A Bayesian hierarchical model for accident and injury surveillance
A Bayesian hierarchical model for accident and injury surveillance
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DOI：
10.1016/s0001-4575(01)00093-8
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发表时间：
2003-01-01
影响因子：
5.9
通讯作者：
MacNab, YC
中科院分区：
文献类型：
--
作者：
MacNab, YC
本文介绍了最近的一项研究，应用贝叶斯分层方法建模和分析事故和伤害监测数据。介绍了一个分层泊松随机效应时空模型，并分析了加拿大不列颠哥伦比亚省0-24岁男孩因机动车事故受伤住院的区域间变化和区域趋势。本文的目的是说明如何建模技术可以实现的事故和伤害的监测和预防系统的一部分，运输和/或卫生当局可能会定期检查事故，伤害和住院治疗的目标高风险，区域预防方案，评估预防策略，并协助卫生规划和资源分配。该方法的创新之处在于它能够揭示和突出数据的重要基础结构。1987年至1996年期间，不列颠哥伦比亚省医院分离登记处登记了10 599例居住在不列颠哥伦比亚省的0-24岁男孩因机动车交通伤害住院的情况，其中大多数（89%）伤害发生在15-24岁的男孩身上。伤害按三个年龄组（0-4岁）汇总。5-14和15-24），20个卫生区（基于居住地），10个日历年（1987年至1996年）和相应的年中人口估计数被用作“高危”人口。一个经验贝叶斯推理技术，采用惩罚准似然估计进行建模率和计数，与样条平滑适应非线性时间效应。结果表明：（a）卫生区域水平的粗伤害率和比率是不稳定的;（B）样条平滑模型使我们能够探索省级和区域水平伤害趋势的可能形状。以及（c）拟合模型提供了有关伤害计数、比率和比率模式（空间和时间）的丰富信息。在10年的时间里，高伤害风险比从东北部演变到中部内陆和西南部。(C)2002爱思唯尔科技有限公司。保留所有权利。
This article presents a recent study which applies Bayesian hierarchical methodology to model and analyse accident and injury surveillance data. A hierarchical Poisson random effects spatio-temporal model is introduced and an analysis of inter-regional variations and regional trends in hospitalisations due to motor vehicle accident injuries to boys aged 0-24 in the province of British Columbia, Canada, is presented. The objective of this article is to illustrate how the modelling technique can be implemented as part of an accident and injury surveillance and prevention system where transportation and/or health authorities may routinely examine accidents, injuries, and hospitalisations to target high-risk, regions for prevention programs, to evaluate prevention strategies, and to assist in health planning and resource allocation. The innovation of the methodology is its ability to uncover and highlight important underlying structure of the data. Between 1987 and 1996, British Columbia hospital separation registry registered 10,599 motor vehicle traffic injury related hospital isations among boys aged 0-24 who resided in British Columbia, of which majority (89%) of the injuries occurred to boys aged 15-24. The injuries were aggregated by three age groups (0-4. 5-14, and 15-24), 20 health regions (based of place-of-residence), and 10 calendar years (1987 to 1996) and the corresponding mid-year population estimates were used as 'at risk' population. An empirical Bayes inference technique using penalised quasi-likelihood estimation was implemented to model both rates and counts, with spline smoothing accommodating non-linear temporal effects. The results show that (a) crude rates and ratios at health region level are unstable, (b) the models with spline smoothing enable us to explore possible shapes of injury trends at both the provincial level and the regional level. and (c) the fitted models provide a wealth of information about the patterns (both over space and time) of the injury counts, rates and ratios. During the 10-year period, high injury risk ratios evolved from northeast to central-interior and the southwest. (C) 2002 Elsevier Science Ltd. All rights reserved.