Lyness J M, Bruce M L, Koenig H G, Parmelee P A, Schulz R, Lawton M P, Reynolds C F
Department of Psychiatry, University of Rochester, New York, USA.
J Am Geriatr Soc. 1996 Feb;44(2):198-203. doi: 10.1111/j.1532-5415.1996.tb02440.x.
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...
医学疾病与老年期抑郁症的高共病率既带来了挑战,也带来了机遇。评估技术、诊断和特定预后方面的挑战对临床护理和研究方法都有影响。然而,将这个棘手的“混杂因素”转化为研究问题的调查可能会有成效。对于为老年人提供服务的临床医生来说,认识到共病的医学疾病在老年期抑郁症中的预后重要性对于治疗规划至关重要。这种共病在诊断抑郁症时也会带来困难,因为医学状况的症状可能与情感障碍的症状重叠。症状评估必须在过度包容(例如,错误地将帕金森病的精神运动迟缓视为抑郁症)和过度排他(例如,错误地将患者的情绪症状视为“可以理解的”而不予理会)之间取得平衡。临床医生还应敏感地认识到情绪和医学障碍严重程度不同的广泛症状表现,不同治疗环境中的变异性就是例证。对于研究人员来说,类似的问题在规划调查策略时也很重要。应考虑以下几点:1. 病例识别是关键的第一步;必须明确界定处理可能被医学疾病混淆的抑郁症状的方法。选择包容性方法可避免过早排除相关现象;探索性分析可研究其他方法对感兴趣关系的影响。2. 在各个样本点使用相似的研究工具将极大地便于结果比较。每个受试者群体都为回答特定问题提供了自身的“优势”。精神科住院患者将突出严重精神病理学的作用(例如,在识别生物标志物方面很有用)。内科住院患者很适合用于研究不同病例识别方法的有效性、调查卫生服务利用情况或突出急性、严重、危及生命的医学疾病对情感疾病的影响。长期护理机构居民适合研究那些受益于随着时间推移对健康过程进行压缩的问题。内科门诊患者在普遍性和公共卫生意义方面有许多优势。需要社区样本以确定上述所有群体的偏差,每个群体都是由服务利用情况界定的。3. 对抑郁症与医学疾病之间关系的研究可能会进一步加深对晚年情绪障碍致病机制的理解。研究问题可能以上述生物心理社会概念框架为指导。一方面,这个框架要求采用多维度研究方法来确定医学疾病在特定患者群体中影响抑郁症的途径。多变量模型应研究医学疾病对抑郁症的直接和间接影响,同时考虑诸如功能残疾、个性和社会支持等干预变量。有指导的多元回归或结构方程建模将有助于确定关联强度。4. 与此同时,如果使用复杂的多变量分析,特别重要的是,特定的理论模型应有助于指导重点研究。此类模型的开发和测试是当前研究应解决的一项重大挑战。最后,从社会角度来看,抑郁症与医学疾病的共病可能对老年人的健康和医疗服务提供产生巨大影响。需要进一步研究以确定更具体的治疗方法。然而,现有数据明确支持在普通医疗环境中对老年人进行常规精神科评估的政策……