Hua Yang Department of Gynecology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, People’s Republic of ChinaCorrespondence: Hua Yang, Department of Gynecology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, 519000, People’s Republic of China, Email yangh353@mail.sysu.edu.cnObjective: This study aimed to evaluate the global, regional, and national impact of the 2014 FDA approval of PARP inhibitors on ovarian cancer burden, leveraging the Global Burden of Disease (GBD) data from 2007 to 2021.Methods: We conducted a segmented regression analysis with breakpoint detection to compare trends in ovarian cancer incidence, prevalence, mortality, disability-adjusted life years (DALYs), years lived with disability (YLDs), and years of life lost (YLLs) before (2007– 2013) and after (2015– 2021) the 2014 landmark.Results: While the absolute global burden of ovarian cancer increased, significant declines in age-standardized incidence, mortality, DALYs, and YLLs were observed in high Socio-demographic Index (SDI) regions and nations. Crucially, breakpoint analysis revealed that these improving trends predominantly began around 2009, years prior to the introduction of PARP inhibitors. In stark contrast, low and middle-SDI regions experienced significant increases across all disease burden metrics. China emerged as a pivotal case, transitioning to the highest absolute death toll and demonstrating a later, unique breakpoint for age-standardized YLDs in 2014.Conclusion: The reduction in ovarian cancer burden in high-income countries was initiated in the pre-PARP inhibitor era, suggesting that earlier advancements in care were the primary drivers. The divergent trends highlight a widening global disparity, underscoring that the benefits of this targeted therapy have not yet been realized at a population level, particularly in developing regions. Our findings call for urgent policy interventions to improve global access to effective ovarian cancer therapies.Keywords: PARP inhibitors, ovarian cancer, global burden of disease, segmented regression analysis
Yang H (Thu,) studied this question.