Purpose Concerns about the risk of osteoporosis and osteoporotic fractures following endocrine therapy have increased with increase in breast cancer survivors. This study aimed to evaluate the incidence of fractures by site between tamoxifen and aromatase inhibitor (AI) users and non-users at premenopausal and postmenopausal ages. Methods This retrospective cohort study used data from the Korea Central Cancer Registry and the Korean National Health Insurance Service. We analyzed 63,021 newly diagnosed breast cancer patients between 2008 and 2016. The risk of fractures was analyzed according to post-surgical endocrine therapy using the multivariable Cox proportional hazard model. Results Herein, 69.1% of the patients received endocrine therapy. The incidence rates of osteoporotic fractures per 1,000 person-years were 7.4 overall, 3.5 in those under 50, and 12.6 in those over 50. After adjusting for confounders, the tamoxifen group showed protection against any fractures in patients over 50 (HR = 0.75, 95% CI 0.62–0.89, p < 0.001) but not in those under 50. The AI ​​group was associated with a significant increase in fractures in those under 50 (HR = 1.54, 95% CI 1.07–2.93, p = 0.019) but not in those over 50. Conclusion Among breast cancer patients aged over 50 years, AI use was not associated with increased risk of compared to non-users. Tamoxifen use was linked to reduced risk of fracture, but not in patients under 50. Considering the relatively young age of breast cancer diagnosis in Korea, long-term follow-up studies are warranted. Summary After adjusting for confounders including age, among breast cancer patients aged over 50 years, AI use was not associated with increased risk of fracture compared to non-users. Tamoxifen use was linked to reduced risk of fracture, but not in patients under 50.

Background: 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. Methods: 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_III (DB_III) and database_DII (DB_DII), 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. Results: The linkage rates for DB_DII and DB_III 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_III 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_III vs. 1.80 [95% CI: 1.18–2.73] in DB_DII). 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_III vs. 1.92 [95% CI: 1.70–2.17] in DB_DII for the localized stage; HR = 1.80 [95% CI: 1.37–2.36] in DB_III vs. 2.05 [95% CI: 1.67–2.52] in DB_DII for the regional stage). Conclusions: 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_DII. 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.

Whether older adults can more likely commit suicide than those in other age groups, after adjusting for other possible causes, remains unknown. We aimed to examine why elderly individuals are more likely to develop first-onset suicidal ideation than individuals in other age groups. We identified 2018 young, 3329 middle-aged, and 2714 elderly individuals without a history of suicidal ideation, from the Korean Welfare Panel Study 2011–2021. To determine key stressors that can induce suicidal ideation, selected groups of variables were adjusted progressively in a generalized estimating equation (GEE) model. Incidence rates of the elderly, middle-aged, and young individuals were 15.9, 22.0, and 11.3 per 1000 person-years, respectively. In GEE analysis, a positive association was not noted between age group and suicidal ideation after adjusting for stressor variables. Furthermore, the overly adjusted model (Full model) showed a strong negative association with aging; young [odds ratio (OR): 1.68, 95% confidence interval (CI): 1.35–2.11] and middle-aged (OR: 1.94, 95% CI: 1.38–2.73) individuals were more likely to develop first-onset suicidal ideation than the elderly. We found that full models, particularly assessing wealth rather than income, can explain why the elderly have higher suicide rates than those in other age groups.

Park KT, et al. J Korean Med Sci. 2025 Sep;40(45):e292. https://doi.org/10.3346/jkms.2025.40.e292