健康寿命年——一个新的测量疾病负担的指标

介绍一种新的测量疾病负担的指标——健康寿命率（ＨｅａＬＹ）。我们用此指标对１９９０～１９９３年北京ＭＯＮＩＣＡ资料进行了重新估算和分析,并将其与几年前ＷＨＯ和世界银行提出的另一个测量疾病负担的指标ＤＡＬＹ进行了比较。     

利用DALY指标分析我国新生儿窒息的疾病负担

目的 利用伤残调整生命年(disability adjusted life years, DALY)指标分析我国新生儿窒息的疾病负担, 为政府合理分配有限的卫生资源提供科学佐证。方法 通过文献检索整理我国新生儿窒息的发生率、死亡率和伤残率数据;采用专家德尔菲法获得我国新生儿窒息所导致的失能权重;依据以上数据资料, 计算新生儿窒息的DALY。结果 我国不同地区新生儿窒息的发生率在1.14%~11.7%之间;全国5岁以下儿童出生窒息死亡率为221.3/10⁵。0~17岁儿童智力残疾率为0.9%, 其中新生儿窒息为第三位致残原因, 占8.6%。0~6岁听力残疾的儿童中, 因新生儿窒息所致残疾占6.34%。新生儿窒息导致的失能权重为0.390。2010年我国5岁以下儿童新生儿窒息DALYs为8 241 093人年, 每千人口DALYs为109.1人年。若新生儿窒息发生率和死亡率同时下降10%, DALYs可下降9.73%。结论 新生儿窒息是导致我国儿童死亡和残疾的主要原因之一, 由出生窒息所引发的疾病负担是巨大的。推广新生儿复苏技术、降低出生窒息的发生率和死亡率具有良好的社会效益。     

Measuring the burden of disease using disability-adjusted life years in Shilin County of Yunnan Province,China

Background  

Few studies calculating burden of disease (BOD) have been carried out in China. Disability-adjusted life years (DALY) is one of the useful methods used to estimate BOD. This study aims to use DALY for evaluating BOD and to provide useful information for health planning for residents in Shilin Yi Nationality Autonomous County (Shilin County) of Yunnan Province, China.     

Regression B-spline smoothing in Bayesian disease mapping: with an application to patient safety surveillance

In the context of Bayesian disease mapping, recent literature presents generalized linear mixed models that engender spatial smoothing. The methods assume spatially varying random effects as a route to partially pooling data and 'borrowing strength' in small-area estimation. When spatiotemporal disease rates are available for sequential risk mapping of several time periods, the 'smoothing' issue may be explored by considering spatial smoothing, temporal smoothing and spatiotemporal interaction. In this paper, these considerations are motivated and explored through development of a Bayesian semiparametric disease mapping model framework which facilitates temporal smoothing of rates and relative risks via regression B-splines with mixed-effect representation of coefficients. Specifically, we develop spatial priors such as multivariate Gaussian Markov random fields and non-spatial priors such as unstructured multivariate Gaussian distributions and illustrate how time trends in small-area relative risks may be explored by splines which vary in either a spatially structured or unstructured manner. In particular, we show that with suitable prior specifications for the random effects ensemble, small-area relative risk trends may be fit by 'spatially varying' or randomly varying B-splines. A recently developed Bayesian hierarchical model selection criterion, the deviance information criterion, is used to assess the trade-off between goodness-of-fit and smoothness and to select the number of knots. The methodological development aims to provide reliable information about the patterns (both over space and time) of disease risks and to quantify uncertainty. The study offers a disease and health outcome surveillance methodology for flexible and efficient exploration and assessment of emerging risk trends and clustering. The methods are motivated and illustrated through a Bayesian analysis of adverse medical events (also known as iatrogenic injuries) among hospitalized elderly patients in British Columbia, Canada.     

Bayesian multivariate disease mapping and ecological regression with errors in covariates: Bayesian estimation of DALYs and ‘preventable’ DALYs

This paper presents Bayesian multivariate disease mapping and ecological regression models that take into account errors in covariates. Bayesian hierarchical formulations of multivariate disease models and covariate measurement models, with related methods of estimation and inference, are developed as an integral part of a Bayesian disability adjusted life years (DALYs) methodology for the analysis of multivariate disease or injury data and associated ecological risk factors and for small area DALYs estimation, inference, and mapping. The methodology facilitates the estimation of multivariate small area disease and injury rates and associated risk effects, evaluation of DALYs and ‘preventable’ DALYs, and identification of regions to which disease or injury prevention resources may be directed to reduce DALYs. The methodology interfaces and intersects the Bayesian disease mapping methodology and the global burden of disease framework such that the impact of disease, injury, and risk factors on population health may be evaluated to inform community health, health needs, and priority considerations for disease and injury prevention. A burden of injury study on road traffic accidents in local health areas in British Columbia, Canada, is presented as an illustrative example. Copyright © 2009 John Wiley & Sons, Ltd.     

On Bayesian shared component disease mapping and ecological regression with errors in covariates

Recent literature on Bayesian disease mapping presents shared component models (SCMs) for joint spatial modeling of two or more diseases with common risk factors. In this study, Bayesian hierarchical formulations of shared component disease mapping and ecological models are explored and developed in the context of ecological regression, taking into consideration errors in covariates. A review of multivariate disease mapping models (MultiVMs) such as the multivariate conditional autoregressive models that are also part of the more recent Bayesian disease mapping literature is presented. Some insights into the connections and distinctions between the SCM and MultiVM procedures are communicated. Important issues surrounding (appropriate) formulation of shared‐ and disease‐specific components, consideration/choice of spatial or non‐spatial random effects priors, and identification of model parameters in SCMs are explored and discussed in the context of spatial and ecological analysis of small area multivariate disease or health outcome rates and associated ecological risk factors. The methods are illustrated through an in‐depth analysis of four‐variate road traffic accident injury (RTAI) data: gender‐specific fatal and non‐fatal RTAI rates in 84 local health areas in British Columbia (Canada). Fully Bayesian inference via Markov chain Monte Carlo simulations is presented. Copyright © 2010 John Wiley & Sons, Ltd.