Two biases in incubation time estimation related to exposure.
Differential recall
Incubation time
Interval censoring
Left truncation
SARS-CoV-2
Journal
BMC infectious diseases
ISSN: 1471-2334
Titre abrégé: BMC Infect Dis
Pays: England
ID NLM: 100968551
Informations de publication
Date de publication:
03 Jun 2024
03 Jun 2024
Historique:
received:
15
02
2024
accepted:
27
05
2024
medline:
4
6
2024
pubmed:
4
6
2024
entrez:
3
6
2024
Statut:
epublish
Résumé
Estimation of the SARS-CoV-2 incubation time distribution is hampered by incomplete data about infection. We discuss two biases that may result from incorrect handling of such data. Notified cases may recall recent exposures more precisely (differential recall). This creates bias if the analysis is restricted to observations with well-defined exposures, as longer incubation times are more likely to be excluded. Another bias occurred in the initial estimates based on data concerning travellers from Wuhan. Only individuals who developed symptoms after their departure were included, leading to under-representation of cases with shorter incubation times (left truncation). This issue was not addressed in the analyses performed in the literature. We performed simulations and provide a literature review to investigate the amount of bias in estimated percentiles of the SARS-CoV-2 incubation time distribution. Depending on the rate of differential recall, restricting the analysis to a subset of narrow exposure windows resulted in underestimation in the median and even more in the 95th percentile. Failing to account for left truncation led to an overestimation of multiple days in both the median and the 95th percentile. We examined two overlooked sources of bias concerning exposure information that the researcher engaged in incubation time estimation needs to be aware of.
Sections du résumé
BACKGROUND
BACKGROUND
Estimation of the SARS-CoV-2 incubation time distribution is hampered by incomplete data about infection. We discuss two biases that may result from incorrect handling of such data. Notified cases may recall recent exposures more precisely (differential recall). This creates bias if the analysis is restricted to observations with well-defined exposures, as longer incubation times are more likely to be excluded. Another bias occurred in the initial estimates based on data concerning travellers from Wuhan. Only individuals who developed symptoms after their departure were included, leading to under-representation of cases with shorter incubation times (left truncation). This issue was not addressed in the analyses performed in the literature.
METHODS
METHODS
We performed simulations and provide a literature review to investigate the amount of bias in estimated percentiles of the SARS-CoV-2 incubation time distribution.
RESULTS
RESULTS
Depending on the rate of differential recall, restricting the analysis to a subset of narrow exposure windows resulted in underestimation in the median and even more in the 95th percentile. Failing to account for left truncation led to an overestimation of multiple days in both the median and the 95th percentile.
CONCLUSION
CONCLUSIONS
We examined two overlooked sources of bias concerning exposure information that the researcher engaged in incubation time estimation needs to be aware of.
Identifiants
pubmed: 38831419
doi: 10.1186/s12879-024-09433-7
pii: 10.1186/s12879-024-09433-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
555Subventions
Organisme : RBG was supported by the Wellcome Trust
ID : 225167/Z/22/Z
Informations de copyright
© 2024. The Author(s).
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