Abstract

Psychosocial factors play on important role in the etiology and course of psychiatric symptoms and syndromes in the elderly. Their effects are both direct and interactive with either factors affecting the mental health of the elderly. Psychosocial factors include a broad range of variables that characterize the individual's location in the life course, their social world, socio-economic and cultural status, and their experiences and perceptions as they move through major late-life transitions, such as medically related disabilities. It is the view of the investigators that virtually all studies of late-life mood disorders, even those focused on biological etiologies and the effects of pharmacological interventions require assessment of the psychosocial characteristics of study participants and their social contexts. Thus, one of the main factions of the Psychosocial Core is to provide measurement tools and conceptual guidance in the design and implementation of studies on late-life affective disorders. The second goal of the Psychosocial Core is to explore specific questions and stimulate research on domains where psychosocial factors are thought to play a primary role ion the etiology and course of late-life mood disorders.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Center Core Grants (P30)
Project #
5P30MH052247-04
Application #
6273527
Study Section
Project Start
1998-04-15
Project End
1999-02-28
Budget Start
1997-10-01
Budget End
1998-09-30
Support Year
4
Fiscal Year
1998
Total Cost
Indirect Cost

  Institution

Name
University of Pittsburgh
Department
Type
DUNS #
053785812
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213

  Publications

Smagula, Stephen F; Beach, Scott; Rosso, Andrea L et al. (2017) Brain Structural Markers and Caregiving Characteristics as Interacting Correlates of Caregiving Strain. Am J Geriatr Psychiatry 25:582-591
Smagula, Stephen F; Wallace, Meredith L; Anderson, Stewart J et al. (2016) Combining moderators to identify clinical profiles of patients who will, and will not, benefit from aripiprazole augmentation for treatment resistant late-life major depressive disorder. J Psychiatr Res 81:112-8
Khalaf, Alexander; Edelman, Kathryn; Tudorascu, Dana et al. (2015) White Matter Hyperintensity Accumulation During Treatment of Late-Life Depression. Neuropsychopharmacology 40:3027-35
Patel, Meenal J; Andreescu, Carmen; Price, Julie C et al. (2015) Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction. Int J Geriatr Psychiatry 30:1056-67
Joel, Ian; Begley, Amy E; Mulsant, Benoit H et al. (2014) Dynamic prediction of treatment response in late-life depression. Am J Geriatr Psychiatry 22:167-76
Diniz, Breno S; Reynolds 3rd, Charles F; Butters, Meryl A et al. (2014) The effect of gender, age, and symptom severity in late-life depression on the risk of all-cause mortality: the Bambuí Cohort Study of Aging. Depress Anxiety 31:787-95
Andreescu, Carmen; Teverovsky, Esther; Fu, Bo et al. (2014) Old worries and new anxieties: behavioral symptoms and mild cognitive impairment in a population study. Am J Geriatr Psychiatry 22:274-84
Chisholm, Denise; Toto, Pamela; Raina, Ketki et al. (2014) Evaluating capacity to live independently and safely in the community: Performance Assessment of Self-care Skills. Br J Occup Ther 77:59-63
Gallo, Joseph J; Morales, Knashawn H; Bogner, Hillary R et al. (2013) Long term effect of depression care management on mortality in older adults: follow-up of cluster randomized clinical trial in primary care. BMJ 346:f2570
Sherer, Eric A; Sale, Mark E; Pollock, Bruce G et al. (2012) Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building. J Pharmacokinet Pharmacodyn 39:393-414

Showing the most recent 10 out of 347 publications