Australian arm of the International Spinal Cord Injury (Aus-InSCI) community survey: 1. population-based design, methodology and cohort profile

Autor: James W. Middleton, Mohit Arora, Annette Kifley, Timothy Geraghty, Samantha J. Borg, Ruth Marshall, Jillian Clark, Andrew Nunn, Anna Ferrante, Christine Fekete, Gerold Stucki, Bamini Gopinath, Ashley Craig, Ian D. Cameron
Rok vydání: 2022
Předmět:
Zdroj: Spinal Cord. 61:194-203
ISSN: 1476-5624
1362-4393
Popis: Study design Cross-sectional survey. Objectives To describe design and methods of Australian arm of International Spinal Cord Injury (Aus-InSCI) community survey, reporting on participation rates, potential non-response bias and cohort characteristics. Setting Survey of community-dwelling people with SCI at least 12 months post-injury, recruited between March 2018 and January 2019, from state-wide SCI services, a government insurance agency and not-for-profit consumer organisations across four Australian states. Methods The Aus-InSCI survey combined data for people with SCI from nine custodians, using secure data-linkage processes, to create a population-based, anonymised dataset. The Aus-InSCI questionnaire comprised 193 questions. Eligibility, response status and participation rates were calculated. Descriptive statistics depict participant characteristics. Logistic regression models were developed for probability of participation, and inverse probability weights generated to assess potential non-response bias. Results 1579 adults with SCI were recruited, a cooperation rate of 29.4%. Participants were predominantly male (73%), with 50% married. Mean age was 57 years (range 19–94) and average time post-injury 17 years (range 1–73). Paraplegia (61%) and incomplete lesions (68%) were most common. Males were more likely than females to have traumatic injuries (p p = 0.0002), and younger age-groups were more likely to have traumatic injuries and tetraplegia (p Conclusions The Aus-InSCI survey made efforts to maximise coverage, avoid recruitment bias and address non-response bias. The distributed, linked and coded (re-identifiable at each custodian level) ‘virtual quasi-registry’ data model supports systematic cross-sectional and longitudinal research.
Databáze: OpenAIRE
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