ABSTRACT
OBJECTIVE: The main goal of the present research is to explore the potential link of body mass index (BMI) with different survival metrics in breast cancer patients. Our aim is to offer the latest and most thorough meta-analysis, assessing the strength and reliability of the connection that BMI has with prognostic indicators in this disease. PATIENTS AND METHODS: As of January 2024, we conducted a systematic literature search across PubMed, Embase, Web of Science, and the Cochrane Library databases. Our search aimed to identify studies examining BMI as an exposure factor, with breast cancer patients constituting the study population, and utilizing adjusted hazard ratio (HR) as the data type of interest. RESULTS: The evidence synthesis incorporated a total of 61 eligible articles involving 201,006 patients. Being underweight posed a risk factor for overall survival (OS) in breast cancer patients compared to normal weight (HR 1.15, 95% CI 0.98-1.35; P = 0.08). Overweight or obesity, in comparison to normal weight, was a risk factor for OS (HR 1.18, 95% CI 1.14-1.23; P < 0.00001), disease-free survival (DFS) (HR 1.11, 95% CI 1.08-1.13; P < 0.00001), relapse-free survival (RFS) (HR 1.14, 95% CI 1.06-1.22; P = 0.03), and breast cancer-specific survival (BCSS) (HR 1.18, 95% CI 1.11-1.26; P < 0.00001), but not for progression-free survival (PFS) (HR 0.91, 95% CI 0.76-1.10; P = 0.33). Notably, in subgroup analyses, overweight patients achieved prolonged PFS (HR 0.80, 95% CI 0.64-0.99; P = 0.04), and compared to the obese population, the overweight cohort exhibited a significant difference in OS (HR 1.11, 95% CI 1.05-1.16; P < 0.00001) and DFS (HR 1.06, 95% CI 1.03-1.10; P = 0.0004), with a considerably stronger association. Furthermore, compared to HER- patients, HER + patients exhibited a greater predictive value for OS (HR 1.23, 95% CI 1.10-1.37; P = 0.0004), RFS (HR 1.30, 95% CI 1.03-1.64; P < 0.00001), and DFS (HR 1.10, 95% CI 1.03-1.17; P = 0.003). CONCLUSIONS: The results of our meta-analysis reveal a notable association between BMI and various survival measures in breast cancer prognosis. These findings provide a solid basis for predicting breast cancer outcomes and implementing more effective therapeutic approaches.
ABSTRACT
A long-term care insurance system provides basic living assistance, medical care cost compensation, and service assurance for people with disabilities. In China, it was first piloted in Qingdao, Shandong Province, in 2012, and has now been implemented in 27 provinces as pilot areas. The locally started policy was adopted by the central government and extended to the whole country in eight years. We focus on answering two questions: How did the diffusion of the policy occur? What were its determining factors? Based on the perspective of policy innovation and diffusion, this study empirically analyzes the main factors influencing the diffusion of the long-term care insurance system using a Cox proportional-hazards model and data from 31 Chinese provinces. The study found that the main internal factors were the local aging coefficient and degree of socioeconomic development, and the main external factors were national competition pressure and central policy pressure.
