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. 2017 Sep 30;17(1):759.
doi: 10.1186/s12889-017-4769-7.

Spatial distribution of HIV, HCV, and co-infections among drug users in the southwestern border areas of China (2004-2014): a cohort study of a national methadone maintenance treatment program

Affiliations

Spatial distribution of HIV, HCV, and co-infections among drug users in the southwestern border areas of China (2004-2014): a cohort study of a national methadone maintenance treatment program

Mingli Li et al. BMC Public Health. .

Abstract

Background: A methadone maintenance treatment (MMT) program to curb the dual epidemics of HIV/AIDS and drug use has been administered by China since 2004. Little is known regarding the geographic heterogeneity of HIV and hepatitis C virus (HCV) infections among MMT clients in the resource-constrained context of Chinese provinces, such as Guangxi. This study aimed to characterize the geographic distribution patterns and co-clustered epidemic factors of HIV, HCV and co-infections at the county level among drug users receiving MMT in Guangxi Zhuang Autonomous Region, located in the southwestern border area of China.

Methods: Baseline data on drug users' demographic, behavioral and biological characteristics in the MMT clinics of Guangxi Zhuang Autonomous Region during the period of March 2004 to December 2014 were obtained from national HIV databases. Residential addresses were entered into a geographical information system (GIS) program and analyzed for spatial clustering of HIV, HCV and co-infections among MMT clients at the county level using geographic autocorrelation analysis and geographic scan statistics.

Results: A total of 31,015 MMT clients were analyzed, and the prevalence of HIV, HCV and co-infections were 13.05%, 72.51% and 11.96% respectively. Both the geographic autocorrelation analysis and geographic scan statistics showed that HIV, HCV and co-infections in Guangxi Zhuang Autonomous Region exhibited significant geographic clustering at the county level, and the Moran's I values were 0.33, 0.41 and 0.30, respectively (P < 0.05). The most significant high-risk overlapping clusters for these infections were restricted to within a 10.95 km2 radius of each of the 13 locations where P county was the cluster center. These infections also co-clustered with certain characteristics, such as being unmarried, having a primary level of education or below, having used drugs for more than 10 years, and receptive sharing of syringes with others. The high-risk clusters for these characteristics were more likely to reside in the areas surrounding P county.

Conclusions: HIV, HCV and co-infections among MMT clients in Guangxi Zhuang Autonomous Region all presented substantial geographic heterogeneity at the county level with a number of overlapping significant clusters. The areas surrounding P county were effective in enrolling high-risk clients in their MMT programs which, in turn, might enable people who inject drugs to inject less, share fewer syringes, and receive referrals for HIV or HCV treatment in a timely manner.

Keywords: Co-infections; Drug users; HCV; HIV; Methadone maintenance treatment; Spatial distribution.

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Conflict of interest statement

Ethics approval and consent to participate

Written informed consent was obtained from all clients. Ethical approval for the MMT was granted by the institutional review board of the National Center for AIDS/STD Control and Prevention, China CDC.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
The location of Guangxi Autonomous Region, China
Fig. 2
Fig. 2
Distribution, LISA cluster map and geographic scan clusters of HIV, HCV and co-infections of MMT clients from 2004 to 2014 in Guangxi. Red circles represent high risk clusters. a Distribution of all MMT clients; b Spatial clusters of HIV infection; c Spatial clusters of HCV infection; d Spatial clusters of HIV/HCV co-infection
Fig. 3
Fig. 3
Co-clustering of HIV, HCV and co-infections among MMT clients from 2004 to 2014 in Guangxi. Red circles represent high risk clusters, blue circles represent low risk clusters. a High risk clusters; b Low risk clusters
Fig. 4
Fig. 4
Distribution and significant spatial clustering of demographic and behavioral variables. a Being unmarried; b Primary level of education or below; c Having used drugs more than 10 years; d Receptive syringe sharing with others

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