Grellety Emmanuel, Golden Michael H
Research Center Health Policy and Systems - International Health, School of Public Health, Université Libre de Bruxelles, Brussels, Belgium.
Department of Medicine and Therapeutics, University of Aberdeen, Aberdeen, Scotland.
PLoS One. 2016 Dec 28;11(12):e0168585. doi: 10.1371/journal.pone.0168585. eCollection 2016.
It is often thought that random measurement error has a minor effect upon the results of an epidemiological survey. Theoretically, errors of measurement should always increase the spread of a distribution. Defining an illness by having a measurement outside an established healthy range will lead to an inflated prevalence of that condition if there are measurement errors.
A Monte Carlo simulation was conducted of anthropometric assessment of children with malnutrition. Random errors of increasing magnitude were imposed upon the populations and showed that there was an increase in the standard deviation with each of the errors that became exponentially greater with the magnitude of the error. The potential magnitude of the resulting error of reported prevalence of malnutrition were compared with published international data and found to be of sufficient magnitude to make a number of surveys and the numerous reports and analyses that used these data unreliable.
The effect of random error in public health surveys and the data upon which diagnostic cut-off points are derived to define "health" has been underestimated. Even quite modest random errors can more than double the reported prevalence of conditions such as malnutrition. Increasing sample size does not address this problem, and may even result in less accurate estimates. More attention needs to be paid to the selection, calibration and maintenance of instruments, measurer selection, training & supervision, routine estimation of the likely magnitude of errors using standardization tests, use of statistical likelihood of error to exclude data from analysis and full reporting of these procedures in order to judge the reliability of survey reports.
人们通常认为随机测量误差对流行病学调查结果影响较小。从理论上讲，测量误差总会增加分布的离散程度。如果存在测量误差，通过将测量值超出既定健康范围来定义疾病会导致该疾病的患病率被高估。
对营养不良儿童的人体测量评估进行了蒙特卡洛模拟。对总体施加了幅度不断增加的随机误差，结果显示，随着每种误差的出现，标准差都会增加，且随着误差幅度的增大呈指数级增长。将由此产生的营养不良报告患病率误差的潜在幅度与已发表的国际数据进行比较，发现其幅度足以使许多调查以及众多使用这些数据的报告和分析变得不可靠。
公共卫生调查中的随机误差以及用于确定“健康”诊断切点的数据的影响被低估了。即使是相当小的随机误差也可能使营养不良等疾病的报告患病率增加一倍以上。增加样本量并不能解决这个问题，甚至可能导致估计结果更不准确。需要更加关注仪器的选择、校准和维护，测量人员的选择、培训与监督，使用标准化测试对可能的误差幅度进行常规估计，利用误差的统计可能性排除分析数据，并全面报告这些程序，以便判断调查报告的可靠性。
