A proposed de-identification framework for a cohort of children presenting at a health facility in Uganda.

ABSTRACT: Data sharing has enormous potential to accelerate and improve the accuracy of research, strengthen collaborations, and restore trust in the clinical research enterprise. Nevertheless, there remains reluctancy to openly share raw data sets, in part due to concerns regarding research participant confidentiality and privacy. Statistical data de-identification is an approach that can be used to preserve privacy and facilitate open data sharing. We have proposed a standardized framework for the de-identification of data generated from cohort studies in children in a low-and-middle income country. We applied a standardized de-identification framework to a data sets comprised of 241 health related variables collected from a cohort of 1750 children with acute infections from Jinja Regional Referra

SUBMITTER: Mawji A 

PROVIDER: S-EPMC9931294 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature