Backgrounds
Whether suicide in China has significant seasonal variations is unclear. The aim of this study is to examine the seasonality of suicide in Shandong China and to assess the associations of suicide seasonality with gender, residence, age and methods of suicide.

Methods
Three types of tests (Chi-square, Edwards' T and Roger's Log method) were used to detect the seasonality of the suicide data extracted from the official mortality data of Shandong Disease Surveillance Point (DSP) system. Peak/low ratios (PLRs) and 95% confidence intervals (CIs) were calculated to indicate the magnitude of seasonality.

Results
A statistically significant seasonality with a single peak in suicide rates in spring and early summer, and a dip in winter was observed, which remained relatively consistent over years. Regardless of gender, suicide seasonality was more pronounced in rural areas, younger age groups and for non-violent methods, in particular, self-poisoning by pesticide.

Conclusions
There are statistically significant seasonal variations of completed suicide for both men and women in Shandong, China. Differences exist between residence (urban/rural), age groups and suicide methods. Results appear to support a sociological explanation of suicide seasonality.

Seasonality of suicide in Shandong China, 1991–2009 : associations with gender, age, area and methods of suicide

This study analyzed the impact of meteorological variables and high-lethality suicide attempts (HLSA) to assess a potential time shift of HLSA affected by climate evolution to predict the suicide attempt cases over different periods of the year. After attempting suicide, 225 subjects were admitted to the emergency ward of the IRCCS Ospedale Policlinico San Martino and later to the psychiatric unit from March 2016 to July 2018. Socio-demographic and clinical characteristics as well as the meteorological variables were collected. The Mann-Kendall test as well as redundancy and cross-correlation analyses were performed to analyze the trends, statistically correlations, and correspondence of the trends, respectively between suicidal behaviors and climatic factors. Sixty-seven (29.8%) committed a HLSA. Our findings indicate a significant association between HLSA and male gender and apparent temperature with a strong correlation of 75% with a phase shift of -1 month. Solar radiation and air pollution (PM2.5) have a positive correlation of 65 and 32%, respectively, with a zero-time lag. Limitations include that the data are limited to a single hospital; psychological factors, or other clinical variables that could be ruled out as a trigger have not been considered. Meteorological variables may not mirror the temperature that the patient is exposed to due to the air conditioning systems. Exploring those environmental factors associated with HLSA in a more detailed manner could lead to early intervention and prevention strategies for such distressing admissions.

/pdf/meteorological-variables-and-suicidal-behavior-air-pollution-24fjpe1kbd.pdf

Meteorological Variables and Suicidal Behavior: Air Pollution and Apparent Temperature Are Associated With High-Lethality Suicide Attempts and Male Gender.

Background Although seasonal variations in mortality have been recognized for millennia, the role of temperature remains unclear. We aimed to assess seasonal variation in mortality and to examine the contribution of temperature. Methods We compiled daily data on all-cause, cardiovascular and respiratory mortality, temperature and indicators on location-specific characteristics from 719 locations in tropical, dry, temperate and continental climate zones. We fitted time-series regression models to estimate the amplitude of seasonal variation in mortality on a daily basis, defined as the peak-to-trough ratio (PTR) of maximum mortality estimates to minimum mortality estimates at day of year. Meta-analysis was used to summarize location-specific estimates for each climate zone. We estimated the PTR with and without temperature adjustment, with the differences representing the seasonal effect attributable to temperature. We also evaluated the effect of location-specific characteristics on the PTR across locations by using meta-regression models. Results Seasonality estimates and responses to temperature adjustment varied across locations. The unadjusted PTR for all-cause mortality was 1.05 [95% confidence interval (CI): 1.00-1.11] in the tropical zone and 1.23 (95% CI: 1.20-1.25) in the temperate zone; adjusting for temperature reduced the estimates to 1.02 (95% CI: 0.95-1.09) and 1.10 (95% CI: 1.07-1.12), respectively. Furthermore, the unadjusted PTR was positively associated with average mean temperature. Conclusions This study suggests that seasonality of mortality is importantly driven by temperature, most evidently in temperate/continental climate zones, and that warmer locations show stronger seasonal variations in mortality, which is related to a stronger effect of temperature.

Seasonal variation in mortality and the role of temperature: a multi-country multi-city study.

An application of multivariate meta-analysis for pooling estimates of non-linear associations from multiple studies. The code included is applicable beyond the specific setting of time series analysis of temperature-health relationships. The example illustrate the analysis in the article: Gasparrini A, Armstrong B, Kenward MG. Multivariate meta-analysis for non-linear and other multi-parameter associations. Statistics in Medicine. 2012;31(29):3821-39. The original example included in the article was based on data from the National Mortality, Morbidity, and Air Pollution Study (NMMAPS), which at the time of the publication was available through the R package NMMAPSlite. Unfortunately, the data are not available any more and the package NMMAPSlite has been archived. In order to provide a working example, the analysis has been replaced with a similar analysis on a publicly available dataset. The material covers: [1] regEngWales.csv stores the dataset used to perform an illustrative example, including time series data for 10 regions in England and Wales for 1993-2006; [2] the numbered files from 01.mkdata.R to 06.addresults.R, to be run sequentially, reproduce the example (different from that in the article). 
The code primarily uses functions in the R package mvmeta, but functions in the R package dlnm are applied as well.

R code and data for: "Multivariate meta-analysis for non-linear and other multi-parameter associations"

We investigated the effect of the coronavirus disease-2019 (COVID-19) pandemic on suicide trends in Korea via a time-series analysis. We used Facebook Prophet to generate forecasting models based on the monthly numbers of suicide deaths in Korea between 1997 and 2018, validated the models by comparison with the 2019 numbers, and predicted the numbers of suicides in 2020. We compared the expected and observed numbers of suicides during the COVID-19 pandemic. The total numbers of suicides during the COVID-19 pandemic did not deviate from projections based on the pre-pandemic period. However, the number of suicides among women and those under the age of 34 years significantly exceeded the expected level. The COVID-19 pandemic did not increase the overall suicide rate significantly. However, suicides among women and young people increased, suggesting that the pandemic might drive more members of these groups to suicide. Further studies are needed to verify the long-term impact of the COVID-19 pandemic on suicide.

Trends in suicide deaths before and after the COVID-19 outbreak in Korea

AIMS: We aimed to investigate the heterogeneity of seasonal suicide patterns among multiple geographically, demographically and socioeconomically diverse populations. METHODS: Weekly time-series data of suicide counts for 354 communities in 12 countries during 1986-2016 were analysed. Two-stage analysis was performed. In the first stage, a generalised linear model, including cyclic splines, was used to estimate seasonal patterns of suicide for each community. In the second stage, the community-specific seasonal patterns were combined for each country using meta-regression. In addition, the community-specific seasonal patterns were regressed onto community-level socioeconomic, demographic and environmental indicators using meta-regression. RESULTS: We observed seasonal patterns in suicide, with the counts peaking in spring and declining to a trough in winter in most of the countries. However, the shape of seasonal patterns varied among countries from bimodal to unimodal seasonality. The amplitude of seasonal patterns (i.e. the peak/trough relative risk) also varied from 1.47 (95% confidence interval [CI]: 1.33-1.62) to 1.05 (95% CI: 1.01-1.1) among 12 countries. The subgroup difference in the seasonal pattern also varied over countries. In some countries, larger amplitude was shown for females and for the elderly population (≥65 years of age) than for males and for younger people, respectively. The subperiod difference also varied; some countries showed increasing seasonality while others showed a decrease or little change. Finally, the amplitude was larger for communities with colder climates, higher proportions of elderly people and lower unemployment rates (p-values < 0.05). CONCLUSIONS: Despite the common features of a spring peak and a winter trough, seasonal suicide patterns were largely heterogeneous in shape, amplitude, subgroup differences and temporal changes among different populations, as influenced by climate, demographic and socioeconomic conditions. Our findings may help elucidate the underlying mechanisms of seasonal suicide patterns and aid in improving the design of population-specific suicide prevention programmes based on these patterns.

/pdf/seasonality-of-suicide-a-multi-country-multi-community-3fbuckispe.pdf

Seasonality of suicide: a multi-country multi-community observational study.

Two-stage meta-analysis has been popularly used in epidemiological studies to investigate an association between environmental exposure and health response by analyzing time-series data collected from multiple locations. The first stage estimates the location-specific association, while the second stage pools the associations across locations. The second stage often incorporates location-specific predictors (i.e., meta-predictors) to explain the between-location heterogeneity and is called meta-regression. The existing second-stage meta-regression relies on parametric assumptions and does not accommodate functional meta-predictors and spatial dependency. Motivated by these limitations, our research proposes a nonparametric Bayesian meta-regression which relaxes parametric assumptions and incorporates functional meta-predictors and spatial dependency. The proposed meta-regression is formulated by jointly modeling the association parameters and the functional meta-predictors using Dirichlet process (DP) or local DP mixtures. In doing so, the functional meta-predictors are represented parsimoniously by the coefficients of the orthonormal basis. The proposed models were applied to (1) a temperature–mortality association study and (2) suicide seasonality study, and validated through a simulation study. Supplementary materials accompanying this paper appear online.

Nonparametric Bayesian Functional Meta-Regression: Applications in Environmental Epidemiology

Nonparametric Bayesian (np Bayes) statistical models are popularly used in a variety of research areas because of their flexibility and computational convenience. This paper reviews the np Bayes models focusing on biomedical research applications. We review key probability models for np Bayes inference while illustrating how each of the models is used to answer different types of research questions using biomedical examples. The examples are chosen to highlight the problems that are challenging for standard parametric inference but can be solved using nonparametric inference. We discuss np Bayes inference in four topics: (1) density estimation, (2) clustering, (3) random effects distribution, and (4) regression.

Jae-Eun Yu

Papers

Seasonality of suicide: a multi-country multi-community observational study.

Nonparametric Bayesian Functional Meta-Regression: Applications in Environmental Epidemiology

Nonparametric Bayesian Statistical Models in Biomedical Research