Sample selection and reasons for non-participation in the PRedictors and Outcomes of incident FRACtures (PROFRAC) study

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Amanda L. Stuart
Julie A. Pasco
Sharon L. Brennan-Olsen
Michael Berk
Amelia G. Betson
Katherine E. Bennett
Elizabeth N. Timney
Lana J. Williams *
(*) Corresponding Author:
Lana J. Williams |


Background. Fragility fractures, associated with osteoporosis, are an escalating public health problem. We aim to describe sample selection, recruitment methods and reasons for non-participation in The PRedictors and Outcomes of incident FRACtures (PROFRAC) study.
Design and Methods. Barwon Statistical Division residents aged 20+ years, with a radiologically-confirmed fracture between June 1st 2012 and May 31st 2013, were eligible. Individuals identified as fracture cases were invited by mail to complete a questionnaire. Reasons for non-participation were documented. Logistic regression techniques were used to determine odds ratios for participation and non-participation reasons.
Results. A total of 1,458 of 2,155 (67.7%) adults with fracture (48.7% men) participated. Individuals were excluded due to inability to give informed consent, death, no knowledge of fracture, or inability to be contacted. The odds of participation decreased with age (OR 0.99, 95%CI 0.99-0.99, P=0.011) and increased among specific fracture groups [clavicle/scapula (OR 2.50, 1.30-4.68, P=0.006), forearm/humerus (OR 2.00, 1.22-3.27, P=0.006), wrist (OR 2.08, 1.31-0.32, P=0.002), hip (OR 2.12, 1.20-3.75, P=0.009), ankle (OR 1.85, 1.20-2.87, P=0.001), compared to face/skull fractures]. The odds of reporting disinterest, time constraints or personal reasons as the reason for non-participation decreased with age, whereas the odds of reporting frailty, language-related issues or illness as the reason for non-participation increased with of age [disinterest (OR 0.98, 0.97-0.98, P<0.001), time constraints (OR 0.97, 0.96-0.98, P<0.001), personal reasons (OR 0.98, 0.97-0.99, P=0.007), frailty (OR 1.12, 1.09- 1.15, P<0.001), language-related issues (OR 1.02, 1.01-1.04, P<0.001), illness (OR 1.03, 1.02-1.05, P<0.001)].
Conclusions. Understanding drivers of research participation can inform study design to achieve optimal participation in health research.

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