The impact on quality of life of diet restrictions and disease symptoms associated with phenylketonuria: a time trade-off and discrete choice experiment study

Author(s):  
Sara Olofsson ◽  
Katarina Gralén ◽  
Christina Hoxer ◽  
Paul Okhuoya ◽  
Ulf Persson



Author(s):  
Charlotte Beaudart ◽  
◽  
Jürgen M. Bauer ◽  
Francesco Landi ◽  
Olivier Bruyère ◽  
...  

Abstract Background and aims To assess experts’ preference for sarcopenia outcomes. Methods A discrete-choice experiment was conducted among 37 experts (medical doctors and researchers) from different countries around the world. In the survey, they were repetitively asked to choose which one of two hypothetical patients suffering from sarcopenia deserves the most a treatment. The two hypothetical patients differed in five pre-selected sarcopenia outcomes: quality of life, mobility, domestic activities, fatigue and falls. A mixed logit panel model was used to estimate the relative importance of each attribute. Results All sarcopenia outcomes were shown to be significant, and thus, important for experts. Overall, the most important sarcopenia outcome was falls (27%) followed by domestic activities and mobility (24%), quality of life (15%) and fatigue (10%). Discussion and conclusion Compared to patient’s preferences, experts considered falls as a more important outcome of sarcopenia, while the outcomes fatigue and difficulties in domestic activities were considered as less important.



2019 ◽  
Vol 233 ◽  
pp. 28-37 ◽  
Author(s):  
Brendan Mulhern ◽  
Richard Norman ◽  
Richard De Abreu Lourenco ◽  
Juliette Malley ◽  
Deborah Street ◽  
...  


2010 ◽  
Vol 26 (2) ◽  
pp. 198-204 ◽  
Author(s):  
Marc A. Koopmanschap ◽  
Elly A. Stolk ◽  
Xander Koolman

Objectives: The aim of this study was to get insight in what criteria as presented in Health technology assessment (HTA) studies are important for decision makers in healthcare priority setting.Methods: We performed a discrete choice experiment among Dutch healthcare professionals (policy makers, HTA experts, advanced HTA students). In twenty-seven choice sets, we asked respondents to elect reimbursement of one of two different healthcare interventions, which represented unlabeled, curative treatments. Both treatments were incrementally compared with usual care. The results of the interventions were normal outputs of HTA studies with a societal perspective. Results were analyzed using a multinomial logistic regression model. Upon completion of the questionnaire, we discussed the exercise with policy makers.Results: Severity of disease, costs per quality-adjusted life-year gained, individual health gain, and the budget impact were the most decisive decision criteria. A program targeting more severe diseases increased the probability of reimbursement dramatically. Uncertainty related to cost-effectiveness was also important. Respondents preferred health gains that include quality of life improvements over extension of life without improved quality of life. Savings in productivity costs were not crucial in decision making, although these are to be included in Dutch reimbursement dossiers for new drugs. Regarding subgroups, we found that policy makers attached relatively more weight to disease severity than others but less to uncertainty.Conclusions: Dutch policy makers and other healthcare professionals seem to have reasonably well articulated preferences: six of seven attributes were significant. Disease severity, budget impact, and cost-effectiveness were very important. The results are comparable to international studies, but reveal a larger set of important decision criteria.



2018 ◽  
Vol 21 (1) ◽  
pp. 69-77 ◽  
Author(s):  
Donna Rowen ◽  
Katherine Stevens ◽  
Alexander Labeit ◽  
Jackie Elliott ◽  
Brendan Mulhern ◽  
...  


2019 ◽  
Vol 39 (6) ◽  
pp. 621-631 ◽  
Author(s):  
Ellen M. Janssen ◽  
Craig E. Pollack ◽  
Cynthia Boyd ◽  
John F. P. Bridges ◽  
Qian-Li Xue ◽  
...  

Background. Older adults with limited life expectancy frequently receive cancer screening, although on average, harms outweigh benefits. We examined the influence of life expectancy on older adults’ cancer screening decisions relative to three other factors. Methods. Adults aged 65+ years ( N = 1272) were recruited from a national online survey panel. Using a discrete choice experiment, we systematically varied a hypothetical patient’s life expectancy, age, quality of life, and physician’s recommendation and asked whether the participant would choose screening. Participants were randomized to questions about colonoscopy or prostate-specific antigen/mammography screenings. Logistic regression produced preference weights that quantified the relative influence of the 4 factors on screening decisions. Results. 879 older adults completed the survey, 660 of whom varied their screening choices in response to the 4 factors we tested. The age of the hypothetical patient had the largest influence on choosing screening: the effect of age being 65 versus 85 years had a preference weight of 2.44 (95% confidence interval [CI]: 2.22, 2.65). Life expectancy (10 versus 1 year) had the second largest influence (preference weight: 1.64, CI: 1.41, 1.87). Physician recommendation (screen versus do not screen) and quality of life (good versus poor) were less influential, with preference weights of 0.90 (CI: 0.72, 1.08) and 0.68 (CI: 0.52, 0.83), respectively. Conclusions. While clinical practice guidelines increasingly use life expectancy in addition to age to guide screening decisions, we find that age is the most influential factor, independent of life expectancy, quality of life, and physician recommendation, in older adults’ cancer screening choices. Strategies to reduce overscreening should consider the importance patients give to continuing screening at younger ages, even when life expectancy is limited.



BMJ Open ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. e038865
Author(s):  
Jackline Oluoch-Aridi ◽  
Mary B Adam ◽  
Francis Wafula ◽  
Gilbert Kokwaro

ObjectiveTo identify what women want in a delivery health facility and how they rank the attributes that influence the choice of a place of delivery.DesignA discrete choice experiment (DCE) was conducted to elicit rural women’s preferences for choice of delivery health facility. Data were analysed using a conditional logit model to evaluate the relative importance of the selected attributes. A mixed multinomial model evaluated how interactions with sociodemographic variables influence the choice of the selected attributes.SettingSix health facilities in a rural subcounty.ParticipantsWomen aged 18–49 years who had delivered within 6 weeks.Primary outcomeThe DCE required women to select from hypothetical health facility A or B or opt-out alternative.ResultsA total of 474 participants were sampled, 466 participants completed the survey (response rate 98%). The attribute with the strongest association with health facility preference was having a kind and supportive healthcare worker (β=1.184, p<0.001), second availability of medical equipment and drug supplies (β=1.073, p<0.001) and third quality of clinical services (β=0.826, p<0.001). Distance, availability of referral services and costs were ranked fourth, fifth and sixth, respectively (β=0.457, p<0.001; β=0.266, p<0.001; and β=0.000018, p<0.001). The opt-out alternative ranked last suggesting a disutility for home delivery (β=−0.849, p<0.001).ConclusionThe most highly valued attribute was a process indicator of quality of care followed by technical indicators. Policymakers need to consider women’s preferences to inform strategies that are person centred and lead to improvements in quality of care during delivery.



2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Katy Tobin ◽  
Sinead Maguire ◽  
Bernie Corr ◽  
Charles Normand ◽  
Orla Hardiman ◽  
...  

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.



2021 ◽  
pp. 135581962110354
Author(s):  
Anthony W Gilbert ◽  
Emmanouil Mentzakis ◽  
Carl R May ◽  
Maria Stokes ◽  
Jeremy Jones

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.





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