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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.

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.

BackgroundFood 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.ObjectiveTo determine the association between food insecurity and several validated HRQoL instruments in US adults.DesignCross-sectional.ParticipantsUS adults (age ≥ 18), weighted to be nationally representative.Main MeasuresFood 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 ResultsIn 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.ConclusionsFood 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.

Background ASCQ-Me®, Neuro-QoL™, NIH Toolbox®, and PROMIS®, which are health-related quality of life measures collectively known as HealthMeasures, have experienced rapid uptake in the scientific community with over 1700 peer-reviewed publications through 2018. Because of their proliferation across multiple research disciplines, there has been significant heterogeneity in the description and reporting of these measures. Here, we provide a publication checklist to promote standardization and comparability across different reports. This checklist can be used across all HealthMeasures systems. Checklist Development: Authors drafted a draft checklist, circulated among the HealthMeasures Steering Committee and PROMIS Health Organization until the members reached consensus. Checklist: The final checklist has 21 entries in 4 categories: measure details, administration, scoring, and reporting. Most entries (11) specify necessary measure-specific details including version number and administration language(s). Administration (4 entries) reminds authors to include details such as use of proxy respondents and the assessment platform. Scoring (3 entries) is necessary to ensure replication and cross-study comparisons. Reporting (3 entries) reminds authors to always report scores on the T-score metric. Conclusion Consistent documentation is necessary to ensure transparent and reproducible methods and support the accumulation of evidence across studies. This checklist promotes standardization and completeness in documentation for ASCQ-Me, Neuro-QoL, PROMIS, and NIH Toolbox measures.

The PROMIS-Preference (PROPr) score is a recently developed summary score for the Patient-Reported Outcomes Measurement Information System (PROMIS). PROPr is a preference-based scoring system for seven PROMIS domains created using multiplicative multi-attribute utility theory. It serves as a generic, societal, preference-based summary scoring system of health-related quality of life. This manuscript evaluates construct validity of PROPr in two large samples from the US general population. We utilized 2 online panel surveys, the PROPr Estimation Survey and the Profiles-Health Utilities Index (HUI) Survey. Both included the PROPr measure, patient demographic information, self-reported chronic conditions, and other preference-based summary scores: the EuroQol-5D (EQ-5D-5L) and HUI in the PROPr Estimation Survey and the HUI in the Profiles-HUI Survey. The HUI was scored as both the Mark 2 and the Mark 3. Known-groups validity was evaluated using age- and gender-stratified mean scores and health condition impact estimates. Condition impact estimates were created using ordinary least squares regression in which a summary score was regressed on age, gender, and a single health condition. The coefficient for the health condition is the estimated effect on the preference score of having a condition vs. not having it. Convergent validity was evaluated using Pearson correlations between PROPr and other summary scores. The sample consisted of 983 respondents from the PROPr Estimation Survey and 3,000 from the Profiles-HUI survey. Age- and gender-stratified mean PROPr scores were lower than EQ-5D and HUI scores, with fewer subjects having scores corresponding to perfect health on the PROPr. In the PROPr Estimation survey, all 11 condition impact estimates were statistically significant using PROPr, 8 were statistically significant by the EQ-5D, 7 were statistically significant by HUI Mark 2, and 9 were statistically significant by HUI Mark 3. In the Profiles-HUI survey, all 21 condition impact estimates were statistically significant using summary scores from all three scoring systems. In these samples, the correlations between PROPr and the other summary measures ranged from 0.67 to 0.70. These results provide evidence of construct validity for PROPr using samples from the US general population.

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.

[This corrects the article DOI: 10.1371/journal.pone.0201093.].

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.

Reliable internet connectivity is crucial for family participation in pediatric digital health care, including telehealth. Lack of internet connectivity is a barrier to pediatric telehealth access. While surveys commonly inquire about metrics, such as internet plan or device ownership, fewer measures exist for the reliability of internet connectivity when needed. There is limited knowledge of the national prevalence of reliable internet connectivity among households with children and how reports of reliable internet connectivity are associated with use of internet plans and devices. We examined the prevalence of reliable internet connectivity among households with children and its association with digital technology access and sociodemographic factors. We performed a secondary data analysis of a US national cross-sectional survey examining parents' health-seeking decisions for children younger than 18 years old. The respondent panel was hosted by the National Opinion Research Center (NORC) AmeriSpeak. This analysis focused on survey items on reliable internet connectivity, digital technology access (internet plan type and device ownership type), and sociodemographic characteristics (education, employment, geographic region, race and ethnicity, and disability) of parent respondents and their children. The dependent variable was a binary indicator of household reliable internet connectivity. Respondents were categorized as having unreliable internet connectivity if they self-reported internet worry or unreliable internet experience. Unadjusted Rao-Scott chi-square tests and adjusted multivariable logistic regressions with sampling weights were applied. The final survey sample (N=1158) comprised 753 (55%) females, 614 (57%) non-Hispanic White, and 948 (81%) metropolitan respondents. There were 125 (12%) parents who reported internet worry, 152 (13%) parents who reported unreliable internet experience, and 76 (7%) parents who reported both. Combining these measures, we identified 201 (19%) parents with unreliable internet connectivity, defined as reporting either internet worry or unreliable internet experience. In contrast, 957 (81%) parents reported reliable internet connectivity in the household. In adjusted analysis, reliable internet connectivity was significantly associated with owning both nonmobile and mobile internet plans combined (86% reliable internet connectivity) versus nonmobile internet plan-only (67%; P=.001); postgraduate (94%) versus high school education (75%; P<.001); employment (84%) versus unemployment (76%; P=<.01); racial and ethnic marginalized status (77%) versus nonmarginalized (85%; P=<.01); and disability (70%) versus without disability (85%; P<.001), but not with device ownership, geographic region, race and ethnicity as separate groups, or parent sex. One-fifth of families with children experienced unreliable internet connectivity, highlighting an important dimension of the digital health divide that appears distinct from internet plan use or device ownership alone. Future research is needed to derive consensus on measuring reliable internet connectivity as a separate metric, including specifying the definition, survey questions, response options, and time frame of unreliability experience. Since reliable internet connectivity is needed for the growing field of digital health care, it is a critical issue for equitable pediatric health care access and delivery.

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. Plain English Summary 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.