Suicide Prediction Models Research Articles

Using Natural Language Processing to develop risk-tier specific suicide prediction models for Veterans Affairs patients

Suicide is a leading cause of death. Suicide rates are particularly elevated among Department of Veterans Affairs (VA) patients. While VA has made impactful suicide prevention advances, efforts primarily target high-risk patients with documented suicide risk. This high-risk population accounts for less than 10% of VA patient suicide deaths. We previously evaluated epidemiological patterns among VA patients that had lower classified suicide risk and derived moderate- and low-risk groupings. Expanding upon VA's leading suicide prediction model, this study uses national VA data to refine high-, moderate-, and low-risk specific suicide prediction methods. We selected all VA patients who died by suicide in 2017 or 2018 (n = 4584), matching each case with five controls who remained alive during treatment year and shared suicide risk percentiles. We extracted all sample unstructured electronic health record notes, analyzed them using natural language processing, and applied machine-learning classification algorithms to develop risk-tier-specific predictive models. We calculated area under the curve (AUC) and suicide risk concentration to evaluate predictive accuracy and analyzed derived words. ResultsOur high-risk model (AUC = 0.621 (95% CI: 0.55–0.68)), moderate-risk (AUC = 0.669 (95% CI: 0.64–0.71)), and low-risk (AUC = 0.673 (95% CI: 0.63–0.72)) models offered significant predictive accuracy over VA's leading suicide prediction algorithm. Derived words varied considerably, the high-risk model including chronic condition service words, moderate-risk model including outpatient care, and low-risk model including acute condition care.Study suggests benefit of leveraging unstructured electronic health records and expands prediction resources for non-high-risk suicide decedents, an historically underserved population.

Accuracy and transportability of machine learning models for adolescent suicide prediction with longitudinal clinical records

Machine Learning models trained from real-world data have demonstrated promise in predicting suicide attempts in adolescents. However, their transportability, namely the performance of a model trained on one dataset and applied to different data, is largely unknown, hindering the clinical adoption of these models. Here we developed different machine learning-based suicide prediction models based on real-world data collected in different contexts (inpatient, outpatient, and all encounters) with varying purposes (administrative claims and electronic health records), and compared their cross-data performance. The three datasets used were the All-Payer Claims Database in Connecticut, the Hospital Inpatient Discharge Database in Connecticut, and the Electronic Health Records data provided by the Kansas Health Information Network. We included 285,320 patients among whom we identified 3389 (1.2%) suicide attempters and 66% of the suicide attempters were female. Different machine learning models were evaluated on source datasets where models were trained and then applied to target datasets. More complex models, particularly deep long short-term memory neural network models, did not outperform simpler regularized logistic regression models in terms of both local and transported performance. Transported models exhibited varying performance, showing drops or even improvements compared to their source performance. While they can achieve satisfactory transported performance, they are usually upper-bounded by the best performance of locally developed models, and they can identify additional new cases in target data. Our study uncovers complex transportability patterns and could facilitate the development of suicide prediction models with better performance and generalizability.

Predicting inmate suicidal behavior with an interpretable ensemble machine learning approach in smart prisons.

The convergence of smart technologies and predictive modelling in prisons presents an exciting opportunity to revolutionize the monitoring of inmate behaviour, allowing for the early detection of signs of distress and the effective mitigation of suicide risks. While machine learning algorithms have been extensively employed in predicting suicidal behaviour, a critical aspect that has often been overlooked is the interoperability of these models. Most of the work done on model interpretations for suicide predictions often limits itself to feature reduction and highlighting important contributing features only. To address this research gap, we used Anchor explanations for creating human-readable statements based on simple rules, which, to our knowledge, have never been used before for suicide prediction models. We also overcome the limitation of anchor explanations, which create weak rules on high-dimensionality datasets, by first reducing data features with the help of SHapley Additive exPlanations (SHAP). We further reduce data features through anchor interpretations for the final ensemble model of XGBoost and random forest. Our results indicate significant improvement when compared with state-of-the-art models, having an accuracy and precision of 98.6% and 98.9%, respectively. The F1-score for the best suicide ideation model appeared to be 96.7%.

Improving prediction of 12-months suicidal attempts in bipolar disorder: a machine learning study

IntroductionBipolar disorder (BD) is a recurrent disorder, causing functional impairment and raised mortality, particularly due to suicide. However, the difficulty in predicting suicidal behaviors relies in the lack of clear biomarkers.Machine learning (ML) has emerged as a promising tool to enhance suicidal prediction. However, most ML studies focused on lifetime attempts, without having a predictive time window, and did not employ time-dependent variables. Moreover, most studies lie on cross-sectional databases, without including more than one time-point.Objectives First, we aimed to predict 12-months suicide attempts in a naturalistic sample of BD patients, using clinical and demographic data.Second, we aimed to improve the prediction by including information from intermediate visits (1, 3, and 6 months), mimicking more closely the clinician’s way of thinking and the multiple observations a patient receives.MethodsA sample of 163 BD patients (53% females, mean age 44.7, SD 15.3) were recruited.Based on EHR, 56 clinical and demographic features were extracted, including hospitalizations, suicidal behaviors lifetime and in the last 12 months, along with comorbidity, family history, work, and therapies. Patients were followed up for 12 months.Support Vector Machine (SVM) was used to differentiate subjects who attempted suicide versus those who did not in a 12-month time window, within a repeated nested Cross-Validation. The SVM was optimized weighting the hyperplane for uneven group sizes.Then, we repeated the analysis including information from intermediate visits (1, 3, 6 months after the first contact). For each visit, we created a composite score based on current therapy, new admissions, and ER presentations. To avoid circularity, all the information (ER, admission etc.) related to a suicide attempt were not included.ResultsDuring the 12-months follow-up, 9.8% of patients attempted suicide. The results from the 12-months suicide prediction model obtained an Area Under the Curve of 0.71(with a Balanced Accuracy (BAC) of 68%).After incorporating the composite scores based on intermediate visits in the model, the prediction raised to an Area Under the Curve of 0.78 (BAC 73%), suggesting that including intermediate visits is a valid method to improve prediction.The features that contributed the most to the prediction were the composite score at 6-month visit, lifetime number of suicide attempts, suicide attempts in the last 12 months, substance of abuse (other than cannabis), and antipsychotics.ConclusionsML proved a good prediction accuracy for suicide in a 12-months time window, and the prediction was improved by including data from intermediate visits. The model showed the importance of time-dependent features, such as attempts in the last 12 months. Our analysis might help in identifying early clinical risk factors and underlies the importance of multiple evaluations in populations at risk.Disclosure of InterestNone Declared

Validation of a Multivariable Model to Predict Suicide Attempt in a Mental Health Intake Sample

Given that suicide rates have been increasing over the past decade and the demand for mental health care is at an all-time high, targeted prevention efforts are needed to identify individuals seeking to initiate mental health outpatient services who are at high risk for suicide. Suicide prediction models have been developed using outpatient mental health encounters, but their performance among intake appointments has not been directly examined. To assess the performance of a predictive model of suicide attempts among individuals seeking to initiate an episode of outpatient mental health care. This prognostic study tested the performance of a previously developed machine learning model designed to predict suicide attempts within 90 days of any mental health outpatient visit. All mental health intake appointments scheduled between January 1, 2012, and April 1, 2022, at Kaiser Permanente Northern California, a large integrated health care delivery system serving over 4.5 million patients, were included. Data were extracted and analyzed from August 9, 2022, to July 31, 2023. Suicide attempts (including completed suicides) within 90 days of the appointment, determined by diagnostic codes and government databases. All predictors were extracted from electronic health records. The study included 1 623 232 scheduled appointments from 835 616 unique patients. There were 2800 scheduled appointments (0.17%) followed by a suicide attempt within 90 days. The mean (SD) age across appointments was 39.7 (15.8) years, and most appointments were for women (1 103 184 [68.0%]). The model had an area under the receiver operating characteristic curve of 0.77 (95% CI, 0.76-0.78), an area under the precision-recall curve of 0.02 (95% CI, 0.02-0.02), an expected calibration error of 0.0012 (95% CI, 0.0011-0.0013), and sensitivities of 37.2% (95% CI, 35.5%-38.9%) and 18.8% (95% CI, 17.3%-20.2%) at specificities of 95% and 99%, respectively. The 10% of appointments at the highest risk level accounted for 48.8% (95% CI, 47.0%-50.6%) of the appointments followed by a suicide attempt. In this prognostic study involving mental health intakes, a previously developed machine learning model of suicide attempts showed good overall classification performance. Implementation research is needed to determine appropriate thresholds and interventions for applying the model in an intake setting to target high-risk cases in a manner that is acceptable to patients and clinicians.

Suicide risk assessment tools and prediction models: new evidence, methodological innovations, outdated criticisms

The number of prediction models for suicide-related outcomes has grown substantially in recent years. These models aim to assist in stratifying risk, improve clinical decision-making, and facilitate a personalised medicine...

Identification of risk factors for attempted suicide by self-poisoning and a nomogram to predict self-poisoning suicide.

Suicide is a global concern, especially among young people. Suicide prediction models have the potential to make it easier to identify patients who are at a high risk of suicide, but they have very little predictive power when there is a positive value for suicide mortality. Therefore, the aim of the study is to uncover potential risk factors associated with suicide by self-poisoning and further to provide a trustworthy nomogram to predict self-poisoning suicide among poisoned patients. This study prospectively enrolled 237 patients who were treated for poisoning at the Fifth Medical Center of PLA General Hospital (Beijing) between May 2021 and May 2022. Patient's basic characteristics, daily activities, mental health status, and history of psychological illnesses were gathered to examine their predictive power for self-poisoning suicide. On developing a prediction model, patients were split 8:2 into a training (n = 196) group and a validation (n = 41) group at random via computer. The training group worked on model development, while the validation group worked on model validation. In this study, the Hosmer and Lemeshow test, accuracy, and area under the curve were the primary evaluation criteria. Shapley Additive exPlanations (SHAP) was determined to evaluate feature importance. To make the prediction model easy for researchers to utilize, it was presented in nomogram format. Two risk groups of patients were identified based on the ideal cut-off value. Of all poisoned patients, 64.6% committed suicide by self-poisoning. With regard to self-poisoning attempted suicide, multivariate analysis demonstrated that female gender, smoking, generalized anxiety disorder-7 (GAD-7), and beck hopelessness scale-20 (BHS-20) were significant risk factors, whereas married status, relatively higher education level, a sedentary time of 1-3 h per day, higher sport frequency per week, higher monthly income were significant protective features. The nomogram contained each of the aforementioned nine features. In the training group, the area under curve (AUC) of the nomogram was up to 0.938 (0.904-0.972), whereas in the validation group, it reached a maximum of 0.974 (0.937-1.000). Corresponding accuracy rates were up to 0.883 and 0.927, respectively, and the P-values for the Hosmer and Lemeshow test were 0.178 and 0.346, respectively. SHAP demonstrated that the top three most important features were BHS-20, GAD-7, and marital status. Based on the best cut-off value of the nomogram (40%), patients in the high-risk group had a nearly six-time larger likelihood of committing suicide by self-poisoning than patients in the low-risk group (88.68 vs. 15.38%, P < 0.001). The dynamic nomogram was made available at the following address: https://xiaobo.shinyapps.io/Nomogramselfpoisoningsuicide/. This study proposes a prediction model to stratify patients at a high risk of suicide by self-poisoning and to guide individual preventive strategies. Patients in the high-risk group require further mental health counseling to alleviate anxiety and hopelessness, healthy lifestyle like quitting smoking and exercising more, and restriction of access to poison and psychiatric drugs.

Genetic liability to suicidal thoughts and behaviors and risk of suicide attempt in US military veterans: moderating effects of cumulative trauma burden.

Little is known about environmental factors that may influence associations between genetic liability to suicidality and suicidal behavior. This study examined whether a suicidality polygenic risk score (PRS) derived from a large genome-wide association study (N = 122,935) was associated with suicide attempts in a population-based sample of European-American US military veterans (N = 1664; 92.5% male), and whether cumulative lifetime trauma exposure moderated this association. Eighty-five veterans (weighted 6.3%) reported a history of suicide attempt. After adjusting for sociodemographic and psychiatric characteristics, suicidality PRS was associated with lifetime suicide attempt (odds ratio 2.65; 95% CI 1.37-5.11). A significant suicidality PRS-by-trauma exposure interaction emerged, such that veterans with higher levels of suicidality PRS and greater trauma burden had the highest probability of lifetime suicide attempt (16.6%), whereas the probability of attempts was substantially lower among those with high suicidality PRS and low trauma exposure (1.4%). The PRS-by-trauma interaction effect was enriched for genes implicated in cellular and developmental processes, and nervous system development, with variants annotated to the DAB2 and SPNS2 genes, which are implicated in inflammatory processes. Drug repurposing analyses revealed upregulation of suicide gene-sets in the context of medrysone, a drug targeting chronic inflammation, and clofibrate, a triacylglyceride level lowering agent. Results suggest that genetic liability to suicidality is associated with increased risk of suicide attempt among veterans, particularly in the presence of high levels of cumulative trauma exposure. Additional research is warranted to investigate whether incorporation of genomic information may improve suicide prediction models.

The Veterans Health Administration REACH VET Program: Suicide Predictive Modeling in Practice.

The U.S. Veterans Health Administration developed a suicide prediction statistical model and implemented a novel clinical program, Recovery Engagement and Coordination for Health-Veterans Enhanced Treatment (REACH VET). This high-value suicide prevention program aims to efficiently identify patients at risk and connect them with care. Starting in April 2017, national REACH VET metric data were collected from electronic health records to evaluate required task completion. By October 2020, 98% of veterans identified (N=6,579) were contacted by providers and had their care evaluated. In the nation's largest health care system, it was feasible to implement a clinical program based on a suicide prediction model.

Predictive Modeling for Suicide-Related Outcomes and Risk Factors among Patients with Pain Conditions: A Systematic Review.

Suicide is a leading cause of death in the US. Patients with pain conditions have higher suicidal risks. In a systematic review searching observational studies from multiple sources (e.g., MEDLINE) from 1 January 2000–12 September 2020, we evaluated existing suicide prediction models’ (SPMs) performance and identified risk factors and their derived data sources among patients with pain conditions. The suicide-related outcomes included suicidal ideation, suicide attempts, suicide deaths, and suicide behaviors. Among the 87 studies included (with 8 SPM studies), 107 suicide risk factors (grouped into 27 categories) were identified. The most frequently occurring risk factor category was depression and their severity (33%). Approximately 20% of the risk factor categories would require identification from data sources beyond structured data (e.g., clinical notes). For 8 SPM studies (only 2 performing validation), the reported prediction metrics/performance varied: C-statistics (n = 3 studies) ranged 0.67–0.84, overall accuracy(n = 5): 0.78–0.96, sensitivity(n = 2): 0.65–0.91, and positive predictive values(n = 3): 0.01–0.43. Using the modified Quality in Prognosis Studies tool to assess the risk of biases, four SPM studies had moderate-to-high risk of biases. This systematic review identified a comprehensive list of risk factors that may improve predicting suicidal risks for patients with pain conditions. Future studies need to examine reasons for performance variations and SPM’s clinical utility.

CO124 Predictive Modeling for Suicide-Related Outcomes and Risk Factors Among Patients with Pain Conditions: A Systematic Review

Suicide is a leading cause of death in the United States, and patients with pain conditions are at an elevated risk. The objective of this study is to evaluate performance of existing suicide prediction models (SPMs) and identify suicide risk factors among patients with pain conditions.

Machine learning based suicide prediction and development of suicide vulnerability index for US counties

Suicide is a growing public health concern in the United States. A detailed understanding and prediction of suicide patterns can significantly boost targeted suicide control and prevention efforts. In this article we look at the suicide trends and geographical distribution of suicides and then develop a machine learning based US county-level suicide prediction model, using publicly available data for the 10-year period from 2010–2019. Analysis of the trends and geographical distribution of suicides revealed that nearly 25% of the total counties experienced at least a 10% increase in suicides from 2010 to 2019, with about 12% of total counties exhibiting an increase of at least 50%. An eXtreme Gradient Boosting (XGBoost) based machine learning model was used with 17 unique features for each of the 3140 counties in the US to predict suicides with an R2 value of 0.98. Using the SHapley Additive exPlanations (SHAP) values, the importance of all the 17 features used in the prediction model training set were identified. County level features, namely Total Population, % African American Population, % White Population, Median Age and % Female Population were found to be the top 5 important features that significantly affected prediction results. The top five important features based on SHAP values were then used to create a Suicide Vulnerability Index (SVI) for US Counties. This newly developed SVI has the potential to detect US counties vulnerable to high suicide rates and can aid targeted suicide control and prevention efforts, thereby making it a valuable tool in an informed decision-making process.

Opioid-related variables did not improve suicide risk prediction models in samples with mental health diagnoses.

Suicide risk prediction models derived from machine learning of electronic health records and insurance claims are an innovation in suicide prevention. Some models do not include opioid-related variables despite the relationship between opioids and suicide. This study evaluated whether inclusion of opioid-related variables improved suicide risk prediction models developed by the Mental Health Research Network. Approximately 630 opioid-related variables and interactions terms were introduced into existing suicide prediction models run in datasets of patient visits in mental health care (n = 27,755,401 visits) or primary care when a mental health diagnosis was given (n = 19,340,461 visits). Training and validation datasets were created. LASSO regression with 10-fold validation identified variables to be added to the models. The new models predicting suicide attempts and suicide deaths in the mental health specialty visit sample performed as well as the existing models (new C-statistic for attempts model = 0.855, CI: 0.853-0.857 versus original C-statistic = 0.851, CI 0.848-0.853; death model = 0.868, CI: 0.856-0.879 versus 0.861, CI 0.848-0.875). The new model for suicide death in the primary care sample improved (0.855, CI: 0.837-0.874 versus 0.833, CI 0.813-0.853) while performance of the new model for suicide attempt in that sample degraded (0.843, CI: 0.839-0.847 versus 0.853, CI 0.849-0.857). Analyses did not include patients without recent care, data did not include illicit opioid use or unrecognized opioid use disorder. Among patients with mental health diagnoses, inclusion of opioid-related variables did not improve prediction of suicide risk beyond mental health predictors.

Privacy concerns with using public data for suicide risk prediction algorithms: a public opinion survey of contextual appropriateness

PurposeExisting algorithms for predicting suicide risk rely solely on data from electronic health records, but such models could be improved through the incorporation of publicly available socioeconomic data – such as financial, legal, life event and sociodemographic data. The purpose of this study is to understand the complex ethical and privacy implications of incorporating sociodemographic data within the health context. This paper presents results from a survey exploring what the general public’s knowledge and concerns are about such publicly available data and the appropriateness of using it in suicide risk prediction algorithms.Design/methodology/approachA survey was developed to measure public opinion about privacy concerns with using socioeconomic data across different contexts. This paper presented respondents with multiple vignettes that described scenarios situated in medical, private business and social media contexts, and asked participants to rate their level of concern over the context and what factor contributed most to their level of concern. Specific to suicide prediction, this paper presented respondents with various data attributes that could potentially be used in the context of a suicide risk algorithm and asked participants to rate how concerned they would be if each attribute was used for this purpose.FindingsThe authors found considerable concern across the various contexts represented in their vignettes, with greatest concern in vignettes that focused on the use of personal information within the medical context. Specific to the question of incorporating socioeconomic data within suicide risk prediction models, the results of this study show a clear concern from all participants in data attributes related to income, crime and court records, and assets. Data about one’s household were also particularly concerns for the respondents, suggesting that even if one might be comfortable with their own being used for risk modeling, data about other household members is more problematic.Originality/valuePrevious studies on the privacy concerns that arise when integrating data pertaining to various contexts of people’s lives into algorithmic and related computational models have approached these questions from individual contexts. This study differs in that it captured the variation in privacy concerns across multiple contexts. Also, this study specifically assessed the ethical concerns related to a suicide prediction model and determining people’s awareness of the publicness of select data attributes, as well as which of these data attributes generated the most concern in such a context. To the best of the authors’ knowledge, this is the first study to pursue this question.

Development of a Suicide Prediction Model for the Elderly Using Health Screening Data.

Suicide poses a serious problem globally, especially among the elderly population. To tackle the issue, this study aimed to develop a model for predicting suicide by using machine learning based on the elderly population. To obtain a large sample, the study used the big data health screening cohort provided by the National Health Insurance Sharing Service. By applying a machine learning technique, a predictive model that comprehensively utilized various factors was developed to select the elderly aged > 65 years at risk of suicide. A total of 48,047 subjects were included in the analysis. Individuals who died by suicide were older, and the number of men was significantly greater. The suicide group had a more prominent history of depression, with the use of medicaments significantly higher. Specifically, the prescription of benzodiazepines alone was associated with a high suicide risk. Furthermore, body mass index, waist circumference, total cholesterol, and low-density lipoprotein level were lower in the suicide group. We developed a model for predicting suicide by using machine learning based on the elderly population. This suicide prediction model can satisfy the performance to some extent by employing only the medical service usage behavior without subjective reports.

Suicide Prediction Models May Lead to Greater Racial Health Disparities

Suicide Prediction Models May Lead to Greater Racial Health Disparities

Reconciling Statistical and Clinicians' Predictions of Suicide Risk.

Statistical models, including those based on electronic health records, can accurately identify patients at high risk for a suicide attempt or death, leading to implementation of risk prediction models for population-based suicide prevention in health systems. However, some have questioned whether statistical predictions can really inform clinical decisions. Appropriately reconciling statistical algorithms with traditional clinician assessment depends on whether predictions from these two methods are competing, complementary, or merely duplicative. In June 2019, the National Institute of Mental Health convened a meeting, "Identifying Research Priorities for Risk Algorithms Applications in Healthcare Settings to Improve Suicide Prevention." Here, participants of this meeting summarize key issues regarding the potential clinical application of suicide prediction models. The authors attempt to clarify the key conceptual and technical differences between traditional risk prediction by clinicians and predictions from statistical models, review the limited evidence regarding both the accuracy of and the concordance between these alternative methods of prediction, present a conceptual framework for understanding agreement and disagreement between statistical and clinician predictions, identify priorities for improving data regarding suicide risk, and propose priority questions for future research. Future suicide risk assessment will likely combine statistical prediction with traditional clinician assessment, but research is needed to determine the optimal combination of these two methods.

Meta-analysis of the strength of exploratory suicide prediction models; from clinicians to computers.

Suicide prediction models have been formulated in a variety of ways and are heterogeneous in the strength of their predictions. Machine learning has been a proposed as a way of improving suicide predictions by incorporating more suicide risk factors. To determine whether machine learning and the number of suicide risk factors included in suicide prediction models are associated with the strength of the resulting predictions. Random-effect meta-analysis of exploratory suicide prediction models constructed by combining two or more suicide risk factors or using clinical judgement (Prospero Registration CRD42017059665). Studies were located by searching for papers indexed in PubMed before 15 August 2020 with the term suicid* in the title. In total, 86 papers reported 102 suicide prediction models and included 20 210 411 people and 106 902 suicides. The pooled odds ratio was 7.7 (95% CI 6.7-8.8) with high between-study heterogeneity (I2 = 99.5). Machine learning was associated with a non-significantly higher odds ratio of 11.6 (95% CI 6.0-22.3) and clinical judgement with a non-significantly lower odds ratio of 4.7 (95% CI 2.1-10.9). Models including a larger number of suicide risk factors had a higher odds ratio when machine-learning studies were included (P = 0.02). Among non-machine-learning studies, suicide prediction models including fewer risk factors performed just as well as those including more risk factors. Machine learning might have the potential to improve the performance of suicide prediction models by increasing the number of included suicide risk factors but its superiority over other methods is unproven.

Prediction of suicide among 372,813 individuals under medical check-up

Suicide is a serious social and public health problem. Social stigma and prejudice reduce the accessibility of mental health care services for high-risk groups, resulting in them not receiving interventions and committing suicide. A suicide prediction model is necessary to identify high-risk groups in the general population. We used national medical check-up data from 2009 to 2015 in Korea. The latest medical check-up data for each subject was set as an index point. Analysis was undertaken for an overall follow-up period (index point to the final tracking period) as well as for a one-year follow-up period. The training set was cross-validated fivefold. The predictive model was trained using a random forest algorithm, and its performance was measured using a separate test set not included in the training. The analysis covered 372,813 individuals, with an average (SD) overall follow-up duration of 1.52 (1.52) years. When we predicted suicide during the overall follow-up period, the area under the receiver operating characteristic curve (AUC) was 0.849, sensitivity was 0.817, and specificity was 0.754. The performance of the predicted suicide risk model for one year from the index point was AUC 0.818, sensitivity 0.788, and specificity 0.657. This is probably the first suicide predictive model using machine learning based on medical check-up data from the general population. It could be used to screen high-risk suicidal groups from the population through routine medical check-ups. Future studies may test preventive interventions such as exercise and alcohol in these high-risk groups.

Income Inequality by Gini-Coefficient on Suicide Death in Iran: A Review of National Data

Research in source of inequality and enhance of knowledge can be reducing the inequalities in the coming decades. Therefore, we aimed to ascertain effects of income inequality measured by Gini-coefficient to death from suicide in Iran. This is an ecological study on the relation of Gini-coefficient and suicide death in Iran. Data were obtained from Iranian Urban and Rural Household Income for Gini-coefficient and Expenditure Survey and Iranian Forensic Medicine Organization for suicide. Concentration Index was used to determine of inequality by Gini-coefficient in suicide death and prediction model was applied by Stata software. Significant level considered less than 5%. A Gini-coefficient between 0.2523 and 0.3755 (mean, 0.3092) was observed. The overall concentration index CI was -0.10 (95% CI= -0.19 to -0.01), therefore our results confirmed a positive inequality in incidence suicide rate result from income inequality in Iran. Our results showed a positive inequality due to Gini-coefficients in suicide death. This study could be a start for investigation of inequality source in geographical units and at the individual level in all provinces.