Suicide Categories by Patterns of Known Risk Factors

Logan, Joseph E.; Hall, Jeffrey E.; Karch, Debra L.

doi:10.1001/archgenpsychiatry.2011.85

Cited by 110 publications

(92 citation statements)

References 41 publications

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“…We found that suicide deaths predominantly occurred among males, age 18 to 65, who suffered from depression or another major mental illness. These demographic patterns are in keeping with the literature on suicide, 17,29,32 and their strong effect helps to explain why the identified clusters all overlap to some degree. Nevertheless, our study did identify 5 clinically Cluster 1 had the highest proportion of females and involves suicidal acts that may have been less well planned and more impulsive.…”

Section: Suicide Clusterssupporting

confidence: 84%

“…A similar study 16 analyzed 148 suicide deaths in Hong Kong and found 2 groups: one associated with psychotic disorders, psychiatric treatment, and acute stress, and one associated with financial problems, chronic stress, and no psychiatric illness. Most recently, Logan et al 17 examined US National Violent Death Reporting System data on 28 703 suicide deaths and identified 9 different suicide clusters that differed based on rates of mental illness, substance abuse, past suicide attempts, stressors, and acute alcohol intoxication. While this is the largest and most comprehensive study examining suicide clusters to date, it is unclear to what extent the results can be generalizable outside the United States, most strikingly because 50% of them occurred by firearms.…”

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confidence: 99%

“…It has previously been argued that the court system could be an important ally in suicide prevention strategies. 17 Perhaps such efforts would be best focused on middle-aged men with substance histories.…”

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confidence: 99%

“…An analysis of the content of suicide notes may be an important next step in understanding the motivation for suicide in this group, and, indeed, in each of the other groups, although some evidence suggests that studies of those who leave notes may not be generalizable to everyone who dies from suicide. 40 Directly comparing these clusters to the 9 clusters identified in the United States by Logan et al 17 is challenging, given that their study separated known mental health diagnoses from depressed mood at the time of death without a prior diagnosis and separated a history of substance abuse from the presence of alcohol at the time of death. The nature of the data in our study meant that we were unable to make these distinctions.…”

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confidence: 99%

“…Cluster 2 in our study includes elements of class 2 (mental health conditions with recent crises), class 4 (current depressed mood with financial problems), and class 6 (medical health problems with depressed mood) from the US study. 17 Cluster 3 in our study includes elements of class 1 (mental health conditions with alcohol problems), class 5 (alcohol problems with other stressful life events, and class 7 (recent stressors with criminal legal problems) from the US study. The US study also distinguished clusters with known histories of mental illness (classes 1-3) from those without known mental illness that had combinations of stressors and substance abuse with or without depressed mood (classes 4-9).…”

mentioning

confidence: 99%

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Characterizing Suicide in Toronto: An Observational Study and Cluster Analysis

2014

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Objective: To determine whether people who have died from suicide in a large epidemiologic sample form clusters based on demographic, clinical, and psychosocial factors. Method:We conducted a coroner's chart review for 2886 people who died in Toronto, Ontario, from 1998 to 2010, and whose death was ruled as suicide by the Office of the Chief Coroner of Ontario. A cluster analysis using known suicide risk factors was performed to determine whether suicide deaths separate into distinct groups. Clusters were compared according to person-and suicide-specific factors.Results: Five clusters emerged. Cluster 1 had the highest proportion of females and nonviolent methods, and all had depression and a past suicide attempt. Cluster 2 had the highest proportion of people with a recent stressor and violent suicide methods, and all were married. Cluster 3 had mostly males between the ages of 20 and 64, and all had either experienced recent stressors, suffered from mental illness, or had a history of substance abuse. Cluster 4 had the youngest people and the highest proportion of deaths by jumping from height, few were married, and nearly one-half had bipolar disorder or schizophrenia. Cluster 5 had all unmarried people with no prior suicide attempts, and were the least likely to have an identified mental illness and most likely to leave a suicide note. Conclusions:People who die from suicide assort into different patterns of demographic, clinical, and death-specific characteristics. Identifying and studying subgroups of suicides may advance our understanding of the heterogeneous nature of suicide and help to inform development of more targeted suicide prevention strategies. W W W Caractérisation du suicide à Toronto : une étude par observation et analyse en grappe (cluster)Objectif : Déterminer si les personnes qui sont mortes par suicide dans un vaste échantillon épidémiologique forment des groupes selon des facteurs démographiques, cliniques et psychosociaux. Méthode :Nous avons mené une revue des dossiers du coroner des 2886 personnes qui sont décédées à Toronto, Ontario, de 1998 à 2010, et dont le décès a été classé comme un suicide par le Bureau du coroner en chef de l'Ontario. Une analyse en grappe a été menée à l'aide des facteurs connus du risque de suicide, afin de déterminer si les décès par suicide se divisaient en groupes distincts. Les grappes ont été comparées en vertu des facteurs propres à la personne et au suicide.

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Section: Suicide Clusterssupporting

confidence: 84%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Characterizing Suicide in Toronto: An Observational Study and Cluster Analysis

2014

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Decoding Suicide Decedent Profiles and Signs of Suicidal Intent Using Latent Class Analysis

Xiao,

Bi,

Yip

et al. 2024

JAMA Psychiatry

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ImportanceSuicide rates in the US increased by 35.6% from 2001 to 2021. Given that most individuals die on their first attempt, earlier detection and intervention are crucial. Understanding modifiable risk factors is key to effective prevention strategies.ObjectiveTo identify distinct suicide profiles or classes, associated signs of suicidal intent, and patterns of modifiable risks for targeted prevention efforts.Design, Setting, and ParticipantsThis cross-sectional study used data from the 2003-2020 National Violent Death Reporting System Restricted Access Database for 306 800 suicide decedents. Statistical analysis was performed from July 2022 to June 2023.ExposuresSuicide decedent profiles were determined using latent class analyses of available data on suicide circumstances, toxicology, and methods.Main Outcomes and MeasuresDisclosure of recent intent, suicide note presence, and known psychotropic usage.ResultsAmong 306 800 suicide decedents (mean [SD] age, 46.3 [18.4] years; 239 627 males [78.1%] and 67 108 females [21.9%]), 5 profiles or classes were identified. The largest class, class 4 (97 175 [31.7%]), predominantly faced physical health challenges, followed by polysubstance problems in class 5 (58 803 [19.2%]), and crisis, alcohol-related, and intimate partner problems in class 3 (55 367 [18.0%]), mental health problems (class 2, 53 928 [17.6%]), and comorbid mental health and substance use disorders (class 1, 41 527 [13.5%]). Class 4 had the lowest rates of disclosing suicidal intent (13 952 [14.4%]) and leaving a suicide note (24 351 [25.1%]). Adjusting for covariates, compared with class 1, class 4 had the highest odds of not disclosing suicide intent (odds ratio [OR], 2.58; 95% CI, 2.51-2.66) and not leaving a suicide note (OR, 1.45; 95% CI, 1.41-1.49). Class 4 also had the lowest rates of all known psychiatric illnesses and psychotropic medications among all suicide profiles. Class 4 had more older adults (23 794 were aged 55-70 years [24.5%]; 20 100 aged ≥71 years [20.7%]), veterans (22 220 [22.9%]), widows (8633 [8.9%]), individuals with less than high school education (15 690 [16.1%]), and rural residents (23 966 [24.7%]).Conclusions and RelevanceThis study identified 5 distinct suicide profiles, highlighting a need for tailored prevention strategies. Improving the detection and treatment of coexisting mental health conditions, substance and alcohol use disorders, and physical illnesses is paramount. The implementation of means restriction strategies plays a vital role in reducing suicide risks across most of the profiles, reinforcing the need for a multifaceted approach to suicide prevention.

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Finding causation in occupational fatalities: A latent class analysis

Farina

Bianco

Bena

et al. 2018

American J Industrial Med

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BackgroundThe method “Learning by mistakes” was developed in Italy to conduct occupational injury investigations and to collect information on the genesis of injuries. The aim is to analyze data classified with this method in order to identify patterns among the factors contributing to injury dynamics.MethodsData regarding 673 factors, corresponding to 354 occupational fatalities that occurred in the Piedmont region (north‐west of Italy) during 2005‐2014 were considered. Latent Class Analysis (LCA) was applied to find patterns among these factors.ResultsThe eight‐class model was selected. Most of the factors fell in the class “Fall from height or vehicle rollover due to incorrect practice” (40.56%) while the remaining factors where heterogeneously distributed in the other classes.ConclusionsAll the classes found allow for a logical interpretation. Systematic use of LCA could aid in uncovering new, unexpected patterns of factors not otherwise detectable by analysis of the single fatal accident.

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Suicide Categories by Patterns of Known Risk Factors

Cited by 110 publications

References 41 publications

Characterizing Suicide in Toronto: An Observational Study and Cluster Analysis

Characterizing Suicide in Toronto: An Observational Study and Cluster Analysis

Decoding Suicide Decedent Profiles and Signs of Suicidal Intent Using Latent Class Analysis

Finding causation in occupational fatalities: A latent class analysis

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