Search

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.

The evidence on the health and economic impacts of air pollution with schizophrenia is scarce, especially in developing countries. In this study, we aimed to systemically examine the short-term effects of PM

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).

Schizophrenia (SCZ) hospital re-admissions constitute a serious disease burden worldwide. Some studies have reported an association between air pollutants and hospital admissions for SCZ. However, evidence is scarce regarding the effects of ambient particulate matter (PM) on SCZ hospital re-admissions, especially in coastal cities in China. The purpose of this study was to examine whether PM affects the risk of SCZ hospital re-admission in the coastal Chinese city of Qingdao. Daily SCZ hospital re-admissions, daily air pollutants, and meteorological factors from 2015 to 2019 were collected. A quasi-Poisson generalized linear regression model combined with distributed lag non-linear model (DLNM) was applied to model the exposure-lag-response relationship between PM and SCZ hospital re-admissions. The relative risks (RRs) were estimated for an inter-quartile range (IQR) increase in PM concentrations. Subgroup analyses by age and gender were conducted to identify the vulnerable subgroups. There were 6220 SCZ hospital re-admissions during 2015-2019. The results revealed that PM, including PM10 (particles with an aerodynamic diameter ≤10 μm), PMc (particles >2.5 μm but

Previous epidemiological studies have reported seasonal variation patterns of depression symptoms, which may be influenced by bad weather conditions, such as a lack of sunlight. However, evidence on the acute effects of sunshine duration on outpatient visits for depression is limited, especially in developing countries, and the results are inconsistent. We collected daily outpatient visits for depression from the local mental health centre in Suzhou, Anhui Province, China, during 2017-2019. We defined the 5th and 95th sunshine percentiles as short and long sunshine durations, respectively. A quasi-Poisson generalized linear regression model combined with a distributed lag nonlinear model was used to quantitatively assess the effects of short and long sunshine durations on outpatient visits for depression. Stratified analyses were further performed by gender, age and number of visits to identify vulnerable populations. A total of 26,343 depression cases were collected during the study period. An approximate U-shaped exposure-response association was observed between sunshine duration and depression outpatient visits. The cumulative estimated relative risks (RRs) for short and long sunshine durations at lag 0-21 days were 1.53 [95% confidence intervals (CI): 1.14, 2.06] and 1.13 (95% CI: 0.88, 1.44), respectively. Moreover, a short sunshine duration was associated with a greater disease burden than a long sunshine duration, with attributable fractions (AFs) of 16.64% (95% CI: 7.8%, 23.89%) and 2.24% (95% CI: -2.65%, 5.74%), respectively. Subgroup analysis showed that males, people aged less than 45 years and first-visit cases may be more susceptible to a lack of sunlight. For a long sunshine duration, no statistically significant associations were found in any population groups. Our study found that a short sunshine duration was associated with an increased risk of depression. The government, medical institutions, family members and patients themselves should fully recognize the important role of sunlight and take active measures to prevent depression.

Motor vehicle exhaust emissions have become the main source of urban air pollution in China, but few studies have explored the association of short-term exposure to traffic-related air pollutants (TRAPs) with anxiety disorders. Thus, we used an overdispersed, generalized additive model (GAM) to investigate the association between TRAPs and hospital admissions (HAs) for anxiety in Qingdao, a coastal Chinese city with high vehicle ownership. In addition, stratified analyses were performed by gender, age, season and hospitalization frequency (first admission and readmission). A positive association between TRAPs and HAs for anxiety was observed. Both inhalable particulate matter (PM

Background Daily air quality index (AQI) forecast can provide early warning information, and it is not clear whether it is appropriate for childhood asthma hospitalizations (CAHs). Furthermore, little is known about the effects of AQI on CAHs, as well as the interactions between temperature, humidity and AQI. Methods We collected 32,238 cases in Hefei from 2013 to 2016 and estimated the association between daily CAHs and AQI by combining the Poisson Generalized Linear Models (PGLMs) with the Distributed Lag Nonlinear Models (DLNMs). The interaction between AQI and temperature was tested by stratifying AQI and temperature, as well as humidity. Results AQI was associated with an increased risk of hospitalizations for childhood asthma. The adverse effect first appeared on the 3rd day, with the RR of 1.011 (95%CI: 1.000–1.023) and continued until the 19th day of lag (RR = 1.010, 95%CI: 1.001–1.020). In the subgroup analysis, the male and pre-school children were more sensitive to AQI, and there are seasonal differences in the effects of AQI on CAHs. Besides, in a stratified analysis with an AQI of 150, we found synergies between temperature, humidity and AQI. The interaction relative risk (IRR) and relative excess risk due to interaction (RERI) for the interaction between temperature and AQI were 1.157 (95%CI: 1.029–1.306) and 0.122 (95%CI: 0.022–0.223) respectively. For the humidity, the IRR and RERI were 1.090 (95%CI: 1.056–1.206) and 0.083 (95%CI: 0.083–0.143) respectively. Exploring different subgroups in the interaction analyses, it was worth noting that female and pre-school children were more sensitive to the interaction between AQI and temperature, while school-age children were more sensitive to the interaction between AQI and humidity. Conclusions The study found that not only AQI can significantly increase the risk of CAHs, but also that under the context of climate change, temperature and humidity have a synergistic effect on AQI, suggesting that considering only the warning information of air pollution is not enough to strengthen the prevention of childhood asthma hospitalization.

This study aimed to evaluate the effectiveness of an intervention to reduce bicycle injuries among rural middle school students in China. A one-year cluster-randomized controlled trial was conducted with seventh grade students from six middle schools in two towns in rural Chaoshan, China. The two towns were randomly assigned to either the intervention or control group. Road safety education materials, two lectures on road safety, and a series of health education activities were delivered to 1312 students in the intervention group over one year, and the content of the intervention included traffic safety knowledge, methods of preventing bicycle injury and management of bicycle injuries. Questionnaires weere administered to the two groups before and after the intervention to measure the incidence, cognitions, and behaviors related to bicycle injuries. The pre-intervention incidence of bicycle injuries exhibited no significant difference between the two groups, while the difference reached significance after the intervention (χ2 = 13.409, p < 0.001). In the intervention group, the incidence decreased significantly after the intervention (χ2 = 8.137, p = 0.004), while no significant change was observed in the control group. Publicity and education intervention measures have certain short-term effects on the prevention of bicycle injuries among rural middle school students; we should approach intervention measures according to the characteristics of traffic injuries in different areas.

The wire mesh tomography system directs at the measurement of some points in flow cross-section, so the image reconstruction algorithms are needed for get complete image of flow cross-section. In this paper, nearest neighbor algorithm, average interpolation algorithm, bilinear interpolation algorithm, bi-cubic interpolation algorithm and inverse distance square weighting method are used for image reconstruction, then the spatial resolution can be increased from 16×16 to 64×64.