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Lamb R, Kougiali ZG. Women and shame: narratives of recovery from alcohol dependence. Psychol Health 2024:1-38. [PMID: 38736242 DOI: 10.1080/08870446.2024.2352191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 04/27/2024] [Indexed: 05/14/2024]
Abstract
OBJECTIVE Existing literature indicates distinct pathways and the key role of shame and stigma into alcohol dependence (AD) and recovery for women. Internationally, there is a paucity of research exploring these factors from women's perspectives. METHODS AND MEASURES Taking a critical realist epistemological position, unstructured life story interviews were analysed via narrative analysis to explore how seven women from the UK, storied shame in their recovery from AD. RESULTS Shame followed a common trajectory across participants' stories, appearing as a reoccurring factor throughout AD and recovery. Participants narrated shame as gendered, contributing to a loss of personal control in defining a valued personal identity. Drinking began as a shame-management strategy but evolved into a source of shame, compounded by fears of being labelled an 'alcoholic woman'. Recovery involved reclaiming the self through de-shaming a shame-based identity and developing a positive, non-drinking identity. By evaluating 'shaming' recovery frameworks, sharing stories and reconstructing their own, participants were able to work through shame, resist pathologising identity labels and internalise esteemed 'sober' identities. CONCLUSION This research provides important insights into the intersection between shame, identity, gender and culture in women's recovery from AD. Implications for clinical practice, future research and policy are considered.
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Affiliation(s)
- Robin Lamb
- Department of Psychology, University of East London, London, UK
| | - Zetta G Kougiali
- School of Psychology, Liverpool John Moores University, Liverpool, UK
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Tauscher JS, DePue MK, Swank J, Salloum RG. Determinants of preference for telehealth versus in-person treatment for substance use disorders: A discrete choice experiment. JOURNAL OF SUBSTANCE USE AND ADDICTION TREATMENT 2023; 146:208938. [PMID: 36880898 DOI: 10.1016/j.josat.2022.208938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 09/06/2022] [Accepted: 12/30/2022] [Indexed: 01/09/2023]
Abstract
INTRODUCTION Access to substance use disorder (SUD) treatment remains a significant issue in the United States. Telehealth has potential for increasing access to services; however, it is underutilized in SUD treatment compared to mental health treatment. This study uses a discrete choice experiment (DCE) to examine stated preferences for telehealth (videoconferencing, text-based + video, text only) versus in-person SUD treatment (community-based, in-home) and the attributes (location, cost, therapist choice, wait time, evidence-based practices) most important when choosing between modalities. Subgroup analyses are reported about preference differences based on type of substance and substance use severity. METHODS Four hundred participants completed a survey containing a DCE with eighteen choice sets, the alcohol use disorders inventory test, drug abuse screening test, and a brief demographic questionnaire. The study collected data between April 15, 2020, and April 22, 2020. Conditional logit regression provided a measure of strength for participant preferences for technology-assisted treatment compared to in-person care. The study provides willingness to pay estimates as a real-world measure for the importance of each attribute in participants' decision-making. RESULTS Telehealth options that include a video conference option were equally preferrable to in-person care modalities. Text-only treatment was significantly less preferable to all other modalities of care. The ability to choose one's own therapist was a significant driver of treatment preference beyond modality, while wait time did not appear significant in making decisions. Participants with the most severe substance use differed in that they were open to text-based care without video conferencing, did not express a preference for evidence-based care, and valued therapist choice significantly more than those with only moderate substance use. CONCLUSIONS Telehealth for SUD treatment is equally preferable to in-person care offered in the community or at home, signifying preference is not a barrier for utilization. Text-only modalities may be enhanced by offering videoconference options for most individuals. Individuals with the most severe substance use issues may be willing to engage in text-based support without synchronous meetings with a provider. This approach may offer a less intensive method to engage individuals in treatment who may not otherwise access services.
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Affiliation(s)
- Justin S Tauscher
- BRiTE Center, Department of Psychiatry & Behavioral Sciences, University of Washington, Seattle, WA, USA; School of Human Development and Organizational Studies, College of Education, University of Florida, Gainesville, FL, USA.
| | - M Kristina DePue
- Department of Human Development, Family Science, and Counseling, University of Nevada- Reno, Reno, NV, USA; School of Human Development and Organizational Studies, College of Education, University of Florida, Gainesville, FL, USA.
| | - Jacqueline Swank
- School of Human Development and Organizational Studies, College of Education, University of Florida, Gainesville, FL, USA; Department of Educational, School, and counseling Psychology, College of Education & Human Development, University of Missouri, Columbia, MO, USA.
| | - Ramzi G Salloum
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.
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Karim S, Craig BM, Vass C, Groothuis-Oudshoorn CGM. Current Practices for Accounting for Preference Heterogeneity in Health-Related Discrete Choice Experiments: A Systematic Review. PHARMACOECONOMICS 2022; 40:943-956. [PMID: 35960434 DOI: 10.1007/s40273-022-01178-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/12/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Accounting for preference heterogeneity is a growing analytical practice in health-related discrete choice experiments (DCEs). As heterogeneity may be examined from different stakeholder perspectives with different methods, identifying the breadth of these methodological approaches and understanding the differences are major steps to provide guidance on good research practices. OBJECTIVES Our objective was to systematically summarize current practices that account for preference heterogeneity based on the published DCEs related to healthcare. METHODS This systematic review is part of the project led by the Professional Society for Health Economics and Outcomes Research (ISPOR) health preference research special interest group. The systematic review conducted systematic searches on the PubMed, OVID, and Web of Science databases, as well as on two recently published reviews, to identify articles. The review included health-related DCE articles published between 1 January 2000 and 30 March 2020. All the included articles also presented evidence on preference heterogeneity analysis based on either explained or unexplained factors or both. RESULTS Overall, 342 of the 2202 (16%) articles met the inclusion/exclusion criteria for extraction. The trend showed that analyses of preference heterogeneity increased substantially after 2010 and that such analyses mainly examined heterogeneity due to observable or unobservable factors in individual characteristics. Heterogeneity through observable differences (i.e., explained heterogeneity) is identified among 131 (40%) of the 342 articles and included one or more interactions between an attribute variable and an observable characteristic of the respondent. To capture unobserved heterogeneity (i.e., unexplained heterogeneity), the studies largely estimated either a mixed logit (n = 205, 60%) or a latent-class logit (n = 112, 32.7%) model. Few studies (n = 38, 11%) explored scale heterogeneity or heteroskedasticity. CONCLUSIONS Providing preference heterogeneity evidence in health-related DCEs has been found as an increasingly used practice among researchers. In recent studies, controlling for unexplained preference heterogeneity has been seen as a common practice rather than explained ones (e.g., interactions), yet a lack of providing methodological details has been observed in many studies that might impact the quality of analysis. As heterogeneity can be assessed from different stakeholder perspectives with different methods, researchers should become more technically pronounced to increase confidence in the results and improve the ability of decision makers to act on the preference evidence.
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Affiliation(s)
- Suzana Karim
- University of South Florida, 4202 E Fowler Ave, Tampa, FL, 33620, USA.
| | - Benjamin M Craig
- University of South Florida, 4202 E Fowler Ave, Tampa, FL, 33620, USA
| | - Caroline Vass
- RTI Health Solutions, Manchester, UK
- The University of Manchester, Manchester, UK
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Hawke LD, Hayes E, Iyer S, Killackey E, Chinnery G, Gariépy G, Thabane L, Darnay K, Alagaratnam A, Tucker-Kilfoil S, Moxness K, Hachimi-Idrissi N, Winkelmann I, Henderson J. Youth-oriented outcomes of education, employment and training interventions for upcoming youth: Protocol for a discrete choice experiment. Early Interv Psychiatry 2021; 15:942-948. [PMID: 32945127 DOI: 10.1111/eip.13039] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 08/06/2020] [Accepted: 08/30/2020] [Indexed: 11/29/2022]
Abstract
AIM The issue of youth who are not engaged in education, employment or training has been a focus of policymakers for decades. Although interventions exist for these youth, they often measure success in ways that fail to capture what youth seek to gain. The project aims to address this gap by assessing youth-oriented outcomes for interventions targeting upcoming youth. Acknowledging the stigma attached to the deficit-based notion of not engaged in education, employment or training, hereafter we refer to 'upcoming youth', a term coined by youth partners on the project. This study asks what youth want to achieve by participating in an intervention for upcoming youth, with a view to guiding service and research design. METHODS A mixed-methods discrete choice experiment will be conducted with youth engaged as partners. A qualitative (focus group) stage will be conducted to design discrete-choice experiment attributes and levels. The experiment will be piloted and administered online to approx. 500 youth (aged 14-29) across Canada to identify the outcomes that youth prioritize for interventions. Latent class analyses will then be conducted to explore clusters of outcomes that different groups of youth prioritize. CONCLUSIONS From a strengths-based recovery-oriented framework, hearing the voices of the target population is important in designing and evaluating services. This youth-oriented research project will identify the intervention outcomes that are the highest priority for upcoming youth. Findings will inform the development, implementation and testing of interventions targeting relevant outcomes for youth who are not engaged in education, employment or training.
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Affiliation(s)
- Lisa D Hawke
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Em Hayes
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | | | | | | | | | | | - Karleigh Darnay
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | | | | | | | | | | | - Joanna Henderson
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
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Larsen A, Tele A, Kumar M. Mental health service preferences of patients and providers: a scoping review of conjoint analysis and discrete choice experiments from global public health literature over the last 20 years (1999-2019). BMC Health Serv Res 2021; 21:589. [PMID: 34144685 PMCID: PMC8214295 DOI: 10.1186/s12913-021-06499-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Accepted: 05/04/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In designing, adapting, and integrating mental health interventions, it is pertinent to understand patients' needs and their own perceptions and values in receiving care. Conjoint analysis (CA) and discrete choice experiments (DCEs) are survey-based preference-elicitation approaches that, when applied to healthcare settings, offer opportunities to quantify and rank the healthcare-related choices of patients, providers, and other stakeholders. However, a knowledge gap exists in characterizing the extent to which DCEs/CA have been used in designing mental health services for patients and providers. METHODS We performed a scoping review from the past 20 years (2009-2019) to identify and describe applications of conjoint analysis and discrete choice experiments. We searched the following electronic databases: Pubmed, CINAHL, PsychInfo, Embase, Cochrane, and Web of Science to identify stakehold,er preferences for mental health services using Mesh terms. Studies were categorized according to pertaining to patients, providers and parents or caregivers. RESULTS Among the 30 studies we reviewed, most were published after 2010 (24/30, 80%), the majority were conducted in the United States (11/30, 37%) or Canada (10/30, 33%), and all were conducted in high-income settings. Studies more frequently elicited preferences from patients or potential patients (21/30, 70%) as opposed to providers. About half of the studies used CA while the others utilized DCEs. Nearly half of the studies sought preferences for mental health services in general (14/30, 47%) while a quarter specifically evaluated preferences for unipolar depression services (8/30, 27%). Most of the studies sought stakeholder preferences for attributes of mental health care and treatment services (17/30, 57%). CONCLUSIONS Overall, preference elicitation approaches have been increasingly applied to mental health services globally in the past 20 years. To date, these methods have been exclusively applied to populations within the field of mental health in high-income countries. Prioritizing patients' needs and preferences is a vital component of patient-centered care - one of the six domains of health care quality. Identifying patient preferences for mental health services may improve quality of care and, ultimately, increase acceptability and uptake of services among patients. Rigorous preference-elicitation approaches should be considered, especially in settings where mental health resources are scarce, to illuminate resource allocation toward preferred service characteristics especially within low-income settings.
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Affiliation(s)
- Anna Larsen
- Department of Global Health, University of Washington, Seattle, WA 98195 USA
| | | | - Manasi Kumar
- Department of Psychiatry, University of Nairobi, (47074), Nairobi, 00100 Kenya
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Williams NJ, Candon M, Stewart RE, Byeon YV, Bewtra M, Buttenheim AM, Zentgraf K, Comeau C, Shoyinka S, Beidas RS. Community stakeholder preferences for evidence-based practice implementation strategies in behavioral health: a best-worst scaling choice experiment. BMC Psychiatry 2021; 21:74. [PMID: 33541301 PMCID: PMC7863375 DOI: 10.1186/s12888-021-03072-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 01/25/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Community behavioral health clinicians, supervisors, and administrators play an essential role in implementing new psychosocial evidence-based practices (EBP) for patients receiving psychiatric care; however, little is known about these stakeholders' values and preferences for implementation strategies that support EBP use, nor how best to elicit, quantify, or segment their preferences. This study sought to quantify these stakeholders' preferences for implementation strategies and to identify segments of stakeholders with distinct preferences using a rigorous choice experiment method called best-worst scaling. METHODS A total of 240 clinicians, 74 clinical supervisors, and 29 administrators employed within clinics delivering publicly-funded behavioral health services in a large metropolitan behavioral health system participated in a best-worst scaling choice experiment. Participants evaluated 14 implementation strategies developed through extensive elicitation and pilot work within the target system. Preference weights were generated for each strategy using hierarchical Bayesian estimation. Latent class analysis identified segments of stakeholders with unique preference profiles. RESULTS On average, stakeholders preferred two strategies significantly more than all others-compensation for use of EBP per session and compensation for preparation time to use the EBP (P < .05); two strategies were preferred significantly less than all others-performance feedback via email and performance feedback via leaderboard (P < .05). However, latent class analysis identified four distinct segments of stakeholders with unique preferences: Segment 1 (n = 121, 35%) strongly preferred financial incentives over all other approaches and included more administrators; Segment 2 (n = 80, 23%) preferred technology-based strategies and was younger, on average; Segment 3 (n = 52, 15%) preferred an improved waiting room to enhance client readiness, strongly disliked any type of clinical consultation, and had the lowest participation in local EBP training initiatives; Segment 4 (n = 90, 26%) strongly preferred clinical consultation strategies and included more clinicians in substance use clinics. CONCLUSIONS The presence of four heterogeneous subpopulations within this large group of clinicians, supervisors, and administrators suggests optimal implementation may be achieved through targeted strategies derived via elicitation of stakeholder preferences. Best-worst scaling is a feasible and rigorous method for eliciting stakeholders' implementation preferences and identifying subpopulations with unique preferences in behavioral health settings.
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Affiliation(s)
| | - Molly Candon
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Rebecca E Stewart
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA
| | - Y Vivian Byeon
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Meenakshi Bewtra
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Division of Gastroenterology, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Alison M Buttenheim
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Family and Community Health, School of Nursing, University of Pennsylvania, Philadelphia, PA, USA
| | - Kelly Zentgraf
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Carrie Comeau
- Department of Behavioral Health and Intellectual disAbility Services (DBHIDS), Philadelphia, PA, USA
| | - Sonsunmolu Shoyinka
- Department of Behavioral Health and Intellectual disAbility Services (DBHIDS), Philadelphia, PA, USA
| | - Rinad S Beidas
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Center for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Implementation Science Center at the Leonard Davis Institute of Health Economics (PISCE@LDI), University of Pennsylvania, 3535 Market Street, 3015, Philadelphia, PA, 19104, USA.
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Cunningham CE, Barwick M, Rimas H, Mielko S, Barac R. Modeling the Decision of Mental Health Providers to Implement Evidence-Based Children's Mental Health Services: A Discrete Choice Conjoint Experiment. ADMINISTRATION AND POLICY IN MENTAL HEALTH AND MENTAL HEALTH SERVICES RESEARCH 2019; 45:302-317. [PMID: 28918498 PMCID: PMC5809569 DOI: 10.1007/s10488-017-0824-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Using an online, cross sectional discrete choice experiment, we modeled the influence of 14 implementation attributes on the intention of 563 providers to adopt hypothetical evidence-based children’s mental health practices (EBPs). Latent class analysis identified two segments. Segment 1 (12%) would complete 100% of initial training online, devote more time to training, make greater changes to their practices, and introduce only minor modifications to EBPs. Segment 2 (88%) preferred fewer changes, more modifications, less training, but more follow-up. Simulations suggest that enhanced supervisor support would increase the percentage of participants choosing the intensive training required to implement EBPs. The dissemination of EBPs needs to consider the views of segments of service providers with differing preferences regarding EBPs and implementation process design.
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Affiliation(s)
- Charles E Cunningham
- Patient Centered Health Care, Department of Psychiatry and Behavioural Neurosciences, Faculty of Health Sciences, Michael G. DeGroote School of Medicine, McMaster University, Hamilton Health Sciences, Hamilton, ON, Canada.
| | - Melanie Barwick
- CHES Research Institute, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, Canada
| | - Heather Rimas
- Patient Centered Health Care, Department of Psychiatry and Behavioural Neurosciences, Faculty of Health Sciences, Michael G. DeGroote School of Medicine, McMaster University, Hamilton Health Sciences, Hamilton, ON, Canada
| | - Stephanie Mielko
- Patient Centered Health Care, Department of Psychiatry and Behavioural Neurosciences, Faculty of Health Sciences, Michael G. DeGroote School of Medicine, McMaster University, Hamilton Health Sciences, Hamilton, ON, Canada
| | - Raluca Barac
- Child and Youth Mental Health Research Unit, The Hospital for Sick Children, Toronto, Canada
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Zhou M, Thayer WM, Bridges JFP. Using Latent Class Analysis to Model Preference Heterogeneity in Health: A Systematic Review. PHARMACOECONOMICS 2018; 36:175-187. [PMID: 28975582 DOI: 10.1007/s40273-017-0575-4] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
BACKGROUND Latent class analysis (LCA) has been increasingly used to explore preference heterogeneity, but the literature has not been systematically explored and hence best practices are not understood. OBJECTIVE We sought to document all applications of LCA in the stated-preference literature in health and to inform future studies by identifying current norms in published applications. METHODS We conducted a systematic review of the MEDLINE, EMBASE, EconLit, Web of Science, and PsycINFO databases. We included stated-preference studies that used LCA to explore preference heterogeneity in healthcare or public health. Two co-authors independently evaluated titles, abstracts, and full-text articles. Abstracted key outcomes included segmentation methods, preference elicitation methods, number of attributes and levels, sample size, model selection criteria, number of classes reported, and hypotheses tests. Study data quality and validity were assessed with the Purpose, Respondents, Explanation, Findings, and Significance (PREFS) quality checklist. RESULTS We identified 2560 titles, 99 of which met the inclusion criteria for the review. Two-thirds of the studies focused on the preferences of patients and the general population. In total, 80% of the studies used discrete choice experiments. Studies used between three and 20 attributes, most commonly four to six. Sample size in LCAs ranged from 47 to 2068, with one-third between 100 and 300. Over 90% of the studies used latent class logit models for segmentation. Bayesian information criterion (BIC), Akaike information criterion (AIC), and log-likelihood (LL) were commonly used for model selection, and class size and interpretability were also considered in some studies. About 80% of studies reported two to three classes. The number of classes reported was not correlated with any study characteristics or study population characteristics (p > 0.05). Only 30% of the studies reported using statistical tests to detect significant variations in preferences between classes. Less than half of the studies reported that individual characteristics were included in the segmentation models, and 30% reported that post-estimation analyses were conducted to examine class characteristics. While a higher percentage of studies discussed clinical implications of the segmentation results, an increasing number of studies proposed policy recommendations based on segmentation results since 2010. CONCLUSIONS LCA is increasingly used to study preference heterogeneity in health and support decision-making. However, there is little consensus on best practices as its application in health is relatively new. With an increasing demand to study preference heterogeneity, guidance is needed to improve the quality of applications of segmentation methods in health to support policy development and clinical practice.
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Affiliation(s)
- Mo Zhou
- Department of Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, 624 N. Broadway, Room 690, Baltimore, MD, 21205, USA.
| | - Winter Maxwell Thayer
- Department of Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, 624 N. Broadway, Room 690, Baltimore, MD, 21205, USA
| | - John F P Bridges
- Department of Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, 624 N. Broadway, Room 690, Baltimore, MD, 21205, USA
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Salloum RG, Shenkman EA, Louviere JJ, Chambers DA. Application of discrete choice experiments to enhance stakeholder engagement as a strategy for advancing implementation: a systematic review. Implement Sci 2017; 12:140. [PMID: 29169397 PMCID: PMC5701380 DOI: 10.1186/s13012-017-0675-8] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2017] [Accepted: 11/15/2017] [Indexed: 01/11/2023] Open
Abstract
Background One of the key strategies to successful implementation of effective health-related interventions is targeting improvements in stakeholder engagement. The discrete choice experiment (DCE) is a stated preference technique for eliciting individual preferences over hypothetical alternative scenarios that is increasingly being used in health-related applications. DCEs are a dynamic approach to systematically measure health preferences which can be applied in enhancing stakeholder engagement. However, a knowledge gap exists in characterizing the extent to which DCEs are used in implementation science. Methods We conducted a systematic literature search (up to December 2016) of the English literature to identify and describe the use of DCEs in engaging stakeholders as an implementation strategy. We searched the following electronic databases: MEDLINE, Econlit, PsychINFO, and the CINAHL using mesh terms. Studies were categorized according to application type, stakeholder(s), healthcare setting, and implementation outcome. Results Seventy-five publications were selected for analysis in this systematic review. Studies were categorized by application type: (1) characterizing demand for therapies and treatment technologies (n = 32), (2) comparing implementation strategies (n = 22), (3) incentivizing workforce participation (n = 11), and (4) prioritizing interventions (n = 10). Stakeholders included providers (n = 27), patients (n = 25), caregivers (n = 5), and administrators (n = 2). The remaining studies (n = 16) engaged multiple stakeholders (i.e., combination of patients, caregivers, providers, and/or administrators). The following implementation outcomes were discussed: acceptability (n = 75), appropriateness (n = 34), adoption (n = 19), feasibility (n = 16), and fidelity (n = 3). Conclusions The number of DCE studies engaging stakeholders as an implementation strategy has been increasing over the past decade. As DCEs are more widely used as a healthcare assessment tool, there is a wide range of applications for them in stakeholder engagement. The DCE approach could serve as a tool for engaging stakeholders in implementation science. Electronic supplementary material The online version of this article (10.1186/s13012-017-0675-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Ramzi G Salloum
- Department of Health Outcomes and Policy, College of Medicine, University of Florida, 2004 Mowry Road, Gainesville, FL, 32610, USA.
| | - Elizabeth A Shenkman
- Department of Health Outcomes and Policy, College of Medicine, University of Florida, 2004 Mowry Road, Gainesville, FL, 32610, USA
| | - Jordan J Louviere
- Institute for Choice, School of Marketing, University of South Australia, Adelaide, SA, Australia
| | - David A Chambers
- Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA
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Frankfurter C, Cunningham C, Morrison KM, Rimas H, Bailey K. Understanding academic clinicians’ intent to treat pediatric obesity. World J Clin Pediatr 2017; 6:60-68. [PMID: 28224097 PMCID: PMC5296631 DOI: 10.5409/wjcp.v6.i1.60] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2016] [Revised: 07/29/2016] [Accepted: 10/27/2016] [Indexed: 02/06/2023] Open
Abstract
AIM To examine the extent to which the theory of planned behavior (TPB) predicts academic clinicians’ intent to treat pediatric obesity.
METHODS A multi-disciplinary panel iteratively devised a Likert scale survey based on the constructs of the TPB applied to a set of pediatric obesity themes. A cross-sectional electronic survey was then administered to academic clinicians at tertiary care centers across Canada from January to April 2012. Descriptive statistics were used to summarize demographic and item agreement data. A hierarchical linear regression analysis controlling for demographic variables was conducted to examine the extent to which the TPB subscales predicted intent to treat pediatric obesity.
RESULTS A total of 198 physicians, surgeons, and allied health professionals across Canada (British Columbia, Alberta, Manitoba, Saskatchewan, Nova Scotia, Ontario and Quebec) completed the survey. On step 1, demographic factors accounted for 7.4% of the variance in intent scores. Together in step 2, demographic variables and TPB subscales predicted 56.9% of the variance in a measure of the intent to treat pediatric obesity. Perceived behavioral control, that is, confidence in one’s ability to manage pediatric obesity, and subjective norms, congruent with one’s context of practice, were the most significant predictors of the intent to treat pediatric obesity. Attitudes and barriers did not predict the intent to treat pediatric obesity in this context.
CONCLUSION Enhancing self-confidence in the ability to treat pediatric obesity and the existence of supportive treatment environments are important to increase clinicians’ intent to treat pediatric obesity.
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Vanniyasingam T, Cunningham CE, Foster G, Thabane L. Simulation study to determine the impact of different design features on design efficiency in discrete choice experiments. BMJ Open 2016; 6:e011985. [PMID: 27436671 PMCID: PMC4964187 DOI: 10.1136/bmjopen-2016-011985] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVES Discrete choice experiments (DCEs) are routinely used to elicit patient preferences to improve health outcomes and healthcare services. While many fractional factorial designs can be created, some are more statistically optimal than others. The objective of this simulation study was to investigate how varying the number of (1) attributes, (2) levels within attributes, (3) alternatives and (4) choice tasks per survey will improve or compromise the statistical efficiency of an experimental design. DESIGN AND METHODS A total of 3204 DCE designs were created to assess how relative design efficiency (d-efficiency) is influenced by varying the number of choice tasks (2-20), alternatives (2-5), attributes (2-20) and attribute levels (2-5) of a design. Choice tasks were created by randomly allocating attribute and attribute level combinations into alternatives. OUTCOME Relative d-efficiency was used to measure the optimality of each DCE design. RESULTS DCE design complexity influenced statistical efficiency. Across all designs, relative d-efficiency decreased as the number of attributes and attribute levels increased. It increased for designs with more alternatives. Lastly, relative d-efficiency converges as the number of choice tasks increases, where convergence may not be at 100% statistical optimality. CONCLUSIONS Achieving 100% d-efficiency is heavily dependent on the number of attributes, attribute levels, choice tasks and alternatives. Further exploration of overlaps and block sizes are needed. This study's results are widely applicable for researchers interested in creating optimal DCE designs to elicit individual preferences on health services, programmes, policies and products.
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Affiliation(s)
- Thuva Vanniyasingam
- Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada
| | - Charles E Cunningham
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
| | - Gary Foster
- Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada
- Biostatistics Unit, Father Sean O'Sullivan Research Centre, St. Joseph's Healthcare, Hamilton, Ontario, Canada
| | - Lehana Thabane
- Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada
- Biostatistics Unit, Father Sean O'Sullivan Research Centre, St. Joseph's Healthcare, Hamilton, Ontario, Canada
- Departments of Paediatrics and Anaesthesia, McMaster University, Hamilton, Ontario, Canada
- Centre for Evaluation of Medicine, St. Joseph's Healthcare, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences, Hamilton, Ontario, Canada
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Novotná G, Dobbins M, Henderson J, Jack S, Sword W, Niccols A. Understanding the Link Between Personal Recovery Experience and Program Delivery Decisions of Administrators Working in Addiction Agencies Serving Women in Canada. ACTA ACUST UNITED AC 2015. [DOI: 10.1080/1556035x.2015.999618] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Clark MD, Determann D, Petrou S, Moro D, de Bekker-Grob EW. Discrete choice experiments in health economics: a review of the literature. PHARMACOECONOMICS 2014; 32:883-902. [PMID: 25005924 DOI: 10.1007/s40273-014-0170-x] [Citation(s) in RCA: 540] [Impact Index Per Article: 49.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
BACKGROUND Discrete choice experiments (DCEs) are increasingly used in health economics to address a wide range of health policy-related concerns. OBJECTIVE Broadly adopting the methodology of an earlier systematic review of health-related DCEs, which covered the period 2001-2008, we report whether earlier trends continued during 2009-2012. METHODS This paper systematically reviews health-related DCEs published between 2009 and 2012, using the same database as the earlier published review (PubMed) to obtain citations, and the same range of search terms. RESULTS A total of 179 health-related DCEs for 2009-2012 met the inclusion criteria for the review. We found a continuing trend towards conducting DCEs across a broader range of countries. However, the trend towards including fewer attributes was reversed, whilst the trend towards interview-based DCEs reversed because of increased computer administration. The trend towards using more flexible econometric models, including mixed logit and latent class, has also continued. Reporting of monetary values has fallen compared with earlier periods, but the proportion of studies estimating trade-offs between health outcomes and experience factors, or valuing outcomes in terms of utility scores, has increased, although use of odds ratios and probabilities has declined. The reassuring trend towards the use of more flexible and appropriate DCE designs and econometric methods has been reinforced by the increased use of qualitative methods to inform DCE processes and results. However, qualitative research methods are being used less often to inform attribute selection, which may make DCEs more susceptible to omitted variable bias if the decision framework is not known prior to the research project. CONCLUSIONS The use of DCEs in healthcare continues to grow dramatically, as does the scope of applications across an expanding range of countries. There is increasing evidence that more sophisticated approaches to DCE design and analytical techniques are improving the quality of final outputs. That said, recent evidence that the use of qualitative methods to inform attribute selection has declined is of concern.
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Affiliation(s)
- Michael D Clark
- Department of Economics, University of Warwick, Coventry, CV4 7AL, UK,
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Cunningham CE, Barwick M, Short K, Chen Y, Rimas H, Ratcliffe J, Mielko S. Modeling the Mental Health Practice Change Preferences of Educators: A Discrete-Choice Conjoint Experiment. SCHOOL MENTAL HEALTH 2013; 6:1-14. [PMID: 24563679 PMCID: PMC3924025 DOI: 10.1007/s12310-013-9110-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Schools are sometimes slow to adopt evidence-based strategies for improving the mental health outcomes of students. This study used a discrete-choice conjoint experiment to model factors influencing the decision of educators to adopt strategies for improving children’s mental health outcomes. A sample of 1,010 educators made choices between hypothetical mental health practice change strategies composed by systematically varying the four levels of 16 practice change attributes. Latent class analysis yielded two segments with different practice change preferences. Both segments preferred small-group workshops, conducted by engaging experts, teaching skills applicable to all students. Participants expressed little interest in Internet options. The support of colleagues, administrators, and unions exerted a strong influence on the practice change choices of both segments. The Change Ready segment, 77.1 % of the sample, was more intent on adopting new strategies to improve the mental health of students. They preferred that schools, rather than the provincial ministry of education, make practice change decisions, coaching was provided to all participants, and participants received post-training follow-up sessions. The Demand Sensitive segment (22.9 %) was less intent on practice change. They preferred that individual teachers make practice change decisions, recommended discretionary coaching, and chose no post-training follow-up support. This study emphasizes the complex social, organizational, and policy context within which educators make practice change decisions. Efforts to disseminate strategies to improve the mental health outcomes of students need to be informed by the preferences of segments of educators who are sensitive to different dimensions of the practice change process. In the absence of a broad consensus of educators, administrators, and unions, potentially successful practice changes are unlikely to be adopted.
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Affiliation(s)
- Charles E Cunningham
- Hamilton Health Sciences, McMaster Children's Hospital, McMaster University, Hamilton, ON L9C 7N4 Canada ; Department of Psychiatry and Behavioural Neurosciences, Faculty of Health Sciences, The Jack Laidlaw Chair in Patient-Centred Health Care, McMaster University, Hamilton, ON Canada
| | - Melanie Barwick
- Hospital for Sick Children, University of Toronto, Toronto, ON Canada
| | - Kathy Short
- Hamilton-Wentworth District School Board, Hamilton, ON Canada
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Gifford EV. Commentary on Cunningham et al. (2012): benefit to clients--outcome monitoring and knowledge translation. Addiction 2012; 107:1525-6. [PMID: 22779419 DOI: 10.1111/j.1360-0443.2012.03964.x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Elizabeth V Gifford
- Center for Health Care Evaluation, VA Palo Alto Health Care System, Menlo Park, CA 94025, USA.
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