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

Race, Ethnicity, and Gender Differences in Patient Reported Well-Being and Cognitive Functioning Within 3 Months of Symptomatic Illness During COVID-19 Pandemic

BACKGROUND

Differences in acute COVID-19 associated morbidity based on race, ethnicity, and gender have been well described; however, less is known about differences in subsequent longer term health-related quality of life and well-being.

METHODS

This prospective cohort study included symptomatic adults tested for SARS-CoV-2 who completed baseline and 3-month follow-up surveys. Using the PROMIS-29 tool, a validated measure of health and well-being, we compared outcomes at 3 months and change in outcomes from baseline to 3 months among groups with different races, ethnicities, and/or sexes.

RESULTS

Among 6044 participants, 4113 (3202 COVID +) were included. Among COVID + participants, compared to non-Hispanic White participants, Black participants had better PROMIS T-scores for cognitive function (3.6 [1.1, 6.2]) and fatigue (- 4.3 [- 6.6, - 2.0]) at 3 months and experienced more improvement in fatigue over 3 months (- 2.7 [- 4.7, - 0.8]). At 3 months, compared with males, females had worse PROMIS T-scores for cognitive function (- 4.1 [- 5.6, - 2.6]), physical function (- 2.1 [- 3.1, - 1.0]), social participation (- 2.8 [- 4.2, - 1.5]), anxiety (2.8 [1.5, 4.1]), fatigue (5.1 [3.7, 6.4]), and pain interference (2.0 [0.9, 3.2]). Females experienced less improvement in fatigue over 3 months (3.1 [2.0, 4.3]). Transgender/non-binary/other gender participants had worse 3-month scores in all domains except for sleep disturbance and pain interference.

CONCLUSIONS

Three months after the initial COVID-19 infection, Black participants reported better cognitive function and fatigue, while females and other gender minoritized groups experienced lower well-being. Future studies are necessary to better understand how and why social constructs, specifically race, ethnicity, and gender, influence differences in COVID-19-related health outcomes. Trials Registration ClinicalTrials.gov Identifier: NCT04610515.

Characteristics of hospital and health system initiatives to address social determinants of health in the United States: a scoping review of the peer-reviewed literature

Background

Despite the incentives and provisions created for hospitals by the US Affordable Care Act related to value-based payment and community health needs assessments, concerns remain regarding the adequacy and distribution of hospital efforts to address SDOH. This scoping review of the peer-reviewed literature identifies the key characteristics of hospital/health system initiatives to address SDOH in the US, to gain insight into the progress and gaps.

Methods

PRISMA-ScR criteria were used to inform a scoping review of the literature. The article search was guided by an integrated framework of Healthy People SDOH domains and industry recommended SDOH types for hospitals. Three academic databases were searched for eligible articles from 1 January 2018 to 30 June 2023. Database searches yielded 3,027 articles, of which 70 peer-reviewed articles met the eligibility criteria for the review.

Results

Most articles (73%) were published during or after 2020 and 37% were based in Northeast US. More initiatives were undertaken by academic health centers (34%) compared to safety-net facilities (16%). Most (79%) were research initiatives, including clinical trials (40%). Only 34% of all initiatives used the EHR to collect SDOH data. Most initiatives (73%) addressed two or more types of SDOH, e.g., food and housing. A majority (74%) were downstream initiatives to address individual health-related social needs (HRSNs). Only 9% were upstream efforts to address community-level structural SDOH, e.g., housing investments. Most initiatives (74%) involved hot spotting to target HRSNs of high-risk patients, while 26% relied on screening and referral. Most initiatives (60%) relied on internal capacity vs. community partnerships (4%). Health disparities received limited attention (11%). Challenges included implementation issues and limited evidence on the systemic impact and cost savings from interventions.

Conclusion

Hospital/health system initiatives have predominantly taken the form of downstream initiatives to address HRSNs through hot-spotting or screening-and-referral. The emphasis on clinical trials coupled with lower use of EHR to collect SDOH data, limits transferability to safety-net facilities. Policymakers must create incentives for hospitals to invest in integrating SDOH data into EHR systems and harnessing community partnerships to address SDOH. Future research is needed on the systemic impact of hospital initiatives to address SDOH.

Construct validity of the Hungarian Version of the Patient-Reported Outcomes Measurement Information System-29 Profile Among Patients with Low Back Pain

OBJECTIVE

We aim to evaluate the psychometric properties of the Hungarian version of the patient-reported outcomes measurement information system (PROMIS)-29 profile domains among patients with chronic low back pain.

METHODS

We used a convenience, cross-sectional sampling of patients recruited at our neurosurgical institution. The participants completed paper-pencil version of the PROMIS-29 profile in addition to validated legacy questionnaires, including the Oswestry disability index, Research and Development Corporation 36-item short-form survey, 7-item general anxiety disorder scale, 9-item patient health questionnaire. Reliability was evaluated by calculating the internal consistency (Cronbach's α). Test-retest reliability was assessed using the intraclass correlation coefficient. The structural validity of PROMIS-29 was assessed using a confirmatory factor analysis. Construct validity was assessed by evaluating convergent and discriminant validity using Spearman's rank correlation. To further corroborate the construct validity, we also performed known-group comparisons.

RESULTS

The mean age of the 131 participants was 54 ± 16 years. Of the 131 patients, 62% were women. The internal consistency of each PROMIS domain was high (Cronbach's α >0.89 for all). The test-retest reliability was excellent (intraclass correlation >0.97). The confirmatory factor analysis showed good structural validity (comparative fit index >0.96; standardized root mean square residual <0.026 for all domains). All measured PROMIS scores correlated strongly with the scores obtained using the corresponding primary legacy instrument, indicating excellent convergent validity. The known-group comparisons demonstrated differences as hypothesized.

CONCLUSIONS

We present data supporting the validity and reliability of the Hungarian PROMIS-29 profile short forms for patients with low back pain. This instrument will be useful for research and clinical applications in spine care.

Measurement properties of the EQ-5D-5L and PROPr in patients with spinal muscular atrophy

OBJECTIVES

Spinal muscular atrophy (SMA) is a rare monogenic neuromuscular disorder caused by loss of function mutations. Measuring health-related quality of life to support economic evaluations in this population is encouraged. However, empirical evidence on the performance of preference-based measures (PBMs) in individuals with SMA is limited. This study aimed to assess the psychometric properties of the EQ-5D-5L and the Patient-Reported Outcomes Measure Information System Preference measure (PROPr) in individuals with SMA.

METHODS

The data used in this study were obtained via a web-based, cross-sectional survey. All participants completed the self-reporting EQ-5D-5L and PROMIS-29 questionnaires. Information about their socioeconomic and health status was also obtained. Ceiling and floor effects, convergent and divergent validity, known-group validity, and the agreement between the two measures were assessed.

RESULTS

Strong ceiling and floor effects were observed for four dimensions of the EQ-5D-5L and three subscales, including pain intensity, pain interference, and physical function, of the PROMIS-29. All hypothesized associations between EQ-5D-5L/PROMIS-29 and other neuromuscular questions were confirmed, supporting good convergent validity. Moreover, both EQ-5D-5L and PROPr scores differentiated between impaired functional groups, demonstrating good discriminative ability. Poor agreement between the EQ-5D-5L and PROPr utility scores was observed.

CONCLUSIONS

The EQ-5D-5L and PROPr both appear to be valid PBMs for individuals with SMA. However, PROPr yielded considerably lower utility scores than EQ-5D-5L and their agreement was poor. Therefore, these two PBMs may not be used interchangeably in economic evaluations of SMA-related interventions.

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.

Cumulative social disadvantage and health-related quality of life: national health interview survey 2013-2017

BACKGROUND

Evidence for the association between social determinants of health (SDoH) and health-related quality of life (HRQoL) is largely based on single SDoH measures, with limited evaluation of cumulative social disadvantage. We examined the association between cumulative social disadvantage and the Health and Activity Limitation Index (HALex).

METHODS

Using adult data from the National Health Interview Survey (2013-2017), we created a cumulative disadvantage index by aggregating 47 deprivations across 6 SDoH domains. Respondents were ranked using cumulative SDoH index quartiles (SDoH-Q1 to Q4), with higher quartile groups being more disadvantaged. We used two-part models for continuous HALex scores and logistic regression for poor HALex (< 20th percentile score) to examine HALex differences associated with cumulative disadvantage. Lower HALex scores implied poorer HRQoL performance.

RESULTS

The study sample included 156,182 respondents, representing 232.8 million adults in the United States (mean age 46 years; 51.7% women). The mean HALex score was 0.85 and 17.7% had poor HALex. Higher SDoH quartile groups had poorer HALex performance (lower scores and increased prevalence of poor HALex). A unit increase in SDoH index was associated with - 0.010 (95% CI [-0.011, -0.010]) difference in HALex score and 20% higher odds of poor HALex (odds ratio, OR = 1.20; 95% CI [1.19, 1.21]). Relative to SDoH-Q1, SDoH-Q4 was associated with HALex score difference of -0.086 (95% CI [-0.089, -0.083]) and OR = 5.32 (95% CI [4.97, 5.70]) for poor HALex. Despite a higher burden of cumulative social disadvantage, Hispanics had a weaker SDoH-HALex association than their non-Hispanic White counterparts.

CONCLUSIONS

Cumulative social disadvantage was associated with poorer HALex performance in an incremental fashion. Innovations to incorporate SDoH-screening tools into clinical decision systems must continue in order to accurately identify socially vulnerable groups in need of both clinical risk mitigation and social support. To maximize health returns, policies can be tailored through community partnerships to address systemic barriers that exist within distinct sociodemographic groups, as well as demographic differences in health perception and healthcare experience.

Utilities Estimated from PROMIS Scales for Cost-Effectiveness Analyses in Stroke

BACKGROUND

The EQ-5D and Health Utilities Index Mark 3 (HUI-3) are preference-based measures used in cost-effectiveness studies. The Patient Reported Outcomes Measurement Information System (PROMIS) Preference scoring system (PROPr) is a new preference-based measure. In addition, algorithms were previously developed to map PROMIS Global Health (PROMIS-GH) items to HUI-3 using linear equating (HUILE) and 3-level EQ-5D using linear (EQ5DLE). We sought to evaluate and compare estimated utilities based on PROPr and PROMIS-GH in adult stroke survivors.

METHODS

We performed a retrospective cohort study of adults diagnosed with 1 of ischemic stroke, intracerebral hemorrhage, or subarachnoid hemorrhage seen in an outpatient clinic between 2015 and 2019. Patients completed PROMIS scales and other measures. We computed a modified version of PROPr (mPROPr) and compared the distributional characteristics and correlations with stroke outcomes for mPROPr, HUILE, and EQ5DLE.

RESULTS

T toal of 4,159 stroke survivors (average age 62.7 ± 14.7 y, 48.4% female, 77.6% ischemic stroke) were included. Mean utility estimates for mPROPr, EQ5DLE, and HUILE were 0.333 ± 0.244, 0.739 ± 0.201, and 0.544 ± 0.301, respectively. Correlations between the modified Rankin Scale and each of mPROPr and HUILE were both -0.48 and -0.43 for EQ5DLE. Regression analyses indicated that mPROPr scores may be too low for stroke patients in good health and that EQ5DLE scores may be too high for stroke patients in poor health.

CONCLUSIONS

All 3 PROMIS-based utilities were associated with measures of stroke disability and severity, but the distributions of utilities were very different. Our study highlights the problem cost-effectiveness researchers face of valuing health states with certainty. For researchers using utilities estimated from PROMIS scales, our study indicates that mapping PROMIS-GH item scores to HUI-3 via linear equating may be most appropriate in stroke patients.

HIGHLIGHTS

A new preference-based measure has been developed from the Patient Reported Outcomes Measurement Information System (PROMIS), known as the PROMIS-Preference (PROPr) scoring system, and published equations mapping PROMIS Global Health (PROMIS-GH) items to the Health Utilities Index Mark 3 (HUI-3) and EQ-5D-3L are available for use in cost-effectiveness studies.Our study provides distributional characteristics and comparisons of utilities estimated using a modified version of PROPr and equations mapping PROMIS-GH items to EQ-5D-3L and HUI-3 in a sample of stroke survivors.The results of our study show large differences in the distributions of utilities estimated using the different health state measures, and these differences highlight the ongoing difficulty researchers face in valuing health states with certainty.

A Direct Comparison of the Measurement Properties of EQ-5D-5L, PROMIS-29+2 and PROMIS Global Health Instruments and EQ-5D-5L and PROPr Utilities in a General Population Sample

OBJECTIVES

We aimed to compare measurement properties of the 5-level version of EQ-5D (EQ-5D-5L) and 2 Patient-Reported Outcomes Measurement Information System (PROMIS) short forms, PROMIS-29+2 and PROMIS Global Health (PROMIS-GH-10), and of EQ-5D-5L and PROMIS-preference scoring system (PROPr) utilities.

METHODS

A cross-sectional survey was conducted in a general population sample in Hungary (N = 1631). We compared the following measurement properties at the level of items, domains, and utilities, the latter using corresponding US value sets: ceiling and floor, informativity (Shannon's indices), agreement, convergent, and known-group validity. For the analyses, PROMIS items/domains were matched to EQ-5D-5L domains that cover similar concepts of health.

RESULTS

The majority of PROMIS items showed enhanced distributional characteristics, including lower ceilings and higher informativity than the EQ-5D-5L. Good convergent validity was established between EQ-5D-5L and PROMIS domains capturing similar aspects of health. Mean EQ-5D-5L utilities were substantially higher than those of PROPr (0.864 vs 0.535). EQ-5D-5L utilities correlated moderately or strongly with PROPr (r = 0.61), PROMIS-GH-10 physical (r = 0.68), and mental health summary scores (r = 0.53). EQ-5D-5L utilities decreased with age, whereas PROPr utilities slightly increased with age. EQ-5D-5L utilities discriminated significantly better in 12/28 (ratio of F-statistics) and 18/26 (area under the receiver-operating characteristics curve ratio) known groups defined by age, self-perceived health status, and self-reported physician-diagnosed health conditions, including hypertension, diabetes, coronary heart disease, chronic kidney disease, and stroke.

CONCLUSIONS

This study provides comparative evidence on the measurement properties of EQ-5D-5L, PROMIS-29+2, and PROMIS-GH-10 and informs decisions about the choice of instruments in population health surveys for assessment of patients' health and for cost-utility analyses.

Health-related quality of life and social determinants of health following COVID-19 infection in a predominantly Latino population

BACKGROUND

As the COVID-19 pandemic evolves, more information is needed on its long-term impacts on health-related quality of life (HRQoL) and social determinants of health (SDoH). The aim of the study was to assess HRQoL and SDoH among a predominantly Latino population of COVID-19 survivors and to compare effects in Latinos versus non-Latinos.

METHODS

This cross-sectional study consisted of a survey (in English and Spanish) of COVID-19 survivors from December 2020 to July 2021. The study assessed sociodemographic data, clinical characteristics, and SDoH, consisting of 10 COVID-19-related concerns. The PROMIS-29 + 2 (PROPr) measure, which captures 8 HRQoL domains and a preference-based health utility, was used to assess HRQoL. Bivariate analyses included chi-square tests and t-tests. Generalized linear models were conducted for multivariable analyses.

RESULTS

Of 230 respondents (6.3% response rate), the mean [SD] age was 43.1 [14.3] years; 83.0% were Latino; the mean [SD] time since diagnosis was 8.1 [3.2] months; and 12.6% had a history of hospitalization with COVID-19. HRQoL scores were slightly worse than population norms on all domains, especially anxiety; the mean [SD] PROPr health utility was 0.36 [0.25]. Domain scores were similar by ethnicity except for cognitive function-abilities, where scores were lower in Latinos. Multivariable analyses revealed that: (1) financial concerns were associated with worse health utility, as well as worse scores on all 8 PROMIS domains; (2) interpersonal conflict was associated with worse health utility and worse scores on 6 of the 8 PROMIS domains (anxiety, depression, fatigue, sleep disturbance, social function, and pain interference); and (3) Latino ethnicity was only associated with 1 PROMIS domain (cognitive function-abilities) after controlling for covariates.

CONCLUSION

COVID-19 infection is associated with HRQoL decrements long after the acute infection, and financial concerns and interpersonal conflict are particularly associated with worse HRQoL.

Survival After Severe COVID-19: Long-Term Outcomes of Patients Admitted to an Intensive Care Unit

BACKGROUND

Understanding the long-term sequelae of severe COVID-19 remains limited, particularly in the United States.

OBJECTIVE

To examine long-term outcomes of patients who required intensive care unit (ICU) admission for severe COVID-19.

DESIGN, PATIENTS, AND MAIN MEASURES

This is a prospective cohort study of patients who had severe COVID-19 requiring an ICU admission in a two-hospital academic health system in Southern California. Patients discharged alive between 3/21/2020 and 12/31/2020 were surveyed approximately 6 months after discharge to assess health-related quality of life using Patient-Reported Outcomes Measurement Information System (PROMIS®)-29 v2.1, post-traumatic stress disorder (PTSD) and loneliness scales. A preference-based health utility score (PROPr) was estimated using 7 PROMIS domain scores. Patients were also asked their attitude about receiving aggressive ICU care.

KEY RESULTS

Of 275 patients admitted to the ICU for severe COVID-19, 205 (74.5%) were discharged alive and 132 (64%, median age 59, 46% female) completed surveys a median of 182 days post-discharge. Anxiety, depression, fatigue, sleep disturbance, ability to participate in social activities, pain interference, and cognitive function were not significantly different from the U.S. general population, but physical function (44.2, SD 11.0) was worse. PROPr mean score of 0.46 (SD 0.30, range -0.02 to 0.96 [<0 is worse than dead and 1 represents perfect health]) was slightly lower than the U.S. general population, with an even distribution across the continuum. Poor PROPr was associated with chronic medical conditions and receipt of life-sustaining treatments, but not demographics or social vulnerability. PTSD was suspected in 20% and loneliness in 29% of patients. Ninety-eight percent of patients were glad they received life-saving treatment.

CONCLUSION

Most patients who survive severe COVID-19 achieve positive outcomes, with health scores similar to the general population at 6 months post-discharge. However, there is marked heterogeneity in outcomes with a substantial minority reporting severely compromised health.

A Comparison of PROPr and EQ-5D-5L Value Sets

OBJECTIVES

The EQ-5D-5L and its value sets are widely used internationally. However, in the US and elsewhere, there is growing use of PROMIS, which has a value set (PROPr) based on the stated preferences of the US population. This paper aims to compare the characteristics of EQ-5D-5L and PROPr value sets and to highlight potential implications for users.

METHODS

US, Australian and English value sets were used for EQ-5D-5L. PROPr utilities were calculated based on PROMIS-29 + 2. We examined, in each case, (i) the characteristics (e.g. range of values, number of unique values) and distribution of all possible 'theoretical' utilities; (ii) dimension/domain importance ranking by the utility of corner states (i.e. health states with the worst level in one domain and the best in all others); (iii) comparisons of utilities for health states hypothesised to be comparable in terms of severity across EQ-5D-5L descriptive systems and PROMIS-29 + 2 domain scores; (iv) the changes in values of adjacent states (i.e. a one-level change in one dimension for EQ-5D-5L and a four-point change in raw scores for PROMIS-29 + 2, with the other dimensions held constant) for dimensions hypothesised to overlap conceptually or be correlated between the two instruments.

RESULTS

EQ-5D-5L and PROPr utilities differ systematically. First, the US EQ-5D-5L utilities range from - 0.573 to 1, whereas PROPr values for PROMIS-29 + 2 range from - 0.022 to 0.954. Second, in the US (and English) EQ-5D-5L value sets, pain is the most important dimension whereas in PROPr pain is one of the least important (apart from sleep disturbance). Third, classified based on severity across EQ-5D-5L descriptive systems and PROMIS-29 + 2 domain scores, PROPr has substantially lower values than EQ-5D-5L values for comparable 'mild' health states, but higher values for more 'severe' health states. Last, when one dimension is considered across its best to worst levels and all other dimensions are held constant at their best or moderate level, in EQ-5D-5L value sets, the greatest changes in utility occur between levels 3 and 4 (moderate and severe) problems; in PROPr that occurred between the most severe states and their descriptively adjacent health states.

CONCLUSION

There are very considerable differences between US EQ-5D-5L and PROPr utilities, despite both in principle representing utility on the same scale anchored at 0 and 1 and both representing the preferences of the US general public. It is important for decision makers and clinical triallists to be aware of these differences. Further work is needed to assess the impact of these differences in value sets using population and patient data, and in longitudinal settings.

Health-Related Quality of Life Measurement in Public Health

Patient-reported outcomes are recognized as essential for the evaluation of medical and public health interventions. Over the last 50 years, health-related quality of life (HRQoL) research has grown exponentially from 0 to more than 17,000 papers published annually. We provide an overview of generic HRQoL measures used widely in epidemiological studies, health services research, population studies, and randomized clinical trials [e.g., Medical Outcomes Study SF-36 and the Patient-Reported Outcomes Measurement Information System (PROMIS^®)-29]. In addition, we review methods used for economic analysis and calculation of the quality-adjusted life year (QALY). These include the EQ-5D, the Health Utilities Index (HUI), the self-administered Quality of Well-being Scale (QWB-SA), and the Health and Activities Limitation Index (HALex). Furthermore, we consider hybrid measures such as the SF-6D and the PROMIS-Preference (PROPr). The plethora of HRQoL measures has impeded cumulative science because incomparable measures have been used in different studies. Linking among different measures and consensus on standard HRQoL measurement should now be prioritized. In addition, enabling widespread access to common measures is necessary to accelerate future progress. Expected final online publication date for the Annual Review of Public Health, Volume 43 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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.

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.