Bipolar disorder is one of the world's most disabling conditions, robbing sufferers of years of healthy functioning. The presence of bipolar disorder is not limited to any nation, race, age, gender, or socioeconomic status, and has a lifetime prevalence of 1% across all populations. While there do not appear to be disparities in who is at risk for bipolar disorder, there are marked disparities in who is likely to be diagnosed and treated. The average person with bipolar disorder waits ten years before receiving the correct diagnosis (National Depression and Manic-Depression Association, 2000). Once a diagnosis of bipolar disorder is made, there are equally marked disparities in treatment outcome.
Also known as manic-depressive illness, bipolar disorder is a recurrent and chronic mental condition associated with substantial morbidity and mortality. The stigma associated with open recognition of this disorder decreases the likelihood of accurate diagnosis and treatment. Considering the impact of this disorder on the most vulnerable populations (youth, elderly, rural populations, and minorities), the challenge is to understand and reverse the current public health crisis. This crisis has created an enormous financial burden on the health, welfare, and disability systems. At the same time, it reduces the likelihood of economic and social productivity that can be achieved by potentially productive individuals.
The primary objective of the study is to test an intervention to reduce health disparities related to bipolar disorder, a vastly more destructive and difficult to treat condition than previously realized. The outcomes of interest include accurate and timely diagnosis, adequacy of prescribed treatment, retention in treatment, suicidality, and a range of treatment benefits including health-related quality of life, employment, treatment satisfaction, medication adherence, utilization of lower levels of intervention (e.g., outpatients versus partial or inpatient care), and reduction of substance use, medical morbidity and mortality. Particular attention has been paid to the collection of service utilization data to track key health care and social services. Costs for medical and psychiatric treatment, medications, inpatient, rehabilitation, and emergency room services are being ascertained for cost assessment, and patients' mood functioning is being tracked to assess the overall effectiveness of the interventions. The study is also using state-of-the-art assessments of phenotypic clinical variables to develop clinically meaningful predictors of treatment response across the age spectrum and across diverse racial groups.
To characterize more precisely the phenotypic complexity of this disorder, we have developed a spectrum model of psychiatric illness using a broader conceptualization of mood disorders and an integrated view of common comorbidities, anchored in the Mood and Anxiety Spectrum Assessments (Cassano et al. 1997; Cassano et al in press). This refined description of patient variability (or phenotypes) should lead to improved understanding of the variability in treatment outcomes among patients suffering from bipolar disorder and eventually to creating appropriate first-line treatments for patients who present with specific clinical phenotypes.
Careful consideration of biological phenotypes, as represented in population pharmacokinetics, turns a second line of attack on the problem of tailoring treatments to patients' specific needs. A key correlate of treatment response that has never been examined in bipolar disorder is consistent and adequate medication exposure. Essential to understanding variability in treatment response is being able to distinguish true non-responders from those who never received adequate exposure to drug. Consistency of drug exposure can be determined using a combination of electronic monitoring of drug-taking and population pharmacokinetic analysis.