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Rivara AC, Galárraga O, Selu M, Arorae M, Wang R, Faasalele-Savusa K, Rosen R, Hawley NL, Viali S. Identifying patient preferences for diabetes care: A protocol for implementing a discrete choice experiment in Samoa. PLoS One 2023; 18:e0295845. [PMID: 38134044 PMCID: PMC10745180 DOI: 10.1371/journal.pone.0295845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 11/27/2023] [Indexed: 12/24/2023] Open
Abstract
In Samoa, adult Type 2 diabetes prevalence has increased within the past 30 years. Patient preferences for care are factors known to influence treatment adherence and are associated with reduced disease progression and severity. However, patient preferences for diabetes care, generally, are understudied, and other patient-centered factors such as willingness-to-pay (WTP) for diabetes treatment have never been explored in this setting. Discrete Choice Experiments (DCE) are useful tools to elicit preferences and WTP for healthcare. DCEs present patients with hypothetical scenarios composed of a series of multi-alternative choice profiles made up of attributes and levels. Patients choose a profile based on which attributes and levels may be preferable for them, thereby quantifying and identifying locally relevant patient-centered preferences. This paper presents the protocol for the design, piloting, and implementation of a DCE identifying patient preferences for diabetes care, in Samoa. Using an exploratory sequential mixed methods design, formative data from a literature review and semi-structured interviews with n = 20 Samoan adults living with Type 2 diabetes was used to design a Best-Best DCE instrument. Experimental design procedures were used to reduce the number of choice-sets and balance the instrument. Following pilot testing, the DCE is being administered to n = 450 Samoan adults living with diabetes, along with associated questionnaires, and anthropometrics. Subsequently, we will also be assessing longitudinally how preferences for care change over time. Data will be analyzed using progressive mixed Rank Order Logit models. The results will identify which diabetes care attributes are important to patients (p < 0.05), examine associations between participant characteristics and preference, illuminate the trade-offs participants are willing to make, and the probability of uptake, and WTP for specific attributes and levels. The results from this study will provide integral data useful for designing and adapting efficacious diabetes intervention and treatment approaches in this setting.
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Affiliation(s)
- Anna C. Rivara
- Department of Epidemiology (Chronic Diseases), Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Omar Galárraga
- Department of Health Services Policy and Practice, and International Health Institute, School of Public Health, Brown University, Providence, Rhode Island, United States of America
| | - Melania Selu
- Obesity Lifestyle and Genetic Adaptations (OLaGA) Research Center, Apia, Samoa
| | - Maria Arorae
- Obesity Lifestyle and Genetic Adaptations (OLaGA) Research Center, Apia, Samoa
| | - Ruiyan Wang
- Department of Epidemiology (Chronic Diseases), Yale School of Public Health, New Haven, Connecticut, United States of America
| | | | - Rochelle Rosen
- Centers for Behavioral and Preventative Medicine, The Miriam Hospital, Providence, Rhode Island, United States of America
- Department of Behavioral and Social Sciences, Brown University, School of Public Health, Providence, Rhode Island, United States of America
| | - Nicola L. Hawley
- Department of Epidemiology (Chronic Diseases), Yale School of Public Health, New Haven, Connecticut, United States of America
| | - Satupaitea Viali
- Department of Epidemiology (Chronic Diseases), Yale School of Public Health, New Haven, Connecticut, United States of America
- School of Medicine, National University of Samoa, Apia, Samoa
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