Background: Patient privacy is a ubiquitous problem around the world. Many existing studies have demonstrated the potential privacy risks associated with sharing of biomedical data. Owing to the increasing need for data sharing and analysis, health care data privacy is drawing more attention. However, to better protect biomedical data privacy, it is essential to assess the privacy risk in the first place. Objective: In China, there is no clear regulation for health systems to deidentify data. It is also not known whether a mechanism such as the Health Insurance Portability and Accountability Act (HIPAA) safe harbor policy will achieve sufficient protection. This study aimed to conduct a pilot study using patient data from Chinese hospitals to understand and quantify the privacy risks of Chinese patients. Methods: We used g-distinct analysis to evaluate the reidentification risks with regard to the HIPAA safe harbor approach when applied to Chinese patients’ data. More specifically, we estimated the risks based on the HIPAA safe harbor and limited dataset policies by assuming an attacker has background knowledge of the patient from the public domain. Results: The experiments were conducted on 0.83 million patients (with data field of date of birth, gender, and surrogate ZIP codes generated based on home address) across 33 provincial-level administrative divisions in China. Under the Limited Dataset policy, 19.58% (163,262/833,235) of the population could be uniquely identifiable under the g-distinct metric (ie, 1-distinct). In contrast, the Safe Harbor policy is able to significantly reduce privacy risk, where only 0.072% (601/833,235) of individuals are uniquely identifiable, and the majority of the population is 3000 indistinguishable (ie the population is expected to share common attributes with 3000 or less people). Conclusions: Through the experiments based on real-world patient data, this work illustrates that the results of g-distinct analysis about Chinese patient privacy risk are similar to those from a previous US study, in which data from different organizations/regions might be vulnerable to different reidentification risks under different policies. This work provides reference to Chinese health care entities for estimating patients’ privacy risk during data sharing, which laid the foundation of privacy risk study about Chinese patients’ data in the future.

Esophageal cancer is one of the most common malignant tumors, with China accounting for 50% of the world’s total incidence. Concurrent chemoradiotherapy (cCRT) with platin-based dual-drug regimen is the standard treatment for inoperable, locally advanced esophageal cancer in patients with a good performance status. However, certain patients possess risk factors that heighten toxicity and reduce their tolerance to cCRT, thereby challenging the feasibility of standard treatment. This study evaluates an alternative therapeutic approach combining programmed cell death protein 1 inhibitor (PD-1 inhibitor), definitive radiotherapy, and immunonutrition support for patients with unresectable non-metastatic esophageal cancer expressing PD-L1 who are intolerant to cCRT. This is a phase II, single-arm, multicenter clinical trial involving patients with histologically confirmed unresectable esophageal squamous cell carcinoma (ESCC), who exhibit positive PD-L1 and are unsuitable for cCRT. Participants will receive a total radiotherapy dose of 50–60 Gy in 25–30 fractions, sintilimab (200 mg every three weeks), alongside, supplemented by enteral nutritional emulsion (600–1600 ml/day). The primary endpoint is the 1-year progression-free survival rate, with secondary endpoints including objective response rate, overall survival and incidence of adverse events. This research has the potential to redefine treatment for inoperable ESCC patients who cannot tolerate conventional therapies. By evaluating a less toxic regimen that combines immunotherapy, radiotherapy, and nutritional support, we aim to determine if this approach can improve both survival rates and quality of life. The synergistic effects of immunonutrition support and PD-1 inhibitor will also be explored. NCT06342167.