The Werther Effect of Two Celebrity Suicides: an Entertainer and a Politician

Purpose

Suicide is a major health problem in Korea. Extensive media exposure of celebrity suicide may induce imitative suicide, a phenomenon called the Werther effect. We examined the increased suicide risk following the suicides of an entertainer and a politician, and identified the relative suicide risks.

Methods

News articles about the celebrity suicides were obtained from three major newspapers and analysed for quantitative and qualitative features. Imitative suicide risk was investigated by applying a Poisson time series autoregression model with suicide mortality data from the National Statistics Office for 1.5 years before and 1.5 years after each celebrity’s suicide. The period with a significantly increased number of suicides immediately after the celebrity’s suicide determined the Werther effect band. The relative risk during this period was examined for different ages, genders, and suicide methods.

Results

News reports were more numerous and they contained more positive definitions about the entertainer’s suicide. The risk of suicide deaths rose markedly after both celebrity suicides. However, the Werther effect band was longer for the entertainer (6 weeks) than for the politician (4 weeks). The relative suicide risk was significant for almost all ages and both genders during that of both individuals. Use of the same suicide method was a prominent risk factor after both celebrity suicides.

Conclusions

Our results confirm the existence of imitative suicide behaviours, suggesting a facilitation effect of media reports. Guidelines for responsible media reporting need to be implemented to enhance public mental health in Korea.     

Public Mood and Consumption Choices: Evidence from Sales of Sony Cameras on Taobao

Previous researchers have tried to predict social and economic phenomena with indicators of public mood, which were extracted from online data. This method has been proved to be feasible in many areas such as financial markets, economic operations and even national suicide numbers. However, few previous researches have examined the relationship between public mood and consumption choices at society level. The present study paid attention to the “Diaoyu Island” event, and extracted Chinese public mood data toward Japan from Sina MicroBlog (the biggest social media in China), which demonstrated a significant cross-correlation between the public mood variable and sales of Sony cameras on Taobao (the biggest Chinese e-business company). Afterwards, several candidate predictors of sales were examined and finally three significant stepwise regression models were obtained. Results of models estimation showed that significance (F-statistics), R-square and predictive accuracy (MAPE) all improved due to inclusion of public mood variable. These results indicate that public mood is significantly associated with consumption choices and may be of value in sales forecasting for particular products.     

Paraquat Prohibition and Change in the Suicide Rate and Methods in South Korea

The annual suicide rate in South Korea is the highest among the developed countries. Paraquat is a highly lethal herbicide, commonly used in South Korea as a means for suicide. We have studied the effect of the 2011 paraquat prohibition on the national suicide rate and method of suicide in South Korea. We obtained the monthly suicide rate from 2005 to 2013 in South Korea. In our analyses, we adjusted for the effects of celebrity suicides, and economic, meteorological, and seasonal factors on suicide rate. We employed change point analysis to determine the effect of paraquat prohibition on suicide rate over time, and the results were verified by structural change analysis, an alternative statistical method. After the paraquat prohibition period in South Korea, there was a significant reduction in the total suicide rate and suicide rate by poisoning with herbicides or fungicides in all age groups and in both genders. The estimated suicide rates during this period decreased by 10.0% and 46.1% for total suicides and suicides by poisoning of herbicides or fungicides, respectively. In addition, method substitution effect of paraquat prohibition was found in suicide by poisoning by carbon monoxide, which did not exceed the reduction in the suicide rate of poisoning with herbicides or fungicides. In South Korea, paraquat prohibition led to a lower rate of suicide by paraquat poisoning, as well as a reduction in the overall suicide rate. Paraquat prohibition should be considered as a national suicide prevention strategy in developing and developed countries alongside careful observation for method substitution effects.     

The Effects of Media Reports on Disease Spread and Important Public Health Measurements

Controlling the spread of influenza to reduce the effects of infection on a population is an important mandate of public health. Mass media reports on an epidemic or pandemic can provide important information to the public, and in turn, can induce positive healthy behaviour practices (i.e., handwashing, social distancing) in the individuals, that will reduce the probability of contracting the disease. Mass media fatigue, however, can dampen these effects. Mathematical models can be used to study the effects of mass media reports on epidemic/pandemic outcomes. In this study we employ a stochastic agent based model to provide a quantification of mass media reports on the variability in important public health measurements. We also include mass media report data compiled by the Global Public Health Intelligence Network, to study the effects of mass media reports in the 2009 H1N1 pandemic. We find that the report rate and the rate at which individuals relax their healthy behaviours (media fatigue) greatly affect the variability in important public health measurements. When the mass media reporting data is included in the model, two peaks of infection result.     

Multiple Linear Regression on Canonical Correlation Variables

When the explanatory variables of a linear model are split into two groups, two notions of collinearity are defined: a collinearity between the variables of each group, of which the mean is called residual collinearity, and a collinearity between the two groups called explained collinearity. Canonical correlation analysis provides information about the collinearity: large canonical correlation coefficients correspond to some small eigenvalues and eigenvectors of the correlation matrix and characterise the explained collinearity. Other small eigenvalues of this matrix correspond to the residual collinearity. A selection of predictors can be performed from the canonical correlation variables, according to their partial correlation coefficient with the explained variable. In the proposed application, the results obtained by the selection of canonical variables are better than those given by classical regression and by principal component regression.     

Feed-forward control of a redundant motor system

We describe a model of feed-forward control of a redundant motor system and validate it using, as examples, tasks of multi-finger force production. The model assumes the existence of two input signals at an upper level of the control hierarchy, related and unrelated to a task variable. Knowledge of the Jacobian of the system is assumed at the level of generation of elemental variables (variables at the level of effectors). Variance at the level of elemental variables is considered as the sum of two components, related and unrelated to variability in the task variable. An index of stabilization of the task variable is similarly introduced as to how it was done in several studies using the framework of the uncontrolled manifold hypothesis. Several phenomena have been simulated including data point distributions corresponding to presence and absence of force-stabilizing synergies in two-finger tasks, changes in synergies with practice, and changes in synergy indices in preparation to a fast action. The model is discussed in comparison to other models of control of multi-element systems based on feedback processes. It shows that patterns of structured variability in the space of elemental variables can result from feed-forward processes. Relations of the model to the equilibrium-point hypothesis are also discussed.     

Using Spatial Analysis to Predict Health Care Use at the Local Level: A Case Study of Type 2 Diabetes Medication Use and Its Association with Demographic Change and Socioeconomic Status

Local health status and health care use may be negatively influenced by low local socio-economic profile, population decline and population ageing. To support the need for targeted local health care, we explored spatial patterns of type 2 diabetes mellitus (T2DM) drug use at local level and determined its association with local demographic, socio-economic and access to care variables. We assessed spatial variability in these associations. We estimated the five-year prevalence of T2DM drug use (2005–2009) in persons aged 45 years and older at four-digit postal code level using the University of Groningen pharmacy database IADB.nl. Statistics Netherlands supplied data on potential predictor variables. We assessed spatial clustering, correlations and estimated a multiple linear regression model and a geographically weighted regression (GWR) model. Prevalence of T2DM medicine use ranged from 2.0% to 25.4%. The regression model included the extent of population ageing, proportion of social welfare/benefits, proportion of low incomes and proportion of pensioners, all significant positive predictors of local T2DM drug use. The GWR model demonstrated considerable spatial variability in the association between T2DM drug use and above predictors and was more accurate. The findings demonstrate the added value of spatial analysis in predicting health care use at local level.     

Linking gene expression data with patient survival times using partial least squares

There is an increasing need to link the large amount of genotypic data, gathered using microarrays for example, with various phenotypic data from patients. The classification problem in which gene expression data serve as predictors and a class label phenotype as the binary outcome variable has been examined extensively, but there has been less emphasis in dealing with other types of phenotypic data. In particular, patient survival times with censoring are often not used directly as a response variable due to the complications that arise from censoring. We show that the issues involving censored data can be circumvented by reformulating the problem as a standard Poisson regression problem. The procedure for solving the transformed problem is a combination of two approaches: partial least squares, a regression technique that is especially effective when there is severe collinearity due to a large number of predictors, and generalized linear regression, which extends standard linear regression to deal with various types of response variables. The linear combinations of the original variables identified by the method are highly correlated with the patient survival times and at the same time account for the variability in the covariates. The algorithm is fast, as it does not involve any matrix decompositions in the iterations. We apply our method to data sets from lung carcinoma and diffuse large B-cell lymphoma studies to verify its effectiveness.     

Bayesian Dynamical Systems Modelling in the Social Sciences

Data arising from social systems is often highly complex, involving non-linear relationships between the macro-level variables that characterize these systems. We present a method for analyzing this type of longitudinal or panel data using differential equations. We identify the best non-linear functions that capture interactions between variables, employing Bayes factor to decide how many interaction terms should be included in the model. This method punishes overly complicated models and identifies models with the most explanatory power. We illustrate our approach on the classic example of relating democracy and economic growth, identifying non-linear relationships between these two variables. We show how multiple variables and variable lags can be accounted for and provide a toolbox in R to implement our approach.     

Latent class regression on latent factors

In the research of public health, psychology, and social sciences, many research questions investigate the relationship between a categorical outcome variable and continuous predictor variables. The focus of this paper is to develop a model to build this relationship when both the categorical outcome and the predictor variables are latent (i.e. not observable directly). This model extends the latent class regression model so that it can include regression on latent predictors. Maximum likelihood estimation is used and two numerical methods for performing it are described: the Monte Carlo expectation and maximization algorithm and Gaussian quadrature followed by quasi-Newton algorithm. A simulation study is carried out to examine the behavior of the model under different scenarios. A data example involving adolescent health is used for demonstration where the latent classes of eating disorders risk are predicted by the latent factor body satisfaction.     

Spatial patterns of white stork (Ciconia ciconia) migratory phenology in the Iberian Peninsula

In contrast to the attention attracted by temporal trends of phenology, the spatial patterns of arrivals, departures or stays of trans-Saharan birds are still nowadays largely unknown in most of their European breeding areas. In the case of the white stork (Ciconia ciconia), some studies have attempted to describe its migratory patterns throughout some European countries but, to our knowledge, no one has related these patterns to some kind of explanatory variable which offers an ecologically-based explanation for the heterogeneous phenology observable among populations. Here, arrivals, departures and stays of this species, recorded in hundreds of Spanish localities, were related to a set of environmental, geographical, biological and spatial predictors, and modeled by multiple regression. The best model for arrival dates accounted for up to 34% of variability of data and pointed towards an earlier arrival in those populations located in south-western Iberia and with higher population densities. This last relationship is probably due to the competition for nest-site fidelity maintenance. However, no variable was able to explain properly the blurred spatial pattern recorded for departure dates. Departure decisions are strongly influenced by social behavior in this species and depend on collective decisions influenced by peculiar local environmental conditions of each year rather than macrogeographical gradients. Environmental, geographical or spatial variables also did not capture much of the observed variability in the length of the stays among populations. However, this variable was strongly related to the arrival and departure dates of populations. White storks stay longer in localities with earlier arrivals and, especially, later departures.     

Climate impact on suicide rates in Finland from 1971 to 2003

Seasonal patterns of death from suicide are well-documented and have been attributed to climatic factors such as solar radiation and ambient temperature. However, studies on the impact of weather and climate on suicide are not consistent, and conflicting data have been reported. In this study, we performed a correlation analysis between nationwide suicide rates and weather variables in Finland during the period 1971–2003. The weather parameters studied were global solar radiation, temperature and precipitation, and a range of time spans from 1 month to 1 year were used in order to elucidate the dose-response relationship, if any, between weather variables and suicide. Single and multiple linear regression models show weak associations using 1-month and 3-month time spans, but robust associations using a 12-month time span. Cumulative global solar radiation had the best explanatory power, while average temperature and cumulative precipitation had only a minor impact on suicide rates. Our results demonstrate that winters with low global radiation may increase the risk of suicide. The best correlation found was for the 5-month period from November to March; the inter-annual variability in the cumulative global radiation for that period explained 40 % of the variation in the male suicide rate and 14 % of the variation in the female suicide rate, both at a statistically significant level. Long-term variations in global radiation may also explain, in part, the observed increasing trend in the suicide rate until 1990 and the decreasing trend since then in Finland.     

Suicide and Social Change in China

Using recently available data from China's Disease Surveillance Points system, we estimate that there are over 300,000 suicides in China per year; this makes suicide one of the most important causes of death in the country and makes the suicide rate in China one of the highest in the world. Moreover, the pattern of suicides in China is quite different than in other parts of the world – there are more completed suicides among females than males and rural rates are three-fold urban rates. The lack of reliable suicide data prior to 1987 makes it difficult to determine whether the rates are currently rising, falling, or staying constant. However, reports of suicides in the Chinese press and case studies conducted by the authors suggest (but do not prove) that the high rates of suicide currently experienced are related to the social changes that have occurred with the economic reforms (which started in 1978). Another possible explanation for the high rates of suicide is the large numbers of persons with depressive illness in China who remain untreated. Single-cause models of suicide (i.e., social factors or mental illness) do not do justice to the complexity of the processes involved and, therefore, do not provide useful information about the etiology and prevention of suicide in China or elsewhere. We describe our own dynamic model of suicide that includes five interacting factors which, we believe, collectively determine the suicide rates in a community.     

Suicide in Recent Onset Psychosis Revisited: Significant Reduction of Suicide Rate over the Last Two Decades — A Replication Study of a Dutch Incidence Cohort

This study aims to compare the suicide risk over the past decade following recent onset psychosis to findings from the eighties and nineties in the same catchment area and to identify predictors of suicide in the context of the Psychosis Recent Onset Groningen-Survey (PROGR-S). A medical file search was carried out to determine the current status of all patients admitted between 2000 and 2009. The suicide rate was compared with a study executed in 1973–1988 in the same catchment area. Predictors of suicide were investigated using Cox regression. The status of 424 of the 614 patients was known in July 2014. Suicide occurred in 2.4% of patients with psychosis disorders (n = 10; mean follow-up 5.6 years); 6 out of 10 suicides took place within two years. Within two decades, the suicide rate dropped from 11% (follow-up 15 years, 8.5% after 5 years) to 2.4%. The Standardized Mortality Rate (SMR) of suicides compared with the general population was 41.6. A higher age was the only significant predictor for suicide. Neuroticism, living situation, disorganized and negative symptoms, and passive coping style all showed a trend for significance. A significant reduction in the suicide rate was found for people with psychosis over the past decades. Given the high SMR, suicide research should be given the highest priority. Identifying predictors may contribute to further reduction of suicide among patients with psychosis.     

A neglected life-history trait: clutch-size variance in snakes

Most analyses of life-history traits have focused on mean values rather than their associated variance. We review published and original data on snakes, including records gathered over many years on single populations, to examine patterns in clutch-size variability in these animals. Within single populations, the coefficient of variation of clutch size did not vary significantly with maternal body size, or among years. The stability of clutch-size variance through time is consistent with experimental studies showing no significant influence of food intake rates on this characteristic. Clutch-size variances did not differ between viviparous and oviparous snakes, but were dependent upon allometric relationships involving maternal body size and the relationship between clutch size and body size. Clutch-size variability was highest in species with relatively variable female sizes, and with a high rate of increase in clutch size with increasing body size. These two factors acted to magnify the extent of clutch-size variability engendered by variability in maternal body sizes. The relationships among these variables were similar in the two squamate Suborders, but the larger body sizes and mean clutch sizes of snakes resulted in clutch-size variances being higher in snakes than in lizards.     

Association between the Density of Physicians and Suicide Rates in Japan: Nationwide Ecological Study Using a Spatial Bayesian Model

Background

Regional disparity in suicide rates is a serious problem worldwide. One possible cause is unequal distribution of the health workforce, especially psychiatrists. Research about the association between regional physician numbers and suicide rates is therefore important but studies are rare. The objective of this study was to evaluate the association between physician numbers and suicide rates in Japan, by municipality.

Methods

The study included all the municipalities in Japan (n = 1,896). We estimated smoothed standardized mortality ratios of suicide rates for each municipality and evaluated the association between health workforce and suicide rates using a hierarchical Bayesian model accounting for spatially correlated random effects, a conditional autoregressive model. We assumed a Poisson distribution for the observed number of suicides and set the expected number of suicides as the offset variable. The explanatory variables were numbers of physicians, a binary variable for the presence of psychiatrists, and social covariates.

Results

After adjustment for socioeconomic factors, suicide rates in municipalities that had at least one psychiatrist were lower than those in the other municipalities. There was, however, a positive and statistically significant association between the number of physicians and suicide rates.

Conclusions

Suicide rates in municipalities that had at least one psychiatrist were lower than those in other municipalities, but the number of physicians was positively and significantly related with suicide rates. To improve the regional disparity in suicide rates, the government should encourage psychiatrists to participate in community-based suicide prevention programs and to settle in municipalities that currently have no psychiatrists. The government and other stakeholders should also construct better networks between psychiatrists and non-psychiatrists to support sharing of information for suicide prevention.     

The North Atlantic Oscillation synchronises fruit production in western European forests

Weather and its lagged effects have been associated with interannual variability and synchrony of fruit production for several tree species. Such relationships are used often in hypotheses relating interannual variability in fruit production with tree resources or favourable pollinating conditions and with synchrony in fruit production among sites through the Moran effect (the synchronisation of biological processes among populations driven by meteorological variability) or the local availability of pollen. Climatic teleconnections, such as the North Atlantic Oscillation (NAO), representing weather packages, however, have rarely been correlated with fruit production, despite often being better predictors of ecological processes than is local weather. The aim of this study was to test the utility of seasonal NAO indices for predicting interannual variability and synchrony in fruit production using data from 76 forests of Abies alba, Fagus sylvatica, Picea abies, Pseudotsuga menziesii, Quercus petraea, and Q. robur distributed across central Europe. Interannual variability in fruit production for all species was significantly correlated with seasonal NAO indices, which were more prominently important predictors than local meteorological variables. The relationships identified by these analyses indicated that proximal causes were mostly responsible for the interannual variability in fruit production, supporting the premise that local tree resources and favourable pollinating conditions are needed to produce large fruit crops. Synchrony in fruit production between forests was mainly associated with weather and geographical distance among sites. Also, fruit production for a given year was less variable among sites during warm and dry springs (negative spring NAO phases). Our results identify the Moran effect as the most likely mechanism for synchronisation of fruit production at large geographical scales and the possibility that pollen availability plays a role in synchronising fruit production at local scales. Our results highlight the influence of the NAO on the patterns of fruit production across western Europe.     

Stochastic Models of Soil Denitrification

Soil denitrification is a highly variable process that appears to be lognormally distributed. This variability is manifested by large sample coefficients of variation for replicate estimates of soil core denitrification rates. Deterministic models for soil denitrification have been proposed in the past, but none of these models predicts the approximate lognormality exhibited by natural denitrification rate estimates. In this study, probabilistic (stochastic) models were developed to understand how positively skewed distributions for field denitrification rate estimates result from the combined influences of variables known to affect denitrification. Three stochastic models were developed to describe the distribution of measured soil core denitrification rates. The driving variables used for all the models were denitrification enzyme activity and CO₂ production rates. The three models were distinguished by the functional relationships combining these driving variables. The functional relationships used were (i) a second-order model (model 1), (ii) a second-order model with a threshold (model 2), and (iii) a second-order saturation model (model 3). The parameters of the models were estimated by using 12 separate data sets (24 replicates per set), and their abilities to predict denitrification rate distributions were evaluated by using three additional independent data sets of 180 replicates each. Model 2 was the best because it produced distributions of denitrification rate which were not significantly different (P > 0.1) from distributions of measured denitrification rates. The generality of this model is unknown, but it accurately predicted the mean denitrification rates and accounted for the stochastic nature of this variable at the site studied. The approach used in this study may be applicable to other areas of ecological research in which accounting for the high spatial variability of microbiological processes is of interest.