Longitudinal Medication Adherence in Older Adults With Multimorbidity and Association With Health Care Utilization: Results From the Irish Longitudinal Study on Ageing

Trajectories in long-term condition accumulation and mortality in older adults: a group-based trajectory modelling approach using the English Longitudinal Study of Ageing

OBJECTIVES

To classify older adults into clusters based on accumulating long-term conditions (LTC) as trajectories, characterise clusters and quantify their associations with all-cause mortality.

DESIGN

We conducted a longitudinal study using the English Longitudinal Study of Ageing over 9 years (n=15 091 aged 50 years and older). Group-based trajectory modelling was used to classify people into clusters based on accumulating LTC over time. Derived clusters were used to quantify the associations between trajectory memberships, sociodemographic characteristics and all-cause mortality by conducting regression models.

RESULTS

Five distinct clusters of accumulating LTC trajectories were identified and characterised as: 'no LTC' (18.57%), 'single LTC' (31.21%), 'evolving multimorbidity' (25.82%), 'moderate multimorbidity' (17.12%) and 'high multimorbidity' (7.27%). Increasing age was consistently associated with a larger number of LTCs. Ethnic minorities (adjusted OR=2.04; 95% CI 1.40 to 3.00) were associated with the 'high multimorbidity' cluster. Higher education and paid employment were associated with a lower likelihood of progression over time towards an increased number of LTCs. All the clusters had higher all-cause mortality than the 'no LTC' cluster.

CONCLUSIONS

The development of multimorbidity in the number of conditions over time follows distinct trajectories. These are determined by non-modifiable (age, ethnicity) and modifiable factors (education and employment). Stratifying risk through clustering will enable practitioners to identify older adults with a higher likelihood of worsening LTC over time to tailor effective interventions to prevent mortality.

Multimorbidity patterns and trajectories in young and middle-aged adults: a large-scale population-based cohort study

Introduction

The presence of multiple chronic conditions, also referred to as multimorbidity, is a common finding in adults. Epidemiologic research can help identify groups of individuals with similar clinical profiles who could benefit from similar interventions. Many cross-sectional studies have revealed the existence of different multimorbidity patterns. Most of these studies were focused on the older population. However, multimorbidity patterns begin to form at a young age and can evolve over time following distinct multimorbidity trajectories with different impact on health. In this study, we aimed to identify multimorbidity patterns and trajectories in adults 18-65 years old.

Methods

We conducted a retrospective longitudinal epidemiologic study in the EpiChron Cohort, which includes all inhabitants of Aragón (Spain) registered as users of the Spanish National Health System, linking, at the patient level, information from electronic health records from both primary and specialised care. We included all 293,923 patients 18-65 years old with multimorbidity in 2011. We used cluster analysis at baseline (2011) and in 2015 and 2019 to identify multimorbidity patterns at four and eight years of follow-up, and we then created alluvial plots to visualise multimorbidity trajectories. We performed age- and sex-adjusted logistic regression analysis to study the association of each pattern with four- and eight-year mortality.

Results

We identified three multimorbidity patterns at baseline, named dyslipidaemia & endocrine-metabolic, hypertension & obesity, and unspecific. The hypertension & obesity pattern, found in one out of every four patients was associated with a higher likelihood of four- and eight-year mortality (age- and sex-adjusted odds ratio 1.11 and 1.16, respectively) compared to the unspecific pattern. Baseline patterns evolved into different patterns during the follow-up.

Discussion

Well-known preventable cardiovascular risk factors were key elements in most patterns, highlighting the role of hypertension and obesity as risk factors for higher mortality. Two out of every three patients had a cardiovascular profile with chronic conditions like diabetes and obesity that are linked to low-grade systemic chronic inflammation. More studies are encouraged to better characterise the relatively large portion of the population with an unspecific disease pattern and to help design and implement effective and comprehensive strategies towards healthier ageing.

Association between nintedanib adherence trajectory and healthcare use among idiopathic pulmonary fibrosis patients

BACKGROUND

Although inverse associations have been found between medication adherence and healthcare use and spending outcomes in many clinical settings, no studies to date have examined these relationships for patients with idiopathic pulmonary fibrosis (IPF) initiating nintedanib. We build on our prior study that used group-based trajectory modeling (GBTM) to compare inpatient hospitalization and medical care spending outcomes between groups of patients with different nintedanib adherence trajectories.

METHODS

This analysis used 100% Medicare data and included beneficiaries with IPF who initiated nintedanib during 10/01/2014-12/31/2018. The sample consisted of community-dwelling older adults (≥ 66 years) with continuous coverage in Medicare Parts A (inpatient care), B (outpatient care) and D (prescription drugs) for one year before (baseline) and after (follow-up) initiating nintedanib. Patients were assigned to the GBTM-derived adherence trajectory group closest to their own nintedanib adherence experience. All-cause and IPF-related hospitalization events and total medical spending were measured during the follow-up period. Unadjusted and adjusted regression models were estimated to compare outcomes between patients in different nintedanib adherence trajectories.

RESULTS

Among the 1,798 patients initiating nintedanib, the mean age was 75.4 years, 61.1% were male, and 91.1% were non-Hispanic white. The best-fitting GBTM had five adherence trajectories: high adherence, moderate adherence, high-then-poor adherence, delayed-poor adherence, and early-poor adherence. All-cause hospitalizations and total all-cause medical spending were higher among patients in the high-then-poor, delayed-poor and early-poor adherence trajectories than those in the high adherence trajectory. For example, adjusted total all-cause medical spending was $4,876 (95% CI: $1,470 to $8,282) higher in the high-then-poor adherence trajectory, $3,639 (95% CI: $1,322 to $5,955) higher in the delayed-poor adherence trajectory and $3,907 (95% CI: $1,658 to $6,156) higher in the early-poor adherence trajectory compared with the high adherence trajectory. IPF-related hospitalizations and medical care spending were higher among those in the high-then-poor adherence trajectory compared with those in the high adherence trajectory.

CONCLUSIONS

Poor adherence to nintedanib was associated with all-cause hospitalizations and medical costs. Therefore, improved adherence programs, such as support programs, can be implemented to reduce economic burden.

Living with Multimorbidity through Time: A Meta-Synthesis of Qualitative Longitudinal Evidence

The growing prevalence of multimorbidity places a strain on primary healthcare globally. The current study's aim was to identify, appraise, and synthesize published qualitative longitudinal research on individuals' experiences concerning living with multimorbidity through time. The authors searched two electronic databases, MEDLINE and CINAHL, and performed an additional literature search in Google Scholar. A thematic synthesis approach was used to analyze the qualitative data across the studies. A total of 10 reports that met the inclusion and exclusion criteria were included in the synthesis. Five descriptive themes emerged from the analysis of the living experiences of individuals with multimorbidity: (1) perceiving multimorbidity, (2) managing chronic conditions, (3) emotional struggles in everyday life with multimorbidity, (4) interactions with the healthcare system and healthcare professionals, and (5) family support. This meta-synthesis provides insights into the diverse perceptions of multimorbidity and how individuals cope with their chronic conditions in their daily lives. The findings highlight the importance of establishing effective patient-centered care that acknowledges and supports the multifaceted needs of this population. It is also recommended to involve a psychological component in the care of individuals with multimorbidity, as part of a collaborative and interprofessional approach.

Factors associated with medication adherence among older adults with multimorbidity: A culture perspective

Multimorbidity is a healthcare concern. To manage diseases, older adults with multimorbidity are expected to practice health behaviors, particularly medication adherence. Studies have examined adherence issues in older patients with multiple diseases, but it remains unclear which factors affect medication adherence. Therefore, this study aimed to identify the factors affecting medication adherence among older adults with multimorbidity. The participants were recruited from the outpatient departments of two hospitals in the Republic of Korea using convenience sampling. Data were collected using structured questionnaires and analyzed using multiple regression analysis. The results showed that those with a lower education level, no side effects, better health literacy, higher medication self-efficacy, and more social support exhibited better medication adherence. In addition, beliefs about medication were not related to medication adherence. These results suggest that providing individualized education, strengthening social support, and decreasing harmful side effects can improve medication adherence.

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.

Association between medication literacy and medication adherence and the mediating effect of self-efficacy in older people with multimorbidity

BACKGROUND

Multimorbidity has a significant impact on public health and primary care. Medication adherence is recognized as the most effective measure for managing and preventing multimorbidity. Studies have shown that medication literacy has a positive effect on medication adherence in patients with multimorbidity. However, limited knowledge exists regarding the underlying mechanisms of this relationship in older adults with multimorbidity. Therefore, the aim of this study was to investigate the mediating role of self-efficacy in the association between medication literacy and medication adherence in this population.

METHODS

This study employed a cross-sectional design and convenience sampling method to survey older patients with multimorbidity in six communities in Zhengzhou, China, from July 12, 2021, to December 15, 2021. Participants were assessed using a demographic questionnaire, the Chinese Version of the Medication Literacy Scale (C-MLS), the Self-Efficacy for Appropriate Medication Use Scale (SEAMS), and the Chinese Version of the Morisky Medication Adherence Scale-8 (C-MMAS-8). Data were analyzed using descriptive statistics, t-tests, one-way analysis of variance, Pearson correlation analysis, and mediation analysis.

RESULTS

A total of 350 elderly patients met the inclusion criteria, and 328 valid questionnaires were collected. The mean age of the participants was 74.90 ± 7.37 years, with a slightly higher proportion of males (55.8%) than females (44.2%). The mean score for medication adherence was 4.85 ± 1.57, indicating poor medication adherence among the participants. Medication adherence scores varied significantly among participants of different ages, education levels, employment statuses and kinds of medication (p < 0.01). Scores for medication literacy and self-efficacy showed a significant positive correlation with medication adherence scores (all p < 0.001). The standardized coefficient for the total effect and direct effect of medication literacy on medication adherence was 0.268 (95% CI: 0.201, 0.335) and 0.187 (95% CI: 0.123, 0.252), respectively. After introducing self-efficacy into the model, the standardized coefficient for the indirect effect was 0.081 (95% CI: 0.049, 0.120), indicating that self-efficacy partially mediated the relationship between medication literacy and medication adherence, accounting for 30.22% of the total effect.

CONCLUSION

This study might suggest that medication literacy indirectly affected medication adherence in older people with multimorbidity through self-efficacy. Health care providers should be aware of the importance of improving medication literacy and implement strategies aimed at increasing self-efficacy to achieve the goal of improving medication adherence in older adults with multimorbidity.

Trajectories of multiple long-term conditions and mortality in older adults: A retrospective cohort study using English Longitudinal Study of Ageing (ELSA)

Objectives

To classify older adults with MLTC into clusters based on accumulating conditions as trajectories over time, characterise clusters and quantify associations between derived clusters and all-cause mortality.

Design

We conducted a retrospective cohort study using the English Longitudinal Study of Ageing (ELSA) over nine years (n=15,091 aged 50 years and older). Group-based trajectory modelling was used to classify people into MLTC clusters based on accumulating conditions over time. Derived clusters were used to quantify the associations between MLTC trajectory memberships, sociodemographic characteristics, and all-cause mortality.

Results

Five distinct clusters of MLTC trajectories were identified and characterised as: "no-LTC" (18.57%), "single-LTC" (31.21%), "evolving MLTC" (25.82%), "moderate MLTC" (17.12%), and "high MLTC" (7.27%). Increasing age was consistently associated with an increased number of MLTC. Female sex (aOR = 1.13; 95%CI 1.01 to 1.27) and ethnic minority (aOR = 2.04; 95%CI 1.40 to 3.00) were associated with the "moderate MLTC" and "high MLTC" clusters, respectively. Higher education and paid employment were associated with a lower likelihood of progression over time towards an increased number of MLTC. All the clusters had higher all-cause mortality than the "no-LTC" cluster.

Conclusions

The development of MLTC and the increase in the number of conditions over time follow distinct trajectories. These are determined by non-modifiable (age, sex, ethnicity) and modifiable factors (education and employment). Stratifying risk through clustering will enable practitioners to identify older adults with a higher likelihood of worsening MLTC over time to tailor effective interventions.

Patterns of statin adherence in primary cardiovascular disease prevention during the pandemic

Background: Study of medication adherence patterns can help identify patients who would benefit from effective interventions to improve adherence. Objectives: To identify and compare groups of statin users based on their adherence patterns before and during the COVID-19 pandemic, to characterize the profile of users in each group, and to analyze predictors of distinct adherence patterns. Methods: Participants of the CARhES (CArdiovascular Risk factors for HEalth Services research) cohort, comprising individuals aged >16 years, residing in Aragón (Spain), with hypertension, diabetes mellitus and/or dyslipidemia, took part in this observational longitudinal study. Individuals who began statin therapy during January-June 2019 were selected and followed up until June 2021. Those with a cardiovascular event before or during follow-up were excluded. Data were obtained from healthcare system data sources. Statin treatment adherence during the implementation phase was estimated bimonthly using the Continuous Medication Availability (CMA9) function in the AdhereR package. Group-based trajectory models were developed to group statin users according to their adherence pattern during July 2019-June 2021. Group characteristics were compared and predictors of each adherence pattern were analyzed using multinomial logistic regression. Results: Of 15,332 new statin users, 30.8% had a mean CMA9 ≥80% for the entire study period. Four distinct adherence patterns were identified: high adherence (37.2% of the study population); poor adherence (35.6%); occasional use (14.9%); and gradual decline (12.3%). The latter two groups included users who showed a change in adherence (increase or decrease) during the pandemic emergence. Users with suboptimal adherence were likely to be younger, not pensioners, not institutionalized, with low morbidity burden and a low number of comorbidities. Female sex and switching between statins of different intensity increased the likelihood of belonging to the occasional use group, in which improved adherence coincided with the pandemic. Conclusion: We identified four distinct adherence patterns in a population of new statin users; two of them modified their adherence during the pandemic. Characterization of these groups could enable more effective distribution of resources in future similar crisis and the routine implementation of patient-centered interventions to improve medication adherence.

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.

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 (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.