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

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 and low and high wind 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 and 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 anxiety 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.


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 EUR 74 billion (USD 75.1 billion) 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. significant disease 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. The effects of meteorological factors may be felt over a period of time, and some people will visit a clinic a few days after 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 causing the episodes and outpatient visits for anxiety. 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. This study also divided the data according to gender, age, and season to identify the vulnerable groups.

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 km 2 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 is shown in Fig. 1.

Data sources
Data on daily outpatient visits for anxiety were obtained from the Suzhou Second People's Hospital, also known as the Suzhou Mental Health Center. The hospital is an earlier and more influential tertiary psychiatric hospital in northern Anhui. The hospital has sufficient medical resources and 500 beds, and patients are particularly willing to seek medical treatment. The annual outpatient visits for anxiety disorders account for 80-90% of the city's visits. Daily anxiety outpatient visit 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.
Daily meteorological data during the study period were downloaded from the China Meteorological Data Sharing Service System (http:// 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 (PM 2.5 ) and nitrogen dioxide (NO 2 ) for the same period from the Suzhou Municipal Bureau of Ecology and Environment, which has four fixed-site monitoring stations. The 24-h average levels of the two air pollutants were averaged and used as individual daily exposure levels (Ji et al. 2022).

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: where t refers to the observation time (days); Y t is the expected number of daily outpatient visits for anxiety at day t; α refers to the intercept of the model; X t, l is the crossbasis matrix produced by DLNM; l represents the lag days; β is the matrix coefficient. Natural cubic spline (ns) with three degrees of freedom (df) were used to adjust for the Log E Y t = + X t,l + ns(Humidity, 3) + ns(Temperature, 3) + ns(Time, 7) + DOW t delayed effects of sunshine duration (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). DOW 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 compared 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 were performed to identify the susceptible groups based on gender (male and female) and age (<45 years old and ≥45 years old) (Gu et al. 2019;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).
where Q 1 and Q 2 are the coefficients in the model for gender, age or season subgroups and ̂2 1 and ̂2 2 are the corresponding standard error.

Sensitivity analyses
Sensitivity analyses were performed as follows: (1) changing the df (6-8) for long-term and seasonal trends, df (3-5) for mean temperature, and df (3-5) for relative humidity; (2) adding air pollutants (PM 2.5 , NO 2 ) into the model to check the stability of the results (Zhou et al. 2021); (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. Table 1 summarizes the daily anxiety outpatient counts, meteorological variables, and the concentrations of air

Descriptive statistics
pollutants. We collected data on a total of 11,323 anxiety outpatient visits in Suzhou 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. 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 (PM 2.5 and NO 2 ). Fig. A.1 shows 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. Table 2 presents the distributed lagged effects of extreme meteorological factors on anxiety outpatient visits from lag0 to lag14 days. Fig. 3 shows 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 are shown in Fig. A 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).  Fig. 3 The distributed lagged effects of low sunshine duration, low wind speed, and high wind speed on anxiety outpatient visits and subgroups 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).

Lagged effects of extreme meteorological factors on anxiety outpatient visits
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 are shown in Fig. A.2. Table 2 also shows the cumulative effects of extreme sunshine duration, wind speed, and precipitation on outpatient visits for anxiety. Fig. 4 shows 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.

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, Fig. 4 The cumulative effects of extreme meteorological factors on anxiety outpatient visits in different gender, age, and season groups at lag 0-14 days 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 (PM 2.5 , NO 2 ) 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. According 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, the regulation of vitamin D metabolism or circadian rhythms may be involved in the relationship between sunlight exposure and anxiety disorders. Neuroinflammation is a key factor in the onset and progression of anxiety disorders (Kim and Jeon 2018;Kouba et al. 2022). Vitamin D has antioxidant, anti-inflammatory, proneurogenic, and neuromodulatory properties that contribute to its anxiolytic effects (Xin et al. 2019;Casseb et al. 2019). Clinical studies also suggest that vitamin D supplementation ameliorates the severity of anxiety disorder (Eid et al. 2019;Borges-Vieira and Cardoso 2022). As sunlight exposure decreases, people may be more likely to become vitamin D deficient and develop symptoms of anxiety disorders. In addition, natural sunlight may affect the hypothalamic suprachiasmatic nuclei (SCN), which regulate the body's circadian rhythm (Kent et al. 2009). Studies have shown that low daytime light is an important environmental risk factor for mood, sleep, and circadian rhythm-related outcomes (Burns et al. 2021). Long-term circadian disruptions are associated with many pathological conditions such as anxiety (Coles et al. 2015;Lyall et al. 2018;Serin and Acar Tek 2019).
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. In 2020, there are 60 cloud computing enterprises above designated size in Suzhou, with an output value of 7.533 billion yuan. 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. In previous studies, wind speed has been associated with mood, violence, suicide, and agitation (Schory et al. 2003;Denissen et al. 2008;McWilliams et al. 2014;Lickiewicz et al. 2020). There are very few empirical studies on the relationship between wind speed and mental disorders. Wind speed is closely related to air ions, and exposure to positive air ions may be harmful, while exposure to negative air ions may be associated with beneficial health effects (Krueger and Reed 1976;Nastos et al. 2017;Della Vecchia et al. 2021). Exposure to high levels of negative air ions may be associated with improvements in depressive symptoms (Perez et al. 2013;Jiang et al. 2018). In addition, high wind speed lead to increased noise from air rushing friction, which may affect the central nervous system, making people feel nervous and irritable. We also 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 a moist, fresh, mild breeze (Yackerson et al. 2012), so experiencing cold and dry winds represents more hassle. In the future, more research is needed to support the association between wind speed and anxiety disorders.
Our study has several advantages. This may be the first study in China to comprehensively explore the short-term effects of extreme meteorological factors (sunshine duration, wind speed, and precipitation) on anxiety outpatient visits by using time series method. We also looked for susceptible groups based on gender, age and season. Males as well as middle-aged and older adults appear 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 short-term effects 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.

Conclusions
This study provides evidence that extremely low sunshine duration and low and high wind speed increase the risk of anxiety outpatient visits in Suzhou, a temperate climate city in Anhui Province, China. 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.