Choi Edmond Pui Hang, Andres Ellie Bostwick, Fan Heidi Sze Lok, Ho Lai Ming, Fung Alice Wai Chi, Lau Kevin Wing Chung, Ng Neda Hei Tung, Yeung Monique, Johnston Janice Mary
School of Nursing, University of Hong Kong, 5/F, 3 Sassoon Road, Pokfulam, Hong Kong.
Duke-NUS Medical School, Outram, Singapore.
BMC Med Inform Decis Mak. 2025 Jun 2;25(1):201. doi: 10.1186/s12911-025-03028-1.
This study aimed to (i) describe the procedures for generating self-generated identification codes (SGICs) in a prospective longitudinal evaluation of a sexual health program for secondary school students in Hong Kong; (ii) outline the matching strategies and processes; (iii) examine rates of successful matching and associated factors; and (iv) compare the responses of participants whose data could be matched to those whose data could not.
A prospective longitudinal cohort study was conducted. The SGIC comprised a 5-element code with 4 digits and 3 letters. A matching algorithm was developed to link baseline and follow-up data collected from students in Years 1 to 3 (n = 1,064) during the 2019-2020 school year. Matching success and associated factors were analyzed, and responses from matched and unmatched participants were compared.
The rate of perfectly matched cases was 49.06%, while 23.59% were partially matched, and 27.35% were unmatched. Logistic regression analysis revealed that male students (adjusted odds ratio [aOR]: 0.63) and Year 1 students (vs. Year 3; aOR: 0.56) were less likely to be perfectly matched. Compared to unmatched cases, perfectly and partially matched cases were less likely to have missing values and more likely to exhibit positive attitudes toward the sexual health program and related topics, such as the importance of sexual health, equal relationships, and condom use.
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.
本研究旨在（i）描述在香港一项针对中学生性健康计划的前瞻性纵向评估中生成自我生成识别码（SGIC）的程序；（ii）概述匹配策略和流程；（iii）检查成功匹配率及相关因素；（iv）比较数据可匹配的参与者与数据不可匹配的参与者的反应。
进行了一项前瞻性纵向队列研究。SGIC由一个包含4位数字和3个字母的5元素代码组成。开发了一种匹配算法，以链接在2019 - 2020学年从1至3年级学生（n = 1064）收集的基线数据和随访数据。分析了匹配成功率及相关因素，并比较了匹配和未匹配参与者的反应。
完全匹配的病例率为49.06%，部分匹配的为23.59%，未匹配的为27.35%。逻辑回归分析显示，男生（调整后的优势比[aOR]：0.63）和1年级学生（与3年级相比；aOR：0.56）完全匹配的可能性较小。与未匹配的病例相比，完全匹配和部分匹配的病例缺失值较少，对性健康计划及相关主题（如性健康的重要性、平等关系和避孕套使用）表现出积极态度的可能性更大。
在一年的时间里，使用SGIC成功匹配了约72.65%的研究样本。这些发现凸显了SGIC作为纵向数据匹配工具的潜力，同时强调需要进一步完善代码生成过程和匹配算法，以尽量减少数据浪费并提高有效性。
