Pharmacoepidemiologic Research Based on Common Data Models: Systematic Review and Bibliometric Analysis.

Background: The adoption of common data models (CDMs) has transformed pharmacoepidemiologic research by enabling standardized data formatting and shared analytical tools across institutions. These models facilitate large-scale, multicenter studies and support timely real-world evidence generation. However, no comprehensive global evaluation of CDM applications in pharmacoepidemiology has been conducted.

Objective: This study aimed to conduct a systematic review and bibliometric analysis to map the landscape of CDM usage in pharmacoepidemiology, including publication trends, institutional authors and collaborations, and citation impacts.

Methods: In total, 5 English databases (PubMed, Web of Science, Embase, Scopus, and Virtual Health Library) and 4 Chinese databases (CNKI, Wan-Fang Data, VIP, and SinoMed) were searched for studies applying CDMs in pharmacoepidemiology from database inception to January 2024. Two reviewers independently screened studies and extracted information about basic publication details, methodological details, and exposure and outcome information. The studies were categorized into 2 groups according to their Total Citations per Year (TCpY), and a comparative analysis was conducted to examine the differences in characteristics between the 2 groups.

Results: A total of 308 studies published between 1997 and 2024 were included, involving 1580 authors across 32 countries and 140 journals. The United States led in both publication volume and citation counts, followed by South Korea. Among the 10 most cited studies, 7 used the Vaccine Safety Datalink, 2 used Sentinel, and one used Observational Medical Outcomes Partnership. Studies were stratified by TCpY to reduce citation bias from publication timing. Comparative analysis showed that high-TCpY studies were significantly more associated with multicenter collaboration (P=.008), United States-based institutions (P=.04), and vaccine-related research (P=.009). These studies commonly featured larger sample sizes, cross-regional data, and enhanced generalizability. International collaborations primarily occurred among North America, Europe, and East Asia, with limited involvement from limited-income countries.

Conclusions: This study presents the first bibliometric overview of CDM-based pharmacoepidemiologic research. The consistent output from United States institutions and increasing engagement from South Korea underscore their central roles in this field. High-TCpY studies tend to be multicenter, collaborative, and vaccine-focused, reflecting structural factors linked to research visibility and influence. Stratified citation analysis supports the value of real-world data integration and international cooperation in producing impactful studies. The dominance of limited-income countries in collaboration networks highlights a need for broader inclusion of underrepresented regions. These findings can help researchers identify key contributors, guide partner selection, and target appropriate journals. As CDM-based methods continue to expand, fostering diverse and collaborative research efforts will be crucial for advancing pharmacoepidemiologic knowledge globally.