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Breast Cancer Epidemiology among Lebanese Women: An 11-Year Analysis.

Mohamad Y FaresHamza A SalhabHussein H KhachfeHassan M Khachfe
Published in: Medicina (Kaunas, Lithuania) (2019)
Background and Objectives: Breast cancer is the most prevalent cancer in women worldwide. Lebanon is a developing country in the Middle East with a prominent breast cancer incidence. The aim of our study was to explore the incidence rates of breast cancer in Lebanon from 2005 to 2015, and compare them to the rates of other countries. Materials and Methods: Breast cancer data for the years 2005-2015 was collected from the National Cancer Registry of Lebanon and stratified by gender and age group. Age-specific and age-standardized incidence rates were calculated and analyzed using joinpoint regression. Age-standardized incidence rates in the world population (ASR(w)) were obtained for other countries, from two online databases. Results: Breast cancer was found to be the most prevalent cancer in Lebanon, accounting for 20% of all cancer cases. The average ASR(w) was 96.5 per 100,000. Over the studied period, breast cancer ASR(w) in Lebanon showed a significantly increasing trend with an annual percent change (APC) of +4.6. Moreover, the APC of breast cancer age-specific rates significantly increased for the age groups 45-49 (p = 0.013), 50-54 (p < 0.001), 55-59 (p = 0.001), 60-64 (p = 0.002), 65-69 (p = 0.003), 70-74 (p < 0.001), and 75+ years (p < 0.001). Lebanon had the highest breast cancer ASR(w), when compared to other regional countries, and trailed only behind Denmark, when compared to selected countries from different parts of the world. Conclusions: Breast cancer incidence in Lebanon is among the highest in the world. Future studies should focus on exploring the genetic profile of the Lebanese population in an aim to extrapolate proper prevention guidelines.
Keyphrases
  • risk factors
  • breast cancer risk
  • childhood cancer
  • mental health
  • metabolic syndrome
  • gene expression
  • polycystic ovary syndrome
  • skeletal muscle
  • deep learning
  • electronic health record
  • artificial intelligence