Short-term effects of extreme meteorological factors on daily outpatient visits for anxiety in Suzhou, Anhui Province, China: A time series study

DOI: https://doi.org/10.21203/rs.3.rs-1681491/v1

Abstract

Anxiety disorders are a major public health concern in China. Previous studies have provided evidence for associations between ambient temperature and anxiety outpatient visits, but no studies have examined short-term effects of other meteorological factors such as sunshine duration, wind speed and precipitation on increased anxiety outpatient visits. We aimed to assess the association between climatic factors and outpatient visits for anxiety in Suzhou, a city with a temperate climate in Anhui Province, China. Daily anxiety outpatient visits, meteorological factors, and air pollutants from 2017 to 2019 were collected. A quasi-Poisson generalized linear regression model combined with distributed lag non-linear model (DLNM) was used to quantify the effects of extreme meteorological factors (sunshine duration, wind speed and precipitation) on anxiety outpatient visits. All effects were presented as relative risk (RR), with the 90th and 10th percentiles of meteorological factors compared to the median. Subgroup analyses by age and gender were performed to identify susceptible subgroups. A total of 11 323 anxiety outpatient visits were reported. Extremely low sunshine duration, low and high speed speed increased the risk of anxiety outpatient visits. The strongest cumulative effects occurred at lag 0–14 days, and the corresponding RRs of extremely low sunshine duration, low and high wind speed were 1.417 (95% CI: 1.056–1.901), 1.529 (95% CI: 1.028–2.275) and 1.396 (95% CI: 1.007–1.935), respectively. Subgroup analyses showed that males and people aged ≥ 45 years appeared to be more susceptible to the cumulative effects of extremely low sunshine duration. In addition, the adverse effects of extreme wind speed were more pronounced in the cold season. This study provides evidence that extreme climatic factors have a lagged effect on anxious outpatient visits. In the context of climate change, these findings may help develop weather-based early warning systems to minimize the effects of extreme meteorological factors on anxiety.

1. Introduction

Anxiety disorders constitute the most common group of mental disorders, and the core features include excessive fear and anxiety, or avoidance of persistent and impairing perceived threats (Penninx et al. 2021; Li et al. 2022). Globally, approximately 301 million people suffered from anxiety disorders in 2019 (GBD 2019 Diseases and Injuries Collaborators 2020). Throughout the world, anxiety disorders severely affect patients and societies, accounting for 3.3% of the global disease burden and costing about 74 billion euros in 30 European countries (Gustavsson et al. 2011). In China, the lifetime weighted prevalence of anxiety disorders was estimated to be 7.6% (Huang et al. 2019). Moreover, anxiety disorders are often comorbid with other psychiatric and somatic disorders, resulting in more severe symptoms, greater clinical burden, and greater difficulty in treatment (Meier et al. 2015; Smaardijk et al. 2020). Given the high prevalence and significant burden of anxiety disorders, it is important to identify anxiety and its associated risk factors.

The onset of anxiety disorder may be influenced by genetic and environmental factors (Arango et al. 2018; Meier and Deckert 2019; Penninx et al. 2021). The heritability of anxiety disorders may vary, but is estimated to be between 35% and 50% (Meier and Deckert 2019). This means that environmental factors may play an important role in anxiety disorders. At present, most studies have reported associations between ambient temperature and anxiety disorders. For example, extreme heat events and ambient high temperature were significantly associated with anxiety disorders (Wang et al. 2014; Lee et al. 2018; Almendra et al. 2019; Zhang et al. 2020; Liu et al. 2021; Yoo et al. 2021; Nori-Sarma et al. 2022). However, no studies have assessed the effects of other meteorological factors such as sunshine duration, wind speed and precipitation on anxiety outpatient visits. Furthermore, the influence of meteorological factors on mental disorders has been studied to some extent. A study showed a relationship between lack of exposure to sunlight and an elevated risk of hospital admissions for schizophrenia (Gu et al. 2019). Another study provided evidence for the delayed effect of temperature, air pressure, and wind speed on the number of psychiatric emergency room patients (Brandl et al. 2018).

To the best of our knowledge, other than extreme temperatures, no studies have been conducted in China to assess the potential relationship between extreme meteorological factors and anxiety outpatient visits. In addition, few studies have examined the lagged effects of outpatient visits for anxiety. The effects of meteorological factors may be felt for some time thereafter, with some people hospitalized within days of exposure. Therefore, the lagged and nonlinear associations between climatic factors and anxiety outpatient visits need to be assessed to better understand the role of extreme climatic factors in exacerbating the occurrence of anxiety disorders. The current study aimed to examine the associations between extreme meteorological factors (sunshine duration, wind speed and precipitation) and outpatient visits for anxiety in Suzhou, China, from 2017 to 2019. In addition, this study also divided the data according to gender, age and season to identify the vulnerable groups.

2. Materials And Methods

2.1 Study areas

This study was conducted in Suzhou, a prefecture-level city in the northeast of Anhui Province in China, which located between 116° 09′-118° 10′ E longitude and 33° 18′-34° 38′ N latitude. Suzhou had a population of 5.32 million and a land area of 9939 km2 in 2021. The city features a semi-humid monsoon climate with an annual average temperature of 15.8°C and an annual mean rainfall of 898 mm. The geographical location of Suzhou was shown in Fig. 1.

2.2 Anxiety outpatient visits data

Data on daily outpatient visits for anxiety were obtained from the Suzhou Second People’s Hospital also known as Suzhou Mental Health Center. This hospital is a specialized hospital with a high medical level, and patients with anxiety disorders are particularly willing to visit this hospital. Daily anxiety outpatient visits data from January 1, 2017 to December 31, 2019 were extracted from the hospital’s medical record information system. The variables included gender, age, date of treatment and residential address. Patients whose residence address was not in Suzhou were excluded. The definition of anxiety was based on the International Classification of Diseases, 10th revision (ICD-10), coded as F40-F41. The number of daily anxiety outpatient visits, meteorological factors, and daily air pollution concentrations were correlated by date for subsequent time series analysis.

2.3 Meteorological and pollutant data

Daily meteorological data during the study period were downloaded from the China Meteorological Data Sharing Service System (www.data.cma.cn), including sunshine duration (h), wind speed (m/s), precipitation (mm), mean temperature (°C) and mean relative humidity (%). To control for potential confounding effects of pollutant variables, we also obtained hourly levels of ambient particulate matter with an aerodynamic diameter < 2.5 µm (PM2.5) and nitrogen dioxide (NO2) for the same period from the Suzhou Municipal Bureau of Ecology and Environment, which has four fixed-site monitoring stations. The 24-hour average levels of the two air pollutants were averaged and used as individual daily exposure levels (Ji et al. 2022).

2.4 Statistical analysis

Because of the delayed and usually nonlinear association between meteorological factors and mental disorders found in previous studies (Zhang et al. 2020), a distributed lag non-linear model (DLNM) with quasi-Poisson distribution was adopted to quantify short-term effects of extreme meteorological factors on outpatient visits for anxiety. The daily number of anxiety outpatient visits was used as the dependent variable, and the three meteorological factors of sunshine duration, wind speed and precipitation were used as independent variables. According to the minimal Akaike Information Criterion (AIC) and previously published studies (Zhao et al. 2016), 14 days was selected as the maximum lag days for meteorological factors. The final model is shown as follows:

$$\text{Log}[\left(\text{E}({Y}_{t}\right)]=\alpha +\beta {X}_{t,l}+ns(Humidity,3)+ns(Temperature,3)+ns(Time,7)+\eta DO{W}_{t}$$

Where \(t\) refers to the observation time (days); \({Y}_{t}\) is the expected number of daily outpatient visits for anxiety at day \(t\); \(\alpha\) refers to the intercept of the model; \({X}_{t,l}\) is the cross-basis matrix produced by DLNM; \(l\) represents the lag days; \(\beta\) is the matrix coefficient. Natural cubic spline (ns) with three degrees of freedom (df) were used to adjust for the delayed effects of sunshine durtaion (lag 0–14), wind speed (lag 0–14) and precipitation (lag 0–14); 3 df were used to adjust for relative humidity and mean temperature (Qiu et al. 2019). The long-term and seasonal trends were controlled by using a natural cubic spline function with 7 df per year (Pan et al. 2022). \(DO{W}_{t}\) was used to control the effect of day of the week by using a categorical variable.

After the model was built, the 90th or 10th percentiles were selected as the cut-off points for extreme meteorological factors (sunshine duration, wind speed and precipitation). The effects on anxiety were presented as relative risk (RR), with 90th or 10th percentiles of meteorological factors compare with their median values. The median value of precipitation was 0 mm, so we only examined the effect of extremely high precipitation with 90th percentile of precipitation relative to no precipitation (Zhang et al. 2019). Meanwhile, the single-day lag effects and cumulative lag effects were both assessed.

Additionally, subgroup analyses was performed to identify the susceptible groups based on gender (male and female) and age (< 45 years old and ≥ 45 years old) (Ji et al. 2021). The effects of each extreme meteorological factor were also estimated for the warm season (April to September) and cold season (October to March) (Zhu et al. 2017). The statistically significant differences between each pair of subgroups were tested by the 95% confidence interval (95% CI) (Yoo et al. 2021).

$$95\text{\%} \text{C}\text{I}=(\hat {{Q}}_{1}-\hat {{Q}}_{2})\pm 1.96\sqrt{(\hat {{\sigma }}_{1}^{2}+\hat {{\sigma }}_{2}^{2})}$$

where \(\hat {{Q}}_{1}\), \(\hat {{Q}}_{2}\) are the coefficients in the model for gender, age or season subgroups, and \(\hat {{\sigma }}_{1}^{2}\), \(\hat {{\sigma }}_{2}^{2}\) are the corresponding standard error.

2.5 Sensitivity analysis

Sensitivity analyses were performed as follows: (1) changing the df (6–8) for the seasonal and long-term trends, df (3–5) for mean temperature and df (3–5) for relative humidity; (2) adding air pollutants (PM2.5, NO2) into the model to check the stability of the results; (3) using alternative cut-off points (95th or 5th percentile) of extreme meteorological factors. All analyses were performed by “splines” and “dlnm” packages in R software version 4.1.3.

3. Results

3.1 Descriptive statistics

Table 1 summarizes the daily anxiety outpatient counts, meteorological variables and the concentrations of air pollutants. We collected data on a total of 11 323 anxiety outpatient visits in Sunzhou from January 1, 2017 to December 31, 2019 (1 095 days). The male-to-female ratio was 1:1.80 (4 046: 7 277), and the majority of the cases were aged ≥ 45 years (65.87%). The percentage of anxiety outpatient visits in cold season (50.40%) was comparable to that in warm season (49.60%). The average values of sunshine duration, wind speed, daily precipitation, mean temperature and relative humidity were 5.85 h, 2.32 m/s, 2.40 mm, 15.75°C, and 73.60%, respectively. The 90th percentiles of sunshine, wind speed and precipitation for the study period were 11.16 h, 3.66 m/s, and 5.40 mm, respectively. Meanwhile, the 10th percentiles of these variables were 0 h, 1.20 m/s, and 0 mm, respectively.

 
Table 1

Descriptive characteristics of anxiety outpatient visits, meteorological factors and air pollutants in Suzhou, China, from 2017 to 2019.

Variables

Total

Mean ± SD

Min

P25

Median

P75

Max

All

11 323

10.35 ± 6.10

1.00

6.00

9.00

13.00

37.00

Gender

             

Male

4 046

3.70 ± 2.95

0.00

2.00

3.00

5.00

16.00

Female

7 277

6.65 ± 4.46

0.00

3.00

6.00

9.00

27.00

Age

             

< 45 years old

3 864

3.53 ± 2.53

0.00

2.00

3.00

5.00

18.00

≥ 45 years old

7 459

6.81 ± 4.96

0.00

3.00

6.00

9.00

26.00

Season

             

Warm (April to September)

5 616

10.23 ± 5.90

1.00

6.00

9.00

13.00

32.00

Cold (October to March)

5 707

10.47 ± 6.30

1.00

6.00

9.00

14.00

37.00

Meteorological variables

             

Sunshine duration (h)

-

5.85 ± 4.26

0.00

0.80

7.10

9.35

12.80

Wind speed (m/s)

-

2.32 ± 1.02

0.40

1.60

2.10

2.90

8.10

Precipitation (mm)

-

2.40 ± 10.68

0.00

0.00

0.00

0.00

232.60

Mean temperature (°C)

-

15.75 ± 9.86

-6.30

6.80

16.30

24.70

34.40

Relative humidity (%)

-

73.60 ± 13.98

27.00

64.00

75.00

84.00

99.00

Air pollutants

             

PM2.5 (µg/m3)

-

58.72 ± 36.35

5.00

33.00

49.00

76.00

250.00

NO2 (µg/m3)

-

34.53 ± 17.95

5.00

22.00

31.00

45.00

121.00


The spearman’s correlations between air pollutants and meteorological factors are shown in Fig. 2. We observed strong negative correlations between meteorological factors and two air pollutants (PM2.5 and NO2). Fig. A.1 showes the time-series distribution of daily sunshine duration,wind speed and precipitation in Suzhou from 2017 to 2019. A distinct seasonal pattern was evident in these meteorological factors.

3.2 Lagged effects of extreme meteorological factors on anxiety outpatient visits

Table 2 presentes the distributed lagged effects of extreme meteorological factors on anxiety outpatient visits from lag0 to lag14 days. Figure 3 showes the distributed lagged effects of extreme meteorological factors for different subgroups at different lag days. The effect of extremely low sunshine duration showed a “U” shape, increasing the risk of anxiety outpatient visits from lag11 to lag14 days, and the effect reached a maximum at lag14 days (RR = 1.078, 95% CI: 1.028–1.130). In contrast, no significant effects were found for extremely high sunshine duration, and the subgroup analyses was shown in Fig. A.2. 

Table 2

The results (relative risk with 95% CI) of anxiety outpatient visits associated with extreme meteorological factors in Suzhou from 2017 to 2019.

Lag days

Low sunshine duration

(10th vs 50th)

High sunshine duration

(90th vs 50th)

Low wind speed

(10th vs 50th)

High wind speed

(90th vs 50th)

High precipitation

(90th vs 50th)

Single-day lag

         

0

1.040 (0.989–1.094)

0.999 (0.951–1.049)

1.047 (0.984–1.113)

0.992 (0.942–1.044)

0.999 (0.988–1.009)

1

1.030 (0.990–1.072)

0.996 (0.958–1.035)

1.043 (0.993–1.096)

0.997 (0.957–1.039)

0.999 (0.991–1.007)

2

1.020 (0.989–1.053)

0.993 (0.963–1.024)

1.040 (0.999–1.083)

1.003 (0.971–1.037)

1.000 (0.994–1.006)

3

1.012 (0.986–1.038)

0.991 (0.966–1.017)

1.037 (1.001–1.073)*

1.009 (0.980–1.037)

1.000 (0.996–1.005)

4

1.005 (0.981–1.030)

0.989 (0.965–1.014)

1.034 (0.999–1.069)

1.014 (0.986–1.042)

1.001 (0.997–1.005)

5

1.000 (0.975–1.026)

0.988 (0.963–1.015)

1.031 (0.994–1.068)

1.019 (0.990–1.049)

1.002 (0.997–1.006)

6

0.998 (0.971–1.026)

0.988 (0.961–1.016)

1.028 (0.990–1.068)

1.023 (0.992–1.055)

1.002 (0.997–1.007)

7

0.999 (0.971–1.027)

0.989 (0.961–1.018)

1.026 (0.986–1.067)

1.027 (0.995–1.060)

1.002 (0.997–1.008)

8

1.003 (0.976–1.031)

0.992 (0.964–1.020)

1.024 (0.986–1.064)

1.030 (0.999–1.062)

1.003 (0.998–1.008)

9

1.010 (0.985–1.036)

0.995 (0.969–1.021)

1.023 (0.987–1.060)

1.033 (1.004–1.063)*

1.003 (0.998–1.008)

10

1.020 (0.997–1.045)

0.999 (0.975–1.024)

1.022 (0.988–1.056)

1.035 (1.008–1.064)*

1.003 (0.999–1.007)

11

1.033 (1.008–1.058)*

1.004 (0.979–1.030)

1.021 (0.987–1.056)

1.037 (1.009–1.066)*

1.003 (0.999–1.008)

12

1.047 (1.017–1.078)*

1.010 (0.980–1.040)

1.020 (0.981–1.060)

1.039 (1.005–1.073)*

1.003 (0.998–1.009)

13

1.062 (1.023–1.102)*

1.016 (0.978–1.055)

1.019 (0.972–1.068)

1.040 (0.999–1.084)

1.003 (0.996–1.010)

14

1.078 (1.028–1.130)*

1.022 (0.974–1.072)

1.019 (0.961–1.080)

1.041 (0.989–1.096)

1.004 (0.995–1.013)

Multi-day lag

         

0–7

1.107 (0.910–1.347)

0.936 (0.769–1.140)

1.323 (1.014–1.726)*

1.086 (0.876–1.346)

1.005 (0.968–1.043)

0–10

1.145 (0.904–1.450)

0.923 (0.726–1.173)

1.415 (1.019–1.966)*

1.196 (0.918–1.558)

1.014 (0.971–1.058)

0–14

1.417 (1.056–1.901)*

0.971 (0.719–1.310)

1.529 (1.028–2.275)*

1.396 (1.007–1.935)*

1.027 (0.975–1.082)

*P < 0.05.


Extremely low wind speed and high wind speed showed significant adverse effects on outpatient visits for anxiety. For extremely low wind speed, we found significant adverse effects at lag3 days (RR = 1.037, 95% CI: 1.001–1.073). The largest and statistically significant effects of extremely low wind speed on anxiety outpatient visits were observed at lag6 days in < 45 years old group (RR = 1.067, 95% CI: 1.015–1.122), and lag6 days in the cold season (RR = 1.051, 95% CI: 1.005-1.100). For extremely high wind speed, it corresponded to higher anxiety outpatient visits from lag9 to lag12 days, and the greatest effect was at lag12 days (RR = 1.039, 95% CI: 1.005–1.073). Meanwhile, there were also significant effects in female, adults aged ≥ 45 years, and cold season. In addition, extremely high precipitation was not significantly associated with anxiety outpatient visits, and the subgroup analyses was shown in Fig. A.2.

3.3 Cumulative effects of extreme meteorological factors on anxiety outpatient visit

Table 2 also showes the cumulative effects of extreme sunshine duration, wind speed, and precipitation on outpatient visits for anxiety. Figure 4 showes the cumulative effects in different gender, age, and season groups at lag 0–14 days. Extremely low sunshine duration corresponded to the highest RR value at lag 0–14 days (RR = 1.417, 95% CI: 1.056–1.901). Males and people aged ≥ 45 years appeared to be more susceptible to the cumulative effect of extremely low sunshine duration than females and people aged < 45 years.

Moreover, extremely low wind speed and high wind speed corresponded to the highest RR value at lag 0–14 days, and the corresponding RRs of extremely low and high wind speed were 1.529 (95% CI: 1.028–2.275) and 1.396 (95% CI: 1.007–1.935), respectively. Meanwhile, the cumulative adverse effects of extremely low and high wind speed on anxiety outpatient visits were more pronounced during the cold season. As for extremely high precipitation, we failed to find any significant association with anxiety outpatient visits. Fig. A.3 presents the overall RR of extreme sunshine duration, wind speed, and precipitation for total anxiety outpatient visits over 14 lag days.

3.4 Sensitivity analyses

The results remained robust after adjusting the dfs of the seasonal and long-term trends (df = 6–8), relative humidity (df = 3–5) and mean temperature (df = 3–5) (Table A.1), and after adding air pollutants (PM2.5, NO2) into the model (Table A.2). In addition, similar results were observed when using alternative cutoff points (95th or 5th percentile) for extreme meteorological factors (Table A.3).

4. Discussion

Global climate change leads to changes in the frequency and/or magnitude of extreme weather and climate events, resulting in significant human morbidity and mortality, and adverse effects on mental health (Hrabok et al. 2020; Ebi et al. 2021). This study is the first to comprehensively examine the relationship between short-term exposure to extreme meteorological factors (including sunshine duration, wind speed, and precipitation) and anxiety outpatient visits in Suzhou, a city with a temperate climate in China. The results showed that extremely low sunshine duration, low wind speed, and high wind speed increase the risk of outpatient visits for anxiety. The associations were robust after adjustment by adding air pollutants (PM2.5, NO2) into the model. Furthermore, the effects were modified by gender, age and season. In the context of climate change, the findings may contribute to the development of weather-based early warning systems to minimize the impact of extreme meteorological factors on anxiety outpatient visits.

While the adverse effects of extreme climatic factors on anxiety disorders have been studied previously, most studies have focused on unfavorable temperatures (Trang et al. 2016; Zhang et al. 2020; Nori-Sarma et al. 2022; Li et al. 2022); studies on the effects of extreme sunshine duration, extreme wind speed and precipitation are limited. A study in Ningbo, China showed a significant and non-linear association between sunshine duration and hospital admissions for schizophrenia, and lack of sunlight increased the risk of hospital admissions for schizophrenia (Gu et al. 2019). However, the association between sunshine duration and outpatient visits for anxiety is largely unclear. Accoding to our study, exposure to extremely low sunshine duration increased the risk of anxiety outpatient visits. This means that people with anxiety disorders should spend appropriate amounts of time outdoors when the sun is full, rather than avoiding the sun.

From a biological perspective, vitamin D metabolism or circadian rhythms regulation could be involved in the relationship link sun exposure and anxiety disorder. A study showed that anxiety groups have an average lower vitamin D levels than the healthy group (Liu et al. 2022). Several studies have suggested that an imbalance in serotonin (5-hydroxytryptamine; 5-HT) neurotransmission may contribute to the development and persistence of anxiety disorders. (Eison 1990; Griebel 1995). Oxidative stress may also play a role in the neurobiology of anxiety disorders (Gautam et al. 2012; Boldrini et al. 2018). Vitamin D supplementation have seen in some studies to improve the severity of anxiety disorders (Eid et al. 2019; Borges-Vieira and Cardoso 2022). This may be due to the increased conversion of serotonin promoted by vitamin D, or the anti-inflammatory effect of vitamin D by reducing oxidative stress (Patrick and Ames 2014; Eid et al. 2019). In addition, natural sunlight may affect the suprachiasmatic nucleus of the hypothalamus, which regulates the body's circadian rhythm. One of its main regulatory functions is to inhibit the conversion of serotonin to melatonin by the pineal gland in sunlight (Kent et al. 2009).

Males and middle-aged and older adults were more sensitive to changes in natural sunlight than other groups, according to subgroup analyses. Previous research has shown that the skin's ability to produce vitamin D is significantly reduced in older adults (Wacker and Holick 2013; Heiskanen et al. 2020), who may be more prone to vitamin D deficiency and symptoms of anxiety disorders. Suzhou, known as the Cloud Capital, is home to East China's largest cloud computing data center. It is one of China's five largest communication node cities and a CG animation cluster rendering base. These industries have historically been male-dominated, which may lead men to doing more indoor computer work and putting them under more mental stress than women.

Our study found that both extremely low and high wind speed increased the risk of outpatient visits for anxiety. However, empirical research on the relationship between wind speed and mental health is scarce. Wind speed is associated with mood, violence, suicide and agitation in previous research (Schory et al. 2003; Denissen et al. 2008; McWilliams et al. 2014; Lickiewicz et al. 2020). Consistent with research showing that weather fluctuations have a particularly strong effect in spring, we found that extreme wind speed had a more adverse effect on outpatient visits for anxiety during winter and spring. Warm temperate semi-humid monsoon climate makes Suzhou hot and humid in summer and cold and dry in winter. This may be due to the calming effect of the moist, fresh, mild sea breeze (Yackerson et al. 2012), so experiencing cold and dry winds represents more of a hassle. Although previous research has shown no association between anxiety and wind speed (Bulbena et al. 2005), the association between anxiety and wind speed needs to be backed up by more research.

Our study has several advantages. This may be the first study to use a time-series approach to comprehensively explore the short-term effects of meteorological factors (including sunshine duration, wind speed and precipitation) on anxiety outpatient visits in a temperate climate city in China. We also looked for susceptible groups based on gender, age and season. Males as well as middle-aged and older adults appeare to be more susceptible to the cumulative effects of extremely low sunshine duration. The adverse effects of extreme wind speed were more pronounced in the cold season. Our study adds to the epidemiological evidence for the effect of extreme meteorological factors on anxiety outpatient visits. At the same time, it provides a reference for the government and medical authorities to formulate targeted intervention measures to protect vulnerable groups.

The study also has some limitations. First, we only studied a typical hospital in Suzhou, and the findings may be limited in generalizability. Second, anxious subjects who did not seek treatment may not have been captured, which may underestimate the effects of air pollution. Third, the use of ecological research may cause ecological bias. Where individual exposures were limited, we used monitoring data from weather stations rather than individual-level exposures. Fourth, we did not further investigate the relationship between extreme weather and various anxiety disorder subtypes due to limited data. Finally, individual confounding factors such as chronic diseases and smoking were not included in this study, and the impact of these factors needs to be addressed in the future.

5. Conclusions

This study provides evidence that extremely low sunshine duration, low and high wind speed increase the risk of anxiety outpatient visits in temperate climates regions. Males and people aged ≥ 45 years appear to be more susceptible to the cumulative effects of extremely low sunshine duration. In addition, the adverse effects of extreme wind speed are more pronounced in the cold season. In the context of climate change, these factors need to be considered in future strategies for the management and prevention of anxiety disorders.

Declarations

Acknowledgments

We are thankful for Suzhou Mental Health Center and National Meteorological Information Center of China for providing the data for the study.

Authors’ contributions

All authors contributed to the study conception and design. Data analysis and writing of the first draft of the manuscript were performed by ZXW and JYH. YZD and LYD reviewed and edited the manuscript. LLP reviewed the manuscript and put forward the comments. All authors commented on previous versions of the manuscript and approved the final manuscript.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data Availability

The authors do not have permission to share data.

Ethics approval Ethical approval was not sought as this paper was based on the analysis of secondary study data provided by Suzhou Mental Health Center.

Consent to participate Not applicable (This study does not contain any individual person’s data in any form).

Consent to publication The authors declare that they agree with the publication of this paper in this journal.

Competing interests The authors declare no competing interests.

References

  1. Almendra R, Loureiro A, Silva G et al (2019) Short-term impacts of air temperature on hospitalizations for mental disorders in Lisbon. Sci Total Environ 647:127–133. https://doi.org/10.1016/j.scitotenv.2018.07.337
  2. Arango C, Díaz-Caneja CM, McGorry PD et al (2018) Preventive strategies for mental health. Lancet Psychiatry 5:591–604. https://doi.org/10.1016/S2215-0366(18)30057-9
  3. Boldrini P, Fusco A, Nicoletti F et al (2018) Potential Use of Modulators of Oxidative Stress as Add-on Therapy in Patients with Anxiety Disorders. Curr Drug Targets 19:636–650. https://doi.org/10.2174/1389450118666170425153356
  4. Borges-Vieira JG, Cardoso CKS (2022) Efficacy of B-vitamins and vitamin D therapy in improving depressive and anxiety disorders: a systematic review of randomized controlled trials. Nutr Neurosci 1–21. https://doi.org/10.1080/1028415X.2022.2031494
  5. Brandl EJ, Lett TA, Bakanidze G et al (2018) Weather conditions influence the number of psychiatric emergency room patients. Int J Biometeorol 62:843–850. https://doi.org/10.1007/s00484-017-1485-z
  6. Bulbena A, Pailhez G, Aceña R et al (2005) Panic anxiety, under the weather? Int J Biometeorol 49:238–243. https://doi.org/10.1007/s00484-004-0236-0
  7. Denissen JJA, Butalid L, Penke L, van Aken MAG (2008) The effects of weather on daily mood: a multilevel approach. Emotion 8:662–667. https://doi.org/10.1037/a0013497
  8. Ebi KL, Vanos J, Baldwin JW et al (2021) Extreme Weather and Climate Change: Population Health and Health System Implications. Annu Rev Public Health 42:293–315. https://doi.org/10.1146/annurev-publhealth-012420-105026
  9. Eid A, Khoja S, AlGhamdi S et al (2019) Vitamin D supplementation ameliorates severity of generalized anxiety disorder (GAD). Metab Brain Dis 34:1781–1786. https://doi.org/10.1007/s11011-019-00486-1
  10. Eison MS (1990) Serotonin: a common neurobiologic substrate in anxiety and depression. J Clin Psychopharmacol 10:26S–30S
  11. Gautam M, Agrawal M, Gautam M et al (2012) Role of antioxidants in generalised anxiety disorder and depression. Indian J Psychiatry 54:244–247. https://doi.org/10.4103/0019-5545.102424
  12. GBD 2019 Diseases and Injuries Collaborators (2020) Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396:1204–1222. https://doi.org/10.1016/S0140-6736(20)30925-9
  13. Griebel G (1995) 5-Hydroxytryptamine-interacting drugs in animal models of anxiety disorders: more than 30 years of research. Pharmacol Ther 65:319–395. https://doi.org/10.1016/0163-7258(95)98597-j
  14. Gu S, Huang R, Yang J et al (2019) Exposure-lag-response association between sunlight and schizophrenia in Ningbo, China. Environ Pollut 247:285–292. https://doi.org/10.1016/j.envpol.2018.12.023
  15. Gustavsson A, Svensson M, Jacobi F et al (2011) Cost of disorders of the brain in Europe 2010. Eur Neuropsychopharmacol 21:718–779. https://doi.org/10.1016/j.euroneuro.2011.08.008
  16. Heiskanen V, Pfiffner M, Partonen T (2020) Sunlight and health: shifting the focus from vitamin D3 to photobiomodulation by red and near-infrared light. Ageing Res Rev 61:101089. https://doi.org/10.1016/j.arr.2020.101089
  17. Hrabok M, Delorme A, Agyapong VIO (2020) Threats to Mental Health and Well-Being Associated with Climate Change. J Anxiety Disord 76:102295. https://doi.org/10.1016/j.janxdis.2020.102295
  18. Huang Y, Wang Y, Wang H et al (2019) Prevalence of mental disorders in China: a cross-sectional epidemiological study. Lancet Psychiatry 6:211–224. https://doi.org/10.1016/S2215-0366(18)30511-X
  19. Ji Y, Liu B, Song J et al (2022) Association between traffic-related air pollution and anxiety hospitalizations in a coastal Chinese city: are there potentially susceptible groups? Environ Res 209:112832. https://doi.org/10.1016/j.envres.2022.112832
  20. Ji Y, Liu B, Song J et al (2021) Particulate matter pollution associated with schizophrenia hospital re-admissions: a time-series study in a coastal Chinese city. Environ Sci Pollut Res Int 28:58355–58363. https://doi.org/10.1007/s11356-021-14816-3
  21. Kent ST, McClure LA, Crosson WL et al (2009) Effect of sunlight exposure on cognitive function among depressed and non-depressed participants: a REGARDS cross-sectional study. Environ Health 8:34. https://doi.org/10.1186/1476-069X-8-34
  22. Lee S, Lee H, Myung W et al (2018) Mental disease-related emergency admissions attributable to hot temperatures. Sci Total Environ 616–617:688–694. https://doi.org/10.1016/j.scitotenv.2017.10.260
  23. Li H, Li M, Zhang S et al (2022) Interactive effects of cold spell and air pollution on outpatient visits for anxiety in three subtropical Chinese cities. Sci Total Environ 817:152789. https://doi.org/10.1016/j.scitotenv.2021.152789
  24. Lickiewicz J, Piotrowicz K, Hughes PP, Makara-StudziƄska M (2020) Weather and Aggressive Behavior among Patients in Psychiatric Hospitals-An Exploratory Study. Int J Environ Res Public Health 17:E9121. https://doi.org/10.3390/ijerph17239121
  25. Liu J, Varghese BM, Hansen A et al (2021) Is there an association between hot weather and poor mental health outcomes? A systematic review and meta-analysis. Environ Int 153:106533. https://doi.org/10.1016/j.envint.2021.106533
  26. Liu X, Zhao W, Hu F et al (2022) Comorbid anxiety and depression, depression, and anxiety in comparison in multi-ethnic community of west China: prevalence, metabolic profile, and related factors. J Affect Disord 298:381–387. https://doi.org/10.1016/j.jad.2021.10.083
  27. McWilliams S, Kinsella A, O’Callaghan E (2014) Daily weather variables and affective disorder admissions to psychiatric hospitals. Int J Biometeorol 58:2045–2057. https://doi.org/10.1007/s00484-014-0805-9
  28. Meier SM, Deckert J (2019) Genetics of Anxiety Disorders. Curr Psychiatry Rep 21:16. https://doi.org/10.1007/s11920-019-1002-7
  29. Meier SM, Petersen L, Mattheisen M et al (2015) Secondary depression in severe anxiety disorders: a population-based cohort study in Denmark. Lancet Psychiatry 2:515–523. https://doi.org/10.1016/S2215-0366(15)00092-9
  30. Nori-Sarma A, Sun S, Sun Y et al (2022) Association Between Ambient Heat and Risk of Emergency Department Visits for Mental Health Among US Adults, 2010 to 2019. JAMA Psychiatry 79:341–349. https://doi.org/10.1001/jamapsychiatry.2021.4369
  31. Pan R, Yao Z, Yi W et al (2022) 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 Int 29:5184–5193. https://doi.org/10.1007/s11356-021-15797-z
  32. Patrick RP, Ames BN (2014) Vitamin D hormone regulates serotonin synthesis. Part 1: relevance for autism. FASEB J 28:2398–2413. https://doi.org/10.1096/fj.13-246546
  33. Penninx BW, Pine DS, Holmes EA, Reif A (2021) Anxiety disorders. Lancet 397:914–927. https://doi.org/10.1016/S0140-6736(21)00359-7
  34. Qiu H, Zhu X, Wang L et al (2019) Attributable risk of hospital admissions for overall and specific mental disorders due to particulate matter pollution: A time-series study in Chengdu, China. Environ Res 170:230–237. https://doi.org/10.1016/j.envres.2018.12.019
  35. Schory TJ, Piecznski N, Nair S, el-Mallakh RS (2003) Barometric pressure, emergency psychiatric visits, and violent acts. Can J Psychiatry 48:624–627. https://doi.org/10.1177/070674370304800909
  36. Smaardijk VR, Maas AHEM, Lodder P et al (2020) Sex and gender-stratified risks of psychological factors for adverse clinical outcomes in patients with ischemic heart disease: A systematic review and meta-analysis. Int J Cardiol 302:21–29. https://doi.org/10.1016/j.ijcard.2019.12.014
  37. Trang PM, Rocklöv J, Giang KB et al (2016) Heatwaves and Hospital Admissions for Mental Disorders in Northern Vietnam. PLoS ONE 11:e0155609. https://doi.org/10.1371/journal.pone.0155609
  38. Wacker M, Holick MF (2013) Sunlight and Vitamin D: A global perspective for health. Dermatoendocrinol 5:51–108. https://doi.org/10.4161/derm.24494
  39. Wang X, Lavigne E, Ouellette-kuntz H, Chen BE (2014) Acute impacts of extreme temperature exposure on emergency room admissions related to mental and behavior disorders in Toronto, Canada. J Affect Disord 155:154–161. https://doi.org/10.1016/j.jad.2013.10.042
  40. Yackerson NS, Bromberg L, Adler B, Aizenberg A (2012) Possible effects of changes in the meteorological state over semi-arid areas on the general well-being of weather-sensitive patients. Environ Health 11:26. https://doi.org/10.1186/1476-069X-11-26
  41. Yoo E-H, Eum Y, Roberts JE et al (2021) Association between extreme temperatures and emergency room visits related to mental disorders: A multi-region time-series study in New York, USA. Sci Total Environ 792:148246. https://doi.org/10.1016/j.scitotenv.2021.148246
  42. Zhang Q, Zhou M, Yang Y et al (2019) Short-term effects of extreme meteorological factors on childhood hand, foot, and mouth disease reinfection in Hefei, China: A distributed lag non-linear analysis. Sci Total Environ 653:839–848. https://doi.org/10.1016/j.scitotenv.2018.10.349
  43. Zhang S, Yang Y, Xie X et al (2020) The effect of temperature on cause-specific mental disorders in three subtropical cities: A case-crossover study in China. Environ Int 143:105938. https://doi.org/10.1016/j.envint.2020.105938
  44. Zhao D, Zhang X, Xie M et al (2016) Is greater temperature change within a day associated with increased emergency admissions for schizophrenia? Sci Total Environ 566–567:1545–1551. https://doi.org/10.1016/j.scitotenv.2016.06.045
  45. Zhu L, Ge X, Chen Y et al (2017) Short-term effects of ambient air pollution and childhood lower respiratory diseases. Sci Rep 7:4414. https://doi.org/10.1038/s41598-017-04310-7