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The relations between socio-demographic information and negative symptoms, mental health, and quality of life: a latent profile analysis with psychotic patients in Kosovo.

ABSTRACT: The current study aims to identify meaningful psychotic patients' profiles by examining certain combinations of patient's demographic and socio-economic variables (sex, age, marital status, number of children, cohabitant and level of education). Moreover, we aim to assess whether there is any significant effect of class membership (profile) on negative symptoms, health state, and quality of life among psychotic patients. A convenience sample of 103 patients (age: M = 22, SD = 1.75), was drawn from the clinical populations of Kosovo. Demographic and socio-economic data was obtained through individual interviews, meanwhile a battery of questionnaires was used to assess negative symptoms, mental health, and quality of life of patients. The 4-class solution was selected as the best fitting model and used in subsequent analyses. Results indicated a significant effect of class membership on health state, quality of life and negative symptoms. Practical implications are discussed.

SUBMITTER: Uka F

PROVIDER: S-EPMC10410071 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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The relations between socio-demographic information and negative symptoms, mental health, and quality of life: a latent profile analysis with psychotic patients in Kosovo.

Uka Fitim F Konjufca Jon J Ramadani Fjolla F Arënliu Aliriza A Bërxulli Dashamir D Jovanović Nikolina N Russo Manuela M

Frontiers in psychiatry 20230726

The current study aims to identify meaningful psychotic patients' profiles by examining certain combinations of patient's demographic and socio-economic variables (sex, age, marital status, number of children, cohabitant and level of education). Moreover, we aim to assess whether there is any significant effect of class membership (profile) on negative symptoms, health state, and quality of life among psychotic patients. A convenience sample of 103 patients (age: <i>M = 22</i>, <i>SD</i> = 1.75) ...[more]

PMID: 37564239

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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.

Project description:BackgroundSome types of sedentary behaviors tend to cluster in individuals or groups of older adults. Insight into how these different types of sedentary behavior cluster is needed, as recent research suggests that not all types of sedentary behavior may have the same negative effects on physical and mental health. Therefore, the aim of this study was to identify sex-specific typologies of older adults' sedentary behavior, and to examine their associations with health-related and socio-demographic factors.MethodsCross-sectional data were collected as part of the BEPAS Seniors, and the Busschaert study among 696 Flemish older adults (60+). Typologies of self-reported sedentary behavior were identified using latent profile analysis, and associations with health-related and sociodemographic factors were examined using analyses of variances.ResultsFive distinct typologies were identified from seven sedentary behaviors (television time, computer time, transport-related sitting time, sitting for reading, sitting for hobbies, sitting for socializing and sitting for meals) in men, and three typologies were identified from six sedentary behaviors (television time, transport-related sitting time, sitting for reading, sitting for hobbies, sitting for socializing and sitting for meals) in women. Typologies that are characterized by high television time seem to be related to more negative health outcomes, like a higher BMI, less grip strength, and a lower physical and mental health-related quality-of-life. Typologies that are represented by high computer time and motorized transport seem to be related to more positive health outcomes, such as a lower body mass index, more grip strength and a higher physical and mental health-related quality-of-life.ConclusionsAlthough causal direction between identified typologies and health outcomes remains uncertain, our results suggests that future interventions should better focus on specific types of sedentary behavior (e.g. television time), or patterns of sedentary behavior, rather than on total sedentary behavior.

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.

Dataset Information

The relations between socio-demographic information and negative symptoms, mental health, and quality of life: a latent profile analysis with psychotic patients in Kosovo.

Publications

The relations between socio-demographic information and negative symptoms, mental health, and quality of life: a latent profile analysis with psychotic patients in Kosovo.

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