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

Abstract

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.

Cumulative Social Disadvantage and Health-Related Quality of Life: National Health Interview Survey 2013-2017

“…Our findings corroborate prior studies which used health utility indices elicited by other more common rating techniques like the standard gamble method (15,16). This adds to the desirability of HALex as a population health tool.…”

“…Third, though a difference of 0.03 has been suggested as potentially a clinically relevant threshold for HALex (40), this has not been ascertained conclusively, limiting the interpretation of the magnitude and relevance of our estimates. However, HALex is strongly congruent with more widely used HRQoL measures (55,56) and our results are consistent with studies that used other HRQoL indices (16,31,49).…”

“…Additionally, these proximal determinants can have differential impact on health which calls for a multidimensional framework in their assessment. Yet, evidence on the association between SDoH and HRQoL is largely based on single assessments of SDoH (11,15,16). Very few indices of cumulative social disadvantage capture burden across an exhaustive range of SDoH domains (17)(18)(19).…”

Finding ikigai: How robots can support meaning in later life

“…The included K-1 scale ( Kondo and Kamada, 2003 ) is widely used with OAs in Japan, while the ikigai-9 scale ( Fido et al, 2020 ) is frequently used in surveys conducted by the municipal/regional governments in Japan. We included four PROMIS subscales selected from the National Institute of Health’s Patient-Reported Outcomes Measurement Information System (PROMIS) to measure meaning and purpose, positive affect, companionship, and emotional support ( Hahn et al, 2010 ; Broderick et al, 2013 ; Hanmer, 2021 ). We used these scales to empirically explore the relationship between PROMIS measures developed in the US and ikigai measures developed in Japan.…”

Socio-Hygienic Determinants Of Health Of Workers In The Production Of Polyacrylonitrile Fibers

“…When studying factors influencing public health, sources of information can be both objective data (results of studies of conditions, lifestyle, the state of the environment, including the working environment, medical documentation, official statistics), on individual health -subjective selfassessments of the subjects, giving the opportunity to identify patterns of health formation at the individual level (Hanmer, 2021;Shapovalova, 2020).…”