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BACKGROUND: Physical function is an important indicator of physical health and predicts mortality. This study identified characteristics associated with limitations in Medicare recipients' activities of daily living. METHODS: 2019 Consumer Assessment of Healthcare Providers and Systems Fee-for-Service Medicare Survey data: 79,725 respondents (34% response rate) who were 65 and older and 53% female; 7% Black, 5% Hispanic, 4% Asian American, Native Hawaiian, or other Pacific Islander, 2% Multiracial, 1% American Indian/Alaskan Native; 35% with high school education or less. Walking, getting in and out of chairs, bathing, dressing, toileting, and eating (scored as having no difficulty versus being able to do with difficulty or unable to do) and a scale of these items were regressed on patient characteristics. RESULTS: After adjustment for all characteristics, function limitations were found for those who smoked (effect sizes of significant associations range .04-.13), had chronic health conditions (.02-.33), were 85 years or older (.09-.46), needed assistance completing the survey (.32-1.29), were female (.05-.07), and had low income and assets (.15-.47). CONCLUSIONS: These nationally representative U.S. estimates of physical function characteristics are useful for interventions for vulnerable population subgroups.
Subject(s)
Activities of Daily Living , Fee-for-Service Plans , Medicare , Self Report , Humans , Female , Male , United States/epidemiology , Aged , Aged, 80 and overABSTRACT
PURPOSE: This study evaluates the interpretability of Patient-Reported Outcomes Measurement Information System® (PROMIS®)-16 profile domain scores (physical function, ability to participate in social roles and activities, anxiety, depression, sleep disturbance, pain interference, cognitive function - abilities, and fatigue) compared to the PROMIS-29 scores and a 5-item PROMIS cognitive function score. The study aims to provide insights into using these measures in clinical and research settings. METHODS: Analyses were conducted using data from 4130 adults from a nationally representative, probability-based internet panel between September and October 2022. A subset of 1256 individuals with back pain was followed up at six months. We compared the PROMIS-16 profile with the corresponding domain scores from the PROMIS-29 and a custom five-item cognitive function measure. We evaluated (1) reliability through inter-item correlations within each domain and (2) criterion validity by comparing PROMIS-16 profile with the corresponding longer PROMIS measures: (a) standardized mean differences in domain scores, (b) correlations, and (c) concordance of change (i.e., got worse, stayed the same, got better) among those with back pain from baseline to six months later using the reliable change index. We report the Kappa coefficient of agreement and the frequency and percentage of participants with concordant classifications. RESULTS: Inter-item correlations for the PROMIS-16 domains ranged from 0.65 in cognitive function to 0.92 in pain interference. Standardized mean differences between PROMIS-16 and the scores for the corresponding longer PROMIS domains were minimal (< 0.2). Correlations among the corresponding domain scores ranged from 0.82 for sleep disturbance to 0.98 for pain interference. The percentage of concordance in change groups ranged from 63% for sleep disturbance to 88% for pain interference. Except for sleep disturbance, the change groups derived from the PROMIS-16 showed moderate to substantial agreement with scores estimated from the longer PROMIS measures (Kappa coefficients ≥ 0.41). CONCLUSION: The PROMIS-16 domain scores perform similarly to the longer PROMIS measures and can be interpreted in the same way. This similarity indicates that PROMIS-16 can be useful for research as a brief health-related quality-of-life profile measure.
The Patient-Reported Outcomes Measurement Information System® (PROMIS®)-16 Profile assesses eight health-related quality of life domains (physical function, ability to participate in social roles and activities, anxiety, depression, sleep disturbance, pain interference, cognitive function abilities, and fatigue) using two items per domain. We evaluated the PROMIS-16 profile in a sample drawn from a nationally representative, probability-based internet panel. The study supports the reliability and criterion validity of the PROMIS-16, showing that the domain scores closely align with and have high concordance in change with the PROMIS-29 scores and a custom five-item cognitive function score. The PROMIS-16 has the potential to be a brief health-related quality-of-life profile measure in research and clinical settings.
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Purpose: Patient experience is a key aspect of care quality. The Consumer Assessment of Healthcare Providers and Systems Clinician and Group (CG-CAHPS®) survey measures experiences with ambulatory care providers to inform public reporting, pay-for-performance initiatives, interventions, patient choice of physicians/practices, and quality improvement. Since the survey's 2007 release, no systematic review of its use in research has been published. Methods: We reviewed English-language, peer-reviewed articles published since 2008 using CG-CAHPS survey data in the U.S. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and used the Checklist for Analytical Cross-Sectional Studies. Results: We examined 126 articles and included 52. Twenty-seven articles focused on general primary care, and the others focused on ambulatory specialty care. Of the 52 studies, 37 were cross-sectional, and the majority conducted patient-level regression analysis, controlling for patient characteristics. The most-used CAHPS measures were overall provider rating and the provider communication composite. CG-CAHPS data were primarily utilized to evaluate interventions (24 studies) and examine cross-sectional associations (21 studies) of site-level (eg, organizational climate), provider-level (physician empathy), and patient-level (medication adherence) factors with patient experience. Four studies reported disparities in patient experience. Conclusions: The widespread use of CG-CAHPS data implies the survey's value in measuring and improving care quality. Unlike facility or plan surveys, the CG-CAHPS survey was designed to allow attribution to medical groups and clinicians, which, as evidence shows, is its main strength. Policymakers, researchers, clinicians, and health care leaders can leverage CG-CAHPS data in quality improvement efforts and interventions supporting patient-centered care.
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BACKGROUND: In contrast to prior research, our study presents longitudinal comparisons of the EQ-5D-5L and Patient-Reported Outcomes Measurement Information System (PROMIS) preference (PROPr) scores. This fills a gap in the literature, providing a much-needed understanding of these preference-based measures and their applications in healthcare research. Furthermore, our study provides equations to estimate one measure from the other, a tool that can significantly facilitate comparisons across studies. METHODS: We administered a health survey to 4,098 KnowledgePanel® members living in the United States. A subset of 1,256 (82% response rate) with back pain also completed the six-month follow-up survey. We then conducted thorough cross-sectional and longitudinal analyses of the two measures, including product-moment correlations between scores, associations with demographic variables, and health conditions. To estimate one measure from the other, we used ordinary least squares (OLS) regression with the baseline data from the general population. RESULTS: The correlation between the EQ-5D-5L and PROPr scores was 0.69, but the intraclass correlation was only 0.34 because the PROPr had lower (less positive) mean scores on the 0 (dead) to 1 (perfect health) continuum than the EQ-5D-5L. The associations between the two preference measures and demographic variables were similar at baseline. The product-moment correlation between unstandardized beta coefficients for each preference measure regressed on 22 health conditions was 0.86, reflecting similar patterns of unique associations. Correlations of change from baseline to 6 months in the two measures with retrospective perceptions of change were similar. Adjusted variance explained in OLS regressions predicting one measure from the other was 48%. On average, the predicted values were within a half-standard deviation of the observed EQ-5D-5L and PROPr scores. The beta-binomial regression model slightly improved over the OLS model in predicting the EQ-5D-5L from the PROPr but was equivalent to the OLS model in predicting the PROPr. CONCLUSION: Despite substantial mean differences, the EQ-5D-5L and PROPr have similar cross-sectional and longitudinal associations with other variables. We provide the OLS regression equations for use in cost-effectiveness research and meta-analyses. Future studies are needed to compare these measures with different conditions and interventions to provide more information on their relative validity.
Subject(s)
Patient Reported Outcome Measures , Quality of Life , Humans , United States , Male , Female , Middle Aged , Cross-Sectional Studies , Adult , Longitudinal Studies , Aged , Surveys and Questionnaires , Patient Preference/statistics & numerical data , Back Pain/psychology , Young Adult , Adolescent , Health SurveysABSTRACT
PURPOSE: Regulatory guidance suggests capturing patient-reported overall side effect impact in cancer trials. We examined whether the Functional Assessment of Cancer Therapy (FACT) GP5 item ("I am bothered by side effects of treatment") post-neoadjuvant chemotherapy/radiotherapy differed between oxaliplatin vs. non- oxaliplatin arms in the National Surgical Adjuvant Breast and Bowel Project (NSABP) R-04 trial of stage II-III rectal cancer patients. METHODS: The R-04 neoadjuvant trial compared local-regional tumor control between patients randomized to receive 5-fluorouracil or capecitabine with radiation, with or without oxaliplatin (4 treatment arms). Participants completed surveys at baseline and immediately after chemoradiotherapy. GP5 has a 5-point response scale: "Not at all" (0), "A little bit" (1), "Somewhat" (2), "Quite a bit" (3), and "Very much" (4). Logistic regression compared the odds of reporting moderate-high side effect impact (GP5 2-4) between patients receiving oxaliplatin or not after chemoradiotherapy, controlling for relevant patient characteristics. We examined associations between GP5 and other patient-reported outcomes reflecting side effects. RESULTS: Analyses were performed among 1132 study participants. Participants receiving oxaliplatin were 1.58 times (95% CI: 1.22-2.05) more likely to report moderate-high side effect bother at post-chemotherapy/radiation. In both arms, worse overall side effect impact was associated with patient-reported diarrhea, nausea, vomiting, and peripheral sensory neuropathy (p < 0.01 for all). CONCLUSION: This secondary analysis of R-04 found that GP5 distinguished between patients receiving oxaliplatin or not as part of their post-neoadjuvant chemoradiotherapy, adding patient-centric evidence on the reduced tolerability of oxaliplatin and demonstrating that GP5 is sensitive to known toxicity differences between treatments. CLINICALTRIALS: GOV: NCT00058474.
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PURPOSE: Longitudinal patient tolerability data collected as part of randomized controlled trials are often summarized in a way that loses information and does not capture the treatment experience. To address this, we developed an interactive web application to empower clinicians and researchers to explore and visualize patient tolerability data. METHODS: We used adverse event (AE) data (Common Terminology Criteria for Adverse Events) and patient-reported outcomes (PROs) from the NSABP-B35 phase III clinical trial, which compared anastrozole with tamoxifen for breast cancer-free survival, to demonstrate the tools. An interactive web application was developed using R and the Shiny web application framework that generates Sankey diagrams to visualize AEs and PROs using four tools: AE Explorer, PRO Explorer, Cohort Explorer, and Custom Explorer. RESULTS: To illustrate how users can use the interactive tool, examples for each of the four applications are presented using data from the NSABP-B35 phase III trial and the NSABP-B30 trial for the Custom Explorer. In the AE and PRO explorers, users can select AEs or PROs to visualize within specified time periods and compare across treatments. In the cohort explorer, users can select a subset of patients with a specific symptom, severity, and treatment received to visualize the trajectory over time within a specified time interval. With the custom explorer, users can upload and visualize structured longitudinal toxicity and tolerability data. CONCLUSION: We have created an interactive web application and tool for clinicians and researchers to explore and visualize clinical trial tolerability data. This adaptable tool can be extended for other clinical trial data visualization and incorporated into future patient-clinician interactions regarding treatment decisions.
Subject(s)
Breast Neoplasms , Internet , Humans , Breast Neoplasms/drug therapy , Female , Patient Reported Outcome Measures , Tamoxifen/therapeutic use , Tamoxifen/adverse effects , User-Computer Interface , SoftwareABSTRACT
PURPOSE: To develop a patient-reported outcome measure to assess the impact of glaucoma and treatment, including minimally invasive glaucoma surgery (MIGS). DESIGN: Observational study before and after concomitant cataract and Food and Drug Administration-approved implantable MIGS device surgery. SETTING: Survey administration was on a computer, iPad, or similar device. PATIENT POPULATION: 184 adults completed the baseline survey, 124 a survey 3 months after surgery, and 106 the 1-month test-retest reliability survey. The age range was 37 to 89 (average age = 72). Most were female (57%), non-Hispanic White (81%), and had a college degree (56%). MAIN OUTCOME MEASURES: The Glaucoma Outcomes Survey (GOS) assesses functional limitations (27 items), vision-related symptoms (7 items), psychosocial issues (7 items), and satisfaction with microinvasive glaucoma surgery (1 item). These multiple-item scales were scored on a 0 to 100 range, with a higher score indicating worse health. RESULTS: Internal consistency reliability estimates ranged from 0.75 to 0.93, and 1-month test-retest intraclass correlations ranged from 0.83 to 0.92 for the GOS scales. Product-moment correlations among the scales ranged from 0.56 to 0.60. Improvement in visual acuity in the study eye from baseline to the 3-month follow-up was significantly related to improvements in GOS functional limitations (r = 0.18, P = .0485), vision-related symptoms (r = 0.19, P = .0386), and psychosocial concerns (r = 0.18, P = .0503). Responders to treatment ranged from 17% for vision-related symptoms to 48% for functional limitations. CONCLUSIONS: This study supports using the GOS for ophthalmic procedures such as MIGS. Further evaluation of the GOS in different patient subgroups and clinical settings is needed.
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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.
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PURPOSE: Adults' comments on patient experience surveys explain variation in provider ratings, with negative comments providing more actionable information than positive comments. We investigate if narrative comments on the Consumer Assessment of Healthcare Providers and Systems (CAHPS®) survey of inpatient pediatric care (Child HCAHPS) account for global perceptions of the hospital beyond that explained by reports about specific aspects of care. METHODS: We analyzed 545 comments from 927 Child HCAHPS surveys completed by parents and guardians of hospitalized children with at least a 24-h hospital stay from July 2017 to December 2020 at an urban children's hospital. Comments were coded for valence (positive/negative/mixed) and actionability and used to predict Overall Hospital Rating and Willingness to Recommend the Hospital along with Child HCAHPS composite scores. RESULTS: Comments were provided more often by White and more educated respondents. Negative comments and greater actionability of comments were significantly associated with Child HCAHPS global rating measures, controlling for responses to closed-ended questions, and child and respondent characteristics. Each explained an additional 8% of the variance in respondents' overall hospital ratings and an additional 5% in their willingness to recommend the hospital. CONCLUSIONS: Child HCAHPS narrative comment data provide significant additional information about what is important to parents and guardians during inpatient pediatric care beyond closed-ended composites. PRACTICE IMPLICATIONS: Quality improvement efforts should include a review of narrative comments alongside closed-ended responses to help identify ways to improve inpatient care experiences. To promote health equity, comments should be encouraged for racial-and-ethnic minority patients and those with less educational attainment.
Subject(s)
Hospitals, Pediatric , Patient Satisfaction , Humans , Male , Child , Female , Patient Satisfaction/statistics & numerical data , Health Care Surveys , Narration , Child, Hospitalized , Inpatients/statistics & numerical data , Adult , Child, Preschool , Adolescent , Surveys and QuestionnairesABSTRACT
OBJECTIVES: Surveys can assist in screening oral diseases in populations to enhance the early detection of disease and intervention strategies for children in need. This paper aims to develop short forms of child-report and proxy-report survey screening instruments for active dental caries and urgent treatment needs in school-age children. METHODS: This cross-sectional study recruited 497 distinct dyads of children aged 8-17 and their parents between 2015 to 2019 from 14 dental clinics and private practices in Los Angeles County. We evaluated responses to 88 child-reported and 64 proxy-reported oral health questions to select and calibrate short forms using Item Response Theory. Seven classical Machine Learning algorithms were employed to predict children's active caries and urgent treatment needs using the short forms together with family demographic variables. The candidate algorithms include CatBoost, Logistic Regression, K-Nearest Neighbors (KNN), Naïve Bayes, Neural Network, Random Forest, and Support Vector Machine. Predictive performance was assessed using repeated 5-fold nested cross-validations. RESULTS: We developed and calibrated four ten-item short forms. Naïve Bayes outperformed other algorithms with the highest median of cross-validated area under the ROC curve. The means of best testing sensitivities and specificities using both child-reported and proxy-reported responses were 0.84 and 0.30 for active caries, and 0.81 and 0.31 for urgent treatment needs respectively. Models incorporating both response types showed a slightly higher predictive accuracy than those relying on either child-reported or proxy-reported responses. CONCLUSIONS: The combination of Item Response Theory and Machine Learning algorithms yielded potentially useful screening instruments for both active caries and urgent treatment needs of children. The survey screening approach is relatively cost-effective and convenient when dealing with oral health assessment in large populations. Future studies are needed to further leverage the customize and refine the instruments based on the estimated item characteristics for specific subgroups of the populations to enhance predictive accuracy.
Subject(s)
Dental Caries , Humans , Dental Caries/diagnosis , Dental Caries/epidemiology , Dental Caries/therapy , Cross-Sectional Studies , Bayes Theorem , Surveys and Questionnaires , Machine LearningABSTRACT
BACKGROUND: Healthcare provided by a bilingual provider or with the assistance of an interpreter improves care quality; however, their associations with patient experience are unknown. We reviewed associations of patient experience with provider-patient language concordance (LC) and use of interpreters for Spanish-preferring patients. METHOD: We reviewed articles from academic databases 2005-2023 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and Joanna Briggs Institute Checklists to rate study quality. We reviewed 217 (of 2193) articles, yielding 17 for inclusion. RESULTS: Of the 17 included articles, most articles focused on primary (n = 6 studies) or pediatric care (n = 5). All were cross-sectional, collecting data by self-administered surveys (n = 7) or interviews (n = 4). Most assessed the relationship between LC or interpreter use and patient experience by cross-sectional associations (n = 13). Two compared subgroups, and two provided descriptive insights into the conversational content (provider-interpreter-patient). None evaluated interventions, so evidence on effective strategies is lacking. LC for Spanish-preferring patients was a mix of null findings (n = 4) and associations with better patient experience (n = 3) (e.g., receiving diet/exercise counseling and better provider communication). Evidence on interpreter use indicated better (n = 2), worse (n = 2), and no association (n = 2) with patient experience. Associations between Spanish-language preference and patient experience were not significant (n = 5) or indicated worse experience (n = 4) (e.g., long waits, problems getting appointments, and not understanding nurses). CONCLUSION: LC is associated with better patient experience. Using interpreters is associated with better patient experience but only with high-quality interpreters. Strategies are needed to eliminate disparities and enhance communication for all Spanish-preferring primary care patients, whether with a bilingual provider or an interpreter.
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PURPOSE: We describe development of a short health-related quality of life measure, the patient-reported outcomes measurement information system® (PROMIS®)-16 Profile, which generates domain-specific scores for physical function, ability to participate in social roles and activities, anxiety, depression, sleep disturbance, pain interference, cognitive function, and fatigue. METHODS: An empirical evaluation of 50 candidate PROMIS items and item pairs was conducted using data from a sample of 5775 respondents from Amazon's Mechanical Turk (MTurk). Results and item response theory information curves for a subset of item pairs were presented and discussed in a stakeholder meeting to narrow the candidate item sets. A survey of the stakeholders and 124 MTurk adults was conducted to solicit preferences among remaining candidate items and finalize the measure. RESULTS: Empirical evaluation showed minimal differences in basic descriptive statistics (e.g., means, correlations) and associations with the PROMIS-29 + 2 Profile, thus item pairs were further considered primarily based on item properties and content. Stakeholders discussed and identified subsets of candidate item pairs for six domains, and final item pairs were agreed upon for two domains. Final items were selected based on stakeholder and MTurk-respondent preferences. The PROMIS-16 profile generates eight domain scores with strong psychometric properties. CONCLUSION: The PROMIS-16 Profile provides an attractive brief measure of eight distinct domains of health-related quality of life, representing an ideal screening tool for clinical care, which can help clinicians quickly identify distinct areas of concern that may require further assessment and follow-up. Further research is needed to confirm and extend these findings.
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BACKGROUND: Burnout among providers negatively impacts patient care experiences and safety. Providers at Federally Qualified Health Centers (FQHC) are at high risk for burnout due to high patient volumes; inadequate staffing; and balancing the demands of patients, families, and team members. OBJECTIVE: Examine associations of provider burnout with their perspectives on quality improvement (QI), patient experience measurement, clinic culture, and job satisfaction. DESIGN: We conducted a cross-sectional provider survey about their perspectives including the single-item burnout measure. We fit separate regression models, controlling for provider type, gender, being multilingual, and fixed effects for clinic predicting outcome measures from burnout. PARTICIPANTS: Seventy-four providers from 44 clinics in large, urban FQHC (52% response rate; n = 174). MAIN MEASURES: Survey included a single-item, self-defined burnout measure adapted from the Physician Worklife Survey, and measures from the RAND AMA Study survey, Heath Tracking Physician survey, TransforMed Clinician and Staff Questionnaire, Physician Worklife Survey, Minimizing Errors Maximizing Outcomes survey, and surveys by Friedberg et al. 31 and Walling et al. 32 RESULTS: Thirty percent of providers reported burnout. Providers in clinics with more facilitative leadership reported not being burned out (compared to those reporting burnout; p-values < 0.05). More pressures related to patient care and lower job satisfaction were associated with burnout (p-values < 0.05). CONCLUSIONS: Creating provider-team relationships and environments where providers have the time and space necessary to discuss changes to improve care, ideas are shared, leadership supports QI, and QI is monitored and discussed were related to not being burned out. Reducing time pressures and improving support needed for providers to address the high-need levels of FQHC patients can also decrease burnout. Such leadership and support to improving care may be a separate protective factor against burnout. Research is needed to further examine which aspects of leadership drive down burnout and increase provider involvement in change efforts and improving care.
Subject(s)
Burnout, Professional , Job Satisfaction , Patient Satisfaction , Quality Improvement , Humans , Burnout, Professional/psychology , Burnout, Professional/epidemiology , Male , Female , Cross-Sectional Studies , Primary Health Care/standards , Adult , Middle Aged , Organizational Culture , Surveys and Questionnaires , Physicians, Primary Care/psychology , Physicians, Primary Care/standardsABSTRACT
BACKGROUND: The Patient-Reported Outcomes Measurement and Information System (PROMIS®) global health items (global-10) yield physical and mental health scale scores and the PROMIS-Preference (PROPr) scoring system estimated from PROMIS domain scores (e.g., PROMIS-29 + 2) produces a single score anchored by 0 (dead or as bad as being dead) to 1 (full health). A link between the PROMIS global-10 and the PROPr is needed. METHODS: The PROMIS-29 + 2 and the PROMIS global-10 were administered to 4102 adults in the Ipsos KnowledgePanel in 2022. The median age was 52 (range 18-94), 50% were female, 70% were non-Hispanic White, and 64% were married or living with a partner. The highest level of education completed for 26% of the sample was a high school degree or general education diploma and 44% worked full-time. We estimated correlations of the PROPr with the PROMIS global health items and the global physical and mental health scales. We examined the adjusted R2 and estimated correlations between predicted and observed PROPr scores. RESULTS: Product-moment correlations between the PROMIS global health items and the PROPr ranged from 0.50 to 0.63. The PROMIS global physical health and mental health scale scores correlated 0.74 and 0.60, respectively, with the PROPr. The adjusted R2 in the regression of the PROPr on the PROMIS global health items was 64%. The equated PROPr preference scores correlated (product-moment) 0.80 (n = 4043; p < 0.0001) with the observed PROPr preference scores, and the intra-class correlation (two-way random effects model) was 0.80. The normalized mean absolute error (NMAE) was 0.45 (SD = 0.43). The adjusted R2 in the OLS regression of the PROPr on the PROMIS global health scales was 59%. The equated PROPr preference scores correlated (product-moment) was 0.77 (n = 4046; p < 0.0001) with the observed PROPr preference scores, and the intra-class correlation was 0.77. The NMAE was 0.49 (SD = 0.45). CONCLUSIONS: Regression equations provide a reasonably accurate estimate of the PROPr preference-based score from the PROMIS global health items or scales for group-level comparisons. These estimates facilitate cost-effectiveness research and meta-analyses. The estimated PROPr scores are not accurate enough for individual-level applications. Future evaluations of the prediction equations are needed.
Subject(s)
Global Health , Marriage , Adult , Female , Humans , Male , Middle Aged , Educational Status , Mental Health , Patient Reported Outcome Measures , United States , Adolescent , Young Adult , Aged , Aged, 80 and overSubject(s)
Chronic Pain , Low Back Pain , Humans , Low Back Pain/diagnosis , Back Pain , Chronic Pain/diagnosisABSTRACT
OBJECTIVE: Assessing functional limitations for adults at high risk of frailty yields valuable information for identifying those in need of therapy. We evaluate a self-report measure used to assess physical function among Medicare recipients in the United States. DESIGN: Secondary analysis of the 2020 Medicare Health Outcomes Survey. SETTING: A random sample of adult enrollees of 510 managed care plans. PARTICIPANTS: 287,476 adults (37% completion rate): 58% women; 16% were <65 years old (entitled via disability), 50% 65-74, and 34% 75 or older; 77% White, 14% Black, and 8% another race; 19% had Subject(s)
Activities of Daily Living
, Medicare
, Adult
, Humans
, Female
, Aged
, United States
, Male
, Reproducibility of Results
, Surveys and Questionnaires
, Outcome Assessment, Health Care
, Disability Evaluation
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BACKGROUND: The extent to which different measures of back pain impact represent an underlying common factor has implications for decisions about which one to use in studies of pain management and estimating one score from others. AIMS: To determine if different self-report back pain impact measures represent an underlying pain latent variable and estimate associations with it. METHOD: Seven pain impact measures completed by Amazon Mechanical Turk adults are used to estimate internal consistency reliability and associations: Oswestry Disability Index (ODI), Roland-Morris Disability Questionnaire (RMDQ), short form of the Örebro Musculoskeletal Pain Questionnaire (OMPQ), Subgroups for Targeted Treatment (STarT) Back Tool, the Graded Chronic Pain Scale (GCPS) disability score, PEG (Pain intensity, interference with Enjoyment of life, interference with General activity), and Impact Stratification Score (ISS). RESULTS: The sample of 1,874 adults with back pain had an average age of 41 and 52% were female. Sixteen percent were Hispanic, 7% non-Hispanic Black, 5% non-Hispanic Asian, and 71% non-Hispanic White. Internal consistency reliability estimates ranged from 0.710 (OMPQ) to 0.923 (GCPS). Correlations among the measures ranged from 0.609 (RMDQ with OMPQ) to 0.812 (PEG with GCPS). Standardized factor loadings on the pain latent variable ranged from 0.782 (RMDQ) to 0.870 (ISS). CONCLUSIONS: Scores of each measure can be estimated from the others for use in research.
Subject(s)
Chronic Pain , Low Back Pain , Musculoskeletal Pain , Adult , Humans , Female , Male , Self Report , Reproducibility of Results , Disability Evaluation , Back Pain , Surveys and QuestionnairesABSTRACT
Objective: To evaluate the associations between baseline demographics, health conditions, pain management strategies, and health-related quality-of-life (HRQoL) measures with pain management strategies at 3-month follow-up in respondents reporting current low-back pain (LBP). Study design: Cohort study of survey data collected from adults with LBP sampled from Amazon Mechanical Turk crowdsourcing panel. Methods: Demographics, health conditions, and the Patient-Reported Outcomes Measurement Information System (PROMIS)-10 were included in the baseline survey. Respondents reporting LBP completed a more comprehensive survey inquiring about pain management strategies and several HRQoL measures. Bivariate then multivariate logistic regression estimated odds ratios (ORs) with 95% confidence intervals (CIs) for the association between baseline characteristics and pain management utilization at 3-month follow-up. Model fit statistics were evaluated to assess the predictive value. Results: The final cohort included 717 respondents with completed surveys. The most prevalent pain management strategy at follow-up was other care (n = 474), followed by no care (n = 94), conservative care only (n = 76), medical care only (n = 51), and medical and conservative care combined (n = 22). The conservative care only group had higher (better) mental and physical health PROMIS-10 scores as opposed to the medical care only and combination care groups, which had lower (worse) physical health scores. In multivariate models, estimated ORs (95% CIs) for the association between baseline and follow-up pain management ranged from 4.6 (2.7-7.8) for conservative care only to 16.8 (6.9-40.7) for medical care only. Additional significant baseline predictors included age, income, education, workman's compensation claim, Oswestry Disability Index score, and Global Chronic Pain Scale grade. Conclusions: This study provides important information regarding the association between patient characteristics, HRQoL measures, and LBP-related pain management utilization.
Subject(s)
Crowdsourcing , Low Back Pain , Adult , Humans , Cohort Studies , Pain Management , Surveys and Questionnaires , Low Back Pain/diagnosis , Low Back Pain/therapyABSTRACT
PURPOSE: The Patient-Reported Outcomes Measurement and Information System (PROMIS®): includes the PROMIS-29 physical and mental health summary and the PROMIS global physical and mental health scores. It is unknown how these scores coincide with one another. This study examines whether the scores yield similar or different information. METHODS: The PROMIS-29 and the PROMIS global health items were administered to 5804 adults from Amazon's Mechanical Turk (MTurk) in 2021-2022 and to 4060 adults in the Ipsos KnowledgePanel (KP) in 2022. RESULTS: The median age of those in MTurk (KP) was 36 (54) and 53% (50%) were male. Mean T-scores on the PROMIS-29 and PROMIS global physical health scales were similar, but PROMIS global mental health was 3-4 points lower than the PROMIS-29 mental health summary score. Product-moment correlations ranged from 0.69 to 0.81 between the PROMIS-29 physical health and PROMIS global physical health scales and 0.56-0.69 between the mental health scales. Multi-trait multimethod analyses indicated that only a small proportion of the correlations between the two methods of measuring mental health were significantly more highly correlated with one another than correlations between physical and mental health. CONCLUSIONS: PROMIS-29 and PROMIS global mental health scales provide different information and, therefore, study conclusions may vary depending on which measure is used. Interpretation of results needs to consider that the PROMIS-29 mental health scale is a weighted combination of specific domains while the PROMIS global mental health scale is based on general mental health perceptions. Further comparisons of methods of assessing mental health are needed.