Mapping of stomach, colorectal, and bladder cancers in Iran, 2004–2009: Applying Bayesian polytomous logit model

Abstract

Background:According to the last report of Iran cancer registry, stomach, colorectal, and bladder cancers are the most prevalent cancers. The present study focused on separating the latent risk surface into shared and disease-specific components.Methods:In this study, data consisting of stomach, colorectal, and bladder cancers in 30 provinces of Iran during 2004–2009 are considered. These data are analyzed by polytomous logit model. The incidence of stomach cancer acts as the reference category (the surrogate for smoking). Then, the log odds are decomposed into shared and specific structured spatial and unstructured spatial components. These latent components help to detect spatial patterns of shared and disease-specific risk factors.Results:Central, Southern, Eastern, and Southwestern provinces are supposed as high-risk regions for shared risk factor for colorectal and bladder cancers. This shared risk factor is slightly associated more with bladder than with colorectal cancer. Northern, northwestern, and central regions and also three borderline provinces in southwestern are high-risk regions for colorectal cancer. Central, eastern, southern, and western strip of the country except Ilam are found as the high-risk regions of bladder cancer.Conclusions:After considering known shared risk factor of the three cancers, it turns out that colorectal and bladder cancers have unknown shared risk factor. The significant difference in their lifestyle and eating habits could be an assumption of the risk factor.

A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research

“…The Bayesian estimation approach was used in more than three-quarters (n = 35, 81.40%) of the included studies [ 32 , 33 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 73 , 79 ]. For Bayesian inference, the MCMC estimation approach was utilised in 27 studies [ 32 , 33 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ,…”

“…To analyse two or more health outcomes simultaneously, several joint spatial and spatiotemporal models were used. To investigate the relative risk of the study variables and their risk factors, joint spatial models were used in 24 studies [ 36 , 38 , 39 , 40 , 42 , 43 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 54 , 56 , 57 , 59 , 60 , 61 , 62 , 64 , 73 , 75 ], joint spatiotemporal models in 12 studies [ 32 , 33 , 35 , 37 , 41 , 44 , 55 , 58 , 63 , 65 , 80 ], and joint spatial and spatiotemporal autocorrelation methods such as Moran Index statistics, Local Indicator Spatial Analysis (LISA), Getis Ord Gi statistics, or Kulldroff spatial and Spatio-temporal scan statistical tests used in 7 studies [ 34 , 72 , 74 , 76 , 77 , 78 , 79 ].…”

“…In joint spatial temporal models, the structured and unstructured spatial random effects were considered to account for the spatial dependence and independent effects, respectively. In more than two-thirds (n = 26, 72.2%) of the studies, prior CAR was considered for the spatially structured random effect [ 32 , 33 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 43 , 44 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 55 , 58 , 60 , 61 , 63 , 64 , 65 ], and all assigned Identical and Independent Distribution (IID) for the unstructured spatial random effects. Out of 12 joint spatiotemporal studies, seven (58.23%) considered prior first-order random walk to account for the temporal dependence in the model [ 32 , 33 , 35 , 41 , 53 , 58 , 65 ].…”

“…Joint spatial and spatiotemporal models, as opposed to univariate models, concurrently account for specific and common spatial and temporal effects by incorporating shared spatial and temporal terms [ 32 ]. The joint spatial and spatiotemporal models were frequently applied to non-communicable diseases (NCDs) including cancer and diabetes mellitus (DM) [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. The development of joint spatial and spatiotemporal methodologies has coincided with a huge advancement in statistical approaches addressing technical and statistical issues related to advanced spatial statistics.…”

“…The development of joint spatial and spatiotemporal methodologies has coincided with a huge advancement in statistical approaches addressing technical and statistical issues related to advanced spatial statistics. For example, authors employed different Bayesian inference techniques such as Markov chain Monte Carlo (MCMC) [ 32 , 33 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ] or Integrated Nested Laplace Approximation (INLA) methods [ 42 , 43 , 61 , 62 , 63 , 64 , 65 ] in Bayesian shared component spatial and spatiotemporal models.…”