OBJECTIVE: Assessment of patient-reported outcome measures (PROMs) for hand and upper-extremity surgery patients using measures such as the Quick Disabilities of the Arm, Shoulder, and Hand (qDASH), as well as general measures including the Patient-Reported Outcomes Measurement Information System Upper Extremity Physical Function domain via a Computer-Adaptive Test (PROMIS UE CAT), has become commonplace. The aim of this study was to link, for crosswalking, the qDASH measure to both versions of the PROMIS UE CAT (v1.2 and v2.0).
METHODS: We included 18,944 hand and upper-extremity patients who completed both versions of the PROMIS UE CAT and the qDASH at the same clinical encounter. Shoulder pathology was excluded. Score linkage was performed using the R package equate, and multiple equating models (linear regression, identity, mean, linear, equipercentile, and circle-arc models) were used to establish crosswalk tables.
RESULTS: Mean qDASH and PROMIS UE CAT v1.2 scores were 38.2 (SD = 23.1) and 36.6 (SD = 9.8), respectively. Mean qDASH and PROMIS UE CAT v2.0 scores were 37.3 (SD = 21.8) and 38.3 (SD = 10.4), respectively. Pearson correlations had very strong linear relationships between the qDASH and the PROMIS UE CAT v1.2 and PROMIS UE CAT v2.0 (r = -0.83 [-0.84, -0.92] and r = -0.80 [-0.81, -0.80], respectively). For the equipercentile equating models, the intraclass correlation coefficient (ICC) had very strong positive relationships to linking measures with ICC = 0.85 (0.84, 0.86) for the qDASH-UE CAT v1.2 crosswalk and ICC = 0.83 (0.82, 0.84) for the qDASH-UE CAT v2.0 crosswalk.
CONCLUSIONS: The linkages establish crosswalk tables using equipercentile equating models to convert the PROMIS UE CAT v1.2 and v2.0 scores to the qDASH and vice versa.
CONCLUSIONS: This study provides crosswalk tables for commonly collected PROMs in hand surgery, increasing the comparability of results between centers using different PROMs to study the same conditions or treatments.

Research on health anxiety has bloomed in recent years, but summaries of the literature are complicated by the use of dissimilar self-report questionnaires. Furthermore, these instruments have rarely been administered in parallel, and especially not in clinical samples. In this study, we aimed to investigate the relationship between five widespread health anxiety measures, and to draft guidelines for the conversion of different sum scores.
Clinical trial participants with principal pathological health anxiety (n = 335) and a sample of healthy volunteers (n = 88) completed the 14-item Whiteley Index (WI-14), the Illness Attitude Scale (IAS), and the 14-, 18-, and 64-item Health Anxiety Inventory (the HAI-64, HAI-18, and HAI-14). Cross-sectional data from all participants were pooled (N = 423) and we conducted a joint factor analysis and approximate equipercentile linking of the WI-14, IAS, HAI-64, HAI-18, and HAI-14.
Inter-scale correlations were high (rs ≥ 0.90 and ≥ 0.88 in adjusted analyses), and the scree plot of the joint factor analysis spoke for a unifactorial solution where 89/105 items (85%) had loadings ≥ 0.40. Most items at the core of this broad trait health anxiety factor pertained to the worry about health, the fear of having or developing a serious disease, and to some extent bodily preoccupation. We present a cross-walk table of observed equipercentile linked sum scores.
This study speaks clearly in favor of the WI-14, IAS, HAI-64, HAI-18, and HAI-14 all tapping into the same trait health anxiety construct, the core of which appears to concern the worry about health, the fear of having or developing a serious disease, and to some extent bodily preoccupation. Based on recently reported cut-offs for the HAI-14, a reasonable cutoff for pathological health anxiety in a psychiatric setting probably lies around 7-8 on the WI-14, 52-53 on the IAS, 82-83 on the HAI-64, and 26-27 on the HAI-18.
ClinicalTrials.gov NCT01966705, NCT02314065.

Most existing facility assessments collect data on a sample of health facilities. Sampling of health facilities may introduce bias into estimates of effective coverage generated by ecologically linking individuals to health providers based on geographic proximity or administrative catchment.
We assessed the bias introduced to effective coverage estimates produced through two ecological linking approaches (administrative unit and Euclidean distance) applied to a sample of health facilities. Our analysis linked MICS household survey data on care-seeking for child illness and childbirth care with data on service quality collected from a census of health facilities in the Savanes region of Cote d\'Ivoire. To assess the bias introduced by sampling, we drew 20 random samples of three different sample sizes from our census of health facilities. We calculated effective coverage of sick child and childbirth care using both ecological linking methods applied to each sampled facility data set. We compared the sampled effective coverage estimates to ecologically linked census-based estimates and estimates based on true source of care. We performed sensitivity analyses with simulated preferential care-seeking from higher-quality providers and randomly generated provider quality scores.
Sampling of health facilities did not significantly bias effective coverage compared to either the ecologically linked estimates derived from a census of facilities or true effective coverage estimates using the original data or simulated random quality sensitivity analysis. However, a few estimates based on sampling in a setting where individuals preferentially sought care from higher-quality providers fell outside of the estimate bounds of true effective coverage. Those cases predominantly occurred using smaller sample sizes and the Euclidean distance linking method. None of the sample-based estimates fell outside the bounds of the ecologically linked census-derived estimates.
Our analyses suggest that current health facility sampling approaches do not significantly bias estimates of effective coverage produced through ecological linking. Choice of ecological linking methods is a greater source of bias from true effective coverage estimates, although facility sampling can exacerbate this bias in certain scenarios. Careful selection of ecological linking methods is essential to minimize the potential effect of both ecological linking and sampling error.

Geographic proximity is often used to link household and health provider data to estimate effective coverage of health interventions. Existing household surveys often provide displaced data on the central point within household clusters rather than household location. This may introduce error into analyses based on the distance between households and providers.
We assessed the effect of imprecise household location on quality-adjusted effective coverage of child curative services estimated by linking sick children to providers based on geographic proximity. We used data on care-seeking for child illness and health provider quality in Southern Province, Zambia. The dataset included the location of respondent households, a census of providers, and data on the exact outlets utilized by sick children included in the study. We displaced the central point of each household cluster point five times. We calculated quality-adjusted coverage by assigning each sick child to a provider\'s care based on three measures of geographic proximity (Euclidean distance, travel time, and geographic radius) from the household location, cluster point, and displaced cluster locations. We compared the estimates of quality-adjusted coverage to each other and estimates using each sick child\'s true source of care. We performed sensitivity analyses with simulated preferential care-seeking from higher-quality providers and randomly generated provider quality scores.
Fewer children were linked to their true source of care using cluster locations than household locations. Effective coverage estimates produced using undisplaced or displaced cluster points did not vary significantly from estimates produced using household location data or each sick child\'s true source of care. However, the sensitivity analyses simulating greater variability in provider quality showed bias in effective coverage estimates produced with the geographic radius and travel time method using imprecise location data in some scenarios.
Use of undisplaced or displaced cluster location reduced the proportion of children that linked to their true source of care. In settings with minimal variability in quality within provider categories, the impact on effective coverage estimates is limited. However, use of imprecise household location and choice of geographic linking method can bias estimates in areas with high variability in provider quality or preferential care-seeking.

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.

The PROsetta Stone Project, summarized in this issue by Schalet et al. (Psychometrika 86, 2021), is a major step forward in enabling comparability between different patient-reported outcomes measures. Schalet et al. clearly describe the psychometric methods used in the PROsetta Stone project and other projects from the Patient-Reported Outcomes Measurement Information System (PROMIS): linking based on unidimensional item response theory (IRT), equipercentile linking, and calibrated projection based on multidimensional IRT. Analyses in a validation data set and simulation studies provide strong support that the linking methods are robust when basic assumptions are fulfilled. The links already established will be of great value to the field, and the methodology described by Schalet et al. will hopefully inspire the next series of linking studies. Among potential improvements that should be considered by new studies are: (1) a thorough evaluation of the content of the measures to be linked to better guide the evaluation of measurement assumptions, (2) improvements in the design of linking studies such as selection of the optimal sample to provide data in the score ranges where linking precision is most critical and using counterbalanced designs to control for order effects. Finally, it may be useful to consider how the linking algorithms are used in subsequent data analyses. Analytic strategies based on plausible values or latent regression IRT models may be preferable to the simple transformation of scores from one patient at the time.