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The health literacy (HL) facet Access to health information is measured in the European Health Literacy Survey (HLS-EU-Q47) by 12 items. To assess Access, we developed adapted item formulations for COVID-19 infection prevention (COVID-19-IP) and early childhood allergy prevention (ECAP) in addition to the original 12 items on General Health (GH). N = 343 (expectant) mothers of infants answered the items in an online assessment. Confirmatory structural analyses for ordinal data were adopted (WLSMV-algorithm). Women’s item ratings varied significantly across domains (η2 = .017–.552). Bi-factor models exhibited the best data fit (GH/COVID-19-IP/ECAP: CFI = .964 /.968/.977; SRMR: .062/.069 /.035): The general factor Access most strongly determined item information. Additionally, three subfactors contributed significantly (but rather weakly) to the item information in each domain. The overall score Access proved to be internally consistent (McDonald’s ωGH/COVID-19-IP/ECAP = .874/.883 /.897) and was associated with socioeconomic state (McArthur scale; rGH/COVID- 19-IP/ECAP = .218 /.210/.146). Access correlated not or only weakly with the other HL facets Understand, Appraise, and Apply. The health domains GH, COVID-19-IP, and ECAP moderated both the difficulty and the dimensional structure of the 12 Access items. This suggests that in the HLS-EU Access reflects not only the search competence but also the availability of health information.
Appropriate parental health literacy (HL) is essential to preventively maintain and promote child health. Understanding health information is assumed to be fundamental in HL models. We developed N = 67 items (multiple-choice format) based on information materials on early childhood allergy prevention (ECAP) and prevention of COVID-19 infections to assess the parental HL facet Understand. N = 343 pregnant women and mothers of infants completed the items in an online assessment. Using exploratory factor analysis for ordinal data (RML estimation) and item response models (1-pl and 2-pl model), we proved the psychometric homogeneity of the item pool. 57 items assess the latent dimension Understand according to the assumptions of the 1-pl model (weighted MNSQ < 1.2; separation reliability = .855). Person parameters of the latent trait Understand correlate specifically with subjective socioeconomic status (r = .27), school graduation (r = .46), allergy status (r = .11), and already infected with COVID-19 (r = .12). The calibrated item pool provides a psychometrically sound, constructvalid assessment of the HL facet Understand Health Information in the areas of ECAP and prevention of COVID-19 infections.