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Using Multivariate Statistics

Structural equation modeling (SEM) is a multivariate statistical framework that is used to model complex relationships between directly observed and indirectly observed (latent) variables. SEM is a general framework that involves simultaneously solving systems of linear equations and encompasses other techniques such as regression, factor analysis, path analysis, and latent growth curve modeling. Recently, SEM has gained popularity in the analysis of complex genetic traits because it can be used to better analyze the relationships between correlated variables (traits), to model genes as latent variables as a function of multiple observed genetic variants, and to assess the association between multiple genetic variants and multiple correlated phenotypes of interest. Though the general SEM framework only allows for the analysis of independent observations, recent work has extended SEM for the analysis of data on general pedigrees. Here, we review the theory of SEM for both unrelated and family data, describe the available software for SEM, and provide examples of SEM analysis.

Structural Equation Modeling

This chapter explores the ecology of human development, those forces in the person's environment that affect and influence development. Urie Bronfenbrenner's model of the human ecosystem guides the discussion, making connections between children in families and in communities and the larger society that surrounds them. The human ecosystem model is much like the study of the natural ecology, focusing on the interactions between subjects at various levels of the environment as they affect each other. The interaction between individual and environment forms the basis of an ecological approach to human development. This view sees the process of development as the expansion of the child's conception of the world and the child's ability to act on that world. Risks to development can come from both direct threats and the absence of opportunities for development. Sociocultural risk refers to the impoverishment in the child's world of essential experiences and relationships.

The Ecology of Human Development

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Applied Missing Data Analysis

Through this international report on the results of its most recent survey, the Health Behaviour in School-aged Children (HBSC) study supplies the up-to-date information needed by policy-makers at various levels of government, nongovernmental organizations, and professionals in sectors such as health, education, social services, justice and recreation
The latest addition to a series of HBSC reports on young people’s health, this report presents findings from the 2009/2010 survey on the demographic and social influences on the health of young people (aged 11, 13 and 15 years) in 43 countries and regions in the WHO European Region and North America Responding to the survey, the young people described their social context (relations with family, peers and school), physical health and satisfaction with life, health behaviours (patterns of eating, tooth brushing and physical activity) and risk behaviours (use of tobacco, alcohol and cannabis, sexual behaviour, fighting and bullying) Statistical analyses were carried out to identify meaningful differences in the prevalence of health and social indicators by gender, age group and levels of family affluence
The aim was to provide a rigorous, systematic statistical base for describing cross-national patterns, in terms of the magnitude and direction of differences between subgroups, thus contributing to a better understanding of the social determinants of health and well-being among young people, and providing the means to help protect and promote their health

Social determinants of health and well-being among young people: Health Behaviour in School-aged Children (HBSC) study: international report from the 2009/2010 survey.

Background: Self-management and adequate consultation behaviour are essential for the successful treatment of chronic heart failure (CHF). Patients with a type-D personality, characterised by high social inhibition and negative affectivity, may delay medical consultation despite increased symptom levels and may be at an increased risk for adverse clinical outcomes. Aim: To examine whether type-D personality predicts poor self-management and failure to consult for evident cardiac symptoms in patients with CHF. Design/methods/patients: 178 outpatients with CHF (aged ⩽80 years) completed the type-D Personality Scale at baseline, and the Health Complaints Scale (symptoms) and European Heart Failure Self-care Behaviour Scale (self-management) at 2 months of follow-up. Medical information was obtained from the patients’ medical records. Results: At follow-up, patients with a type-D personality experienced more cardiac symptoms (OR 6.4; 95% CI 2.5 to 16.3, p Conclusion: Patients with CHF with a type-D personality display inadequate self-management. Failure to consult for increased symptom levels may partially explain the adverse effect of type-D personality on cardiac prognosis.

/pdf/failure-to-consult-for-symptoms-of-heart-failure-in-patients-1qd92ot2he.pdf

Failure to consult for symptoms of heart failure in patients with a type-D personality

Inadequate consultation behavior modulates the relationship between type D personality and impaired health status in chronic heart failure.

Objective:

To examine the role of clinical and psychological characteristics as predictors of fatigue in CHF.



Background:

Little is known about predictors of fatigue in CHF. Next to heart failure characteristics, depressive symptoms and type-D personality may explain individual differences in fatigue.



Methods:

At baseline, 136 CHF outpatients (age≤80 years) completed a questionnaire to assess depressive symptoms, type-D personality and cardiac symptoms. At one-year follow-up, they completed the Dutch Exertion Fatigue Scale and the Fatigue Assessment Scale to assess symptoms of fatigue. Medical information was obtained from the patients' medical records.



Results:

Exertion fatigue and general fatigue were identified as different manifestations of fatigue. We found that exertion fatigue at 12-month follow-up was predicted by decreased exercise capacity (β=−.35; p<.001), dyspnoea (β=24; p=.002), hypertension (β=.16; p=.03), and depressive symptoms (β=.16; p=.05). In contrast, general fatigue at 12-month follow-up was predicted by dyspnoea (β=.24; p=.003), depressive symptoms (β=.27; p<.001), type-D personality (β=17; p=.03), and sleep problems (β=.20; p=.01). Together, these variables explained 32% and 37% of the variance, respectively.



Conclusion:

The present study showed that fatigue was related to both clinical and psychological characteristics. The use of this knowledge may lead to a better understanding and treatment of the clinical manifestations of fatigue in CHF.

https://onlinelibrary.wiley.com/doi/pdf/10.1016/j.ejheart.2007.05.016

Symptoms of fatigue in chronic heart failure patients: clinical and psychological predictors.

OBJECTIVE Depression is a predictor of adverse health outcomes in chronic heart failure (CHF), but it is not known whether specific symptoms drive this relationship. We examined the impact of somatic/affective, cognitive/affective, and total depressive symptoms on all-cause mortality and health status in CHF. METHOD Consecutive CHF outpatients (n = 366) completed the Beck Depression Inventory. The primary endpoint was all-cause mortality; the secondary endpoint was disease-specific health status, as measured by the Minnesota Living with Heart Failure Questionnaire (n = 285) at inclusion and 1-year follow-up. The study was conducted between October 2003 and March 2007. RESULTS There were 68 (18.6%) deaths (mean +/- SD follow-up, 37.2 +/- 10.6 months). Patients high on somatic/affective depressive symptoms had a greater incidence of mortality compared to patients low on somatic/affective depressive symptoms (31% vs 15%; hazard ratio [HR] = 2.3; 95% CI, 1.38-3.69; P = .001). There was no significant difference in the incidence of mortality between patients high versus low on cognitive/affective depressive symptoms (23% vs 18%; HR = 1.4; 95% CI, 0.80-2.40; P = .25), but there was a significant difference between patients high versus low on total depressive symptoms (24% vs 16%; HR = 1.6; 95% CI, 1.01-2.63; P < .05). After adjusting for demographic and clinical characteristics, we found that somatic/affective depressive symptoms predicted all-cause mortality (HR = 1.8; 95% CI, 1.03-3.07; P = .04), while cognitive/affective and total depressive symptoms did not. Both dimensions of depressive symptoms predicted disease-specific health status at 1 year. CONCLUSIONS Only somatic/affective depressive symptoms significantly predicted all-cause mortality in CHF. In the context of diagnosing and intervening, awareness of subtypes of depressive symptoms is important.

Otto R.F. Smith

Papers

Social determinants of health and well-being among young people: Health Behaviour in School-aged Children (HBSC) study: international report from the 2009/2010 survey.

Failure to consult for symptoms of heart failure in patients with a type-D personality

Inadequate consultation behavior modulates the relationship between type D personality and impaired health status in chronic heart failure.

Symptoms of fatigue in chronic heart failure patients: clinical and psychological predictors.

Somatic versus cognitive symptoms of depression as predictors of all-cause mortality and health status in chronic heart failure.