Number of suicides and suicide rates by sex and age in England and Wales. Includes information on conclusion type, the proportion of suicides by method, and the median registration delay.

This report uses 2009 to 2014 NSDUH data, and 1999 and 2009 to 2014 data from the National Vital Statistics System to examine the percentages of suicidal thoughts and behaviors versus suicidal death rates among the middle-aged.

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.

this project to realized in Power Bi:

Suicide rates vary around the world Suicide rates vary widely between countries. The map shows this.

For some countries in Southern Africa and Eastern Europe, the estimated rates of suicide are high, with over 15 annual deaths per 100,000 people.

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Meanwhile for other countries in Europe, South America and Asia, the estimated rates of suicide are lower, with under 10 annual deaths per 100,000 people.

The wide range in suicide rates around the world is likely the result of many factors. This includes differences in underlying mental health and treatment, personal and financial stress, restrictions on the means of suicide, recognition and awareness of suicide, and other factors.

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WHAT YOU SHOULD KNOW ABOUT THIS DATA Suicide estimates come from death certificate data, using deaths that were classified under death codes for 'intentional self-harm' in the International Classification of Diseases (ICD). This includes people who had self-harmed but had not intended to die, and they may not be considered suicides by the country's particular legal definition. In many countries, deaths due to self-harm are highly underreported due to social stigma, cultural and legal concerns. Instead, these deaths are often misclassified in reported data, especially as deaths due to "events of undetermined intent", accidents, homicides, or unknown causes. To account for this, the WHO's Global Health Observatory reclassifies a proportion of deaths reported with those causes as suicides, according to the fraction that are estimated to be deaths by suicide. As a result, data on suicide rates represent a better estimate of how many people die from suicide.

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Suicides may still be underestimated after this adjustment, especially if they are misclassified as other types of deaths.2 This can also be why some countries appear to have rising suicide rates, if the rates of misclassification decline.

Global Suicide, Mental Health, Substance Use Disorders Trends

Analyzing the Impact Across Countries

By [source]

About this dataset

This dataset contains comprehensive data on global suicide, mental health, substance use disorders, and economic trends from 1990 to 2017. Using this data, researchers can delve deep into the effects of these trends across countries and ultimately uncover important insights about the state of global health. The dataset contains information about suicide rates (per 100,000 people), mental disorder prevalence (as a percentage of population size in 2017), population share with substance use disorders (as a percentage from 1990-2016), GDP per capita by purchasing power parity (in terms of current US$ for 1990-2017) and net national income per capita adjusted for inflation effects(in current US$, as in 2016). Additionally it tracks unemployment rate among populations over time(populaton%, 1991-2017). All this will help us to better understand how issues such as suicide, mental health and substance use disorders are affecting the lives of people globally

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How to use the dataset

This dataset offers insights into how mental health, substance use disorders, and economic status can impact global suicide trends. To get the most out of this data set, it is important to note the various columns available and their purpose as outlined above.

To analyze global suicide rates, look at the column “Probability (%) of dying between age 30 and exact age 70 from any of cardiovascular disease, cancer, diabetes or chronic respiratory disease” for a summary of estimated suicide rates for different countries over time. Additionally the columns “Entity” and “Code” provide useful information on which country is being discussed in each row.

The column “Prevalence- Alcohol and Substance Use Disorders” provides an overview of substance use disorders across different countries while the year column indicates when these trends are taking place.

For economic indicators related to mental health there is data available on national income per capita (current US$, 2016) as well as unemployment rate (population % 1991-2017). Together these metrics give a detailed picture into how economics can be interlinked with mental health and potentially suicide rates.

Finally this dataset also allows you to investigate varying trends overtime between different countries by looking at any common metrics but only in one specific year using appropriate filters when exploring the data set in more detail

Research Ideas

• Analyzing the correlation between mental health and economic indicators.
• Identifying countries with the highest prevalence of substance use disorders and developing targeted interventions for those populations.
• Examining the impact of global suicide rates over time to increase awareness and reduce stigma surrounding mental health issues in different countries

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: share-with-alcohol-and-substance-use-disorders 1990-2016.csv | Column name | Description | |:-----------------------------------------------------|:-----------------------------------------------------------------------------------| | Entity | The name of the country. (String) | | Code | The ISO code of the country. (String) | | Year | The year of the data. (Integer) | | Prevalence - Alcohol and substance use disorders | The percentage of the population with alcohol and substance use disorders. (Float) | | **Prevalence ** | Both (age-standardized percent) (%) |

**File: crude suicide rate...

Years of potential life lost under 75 (YPLL-75) and percent (%) change by firearm homicide and suicide deaths, 1981 to 2020 by 10-year interval periods.

Context

In light of Suicide Awareness Month, relationships between overall health investment (as a mental health proxy) and suicide rates are a key index to watch.

We expect a positive effect of more expenditure in health topics, giving away less rates of suicides in countries worldwide.

The hope of less suicides brings us a light of answers of where to put our efforts, even though this job does not consider other relevant factors such as socioeconomical development, private and public actions, and individuals characteristics.

Content

This data was downloaded from the WHO Mortality stats published and ICD10 death classification codes, then edited for format and condensation of ICD (more than 5 million rows) in SQL (Query attached). Finally, a tableau visualization was also developed. The following links are the primary content sources: 1. https://www.who.int/data/data-collection-tools/who-mortality-database 2. https://www.who.int/data/gho/data/indicators/indicator-details/GHO/current-health-expenditure-(che)-as-percentage-of-gross-domestic-product-(gdp)-(-) 3. https://health.mo.gov/data/documentation/death/death-icd10.php

"The data available for download from this web site are official national statistics in the sense that they have been transmitted to the World Health Organization by the competent authorities of the countries concerned.

The database contains number of deaths by country, year, sex, age group and cause of death as far back from 1950. Data are included only for countries reporting data properly coded according to the International Classification of Diseases (ICD). "

Tableau Visualization

Acknowledgements

Many thanks to Ken Jee guidelines on analytics: https://www.kaggle.com/code/kenjee/kaggle-project-from-scratch/notebook#Part-1---How-to-Start-a-Kaggle-Competition.

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.

Methods of suicide/self-inflicted injuries for Santa Clara County residents. The methods of injury for suicide deaths are provided for the total county population and by race/ethnicity. Data for emergency department utilization and hospital discharges are summarized only for total county population. Data are presented for pooled years combined. Missing data are not included in the analysis. Source: Santa Clara County Public Health Department, VRBIS, 2007-2016. Data as of 05/26/2017; Office of Statewide Planning and Development, 2007-2014 Emergency Department Data; Office of Statewide Planning and Development, 2007-2014 Patient Discharge Data.METADATA:Notes (String): Lists table title, notes and sourceYear (String): Year of eventData element (String): Lists data represents deaths, hospital discharges or emergency department visitsCategory (String): Lists the category representing the data. Suicide death data are presented as: 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). Suicide attempt/ideation data are presented as: Santa Clara County is for total population.Means of injury (String): Methods are categorized as: Poisoning, Suffocation, Firearms, Fall, Cut/pierce, Fire/flame and other.Percentage (Numeric): Percentage

Number of deaths and age-specific mortality rates for selected grouped causes, by age group and sex, 2000 to most recent year.

This table presents the 2008 to 2010 National Survey on Drug Use and Health (NSDUH) estimates of past year serious thoughts of suicide among those aged 18 or older by State and substate regions.

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.

BackgroundOur current understanding of Asian American mortality patterns has been distorted by the historical aggregation of diverse Asian subgroups on death certificates, masking important differences in the leading causes of death across subgroups. In this analysis, we aim to fill an important knowledge gap in Asian American health by reporting leading causes of mortality by disaggregated Asian American subgroups.Methods and FindingsWe examined national mortality records for the six largest Asian subgroups (Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese) and non-Hispanic Whites (NHWs) from 2003-2011, and ranked the leading causes of death. We calculated all-cause and cause-specific age-adjusted rates, temporal trends with annual percent changes, and rate ratios by race/ethnicity and sex. Rankings revealed that as an aggregated group, cancer was the leading cause of death for Asian Americans. When disaggregated, there was notable heterogeneity. Among women, cancer was the leading cause of death for every group except Asian Indians. In men, cancer was the leading cause of death among Chinese, Korean, and Vietnamese men, while heart disease was the leading cause of death among Asian Indians, Filipino and Japanese men. The proportion of death due to heart disease for Asian Indian males was nearly double that of cancer (31% vs. 18%). Temporal trends showed increased mortality of cancer and diabetes in Asian Indians and Vietnamese; increased stroke mortality in Asian Indians; increased suicide mortality in Koreans; and increased mortality from Alzheimer’s disease for all racial/ethnic groups from 2003-2011. All-cause rate ratios revealed that overall mortality is lower in Asian Americans compared to NHWs.ConclusionsOur findings show heterogeneity in the leading causes of death among Asian American subgroups. Additional research should focus on culturally competent and cost-effective approaches to prevent and treat specific diseases among these growing diverse populations.

Global Trends in Mental Health Disorder

From Schizophrenia to Depression

By Amit [source]

About this dataset

This dataset contains informative data from countries across the globe about the prevalence of mental health disorders including schizophrenia, bipolar disorder, eating disorders, anxiety disorders, drug use disorders, depression and alcohol use disorders. By providing this data in an easy to visualise format you can gain an insight into how these issues are impacting lives; allowing for a deeper understanding of these conditions and the implications. Through this reflection you may be able to answer some important questions: - What are the types of mental health disorder that people around the world suffer? - How many people in each country suffer mental health problems? - Are men or women more likely to have depression? - Is depression linked with suicide and what is the percentage rate? - In which age groups is depression more common?
From exploring patterns between prevalence rates through in-depth data visualisation you’ll be able to further understand these complex issues. The knowledge gained from this dataset can help bring valuable decision making skills such as research grants, policy making or preventative intervention plans across various countries. So if you wish to create meaningful data viz then start with this global prevalence of mental health disorder’s together with accompanying videos for extra context - Deepen your understanding about Mental Health Disorders today!

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How to use the dataset

Using this dataset is quite straightforward. Each row of the table contains information about a certain country or region for a certain year. The following columns are provided: Entity (the country or region name), Code (the code for the country or region), Year (the year the data was collected) Schizophrenia (% - percentage of people with schizophrenia), Bipolar Disorder (%) - percentage of people with bipolar disorder) Eating Disorders (%) - Percentage of individuals with disordered eating patterns Anxiety Disorders (%) - Percentage of individuals with anxiety Drug Use Disorders (%) - Percentage figures for those struggling with substance abuse Depression (%) – Percentages relating to those struggling with depressive illness Alcohol Use Disorders (%) – Percentages relating to those battling alcoholism

Using this dataset requires no special skills; however it is best suited for those comfortable navigating spreadsheets and tables as well as analyzing numerical information quickly and accurately. Many software suites like excel are useful here but simple internet searches will reveal free alternatives if your preference is web-based solutions!

By piecing together these different columns’ values we can get an idea if prevalence rates across different types of mental illnesses increase or decrease over time. For example we could compare depression levels between 2015 and 2018 by creating two separate sets containing information filtered just within our parameters respectively only reading records from 2015 then 2018). From here we can see whether numbers changed very much or stayed stagnant supefying any sort of patterns that could exist

Research Ideas

• Visualizing the prevalence of mental health disorders - Create a data visualization that compares and contrasts the prevalence of depression, anxiety, bipolar disorder, schizophrenia, eating disorders, alcohol use disorder and drug use disorder across different countries. This could provide insight into global differences in mental health and potential causes of those differences.

• Mapping depression rates - Create an interactive map that shows both regional and national variations in depression rates within a specific country or region. This would allow people to easily identify areas with higher or lower than average prevalence of depression which could help inform decision-makers when it comes to policy-making related to mental healthcare services provisioning.

• Developing predictive models for mental health - Use the data from this dataset as part of a larger machine learning project to build predictive models for mental health across countries or regions based on various factors such as demographics, economic indicators etc., This can be helpful for researchers working on understanding populations’ susceptibility towards developing certain disorders so as to craft appropriate preventive strategies accordingly

Acknowledgements

If you use this dataset in your research, please credit the original aut...

Contains covariate data for "The association between alcohol consumption per capita and suicide mortality across 30 European countries" which were extracted from the Pew Research Center (pewresearch.org), World Bank Group (worldbank.org), and Eurostat (ec.europa.eu/eurostat). Also contains dummy variables to represent: the 2008 global economic recession, changes from ICD-9 to ICD-10, and the COVID-19 pandemic. All covariates which were initially considered are included in this dataset. However, data were further cleaned according to methods described in the associated publication prior to analysis. Within the dataset: edu = Educational attainment (completion of post-secondary or equivalent) lit = Literacy, adult total (% of people ages 15 and above) unemp = Unemployment, total (% of total labor force) (modeled ILO estimate) divorce = Divorce rate migration = Net migration rate relig.muslim = Proportion of the population who identified as Muslim relig.buddhist = Proportion of the population who identified as Buddhist lff = Female labour force participation (% of total labor force) gdp = Gross domestic product based on purchasing power parity (GDP (PPP)) gini = Gini index density = Population density urban = Proportion of the population living in urban areas recession, covid, icd: Dummy variables detailed above.

Depression is common, especially in women of child-bearing age; prevalence estimates for this group range from 8% to 12%, and there is robust evidence that maternal depression is associated with mental health problems in offspring. Suicidal behaviour is a growing concern amongst young people and those exposed to maternal depression are likely to be especially at high risk. The aim of this study was to utilise a large, prospective population cohort to examine the relationship between depression symptom trajectories in mothers over the first eleven years of their child’s life and subsequent adolescent suicidal ideation. An additional aim was to test if associations were explained by maternal suicide attempt and offspring depressive disorder. Data were utilised from a population-based birth cohort: the Avon Longitudinal Study of Parents and Children. Maternal depression symptoms were assessed repeatedly from pregnancy to child age 11 years. Offspring suicidal ideation was assessed at age 16 years. Using multiple imputation, data for 10,559 families were analysed. Using latent class growth analysis, five distinct classes of maternal depression symptoms were identified (minimal, mild, increasing, sub-threshold, chronic-severe). The prevalence of past-year suicidal ideation at age 16 years was 15% (95% CI: 14-17%). Compared to offspring of mothers with minimal symptoms, the greatest risk of suicidal ideation was found for offspring of mothers with chronic-severe symptoms [OR 3.04 (95% CI 2.19, 4.21)], with evidence for smaller increases in risk of suicidal ideation in offspring of mothers with sub-threshold, increasing and mild symptoms. These associations were not fully accounted for by maternal suicide attempt or offspring depression diagnosis. Twenty-six percent of non-depressed offspring of mothers with chronic-severe depression symptoms reported suicidal ideation. Risk for suicidal ideation should be considered in young people whose mothers have a history of sustained high levels of depression symptoms, even when the offspring themselves do not have a depression diagnosis.

Estimated Annual Percent Change (APC) and change timepoint (if applicable) by trend segment.

This is the thirtieth in a series of regular E-Bulletins that provide an overview of the injury profile for Victoria. This E-Bulletin provides a detailed overview of Victorian injury deaths in the four-year period 2020–2023: the latest available cause of death data held by the Victorian Injury Surveillance Unit (VISU). The E-Bulletin shows trends in injury deaths for the period 2007–2023, although the focus is the latest four-year period. Summary - All Ages In the four-year period 2020–2023, 13037 Victorians died as a result of injury. Seventy-five percent of these deaths were unintentional (n=9744, 74.7%), 23.8% were intentional (n=3107: suicide=2872 & homicide=235) and the remaining 1.4% were classified as undetermined intent (n=186). The overall average annual injury death rate was 49.0 per 100,000 population.Males were overrepresented accounting for 57.3% (n=5581) of unintentional injury deaths, 74.3% (n=2307) of intentional injury deaths and 63.4% (n=118) of undetermined intent injury deaths.Three causes: falls (n=5839, 44.8%), suicide (n=2872, 22.0%) and unintentional poisoning (n=1711, 13.1%) combined accounted for 79.9% of injury deaths.Data SourceData have been extracted from the VISU-held Cause of Death (COD) dataset supplied by the Australian Coordinating Registry (ACR) and based on the Australian Bureau of Statistics (ABS) cause of death data.

Demographic characteristics of New South Wales men diagnosed with prostate cancer in 1997 to 2007, comparing those who committed suicide with all men diagnosed with prostate cancer, number, percent, person years at risk and crude rate per 100,000 person years at risk.

Select demographic characteristics in generations study by cohort, estimated population percentage and 95% confidence interval (N = 1,518).