@article{ref1,
title="Prospective outcomes of injury study",
journal="Injury prevention",
year="2009",
author="Derrett, Sarah and Langley, John Desmond and Hokowhitu, Brendan and Ameratunga, Shanthi N. and Hansen, P. and Davie, Gabrielle S. and Wyeth, E. and Lilley, R.",
volume="15",
number="5",
pages="e3-e3",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="1353-8047",
doi="10.1136/ip.2009.022558a",
url="http://dx.doi.org/10.1136/ip.2009.022558a"
}