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The Relationship between Suicidality and Socio-Demographic Variables, Physical Disorders, and Psychiatric Disorders: Results from the Singapore Mental Health Study 2016.

ABSTRACT: Suicidality encompasses suicidal ideation, plans, and attempts. This paper aims to establish associations between suicidality and sociodemographic variables, physical disorders, and psychiatric disorders. The Singapore Mental Health Study 2016 was a population-level epidemiological survey, which determined the prevalence of physical disorders, psychiatric disorders, and suicidality. Questionnaires were used to determine socio-demographic information. A total of 6216 respondents were interviewed. Lifetime prevalence of suicidal ideation, planning, and attempts were 7.8%, 1.6%, and 1.6%, respectively. All components of suicidality were more likely in those with major depressive disorder, bipolar disorder, generalized anxiety disorder, alcohol use disorder, and chronic pain. Suicidal ideation and attempts were more likely in those with diabetes. Age above 65, being male, and a monthly household income of ≥ SGD 10,000 were associated with a lower likelihood of suicidal ideation. These findings indicate that there are high-risk groups for whom suicidality is a concern, and for whom interventions may be needed.

SUBMITTER: Ganesh Kudva K

PROVIDER: S-EPMC8074258 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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The Relationship between Suicidality and Socio-Demographic Variables, Physical Disorders, and Psychiatric Disorders: Results from the Singapore Mental Health Study 2016.

Ganesh Kudva Kundadak K Abdin Edimansyah E Vaingankar Janhavi Ajit JA Chua Boon Yiang BY Shafie Saleha S Verma Swapna Kamal SK Fung Daniel Shuen Sheng DSS Heng Mok Kwee Derrick D Chong Siow Ann SA Subramaniam Mythily M

International journal of environmental research and public health 20210420 8

Suicidality encompasses suicidal ideation, plans, and attempts. This paper aims to establish associations between suicidality and sociodemographic variables, physical disorders, and psychiatric disorders. The Singapore Mental Health Study 2016 was a population-level epidemiological survey, which determined the prevalence of physical disorders, psychiatric disorders, and suicidality. Questionnaires were used to determine socio-demographic information. A total of 6216 respondents were interviewed. ...[more]

PMID: 33924079

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Project description:BackgroundNeurocognitive impairments are associated with poor clinical and employment outcomes in individuals with affective disorders. However, little is known about their associations with long-term clinical outcomes such as psychiatric hospitalizations, and with socio-demographic indicators other than employment. In the largest longitudinal study of neurocognition in affective disorders to date, we investigate the role of neurocognitive impairments on psychiatric hospitalizations and socio-demographic conditions.MethodsThe study included 518 individuals with bipolar or major depressive disorder. Neurocognitive assessments covered executive function and verbal memory domains. Longitudinal data on psychiatric hospitalization and socio-demographic conditions (employment, cohabitation, and marital status) for up to 11 years were obtained using National population-based registers. The primary and secondary outcomes were psychiatric hospitalizations (n = 398) and worsening of socio-demographic conditions (n = 518), in the follow-up period since study inclusion, respectively. Cox regression models were used to examine the association of neurocognition with future psychiatric hospitalizations and the worsening of socio-demographic conditions.FindingsClinically significant impairment in verbal memory (z-score ≤ -1; defined by the ISBD Cognition Task Force), but not in executive function, was associated with a higher risk of future hospitalization, when adjusted for age, sex, hospitalization in the year preceding inclusion, depression severity, diagnosis, and type of clinical trial (HR = 1.84, 95% CI:1.05-3.25, p = 0.034; n = 398). The results remained significant even after accounting for illness duration. Neurocognitive impairments were not associated with the worsening of socio-demographic conditions (p ≥ 0.17; n = 518).InterpretationPromoting neurocognitive function, especially verbal memory, may mitigate the risk of future psychiatric hospitalization in individuals with affective disorders.FundingLundbeckfonden (R279-2018-1145).

Project description:Questionnaires are among the most basic and widespread tools to assess the mental health of a population in epidemiological and public health studies. Their most obvious advantage (firsthand self-report) is also the source of their main problems: the raw data requires interpretation, and are a snapshot of the specific sample's status at a given time. Efforts to deal with both issues created a bi-dimensional space defined by two orthogonal axes, in which most of the quantitative mental health research can be located. Methods aimed to assure that mental health diagnoses are solidly grounded on existing raw data are part of the individual validity axis. Tools allowing the generalization of the results across the entire population compose the collective validity axis. This paper raises a different question. Since one goal of mental health assessments is to obtain results that can be generalized to some extent, an important question is how robust is a questionnaire result when applied to a different population or to the same population at a different time. In this case, there is deep uncertainty, without any a priori probabilistic information. The main claim of this paper is that this task requires the development of a new robustness to deep uncertainty axis, defining a three-dimensional research space. We demonstrate the analysis of deep uncertainty using the concept of robustness in info-gap decision theory. Based on data from questionnaires collected before and during the Covid-19 pandemic, we first locate a mental health assessment in the space defined by the individual validity axis and the collective validity axis. Then we develop a model of info-gap robustness to uncertainty in mental health assessment, showing how the robustness to deep uncertainty axis interacts with the other two axes, highlighting the contributions and the limitations of this approach. The ability to measure robustness to deep uncertainty in the mental health realm is important particularly in troubled and changing times. In this paper, we provide the basic methodological building blocks of the suggested approach using the outbreak of Covid-19 as a recent example.

Project description:BackgroundPrevious studies have shown a relationship between socio-demographic variables and the mental health of children and adolescents. However, no research has been found on a model-based cluster analysis of socio-demographic characteristics with mental health. This study aimed to identify the cluster of the items representing the socio-demographic characteristics of Australian children and adolescents aged 11-17 years by using latent class analysis (LCA) and examining the associations with their mental health.MethodsChildren and adolescents aged 11-17 years (n = 3152) were considered from the 2013-2014 Young Minds Matter: The Second Australian Child and Adolescent Survey of Mental Health and Wellbeing. LCA was performed based on relevant socio-demographic factors from three levels. Due to the high prevalence of mental and behavioral disorders, the generalized linear model with log-link binomial family (log-binomial regression model) was used to examine the associations between identified classes, and the mental and behavioral disorders of children and adolescents.ResultsThis study identified five classes based on various model selection criteria. Classes 1 and 4 presented the vulnerable class carrying the characteristics of "lowest socio-economic status and non-intact family structure" and "good socio-economic status and non-intact family structure" respectively. By contrast, class 5 indicated the most privileged class carrying the characteristics of "highest socio-economic status and intact family structure". Results from the log-binomial regression model (unadjusted and adjusted models) showed that children and adolescents belonging to classes 1 and 4 were about 1.60 and 1.35 times more prevalent to be suffering from mental and behavioral disorders compared to their class 5 counterparts (95% CI of PR [prevalence ratio]: 1.41-1.82 for class 1; 95% CI of PR [prevalence ratio]: 1.16-1.57 for class 4). Although children and adolescents from class 4 belong to a socio-economically advantaged group and shared the lowest class membership (only 12.7%), the class had a greater prevalence (44.1%) of mental and behavioral disorders than did class 2 ("worst education and occupational attainment and intact family structure") (35.2%) and class 3 ("average socio-economic status and intact family structure") (32.9%).ConclusionsAmong the five latent classes, children and adolescents from classes 1 and 4 have a higher risk of developing mental and behavioral disorders. The findings suggest that health promotion and prevention as well as combating poverty are needed to improve mental health in particular among children and adolescents living in non-intact families and in families with a low socio-economic status.

Dataset Information

The Relationship between Suicidality and Socio-Demographic Variables, Physical Disorders, and Psychiatric Disorders: Results from the Singapore Mental Health Study 2016.

Publications

The Relationship between Suicidality and Socio-Demographic Variables, Physical Disorders, and Psychiatric Disorders: Results from the Singapore Mental Health Study 2016.

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