Dataset Information

Adherence and Persistence Across Antidepressant Therapeutic Classes: A Retrospective Claims Analysis Among Insured US Patients with Major Depressive Disorder (MDD).

ABSTRACT:

Background

Adherence and persistence to therapy, or how well a patient follows provider directions on frequency and time to discontinuation of prescribed medications, is associated with positive health outcomes, including decreased healthcare costs and patient mortality. A clear literature gap exists assessing adherence and persistence to antidepressants (ADs) in the major depressive disorder (MDD) population at clinically relevant time points and at the therapeutic class level.

Objective

This study assessed adherence and persistence to specific ADs, therapeutic classes, and AD therapy overall at multiple time points among US individuals from commercial, Medicare supplemental, and Medicaid insurance plans.

Methods

Patients with MDD without AD or MDD claims in the prior 6 months who initiated therapy in 2003-2014 with a selective serotonin reuptake inhibitor (SSRI), serotonin and norepinephrine reuptake inhibitor (SNRI), tricyclic AD (TCA), monoamine oxidase inhibitor (MAOI), or other AD were identified using MarketScan^® databases. These databases contain information on diagnoses, billing codes, and dates of service. Adherence (proportion of days covered) and persistence (days until a 30-day gap in therapy) were calculated to AD medication, AD therapeutic class, and AD therapy overall over the first 3, 6, 9, and 12 months from the index prescription date. Multivariable logistic regression estimated the adjusted odds ratios (ORs) of adherence to initial AD medication comparing AD therapeutic classes.

Results

For 527,907 patients, adherence to initial AD medication decreased over 3, 6, 9, and 12 months (41, 31, 24, and 21%, respectively). Similar patterns were observed for adherence to initial AD therapeutic class, AD therapy overall, and all three persistence calculations. The odds of adherence to SNRIs versus SSRIs were 20-27% greater at 3, 6, 9, and 12 months (ORs 1.20, 1.23, 1.25, 1.27, respectively; p-values all <0.0001). Similar or significantly lower odds of adherence were demonstrated for other classes versus SSRIs at 3, 6, 9, and 12 months [ORs for other ADs 0.80, 0.77, 0.74, 0.72, respectively (p-values all <0.0001); ORs for TCAs 0.46, 0.45, 0.47, 0.49, respectively (p-values all <0.0001); ORs for MAOIs 1.13, 1.0, 0.77, 0.69, respectively (p-values all >0.05)].

Conclusion

We found low adherence and persistence to ADs in the MDD population. Within the limitations of the insurance claims data we analysed, our results suggest that adherence may differ based on therapeutic class, as patients initiating SNRI therapy appeared to have a higher likelihood of adherence versus SSRIs over the year assessed, while the odds of adherence appeared similar or lower for other classes versus SSRIs. Further prospective research is needed to confirm these findings and determine additional drivers of these apparent differences by AD therapeutic class.

SUBMITTER: Keyloun KR

PROVIDER: S-EPMC5425490 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Adherence and Persistence Across Antidepressant Therapeutic Classes: A Retrospective Claims Analysis Among Insured US Patients with Major Depressive Disorder (MDD).

Keyloun Katelyn R KR Hansen Ryan N RN Hepp Zsolt Z Gillard Patrick P Thase Michael E ME Devine Emily Beth EB

CNS drugs 20170501 5

<h4>Background</h4>Adherence and persistence to therapy, or how well a patient follows provider directions on frequency and time to discontinuation of prescribed medications, is associated with positive health outcomes, including decreased healthcare costs and patient mortality. A clear literature gap exists assessing adherence and persistence to antidepressants (ADs) in the major depressive disorder (MDD) population at clinically relevant time points and at the therapeutic class level.<h4>Objec ...[more]

PMID: 28378157

Similar Datasets

Project description:Objective: To examine the dose-dependent relationship of different types of statins with the occurrence of major depressive disorder (MDD) and prescription of antidepressant medication. Methods: This cross-sectional study used medical claims data for the general Austrian population (n = 7,481,168) to identify all statin-treated patients. We analyzed all patients with MDD undergoing statin treatment and calculated the average defined daily dose for six different types of statins. In a sub-analysis conducted independently of inpatient care, we investigated all patients on antidepressant medication (statin-treated patients: n = 98,913; non-statin-treated patients: n = 789,683). Multivariate logistic regression analyses were conducted to calculate the risk of diagnosed MDD and prescription of antidepressant medication in patients treated with different types of statins and dosages compared to non-statin-treated patients. Results: In this study, there was an overrepresentation of MDD in statin-treated patients when compared to non-statin-treated patients (OR: 1.22, 95% CI: 1.20-1.25). However, there was a dose dependent relationship between statins and diagnosis of MDD. Compared to controls, the ORs of MDD were lower for low-dose statin-treated patients (simvastatin>0- < =10 mg:OR: 0.59, 95% CI: 0.54-0.64; atorvastatin>0- < =10 mg:OR:0.65, 95%CI: 0.59-0.70; rosuvastatin>0- < =10 mg:OR: 0.68, 95% CI: 0.53-0.85). In higher statin dosages there was an overrepresentation of MDD (simvastatin>40- < =60 mg:OR: 2.42, 95% CI: 2.18-2.70, >60-80 mg:OR: 5.27, 95% CI: 4.21-6.60; atorvastatin>40- < =60 mg:OR: 2.71, 95% CI: 1.98-3.72, >60- < =80 mg:OR: 3.73, 95% CI: 2.22-6.28; rosuvastatin>20- < =40 mg:OR: 2.09, 95% CI: 1.31-3.34). The results were confirmed in a sex-specific analysis and in a cohort of patients taking antidepressants, prescribed independently of inpatient care. Conclusions: This study shows that it is important to carefully re-investigate the relationship between statins and MDD. High-dose statin treatment was related to an overrepresentation, low-dose statin treatment to an underrepresentation of MDD.

Project description:Depressive symptoms are related to abnormalities in the autonomic nervous system (ANS), and physiological signals that can be used to measure and evaluate such abnormalities have previously been used as indicators for diagnosing mental disorder, such as major depressive disorder (MDD). In this study, we investigate the feasibility of developing an objective measure of depressive symptoms that is based on examining physiological abnormalities in individuals when they are experiencing mental stress. To perform this, we recruited 30 patients with MDD and 31 healthy controls. Then, skin conductance (SC) was measured during five 5-min experimental phases, comprising baseline, mental stress, recovery from the stress, relaxation, and recovery from the relaxation, respectively. For each phase, the mean amplitude of the skin conductance level (MSCL), standard deviations of the SCL (SDSCL), slope of the SCL (SSCL), mean amplitude of the non-specific skin conductance responses (MSCR), number of non-specific skin conductance responses (NSCR), and power spectral density (PSD) were evaluated from the SC signals, producing 30 parameters overall (six features for each phase). These features were used as input data for a support vector machine (SVM) algorithm designed to distinguish MDD patients from healthy controls based on their physiological responses. Statistical tests showed that the main effect of task was significant in all SC features, and the main effect of group was significant in MSCL, SDSCL, SSCL, and PSD. In addition, the proposed algorithm achieved 70% accuracy, 70% sensitivity, 71% specificity, 70% positive predictive value, 71% negative predictive value in classifying MDD patients and healthy controls. These results demonstrated that it is possible to extract meaningful features that reflect changes in ANS responses to various stimuli. Using these features, detection of MDD was feasible, suggesting that SC analysis has great potential for future diagnostics and prediction of depression based on objective interpretation of depressive states.

Project description:BackgroundDepression is the greatest contributor to worldwide disability. The purpose of this study was to understand the influence of antidepressant and psychotherapy treatment adherence on future work leaves for patients with major depressive disorder.MethodsPatients with a newly diagnosed major depressive disorder (n = 26,256) were identified in IBM® Watson™ MarketScan® medical and disability claims databases. Antidepressant and psychotherapy adherence metrics were evaluated in the acute phase of treatment, defined as the 114 days following the depression diagnosis. Multiple variable Cox proportional hazards regression models evaluated the influence of antidepressant and/or psychotherapy adherence on future injury or illness work leaves.ResultsThe majority of work leaves in the 2-year follow-up period occurred in the acute phase of treatment (71.2%). Among patients without a work leave in the acute phase and who received antidepressants and/or psychotherapy (n = 19,994), those who were adherent to antidepressant or psychotherapy treatment in the acute phase had a 16% (HR = 0.84, 95% CI = 0.77-0.91) reduced risk of a future work leave compared to treatment non-adherent patients. Patients who were non-adherent or adherent to antidepressant treatment had a 22% (HR = 1.22, 95% CI = 1.11-1.35) and 13% (HR = 1.13, 95% CI = 1.01-1.27) greater risk of a future work leave, respectively, than patients not receiving antidepressant treatment. Conversely, patients who were non-adherent or adherent to psychotherapy treatment had a 9% (HR = 0.91, 95% CI = 0.81-1.02) and 28% (HR = 0.72, 95% CI = 0.64-0.82) reduced risk of a future work leave, respectively, than patients not receiving psychotherapy treatment.ConclusionsThis analysis suggests that treatment adherence may reduce the likelihood of a future work leave for patients with newly diagnosed major depressive disorder. Psychotherapy appears more effective than antidepressants in reducing the risk of a future work leave.

Dataset Information

Adherence and Persistence Across Antidepressant Therapeutic Classes: A Retrospective Claims Analysis Among Insured US Patients with Major Depressive Disorder (MDD).

Background

Objective

Methods

Results

Conclusion

Publications

Adherence and Persistence Across Antidepressant Therapeutic Classes: A Retrospective Claims Analysis Among Insured US Patients with Major Depressive Disorder (MDD).

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets