Space-time analysis of ovarian cancer mortality rates by age groups in spanish provinces (1989–2015)

Abstract

Background Ovarian cancer is a silent and largely asymptomatic cancer, leading to late diagnosis and worse prognosis. The late-stage detection and low survival rates, makes the study of the space-time evolution of ovarian cancer particularly relevant. In addition, research of this cancer in small areas (like provinces or counties) is still scarce. Methods The study presented here covers all ovarian cancer deaths for women over 50 years of age in the provinces of Spain during the period 1989-2015. Spatio-temporal models have been fitted to smooth ovarian cancer mortality rates in age groups [50,60), [60,70), [70,80), and [80,+), borrowing information from spatial and temporal neighbours. Model fitting and inference has been carried out using the Integrated Nested Laplace Approximation (INLA) technique. Results Large differences in ovarian cancer mortality among the age groups have been found, with higher mortality rates in the older age groups. Striking differences are observed between northern and southern Spain. The global temporal trends (by age group) reveal that the evolution of ovarian cancer over the whole of Spain has remained nearly constant since the early 2000s. Conclusion Differences in ovarian cancer mortality exist among the Spanish provinces, years, and age groups. As the exact causes of ovarian cancer remain unknown, spatio-temporal analyses by age groups are essential to discover inequalities in ovarian cancer mortality. Women over 60 years of age should be the focus of follow-up studies as the mortality rates remain constant since 2002. High-mortality provinces should also be monitored to look for specific risk factors.

m6A-Related lncRNA Signature Is Involved in Immunosuppression and Predicts the Patient Prognosis of the Age-Associated Ovarian Cancer

“…For robustness, our MS outperformed the current m6A risk scores for ovarian cancer survival prediction [ 31 , 32 ] according to criteria for genetic model estimation [ 33 ], and our clinical nomogram also achieved high calibration for short-term or long-term survival prediction. Apart from MS, we noticed that age was also a risky factor for ovarian cancers, following the finding that the older age group has been found to harbor higher mortality [ 3 ]. This correlation demonstrated ovarian cancer an age-associated disease with the participation of m6A.…”

“…Ovarian cancer is one of the most predominant gynecological diseases in the world, containing a highly heterogeneous group of malignant tumors in etiology and genetic features. The largest proportion of ovarian cancer is epithelial, presenting an advanced stage at the time of first diagnosis [ 1 ] and higher mortality rates in aged patients [ 2 , 3 ]. Serous carcinomas are the most common epithelial ovarian cancer; their routine treatments for serous ovarian cancers are surgical resection and adjuvant therapy.…”

A measurement error Rao–Yu model for regional prevalence estimation over time using uncertain data obtained from dependent survey estimates

“…This approach allows for efficient estimates when the model has sufficient explanatory power (Münnich et al 2019;Burgard et al 2020b). Some recent papers in small area estimation for regional estimation are Trandafir et al (2020), Bernal et al (2020), Mills et al (2020) and Saeedi et al (2019), as well as Adin et al (2019).…”