Bayesian disease mapping and the 'High-Risk' oral cancer population in Hong Kong.

Abstract

Preventive and early diagnostic methods such as health promotion and disease screening are increasingly advocated to improve detection and survival rates for oral cancer. These strategies are most effective when targeted at "high-risk" individuals and populations. Bayesian disease-mapping modelling is a statistical method to quantify and explain spatial and temporal patterns for risk and covariate factor influence, thereby identifying "high-risk" sub-regions or "case clustering" for targeted intervention. Rarely applied to oral cancer epidemiology, this paper highlights the efficacy of disease mapping for the Hong Kong population. Following ethical approval, anonymized individual-level data for oral cancer diagnoses were obtained retrospectively from the Clinical Data Analysis and Reporting System (CDARS) of the Hong Kong Hospital Authority (HA) database for a 7-year period (January 2013 to December 2019). Data facilitated disease mapping and estimation of relative risks of oral cancer incidence and mortality. A total of 3,341 new oral cancer cases and 1,506 oral cancer-related deaths were recorded during the 7-year study period. Five districts, located in Hong Kong Island and Kowloon, exhibited considerably higher relative incidence risks with 1 significant "case cluster" hotspot. Six districts displayed higher mortality risks than expected from territory-wide values, with highest risk identified for two districts of Hong Kong Island. Bayesian disease mapping is successful in identifying and characterizing "high-risk" areas for oral cancer incidence and mortality within a community. This should facilitate targeted preventive and interventional strategies. Further work is encouraged to enhance global-level data and comprehensive mapping of oral cancer incidence, mortality and survival.