Group-Based Trajectory Models: Assessing Adherence to Antihypertensive Medication in Older Adults in a Community Pharmacy Setting

Using large administrative data for mining patients' trajectories for risk stratification: An example from urological diseases

OBJECTIVE

To identify latent clusters among urological patients by examining hospitalisation rate trajectories and their association with risk factors and outcome quality indicators.

MATERIALS AND METHODS

Victorian Admitted Episodes Dataset, containing information on all hospital admissions in Victoria from 2009 to 2019. The top twenty ICD-10 primary diagnosis codes in urology were used to select patients (n = 98,782) who were included in the study. Latent class trajectory modelling (LCTM) was used to cluster urological patient hospitalisation trajectories. Logistic regression was used to find baseline factors that influence cluster membership, the variables tested included comorbidities, baseline diagnosis codes, and socio-demographic factors. The analysis was further stratified into non-surgical procedures and surgical procedures.

RESULTS

Five clusters of hospitalisation trajectories were identified based on clustering hospitalisation rates over time. Higher hospitalisation clusters were strongly associated with longer length of stay, higher readmission rates and higher complication rates. Higher-risk groups were strongly associated with comorbidities such as renal disease and diabetes. For surgical procedures, urological cancers (kidney, prostate and bladder cancer) and irradiation cystitis were associated with higher-risk groups. For non-surgical procedures, calculus of the bladder, urethral stricture and bladder neck obstruction were associated with higher-risk groups. For patients with two or more admissions, liver cardiovascular disease and being diagnosed with benign prostatic hyperplasia were also associated with higher risk groups.

CONCLUSION

A novel statistical approach to cluster hospitalisation trajectories for urological patients was used to explore potential clusters of patient risks and their associations with outcome quality indicators. This study supports the observation that baseline comorbidities and diagnosis can be predictive of higher hospitalisation rates and, therefore, poorer health outcomes. This demonstrates that it is possible to identify patients at risk of developing complications, higher length of stay and readmissions by using baseline comorbidities and diagnosis from administrative data.

Trajectory and predictors of adherence to Nucleos(t)ide analogues medication among patients with chronic hepatitis B

Objectives

To investigate the developmental trajectory of medication adherence and its predictors in chronic hepatitis B (CHB) patients taking nucleos(t)ide analogues.

Methods

A longitudinal study was conducted. Patients with CHB who met the inclusion criteria were selected using convenience sampling. Follow-ups were conducted at baseline, 3 months, 6 months, 9 months, and 12 months. Medication adherence was assessed using a medication adherence scale. Group-based trajectory modeling (GBTM) was used to explore medication adherence trajectories, and repeated measures ANOVA was used to describe changes in each trajectory. Unordered multinomial logistic regression analysis was used to explore predictive factors.

Results

A total of 305 patients completed all follow-ups. Medication adherence was categorized into four trajectory groups: low adherence (4.9 %), decreasing adherence (24.3 %), increasing adherence (48.2 %), and high adherence (22.6 %). Multinomial logistic regression results showed that HBV-infected discrimination, depression, self-efficacy, and social support were significantly different among different medication adherence levels (p < 0.05).

Conclusions

Medication adherence trajectories in patients with CHB exhibit heterogeneity. Healthcare professionals can develop personalized treatment plans based on patients' social and psychological characteristics to improve medication adherence.

Enhancing DHA supplementation adherence: A Bayesian approach with finite mixture models and irregular interim schedules in adaptive trial designs

Docosahexaenoic acid (DHA) supplementation has proven beneficial in reducing preterm births. However, the challenge lies in addressing nonadherence to prescribed supplementation regimens-a hurdle that significantly impacts clinical trial outcomes. Conventional methods of adherence estimation, such as pill counts and questionnaires, usually fall short when estimating adherence within a specific dosage group. Thus, we propose a Bayesian finite mixture model to estimate adherence among women with low baseline red blood cell phospholipid DHA levels (<6%) receiving higher DHA doses. In our model, adherence is defined as the proportion of participants classified into one of the two distinct components in a normal mixture distribution. Subsequently, based on the estimands from the adherence model, we introduce a novel Bayesian adaptive trial design. Unlike conventional adaptive trials that employ regularly spaced interim schedules, the novelty of our proposed trial design lies in its adaptability to adherence percentages across the treatment arm through irregular interims. The irregular interims in the proposed trial are based on the effect size estimation informed by the finite mixture model. In summary, this study presents innovative methods for leveraging the capabilities of Bayesian finite mixture models in adherence analysis and the design of adaptive clinical trials.

Uses of antiseizure medication among pregnant women with epilepsy and risk of adverse obstetric outcomes: A group-based trajectory analysis

OBJECTIVE

This study was undertaken to examine the association between different patterns of antiseizure medication (ASM) use during pregnancy and adverse obstetric outcomes (preterm birth, low birth weight [LBW], and small for gestational age [SGA]).

METHODS

This retrospective cohort study used the Birth Certificate Application and National Health Insurance data in Taiwan (January 1, 2004 through December 31, 2018). We retrieved weekly ASM among pregnant women with epilepsy who were prepregnancy chronic users and used group-based trajectory modeling to identify distinct patterns of use. Logistic regressions were adopted to examine the association between patterns of ASM use and risk of preterm birth, LBW, and SGA. In addition, we revealed postnatal ASM utilization pattern among these prepregnancy chronic users as an exploratory study.

RESULTS

Of 2175 pregnant women with epilepsy, we identified four patterns of ASM use during pregnancy: frequent and continuous (64.87%), frequent but discontinuous (7.08%), intermittent (19.72%), and intermittent and discontinuous users (8.32%). Compared to frequent and continuous users, the adjusted odds ratios for preterm birth in frequent but discontinuous, intermittent, and intermittent and discontinuous users were .83 (95% confidence interval [CI] = .47-1.48), .71 (95% CI = .47-1.05), and .88 (95% CI = .52-1.49), respectively. Similar results were observed for LBW and SGA. In the exploratory study, we found that most of our study subjects maintained the same patterns before and after delivery.

SIGNIFICANCE

After considering duration and timing of exposure, our study did not find an association between four distinct patterns of ASM use and adverse obstetric outcomes among women with epilepsy. The findings suggested that optimal seizure control could be received for pregnant women with epilepsy after evaluating the risks and benefits.

Analysis of Longitudinal Patterns and Predictors of Medicine Use in Residential Aged Care Using Group-Based Trajectory Modeling: The "MEDTRAC-Cardiovascular" Longitudinal Cohort Study

AIM

Cardiovascular diseases are the leading cause of death globally. Ensuring ongoing use of medicines-medication persistence-is crucial, yet no prior studies have examined this in residential aged care facilities (RACFs). We aimed to identify long-term trajectories of persistence with cardiovascular medicines and determine predictors of persistence trajectories.

METHOD

A longitudinal cohort study of 2837 newly admitted permanent residents from 30 RACFs in New South Wales, Australia. We monitored weekly exposure to six cardiovascular medicine classes-lipid modifiers, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (ACEI/ARBs), beta-blockers, diuretics, calcium channel blockers (CCB), and cardiac therapy-over 3 years. Group-based trajectory modeling was employed to determine persistence trajectories for each class.

RESULTS

At baseline, 76.6% (n = 2172) received at least one cardiovascular medicine with 41.2% receiving lipid modifiers, 31.4% ACEI/ARBs, 30.2% beta-blockers, 24.4% diuretics, 18.7% CCBs, and 14.8% cardiac therapy. The model identified two persistence trajectories for CCBs and three trajectories for all other classes. Sustained high persistence rates ranged from 68.4% (ACEI/ARBs) to 79.8% (beta-blockers) while early decline in persistence and subsequent discontinuation rates ranged from 7.6% (cardiac therapy) to 25.3% (CCBs). Logistic regressions identified 11 predictors of a declining persistence across the six medicine classes.

CONCLUSION

Our study revealed varied patterns of cardiovascular medicine use in RACFs, with 2-3 distinctive medicine use trajectories across different classes, each exhibiting a unique clinical profile, and up to a quarter of residents discontinuing a medicine class. Future studies should explore the underlying reasons and appropriateness of nonpersistence to aid in identifying areas for improvement.

Adherence to istradefylline in patients with Parkinson's disease: A group-based trajectory analysis

BACKGROUND

Understanding the different patterns of adherence to istradefylline treatment is essential to identifying Parkinson's disease (PD) patients who might benefit from targeted interventions.

OBJECTIVES

This descriptive study aimed to identify longitudinal istradefylline adherence patterns and to characterize factors associated with them.

METHODS

We identified PD patients aged 21-99 years who initiated istradefylline treatment in a Japanese hospital administrative database. Group-based trajectory modeling was used to model the monthly proportion of days covered over time to identify distinct 360-day adherence patterns. Factors associated with each adherence pattern were assessed using univariable multinomial logistic regression models.

RESULTS

Of 2088 eligible PD patients, 4 distinct adherence groups were identified: consistently high adherence (56.8%); rapidly declining adherence (25.8%); gradually declining adherence (8.5%); and gradually declining and then recovering adherence (9.0%). Compared to the consistently high adherence group, the other groups had the following characteristics associated with a likelihood of lower adherence: the rapidly declining adherence group received fewer dopamine agonists (63.8% vs. 69.4%), monoamine oxidase B (MAO-B) inhibitors (26.8% vs. 31.6%), and catechol-O-methyl transferase inhibitors (31.6% vs. 37.0%) and had a higher prevalence of anxiety/mood disorders (29.9% vs. 24.6%); the gradually declining adherence group received fewer MAO-B inhibitors (22.5% vs. 31.6%) and amantadine (8.4% vs. 16.1%) and had a higher prevalence of mild cognitive impairment/dementia (27.0% vs. 18.8%); and the declining and then recovering adherence group had a higher prevalence of anxiety/mood disorders (34.2% vs. 24.6%).

CONCLUSIONS

Clinicians should be aware of the heterogeneous patterns of adherence to istradefylline.

Longitudinal medication adherence group-based trajectories of aging adults in the US: A retrospective analysis using monthly proportion of days covered calculations

BACKGROUND

It is thought that half of the patients with chronic conditions are not adherent to their medications, which contributes to significant health and economic burden. Many studies estimate medication non-adherence by implementing a threshold of ≥80% of Proportion of Days Covered (PDC), categorizing patients as either adherent or non-adherent. Healthcare quality metrics pertaining to medication use are based on this dichotomous approach of medication adherence, including the Medicare Part D Star Ratings. Among others, the Medicare Part D Star Ratings rewards part D plan sponsors with quality bonus payments based on this dichotomous categorization of beneficiaries' medication adherence.

OBJECTIVES

Describe the longitudinal adherence trajectories of adults ≥65 years of age covered by Medicare for 3 classes of drugs in the Part D Star Ratings: diabetes medications, statins, and select antihypertensives.

METHODS

This study used Medicare healthcare administrative claims data linked to participants from the Health Retirement Study between 2008 and 2016. Group-based trajectory models (GBTM) elicited the number and shape of adherence trajectories from a sample of N = 11,068 participants for the three pharmacotherapeutic classes considered in this study. Medication adherence was estimated using monthly PDC.

RESULTS

GBTM were estimated for the sample population taking antihypertensives (n = 7,272), statins (n = 8,221), and diabetes medications (n = 3,214). The hypertension model found three trajectories: high to very high adherence (47.55%), slow decline (32.99%), and rapid decline (19.47%) trajectories. The statins model found 5 trajectories: high to very high adherence (35.49%), slow decline (17.12%), low then increasing adherence (23.58%), moderate decline (12.62%), and rapid decline (11.20%). The diabetes medications model displayed 6 trajectories: high to very high adherence (24.15%), slow decline (16.84%), high then increasing adherence (25.56%), low then increasing (13.58%), moderate decline (10.60%), and rapid decline (9.27%).

CONCLUSIONS

This study showed the fluid nature of long-term medication adherence to the medications considered in the Medicare Part D Star Ratings and how it varies by pharmacotherapeutic class. These challenge previous assumptions about which patients were considered adherent to chronic medications. Policy and methodological implications about medication adherence are discussed.

Trajectories of adherence to ACEI/ARB medications following a motivational interviewing intervention among Medicare Advantage beneficiaries in Texas

OBJECTIVES

To assess the impact of student telephone motivational interviewing intervention on angiotensin-converting enzyme inhibitors/angiotensin-receptor blockers (ACEI/ARBs) adherence trajectories and identify predictors of each trajectory.

METHODS

The intervention group included continuously enrolled Medicare Advantage Plan patients non-adherent to ACEI/ARBs vs the control group (1:2 ratio). The intervention was tailored by pre-intervention trajectories and included an initial and five follow-up calls. Adherence was measured 6 months after initial calls using the proportion of days covered (PDC). Monthly PDCs were integrated into a group-based trajectory model and categorized patients into 4-groups. A multinomial logistic regression model was used to evaluate trajectory predictors.

RESULTS

The study comprised 240 intervention patients and 480 controls with four trajectories: adherent trajectory 44.2%, gradual improvement in adherence 13.4%, slow decline in adherence 24.1%, and discontinuation 18.3%. Patients with the intervention were less likely to experience a slow decline in adherence than controls (OR: 0.627 [0.401-0.981]). Patients with specialty prescribers' visits, ≥ 1 previous hospitalization, rapid decline in adherence as pre-intervention trajectory, and higher CMS risk score were associated with discontinuation trajectory.

CONCLUSION

Intervention patients vs controls had a lower likelihood of following a slow decline in adherence pattern.

PRACTICE IMPLICATIONS

This study underscores the importance of individualized interventions and the association between past adherence patterns and post-intervention trajectories.

An Improved Statistical Modeling Approach to Individual Anticholinergic Drug Use Trend Analysis

Anticholinergic (AC) drugs are commonly prescribed to older adults for treating diseases and chronic conditions, such as chronic obstructive pulmonary disease, urinary incontinence, gastrointestinal disorder, or simply pain and allergy. The high prevalence of AC drug use can have a detrimental effect on the mental health of older adults. We aim to improve the prediction of future trends of AC drug use at the individual level, with pharmacy refill data. The individual drug use data presents challenges in the modeling, such as data being discrete-valued with excess zeros and having significant unobserved heterogeneity in the trend pattern. To address these challenges, we propose a statistical model of hierarchical structure and an EM scheme for the model parameter estimation. We evaluate the proposed modeling approach through a numerical study with synthetic data and a case study with real-world pharmacy refill data. The simulation study show that our analysis method outperforms the existing ones (e.g., reducing MSE significantly), particularly in terms of accurately predicting the trend pattern. The real-world case study further verifies the out-performance and demonstrate the advantageous features of our method. We expect the prediction tool developed based on our study can assist pharmacists' decision on initiating or strengthening behavioral interventions with the hope of discontinuing AC drug misuse.

Medication adherence trajectory of patients with chronic diseases and its influencing factors: A systematic review

AIMS

To synthesize the published studies on medication adherence trajectories among patients with chronic diseases and identify the influencing factors.

DESIGN

Systematic review.

DATA SOURCES

Medline (Ovid), Embase (Ovid) and Web of Science core collection were searched from database inception to 1 July 2022.

REVIEW METHODS

Potentially eligible articles were independently screened by three reviewers using set inclusion and exclusion criteria. The Joanna Briggs Institute critical appraisal checklist for cohort studies was used to appraise the quality of the included articles. Three reviewers independently evaluated the quality, extracted data and resolved differences by consensus. Results were presented using descriptive synthesis, and the prevalence of recategorised medication adherence trajectories was calculated from the published data.

RESULTS

Fifty studies were included. Medication adherence trajectories among patients with chronic diseases were synthesized into six categories: adherence, non-adherence, decreasing adherence, increasing adherence, fluctuating adherence and moderate adherence. Low and moderate evidence showed that (1) patient-related factors, including age, sex, race, marital status and mental status; (2) healthcare team and system-related factors, including healthcare utilization, insurance and primary prescriber specialty; (3) socioeconomic factors including education, income and employment status; (4) condition-related factors including complications and comorbidities and (5) therapy-related factors including the number of medications, use of other medications, and prior medication adherence behaviours were factors influencing the medication adherence trajectory. Marital status and prior medication adherence behaviour were the only influencing factors with moderate evidence of an effect.

CONCLUSION

The medication adherence trajectory among patients with chronic diseases varied widely. Further studies are warranted to determine contributory factors.

IMPLICATIONS FOR THE PROFESSION

Healthcare providers should be aware that patients' medication adherence has different trajectories and should take appropriate measures to improve patients' medication adherence patterns.

PATIENT OR PUBLIC CONTRIBUTION

None. As a systematic review, patients and the public were not involved.

HOW TO IDENTIFY SUBGROUPS IN LONGITUDINAL CLINICAL DATA: TREATMENT RESPONSE PATTERNS IN PATIENTS WITH A SHORTENED DENTAL ARCH

BACKGROUND

When dental patients seek care, treatments are not always successful,that is patients' oral health problems are not always eliminated or substantially reduced. Identifying these patients (treatment non-responders) is essential for clinical decision-making. Group-based trajectory modeling (GBTM) is rarely used in dentistry, but a promising statistical technique to identify non-responders in particular and clinical distinct patient groups in general in longitudinal data sets.

AIM

Using group-based trajectory modeling, this study aimed to demonstrate how to identify oral health-related quality of life (OHRQoL) treatment response patterns by the example of patients with a shortened dental arch (SDA).

METHODS

This paper is a secondary data analysis of a randomized controlled clinical trial. In this trial SDA patients received partial removable dental prostheses replacing missing teeth up to the first molars (N = 79) either or the dental arch ended with the second premolar that was present or replaced by a cantilever fixed dental prosthesis (N = 71). Up to ten follow-up examinations (1-2, 6, 12, 24, 36, 48, 60, 96, 120, and 180 months post-treatment) continued for 15 years. The outcome OHRQoL was assessed with the 49-item Oral Health Impact Profile (OHIP). Exploratory GBTM was performed to identify treatment response patterns.

RESULTS

Two response patterns could be identified - "responders" and "non-responders." Responders' OHRQoL improved substantially and stayed primarily stable over the 15 years. Non-responders' OHRQoL did not improve considerably over time or worsened. While the SDA treatments were not related to the 2 response patterns, higher levels of functional, pain-related, psychological impairment in particular, and severely impaired OHRQoL in general predicted a non-responding OHRQoL pattern after treatment. Supplementary, a 3 pattern approach has been evaluated.

CONCLUSIONS

Clustering patients according to certain longitudinal characteristics after treatment is generally important, but specifically identifying treatment in non-responders is central. With the increasing availability of OHRQoL data in clinical research and regular patient care, GBTM has become a powerful tool to investigate which dental treatment works for which patients.

Adherence levels and patterns for multiple cardiac medications prescribed to patients with incident atrial fibrillation events

AIMS

Using advanced longitudinal analyses, this real-world investigation examined medication adherence levels and patterns for incident atrial fibrillation (AF) patients with significant cardiovascular and noncardiovascular multimorbid conditions for each of 5 medication classes (β-blockers, calcium channel blockers/digoxin, antiarrhythmics, anticoagulants, antiplatelets). The population was derived from a large cohort covering a wide age spectrum/diversified US geographical areas/wide range of socioeconomic-disability status.

METHODS

The patients were drawn from 3 different health plans. Adherence was defined in terms of the proportion of day covered (PDC), and its patterns were modelled in terms of group-based trajectory, with each pattern profiled in terms of comorbid history, demographic variables and health plan factors using multinomial regression modelling.

RESULTS

The total population consisted of 1 978 168 patients, with the AF cohort being older (average age of 64.6 years relative to 44.7 years for the non-AF cohort) and having fewer females (47.8% relative to 55.4 for the non-AF cohort). The AF cohort had significant cardiovascular/noncardiovascular multimorbidities and was much sicker than the non-AF cohort. A 6-group based trajectory solution appears to be the most logical outcome for each medication class according to assessed criteria. For each medication class, it consisted of one consistent adherent group (PDC ≥ 0.84), one fast declining group (PDC ≤ 0.11) and 4 intermediate nonadherence groups (slow decline [0.30-0.74 PDC range], occasional users [0.24-0.55 PDC range] and early gap/increased adherence [0.62-0.75]). The most consistent adherent groups were much lower than 50% of the total population and equal to 12.5-27.0% of the population, with the fast declining nonadherent pattern in the 5.6-35.0% of the population and the intermediate nonadherence equal to ~61% of the population.

CONCLUSION

Our findings confirm that medication adherence is of major concern among multimorbid patients, with adherence levels lower much than those reported in the literature. There are 3 patterns of intermediate nonadherence (slow decline, occasional users, early gap/increased adherence), which were found to be eligible for interventions aimed at improving their adherence levels for each medication class. This may help improve cardiovascular medication adherence using large medication nonadherence improvement programmes.

Group based trajectory modeling to assess adherence to oral anticoagulants among atrial fibrillation patients with comorbidities: a retrospective study

BACKGROUND

Poor adherence to oral anticoagulants is a significant problem in atrial fibrillation (AF) patients with comorbidities as it increases the risk for cardiac and thromboembolic events.

AIM

The primary objective was to evaluate adherence to direct oral anticoagulants (DOACs) or warfarin using group-based trajectory modeling (GBTM). The secondary objective was to identify the predictors of adherence to oral anticoagulants. Finally, to report the drug interactions with DOACs/warfarin.

METHOD

This retrospective study was conducted among continuously enrolled Medicare Advantage Plan members from January 2016-December 2019. AF patients with comorbid hypertension, diabetes and hyperlipidemia using warfarin/DOACs were included. Monthly adherence to DOAC/warfarin was measured using proportion of days covered (PDC) and then modeled in a logistic GBTM to identify the distinct patterns of adherence. Logistic regression model was conducted to identify the predictors of adherence to oral anticoagulants adjusting for all baseline characteristics. Concomitant use of DOACs/warfarin with CYP3A4,P-gp inhibitors were measured.

RESULTS

Among 317 patients, 137 (43.2%) and 79 (24.9%) were DOAC, and warfarin users, respectively. The adherence trajectory model for DOACs included gradual decline (40.4%), adherent (38.8%), and rapid decline (20.8%). The adherence trajectories for warfarin adherence included gradual decline (8.9%), adherent (59.4%), and gaps in adherence (21.7%). Predictors of adherence included type of oral anticoagulant, stroke risk score, low-income subsidy, and baseline PDC. CYP3A4,P-gp drugs were co-administered with DOACs /warfarin resulting in adverse events.

CONCLUSION

Adherence to oral anticoagulants is suboptimal. Interventions tailored according to past adherence trajectories may be effective in improving patient's adherence.

Assessing Concurrent Adherence to Combined Essential Medication and Clinical Outcomes in Patients With Acute Coronary Syndrome. A Population-Based, Real-World Study Using Group-Based Trajectory Models

Aim

Adherence to multiple medications recommended for secondary prevention of cardiovascular conditions represents a challenge. We aimed to identify patterns of concurrent adherence to combined therapy and assess their impact on clinical outcomes in a cohort of patients with acute coronary syndrome (ACS).

Methods

Population-based retrospective cohort of all patients discharged after hospitalization for ACS (2009–2011), prescribed ≥3 therapeutic groups within the first month. We assessed monthly concurrent adherence (≥24 days of medication out of 30) to ≥3 medications during the first year, and patterns were identified through group-based trajectory models. A composite clinical outcome during the second year was constructed. The association between adherence patterns and traditional refill adherence metrics [e.g., the proportion of days covered (PDC)], and outcomes were assessed through a multivariable Cox proportional hazards model.

Results

Among 15,797 patients discharged alive, 12,057 (76.32%) initiated treatment with ≥3 therapeutic groups after discharge. We identified seven adherence trajectories to ≥3 medications: Adherent (52.94% of patients); Early Gap (6.64%); Middle Gap (5.67%); Late Decline (10.93%); Occasional Users (5.45%); Early Decline (8.79%); Non-Adherent (9.58%). Compared to the Adherent group, patients belonging to Early Gap (HR:1.30, 95%CI 1.07;1.60), Late decline (hazards ratio (HR): 1.31, 95% CI 1.1; 1.56), and Non-Adherent trajectories (HR: 1.36, 95% CI 1.14; 1.63) had a greater risk of adverse clinical outcomes, which was also different to the risk ascertained through concurrent PDC < 80 (HR: 1.13, 95% CI 1.01; 1.27).

Conclusion

Overall, seven adherence trajectories to ≥3 drugs were identified, with three distinct adherence patterns being at higher risk of adverse outcomes. The identification of patterns of concurrent adherence, a more comprehensive approach than traditional measurements, may be useful to target interventions to improve adherence to multiple medications.

Static and group-based trajectory analyses of factors associated with non-adherence in patients with multiple sclerosis newly-initiating once- or twice-daily oral disease-modifying therapy

Background

Increased understanding of adherence may facilitate optimal targeting of interventions.

Objective

To utilize group-based trajectory modeling (GBTM) to understand longitudinal patterns of adherence and factors associated with non-adherence in patients with multiple sclerosis (MS) newly-initiating once-/twice-daily oral disease-modifying therapy (DMT) (fingolimod, dimethyl fumarate, or teriflunomide).

Methods

Commercial plan data were analyzed using proportion of days covered (PDC) to evaluate factors associated with non-adherence. GBTM clustered patient subgroups with similar longitudinal patterns of adherence measured by monthly PDC (≥80%) and multinomial logistic regression identified factors associated with adherence trajectory subgroups.

Results

Among 7689 patients, 39.5% were non-adherent to once-/twice-daily oral DMTs. Characteristics associated with non-adherence (PDC<80%) included younger age, female, depression or migraine, switching during follow-up, more frequent dosing, relapse, and absence of magnetic resonance imaging. GBTM elucidated three adherence subgroups: Immediately Non-Adherent (14.9%); Gradually Non-Adherent (19.5%), and Adherent (65.6%). Additional factors associated with adherence (i.e. region, chronic lung disease) were identified and factors differed among trajectory subgroups.

Conclusion

These analyses confirmed that a significant proportion of patients with MS are non-adherent to once-/twice-daily oral DMTs. Unique patterns of non-adherence and factors associated with patterns of adherence emerged. The approach demonstrated how quantitative trajectories can help clinicians develop tailored interventions.

Identifying temporal patterns of adherence to antidepressants, bisphosphonates and statins, and associated patient factors

Background

Group-based trajectory modelling (GBTM) has recently been explored internationally as an improved approach to measuring medication adherence (MA) by differentiating between alternative temporal patterns of nonadherence. To build on this international research, we use the method to identify temporal patterns of medication adherence to antidepressants, bisphosphonates or statins, and their associations with patient characteristics.

Objectives

The objectives include identification of MA types using GBTM, exploration of features and associated patient characteristics of each MA type, and identification of the advantages of GBTM compared to the traditional proportion of days covered (PDC) measure.

Data and methods

We used 45 and Up Study survey data which contains information about demographics, family, health, diet, work and lifestyle of 267,153 participants aged at least 45 years across New South Wales, Australia. This data was linked to participant records of medication use, outpatient and inpatient care, and death. Our study participants initiated use of antidepressants (9287 participants), bisphosphonates (1660 participants) or statins (10,242 participants) during 2012–2016. MA types were identified from 180-day patterns of medication use for antidepressants and 360-day patterns for bisphosphonates and statins. Multinomial and binomial logistic regressions were performed to estimate participant characteristics associated with GBTM MA and PDC MA, respectively.

Results

Three GBTM MA types were identified for antidepressants and six for bisphosphonates and statins. For all three medications, MA types included: almost fully adherent; decreasing adherence and early discontinuation. The additional nonadherent types for bisphosphonates and statins were improved adherence, low adherence and later discontinuation. Participant characteristics impacting GBTM MA and PDC MA were consistent. However, several associations were uniquely found for GBTM MA as compared to PDC MA.

Conclusion

GBTM permits clinicians, policy-makers and researchers to differentiate between alternative nonadherence patterns, allowing them to better identify patients at risk of poor adherence and tailor interventions accordingly.

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Medication adherence was categorised using group-based trajectory modelling (GBTM).

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GBTM categories include adherence, early discontinuation and decreasing adherence.

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Demographic, economic, health and other factors determined GBTM categories.

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GBTM provides additional information to better target adherence interventions.

Trajectories of Prescription Opioid Utilization During Pregnancy Among Prepregnancy Chronic Users and Risk of Neonatal Opioid Withdrawal Syndrome

Little is known about the impact of dose, duration, and timing of prenatal prescription opioid exposure on the risk of neonatal opioid withdrawal syndrome (NOWS). Using a cohort of 18,869 prepregnancy chronic opioid users nested within the 2000-2014 Medicaid Analytic eXtract, we assessed average opioid dosage within biweekly gestational age intervals, created group-based trajectory models, and evaluated the association between trajectory groups and NOWS risk. Women were grouped into 6 distinct opioid use trajectories which, based on observed patterns, were categorized as 1) continuous very low-dose use, 2) continuous low-dose use, 3) initial moderate-dose use with a gradual decrease to very low-dose/no use, 4) initial high-dose use with a gradual decrease to very low-dose use, 5) continuous moderate-dose use, and 6) continuous high-dose use. Absolute risk of NOWS per 1,000 infants was 7.7 for group 1 (reference group), 28.8 for group 2 (relative risk (RR) = 3.7, 95% confidence interval (CI): 2.8, 5.0), 16.5 for group 3 (RR = 2.1, 95% CI: 1.5, 3.1), 64.9 for group 4 (RR = 8.4, 95% CI: 5.6, 12.6), 77.3 for group 5 (RR = 10.0, 95% CI: 7.5, 13.5), and 172.4 for group 6 (RR = 22.4, 95% CI: 16.1, 31.2). Trajectory models-which capture information on dose, duration, and timing of exposure-are useful for gaining insight into clinically relevant groupings to evaluate the risk of prenatal opioid exposure.

Comparing Continuous and Binary Group-based Trajectory Modeling Using Statin Medication Adherence Data

BACKGROUND

Of 58 medication adherence group-based trajectory modeling (GBTM) published studies, 74% used binary and 26% used continuous GBTM. Few studies provided a rationale for this choice. No medication adherence studies have compared continuous and binary GBTM.

OBJECTIVE

The objective of this study was to assess whether continuous versus binary GBTM: (1) impacts adherence trajectory shapes; and (2) results in the differential classification of patients into adherence groups.

METHODS

Patients were prevalent statin users with myocardial infarction hospitalization, 66+ years old, and continuously enrolled in fee-for-service Medicare. Statin medication adherence was measured 6 months prehospitalization using administrative claims. Final GBTM specifications beyond default settings were selected using a previously defined standardized procedure and applied separately to continuous and binary (proportion of days covered ≥0.80) medication adherence measures. Assignment to adherence groups was compared between continuous and binary models using percent agreement of patient classification and the κ coefficient.

RESULTS

Among 113,296 prevalent statin users, 4 adherence groups were identified in both models. Three groups were consistent: persistently adherent, progressively nonadherent, and persistently nonadherent. The fourth continuous group was moderately adherent (progressively adherent in the binary model). When comparing patient assignment into adherence groups between continuous and binary trajectory models, only 78.4% of patients were categorized into comparable groups (κ=0.641; 95% confidence interval: 0.638-0.645). The agreement was highest in the persistently adherent group (∼94%).

CONCLUSIONS

Continuous and binary trajectory models are conceptually different measures of medication adherence. The choice between these approaches should be guided by study objectives and the role of medication adherence within the study-exposure, outcome, or confounder.

Identifying adherent patients to newly initiated statins using previous adherence to chronic medications

BACKGROUND: Statins are one of the most frequently prescribed medications in the United States. Despite well-documented benefits in managing hyperlipidemia and reducing cardiovascular risks, statin adherence remains suboptimal. Several effective interventions have been implemented to improve adherence to statins. However, identifying patients who are at risk for developing poor medication adherence at the time of treatment initiation could assist in planning early targeted interventions. Studies have suggested that previous adherence to chronic medications is a strong predictor of future adherence to newly initiated medications. OBJECTIVE: To investigate patients' adherence to newly initiated statins by measuring previous adherence to angiotensin-converting enzyme inhibitors (ACEIs), angiotensin II receptor blockers (ARBs), and oral antidiabetic drugs (OADs). METHODS: A retrospective cohort study was conducted using administrative claims data from January 2016 to May 2018. New statin initiators were identified and included in the study if they were continuously enrolled in the health plan and had at least 1 prescription for ACEIs, ARBs, or OADs 1 year before statin initiation (pre-index period). Baseline adherence to ACEIs/ARBs, OADs, or both was calculated during a 1-year pre-index period using proportion of days covered (PDC) and defined as PDC ≥ 0.80. Adherence to statins was assessed 1 year after statin initiation and was the primary outcome, with a PDC ≥ 0.80 considered as adherent. Patient demographics were measured during the pre-index period. Multivariable logistic regression was conducted for each cohort separately to determine an association between baseline adherence and future statin adherence controlling for various demographic and clinical characteristics. RESULTS: 1,223 ACEI/ARB users, 714 OAD users, and 452 concomitant ACEI/ARB and OAD users were identified. In the regression model, adherence to baseline medications was significantly associated with 1-year adherence to statins (ACEI/ARB users: OR = 1.75, 95% CI = 1.37-2.25; OAD users: OR = 2.02, 95% CI = 1.46-2.79; concomitant ACEI/ARB and OAD users: OR = 1.73, 95% CI = 1.16-2.58). CONCLUSIONS: Past adherence to baseline medications may predict future adherence to newly initiated statins. Identifying patients likely to become nonadherent during treatment initiation could enable health care providers in recognizing individuals at risk of nonadherence and intervene earlier to enhance future adherence. DISCLOSURES: No funding was received for this study. Abughosh reports grants from Regeneron-Sanofi, BMS-Pfizer, and Valeant, unrelated to this work. Majd, Mohan, and Paranjpe have nothing to disclose.

Exploring Culture, Religiosity and Spirituality Influence on Antihypertensive Medication Adherence Among Specialised Population: A Qualitative Ethnographic Approach

BACKGROUND

Hypertension is one of the major risk factors of stroke and leading risk factors for global death. Inadequate control of blood pressure due to medication non-adherence remains a challenge and identifying the underlying causes will provide useful information to formulate suitable interventions.

PURPOSE

This study aimed to explore the roles of culture, religiosity, and spirituality on adherence to anti-hypertensive medications.

METHODOLOGY

A semi-structured qualitative interview was used to explore promoters and barriers to medication adherence among hypertensive individuals residing in urban and rural areas of Perak State, West Malaysia. Study participants were individuals who are able to comprehend either in Malay or English, above 18 years old and on antihypertensive medications. Interview transcriptions from 23 participants were coded inductively and analyzed thematically. Codes generated were verified by three co-investigators who were not involved in transcribing process. The codes were matched with quotations and categorized using three levels of themes named as organizing, classifying and general themes.

RESULTS

Cultural aspects categorized as societal and communication norms were related to non-adherence. The societal norms related to ignorance, belief in testimony and anything "natural is safe" affected medication adherence negatively. Communication norms manifested as superficiality, indirectness and non-confrontational were also linked to medication non-adherence. Internal and organizational religiosity was linked to increased motivation to take medication. In contrast, religious misconception about healing and treatment contributed towards medication non-adherence. The role of spirituality remains unclear and seemed to be understood as related to religiosity.

CONCLUSION

Culture and religiosity (C/R) are highly regarded in many societies and shaped people's health belief and behaviour. Identifying the elements and mechanism through which C/R impacted adherence would be useful to provide essential information for linking adherence assessment to the interventions that specifically address causes of medication non-adherence.

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

OBJECTIVE

Despite 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.

METHODS

The 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.

RESULTS

The 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.

CONCLUSION

This 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.

A data-driven typology of asthma medication adherence using cluster analysis

Asthma preventer medication non-adherence is strongly associated with poor asthma control. One-dimensional measures of adherence may ignore clinically important patterns of medication-taking behavior. We sought to construct a data-driven multi-dimensional typology of medication non-adherence in children with asthma. We analyzed data from an intervention study of electronic inhaler monitoring devices, comprising 211 patients yielding 35,161 person-days of data. Five adherence measures were extracted: the percentage of doses taken, the percentage of days on which zero doses were taken, the percentage of days on which both doses were taken, the number of treatment intermissions per 100 study days, and the duration of treatment intermissions per 100 study days. We applied principal component analysis on the measures and subsequently applied k-means to determine cluster membership. Decision trees identified the measure that could predict cluster assignment with the highest accuracy, increasing interpretability and increasing clinical utility. We demonstrate the use of adherence measures towards a three-group categorization of medication non-adherence, which succinctly describes the diversity of patient medication taking patterns in asthma. The percentage of prescribed doses taken during the study contributed to the prediction of cluster assignment most accurately (84% in out-of-sample data).

Medication Adherence Trajectories: A Systematic Literature Review

BACKGROUND

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.

OBJECTIVES

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

DISCLOSURES

No outside funding supported this study. The authors have no conflicts of interest to declare.

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.

Identifying Adherence Patterns Across Multiple Medications and Their Association With Health Outcomes in Older Community-Dwelling Adults With Multimorbidity

OBJECTIVES

To classify older people with multimorbidity according to their adherence patterns and to examine the association between medication adherence and health outcomes.

METHODS

This is a secondary analysis of a cohort study. Community-dwelling adults aged ≥70 years were recruited from 15 general practices in Ireland in 2010 (wave 1) and followed up 2 years later (wave 2). Participants had ≥2 RxRisk-V multimorbidity conditions at wave 1 and had ≥2 dispensations of RxRisk-V medications (wave 1-wave 2). Average adherence across RxRisk-V conditions was estimated based on continuous multiple-interval measure of medication availability (CMA7 function in AdhereR). Group-based trajectory models were used to group participants' adherence patterns for RxRisk-V medications. Multilevel regression was used to examine the association between adherence and (1) EuroQol 5-dimension (EQ-5D) utility (linear) and (2) vulnerability, using the Vulnerable Elders Survey (≥3 defined as vulnerable; logistic) at wave 2, controlling for potential confounders.

RESULTS

Average adherence (CMA7) was 77% across 501 participants. Group-based trajectory models identified 5 adherence groups: (1) initial low adherers, gradual increase; (2) high adherers, sharp decline; (3) steady adherers, gradual decline; (4) consistent high adherers; and (5) consistent nonadherers. Higher average adherence was associated with a significant increase in EQ-5D utility (adjusted β = 0.11, robust standard error 0.04). Group 5 was associated with significantly increased vulnerability compared to group 4 (adjusted odds ratio = 1.88; 95% confidence interval 1.01-3.50).

CONCLUSION

Increased average adherence was associated with higher EQ-5D utility. Adherence grouping did not significantly impact utility. Suboptimal adherence to multiple medications in older adults with multimorbidity was associated with vulnerability.

Evaluating trajectories of statin adherence after a motivational interviewing intervention

OBJECTIVE

The 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.

DESIGN

Retrospective study design using group-based trajectory modeling.

SETTINGS AND PARTICIPANTS

A 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 MEASURES

Group-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.

RESULTS

There 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.

CONCLUSION

The postintervention adherence trajectory patterns differed from the preintervention trajectory patterns with many patients moving into an adherent trajectory especially among those receiving the intervention.

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

The use of group-based trajectory (GBTM) modelling 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 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.

Adherence trajectories of adjuvant endocrine therapy in the five years after its initiation among women with non-metastatic breast cancer: a cohort study using administrative databases

PURPOSE

Despite the benefits of adjuvant endocrine therapy (AET) for reducing recurrence and mortality risks after hormone-sensitive breast cancer, AET adherence is sub-optimal for a high proportion of women. However, little is known about long-term patterns of AET adherence over the minimally recommended 5 years. Our objectives were to: (1) identify 5-year AET adherence trajectory groups; (2) describe trajectory groups according to adherence measures traditionally used (i.e., Proportion of Days Covered); and (3) explore factors associated with trajectories.

METHODS

We conducted a 5-year cohort study using data from a French national study that included AET dispensing data. Women diagnosed with first non-metastatic breast cancer and having at least 1 AET dispensing in the 12 months after diagnosis were included. Group-based trajectory modeling was used to identify adherence trajectory groups by clustering similar patterns of monthly AET dispensing. Multinomial logistic regressions were used to identify factors associated with trajectories.

RESULTS

Among 674 women, five AET adherence trajectory groups were identified: (1) quick decline and stop (5.2% of women); (2) moderate decline and stop (6.4%); (3) slow decline (17.2%); (4) high adherence (30.0%); and (5) maintenance of very high adherence (41.2%). Mean 5-year Proportion of Days Covered varied from 10 to 97% according to trajectories. Women who did not receive chemotherapy or a personalized care plan were more likely to belong to trajectories where AET adherence declined and stopped.

CONCLUSION

Our results provide information on the diversity of longitudinal AET adherence patterns, the timing of decline and discontinuation and associated factors that could inform healthcare professionals.

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)

PURPOSE

Commonly 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 METHODS

Patients 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.

RESULTS

A 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.

CONCLUSION

Identifying various patient characteristics associated with non-adherent trajectories can guide the development of tailored interventions to enhance adherence to ACEI/ARBs.

Opening the black box of the group-based trajectory modeling process to analyze medication adherence patterns: An example using real-world statin adherence data

PURPOSE

The rationale for choosing a final group-based trajectory modeling (GBTM) specification and evaluations of patient adherence patterns within groups are often omitted in the GBTM medication adherence literature. We aimed to (1) reveal the complexity of GBTM and (2) assess model discrimination of patient medication adherence patterns.

METHODS

Medicare administrative claims were used to measure statin medication adherence as a continuous value in the 6 months before an acute myocardial infarction (AMI) hospitalization. Different GBTM specifications beyond default settings were constructed and compared with the Bayesian information criterion. Spaghetti plots were used to compare individual adherence patterns with group averages.

RESULTS

Overall, 113,296 prevalent statin users met eligibility criteria. Four adherence groups were identified: persistently adherent, moderately adherent, progressively nonadherent, and persistently nonadherent. Spaghetti plots showed the persistently adherent and persistently nonadherent groups had relatively homogeneous adherence patterns that matched predicted trajectories well. Spaghetti plots also showed that, while adherence patterns in the progressively nonadherent group were not as homogeneous, most patients in this group appeared to be discontinuing statin therapy pre-AMI.

CONCLUSIONS

Subjective decisions are necessary to identify a final trajectory model. Greater transparency and disclosure of these decisions in the medication adherence literature are needed. Individual patient adherence patterns from spaghetti plots provided additional diagnostic information about trajectory models beyond standard model-fit assessments to determine if group-average adherence estimates represent homogeneous patterns of medication adherence.

Trajectories of Adherence to Low-Dose Aspirin Treatment Among the French Population

BACKGROUND

Previous studies have shown that adherence to low-dose aspirin (LDA) is suboptimal. However, these studies were based on an average measure of adherence during follow-up, ignoring its dynamic process over time. We described the trajectories of adherence to LDA treatment among the French population over 3 years of follow-up.

METHODS

We identified a cohort of 11 793 new LDA users, aged ≥50 years in 2010, by using the French national health-care database. Patients included had at least 3 years of history in the database before study entry to exclude prevalent aspirin users and to assess baseline comorbidities. They were followed from the first date of LDA supply (the index date) until the first date among death, exit from the database, or 3 years after the index date. Adherence to LDA was assessed every 3 months by using the proportion of days covered (PDC) and dichotomized with a cutoff of PDC of 0.8. We used group-based trajectory modeling to identify trajectories of LDA adherence. Predictors of LDA adherence trajectory membership were identified by multinomial logistics regression.

RESULTS

We identified 4 trajectories of adherence among new LDA users: the not-adherents (4737 [40.2%]), the delayed not-adherents (gradual decrease in adherence probability, 1601 [13.6%]), the delayed adherents (gradual increase in adherence probability, 1137 [9.6%]), and the persistent adherents (4318 [36.6%]). The probability of belonging to the not-adherent group was increased with female sex, low socioeconomic status, and polymedication and was reduced with a secondary indication for LDA use, such as diabetes, hypertension, and dementia, at least 4 consultations in the previous year, or 1 hospitalization or a cardiologist consultation in the 3 months before the index date.

CONCLUSION

This study provides a dynamic picture of adherence behaviors among new LDA users and underlines the presence of critical trajectories that intervention could target to improve adherence.

The association between pharmacy refill-adherence metrics and healthcare utilisation: a prospective cohort study of older hypertensive adults

AIMS

Methods that enable targeting and tailoring of adherence interventions may facilitate implementation in clinical settings. We aimed to determine whether community pharmacy refill-adherence metrics are useful to identify patients at higher risk of healthcare utilisation due to low antihypertensive adherence, who may benefit from an adherence intervention.

METHODS

We conducted a prospective cohort study, recruiting participants (n = 905) from 106 community pharmacies across the Republic of Ireland. Participants completed a structured interview at baseline and 12 months. Antihypertensive medication adherence was evaluated from linked pharmacy records using group-based trajectory modelling (GBTM) and proportion of days covered (PDC). Healthcare utilisation included self-reported number of hospital visits (emergency department visits and inpatient admissions) and general practitioner (GP) visits, over a 6-month period. Separate regression models were used to estimate the association between adherence and number of hospital/GP visits. The relative statistical fit of each model using different adherence metrics was determined using the Bayesian information criterion (BIC).

RESULTS

For the number of hospital visits, significant associations were observed only for PDC but not for GBTM. Each 10% increase in refill-adherence by PDC was significantly associated with a 16% lower rate of hospital visits (adjusted incidence rate ratio 0.84, 95% CI 0.72-0.98, P = 0.036). Poorer adherence using both measures was associated with higher GP visits. Improvements in BIC favoured models using PDC.

CONCLUSIONS

Medication refill-adherence, measured using PDC in community pharmacy settings, could be used to recognise poor antihypertensive adherence to enable effective targeting of clinical interventions to improve hypertension management and outcomes.

Maternal use of thyroid hormone replacement therapy before, during, and after pregnancy: agreement between self-report and prescription records and group-based trajectory modeling of prescription patterns

Purpose

A reliable definition of exposure and knowledge about long-term medication patterns is important for drug safety studies during pregnancy. Few studies have investigated these measures for thyroid hormone replacement therapy (THRT). The purpose of this study was to 1) calculate the agreement between self-report and dispensed prescriptions of THRT and 2) classify women with similar adherence patterns to THRT into disjoint longitudinal trajectories.

Methods

Our analysis used data from the Norwegian Mother and Child Cohort Study (MoBa), a prospective population-based cohort study. MoBa was linked to prescription records from the Norwegian Prescription Database (NorPD). We estimated Cohen’s kappa coefficients (k) and approximate 95% CIs for agreement between self-report and prescription records for the 6-month period prior to pregnancy and for each pregnancy trimester. Using group-based trajectory models (GBTMs), we estimated adherence trajectories among women who self-reported and had a THRT prescription.

Results

There were 56,148 women in MoBa, who had both a record in NorPD and available prescription history up to 1 year prior to pregnancy. Of these, 1,171 (2.1%) self-reported and received a prescription for THRT. Agreement was “perfect” in the 6-month period prior to pregnancy (k=0.86; CI 0.85–0.88), in the first (k=0.83; CI 0.82–0.85) and in the second trimesters (k=0.89; CI 0.87–0.90), while this was moderate (k=0.57; CI 0.54–0.59) in the third trimester. Among the subset of the 1,171 women, we identified four disjoint GBTM adherence groups: Constant-High (50.2%), Constant-Medium (32.9%), Increasing-Medium (11.0%), and Decreasing-Low (5.8%).

Conclusion

Agreement between self-report and prescription records was high for THRT in the early pregnancy period. Based on our GBTM results, about one in two women with hypothyroidism had adequate adherence to prescribed THRT throughout pregnancy. Given the potential consequences, evidence of low adherence in 5.8% of pregnant women with hypothyroidism is of concern.

Impact of financial burden, resulting from prescription co-payments, on antihypertensive medication adherence in an older publically insured population

Introduction

Medication co-payments represent a financial barrier to antihypertensive medication adherence. The introduction of co-payments for Irish publically insured patients was associated with a 5% reduction in adherence. However there is socioeconomic variability within this population, and the impact may be greater for those on lower income. We evaluated medication-related financial burden of the co-payment in a cohort of Irish publically insured antihypertensive users and tested its association with adherence at 12 months.

Methods

This was a prospective cohort study of community dwelling older (> 65 yrs) adults (n = 1152) from 106 Irish community pharmacies. Participants completed a structured telephone interview at baseline, and a follow-up interview at 12-months, which we linked to pharmacy records. We assessed medication-related financial burden at baseline using a single questionnaire item, and adherence at 12 months via questionnaire and refill-adherence as Proportion of Days Covered (PDC).

Results

A third of participants (30.1%) reported financial burden due to medication costs. In adjusted linear regression models financially burdened participants had significantly lower self-reported adherence (β = − 0.29, 95% CI -0.48 to − 0.11), although this was not evident with PDC (β = − 2.76, 95% CI -5.65 to 0.14).

Conclusion

This co-payment represents a financial barrier to antihypertensive adherence for many older Irish publically insured patients. The negative impact to adherence will potentially increase the risk of adverse outcomes, such as stroke, and increase long-term healthcare costs.

Electronic supplementary material

The online version of this article (10.1186/s12889-018-6209-8) contains supplementary material, which is available to authorized users.