A person-reported cumulative social risk measure does not show bias by income and education

Autor: Salene M.W. Jones, Katherine J. Briant, David R. Doody, Ronaldo Iachan, Jason A. Mendoza
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Journal of Patient-Reported Outcomes, Vol 8, Iss 1, Pp 1-8 (2024)
Druh dokumentu: article
ISSN: 2509-8020
DOI: 10.1186/s41687-024-00772-2
Popis: Abstract Background Social risk such as housing instability, trouble affording medical care and food insecurity are a downstream effect of social determinants of health (SDOHs) and are frequently associated with worse health. SDOHs include experiences of racism, sexism and other discrimination as well as differences in income and education. The collective effects of each social risk a person reports are called cumulative social risk. Cumulative social risk has traditionally been measured through counts or sum scores that treat each social risk as equivalent. We have proposed to use item response theory (IRT) as an alternative measure of person-reported cumulative social risk as IRT accounts for the severity in each risk and allows for more efficient screening with computerized adaptive testing. Methods We conducted a differential item functioning (DIF) analysis comparing IRT-based person-reported cumulative social risk scores by income and education in a population-based sample (n = 2122). Six social risk items were analyzed using the two-parameter logistic model and graded response model. Results Analyses showed no DIF on an IRT-based cumulative social risk score by education level for the six items examined. Statistically significant DIF was found on three items by income level but the ultimate effect on the scores was negligible. Conclusions Results suggest an IRT-based cumulative social risk score is not biased by education and income level and can be used for comparisons between groups. An IRT-based cumulative social risk score will be useful for combining datasets to examine policy factors affecting social risk and for more efficient screening of patients for social risk using computerized adaptive testing.
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