Development of an ultra-short measure of eight domains of health-related quality of life for research and clinical care: the patient-reported outcomes measurement information system® PROMIS®-16 profile.

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.

Eliminating Food Insecurity in the USA: a Target Trial Emulation Using Observational Data to Estimate Effects on Health-Related Quality of Life.

BACKGROUND: Food insecurity is associated with many aspects of poor health. However, trials of food insecurity interventions typically focus on outcomes of interest to funders, such as healthcare use, cost, or clinical performance metrics, rather than quality of life outcomes that may be prioritized by individuals who experience food insecurity. OBJECTIVE: To emulate a trial of a food insecurity elimination intervention, and quantify its estimated effects on health utility, health-related quality of life, and mental health. DESIGN: Target trial emulation using longitudinal, nationally representative data, from the USA, 2016-2017. PARTICIPANTS: A total of 2013 adults in the Medical Expenditure Panel Survey screened positive for food insecurity, representing 32 million individuals. MAIN MEASURES: Food insecurity was assessed using the Adult Food Security Survey Module. The primary outcome was the SF-6D (Short-Form Six Dimension) measure of health utility. Secondary outcomes were mental component score (MCS) and physical component score (PCS) of the Veterans RAND 12-Item Health Survey (a measure of health-related quality of life), Kessler 6 (K6) psychological distress, and Patient Health Questionnaire 2-item (PHQ2) depressive symptoms. KEY RESULTS: We estimated that food insecurity elimination would improve health utility by 80 QALYs per 100,000 person-years, or 0.008 QALYs per person per year (95% CI 0.002 to 0.014, p = 0.005), relative to the status quo. We also estimated that food insecurity elimination would improve mental health (difference in MCS [95% CI]: 0.55 [0.14 to 0.96]), physical health (difference in PCS: 0.44 [0.06 to 0.82]), psychological distress (difference in K6: -0.30 [-0.51 to -0.09]), and depressive symptoms (difference in PHQ-2: -0.13 [-0.20 to -0.07]). CONCLUSIONS: Food insecurity elimination may improve important, but understudied, aspects of health. Evaluations of food insecurity interventions should holistically investigate their potential to improve many different aspects of health.

Two-step screening for depressive symptoms in patients treated with kidney replacement therapies: a cross-sectional analysis.

BACKGROUND: Systematic screening for depressive symptoms may identify patients who may benefit from clinical assessment and psychosocial support. Here we assess a two-step screening using ultrabrief pre-screeners [Edmonton Symptom Assessment Survey-revised Depression item (ESASr-D) or Patient Health Questionnaire-2 (PHQ-2)] followed by the Patient-Reported Outcomes Measurement Information System Depression questionnaire (PROMIS-D) to identify depressive symptoms in patients on kidney replacement therapies. METHODS: We conducted a cross-sectional study of adults (kidney transplant recipients or treated with dialysis) in Toronto, ON, Canada. We simulated various two-step screening scenarios where only patients above a pre-screening cut-off score on the ESASr-D or PHQ-2 would move to step 2 (PROMIS-D). Screening performance was evaluated by sensitivity, specificity and positive and negative predictive values using the Patient Health Questionnaire-9 (PHQ-9) as the referent. The average number of items completed by patients in different scenarios was reported. RESULTS: Of 480 participants, 60% were male with a mean age of 55 years. Based on PHQ-9, 19% of patients had moderate or severe depressive symptoms. Pre-screening with a PHQ-2 score ≥1 combined with a PROMIS-D score of ≥53 provided the best two-step results (sensitivity 0.81, specificity 0.84, NPV 0.95). Two-step screening also reduces question burden. CONCLUSIONS: A two-step screening using a PHQ-2 score ≥1 followed by a PROMIS-D score ≥53 has good sensitivity and specificity for identifying potentially significant depressive symptoms among patients on kidney replacement therapies. This approach has lower question burden. Screened-in patients will need further clinical assessment to establish a diagnosis.

A Roadmap for Increasing the Usefulness and Impact of Patient-Preference Studies in Decision Making in Health: A Good Practices Report of an ISPOR Task Force.

Many qualitative and quantitative methods are readily available to study patient preferences in health. These methods are now being used to inform a wide variety of decisions, and there is a growing body of evidence showing studies of patient preferences can be used for decision making in a wide variety of contexts. This ISPOR Task Force report synthesizes current good practices for increasing the usefulness and impact of patient-preference studies in decision making. We provide the ISPOR Roadmap for Patient Preferences in Decision Making that invites patient-preference researchers to work with decision makers, patients and patient groups, and other stakeholders to ensure that studies are useful and impactful. The ISPOR Roadmap consists of 5 key elements: (1) context, (2) purpose, (3) population, (4) method, and (5) impact. In this report, we define these 5 elements and provide good practices on how patient-preference researchers and others can actively contribute to increasing the usefulness and impact of patient-preference studies in decision making. We also present a set of key questions that can support researchers and other stakeholders (eg, funders, reviewers, readers) to assess efforts that promote the ongoing impact (both intended and unintended) of a particular preference study and additional studies in the future.

Electronic health record (EHR)-based PROMIS measures among neurology clinic decedents and survivors: a retrospective cohort analysis.

BACKGROUND: In addition to their standard use to assess real-time symptom burden, patient-reported outcomes (PROs), such as the Patient-Reported Outcomes Measurement Information System (PROMIS), measures offer a potential opportunity to understand when patients are experiencing meaningful clinical decline. If PROs can be used to assess decline, such information can be used for informing medical decision making and determining patient-centered treatment pathways. We sought to use clinically implemented PROMIS measures to retrospectively characterize the final PROMIS report among all patients who completed at least one PROMIS assessment from December 2017-March 2020 in one large health system, stratified by decedents vs. survivors. We conducted a retrospective cohort analysis of decedents (N = 1,499) who received care from outpatient neurology clinical practice within a single, large health system as part of usual care. We also compared decedents to survivors (360 + days before death; N = 49,602) on PROMIS domains and PROMIS-Preference (PROPr) score, along with demographics and clinical characteristics. We used electronic health record (EHR) data with built-in PROMIS measures. Linear regressions assessed differences in PROMIS domains and aggregate PROPr score by days before death of the final PROMIS completion for each patient. RESULTS: Among decedents in our sample, in multivariable regression, only fatigue (range 54.48-59.38, p < 0.0029) and physical function (range 33.22-38.38, p < 0.0001) demonstrated clinically meaningful differences across time before death. The overall PROPr score also demonstrated statistically significant difference comparing survivors (0.19) to PROPr scores obtained 0-29 days before death (0.29, p < 0.0001). CONCLUSIONS: Although clinic completion of PROMIS measures was near universal, very few patients had more than one instance of PROMIS measures reported, limiting longitudinal analyses. Therefore, patient-reported outcomes in clinical practice may not yet be robust enough for incorporation in prediction models and assessment of trajectories of decline, as evidenced in these specialty clinics in one health system. PROMIS measures can be used to effectively identify symptoms and needs in real time, and robust incorporation into EHRs can improve patient-level outcomes, but further work is needed for them to offer meaningful inputs for defining patient trajectories near the end of life. Assessing symptom burden provides an opportunity to understand clinical decline, particularly as people approach the end of life. We sought to understand whether symptoms reported by patients can be used to assess decline in health. Such information can inform decision-making about care and treatments. Of eight symptoms that we assessed, patient reports of fatigue and physical function were associated with clinical decline, as was an overall score of symptom burden. Because few symptoms were associated with decline, patient-reported outcomes in clinical practice may not yet be robust enough for incorporation in prediction models and assessment of trajectories of decline.

Understanding the measurement relationship between EQ-5D-5L, PROMIS-29 and PROPr.

PURPOSE: Many generic patient-reported instruments are available for the measurement of health outcomes, including EQ-5D-5L, and the Patient-Reported Outcome Measurement Information System (PROMIS). Assessing their measurement characteristics informs users about the consistency between, and limits of, evidence produced. The aim was to assess the measurement relationship between the EQ-5D-5L descriptive system and value sets, the PROMIS-29 and PROPr (PROMIS value set). METHODS: Data were extracted from a cross-sectional survey administering measures of quality of life online in Australia. Descriptive analysis, agreement and construct validity assessment methods were used to compare instruments at the item, domain and value set level. RESULTS: In total, 794 Australians completed the survey. Convergent validity analysis found that similar dimensions across instruments were highly correlated (> 0.50), but the PROMIS-29 assesses additional health concepts not explicitly covered by EQ-5D (sleep and fatigue). Known-group assessment found that EQ-5D-5L and PROPr were able to detect those with and without a condition (ES range 0.78-0.83) but PROPr could more precisely detect differing levels of self-reported health. Both instruments were sensitive to differences in levels of pain. DISCUSSION: There is some consistency in what the EQ-5D-5L, PROMIS-29 and PROPr measure. Differences between value set characteristics can be linked to differences what is measured and the valuation approaches used. This has implications for the use of each in assessing health outcomes, and the results can inform decisions about which instrument should be used in which context.

CAPER: patient preferences to inform nonsurgical treatment of chronic low back pain: a discrete-choice experiment.

OBJECTIVE: We developed and used a discrete-choice measure to study patient preferences with regard to the risks and benefits of nonsurgical treatments when they are making treatment selections for chronic low back pain. METHODS: "CAPER TREATMENT" (Leslie Wilson) was developed with standard choice-based conjoint procedures (discrete-choice methodology that mimics an individual's decision-making process). After expert input and pilot testing, our final measure had 7 attributes (chance of pain relief, duration of relief, physical activity changes, treatment method, treatment type, treatment time burden, and risks of treatment) with 3-4 levels each. Using Sawtooth software (Sawtooth Software, Inc., Provo, UT, USA), we created a random, full-profile, balanced-overlap experimental design. Respondents (n = 211) were recruited via an emailed online link and completed 14 choice-based conjoint choice pairs; 2 fixed questions; and demographic, clinical, and quality-of-life questions. Analysis was performed with random-parameters multinomial logit with 1000 Halton draws. RESULTS: Patients cared most about the chance of pain relief, followed closely by improving physical activity, even more than duration of pain relief. There was comparatively less concern about time commitment and risks. Gender and socioeconomic status influenced preferences, especially with relation to strength of expectations for outcomes. Patients experiencing a low level of pain (Pain, Enjoyment, and General Activity Scale [PEG], question 1, numeric rating scale score<4) had a stronger desire for maximally improved physical activity, whereas those in a high level of pain (PEG, question 1, numeric rating scale score>6) preferred both maximum and more limited activity. Highly disabled patients (Oswestry Disability Index score>40) demonstrated distinctly different preferences, placing more weight on achieving pain control and less on improving physical activity. CONCLUSIONS: Individuals with chronic low back pain were willing to trade risks and inconveniences for better pain control and physical activity. Additionally, different preference phenotypes exist, which suggests a need for clinicians to target treatments to particular patients.

Parent-Reported Use of Pediatric Primary Care Telemedicine: Survey Study.

BACKGROUND: Telemedicine delivered from primary care practices became widely available for children during the COVID-19 pandemic. OBJECTIVE: Focusing on children with a usual source of care, we aimed to examine factors associated with use of primary care telemedicine. METHODS: In February 2022, we surveyed parents of children aged ≤17 years on the AmeriSpeak panel, a probability-based panel of representative US households, about their children's telemedicine use. We first compared sociodemographic factors among respondents who did and did not report a usual source of care for their children. Among those reporting a usual source of care, we used Rao-Scott F tests to examine factors associated with parent-reported use versus nonuse of primary care telemedicine for their children. RESULTS: Of 1206 respondents, 1054 reported a usual source of care for their children. Of these respondents, 301 of 1054 (weighted percentage 28%) reported primary care telemedicine visits for their children. Factors associated with primary care telemedicine use versus nonuse included having a child with a chronic medical condition (87/301, weighted percentage 27% vs 113/753, 15%, respectively; P=.002), metropolitan residence (262/301, weighted percentage 88% vs 598/753, 78%, respectively; P=.004), greater internet connectivity concerns (60/301, weighted percentage 24% vs 116/753, 16%, respectively; P=.05), and greater health literacy (285/301, weighted percentage 96% vs 693/753, 91%, respectively; P=.005). CONCLUSIONS: In a national sample of respondents with a usual source of care for their children, approximately one-quarter reported use of primary care telemedicine for their children as of 2022. Equitable access to primary care telemedicine may be enhanced by promoting access to primary care, sustaining payment for primary care telemedicine, addressing barriers in nonmetropolitan practices, and designing for lower health-literacy populations.

Changes in Food Insecurity and Changes in Patient-Reported Outcomes: a Nationally Representative Cohort Study.

BACKGROUND: Cross-sectional studies have found that health-related quality of life and mental health are worse among food-insecure compared with food-secure individuals. However, how these outcomes change as food insecurity changes is unclear. OBJECTIVE: To evaluate how common patient-reported health-related quality of life and mental health scales change in response to changes in food security. DESIGN: Retrospective cohort study using data representative of the civilian, adult, non-institutionalized population of the USA. PARTICIPANTS: Food insecure adults who completed the 2016-2017 Medical Expenditure Panel Survey. MAIN MEASURES: Mental health, as measured by the mental component score of the Veterans Rand 12-Item Health Survey (VR-12) (primary outcome), along physical health (physical component score of the VR-12), self-rated health status, psychological distress (Kessler 6), depressive symptoms (PHQ2), and the SF-6D measure of health utility. We fit linear regression models adjusted for baseline outcome level, age, gender, race/ethnicity, education, health insurance, and family size followed by predictive margins to estimate the change in outcome associated with a 1-point improvement in food security. KEY RESULTS: A total of 1,390 food-insecure adults were included. A 1-point improvement in food security was associated with a 0.38 (95%CI 0.62 to 0.14)-point improvement in mental health, a 0.15 (95%CI 0.02 to 0.27)-point improvement in psychological distress, a 0.05 (95%CI 0.01 to 0.09)-point improvement in depressive symptoms, and a 0.003 (95%CI 0.000 to 0.007)-point improvement in health utility. Point estimates for physical health and self-rated health were in the direction of improvement, but were not statistically significant. CONCLUSIONS: Improvement in food insecurity was associated with improvement in several patient-reported outcomes. Further work should investigate whether similar changes are seen in food insecurity interventions, and the most useful scales for assessing changes in health-related quality of life and mental health in food insecurity interventions.

Common Patient-Reported Outcomes Within the Food and Drug Administration Voice of the Patient Reports.

OBJECTIVES: Proponents of disease-specific patient-reported outcome measurements (PROMs) often argue disease-agnostic measures do not adequately capture their patient population's experience. Patient-Reported Outcomes Measurement Information System (PROMIS) provides a disease-agnostic domain set that may adequately cover many diseases. This study seeks to investigate whether PROMIS's quality of life domain coverage can span patient-reported outcomes (PROs) elicited from patients across unrelated diseases. METHODS: The Food and Drug Administration Voice of the Patient reports were an initiative to elevate patient voices regarding their condition and associated treatments. Two reviewers extracted patient-reported health-related (quality of life) domains from the reports and categorized them into PROMIS domains or non-PROMIS domains. Domain coverage was summarized for each report. Any extracted PROs not covered by PROMIS domains were placed in an "other" category and analyzed for common themes. RESULTS: Across 26 reports, PROMIS covered 216 of 374 (70%) of the reports' PRO domains. The heritable bleeding disorders report had the highest coverage (82%). Human immunodeficiency virus had the lowest coverage (50%). The most common PROMIS domain, "ability to participate in social roles," appeared in 25 reports (96%). The most common domains not included in PROMIS were stigma, sensitivities, and sensory deficits as evident in 19 (73%), 18 (69%), and 18 reports (69%), respectively. If the top 3 unincluded domains were amended into PROMIS, the total domain coverage would increase to 84%. CONCLUSIONS: PRO domains elicited in the Food and Drug Administration Voice of the Patient reports were widely captured by PROMIS, suggesting domains patients experience contain enough overlap to be recorded by appropriate PROMIS domains. PROMIS could increase its coverage by adding domains.

Evidence on the relationship between PROMIS-29 and EQ-5D: a literature review.

PURPOSE: EQ-5D and PROMIS-29 are both concise, generic measures of patient-reported outcomes accompanied by preference weights that allow the estimation of quality-adjusted life years (QALYs). Both instruments are candidates for use in economic evaluation. However, they have different features in terms of the domains selected to measure respondents' self-perceived health and the characteristics of (and methods used to obtain) the preference weights. It is important to understand the relationship between the instruments and the implications of choosing either for the evidence used in decision-making. This literature review aimed to synthesise existing evidence on the relationship between PROMIS-29 (and measures based on it, such as PROMIS-29+2) and EQ-5D (both EQ-5D-3L and EQ-5D-5L). METHODS: A literature review was conducted in PubMed and Web of Science to identify studies investigating the relationship between PROMIS-29 and EQ-5D-based instruments. RESULTS: The literature search identified 95 unique studies, of which nine studies met the inclusion criteria, i.e. compared both instruments. Six studies examined the relationship between PROMIS-29 and EQ-5D-5L. Three main types of relationship have been examined in the nine studies: (a) comparing PROMIS-29 and EQ-5D as descriptive systems; (b) mapping PROMIS-29 domains to EQ-5D utilities; and (c) comparing and transforming PROMIS-29 utilities to EQ-5D utilities. CONCLUSION: This review has highlighted the lack of evidence regarding the relationship between PROMIS-29 and EQ-5D. The impact of choosing either instrument on the evidence used in cost-effectiveness analysis is currently unclear. Further research is needed to understand the relationship between the two instruments.

Do patients want clinicians to ask about social needs and include this information in their medical record?

BACKGROUND: Social needs screening in primary care may be valuable for addressing non-medical health-related factors, such as housing insecurity, that interfere with optimal medical care. Yet it is unclear if patients welcome such screening and how comfortable they are having this information included in electronic health records (EHR). OBJECTIVE: To assess patient attitudes toward inclusion of social needs information in the EHR and key correlates, such as sociodemographic status, self-rated health, and trust in health care. DESIGN, PARTICIPANTS, AND MAIN MEASURES: In a cross-sectional survey of patients attending a primary care clinic for annual or employment exams, 218/560 (38%) consented and completed a web survey or personal interview between 8/20/20-8/23/21. Patients provided social needs information using the Accountable Care Communities Screening Tool. For the primary outcome, patients were asked, "Would you be comfortable having these kinds of needs included in your health record (also known as your medical record or chart)?" ANALYSES: Regression models were estimated to assess correlates of patient comfort with including social needs information in medical records. KEY RESULTS: The median age was 45, 68.8% were female, and 78% were white. Median income was $75,000 and 84% reported education beyond high school. 85% of patients reported they were very or somewhat comfortable with questions about social needs, including patients reporting social needs. Social need ranged from 5.5% (utilities) to 26.6% (housing), and nonwhite and gender-nonconforming patients reported greater need. 20% reported "some" or "complete" discomfort with social needs information included in the EHR. Adjusting for age, gender, race, education, trust, and self-rated health, each additional reported social need significantly increased discomfort with the EHR for documenting social needs. CONCLUSIONS: People with greater social needs were more wary of having this information placed in the EHR. This is a concerning finding, since one rationale for collecting social need data is to use this information (presumably in the EHR) for addressing needs.

Perceived Usefulness of Increased Telemedicine Use by Pediatric Subspecialists: A National Survey.

Introduction: Although many studies have explored the perceived ease-of-use of telemedicine, the perceived usefulness of telemedicine for pediatric subspecialty care is less clear. Methods: We invited a national sample of 840 general pediatricians and 840 pediatric subspecialists to participate in a survey fielded in May-June 2020. Respondents ranked perceptions of usefulness of telemedicine for pediatric subspecialty care on a 5-point Likert scale and prioritization of potential strategies to support telemedicine use on a 4-point scale. Results: Of 285 respondents (18% response rate), physicians perceived that increased telemedicine use by pediatric subspecialists would modestly improve child health (mean = 3.5, standard deviation [SD] = 0.7), and access to care (mean = 3.9, SD = 0.6), but would slightly worsen the clinician experience (mean = 2.8, SD = 0.8). Most respondents highly prioritized payment-related strategies to support use of telemedicine. Conclusions: Pediatric clinicians anticipated that increased telemedicine use by pediatric subspecialists would improve child health and health care access but would worsen clinician experience.

Predicting Unplanned Health Care Utilization and Cost: Comparing Patient-reported Outcomes Measurement Information System and Claims.

OBJECTIVES: There is little literature describing if and how payers are utilizing patient-reported outcomes to predict future costs. This study assessed if Patient-reported Outcomes Measurement Information System (PROMIS) domain scores, collected in routine practice at neurology clinics, improved payer predictive models for unplanned care utilization and cost. STUDY DESIGN: Retrospective cohort analysis of private Health Plan-insured patients with visits at 18 Health Plan-affiliated neurology clinics. METHODS: PROMIS domains (Anxiety v1.0, Cognitive Function Abilities v2.0, Depression v1.0, Fatigue v1.0, Pain Interference v1.0, Physical Function v2.0, Sleep Disturbance v1.0, and Ability to Participate in Social Roles and Activities v2.0) are collected as part of routine care. Data from patients' first PROMIS measures between June 27, 2018 and April 16, 2019 were extracted and combined with claims data. Using (1) claims data alone and (2) PROMIS and claims data, we examined the association of covariates to utilization (using a logit model) and cost (using a generalized linear model). We evaluated model fit using area under the receiver operating characteristic curve (for unplanned care utilization), akaike information criterion (for unplanned care costs), and sensitivity and specificity in predicting top 15% of unplanned care costs. RESULTS: Area under the receiver operating curve values were slightly higher, and akaike information criterion values were similar, for PROMIS plus claims covariates compared with claims alone. The PROMIS plus claims model had slightly higher sensitivity and equivalent specificity compared with claims-only models. CONCLUSION: One-time PROMIS measure data combined with claims data slightly improved predictive model performance compared with claims alone, but likely not to an extent that indicates improved practical utility for payers.

Association between Food Insecurity and Health-Related Quality of Life: a Nationally Representative Survey.

BACKGROUND: Food insecurity, limited or uncertain access to enough food for an active, healthy life, affected over 37 million Americans in 2018. Food insecurity is likely to be associated with worse health-related quality of life (HRQoL), but this association has not been measured with validated instruments in nationally representative samples. Given growing interest understanding food insecurity's role in health outcomes, it would be useful to learn what HRQoL measures best capture the experience of those with food insecurity. OBJECTIVE: To determine the association between food insecurity and several validated HRQoL instruments in US adults. DESIGN: Cross-sectional. PARTICIPANTS: US adults (age ≥ 18), weighted to be nationally representative. MAIN MEASURES: Food insecurity was assessed with three items derived from the USDA Household Food Security Survey Module. HRQoL was assessed using PROMIS-Preference (PROPr), which contains 7 PROMIS domains, self-rated health (SRH), Euroqol-5D-5L (EQ-5D), Health Utilities Index (HUI), and Short Form-6D (SF-6D). KEY RESULTS: In December 2017, 4142 individuals completed at least part of the survey (31% response rate), of whom 4060 (98.0%) reported food security information. Of survey respondents, 51.7% were women, 12.5% self-identified as black, 15.8% were Hispanic, and 11.0% did not have a high school diploma. 14.1% of respondents reported food insecurity. In adjusted analyses, food insecurity was associated with worse HRQoL across all instruments and PROMIS domains (p < .0001 for all). The magnitude of the difference between food-insecure and food-secure participants was largest with the SF-6D, EQ-5D, and PROPr; among individual PROMIS domain scores, the largest difference was for ability to participate in social roles. CONCLUSIONS: Food insecurity is strongly associated with worse HRQoL, with differences between food-secure and food-insecure individuals best measured using the SF-6D, EQ-5D, and PROPr. Future work should develop a specific instrument to measure changes in HRQoL in food insecurity interventions.

Measuring population health: association of self-rated health and PROMIS measures with social determinants of health in a cross-sectional survey of the US population.

BACKGROUND: Self-reported health-related quality of life is an important population health outcome, often assessed using a single question about self-rated health (SRH). The Patient Reported Outcomes Measurement Information System (PROMIS) is a new set of measures constructed using item response theory, so each item contains information about an underlying construct. This study's objective is to assess the association between SRH and PROMIS scores and social determinants of health (SDoH) to evaluate the use of PROMIS for measuring population health. METHODS: A cross sectional survey of 4142 US adults included demographics, 7 PROMIS domains with 2 items each, the PROMIS-preference (PROPr) score, self-rated health (SRH), 30 social determinants of health (SDoH), and 12 chronic medical conditions. SDoH and chronic condition impact estimates were created by regressing the outcome (PROMIS domain, PROPr, or SRH) on demographics and SDoH or a single chronic condition. Linear regression was used for PROMIS domains and PROPr; ordinal logistic regression was used for SRH. RESULTS: Both SRH and PROPr detected statistically significant differences for 11 of 12 chronic conditions. Of the 30 SDoH, 19 statistically significant differences were found by SRH and 26 statistically significant differences by PROPr. The SDoH with statistically significant differences included those addressing education, income, financial insecurity, and social support. The number of statistically significant differences found for SDoH varies by individual PROMIS domains from 13 for Sleep Disturbance to 25 for Physical Function. CONCLUSIONS: SRH is a simple single question that provides information about health-related quality of life. The 14 item PROMIS measure used in this study detects more differences in health-related quality of life for social determinants of health than SRH. This manuscript illustrates the relative costs and benefits of each approach to measuring health-related quality of life.

Cross-sectional validation of the PROMIS-Preference scoring system by its association with social determinants of health.

PURPOSE: PROMIS-Preference (PROPr) is a generic, societal, preference-based summary score that uses seven domains from the Patient-Reported Outcomes Measurement Information System (PROMIS). This report evaluates construct validity of PROPr by its association with social determinants of health (SDoH). METHODS: An online panel survey of the US adult population included PROPr, SDoH, demographics, chronic conditions, and four other scores: the EuroQol-5D-5L (EQ-5D-5L), Health Utilities Index (HUI) Mark 2 and Mark 3, and the Short Form-6D (SF-6D). Each score was regressed on age, gender, health conditions, and a single SDoH. The SDoH coefficient represents the strength of its association to PROPr and was used to assess known-groups validity. Convergent validity was evaluated using Pearson correlations between different summary scores and Spearman correlations between SDoH coefficients from different summary scores. RESULTS: From 4142 participants, all summary scores had statistically significant differences for variables related to education, income, food and financial insecurity, and social interactions. Of the 42 SDoH variables tested, the number of statistically significant variables was 27 for EQ-5D-5L, 17 for HUI Mark 2, 23 for HUI Mark 3, 27 for PROPr, and 27 for SF-6D. The average SDoH coefficients were - 0.086 for EQ-5D-5L, - 0.039 for HUI Mark 2, - 0.063 for HUI Mark 3, - 0.064 for PROPr, and - 0.037 for SF-6D. Despite the difference in magnitude across the measures, Pearson correlations were 0.60 to 0.76 and Spearman correlations were 0.74 to 0.87. CONCLUSIONS: These results provide evidence of construct validity supporting the use of PROPr monitor population health in the general US population.

Linking AM-PAC Cognition to PROMIS Cognitive Function.

OBJECTIVE: To link the Activity Measure for Post-Acute Care (AM-PAC) Applied Cognition to the Patient-Reported Outcomes Measurement Information System (PROMIS) Cognitive Function, allowing for a common metric across scales. DESIGN: Cross-sectional survey study. SETTING: Outpatient rehabilitation clinics. PARTICIPANTS: Consecutive sample of 500 participants (N=500) aged ≥18 years presenting for outpatient therapy (physical, occupation, speech). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: AM-PAC Medicare and Generic Cognition short forms and PROMIS Cognitive Function items representing the PROMIS Cognitive Function item bank. RESULTS: The calibration of 25 AM-PAC cognition items with 11 fixed PROMIS cognitive function item parameters using item-response theory indicated that items were measuring the same underlying construct (cognition). Both scales measured a wide range of functioning. The AM-PAC Generic Cognitive assessment showed more reliability with lower levels of cognition, whereas the PROMIS Cognitive Function full-item bank was more reliable across a larger distribution of scores. Data were appropriate for a fixed-anchor item response theory-based crosswalk and AM-PAC Cognition raw scores were mapped onto the PROMIS metric. CONCLUSIONS: The crosswalk developed in this study allows for converting scores from the AM-PAC Applied Cognition to the PROMIS Cognitive Function scale.

Computing PROPr Utility Scores for PROMIS® Profile Instruments.

OBJECTIVES: The Patient-Reported Outcomes Measurement Information System® (PROMIS) Profile instruments measure health status on 8 PROMIS domains. The PROMIS-Preference (PROPr) score provides a preference-based summary score for health states defined by 7 PROMIS domains. The Profile and PROPr share 6 domains; PROPr has 1 unique domain (Cognitive Function-Abilities), and the Profile has 2 unique domains (Anxiety and Pain Intensity). We produce an equation for calculating PROPr utility scores with Profile data. METHODS: We used data from 3982 members of US online survey panels who have scores on all 9 PROMIS domains. We used a 70%/30% split for model fit/validation. Using root-mean-square error and mean error on the utility scale, we compared models for predicting the missing Cognitive Function score via (A) the population average; (B) a score representing excellent cognitive function; (C) a score representing poor cognitive function; (D) a score predicted from linear regression of the 8 profile domains; and (E) a score predicted from a Bayesian neural network of the 8 profile domains. RESULTS: The mean errors in the validation sample on the PROPr scale (which ranges from -0.022 to 1.00) for the models were: (A) 0.025, (B) 0.067, (C) -0.23, (D) 0.018, and (E) 0.018. The root-mean-square errors were: (A) 0.097, (B) 0.12, (C) 0.29, (D) 0.095, and (E) 0.094. CONCLUSION: Although the Bayesian neural network had the best root-mean-square error for producing PROPr utility scores from Profile instruments, linear regression performs almost as well and is easier to use. We recommend the linear model for producing PROPr utility scores for PROMIS Profiles.