Impact of linkage level on inferences from big data analyses in health and medical research: an empirical study

• Published in: BMC medical informatics and decision making 2024-07, Vol.24 (1), p.193-11, Article 193
• Main Authors: Lee, Bora, Lee, Young-Kyun, Kim, Sung Han, Oh, HyunJin, Won, Sungho, Jang, Suk-Yong, Jeon, Ye Jin, Yoo, Bit-Na, Bak, Jean-Kyung
• Format: Article
• Language: English
• Subjects: Accuracy; Analysis; Big Data; Biomedical Research; Breast cancer; Cervical cancer; Confidence intervals; Data analysis; Data entry; Datasets; Directly identifiable information; Disease; Empirical analysis; Empirical Research; Epidemiology; Evaluation; Female; Fractures; Gastric cancer; Health care; Health services; Humans; Impact analysis; Indirectly identifiable information; Information management; Linkage analysis; Linkage levels; Linked Data; Male; Medical Record Linkage; Medical research; Medical screening; Medicine, Experimental; Mortality; National health insurance; Patients; Personal information; Pilot projects; Prostate cancer; Statistical analysis; Thyroid gland
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-024-02586-0

Linkage errors that occur according to linkage levels can adversely affect the accuracy and reliability of analysis results. This study aimed to identify the differences in results according to personally identifiable information linkage level, sample size, and analysis methods through empirical analysis. The difference between the results of linkage in directly identifiable information (DII) and indirectly identifiable information (III) linkage levels was set as III linkage based on name, date of birth, and sex and DII linkage based on resident registration number. The datasets linked at each level were named as database (DB ) and database (DB ), respectively. Considering the analysis results of the DII-linked dataset as the gold standard, descriptive statistics, group comparison, incidence estimation, treatment effect, and moderation effect analysis results were assessed. The linkage rates for DB and DB were 71.1% and 99.7%, respectively. Regarding descriptive statistics and group comparison analysis, the difference in effect in most cases was "none" to "very little." With respect to cervical cancer that had a relatively small sample size, analysis of DB resulted in an underestimation of the incidence in the control group and an overestimation of the incidence in the treatment group (hazard ratio [HR] = 2.62 [95% confidence interval (CI): 1.63-4.23] in DB vs. 1.80 [95% CI: 1.18-2.73] in DB ). Regarding prostate cancer, there was a conflicting tendency with the treatment effect being over or underestimated according to the surveillance, epidemiology, and end results summary staging (HR = 2.27 [95% CI: 1.91-2.70] in DB vs. 1.92 [95% CI: 1.70-2.17] in DB for the localized stage; HR = 1.80 [95% CI: 1.37-2.36] in DB vs. 2.05 [95% CI: 1.67-2.52] in DB for the regional stage). To prevent distortion of the analyses results in health and medical research, it is important to check that the patient population and sample size by each factor of interest (FOI) are sufficient when different data are linked using DB . In cases involving a rare disease or with a small sample size for FOI, there is a high likelihood that a DII linkage is unavoidable.