Patient-Patient Centered Outcomes Research最新文献

Objectives: No guidance currently exists as to the cognition threshold beyond which self-reported quality of life for older people with cognitive impairment and dementia is unreliable.

Methods: Older aged care residents (≥ 65 years) were randomly assigned to complete the EQ-5D-5L in computer-based (eye movements were tracked) or hard copy (participants were encouraged to 'think aloud') format. Cognition was assessed using the Mini-Mental State Examination (MMSE). Think aloud and eye tracking data were analysed by two raters, blinded to MMSE scores. At the participant level, predefined criteria were used to assign traffic light grades (green, amber, red). These grades indicate the extent to which extracted data elements provided evidence of self-report reliability. The MMSE-defined cognition threshold was determined following review of the distributions of assigned traffic light grades.

Results: Eighty-one residents participated and provided complete data (38 eye tracking, 43 think aloud). In the think aloud cohort, all participants with an MMSE score ≤ 23 (n = 10) received an amber or red grade, while 64% of participants with an MMSE score ≥ 24 (21 of 33) received green grades. In the eye tracking cohort, 68% of participants with an MMSE score ≥ 24 (15 of 22) received green grades. Of the 16 eye tracking participants with an MMSE score ≤ 23, 14 (88%) received an amber or red grade.

Conclusions: Most older residents with an MMSE score ≥ 24 have sufficient cognitive capacity to self-complete the EQ-5D-5L. More research is needed to better understand self-completion reliability for other quality-of-life instruments in cognitively impaired populations.

Background and objective: Medical advancement has enabled children to survive congenital airway anomalies, rare diseases and critical illnesses with medical technology including tracheostomies and long-term ventilation to support breathing. This study aimed to assess (1) the validity of the EQ-TIPS and EQ-5D-Y-3L in children dependent on technology and (2) the impact of caring for these children on the EQ-5D-5L and CarerQoL.

Methods: Caregivers of children aged 1 month to 18 years completed the EQ-TIPS or EQ-5D-Y-3L, Pediatric Quality of Life Inventory (PedsQL) and Paediatric Tracheostomy Health Status Instrument (PTHSI) to reflect the child's health. In addition, caregivers self-completed the EQ-5D-5L and CarerQoL. Reports of problems on EQ dimensions were compared across age groups with the Fisher's exact test. Spearman and Pearson's correlation coefficients and Kruskal-Wallis H-test were used to explore the association between caregiver and child scores, concurrent validity, and known-group validity of the EQ-TIPS and EQ-5D-Y-3L.

Results: Responses from 144 caregivers were collected, 66 for children aged 1 month to 4 years completing EQ-TIPS and 78 for children aged 5-18 years completing EQ-5D-Y-3L. The EQ-TIPS showed a higher report of no problems for social interaction for children aged 1-12 months (p = 0.040) than the older age groups, there were however no differences in the level sum score (LSS) or EQ Visual Analogue Scale scores between the age groups. The EQ-5D-Y-3L showed a significantly less report of problems for mobility (p = 0.013) and usual activities (p = 0.006) for children aged 5-7 years compared with children aged 8-12 and children aged 13-18 years. Similarly, the 5-7 years of age group had a significantly lower EQ-5D-Y-3L LSS compared with the older groups (H = 12.08, p = 0.002). The EQ-TIPS and EQ-5D-Y-3L showed moderate-to-strong associations with the PedsQL. EQ-TIPS median LSS was able to differentiate between groups on the clinical prognosis with a better health-related quality of life (HRQoL) in those where weaning from technology is possible compared with those where weaning is not possible (H = 18.98, p = 0.011). The EQ-5D-Y-3L can discriminate between breathing technology, where those with only a tracheostomy reported better HRQoL (H = 8.92, p = 0.012), and between mild and moderate clinical severity (H = 19.42, p < 0.001). Neither the PedsQL nor the PTHSI was able to discriminate between these groups across the age range. Caregiver and child HRQoL scores showed moderate-to-strong associations.

Conclusions: The EQ-TIPS and EQ-5D-Y-3L showed good validity in children dependent on the technology for breathing. The EQ-TIPS and EQ-5D-Y-3L LSS were all able to differentiate between children with known clinical variables and outperformed both the PedsQL and PTHSI, making them preferable for intervention research. The caregiver scores ar

Background and objective: Preference-based outcome measures are commonly applied in economic analyses to inform healthcare resource allocation decisions. Few preference-based outcome measures have been specifically developed for palliative and end-of-life settings. This study aimed to identify which quality-of-life domains are most important to Australians receiving specialised palliative care services to help determine if the development of a new condition-specific preference-based outcome measure is warranted.

Methods: In-depth face-to-face interviews were conducted with 18 participants recruited from palliative care services in South Australia. Data were analysed using a framework analysis drawing on findings from a systematic review of international qualitative studies investigating the quality-of-life preferences of patients receiving palliation (domains identified included cognitive, emotional, healthcare, personal autonomy, physical, preparatory, social, spiritual). Participants identified missing or irrelevant domains in the EQ-5D and QLU-C10D questionnaires and ranked the importance of domains.

Results: A priori domains were refined into cognitive, environmental, financial, independence, physical, psychological, social and spiritual. The confirmation of the eight important quality-of-life domains across multiple international studies suggests there is a relatively high degree of convergence on the perspectives of patients in different countries. Four domains derived from the interviews are not covered by the EQ-5D and QLU-C10D (cognitive, environmental, financial, spiritual), including one of the most important (spiritual).

Conclusions: Existing, popular, preference-based outcome measures such as the EQ-5D do not incorporate the most important, patient-valued, quality-of-life domains in the palliative and end-of-life settings. Development of a new, more relevant and comprehensive preference-based outcome measure could improve the allocation of resources to patient-valued services and have wide applicability internationally.

Health preference research (HPR) is being increasingly conducted to better understand patient preferences for medical decisions. However, patients vary in their desire to play an active role in medical decisions. Until now, few studies have considered patients' preferred roles in decision making. In this opinion paper, we advocate for HPR researchers to assess and account for role preferences in their studies, to increase the relevance of their work for medical and shared decision making. We provide recommendations on how role preferences can be elicited and integrated with health preferences: (1) in formative research prior to a health preference study that aims to inform medical decisions or decision makers, (2a) in the development of health preference instruments, for instance by incorporating a role preference instrument and (2b) by clarifying the respondent's role in the decision prior to the preference elicitation task or by including role preferences as an attribute in the task itself, and (3) in statistical analysis by including random parameters or latent classes to raise awareness of heterogeneity in role preferences and how it relates to health preferences. Finally, we suggest redefining the decision process as a model that integrates the role and health preferences of the different parties that are involved. We believe that the field of HPR would benefit from learning more about the extent to which role preferences relate to health preferences, within the context of medical and shared decision making.

Objective: To measure preference heterogeneity for monitoring systems among patients with a chronic heart failure.

Methods: A best-worst scaling experiment (BWS case 3) was conducted among patients with chronic heart failure to assess preferences for hypothetical monitoring care scenarios. These were characterized by the attributes mobility, risk of death, risk of hospitalization, type and frequency of monitoring, risk of medical device, and system-relevant complications. A latent class analysis (LCA) was used to analyze and interpret the data. In addition, a market simulator was used to examine which treatment configurations participants in the latent classes preferred.

Results: Data from 278 respondents were analyzed. The LCA identified four heterogeneous classes. For class 1, the most decisive factor was mobility with a longer distance covered being most important. Class 2 respondents directed their attention toward attribute "monitoring," with a preferred monitoring frequency of nine times per year. The attribute risk of hospitalization was most important for respondents of class 3, closely followed by risk of death. For class 4, however, risk of death was most important. A market simulation showed that, even with high frequency of monitoring, most classes preferred therapy with high improvement in mobility, mortality, and hospitalization.

Conclusion: Using LCA, variations in preferences among different groups of patients with chronic heart failure were examined. This allows treatment alternatives to be adapted to individual needs of patients and patient groups. The findings of the study enhance clinical and allocative decision-making while elevating the quality of clinical data interpretation.

Objective: We aimed to empirically compare maximum acceptable risk results estimated using both a discrete choice experiment (DCE) and a probabilistic threshold technique (PTT).

Methods: Members of the UK general public (n = 982) completed an online survey including a DCE and a PTT (in random order) measuring their preferences for preventative treatment for rheumatoid arthritis. For the DCE, a Bayesian D-efficient design consisting of four blocks of 15 choice tasks was constructed including six attributes with varying levels. The PTT used identical risk and benefit attributes. For the DCE, a panel mixed-logit model was conducted, both mean and individual estimates were used to calculate maximum acceptable risk. For the PTT, interval regression was used to calculate maximum acceptable risk. Perceived complexity of the choice tasks and preference heterogeneity were investigated for both methods.

Results: Maximum acceptable risk confidence intervals of both methods overlapped for serious infection and serious side effects but not for mild side effects (maximum acceptable risk was 32.7 percent-points lower in the PTT). Although, both DCE and PTT tasks overall were considered easy or very easy to understand and answer, significantly more respondents rated the DCE choice tasks as easier to understand compared with those who rated the PTT as easier (7-percentage point difference; p < 0.05).

Conclusions: Maximum acceptable risk estimate confidence intervals based on a DCE and a PTT overlapped for two out of the three included risk attributes. More respondents rated the DCE as easier to understand. This may suggest that the DCE is better suited in studies estimating maximum acceptable risk for multiple risk attributes of differing severity, while the PTT may be better suited when measuring heterogeneity in maximum acceptable risk estimates or when investigating one or more serious adverse events.

Background: Addressing the increasing prevalence of obesity is a global public health priority. Severe obesity (body mass index > 40) reduces life expectancy, due to its association with people developing complications (e.g. diabetes, cancer, cardiovascular disease), and greatly impairs quality of life. The National Health Service (NHS) in the UK provides specialist weight management services (SWMS) for people with severe obesity, but key uncertainties remain around patient access to and engagement with weight management services, as well as pathways beyond the service.

Methods: In this multiple methods study, using online forum data and semi-structured interviews, stakeholders' experiences of delivering and receiving SWMS were explored. Using the web search engine Google with keywords and web address (URL) identifiers, relevant public online platforms were sourced with snowball sampling and search strings used to identify threads related to people's experiences of accessing SWMS (n = 57). Interviews were conducted with 24 participants (nine patients, 15 staff), and data from all sources were analysed thematically using the framework approach.

Results: Six themes related to access to and engagement with SWMS emerged during data analysis: (1) making the first move, (2) uncertainty and confusion, (3) resource issues, (4) respect and understanding, (5) mode of delivery, and (6) desire for ongoing support.

Conclusion: There is a mixed and varied picture of SWMS provision across the UK. The service offered is based on local clinical decision making and available resources, resulting in a range of patient experiences and perspectives. Whilst service capacity issues and patient anxiety were seen as barriers to accessing care, peer support and positive clinical and group interactions (connectedness between individuals) were considered to increase engagement.

Introduction: Blood donation rates remain suboptimal in China necessitating the reform of the current nonmonetary incentive system to motivate donors. This study aims to identify relevant attributes and levels for nonmonetary incentives in repeated blood donation and provide insights for the development of preference elicitation instruments.

Methods: A qualitative research process was employed, including a literature review, in-depth interviews, attribute ranking, focus group discussions, and cognitive interviews, to identify potential nonmonetary incentives for blood donation. The identified attributes were then incorporated into a discrete choice experiment (DCE) study design. The comprehensibility and acceptability of the DCE questionnaire were assessed through cognitive interviews and a pilot study.

Results: Five nonmonetary incentive attributes were identified, including health examination, designated blood recipient, honor for donation, travel time, and gifts. The designated recipient of blood donation emerged as the most important motivator for future donations among the participants. The cognitive interviews and pilot study provided valuable feedback for refining the DCE questionnaire and ensuring its reliability.

Conclusion: This study contributes to the understanding of nonmonetary incentives for blood donation and highlights the importance of designated blood recipients, health examination, honor for donation, travel time, and gifts as potential motivators. Moreover, it emphasizes the value of employing cognitive interviews and pilot studies in the development and refinement of DCE questionnaires, ultimately enhancing the reliability and validity of preference elicitation instruments.

The Chronic Care Model has guided quality improvement in health care for almost 20 years, using a patient-centered, disease management approach to systems and care teams. To further advance efforts in person-centered care, we propose strengthening the Chronic Care Model with the goal-oriented care approach. Goal-oriented care is person-centered in that it places the focus on what matters most to each person over the course of their life. The person's goals inform care decisions, which are arrived at collaboratively between clinicians and the person. In this paper, we build on each of the elements of the Chronic Care Model with person-centered, goal-oriented care and provide clinical examples on how to operationalize this approach. We discuss how this adapted approach can support our health care systems, in particular in the context of growing multi-morbidity.