scholarly journals Exploring the Factors that Influence Workforce Participation for People with Multiple Sclerosis: A Discrete Choice Experiment

2021 ◽  
Author(s):  
Elizabeth Goodwin ◽  
Annie Hawton ◽  
Jennifer A. Whitty ◽  
Colin Green
Keyword(s):  
Multiple Sclerosis ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Latent Class ◽  
Latent Class Models ◽  
Relative Importance ◽  
Workforce Participation ◽  
Job Flexibility ◽  
The Impact

AbstractPurpose Research indicates that employment is beneficial for people with multiple sclerosis (MS). However, people with MS typically face reduced workforce participation compared to the general population. Using a discrete choice experiment (DCE) we explored which factors are most important in influencing employment choices of people with MS, and whether the relative importance of factors differs between subgroups. Methods Attributes and levels for the DCE were developed using a systematic literature review and public involvement techniques with people with MS. In an online survey, respondents were asked to choose between two hypothetical job scenarios described using six attributes. We used a large, national register (the UK MS Register), to recruit participants aged 18–64 years with a diagnosis of MS. Choice data were analysed using multinomial logit and latent class models. Results Analyses were based on responses from 2350 people with MS. The preferred model specification was a latent class model, with three classes of respondent. The relative importance of attributes varied between classes, with one giving the greatest weight to the impact of work on other aspects of their lives, the second to having supportive bosses and colleagues, and the third to job flexibility. The classes differed significantly in terms of age and gender, type of MS, and socio-economic status. Conclusions Significant heterogeneity was apparent among people with MS regarding the factors that influence their employment decisions. Attributes concerning the impact of work, attitudes in the workplace and job flexibility appear more influential than those concerning physical workplace adaptations.

scholarly journals Discrete choice experiment for eliciting preference for health services for patients with ALS and their informal caregivers

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Katy Tobin ◽  
Sinead Maguire ◽  
Bernie Corr ◽  
Charles Normand ◽  
Orla Hardiman ◽  
...  
Keyword(s):  
Health Services ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Strong Preference ◽  
Multidisciplinary Teams ◽  
Relative Importance ◽  
Trade Offs ◽  
Als Patients ◽  
The Impact

Abstract Background Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative condition with a mean life expectancy of 3 years from first symptom. Understanding the factors that are important to both patients and their caregivers has the potential to enhance service delivery and engagement, and improve efficiency. The Discrete Choice Experiment (DCE) is a stated preferences method which asks service users to make trade-offs for various attributes of health services. This method is used to quantify preferences and shows the relative importance of the attributes in the experiment, to the service user. Methods A DCE with nine choice sets was developed to measure the preferences for health services of ALS patients and their caregivers and the relative importance of various aspects of care, such as timing of care, availability of services, and decision making. The DCE was presented to patients with ALS, and their caregivers, recruited from a national multidisciplinary clinic. A random effects probit model was applied to estimate the impact of each attribute on a participant’s choice. Results Patients demonstrated the strongest preferences about timing of receiving information about ALS. A strong preference was also placed on seeing the hospice care team later rather than early on in the illness. Patients also indicated their willingness to consider the use of communication devices. Grouping by stage of disease, patients who were in earlier stages of disease showed a strong preference for receipt of extensive information about ALS at the time of diagnosis. Caregivers showed a strong preference for engagement with healthcare professionals, an attribute that was not prioritised by patients. Conclusions The DCE method can be useful in uncovering priorities of patients and caregivers with ALS. Patients and caregivers have different priorities relating to health services and the provision of care in ALS, and patient preferences differ based on the stage and duration of their illness. Multidisciplinary teams must calibrate the delivery of care in the context of the differing expectations, needs and priorities of the patient/caregiver dyad.

scholarly journals Incentivising doctor attendance in rural Bangladesh: a latent class analysis of a discrete choice experiment

2021 ◽  
Vol 6 (7) ◽  
pp. e006001
Author(s):  
Blake Angell ◽  
Mushtaq Khan ◽  
Raihanul Islam ◽  
Kate Mandeville ◽  
Nahitun Naher ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Latent Class ◽  
Latent Class Models ◽  
Class Analysis ◽  
Sustainable Solutions ◽  
Rural Bangladesh ◽  
Incentive Payments

ObjectiveDoctor absenteeism is widespread in Bangladesh, and the perspectives of the actors involved are insufficiently understood. This paper sought to elicit preferences of doctors over aspects of jobs in rural areas in Bangladesh that can help to inform the development of packages of policy interventions that may persuade them to stay at their posts.MethodsWe conducted a discrete choice experiment with 308 doctors across four hospitals in Dhaka, Bangladesh. Four attributes of rural postings were included based on a literature review, qualitative research and a consensus-building workshop with policymakers and key health-system stakeholders: relationship with the community, security measures, attendance-based policies and incentive payments. Respondents’ choices were analysed with mixed multinomial logistic and latent class models and were used to simulate the likely uptake of jobs under different policy packages.ResultsAll attributes significantly impacted doctor choices (p<0.01). Doctors strongly preferred jobs at rural facilities where there was a supportive relationship with the community (β=0.93), considered good attendance in education and training (0.77) or promotion decisions (0.67), with functional security (0.67) and higher incentive payments (0.5 per 10% increase of base salary). Jobs with disciplinary action for poor attendance were disliked by respondents (−0.63). Latent class analysis identified three groups of doctors who differed in their uptake of jobs. Scenario modelling identified intervention packages that differentially impacted doctor behaviour and combinations that could feasibly improve doctors’ attendance.ConclusionBangladeshi doctors have strong but varied preferences over interventions to overcome absenteeism. We generated evidence suggesting that interventions considering the perspective of the doctors themselves could result in substantial reductions in absenteeism. Designing policy packages that take account of the different situations facing doctors could begin to improve their ability and motivation to be present at their job and generate sustainable solutions to absenteeism in rural Bangladesh.

scholarly journals Public Preferences for Social Distancing Behaviors to Mitigate the Spread of COVID-19: A Discrete Choice Experiment

2020 ◽  
Author(s):  
Ingrid Eshun Wilson ◽  
Aaloke Mody ◽  
Ginger McKay ◽  
Mati Hlatshwayo ◽  
Cory Bradley ◽  
...  
Keyword(s):  
Public Health ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Latent Class ◽  
Latent Class Models ◽  
Second Phase ◽  
Sampling Weights ◽  
Social Distancing ◽  
The Public

AbstractPolicies to promote social distancing can minimize COVID-19 transmission, but come with substantial social and economic costs. Quantifying relative preferences of the public for such practices can inform policy prioritization and optimize uptake. We used a discrete choice experiment (DCE) to quantify relative “utilities” (preferences) for five COVID-19 pandemic social distances strategies (e.g., closure of restaurants, restriction of large gatherings) against the hypothetical risk of acquiring COVID-19 and anticipated income loss. The survey was distributed in Missouri in May-June, 2020. We applied inverse probability sampling weights to mixed logit and latent class models to generate mean preferences and identify preference classes. Overall (n=2,428), the strongest preference was for the prohibition of large gatherings, followed by preferences to keep outdoor venues, schools, and social and lifestyle venues open, 75% of the population showing probable support for a strategy that prohibited large gatherings and closed lifestyle and social venues. Latent class analysis, however revealed four preference sub-groups in the population - “risk eliminators”, “risk balancers”, “altruistic” and “risk takers”, with men twice as likely as women to belong to the risk-taking group. In this setting, public health policies which as a first phase prohibit large gatherings, as well as close social and lifestyle venues may be acceptable and adhered to by the public. In addition, policy messages that address preference heterogeneity, for example by targeting public health messages at men, could improve adherence to social distancing measures and prevent further COVID-19 transmission prior to vaccine distribution and in the event of future pandemics.Significance StatementPreferences drive behavior – DCE’s are a novel tool in public health that allow examination of preferences for a product, service or policy, identifying how the public prioritizes personal risks and cost in relation to health behaviors. Using this method to establish preferences for COVID-19 mitigation strategies, our results suggest that, firstly, a tiered approach to non-essential business closures where large gatherings are prohibited and social and lifestyle venues are closed as a first phase, would be well aligned with population preferences and may be supported by the public, while school and outdoor venue closures may require more consideration prior to a second phase of restrictions. And secondly, that important distinct preference phenotypes - that are not captured by sociodemographic (e.g., age, sex, race) characteristics - exist, and therefore that messaging should be target at such subgroups to enhance adherence to prevention efforts.

scholarly journals Public Preferences for Exit Strategies From COVID-19 Lockdown in Germany—A Discrete Choice Experiment

2021 ◽  
Vol 66 ◽  
Author(s):  
Christian Krauth ◽  
Carina Oedingen ◽  
Tim Bartling ◽  
Maren Dreier ◽  
Anke Spura ◽  
...  
Keyword(s):  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Latent Class ◽  
Latent Class Models ◽  
Fractional Factorial ◽  
Elderly Persons ◽  
Protection Measures ◽  
Trade Offs ◽  
Exit Strategies

Objectives: To decrease the rapid growth of SARS-CoV-2 in Germany, a stepped lockdown was conducted. Acceptance and compliance regarding entering and exiting lockdown measures are key for their success. The aim of the present study was to analyse the population's preferences for exiting lockdown measures.Methods: To evaluate population’s preferences and identify trade-offs between different exit strategies, a discrete choice experiment was conducted on 28–29 April (n = 1,020). Overall, six attributes and 16 choice sets (fractional-factorial design) without an opt-out were chosen. Conditional logit and latent class models were conducted.Results: Most attributes proved to be significant. Two attributes dominated all others: Avoiding a mandatory tracing app, and providing sufficient intensive care capacities. Preventing a high long-term unemployment rate and avoiding the isolation of persons aged 70+, were relevant, though utilities were comparatively lower. We identified subgroups (elderly persons and persons with school children) with different utilities, which indicates specific attributes affecting them dissimilarly.Conclusions: The population prefers cautious re-opening strategies and is at least sceptical about the adoption of severe protection measures. Government should balance interests between subgroups.

scholarly journals Economic considerations and patients' preferences affect treatment selection for patients with rheumatoid arthritis: a discrete choice experiment among European rheumatologists

2016 ◽  
Vol 76 (1) ◽  
pp. 126-132 ◽  
Author(s):  
M Hifinger ◽  
M Hiligsmann ◽  
S Ramiro ◽  
V Watson ◽  
J L Severens ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Cost Effectiveness ◽  
Disease Activity ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Latent Class ◽  
Latent Class Models ◽  
Economic Considerations ◽  
Class Models

ObjectiveTo compare the value that rheumatologists across Europe attach to patients' preferences and economic aspects when choosing treatments for patients with rheumatoid arthritis.MethodsIn a discrete choice experiment, European rheumatologists chose between two hypothetical drug treatments for a patient with moderate disease activity. Treatments differed in five attributes: efficacy (improvement and achieved state on disease activity), safety (probability of serious adverse events), patient's preference (level of agreement), medication costs and cost-effectiveness (incremental cost-effectiveness ratio (ICER)). A Bayesian efficient design defined 14 choice sets, and a random parameter logit model was used to estimate relative preferences for rheumatologists across countries. Cluster analyses and latent class models were applied to understand preference patterns across countries and among individual rheumatologists.ResultsResponses of 559 rheumatologists from 12 European countries were included in the analysis (49% females, mean age 48 years). In all countries, efficacy dominated treatment decisions followed by economic considerations and patients’ preferences. Across countries, rheumatologists avoided selecting a treatment that patients disliked. Latent class models revealed four respondent profiles: one traded off all attributes except safety, and the remaining three classes disregarded ICER. Among individual rheumatologists, 57% disregarded ICER and these were more likely from Italy, Romania, Portugal or France, whereas 43% disregarded uncommon/rare side effects and were more likely from Belgium, Germany, Hungary, the Netherlands, Norway, Spain, Sweden or UK.ConclusionsOverall, European rheumatologists are willing to trade between treatment efficacy, patients' treatment preferences and economic considerations. However, the degree of trade-off differs between countries and among individuals.

scholarly journals Discrete choice experiment (DCE) to quantify the influence of trial features on the decision to participate in cystic fibrosis (CF) clinical trials

BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e045803
Author(s):  
Rebecca Anne Dobra ◽  
Marco Boeri ◽  
Stuart Elborn ◽  
Frank Kee ◽  
Susan Madge ◽  
...  
Keyword(s):  
Cystic Fibrosis ◽  
Clinical Trials ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Latent Class Models ◽  
Study Patient ◽  
Relative Importance ◽  
Preference Weight ◽  
Patient Consent

IntroductionEngaging people with cystic fibrosis (CF) in clinical trials is critical to improving outcomes for this fatal disease. Following extensive exploration of engagement in CF trials we believe six key concepts require a quantitative understanding of their influence in the current CF trials landscape including how controversial issues like placebos, washouts, stipend provision and location of trial visits are viewed by the CF community and how these might be modified depending on the type of medicine being investigated and the mechanism of access to the drug on trial completion.Methods and analysisWe have designed and will administer an online discrete choice experiment to elicit and quantify preferences of people with CF for these trials’ attributes and estimate the relative importance of an attribute when choosing to participate in a trial. The cross-sectional data generated will be explored using conditional multinomial logit model. Mixed logit models such as the random-parameters logit and a latent class models will be used to explore preference heterogeneity. To determine the relative importance of an attribute, the difference between the attribute level with the highest preference weight and the level with the lowest preference weight will be calculated.Ethics and disseminationImperial College London Joint Research Compliance Office has granted ethical approval for this study. Patient consent will be sought following full explanation. No identifying information will be collected. Dissemination will be via international conferences, peer-review publication and patient accessible forums. Major CF trials networks have agreed to incorporate our findings into their review process, meaning our results can realistically influence and optimise CF trial delivery.PROSPERO registration numberCRD42020184886.

scholarly journals Who Gets the Last Bed? A Discrete-Choice Experiment Examining General Population Preferences for Intensive Care Bed Prioritization in a Pandemic

2021 ◽  
pp. 0272989X2199661
Author(s):  
Amelia E. Street ◽  
Deborah J. Street ◽  
Gordon M. Flynn
Keyword(s):  
Decision Making ◽  
Intensive Care ◽  
General Population ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Latent Class ◽  
Latent Class Model ◽  
Latent Class Models ◽  
Bed Allocation

Objective To explore the key patient attributes important to members of the Australian general population when prioritizing patients for the final intensive care unit (ICU) bed in a pandemic over-capacity scenario. Methods A discrete-choice experiment administered online asked respondents ( N = 306) to imagine the COVID-19 caseload had surged and that they were lay members of a panel tasked to allocate the final ICU bed. They had to decide which patient was more deserving for each of 14 patient pairs. Patients were characterized by 5 attributes: age, occupation, caregiver status, health prior to being infected, and prognosis. Respondents were randomly allocated to one of 7 sets of 14 pairs. Multinomial, mixed logit, and latent class models were used to model the observed choice behavior. Results A latent class model with 3 classes was found to be the most informative. Two classes valued active decision making and were slightly more likely to choose patients with caregiving responsibilities over those without. One of these classes valued prognosis most strongly, with a decreasing probability of bed allocation for those 65 y and older. The other valued both prognosis and age highly, with decreasing probability of bed allocation for those 45 y and older and a slight preference in favor of frontline health care workers. The third class preferred more random decision-making strategies. Conclusions For two-thirds of those sampled, prognosis, age, and caregiving responsibilities were the important features when making allocation decisions, although the emphasis varies. The remainder appeared to choose randomly.

scholarly journals How to incentivise doctor attendance in Bangladesh: a latent class analysis of a discrete choice experiment

2021 ◽  
Author(s):  
Blake Angell ◽  
Mushtaq Khan ◽  
Mir Raihanul Islam ◽  
Kate Mandeville ◽  
Nahitun Naher ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Latent Class ◽  
Latent Class Models ◽  
Disciplinary Action ◽  
Class Analysis ◽  
Sustainable Solutions ◽  
Incentive Payments

AbstractObjectiveTo elicit preferences of doctors over interventions to address doctor absenteeism in rural facilities in Bangladesh, a pervasive form of corruption across the country.MethodsWe conducted a discrete choice experiment with 308 doctors across four tertiary hospitals in Dhaka, Bangladesh. Four attributes of rural postings were included based on a literature review, qualitative research and a consensus-building workshop with policymakers and key health-system stakeholders: relationship with the community, security measures, attendance-based policies, and incentive payments. Respondents’ choices were analysed with mixed multinomial logistic and latent class models and were used to simulate the likely uptake of jobs under different policy packages.ResultsAll attributes significantly impacted doctor choices (p<0.01). Doctors strongly preferred jobs at rural facilities where there was a supportive relationship with the community (β=0.93), considered good attendance in education and training (0.77) or promotion decisions (0.67), with functional security (0.67) and higher incentive payments (0.5 per 10% increase of base salary). Jobs with disciplinary action for poor attendance were disliked by respondents (-.63). Latent class analysis identified three groups of doctors that differed in their uptake of jobs. Scenario modelling identified intervention packages that differentially impacted doctor behaciour and combinations that could feasibly improve doctors’ attendance.ConclusionBangladeshi doctors have strong but varied preferences over interventions to overcome absenteeism. Some were unresponsive to intervention but a substantial number appear amenable to change. Designing policy packages that consider these differences and target particular doctors could begin to generate sustainable solutions to doctor absenteeism in rural Bangladesh.

scholarly journals Patient preferences for use of virtual consultations in an orthopaedic rehabilitation setting: Results from a discrete choice experiment

2021 ◽  
pp. 135581962110354
Author(s):  
Anthony W Gilbert ◽  
Emmanouil Mentzakis ◽  
Carl R May ◽  
Maria Stokes ◽  
Jeremy Jones
Keyword(s):  
Conceptual Model ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Patient Preferences ◽  
Choice Experiment ◽  
Best Practice ◽  
Time Of Day ◽  
Problem Area ◽  
Fractional Factorial ◽  
The Impact

Objective Virtual Consultations may reduce the need for face-to-face outpatient appointments, thereby potentially reducing the cost and time involved in delivering health care. This study reports a discrete choice experiment (DCE) that identifies factors that influence patient preferences for virtual consultations in an orthopaedic rehabilitation setting. Methods Previous research from the CONNECT (Care in Orthopaedics, burdeN of treatmeNt and the Effect of Communication Technology) Project and best practice guidance informed the development of our DCE. An efficient fractional factorial design with 16 choice scenarios was created that identified all main effects and partial two-way interactions. The design was divided into two blocks of eight scenarios each, to reduce the impact of cognitive fatigue. Data analysis were conducted using binary logit regression models. Results Sixty-one paired response sets (122 subjects) were available for analysis. DCE factors (whether the therapist is known to the patient, duration of appointment, time of day) and demographic factors (patient qualifications, access to equipment, difficulty with activities, multiple health issues, travel costs) were significant predictors of preference. We estimate that a patient is less than 1% likely to prefer a virtual consultation if the patient has a degree, is without access to the equipment and software to undertake a virtual consultation, does not have difficulties with day-to-day activities, is undergoing rehabilitation for one problem area, has to pay less than £5 to travel, is having a consultation with a therapist not known to them, in 1 weeks’ time, lasting 60 minutes, at 2 pm. We have developed a simple conceptual model to explain how these factors interact to inform preference, including patients’ access to resources, context for the consultation and the requirements of the consultation. Conclusions This conceptual model provides the framework to focus attention towards factors that might influence patient preference for virtual consultations. Our model can inform the development of future technologies, trials, and qualitative work to further explore the mechanisms that influence preference.

scholarly journals Assessing cigarette packaging and labelling policy effects on early adolescents: results from a discrete choice experiment

2020 ◽  
pp. tobaccocontrol-2019-055463
Author(s):  
Inti Barrientos-Gutierrez ◽  
Farahnaz Islam ◽  
Yoo Jin Cho ◽  
Ramzi George Salloum ◽  
Jordan Louviere ◽  
...  
Keyword(s):  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Early Adolescents ◽  
Interactive Effects ◽  
Brand Name ◽  
Warning Label ◽  
Plain Packaging ◽  
Tobacco Marketing ◽  
The Impact

IntroductionCigarette packaging is a primary channel for tobacco advertising, particularly in countries where traditional channels are restricted. The current study evaluated the independent and interactive effects of cigarette packaging and health warning label (HWL) characteristics on perceived appeal of cigarette brands for early adolescents in Mexico.MethodsA discrete choice experiment (DCE) was conducted with early adolescents, aged 12–14 years (n=4251). The DCE involved a 3×25 design with six attributes: brand (Marlboro, Pall Mall, Camel), tobacco flavour (regular, menthol), flavour capsule (none, 1 or 2 capsules), presence of descriptive terms, branding (vs plain packaging), HWL size (30%, 75%) and HWL content (emphysema vs mouth cancer). Participants viewed eight sets of three cigarette packs and selected a pack in each set that: (1) is most/least attractive, (2) they are most/least interested in trying or (3) is most/least harmful, with a no difference option.ResultsParticipants perceived packs as less attractive, less interesting to try and more harmful if they had plain packaging or had larger HWLs, with the effect being most pronounced when plain packaging is combined with larger HWLs. For attractiveness, plain packaging had the biggest influence on choice (43%), followed by HWL size (19%). Interest in trying was most influenced by brand name (34%), followed by plain packaging (29%). Perceived harm was most influenced by brand name (30%), followed by HWL size (29%).ConclusionIncreasing the size of HWLs and implementing plain packaging appear to reduce the appeal of cigarettes to early adolescents. Countries should adopt these policies to minimise the impact of tobacco marketing.

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