Group-based trajectory models to identify sociodemographic and clinical predictors of adherence patterns to statin therapy among older adults

Aisha Vadhariya, Marc Labaron Fleming, Michael L. Johnson, E. James Essien, Omar Serna, Tara Esse, Jeannie Choi, Susan H. Boklage, Susan M. Abughosh

Research output: Contribution to journalArticleResearchpeer-review

Abstract

BACKGROUND: The benefits of statins in the prevention of primary and secondary atherosclerotic cardiovascular (CV) disease events have been well documented. Suboptimal adherence is a persistent problem associated with increased CV events and increased healthcare utilization. Proportion of days covered (PDC) is widely used to measure medication adherence, and provides a single value that does not adequately depict different adherence behavior patterns. Group-based trajectory modeling has been used to identify adherence patterns (or trajectories) over time. The identification of characteristics unique to each pattern can help in the early identification of patients who are likely to be poor adherents and can inform the development of interventions. OBJECTIVES: To identify distinct trajectories of statin adherence in patients enrolled in a Medicare Advantage plan and the sociodemographic and clinical predictors associated with each trajectory. METHODS: Patients were included in the study if they were continuously enrolled in a Medicare Advantage plan between 2013 and 2016 and had a statin prescription between January 2015 and June 2015. We observed each patient for 360 days and computed the monthly PDC. The monthly PDC was incorporated into a group-based trajectory model to provide distinct patterns of adherence. Using group-based trajectory modeling, the patients were categorized into groups based on their adherence patterns. Multinomial logistic regression was performed to identify the sociodemographic and clinical factors associated with each group. RESULTS: A total of 7850 patients were included in the analysis and were categorized into 4 distinct groups based on statin adherence—rapid discontinuation (7.8%), gradual decline (16.8%), gaps in adherence (17.2%), and high or nearly perfect adherence (58.2%). Significant predictors of being placed into one or more of the low-adherence trajectories compared with the high-adherence trajectory included sex, age, low-income subsidy, language, Charlson Comorbidity Index score, statin intensity, and 90-day refills. CONCLUSIONS: The predictors identified in this study provide valuable insight into patient characteristics that increase the risk for statin nonadherence, which has the potential to inform targeted interventions. Identifying patient trajectories can inform the future development of protocols to individualize appropriate interventions for these patients.

Original languageEnglish
Pages (from-to)202-211
Number of pages10
JournalAmerican Health and Drug Benefits
Volume12
Issue number4
StatePublished - 1 Jun 2019

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Hydroxymethylglutaryl-CoA Reductase Inhibitors
Medicare Part C
Therapeutics
Medication Adherence
Therapy
Trajectory
Adherence
Predictors
Primary Prevention
Patient Compliance
Secondary Prevention
Prescriptions
Comorbidity
Cardiovascular Diseases
Language
Logistic Models
Delivery of Health Care

Keywords

  • Adherence
  • Cardiovascular disease
  • Elderly patients
  • Nonadherence
  • Predictors of statin adherence
  • Statin therapy
  • Trajectory modeling

Cite this

Vadhariya, Aisha ; Fleming, Marc Labaron ; Johnson, Michael L. ; Essien, E. James ; Serna, Omar ; Esse, Tara ; Choi, Jeannie ; Boklage, Susan H. ; Abughosh, Susan M. / Group-based trajectory models to identify sociodemographic and clinical predictors of adherence patterns to statin therapy among older adults. In: American Health and Drug Benefits. 2019 ; Vol. 12, No. 4. pp. 202-211.
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title = "Group-based trajectory models to identify sociodemographic and clinical predictors of adherence patterns to statin therapy among older adults",
abstract = "BACKGROUND: The benefits of statins in the prevention of primary and secondary atherosclerotic cardiovascular (CV) disease events have been well documented. Suboptimal adherence is a persistent problem associated with increased CV events and increased healthcare utilization. Proportion of days covered (PDC) is widely used to measure medication adherence, and provides a single value that does not adequately depict different adherence behavior patterns. Group-based trajectory modeling has been used to identify adherence patterns (or trajectories) over time. The identification of characteristics unique to each pattern can help in the early identification of patients who are likely to be poor adherents and can inform the development of interventions. OBJECTIVES: To identify distinct trajectories of statin adherence in patients enrolled in a Medicare Advantage plan and the sociodemographic and clinical predictors associated with each trajectory. METHODS: Patients were included in the study if they were continuously enrolled in a Medicare Advantage plan between 2013 and 2016 and had a statin prescription between January 2015 and June 2015. We observed each patient for 360 days and computed the monthly PDC. The monthly PDC was incorporated into a group-based trajectory model to provide distinct patterns of adherence. Using group-based trajectory modeling, the patients were categorized into groups based on their adherence patterns. Multinomial logistic regression was performed to identify the sociodemographic and clinical factors associated with each group. RESULTS: A total of 7850 patients were included in the analysis and were categorized into 4 distinct groups based on statin adherence—rapid discontinuation (7.8{\%}), gradual decline (16.8{\%}), gaps in adherence (17.2{\%}), and high or nearly perfect adherence (58.2{\%}). Significant predictors of being placed into one or more of the low-adherence trajectories compared with the high-adherence trajectory included sex, age, low-income subsidy, language, Charlson Comorbidity Index score, statin intensity, and 90-day refills. CONCLUSIONS: The predictors identified in this study provide valuable insight into patient characteristics that increase the risk for statin nonadherence, which has the potential to inform targeted interventions. Identifying patient trajectories can inform the future development of protocols to individualize appropriate interventions for these patients.",
keywords = "Adherence, Cardiovascular disease, Elderly patients, Nonadherence, Predictors of statin adherence, Statin therapy, Trajectory modeling",
author = "Aisha Vadhariya and Fleming, {Marc Labaron} and Johnson, {Michael L.} and Essien, {E. James} and Omar Serna and Tara Esse and Jeannie Choi and Boklage, {Susan H.} and Abughosh, {Susan M.}",
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Vadhariya, A, Fleming, ML, Johnson, ML, Essien, EJ, Serna, O, Esse, T, Choi, J, Boklage, SH & Abughosh, SM 2019, 'Group-based trajectory models to identify sociodemographic and clinical predictors of adherence patterns to statin therapy among older adults', American Health and Drug Benefits, vol. 12, no. 4, pp. 202-211.

Group-based trajectory models to identify sociodemographic and clinical predictors of adherence patterns to statin therapy among older adults. / Vadhariya, Aisha; Fleming, Marc Labaron; Johnson, Michael L.; Essien, E. James; Serna, Omar; Esse, Tara; Choi, Jeannie; Boklage, Susan H.; Abughosh, Susan M.

In: American Health and Drug Benefits, Vol. 12, No. 4, 01.06.2019, p. 202-211.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Group-based trajectory models to identify sociodemographic and clinical predictors of adherence patterns to statin therapy among older adults

AU - Vadhariya, Aisha

AU - Fleming, Marc Labaron

AU - Johnson, Michael L.

AU - Essien, E. James

AU - Serna, Omar

AU - Esse, Tara

AU - Choi, Jeannie

AU - Boklage, Susan H.

AU - Abughosh, Susan M.

PY - 2019/6/1

Y1 - 2019/6/1

N2 - BACKGROUND: The benefits of statins in the prevention of primary and secondary atherosclerotic cardiovascular (CV) disease events have been well documented. Suboptimal adherence is a persistent problem associated with increased CV events and increased healthcare utilization. Proportion of days covered (PDC) is widely used to measure medication adherence, and provides a single value that does not adequately depict different adherence behavior patterns. Group-based trajectory modeling has been used to identify adherence patterns (or trajectories) over time. The identification of characteristics unique to each pattern can help in the early identification of patients who are likely to be poor adherents and can inform the development of interventions. OBJECTIVES: To identify distinct trajectories of statin adherence in patients enrolled in a Medicare Advantage plan and the sociodemographic and clinical predictors associated with each trajectory. METHODS: Patients were included in the study if they were continuously enrolled in a Medicare Advantage plan between 2013 and 2016 and had a statin prescription between January 2015 and June 2015. We observed each patient for 360 days and computed the monthly PDC. The monthly PDC was incorporated into a group-based trajectory model to provide distinct patterns of adherence. Using group-based trajectory modeling, the patients were categorized into groups based on their adherence patterns. Multinomial logistic regression was performed to identify the sociodemographic and clinical factors associated with each group. RESULTS: A total of 7850 patients were included in the analysis and were categorized into 4 distinct groups based on statin adherence—rapid discontinuation (7.8%), gradual decline (16.8%), gaps in adherence (17.2%), and high or nearly perfect adherence (58.2%). Significant predictors of being placed into one or more of the low-adherence trajectories compared with the high-adherence trajectory included sex, age, low-income subsidy, language, Charlson Comorbidity Index score, statin intensity, and 90-day refills. CONCLUSIONS: The predictors identified in this study provide valuable insight into patient characteristics that increase the risk for statin nonadherence, which has the potential to inform targeted interventions. Identifying patient trajectories can inform the future development of protocols to individualize appropriate interventions for these patients.

AB - BACKGROUND: The benefits of statins in the prevention of primary and secondary atherosclerotic cardiovascular (CV) disease events have been well documented. Suboptimal adherence is a persistent problem associated with increased CV events and increased healthcare utilization. Proportion of days covered (PDC) is widely used to measure medication adherence, and provides a single value that does not adequately depict different adherence behavior patterns. Group-based trajectory modeling has been used to identify adherence patterns (or trajectories) over time. The identification of characteristics unique to each pattern can help in the early identification of patients who are likely to be poor adherents and can inform the development of interventions. OBJECTIVES: To identify distinct trajectories of statin adherence in patients enrolled in a Medicare Advantage plan and the sociodemographic and clinical predictors associated with each trajectory. METHODS: Patients were included in the study if they were continuously enrolled in a Medicare Advantage plan between 2013 and 2016 and had a statin prescription between January 2015 and June 2015. We observed each patient for 360 days and computed the monthly PDC. The monthly PDC was incorporated into a group-based trajectory model to provide distinct patterns of adherence. Using group-based trajectory modeling, the patients were categorized into groups based on their adherence patterns. Multinomial logistic regression was performed to identify the sociodemographic and clinical factors associated with each group. RESULTS: A total of 7850 patients were included in the analysis and were categorized into 4 distinct groups based on statin adherence—rapid discontinuation (7.8%), gradual decline (16.8%), gaps in adherence (17.2%), and high or nearly perfect adherence (58.2%). Significant predictors of being placed into one or more of the low-adherence trajectories compared with the high-adherence trajectory included sex, age, low-income subsidy, language, Charlson Comorbidity Index score, statin intensity, and 90-day refills. CONCLUSIONS: The predictors identified in this study provide valuable insight into patient characteristics that increase the risk for statin nonadherence, which has the potential to inform targeted interventions. Identifying patient trajectories can inform the future development of protocols to individualize appropriate interventions for these patients.

KW - Adherence

KW - Cardiovascular disease

KW - Elderly patients

KW - Nonadherence

KW - Predictors of statin adherence

KW - Statin therapy

KW - Trajectory modeling

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VL - 12

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JO - American Health and Drug Benefits

JF - American Health and Drug Benefits

SN - 1942-2962

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