How Are Age, Gender, and Country Differences Associated With PROMIS Physical Function, Upper Extremity, and Pain Interference Scores?

BACKGROUND: The interpretation of patient-reported outcomes requires appropriate comparison data. Currently, no patient-specific reference data exist for the Patient-Reported Outcome Measurement Information System (PROMIS) Physical Function (PF), Upper Extremity (UE), and Pain Interference (PI) scales for individuals 50 years and older. QUESTIONS/PURPOSES: (1) Can all PROMIS PF, UE, and PI items be used for valid cross-country comparisons in these domains among the United States, the United Kingdom, and Germany? (2) How are age, gender, and country related to PROMIS PF, PROMIS UE, and PROMIS PI scores? (3) What is the relationship of age, gender, and country across individuals with PROMIS PF, PROMIS UE, and PROMIS PI scores ranging from very low to very high? METHODS: We conducted telephone interviews to collect custom PROMIS PF (22 items), UE (eight items), and PI (eight items) short forms, as well as sociodemographic data (age, gender, work status, and education level), with participants randomly selected from the general population older than 50 years in the United States (n = 900), United Kingdom (n = 905), and Germany (n = 921). We focused on these individuals because of their higher prevalence of surgeries and lower physical functioning. Although response rates varied across countries (14% for the United Kingdom, 22% for Germany, and 12% for the United States), we used existing normative data to ensure demographic alignment with the overall populations of these countries. This helped mitigate potential nonresponder bias and enhance the representativeness and validity of our findings. We investigated differential item functioning to determine whether all items can be used for valid crosscultural comparisons. To answer our second research question, we compared age groups, gender, and countries using median regressions. Using imputation of plausible values and quantile regression, we modeled age-, gender-, and country-specific distributions of PROMIS scores to obtain patient-specific reference values and answer our third research question. RESULTS: All items from the PROMIS PF, UE, and PI measures were valid for across-country comparisons. We found clinically meaningful associations of age, gender, and country with PROMIS PF, UE, and PI scores. With age, PROMIS PF scores decreased (age ß Median = -0.35 [95% CI -0.40 to -0.31]), and PROMIS UE scores followed a similar trend (age ß Median = -0.38 [95% CI -0.45 to -0.32]). This means that a 10-year increase in age corresponded to a decline in approximately 3.5 points for the PROMIS PF score-a value that is approximately the minimum clinically important difference (MCID). Concurrently, we observed a modest increase in PROMIS PI scores with age, reaching half the MCID after 20 years. Women in all countries scored higher than men on the PROMIS PI and 1 MCID lower on the PROMIS PF and UE. Additionally, there were higher T-scores for the United States than for the United Kingdom across all domains. The difference in scores ranged from 1.21 points for the PROMIS PF to a more pronounced 3.83 points for the PROMIS UE. Participants from the United States exhibited up to half an MCID lower T-scores than their German counterparts for the PROMIS PF and PROMIS PI. In individuals with high levels of physical function, with each 10-year increase in age, there could be a decrease of up to 4 points in PROMIS PF scores. Across all levels of upper extremity function, women reported lower PROMIS UE scores than men by an average of 5 points. CONCLUSION: Our study provides age-, gender-, and country-specific reference values for PROMIS PF, UE, and PI scores, which can be used by clinicians, researchers, and healthcare policymakers to better interpret patient-reported outcomes and provide more personalized care. These findings are particularly relevant for those collecting patient-reported outcomes in their clinical routine and researchers conducting multinational studies. We provide an internet application ( www.common-metrics.org/PROMIS_PF_and_PI_Reference_scores.php ) for user-friendly accessibility in order to perform age, gender, and country conversions of PROMIS scores. Population reference values can also serve as comparators to data collected with other PROMIS short forms or computerized adaptive tests. LEVEL OF EVIDENCE: Level II, diagnostic study.

What's in a score: A longitudinal investigation of scores based on item response theory and classical test theory for the Amsterdam Instrumental Activities of Daily Living Questionnaire in cognitively normal and impaired older adults.

OBJECTIVE: We aimed to investigate whether item response theory (IRT)-based scoring allows for a more accurate, responsive, and less biased assessment of everyday functioning than traditional classical test theory (CTT)-based scoring, as measured with the Amsterdam Instrumental Activities of Daily Living Questionnaire. METHOD: In this longitudinal multicenter study including cognitively normal and impaired individuals, we examined IRT-based and CTT-based score distributions and differences between diagnostic groups using linear regressions, and investigated scale attenuation. We compared change over time between scoring methods using linear mixed models with random intercepts and slopes for time. RESULTS: Two thousand two hundred ninety-four participants were included (66.6 ± 7.7 years, 54% female): n = 2,032 (89%) with normal cognition, n = 93 (4%) with subjective cognitive decline, n = 79 (3%) with mild cognitive impairment, and n = 91 (4%) with dementia. At baseline, IRT-based and CTT-based scores were highly correlated (r = -0.92). IRT-based scores showed less scale attenuation than CTT-based scores. In a subsample of n = 1,145 (62%) who were followed for a mean of 1.3 (SD = 0.6) years, IRT-based scores declined significantly among cognitively normal individuals (unstandardized coefficient [B] = -0.15, 95% confidence interval, 95% CI [-0.28, -0.03], effect size = -0.02), whereas CTT-based scores did not (B = 0.20, 95% CI [-0.02, 0.41], effect size = 0.02). In the other diagnostic groups, effect sizes of change over time were similar. CONCLUSIONS: IRT-based scores were less affected by scale attenuation than CTT-based scores. With regard to responsiveness, IRT-based scores showed more signal than CTT-based scores in early disease stages, highlighting the IRT-based scores' superior suitability for use in preclinical populations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Evaluating the agreement between different substance use recall periods in multiple HIV cohorts.

BACKGROUND: This study aims to evaluate the agreement in substance use on both binary and ordinal scales between 3-month and 6-month recall periods with samples from different communities, demographic backgrounds, and HIV status. METHODS: We administered the Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) to 799 participants from three different North American cohorts focused on substance use and HIV. We conducted a within-person agreement analysis by calculating the agreement levels and Kappa statistic between data collected using the 3-month recall ASSIST and 6-month custom substance use surveys as well as different terminology for each substance in multiple cohorts. RESULTS: For all drugs studied, the agreement on the binary use or ordinal frequency of use metrics showed a high agreement level between 80.4% and 97.9% and an adequate adjusted kappa value between 0.61 and 0.96, suggesting substantial agreement. According to the agreement criteria we proposed, substance use data collected using different recall periods and with variation in drug names can be harmonized across cohorts. CONCLUSIONS: This study is the first to evaluate the feasibility of data harmonization of substance use by demonstrating high level of agreement between different recall periods in different cohorts. The results can inform data harmonization efforts in consortia where data are collected from cohorts using different questions and recall periods.

Can severity of substance use be measured across drug classes? Estimating differential item functioning by drug class in two general measures of substance use severity.

BACKGROUND: Substance use severity is frequently measured using generic (i.e., non-drug specific) items. Yet, the measurement properties of these items must be evaluated for measurement invariance across inidividuals who use differing substances to ensure total scores can be compared across groups. METHOD: This study used data from two independent samples (n1 = 474; n2 = 5183) and two measures of general substance use severity with generic items, the Patient Reported Outcomes Measurement Information System (PROMIS) Severity of Substance Use and DAST-10, to examine for differential item functioning (DIF) across substances (i.e., sedatives, opioids, amphetamines, cocaine, and cannabis). We utilized moderated nonlinear factor analysis to estimate DIF. Finally, we compared factor scores across estimation methods with and without accounting for DIF to examine the impact of DIF. RESULTS: A minority of items showed statistically significant DIF in each scale (Items with DIF: PROMIS Sample 1: 5/37; PROMIS Sample 2: 7/20; DAST-10 Sample 2: 3/10). Factor scores across scoring methods showed extremely high correlations (0.994 - 0.999), estimates of mean differences across substance groups did not vary considerably across scoring methods, but measurement differences were correlated with factor scores. DISCUSSION: These findings suggest that these two measures of substance use severity can be used across individuals using different substances. Factor scores appear similar across scoring methods and mean differences do not appear to be substantially biases. Measures with generic items may offer a parsimonious alternative to measures with drug specific items but more research is needed to evaluate the robustness of these findings.

Using bookmarking methods with orthopedic clinicians and patients with fractures produces score interpretation labels for patient-reported outcome measures.

OBJECTIVE: The objective of this study was to determine the patient-reported outcome measure (PROM) score ranges associated with descriptive labels (i.e., within normal limits, mild, moderate, severe) by using bookmarking methods with orthopedic clinicians and patients who have experienced a bone fracture. STUDY DESIGN AND SETTING: We created vignettes comprised of six items and responses from the Patient-Reported Outcomes Measurement Information System (PROMIS) Upper Extremity Function, Physical Function, and Pain Interference item banks reflecting different levels of severity. Two groups of patients with fractures (n = 11) and two groups of orthopedic clinicians (n = 16) reviewed the vignettes and assigned descriptive labels independently and then discussed as a group until reaching consensus via a videoconference platform. RESULTS: PROMIS Physical Function and Pain Interference thresholds (T = 50, 40, 25/30 and T = 50/55, 60, 65/70, respectively) for patients with bone fractures were consistent with the results from other patient populations. Upper Extremity thresholds were about 10 points (1 SD) more severe (T = 40, 30, 25/20) compared to the other measures. Patient and clinician perspectives were similar. CONCLUSION: Bookmarking methods generated meaningful score thresholds for PROMIS measures. These thresholds between severity categories varied by domain. Threshold values for severity represent important supplemental information to interpret PROMIS scores clinically.

Clinical Validity of the PROMIS Healthcare Engagement 8-Item Short Form.

BACKGROUND: Healthcare engagement is a key measurement target for value-based healthcare, but a reliable and valid patient-reported measure has not yet been widely adopted. OBJECTIVE: To assess the validity of a newly developed patient-reported measure of healthcare engagement, the 8-item PROMIS Healthcare Engagement (PHE-8a). DESIGN: Prospective cohort study of the association between healthcare engagement and quality of care over 1 year. We fit mixed effects models of quality indicators as a function of engagement scores, adjusting for age, race/ethnicity, rural residence, and risk scores. PARTICIPANTS: National stratified random sample of 9552 Veterans receiving Veterans Health Administration care for chronic conditions (hypertension, diabetes) or mental health conditions (depression, post-traumatic stress disorder). MAIN MEASURES: Patient experience: Consumer Assessment of Health Plans and Systems communication and self-management support composites; no-show rates for primary care and mental health appointments; use of patient portal My HealtheVet; and Healthcare Effectiveness Data and Information Set electronic quality measures: HbA1c poor control, controlling high blood pressure, and hyperlipidemia therapy adherence. KEY RESULTS: Higher engagement scores were associated with better healthcare quality across all outcomes, with each 5-point increase (1/2 standard deviation) in engagement scores associated with statistically significant and clinically meaningful gains in quality. Across the continuum of low to high engagement scores, we observed a concomitant reduction in primary care no-show rates of 37% and 24% for mental health clinics; an increased likelihood of My HealtheVet use of 15.4%; and a decreased likelihood of poor diabetes control of 44%. CONCLUSIONS: The PHE-8a is a brief, reliable, and valid patient-reported measure of healthcare engagement. These results confirm previously untested hypotheses that patient engagement can promote healthcare quality.

Response Category Functioning on the Health Care Engagement Measure Using the Nominal Response Model.

As part of a scale development project, we fit a nominal response item response theory model to responses to the Health Care Engagement Measure (HEM). When using the original 5-point response format, categories were not ordered as intended for six of the 23 items. For the remaining, the category boundary discrimination between Categories 0 (not at all true) and 1 (a little bit true) was only weakly discriminating, suggesting uninformative categories. When the lowest two categories were collapsed, psychometric properties improved greatly. Category boundary discriminations within items, however, varied significantly. Specifically, higher response category distinctions, such as responding 3 (very true) versus 2 (mostly true) were considerably more discriminating than lower response category distinctions. Implications for HEM scoring and for improving measurement precision at lower levels of the construct are presented as is the unique role of the nominal response model in category analysis.

Can a linking crosswalk table be applied to a different population? An independent validation study for a crosswalk between BSI depression and PROMIS depression scales.

A linking procedure establishes a "bridge" between the scores from different patient-reported outcome (PRO) instruments that measure similar constructs. After developing a linking relationship however, it is critical to evaluate whether this relationship can be generalized to different groups. Our study aims to validate a published crosswalk for score conversion between the Brief Symptom Inventory Depression subscale and the Patient-Reported Outcomes Measurement Information System Depression 8a using an independent sample. Data were from a sample of young men who have sex with men (MSM), which differs in terms of participant age, race, and ethnicity from the sample used to develop the existing crosswalk. The validity of the newly derived crosswalk was evaluated in terms of the correlation, mean difference and standard deviation between the observed and the linked scores. The two crosswalks were further compared to evaluate if the difference was within an acceptable range. More than half of the item parameters obtained from the two samples were found to overlap in their confidence intervals. Differences between each pair of scores in the two crosswalks was within three T-score points, well within the range of each crosswalk score's standard error. This study concludes that an existing crosswalk is replicable on a sample that differs from that used for crosswalk development, but future research should continue to examine the generalizability of the linked parameters and evaluate the reproducibility of this crosswalk to other populations.

Linking Hip Disability and Osteoarthritis Outcome Score-Physical Function Short Form and PROMIS Physical Function.

INTRODUCTION: Linking scores on patient-reported outcome measures can enable data aggregation for research, clinical care, and quality. We aimed to link scores on the Hip Disability and Osteoarthritis Outcome Score-Physical Function Short Form (HOOS-PS) and the Patient-reported Outcomes Measurement Information System Physical Function (PROMIS PF). METHODS: A retrospective study was conducted from 2017 to 2020 evaluating patients with hip osteoarthritis who received routine clinical care from an orthopaedic surgeon. Our sample included 3,382 unique patients with 7,369 pairs of HOOS-PS and PROMIS PF measures completed at a single nonsurgical, preoperative, or postoperative time point. We included one randomly selected time point of scores for each patient in our linking analysis sample. We compared the accuracy of linking using four methods, including equipercentile and item response theory-based approaches. RESULTS: PROMIS PF and HOOS-PS scores were strongly correlated ( r = -0.827 for raw HOOS-PS scores and r = 0.820 for summary HOOS-PS scores). The assumptions were met for equipercentile and item response theory approaches to linking. We selected the item response theory-based Stocking-Lord approach as the optimal crosswalk and estimated item parameters for the HOOS-PS items on the PROMIS metric. A sensitivity analysis demonstrated overall robustness of the crosswalk estimates in nonsurgical, preoperative, and postoperative patients. CONCLUSION: These crosswalks can be used to convert scores between HOOS-PS and PROMIS PF metric at the group level, which can be valuable for data aggregation. Conversion of individual patient-level data is not recommended secondary to increased risk of error.

Personality and health: Disentangling their between-person and within-person relationship in three longitudinal studies.

Personality traits and physical health both change over the life span. Theoretical models and empirical evidence suggest that these changes are related. The current study investigated the dynamic relations between personality traits and physical health at both the between-person and the within-person levels. Data were drawn from three longitudinal studies: the Veterans Affairs Normative Aging Study (NAS; N = 1,734), the Longitudinal Internet Studies for the Social Sciences (LISS; N = 13,559), and the Swedish Adoption/Twin Study of Aging (SATSA, N = 2,209). Using random intercept cross-lagged panel models (RI-CLPMs) and the continuous time (CT) models, after controlling the between-person variance, generally, evidence was found for bidirectional associations between changes in neuroticism and extraversion and changes in self-rated health and general disease level. Bidirectional associations between changes in neuroticism and change in cardiovascular diseases and central nervous system diseases were observed only when time was modeled as continuous. We also found within-person associations between changes in neuroticism and extraversion and changes in performance-based ratings of motor functioning impairment. According to the current findings, the dynamic within-person relations between personality traits and health outcomes were largely in the direction consistent with their between-person connections, although the within-person relationships were substantially smaller in strength when compared their between-person counterparts. Findings from the current study highlight the importance of distinguishing between-person and within-person effects when examining the longitudinal relationship between personality traits and health. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Linking the KOOS-PS to PROMIS Physical Function in Knee Patients Evaluated for Surgery.

INTRODUCTION: The Knee Injury and Osteoarthritis Outcome Score-Physical Function Short-form and the Patient-Reported Outcomes Measurement Information System Physical Function are widely used patient-reported outcome measures in orthopaedic practice and research. It would be helpful for clinicians and researchers to compare scores obtained on one instrument with those collected on another. To achieve this goal, this study conducted a linking analysis and computed a crosswalk table between these two scales. DATA: The data of this study were collected as part of the clinical care of total knee arthroplasty patients in a large urban and suburban health system. The sample was a mix of responses from nonsurgical (no surgery performed), preoperative (before surgical intervention), and postoperative (after surgical intervention) groups. METHODS: This study applied five linking methods: the item response theory (IRT)-based linking methods including fixed-parameter calibration, separate-parameter calibration with Stocking-Lord constants, and calibrated projection; and the equipercentile methods with log-linear smoothing and nonsmoothing approaches. Before conducting the linking analysis, we checked the linking assumptions including the similar content of the two scales, the unidimensionality of the combined scales, and the population invariance. The results of the five linking methods were evaluated by mean difference, SD, root-mean-squared deviation, intraclass correlation coefficient of the observed T scores and the crosswalk-derived T scores. RESULTS: The linking assumptions were all met. T scores generated from the Stocking-Lord crosswalk had the smallest mean difference (= -0.03) and relatively small SD (= 4.91) and root-mean-squared deviation (= 4.91) among the five linking methods. We validated this crosswalk in a larger sample with the nonsurgical, preoperative, and postoperative groups and in an external sample. DISCUSSION: This study provides clinicians and researchers a practical tool (ie, a crosswalk table) to link scores from two popular physical function measures. Given the diversity of patient-reported outcome measures in use for knee conditions, these crosswalk tables would accelerate clinical and research interpretation of aggregating functional outcomes among the patients evaluated for knee surgery each year.

Do You Recall?: Results From a Within-Person Recall Study of the Patient-Reported Outcomes Measurement Information System (PROMIS) Short Form v2.0 - Physical Function 8c.

OBJECTIVES: This study aimed to determine whether responses to Patient-Reported Outcomes Measurement Information System Short Form v2.0 - Physical Function 8c (PROMIS PF8c) items differed when the use of a 7-day recall period was compared with no specified recall period. METHODS: Using a within-subject design, we surveyed 1810 individuals from the US general population, administering PROMIS PF8c at survey beginning and end. The order of measure presentation was randomly assigned. We calculated recall difference scores (RDSs) as no recall score minus 7-day recall score using both item response theory-based T scores and raw summed scores. We examined the distribution and created Bland-Altman plots for both RDSTscore and RDSRaw. We also calculated correlations between no recall versus 7-day recall T score and raw scores. Finally, we determined whether differences in no recall versus 7-day recall scores were associated with patient-reported PF. RESULTS: RDSTscore and RDSRaw had means (root mean square differences) of 0.00 (5.43) and -0.04 (3.79), respectively. The vast majority (%) of RDSTscore and RDSRaw values fell between the Bland-Altman limits of agreement (-10.65 to 10.66 and -7.46 to 7.38, respectively). Pearson's correlations between no recall and 7-day recall for T scores and raw scores were 0.88 and 0.87, respectively. Effect sizes for mean RDSTscore and RDSRaw compared across level of Eastern Oncology Cooperative Group performance status, patient global impression of PF severity, and single PF items were near 0. CONCLUSIONS: We did not find any significant recall period effect on PF8c responses. Therefore, we recommend the use of the PROMIS physical function standard, with no specified recall time period.

The Impact of Education on Depression Assessment: Differential Item Functioning Analysis.

A person's level of education can affect their access to health care, and their health outcomes. Increasing rates of depression are another looming public health concern. Therefore, vulnerability is compounded for individuals who have a lower level of education and depression. Assessment of depressive symptoms is integral to many domains of health care including primary care and mental health specialty care. This investigation examined the degree to which education influences the psychometric properties of self-report items that measure depressive symptoms. This study was a secondary data analysis derived from three large internet panel studies. Together, the studies included the Beck Depression Inventory-II, the Center for Epidemiologic Studies Depression Scale, the Patient Health Questionnaire, and the Patient Reported Outcomes Measurement Information System measures of depression. Using a differential item functioning (DIF) approach, we found evidence of DIF such that some items on each of the questionnaires were flagged for DIF with effect sizes ranging from McFadden's Pseudo R2 = .005 to .022. For example, results included several double-barreled questions flagged for DIF. Overall, questionnaires assessing depression vary in level of complexity, which interacts with the respondent's level of education. Measurement of depression should include consideration of possible educational disparities, to identify people who may struggle with a written questionnaire, or may be subject to subtle psychometric biases associated with education.

The Development of iManage-PC, an Online Symptom Monitoring and Self-management Tool for Men With Clinically Localized Prostate Cancer.

BACKGROUND: Prostate cancer (PC) often impacts 4 major aspects of health-related quality of life (HRQL): urinary, sexual, and bowel dysfunction, and anxiety. Online tools may be helpful in supporting the development of self-management skills that can improve HRQL. OBJECTIVE: The aim of this study was to develop and pilot-test an online symptom monitoring and self-management program, iManage-PC. METHODS: A literature search, input from experts, and feedback from patients were used to develop iManage-PC. A 4-week, single-arm pilot study was conducted with 96 men with prostate cancer. We evaluated system usability, acceptance, and satisfaction and examined preliminary effects on patient-reported outcomes. RESULTS: Rates of retention (94.8%) and adherence to symptom monitoring (95.0%-97.0%) were high. Most participants rated the tool as satisfactory and acceptable (81.2%-94.3%). Related-samples Wilcoxon signed rank tests revealed that participants reported increased self-efficacy related to their ability to manage their adverse effects (T = 1772.0, P < .001, r = 0.39), physical discomfort (T = 1259.0, P < .001, r = 0.40), and stress and worry (T = 1108.5, P = .001, r = 0.34). Global mental and physical health also improved (T = 1322.0, P = .032, r = 0.23, and T = 1409.0, P = .001, r = 0.35, respectively). CONCLUSIONS: Future research with such tools should examine the potential role of cut-score-derived management interventions to improve engagement, symptom management self-efficacy, and HRQL. IMPLICATIONS FOR PRACTICE: Our findings are consistent with a growing body of literature that supports the feasibility and acceptability of remotely delivered interventions.

Linking Scores with Patient-Reported Health Outcome Instruments:A VALIDATION STUDY AND COMPARISON OF THREE LINKING METHODS.

The psychometric process used to establish a relationship between the scores of two (or more) instruments is generically referred to as linking. When two instruments with the same content and statistical test specifications are linked, these instruments are said to be equated. Linking and equating procedures have long been used for practical benefit in educational testing. In recent years, health outcome researchers have increasingly applied linking techniques to patient-reported outcome (PRO) data. However, these applications have some noteworthy purposes and associated methodological questions. Purposes for linking health outcomes include the harmonization of data across studies or settings (enabling increased power in hypothesis testing), the aggregation of summed score data by means of score crosswalk tables, and score conversion in clinical settings where new instruments are introduced, but an interpretable connection to historical data is needed. When two PRO instruments are linked, assumptions for equating are typically not met and the extent to which those assumptions are violated becomes a decision point around how (and whether) to proceed with linking. We demonstrate multiple linking procedures-equipercentile, unidimensional IRT calibration, and calibrated projection-with the Patient-Reported Outcomes Measurement Information System Depression bank and the Patient Health Questionnaire-9. We validate this link across two samples and simulate different instrument correlation levels to provide guidance around which linking method is preferred. Finally, we discuss some remaining issues and directions for psychometric research in linking PRO instruments.

Can the Knee Outcome and Osteoarthritis Score (KOOS) Function Subscale Be Linked to the PROMIS Physical Function to Crosswalk Equivalent Scores?

BACKGROUND: An increased focus on patient-reported outcome measures (PROMs) has led to a proliferation of these measures in orthopaedic surgery. Mandating a single PROM in clinical and research orthopaedics is not feasible given the breadth of data already collected with older measures and the emergence of psychometrically superior measures. Creating crosswalk tables for scores between measures allows providers to maintain control of measure choice. Furthermore, crosswalk tables permit providers to compare scores collected with older outcome measures with newly collected ones. Given the widespread use of the newer Patient-reported Outcome Measure Information System Physical Function (PROMIS PF) and the established Knee Outcome and Osteoarthritis Score (KOOS), it would be clinically useful to link these two measures. QUESTION/PURPOSE: Can the KOOS Function in Activities of Daily Living (ADL) subscale be robustly linked to the PROMIS PF to create a crosswalk table of equivalent scores that accurately reflects a patient's reported physical function level on both scales? METHODS: We sought to establish a common standardized metric for collected responses to the PROMIS PF and the KOOS ADL to develop equations for converting a PROMIS PF score to a score for the KOOS-ADL subscale and vice versa. To do this, we performed a retrospective, observational study at two academic medical centers and two community hospitals in an urban and suburban healthcare system. Patients 18 years and older who underwent TKA were identified. Between January 2017 and July 2020, we treated 8165 patients with a TKA, 93% of whom had a diagnosis of primary osteoarthritis. Of those, we considered patients who had completed a full KOOS and PROMIS PF 10a on the same date as potentially eligible. Twenty-one percent (1708 of 8165) of patients were excluded because no PROMs were collected at any point, and another 67% (5454 of 8165) were excluded because they completed only one of the required PROMs, leaving 12% (1003 of 8165) for analysis here. PROMs were collected each time they visited the health system before and after their TKAs. Physical function was measured by the PROMIS PF version 1.0 SF 10a and KOOS ADL scale. Analyses to accurately create a crosswalk of equivalent scores between the measures were performed using the equipercentile linking method with both unsmoothed and log linear smoothed score distributions. RESULTS: Crosswalks were created, and adequate validation results supported their validity; we also created tables to allow clinicians and clinician scientists to convert individual patients' scores easily. The mean difference between the observed PROMIS PF scores and the scores converted by the crosswalk from the KOOS-ADL scores was -0.08 ± 4.82. A sensitivity analysis was conducted, confirming the effectiveness of these crosswalks to link the scores of two measures from patients both before and after surgery. CONCLUSION: The PROMIS PF 10a can be robustly linked to the KOOS ADL measure. The developed crosswalk table can be used to convert PROMIS PF scores from KOOS ADL and vice versa. CLINICAL RELEVANCE: The creation of a crosswalk table between the KOOS Function in ADL subscale and PROMIS PF allows clinicians and researchers to easily convert scores between the measures, thus permitting greater choice in PROM selection while preserving comparability between patient cohorts and PROM data collected from older outcome measures. Creating a crosswalk, or concordance table, between the two scales will facilitate this comparison, especially when pooling data for meta-analyses.

Developing a common metric for depression across adulthood: Linking PROMIS depression with the Edinburgh Postnatal Depression Scale.

Depression is a leading mental health concern across the U.S. and worldwide. There are myriad assessments to evaluate depressive symptoms, including the Edinburgh Postnatal Depression Scale (EPDS), which is widely used to evaluate women's pre- and postnatal depression but not as prevalent at other timepoints in adulthood, limiting its utility for longitudinal research. As part of the National Institutes of Health's (NIH) Environmental influences on Child Health Outcomes (ECHO) Research Program, the current study sought to develop a common metric so that scores on the EPDS can be converted to the standardized Patient-Reported Measurement Information System (PROMIS®) T-score metric. Drawing on data from the ECHO-Prenatal Alcohol in SIDS and Stillbirth cohort, this study used a single-group linking design, where 1,263 mothers completed the EPDS and PROMIS-Depression measures at the same time. Score linking was conducted using equipercentile and item response theory (IRT) methods. Results showed both linking methods provide robust, congruent results, and subgroup invariance held across age, race, ethnicity, education, and geographic location. The IRT-based unidimensional fixed-parameter calibration was selected due to its model simplicity, and a crosswalk table was established to convert scores from the EPDS to PROMIS T-scores. Overall, this study provides a way to aggregate data across various depression measures and timepoints, such that researchers and clinicians now have the ability to directly compare and combine EPDS data with PROMIS and other depression measures already score-linked to PROMIS. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Psychometric evaluation of a patient-reported item bank for healthcare engagement.

PURPOSE: Healthcare engagement is a core measurement target for efforts to improve healthcare systems. This construct is broadly defined as the extent to which healthcare services represent collaborative partnerships with patients. Previous qualitative work operationalized healthcare engagement as generalized self-efficacy in four related subdomains: self-management, collaborative communication, health information use, and healthcare navigation. Building on this work, our objective was to establish a healthcare engagement instrument that is sufficiently unidimensional to yield a single score. METHOD: We conducted cognitive interviews followed by a nation-wide mail survey of US Veteran Administration (VA) healthcare users. Data were collected on 49 candidate healthcare engagement items, as well as measures of self-efficacy for managing symptoms, provider communication, and perceived access. Items were subjected to exploratory bifactor, statistical learning, and IRT analyses. RESULTS: Cognitive interviews were completed by 56 patients and 9552 VA healthcare users with chronic conditions completed the mail survey. Participants were mostly white and male but with sizable minority participation. Psychometric analyses and content considerations reduced the item pool to 23 items, which demonstrated a strong general factor (OmegaH of .89). IRT analyses revealed a high level of reliability across the trait range and little DIF across groups. Most health information use items were removed during analyses, suggesting a more independent role for this domain. CONCLUSION: We provide quantitative evidence for a relatively unidimensional measure of healthcare engagement. Despite developed with VA healthcare users, the measure is intended for general use. Future work includes short-form development and validation with other patient groups.