石家庄市2006—2007年流感疫情暴发分析

对石家庄市2006-2007年19起流感疫情暴发的监测资料进行分析,探讨全市流感活动状况.     

痴呆危险因素人群归因分值的城乡差异

目的 使用人群归因分值(population attributable fractions,PAF)探讨痴呆危险因素所造成痴呆患者的比例,为今后痴呆的早期预防和干预提供建议.方法 采用随机整群抽样的方法选取3 361名≥65岁的老年人进行调查,使用x2检验分析危险因素的城乡差异.在主成分分析探索危险因素之间重叠效应的基...     

脑出血归因危险度分析

根据脑出血危险因素的病例对照研究资料，初步估计并分析了脑出血的归因危险度和人群归因危险度。结果表明高血压、短暂脑缺血发作（ＴＩＡ）和打鼾的归因危险度分别为９１．４％、９３．８％和５２．７％。以上３种因素的人群归因危险度分别为６８．６％、１８．５％和１９．５％，提示在一般人群中控制高血压、ＴＩＡ和打鼾对预防脑出血具有重要的卫生学意义。     

病例对照研究资料人群归因危险度估计的改进法

在病例对照研究中由暴露因素为等级变量出发推导出估计人群归因危险度的改进公式, 并以医学实例介绍了这一公式的使用方法。文中对改进公式与Whittem ore及Walter等提出的归因危险度估计法作了必要的讨论     

构建个体化预测医务人员职业暴露风险的列线图模型

目的探究医护人员职业暴露的影响因素,构建个体化预测职业暴露风险的列线图模型。方法调查2016年7月—2017年7月医院医护人员职业暴露发生情况,使用二分类Logistic回归方法探索医务人员职业暴露风险的危险因素,应用R软件构建预测职业暴露风险的列线图模型。结果 962名医务人员中发生职业暴露有142例,职业暴露发生率为14. 76%; Logistic回归分析发现:夜班间隔时间、日均工作时间、防护用品满意度及职业防护培训是医护人员发生职业暴露的独立危险因素(P <0. 05),根据多因素分析结果绘制出职业暴露风险列线图。结论不参加单位职业防护培训、夜班间隔时间≤5 d是医护人员发生职业暴露的高危因素;基于以上危险因素建立个体化预测医务人员职业暴露风险的列线图模型,对医疗机构制定有针对性的职业暴露防控方案具有指导意义。     

脑出血与脑梗塞人群归因危险度的分析及比较

采用配比病例对照研究方法,对经颅脑CT确诊的162例脑出血病人和158例脑梗塞病人分别配以同等数量的对照作1:1配比分析,探讨中老年人脑出血和脑梗塞发病的危险因素.多因素条件logistic回归分析结果表明,脑出血的主要危险因素为高血压、短暂脑缺血发作(TIA)、父母脑卒中病史及打鼾,相应的调整人群归因危险度(PARs)分别为0.7004、0.1862、0.1357、0.2020;4个因素综合的人群归因危险度(PAR)为0.8318.脑梗塞的主要危险因素为高血压、TIA、习惯性饮酒和打鼾,相应的PARs分别为0.7473、0.1229、0.2403、0.4959;四因素综合的PAR为0.9137,经常参加体育锻炼对脑梗塞具有保护作用(OR为0.30).     

应用Meta分析有效估计人群归因危险度百分比的探讨

人群归因危险度百分比 (populationattributableriskproportion ,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…     

基于列线图的中国西部MSM人群暴露前预防用药依从性预测研究

目的 本研究旨在建立男男性行为者(men who have sex with men,MSM)艾滋病暴露前预防用药(pre-exposure prophylaxis,PrEP)依从性的列线图预测模型,为基层防控人员提供可直观判断服药依从性的测量工具,以提醒服药依从性差者,从而降低其HIV新发感染.方法 采用非概率抽样法...     

Selecting participants that raise a clinical trial's population attributable fraction can increase the treatment effect within the trial and reduce the required sample size

Background

Detection of modest but worthwhile treatment effects in randomized controlled trials (RCTs) demands trials of large sample size. Approaches to decreasing required size of RCTs while maintaining power are needed.

Objective

The epidemiological concept of population attributable fraction (AF_p) was applied to the population selected for an RCT to assess its role in determining the size of treatment effect and the required sample size. The additional effect of efficacy of treatment specifically among participants at risk for attributable target events (relative risk reduction_{at risk} [RRR_{at risk}]) was also examined.

Results

A model is described which accounts for size of treatment effect in an RCT based on AF_p and RRR_{at risk}: RRR_trial = (AF_p) (RRR_at risk). The increase in RRR_trial resulting from raising AF_p exceeds that possible under the traditional high risk/high response approach to trial design and allows a reduction in required trial sample size. AF_p can be estimated from studies of causation that determine both risk and attributable risk (AR) associated with specific risk factors.

Conclusion

Larger treatment effects within RCTs are enabled by choosing a target outcome having a specific cause and selecting participants at specific risk for that outcome. Using information about phenotypic and genetic predictors of AR may increase our capacity to select trial populations having high AF_p.     

Decomposition of the population attributable fraction for two exposures

Purpose

The population attributable fraction (AF) is frequently used to quantify disease burden attributable to exposures. AF is interpreted as the fractional reduction of disease events that would occur if exposures were eliminated. This article aims to provide a decomposition of the overall AF for two exposures into AFs for each of two exposures and AF for their interaction, using potential outcomes framework.

The extrapolation of attributable risk to new populations

I develop a method for extrapolation of attributable risk estimated from one population, to other populations with a different rate of risk factor exposure and/or rate of outcome. The method uses the relationship between attributable risk and the product moment correlation.     

A heuristic approach to the formulas for population attributable fraction

BACKGROUND: As the definitional formula for population attributable fraction is not usually directly usable in applications, separate estimation formulas are required. However, most epidemiology textbooks limit their coverage to Levin's formula, based on the (dichotomous) distribution of the exposure of interest in the population. Few present or explain Miettinen's formula, based on the distribution of the exposure in the cases; and even fewer present the corresponding formulas for situations with more than two levels of exposure. Thus, many health researchers and public health practitioners are unaware of, or are not confident in their use of, these formulas, particularly when they involve several exposure levels, or confounding factors. METHODS/RESULTS: A heuristic approach, coupled with pictorial representations, is offered to help understand and interconnect the structures behind the Levin and Miettinen formulas. The pictorial representation shows how to deal correctly with several exposure levels, and why a commonly used approach is incorrect. Correct and incorrect approaches are also presented for situations where estimates must be aggregated over strata of a confounding factor.     

Cancers in Australia in 2010 attributable to infectious agents

Objectives: To estimate the proportion and numbers of cancers in Australia in 2010 attributable to infectious agents. Methods: The population attributable fraction (PAF) and number of cancers caused by hepatitis B and C viruses (HBV, HCV), Helicobacter pylori and human immunodeficiency virus (HIV) were calculated using standard formulae incorporating prevalence of infection in the Australian population, the relative risks associated with that infection and cancer incidence. For cancers with very strong associations to the infectious agent (Epstein‐Barr virus [EBV], human papillomavirus [HPV] and HIV/Kaposi's sarcoma herpes virus [KSHV]), calculations were based on viral prevalence in the tumour. Results: An estimated 3,421 cancers (2.9% of all cancers) in Australia in 2010 were attributable to infections. Infectious agents causing the largest numbers of cancers were HPV (n=1,706), H. pylori (n=793) and HBV/HCV (n=518). Cancer sites with the greatest number of cancers caused by infections were cervix (n=818), stomach (n=694) and liver (n=483). Cancers with highest proportions attributable to infectious agents were Kaposi's sarcoma (100%), cervix (100%), nasopharynx (87%), anus (84%) and vagina (70%). Conclusions: Infectious agents cause more than 3,000 cancers annually in Australia. Implications: Opportunities for cancer prevention through infection control are considerable, even in a ‘first world’ nation like Australia.     

Effect of exposure misclassification on the mean squared error of population attributable risk and prevented fraction estimates

Previous work has considered the effect of exposure misclassification on the bias of population attributable risk (AR) estimates, but little is known about the corresponding effects on their precision or mean squared error (MSE). This paper considers AR estimation in typical scenarios for case-control and cohort studies. The analogous index used when exposure reduces the risk--the prevented fraction (PF)--is also investigated. It is shown, through both theoretical and simulation results, that even with quite modest levels of exposure misclassification, the MSE can increase substantially, relative to the variance of AR estimated without measurement error. When exposure assessment is perfectly sensitive, there is no bias in AR but lack of measurement specificity can still cause a substantial loss of precision. In a few cases, the AR or PF with misclassified exposure can actually have smaller MSE; these exceptional cases arise when sensitivity is poor and the bias in AR or PF is relatively large. We conclude that while bias can be reduced by defining exposure on a highly sensitive basis, one must also consider the deleterious effect on precision by doing so. Loss of precision in the AR and PF estimates can be safely ignored only when the exposure measure is very accurate.     

Cancers in Australia in 2010 attributable to the consumption of alcohol

Objective: To estimate the proportion and numbers of cancers occurring in Australia in 2010 that are attributable to alcohol consumption. Methods: We estimated the population attributable fraction (PAF) of cancers causally associated with alcohol consumption using standard formulae incorporating prevalence of alcohol consumption and relative risks associated with consumption and cancer. We also estimated the proportion change in cancer incidence (potential impact fraction [PIF]) that might have occurred under the hypothetical scenario that an intervention reduced alcohol consumption, so that no‐one drank >2 drinks/day. Results: An estimated 3,208 cancers (2.8% of all cancers) occurring in Australian adults in 2010 could be attributed to alcohol consumption. The greatest numbers were for cancers of the colon (868) and female breast cancer (830). The highest PAFs were for squamous cell carcinomas of the oral cavity/pharynx (31%) and oesophagus (25%). The incidence of alcohol‐associated cancer types could have been reduced by 1,442 cases (4.3%) – from 33,537 to 32,083 – if no Australian adult consumed >2 drinks/day. Conclusions: More than 3,000 cancers were attributable to alcohol consumption and thus were potentially preventable. Implications: Strategies that limit alcohol consumption to guideline levels could prevent a large number of cancers in Australian adults.     

Mortality attributable to smoking in Vietnamese men in 2008

Objective

Smoking prevalence among Vietnamese men is among the highest in the world. Our aim was to provide estimates of tobacco attributable mortality to support tobacco control policies.

Method

We used the Peto–Lopez method using lung cancer mortality to derive a Smoking Impact Ratio (SIR) as a marker of cumulative exposure to smoking. SIRs were applied to relative risks from the Cancer Prevention Study, Phase II. Prevalence-based and hybrid methods, using the SIR for cancers and chronic obstructive pulmonary disease and smoking prevalence for all other outcomes, were used in sensitivity analyses.

Results

When lung cancer was used to measure cumulative smoking exposure, 28% (95% uncertainty interval 24–31%) of all adult male deaths (> 35 years) in Vietnam in 2008 were attributable to smoking. Lower estimates resulted from prevalence-based methods [24% (95% uncertainty interval 21–26%)] with the hybrid method yielding intermediate estimates [26% (95% uncertainty interval 23–28%)].

Conclusion

Despite uncertainty in these estimates of attributable mortality, tobacco smoking is already a major risk factor for death in Vietnamese men. Given the high current prevalence of smoking, this has important implications not only for preventing the uptake of tobacco but also for immediate action to adopt and enforce stronger tobacco control measures.     

Cancers in Australia in 2010 attributable to modifiable factors: introduction and overview

Objective: To describe the approach underpinning a national project to estimate the numbers and proportions of cancers occurring in Australia in 2010 that are attributable to modifiable causal factors. Methods: We estimated the population attributable fraction (PAF) (or prevented fraction) of cancers associated with exposure to causal (or preventive) factors using standard formulae. Where possible, we also estimated the potential impact on cancer incidence resulting from changes in prevalence of exposure. Analyses were restricted to factors declared causal by international agencies: tobacco smoke; alcohol; solar radiation; infectious agents; obesity; insufficient physical activity; insufficient intakes of fruits, vegetables and fibre; red and processed meat; menopausal hormone therapy (MHT); oral contraceptive pill (OCP); and insufficient breast feeding. Separately, we estimated numbers of cancers prevented by: aspirin; sunscreen; MHT; and OCP use. We discuss assumptions pertaining to latent periods between exposure and cancer onset, choices of prevalence data and risk estimates, and approaches to sensitivity analyses. Results: Numbers and population attributable fractions of cancer are presented in accompanying papers. Conclusions: This is the first systematic assessment of population attributable fractions of cancer in Australia.