Using self-generated identification codes to match anonymous longitudinal data in a sexual health study of secondary school students: a cohort study
The use of SGICs successfully matched approximately 72.65% of the study sample over a one-year period. These findings highlight the potential of SGICs as a tool for longitudinal data matching while underscoring the need for further refinement of code generation processes and matching algorithms to minimize data wastage and improve effectiveness.
Authors
- LM Ho (ORCID: https://orcid.org/0000-0002-2709-927X)
- Edmond Pui Hang Choi (ORCID: https://orcid.org/0000-0001-9062-3540)
- Kevin Wing Chung Lau
- Alice Wai Chi Fung
- Monique Yeung
- JM Johnston (ORCID: https://orcid.org/0000-0002-6624-867X)
- Ellie Bostwick Andres (ORCID: https://orcid.org/0000-0001-9086-8222)
- Heidi Sze Lok Fan (ORCID: https://orcid.org/0000-0001-5042-650X)
- Neda Hei Tung Ng
Institutions
- AIDS United (US)
- Duke-NUS Medical School (SG)
- Okanagan University College (CA)
- University of Hong Kong (HK)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-06-02
- DOI
- https://doi.org/10.1186/s12911-025-03028-1
- Citations
- 1
- Primary Topic
- Survey Methodology and Nonresponse
- Type
- article
- Field-Weighted Citation Impact
- 4.20
Funders
- Food and Health Bureau
- Health and Medical Research Fund