Patient-Patient Centered Outcomes Research最新文献

Background: Health technology assessment agencies typically recommend generic measures of health to generate quality-adjusted life-years. Most agencies provide recommendations on which measure to use for adults, whereas few make recommendations for children. Two widely used preference-weighted measures of child and adolescent health that have evidence of good psychometric performance are the EQ-5D-Y-3L and the Child Health Utility 9D Index (CHU9D). The EQ-5D-5L has also been used to assess adolescent health. However, evidence on their content validity-a core measurement property-is limited. The objective of this study was to explore the content validity of the EQ-5D-5L, EQ-5D-Y-3L, and CHU9D measures, including their relevance, comprehensiveness, and comprehensibility.

Methods: We assessed the content validity of the EQ-5D-5L, EQ-5D-Y-3L, and CHU9D using online semi-structured cognitive interviews in the UK. Participants were asked to comment on the relevance, comprehensibility, and comprehensiveness of the measures, including response options, recall period, and completion instructions. Interviews were informed by a topic guide. Purposive sampling allowed for appropriate breadth in the sample, with variation in gender, and presence of health conditions, disease, or disability. Interviews were recorded and transcribed verbatim before thematic content analysis.

Results: In total, we conducted 49 interviews between August 2022 and June 2023: 21 children/adolescents aged 8-17 years and 28 parents/guardians of children aged 4-17 years. The mean duration of the interviews was 45 min. Relevance was broadly supported, but issues were identified. Comprehensibility was inconsistent on some items, and participants expressed difficulty with grouped items (e.g., 'anxiety/depression'). Participants had difficulty distinguishing qualitatively between some response options (e.g., 'a little bit/a bit'). Some participants noted that instrument comprehensiveness was insufficient.

Conclusions: Although the content of the EQ-5D-5L, EQ-5D-Y-3L, and CHU9D was broadly supported, potential problems were identified in aspects of comprehensibility, relevance, and comprehensiveness. These present opportunities for future research and refinement to ultimately improve the content validity of these measures for assessing child and adolescent health.

This paper provides an introduction to statistical analysis of choice data using example data from a simple discrete-choice experiment (DCE). It describes the layout of the analysis dataset, types of variables contained in the dataset, and how to identify response patterns in the data indicating data quality. Model-specification options include linear models with continuous attribute levels and non-linear continuous and categorical attribute levels. Advantages and disadvantages of conditional logit, mixed logit, and latent-class analysis are discussed and illustrated using the example DCE data. Readers are provided with links to various software programs for analyzing choice data. References are provided on topics for which there currently is limited consensus and on more advanced techniques to guide readers interested in exploring choice-modeling challenges in greater depth. Supplementary materials include the simulated example data used to illustrate modeling approaches, together with R and Matlab code to reproduce the estimates shown.

Background: Globally, over 21 million children need palliative care each year. Although guidelines exist to support paediatric palliative care delivery, they are not informed by the experiences of children themselves.

Objective: We aimed to determine what constitutes good quality palliative care from the perspectives of children with life-limiting or life-threatening conditions and their parents.

Methods: We analysed semi-structured qualitative interviews using reflexive thematic analysis informed by the European Association for Palliative Care charter of palliative care for children and young people, and Bronfenbrenner's bioecological model. Participants included 26 children aged 5-17 years, and 40 parents of children aged 0-17 years, with a range of cancer and non-cancer diagnoses in nine UK paediatric palliative care services (hospitals and hospices).

Results: Quality paediatric palliative care can be both enacted or interrupted across the five domains of the bioecological model. Honest timely communication with the child and family (microsystem), and collaborative relationships between care teams and others in the child's life (mesosystem), are vital. Care experiences are negatively affected by inequities in care provision (exosystems), and society's reluctance to discuss mortality in childhood (macrosystem). Children need to enjoy what matters to them, and maintain social connections, and plan for the future, even if facing a shortened life (chronosystem).

Conclusions: Children and parents are experts in their condition and should be actively involved in care discussions, through communication tailored to the child's pace and preferences, and support advocating for and coordinating care services. Fostering strong and collaborative relationships builds trust and helps children and families to feel safe, included and supported.

Background: Discrete choice experiments (DCEs) and best-worst scaling (BWS) profile cases (BWS case 2, or BWS-2) have been increasingly used in eliciting preferences towards health care interventions. However, it remains unclear which method is more suitable for preference elicitation, particularly in the mental health context. This study aims to compare: (1) the preference results elicited from a DCE and BWS-2; and (2) the acceptability of the two methods in the context of web-based mental health interventions (W-MHIs) for managing anxiety and depression in young people.

Methods: Participants were aged 18-25 years, lived in Australia, and self-reported experiencing anxiety and/or depression in the past 12 months. They also had either an intention to use W-MHIs or previous experience with W-MHIs for managing anxiety and/or depression. Recruitment was conducted online via social media and Deakin University notice boards. Eligible participants completed an online survey containing eight DCE and eight BWS-2 choice tasks. Both types of choice tasks comprised six attributes. A multinominal logit model was used to estimate the preference weights and relative importance of attributes. Acceptability was assessed on the basis of dropout rate, completion time, task difficulty, understanding, and participants' preferred type of choice task.

Results: A total of 198 participants (mean age: 21.42 ± 2.3 years, 64.65% female) completed the survey. Both DCE and BWS-2 predicted that cost was the most important attribute in young people's decision to engage with W-MHIs. However, the two methods differed in the relative importance of attributes and the preference ranking of levels within attributes. The DCE was perceived as easier to understand and answer, with nearly 64% of the participants preferring it over the BWS-2.

Conclusions: While both methods found cost was the most important attribute associated with engagement with W-MHIs, differences in the ranking of other attributes suggest that DCE and BWS-2 are not necessarily interchangeable. Increased acceptability by study participants of the DCE format suggests that this technique may have more merit than BWS-2-at least in the current study's context. Further research is required to identify the optimal method for determining the relative importance of attributes.

In health preference research (HPR) studies, data are generated by participants'/subjects' decisions. When developing an HPR study, it is therefore important to have a clear understanding of the components of a decision and how those components stimulate participant behavior. To obtain valid and reliable results, study designers must sufficiently describe the decision model and its components. HPR studies require a detailed examination of the decision criteria, detailed documentation of the descriptive framework, and specification of hypotheses. The objects that stimulate subjects' decisions in HPR studies are defined by attributes and attribute levels. Any limitations in the identification and presentation of attributes and levels can negatively affect preference elicitation, the quality of the HPR data, and study results. This practical guide shows how to link the HPR question to an underlying decision model. It covers how to (1) construct a descriptive framework that presents relevant characteristics of a decision object and (2) specify the research hypotheses. The paper outlines steps and available methods to achieve all this, including the methods' advantages and limitations.

Background: There is limited evidence and guidance in health preferences research to prevent, identify, and manage fraudulent respondents and data fraud, especially for best-worst scaling (BWS) and discrete choice experiments with nonordered attributes. Using an example from a BWS survey in which we experienced data fraud, we aimed to: (1) develop an approach to identify, verify, and categorize fraudulent respondents; (2) assess the impact of fraudulent respondents on data and results; and (3) identify variables associated with fraudulent respondents.

Methods: An online BWS survey on healthcare services for inflammatory bowel disease (IBD) was administered to Canadian IBD patients. We used a three-step approach to identify, verify, and categorize respondents as likely fraudulent (LF), likely real (LR), and unsure. First, responses to 12 "red flag" variables (variables identified as indicators of fraud) were coded 0 (pass) or 1 (fail) then summed to generate a "fraudulent response score" (FRS; range: 0-12 (most likely fraudulent)) used to categorize respondents. Second, respondents categorized LR or unsure underwent age verification. Third, categorization was updated on the basis of age verification results. BWS data were analyzed using conditional logit and latent class analysis. Subgroup analysis was done by final categorization, FRS, and red flag variables.

Results: Overall, n = 4334 respondents underwent initial categorization resulting in 24% (n = 1019) LF and 76% (n = 3315) needing further review. After review, 75% (n = 3258) were categorized as LF and n = 484 underwent age verification. Respondent categorization was updated on the basis of age verification, with final categorization of 76% (n = 3297) LF, 14% (n = 592) unsure, 10% (n = 442) LR, and < 1% (n = 3) duplicates of LR. BWS item rankings differed most by respondent category. Latent class analysis demonstrated final categorization was significantly associated with class membership; class 1 had characteristics consistent with LR respondents and item ranking order for class 1 closely aligned with LR respondent conditional logit results. Suspicious email was the most frequently failed red flag variable and was associated with fraudulent respondents.

Conclusions: Additional steps to review data and verify age resulted in better categorization than only FRS or single red flag variables. Email authentication, single use/unique survey links, and built-in identification verification may be most effective for fraud prevention. Guidance is needed on good research practices for most effective and efficient approaches for preventing, identifying, and managing fraudulent data in health preferences research, specifically in studies with nonordered attributes.

Introduction: Like in many countries, coverage of sexual and reproductive health (SRH) services among adolescents and young people (AYP) aged 15-24 remains low in Zambia. Increasing coverage of SRH services requires interventions that are responsive to the needs and preferences of AYP. We conducted a discrete choice experiment (DCE) to elicit AYP's preferences for SRH service delivery in Lusaka, Zambia.

Methods: A cross-sectional DCE was conducted with AYP aged 15-24 years. Consenting participants were presented with alternative SRH service delivery strategies represented by six attributes, namely: location, type of provider, type of services, service differentiation by sex, availability of edutainment, and opening hours. Multinomial logit and random parameters logit models were used to analyse the data. All variables were effect coded.

Results: A total of 423 AYP aged 15-24 years (61% female) completed the DCE. Respondents preferred SRH services that were integrated with other healthcare services (b = 0.65, p < 0.001), delivered by medical staff and peer support workers (b = 0.44, p < 0.001), and provided at a hub within a health facility (b = 0.62, p < 0.001). AYP also preferred services to be available on weekends during the daytime (b = 0.37, p < 0.001). Participants also preferred alternatives which included edutainment (b = 0.22, p < 0.001). Service differentiation by sex had little effect on the preference for SRH service delivery (b = - 0.05, p = 0.08). The coefficient for the "neither" option was negative and statistically significant (b = - 5.31, p < 0.001), implying that AYP did not favor routine SRH service delivery in an outpatient department.

Conclusion: Efforts to increase SRH service utilization among AYP should focus on providing comprehensive SRH services that are integrated with other healthcare services. These services should be delivered by a combination of medical staff and peer supporter workers in youth-friendly spaces. Careful attention should be paid to opening times to ensure that these are convenient to AYP.

Introduction: Discrete choice experiments (DCEs) are increasingly applied to develop value sets for health-related quality-of-life instruments, but respondents may adopt various simplifying heuristics that affect the resulting health state values. Attribute level overlap can make these DCE tasks easier and thereby increase respondent engagement. This study uses choice tasks involving EQ-5D-5L health states to compare designs with and without overlap, constructed using different methods (generator-developed design, Ngene, SAS, and Bayesian D-efficient design) to assess respondent non-attendance to attributes.

Methods: A multi-arm DCE using the EQ-5D-5L was conducted in the Australian general population. The performance of designs with various properties was compared using the level of respondent engagement. Respondent engagement was quantified through the inferred attribute non-attendance (ANA) estimated by the equality constrained latent class model. Utility decrements derived using all respondents (i.e., including non-attendees) were compared with estimates obtained only from those who attended to all EQ-5D-5L attributes.

Results: The inclusion of overlap improved full attendance rates from 22.3-28.4% to 28.2-54.2%. Within designs with overlap, modified Fedorov designs (constructed using either Ngene or SAS macros) had higher full attendance rates than other designs. The relative attribute importance of the EQ-5D-5L also differed significantly before and after data exclusion using ANA analysis, but there was no clear pattern in the differences.

Conclusions: This study found evidence to support the use of modified Fedorov designs (constructed using Ngene or SAS) with attribute overlap to reduce ANA and improve respondent engagement in DCE studies. It highlights the potential value of ANA analysis as a quality-control tool for the inclusion and exclusion of respondents in future health valuation work for the EQ-5D-5L.

Objectives: Frailty dimensions can be evaluated in a comprehensive geriatric assessment (CGA). As frailty is often reversible, early detection can help to maintain a person's independence. The objective of the present study was to identify organizational factors that would encourage people aged 60 years or over to agree to frailty screening.

Methods: A face-to-face discrete choice experiment was conducted to elicit older adults' preferences for five attributes of the CGA: the initiator, pre-assessment, location, schedules, and announcement of the results. To assess the relative importance of attributes and explore relationships between preferences and participant characteristics, the data were analyzed using conditional logit, mixed logit, and bivariate probit models.

Results: A total of 224 people (women: 61.2%; mean age: 72.2 years; urban dwellers: 61.6%; caregivers for other people: 35.6%) completed the survey. The CGA initiator, schedules, and location significantly influenced the respondents' preferences. The organizational preferences varied according to the respondent's quality of life and relationship with the family physician, as well as knowledge of the CGA; while agreement to attend a CGA depended on the person's gender, living environment, socioprofessional category, and caregiver status. Ideally, the CGA should be initiated by the family physician, carried out at the family physician's office or in hospital (but not at home), and split into two parts on the same day. The CGA results should preferably be presented by the healthcare professional who carried out the assessment.

Conclusions: Early frailty screening by a mobile geriatric team or specifically trained professionals in a neutral location close to the older person's home might be envisaged.