Pathways to Psychiatric & Medical Comorbidity in Adults

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DESCRIPTION (provided by applicant): Recent studies suggest that an exposure to perinatal problems and social adversity in conjunction with maternal depression explain, at least in part, the increased risk of behavioral, emotional, and medical problems in offspring in their infancy, childhood, and adulthood. Individuals with psychiatric illness such as depression who have a comorbid medical problem have increased functional impairment, more frequent use of health care services, and a higher prevalence of suicidal ideation. Elucidation of the mechanism through which the phenomenon occurs, through examination of problems across the life cycle (birth, infancy, childhood, and adulthood), will help clarify the temporal sequences of the selected psychiatric illness (depression and substance use) and selected comorbid medical illness (allergies, headaches, and heart disease). Exploiting an unparalleled opportunity to utilize the dataset from a community-based longitudinal study (N=1758) that followed parents and their offspring for over 30 years, the proposed study has 5 aims: 1) to investigate direct and indirect (through social adversity) effects of perinatal risk on different problems (emotional/behavioral, medical, and neurological) in infancy and childhood, as well as psychiatric and medical illness in adulthood; 2) to elucidate pathways from social adversity and perinatal risk to adult psychiatric and medical illness, mediated through different problems in infancy and childhood; 3) to examine the temporal sequence of psychiatric and medical illness from childhood to adulthood; 4) to compare the level of social and functional impairment, such as suicidal ideation, hospital use, and personal resources among those with both psychiatric and comorbid medical illness, those with one and those with neither illness; 5) to examine the role of maternal depression and family support as moderators. These aims will be implemented by analyzing the data using rigorous statistic techniques such as structural equation model, log linear model, and proportional hazards model. The findings of this project will provide important information for both clinicians and policy makers in order to plan more cost-effective and time-effective early intervention.