Suicide is one of the leading causes of death. Major Depressive Disorder (MDD) is one of the risk factors for committing suicide. Epigenetic data may help to distinguish suicidal and non-suicidal deaths. In particular, DNA methylation is a process involving a chemical modification on DNA which can change gene activity without changing the sequence. It has successfully been used for monitoring suicide progression and prediction. The objective of this study is to predict suicidal and non-suicidal deaths from DNA methylation data using a modern machine learning algorithm. We used support-vector machines to classify existing secondary data consisting of normalized values of methylated DNA probe intensities from tissues of two cortical brain regions (Brodmann Area 11 [BA11] and Brodmann Area 25 [BA25]) to distinguish 20 suicide cases (who died following a major depressive disorder) from 20 control cases (non-psychiatric sudden death). Prior to classification, we employed Principal component analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) to reduce the dimension of the data. In comparison to PCA, the modern data visualization method t-SNE performs better in dimensionality reduction. t-SNE accounts for the possible non-linear patterns in low-dimensional data. By contrast, PCA failed to group the cases according to different types of deaths. We applied four-fold cross-validation in which the resulting output from t-SNE was used as training data for the Support Vector Machine (SVM). For BA25, two-dimensional t-SNE better classified the suicidal vs. non-suicidal deaths compared to three-dimensional t-SNE. The Receiver Operating Characteristic Curve (ROC) for the classification exhibited an 80% Area Under the Curve (AUC) for BA25 data and 100% AUC for BA11. Despite the use of cross-validation, the nominally perfect prediction of suicidal deaths for BA11 data suggests possible over-fitting of the model. The study also may have suffered from 'spectrum bias' since the individuals were only studied from two extreme scenarios. This research constitutes a baseline study for classifying suicidal and non-suicidal deaths from DNA methylation data. Future studies with larger sample size, while possibly incorporating methylation data from living individuals, may reduce the bias and improve the accuracy of the results.

Background Young people are more likely to be affected by suicide contagion, and there are concerns about the role social media plays in the development and maintenance of suicide clusters or in facilitating imitative suicidal behavior. However, social media also presents an opportunity to provide real-time and age-appropriate suicide prevention information, which could be an important component of suicide postvention activities. Objective This study aimed to test an intervention designed to equip young people to communicate safely online about suicide (#chatsafe) with a sample of young people who had recently been exposed to a suicide or suicide attempt, with a view to determining the role social media can play as part of a postvention response. Methods A sample of 266 young people from Australia, aged 16 to 25 years, were recruited to participate in the study. They were eligible if they had been exposed to a suicide or knew of a suicide attempt in the past 2 years. All participants received the #chatsafe intervention, which comprised 6 pieces of social media content that were sent to them weekly via direct message through Instagram, Facebook, or Snapchat. Participants were assessed on a range of outcome measures (social media use, willingness to intervene against suicide, internet self-efficacy, confidence, and safety when communicating about suicide on social media platforms) at baseline, immediately after the intervention, and at 4-week follow-up. Results After the 6-week #chatsafe intervention, participants reported substantial improvements in their willingness to intervene against suicide online, their internet self-efficacy, and their perceived confidence and safety when communicating about suicide online. Overall, the participants reported that it was appropriate to receive the #chatsafe intervention via social media, and no iatrogenic effects were recorded. Conclusions The findings suggest that it is safe and acceptable to disseminate suicide prevention information entirely via social media among young people who have recently been exposed to a suicide or suicide attempt. Interventions such as #chatsafe could potentially mitigate the risk of distress and future suicidal behavior in young people by improving the quality and safety of online communication about suicide and, as such, can be an important component of delivering a postvention response to young people.

Background: Unsafe pregnancy termination is a major public health concern among reproductive-aged women in many developing countries. This study evaluated the socio-demographic characteristics, as well as residual spatial correlation in pregnancy termination among Bangladeshi women. Methods: Secondary data was obtained from the Bangladesh Demographic and Health Survey for the survey year 2014. Data included 17,863 samples of ever-married women between the ages of 15-49 years, which is a national representative sample in Bangladesh. Bayesian spatial logistic regression was used to assess the associations between socio-demographic characteristics and pregnancy termination. We flexibly modeled the non-linear effects of the continuous covariates while accounting for residual spatial correlation at the district level. Results: Our findings revealed that about 19% of the respondents in Bangladesh reported ever had a pregnancy terminated. The risk of pregnancy termination was higher among women who had been working, had a higher wealth index, were in a conjugal relationship, had no children, were older and started their cohabitation earlier. Residual spatial patterns revealed the areas at a higher risk of pregnancy termination, including Panchagarh, Habiganj, and Sylhet after adjusting for covariates. Conclusions: Prevalence of pregnancy termination remains considerably high in Bangladesh. The study revealed significant associations of women's age at survey time, age at first cohabitation, occupational status, socioeconomic status, marital status and the total number of children ever born with reporting having a history of terminated pregnancy among Bangladeshi ever-married women. The identified socio-demographic characteristics and districts at an increased likelihood of pregnancy termination can inform localized intervention and prevention strategies to improve the reproductive healthcare of women in Bangladesh.

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