EHESP Rennes, Université Sorbonne Paris Cité, France.
Aix Marseille University, IRD French Institute of Research for Development, EHESP French School of Public Health, UMR_D 190 Emergence des Pathologies Virales, Marseille, France.
Med Decis Making. 2018 Apr;38(3):377-389. doi: 10.1177/0272989X17750845. Epub 2018 Feb 13.
Although people are likely to underestimate the frequencies of risks to health from common diseases and overestimate those from rare diseases, we still do not know much about reasons for this systematic bias, which is also referred to as "primary bias" in the literature. In this study, we take advantage of a series of large epidemics of mosquito-borne diseases to examine the accuracy of judgments of risk frequencies. In this aim, we assessed the perceived v. observed prevalence of infection by Zika, chikungunya or dengue fever during these outbreaks, as well as their variations among different subpopulations and epidemiological settings.
We used data drawn from 4 telephone surveys, conducted between 2006 and 2016, among representative samples of the adult population in tropical regions (Reunion, Martinique, and French Guiana). The participants were asked to estimate the prevalence of these infections by using a natural frequency scale.
The surveys showed that 1) most people greatly overestimated the prevalence of infection by arbovirus, 2) these risk overestimations fell considerably as the actual prevalence of these diseases increased, 3) the better-educated and male participants consistently yielded less inaccurate risk estimates across epidemics, and 4) these biases in the perception of prevalence of these infectious diseases are relatively well predicted by the probability weighting function developed in the field of behavioral decision making.
These findings suggest that the primary bias, which has been found in laboratory experiments to characterize a variety of probabilistic judgments, equally affects perception of prevalence of acute infectious diseases in epidemic settings. They also indicate that numeracy may play a considerable role in people's ability to transform epidemiological observations from their social environment to more accurate risk estimates.
尽管人们可能会低估常见疾病对健康的风险频率，而高估罕见疾病的风险频率，但我们对这种系统性偏差的原因仍知之甚少，这种偏差在文献中也被称为“原发性偏差”。在这项研究中，我们利用一系列大规模的蚊媒疾病流行来检验对风险频率判断的准确性。为此，我们评估了在这些疫情中，人们对感染寨卡病毒、基孔肯雅热或登革热的感知流行率与实际流行率的差异，以及它们在不同亚人群和流行病学环境中的变化。
我们使用了 2006 年至 2016 年期间在热带地区（留尼汪岛、马提尼克岛和法属圭亚那）的代表性成年人群体中进行的 4 次电话调查的数据。参与者被要求使用自然频率量表来估计这些感染的流行率。
调查显示：1）大多数人大大高估了虫媒病毒感染的流行率；2）随着这些疾病实际流行率的增加，这些风险高估的程度大大降低；3）受教育程度较高和男性参与者在各次疫情中始终产生的风险估计误差较小；4）在行为决策领域开发的概率加权函数可以较好地预测这些传染病流行率感知的偏差。
这些发现表明，在实验室实验中发现的原发性偏差同样影响了人们对流行环境中急性传染病流行率的感知。它们还表明，计算能力可能在人们将其社会环境中的流行病学观察结果转化为更准确的风险估计方面发挥重要作用。
