Nuestro grupo organiza más de 3000 Series de conferencias Eventos cada año en EE. UU., Europa y América. Asia con el apoyo de 1.000 sociedades científicas más y publica más de 700 Acceso abierto Revistas que contienen más de 50.000 personalidades eminentes, científicos de renombre como miembros del consejo editorial.

Revistas de acceso abierto que ganan más lectores y citas
700 revistas y 15 000 000 de lectores Cada revista obtiene más de 25 000 lectores

Indexado en
  • Índice Copérnico
  • Google Académico
  • sherpa romeo
  • Revista GenámicaBuscar
  • SeguridadIluminado
  • Acceso a la Investigación Global en Línea en Agricultura (AGORA)
  • Centro Internacional de Agricultura y Biociencias (CABI)
  • Búsqueda de referencia
  • Universidad Hamdard
  • EBSCO AZ
  • OCLC-WorldCat
  • CABI texto completo
  • cabina directa
  • publones
  • Fundación de Ginebra para la educación y la investigación médicas
  • Pub Europeo
  • ICMJE
Comparte esta página

Abstracto

Predictive Models for Incidence and Economic Burden of Liver Cancer in Saudi Arabia

Shoukri MM, Elsiesy HA, Khafaga Y, Bazarbashi S, Al-Sebayel M, Collison K, Al-Mohanna F

Hepatocellular carcinoma (HCC) is a major cause of cancer-related death worldwide, and the burden of this devastating disease is expected to increase. The variability in the incidence and prevalence of this disease is documented in many epidemiological studies. This variation may be attributed to the variation in the prevalence of major risk factors such as, smoking, drinking, gender, hepatitis B and C viral infection and the Nonalcoholic Fatty Liver Disease (NAFLD). In order to understand the role of such risk factors in the disease etiology a surveillance system with rich data should be available. We intend to use the Saudi Cancer Registry (SCR) data to establish the relationship between age, gender, and the HCC incidence, and the future burden in terms of the forecasted number of liver cancer cases. Moreover we shall link the information available from the Saudi Transplant Registry (STR) with a model that utilizes the number of forecasted HCC cases to predict the future number of needed liver transplants, and hence the economic burden for the next 10 years. This is done by using the Poisson regression model for count data. The projected information will be reported (within limits of uncertainty) and is expected to play a critical role in guiding health officials on future disease patient management.