Undiagnosed depression: A community diagnosis

Abstract

Many large provider networks are investing heavily in preventing disease within the communities that they serve. We explore the potential benefits and challenges associated with tackling depression at the community level using a unique dataset designed for one such provider network. The economic costs of having depression (increased medical care use, lower quality of life, and decreased workplace productivity) are among the highest of any disease. Depression often goes undiagnosed, yet many believe that depression can be treated or prevented altogether. We explore the prevalence, distribution, economic burden, and the psychosocial and economic factors associated with undiagnosed depression in a lower-income neighborhood in northern Manhattan. Even using state-of-the art data to “diagnose” the risk factors within a community, it can be challenging for provider networks to act against such risk factors.