Dissecting the determinants of depressive disorders outcome: an in depth analysis of two clinical cases

Clinicians face everyday the complexity of depression. Available pharmacotherapies and psychotherapies improve patients suffering in a large part of subjects, however up to half of patients do not respond to treatment. Clinicians may forecast to a good extent if a given patient will respond or not, based on a number of data and sensations that emerge from face to face assessment. Conversely, clinical predictors of non response emerging from literature are largely unsatisfactory. Here we try to fill this gap, suggesting a comprehensive assessment of patients that may overcome the limitation of standardized assessments and detecting the factors that plausibly contribute to so marked differences in depressive disorders outcome. For this aim we present and discuss two clinical cases. Mr. A was an industrial manager who came to psychiatric evaluation with a severe depressive episode. His employment was demanding and the depressive episode undermined his capacity to manage it. Based on standardized assessment, Mr. A condition appeared severe and potentially dramatic. Mrs. B was a housewife who came to psychiatric evaluation with a moderate depressive episode. Literature predictors would suggest Mrs. B state as associated with a more favourable outcome. However the clinician impression was not converging with the standardized assessment and in fact the outcome will reverse the prediction based on the initial formal standard evaluation. Although the present report is based on two clinical cases and no generalizability is possible, a more detailed analysis of personality, temperament, defense mechanisms, self esteem, intelligence and social adjustment may allow to formalize the clinical impressions used by clinicians for biologic and pharmacologic studies

Neighborhood Age Structure and its Implications for Health

Age structure at the neighborhood level is rarely considered in contextual studies of health. However, age structure can play a critical role in shaping community life, the availability of resources, and the opportunities for social engagement—all factors that, research suggests, have direct and indirect effects on health. Age structure can be theorized as a compositional effect and as a contextual effect. In addition, the dynamic nature of age structure and the utility of a life course perspective as applied to neighborhood effects research merits attention. Four Chicago neighborhoods are summarized to illustrate how age structure varies across small space, suggesting that neighborhood age structure should be considered a key structural covariate in contextual research on health. Considering age structure implies incorporating not only meaningful cut points for important age groups (e.g., proportion 65 years and over) but attention to the shape of the distribution as well