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Development and validation of the Symptom Burden Questionnaire™ for Long COVID: a Rasch analysis (preprint)
medrxiv; 2022.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2022.01.16.22269146
ABSTRACT
ObjectiveTo describe the development and initial validation of a novel patient-reported outcome measure of Long COVID symptom burden, the Symptom-Burden Questionnaire for Long COVID (SBQ-LC). Method and FindingsThis multi-phase, prospective mixed-methods study took place between April and August 2021 in the United Kingdom (UK). A conceptual framework and initial item pool were developed from published systematic reviews. Further concept elicitation and content validation was undertaken with adults with lived experience (n = 13) and clinicians (n = 10), and face validity was confirmed by the Therapies for Long COVID Study Patient and Public Involvement group (n = 25). The draft SBQ-LC was field tested by adults with self-reported Long COVID recruited via social media and international Long COVID support groups (n = 274). Thematic analysis of interview and survey transcripts established content validity and informed construction of the draft questionnaire. Rasch analysis of field test data guided item and scale refinement and provided evidence of the final SBQ-LCs measurement properties. The Rasch-derived SBQ-LC is composed of 17 independent scales with promising psychometric properties. Respondents rate symptom burden during the past 7-days using a dichotomous response or 4-point rating scale. Each scale provides coverage of a different symptom domain and returns a summed raw score that may be converted to a linear (0 - 100) score. Higher scores represent higher symptom burden. ConclusionsThe SBQ-LC is a comprehensive patient-reported assessment of Long COVID symptom burden developed using modern psychometric methods. It measures symptoms of Long COVID important to individuals with lived experience and may be used to evaluate the impact of interventions and inform best practice in clinical management.

Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2022 Document Type: Preprint

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Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2022 Document Type: Preprint