Antihypertensive Adherence Trajectories Among Older Adults in the First Year After Initiation of Therapy

Hargrove, Jennifer; Pate, Virginia; Casteel, Carri; Golightly, Yvonne M.; Loehr, Laura R.; Stürmer, Til

doi:10.1093/ajh/hpx086

Cited by 24 publications

(62 citation statements)

References 38 publications

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“…Spaghetti plots have been mentioned in a previous GBTM study, and we recommend all GBTM studies modeling medication adherence as a continuous variable report these plots in the manuscript or appendix. The moderately adherent group's spaghetti plot—supported by this group's distribution of maximum PPs—suggested that these patients did not have a homogeneous pattern of adherence.…”

Section: Discussionmentioning

confidence: 99%

“…Finally, we evaluated spaghetti plots for 200 randomly selected patients within each group . A single spaghetti plot “strand” represents the longitudinal adherence for one patient; spaghetti plots help visualize adherence patterns of individual patients within each group to assess how well those individual patterns match the group's average adherence.…”

Section: Methodsmentioning

confidence: 99%

“…6,8,14 Finally, we evaluated spaghetti plots for 200 randomly selected patients within each group. 7 A single spaghetti plot "strand" represents the longitudinal adherence for one patient; spaghetti plots help visualize adherence patterns of individual patients within each group to assess how well those individual patterns match the group's average adherence. If adherence patterns of most patients are explained well by the group's average, most "strands" will be concentrated close to group-average regression lines.…”

Section: Gbtm Model-building Processmentioning

confidence: 99%

“…Finally, while individual patients' adherence patterns are not concentrated close to the progressively nonadherent group average ( Figure 2D), a consistent statin discontinuation pattern was observed. Spaghetti plots have been mentioned in a previous GBTM study, 7 and we recommend all GBTM studies modeling medication adherence as a continuous variable report these plots in the manuscript or appendix. The moderately adherent group's spaghetti plot-supported by this group's distribution of maximum PPs-suggested that these patients did not have a homogeneous pattern of adherence.…”

Section: Assessment Of Model Fitnessmentioning

confidence: 99%

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

Hickson

Annis

Killeya‐Jones

et al. 2019

Pharmacoepidemiology and Drug

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Gbtm Model-building Processmentioning

confidence: 99%

Section: Assessment Of Model Fitnessmentioning

confidence: 99%

See 2 more Smart Citations

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

Hickson

Annis

Killeya‐Jones

et al. 2019

Pharmacoepidemiology and Drug

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“…Various factors influence acceptance and persistence with life-long pharmacotherapy, and its patterns are heterogenous. [20] Detection-to-initiation time interval for anti-hypertensive drug therapy is longer in younger, as compared to older hypertensive individuals. [21] Poor economic status, and poor disease control are strong predictors of discontinuation of pharmacotherapy.…”

Section: Initiation Of Therapy For Hypertension and Diabetes Is Necesmentioning

confidence: 99%

Linkage to primary care public health facilities for cardiovascular disease prevention : A community based cohort study from urban slums in India

Pakhare

Joshi

Anwar

et al. 2020

Preprint

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We designed and conducted a community based longitudinal study in 16 urban slum clusters in context of a community health worker (CHW) led screening and preventive therapy initiation initiative for CVD prevention. Linkage to public health systems primary care facilities was a key outcome indicator for this initiative. In this paper, we have investigated predictors and barriers to non-linkage. CHWs screened all adults aged 30 years through for hypertension as well as diabetes. Referrals were advised and facilitated to nearby Urban Primary Health Centre (PHC) clinic for either treatment initiation or continuation or optimization. CHWs screened a total of 6174 individuals, and physicians identified 1449 participants (23.46%; 95% CI 22.42-24.54) as high-risk who required linkage to public-health facilities for pharmacotherapy . Out of these, 943(65%) attended health facilities with 801(55.2 %) being adherent to pharmacotherapy. Those who were not linked were young men belonging to low socio-economic position, living farther from UPHC, engaged late by CHW and identified to be in denial mode and reported lack of family support. This study highlights importance of early engagement through CHWs after positive screening, necessity to address denial of newly diagnosed and increase male participation in order to reduce detection to treatment initiation gap. Keywords- hypertension, diabetes, adherence, community health worker, cardio vascular diseases, population-based screening

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Effect of Electronic Health Record–Based Medication Support and Nurse-Led Medication Therapy Management on Hypertension and Medication Self-management

et al. 2018

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IMPORTANCE Complex medication regimens pose self-management challenges, particularly among populations with low levels of health literacy. OBJECTIVE To test medication management tools delivered through a commercial electronic health record (EHR) with and without a nurse-led education intervention. DESIGN, SETTING, AND PARTICIPANTS This 3-group cluster randomized clinical trial was performed in community health centers in Chicago, Illinois. Participants included 794 patients with hypertension who self-reported using 3 or more medications concurrently (for any purpose). Data were collected from April 30, 2012, through February 29, 2016, and analyzed by intention to treat. INTERVENTIONS Clinics were randomly assigned to to groups: electronic health record-based medication management tools (medication review sheets at visit check-in, lay medication information sheets printed after visits; EHR-alone group), EHR-based tools plus nurse-led medication management support (EHR plus education group), or usual care. MAIN OUTCOMES AND MEASURES Outcomes at 12 months included systolic blood pressure (primary outcome), medication reconciliation, knowledge of drug indications, understanding of medication instructions and dosing, and self-reported medication adherence. Medication outcomes were assessed for all hypertension prescriptions, all prescriptions to treat chronic disease, and all medications. RESULTS Among the 794 participants (68.6% women; mean [SD] age, 52.7 [9.6] years), systolic blood pressure at 12 months was greater in the EHR-alone group compared with the usual care group by 3.6 mm Hg (95% CI, 0.3 to 6.9 mm Hg). Systolic blood pressure in the EHR plus education group was not significantly lower compared with the usual care group (difference, −2.0 mm Hg; 95% CI, −5.2 to 1.3 mm Hg) but was lower compared with the EHR-alone group (−5.6 mm Hg; 95% CI, −8.8 to −2.4 mm Hg). At 12 months, hypertension medication reconciliation was improved in the EHR-alone group (adjusted odds ratio [OR], 1.8; 95% CI, 1.1 to 2.9) and the EHR plus education group (adjusted odds ratio [OR], 2.0; 95% CI, 1.3 to 3.3) compared with usual care. Understanding of medication instructions and dosing was greater in the EHR plus education group than the usual care group for hypertension medications (OR, 2.3; 95% CI, 1.1 to 4.8) and all medications combined (OR, 1.7; 95% CI, 1.0 to 2.8). Compared with usual care, the EHR tools alone and EHR plus education interventions did not improve hypertension medication adherence (OR, 0.9; 95% CI, 0.6-1.4 for both) or knowledge of chronic drug indications (OR for EHR tools alone, 1.0 [95% CI, 0.6 to 1.5] and OR for EHR plus education, 1.1 [95% CI, 0.7-1.7]). CONCLUSIONS AND RELEVANCE The study found that EHR tools in isolation improved medication reconciliation but worsened blood pressure. Combining these tools with nurse-led support suggested improved understanding of medication instructions and dosing but did not lower blood pressure compared with usual care. TRIAL REGISTRATION ClinicalTrials.gov identifier: N...

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Antihypertensive Adherence Trajectories Among Older Adults in the First Year After Initiation of Therapy

Abstract: There is substantial variation in antihypertensive adherence among older adults. Certain patient characteristics are likely determinants of antihypertensive adherence trajectories.

Cited by 24 publications

References 38 publications

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

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

Linkage to primary care public health facilities for cardiovascular disease prevention : A community based cohort study from urban slums in India

Effect of Electronic Health Record–Based Medication Support and Nurse-Led Medication Therapy Management on Hypertension and Medication Self-management

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