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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 Li1,
  • Rongjian Li2,
  • Zhiyong Shen2,
  • Chunying Li2,
  • Nengxiu Liang2,
  • Zhenren Peng2,
  • Wenbo Huang2,
  • Chongwei He2,
  • Feng Zhong2,
  • Xianyan Tang3 and
  • Guanghua Lan2Email author
Contributed equally
BMC Public HealthBMC series – open, inclusive and trusted201717:759

https://doi.org/10.1186/s12889-017-4769-7

Received: 23 January 2017

Accepted: 18 September 2017

Published: 30 September 2017

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

Spatial distribution HIV HCV Co-infections Drug users Methadone maintenance treatment

Background

HIV and hepatitis C virus (HCV) infections are major global public health concerns, with overlapping routes of transmission, populations most affected and geographical areas. Data from 2014 suggested that more than 36.9 million people worldwide were living with HIV [1], 115 million people were estimated to be HCV antibody-positive [2], and approximately 2.3 million were estimated to have HIV/HCV co-infection, of whom 59% were people who inject drugs (PWID) [3]. Similar to other oversea countries, China has witnessed the fastest-growing HIV and HCV epidemics fueled by injecting drug users (IDUs) over the past three decades and is experiencing the highest burden of these infections in PWID at present [46]. Methadone maintenance treatment (MMT) programs were first initiated in China as a small pilot project of only eight sites serving 1029 drug users in 2004. Since then, it has rapidly expanded into a nationwide program covering 738 clinics and serving some 344,254 heroin users by the end of 2011, which accounted for approximately 30% of registered IDUs in China [7, 8]. The MMT program in China is believed to have made impressive progress in HIV infection and drug use among PWID [7] as a result of offering various ancillary services, including testing for HIV, syphilis and HCV, psychosocial support, and referrals for the treatment of HIV, tuberculosis and sexually transmitted diseases.

Because it borders the drug-trafficking route known as the ‘Golden Triangle’ and connects China with the Association of Southeast Asian Nations (ASEAN) countries, Guangxi Zhuang Autonomous Region (referred to hereafter as ‘Guangxi’) detected the first outbreak of HIV-1 infection among IDUs in 1996, and transmission through IDUs accounted for 69% of reported HIV cases in 2003, with the second-highest accumulated number of HIV cases in China since 2009 [911]. Guangxi therefore launched an MMT program in 2004 as one of the first eight national MMT pilot clinics [12], covering 72 clinics serving more than 30,000 heroin users by the end of 2014. Numerous studies conducted in China [1316] showed that variations toward the prevalence of HIV and HCV infections differed dramatically across geographic locations. Nevertheless, the spatial distribution of these infections among MMT clients in border areas of China, such as Guangxi, is poorly understood, and most previously published studies [1719] have concentrated on descriptive analysis. These findings could not visualize the geographic heterogeneity of these infections and detect the presence and location of a cluster in confined regions. We therefore undertook a spatial analysis of the prevalence of HIV and HCV infections among MMT clients from baseline data of treatment between 2004 and 2014, to characterize the geographic distribution patterns and co-clustered epidemic factors of HIV and HCV infections among drug injectors receiving MMT. This study might also have critical implications for policy-making and resource allocation based on the needs of each region, as well as for future MMT program implementation.

Methods

Study area

The study site is located in Guangxi, one of the areas most severely affected by HIV/AIDS in China, along with neighboring areas such as Vietnam and Yunnan (Fig. 1). Guangxi, which is located on the southwestern coast of China (104.26° ~ 112.04°E, 20.54° ~ 26.24°N), has an area of 236.7 thousand square kilometers and a population of approximately 52.82 million and encompasses 14 cities, 7 county level cities, 12 autonomic counties and 55 counties (Guangxi Statistical Yearbook in 2015). We examined the city-governed region and designated the others as county-level areas; there were thus 88 county-level areas in our study.
Fig. 1

The location of Guangxi Autonomous Region, China

Study population and data collection

Since the Chinese National Comprehensive AIDS Response Policy and the ‘Four Frees and One Care’ program (‘four frees’ refers to free HIV voluntary counseling and testing, free antiretroviral treatment for rural HIV patients and poor urban patients, free antiretroviral treatment for pregnant women living with HIV/AIDS, and free schooling for orphaned children of AIDS patients; ‘one care’ refers to financial subsidies for low-income AIDS patients and their families) were launched in 2003 [20], the Chinese Centers for Disease Control and Prevention (China CDC) has established national HIV databases. These cohort study databases can be used to study the prevalence of HIV and HCV infections among MMT clients, and details on the eligibility criteria to participate in the national MMT program have been reported elsewhere [7, 12, 21].

All clients attending the 72 MMT clinics of Guangxi from March 2004 to December 2014 were selected as our study population. Baseline data were collected upon inclusion in the MMT program using an interview questionnaire. Demographic characteristics focused on gender, residential address, ethnicity, marital status, occupation, education, and living status. Drug use questions focused on the initial age and length of drug use, current drugs injected and substances used, method of drug-taking, injection frequency, and syringe sharing. Biological data included urine morphine testing and HIV and HCV testing. Most of the data were self-reported by clients, such as demographic characteristics and drug use questions. Only MMT clients with both HIV or HCV infection and a residential address were analyzed.

A total of 35,387 records (35,008 clients) from 2004 to 2014 were obtained from the baseline database of the 72 MMT clinics in Guangxi. We excluded 3993 records without HIV or HCV testing results and another 379 repeated records; therefore, approximately 31,015 MMT clients were included in our study, which accounted for 88.59% (31,015/35,008) of all MMT clients. To exclude the possibility that the exclusion of clients unmasked correlations among the remaining observations, we compared included clients (N = 31,015) and excluded clients (N = 4372) for all variables, and no differences were found between these groups (not shown in this report).

Laboratory methods

Urine samples were collected from all participants for morphine testing with colloidal gold diagnostics, and a positive urine result was indicative of a current heroin user. Blood samples were collected from all participants for HIV and HCV serologic testing with an enzyme-linked immune sorbent assay (ELISA). Western blot testing was conducted to confirm positive HIV ELISA results. All clients with HIV- and HCV-positive results received counseling and referral to the local China CDC, based on the city/county level of the client’s residence, for follow-up testing and treatment. Seronegative clients were also advised to proceed for follow-up testing in the future as clinically indicated.

Statistical analysis

Spatial analysis was initiated by geolocating residential addresses of MMT clients using electronic maps of Guangxi (Bureau of Surveying and Mapping, Guangxi, China). We confirmed that all addresses were located within residential areas and not in non-residential locations, such as rail yards or industrial areas.

Baseline characteristics of the prevalence of HIV, HCV and co-infections were compared using chi-square tests, and the statistical analysis was performed using the SPSS Statistical Package for Social Sciences (SPSS Inc., Chicago, IL, USA). Geographic autocorrelation analysis was conducted using ArcGIS version 10.2 (ESRI Inc., Redlands, California, USA), and geographic scan statistics were performed using SaTScan™ v9.1.1 software (Martin Kulldorff together with information Management Services Inc., Boston, USA).

Geographic autocorrelation analysis was applied to describe the correlation of a single variable between pairs of neighboring observations, with the standard measure of the Moran statistic. First, global spatial autocorrelation was used to explore the distribution of infections, in which all counties were seen as a whole [22]. The values of Moran’s I ranged from +1 (indicating strong autocorrelation) to 0 (indicating a random pattern) to −1 (indicating over-dispersion and uniformity). Second, Local Indicators of Spatial Association (LISA) was applied to identify significant spatial outliers and generate four geographic patterns, including high-high, high-low, low-high and low-low [14]. A high-high pattern indicated that a county and its surroundings collectively had a higher infection rate than the average. A low-low pattern showed that a county and its surroundings collectively had a lower infection rate than the average. A high-low pattern indicated that a county with an above-average infection rate was surrounded by counties with below-average infection rates. A low-high pattern showed that a county with a below-average infection rate was surrounded by counties with above-average infection rates.

Geographic scan statistics were applied to test for the presence and location of clusters. This analysis imposes a circular window of varying radii on the map surface and allows its center to move, so that at any given position and size, the window includes different sets of adjacent neighboring areas. As the window is placed at each neighborhood center, its radius varies continuously, from zero to a maximum radius that never exceeds 50% of the total study population. The method allows the circular window to continuously vary in both location and size, thereby creating a large number of distinct circular clusters. The significance of the identified clusters was tested with a likelihood ratio test against a null distribution obtained from Monte Carlo simulations [23]. Details of how the likelihood function is maximized over all windows under the Poisson assumption have been described elsewhere [2426]. For the Monte Carlo inference, 999 replications were performed for ordinal or nominal variables, and 9999 replications were performed for dichotomous variables. After a cluster was identified, the strength of the clustering was estimated using the relative risk of infections within the cluster versus outside the cluster. The null hypothesis of no clusters was rejected when the P-value was less than or equal to 0.05.

Results

Study population

Approximately 31,015 clients with valid data for both demographic characteristics and HIV or HCV testing results from 2004 to 2014 were obtained from the database of the 72 MMT clinics in Guangxi. The majority of clients were males (90.39% of the sample), unemployed (55.22%), Han ethnic groups (67.26%), had a junior secondary school education (62.75%) and were living with family or relatives (79.75%). In total, 48.45% reported never having been married, 43.12% were married, and 8.33% were divorced, separated or widowed. Most of the clients obtained their living expenses in the past 6 months from family or friends (53.46%), followed by casual wages (26.01%) and fixed wages (4.51%). The remaining portion of the sample obtained their living expenses from social welfare, criminal activity or other means. In terms of drug use, the average age of initial drug use and length of drug use were 24.18 ± 6.64 years and 8.91 ± 5.03 years, respectively. The main drug currently injected was heroin (87.70%), and 68.28% used drugs by injection only. The average frequency of drug use was 3.06 ± 1.25 times per day in the past month, and 23.25% self-reported that they had shared needles with others. Of all MMT clients at baseline, 56.05% (17,383/31,015) had positive urine morphine testing results, 13.05% (4046/31,015) were infected with HIV (including the number of individuals who were infected with HIV only and those who were HIV/HCV co-infected), 72.51% (22,488/31,015) were infected with HCV (including the number of individuals who were infected with HCV only and those who were HIV/HCV co-infected), and 11.96% (3708/31,015) were HIV/HCV co-infected. There were significant differences in terms of demographic and behavioral characteristics among HIV, HCV, and HIV/HCV co-infected clients (Table 1).
Table 1

Baseline characteristics of MMT clients with HIV, HCV and co-infections in Guangxi (2004 ~ 2014)

Characteristic

Total (n = 31,015)

HIV clients (n = 4046)

HCV clients (n = 22,488)

Co-infected clients (n = 3708)

No. of clients

Proportion (%)

No. of clients

Proportion (%)

No. of clients

Proportion (%)

No. of clients

Proportion (%)

Gender

  

χ2 = 37.75, P < 0.001

χ2 = 96.01, P < 0.001

χ2 = 118.25, P < 0.001

 Male

28,036

90.39

3550

87.74

20,101

89.39

3249

87.62

 Female

2979

9.61

496

12.26

2387

10.61

459

12.38

Occupation

  

χ2 = 49.68, P < 0.001

χ2 = 505.12, P < 0.001

χ2 = 558.71, P < 0.001

 Unemployed

17,125

55.21

2415

59.69

13,143

58.44

2216

59.76

 Farmers

9109

29.37

1135

28.05

5812

25.85

1034

27.89

 Others

4781

15.42

496

12.26

3533

15.71

458

12.35

Ethnic groups

  

χ2 = 111.43, P < 0.001

χ2 = 419.45, P < 0.001

χ2 = 472.78, P < 0.001

 Han

20,860

67.26

3004

74.24

15,876

70.60

2779

74.95

 Zhuang

9399

30.30

990

24.47

6093

27.09

880

23.73

 Others

756

2.44

52

1.29

519

2.31

49

1.32

Marital status

  

χ2 = 144.04, P < 0.001

χ2 = 141.98, P < 0.001

χ2 = 242.64, P < 0.001

 Married

13,375

43.12

1426

35.25

9329

41.48

1305

35.19

 Unmarried

15,055

48.54

2155

53.26

11,079

49.27

1972

53.18

 Others

2585

8.34

465

11.49

2080

9.25

431

11.63

Education

  

χ2 = 74.10, P < 0.001

χ2 = 27.92, P < 0.001

χ2 = 89.74, P < 0.001

 Primary level education or below

8274

26.68

1276

31.54

6129

27.25

1168

31.50

 Junior secondary school

19,461

62.75

2446

60.45

13,911

61.86

2238

60.36

 Senior school or above

3280

10.57

324

8.01

2448

10.89

302

8.14

Living status

  

χ2 = 81.61, P < 0.001

χ2 = 183.78, P < 0.001

χ2 = 230.68, P < 0.001

 With family or relatives

24,734

79.75

3045

75.26

17,630

78.40

2796

75.40

 With friends

971

3.13

108

2.67

644

2.86

96

2.59

 Alone

2138

6.89

363

8.97

1623

7.22

321

8.66

 Others

3172

10.23

530

13.10

2591

11.52

495

13.35

Living expense source in the past six months

  

χ2 = 103.70, P < 0.001

χ2 = 144.68, P < 0.001

χ2 = 212.56, P < 0.001

 From family or friends

16,582

53.46

2257

55.78

11,950

53.14

2051

55.31

 From casual wages

8067

26.01

877

21.68

5623

25.00

805

21.71

 From fixed wages

1399

4.51

117

2.89

986

4.39

108

2.91

 Others

4967

16.02

795

19.65

3929

17.47

744

20.07

Age of initial drug use (years)

  

χ2 = 194.65, P < 0.001

χ2 = 223.97, P < 0.001

χ2 = 358.48, P < 0.001

  < 24

17,372

56.01

2677

66.16

13,180

58.61

2476

66.77

  ≥ 24

13,643

43.99

1369

33.84

9308

41.39

1232

33.23

Length of drug use at baseline (years)

  

χ2 = 1250.60, P < 0.001

χ2 = 3413.13, P < 0.001

χ2 = 4156.72, P < 0.001

  < 5

9763

31.48

411

10.16

5011

22.28

340

9.17

 5–10

7655

24.68

922

22.79

5846

26.00

852

22.98

  > 10

13,597

43.84

2713

67.05

11,631

51.72

2516

67.85

Route of drug use in the past six months

  

χ2 = 674.72, P < 0.001

χ2 = 6329.18, P < 0.001

χ2 = 6595.77, P < 0.001

Injection intravenously only

21,177

68.28

3446

85.17

18,015

80.11

3209

86.54

Smoking or sniffing

7957

25.66

382

9.44

3055

13.58

297

8.01

Injection mixed with other

1881

6.06

218

5.39

1418

6.31

202

5.45

Receptive sharing of syringes with others

  

χ2 = 2840.14, P < 0.001

χ2 = 1566.26, P < 0.001

χ2 = 3753.07, P < 0.001

 Yes

7211

23.25

2276

56.25

6543

29.10

2137

57.63

 No

23,804

76.75

1770

43.75

1,5945

70.90

1571

42.37

Frequency of drug use in the past month

  

χ2 = 208.72, P < 0.001

χ2 = 196.19, P < 0.001

χ2 = 195.95, P < 0.001

 0 times/day

2485

8.01

154

3.81

1615

7.18

143

3.86

 1–3 times/day

18,386

59.28

2278

56.30

13,147

58.46

2077

56.01

 4–6 times/day

8303

26.77

1289

31.86

6322

28.11

1220

32.90

  > 6 times/day

1489

4.80

288

7.12

1188

5.29

245

6.61

 Missing

352

1.14

37

0.91

216

0.96

23

0.62

HIV status at baseline

  

χ2 = 855.02, P < 0.001

 Positive

4046

13.05

  

3708

16.49

  

 Negative

26,969

86.95

  

18,780

83.51

  

HCV status at baseline

  

χ2 = 855.02, P < 0.001

 Positive

22,488

72.51

3708

91.65

    

 Negative

8527

27.49

338

8.35

    

HIV/HCV co-infection at baseline

  

 Double positive

3708

11.96

      

 Double negative

8189

26.40

      

 Single positive and negative

19,118

61.64

      

Urine morphine testing results at baseline

  

χ2 = 24.79, P < 0.001

χ2 = 13.21, P = 0.001

χ2 = 33.50, P < 0.001

 Positive

17,383

56.05

2442

60.36

12,622

56.13

2190

59.06

 Negative

13,085

42.19

1562

38.61

9507

42.28

1445

38.97

 Missing

547

1.76

42

1.04

359

1.59

73

1.97

Of the 88 county-level areas in Guangxi, 63 locations were covered by MMT clinics, with the cumulative number of clients ranging from 45 to 2634. Clinics with more than a cumulative number of 400 clients were mostly distributed in the southern areas, with A county as the center. A county accounted for 41.55% of the total clients in these clinics (Fig. 2a). The residential addresses for all MMT clients were distributed throughout Guangxi in a pattern that was more closely clustered than random (Moran’s I = 0.14, P = 0.022).
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

Spatial clusters of HIV infection

The HIV infection rate of MMT clients was found to be strongly geographically clustered (Moran’s I = 0.33, P < 0.001). For high HIV infection rates, the results of LISA were similar to those of the geographic scan statistic (Fig. 2b and Table 2). LISA analysis identified three high-high locations distributed in B and C cities and D county and two high-low locations in A and E counties, which included 2844 MMT clients and 1041 HIV cases, with an HIV infection rate of 36.60% (1041/2844). Apart from these clusters, there were only 3005 HIV cases, with a 10.67% (3005/28,171) infection rate. The geographic scan statistic identified four significant high-risk clusters, which included 19 locations with a 23.73% (2264/9542) infection rate. The high-risk clusters were mainly concentrated in the northeastern parts where P county was the cluster center, along with the surrounding areas of E and F cities or A county alone. Apart from these clusters, there were only 1782 HIV cases with an 8.30% (1782/21,473) infection rate. For low HIV infection rates, the geographic scan statistic identified three significant low-risk clusters, which included 19 locations with a 3.66% (334/9131) infection rate. These low-risk areas were mainly distributed in the northwestern parts, where H city was the cluster center, or in the southern parts, where I county and J city were the cluster centers. LISA analysis showed that there were no significant low-low locations for HIV infection rates.
Table 2

General description of the clusters with high and low prevalence of HIV, HCV and co-infections among MMT clients in Guangxi (2004 ~ 2014)

Type of infection

Type of cluster

No. of counties/cities

Radius (km2)

MMT clients

No. of infections

Relative risk

Log likelihood ratio

P value

HIV infection

High risk cluster

14

11.95

7074

1405

1.80

147.72

<0.001

  

2

6.24

627

220

2.79

81.90

<0.001

  

2

6.14

708

173

1.91

28.76

<0.001

  

1

0.00

1133

466

3.43

230.27

<0.001

 

Low risk cluster

7

11.23

2556

74

0.21

156.90

<0.001

  

7

8.91

4209

144

0.24

234.95

<0.001

  

5

9.29

2366

116

0.36

84.01

<0.001

HCV infection

High risk cluster

14

11.95

7074

5626

1.13

30.50

<0.001

  

8

9.48

6692

5655

1.22

81.59

<0.001

 

Low risk cluster

12

12.01

4224

2392

0.75

90.92

<0.001

  

4

7.61

1142

684

0.82

13.80

0.002

  

1

0.00

680

381

0.77

14.11

0.008

  

1

0.00

342

5

0.02

224.78

<0.001

HIV/HCV co-infection

High risk cluster

13

10.95

4888

1008

2.00

156.03

<0.001

  

2

6.24

627

201

2.78

74.43

<0.001

  

2

6.14

708

160

1.93

27.31

<0.001

  

1

0.00

1133

466

3.43

230.27

<0.001

 

Low risk cluster

7

8.91

4209

129

0.23

219.66

<0.001

  

7

11.32

2556

68

0.21

143.51

<0.001

  

5

9.29

2366

102

0.34

81.52

<0.001

  

1

0.00

342

0

0.00

25.11

<0.001

Spatial clusters of HCV infection

Significant spatial clustering was detected for the HCV infection of MMT clients (Moran’s I = 0.41, P < 0.001). For high HCV infection rates, LISA analysis observed two high-high locations distributed in M and N cities and one high-low location in F city, which included 3490 MMT clients and 3105 HCV cases, with an HCV infection rate of 88.97% (3105/3490). Apart from these clusters, there were only 19,383 HCV cases, with a 70.72% (19,383/27,525) infection rate. The geographic scan statistic identified two significant high-risk clusters, which included 22 locations with an 81.95% (11,281/13,766) infection rate. One cluster included eight locations distributed in southern areas where M city was the cluster center, and the other cluster included 14 locations distributed in the northeastern parts where P county was the cluster center. Apart from these clusters, there were only 11,207 HCV cases, with a 64.97% (11,207/17,249) infection rate. For low HCV infection rates, LISA analysis observed three low-low locations distributed in X, Y, and Z counties and one low-high location in Q county, with a 29.42% (373/1268) infection rate. The geographic scan statistic identified four significant low-risk clusters, which included 18 locations with a 54.20% (3462/6388) infection rate. These low-risk areas were mainly located in western parts, where T county was the cluster center, and in the central parts, where L city was the cluster center, along with sporadic counties, such as I and Q counties (Fig. 2c and Table 2).

Spatial clusters of HIV/HCV co-infection

The co-infection rate of HIV/HCV among MMT clients was more closely clustered than resembling a random pattern (Moran’s I = 0.30, P = 0.003). The clustering pattern was closely similar to that of the HIV infection rate (Fig. 2d and Table 2). Except for Q county, the clustering areas for co-infection rates detected by the geographic scan statistic were identical with those of LISA analysis. The geographic scan statistic identified four significant high-risk clusters, with a 24.95% (1835/7356) co-infection rate of 18 locations, and four significant low-risk clusters, with a 3.16% (299/9473) co-infection rate of 20 locations.

Co-clustering of HIV, HCV and co-infections

As shown in Fig. 3, many significant clusters of HIV, HCV, or co-infections overlapped. For the significant high-risk clusters, the overlaps for these infections were located in the northeastern parts where P county was the cluster center, which covered 13 locations where the radius was 10.95 km2. In addition, the overlaps for HIV and co-infections were also located in the surrounding areas where E and F cities were the cluster centers, as well as A county (Fig. 3a). For the significant low-risk clusters, the overlap for HIV, HCV and co-infections was only I county, while the overlaps for HIV and co-infections were also located in the surrounding areas where H and J cities were the cluster centers, and the overlap for HCV and co-infections also contained Q county (Fig. 3b).
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

Spatial clusters of epidemic factors

Significant spatial clustering was also detected for several of demographic and behavioral factors, and most of them were likely to reside within the clusters of HIV, HCV, or co-infections (Fig. 4 and Table 3). Of the demographic factors, only being unmarried and having a primary level education or below were geographically clustered. Of risky injection practices, injectors who reported more than 10 years of drug use and receptive sharing of syringes with others were geographically clustered. As shown in Fig. 4, the high-risk clusters for these significant clustering characteristics were mainly located within or surrounding the northeastern parts where P county was the cluster center. In addition, two characteristics of injection practices were also located near one of the overlaps for HIV and co-infections (such as the surrounding areas where E city was the cluster center) or one of the high-risk clusters of HCV infection (such as the southern parts where M city was the cluster center). For the low-risk clusters, most of them were distributed in the western and southeastern parts, which are located near one of the low-risk clusters for HIV, HCV, or co-infections.
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

Table 3

Significant high risk clusters of demographic and behavioral characteristics of MMT clients in Guangxi

Characteristic

Moran’s I ± SD expected I = −0.0161

P value for clustering

No. of counties/cities

Radius (km2)

Relative risk

Being unmarried

0.2002 ± 0.0024

<0.001

5

7.50

1.38

  

3

6.77

1.21

Primary level education or below

0.1167 ± 0.0024

0.007

4

7.40

1.43

  

2

6.77

1.48

Having used drugs for more than 10 years

0.0979 ± 0.0024

0.020

3

7.93

1.63

  

3

7.56

1.69

Receptive sharing of syringes with others

0.1091 ± 0.0024

0.010

6

8.03

1.62

  

4

7.73

1.34

  

3

7.34

1.74

  

1

0.00

1.99

Discussion

To our knowledge, this is the first study to investigate the spatial distribution patterns of HIV and HCV epidemics in relation to MMT clients and their possible interactions in Guangxi. The overall infection rates of HIV, HCV, and HIV/HCV among MMT clients at treatment baseline were 13.05%, 72.51% and 11.96% respectively, which were similar to the rates of high-transmission areas (including Yunnan, Guizhou, Sichuan and Xinjiang) of China, but distinctly higher than those of the other provinces [8]. This finding indicated that the rates of these infections remained highly concentrated among provinces along the traditional drug-trafficking routes, and MMT clinics have recruited more HIV- or HCV-infected drug users as a result of the 2006 national policy to relax the eligibility criteria for MMT enrollment. The similar infection rates of HIV and HIV/HCV suggest that HIV-infected MMT clients were at high risk of co-infected HCV infection. Additionally, this finding showed the important role of IDUs in driving the HCV epidemic among PWID and HIV-infected individuals, which was consistent with previously published evidence [3, 6, 27].

Our study demonstrated that HIV, HCV and co-infections in Guangxi all exhibited significant geographic clustering at the county level, and their distribution patterns overlapped to some degree, particularly for HIV and HIV/HCV co-infection. The most significant high-risk overlapping clusters for these infections surrounded P county in the northeastern area of Guangxi. The overlaps for HIV and co-infections were also located in the area surrounding E and F cities in the southwestern areas of Guangxi, as well as A county. Several important points are considered in interpreting the geographic distribution patterns of these infections. First, E city is adjacent to Vietnam, where the HIV epidemic was driven by IDUs in the early phase and formed the spreading trends from the South to the North since 1990. Guangxi thus first detected a domestic HIV-1 case among IDUs in one county of E city in 1996, which gradually led to an HIV outbreak among drug users in the surrounding border areas [9, 28, 29]. Second, because of its shared border with the ‘Golden Triangle’ and Yunnan province, which were the earliest and most severe HIV/AIDS epidemic areas in China, F city became one of the most severely affected HIV/AIDS areas initially fueled by IDUs in Guangxi [30]. Third, as previous studies have shown, the geographic concentrations of HIV in poor and underserved areas and the dispersion of HIV along roads and highways [3134] are high, and it is indeed the case that most of the locations surrounding P county, including E and F cities, belong to economically depressed areas. These findings corroborated that HIV and HCV epidemics first broke out within the border areas adjacent to the ‘Golden Triangle’ and then along drug trafficking routes to other parts of Guangxi, and poverty might play an important role in accelerating these epidemics. Meanwhile, the most significant low-risk overlapping clusters for HIV, HCV or co-infections were mainly located in areas surrounding H and J cities or I county, which was consistent with the trend from routine monitoring data of Guangxi. This information suggested that some behavioral or biological protective factors appear to have slowed the transmission of these infections in the low-risk clusters.

The distribution patterns of HIV, HCV and co-infections are similar to those across different counties, which might be attributed to the similarities in the features of the epidemics. Compared with related studies [3537], our findings showed that several epidemic factors, 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, were geographically clustered. Most of the individuals with these factors were more likely to reside in one of clusters for HIV, HCV and co-infections, especially for the high-risk clusters (e.g., the areas surrounding P county as the cluster center). Numerous studies have reported that the emergence of MMT and other harm reduction programs have resulted in lower levels of risky behaviors and reductions in the HIV epidemic [7, 12, 38, 39]. However, not all previous studies have found such a tight linkage, as one study from the San Francisco Bay Area [40] found that risky injection practices were indeed lower among drug users from poorer communities targeting harm reduction programs, but the prevalence of HIV remained high. Therefore, additional studies should be conducted to evaluate the spatial distribution of these infections and their association with epidemic features after MMT programs have been more widely established.

The similar prevalence and overlapping spatial clusters found in our study between HIV and HIV/HCV co-infection suggest a higher prevalence of HCV co-infection among HIV-infected PWID in Guangxi. Co-infection of HIV and HCV interact synergistically by affecting the transmission history and reducing the immune clearance of the other. Individuals co-infected with HCV could boost the occurrence of HIV infection, with the perinatal transmission risk doubling in HIV-infected mothers [41, 42], thus altering immunological responses to antiretroviral therapy and accelerating the risk of drug-related hepatotoxicity and consequently cirrhosis, liver failure, and hepatocellular carcinoma [4345]. Meanwhile, HIV-infected individuals without treatment are less likely to spontaneously clear HCV infection; they may then experience more rapid HCV disease progression than HIV-negative individuals [3, 46]. Given the fact that a large proportion of HIV cases are acquired through IDUs in Guangxi, there is an urgent need to comply with international guidelines that recommend HCV screening for HIV-infected individuals, investment in building HCV surveillance and care, and access to direct-acting antiviral treatment for those with chronic active infection [4750]. Comprehensive measures in parallel to MMT, such as antiretroviral therapy, 100% use of condoms, needle exchanges and sexually transmitted disease management services should be promoted as well.

Several limitations in this study should be noted. First, it is difficult to determine whether the distribution patterns and associations of the MMT clients described in this report are true for the entire population of injectors in this region. However, one study reported by national sentinel surveillance has shown that the national HIV prevalence among MMT clients was not significantly different from that of non-MMT drug users from 2004 to 2009 [8]. Second, most of the data included were obtained from 2008 to 2014, given that a national MMT program database was developed in 2004 to monitor the pilot and was later upgraded to a web-based management database in 2008. Third, most of the data, including residential addresses and drug use behaviors, were self-reported, and we have no way knowing the proportion of the clients giving false or misleading information. Nevertheless, the information bias might be narrowed given that the staff members of local MMT clinics needed to receive a series of professional trainings on assessment surveys, after which they could upload clients’ information to the national web-based management database prior to their assignment. Most of the clients were later contacted at the addresses they provided as well. Finally, MMT clients without a fixed address (and who were thus more likely to be at high risk) were excluded, and drug use is likely to occur in venues or areas of the city that might not coincide with an address. Therefore, the location of consumption would have been a better approach for the analysis.

Conclusions

Using spatial analysis for detecting the HIV and HCV epidemics among drug users from a cohort study of MMT programs from 2004 to 2014, we revealed two important findings. First, 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. Second, areas surrounding P county were effective in enrolling high-risk clients in their MMT programs, which in turn might allow PWID to inject less, share fewer syringes, and receive referrals for HIV or HCV treatment in a timely manner.

Abbreviations

AIDS: 

Acquired Immune Deficiency Syndrome

ASEAN: 

The Association of Southeast Asian Nations

CDC: 

Center for Disease Control and Prevention

ELISA: 

Enzyme-linked immune sorbent assay

GIS: 

Geographical information system

HCV: 

Hepatitis C virus

HIV: 

Human immunodeficiency virus

IDUs: 

Injecting drug users

LISA: 

Indicators of Spatial Association

MMT: 

Methadone maintenance treatment

PWID: 

People who inject drugs

SPSS: 

The SPSS Statistical Package for Social Sciences

Declarations

Acknowledgments

The authors thank staff members who have been involved in the surveillance, laboratory testing and treatment of HIV/AIDS patients in Guangxi.

Funding

The study was supported by grants from the Guangxi Bagui Honor Scholars, Ministry of Science and Technology of China (2012ZX10001–002).

Availability of data and materials

The data of HIV and HCV epidemics, incorporating demographic information of MMT clients in Guangxi were obtained from Guangxi Center for Disease Prevention and Control (http://10.249.1.170/).

Authors’ contributions

GHL, MLL and RJL conceived and designed the study. MLL, CYL, NXL, HBH, ZRP, CWH, FZ and XYT conducted data processing and statistical analysis. MLL and RJL wrote the manuscript. GHL revised the manuscript. All authors read and approved the final version.

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

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Institute of Vaccine Clinical Research, Guangxi Zhuang Autonomous Region Center for Disease Control and Prevention
(2)
Institute of HIV/AIDS Prevention and Control, Guangxi Zhuang Autonomous Region Center for Disease Control and Prevention
(3)
School of Public Health, Guangxi Medical University

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Copyright

© The Author(s). 2017

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