Is there a nonadherent subtype of hypertensive patient? A latent class analysis approach

Trivedi, Ranak; Ayotte, Brian J.; Thorpe, Carolyn T.; Edelman, David; Bosworth, Hayden B.

doi:10.2147/ppa.s11335

Cited by 7 publications

(8 citation statements)

References 39 publications

(31 reference statements)

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“…Latent class analysis has been applied to other health-related observational studies and clinical data to identify subgroups based on behavioral and psychosocial characteristics [28–31]. However, to our knowledge, this is the first application of RMLCA to use randomized clinical trial data to identify responders and non-responders to a behavioral lifestyle intervention.…”

Section: Discussionmentioning

confidence: 99%

Application of Latent Class Analysis to Identify Behavioral Patterns of Response to Behavioral Lifestyle Interventions in Overweight and Obese Adults

Fitzpatrick

Coughlin²,

Appel

et al. 2014

Int.J. Behav. Med.

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Background Examining responders and non-responders to behavioral lifestyle interventions among overweight/obese adults with additional comorbidities may aid in refining and tailoring obesity treatment. Purpose The purpose of this study is to demonstrate the use of latent class analysis to identify patterns of response to behavioral lifestyle interventions based on adherence to diet and exercise recommendations. Method Repeated measures latent class analysis was applied to two clinical trial datasets, combination of two active interventions in the PREMIER Trial (n=501) and phase 1 of the Weight Loss Maintenance Trial (WLM; n=1685), to identify patterns of response to behavioral lifestyle interventions. Treatment response was based on adherence to daily recommendations for fruit/vegetable, fat, saturated fat, sodium, and exercise at baseline and 6 months. Results In PREMIER, three distinct latent classes emerged: responders (45.9 %), non-responders (23.6 %), and early adherers (30.5 %). Responders and Early Adherers had greater weight loss at 6 and 18 months and were more likely to meet behavioral recommendations at 18 months than Non-responders. For WLM, there were four latent classes: partial responders (16 %), non-responders (40 %), early adherers (2 %), and fruit/veggie only responders (41 %). Non-responders in WLM had significantly less weight loss at 6 months compared to that of the other three latent classes. Conclusion Latent class analysis is a useful method to apply to clinical trial data to identify distinct patterns of response to behavioral interventions. Overweight/ obese participants who respond to behavioral lifestyle treatment (i.e., meet behavioral recommendations) have significantly greater weight loss than that of participants who do not make behavioral changes.

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Section: Discussionmentioning

confidence: 99%

Application of Latent Class Analysis to Identify Behavioral Patterns of Response to Behavioral Lifestyle Interventions in Overweight and Obese Adults

Fitzpatrick

Coughlin²,

Appel

et al. 2014

Int.J. Behav. Med.

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“…A strength of the LPA is that it provides insight into the presence of relevant subpopulations of patients as was previously examined for hypertensive patients with regard to adherence to medications, diet, exercise, smoking, and blood pressure [ 4 ]. LPA provides a more formal approach to assess the most likely number of classes compared to methods such as cluster-analysis or classifying individuals by face-validity, because it is governed by “goodness-of-fit” statistics.…”

Section: Discussionmentioning

confidence: 99%

“…These provide an empirically based approach to assess the most likely number of patient classes with different adherence profiles. LPA or related approaches have been used to study amongst others adherence subtypes in hypertensive patients [ 4 ], depression subtypes among patients treated with citalopram [ 5 ], examination of the nosological status of generalized anxiety disorders versus dysthymic disorder [ 6 ], and course trajectories of unipolar depressive disorders [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Distinct Profiles on Subjective and Objective Adherence Measures in Patients Prescribed Antidepressants

Wouters

Rhebergen

Vervloet

et al. 2019

Drugs

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Objective A recurrent observation is that associations between self-reported and objective medication adherence measures are often weak to moderate. Our aim was therefore to identify patients with different profiles on self-reported and objective adherence measures. Study Design and Setting This was an observational study of 221 community pharmacy patients who were dispensed antidepressants. Adherence profiles were estimated with Latent Profile Analysis (LPA) using data on self-reported adherence (Medication Adherence Rating Scale) complemented with data on medication beliefs (perceived necessity and concerns measured with the Beliefs about Medicines Questionnaire) and data from objective adherence measures (electronic monitoring of medication taking and the Medication Possession Ratio calculated from pharmacy dispensing data). Results ‘Goodness-of-fit’ statistics indicated the presence of three classes: “concordantly high adherent” (83%, high adherence on all measures), “concordantly suboptimal adherent” (11%, low adherence on all measures), and “discordant” (6%, high self-reported adherence but lower adherence on objective measures). Conclusion Most patients had concordant outcomes on self-reported and objective measures of adherence. A small discordant class had high self-reported but low objective adherence. LPA will enable sensitivity analyses in future studies, for example excluding patients from the discordant class.

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“…Therefore, a combination of criteria was used, including: interpretability, clinical relevance, and the following statistical tests of model fit: log-likelihood, Bayesian Information Criterion (BIC), entropy, and boot-strapped likelihood ratio tests (LRT). 38–40 In our final model, we assigned Veterans to the class that most closely matched their pattern of diabetes healthcare utilization (i.e., assigned to the class with the highest posterior class membership probability).…”

Section: Methodsmentioning

confidence: 99%

VA and Medicare Utilization Among Dually Enrolled Veterans with Type 2 Diabetes: A Latent Class Analysis

et al. 2016

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Background Many Veterans treated within the VA Healthcare System (VA) are also enrolled in fee-for-service (FFS) Medicare and receive treatment outside the VA. Prior research has not accounted for the multiple ways that Veterans receive services across healthcare systems. Objective To establish a typology of VA and Medicare utilization among dually-enrolled Veterans with type 2 diabetes. Design Retrospective cohort. Participants 316,775 community-dwelling Veterans age ≥65 with type 2 diabetes who were dually-enrolled in the VA and FFS Medicare in 2008–2009. Methods Using latent class analysis, we identified classes of Veterans based upon their probability of using VA and Medicare diabetes care services, including patient visits, laboratory tests, glucose test strips, and medications. We compared amount of healthcare use between classes and identified factors associated with class membership using multinomial regression. Key Results We identified 4 distinct latent classes: class 1 (53.9%) had high probabilities of VA use and low probabilities of Medicare use; classes 2 (17.2%), 3 (21.8%), and 4 (7.0%) had high probabilities of VA and Medicare use, but differed in their Medicare services used. For example, Veterans in class 3 received test strips exclusively through Medicare, while Veterans in class 4 were reliant on Medicare for medications. Living ≥40 miles from a VA predicted membership in classes 3 (OR 1.1, CI 1.06–1.15) and 4 (OR 1.11, CI 1.04–1.18), while Medicaid eligibility predicted membership in class 4 (OR 4.30, CI 4.10–4.51). Conclusions Veterans with diabetes can be grouped into 4 distinct classes of dual health system use, representing a novel way to characterize how patients use multiple services across healthcare systems. This classification has applications for identifying patients facing differential risk from care fragmentation.

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Is there a nonadherent subtype of hypertensive patient? A latent class analysis approach

Cited by 7 publications

References 39 publications

Application of Latent Class Analysis to Identify Behavioral Patterns of Response to Behavioral Lifestyle Interventions in Overweight and Obese Adults

Application of Latent Class Analysis to Identify Behavioral Patterns of Response to Behavioral Lifestyle Interventions in Overweight and Obese Adults

Distinct Profiles on Subjective and Objective Adherence Measures in Patients Prescribed Antidepressants

VA and Medicare Utilization Among Dually Enrolled Veterans with Type 2 Diabetes: A Latent Class Analysis

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