弗吉尼亚各县自杀相对风险的空间分析，纳入变量估计的不确定性。
Spatial analysis of the relative risk of suicide for Virginia counties incorporating uncertainty of variable estimates.
作者信息
Orndahl Christine M, Wheeler David C
机构信息
Department of Biostatistics, Virginia Commonwealth University, One Capitol Square, Seventh Floor, 830 East Main Street, P.O. Box 980032, Richmond, VA 23219, USA.
出版信息
Spat Spatiotemporal Epidemiol. 2018 Nov;27:71-83. doi: 10.1016/j.sste.2018.10.001. Epub 2018 Oct 13.
PURPOSE
This research aimed to identify significantly elevated areas of risk for suicide in Virginia adjusting for risk factors and risk factor uncertainty.
METHODS
We fit three Bayesian hierarchical spatial models for relative risk of suicide adjusting for risk factors and considering different random effects. We compared models with and without incorporating parameter estimates' margin of error (MOE) from the American Community Survey and identified counties with significantly elevated risk and highly significantly elevated risk for suicide.
RESULTS
Incorporating MOEs and using a mixing parameter between unstructured and spatially structured random effects achieved the best model fit. Fifty-two counties had significantly elevated risk and 18 had highly significantly elevated risk of suicide. Models without MOEs underestimated relative risk and over-identified counties with elevated risk.
CONCLUSIONS
Accounting for uncertainty in parameter estimates achieved better model fit. Efficient allocation of resources for suicide prevention can be attained by targeting clusters of counties with elevated risk.
目的
本研究旨在确定弗吉尼亚州经风险因素及风险因素不确定性调整后的自杀风险显著升高区域。
方法
我们拟合了三个贝叶斯分层空间模型，用于经风险因素调整并考虑不同随机效应的自杀相对风险。我们比较了纳入和未纳入美国社区调查参数估计误差幅度（MOE）的模型，并确定了自杀风险显著升高和高度显著升高的县。
结果
纳入误差幅度并使用非结构化和空间结构化随机效应之间的混合参数实现了最佳模型拟合。52个县的自杀风险显著升高，18个县的自杀风险高度显著升高。未纳入误差幅度的模型低估了相对风险，并过度识别了风险升高的县。
结论
考虑参数估计中的不确定性可实现更好的模型拟合。通过针对风险升高的县集群进行资源有效分配，可实现自杀预防。
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