Adherence Trajectories Research Articles

Chemoprotective effect of metformin against HR+/HER2- breast cancer among women with type-2 diabetes.

10518 Background: Type-2 diabetes mellitus (T2DM) increases the risk of breast cancer among postmenopausal women. Metformin has demonstrated a chemoprotective effect in breast cancer, however its role in HR+/HER2- breast cancer (HR+/HER2- BC), the most common subtype, has not been studied among older women with T2DM in the United States. This study evaluated if increased exposure to metformin is associated with a reduced risk of HR+/HER2- BC among postmenopausal women with T2DM. Methods: A case-control study was performed using the Surveillance, Epidemiology, and End Results (SEER)-Medicare data (2008-2015). Those diagnosed with HR+/HER2- BC as their first/only cancer after incident T2DM diagnosis were cases. The event date was the date of HR+/HER2- BC diagnosis in cases, and randomly assigned to non-cancer T2DM controls based on the distribution in cases. Cases were matched to up to 4 controls each using incidence density sampling with replacement. Metformin exposures were defined as cumulative dose, average intensity and adherence, measured during the 1-year lookback period prior to the event date. Dose (mg) was categorized as: 0, 0-30,000, 30,001-136,000, 136,001-293,000, and > 293,000. Average intensity per day (mg/day) was categorized as: 0, 1-500, and > 500. To evaluate adherence, those without metformin claims during the lookback period were excluded. Adherence measures were: binary proportion of days covered (PDC) (≥0.80, < 0.80) and adherence trajectories. Group based trajectory modeling was used to identify trajectories (adherent, slow decline, rapid decline, and early discontinuation). The Anderson Behavioral Model was used to guide selection of covariables: demographic and clinical variables (diabetes severity, metabolic syndrome, comedications, and health status). Conditional logistic regression was used to evaluate the association between exposure to metformin and the risk of HR+/HER2- BC. Results: The main cohort included 690 cases and 2747 controls. A decremental reduction in odds of HR+/HER2- BC in the highest cumulative dose (OR = 0.72, 95% CI: 0.55-0.95; OR = 0.60, 95% CI: 0.42-0.85) and intensity (OR = 0.61, 95% CI:0.46-0.82) categories of metformin was observed compared to the no-metformin group. Those non-adherent to metformin had 45% (OR = 1.45, 95% CI: 1.08-1.94) increased odds of HR+/HER2- BC compared to those adherent. The risk of HR+/HER2- BC in the adherent (OR = 0.67, 95% CI: 0.39-1.14), slow decline (OR = 0.75, 95% CI: 0.43-1.32) and rapid decline (OR = 0.73, 95% CI: 0.41-1.31) trajectories was not statistically significant compared to the early discontinuation trajectory. Conclusions: This retrospective study based on SEER-Medicare found an association between high dose and intensity of metformin use with reduced odds of incidence of HR+/HER2- BC among postmenopausal women with T2DM. Adherence to metformin also showed protective effect against HR+/HER2- BC.

Drug attitude inventory is relevant to LAI treatment persistence in schizophrenia: Preliminary results

IntroductionPatients’ attitudes and subjective experience are crucial in the management of severe mental illness, but their practical value is overlooked.ObjectivesTo identify predictors of future adherence to LAI antipsychotic maintenance treatment of schizophrenia among socio-demographic, clinical, and psychometric characteristics – including Drug Attitude Inventory-10 (DAI-10) and Subjective Well-being under Neuroleptics short form (SWN-K) scores.MethodsRetrospective baseline data from 53 clinically stable outpatients with schizophrenia switched from oral to LAI therapy were collected. Patients continuing treatment at the time of analysis (n=29) were compared to those who had discontinued it (n=24). Selected variables were further evaluated in survival analyses.ResultsBetween-group differences are presented in Table 1 (**: p<0.01; *: p<0.05).Continued treatmentDiscontinued treatmentχ2 or tTreatment persistence (months)63.79±21.0123.88±25.806.21**Age (years)39.17±10.1135.58±13.391.11Male15 (51.7%)13 (54.1%)0.03Single20 (69.0%)15 (62.5%)0.25Instruction (years)13.28±3.3111.83±3.561.53Employed20 (69.0%)7 (29.2%)8.32**Illness duration (years)17.69±10.5313.42±11.361.42Previous hospitalisations2.10±1.322.67±1.86-1.29MADRS13.59±9.0614.67±8.99-0.43YMRS5.52±5.576.00±9.94-0.22p-PANSS12.17±5.2014.38±6.13-1.41n-PANSS10.90±5.3915.63±7.93-2.48*g-PANSS29.38±10.3333.63±10.26-1.49PANSS52.66±17.5763.96±20.61-2.15*DAI-103.86±4.96-1.13±5.803.38**SWN-K74.93±23.0781.00±15.60-1.09Cox regression analysis included instruction, employment, hospitalisations, PANSS subscales and DAI-10 scores: a protective role against treatment discontinuation was outlined only for employment (HR 0.16; 95%CI 0.05-0.50) and higher DAI-10 scores (HR 0.85; 95%CI 0.78-0.94). DAI-10 scores delineated distinct adherence trajectories (Figure 1).ConclusionsBaseline DAI-10 scores may identify patients at risk of dropout after switching to LAI.DisclosureNo significant relationships.

A ‘case-mix’ approach to understand adherence trajectories for a falls prevention exercise intervention: A longitudinal cohort study

ObjectiveWe identified adherence-based case-mixes from participants’ longitudinal adherence to falls prevention exercise interventions over 12 months. Second, we identified modifiable baseline predictors (cognition, mobility and functional status) based on participants' case-mix adherence trajectories. Study design and outcome measuresThis study was a 12-month longitudinal secondary analysis of data from 172 participants who received the Otago Exercise Program (OEP) in a randomized controlled trial. Adherence to the OEP was ascertained monthly via self-report. Case-mixes, groups of individuals who followed similar adherence trajectories, were visually defined using 12-month longitudinal trajectories; we used latent class growth modeling. Baseline predictors of adherence were examined for the following categories: 1) cognition, 2) mobility and 3) functional status. ResultsFour distinct case-mixes were identified. The “non-adherent” case-mix (18 %) was distinguished by a non-adherent and decreasing adherence trajectory over time. The “low adherence” case-mix (45 %) did not have complete adherence or consistent adherence over the 12-month follow-up. The “moderate adherence” case-mix (27 %) was characterized by a stable (i.e., non-variable) adherence trajectory with a slightly increasing pattern at midpoint. The “high adherence” case-mix (10 %) demonstrated consistent and high adherence over the 12-month follow-up. For individuals with “moderate adherence”, the Digit Symbol Substitution Test (DSST) significantly predicted adherence (relative risk ratio (RRR) = 1.12 (0.95 CI: 1.0–1.26); p = 0.049). For individuals with “high adherence”, the Digits Forward minus Digits Backward (RRR = 0.43 (0.95 CI: 0.23−0.79); p = 0.002) and Instrumental Activities of Daily Living (RRR = 0.36 (0.95 CI: 0.16−0.81); p = 0.01) significantly predicted adherence. ConclusionsCognitive profile and activities of daily living at baseline may predict the longitudinal pattern of adherence.

The PrEP Journey: Understanding How Internal Drivers and External Circumstances Impact The PrEP Trajectory of Adolescent Girls and Young Women in Cape Town, South Africa.

Despite high risk for HIV, South African adolescent girls and young women (AGYW) experience numerous challenges with adherence to PrEP. To better understand AGYW's motivations for PrEP and factors that impact PrEP adherence, we conducted serial in-depth interviews with 22 South African AGYW during a 12 month prospective study. Interviews explored motivations and initial experiences of PrEP use, patterns of adherence, social support, and reasons for stopping or persisting with PrEP. We also assessed drug levels as a biomarker of adherence; dried blood spots were collected at months 1, 2, 3, 6, and 12 to assess intracellular tenofovir diphosphate levels. An end-user journey analytical approach revealed themes related to behavioral and emotional aspects of use, including multilevel factors leading to divergent PrEP adherence trajectories. Our findings highlight how internal versus external motivations drive PrEP use, as well as how positive identity formation and challenges are handled, which are essential to understand AGYW in their PrEP journeys.

Five-Year Adjuvant Endocrine Therapy Adherence Trajectories Among Women With Breast Cancer: A Nationwide French Study Using Administrative Data

BackgroundAdjuvant endocrine therapy (AET) improves long-term survival of breast cancer patients, yet many women are nonadherent or discontinue this treatment. In this study we aimed to describe AET adherence trajectories over 5 years after treatment initiation and to identify factors associated with these trajectories, in a nationwide French cohort of breast cancer survivors. Patients and MethodsEvery woman diagnosed with a first nonmetastatic breast cancer in 2011 in France who initiated AET in the 12 months after surgery was included from the French cancer cohort. We identified all reimbursements for AET from national health administrative data sets and modeled AET adherence trajectories over 5 years, using group-based trajectory modeling on the basis of the monthly proportion of days covered by AET. Associated factors were identified using multinomial logistic regressions. ResultsWe included 33,260 women. A 6-trajectory model was selected: 1, immediate discontinuation (6.6%); 2, continuous suboptimal adherence (4.3%); 3, progressive nonadherence then discontinuation (6.3%); 4, early nonadherence then discontinuation (5.7%); 5, continuous optimal adherence (68.8%); and 6, late nonadherence then discontinuation (8.3%). The main factors associated with nonadherence trajectories were extreme age (younger than 50 and older than 70 years) and switching AET. ConclusionApproximately 70% of women had optimal adherence over all 5 years. The original nationwide approach enabled us to identify the “continuous suboptimal adherence trajectory” never previously described.

Association of the Healthcare Effectiveness Data and Information Set (HEDIS) Follow-Up Care Measures and Medication Adherence Among Medicaid Insured Children with ADHD

Objectives: To assess the association of HEDIS C&M phase follow-up measure with ADHD medication adherence. Methods: A retrospective study was conducted using Medicaid data (2014–2016) of children initiated with ADHD medication. Patients were categorized as meeting the C&M follow-up measure or not according to the HEDIS specifications. Medication adherence was measured using both Proportion of Days Covered (PDC) & Group-Based Trajectory Modeling (GBTM). The association of medication adherence with HEDIS measure was assessed using Chi-square test. Results: Nearly all (97%) children with PDC≥0.7 and 72% of patients following the late decliner adherence trajectory met the HEDIS C&M follow-up criteria. In contrast, none of the children with PDC<0.7 and only two following rapid and slow decliner trajectories met the criteria. Conclusion: ADHD medication adherence, regardless of how it was measured, clearly differentiated children with and without C&M phase follow-up, which essentially questions the implication of HEDIS C&M phase follow-up care measure.

Trajectories of PrEP Adherence Among Young Women Aged 16 to 25 in Cape Town, South Africa.

Despite demonstrated efficacy for HIV prevention, substantial challenges remain for the successful rollout of oral pre-exposure prophylaxis (PrEP), especially among adolescent girls and young women (AGYW). We characterized trajectories of PrEP adherence among 200 AGYW in South Africa and analyzed data from 22 qualitative interviews and 3 focus group discussions for explanatory purposes. Two adherence trajectory groups were identified: 52% with high early adherence that declined after month three and 48% with low adherence throughout. Adherence in the "consistently low" group was related to social support and logistical concerns, while the decrease in the "high declining" group corresponded to a change in the frequency of study visits from monthly to quarterly. PrEP support should be differentiated for those who need more frequent visits and adherence support initially versus later in PrEP use. Visits every month, when needed, should be considered for AGYW who need sustained support later into use.

Trajectories of medication adherence in patients with Bipolar Disorder along 2 years-follow-up

BackgroundBipolar disorder (BD) is a chronic and severe mental illness. It requires a non-discontinued pharmacological treatment to prevent mood recurrences but nonadherence to medication is frequent. To this date, medication adherence in BD has been mostly evaluated in cross-sectional studies and often considered as a stable trait. We aimed to study medication adherence using a prospective person-oriented approach. Methods1627 BD patients were followed on a 2 years period and assessed every 6 months. Medication adherence was evaluated at each visit with the Medication Adherence Rating Scale (MARS). A latent class mixed model (LCMM) was used to identify trajectory classes of adherence over time. Regression analyses and linear mixed model were used to search for predictors and covariables of the trajectories. ResultsThree distinct and robust trajectories of medication adherence have been identified: one that starts poorly and keeps deteriorating (4.8%), one that starts poorly but improves (9%) and one that starts well and keeps improving (86.2%). A good tolerance to psychotropic medications, low depressive symptoms, the absence of comorbid eating disorders and anticonvulsant medication were associated to a better prognosis of adherence. Along the follow-up, the lower were the depressive symptoms, the better was the medication adherence (p < .001) LimitationsThe use of a single measure of medication adherence although it is a validated instrument and a possible positive selection bias that might limit the generalization of our findings. ConclusionsThis study demonstrates that medication adherence in BD patients is a heterogeneous and potentially variable phenomenon.

PMU88 Mapping the Use of Group-Based Trajectory Modelling in Medication Adherence Research: A Scoping Review Protocol

The use of group-based trajectory modelling (GBTM) within medication adherence research is rapidly growing. Researchers are adopting enhanced methods to analyse and visualise dynamic behaviours, such as medication adherence, within ‘real-world’ populations. Application of GBTM based on longitudinal adherence behaviour allows for the identification of adherence trajectories or groups. A group is conceptually thought of a collection of individuals who follow a similar pattern of adherence behaviour over a period of time. A common obstacle faced by researchers when implementing GBTM is deciding on the number of trajectory groups that may exist within a population. Decision-making can introduce subjectivity, as there is no ‘gold standard’ for model selection criteria. This study aims to examine the extent and nature of existing evidence on the application of GBTM for medication adherence assessment, providing an overview of the different GBTM techniques used in the literature. The methodological framework will consist of five stages: i) identify the research question(s); ii) identify relevant studies; iii) select studies; iv) chart the data and finally, v) collate, summarise and report the results. Original peer-reviewed articles, published in English, describing observational and interventional studies including both concepts and/or sub-concepts of GBTM and medication adherence will be included. The following databases will be queried: PubMed/MEDLINE; Embase (Ovid); SCOPUS; ISI Web of Science; PsychInfo. The PRISMA extension for Scoping Reviews (PRISMA-ScR) tool will be used to report results.This scoping review will collect and schematise different techniques in the application of GBTM for medication adherence assessment available in literature to date, identifying research and knowledge gaps in this area. Datbase searches have identified >6000 potentially relevant abstract results. Screening is ongoing with results to follow shortly. This review will represent an important tool for future research, providing methodological support to researchers carrying out GBTM analysis to assess medication adherence

Predictors of tyrosine kinase inhibitor adherence trajectories in patients with newly diagnosed chronic myeloid leukemia.

Although consistent use of tyrosine kinase inhibitors (TKIs) confers significant improvements in long-term survival for individuals with chronic myeloid leukemia (CML), only 70% of CML patients are adherent to TKIs. Understanding the factors that contribute to non-adherence and establishing dynamic adherence patterns in this population are essential aspects of targeted drug monitoring and intervention strategies. Newly diagnosed CML patients were identified in the MarketScan database and relevant covariate values extracted. Proportion of days covered (PDC) per 30-day interval was used to calculate adherence over a 12-month follow-up period. We conducted a latent profile analysis (LPA) on these PDC estimates to identify distinct, dynamic patterns of TKI adherence. Identified trajectories were grouped into four clinically relevant categories and predictors of membership in these categories were determined via multinomial logistic regression. Four broad adherence categories were identified from the LPA: never adherent, initially non-adherent becoming adherent, initially adherent becoming non-adherent, and stable adherent. Results from the subsequent multinomial logistic regression indicated that younger age, female sex, greater monthly financial burden, fewer comorbidities, fewer concomitant medications, year of diagnosis, higher starting dose, TKI type, and a longer duration from diagnosis to treatment were significantly associated with membership in at least one of the three non-stable adherent groups. Select sociodemographic and clinical characteristics were found to predict membership in clinically meaningful groups of longitudinal TKI adherence. These findings could have major implications for informing personalized monitoring and intervention strategies for individuals who are likely to be non-adherent.

Depression and antecedent medication adherence in a cohort of new metformin users.

The association between depression and poor medication adherence is based on cross-sectional studies and cohort studies that measure adherence rates after depression status is determined. However, depressive symptoms occur well before diagnosis. This study examined adherence patterns in the year before a depressive episode. This retrospective cohort study followed new metformin users identified in Alberta Health's administrative data between 2008 and 2018. Depressive episodes starting ≥1year after metformin initiation were identified using a validated case definition. Controls were randomly assigned a pseudo depression date. Adherence to oral antihyperglycemic medications was estimated using proportion of days covered (PDC) and group-based trajectory models to explore the association between depression and poor adherence (PDC<0.8). A depressive episode occurred in 17,418 (10.6%) of 165,056 new metformin users. Individuals with depression were more likely to have poor adherence compared to controls (adjusted odds ratio 1.21; 95% CI 1.17, 1.26). Five trajectories were identified: nearly perfect adherence (PDC >0.95 [34.8% of cohort]), discontinued (PDC=0 [18.3% of cohort], poor initial adherence (PDC 0.75) that declined either rapidly (9.2% of cohort) or gradually (30.1% of cohort), and poor initial adherence (PDC 0.26) that increased gradually (7.6% of cohort). Individuals with depression were more likely to be in one of the four trajectories of poor adherence compared to controls (adjusted odds ratio 1.24; 95% CI 1.19-1.29). Poor medication adherence occurs in the year before a depressive episode; therefore, poor medication use patterns could be used as an early warning sign for depression.

Comparative Adherence Trajectories of Oral Fingolimod and Injectable Disease Modifying Agents in Multiple Sclerosis.

BackgroundOral fingolimod is convenient to use than injectable disease modifying agents (DMAs) in patients with multiple sclerosis (MS). However, the existing literature regarding the comparative adherence trajectories between oral fingolimod and injectable DMAs is limited.ObjectiveTo compare the adherence trajectories between oral DMA, fingolimod, and injectable DMAs in patients with MS.MethodsA retrospective longitudinal study was conducted using adults (≥18 years) with MS (ICD-9-CM: 340 and a DMA prescription) from the IBM MarketScan Commercial Claims and Encounters Database between 2010 and 2012. Patients were grouped into oral fingolimod or injectable DMA users based on the index DMA among patients with MS. The annual DMA adherence trajectories, based on the proportion of days covered (PDC), were examined using group-based trajectory modeling (GBTM) during the one-year follow-up period after treatment initiation. Multivariable multinomial logistic regression using stabilized inverse probability treatment weights (IPTW) was performed to assess the association between the DMA route of administration (Oral vs Injectable) and the adherence trajectory groups. The balance of covariates between oral and injectable DMAs before and after IPTW was checked against a standardized difference threshold of 0.25.ResultsThe study cohort consisted of 1,700 MS patients who were initiated with oral (15.8%) or injectable (84.2%) DMAs between 2010 and 2012. The adherence rates (PDC≥0.8) in oral fingolimod and injectable DMA users were found to be 64.7% and 50.8%, respectively. The GBTM grouped individuals in the study cohort into three adherence trajectories – rapid discontinuers (23.5%), complete adherers (49.9%), and slow decliners (26.6%). The multinomial logistic regression model with stabilized IPTW revealed that oral fingolimod users had higher odds to be a complete adherer (adjusted odds ratio [AOR]: 2.78, 95% CI: 1.85–4.16) or a slow discontinuer (AOR: 2.62, 95% CI: 1.70–4.05) than injectable DMA users.ConclusionsOral DMA fingolimod was associated with better adherence than injectable DMAs across group-based trajectories. Further research is warranted to evaluate the adherence trajectories with newer oral DMAs introduced in the last decade for MS.

Patient-reported barriers to statin adherence: Excerpts from a motivational interviewing intervention in older adults

ObjectiveDespite a known benefit in the reduction of cardiovascular risk, adherence to statins remains suboptimal. A qualitative analysis was conducted within an intervention that identified trajectories of statin adherence in patients and used motivational interviewing (MoI) to improve adherence. The objective of this qualitative study was to evaluate transcripts of an MoI telephonic intervention to identify potential, past, and current barriers to statin adherence and barriers specific to distinct adherence trajectories. MethodsThe MoI intervention was customized by past 1-year adherence trajectories (rapid discontinuation, gradual decline, and gaps in adherence). Two authors independently extracted and documented barriers from phone transcripts. Themes were derived from literature a priori and by cataloging recurring themes from the transcripts. ResultsThe transcripts of calls made to 157 patients were reviewed of which 25.2% did not communicate a specific adherence barrier despite falling into a low-adherence trajectory when examining refill data. The most commonly reported barriers to statin adherence included adverse effects (40.1%), forgetfulness (30.0%), and lack of skills or knowledge pertaining to statins (25%). More patients in the rapid discontinuation group perceived medication as unnecessary, whereas more patients in the gaps in adherence group reported a communication barrier with their health care provider. Several barriers among patients who fell into low-adherence trajectories were reported. Some patients did not report any barriers, which may have indicated denial. MoI phone calls were useful in providing knowledge, clarifying medication regimens, and reinforcing the need to take statins. ConclusionThis study identified patient-reported barriers to statin adherence elicited during an MoI telephonic intervention conducted by student pharmacists. There were differences in barriers reported by patients from each trajectory, which emphasize the need for additional tailored interventions to improve patient adherence.

&lt;p&gt;Group-Based Trajectory Modeling to Identify Patterns of Adherence and Its Predictors Among Older Adults on Angiotensin-Converting Enzyme Inhibitors (ACEIs)/Angiotensin Receptor Blockers (ARBs)&lt;/p&gt;

PurposeCommonly prescribed medications among patients with comorbid diabetes mellitus and hypertension include ARBs and ACEIs. However, these medications are associated with suboptimal adherence leading to inadequately controlled blood pressure. Unlike traditional single estimates of proportion of days covered (PDC), group-based trajectory modeling (GBTM) can graphically display the dynamic nature of adherence. The objective of this study was to evaluate adherence using GBTMs among patients prescribed ACEI/ARBs and identify predictors associated with each adherence trajectory.Patients and MethodsPatients with an ACEI/ARBs prescription were identified between July 2017 and December 2017 using a Medicare Advantage dataset. PDC was used to measure monthly patient adherence during the one-year follow-up period. The monthly PDC was added to a logistic group-based trajectory model to provide distinct patterns of adherence. Further, a multinomial logistic regression was conducted to determine predictors of each identified adherence trajectory. Predictors included various socio-demographic and clinical patient characteristics.ResultsA total of 22,774 patients were included in the analysis and categorized into 4 distinct adherence trajectories: rapid decline (12.6%); adherent (58.5%); gaps in adherence (12.2%), and gradual decline (16.6%). Significant predictors associated with all lower adherence trajectories included 90 days refill, >2 number of other medications, ≥1 hospitalizations, and prevalent users. Significant predictors associated with the rapid decline trajectory included male sex, comorbidities, and increased CMS risk score. Further, significant predictors associated with the gaps in adherence trajectory included increasing age, and comorbidities. Lastly, significant predictors associated with the gradual decline trajectory included increasing age, no health plan subsidy, comorbidities, and increasing CMS risk score.ConclusionIdentifying various patient characteristics associated with non-adherent trajectories can guide the development of tailored interventions to enhance adherence to ACEI/ARBs.

Medication Adherence Trajectories: A Systematic Literature Review.

Traditional adherence measures such as proportion of days covered (PDC) and medication possession ratio (MPR) are limited in their ability to explain patient medication adherence over time. Group-based trajectory modeling (GBTM) is a new methodological approach that visually describes the dynamics of long-term medication adherence and classifies adherence behavior into groups. To describe and compare trajectories of medication nonadherence reported in the medical literature, including identifying consistent trends in adherence trajectories and disease and patient characteristics that predict trajectory group membership. A systematic literature review was conducted in April 2020 in PubMed and CINAHL using MeSH terms and key words in appropriate combinations. Citations were screened for relevance using predefined inclusion and exclusion criteria and evaluated according to variables associated with group-based trajectory models. 21 articles met the study criteria and were reviewed. Generally, studies identified 4 to 6 trajectory groups that described longitudinal medication adherence behavior. Most commonly identified trajectories were labeled as (a) consistent, high adherence, (b) declining adherence, (c) early and consistent nonadherence, and (d) initial nonadherence followed by an increase. Several predictors, including socioeconomic status, disease characteristics, and therapy initiation were routinely associated with group membership. This review suggests that adherence trajectories and predictors of specific group membership may be similar across diverse disease states. GBTM describes longitudinal, dynamic patterns of medication adherence that may facilitate the development of targeted interventions to promote adherence. Implications for value-based payment systems are discussed in this review. No outside funding supported this study. The authors have no conflicts of interest to declare.

Trajectories of adherence to adjuvant endocrine therapy for 5 years in women with breast cancer

Abstract Background Adjuvant endocrine therapy (AET) is a daily oral medication prescribed for women with hormone-sensitive breast cancer (BC) to reduce recurrence and mortality risks. However, many women do not take AET daily or do not persist with AET for the recommended duration of at least 5 years. Our aims were to identify: 1) trajectories of AET adherence for the 5 years; 2) factors associated with these trajectories. Methods The French Cancer Cohort includes data on hospitalizations, ambulatory care and drug claims for all cancers diagnosed in France (SNDS database). Women diagnosed with a 1st non-metastatic BC in 2011 who had ≥ 1 AET claim within 12 months of surgery were included. For each woman, we estimated the monthly proportion of days covered (PDC) by an AET for 5 years after the first AET. Monthly PDCs were used to model AET adherence trajectories using group-based trajectory modeling. Statistical criteria were used to assess the suitability of the selected model. The factors associated with the trajectories were identified using multinomial logistic regressions. Results 33,260 women were included. A 6-trajectory model was selected: 1) Stop of AET in the 1st year (6.6%), 2) Adherence for 1 year and stop (5.7%), 3) Adherence for 2.5y and stop (6.3%), 4) High adherence for 4.5y and stop (8.3%), 5) Sub-optimal adherence for 5y (4.3%), 6) Very high adherence for 5y (68.8%). Factors associated with non-adherence trajectories are mainly extreme age (&amp;gt;70y) and switch in AET. Conclusions About 70% of women had an optimal adherence for 5 years. Our results showed that women who changed AET during the treatment course were at higher risk of non-adherence. Among non-adherent women, the switch in AET is frequent and probably often related to the management of side effects. Interventions to detect and manage these side effects may help to support women with AET use. Effective management of these effects during all the 5 years could be needed to maintain adherence. Key messages About 70% of women had an optimal adherence for 5 years. Women who changed AET during the treatment course were at higher risk of non-adherence.

Adherence trajectories in oral therapy for chronic myeloid leukemia: Overview of a research protocol.

Over a quarter of chemotherapy regimens now include oral agents. Individuals living with cancer are now responsible for administering this lifesaving therapy at home by taking every dose as prescribed. One type of oral chemotherapy, tyrosine kinase inhibitors (TKIs), is the current recommended treatment for chronic myeloid leukemia. This targeted therapy has markedly improved survival but comes with significant side effects and financial costs. In the study described in this protocol, the investigators seek to understand the dynamic nature of TKI adherence experienced by individuals diagnosed with CML. Using a mixed-method approach in this prospective observational study, funded by the National Cancer Institute, we seek to describe subjects' adherence trajectories over 1 year. We aim to characterize adherence trajectories in individuals taking TKIs using model-based cluster analysis. Next, we will determine how side effects and financial toxicity influence adherence trajectories. Then we will examine the influence of TKI adherence trajectories on disease outcomes. Additionally, we will explore the experience of patients taking TKIs by interviewing a subset of participants in different adherence trajectories. The projected sample includes 120 individuals taking TKIs who we will assess monthly for 12 months, measuring adherence with an objective measure (Medication Event Monitoring System). Identifying differential trajectories of adherence for TKIs is important for detecting subgroups at the highest risk of nonadherence and will support designing targeted interventions. Results from this study can potentially translate to other oral agents to improve care across different types of cancer.

Mapping the use of Group-Based Trajectory Modelling in medication adherence research: A scoping review protocol.

The use of group-based trajectory modelling (GBTM) within the medication adherence literature is rapidly growing. Researchers are adopting enhanced methods to analyse and visualise dynamic behaviours, such as medication adherence, within 'real-world' populations. Application of GBTM based on longitudinal adherence behaviour allows for the identification of adherence trajectories or groups. A group is conceptually thought of a collection of individuals who follow a similar pattern of adherence behaviour over a period of time. A common obstacle faced by researchers when implementing GBTM is deciding on the number of trajectory groups that may exist within a population. Decision-making can introduce subjectivity, as there is no 'gold standard' for model selection criteria. This study aims to examine the extent and nature of existing evidence on the application of GBTM for medication adherence assessment, providing an overview of the different GBTM techniques used in the literature. The methodological framework will consist of five stages: i) identify the research question(s); ii) identify relevant studies; iii) select studies; iv) chart the data and finally, v) collate, summarise and report the results. Original peer-reviewed articles, published in English, describing observational and interventional studies including both concepts and/or sub-concepts of GBTM and medication adherence or any other similar terms, will be included. The following databases will be queried: PubMed/MEDLINE; Embase (Ovid); SCOPUS; ISI Web of Science and PsychInfo. This scoping review will utilise the PRISMA extension for Scoping Reviews (PRISMA-ScR) tool to report results. This scoping review will collect and schematise different techniques in the application of GBTM for medication adherence assessment available in the literature to date, identifying research and knowledge gaps in this area. This review can represent an important tool for future research, providing methodological support to researchers carrying out a group-based trajectory analysis to assess medication adherence in a real-world context.

Mapping the use of Group-Based Trajectory Modelling in medication adherence research: A scoping review protocol

The use of group-based trajectory modelling (GBTM) within the medication adherence literature is rapidly growing. Researchers are adopting enhanced methods to analyse and visualise dynamic behaviours, such as medication adherence, within ‘real-world’ populations. Application of GBTM based on longitudinal adherence behaviour allows for the identification of adherence trajectories or groups. A group is conceptually thought of a collection of individuals who follow a similar pattern of adherence behaviour over a period of time. A common obstacle faced by researchers when implementing GBTM is deciding on the number of trajectory groups that may exist within a population. Decision-making can introduce subjectivity, as there is no ‘gold standard’ for model selection criteria. This study aims to examine the extent and nature of existing evidence on the application of GBTM for medication adherence assessment, providing an overview of the different GBTM techniques used in the literature. The methodological framework will consist of five stages: i) identify the research question(s); ii) identify relevant studies; iii) select studies; iv) chart the data and finally, v) collate, summarise and report the results. Original peer-reviewed articles, published in English, describing observational and interventional studies including both concepts and/or sub-concepts of GBTM and medication adherence or any other similar terms, will be included. The following databases will be queried: PubMed/MEDLINE; Embase (Ovid); SCOPUS; ISI Web of Science and PsychInfo. This scoping review will utilise the PRISMA extension for Scoping Reviews (PRISMA-ScR) tool to report results. This scoping review will collect and schematise different techniques in the application of GBTM for medication adherence assessment available in the literature to date, identifying research and knowledge gaps in this area. This review can represent an important tool for future research, providing methodological support to researchers carrying out a group-based trajectory analysis to assess medication adherence in a real-world context.

Evaluating trajectories of statin adherence after a motivational interviewing intervention

ObjectiveThe objective of the current study was to compare postintervention adherence trajectories with preintervention trajectories for those receiving a telephonic motivational interviewing (MoI) intervention to determine predictors associated with each distinct postintervention trajectory and any association between pre- and postintervention trajectories. DesignRetrospective study design using group-based trajectory modeling. Settings and participantsA telephonic MoI intervention was conducted by trained student pharmacists to improve statin adherence in a Medicare Advantage plan. Four preintervention adherence trajectories were previously identified: rapid decline (RD), gradual decline (GD), gaps in adherence (GA), and adherent and were used to customize the MoI intervention. Patients from the 3 nonadherent preintervention trajectories were randomized to control or intervention groups and were followed for 6 months from the date of MoI intervention. Outcome measuresGroup-based trajectory modeling was performed to identify 3 relevant postintervention trajectories. Descriptive statistics were used to assess differences in pre- and postintervention adherence trajectories. Multinomial logistic regression was conducted to determine predictors associated with each identified postintervention trajectory. ResultsThere were 152 intervention patients and 304 randomly selected controls. The prominent postintervention trajectories that were identified differed from the preintervention trajectories and were (1) GD (17.2%), (2) adherent (61.9%), and (3) discontinuation (20.9%). Among the intervention group, more patients in the GA preintervention trajectory (58.65%) moved to the adherent trajectory postintervention than those in the RD and GD preintervention trajectories. Furthermore, the predictors associated with the postintervention trajectories included MoI intervention, prescriber specialty, presence of diabetes, presence of congestive heart failure, Centers for Medicare & Medicaid Services risk score, and preintervention trajectories. ConclusionThe postintervention adherence trajectory patterns differed from the preintervention trajectory patterns with many patients moving into an adherent trajectory especially among those receiving the intervention.