Abstract Background This study evaluates the psychometric properties of the COmprehensive Score for financial Toxicity (COST) measure in a multicenter cohort of patients with advanced liver disease (AdvLD). Methods Between April 12, 2023, and April 30, 2024, a sample of adults undergoing liver transplantation evaluation was recruited from 13 U.S. transplant centers. We evaluated structural validity using factor analysis and internal consistency reliability using Cronbach’s alpha. Construct validity was assessed by examining correlations between the COST measure, health-related unmet social needs, and EQ-5D-5L scores. Results Among the 459 participants, more than half were male (59%), White (82%), and not Hispanic or Latino (77%). Approximately 44% were unable to work due to illness or disability, and over 44% had an annual household income below $50,000. The COST measure demonstrated suboptimal structural validity based on both one- and two-factor models. Cronbach’s alpha for the COST measure was 0.84. The total COST score showed mild to moderate correlations with health-related unmet social needs (r: -0.21 to -0.41) and small correlations with the EQ-5D-5 L visual analogue scale ( r = 0.24) and pattern scores ( r = 0.34). Discussion Caution may be warranted when applying COST to patients with AdvLD, as suboptimal structural and construct validity compromise score interpretation and clinical utility. Future studies should systematically review evidence on its psychometric properties and evaluate content validity through cognitive interviews with patients with AdvLD.

Abstract Given the growing crisis in youth mental health, there is a critical need to rebuild and sustain healthy social environments. Cooperative experiences (e.g., sports, clubs) may promote mental health but we lack rigorously tested measures to drive research and evaluation. This study sought to develop a measure of cooperative experiences and test associations with health. We developed and revised a measure of cooperative experiences based on interdisciplinary literature and 20 cognitive interviews. We recruited youth aged 13-25 years (N = 262) through youth-serving organizations and snowball sampling to complete an investigator-administered (n = 50) or self-administered (n = 212) survey assessing cooperative experiences (48 items), mental and physical health, and demographics. We assessed item characteristics, dimensionality, reliability, and construct validity. Multivariable linear regressions were used to estimate the association between the total score and self-reported health. Participants were 57% female, 69% Latino, 55% high school students, and 25% college students. The measure was reduced to 35 items (alpha = 0.90) with six subscales: sense of a unified group (7 items, alpha = 0.83), goal alignment (3 items, alpha = 0.80), inclusion and shared purpose (10 items, alpha = 0.88), social exclusion (2 items, alpha = 0.91), positive interdependence (7 items, alpha = 0.77), and negative interdependence (6 items, alpha = 0.87). A higher total score was associated with better self-reported mental health (beta = 0.25 standard deviation change in health score for each standard deviation change in cooperation scale, 95% CI [0.108, 0.394], p = 0.001) and self-reported general health (beta = 0.25, 95% CI [0.107, 0.395], p = 0.001). The study provides preliminary support for the reliability and validity of a new measure of exposure to cooperative experiences among youth. The measure holds promise as a tool to examine the relationship between social environments and health outcomes in real-world settings.

BACKGROUND AND OBJECTIVES:Many healthcare organizations administer Consumer Assessment of Healthcare Providers and Systems (CAHPS) surveys and use both quantitative ratings and qualitative narrative comments. While narrative data offer rich insights into patient experience, their time-intensive analysis often limits systematic use in quality improvement (QI). Healthcare organizations often underutilize this data, reading comments and not analyzing them systematically. This study examined the feasibility and accuracy of engaging clinicians in systematically coding narrative CAHPS data for QI. We conducted a coding exercise using patient-level narrative comments and compared the inter-rater reliability of clinicians and experienced qualitative researchers. METHODS:We partnered with a large, urban freestanding children's hospital that routinely collects Child HCAHPS data. Our six-person coding team comprised two experienced qualitative researchers, two pediatric inpatient physicians, and two pediatric inpatient nurses. Following standardized training on the coding process and a predefined codebook, the comments were coded independently. The coding framework included valence (positive, negative, mixed), identification of potential patient safety issues (safety flag), staff type mentioned (e.g., doctor, nurse), setting mentioned (e.g., emergency room), overall actionability (yes/no), and, if actionable, the level of action needed (individual clinician/provider or organizational). We calculated overall and pairwise inter-rater agreement (Kappa) to assess coding consistency across and between the three professional groups and for different code combinations to identify patterns. RESULTS:Researchers had excellent inter-rater agreement (pooled Kappa = 0.88). There was very good agreement among clinicians (Kappa = 0.68), whereas nurses had good agreement (Kappa = 0.60). Valence and mentioned staff type were coded with high consistency across all groups. Agreement on the dichotomous 0/1 indicator of actionability (yes/no) was higher than for the three-level coding of actionability (not actionable/actionable at provider-level/actionable at organizational-level). Notably, nurses displayed slightly higher agreement than physicians, whereas researchers were consistently more concordant than both clinician groups. Nurses also demonstrated the strongest sensitivity in identifying potential patient safety issues within the narrative comments. CONCLUSION:Our findings suggest that training nurses to code narrative CAHPS comments for valence, mentioned staff, overall actionability, and patient safety flags is a feasible and potentially effective strategy for QI. This focused coding approach, implemented regularly (e.g., quarterly), could efficiently generate a substantial dataset for integration into routine QI initiatives and staff meetings.

In this commentary, we expressed concerns about the design and findings of a recent study that directly compared the measurement properties of PROMIS-16 and EQ-5D-5 L in the U.S. general population.

Background: Income is closely linked to morbidity and mortality in the United States, potentially due in part to differences in patient experience. However, existing studies on income and care experiences are outdated and have other important limitations. Methods: Using data from a recent national sample of adults (N=5016), we conducted a mixed-methods investigation of the relationship between income and primary care experiences. Patient experience was measured using the CAHPS Clinician and Group survey (CG-CAHPS) and its associated Narrative Item Set (NIS). Closed-ended responses were used to create 4 composite measures, for example, access to care, while open-ended NIS responses were coded for positive and negative mentions of 7 aspects of care: access, coordination, communication, office staff, efficiency, thoroughness, and emotional rapport. Results: Contingent on mentioning an aspect of care in their narratives, low-income participants had lower unadjusted odds of making positive mentions of access, coordination, communication, office staff, efficiency, and emotional rapport (all P- values ≤0.02). Conversely, they had higher unadjusted odds of making negative mentions of coordination, communication, efficiency, and thoroughness (all P -values ≤0.006). Patterns were similar after controlling for education and other characteristics. Low-income participants also had scores on all CG-CAHPS composite measures that were 3–5 points lower than scores for higher-income participants (all P- values <0.001). Conclusion: Low-income patients report fewer positive and more negative health care experiences than higher-income patients across multiple aspects of care. These deficits may contribute to their higher morbidity and mortality. Further research is needed to uncover underlying causes and inform policies and practices to ensure high-quality care for all patients.

Patient-Centered Outcomes Research Institute.

Importance:Advance care planning (ACP) is recommended to ensure that medical care for patients with serious illness aligns with their goals; however, effective pragmatic strategies have been elusive. Objective:To compare the association of an appointment-based ACP behavioral intervention delivered with the patient just before a primary care visit vs a non-appointment-based intervention. Design, Setting, and Participants:Retrospective cohort study of patients from a cluster randomized trial of ACP interventions conducted October 2019 to June 2022 (data analysis June to November 2025). This included 50 primary care clinics across 3 academic health systems. Participants were primary care patients with serious illness 18 years and older without an advance directive (AD) or Physician Order for Life Sustaining Treatment (POLST) in the electronic health record (EHR). Exposure:Three different appointment-based automated ACP interventions delivered to patients 7 to 21 days before a primary care visit. Eligible patients who did not receive an appointment-based intervention received a non-appointment-based intervention after 6 months. Main Outcomes and Measures:Posthoc analysis of AD or POLST in the EHR at 12 and 24 months and documented patient-clinician ACP discussions by 24 months. Generalized estimating equation logistic regression models were used to compute adjusted differences. Results:Among the original study sample of 8707 patients, there were 5810 patients with no AD or POLST in the EHR at baseline (mean age 71 [15] years; 3017 [51.9%] male). Of these, 5435 (93.5%) received an ACP intervention by 24 months: 2842 patients (52.3%) received at least 1 appointment-based intervention, and 2593 (47.7%) received only non-appointment-based interventions. After 24 months, 475 patients (16.7%) receiving any appointment-based intervention had an AD or POLST in the EHR compared with 254 (9.8%) receiving only non-appointment-based interventions (adjusted difference, 6.5 percentage points [pp]; 95% CI, 4.9-8.2%. Among patients receiving appointment-based interventions, 1143 (40.2%) had ACP discussion documentation in the EHR compared with 714 (27.5%) of patients receiving only non-appointment-based interventions (adjusted difference, 12.2 pp; 95% CI, 8.7-15.6). Appointment-based interventions were associated with more AD or POLST and documented ACP discussions than non-appointment-based interventions across all 3 ACP intervention groups. Conclusions and Relevance:In this cohort of primary care patients with serious illness, an ACP intervention designed to engage patients and promote ACP discussions was associated with greater uptake if delivered before an office visit.

Patient experience data are used to set performance targets and monitor effectiveness of quality improvement (QI) activities. However, assessing and determining areas that need improvement can be challenging, especially when there are many measures, making it harder to synthesize and identify clear priorities. We describe a QI priority metric and technique for assessing cross-sectional patient experience data that explicitly examine subgroup performance and priorities. The QI priority metric combines 2 patient experience metrics (case-mix-adjusted mean for a patient experience measure and partial correlation of that measure with an overall rating) into a single priority value, allowing leaders to quickly identify improvement areas across multiple measures and patient groups. We examined the priority metric overall and by patient groups (ie, by race, ethnicity, and language preference). We found the priority metric synthesized and identified 2 priority areas for improvement for the entire patient population and revealed several additional improvement priorities specific to patient groups. This metric has the potential to be an informative and self-educating technique for promoting uniformly high-quality care and enhancing performance.

BACKGROUND:Income is closely linked to morbidity and mortality in the United States, potentially due in part to differences in patient experience. However, existing studies on income and care experiences are outdated and have other important limitations. METHODS:Using data from a recent national sample of adults (N=5016), we conducted a mixed-methods investigation of the relationship between income and primary care experiences. Patient experience was measured using the CAHPS Clinician and Group survey (CG-CAHPS) and its associated Narrative Item Set (NIS). Closed-ended responses were used to create 4 composite measures, for example, access to care, while open-ended NIS responses were coded for positive and negative mentions of 7 aspects of care: access, coordination, communication, office staff, efficiency, thoroughness, and emotional rapport. RESULTS:Contingent on mentioning an aspect of care in their narratives, low-income participants had lower unadjusted odds of making positive mentions of access, coordination, communication, office staff, efficiency, and emotional rapport (all P- values ≤0.02). Conversely, they had higher unadjusted odds of making negative mentions of coordination, communication, efficiency, and thoroughness (all P -values ≤0.006). Patterns were similar after controlling for education and other characteristics. Low-income participants also had scores on all CG-CAHPS composite measures that were 3-5 points lower than scores for higher-income participants (all P- values <0.001). CONCLUSION:Low-income patients report fewer positive and more negative health care experiences than higher-income patients across multiple aspects of care. These deficits may contribute to their higher morbidity and mortality. Further research is needed to uncover underlying causes and inform policies and practices to ensure high-quality care for all patients.

Rationale & Objective:Symptom burden is distressing for patients living with kidney failure, but there is limited information about the combination of symptoms and individual symptoms that most strongly predict health care use in this group. We classified and summarized patients' symptom burden levels and changes over time and estimated associations with hospitalizations among patients receiving incident hemodialysis. Study Design:Longitudinal, observational. Setting & Participants:Individuals initiating dialysis in the United States. Exposure:Kidney Disease Quality of Life-36 (KDQOL-36) measure. Outcome:First hospitalization after dialysis initiation. Analytical Approach:Latent transition analysis was used to identify symptom burden classes using the KDQOL-36. Cox regression models were used to assess whether individual KDQOL-36 symptoms and symptom burden groups were associated with hospitalization risk after dialysis initiation, independent of demographics and comorbid conditions. Results:1,818 participants were Black (29%), were aged >65 years (59%), were women (42%), had diabetes (49%), and had hypertension (74%). Latent transition analysis identified the following 3 symptom burden groups: (1) low (low severity of all symptoms and kidney disease impacts), (2) moderate (high physical health impact and overall burden of kidney disease), and (3) high (high levels of all symptoms and kidney disease impact). After adjusting for patient characteristics, all KDQOL-36 scales except the Effects of Kidney Disease scale were associated with a higher hazard of hospitalization. Using the symptom burden groups, a high symptom burden was associated with a 20% increase in the hazard of hospitalization. A 1-category worsening in pain interference and in fatigue was associated with a 12% and an 8% increased hazard of hospitalization, respectively. Limitations:Findings may not generalize outside the United States. Conclusions:Pain interference and fatigue, as well as an overall symptom burden, are useful prognostic indicators in patients receiving in-center hemodialysis. Symptom burden should remain a treatment target in hemodialysis.

ObjectivesThe EQ-5D-5L and Patient-Reported Outcomes Measurement Information System (PROMIS®) preference score (PROPr) are preference-based measures. This study compares mapping and linking approaches to align the PROPr and the PROMIS domains included in PROPr plus Anxiety with EQ-5D-5L item responses and preference scores.MethodsA general population sample of 983 adults completed the online survey. Regression-based mapping methods and item response theory (IRT) linking methods were used to align scores. Mapping was used to predict EQ-5D-5L item responses or preference scores using PROMIS domain scores. Equating strategies were applied to address regression to the mean. The linking approach estimated item parameters of EQ-5D-5L based on the PROMIS score metric and generated bidirectional crosswalks between EQ-5D-5L item responses and relevant PROMIS domain scores.ResultsEQ-5D-5L item responses were significantly accounted for by PROMIS domains of Anxiety, Depression, Fatigue, Pain Interference, Physical Function, Social Roles, and Sleep Disturbance. EQ-5D-5L preference scores were accounted for by the same PROMIS domains, excluding Anxiety and Fatigue, and by the PROPr preference scores. IRT-linking crosswalks were generated between EQ-5D-5L item responses and PROMIS domains of Physical Function, Pain, and Depression. Small differences were found between observed and predicted scores for all 3 methods. The direct mapping approach (directly predicting EQ-5D-5L scores) with the equipercentile equating strategy proved superior to the linking method due to improved prediction accuracy and comparable score range coverage.ConclusionsThe PROPr and the PROMIS domains included in the PROMIS-29+2 predict EQ-5D-5L preference scores or item responses. Both methods can generate acceptably precise EQ-5D-5L preference scores, with the direct mapping approach using the equating strategy offering better precision. We summarized recommended score conversion tables based on available and desired scores.HighlightsThis study compares mapping (score prediction) and IRT-based linking approaches to align the PROPr and the PROMIS domains with EQ-5D-5L item responses and preference scores.Researchers, clinicians, and stakeholders can use this study's regression formulas and score crosswalks to convert scores between PROMIS and EQ-5D-5L.Mapping can generate more precise scores, while linking offers greater flexibility in score estimation when fewer PROMIS domain scores are collected.

PURPOSE:The Patient-Reported Outcomes Measurement Information System® (PROMIS)-16 assesses the same multi-item domains but does not include the pain intensity item in the PROMIS-29. We evaluate how well physical and mental health summary scores estimated from the PROMIS-16 reproduce those estimated using the PROMIS-29. METHODS:An evaluation of data collected from 4130 respondents from the KnowledgePanel. Analyses include confirmatory factor analysis to assess physical and mental health latent variables based on PROMIS-16 scores, reliability estimates for the PROMIS measures, mean differences and correlations of scores estimated by the PROMIS-16 with those estimated by the PROMIS-29, and associations between differences in corresponding PROMIS-16 and PROMIS-29 scores by sociodemographic characteristics. RESULTS:A two-factor (physical and mental health) model adequately fits the PROMIS-16 scores. Reliability estimates for the PROMIS-16 measures were slightly lower than for the PROMIS-29 measures. There were minimal differences between PROMIS physical and mental health summary scores estimated using the PROMIS-16 or the PROMIS-29. PROMIS-16 and PROMIS-29 score differences by sociodemographic characteristics were small. Using the PROMIS pain intensity item when scoring the PROMIS-16 produced similar estimates of physical and mental health summary scores. CONCLUSION:The PROMIS-16 provides similar estimates of the PROMIS-29 physical and mental health summary scores. The high reliability of these scores indicates they are accurate enough for use with individual patients.

Supplementary Methods S1 provides a comprehensive statistical description of the two applied predictive survival models (PC Cox model and PC Cox BLUP model).

Supplementary Figure S7 presents a screenshot of the web tool; the supplementary data provides instructions for utilizing the web-based treatment discontinuation predictive tool developed based on the predictive survival models.

Supplementary Figure S6 (a) shows the actual eight PROs of patient 1 at two time points, baseline, and 6 months (shown by the black dotted line). This patient is also overweight at baseline (25 < BMI < 30). In the clinical data set, this patient discontinued treatment at 14 months, but this information was not used in the model. Supplementary Figure S6 (b) shows the estimated probability of treatment discontinuation for patient 1 anytime after 6 months but before 18 months. Supplementary Figure S6 (c) shows similar information for patient 2 who is also overweight. However, patient 2 completed treatment by 60 months. Supplementary Figure S6 (d) shows the estimated probability of treatment discontinuation for patient 2 anytime after 6 months but before 18 months.

BACKGROUND AND OBJECTIVES:We assess differences in retirement knowledge between older Hispanic and non-Hispanic White adults, and the extent to which individual, household, and neighborhood characteristics account for these differences. We also evaluate whether the relationships of retirement knowledge with financial outcomes (i.e., wealth) and health differ between Hispanic and non-Hispanic White older adults. RESEARCH DESIGN AND METHODS:We analyzed the Retirement Knowledge Scale (RKS) included in the 2020 Health and Retirement Study (N = 1,350). We use a regression approach with a Blinder-Oaxaca decomposition analysis. RESULTS:The average RKS was significantly lower among Hispanic than among White older adults. The top three factors explaining differences in retirement knowledge between Hispanic and White older adults were educational attainment, financial literacy, and neighborhood socioeconomic characteristics. RKS was significantly (p < .05) and positively associated with financial account balances and the likelihood of reporting very good or excellent health. We find that the association of RKS with wealth is of smaller magnitude for Hispanic than for White older adults. However, the relationship between retirement knowledge and health did not differ across racial/ethnic groups. Finally, differences between Hispanic and White older adults on the RKS were larger for men than for women. DISCUSSION AND IMPLICATIONS:The results suggest that reducing gaps in financial literacy and education, and acknowledging the role of neighborhood characteristics, can be helpful channels for reducing differences in retirement preparedness among Hispanic and White older adults. It is also significant that retirement knowledge is more strongly linked to wealth among White older adults.