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Published on: July 24, 2012
Prospective outcomes of injury study
S Derrett1, J Langley, B Hokowhitu
1Injury Prevention Research Unit, Department of Preventive and Social Medicine, Dunedin School of Medicine, University of Otago, New Zealand.
Background:
In New Zealand (NZ), 20% of adults report a disability, of which one-third is caused by injury. No prospective epidemiological studies of predictors of disability following all-cause injury among New Zealanders have been undertaken. Internationally, studies have focused on a limited range of predictors or specific injuries. Although these studies provide useful insights, applicability to NZ is limited given the importance of NZ's unique macro-social factors, such as NZ's no-fault accident compensation and rehabilitation scheme, the Accident Compensation Corporation (ACC).
Objectives:
(1) To quantitatively determine the injury, rehabilitation, personal, social and economic factors leading to disability outcomes following injury in NZ. (2) To qualitatively explore experiences and perceptions of injury-related outcomes in face-to-face interviews with 15 Māori and 15 other New Zealanders, 6 and 12 months after injury.
Setting:
Four geographical regions within NZ.
Design:
Prospective cohort study with telephone interviews 1, 4 and 12 months after injury.
Participants:
2500 people (including 460 Māori), aged 18-64 years, randomly selected from ACC's entitlement claims register (people likely to be off work for at least 1 week or equivalent).
Data:
Telephone interviews, electronic hospital and ACC injury data. Exposures include demographic, social, economic, work-related, health status, participation and/or environmental factors.
Outcome Measures:
Primary: disability (including WHODAS II) and health-related quality of life (including EQ-5D). Secondary: participation (paid and unpaid activities), life satisfaction and costs.
Analysis:
Separate regression models will be developed for each of the outcomes. Repeated measures outcomes will be modelled using general estimating equation models and generalised linear mixed models.
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