Bayesian spatio-temporal modelling of tobacco-related cancer mortality in Switzerland

Abstract

Abstract. Tobacco smoking is a main cause of disease in Switzerland; lung cancer being the most common cancer mortality in men and the second most common in women. Although disease-specific mortality is decreasing in men, it is steadily increasing in women. The four language regions in this country might play a role in this context as they are influenced in different ways by the cultural and social behaviour of neighbouring countries. Bayesian hierarchical spatio-temporal, negative binomial models were fitted on subgroup-specific death rates indirectly standardized by national references to explore age-and gender-specific spatio-temporal patterns of mortality due to lung cancer and other tobacco-related cancers in Switzerland for the time period 1969-2002. Differences influenced by linguistic region and life in rural or urban areas were also accounted for. Male lung cancer mortality was found to be rather homogeneous in space, whereas women were confirmed to be more affected in urban regions. Compared to the German-speaking part, female mortality was higher in the French-speaking part of the country, a result contradicting other reports of similar comparisons between France and Germany. The spatio-temporal patterns of mortality were similar for lung cancer and other tobacco-related cancers. The estimated mortality maps can support the planning in health care services and evaluation of a national tobacco control programme. Better understanding of spatial and temporal variation of cancer of the lung and other tobacco-related cancers may help in allocating resources for more effective screening, diagnosis and therapy. The methodology can be applied to similar studies in other settings.

A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research

“…Of the total of 52 studies, 26 (50.0%) were on infectious diseases, 11 (21.1%) on malaria [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ], 8 (15.3%) on Dengue fever [ 40 , 41 , 42 , 43 , 44 , 45 ], 4 (7.6%) on HIV [ 46 , 47 , 48 , 49 ], and 3 (5.7%) on TB [ 50 , 51 , 52 ]. Overall, 10 (19.2%) studies were on chronic diseases [ 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ], 8 (15.5%) on maternal and child health [ 63 , 64 , 65 , 66 ], 3 (5.7%) on nutrition [ 67 , 68 , 69 ] and 5 (9.6%) were applied to other health-related outcomes [ 58 , 70 , 71 , 72 , 73 ]. Most of the studies (42, 80.7%) used an ecological study design [ 30 , 31 , 33 , 34 , 37 , 38 , ...…”

“…Overall, 10 (19.2%) studies were on chronic diseases [ 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ], 8 (15.5%) on maternal and child health [ 63 , 64 , 65 , 66 ], 3 (5.7%) on nutrition [ 67 , 68 , 69 ] and 5 (9.6%) were applied to other health-related outcomes [ 58 , 70 , 71 , 72 , 73 ]. Most of the studies (42, 80.7%) used an ecological study design [ 30 , 31 , 33 , 34 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 51 , 52 , 53 , 54 , 55 , 56 , 58 , 59 , 60 , 61 , 62 , 64 , 65 , 66 , 67 , 69 ,…”

“…The number of spatial units in the reviewed studies ranged from a minimum of 10 to a maximum of 1325. About 9 (17.3%), 7 (13.1%), 6 (11.5%), and 6 (11.5%) studies’ spatial unit of analysis were districts [ 37 , 44 , 46 , 56 , 62 , 68 , 74 , 81 ], counties [ 32 , 34 , 48 , 49 , 52 , 75 , 77 , 78 ], regions [ 30 , 31 , 38 , 59 , 72 , 80 ], and provinces [ 35 , 41 , 45 , 50 , 51 , 53 ], respectively. The majority of the studies (80.7%) used years as a temporal unit measure and 9 (17.3%) studies used months as a temporal unit measure.…”

Long-term air pollution exposure and COVID-19 case-severity: An analysis of individual-level data from Switzerland

“…Thus, in the French region of Switzerland, the odds of dying or ending up in ICU after hospitalization with COVID-19 are higher than in the German part. Different language regions in Switzerland reflect cultural and lifestyle differences and have been shown to be related to the geographical variation of other health outcomes in the country, including cancer ( Jürgens et al, 2013 ) and other cause-specific mortality ( Chammartin et al, 2016 ). Another plausible explanation is that people in the French-speaking part were more likely to die at the hospital rather than at the nursing home.…”

40 years of progress in female cancer death risk: a Bayesian spatio-temporal mapping analysis in Switzerland

“…Space and temporal random effects as well as possible non-linear temporal trends were modelled on the log of the mean standardised mortality rate following model formulations of Jürgens et al [ 6 ] (cf. Appendix 1 ).…”