Abdullah Noraidatulakma, Husin Nurul Faeizah, Goh Ying-Xian, Kamaruddin Mohd Arman, Abdullah Mohd Shaharom, Yusri Aiman Fitri, Kamalul Arifin Azwa Shawani, Jamal Rahman
UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.
Digit Health. 2024 Sep 12;10:20552076241277481. doi: 10.1177/20552076241277481. eCollection 2024 Jan-Dec.
The management of extensive longitudinal data in cohort studies presents significant challenges, particularly in middle-income countries like Malaysia where technological resources may be limited. These challenges include ensuring data integrity, security, and scalability of storage solutions over extended periods.
This article outlines innovative methods developed and implemented by The Malaysian Cohort project to effectively manage and maintain large-scale databases from project inception through the follow-up phase, ensuring robust data privacy and security.
We describe the comprehensive strategies employed to develop and sustain the database infrastructure necessary for handling large volumes of data collected during the study. This includes the integration of advanced information management systems and adherence to stringent data security protocols.
Key achievements include the establishment of a scalable database architecture and an effective data privacy framework that together support the dynamic requirements of longitudinal healthcare research. The solutions implemented serve as a model for similar cohort studies in resource-limited settings. The article also explores the broader implications of these methodologies for public health and personalized medicine, addressing both the challenges posed by big data in healthcare and the opportunities it offers for enhancing disease prevention and management strategies.
By sharing these insights, we aim to contribute to the global discourse on improving data management practices in cohort studies and to assist other researchers in overcoming the complexities associated with longitudinal health data.
队列研究中广泛纵向数据的管理面临重大挑战，尤其是在马来西亚等技术资源可能有限的中等收入国家。这些挑战包括确保数据完整性、安全性以及存储解决方案在较长时期内的可扩展性。
本文概述了马来西亚队列项目开发和实施的创新方法，以从项目启动到随访阶段有效管理和维护大规模数据库，确保强大的数据隐私和安全。
我们描述了为开发和维持处理研究期间收集的大量数据所需的数据库基础设施而采用的综合策略。这包括先进信息管理系统的整合以及对严格数据安全协议的遵守。
主要成就包括建立了可扩展的数据库架构和有效的数据隐私框架，共同支持纵向医疗研究的动态需求。所实施的解决方案为资源有限环境下的类似队列研究提供了一个范例。本文还探讨了这些方法对公共卫生和个性化医疗的更广泛影响，既解决了医疗大数据带来的挑战，也探讨了其为加强疾病预防和管理策略提供的机遇。
通过分享这些见解，我们旨在为全球关于改善队列研究数据管理实践的讨论做出贡献，并帮助其他研究人员克服与纵向健康数据相关的复杂性。
