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Social scientists generally agree that health disparities are produced, at least in part, by adverse social experiences, especially during childhood and adolescence. Building on this research, we use an innovative method to measure early adversity while drawing upon a biopsychosocial perspective on health to formulate a model that specifies indirect pathways whereby childhood and adolescent adversity become biologically embedded and influence adult health.
Using nearly 20 years of longitudinal data from 382 Black Americans, we use repeated-measures latent class analysis (RMLCA) to construct measures of childhood/adolescent adversities and their trajectories. Then, we employ structural equation modeling to examine the direct and indirect effects of childhood/adolescent adversity on health outcomes in adulthood through psychosocial maladjustment.
RMLCA identified two classes for each component of childhood/adolescent adversity across the ages of 10 to 18, suggesting that childhood/adolescent social adversities exhibit a prolonged heterogeneous developmental trajectory. The models controlled for early and adult mental health, sociodemographic and health-related covariates. Psychosocial maladjustment, measured by low self-esteem, depressive and anxiety symptoms, and lack of self-control, mediated the relationship between childhood/adolescent adversity, especially parental hostility, racial discrimination, and socioeconomic class, and both self-reported illness and blood-based accelerated biological aging (with proportion mediation ranging from 8.22% to 79.03%).
The results support a biopsychosocial model of health and provide further evidence that, among Black Americans, early life social environmental experiences, especially parenting, financial stress, and racial discrimination, are associated with adult health profiles, and furthermore, psychosocial mechanisms mediate this association.
A decline in everyday cognitive functioning is important for diagnosing dementia. Informant questionnaires, such as the informant questionnaire on cognitive decline in the elderly (IQCODE), are used to measure this. Previously, conflicting results on the IQCODEs ability to discriminate between Alzheimer's disease (AD), mild cognitive impairment (MCI), and cognitively healthy elderly were found. We aim to investigate whether specific groups of items are more useful than others in discriminating between these patient groups. Informants of 180 AD, 59 MCI, and 89 patients with subjective memory complaints (SMC) completed the IQCODE. To investigate the grouping of questionnaire items, we used a two-dimensional graded response model (GRM).The association between IQCODE, age, gender, education, and diagnosis was modeled using structural equation modeling. The GRM with two groups of items fitted better than the unidimensional model. However, the high correlation between the dimensions (r=.90) suggested unidimensionality. The structural model showed that the IQCODE was able to differentiate between all patient groups. The IQCODE can be considered as unidimensional and as a useful addition to diagnostic screening in a memory clinic setting, as it was able to distinguish between AD, MCI, and SMC and was not influenced by gender or education. (JINS, 2011, 17, 674–681)
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