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

The Youth Risk Behavior Surveillance System (YRBSS) is a set of surveys that monitor priority health risk behaviors and experiences that contribute markedly to the leading causes of death, disability, and social problems among youth of grade 9 -12 in the United States. The surveys are administered every other year and it is maintained by the Centers for Disease Control and Prevention (CDC). A total of 107 questionnaire are asked. Some of the health-related behaviors and experiences monitored are: * Student demographics: sex, sexual identity, race and ethnicity, and grade * Youth health behaviors and conditions: sexual, injury and violence, bullying, diet and physical activity, obesity, and mental health, suicide attempt * Substance use behaviors: electronic vapor product and tobacco product use, alcohol use, and other drug use * Student experiences: parental monitoring, school connectedness, unstable housing, and exposure to community violence The dataset is used by a group of graduate students from Texas State University for 2025 TXST Open Datathon. The main YRBSS dataset includes data of multiple years, various states, district. For analyzing demographic variations associated with suicide, the 1991–2023 combined district dataset (https://www.cdc.gov/yrbs/files/sadc_2023/HS/sadc_2023_district.dat) is used, which offers a broad historical perspective on trends across different groups. To examine the preventive measures and develop a predictive model for suicide risk, the 2023 dataset (https://www.cdc.gov/yrbs/files/2023/XXH2023_YRBS_Data.zip) was used, ensuring the inclusion of the most recent behavioral and attributes. Please review the 2023 YRBS Data User's Guide by CDC for further information.

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.

ObjectiveThe majority of suicide decedents have had contact with health services in the months before their death. Contacts for mental health services present potential suicide prevention opportunities. This study aims to compare contact-based characteristics among suicide decedents and living controls in the year subsequent to clinical mental health contact with the public health system in Victoria, Australia.MethodsA population-based nested case-control study of those who had mental health-related hospital and community contacts with the public health system was conducted. Cases (suicide decedents) were age and gender-matched to living controls (suicide non-decedents). These records were linked to records of suicides that occurred in the 12 months following the health service contact, between January 1, 2011, and December 31, 2016. Victorian residents aged 10 years and above were selected at the time of contact (483,933 clients). In the study population, conditional logistic regression models were used to assess the relationship between contact-based characteristics and suicide. Socio-demographics and mental health-related hospital and community contact data was retrieved from the Victorian Admitted Episodes Dataset, the Victorian Emergency Minimum Dataset and the Public Clinical Mental Health database and suicide data from the Victorian Suicide Register.ResultsDuring a six-year period, 1,091 suicide decedents had at least one mental health contact with the public health system in the 12 months preceding the suicide. Overall, controls used more mental health services than cases; however, cases used more mental health services near the event. The relationship between the type of service and suicide differed by service type: hospital admissions and emergency department presentations had a significant positive association with suicide with an OR of 2.09 (95% CI 1.82–2.40) and OR of 1.13 (95% CI 1.05–1.22), and the effect size increased as the event approached, whereas community contacts had a significant negative association with an OR of 0.93 (95% CI 0.92–0.94), this negative association diminished in magnitude as the event approached (OR∼1).ConclusionSuicide decedents had less contact with mental health services than non-decedents; however, evidence suggests suicide decedents reach out to mental health services proximal to suicide. An increase in mental health service contact by an individual could be an indication of suicide risk and therefore an opportunity for intervention. Further, community level contact should be further explored as a possible prevention mechanism considering the majority of suicide decedents do not access the public clinical mental health services.

Average Annual Numbers and Rates of Suicide Fatalities in NS by age group and sex, time periods: 2010-2012, 2013-2015, 2016-2018, 2019-2021.

This paper presents data on mental health, suicidal behaviors, and suicide literacy and suicide stigma. A cross-sectional, face to face interview project was carried out between August and October 2019 within a total 5- Bangladeshi undergraduate dental institutes. Using a convenient sampling technique, a total of 487 students participated in the survey and 468 were kept for final analysis. The questionnaire included questions on (i) socio-demographics, (ii) family and personal psychiatric history, (iii) depression, (iv) anxiety, (v) suicidal behaviors, (vi) suicide literacy, and (vii) suicide stigma. Data was analyzed by using Statistical Package for Social Science (SPSS) version 22 and represented as frequencies, percentages herein. Further study from this data may help providing necessary information to develop effective healthcare policy and reduce adverse psychological effects and unexpected suicidal behaviors among dental students.

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.

Age-adjusted rate of suicide deaths for Santa Clara County residents. The data are provided for the total county population and by sex and race/ethnicity. Data trends are presented from 2007 to 2016. Source: Santa Clara County Public Health Department, VRBIS, 2007-2016. Data as of 05/26/2017; U.S. Census Bureau, 2010 Census.METADATA:Notes (String): Lists table title, notes and sourceYear (String): Year of death Category (String): Lists the category representing the data: Santa Clara County is for total population, sex: Male and Female, race/ethnicity: African American, Asian/Pacific Islander, Latino and White (non-Hispanic White only) and Asian/Pacific Islander subgroups: Asian Indian, Chinese. Filipino, Korean and Vietnamese.Age adjusted rate per 100,000 people (Numeric): The Tenth Revision of the International Classification of Diseases codes (ICD-10) are used for coding causes of death. Age-adjusted rate is calculated using 2000 U.S. Standard Population. Suicide rate is number of suicide deaths in a year per 100,000 people in the same time period.

This study was initiated by the administrator of a county jail in the Northern Plains of the United States who was concerned about the incidence of suicide behaviors in that facility, particularly among the American Indian population. It was a two-year project designed to evaluate the existing admissions suicide screening tool and to improve the instrument's cultural relevance for the American Indian population. The existing screening instrument used in the county jail to interview inmates at their intake was developed in New York. The main objective of the first year of the project was to determine if that instrument was culturally appropriate for the jailed American Indian population. The principal objective of the second year of the project was to determine whether the employment of different suicide screening protocols would make a difference in the responses of new detainees with regard to the likelihood of securing their honest reports of experiencing suicide ideation and its associated risk factors. For the duration of the project, all male and female inmates aged 18 and older who were booked into the jail went through the customary booking procedure that included the administration of the New York Suicide Prevention Screening Guidelines. In the first year of the project, researchers also administered a short self-report survey consisting of measures commonly associated with suicidal ideation. The self-report survey measured stress, anxiety, suicide ideation, hopelessness, and suicidal behavior history. The protocols in the second year of the project reflected efforts to test different screening conditions for four experimental groups and one control group of new detainees. The outcome variables of the short self-report survey consisted of measures of demographics, comfort experience during booking and the screening process, self-efficacy and management of depression, knowledge of mental health support available within the jail, and general well-being. In addition to the quantitative data collection, qualitative data were also collected to develop a straightforward assessment of suicide ideation criteria in this specific jail setting using semi-structured focus group interviews.

This dataset provides comprehensive information on the total number of suicides in Mexico from 1990 to 2023, categorized by sex and state.The dataset adheres to the government methodology by using the year of registration and the state of residence of the deceased as key variables. It includes the following data points:The total male and female populations.Suicide counts for males and females.Suicide rates for each sex.Data SourcesSuicide Data: Extracted from the INEGI database of registered deaths.Source: INEGI - Microdata on DeathsPopulation Data: Sourced from Mexican government population projections for 2020-2070.Source: Gob.mx - Population ProjectionsThis dataset is a valuable resource for understanding trends in suicide across Mexico and offers insights into differences by sex and state-level demographics.

Age-adjusted rate of patient discharges after being hospitalized due to suicide attempts/ideation for Santa Clara County residents. The data are provided for the total county population and by sex and race/ethnicity. The data trends are presented from 2007 to 2014. Source: Office of Statewide Planning and Development, 2007-2014 Patient Discharge Data; U.S. Census Bureau, 2010 Census.METADATA:Notes (String): Lists table title, notes and sourceYear (Numeric): Year of hospital dischargeCategory (String): Lists the category representing the data: Santa Clara County is for total population, sex: Male and Female, and race/ethnicity: African American, Asian/Pacific Islander, Latino and White (non-Hispanic White only).Age adjusted rate per 100,000 people (String): The Ninth Revision of the International Classification of Diseases codes (ICD-9) are used for coding patient discharge data. Age-adjusted rate is calculated using 2000 U.S. Standard Population. Rate of hospitalization due to suicide attempt/ideation is number of related hospital discharges in a year per 100,000 people in the same time period. Data are not presented if the number of hospital discharges is 15 or less.

Abstract copyright UK Data Service and data collection copyright owner. The aims of the project were to examine alcohol- and suicide-related beliefs among UK Protestants and Jews, both men and women, to investigate the so-called alcohol-suicide-depression hypothesis. This hypothesis suggests that attitudes to alcohol use and suicide will be more favourable among Protestants than Jews, and among men more than women. Questionnaire measures of alcohol- and suicide-related beliefs and behaviour assessed the dependent variables in an analysis of covariance design. The independent variables were cultural-religious group (Protestant vs. Jewish background or affiliation). Covariates, assessed by questionnaire measures, were religiosity, depression, anxiety, and (a new measure of) tolerance for depression. Main Topics: The data cover: demographics - participant's age, other demographic factors, religious practice; alcohol - consumption, beliefs about alcoholism, expectations about alcohol's effects, attitudes to alcohol use; suicide - attempts, ideation, reasons for living; tolerance for depression; depression, anxiety. Standard Measures Reasons for Living inventory (RFL): Linehan, M. M. et al (1983) 'Reasons for staying alive when you are thinking of killing yourself: the Reasons for Living inventory' Journal of Consulting and Clinical Psychology, 52, pp.276-286. Religious Activity Measure, from: Loewenthal, K. M., Macleod, A. K. and Cinnirella, M. (2001) 'Are women more religious than men? Gender differences in religious activity among different religious groups in the UK' Personality and Individual Differences. Biphasic Alcohol Effects Scale (BAES): Martin, C. S. et al (1993) 'Development and validation of the Biphasic Alcohol Effects Scale' Alcoholism - Clinical and Experimental Research, 17, pp.140-146. Alcohol consumption, from: Weiss, S. and Moore, M. (1992) 'Perception of alcoholism among Jewish, Moslem and Christian teachers in Israel' Journal of Drug Education, 22, pp.253-260. Suicide ideation and attempts, from the Present State Examination: Wing, J. K., Cooper, J. E. and Sartorius, N. (1973) The measurement and classification of psychiatric symptoms, London: Cambridge University Press. Anxiety, depression: Zigmond, A. S. and Snaith, R. P. (1993) 'The Hospital Anxiety and Depression Scale' Acta Psychiatrica Scandinavia, 67, pp.361-370.

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.

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

The factsheet gives a brief overview of the key demographics and risk factors related to suicides, and how understanding of these factors supports local prevention measures in Camden.

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.

The Transdiagnostic Connectome Project

A richly phenotyped transdiagnostic dataset with behavioral and Magnetic Resonance Imaging (MRI) data from 241 individuals aged 18 to 70, comprising 148 individuals meeting diagnostic criteria for a broad range of psychiatric illnesses and a healthy comparison group of 93 individuals.

These data include high-resolution anatomical scans and 6 x resting-state, and 3 x task-based (2 x Stroop, 1 x Faces/Shapes) functional MRI runs. Participants completed over 50 psychological and cognitive questionnaires, as well as a semi-structured clinical interview.

Data was collected at the Brain Imaging Center, Yale University, New Haven, CT and McLean Hospital, Belmont, MA. This dataset will allow investigation into brain function and transdiagnostic psychopathology in a community sample.

Inclusion Criteria

Participants in the study met the following inclusion criteria:

• Aged 18 to 64 years and spoke English
• No metal contraindications, no history of concussion or prior neurological problems, no color-blindness
• Prior history of affective or psychotic illness or no psychiatric history

Exclusion criteria

Participants meeting any of the criteria listed below were excluded from the study: * Neurological disorders * Pervasive developmental disorders (e.g., autism spectrum disorder) * Any medical condition that increases risk for MRI (e.g., pacemaker, dental braces) * MRI contraindications (e.g., claustrophobia pregnancy)

Consent

Institutional Review Board approval and consent were obtained. To characterise the sample, we collected data on race/ethnicity, income, use of psychotropic medication, and family history of medical or psychiatric conditions.

Clinical Measures

Completed by Participants:

• Health and demographics questionnaire
• Alcohol Tobacco Caffeine Use Questionnaire (ATC)
• Broad Autism Phenotype Questionnaire (BAPQ-2)
• Barratt Impulsiveness Scale (BIS)
• Behavioral Inhibition/Activation Scale (BISBAS)
• Childhood Trauma Questionnaire (CTQ)
• Domain Specific Risk Taking (DOSPERT)
• Fagerstrom Test for Nicotine Dependence (FTND)
• NEO Five Factor Inventory (NEO-FFI)
• Quick Inventory of Depressive Symptomatology (QIDS)
• Multidimensional Scale for Perceived Social Support (MSPSS)
• State-Trait Anxiety Inventory (STAI)
• Temperament Character Inventory (TCI)
• Anxiety Sensitivity Index (ASI)
• Depression Anxiety Stress Scale (DASS)
• Profile of Mood States (POMS)
• Perceived Stress Scale (PSS)
• Shipley Institute of Living Scale (Shipley)
• Temporal Experience of Pleasure Scale (TEPS)
• Cognitive Emotion Regulation Questionnaire (CERQ)
• Cognitive Failures Questionnaire (CFQ)
• Cognitive Reflections Test (CRT)
• Experiences in Close Relationships Inventory (ECR)
• Positive Urgency Measure (PUM)
• Ruminative Responses Scale (RRS)
• Retrospective Self-Report of Inhibition (RSRI)
• Snaith-Hamilton Pleasure Scale (SHAPS)
• Test My Brain (TMB)
• Stroop Task (during fMRI)
• Hammer Task (during fMRI)

Completed by Clinicians:

• Structured Clinical Interview for DSM-5 Disorder (SCID-5)
• Clinical Global Impression (CGI)
• Anxiety Symptom Chronicity (ASC)
• Columbia Suicide Severity Rating Scale (CSSR-S)
• Range of Impaired Functioning Tool (LIFE-RIFT)
• Montgomery-Asberg Depression Rating Scale (MADRS)
• Multnomah Community Ability Scale (MCAS)
• Positive and Negative Syndrome Scale (PANSS)
• Panic Disorder Severity Scale (PDSS)
• Young Mania Rating Scale (YMRS)

Clinical Measures Data

Relevant clinical measures can be found in the phenotype folder, with each measure and its items described in the relevant _definition .csv file. The 'qc' columns indicate quality control checks done on each members (i.e., number of unanswered items by a participant.) '999' values indicate missing or skipped data.

Detailed information and imaging protocols regarding the dataset can be found here: [Add preprint Link]

Abstract copyright UK Data Service and data collection copyright owner. The UCL COVID-19 Social Study at University College London (UCL) was launched on 21 March 2020. Led by Dr Daisy Fancourt and Professor Andrew Steptoe from the Department of Behavioural Science and Health, the team designed the study to track in real-time the psychological and social impact of the virus across the UK. The study quickly became the largest in the country, growing to over 70,000 participants and providing rare and privileged insight into the effects of the pandemic on people’s daily lives. Through our participants’ remarkable two-year commitment to the study, 1.2 million surveys were collected over 105 weeks, and over 100 scientific papers and 44 public reports were published. During COVID-19, population mental health has been affected both by the intensity of the pandemic (cases and death rates), but also by lockdowns and restrictions themselves. Worsening mental health coincided with higher rates of COVID-19, tighter restrictions, and the weeks leading up to lockdowns. Mental health then generally improved during lockdowns and most people were able to adapt and manage their well-being. However, a significant proportion of the population suffered disproportionately to the rest, and stay-at-home orders harmed those who were already financially, socially, or medically vulnerable. Socioeconomic factors, including low SEP, low income, and low educational attainment, continued to be associated with worse experiences of the pandemic. Outcomes for these groups were worse throughout many measures including mental health and wellbeing; financial struggles;self-harm and suicide risk; risk of contracting COVID-19 and developing long Covid; and vaccine resistance and hesitancy. These inequalities existed before the pandemic and were further exacerbated by COVID-19, and such groups remain particularly vulnerable to the future effects of the pandemic and other national crises.Further information, including reports and publications, can be found on the UCL COVID-19 Social Study website. Main Topics: The study asked baseline questions on the following: Demographics, including year of birth, sex, ethnicity, relationship status, country of dwelling, urban/rural dwelling, type of accommodation, housing tenure, number of adults and children in the household, household income, education, employment status, pet ownership, and personality. Health and health behaviours, including pre-existing physical health conditions, diagnosed mental health conditions, pregnancy, smoking, alcohol consumption, physical activity, caring responsibilities, usual social behaviours, and social network size. It also asked repeated questions at every wave on the following: COVID-19 status, including whether the respondent had had COVID-19, whether they had come into likely contact with COVID-19, current isolation status and motivations for isolation, length of isolation, length of time not leaving the home, length of time not contacting others, trust in government, trust in the health service, adherence to health advice, and experience of adverse events due to COVID-19 (including severe illness within the family, bereavement, redundancy, or financial difficulties). Mental health, including wellbeing, depression, anxiety, which factors were causing stress, sleep quality, loneliness, social isolation, and changes in health behaviours such as smoking, drinking and exercise. How people were spending their time whilst in isolation, including questions on working, functional household activities, care, and schooling of any children in the household, hobbies, and relaxation. Certain waves of the study also included one-off modules on topics including volunteering behaviours, locus of control, frustrations and expectations, coping styles, fear of COVID-19, resilience, arts and creative engagement, life events, weight, gambling behaviours, mental health diagnosis, use of financial support, faith and religion, relationships, neighbourhood satisfaction, healthcare usage, discrimination experiences, life changes, optimism, long COVID and COVID-19 vaccination.

Estimated hazard ratios from a survival analysis comparing time-to-first-suicide-attempt or intentional self-harm between vitamin D supplemented veterans and matched control veterans.

BackgroundConstruction workers are a population that is at risk for mental illnesses such as depression, anxiety, and even suicide due to the high stress and physical demands of their work. This study aimed to determine the prevalence and risk factors for depression, anxiety, and stress among Bangladeshi construction workers.MethodsFrom February 2022 to June 2022, community-based cross-sectional research was conducted among construction workers. Survey data was gathered using interviewer administered questionnaires with 502 participants from the construction sites. Data were collected based on the information related to socio-demographics, lifestyle, occupation, health hazards, and mental health (i.e., depression, anxiety, and stress). The results were interpreted using the chi-square test and logistic regression utilizing SPSS statistical software.ResultsThe study revealed the prevalence rates of depression, anxiety, and stress among construction workers to be 17.9%, 30.3%, and 12%, respectively. Key findings indicate that construction workers who maintained a healthy sleep duration were 64% less likely to be depressed compared to those with poor sleep (AOR = 0.36; 95% CI: 0.21–0.61, p