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. 2023 Nov 24;23(1):2329.
doi: 10.1186/s12889-023-17234-x.

Determining the risk-factors for molecular clustering of drug-resistant tuberculosis in South Africa

Halima Said  1 , Elizabeth Kachingwe  2 , Yasmin Gardee  2 , Zaheda Bhyat  2 , John Ratabane  2 , Linda Erasmus  3 , Tiisetso Lebaka  4 , Minty van der Meulen  2 , Thabisile Gwala  2 , Shaheed Omar  2 , Farzana Ismail  2 , Nazir Ismail  2
Affiliations

Determining the risk-factors for molecular clustering of drug-resistant tuberculosis in South Africa

Halima Said et al. BMC Public Health. .

Abstract

Background: Drug-resistant tuberculosis (DR-TB) epidemic is driven mainly by the effect of ongoing transmission. In high-burden settings such as South Africa (SA), considerable demographic and geographic heterogeneity in DR-TB transmission exists. Thus, a better understanding of risk-factors for clustering can help to prioritise resources to specifically targeted high-risk groups as well as areas that contribute disproportionately to transmission.

Methods: The study analyzed potential risk-factors for recent transmission in SA, using data collected from a sentinel molecular surveillance of DR-TB, by comparing demographic, clinical and epidemiologic characteristics with clustering and cluster sizes. A genotypic cluster was defined as two or more patients having identical patterns by the two genotyping methods used. Clustering was used as a proxy for recent transmission. Descriptive statistics and multinomial logistic regression were used.

Result: The study identified 277 clusters, with cluster size ranging between 2 and 259 cases. The majority (81.6%) of the clusters were small (2-5 cases) with few large (11-25 cases) and very large (≥ 26 cases) clusters identified mainly in Western Cape (WC), Eastern Cape (EC) and Mpumalanga (MP). In a multivariable model, patients in clusters including 11-25 and ≥ 26 individuals were more likely to be infected by Beijing family, have XDR-TB, living in Nelson Mandela Metro in EC or Umgungunglovo in Kwa-Zulu Natal (KZN) provinces, and having history of imprisonment. Individuals belonging in a small genotypic cluster were more likely to infected with Rifampicin resistant TB (RR-TB) and more likely to reside in Frances Baard in Northern Cape (NC).

Conclusion: Sociodemographic, clinical and bacterial risk-factors influenced rate of Mycobacterium tuberculosis (M. tuberculosis) genotypic clustering. Hence, high-risk groups and hotspot areas for clustering in EC, WC, KZN and MP should be prioritized for targeted intervention to prevent ongoing DR-TB transmission.

Keywords: Clustering; Drug resistant TB; Risk-factors; South Africa; Transmission.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Coefficient plots of adjusted odds ratios with 95% confidence intervals from multinomial logistic regression analysis (Cluster = 2–5 cases)
Fig. 2
Fig. 2
Coefficient plots of adjusted odds ratios with 95% confidence intervals from multinomial logistic regression analysis (Cluster = 6–10 cases)
Fig. 3
Fig. 3
Coefficient plots of adjusted odds ratios with 95% confidence intervals from multinomial logistic regression analysis (Cluster = 11–25 cases)
Fig. 4
Fig. 4
Coefficient plots of adjusted odds ratios with 95% confidence intervals from multinomial logistic regression analysis (Cluster = ≥ 26 cases)
See this image and copyright information in PMC

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