Immediate and delayed effects of climatic factors on hospital admissions for schizophrenia in Queensland Australia: A time series analysis

Climate change and disorders of the nervous system

Anthropogenic climate change is affecting people's health, including those with neurological and psychiatric diseases. Currently, making inferences about the effect of climate change on neurological and psychiatric diseases is challenging because of an overall sparsity of data, differing study methods, paucity of detail regarding disease subtypes, little consideration of the effect of individual and population genetics, and widely differing geographical locations with the potential for regional influences. However, evidence suggests that the incidence, prevalence, and severity of many nervous system conditions (eg, stroke, neurological infections, and some mental health disorders) can be affected by climate change. The data show broad and complex adverse effects, especially of temperature extremes to which people are unaccustomed and wide diurnal temperature fluctuations. Protective measures might be possible through local forecasting. Few studies project the future effects of climate change on brain health, hindering policy developments. Robust studies on the threats from changing climate for people who have, or are at risk of developing, disorders of the nervous system are urgently needed.

Immediate and delayed effects of environmental temperature on schizophrenia admissions in Liuzhou, China, 2013-2020: a time series analysis

This study aimed to investigate the associations between environmental temperature and schizophrenia admissions in Liuzhou, China. A Poisson generalized linear model combined with a distributed lag nonlinear model was used to analyze the effects of daily mean temperature on schizophrenia admissions from 2013 to 2020 in Liuzhou. Additionally, subgroup analyses were conducted to investigate possible modifications stratified by gender, marital status, and age. In this study, 10,420 schizophrenia admissions were included. The relative risks of schizophrenia admissions increased as the temperature rose, and the lag effects of high temperature on schizophrenia admissions were observed when the daily mean temperature reached 21.65°C. The largest single effect was observed at lag0, while the largest cumulative effect was observed at lag6. The single effects of high temperatures on schizophrenia admissions were statistically significant in both males and females, but the cumulative effects were statistically significant only in males, with the greatest effect at lag0-7. The single effect of high temperatures on admissions for unmarried schizophrenics was greatest at lag5, while the maximum cumulative effect for unmarried schizophrenia was observed at lag0-7. The single effects of high temperatures on schizophrenia admissions were observed in those aged 0-20, 21-40, and 41-60. The cumulative effects for schizophrenics aged 21-40 were observed from lag0-3 to lag0-7, with the maximum effect at lag0-7. In conclusion, the risk of schizophrenia admissions increased as the environmental temperature increased. The schizophrenics who were unmarried appeared to be more vulnerable to the single and cumulative effects of high temperature.

Short-term effects of PM2.5 and its components exposure on endothelial function in Chinese elders

Particulate matter (PM2.5) and its components have been studied widely around the world and are associated with many adverse health events (e.g. cardiovascular diseases and death). Flow-mediated dilation (FMD) is a non-invasive assessment that is able to detect endothelial damage at an early stage, therefore, improving the prognosis of atherosclerotic cardiovascular disease. The current study used data from Shanghai to explore the relationship between PM2.5 and its components and FMD using multiple statistical models. The results of the analysis of 812 patients' data (age ≥ 65) suggested that as PM2.5 level rises, endothelial function reduces. Among the five PM2.5 components included in this study, black carbon was shown by both models to be the dominating factor three days post-exposure (lag3). However, results from lag4 and lag5 were inconclusive in the two models with some evidence proposing the significance of sulphate, organic matter, and ammonium. Our results are in concordance with previous literature and further prove the significance of black carbon as an individual pollutant in the atmosphere. More research is needed to confirm the role of sulphate, organic matter, and ammonium as independent pollutants in relation to health.

Multilevel and general linear modeling of weather and time effects on the emotional and behavioral states of children with profound intellectual and multiple disabilities

Introduction

Eliciting the emotional and behavioral states of children with severe or profound intellectual disabilities (IDs) and profound intellectual and multiple disabilities (PIMD) due to their complex and atypical developmental trajectories has become increasingly elusive. It is evident that the environment, influenced by weather conditions and time of the day, plays a pivotal role in molding children's behaviors, emotions, and interactions. This underscores the significance of the environment as a critical factor in exploring the communication dynamics of children with PIMD/IDs.

Methods

Over five months during fall and winter seasons, we conducted 105 video-recorded sessions with 20 children aged 8 to 16 with PIMD/IDs. These sessions aimed to capture the emotional and behavioral states interpreted by caregivers while simultaneously collecting indoor and outdoor weather indices, location, and time data. Using cross-classified multilevel and general linear models adjusted for individual characteristics and location variability with subsequent simple slope analyses, we examined the main and seasonal interaction effects of indoor and outdoor weather indices and time of the day on the emotional and behavioral states of children with PIMD/IDs.

Results

The models revealed that higher atmospheric pressure (atm), indicative of pleasant and favorable weather conditions, was associated with increased engagement (indoor: p < 0.01; outdoor: p < 0.01) and interest (outdoor: p < 0.01) behaviors. In contrast, engagement levels decreased before lunchtime (p < 0.01; p < 0.001), and inclement or unstable weather conditions characterized by low-pressure systems (p < 0.05) and stronger wind speed (p < 0.05) led to more refusal or disagreement. During winter, children displayed significantly more agreement with their caregivers (p < 0.001). Interestingly, they also engaged more on cloudy days (p < 0.05). Furthermore, simple slope analyses revealed that high atm conditions in fall were linked to more engagement (p < 0.05) while humid conditions predicted more assent behaviors (p < 0.001). However, cloudy weather predicted less attentional focusing (p < 0.05) and interest (p < 0.01) behaviors in winter.

Conclusion

This study confirms that fluctuations in weather indices, including seasonal changes and time of the day, can provide potential pathway indicators and supplement behavioral observations to elicit the behavioral states of children with PIMD/IDs. These findings highlight the importance of considering these factors when designing meaningful interactions and communication interventions for this population.

Seasonal peak and the role of local weather in schizophrenia occurrence: A global analysis of epidemiological evidence

BACKGROUND

Many studies have shown that the onset of schizophrenia peaked in certain months within a year and the local weather conditions could affect the morbidity risk of schizophrenia. This study aimed to conduct a systematic analysis of schizophrenia seasonality in different countries of the world and to explore the effects of weather factors globally.

METHODS

We searched three databases (PubMed, Web of Science, and China National Knowledge Infrastructure) for eligible studies published up to September 2022. Schizophrenia seasonality was compared between hemispheres and within China. A meta-analysis was conducted to pool excess risk (ER, absolute percentage increase in risk) of the onset of schizophrenia associated with various weather factors including temperature (an increase or decrease of temperature as a reflection of high or low temperature; heatwave; temperature variation), precipitation, etc. RESULTS: We identified 84 relevant articles from 22 countries, mainly in China. The seasonality analysis found that the onset of schizophrenia mostly peaked in the cold season in the southern hemisphere but in the warm season in the northern hemisphere. Interestingly in China, schizophrenia seasonality presented two peaks, respectively in the late cold and warm seasons. The meta-analysis further revealed an increased risk of schizophrenia after short-term exposure to high temperature [ER%: 0.45 % (95 % confidence interval (CI): 0.14 % to 0.76 %)], low temperature [ER%: 0.52 % (95%CI: 0.29 % to 0.75 %)], heatwave [ER%: 7.26 % (95%CI: 4.45 % to 10.14 %)], temperature variation [ER%: 1.02 % (95%CI: 0.55 % to 1.50 %)], extreme precipitation [ER%: 3.96 % (95%CI: 2.29 % to 5.67 %)]. The effect of other weather factors such as sunlight on schizophrenia was scarcely investigated with inconsistent findings.

CONCLUSION

This study provided evidence of intra- and inter-country variations in schizophrenia seasonality, especially the double-peak seasons in China. Exposure to local weather conditions mainly temperature changes and precipitation could affect the onset risk of schizophrenia.

Explainable prediction of daily hospitalizations for cerebrovascular disease using stacked ensemble learning

BACKGROUND

With the prevalence of cerebrovascular disease (CD) and the increasing strain on healthcare resources, forecasting the healthcare demands of cerebrovascular patients has significant implications for optimizing medical resources.

METHODS

In this study, a stacking ensemble model comprised of four base learners (ridge regression, random forest, gradient boosting decision tree, and artificial neural network) and a meta learner (elastic net) was proposed for predicting the daily number of hospital admissions (HAs) for CD using the historical HAs data, air quality data, and meteorological data in Chengdu, China from 2015 to 2018. To solve the label imbalance problem, a re-weighting method based on label distribution smoothing was integrated into the meta learner. We trained the model using the data from 2015 to 2017 and evaluated its predictive ability using the data in 2018 based on four metrics, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²). In addition, the SHapley Additive exPlanations (SHAP) framework was applied to provide explanation for the prediction of our stacking model.

RESULTS

Our proposed model outperformed all the base learners and long short-term memory (LSTM) on two datasets. Particularly, compared with the optimal results obtained by individual models, the MAE, RMSE, and MAPE of the stacking model decreased by 13.9%, 12.7%, and 5.8%, respectively, and the R² improved by 6.8% on CD dataset. The model explanation demonstrated that environmental features played a role in further improving the model performance and identified that high temperature and high concentrations of gaseous air pollutants might strongly associate with an increased risk of CD.

CONCLUSIONS

Our stacking model considering environmental exposure is efficient in predicting daily HAs for CD and has practical value in early warning and healthcare resource allocation.

Explainable Gated Recurrent Unit to explore the effect of co-exposure to multiple air pollutants and meteorological conditions on mental health outcomes

Mental health conditions have the potential to be worsened by air pollution or other climate-sensitive factors. Few studies have empirically examined those associations when we faced to co-exposures, as well as interaction effects. There would be an urgent need to use deep learning to handle complex co-exposures that might interact in multiple ways, and the model performance reinforced by SHapely Additive exPlanations (SHAP) enabled our predictions interpretable and hence actionable. Here, to evaluate the mixed effect of short-term co-exposure, we conducted a time-series analysis using approximately 1.47 million hospital outpatient visits of mental disorders (i.e., depressive disorder-DD, Schizophrenia-SP, Anxiety Disorder-AD, Bipolar Disorder-BD, Attention Deficit and Hyperactivity Disorder-ADHD, Autism Spectrum Disorder-ASD), with matched meteorological observations from 2015 through 2019 in Nanjing, China. The global insights of gated recurrent unit model revealed that most of input features with similar effect size caused the illness risk of SP and ASD increase, and most markedly, 73% of relative humidity, 44.6 µg/m³ of NO₂, and 14.1 µg/m³ of SO₂ at 5-year average level associated with 2.27, 1.14, and 1.29 visits increase for DD, SP, and AD, respectively. Both synergic and antagonistic effect among informative paired-features were distinguished from local feature dependence. Interestingly, variation tendencies of excessive visits of bipolar disorder when atmospheric pressure, PM_2.5, and O₃ interacted with one another were inconsistent. Our results provided added qualitative and quantitative support for the conclusion that short-term co-exposure to ambient air pollutants and meteorological conditions posed threats to human mental health.