Severity and Lethality of Cancers in Tunisia: Empirical Modeling Insights from GLOBOCAN 2020 Estimates

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.