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

Pan, Rubing; Yao, Zhenhai; Yi, Weizhuo; Wei, Qiannan; He, Yangyang; Tang, Chao; Son, Shasha; Ji, Yanhu; Song, Jian; Cheng, Jian; Ji, Yifu; Su, Hong

doi:10.1007/s11356-021-15797-z

Cited by 10 publications

(3 citation statements)

References 35 publications

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“…Firstly, we discovered that the distribution of scatter plot is a non-linear wavy curve; Secondly, the DLNM takes account of both lag effects and the exposure-reaction non-linear relationship, and initially applied in epidemiological studies in 2006 ( 50 ). Multiple studies have suggested that the effects of air pollution and meteorological factors on outcomes have lag effects, while traditional linear models, which only study effects on certain time points without considering lag effects, are likely to produce high linearity, biasing the results ( 51 , 52 ). Therefore, DLNM is a better choice to study the lag effects of exposure, especially in the analysis of air pollution.…”

Section: Discussionmentioning

confidence: 99%

Association Between Air Pollutants and Pediatric Respiratory Outpatient Visits in Zhoushan, China

Liu

Yi²,

Shi

et al. 2022

Front. Public Health

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ObjectiveThis study aimed to explore the time-series relationship between air pollutants and the number of children's respiratory outpatient visits in coastal cities.MethodsWe used time series analysis to investigate the association between air pollution levels and pediatric respiratory outpatient visits in Zhoushan city, China. The population was selected from children aged 0–18 who had been in pediatric respiratory clinics for eight consecutive years from 2014 to 2020. After describing the population and weather characteristics, a lag model was used to explore the relationship between outpatient visits and air pollution.ResultsWe recorded annual outpatient visits for different respiratory diseases in children. The best synergy lag model found a 10 μg/m3 increase in PM2.5 for every 4–10% increase in the number of pediatric respiratory outpatient visits (P < 0.05). The cumulative effect of an increase in the number of daily pediatric respiratory clinics with a lag of 1–7 days was the best model.ConclusionsPM2.5 is significantly related to the number of respiratory outpatient visits of children, which can aid in formulating policies for health resource allocation and health risk assessment strategies.

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Section: Discussionmentioning

confidence: 99%

Association Between Air Pollutants and Pediatric Respiratory Outpatient Visits in Zhoushan, China

Liu

Yi²,

Shi

et al. 2022

Front. Public Health

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“…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). DOW t was used to control the effect of day of the week by using a categorical variable.…”

Section: Discussionmentioning

confidence: 99%

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

Zhang

Yang

et al. 2022

Environ Sci Pollut Res

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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 ndings may help develop weather-based early warning systems to minimize the effects of extreme meteorological factors on anxiety.

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“…According to the quasi-Poisson Akaike information criterion (Q-AIC) for temperature and lag with 3 degrees of freedom. We control long-term trends with 7 degrees of freedom per year (Pan et al, 2021). The basic model is shown as below:…”

Section: Data Resourcementioning

confidence: 99%

The cold effect of ambient temperature on acute aortic dissection hospital admission: utilizing distributed lag nonlinear analysis

Zhang

Wang

et al. 2022

Preprint

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The effects of ambient temperature on acute aortic dissection (AAD) admissions in China have not been well addressed. So, the aim of study was to explore the effect of ambient daily mean temperature for AAD admissions, especially in Hefei, China. Data on daily mean temperature, daily maximum temperature, daily minimum temperature, air pressure, relative humidity and AAD admissions in Hefei were obtained between 2015 and 2020. Distributed lag non-linear model was employed to determine the association between daily ambient temperature and AAD admissions. Relative risk (RR) with 95% confidence interval (CI) were calculated based on gender, age group and hypertensive status. A total number of 1648 AAD admitted cases were documented. A non-linear acute effect of cold temperature on AAD was evaluated. Compared with median reference temperature (17.56 °C), the cumulative RR of extreme low temperature increased the risk of AAD admissions. The cumulative effect was at lag 0 day (RR: 1.639, 95% CI: 1.120, 2.398), and got the peak at lag 3 days (RR: 2.176, 95% CI: 1.345, 3.519). Then the effect size was gradually reduced until lag 8 days (RR: 1.953, 95% CI: 1.010, 3.776). The impact of extreme low temperature on AAD admissions was found to be more obvious in male, the elderly, and hypertensive patients. Overall, exposure to extreme low temperature was associated with increasing AAD daily admissions risk in Hefei, China.

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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

Cited by 10 publications

References 35 publications

Association Between Air Pollutants and Pediatric Respiratory Outpatient Visits in Zhoushan, China

Association Between Air Pollutants and Pediatric Respiratory Outpatient Visits in Zhoushan, China

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

The cold effect of ambient temperature on acute aortic dissection hospital admission: utilizing distributed lag nonlinear analysis

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