么鸿雁,施侣元.应用Meta分析有效估计人群归因危险度百分比的探讨[J].Chinese journal of Epidemiology,2003,24(4):320-321 |
应用Meta分析有效估计人群归因危险度百分比的探讨 |
Application of Meta analysis effectively estimate the population attributable risk percentage |
Received:February 05, 2002 |
DOI: |
KeyWord: 人群归因危险度百分比 Meta分析 病例对照研究 危险因素 有效估计 PARP 分析方法 抽样调查 暴露率 肺癌危险 |
English Key Word: The population attributable risk percentage Meta analysis A case-control study Risk factors for Effective estimation PARP Analysis method Sampling survey Exposure rate Lung cancer risk |
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Abstract: |
人群归因危险度百分比(population attributable risk proportion, PARP)是总体人群中某种疾病归因于某种因素的暴露所引起的发病(死亡)占全部发病(死亡)的百分比,反映该因素所引起的发病(死亡)占全部发病(死亡)的比重.通过PARP可了解各危险因素对人群中某疾病的发病所产生的影响,亦即消除某危险因素后,所产生的对预防该疾病的效果将占有多大比重.它能够为卫生政策的制订提供依据,有着重要的公共卫生的实际意义.目前常用的估计PARP的方法有两种:一种是利用全国人群抽样调查获得的人群总暴露率来估计;另一种是利用某地区病例对照资料的单个研究的人群暴露率作为人群总暴露率来估计.这两种方法各有特点,但也明显地存在着不足,或者所需样本量大、实际操作费时、费力;或者方法上不够严谨准确. |
English Abstract: |
Population attributable risk percentage (population attributable risk proportion, PARP) is a disease in the general population due to some factors caused by the exposure of the disease (death) as a percentage of total incidence (death), reflect the factors caused (death) of the proportion of total incidence (death). Through PARP can understand the risk factors on the impact of the onset of certain diseases in the crowd, i.e. to eliminate some risk factors, the proportion will have much effect on the disease prevention. It can provide the basis for the formulation of health policies, has important practical significance of the public health. The common method to estimate the PARP has two kinds: one kind is to use the national population sampling survey of population total exposure rate to estimate;Another kind is to use case-control data in a given area of a single study population exposure rate as the crowd to estimate total exposure rate. These two methods have their own characteristics, but also clearly there is insufficient, or the large sample size required, the actual operation is time-consuming and tedious;Or not rigorous accurate methods. |
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