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01.12.2018 | Research article | Ausgabe 1/2018 Open Access

BMC Public Health 1/2018

Risk patterns of lung cancer mortality in northern Thailand

Zeitschrift:
BMC Public Health > Ausgabe 1/2018
Autoren:
Apinut Rankantha, Imjai Chitapanarux, Donsuk Pongnikorn, Sukon Prasitwattanaseree, Walaithip Bunyatisai, Patumrat Sripan, Patrinee Traisathit
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1186/​s12889-018-6025-1) contains supplementary material, which is available to authorized users.

Abstract

Background

Over the past decade, lung cancers have exhibited a disproportionately high mortality and increasing mortality trend in Thailand, especially in the northern region, and prevention strategies have consequently become more important in this region. Spatial analysis studies may be helpful in guiding any strategy put in place to respond to the risk of lung cancer mortality in specific areas. The aim of our study was to identify risk patterns for lung cancer mortality within the northern region of Thailand.

Methods

In the spatial analysis, the relative risk (RR) was used as a measure of the risk of lung cancer mortality in 81 districts of northern Thailand between 2008 and 2017. The RR was estimated according to the Besag-York-Mollié autoregressive spatial model performed using the OpenBUGS routine in the R statistical software package. We presented the overall and gender specific lung cancer mortality risk patterns of the region using the Quantum Geographic Information System.

Results

The overall risk of lung cancer mortality was the highest in the west of northern Thailand, especially in the Hang Dong, Doi Lo, and San Pa Tong districts. For both genders, the risk patterns of lung cancer mortality indicated a high risk in the west of northern Thailand, with females being at a higher risk than males.

Conclusions

There was distinct geographical variation in risk patterns of lung cancer mortality in Thailand. Differences could be related to differences in risk factors such as ground-based radon and air pollution. This study provides a starting point for estimating the spatial pattern of the risk of lung cancer mortality and for examining associations between geographic risk factors and lung mortality for further studies.
Zusatzmaterial
Additional file 1: Appendix 1. The OpenBugs syntax for the BYM model fitting. (DOCX 13 kb)
12889_2018_6025_MOESM1_ESM.docx
Additional file 2: Table S1. Relative risks of lung cancer mortality in each district. (DOCX 26 kb)
12889_2018_6025_MOESM2_ESM.docx
Literatur
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