Psychosocial stratification of antenatal indicators to guide population-based programs in perinatal depression

Abstract Background There is increasing awareness that perinatal psychosocial adversity experienced by mothers, children, and their families, may influence health and well-being across the life course. To maximise the impact of population-based interventions for optimising perinatal wellbeing, health services can utilise empirical methods to identify subgroups at highest risk of poor outcomes relative to the overall population. Methods This study sought to identify sub-groups using latent class analysis within a population of mothers in Sydney, Australia, based on their differing experience of self-reported indicators of psychosocial adversity. This study sought to identify sub-groups using latent class analysis within a population of mothers in Sydney, Australia, based on their differing experience of self-reported indicators of psychosocial adversity. Subgroup differences in antenatal and postnatal depressive symptoms were assessed using the Edinburgh Postnatal Depression Scale. Results Latent class analysis identified four distinct subgroups within the cohort, who were distinguished empirically on the basis of their native language, current smoking status, previous involvement with Family-and-Community Services (FaCS), history of child abuse, presence of a supportive partner, and a history of intimate partner psychological violence. One group consisted of socially supported ‘local’ women who speak English as their primary language (Group L), another of socially supported ‘migrant’ women who speak a language other than English as their primary language (Group M), another of socially stressed ‘local’ women who speak English as their primary language (Group Ls), and socially stressed ‘migrant’ women who speak a language other than English as their primary language (Group Ms.). Compared to local and not socially stressed residents (L group), the odds of antenatal depression were nearly three times higher for the socially stressed groups (Ls OR: 2.87 95%CI 2.10–3.94) and nearly nine times more in the Ms. group (Ms OR: 8.78, 95%CI 5.13–15.03). Antenatal symptoms of depression were also higher in the not socially stressed migrant group (M OR: 1.70 95%CI 1.47–1.97) compared to non-migrants. In the postnatal period, Group M was 1.5 times more likely, while the Ms. group was over five times more likely to experience suboptimal mental health compared to Group L (OR 1.50, 95%CI 1.22–1.84; and OR 5.28, 95%CI 2.63–10.63, for M and Ms. respectively). Conclusions The application of empirical subgrouping analysis permits an informed approach to targeted interventions and resource allocation for optimising perinatal maternal wellbeing.

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Psychosocial Stratification of Antenatal Indicators to Guide Population-Based Programs in Perinatal Depression

10.21203/rs.3.rs-97766/v1 ◽

2020 ◽

Author(s):

John Eastwood ◽

Andy Wang ◽

Sarah Khanlari ◽

Alicia Montgomery ◽

Jean Yee Hwa Yang

Keyword(s):

Mental Health ◽

Latent Class Analysis ◽

Latent Class ◽

Population Based ◽

Primary Language ◽

Language Group ◽

Class Analysis ◽

Migrant Women ◽

History Of ◽

Psychosocial Adversity

Abstract Background: There is increasing awareness that perinatal psychosocial adversity experienced by mothers, children, and their families, may influence health and well-being across the life course. To maximise the impact of population-based interventions for optimising perinatal wellbeing, health services can utilise empirical methods to identify subgroups at highest risk of poor outcomes relative to the overall population. Methods: This study sought to identify sub-groups using latent class analysis within a population of mothers in Sydney, Australia, based on their differing experience of self-reported indicators of psychosocial adversity. Subgroups differences in antenatal and postnatal depressive symptoms were then assessed, as measured by Edinburgh Postnatal Depression Scale scores recorded at antenatal booking and early postnatal assessments, respectively.Results: Latent class analysis identified four distinct subgroups within the cohort, who were distinguished empirically on the basis of their native language, current smoking status, previous involvement with Family-and-Community Services (FaCS), history of child abuse, presence of a supportive partner, and a history of intimate partner psychological violence. One group consisted of socially supported ‘local’ women who speak English as their primary language (Group L), another of socially supported ‘migrant’ women who speak a language other than English as their primary language (Group M), another of socially stressed ‘local’ women who speak English as their primary language (Group Ls), and socially stressed ‘migrant’ women who speak a language other than English as their primary language (Group Ms). Compared to local and not socially stressed residents (L group), the odds of antenatal depression were nearly three times higher for the socially stressed groups (Ls OR: 2.87 95%CI 2.10-3.94) and nearly nine times more in the Ms group (Ms OR: 8.78, 95%CI 5.13-15.03). Antenatal symptoms of depression were also higher in the not socially stressed migrant group (M OR: 1.70 95%CI 1.47-1.97) compared to non-migrants. In the postnatal period, Group M was 1.5 times more likely, while Ms group was over five times more likely, to experience suboptimal mental health compared to Group L (OR 1.50, 95%CI 1.22-1.84; and OR 5.28, 95%CI 2.63-10.63, for M and Ms respectively). Conclusions: This study demonstrates that it is possible to stratify pregnant women into subpopulations using their demographic and psychosocial characteristics, to identify those at greatest risk of suboptimal mental health in the antenatal and postnatal periods. The application of empirical subgrouping analysis permits an informed approach to targeted resource allocation for optimising perinatal maternal wellbeing.

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Estimates and Determinants of SARS-Cov-2 Seroprevalence and Infection Fatality Ratio Using Latent Class Analysis: The Population-Based Tirschenreuth Study in the Hardest-Hit German County in Spring 2020

Viruses ◽

10.3390/v13061118 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1118

Author(s):

Ralf Wagner ◽

David Peterhoff ◽

Stephanie Beileke ◽

Felix Günther ◽

Melanie Berr ◽

...

Keyword(s):

Latent Class Analysis ◽

Latent Class ◽

Care Home ◽

Case Fatality ◽

The Elderly ◽

Population Based ◽

Class Analysis ◽

Population Based Study ◽

Targeted Testing ◽

June July

SARS-CoV-2 infection fatality ratios (IFR) remain controversially discussed with implications for political measures. The German county of Tirschenreuth suffered a severe SARS-CoV-2 outbreak in spring 2020, with particularly high case fatality ratio (CFR). To estimate seroprevalence, underreported infections, and IFR for the Tirschenreuth population aged ≥14 years in June/July 2020, we conducted a population-based study including home visits for the elderly, and analyzed 4203 participants for SARS-CoV-2 antibodies via three antibody tests. Latent class analysis yielded 8.6% standardized county-wide seroprevalence, a factor of underreported infections of 5.0, and 2.5% overall IFR. Seroprevalence was two-fold higher among medical workers and one third among current smokers with similar proportions of registered infections. While seroprevalence did not show an age-trend, the factor of underreported infections was 12.2 in the young versus 1.7 for ≥85-year-old. Age-specific IFRs were <0.5% below 60 years of age, 1.0% for age 60–69, and 13.2% for age 70+. Senior care homes accounted for 45% of COVID-19-related deaths, reflected by an IFR of 7.5% among individuals aged 70+ and an overall IFR of 1.4% when excluding senior care home residents from our computation. Our data underscore senior care home infections as key determinant of IFR additionally to age, insufficient targeted testing in the young, and the need for further investigations on behavioral or molecular causes of the fewer infections among current smokers.

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Distinguishing Complex PTSD from Borderline Personality Disorder among individuals with a history of sexual trauma: A latent class analysis

European Journal of Trauma & Dissociation ◽

10.1016/j.ejtd.2018.08.004 ◽

2020 ◽

Vol 4 (1) ◽

pp. 100080 ◽

Cited By ~ 6

Author(s):

R. Frost ◽

P. Hyland ◽

M. Shevlin ◽

J. Murphy

Keyword(s):

Borderline Personality Disorder ◽

Personality Disorder ◽

Latent Class Analysis ◽

Sexual Trauma ◽

Latent Class ◽

Borderline Personality ◽

Complex Ptsd ◽

Class Analysis ◽

History Of

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Clusters of the Risk Markers and the Pattern of Premature Coronary Heart Disease: An Application of the Latent Class Analysis

Frontiers in Cardiovascular Medicine ◽

10.3389/fcvm.2021.707070 ◽

2021 ◽

Vol 8 ◽

Author(s):

Leila Jahangiry ◽

Mahdieh Abbasalizad Farhangi ◽

Mahdi Najafi ◽

Parvin Sarbakhsh

Keyword(s):

Risk Factors ◽

Coronary Heart Disease ◽

Latent Class Analysis ◽

High Probability ◽

Latent Class ◽

Class Analysis ◽

Risk Markers ◽

History Of ◽

Opium Addiction ◽

The Mean

Background: Coronary heart disease (CHD) is the major cause of mortality in the world with a significant impact on the younger population. The aim of this study was to identify prematurity among patients with coronary artery bypass graft surgery (CABG) based on the clustering of CHD risk factors.Methods: Patients were recruited from an existing cohort of candidates for CABG surgery named Tehran Heart Center Coronary Outcome Measurement (THC-COM). A latent class analysis (LCA) model was formed using 11 potential risk factors as binary variables: cigarette smoking, obesity, diabetes, family history of CHD, alcohol use, opium addiction, hypertension, history of stroke, history of myocardial infarction (MI), peripheral vascular disease (PVD), and hyperlipidemia (HLP). We analyzed our data to figure out how the patients are going to be clustered based on their risk factors.Results: For 566 patients who were studied, the mean age (SD) and BMI of patients were 59.1 (8.9) and 27.3 (4.1), respectively. The LCA model fit with two latent classes was statistically significant (G2 = 824.87, df = 21, p < 0.0001). The mean (SD) age of patients for Class I and Class II was 55.66 (8.55) and 60.87 (8.66), respectively. Class I (premature) was characterized by a high probability of smoking, alcohol consumption, opium addiction, and a history of MI (P < 0.05), and class II by a high probability of obesity, diabetes, and hypertension.Conclusion: Latent class analysis calculated two groups of severe CHD with distinct risk markers. The younger group, which is characterized by smoking, addiction, and the history of MI, can be regarded as representative of premature CHD.

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Latent class analysis of the Apathy Scale does not identify subtypes of apathy in general population-based older persons

International Journal of Geriatric Psychiatry ◽

10.1002/gps.4413 ◽

2016 ◽

Vol 31 (9) ◽

pp. 1021-1028 ◽

Cited By ~ 2

Author(s):

Isis Groeneweg-Koolhoven ◽

Lotte J. Huitema ◽

Margot W. M. de Waal ◽

Max L. Stek ◽

Jacobijn Gussekloo ◽

...

Keyword(s):

General Population ◽

Latent Class Analysis ◽

Older Persons ◽

Latent Class ◽

Population Based ◽

Class Analysis

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Latent class analysis identifies three subtypes of aggressive end-of-life care: A population-based study in Taiwan

European Journal of Cancer ◽

10.1016/j.ejca.2013.05.005 ◽

2013 ◽

Vol 49 (15) ◽

pp. 3284-3291 ◽

Cited By ~ 4

Author(s):

Mei-Ling Chen ◽

Yun-Yi Chen ◽

Siew Tzuh Tang

Keyword(s):

Latent Class Analysis ◽

End Of Life ◽

End Of Life Care ◽

Latent Class ◽

Population Based ◽

Class Analysis ◽

Life Care ◽

Population Based Study

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Health Information-Seeking Behaviors of a German Speaking Minority in Italy: Latent Class Analysis of a Population-Based Cross-Sectional Survey

10.21203/rs.3.rs-483915/v2 ◽

2021 ◽

Author(s):

Dietmar Ausserhofer ◽

Wolfgang Wiedermann ◽

Christian J. Wiedermann ◽

Ulrich Becker ◽

Anna Vögele ◽

...

Keyword(s):

Latent Class Analysis ◽

Health Information ◽

Information Seeking ◽

Latent Class ◽

Population Based ◽

Health Information Seeking ◽

Class Analysis ◽

Online Class ◽

German Speaking ◽

Information Seeking Behaviors

Abstract Latent classes of health information-seeking behaviors of adults in a German-speaking minority of Italy were explored in a population-based, telephone survey on 10 health information sources conducted in South Tyrol, Italy. Data were collected from 504 adults (primary language German 68%, Italian 28%) and analyzed using latent class analysis and latent class multinomial logistic regression models. Three classes of health information-seeking behaviors emerged: “multidimensional” (23.3%), “interpersonal” (38.6%) and “technical/online” (38.1%). Compared to the “technical/online” class, “interpersonal” class members were older, had lower education than high school, and were less likely to be of Italian ethnicity. “Multidimensional” class members were more likely to be female, older, and of German ethnicity than those in the “technical/online” class. Linguistic ethnicity explains membership in classes of health-information-seeking behaviour. Policy makers and healthcare providers need to consider the health information-seeking behaviors of population subgroups to promote the health literacy skills of language minority groups.

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Schizotypal traits in adolescents: Links to family history of psychosis and psychological distress

European Psychiatry ◽

10.1016/j.eurpsy.2012.04.002 ◽

2012 ◽

Vol 28 (4) ◽

pp. 247-253 ◽

Cited By ~ 31

Author(s):

M. Cella ◽

M. Serra ◽

A. Lai ◽

O.J. Mason ◽

D. Sisti ◽

...

Keyword(s):

Psychological Distress ◽

Family History ◽

Latent Class Analysis ◽

Latent Class ◽

General Health Questionnaire ◽

Positive Association ◽

Total Sample ◽

Class Analysis ◽

Item Endorsement ◽

History Of

AbstractObjectiveStudies in the general population report that unusual subjective experiences are relatively common. Such experiences have been conceptualized either as extreme personality traits or as vulnerability markers for psychosis, and often grouped under the expression “schizotypal experiences”. This study investigates the heterogeneity of schizotypal traits using factor and latent class analysis.MethodsOne thousand and thirty-two adolescents were recruited for this study. Schizotypal experiences were assessed with the Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE); psychological distress was assessed with the General Health Questionnaire (GHQ). Confirmatory Factorial Analysis (CFA) and Latent Class Analysis (LCA) were performed on the O-LIFE and on the association with the GHQ and demographic variables.ResultsCFA replicated the original 4-factor structure of the O-LIFE. Three latent classes (LC) of schizotypal features were identified: participants in LC1 (26% of the total sample) showed minimal level of item endorsement; LC2 accounted for 52% of the sample and showed overall higher item endorsement compared to LC1, especially for disorganization and positive signs of schizotypy, but not for negative affective items. LC3 (22%) showed an overall higher level of item endorsement across schizotypal dimensions, and positive association with psychological distress and family history of psychosis.DiscussionDifferent latent class of schizotypal features can be empirically defined in adolescent community samples. The most extreme class is defined not only by a profile of higher positive replies to the items, but also by anhedonia, high psychological distress, and family history of psychosis. These findings can inform prevention research in schizophrenia.

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