Number of suicides and suicide rates, by sex and age, in England and Wales. Information on conclusion type is provided, along with the proportion of suicides by method and the median registration delay.

Over *** thousand deaths due to suicides were recorded in India in 2022. Furthermore, majority of suicides were reported in the state of Tamil Nadu, followed by Rajasthan. The number of suicides that year had increased from the previous year. Some of the causes for suicides in the country were due to professional problems, abuse, violence, family problems, financial loss, sense of isolation and mental disorders. Depressive disorders and suicide As of 2015, over ****** million people worldwide suffered from some kind of depressive disorder. Furthermore, over ** percent of the total population in India suffer from different forms of mental disorders as of 2017. There exists a positive correlation between the number of suicide mortality rates and people with select mental disorders as opposed to those without. Risk factors for mental disorders Every ******* person in India suffers from some form of mental disorder. Today, depressive disorders are regarded as the leading contributor not only to disease burden and morbidity worldwide, but even suicide if not addressed. In 2022, the leading cause for suicide deaths in India was due to family problems. The second leading cause was due to illness. Some of the risk factors, relative to developing mental disorders including depressive and anxiety disorders, include bullying victimization, poverty, unemployment, childhood sexual abuse and intimate partner violence.

Download data on suicides in Massachusetts by demographics and year. This page also includes reporting on military & veteran suicide, and suicides during COVID-19.

This table contains the number of victims of suicide arranged by marital status, method, motives, age and sex. They represent the number deaths by suicide in the resident population of the Netherlands.

The figures in this table are equal to the suicide figures in the causes of death statistics, because they are based on the same files. The causes of death statistics do not contain information on the motive of suicide. For the years 1950-1995, this information is obtained from a historical data file on suicides. For the years 1996-now the motive is taken from the external causes of death (Niet-Natuurlijke dood) file. Before the 9th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD), i.e. for the years 1950-1978, it was not possible to code "jumping in front of train/metro". For these years 1950-1978 "jumping in front of train/metro" has been left empty, and it has been counted in the group "other method".

Relative figures have been calculated per 100 000 of the corresponding population group. The figures are calculated based on the average population of the corresponding year.

Data available from: 1950

Status of the figures: The figures up to and including 2023 are final.

Changes as of January 23rd 2025: The figures for 2023 are made final.

When will new figures be published: In the third quarter of 2025 the provisional figures for 2024 will be published.

BackgroundIn Europe, men have lower rates of attempted suicide compared to women and at the same time a higher rate of completed suicides, indicating major gender differences in lethality of suicidal behaviour. The aim of this study was to analyse the extent to which these gender differences in lethality can be explained by factors such as choice of more lethal methods or lethality differences within the same suicide method or age. In addition, we explored gender differences in the intentionality of suicide attempts.Methods and FindingsMethods. Design: Epidemiological study using a combination of self-report and official data. Setting: Mental health care services in four European countries: Germany, Hungary, Ireland, and Portugal. Data basis: Completed suicides derived from official statistics for each country (767 acts, 74.4% male) and assessed suicide attempts excluding habitual intentional self-harm (8,175 acts, 43.2% male).Main Outcome Measures and Data Analysis. We collected data on suicidal acts in eight regions of four European countries participating in the EU-funded “OSPI-Europe”-project (www.ospi-europe.com). We calculated method-specific lethality using the number of completed suicides per method * 100 / (number of completed suicides per method + number of attempted suicides per method). We tested gender differences in the distribution of suicidal acts for significance by using the χ2-test for two-by-two tables. We assessed the effect sizes with phi coefficients (φ). We identified predictors of lethality with a binary logistic regression analysis. Poisson regression analysis examined the contribution of choice of methods and method-specific lethality to gender differences in the lethality of suicidal acts.Findings Main ResultsSuicidal acts (fatal and non-fatal) were 3.4 times more lethal in men than in women (lethality 13.91% (regarding 4106 suicidal acts) versus 4.05% (regarding 4836 suicidal acts)), the difference being significant for the methods hanging, jumping, moving objects, sharp objects and poisoning by substances other than drugs. Median age at time of suicidal behaviour (35–44 years) did not differ between males and females. The overall gender difference in lethality of suicidal behaviour was explained by males choosing more lethal suicide methods (odds ratio (OR) = 2.03; 95% CI = 1.65 to 2.50; p < 0.000001) and additionally, but to a lesser degree, by a higher lethality of suicidal acts for males even within the same method (OR = 1.64; 95% CI = 1.32 to 2.02; p = 0.000005). Results of a regression analysis revealed neither age nor country differences were significant predictors for gender differences in the lethality of suicidal acts. The proportion of serious suicide attempts among all non-fatal suicidal acts with known intentionality (NFSAi) was significantly higher in men (57.1%; 1,207 of 2,115 NFSAi) than in women (48.6%; 1,508 of 3,100 NFSAi) (χ2 = 35.74; p < 0.000001).Main limitations of the studyDue to restrictive data security regulations to ensure anonymity in Ireland, specific ages could not be provided because of the relatively low absolute numbers of suicide in the Irish intervention and control region. Therefore, analyses of the interaction between gender and age could only be conducted for three of the four countries. Attempted suicides were assessed for patients presenting to emergency departments or treated in hospitals. An unknown rate of attempted suicides remained undetected. This may have caused an overestimation of the lethality of certain methods. Moreover, the detection of attempted suicides and the registration of completed suicides might have differed across the four countries. Some suicides might be hidden and misclassified as undetermined deaths.ConclusionsMen more often used highly lethal methods in suicidal behaviour, but there was also a higher method-specific lethality which together explained the large gender differences in the lethality of suicidal acts. Gender differences in the lethality of suicidal acts were fairly consistent across all four European countries examined. Males and females did not differ in age at time of suicidal behaviour. Suicide attempts by males were rated as being more serious independent of the method used, with the exceptions of attempted hanging, suggesting gender differences in intentionality associated with suicidal behaviour. These findings contribute to understanding of the spectrum of reasons for gender differences in the lethality of suicidal behaviour and should inform the development of gender specific strategies for suicide prevention.

BackgroundThe rise of depression, anxiety, and suicide rates has led to increased demand for telemedicine-based mental health screening and remote patient monitoring (RPM) solutions to alleviate the burden on, and enhance the efficiency of, mental health practitioners. Multimodal dialog systems (MDS) that conduct on-demand, structured interviews offer a scalable and cost-effective solution to address this need.ObjectiveThis study evaluates the feasibility of a cloud based MDS agent, Tina, for mental state characterization in participants with depression, anxiety, and suicide risk.MethodSixty-eight participants were recruited through an online health registry and completed 73 sessions, with 15 (20.6%), 21 (28.8%), and 26 (35.6%) sessions screening positive for depression, anxiety, and suicide risk, respectively using conventional screening instruments. Participants then interacted with Tina as they completed a structured interview designed to elicit calibrated, open-ended responses regarding the participants' feelings and emotional state. Simultaneously, the platform streamed their speech and video recordings in real-time to a HIPAA-compliant cloud server, to compute speech, language, and facial movement-based biomarkers. After their sessions, participants completed user experience surveys. Machine learning models were developed using extracted features and evaluated with the area under the receiver operating characteristic curve (AUC).ResultsFor both depression and suicide risk, affected individuals tended to have a higher percent pause time, while those positive for anxiety showed reduced lip movement relative to healthy controls. In terms of single-modality classification models, speech features performed best for depression (AUC = 0.64; 95% CI = 0.51–0.78), facial features for anxiety (AUC = 0.57; 95% CI = 0.43–0.71), and text features for suicide risk (AUC = 0.65; 95% CI = 0.52–0.78). Best overall performance was achieved by decision fusion of all models in identifying suicide risk (AUC = 0.76; 95% CI = 0.65–0.87). Participants reported the experience comfortable and shared their feelings.ConclusionMDS is a feasible, useful, effective, and interpretable solution for RPM in real-world clinical depression, anxiety, and suicidal populations. Facial information is more informative for anxiety classification, while speech and language are more discriminative of depression and suicidality markers. In general, combining speech, language, and facial information improved model performance on all classification tasks.

The dataset contains World Bank Suicide mortality rate WDI (world development indicator) (2000-2019) world-wide data in original and processed form. In addition to the statistical data this dataset also contains bibliographic records of articles published on the topic of suicide in relation to individual countries during (2000-2019) in original and processed form.

The data consists of six archives:

World development indicator suicide mortality rate SH.STA.SUIC.P5. This archive contains suicide mortality rate of 159 countries during the period of 2000-2019 per 100,000 population including males and females as of November, 2023.

Web of science records country and suicide. This archive contains bibliographic records organized by country on the topic of suicide related to that country published during 2000-2019 as of November, 2023.

Suicide mortality rate statistics and keywords. This archive contains processed data of 1 and 2 archives in three files. The 'Countries suicide rates and WOS records' contains organized temporal suicide mortality rate data for each country and each year for males and females including counts of articles on suicide related in that country. The 'words and countries matrix' file contains information about how many times author and paper keywords from suicide related publications were seen in articles associated with each country. This data is organized as matrix in which rows are keywords, columns are countries and cells are counts of the keyword. The 'words and countries pairs' file contains same information only organized as keyword country pairs.

Suicide mortality rate clusters countries keywords titles. This archive contains bibliographic data organized by country clusters. These clusters group countries with similar suicide mortality rate dynamics in males and females shown in two included figures. Each folder of the cluster contains a section with bibliographic records; a section with keywords associated with each country; and a section in which each publication associated with the country has a separate filecontaining its title and keywords.

Suicide keywords embedding data. This archive contains word embedding vectors and metadata learned by recurrent neural network trained to classify countries from suicide related keywords of articles associated with those countries. Folder 'trained with keywords' contains embeddings learned in classifying countries in which training samples are keyword strings of publications. Folder 'trained with titles' contains embeddings learned in classifying countries in which training samples are strings containing titles of publication plus keywords.

Suicide keywords association rule mining. This archive contains files of subsets of keywords frequently mentioned together in suicide related publications. Folder 'Mining in clusters' has frequent keyword itemsets in country clusters. Folder 'Mining in individual countries' has frequent keyword itemsets in countries. Examples of keyword networks connecting clusters and networks connecting countries in individual clusters are included which helps to identify specific and shared keywords by country clusters and by countries in the individual clusters.

These datasets support a data availability statements for upcoming articles.

Context

This dataset contains information on suicides which happened in India during 2015.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4208638%2Ffab2e99b439f9780daf358511060f514%2FWorld-Suicide-Prevention-Day.jpg?generation=1598114750200382&alt=media" alt="">

The singular age-old social precept of 'Lok Kya Kahenge?' (loosely translated: "What will people say?") suppresses the much-needed psychological care in India. It's high time that we understand why suicides happen and what are the reasons behind it. This dataset aims to spread awareness about suicides in India.

Content

I acquired this dataset from here. Have a look at the website.

This dataset contains 9 files in .csv format. You can find a description for each column. Let me summarize it here as well.

1. Cause-wise distribution of suicides in Central Armed Police Force (CAPF) during 2015.
2. Economic Status-wise distribution of suicides during 2015.
3. Educational Status-wise distribution of suicides during 2015.
4. Farmer or Cultivators distribution of suicides during 2015.
5. Profession-wise distribution of suicides during 2015.
6. Social status-wise distribution of suicides during 2015.
7. Cause-wise distribution of suicides during 2015.
8. Suicides by Agricultural labourers during 2015.
9. Suicides by means adopted during 2015.

Inspiration

We now have plenty of data to explore to draw some conclusions about suicides which happened in India during 2015. Let's start by answering these questions: - What are the top 5 states where Farmers' suicides occurred the most? - What's the top reason that agricultural labourers committed suicide? - Which Profession has the most suicides? What could be the reason? - How many Transgender suicides have occurred in different categories?

I hope these questions interest you in starting to explore this dataset.

Acknowledgements

I thank the Indian Government for making it public under their Open Government Data (OGD) Platform India. Please use this dataset strictly for educational purposes. Thank you.

Historical chart and dataset showing Nigeria suicide rate by year from 2000 to 2021.

Numbers and rates of suicide fatalities in NS by year, month, sex, and health zone of residence.

This dataset contains counts of deaths for California as a whole based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in California regardless of the place of residence (by occurrence) and deaths to California residents (by residence), whereas the provisional data table only includes deaths that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

Abstract (en): The Drug Abuse Warning Network (DAWN) is a nationally representative public health surveillance system that has monitored drug related emergency department (ED) visits to hospitals since the early 1970s. First administered by the Drug Enforcement Administration (DEA) and the National Institute on Drug Abuse (NIDA), the responsibility for DAWN now rests with the Substance Abuse and Mental Health Services Administration's (SAMHSA) Center for Behavioral Health Statistics and Quality (CBHSQ). Over the years, the exact survey methodology has been adjusted to improve the quality, reliability, and generalizability of the information produced by DAWN. The current approach was first fully implemented in the 2004 data collection year. DAWN relies on a longitudinal probability sample of hospitals located throughout the United States. To be eligible for selection into the DAWN sample, a hospital must be a non-Federal, short-stay, general surgical and medical hospital located in the United States, with at least one 24-hour ED. DAWN cases are identified by the systematic review of ED medical records in participating hospitals. The unit of analysis is any ED visit involving recent drug use. DAWN captures both ED visits that are directly caused by drugs and those in which drugs are a contributing factor but not the direct cause of the ED visit. The reason a patient used a drug is not part of the criteria for considering a visit to be drug-related. Therefore, all types of drug-related events are included: drug misuse or abuse, accidental drug ingestion, drug-related suicide attempts, malicious drug poisonings, and adverse reactions. DAWN does not report medications that are unrelated to the visit. The DAWN public-use dataset provides information for all types of drugs, including illegal drugs, prescription drugs, over-the-counter medications, dietary supplements, anesthetic gases, substances that have psychoactive effects when inhaled, alcohol when used in combination with other drugs (all ages), and alcohol alone (only for patients aged 20 or younger). Public-use dataset variables describe and categorize up to 22 drugs contributing to the ED visit, including toxicology confirmation and route of administration. Administrative variables specify the type of case, case disposition, categorized episode time of day, and quarter of year. Metropolitan area is included for represented metropolitan areas. Created variables include the number of unique drugs reported and case-level indicators for alcohol, non-alcohol illicit substances, any pharmaceutical, non-medical use of pharmaceuticals, and all misuse and abuse of drugs. Demographic items include age category, sex, and race/ethnicity. Complex sample design and weighting variables are included to calculate various estimates of drug-related ED visits for the Nation as a whole, as well as for specific metropolitan areas, from the ED visits classified as DAWN cases in the selected hospitals. DAWN includes a set of complex sample design variables to calculate estimates for the entire universe of DAWN-eligible hospitals in the United States from the sampled hospitals participating in DAWN. The primary sampling weights reflect the probability of selection, and separate adjustment factors are included to account for sampling of ED visits, nonresponse, data quality, and the known total of ED visits delivered by the universe of eligible hospitals. DAWN design variables include: variance estimation stratum (STRATA), PSU, replicate (REPLICATE), PSU frame count (PSUFRAME), and case weight (CASEWGT). ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Created variable labels and/or value labels.; Standardized missing values.; Created online analysis version with question text.; Performed recodes and/or calculated derived variables.; Checked for undocumented or out-of-range codes.. Response Rates: For 2009, 242 hospitals submitted data that were used for estimation. The overall weighted response rate was 31.8 percent. For the 12 oversampled metropolitan areas and divisions, the individual response rates ranged from 28.5 percent in the Ho...

BackgroundWhen a person dies by suicide, it takes a reverberating emotional, physical, and economic toll on families and communities. The widespread use of social media among youth and adolescents, disclosures of emotional distress, suicidal ideation, intent to self-harm, and other mental health crises posted on these platforms have increased. One solution to address the need for responsive suicide prevention and mental health services is to implement a culturally-tailored gatekeeper training. The Northwest Portland Area Indian Health Board (NPAIHB) developed Mind4Health, an online gatekeeper training (90 min) and text message intervention for caring adults of American Indian/Alaska Native (AI/AN) youth.MethodsThe Mind4Health intervention was a multi-phase, single-arm, pre-and post-test study of users enrolled in the intervention that is available via text message (SMS) or via a 90 min online, self-paced training. We produced four datasets in this study: Mobile Commons, pre-survey data, post-survey data, and Healthy Native Youth website’s Google Analytics. The analysis included data cleaning, basic frequency counts, percentages, and descriptive statistics. Qualitative data were analyzed using thematic content analysis methods and hand-coding techniques with two independent coders.ResultsFrom 2022 to 2024, 280 people enrolled in the Mind4Health SMS training, and 250 completed the 8-week intervention. Many messages in the sequence were multi-part text messages and over 21,500 messages were sent out during the timeframe. Of the 280 subscribers, 52 participated in the pre-survey. Pre-survey data show that 94% of participants were female, and nearly one-fourth lived in Washington state, 92% of participants in the pre-survey were very to moderately comfortable talking with youth about mental health (n = 48). Most participants interact with youth in grades K–12. Post-survey data demonstrate changes in knowledge, beliefs, comfort talking about mental health, and self-efficacy among participants. Mind4Health improved participant’s skills to have mental health conversations with youth and refer youth to resources in their community.

Frequency of factors related to Suicide Risk Assessment (SRA).