Severity and Lethality of Cancers in Tunisia: Empirical Modeling Insights from GLOBOCAN 2020 Estimates
Nesrine Mejri
Department of Medical Oncology, University Hospital Abderrahmen Mami, 2080 Ariana, Tunis, Tunisia.
Myriam Saadi
Department of Medical Oncology, University Hospital Abderrahmen Mami, 2080 Ariana, Tunis, Tunisia.
Lilia Ghorbal
Department of Radiation Oncology, University Hospital Abderrahmen Mami, 2080 Ariana, Tunis, Tunisia.
Senyefia Bosson-Amedenu
Department of Mathematics, Statistics and Actuarial Sciences Takoradi Technical University, Sekondi-Takoradi, P.O. Box 256, Ghana.
Theodore Oduro-Okyireh
Department of Mathematics and Statistics, Cape Coast Technical University, Cape Coast, Ghana.
Anthony Joe Turkson
Department of Mathematics, Statistics and Actuarial Sciences Takoradi Technical University, Sekondi-Takoradi, P.O. Box 256, Ghana.
Noureddine Ouerfelli *
Institut Supérieur des Technologies Médicales de Tunis, LR13SE07, Laboratoire de Biophysique et Technologies Médicales, Université de Tunis El Manar, Tunis, Tunisia.
*Author to whom correspondence should be addressed.
Abstract
Background: Cancer incidence and mortality impose a substantial health burden in Tunisia. Understanding the relationships among key epidemiological indicators is essential for assessing cancer patterns and their public health implications.
Aim: This study examined patterns in national cancer estimates for 2020 using empirical mathematical relationships between epidemiological indicators.
Methods: Aggregated GLOBOCAN 2020 estimates were analysed for a population of approximately 11.8 million. Cancer-site incidence and mortality were examined using linear regression, logarithmic transformations, power-law modelling, cancer rankings, and a rank-derived severity parameter. Cumulative risks and selected age-standardised incidence and mortality rates were also compared.
Results: The dataset reported 19,446 new cancer cases and 11,855 cancer-related deaths. Breast cancer accounted for the largest proportion of new cases (3,092; 15.9%), followed by lung cancer (2,929; 15.1%). Lung cancer accounted for the largest proportion of deaths (2,634; 22.2%), followed by breast cancer (986; 8.3%). The fitted linear relationship between cancer-site deaths and new cases had a slope of 0.5897. Logarithmic modelling indicated a strong positive association (R = 0.9636), with an estimated power-law exponent of 1.06319. The rank-based cumulative-risk models identified estimated inflection ranks of 16.76 for incidence and 13.24 for mortality.
Conclusions: The analysis provides descriptive comparisons of cancer-site patterns and empirical modelling parameters for Tunisia in 2020. These population-level associations do not establish causal relationships or individual survival probabilities. Further evaluation using additional datasets would be required to assess the stability and broader applicability of the models.
Keywords: Cancer burden, cancer incidence, cancer mortality, Tunisia, GLOBOCAN 2020, empirical modelling, age-standardised rates, mortality-to-incidence relationship, rank-based severity, cumulative risk