Fig 1.
Map of five Regional Health Authorities (RHA) and the corresponding Regional Health Authority Districts in Manitoba.
Number of districts for each RHA is shown in parenthesis in the legend. *Note: The numbers on the map and subsequent ones in this study represents area identification tag.
Table 1.
Description of all GC topographies using ICD-O3.
Fig 2.
GC distribution by age group.
Fig 3.
Geographical distribution of Indigenous population across the 96 RHADs based on 2016 Canadian Census data.
Numbers in the map are the district labels from 1 to 96. Values in parenthesis represents the counts in each distribution category.
Fig 4.
Geographical distribution of immigrant population across the 96 RHADs based on 2016 Canadian Census data.
Numbers in the map are the district labels from 1 to 96. Values in parenthesis represents the counts in each distribution category.
Fig 5.
Geographical distribution of socio-economic status score index across the 96 RHADs based on 2016 Canadian Census data.
Numbers in the map are the district labels from 1 to 96. Values in parenthesis represents the counts in each distribution category.
Fig 6.
Map of unexplained variation in overall GC incidence risk ratio.
Numbers in the map are the district labels from 1 to 96. Number of districts in each risk category is shown in parenthesis in the legend.
Table 2.
Incidence Risk Ratio (IRR) and 95% credible interval for overall, male and female GC dataset using spatial Poisson regression model.
Table 3.
Incidence Risk Ratio (IRR) and 95% credible interval for cardia and non-cardia GC dataset using spatial Poisson regression model.
Table 4.
Incidence Risk Ratio (IRR) and 95% credible interval for cardia GC dataset stratified by sex using spatial Poisson regression model.
Table 5.
Incidence Risk Ratio (IRR) and 95% credible interval for Non-cardia GC dataset stratified by sex using spatial Poisson regression model.
Fig 7.
Map of standardized IRR of overall GC in Manitoba men using spatial Poisson regression model.
Numbers in the map are the district labels from 1 to 96. Number of districts in each risk category is shown in parenthesis in the legends.
Fig 8.
Map of standardized IRR of overall GC in Manitoba women using spatial Poisson regression model.
Numbers in the map are the district labels from 1 to 96. Number of districts in each risk category is shown in parenthesis in the legends.
Fig 9.
Map of standardized IRR of overall CGC in Manitoba using Poisson regression model.
Numbers in the map are the district labels from 1 to 96. Number of districts in each risk category is shown in parenthesis in the legends.
Fig 10.
Map of standardized IRR of overall NCGC in Manitoba using Poisson regression model.
Numbers in the map are the district labels from 1 to 96. Number of districts in each risk category is shown in parenthesis in the legends.
Fig 11.
Maps of district-specific risk of overall men GC for the 96 RHADs in Manitoba for five time periods using spatio-temporal Poisson regression model.
Numbers in the map are the district labels from 1 to 96. Number of districts in each risk category is shown in parenthesis in the legends.
Fig 12.
Maps of district-specific risk of overall women GC for 96 RHADs in Manitoba for five time periods, using spatio-temporal Poisson regression model.
Numbers in the map are the district labels from 1 to 96. Number of districts in each risk category is shown in parenthesis in the legends.
Fig 13.
Maps of district-specific risk of CGC for 96 RHADs in Manitoba for five time periods using spatio-temporal Poisson regression model.
Numbers in the map are the district labels from 1 to 96. Number of districts in each risk category is shown in parenthesis in the legends.
Fig 14.
Maps of district-specific risk of NCGC for 96 RHADs in Manitoba for five time periods using spatio-temporal Poisson regression model.
Numbers in the map are the district labels from 1 to 96. Number of districts in each risk category is shown in parenthesis in the legends.