Big data in severe mental illness: the role of electronic monitoring tools and metabolomics

There is an increasing interest in the development of effective early detection and intervention strategies in severe mental illness (SMI). Ideally, these efforts should lead to the delineation of accurate staging models of SMI enabling personalized interventions. It is plausible that big data approaches will be instrumental in describing the developmental trajectories of SMI by facilitating the incorporation of data from multiple sources, including those pertaining to the biological make-up of affected subjects. In this review, we first aimed to offer a perspective on how big data are helping the delineation of personalized approaches in SMI, and, second, to offer a quantitative synthesis of big data approaches in metabolomics of SMI. We finally described future directions of this research area.

Controlling for selection effects in the relationship between child behavior problems and exposure to intimate partner violence

This article used the Project on Human Development in Chicago Neighborhoods (PHDCN) data to examine the relationship between exposure to intimate partner violence (IPV) and child behavior problems (externalizing and internalizing), truancy, grade repetition, smoking, drinking, and use of marijuana. Longitudinal data analysis was conducted on 1,816 primary caregivers and their children. Fixed-effects regression models were employed to address concerns with selection bias. IPV was associated with significantly greater internalizing behavior, externalizing behavior, and truancy. Findings from age interaction models suggested that the relationship between IPV and child behavior problems may attenuate as the age of the child at time of exposure increases.

Social network determinants of depression

The etiology of depression has long been thought to include social environmental factors. To quantitatively explore the novel possibility of person-to-person spread and network-level determination of depressive symptoms, analyses were performed on a densely interconnected social network of 12,067 people assessed repeatedly over 32 years as part of the Framingham Heart Study. Longitudinal statistical models were used to examine whether depressive symptoms in one person were associated with similar scores in friends, co-workers, siblings, spouses and neighbors. Depressive symptoms were assessed using CES-D scores that were available for subjects in three waves measured between 1983 and 2001. Results showed both low and high CES-D scores (and classification as being depressed) in a given period were strongly correlated with such scores in one's friends and neighbors. This association extended up to three degrees of separation (to one's friends' friends' friends). Female friends appear to be especially influential in the spread of depression from one person to another. The results are robust to multiple network simulation and estimation methods, suggesting that network phenomena appear relevant to the epidemiology of depression and would benefit from further study.

The impact of nonclinical factors on care use for patients with depression: a STAR*D report

INTRODUCTION

This article presents baseline findings that describe how nonclinical factors were associated with patient use of psychiatric and general medical care and how those relationships changed after patients enrolled in the 41-site Sequenced Treatment Alternatives to Relieve Depression study (STAR*D).

AIMS

STAR*D offered adult outpatients with major depression diligently delivered, measurement-based care. To achieve full remission within a tolerable medication dose, recommendations for treatment based on routine symptom and side-effect measurements were discussed with patients by clinical research coordinators and offered to clinicians who could flexibly tailor that guidance to accommodate individual patient needs. Medications were provided gratis. Pre- and post-enrollment data came from provider records and from patient face-to-face, telephone, and computer-assisted surveys. Two-part nested mixed models assessed patient likelihood and volume of mental and general medical care services.

RESULTS

Prior to enrollment, predisposing (gender, race, education, and care attitude), affordability (private insurance), and clinical factors (depressive symptoms and mental and physical functioning) were found to be important drivers of patient use of psychiatric and general medical care. After STAR*D enrollment, however, predisposing factors were less important drivers of psychiatric service use but remained important drivers of general medical care.

CONCLUSIONS

Data suggest diligent, measurement-based mental health programs may reduce race, gender, and education disparities in the use of needed mental health care.

Aripiprazole augmentation in major depressive disorder: a double-blind, placebo-controlled study in patients with inadequate response to antidepressants

INTRODUCTION

Effective management of major depressive disorder (MDD) continues to be a challenging task for psychiatrists and primary care physicians. This trial evaluated the efficacy and safety of adjunctive aripiprazole versus antidepressant monotherapy in patients with MDD and independently replicated the positive findings of two similar trials.

METHODS

Patients (N=1,147) with MDD experiencing a major depressive episode and a history of inadequate response to antidepressant monotherapy were enrolled (week 0); 827 received single-blind adjunctive placebo plus open-label antidepressant (escitalopram, fluoxetine, paroxetine controlled release, sertraline, or venlafaxine extended release) for 8 weeks to confirm inadequate response to antidepressants; 349 patients with inadequate response were randomized (1:1) to double-blind, adjunctive placebo (n=172) or adjunctive aripiprazole (n=177; 2-20 mg/day). Primary outcome was the mean change in Montgomery-Asberg Depression Rating Scale (MADRS) Total score from baseline (week 8) to endpoint (week 14).

RESULTS

Clinically significant improvements in depressive symptoms as assessed by decreases in the MADRS Total score were greater with adjunctive aripiprazole (-10.1) than placebo (-6.4; P<.001). Remission rates were greater for adjunctive aripiprazole than for adjunctive placebo (week 14, 36.8% vs 18.9%; P<.001). Completion rates with adjunctive aripiprazole and placebo were high (83% vs. 87%) and discontinuations due to adverse events were low (6.2% vs 1.7%).

CONCLUSION

For some patients with MDD who do not obtain adequate symptom relief with antidepressant monotherapy, adjunctive therapies can significantly improve depressive symptoms. As reported, adjunctive aripiprazole was associated with a two-fold higher remission rate than adjunctive placebo. This, and previous studies, have shown that discontinuations due to adverse events were low and completion rates were high, and has indicated that both antidepressant and aripiprazole in combination were relatively well-tolerated and safe. This is the third consecutive clinical trial, in the absence of a failed trial, to demonstrate that aripiprazole augmentation to antidepressants is an efficacious and well-tolerated treatment for patients with MDD who do not respond adequately to standard antidepressant monotherapy (ClinicalTrials.gov study NCT00105196).

Measuring use and cost of care for patients with mood disorders: the utilization and cost inventory

BACKGROUND

Researchers conducting cost-outcome studies must account for all materially relevant care that subjects receive from their care providers. However, access to provider records is often limited. This article describes and tests the Utilization and Cost Inventory (UAC-I), a structured patient interview designed to measure costs of care when access to provider records is limited.

METHODS

UAC-I was tested on 212 consenting adult veterans with mood disorder attending a VA medical center. Counts (inpatient days and outpatient encounters) and costs (dollars) computed from survey responses were compared with estimates from medical records and an alternative structured questionnaire.

RESULTS

The agreement between inpatient costs computed from provider records and from UAC-I responses, assessed using the intraclass correlation coefficient (ICC), was 0.66, 95% confidence interval (CI), 0.30-0.84; the bias was -3.7%, 95% CI, -48 to 41. The ICC for the service data (inpatient days) was 0.97, 95% CI, 0.95-0.99; the bias was <1%, 95% CI, -14 to 15. The ICC for outpatient costs computed from provider records and from UAC-I responses was 0.53 95% CI, 0.38-0.65; the bias was <1%, 95% CI, -27 to 27. The ICC for outpatient encounters was 0.74, 95% CI, 0.65-0.80; the bias was <1%, 95% CI, -16 to 18.

CONCLUSIONS

These results indicate that it may be feasible for cost-outcome studies to compare patient groups for inpatient and outpatient costs computed from patient self-reports.

Assessing the preventive effects of cognitive therapy following relief of depression: A methodological innovation

BACKGROUND

Strategies to compute benefits from continuing cognitive therapy for patients with recurrent major depression do not take into account whether discontinuing treatments may induce temporary increases in the risk that symptoms return (discontinuation-effect).

METHODS

We apply varying-effects analyses and compare findings with traditional methods to assess the effects of continuation-phase cognitive therapy. Two years of data came from 79 patients with recurrent major depression who responded to acute cognitive therapy. Patients were randomized to either an experimental cohort (n=39) who received 10-session, protocol continuation-phase therapy for 8 months, or a control cohort (n=40) who stopped protocol treatment after the acute-phase. Symptoms were assessed using the Longitudinal Interval Follow-up Evaluation (LIFE). Symptom risk rates were computed weekly by cohort as the proportion of patients at risk who were suffering from a major depressive episode.

RESULTS

Significant discontinuation-effects occurred when protocol treatments stopped for both experimental and control cohorts. Following acute-phase care, traditional computation methods (week 1-35) revealed treated patients had 18% of the risk for symptoms as controls. Expanding the observation period (week 1-74) to include these discontinuation-effects revealed more modest initial effect sizes (43%), but significant long-term effects (54% for week 75-101).

LIMITATIONS

Limitations include limited sample size, one-site study, confounds from patient-level interactions, and off-protocol use of depression-related care.

CONCLUSIONS

Varying-effects analyses can describe how outcomes from cognitive therapy may unfold over time for patients with major depression. These analyses reveal complex longitudinal patterns that otherwise are not detectable with traditional time-to-event methods. Specifically, we observed discontinuation-effects, or temporary spikes in symptom risks that occur after treatment ends. Further research is needed to identify the mechanisms driving these effects. Future studies are needed to determine if higher risks result from the patients' anxiety as they attempt to maintain gains independent of ongoing therapy, or reflect residual symptoms previously suppressed by treatment. We also observed longer-term preventive effects from therapy. Again, further research is recommended to determine the extent to which lower risks result from coping and compensatory skills learned during cognitive therapy. These findings suggest that varying-effects analyses may provide an appealing paradigm for understanding treatment-related effects in episodic illness.

Economic implications of treatment-resistant depression among employees

BACKGROUND

Conservative estimates indicate between 10% and 20% of all individuals with major depressive disorders (MDDs) fail to respond to conventional antidepressant therapies. Amongst those with MDD, individuals with treatment-resistant depression (TRD) have been found to be frequent users of healthcare services and to incur significantly greater costs than those without TRD. Given the prevalence of the disorder, it is understandable that MDDs are responsible for a significant amount of both direct and indirect healthcare costs.

OBJECTIVE

To provide empirical findings for employees likely to have TRD based on analysis of employer claims data, in the context of previous research.

METHODS

We conducted a claims data analysis of employees of a large national (US) employer. The data source consisted of medical, pharmaceutical and disability claims from a Fortune 100 manufacturer for the years 1996-1998 (total beneficiaries >100000). The employee sample included individuals with medical or disability claims for MDDs (n = 1692). A treatment pattern algorithm was applied to classify MDD patients into TRD-likely (n = 180) and TRD-unlikely groups. Treated prevalence of select comorbid conditions and the patient costs (direct and indirect) from the employer perspective by condition were compared among TRD-likely and TRD-unlikely employees, and with a 10% random sample of the overall employee population for 1998.

RESULTS

The average annual cost of employees considered TRD-likely was dollars US 14490 per employee, while the cost for depressed but TRD-unlikely employees was dollars US 6665 per employee, and dollars US 4043 for the employee from the random sample. TRD beneficiaries used more than twice as many medical services compared with TRD-unlikely patients, and incurred significantly greater work loss costs.

CONCLUSION

TRD has gained increasing recognition due to both the clinical challenges and economic burdens associated with the condition. TRD imposes a significant economic burden on an employer. TRD-likely employees are more likely to be treated for selected comorbid conditions and have higher medical and work loss costs across all conditions.