Assessment of Neonate's Congenital Hypothyroidism Pattern Using Poisson Spatio-temporal Model in Disease Mapping under the Bayesian Paradigm during 2011-18 in Guilan, Iran

Congenital Hypothyroidism (CH) is one of the reasons for mental retardation and defective growth in neonates. It can be treated if it is diagnosed early. The congenital hypothyroidism can be diagnosed using newborn screening in the first days after birth. Disease mapping helps to identify high-risk areas of the disease. This study aimed to evaluate the pattern of CH using the Poisson Spatio-temporal model in disease mapping under the Bayesian paradigm. The recorded data of all infants diagnosed with CH between 2011 and 2018 in Guilan, Iran were used in this study. The Poisson Spatio-temporal model under the Bayesian paradigm was run using the Markov Chain Monte Carlo method in Open BUGS software. Moreover, the maps of the towns in Guilan were prepared via Arc GIS software. Out of 219800 live births in Guilan, Iran, the incidence of CH was 2:1000 in this time period. The pattern of disease mapping for the posterior mean of relative risk for CH was identical in this 7-year period. Furthermore, the pattern of disease mapping with spatial model excluding time dependence was similar to the maps of the Spatio-temporal model. The incidence rate of CH was approximately constant during this time, and disease mapping revealed no rising trends in this period. This probably can be due to resolving iodine deficiency as one of the main causes of CH incidence by consuming kinds of seafood and iodized salt in Guilan province.