A Quantitative Framework for Medication Non-Adherence: Integrating Patient Treatment Expectations and Preferences

Charles Muiruri; Eline M van den Broek-Altenburg; Hayden B Bosworth; Crystal W Cené; Juan Marcos Gonzalez

doi:10.2147/PPA.S434640

A Quantitative Framework for Medication Non-Adherence: Integrating Patient Treatment Expectations and Preferences

Patient Prefer Adherence. 2023 Dec 4:17:3135-3145. doi: 10.2147/PPA.S434640. eCollection 2023.

Authors

Charles Muiruri^{1

2}, Eline M van den Broek-Altenburg³, Hayden B Bosworth^{1

4}, Crystal W Cené⁵, Juan Marcos Gonzalez^{1

6}

Affiliations

¹ Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA.
² Duke Global Health Institute, Duke University, Durham, NC, USA.
³ University of Vermont Larner College of Medicine, Burlington, VT, USA.
⁴ Durham Veterans Affairs Medical Center, Durham, NC, USA.
⁵ University of California San Diego Health, San Diego, CA, USA.
⁶ Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA.

Abstract

Introduction: Medication non-adherence remains a significant challenge in healthcare, impacting treatment outcomes and the overall effectiveness of medical interventions. This article introduces a novel approach to understanding and predicting medication non-adherence by integrating patient beliefs, efficacy expectations, and perceived costs. Existing theoretical models often fall short in quantifying the impact of barrier removal on medication adherence and struggle to address cases where patients consciously choose not to follow prescribed medication regimens. In response to these limitations, this study presents an empirical framework that seeks to provide a quantifiable model for both individual and population-level prediction of non-adherence under different scenarios.

Methods: We present an empirical framework that includes a health production function, specifically applied to antihypertensive medications nonadherence. Data collection involved a pilot study that utilized a double-bound contingent-belief (DBCB) questionnaire. Through this questionnaire, participants could express how efficacy and side effects were affected by controlled levels of non-adherence, allowing for the estimation of sensitivity in health outcomes and costs.

Results: Parameters derived from the DBCB questionnaire revealed that on average, patients with hypertension anticipated that treatment efficacy was less sensitive to non-adherence than side effects. Our derived health production function suggests that patients may strategically manage adherence to minimize side effects, without compromising efficacy. Patients' inclination to manage medication intake is closely linked to the relative importance they assign to treatment efficacy and side effects. Model outcomes indicate that patients opt for full adherence when efficacy outweighs side effects. Our findings also indicated an association between income and patient expectations regarding the health of antihypertensive medications.

Conclusion: Our framework represents a pioneering effort to quantitatively link non-adherence to patient preferences. Preliminary results from our pilot study of patients with hypertension suggest that the framework offers a viable alternative for evaluating the potential impact of interventions on treatment adherence.

Keywords: behavioral model; health production; medication adherence; patient preferences; quantitative framework; side effects; treatment efficacy.

Abstract

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