Predicting National Suicide Numbers with Social Media Data

<div><p>Suicide is not only an individual phenomenon, but it is also influenced by social and environmental factors. With the high suicide rate and the abundance of social media data in South Korea, we have studied the potential of this new medium for predicting completed suicide at the population level. We tested two social media variables (suicide-related and dysphoria-related weblog entries) along with classical social, economic and meteorological variables as predictors of suicide over 3 years (2008 through 2010). Both social media variables were powerfully associated with suicide frequency. The suicide variable displayed high variability and was reactive to celebrity suicide events, while the dysphoria variable showed longer secular trends, with lower variability. We interpret these as reflections of social affect and social mood, respectively. In the final multivariate model, the two social media variables, especially the dysphoria variable, displaced two classical economic predictors – consumer price index and unemployment rate. The prediction model developed with the 2-year training data set (2008 through 2009) was validated in the data for 2010 and was robust in a sensitivity analysis controlling for celebrity suicide effects. These results indicate that social media data may be of value in national suicide forecasting and prevention.</p></div

Association between Air Pollution and Suicide in South Korea: A Nationwide Study

<div><p>Suggestive associations of suicide with air pollutant concentrations have been reported. Recognizing regional and temporal variability of pollutant concentrations and of suicide, we undertook a detailed meta-analysis of completed suicides in relation to 5 major pollutants over 6 years in the 16 administrative regions of the Republic of Korea, while also controlling for other established influences on suicide rates. Of the 5 major pollutants examined, ozone concentrations had a powerful association with suicide rate, extending back to 4 weeks. Over the range of 2 standard deviations (SD) around the annual mean ozone concentration, the adjusted suicide rate increased by an estimated 7.8% of the annual mean rate. Particulate matter pollution also had a significant effect, strongest with a 4-week lag, equivalent to 3.6% of the annual mean rate over the same 2 SD range that approximated the half of annual observed range. These results strongly suggest deleterious effects of ozone and particulate matter pollution on the major public health problem of suicide.</p></div

Variables selected for multivariate prediction model development.

<p>t indicates the predicted time point, and t-1 indicates a previous time point (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061809#s2" target="_blank">Methods</a> for details). With the exception of the consumer price index, all variables were significant after Bonferroni correction for multiple testing (P<0.0002 or lower). The Table displays uncorrected <i>P</i> values.</p

Final variables included in the prediction model (adjusted R-squared = 0.66).

<p>t indicates the predicted time point, and t-1 indicates a previous time point (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061809#s2" target="_blank">Methods</a> for details).</p

Prediction of nation-wide suicide number occurring in three-day epochs.

<p>Vertical bars denote the one month period following each celebrity suicide case (N = 6) (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061809#s2" target="_blank">Methods</a>). These intervals overlapped for the first 2 celebrity suicide cases. Data of 2008 and 2009 were used as a training set and data of 2010 were used as a validation set. (<b>A</b>) Prediction range accuracy. Observed suicides (blue solid line) and prediction intervals (red dashed lines). The prediction range was computed for 85% probability. Prediction range accuracy was 0.88 for the training set and 0.79 for the validation set. (<b>B</b>) Predicted suicides (red) and observed suicides (blue). Correlations of 0.82 and 0.74 were obtained for 243 epochs of the training set and 121 epochs of the validation set, respectively. (<b>C</b>) Celebrity suicides and social media data. Suicide weblog counts (blue) and dysphoria weblog counts (black) are presented. The dysphoria weblog count was divided by 5 to adjust the ordinate axis scale with the suicide weblog count.</p

Associations of Pollutants with Averaged Weekly Suicide Rate per 10 million Persons from Lag 0 to Lag 4.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0117929#pone.0117929.t001" target="_blank">Table 1</a>. are results of meta-analyses of regional data in South Korea from 2006 through 2011.</p><p>Abbreviations: S.E., Standard Error; SD, Standard Deviation; CI, Confidence Interval; PM-10, Particulate Matter (particulates with size of 10 μm in diameter or smaller).</p><p>^a. Increased weekly suicides per 10 million persons when the level of air pollution increases by 1 unit.</p><p>^b. Calculated by multiplication of beta, 2 SD range of national level of air pollution and inverse number of national weekly suicide rate per 10 million persons.</p><p>^c. Corrected by Bonferroni’s method for the tests of the number of time lags.</p><p>^d. I-square heterogeneity test and Cochran’s Q test were employed for testing the presence of statistical heterogeneity in meta-analyses.</p><p>Associations of Pollutants with Averaged Weekly Suicide Rate per 10 million Persons from Lag 0 to Lag 4.</p

Suicide Increase Associated with Air Pollution Increase According To Weeks Prior to suicide.

<p>(<i>A</i>) Ozone; (<i>B</i>) Particulate matter; (<i>C</i>) Nitrogen dioxide; (<i>D</i>) Carbon monoxide; (<i>E</i>) Sulfur dioxide. *Corrected <i>P</i>< 0.05, **corrected <i>P</i>< 0.01, ***corrected <i>P</i>< 0.001, ****corrected <i>P</i>< 0.0001. Percentage suicide increase was calculated by multiplication of beta, range of pollutant concentration from-1SD to +1SD relative to the annual mean value (2 SD range) and inverse number of national weekly suicide rate per 10 million persons.</p

Genetic Prediction of Antidepressant Drug Response and Nonresponse in Korean Patients

<div><p>Genetic polymorphism contributes to variation in response to drug treatment of depression. We conducted three independent 6-week treatment studies in outpatients with major depressive disorder (MDD) to develop a pharmacogenomic model predicting response and nonresponse. We screened candidate genomic markers for association with response to selective serotonin reuptake inhibitors (SSRIs). No patients had received any antidepressant drug treatment in the current episode of depression. Outcome evaluation was blinded to drug and genotype data. The prediction model derived from a development sample of 239 completer cases treated with SSRIs comprised haplotypes and polymorphisms related to serotonin synthesis, serotonin transport, glutamate receptors, and GABA synthesis. The model was evaluated prospectively for prediction of outcome in a validation sample of 176 new SSRI-treated completer cases. The model gave a prediction in 60% of these cases. Predictive values were 85% for predicted responders and 86% for predicted nonresponders, compared to prior probabilities of 66% for observed response and 34% for observed nonresponse in those cases (both <i>P</i><0.001). Convergent cross-validation was obtained through failure of the model to predict outcomes in a third independent sample of 189 completer cases who received non-SSRI antidepressants. We suggest proof of principle for genetic guidance to use or avoid SSRIs in a majority of Korean depressed patients.</p></div

Genotypic combinations of haplotype-SNP (HAP-SNP) prediction model.

<p>Abbreviations: SNP, single-nucleotide polymorphism; <i>5-HTTLPR,</i> serotonin-transporter-linked polymorphic region.</p><p>*H3-A is defined as a pair of two haplotypes (GCATGG and GCATGG), and H3-B as the other cases.</p>†<p>H1-A is defined as any pairs constituting of the CATAGGGATGCC, CATAGGGACGCC, CATAGGAACGTC, CCTAGGGATGCC, AATAGGGATGCC, AACGAGGCCCCT, AACGAGAATGCC and AACGAAGCCCCT haplotypes, and H1-B as any pairs including at least one haplotype among the AACGAGAACGTC, CATAGGGCCCCC and CATGAGGATGCC haplotypes.</p><p>Genotypic combinations of haplotype-SNP (HAP-SNP) prediction model.</p

Enrollment, attrition, drug treatments, and outcomes of patients in all samples.

<p>Enrollment, attrition, drug treatments, and outcomes of patients in all samples.</p