Spatial clustering of drug-resistant tuberculosis in Hunan province, China: an ecological study

Kefyalew Addis Alene; Zuhui Xu; Liqiong Bai; Hengzhong Yi; Yunhong Tan; Darren Gray; Kerri Viney; Archie Ca Clements

doi:10.1136/bmjopen-2020-043685

Spatial clustering of drug-resistant tuberculosis in Hunan province, China: an ecological study

BMJ Open. 2021 Apr 1;11(4):e043685. doi: 10.1136/bmjopen-2020-043685.

Authors

Kefyalew Addis Alene^{1

2}, Zuhui Xu³, Liqiong Bai⁴, Hengzhong Yi⁵, Yunhong Tan⁵, Darren Gray⁶, Kerri Viney^{6

7

8}, Archie Ca Clements^{9

2}

Affiliations

¹ Faculty of Health Sciences, Curtin University, Bentley, Western Australia, Australia kefyalew.alene@telethonkids.org.au.
² Telethon Kids Institute, Nedlands, Western Australia, Australia.
³ Department of Tuberculosis Control, Tuberculosis Control Institute of Hunan Province, Changsha, Hunan, China.
⁴ Department of Director's Office, Hunan Tuberculosis Control Institute, Changsha, Hunan, China.
⁵ Department of MDR-TB, Internal Medicine, Hunan Chest Hospital, Changsha, China.
⁶ Research School of Population Health, Australian National University, Canberra, ACT, Australia.
⁷ Public Health Sciences, Karolinska Institute, Stockholm, Sweden.
⁸ School of Public Health, The University of Sydney, Sydney, New South Wales, Australia.
⁹ Faculty of Health Sciences, Curtin University, Bentley, Western Australia, Australia.

Abstract

Objective: This study aimed to investigate the spatial distribution of drug-resistant tuberculosis (DR-TB) in Hunan province, China.

Methods: An ecological study was conducted using DR-TB data collected from the Tuberculosis Control Institute of Hunan Province between 2012 and 2018. Spatial clustering of DR-TB was explored using the Getis-Ord statistic. A Poisson regression model was fitted with a conditional autoregressive prior structure, and with posterior parameters estimated using a Bayesian Markov chain Monte Carlo simulation, to quantify associations with possible risk factors and identify clusters of high DR-TB risk.

Results: A total of 2649 DR-TB patients were reported to Hunan TB Control Institute between 2012 and 2018. The majority of the patients were male (74.8%, n=1983) and had a history of TB treatment (88.53%, n=2345). The proportion of extensively DR-TB among all DR-TB was 3.3% (95% CI 2.7% to 4.1%), which increased from 2.8% in 2012 to 4.4% in 2018. Of 1287 DR-TB patients with registered treatment outcomes, 434 (33.8%) were cured, 198 (15.3%) completed treatment, 92 (7.1%) died, 108 (8.3%) had treatment failure and 455 (35.3%) were lost to follow-up. Half (50.9%, n=655) had poor treatment outcomes. The annual cumulative incidence rate of notified DR-TB increased over time from 0.25 per 100 000 people in 2012 to 0.83 per 100 000 people in 2018. Substantial spatial heterogeneity was observed, and hotspots were detected in counties located in the North and East parts of Hunan province. The cumulative incidence of notified DR-TB was significantly associated with urban communities.

Conclusion: The annual incidence of notified DR-TB increased over time in Hunan province. Spatial clustering of DR-TB was detected and significantly associated with urbanisation. This finding suggests that targeting interventions to the highest risk areas and population groups would be effective in reducing the burden and ongoing transmission of DR-TB.

Keywords: epidemiology; public health; tuberculosis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bayes Theorem
China / epidemiology
Cluster Analysis
Female
Humans
Incidence
Male
Spatial Analysis
Tuberculosis, Multidrug-Resistant* / drug therapy
Tuberculosis, Multidrug-Resistant* / epidemiology