ePBRN 2019 GP + Hospital linked Dataset

The University of New South Wales (UNSW) electronic Practice Based Research Network (ePBRN) was established in 2010 in the Fairfield local government area of South Western Sydney (SWS) to conduct clinical and health services research by extracting and linking routinely collected data from 18 participating General Practices (GPs) and local health services, including the five hospitals in the Fairfield and Wollondilly integrated health neighbourhoods.  The SWS region has a diverse cultural and linguistic (CALD) population with a low socioeconomic profile. The ePBRN is a representative sample of SWS patients who use health services. 

The 2012-2019 Electronic Practice-based Research Network (ePBRN) Linked Dataset from South Western Sydney Local Health District (SWSLHD) comprises anonymized electronic health records sourced from an integrated care network consisting of 18 general practitioner clinics and hospitals situated in South Western Sydney, New South Wales, Australia. These records have undergone rigorous anonymization processes to ensure the protection of patient identities and privacy. Notably, the dataset's strength lies in its capacity to link these primary care records to hospital data through a probabilistic linkage approach, resulting in a unified and coherent dataset. In total, this linked dataset encompasses a substantial cohort of 158,159 patients and incorporates a diverse range of healthcare information, including patient demographics, medication histories, documented medical conditions, and detailed records of visits to both general practices and hospitals.

Furthermore, to facilitate seamless compatibility and standardization for research and analysis, the dataset has been meticulously converted into the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM). Researchers and healthcare professionals can leverage the richness and comprehensiveness of the ePBRN SWSLHD Linked Dataset to conduct a wide spectrum of studies, from investigating healthcare trends to evaluating treatment outcomes and conducting epidemiological research. This resource serves as a valuable asset not only for enhancing healthcare within South Western Sydney but also for contributing to broader healthcare research endeavors. Its ability to integrate general practice and hospital data offers a holistic view of patients' healthcare experiences, enabling comprehensive analyses and the generation of meaningful insights. 

Dataset access fees

• ePBRN 2019 data access fee - A$12,500.00 

• SREDH Platform (data storage and linkage) license for 1 year - A$1,900.00 

• ePBRN linkage and data preparation costs - A$5,000

• Payment via bank transfer to UNSW Sydney

Dataset Access Instruction

• Fill out this form to get access to the Dataset

• Once the request is approved, please sign and return the SREDH Consortium membership, data usage and project description forms that will be sent up on filling out the data request form above. 

• Pay data access and associated fees, if applicable 

• Download the dataset from the SREDH secure server. 

• Submit progress report every 6 months until the completion of the project

Access Criteria

• Available to researchers( academic and non-academic) for non-commercial purposes.

• Researchers need to have experience handling sensitive patient data and training in ethics.

• Researchers are required to report biannually to the SREDH Consortium on any research outputs that arise.

• Any output that arises from this dataset needs to be reviewed by the data custodian (SREDH Consortium) before submission. 

Frequently Asked Questions

• Please refer to FAQs page and following presentations and publications.

Selected Presentations

• Wang J,  Sitas F , Jonnagaddala J (2023) . Systematic data quality assessment of general practitioner collected smoking information on smoking-associated disease risks. OHDSI APAC 2023.  https://www.ohdsi.org/wp-content/uploads/2023/08/APAC-2-Jiayue-Wang_Smoking.pdf  

• Kadappu, P., Jonnagaddala, J., Liaw, S. T., Cochran, B. J., Rye, K. A., & Ong, K. L. (2022) https://www.ohdsi.org/wp-content/uploads/2020/12/Statin-Prescribing-Patterns-and-Effects-on-Inflammation-Kadappu-P.pdf 

• Siaw-Teng Liaw, Aldir Borelli, Guan-Nam Guo, Jitendra Jonnagaddala (2020)  https://www.ohdsi.org/wp-content/uploads/2020/05/OHDI_symposium_2020_poster_Liaw.pdf 

• Jitendra Jonnagaddala, Selva Muthu Kumaran Sathappan, Su Chi Lim, E Shyong Tai, Mengling Feng, Siaw-Teng Liaw  (2020) https://www.ohdsi.org/wp-content/uploads/2020/05/OHDI_symposium_2020_T2DM_poster_JJ.pdf 

• Liaw, S. T., Jonnagaddala, J (2019) UNSW ePBRN & OMOP-CDM  - https://www.ohdsi.org/web/wiki/lib/exe/fetch.php?media=projects:workgroups:20190612_ohdsi_epbrn_presentation_v0.3.pdf 

Selected Publications

• Liyanage, H.,Liaw, S. T., Jonnagaddala, J., Hinton, W., & de LUSIGNAN, S. (2018). Common Data Models (CDMs) to Enhance International Big Data Analytics: A Diabetes UseCase to Compare Three CDMs. EFMI-STC, 60-64. https://doi.org/10.3233/978-1-61499-921-8-60

• Kadappu, P., Jonnagaddala, J., Liaw, S. T., Cochran, B. J., Rye, K. A., & Ong, K. L. (2022). Statin Prescription Patterns and Associations with Subclinical Inflammation. Medicina (Kaunas, Lithuania), 58(8), 1096. https://doi.org/10.3390/medicina58081096 

• Vo, K., Jonnagaddala, J., & Liaw, T. (2019). Statistical supervised meta-ensemble algorithm for medical record linkage. Journal of Biomedical Informatics, 95, 103220. https://doi.org/10.1016/j.jbi.2019.103220 

• Guo, G. N., Jonnagaddala, J., Farshid, S., Huser, V., Reich, C., & Liaw, S. T. (2019). Comparison of the cohort selection performance of Australian Medicines Terminology to Anatomical Therapeutic Chemical mappings. Journal of the American Medical Informatics Association : JAMIA, 26(11), 1237–1246. https://doi.org/10.1093/jamia/ocz143 

•  Lu, Y., Van Zandt, M., Liu, Y., Li, J., Wang, X., Chen, Y., Chen, Z., Cho, J., Dorajoo, S. R., Feng, M., Hsu, M. H., Hsu, J. C., Iqbal, U., Jonnagaddala, J., Li, Y. C., Liaw, S. T., Lim, H. S., Ngiam, K. Y., Nguyen, P. A., Park, R. W., … Xu, H. (2022). Analysis of Dual Combination Therapies Used in Treatment of Hypertension in a Multinational Cohort. JAMA network open, 5(3), e223877. https://doi.org/10.1001/jamanetworkopen.2022.3877 

• Williams, R. D., Markus, A. F., Yang, C., Duarte-Salles, T., DuVall, S. L., Falconer, T., Jonnagaddala, J., Kim, C., Rho, Y., Williams, A. E., Machado, A. A., An, M. H., Aragón, M., Areia, C., Burn, E., Choi, Y. H., Drakos, I., Abrahão, M. T. F., Fernández-Bertolín, S., Hripcsak, G., … Rijnbeek, P. R. (2022). Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network. BMC medical research methodology, 22(1), 35. https://doi.org/10.1186/s12874-022-01505-z 

• Reps, J. M., Kim, C., Williams, R. D., Markus, A. F., Yang, C., Duarte-Salles, T., Falconer, T., Jonnagaddala, J., Williams, A., Fernández-Bertolín, S., DuVall, S. L., Kostka, K., Rao, G., Shoaibi, A., Ostropolets, A., Spotnitz, M. E., Zhang, L., Casajust, P., Steyerberg, E. W., Nyberg, F., … Rijnbeek, P. R. (2021). Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study. JMIR medical informatics, 9(4), e21547. https://doi.org/10.2196/21547