暴露测量错分对研究真实性的影响

通过数据模拟的方式,假定某人群的总人口为2 000万,人群疾病感染率为0.1%~5.0%,模拟不同灵敏度（99.0%、99.5%、100.0%）和特异度（97.0%、97.5%、98.0%、98.5%、99.0%、99.5%、99.9%）的组合情况,计算各组合的PPV、真阳性数和假阳性数。

在低感染率（≤5.0%）状态下,与灵敏度相比,特异度对PPV的影响更大,且随着感染率的下降,特异度提高时PPV的增幅加大。当感染率>1.0%时,特异度越趋近99.9%,PPV越接近100.0%;而当感染率<1.0%时,PPV的最大值也仅约为90.0%。当人群感染率为0.1%、灵敏度≥99.0%时,在2 000万的人群中筛检试验可以发现的真阳性人数约为2万人;当特异度为97.0%时,假阳性人数约为 59.9万人,PPV为3.2%;当特异度达到99.9%时,假阳性人数降到约2万人,PPV升高到50.0%。当人群感染率为1.0%时,灵敏度≥99.0%和特异度≥97.0%的筛检试验在2 000万的人群中可发现的真阳性数为19.8万~20.0万人;当特异度由97.0%升高至99.9%时,假阳性人数由59.4万人降至2万人。当人群感染率为5.0%时,灵敏度≥99.0%和特异度≥97.0%的筛检试验在2 000万的人群中可发现的真阳性数为99.0万~100.0万人;当特异度由97.0%升高至99.9%时,假阳性人数由57.0万人降至1.9万人。在总人口为 2 000万的人群中,当感染率≤5.0%时,即使灵敏度和特异度分别达到最大值100.0%和99.9%,仍存在约2万例的假阳性。

当人口数量较多而感染率较低时,运用筛检试验进行大规模人群筛查除了要提高特异度外,大量假阳性所产生的问题不可忽视。

     

测量误差变量与准确测量变量混合对研究真实性的影响

目的探讨测量误差变量与准确测量变量混合情况下测量误差对联系效应估计的影响。方法利用测量误差大小、准确测量变量与测量误差变量之间的相关性、准确测量变量的个数和联系效应之间的函数，采用R软件做图来讨论分析测量误差对研究真实性的影响。结果当连续变量Y和Z能准确测量，连续变量X不能准确测量时，无差异性测量误差使所估计的联系效应值总低于实际值，并随X与Z的相关程度的增加，测量误差所致的偏倚会进一步地恶化。在一个错分二分类变量X和一个准确测量连续变量Z混合的情况下，测量误差所致的偏倚不仅跟暴露测量的灵敏度和特异度有关，而且跟X与Z的相关系数以及X的暴露比例有关，并且随着相关系数的增加，AF值逐渐减少。在ρ=0．5时，AF值为1．419，变量X对应变量Y的联系效应估计值大于实际值，但当ρ增至0．9时，AF值为0．474，其联系效应估计值低于实际值，改变了错分偏倚的方向。结论在准确测量变量和测量误差变量混杂的研究中，用线性回归模型来分析估计多个自变量与应变量之间的联系时，对测量误差所致偏倚的识别、控制和评估是十分必要的，对结果的解释要谨慎。     

环境暴露对健康影响的非随机化研究偏倚评价工具ROBINS-E介绍

针对非随机化环境暴露研究Cochrane偏倚评估工具ROBINS-E的主要内容进行详细介绍,并举例说明ROBINS-E的使用方法和注意事项。ROBINS-E针对非随机化暴露研究（non-randomized studies of exposures,NRSE）的特点,设置了相应的评估领域和信号问题,为将NRSE纳入到系...     

暴露对结局影响的非随机对照研究偏倚分析工具ROBINS-E（2022）的介绍

本文介绍了2022年6月最新版非随机对照研究偏倚分析工具ROBINS-E（2022）的内容并举例说明其使用方法。ROBINS-E是一种评估暴露相关非随机对照研究偏倚风险的工具。与ROBINS-E（2019）相比,ROBINS-E（2022）补充了更多适用于观察性研究的偏倚,涵盖的偏倚更加全面,同时增加了针对研究外部真实性的评估。ROBINS-E（2022）增加了初步评估环节,便于提高评估的效率。此外ROBINS-E（2022）使用路径图的形式将信号问题的使用进行了可视化和工具化,使用更加便捷。ROBINS-E（2022）虽然对共暴露的问题有了更多的考虑,但仍然没有解决共暴露中的效应修饰问题,仍有扩展适用的研究范围的空间。     

职业性锰暴露对电焊工脂质过氧化的影响研究

[目的]探讨锰暴露对机体脂质过氧化的影响。[方法]2004年,在淮安市选择84名锰焊作业人员,测定作业场所空气锰浓度,检测血清丙二醛(MDA)含量及超氧化物岐化酶(SOD)、谷胱甘肽过氧化物酶(GSHPx)活性,并以84名服务行业人员为对照。[结果]锰暴露组MDA含量高于对照组,血清SOD、GSHPx活性低于对照组(P<0.01);MDA、SOD与作业场所空气锰浓度呈现一定的剂量反应关系。[结论]锰能使机体脂质过氧化增加,抗氧化氧能力下降,MDA、SOD可以作为锰中毒的早期敏感指标。     

氟暴露对工人牙齿影响的研究

目的 研究氟暴露对工人牙齿的影响。方法 对651名作业工人和268名对照工人进行了专科检查;其中对工龄5年以上的104名工人进行了微量牙表层组织氟浓度的检测,结果 氟暴露工人牙齿疾患的患百闻不如一见 率为39.3%、疾患率为71.6%,工人牙表层组织氟浓度增高,均明显高于对照组。结论 工人牙疾患的增高与氟暴露有一定的关系,随着氟暴露工龄的增加而明显,这对防治措施提供了依据。     

农药暴露对女性生殖健康的影响

目的：研究不同程度农药暴露对女性生殖健康的影响。方法选择长期接触农药的育龄期女性319人为观察组,其中低暴露强度104人,中暴露强度112人,高暴露强度103人;以未长期接触农药的育龄期女性109人为对照组,通过问卷调查的方式搜集资料并进行数据之间的比较。结果不同强度的农药暴露对女性的月经功能、性功能状况、乳腺疾病、阴道炎、不良妊娠结局的差异有统计学意义（P＜0.05）,且与农药暴露的相关系数分别是0.257,0.113,0.127,0.125和0.119,其中长期农药暴露对月经功能和乳腺疾病影响显著（ P＜0.01）,但不同农药暴露对子宫疾病、孕期并发症影响较小（P＞0.05）。结论长期农药暴露对女性生殖系统健康有一定影响,其中对月经功能、性功能状况、阴道炎、乳腺病、不良妊娠结局等方面影响比较显著。应增强农药使用者的安全意识,促进女性农药使用者的生殖健康。     

对噪声个体暴露测量与评价的思考和探索

笔者结合近年来的工作和体会 ,对噪声个体暴露测量和评价中存在的问题及解决方法作一简要的回顾和总结。1 噪声测量和评价主体的转变 :经典的噪声测量和评价的主体是环境中某些重要的、有代表性的点 (如十字路口交通警察的工作位 )或某些特定的噪声源 (如某台空压机 )。这种测量和评价方法对于噪声控制非常有用 ,但对于分析和评价噪声与人体健康的关系却不尽人意。在实际工作和生活中 ,人们在环境中经常处于不同的地方 ,声环境各不相同。同时 ,环境中噪声源发出的噪声有时是不稳定的 ,如汽车在不同的道路、负荷和速度下发出的噪声不同。显然…     

稀土暴露对儿童智商的影响

目的探讨稀土暴露与儿童智商的关系。方法采用绘人智能法对江西省某稀土矿区(暴露组,n=229)和对照区(对照组,n=235)7～10岁儿童进行智商测定,并使用电感耦合等离子体质谱法(ICP-MS)测定暴露组(n=69)和对照组(n=43)儿童静脉血稀土浓度,同时对儿童稀土暴露与智商的关系进行多元逐步回归分析。结果儿童血中15种稀土元素均可检出,且暴露组儿童血总稀土浓度(2.18±1.08)ng/g,比对照组(1.26±0.35)ng/g高出0.73倍,差异有统计学意义(P<0.01)。暴露组儿童智商犤(82.63±12.16)分犦低于对照组犤(87.08±15.28)分犦,差异有统计学意义(P<0.01)。多因素分析显示,家与稀土矿区距离及家中是否堆放稀土均可影响儿童智商水平。结论稀土暴露对儿童智商有一定的影响。     

The effect of non-differential exposure misclassification on estimates of the attributable and prevented fraction

This paper considers the effect of non-differential exposure misclassification on the population attributable fraction and the population prevented fraction as a function of the sensitivity and specificity of the exposure classification, the true relative risk, and the true prevalence of the exposure. Given a certain set of sensitivity, specificity, and prevalence of the exposure, the apparent attributable fraction is a constant proportion of the true attributable fraction regardless of the true relative risk. This observation does not hold for the apparent prevented fraction and the apparent relative risk, both of which vary with the true relative risk. For both the attributable and the prevented fraction, the sensitivity of the exposure classification has a greater influence on the magnitude of the bias than the specificity; also, the higher the prevalence of the exposure, the larger is the bias caused by the imperfect exposure classification.     

Reducing classification errors in cohort studies: the approach and a practical application

Classification errors of dependent variables can distort the results of observational studies. To reduce misclassification from our multicentre observational study of abortion complications, we extended the methodology of Lawrence and Greenwald for use in situations of unequal sample sizes and implemented both an office review and a field review. We reabstracted 424 reported complications and a random sample of 364 reported non-serious cases from 12 institutions participating in our study. In total, 30 per cent of the reported serious complications turned out to be misclassified: the office review detected 74 per cent of the total number of misclassifications with the remainder found in the field review. Because, with our particular data base, we estimated expending only 15 per cent of the total resources with our office effort, this represented the most cost-efficient approach to reducing classification errors. By eliminating the false positives from our study, we forced the specificity to 1.00 which produced both an unbiased estimate of the relative risk and an increase of 4 per cent to 63 per cent in the power of our study.     

Curious phenomena in Bayesian adjustment for exposure misclassification

Many epidemiologic investigations involve some discussion of exposure misclassification, but rarely is there an attempt to adjust for misclassification formally in the statistical analysis. Rather, investigators tend to rely on intuition to comment qualitatively on how misclassification might impact their findings. We point out several ways in which intuition might fail, in the context of unmatched case-control analysis with non-differential exposure misclassification. Particularly, we focus on how intuition can conflict with the results of a Bayesian analysis that accounts for the various uncertainties at hand. First, the Bayesian adjustment for misclassification can weaken the evidence about the direction of an exposure-disease association. Second, admitting uncertainty about the misclassification parameters can lead to narrower interval estimates concerning the association. We focus on the simple setting of unmatched case-control analysis with binary exposure and without adjustment for confounders, though much of our discussion should be relevant more generally.     

The diuretic effects of alcohol and caffeine and total water intake misclassification

Data from the 1994 USDA nationwide survey (CSFII) on 190 non- smoking males (aged 20–29) were used to propose a method for adjusting total water intake for the diuretic effects of caffeine and alcohol, and evaluate the potential for related misclassification bias. The data were processed on a per meal basis. Under the assumption that subjects were in water balance at the start of the survey day, water losses due to caffeine (1.17 ml/mg caffeine) and alcohol (10ml/g alcohol) were subtracted from crude intake estimates. If water intake for one meal was inadequate for excretion of the associated osmotic load at 750 mosmol/l, water losses for the subsequent meal were reduced by 32%. Unadjusted and adjusted mean total water intakes differed by 321.5g. Misclassification appeared worst at higher water intakes. Linear regression models, each with a water intake variable as an independent variable and body mass index as the outcome, were fit to evaluate the potential for alcohol- and caffeine-related misclassification bias. Misclassification resulted in large changes (all >10%) in linear regression estimates of effect. Future studies of water–disease relationships, especially those intending to compare extremes of total water intake, should consider caffeine- and alcohol-related misclassification bias.     

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.     

Statistical analysis with missing exposure data measured by proxy respondents: a misclassification problem within a missing‐data problem

In studies of older adults, researchers often recruit proxy respondents, such as relatives or caregivers, when study participants cannot provide self‐reports (e.g., because of illness). Proxies are usually only sought to report on behalf of participants with missing self‐reports; thus, either a participant self‐report or proxy report, but not both, is available for each participant. Furthermore, the missing‐data mechanism for participant self‐reports is not identifiable and may be nonignorable. When exposures are binary and participant self‐reports are conceptualized as the gold standard, substituting error‐prone proxy reports for missing participant self‐reports may produce biased estimates of outcome means. Researchers can handle this data structure by treating the problem as one of misclassification within the stratum of participants with missing self‐reports. Most methods for addressing exposure misclassification require validation data, replicate data, or an assumption of nondifferential misclassification; other methods may result in an exposure misclassification model that is incompatible with the analysis model. We propose a model that makes none of the aforementioned requirements and still preserves model compatibility. Two user‐specified tuning parameters encode the exposure misclassification model. Two proposed approaches estimate outcome means standardized for (potentially) high‐dimensional covariates using multiple imputation followed by propensity score methods. The first method is parametric and uses maximum likelihood to estimate the exposure misclassification model (i.e., the imputation model) and the propensity score model (i.e., the analysis model); the second method is nonparametric and uses boosted classification and regression trees to estimate both models. We apply both methods to a study of elderly hip fracture patients. Copyright © 2014 John Wiley & Sons, Ltd.     

Bayesian analysis of a matched case–control study with expert prior information on both the misclassification of exposure and the exposure–disease association

We propose a Bayesian adjustment for the misclassification of a binary exposure variable in a matched case–control study. The method admits a priori knowledge about both the misclassification parameters and the exposure–disease association. The standard Dirichlet prior distribution for a multinomial model is extended to allow separation of prior assertions about the exposure–disease association from assertions about other parameters. The method is applied to a study of occupational risk factors for new‐onset adult asthma. Copyright © 2009 John Wiley & Sons, Ltd.