Data quality assessment and subsampling strategies to correct distributional bias in prevalence studies

Data quality assessment and subsampling strategies to correct distributional bias in prevalence studies

Abstract Background Healthcare-associated infections (HAIs) represent a major Public Health issue. Hospital-based prevalence studies are a common tool of HAI surveillance, but data quality problems and non-representativeness can undermine their reliability. Methods This study proposes three algorith...

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Bibliographic Details
Main Authors: A. D’Ambrosio, J. Garlasco, F. Quattrocolo, C. Vicentini, C. M. Zotti
Format: Article
Language:English
Published: BMC 2021-04-01
Series:BMC Medical Research Methodology
Subjects:
Healthcare associated infections
Prevalence studies
Sampling
Data quality
Methodology
Bias correction
Online Access:https://doi.org/10.1186/s12874-021-01277-y

Internet

https://doi.org/10.1186/s12874-021-01277-y

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