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Predicting persistence to antidepressant treatment in administrative claims data: Considering the influence of refill delays and prior persistence on other medications.

ABSTRACT: Many patients with major depressive disorder (MDD) who begin antidepressant treatment discontinue use before for six months, the recommended minimum treatment length. This study sought to identify predictors of six-month antidepressant persistence including predictors utilizing patients' electronic prescription records.Commercially insured children (3-17 years) and adults (18-64 years) with MDD who initiated antidepressant treatment, 1/1/2003-2/28/2010, were assessed for six-month persistence (based on prescriptions' days supply, allowing a 30-day grace period). Antidepressant persistence prediction models were developed separately for children and adults. Two additional measures, days without medication between the first and second antidepressant fill (children and adults) and prior persistence on other medications (adults only), were added to the models, concordance (c) statistics were compared and risk reclassification evaluated.Among children (n=8837 children) and adults (n=47,495) with MDD, six-month antidepressant persistence was low and varied by age (37%, 18-24 years to 52%, 3-12 and 50-64 years). Independent baseline predictors of persistence were identified, with model c-statistics: children=0.582, adults=0.584. Patients with more days without medication between fills were less likely to be persistent (10-30 vs. 0 days, children: RR=0.72, adults: RR=0.74), as were adults not previously persistent to other medications (RR=0.73).The definition of six-month persistence is dependent on correct days supply values and the grace period utilized; potential predictors were limited to measures available in claims data.Six-month antidepressant persistence was low and overall prediction of persistence was poor; however, days without medication between fills and prior persistence on other medications marginally improved the ability to predict antidepressant persistence.

SUBMITTER: Bushnell GA

PROVIDER: S-EPMC5033109 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Predicting persistence to antidepressant treatment in administrative claims data: Considering the influence of refill delays and prior persistence on other medications.

Bushnell Greta A GA Stürmer Til T White Alice A Pate Virginia V Swanson Sonja A SA Azrael Deborah D Miller Matthew M

Journal of affective disorders 20160209

<h4>Background</h4>Many patients with major depressive disorder (MDD) who begin antidepressant treatment discontinue use before for six months, the recommended minimum treatment length. This study sought to identify predictors of six-month antidepressant persistence including predictors utilizing patients' electronic prescription records.<h4>Methods</h4>Commercially insured children (3-17 years) and adults (18-64 years) with MDD who initiated antidepressant treatment, 1/1/2003-2/28/2010, were as ...[more]

PMID: 26921866

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Project description:BackgroundAdherence 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.ObjectiveThis 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.MethodsPatients 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.ResultsFor 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)].ConclusionWe 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.

Dataset Information

Predicting persistence to antidepressant treatment in administrative claims data: Considering the influence of refill delays and prior persistence on other medications.

Publications

Predicting persistence to antidepressant treatment in administrative claims data: Considering the influence of refill delays and prior persistence on other medications.

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