6 to 8 (PNG 1962 kb)
Fig. S3
Exposure–response relationships between diurnal temperature range (DTR) and hospitalizations for schizophrenia by controlling the effect of air pollutions (PNG 3684 kb)
Fig. S4
Exposure–response relationships between diurnal temperature range (DTR) and hospitalizations for schizophrenia by changing the moving average of humidity from 7 to 21 days. (PNG 1962 kb)
Fig. S5
Exposure–response relationships between diurnal temperature range (DTR) and hospitalizations for schizophrenia by changing the moving average of temperature from 7 to 21 days (PNG 1962 kb)
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Pan, R., Yao, Z., Yi, W. et al. Temporal trends of the association between temperature variation and hospitalizations for schizophrenia in Hefei, China from 2005 to 2019: a time-varying distribution lag nonlinear model. Environ Sci Pollut Res 29, 5184–5193 (2022). https://doi.org/10.1007/s11356-021-15797-z
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DOI: https://doi.org/10.1007/s11356-021-15797-z