Spatiotemporal analysis of tuberculosis drug resistance and associated risk factors in Tanzania.

Abstract

Background: The prevalence of tuberculosis (TB) multi-drug resistance is increasing worldwide, including in Tanzania. This trend hinders the attainment of sustainable development goal number three as it increases the number of cases of the disease and treatment costs. Fewer cases of drug resistance have been reported over time, making it necessary to demand models that can handle an excessive number of zero counts. This study employed the zero-inflated Poisson (ZIP) models suitable for such data to assess drug resistance patterns.

Objective: To examine the TB drug resistance spatiotemporal risk patterns and associated risk factors using health facility case notification data.

Design: A retrospective cohort study utilizing TB drug resistance case notification data from the District Health Information System 2 for Tanzania Mainland between 2018 and 2020.

Methods: The study was conducted in Tanzania Mainland and utilized TB drug resistance case data from 184 councils. Six hundred fifty-two (652) TB drug resistance cases were analyzed using the Bayesian ZIP spatiotemporal model to identify high-risk areas and risk factors for TB drug resistance. The deviance information criterion guided model selection.

Results: The findings revealed a higher prevalence of drug resistance among males (65.2%), individuals aged 35-49 years (33.7%), persons living without HIV (66.4%) and new TB cases (70.7%). Spatiotemporal modelling indicated significant relationships between drug resistance and sex, age, TB treatment history and HIV status. Males were 1.4 times more likely to develop drug resistance than females. Children aged 0-4 and 5-14 years were 25 and 8.3 times less likely to develop drug resistance than adults aged 35-49. Persons living with HIV and those with unknown HIV status were 1.2 and 3.4 times less likely to develop drug resistance, respectively, than persons living without HIV. Individuals with a previous TB treatment history were three times more likely to develop drug resistance compared to new cases.

Conclusion: The Bayesian ZIP spatiotemporal models provide critical insights by identifying high-risk populations and areas, enabling targeted interventions to control multi-drug resistant TB. The study further concludes that resistance to anti-TB drugs is highly associated with sex, age and previous treatment history. To mitigate its spread and impact, the study recommends strengthening awareness campaigns on adherence to treatment guidelines and understanding the risk factors associated with TB drug resistance.