title: Depression and medical illness in late life: report of a symposium.
abstract: The high comorbidity of medical illnesses and late life depression poses both challenges and opportunities. Challenges in assessment techniques, diagnosis, and specific prognosis affect clinical care and research methodology alike. However, investigations that turn this vexing "confound" into research questions may prove fruitful. For clinicians working with older persons, recognizing the prognostic import of comorbid medical illnesses in late-life depression is essential to treatment planning. This comorbidity also poses difficulties in diagnosing depression inasmuch as symptoms of the medical conditions may overlap with those of an affective disorder. Symptom assessments must strike a balance between overly inclusive (e.g., mistakenly treating the psychomotor slowing of Parkinson's disease as depression) and overly exclusive (e.g., erroneously dismissing the patient's mood symptoms as "understandable"). Clinicians also should be sensitive to the broad range of symptomatic presentations with varying severities of both mood and medical disorders, as exemplified by variability across treatment settings. For researchers, similar issues are of relevance in planning investigative strategies. Consideration should be given to the following: 1. Case identification is a crucial first step; the approach to depressive symptoms potentially confounded by medical illnesses must be defined explicitly. Choice of an inclusive approach avoids premature exclusion of relevant phenomena; exploratory analyses can examine the effects of other approaches to the relationships of interest. 2. The use of similar research instruments across sample sites would greatly facilitate comparisons of results. Each subject group offers its own "leverage" for answering particular questions. Psychiatric inpatients will highlight the contributions of severe psychopathology (useful, for example, in identifying biologic markers). Medical inpatients are well suited to studies examining validity of different approaches to case identification, investigating health service utilization, or highlighting the contribution of acute, severe, life-threatening medical disorders to affective illness. Long-term care residents lend themselves to issues that benefit from compression of health processes over time. Medical outpatients have many advantages regarding generalizability and public health significance. Community samples are needed to determine the biases of all the above groups, which are each defined by service utilization. 3. Study of the relationships between depression and medical illness may further understanding of pathogenic mechanisms in late life mood disorders. Research questions might be guided by the biopsychosocial conceptual context described above. On the one hand, this context demands multidimensional study methodology to identify the routes by which medical illness influences depression in particular patient groups. Multivariate models should examine direct and indirect effects of medical illness on depression while, at the same time, considering intervening variables such as functional disability, personality, and social support. Guided multiple regressions or structural equation modeling will allow for determination of strengths of associations. 4. At the same time, and of particular importance if complex multivariate analyses are used, specific theoretic models should help direct focused investigations. The development and testing of such models is a major challenge that should be addressed by current research. Finally, from a societal perspective, the comorbidity of depression and medical illness likely has a tremendous impact on both health and health care delivery for older adults. Further study is needed to identify more specific approaches to treatment. Yet existing data clearly support a policy of routine psychiatric assessment of older people in general medical settings...
authors.affiliation: Department of Psychiatry, University of Rochester, New York, USA.
authors.forename: J M
authors.initials: JM
authors.lastname: Lyness
authors.forename: M L
authors.initials: ML
authors.lastname: Bruce
authors.forename: H G
authors.initials: HG
authors.lastname: Koenig
authors.forename: P A
authors.initials: PA
authors.lastname: Parmelee
authors.forename: R
authors.initials: R
authors.lastname: Schulz
authors.forename: M P
authors.initials: MP
authors.lastname: Lawton
authors.forename: C F
authors.initials: CF
authors.lastname: Reynolds
mesh_terms: D000367:Age Factors; D000368:Aged; D015897:Comorbidity; D015986:Confounding Factors, Epidemiologic; D003866:Depressive Disorder / Q000150:complications / Q000175:diagnosis / Q000453:epidemiology; D015577:Geriatric Assessment; D006801:Humans; D011237:Predictive Value of Tests; D015995:Prevalence; D011379:Prognosis; D012107:Research Design; D012307:Risk Factors; D015983:Selection Bias