Bayesian disease mapping and the 'High-Risk' oral cancer population in Hong Kong.
Hong Kong population study
disease mapping
oral cancer
Journal
Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology
ISSN: 1600-0714
Titre abrégé: J Oral Pathol Med
Pays: Denmark
ID NLM: 8911934
Informations de publication
Date de publication:
Oct 2020
Oct 2020
Historique:
received:
06
05
2020
accepted:
08
05
2020
pubmed:
26
5
2020
medline:
22
12
2020
entrez:
26
5
2020
Statut:
ppublish
Résumé
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.
Sections du résumé
BACKGROUND
BACKGROUND
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.
METHODS
METHODS
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.
RESULTS
RESULTS
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.
CONCLUSION
CONCLUSIONS
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.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
907-913Informations de copyright
© 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Références
Thomson PJ. Oral Cancer: From Prevention to Intervention. Newcastle upon Tyne: Cambridge Scholars Publishing; 2019.
Anderson C, Lee D, Dean N. Identifying clusters in Bayesian disease mapping. Biostatistics. 2014;15:457-469.
Cramb SM, Mengersen KL, Baade PD. Developing the atlas of cancer in Queensland: methodological issues. Int J Health Geogr. 2011;10:9.
Kang SY, Cramb SM, White NM, Ball SJ, Mengersen KL. Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data. Geospat Health. 2016;11:428.
Adeyemi R, Zewotir T, Ramroop S. A bayesian hierarchical analysis of geographical patterns for child mortality in Nigeria. Open Public Health J. 2019;12:247-261.
Lee D. A comparison of conditional autoregressive models used in Bayesian disease mapping. Spat Spatiotemporal Epidemiol. 2011;2:79-89.
Bilancia M, Fedespina A. Geographical clustering of lung cancer in the province of Lecce, Italy: 1992-2001. Int J Health Geogr. 2009;8:40.
Duncan EW, Cramb SM, Aitken JF, Mengersen KL, Baade PD. Development of the Australian Cancer Atlas: spatial modelling, visualisation, and reporting of estimates. Int J Health Geogr. 2019;18:21.
Johnson GD. Small area mapping of prostate cancer incidence in New York State (USA) using fully Bayesian hierarchical modelling. Int J Health Geogr. 2004;3:29.
Conway DI, Purkayastha M, Chestnutt IG. The changing epidemiology of oral cancer: definitions, trends, and risk factors. Br Dent J. 2018;225:867-873.
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394-424.
Sankaranarayanan R, Ramadas K, Thomas G, et al. Effect of screening on oral cancer mortality in Kerala, India: a cluster-randomised controlled trial. Lancet. 2005;365:1927-1933.
Ushida K, McGrath CP, Lo EC, Zwahlen RA. Oral cavity cancer trends over the past 25 years in Hong Kong: a multidirectional statistical analysis. BMC Oral Health. 2015;15:83.
Thomson P, Su R, Choi S. Oral cancer in Hong Kong: identifying and managing the ‘high-risk’ population. Fac Dent J. 2018;9:116-121.
Choi SW, Thomson P. Increasing incidence of oral cancer in Hong Kong-Who, where and why? J Oral Pathol Med. 2019;48:483-490.
GOV.HK. Hong Kong - the facts. Hong Kong: Hong Kong Special Administrative Region Government; 2020 Available from: https://www.gov.hk/en/about/abouthk/facts.htm
Census and Statistics Department HG. Hong Kong 2016 population by census - District profiles 2020 Available from: https://www.bycensus2016.gov.hk/en/bc-dp.html
Census and Statistics Department HG. The mortality trend in Hong Kong from 1986 to 2018 Hong Kong: CSD HKSAR Govt; 2020 Available from: https://www.censtatd.gov.hk/hkstat/sub/sp160.jsp?productCode=FA100094
Hospital Authority HKCR. Cancer Statistics Query Systems - all ages 2020 Available from: https://www3.ha.org.hk/cancereg/allages.asp
Hospital Authority HKCR. Cancer Statistics Query Systems - all ages Available from: https://www3.ha.org.hk/cancereg/pdf/overview/Summary%20of%20CanStat%202017.pdf
Hong Kong Anti-Cancer Society. Cancer Screening Guidelines (Hong Kong) 2018 Available from: https://www.hkacs.org.hk/en/screening_guideline.php
Gomez-Rubio V, Lopez-Quilez A editors. Empirical and full Bayes estimators for disease mapping. Proceedings of the International workshop on spatio-temporal modelling (METMA3) 2006 September 27 - 29; Pamplona, Spain. Spain: Universidad Publica de Navarra; 2006.
Waller LA, Carlin BP. Disease mapping. Chapman Hall CRC Handb Mod Stat. Methods. 2010;217-43.
Cramb S, Duncan E, Baade P, Mengersen K. Investigation of Bayesian Spatial Models. Brisbane, Australia: Cancer Council Queensland and Queensland University of Technology (QUT); 2018.
Best N, Richardson S, Thomson A. A comparison of Bayesian spatial models for disease mapping. Stat Methods Med Res. 2005;14:35-39.
Bivand R, Altman M, Anselin L, Assuncao R, Berke O, Bernat A, et al. Spatial dependence: Weighting Schemes and Statistics 2014 Available from: https://cran.r-project.org/web/packages/spdep/spdep.pdf
Lee D. CARBayes: An R package for Bayesian spatial modeling with conditional autoregressive priors. J Stat Software. 2013;55:1-24.
Anselin L, Syabri I, Kho Y. GeoDa: an introduction to spatial data analysis. Geogr Anal. 2006;38:5-22.
Tsai WC, Kung PT, Wang YH, Huang KH, Liu SA. Influence of time interval from diagnosis to treatment on survival for oral cavity cancer: a nationwide cohort study. PLoS One. 2017;12:e0175148.
Brenner H, Söderman B, Hakulinen T. Use of period analysis for providing more up-to-date estimates of long-term survival rates: empirical evaluation among 370,000 cancer patients in Finland. Int J Epidemiol. 2002;31:456-462.