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Warning of Immortal Time Bias When Studying Drug Safety in Pregnancy: Application to Late Use of Antibiotics and Preterm Delivery

This study aimed to illustrate and account for immortal time bias in pregnancy observational investigations, using the relationship between late use of antibiotics and risk of preterm birth as an example. We conducted a population-based cohort study including 549,082 deliveries between 2007 and 2017...

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Detalles Bibliográficos
Autores principales: Corrao, Giovanni, Rea, Federico, Franchi, Matteo, Beccalli, Benedetta, Locatelli, Anna, Cantarutti, Anna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7558278/
https://www.ncbi.nlm.nih.gov/pubmed/32899455
http://dx.doi.org/10.3390/ijerph17186465
Descripción
Sumario:This study aimed to illustrate and account for immortal time bias in pregnancy observational investigations, using the relationship between late use of antibiotics and risk of preterm birth as an example. We conducted a population-based cohort study including 549,082 deliveries between 2007 and 2017 in Lombardy, Italy. We evaluated the risk of preterm births, low birth weight, small for gestational age, and low Apgar score associated with antibiotic dispensing during the third trimester of pregnancy. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CI) of the outcomes, considering the use of antibiotics as time-fixed (with biased classification of exposure person-time) and time-varying (with proper classification of exposure person-time) exposure. There were 23,638 (4.3%) premature deliveries. There was no association between time-fixed exposure to antibiotics and preterm delivery (adjusted HR 0.96; 95% CI 0.92 to 1.01) but an increased risk of preterm birth when time-varying exposure to antibiotics was considered (1.27; 1.21 to 1.34). The same trend was found for low birth weight and low Apgar score. Immortal time bias is a common and sneaky trap in observational studies involving exposure in late pregnancy. This bias could be easily avoided with suitable design and analysis.