背景:在COVID-19大流行期间,CoMix研究,纵向行为调查,旨在监测多个国家的社会接触和公众意识,包括比利时。作为纵向测量,它容易受到参与者的“调查疲劳”,这可能会影响推论。
方法:位置的负二项广义加性模型,scale,和形状(NBIGAMLSS)用于估计年龄组之间报告的接触数量,并处理由于研究中的疲劳导致的报告不足。使用一阶自回归逻辑回归分析辍学过程,以确定影响辍学的因素。利用所谓的下一代原理,我们计算了由于疲劳导致的报告不足对估计繁殖数的影响。
结果:据报道,随着参与调查的时间延长,接触人数减少,这表明由于调查疲劳导致报告不足。参与者辍学受家庭规模和年龄类别的影响很大,但不受最近两次浪潮中报告的接触人数的影响。这表明协变量依赖性完全随机缺失(MCAR),当随机缺失(MAR)是替代方案。然而,我们不能排除更复杂的机制,例如非随机缺失(MNAR)。此外,由于疲劳导致的漏报被发现随着时间的推移是一致的,这意味着在纠正和不纠正漏报之间,联系人数量和复制数量([公式:见正文])的比率减少了15-30%。最后,我们发现,在考虑易感性和传染性的年龄特异性异质性时,纠正疲劳并没有改变各年龄组之间的相对发病率模式.
结论:CoMix数据强调了不同年龄段和不同时间的接触模式的可变性,揭示了控制COVID-19/空气传播疾病在人群中传播/传播的机制。尽管这种纵向接触调查由于参与者疲劳和辍学而容易出现报告不足的情况,我们表明,这些因素可以使用NBIGAMLSS识别和校正。这些信息可以用来改进类似的设计,未来的调查。
During the COVID-19 pandemic, the CoMix study, a longitudinal behavioral survey, was designed to monitor social contacts and public awareness in multiple countries, including Belgium. As a longitudinal survey, it is vulnerable to participants\' \"survey fatigue\", which may impact inferences.
A negative binomial generalized additive model for location, scale, and shape (NBI GAMLSS) was adopted to estimate the number of contacts reported between age groups and to deal with under-reporting due to fatigue within the study. The dropout process was analyzed with first-order auto-regressive logistic regression to identify factors that influence dropout. Using the so-called next generation principle, we calculated the effect of under-reporting due to fatigue on estimating the reproduction number.
Fewer contacts were reported as people participated longer in the survey, which suggests under-reporting due to survey fatigue. Participant dropout is significantly affected by household size and age categories, but not significantly affected by the number of contacts reported in any of the two latest waves. This indicates covariate-dependent missing completely at random (MCAR) in the dropout pattern, when missing at random (MAR) is the alternative. However, we cannot rule out more complex mechanisms such as missing not at random (MNAR). Moreover, under-reporting due to fatigue is found to be consistent over time and implies a 15-30% reduction in both the number of contacts and the reproduction number ([Formula: see text]) ratio between correcting and not correcting for under-reporting. Lastly, we found that correcting for fatigue did not change the pattern of relative incidence between age groups also when considering age-specific heterogeneity in susceptibility and infectivity.
CoMix data highlights the variability of contact patterns across age groups and time, revealing the mechanisms governing the spread/transmission of COVID-19/airborne diseases in the population. Although such longitudinal contact surveys are prone to the under-reporting due to participant fatigue and drop-out, we showed that these factors can be identified and corrected using NBI GAMLSS. This information can be used to improve the design of similar, future surveys.