Abstract
Introduction: Pulmonary tuberculosis (PTB) remains one of the top ten causes of death globally. A crucial component of controlling this infection is by limiting the spread of the disease. This study aimed to identify hot spot geographical areas with PTB incidence and to evaluate spatial global autocorrelation using geographical information science (GIS) technology. Methods: Using tuberculosis register software, cases of PTB were recorded by Chest and Respiratory illnesses center in Kerbala governorate. Seven years’ data records were used to investigate hot spots and spatial distribution of PTB cases in Kerbala. Patients whom reside outside Kerbala were excluded from the current analysis. Loess smoothing Seasonal decomposition trend was applied to analyze temporal patterns and clusters mapping of tuberculosis, using R statistical
software version 3.5.1. Correlation analysis (pairwise Spearman) was used to observe the association among the factors contributing to PTB prevalence. Standard morbidity ratio (SMRs) was used to find the morbidity rate Bayesian conditional auto‐regressive model was used in the analysis and estimation of the parameters was done through Markov Chain Monte Carlo methods to estimate the space mapping variability cluster of disease risk and covariates effect. Results: A significant correlation was found between PTB prevalence and the age (r = 0.731) and was also found in gender (r = 0.822). Most of the cases were distributed within the age of 17–50 years (68.4%). Tuberculosis cases were not randomly distributed in which the variables occur with asymptotic probabilities with unpredictable spacing and that there was the presence
of high global autocorrelation among PTB cases in the City of Kerbala. Approximately, 59% of all PTB cases were seen in six quarters.
Conclusion: Spatial analysis using GIS reveals useful information about epidemiological situation of PTB cases in Kerbala Province, Iraq.
Additionally, this study predicted possible places for PTB transmission based on hot spot analysis and the continuous presence of infection during the studied period.
Keywords: Autoregressive effects, Bayesian approach, cluster mapping, loess smoothing, nonspatial effects
