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Apolo AB, Michaels-Igbokwe C, Simon NI, Benjamin DJ, Farrar M, Hepp Z, Mucha L, Heidenreich S, Cutts K, Krucien N, Ramachandran N, Gore JL. Patient Preferences for First-Line Treatment of Locally Advanced or Metastatic Urothelial Carcinoma: An Application of Multidimensional Thresholding. THE PATIENT 2025; 18:77-87. [PMID: 39198374 PMCID: PMC11717873 DOI: 10.1007/s40271-024-00709-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/25/2024] [Indexed: 09/01/2024]
Abstract
OBJECTIVES Patient preferences have the potential to influence the development of new treatments for locally advanced/metastatic urothelial carcinoma (la/mUC), and therefore we explored how patients with la/mUC value different attributes of first-line treatments. METHODS An online preference survey and multidimensional thresholding (MDT) exercise were developed following a targeted literature review and qualitative interviews with physicians, patients with la/mUC, and their caregivers. Treatment attributes included two benefits (overall response rate [ORR], pain related to bladder cancer [scored 0-100; 100 being the worst pain possible]) and four treatment-related risks (peripheral neuropathy, severe side effects, mild to moderate nausea, mild to moderate skin reactions). A Dirichlet regression was used to estimate average preference weights. Marginal utility and the reduction in ORR that patients would accept in exchange for a 10-point decrease or a 10% decrease in other attributes were calculated. RESULTS A total of 100 patients were recruited and self-completed the survey and MDT. Mean patient age was 64.9 years (standard deviation, 7.6), 54% were female, and 38% identified as white. All included treatment attributes had a statistically significant impact on preferences. Changes in ORR had the largest impact, followed by cancer-related pain and treatment-related risks. Patients were willing to accept an 8.4% decrease in ORR to reduce their pain level by 10 points or a 7.8% decrease in ORR to reduce the risk of peripheral neuropathy by 10%. For a 10% decrease in severe side effects, mild to moderate nausea, or skin reaction, patients would accept decreases in ORR of 5.5%, 3.7%, or 3.4%, respectively. CONCLUSIONS Of the attributes tested, changes in ORR were most important to patients. Patients made tradeoffs between treatment attributes indicating that a lower ORR may be acceptable for an improvement in other attributes such as reduced cancer-related pain or the risk of treatment-related adverse events.
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Affiliation(s)
- Andrea B Apolo
- Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Nicholas I Simon
- Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | | | | | | | - Lisa Mucha
- Astellas Pharma, Inc, Northbrook, IL, USA
| | | | | | | | | | - John L Gore
- Department of Urology, University of Washington, Seattle, WA, USA
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Yiu HHE, Deng K, Fung LWY, Ye X, Blais JE, Tse HF, Wong MCS, Yan BP, Wong WCW, Li X, Wong CKH, Wong CK, Chan EW. Lipid-lowering agent preferences among patients with hypercholesterolemia: a focus group study. J Pharm Policy Pract 2024; 17:2421261. [PMID: 39664865 PMCID: PMC11632947 DOI: 10.1080/20523211.2024.2421261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Accepted: 10/14/2024] [Indexed: 12/13/2024] Open
Abstract
Background Non-adherence to lipid-lowering agents poses significant risks to patients and diminishes treatment effectiveness. Current understanding of patients' preferences regarding the characteristics of these agents is limited. This study aims to qualitatively identify the barriers to lipid-lowering medication adherence and the factors considered by patients with hypercholesterolemia when choosing lipid-lowering agents, and to inform the design of a medication preference study. Methods Face-to-face focus group interviews were conducted with Cantonese-speaking patients diagnosed with hypercholesterolemia in Hong Kong. Patients were recruited by cardiologists at a university-affiliated hospital using convenience sampling. The interviews consisted of three parts: gathering patients' perceptions of disease and medication, identifying important factors in selecting lipid-lowering agents, and completing the medication preference tasks designed using the Discrete Choice Experiment (DCE) method. Thematic analysis was used to categorise the codes derived from the transcripts into higher-order themes. Results Twenty patients completed the focus group interviews on the university campus between January and March 2023. Four main themes emerged: medication management issues, patients' medication preferences, structure, and comprehension of preference tasks. Barriers to medication adherence included lack of knowledge, a high pill burden, poor communication with healthcare providers, minimal treatment decision involvement, limited access to medication information, side effects, and forgetfulness. Factors influencing medication choice were treatment regimen (i.e. the route and frequency of administration), effectiveness, side effects, doctors' opinions, drug interactions, and out-of-pocket costs. Despite suggestions for modifying attributes and levels, the medication preference tasks effectively reflected patients' trade-offs. Conclusions The identified barriers to medication adherence and the factors influencing medication choice highlight the importance of considering patients' perspectives. These insights could assist decision-makers in selecting medications that align with patient preferences, thereby promoting medication adherence. A large-scale DCE preference study will be conducted in Hong Kong to quantify the relative importance of the attributes of lipid-lowering agents.
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Affiliation(s)
- Hei Hang Edmund Yiu
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Kehui Deng
- Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Parkville, Australia
| | - Lydia WY Fung
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science and Technology Park, Hong Kong SAR, People’s Republic of China
| | - Xuxiao Ye
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Joseph Edgar Blais
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science and Technology Park, Hong Kong SAR, People’s Republic of China
| | - Hung Fat Tse
- Department of Medicine, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Martin Chi Sang Wong
- JC School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Bryan P. Yan
- Department of Medicine & Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - William Chi Wai Wong
- Department of Family Medicine and Primary Care, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Xue Li
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science and Technology Park, Hong Kong SAR, People’s Republic of China
- Department of Medicine, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Carlos King Ho Wong
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science and Technology Park, Hong Kong SAR, People’s Republic of China
- Department of Family Medicine and Primary Care, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Chun Ka Wong
- Department of Medicine, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Esther W. Chan
- Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People’s Republic of China
- Laboratory of Data Discovery for Health (D4H), Hong Kong Science and Technology Park, Hong Kong SAR, People’s Republic of China
- Department of Pharmacy, The University of Hong Kong-Shenzhen Hospital, Shenzhen, People’s Republic of China
- The University of Hong Kong Shenzhen Institute of Research and Innovation, Shenzhen, People’s Republic of China
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Ride J, Goranitis I, Meng Y, LaBond C, Lancsar E. A Reporting Checklist for Discrete Choice Experiments in Health: The DIRECT Checklist. PHARMACOECONOMICS 2024; 42:1161-1175. [PMID: 39227559 PMCID: PMC11405421 DOI: 10.1007/s40273-024-01431-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/18/2024] [Indexed: 09/05/2024]
Abstract
BACKGROUND Reporting standards of discrete choice experiments (DCEs) in health have not kept pace with the growth of this method, with multiple reviews calling for better reporting to improve transparency, assessment of validity and translation. A key missing piece has been the absence of a reporting checklist that details minimum standards of what should be reported, as exists for many other methods used in health economics. METHODS This paper reports the development of a reporting checklist for DCEs in health, which involved a scoping review to identify potential items and a Delphi consensus study among 45 DCE experts internationally to select items and guide the wording and structure of the checklist. The Delphi study included a best-worst scaling study for prioritisation. CONCLUSIONS The final checklist is presented along with guidance on how to apply it. This checklist can be used by authors to ensure that sufficient detail of a DCE's methods are reported, providing reviewers and readers with the information they need to assess the quality of the study for themselves. Embedding this reporting checklist into standard practice for health DCEs offers an opportunity to improve consistency of reporting standards, thereby enabling transparency of review and facilitating comparison of studies and their translation into policy and practice.
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Affiliation(s)
- Jemimah Ride
- Monash University Health Economics Group, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
- Health Economics Unit, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Australia.
| | - Ilias Goranitis
- Health Economics Unit, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Australia
| | | | - Christine LaBond
- Department of Health Economics Wellbeing and Society, The Australian National University, Canberra, Australia
| | - Emily Lancsar
- Department of Health Economics Wellbeing and Society, The Australian National University, Canberra, Australia
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Vass C, Boeri M, Shields G, Seo J. Making Use of Technology to Improve Stated Preference Studies. THE PATIENT 2024; 17:483-491. [PMID: 38632181 DOI: 10.1007/s40271-024-00693-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/21/2024] [Indexed: 04/19/2024]
Abstract
The interest in quantifying stated preferences for health and healthcare continues to grow, as does the technology available to support and improve health preference studies. Technological advancements in the last two decades have implications and opportunities for preference researchers designing, administering, analysing, interpreting and applying the results of stated preference surveys. In this paper, we summarise selected technologies and how these can benefit a preference study. We discuss empirical evaluations of the technology in preference research, with examples from health where possible. The technologies reviewed include serious games, virtual reality, eye tracking, innovative formats and decision aids with values clarification components. We conclude with a critical reflection on the benefits and limitations of implementing (often costly) technology alongside stated preference studies.
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Affiliation(s)
| | - Marco Boeri
- Open Health, Belfast, UK
- Queen's University of Belfast, Belfast, UK
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Loría-Rebolledo LE, Abbott M, Antunes M, Norwood P, Ryan M, Watson V, Wu H. Public preferences and willingness to pay for a net zero NHS: a protocol for a discrete choice experiment in England and Scotland. BMJ Open 2024; 14:e082863. [PMID: 38908844 PMCID: PMC11328650 DOI: 10.1136/bmjopen-2023-082863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/24/2024] Open
Abstract
INTRODUCTION Climate change poses a major threat to our health, livelihoods and the planet. In 2020, the UK National Health Service (NHS) committed to reducing its Scope 1, 2 and 3 emissions to reach net zero by 2045. Although a net zero NHS would help to limit the consequences of climate change, little is known about the UK general public's values and preferences for the proposed service changes needed to reach net zero. METHODS This study will elicit the public's preferences for actions to help achieve net zero NHS in England and Scotland using a discrete choice experiment (DCE). The DCE attributes and levels describe actions that can be taken by the NHS across key areas: buildings and estates, outdoor space, travel and transport, provision of care, goods and services and food and catering. The survey was designed using online think-aloud interviews with 17 members of the public. Two versions of the survey will be administered to a sample of up to 2200 respondents. One will include a payment vehicle as income tax increases. We will estimate the relative importance of each attribute and, for the former survey, the monetary trade-offs which individuals are willing to make between attributes. Where possible, we will match both samples to gauge preference robustness with the inclusion of the monetary payment. We will test whether respondents' preferences differ based on their socioeconomic circumstances and attitudes toward the NHS and climate change. ETHICS AND DISSEMINATION The University of Aberdeen's School of Medicine, Medical Sciences and Nutrition Ethics Research Board has approved the study (reference: SERB/690090). All participants will provide informed consent. Results will be submitted to peer-reviewed publications and presented at relevant conferences and seminars. A lay summary of the research will be published on the Health Economics Research Unit website.
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Affiliation(s)
| | - Michael Abbott
- Health Economics Research Unit, University of Aberdeen, Aberdeen, UK
| | - Mélanie Antunes
- Health Economics Research Unit, University of Aberdeen, Aberdeen, UK
| | - Patricia Norwood
- Health Economics Research Unit, University of Aberdeen, Aberdeen, UK
| | - Mandy Ryan
- Health Economics Research Unit, University of Aberdeen, Aberdeen, UK
| | - Verity Watson
- Health Economics Research Unit, University of Aberdeen, Aberdeen, UK
| | - Hangjian Wu
- Health Economics Research Unit, University of Aberdeen, Aberdeen, UK
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Zhao T, Cai X, Zhang S, Wang M, Chen L, Wang J, Yu Y, Tao L, Xu X, Luo J, Wang C, Du J, Liu Y, Lu Q, Cui F. Differences in Vaccination Consultation Preferred by Primary Health Care Workers and Residents in Community Settings. Vaccines (Basel) 2024; 12:534. [PMID: 38793785 PMCID: PMC11126119 DOI: 10.3390/vaccines12050534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 04/24/2024] [Accepted: 04/30/2024] [Indexed: 05/26/2024] Open
Abstract
OBJECTIVE To evaluate the preference of primary HCWs and residents on vaccination consultation in community health services to provide evidence for vaccine hesitancy intervention strategies. METHODS A discrete choice model (DCM) was constructed to evaluate the preference difference between primary HCWs and residents on vaccination consultation in community health services in China during May-July 2022. RESULTS A total of 282 residents and 204 HCWs were enrolled in this study. The residents preferred consulting with an HCW-led approach (β = 2.168), with specialized content (β = 0.954), and accompanied by telephone follow-up (β = 1.552). In contrast, the HCWs preferred face-to-face consultation (β = 0.540) with an HCW-led approach (β = 0.458) and specialized content (β = 0.409), accompanied by telephone follow-up (β = 0.831). College residents and residents with underlying self-reported disease may be near-critically inclined to choose traditional consultation (an offline, face-to-face consultation with standardized content and more prolonged duration) rather than a new-media consulting group (an online consultation with specialized content within 5 min). Urban HCWs preferred long-term consultation groups (the resident-led offline consultation with follow-up lasting more than 5 min). In contrast, rural HCWs preferred efficient consultation (the HCW-led, short-duration, standardized offline consultation mode). CONCLUSION The selection preference for vaccine consultation reveals a gap between providers and demanders, with different groups exhibiting distinct preferences. Identifying these targeted gaps can help design more acceptable and efficient interventions, increasing their likelihood of success and leading to better resource allocation for policymakers to develop targeted vaccination policies.
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Affiliation(s)
- Tianshuo Zhao
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing 100191, China
- Center for Infectious Diseases and Policy Research & Global Health and Infectious Diseases Group, Peking University, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Xianming Cai
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing 100191, China
- Center for Infectious Diseases and Policy Research & Global Health and Infectious Diseases Group, Peking University, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Sihui Zhang
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing 100191, China
- Center for Infectious Diseases and Policy Research & Global Health and Infectious Diseases Group, Peking University, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Mingting Wang
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing 100191, China
- Center for Infectious Diseases and Policy Research & Global Health and Infectious Diseases Group, Peking University, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Linyi Chen
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Juan Wang
- Jiuzhaigou Center for Disease Control and Prevention, Ngawa 623099, China;
| | - Yajie Yu
- Yilan Center for Disease Control and Prevention, Harbin 154899, China
| | - Liandi Tao
- Longxi Center for Disease Control and Prevention, Longxi 748199, China
| | - Xiaoxia Xu
- Chengguan Center for Disease Control and Prevention, Lanzhou 730030, China;
| | - Jing Luo
- Suzhou Center for Disease Control and Prevention, Suzhou 234099, China
| | - Chao Wang
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing 100191, China
- Center for Infectious Diseases and Policy Research & Global Health and Infectious Diseases Group, Peking University, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Juan Du
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing 100191, China
- Center for Infectious Diseases and Policy Research & Global Health and Infectious Diseases Group, Peking University, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Yaqiong Liu
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing 100191, China
- Center for Infectious Diseases and Policy Research & Global Health and Infectious Diseases Group, Peking University, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Qingbin Lu
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing 100191, China
- Center for Infectious Diseases and Policy Research & Global Health and Infectious Diseases Group, Peking University, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
| | - Fuqiang Cui
- Department of Laboratorial Science and Technology & Vaccine Research Center, School of Public Health, Peking University, Beijing 100191, China
- Center for Infectious Diseases and Policy Research & Global Health and Infectious Diseases Group, Peking University, Beijing 100191, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
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Zhou Q, Liu J, Zheng F, Wang Q, Zhang X, Li H, Tan L, Luo W. Nurses' preferences for interventions to improve infection prevention and control behaviors based on systems engineering initiative to patient safety model: a discrete choice experiment. BMC Nurs 2024; 23:29. [PMID: 38200529 PMCID: PMC10777601 DOI: 10.1186/s12912-024-01701-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 01/01/2024] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND The evidence of preferences for infection prevention and control (IPC) intervention from system perspective was lacked. This study aimed to elicit nurses' preferences for the intervention designed to improve IPC behaviors based on the Systems Engineering Initiative to Patient Safety (SEIPS) model using Discrete Choice Experiment (DCE). METHODS A DCE was conducted among nurses who were on active duty and willing to participate from July 5th to 10th, 2021 in a tertiary hospital in Ganzhou City, Jiangxi Province, using convenience sampling. A self-administered questionnaire included scenarios formed by six attributes with varying levels based on SEIPS model: person, organization, tools and technology, tasks, internal environment and external environment. A conditional logit and latent class logit model were performed to analyze the data. RESULTS A total of 257 valid questionnaires were analyzed among nurses. The results from the latent class logit model show that nurses' preferences can be divided into three classes. For nurses in multifaceted-aspect-preferred class (41.9%), positive coefficients were obtained in those six attributes. For person-preferred class (19.7%), only person was positively significant. For environment-preferred class (36.4%), the most important attribute were tasks, tools and technology, internal environment and external environment. CONCLUSIONS This finding suggest that nurses have three latent-class preferences for interventions. Multifaceted interventions to improve IPC behaviors based on the SEIPS model are preferred by most nurses. Moreover, relevant measured should be performed targeted the latent class of person-preferred and external-environment-preferred nurses.
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Affiliation(s)
- Qian Zhou
- Department of Hospital Infection Management, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology , No.100 Xianggang Rd, Wuhan, Hubei Province, China
| | - Junjie Liu
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Feiyang Zheng
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qianning Wang
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xinping Zhang
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Hui Li
- Children's Oncology Department, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Li Tan
- Department of Hospital Infection Management, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095 Jie Fang Avenue, 430030, Hankou, Wuhan, China.
| | - Wanjun Luo
- Department of Hospital Infection Management, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology , No.100 Xianggang Rd, Wuhan, Hubei Province, China.
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van Til JA, Pearce A, Ozdemir S, Hollin IL, Peay HL, Wu AW, Ostermann J, Deal K, Craig BM. Role Preferences in Medical Decision Making: Relevance and Implications for Health Preference Research. THE PATIENT 2024; 17:3-12. [PMID: 37874464 PMCID: PMC10769916 DOI: 10.1007/s40271-023-00649-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/27/2023] [Indexed: 10/25/2023]
Abstract
Health preference research (HPR) is being increasingly conducted to better understand patient preferences for medical decisions. However, patients vary in their desire to play an active role in medical decisions. Until now, few studies have considered patients' preferred roles in decision making. In this opinion paper, we advocate for HPR researchers to assess and account for role preferences in their studies, to increase the relevance of their work for medical and shared decision making. We provide recommendations on how role preferences can be elicited and integrated with health preferences: (1) in formative research prior to a health preference study that aims to inform medical decisions or decision makers, (2a) in the development of health preference instruments, for instance by incorporating a role preference instrument and (2b) by clarifying the respondent's role in the decision prior to the preference elicitation task or by including role preferences as an attribute in the task itself, and (3) in statistical analysis by including random parameters or latent classes to raise awareness of heterogeneity in role preferences and how it relates to health preferences. Finally, we suggest redefining the decision process as a model that integrates the role and health preferences of the different parties that are involved. We believe that the field of HPR would benefit from learning more about the extent to which role preferences relate to health preferences, within the context of medical and shared decision making.
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Affiliation(s)
- Janine A van Til
- Department of Health Technology and Services Research, Technical Medical Center, Faculty of Behavioural, Management and Social Sciences (BMS), University of Twente, Technohal, Room 3304, P.O. Box 217, 7500 AE, Enschede, The Netherlands.
| | - Alison Pearce
- The Daffodil Centre, The University of Sydney, a Joint Venture with Cancer Council NSW, Sydney, Australia
- Sydney School of Public Health, The University of Sydney, Sydney, Australia
| | - Semra Ozdemir
- Department of Population Health Sciences, Duke University, Durham, NC, USA
| | - Ilene L Hollin
- Department of Health Services Administration and Policy, College of Public Health, Temple University, Philadelphia, PA, USA
| | - Holly L Peay
- Genomics and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Albert W Wu
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jan Ostermann
- Department of Health Services, Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Ken Deal
- DeGroote School of Business, McMaster University, Hamilton, ON, Canada
| | - Benjamin M Craig
- Department of Economics, University of South Florida, Tampa, FL, USA
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Sharma P, Kularatna S, Abell B, Eagleson K, Vo LK, Halahakone U, Senanayake S, McPhail SM. Preferences in the Design and Delivery of Neurodevelopmental Follow-Up Care for Children: A Systematic Review of Discrete Choice Experiments. Patient Prefer Adherence 2023; 17:2325-2341. [PMID: 37745632 PMCID: PMC10517687 DOI: 10.2147/ppa.s425578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 08/31/2023] [Indexed: 09/26/2023] Open
Abstract
Neurodevelopmental disorders are a significant cause of morbidity. Early detection of neurodevelopmental delay is essential for timely diagnosis and intervention, and it is therefore important to understand the preferences of parents and clinicians for engaging with neurodevelopmental surveillance and follow-up care. Discrete choice experiment (DCE) may be an appropriate method for quantifying these preferences. This review systematically examined how DCEs have been designed and delivered in studies examining neurodevelopmental care of children and identified the preferred attributes that have been reported. PubMed, Embase, CINAHL, and Scopus databases were systematically searched. Studies were included if they used DCE to elicit preferences for a neurodevelopmental follow-up program for children. Two independent reviewers conducted the title and abstract and full-text screening. Risk of bias was assessed using a DCE-specific checklist. Findings were presented using a narrative synthesis. A total of 6618 records were identified and 16 papers were included. Orthogonal (n=5) and efficient (n=5) experimental designs were common. There was inconsistent reporting of design-related features. Analysis was primarily completed using mixed logit (n=6) or multinomial logit (n=3) models. Several key attributes for neurodevelopmental follow-up care were identified including social, behavioral and emotional support, therapy, waiting time, and out-of-pocket costs. DCE has been successfully used as a preference elicitation method for neurodevelopmental-related care. There is scope for improvement in the design and analysis of DCE in this field. Nonetheless, attributes identified in these studies are likely to be important considerations in the design and implementation of programs for neurodevelopmental care.
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Affiliation(s)
- Pakhi Sharma
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia
| | - Sanjeewa Kularatna
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia
| | - Bridget Abell
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia
| | - Karen Eagleson
- Queensland Paediatric Cardiac Service, Queensland Children’s Hospital, Brisbane, QLD, Australia
- Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Linh K Vo
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia
| | - Ureni Halahakone
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia
| | - Sameera Senanayake
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia
| | - Steven M McPhail
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia
- Digital Health and Informatics Directorate, Metro South Health, Brisbane, QLD, Australia
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