As the tagline of ‘American Association of Suicidology’ says I strongly believe that suicide prevention is everyone’s business. The act of ending one’s own life stating the reasons to be depression, alcoholism or any other mental disorders for that matter is not a considerable idea keeping in mind that anything can be overcome with reliable help and lifestyle. We can choose to stand together in the face of a society which may often feel like a lonely and disconnected place, and we can choose to make a difference by making lives more livable for those who struggle to cope. Through this project, I am hoping to identify the trends of suicidal rates by country, gender, age and ethnicity. And relate the trends to the possible reasons that leads to the drastic decision, which might help us to curb the thought in the very beginning.

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Data on suicides is deficient for two reasons, first of all, there is a problem with the frequency and reliability of vital registration data in many countries – an issue that undermine the quality of mortality estimates in general, not just suicide. Secondly, there are problems with the accuracy of the official figures made available, since suicide registration is a complicated process involving several responsible authorities with medical and legal concerns. Moreover, the illegality of suicidal behavior in some countries contributes to under reporting and misclassification. I was lucky enough to obtain enough data from different reliable resources. I will be starting off the project with the most reliable datasets available for us on suicide.

•World Health Organization (WHO) dataset which contains entity wise suicide rates, crude suicide rates per gender and country which are age standardized which has a geographical coverage of 198 countries. The time spanning from 1950-2011.

•Samaritans statistics report 2017 including data for 2013-2015, in order to reduce the time, it takes to register deaths, the maximum time between a death and registration is eight days.

•American Association of Suicidology facts and statistics which are categorized by age, gender, region and ethnicity.

Inspiration: To visualize the trends and patterns by merging different datasets available regarding the subject matter from different organizations, deriving the major causes for the drastic stride. And also observing the changes in patterns over the years by country, sex and ethnicity

Understanding the data: It is always tricky to understand the suicide statistics as they may not be so straight forward as they appear to be. Generally, the rate is per 100,000. It is done this way to adjust the underlying population size. ‘Age-standardized’ rates have been standardized to the world population to increase the confidence while making the comparisons. On the other hand, ‘Crude rates’ have not been standardized like the prior, so they are just the basic calculation of number of deaths divided by the population (x100,000). The size of the population and specific cohort is also to be taken into account as smaller groups often produce less reliable rates per 100,000. When examining the suicide trends over a period of time it is also important to look over a relatively long period. Increases and decreases for a year at a time should not be considered in isolation.

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.

Uzbekistan UZ: Suicide Mortality Rate: per 100,000 Population data was reported at 7.400 Number in 2016. This stayed constant from the previous number of 7.400 Number for 2015. Uzbekistan UZ: Suicide Mortality Rate: per 100,000 Population data is updated yearly, averaging 7.400 Number from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 7.600 Number in 2000 and a record low of 5.800 Number in 2005. Uzbekistan UZ: Suicide Mortality Rate: per 100,000 Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Uzbekistan – Table UZ.World Bank: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted Average;

BackgroundPeople in late adulthood die by suicide at the highest rate worldwide. However, there are still no tools to help predict the risk of death from suicide in old age. Here, we leveraged the Survey of Health, Ageing, and Retirement in Europe (SHARE) prospective dataset to train and test a machine learning model to identify predictors for suicide in late life.MethodsOf more than 16,000 deaths recorded, 74 were suicides. We matched 73 individuals who died by suicide with people who died by accident, according to sex (28.8% female in the total sample), age at death (67 ± 16.4 years), suicidal ideation (measured with the EURO-D scale), and the number of chronic illnesses. A random forest algorithm was trained on demographic data, physical health, depression, and cognitive functioning to extract essential variables for predicting death from suicide and then tested on the test set.ResultsThe random forest algorithm had an accuracy of 79% (95% CI 0.60-0.92, p = 0.002), a sensitivity of.80, and a specificity of.78. Among the variables contributing to the model performance, the three most important factors were how long the participant was ill before death, the frequency of contact with the next of kin and the number of offspring still alive.ConclusionsProspective clinical and social information can predict death from suicide with good accuracy in late adulthood. Most of the variables that surfaced as risk factors can be attributed to the construct of social connectedness, which has been shown to play a decisive role in suicide in late life.

South Africa ZA: Suicide Mortality Rate: Female data was reported at 4.700 NA in 2016. This records a decrease from the previous number of 4.800 NA for 2015. South Africa ZA: Suicide Mortality Rate: Female data is updated yearly, averaging 5.400 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 5.700 NA in 2000 and a record low of 4.700 NA in 2016. South Africa ZA: Suicide Mortality Rate: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s South Africa – Table ZA.World Bank.WDI: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Sri Lanka LK: Suicide Mortality Rate: per 100,000 Population data was reported at 14.600 Number in 2016. This records a decrease from the previous number of 14.800 Number for 2015. Sri Lanka LK: Suicide Mortality Rate: per 100,000 Population data is updated yearly, averaging 19.100 Number from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 28.800 Number in 2000 and a record low of 14.600 Number in 2016. Sri Lanka LK: Suicide Mortality Rate: per 100,000 Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sri Lanka – Table LK.World Bank.WDI: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

BackgroundSuicide is a prominent source of harm and death globally, and it is the leading cause of premature death among prisoners. Therefore, the main aim of this study was to determine the prevalence and factors associated with suicidal ideation and attempt among prisoners in Northwest Ethiopia.MethodsAn institution-based cross-sectional study design was performed from May 23 to June 22, 2022. After proportional allocation to the three correctional institutions, a total of 788 study participants were randomly recruited. The World Health Organization Composite International Diagnostic Interview (CIDI) was used to evaluate suicide ideation and attempt. To determine factors associated with suicidal ideation and attempt, multivariate logistic regression analyses were conducted. At a 95% confidence interval (CI) of P-value

Suicide is a complex, multidimensional event, and a significant challenge for prevention globally. Artificial intelligence (AI) and machine learning (ML) have emerged to harness large-scale datasets to enhance risk detection. In order to trust and act upon the predictions made with ML, more intuitive user interfaces must be validated. Thus, Interpretable AI is one of the crucial directions which could allow policy and decision makers to make reasonable and data-driven decisions that can ultimately lead to better mental health services planning and suicide prevention. This research aimed to develop sex-specific ML models for predicting the population risk of suicide and to interpret the models. Data were from the Quebec Integrated Chronic Disease Surveillance System (QICDSS), covering up to 98% of the population in the province of Quebec and containing data for over 20,000 suicides between 2002 and 2019. We employed a case-control study design. Individuals were considered cases if they were aged 15+ and had died from suicide between January 1st, 2002, and December 31st, 2019 (n = 18339). Controls were a random sample of 1% of the Quebec population aged 15+ of each year, who were alive on December 31st of each year, from 2002 to 2019 (n = 1,307,370). We included 103 features, including individual, programmatic, systemic, and community factors, measured up to five years prior to the suicide events. We trained and then validated the sex-specific predictive risk model using supervised ML algorithms, including Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Multilayer perceptron (MLP). We computed operating characteristics, including sensitivity, specificity, and Positive Predictive Value (PPV). We then generated receiver operating characteristic (ROC) curves to predict suicides and calibration measures. For interpretability, Shapley Additive Explanations (SHAP) was used with the global explanation to determine how much the input features contribute to the models’ output and the largest absolute coefficients. The best sensitivity was 0.38 with logistic regression for males and 0.47 with MLP for females; the XGBoost Classifier with 0.25 for males and 0.19 for females had the best precision (PPV). This study demonstrated the useful potential of explainable AI models as tools for decision-making and population-level suicide prevention actions. The ML models included individual, programmatic, systemic, and community levels variables available routinely to decision makers and planners in a public managed care system. Caution shall be exercised in the interpretation of variables associated in a predictive model since they are not causal, and other designs are required to establish the value of individual treatments. The next steps are to produce an intuitive user interface for decision makers, planners and other stakeholders like clinicians or representatives of families and people with live experience of suicidal behaviors or death by suicide. For example, how variations in the quality of local area primary care programs for depression or substance use disorders or increased in regional mental health and addiction budgets would lower suicide rates.

Mongolia MN: Suicide Mortality Rate: per 100,000 Population data was reported at 13.000 Number in 2016. This records a decrease from the previous number of 13.100 Number for 2015. Mongolia MN: Suicide Mortality Rate: per 100,000 Population data is updated yearly, averaging 16.600 Number from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 17.900 Number in 2000 and a record low of 13.000 Number in 2016. Mongolia MN: Suicide Mortality Rate: per 100,000 Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mongolia – Table MN.World Bank.WDI: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Tajikistan TJ: Suicide Mortality Rate: Male data was reported at 3.700 NA in 2016. This stayed constant from the previous number of 3.700 NA for 2015. Tajikistan TJ: Suicide Mortality Rate: Male data is updated yearly, averaging 3.700 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 4.300 NA in 2000 and a record low of 3.600 NA in 2010. Tajikistan TJ: Suicide Mortality Rate: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Tajikistan – Table TJ.World Bank.WDI: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

BackgroundThe early stages of psychotic disorders correspond to the early phases of the disease and include the prodromal phase and first-episode psychosis; they constitute a period at high risk of suicidal behaviour. A long duration of untreated psychosis (DUP) is among the risk factors of suicidal behaviour identified in this early period. Many studies have shown the effectiveness of early interventions on the overall prognosis of psychotic disorders in the early stages, and early intervention strategies have been developed and tested worldwide. Several authors reported an improvement in suicidal behaviours; however, all these data have not been systematically analysed yet. The main objective of this systematic review was to collect evidence on the effect on suicidal behaviour of early interventions for patients in the early stages of psychotic disorders.MethodsWe will carry out a systematic review of the literature according to the PRISMA criteria by searching articles in five databases (PubMed, Cochrane, PsycINFO, Scopus, EMBASE), without restriction on the publication date. The selection criteria are: articles (any type; e.g. prospective, retrospective, controlled or uncontrolled, and literature reviews) on early interventions for psychotic disorders in the early stages with data on suicide attempts, death by suicide, suicidal ideation; articles written in English or French. Exclusion criteria are: articles on suicidal behaviours in patients with psychotic disorders in the early stages, but without early intervention, and articles on early-stage psychotic disorders without data on suicidal behaviours.DiscussionIf this review confirms the effectiveness on suicidal behaviours of early interventions for young patients with psychotic disorders, the development/implementation of such intervention programmes should be better promoted.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42021237833.

Myanmar MM: Suicide Mortality Rate: Male data was reported at 5.900 NA in 2016. This records an increase from the previous number of 5.800 NA for 2015. Myanmar MM: Suicide Mortality Rate: Male data is updated yearly, averaging 5.600 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 5.900 NA in 2016 and a record low of 4.200 NA in 2000. Myanmar MM: Suicide Mortality Rate: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Myanmar – Table MM.World Bank.WDI: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Depression and suicidal thoughts and behaviour are remarkably common among people living with HIV worldwide, leading to a higher burden of disease, poor HIV care engagement, and death. Suicidal behaviour is criminalized in 20 countries worldwide, including Tanzania, where culturally appropriate interventions are lacking. We describe the experiences of counsellors who screened patients as the initial procedure in a randomized controlled clinical trial aimed to reduce suicide and depression, and improve HIV care engagement in Kilimanjaro, Tanzania. The clinical trial was registered at clinicaltrials.gov (ID: NCT04696861). We conducted in-depth interviews (IDIs) with 10 HIV counsellors and four mental health workers. Interviews were held 3 months post-enrollment of participants. Data was collected from March to August 2023. We referred to a brief screener developed for the trial, combining the PHQ-2 for depression and one question on suicidal ideation. IDIs focused on the frequency of depression and suicide assessments before and after the trial; the nature of assessments and referrals; perceived significance, acceptability, and feasibility of the screening process; and opinions on the criminalization of suicide. Data was analyzed using NVivo. Themes were identified, collected, compared, combined, and tabulated. Differences were resolved by the first three and final authors. Our findings revealed an increased focus on mental health assessments and referrals since the start of the trial, perceived high necessity of integration of mental health screening, and a high acceptability and feasibility of screening. Participants consistently reported increased mental health awareness and a positive overall experience of screening. Counsellors favoured abolishment of laws against suicide due to their hindering support-seeking. In a mental health resource-limited setting, these findings highlight the need for targeted and integrated non-specialist interventions. Feedback from counsellors indicated that screening was acceptable and feasible; further research is needed to assess the sustainability of screening.