Bayesian Spatial Survival Analysis of Duration to Cure among New Smear-Positive Pulmonary Tuberculosis (PTB) Patients in Iran, during 2011–2018

Eisa Nazar, Hossein Baghishani, Hassan Doosti, Vahid Ghavami, Ehsan Aryan, Mahshid Nasehi, Saeid Sharafi, Habibollah Esmaily and Jamshid Yazdani Charati
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Eisa Nazar: Department of Biostatistics, Faculty of Health, Mashhad University of Medical Sciences, Mashhad 913767-3119, Iran
Hossein Baghishani: Department of Statistics, Faculty of Mathematical Sciences, Shahrood University of Technology, Shahrood 316-3619995161, Iran
Hassan Doosti: Department of Mathematics and Statistics, Macquarie University, Sydney, NSW 2109, Australia
Vahid Ghavami: Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad 913767-3119, Iran
Ehsan Aryan: Antimicrobial Resistance Research Center, Mashhad University of Medical Sciences, Mashhad 917669-9199, Iran
Mahshid Nasehi: Centre for Communicable Diseases Control, Ministry of Health and Medical Education, Tehran 141994-3471, Iran
Saeid Sharafi: Centre for Communicable Diseases Control, Ministry of Health and Medical Education, Tehran 141994-3471, Iran
Habibollah Esmaily: Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad 913767-3119, Iran
Jamshid Yazdani Charati: Department of Biostatistics, Health Sciences Research Center, Addiction Institute, Mazandaran University of Medical Sciences, Sari 484711-6548, Iran

IJERPH, 2020, vol. 18, issue 1, 1-18

Abstract: Mycobacterium tuberculosis is the causative agent of tuberculosis (TB), and pulmonary TB is the most prevalent form of the disease worldwide. One of the most concrete actions to ensure an effective TB control program is monitoring TB treatment outcomes, particularly duration to cure; but, there is no strong evidence in this respect. Thus, the primary aim of this study was to examine the possible spatial variations of duration to cure and its associated factors in Iran using the Bayesian spatial survival model. All new smear-positive PTB patients have diagnosed from March 2011 to March 2018 were included in the study. Out of 34,744 patients, 27,752 (79.90%) patients cured and 6992 (20.10%) cases were censored. For inferential purposes, the Markov chain Monte Carlo algorithms are applied in a Bayesian framework. According to the Bayesian estimates of the regression parameters in the proposed model, a Bayesian spatial log-logistic model, the variables gender (male vs. female, TR = 1.09), altitude (>750 m vs. ≤750 m, TR = 1.05), bacilli density in initial smear (3+ and 2+ vs. 1–9 Basil & 1+, TR = 1.09 and TR = 1.02, respectively), delayed diagnosis (>3 months vs. <1 month, TR = 1.02), nationality (Iranian vs. other, TR = 1.02), and location (urban vs. rural, TR = 1.02) had a significant influence on prolonging the duration to cure. Indeed, pretreatment weight (TR = 0.99) was substantially associated with shorter duration to cure. In summary, the spatial log-logistic model with convolution prior represented a better performance to analyze the duration to cure of PTB patients. Also, our results provide valuable information on critical determinants of duration to cure. Prolonged duration to cure was observed in provinces with low TB incidence and high average altitude as well. Accordingly, it is essential to pay a special attention to such provinces and monitor them carefully to reduce the duration to cure while maintaining a focus on high-risk provinces in terms of TB prevalence.

Keywords: pulmonary tuberculosis; duration to cure; altitude; spatial survival models; interval censored; Iran (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
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