Daily changes in ambient air pollution concentrations and temperature and suicide mortality in Canada: Findings from a national time-stratified case-crossover study

Association between a large change between the minimum and maximum monthly values of solar insolation and a history of suicide attempts in bipolar I disorder

BACKGROUND

The rate of suicide attempts by patients with bipolar disorder is high. In addition to patient and country specific factors, environmental factors may contribute to suicidal behavior. Sunlight has multiple diverse impacts on human physiology and behavior. Solar insolation is defined as the electromagnetic energy from the sun striking a surface area on earth. We previously found that a large change in solar insolation between the minimum and maximum monthly values was associated with an increased risk of suicide attempts in patients with bipolar I disorder.

METHODS

The association between solar insolation and a history of suicide attempts in bipolar disorder was again investigated using an international database with 15% more data and more sites at diverse locations and countries.

RESULTS

Data were available from 5641 patients with bipolar I disorder living at a wide range of latitudes in 41 countries in both hemispheres. A large change in solar insolation between the minimum and maximum monthly values was associated with a history of suicide attempts in patients with bipolar I disorder, a replication of our prior analysis. The estimated model also associated state sponsored religion in the onset country, female gender, a history of alcohol or substance abuse, and being part of a younger birth cohort with a history of suicide attempts.

CONCLUSIONS

A large change between the minimum and maximum monthly values of solar insolation was associated with a history of suicide attempts in bipolar I disorder, replicating our prior research. Physicians should be aware that daylight has wide ranging physiological and psychiatric impacts, and that living with large changes in solar insolation may be associated with an increased suicide risk.

Association of short-term exposure to ambient air pollution and temperature with bronchiectasis mortality: a nationwide time-stratified case-crossover study

BACKGROUND

Ambient pollution and non-optimal temperature are major risk factors for respiratory health. However, the relationships between short-term exposure to these factors and bronchiectasis mortality remain unknown.

METHODS

A nationwide, time-stratified case-crossover study across Mainland China was conducted from 2013 to 2019. Records of bronchiectasis deaths were extracted from the National Death Registration Reporting Information System. Daily concentrations of fine particulate matter (PM2.5), coarse particulate matter (PM2.5-10), nitrogen dioxide (NO2), ozone (O3), and daily temperature were obtained from high-resolution prediction models. We utilized conditional logistic regression model and distributed lag nonlinear model to explore the associations of these exposures with bronchiectasis mortality.

FINDINGS

We included a total of 19,320 bronchiectasis deaths. Air pollutant was associated with bronchiectasis mortality within the first 3 days after exposure and the exposure-response relationships were almost linear. An interquartile range increase in PM2.5, PM2.5-10, and O3 was associated with increments of 3.18%, 4.14%, and 4.36% in bronchiectasis mortality at lag 02 d, respectively. Additionally, lower temperature was associated with higher odds of bronchiectasis mortality. Compared to referent temperature (23.6 °C), the odds ratio for bronchiectasis mortality associated with extremely low temperature (P1: -13.4 °C) was 1.54 (95% CI: 1.05, 2.25).

INTERPRETATION

This national study provides compelling evidence, and highlights the necessity and importance of reducing air pollution exposures and keeping warm for susceptible populations.

FUNDING

National Natural Science Foundation of China (81925001; 82330070); Innovation Program of Shanghai Municipal Education Commission (202101070007-E00097); Program of Shanghai Municipal Science and Technology Commission (21DZ2201800); Program of Shanghai Shenkang Development Center (SHDC12023110); and Major Project of National Health Commission of China.

Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models

BACKGROUND

Suicidal behaviors, which may lead to death (suicide) or survival (suicide attempt), are influenced by various factors. Identifying the specific risk factors for suicidal behavior mortality is critical for improving prevention strategies and clinical interventions. Predicting the outcomes of suicidal behaviors can help identify individuals at higher risk of death, enabling timely and targeted interventions. This study aimed to determine the critical risk factors associated with suicidal behavior mortality and identify an effective classification model for predicting suicidal behavior outcomes.

MATERIALS AND METHODS

This study utilized data recorded in the suicidal behavior registry system of hospitals in Ilam Province. In the first phase, duplicate records were removed, and the data was numerically encoded via Python version 3.11; then, the data was analyzed using chi-square and Fisher's exact tests in SPSS version 22 software to identify the factors influencing suicidal behavior mortality. In the second phase, missing data were removed, and the dataset was standardized. Five binary classification algorithms were utilized, including Random Forest, Logistic Regression, and Decision Trees, with hyperparameters optimized using the area under the receiver operating characteristic curve (AUC) and F1 score metrics. These models were compared based on accuracy, recall, precision, F1 score, and AUC.

RESULTS

Among 3833 cases of suicidal behavior in various hospitals in Ilam Province, the results indicated that the method of suicidal behavior (P < 0.001), reason for suicidal behavior (P < 0.001), age group (P < 0.001), education level (P < 0.001), marital status (P = 0.004), and employment status (P = 0.042) were significantly associated with suicide. Variables such as the season of suicidal behavior, gender, father's education, and mother's education were not significantly related to suicidal behavior mortality. Furthermore, the random forest model demonstrated the highest area under the ROC curve (0.79) and the highest classification accuracy and F1 score on both the training data (0.85 and 0.2, respectively) and test data (0.86 and 0.31, respectively) for predicting suicidal behaviors outcomes among the models tested.

CONCLUSION

This study identified key factors such as older age, lower education, divorce or widowhood, employment, physical methods, and socioeconomic issues as significant predictors of suicidal behavior outcomes. A combination of statistical models for feature selection and machine learning algorithms for prediction was used, with Random Forest showing the best performance. This approach highlights the potential of integrating statistical methods with machine learning to improve suicide risk prediction and intervention strategies.

The short-term effects of individual and mixed ambient air pollutants on suicide mortality: A case-crossover study

It is critical to explore intervenable environmental factors in suicide mortality. Based on 30,688 suicide cases obtained from the Mortality Surveillance System of the Jiangsu Provincial Centre for Disease Control and Prevention, we utilized a case-crossover design, and found that the OR of suicide deaths increased by a maximum of 0.71 % (95 % CI: 0.09 %, 1.32 %), 0.68 % (95 % CI: 0.12 %, 1.25 %), 0.77 % (95 % CI: 0.19 %, 1.37 %), 2.95 % (95 % CI: 1.62 %, 4.29 %), 4.18 % (95 % CI: 1.55 %, 6.88 %), and 0.93 % (95 % CI: 0.10 %, 1.77 %), respectively, for per 10 μg/m3 increase in the particulate matter (PM) with diameters ≤ 2.5 µm (PM2.5), PM with diameters ≤ 10 µm (PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and per 0.1 mg/m3 increase in carbon monoxide (CO) concentrations with the conditional logistic regression analysis. People living in county-level cities were more susceptible. Particularly, a significant positive association was found between air pollutant mixture exposure and suicide deaths (OR=1.04,95 % CI: 1.01, 1.06). The excess fraction of suicide deaths due to air pollution reached a maximum of 8.07 %. In conclusion, we found associations between individual and mixed ambient air pollutants and suicide deaths, informing the development of integrated air pollution management and targeted measures for suicide prevention and intervention. ENVIRONMENTAL IMPLICATION: As a major contributor to the global burden of disease, air pollution was confirmed by accumulating studies to have adverse impact on mental health, and potentially lead to suicide deaths. However, systematic studies on the association between air pollution and suicide mortality are lacking. We explored the associations of multiple air pollutants and pollution mixtures with suicide deaths and assessed excess suicide mortality due to air pollution, emphasizing the importance of air pollution control on suicide prevention. Our study provides evidence to support mechanistic studies on the association between air pollution and suicide, and informs comprehensive air pollution management.

Global climate change: Effects of future temperatures on emergency department visits for mental disorders in Beijing, China

Rising temperatures can increase the risk of mental disorders. As climate change intensifies, the future disease burden due to mental disorders may be underestimated. Using data on the number of daily emergency department visits for mental disorders at 30 hospitals in Beijing, China during 2016-2018, the relationship between daily mean temperature and such visits was assessed using a quasi-Poisson model integrated with a distributed lag nonlinear model. Emergency department visits for mental disorders attributed to temperature changes were projected using 26 general circulation models under four climate change scenarios. Stratification analyses were then conducted by disease subtype, sex, and age. The results indicate that the temperature-related health burden from mental disorders was projected to increase consistently throughout the 21st century, mainly driven by high temperatures. The future temperature-related health burden was higher for patients with mental disorders due to the use of psychoactive substances and schizophrenia as well as for women and those aged <65 years. These findings enhance our knowledge of how climate change could affect mental well-being and can be used to advance and refine targeted approaches to mitigating and adapting to climate change with a view on addressing mental disorders.

The short-term effect of ambient particulate matter on suicide death

BACKGROUND

Air pollution can cause various respiratory and neurological diseases and continuous exposure can lead to death. Previous studies have reported that particulate matter (PM) exposure increases the risk of depression, suicidal thoughts, and suicidal death; however, the results are inconsistent and limited. This study aimed to examine the relationship between short-term PM exposure and suicide deaths, as well as investigate the short-term effects of PM on suicide death within vulnerable groups based on factors such as sex, age group, suicide-related information (note, method, and cause), psychiatric disorders, and physical diseases.

METHODS

Data on a total of 28,670 suicide deaths from 2013 to 2017, provided by the Korea Foundation for Suicide Prevention, were analyzed. The study design employed a time-series analysis with a two-stage approach. In the first step, a generalized additive model combined with a distributed lag nonlinear model was used to estimate the short-term effect of PM exposure on suicide risk specific to each city. In the second step, the estimated results from each city were pooled through a meta-analysis to derive the overall effect. We determined the effects of single lag, cumulative lag, and moving average PM concentrations from days 0-7 before suicide.

RESULTS

We confirmed an association between exposure to PM10 (≤ 10 μm in diameter) and deaths due to suicide. In particular, among individuals with psychiatric disorders and those who employed non-violent suicide methods, increased exposure to PM10 was associated with a higher risk of death by suicide, with percentage changes of 5.92 (95% confidence interval [CI]: 3.95-7.92) and 11.47 (95% CI: 7.95-15.11), respectively. Furthermore, in the group with psychiatric disorders, there was an observed tendency of increasing suicide risk as PM10 levels increased up to 120 µg/m3, whereas in the group with non-violent suicide deaths, there was a pronounced trend of rapid increase in suicide risk with an increase in PM10 up to 100 µg/m3.

CONCLUSIONS

These results show an association between short-term exposure to PM and suicide. Our study adds evidence for the benefits of reducing PM in preventing diseases and improving mental health.

Short-term exposure to PM2.5 and 1.5 million deaths: a time-stratified case-crossover analysis in the Mexico City Metropolitan Area

BACKGROUND

Satellite-based PM2.5 predictions are being used to advance exposure science and air-pollution epidemiology in developed countries; including emerging evidence about the impacts of PM2.5 on acute health outcomes beyond the cardiovascular and respiratory systems, and the potential modifying effects from individual-level factors in these associations. Research on these topics is lacking in low and middle income countries. We aimed to explore the association between short-term exposure to PM2.5 with broad-category and cause-specific mortality outcomes in the Mexico City Metropolitan Area (MCMA), and potential effect modification by age, sex, and SES characteristics in such associations.

METHODS

We used a time-stratified case-crossover study design with 1,479,950 non-accidental deaths from the MCMA for the period of 2004-2019. Daily 1 × 1 km PM2.5 (median = 23.4 μg/m3; IQR = 13.6 μg/m3) estimates from our satellite-based regional model were employed for exposure assessment at the sub-municipality level. Associations between PM2.5 with broad-category (organ-system) and cause-specific mortality outcomes were estimated with distributed lag conditional logistic models. We also fit models stratifying by potential individual-level effect modifiers including; age, sex, and individual SES-related characteristics namely: education, health insurance coverage, and job categories. Odds ratios were converted into percent increase for ease of interpretation.

RESULTS

PM2.5 exposure was associated with broad-category mortality outcomes, including all non-accidental, cardiovascular, cerebrovascular, respiratory, and digestive mortality. A 10-μg/m3 PM2.5 higher cumulative exposure over one week (lag06) was associated with higher cause-specific mortality outcomes including hypertensive disease [2.28% (95%CI: 0.26%-4.33%)], acute ischemic heart disease [1.61% (95%CI: 0.59%-2.64%)], other forms of heart disease [2.39% (95%CI: -0.35%-5.20%)], hemorrhagic stroke [3.63% (95%CI: 0.79%-6.55%)], influenza and pneumonia [4.91% (95%CI: 2.84%-7.02%)], chronic respiratory disease [2.49% (95%CI: 0.71%-4.31%)], diseases of the liver [1.85% (95%CI: 0.31%-3.41%)], and renal failure [3.48% (95%CI: 0.79%-6.24%)]. No differences in effect size of associations were observed between age, sex and SES strata.

CONCLUSIONS

Exposure to PM2.5 was associated with non-accidental, broad-category and cause-specific mortality outcomes beyond the cardiovascular and respiratory systems, including specific death-causes from the digestive and genitourinary systems, with no indication of effect modification by individual-level characteristics.