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. 2017 Jul 7;7:4890. doi: 10.1038/s41598-017-05138-x

Cancer risk factors among people living with HIV/AIDS in China: a systematic review and meta-analysis

Zi-Yi Jin 1,#, Xing Liu 1,#, Ying-Ying Ding 1, Zuo-Feng Zhang 2, Na He 1,✉
PMCID: PMC5501798  PMID: 28687813

Abstract

Cancer is a leading cause of death among people living with HIV/AIDS (PLWHA). We conducted a systematic review and meta-analysis to evaluate prevalence of cancer risk factors among Chinese PLWHA based on 102 articles. Random effects meta-analysis was used to calculate the summary prevalence estimate (sPrev) and 95% confidence interval (CI) for each cancer risk factor by demographic group. Overall, the sPrev for each risk factor among Chinese PLWHA was: 41.1% (95% CI: 35.3–46.9%) for current smoking; 30.3% (95% CI: 23.3–37.4%) for current alcohol consumption; 24.4% (95% CI: 14.7–30.2%) for overweight and obesity; 12.5% (95% CI: 10.6–14.3%) for hepatitis B virus infection; 29.1% (95% CI: 23.6–34.5%) for hepatitis C virus infection; 33.9% (95% CI: 24.3–43.5%) for high-risk human papillomavirus infection from cervical samples and 78.6% (95% CI: 69.4–87.7%) from anal samples; 2.7% (95% CI: 0.7–4.7%) for Epstein-Barr virus (EBV) immunoglobulin M (IgM) positivity, 94.7% (95% CI: 90.7–98.8%) for EBV IgG positivity and 25.6% (95% CI: 12.4–38.8%) for EBV DNA positivity; 14.9% (95% CI: 12.4–17.4%) for human herpes virus 8 infection. The prevalence of major cancer risk factors was high among PLWHA in China, suggesting an urgent need for interventions to reduce cancer risk in this high-risk group.

Introduction

Highly active antiretroviral therapy (HAART) or combination antiretroviral therapy (cART) has drastically reduced mortality in people living with HIV/AIDS (PLWHA)1–3. For PLWHA, cancer is now one of the leading causes of death with higher incidence of both AIDS-defining cancers (ADCs) and non-AIDS-defining cancers (NADCs) compared with the general population4–8. In particular, NADCs have shown increasing incidence and account for a rising proportion of all cancers in this population4. China has implemented HAART nationally since 2003. In a Chinese cohort, substantially elevated standardized incidence ratios of NADCS and ADCs were observed, and cancers occurred at a younger age for PLWHA compared to the general population9. Accumulated evidence suggests that HIV-induced immunodeficiency and prevalent risk factors other than HIV infection independently contribute to the increased cancer risk among PLWHA10, 11. The major risk factors associated with leading causes of cancer deaths include tobacco use (associated with lung, colorectal, stomach, and liver cancer), alcohol consumption (colorectal and liver cancer), overweight/obesity (breast and colorectal cancer), and viral infections (liver, stomach, and cervical cancer)12, 13. Numerous independent studies have reported prevalence of cancer risk factors among PLWHA in China. However, the researches vary in time and geographic areas, and do not provide a comprehensive overview of cancer risk factors epidemics for PLWHA in China. We conducted a meta-analysis among PLWHA in China to describe the prevalence of major cancer risk factors including tobacco smoking, alcohol consumption, overweight and obesity, and cancer-related viral infections such as hepatitis B virus (HBV), hepatitis C virus (HCV), human papillomavirus (HPV), Epstein-Barr virus (EBV) and human herpes virus 8 (HHV8). Prevalence estimates among Chinese PLWHA were compared with the general population or uninfected comparison groups from the same study when available.

Results

The process of study selection

The process of selection of publications and the numbers of articles for each cancer risk factor are presented in Table 1. A total of 3,955 peer-reviewed research articles were identified from four databases: PubMed (219), CNKI (2,381), Wanfang Data (586), and CQVIP (769). We excluded 3,407 articles after screening titles and abstracts. Among the remaining 548 studies (101 in English and 447 in Chinese), we further excluded 446 studies during full-text review for the following reasons: 171 were studies with an irrelevant population, 138 had no prevalence estimate, 33 did not present original data, 44 were reviews, 39 had a small sample size (less than 100), and 21 were redundant articles. Finally, 102 eligible publications (35 in English and 67 in Chinese) contributed to the meta-analyses14–115. One additional study with unique data on HIV uninfected population was included for comparison analysis116.

Table 1.

The process for selection of studies, by cancer risk factor.

Flow Category Cancer risk factor
Smoking Alcohol Consumption Overweight/obesity Hepatitis B or C virus infection Human papilloma virus infection Epstein-Barr virus Human herpes virus 8 Total
Identified articlesa PubMed 29 73 12 65 20 6 14 219
CNKI 387 407 149 849 170 291 128 2381
Wanfang 40 44 15 396 46 20 25 586
CQVIP 75 68 43 486 74 1 22 769
Total 531 592 219 1796 310 318 189 3955
Excluded after screening of title and abstract Total 383 435 189 1661 267 298 174 3407
Full-text review English article 10 29 5 28 16 2 11 101
Chinese article 138 128 25 107 27 18 4 447
Total 148 157 30 135 43 20 15 548
Excluded after full-text review with reasons for exclusion No relevant population 29 62 2 67 6 1 4 171
No prevalence estimate 63 45 7 14 2 7 0 138
No original data 14 4 15 0 0 0 0 33
Review 14 20 2 2 4 1 1 44
Sample size < 100 7 6 1 6 13 1 5 39
Redundant article 2 4 0 2 9 3 1 21
Total 129 141 27 91 34 13 11 446
Eligible articles English article 1 6 1 17 6 1 2 34
Chinese article 18 10 2 27 3 6 2 68
Total 19 16 3 44 9 7 4 102
Contributed to the meta-analysisb 26 32 6 HBV:29; HCV:42 9 9 4 102
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aDate searched from database inception to February 29, 2016 for all cancer risk factors, except for hepatitis B or C virus infection (January 1, 2013–February 29, 2016).

bIncluding eligible articles identified from both the specific risk factor and other risk factors.

Characteristics of eligible publications

Table 2 shows the characteristics of the eligible articles. Ninety-two cross-sectional studies, 7 prospective cohort studies38, 52, 60, 80, 94, 100, 101, 2 retrospective cohort studies68, 79 and 1 intervention trial36 were included. Most studies (99) were based in clinics or hospitals for PLWHA recruitment, 2 were population-based72, 73 and 1 study sampled was recruited from jail75. The 102 eligible studies covered 20 provinces and 4 municipalities in China. The median sample size for PLWHA was 295 [interquartile range (IQR): 178–551]. Seventy-five articles reported the median or mean age, and the median of the median or mean age was 37.2(IQR: 34.0–41.0). Of the 95 articles reporting gender distribution, the median proportion of male was 72.6% (IQR: 61.3–86.9%). Of the 16 articles reporting the proportions of MSM, the median was 79.1% (IQR: 63.9–100.0%). Of the 23 articles reporting the proportions of IDU, the median was 74.8% (IQR: 40.4–100.0%). Of 26 articles reporting the proportions on ART, the median was 67.5% (IQR: 45.3–100.0%).

Table 2.

Characteristics of eligible articles.

Author (Publication year) Province Calendar years Age (Mean/Median) N Male (%) MSM (%) IDU (%) ART (%)
Yao (2015)14 Shaanxi … 35.5 535 93.5 71.8 … 100.0
Wei (2015)15 Jiangsu 2014–2014 39.2 276 91.7 … 2.5 100.0
Pu (2011)16 Guangxi 2008–2010 34.3 184 84.8 … 42.9 …
Xiao (2014)17 Anhui 2012–2012 44.5 801 59.9 … … 100.0
Nurbiya (2014)18 Xinjiang 2013–2013 40.1 109 59.6 … … …
Zhang (2012)19 Shanxi 2004–2005 42.0 316 53.8 … … …
Luo (2014)20 Yunan 2012–2012 38.1 455 66.2 … 55.4 67.5
Bao (2015)21 Shanghai 2014–2014 38.3 284 94.0 … … …
Sun (2014)22 Liaoning 2010–2011 37.4 772 89.5 … … 35.2
Cheng (2014)23 Beijing 2013–2013 34.0 504 91.7 … … …
Cheng (2012)24 Taiwan 2010–2010 31.0 305 100.0 100.0 … 47.5
Cheng (2014)25 Taiwan 2011–2011 32.9 230 100.0 78.7 … …
Zhang (2014)26 Yunan 2009–… 34.0 301 0.0 … … 64.1
Wu (2012)27 Taiwan 2008–2009 38.8 803 94.9 75.2 4.9 …
Luo (2014)28 Chongqing 2012–2013 38.3 124 75.0 … 8.1 54.0
Dong (2006)29 Guangxi 2005–2005 … 321 69.8 … 30.8 …
Li (2011)30 Shanghai 2005–2009 42.0 348 84.5 … 2.9 20.7
Zhou (2015)31 Yunan … 59.8 201 … … … …
Tsai (2014)32 Taiwan 2002–2006 37.3 320 90.6 62.8 … 94.4
Zhou (2015)33 Hunan 2010–2015 … 240 44.6 … … 48.3
Jiang (2014)34 Anhui 2012–2012 49.0 261 55.6 … … 100.0
Su (2010)35 Anhui 2006–… 40.4 153 43.8 … … …
Xie (2015)36 Heilongjiang 2010–2012 36.0 240 92.5 … … …
Zhao (2012)37 Guangxi 2010–2011 54.6 100 75.0 … … …
Lin (2015)38 Chongqing 2008–2011 48.6 188 66.5 36.2 61.7 62.8
Zhang (2008)39 Anhui 2005–… 41.9 219 48.4 … … …
Gu (2013)40 Sichuan 2005–2011 43.2 506 78.7 … … …
Wang (2011)41 Henan 2005–2009 45.2 162 50.0 … … …
Lin (2010)42 Shandong 2009–2009 37.9 324 61.4 … … 100.0
Ge (2015)43 Zhejiang … 42.4 123 83.7 … … …
Li (2014)44 Shandong …–2013 … 184 92.9 … … …
Liu (2014)45 Henan … 45.0 706 57.8 … … …
Li (2009)46 Beijing 2005–2006 … 179 60.9 … … …
Muessig (2014)47 Guangdong 2011–2011 31–40 721 61.3 … … 100.0
Luo (2013)48 Yunan 2012–2012 38.1 455 66.2 … 55.4 67.5
Hu (2013)49 Beijing 2010–2011 30.4 212 100.0 100.0 … …
Yang (2010)50 Sichuan 2007–2009 33.2 102 100.0 100.0 100.0 46.1
Jin (2014)51 Yunan … 36.8 204 65.7 … 100.0 …
Yen (2012)52 Taiwan 2007–2010 40.0 194 83.0 … 100.0 …
Zhang (2013)53 Tianjin 2011–2012 42.1 100 89.0 … … …
Dong (2011)54 Guangxi 2010–2010 39.3 400 63.8 … … …
Zhou (2013)55 Guangxi … … 125 0.0 … … …
Pan (2015)56 Guangxi 2012–2014 … 158 72.2 … 100.0 …
Shen (2015)57 Yunan …–2013 … 663 96.1 … 100.0 …
Dong (2015)58 Yunan … 34.0 498 86.9 … 100.0 …
Zhou (2014)59 Zhejiang 2009–2012 38.5 572 72.6 … … …
Liu (2014)60 Sichuan 1995–2010 32.6 614 81.4 … … …
Zhang (2014)61 China 2010–2012 … 33861 66.5 … … …
Chen (2013)62 Yunan 2006–2008 39.0 978 78.0 … … …
Chen (2016)63 Sichuan 2011–2014 … 126 54.8 … … …
Wu (2015)64 Jiangsu 2010–2014 … 145 89.7 … … …
Pan (2015)65 Jiangxi … 35.1 134 68.7 … … …
Ye (2013)66 Yunan 2005–2006 … 544 65.6 … … …
Li (2013)67 Chongqing 2009–2012 30–40 994 68.9 … … …
Pererdun (2015)68 Xinjiang 2005–2011 35.1 2357 54.5 … … …
Zhang (2013)69 Shandong 2000–2010 35.0 2021 62.7 … … …
Zhang (2014)70 Shaanxi … … 164 64.6 … … 78.7
Zhao (2013)71 Hubei 2007–2010 38.6 356 73.6 … … 29.2
Dong (2014)72 Sichuan 2010–2010 … 694 69.5 … … …
Liu (2014)73 Henan … 29.1 164 55.5 … … …
Zhou (2014)74 Sichuan 2004–2012 … 1633 9.8 … 100.0 …
Hsieh (2014)75 Taiwan 2008–2010 36.4 297 85.5 … 100.0 …
Liu (2015)76 Xinjiang … … 129 … … 100.0 …
Ng (2013)77 Taiwan 2006–2010 … 162 … … 100.0 …
Zhang (2013)78 Guangdong 2006–2011 … 145 … … 100.0 …
Li (2016)79 Multiple 2005–2014 34.0 1105 71.7 … … 100.0
Peierdun (2015)80 Xinjiang 2006–2011 36.6 2252 54.2 … … …
Pan (2014)81 Zhejiang 2013–2013 31–40 321 84.7 … … …
Duan (2014)82 Jiangxi 2004–2012 42.7 2194 71.4 … … …
Tang (2013)83 Sichuan 2012–2013 … 378 … … … …
Wang (2015)84 Anhui 2014–2014 37.0 122 50.0 … … 100.0
He (2015)85 Sichuan 2012–2014 37.1 285 60.4 … … …
Liu (2013)86 Yunan 2008–2011 … 1025 … … … …
Zheng (2013)87 Shanghai 2005–2010 39.7 1153 82.8 … … …
Zhang (2015)88 Shaanxi 2005–2013 36.0 1243 91.7 … … …
Shen (2013)89 Multiple 2009–2010 41.0 2040 75.7 … … …
Lee (2014)90 Taiwan 2009–2012 38.0 171 93.6 79.5 … …
Cao (2013)91 Multiple 2009–2010 36.5 538 74.2 … … …
Feng (2013)92 Yunan 2009–2011 41.3 449 66.4 … … …
Li (2013)93 Sichuan 2006–2009 42.6 133 76.7 … … …
Kou (2013)94 Multiple 2009–2010 37.0 532 74.6 … … …
Wang (2014)95 Guizhou 2013–2013 … 204 74.0 … … …
Xiao (2014)96 Anhui 2011–2013 … 234 65.4 … … …
Liu (2014)97 Henan 2012–2013 … 1887 73.3 … … …
Zhao (2013)98 Hubei 2011–2012 … 332 100.0 100.0 … …
An (2013)99 Multiple 2010–2012 31.5 513 100.0 100.0 … …
Guo (2013)100 Guangdong 2009–2012 31.6 166 0.0 … … …
Zhang (2012)101 Hubei 2009–2011 41.0 293 0.0 … … …
Luo (2013)102 Multiple 2009–2010 … 182 0.0 … … …
Yu (2012)103 Taiwan 2010–2011 … 194 100.0 67.0 … 84.5
Li (2016)104 Multiple … 32.2 104 100.0 100.0 … …
Yang (2015)105 Hubei 2007–2014 37.6 157 72.6 … … …
Zhang (2014)106 Beijing 2005–2011 … 731 86.6 … … 100.0
Hu (2014)107 Hubei 2010–2013 41.0 217 77.4 … … …
Wu (2012)108 Guangdong 2010–2011 36.5 257 72.8 … … …
Zhou (2011)109 Shanxi 2006–2006 … 175 64.6 … … …
Shi (2012)110 Yuan 2008–2010 35.0 245 66.5 1.6 43.7 40.8
He (2011)111 Multiple 2008–2009 30–39 1110 61.4 … … 42.7
Yang (2009)112 Xinjiang 2006–2006 31.4 468 51.1 … 40.4 …
Li (2014)113 Yuan 2008–2010 … 245 … … … …
Lee (2014)114 Shanxi 2004–2006 33.8 504 86.7 25.2 74.8 29.8
Yang (2013)115 Jiangsu 2012–2012 37.2 264 100.0 100.0 … …
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Prevalence estimates for cancer risk factors

Table 3 presents the group-specific sPrev estimates for each cancer risk factor among PLWHA and the corresponding prevalence in the Chinese general population. Selected forest plots for important cancer risk factors are shown in Fig. 1. Moreover, Table 4 presents the results comparing the prevalence between PLWHA and HIV uninfected groups if data were available from the same studies.

Table 3.

Pooled and single prevalence of cancer risk factors among persons living with HIV/AIDS by demographic group and prevalence in general population, in China.

Risk factor, demographic group Prevalence (%) (95% CI) I2 (%), P-value N References Prevalence (%, 95% CI) in general population
Smoking
Current
 Overalla 41.1 (35.3–46.9) 96.0, <0.001 21 14–21, 27–39 28.3 (27.2–29.4)117, 119
 Female 3.4 (2.2–4.6) 0.0, 0.709 5 17–20, 26 2.5 (1.9–3.0)117, 119
 Maleb 63.2 (39.7–86.8) 98.9, <0.001 5 17–20, 25 53.3 (51.4–55.2)117, 119
 MSM 47.0 (41.4–52.7) — 1 24
Ever
 Overall 51.9 (42.8–61.1) 95.9, <0.001 5 20–23, 27
 Female 8.0 (4.8–11.2) — 1 26
Former
 Overall 13.9 (0.3–27.6) 98.6, <0.001 3 20, 21, 27 15.3 (14.3–16.4)117, 119
 Female 5.1 (2.5–7.7) — 1 26 25.6 (21.0–30.3)117, 119
Alcohol Consumption
Current
 Overall 30.3 (23.3–37.4) 98.6, <0.001 25 14–23, 33–47 28.8119
 Female 3.3 (1.2–5.4) 67.8, 0.045 3 17, 19, 20 9.4119
 Male 26.9 (18.5–35.4) 91.8, <0.001 5 17–20, 25 47.6119
MSM 35.7 (18.9–52.5) 95.1, <0.001 3 24, 49, 50
 IDU 13.1 (8.3–17.9) 51.5, 0.151 2 51, 52
Ever
 Overall 64.3 (60.8–67.7) 0.0, 0.577 2 21, 48 36.4119
 Female 16.9 (11.0–22.8) — 1 48 14.5119
 Male 89.7 (86.3–93.1) — 1 48 57.7119
Former
 Overall 17.8 (3.6–32.0) 96.5, <0.001 2 20, 21
 Female 64.3 (56.7–71.9) — 1 20
 Male 5.0 (2.5–7.4) — 1 20
Overweight and obesity
 Overall 22.4 (14.7–30.2) 94.3, <0.001 6 23, 32–34, 53, 54 42.6119
Hepatitis B virus infection (HBsAg)
 Overall 12.5 (10.6–14.3) 96.1, <0.001 25 40, 59–66, 79–94 7.2 (6.7–7.7)120
 Female 10.4 (8.1–12.8) 66.2, 0.003 9 59–66, 79 5.7120
 Male 13.4 (11.0–15.8) 89.0, <0.001 10 57, 59–66, 79 8.6120
 MSM 12.7 (9.1–16.2) — 1 98
 IDU 32.8 (0.0–70.7) 99.7, <0.001 3 57, 58, 75
Hepatitis C virus infection
 Overall 29.1 (23.6–34.5) 99.5, <0.001 31 40, 61–73, 81–97 0.43 (0.33–0.53)121
 Female 29.9 (23.2–36.6) 98.4, <0.001 16 55–57, 61–74 0.40 (0.27–0.54)121
 Male 47.1 (32.2–62) 99.8, <0.001 15 56, 57, 61–74 0.46 (0.31–0.61)121
 MSM 2.0 (1.1–3.0) 0.0, 0.873 2 98, 99
 IDU 83.4 (63.8–100.0) 99.8, <0.001 6 57, 58, 74–76, 78
Human papillomavirus infection
Cervical
 Any type 42.0 (37.7–46.3) 34.3, 0.218 3 26, 101, 102 9.9–27.5123
 High-risk types 33.9 (24.3–43.5) 90.2, <0.001 4 26, 100–102 21.1 (20.8–21.3)122
Anal
 Any type, Male 78.6 (69.4–87.7) 81.5, 0.020 2 25, 103 17.8 (16.2–19.3)124
 Any type, MSM 71.7 (52.8–90.6) 97.3, <0.001 4 24, 49, 103, 104
 High-risk, Male 52.1 (27.9–76.2) 96.3, <0.001 2 25, 103 6.4 (5.4–7.3)124
 High-risk, MSM 50.7 (32.3–69.2) 96.4, <0.001 4 24, 49, 103, 104
Epstein-Barr virus
 Overall, IgM 2.7 (0.7–4.7) 89.7, <0.001 4 81, 105–107
 Overall, DNA, PB 25.6 (12.4–38.8) 90.4, 0.001 2 108, 109
 Female, DNA, PB 27.1 (16.7–37.6) — 1 108
 Male, DNA, PB 34.2 (27.4–41.0) — 1 108
 Overall, DNA, saliva 82.0 (77.2–86.8) — 1 110
 Overall, IgG 94.7 (90.7–98.8) 85.5, 0.009 2 19, 111 97.6125
 Female, IgG 92.5 (88.3–96.7) 61.1, 0.109 2 19, 111
 Male, IgG 96.8 (93.6–100.0) 71.9, 0.059 2 19, 111
Human herpes virus 8
 Overall, saliva 14.3 (9.9–18.7) — 1 113
 Overall, Sera 14.9 (12.4–17.4) 0.0, 0.564 2 19, 112 11.3 (7.2–15.5)126
 Female, Sera 12.1 (0.0–25.3) 92.9, <0.001 2 19, 112
 Male, Sera 17.7 (8.3–27.2) 85, 0.010 2 19, 112
 MSM, Sera 32.3 (24.2–40.4) — 1 114
 IDU, Sera 16.3 (0.0–35.7) 97.0, <0.001 2 112, 114
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a‘Overall’ means PLWHA were not restricted by sex or HIV transmission category.

b‘Male’ means PLWHA were not restricted by HIV transmission category.

Figure 1.

Figure 1

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Selected forest plots showing the results of meta-analysis of cancer risk factor prevalence among persons living with HIV/AIDS. (a) Current smoking prevalence among overall; (b) Current alcohol consumption prevalence among overall; (c) Overweight/obesity prevalence among overall; (d) Hepatitis B virus infection prevalence among overall; (e) Hepatitis C virus infection prevalence among overall; (f) Human papillomavirus infection prevalence; (g) Epstein-Barr virus infection prevalence; (h) Human herpes virus 8 infection prevalence.

Table 4.

Comparison of prevalence of cancer risk factors between HIV infected and uninfected groups from the same study.

Risk factor, study Study design Matching conditions Category, Demographic groupa HIV infected HIV uninfected P-value
N Prevalence (%) N Prevalence (%)
Smoking
Su (2010)35 Cross-sectional Age, gender Current, Overall 153 41.2 153 25.5 0.004
Cheng (2012)24 Cross-sectional None Current, MSM 304 47.0 100 32.0 0.008
Alcohol Consumption
Su (2010)35 Cross-sectional Sex, age Current, Overall 153 47.7 153 54.9 0.208
Jin (2014)51 Cross-sectional None Current, IDU 204 15.7 202 19.3 0.337
Jin (2014)51 b Cross-sectional Age, gender, education, and ethnicity Current, IDU 204 15.7 201 12.4 0.883
Yen (2012)52 Prospective cohort None Current, IDU 194 10.8 1250 10.6 0.938
Cheng (2012)24 Cross-sectional None Current, MSM 305 33.8 100 50.0 0.004
Hu (2013)49 Cross-sectional None Current, MSM 212 19.8 453 26.5 0.062
Hepatitis B virus infection
Hsieh (2014)75 Cross-sectional None IDU 297 18.9 265 11.3 0.013
Hepatitis C virus infection
Hsieh (2014)75 Cross-sectional None IDU 297 98.7 265 83.0 <0.001
Human papillomavirus infection
Zhang (2012)101 Prospective cohort None Cervical-any type, Female 293 44.4 200 20.0 <0.001
Luo (2013)102 Cross-sectional None Cervical-any type, Female 182 36.8 300 19.3 <0.001
Guo (2013)100 Prospective cohort None Cervical-high-risk, Female 166 38.6 476 10.5 <0.001
Zhang (2012)101 Prospective cohort None Cervical-high-risk, Female 293 39.9 200 17.5 <0.001
Luo (2013)102 Cross-sectional None Cervical-high-risk, Female 182 19.8 300 1.7 <0.001
Cheng (2012)24 Cross-sectional None Anal-any type, MSM 305 45.2 100 18.0 <0.001
Hu (2013)49 Cross-sectional None Anal-any type, MSM 212 82.1 459 57.5 <0.001
Li (2016)104 Cross-sectional None Anal-any type, MSM 104 82.7 718 62.8 <0.001
Cheng (2012)24 Cross-sectional None Anal-high-risk, MSM 305 31.1 100 13.0 <0.001
Hu (2013)49 Cross-sectional None Anal-high-risk, MSM 212 61.3 459 39.7 <0.001
Li (2016)104 Cross-sectional None Anal-high-risk, MSM 104 70.2 718 48.2 <0.001
Epstein-Barr virus
Shi (2012)110 Cross-sectional Age, gender Saliva (EB-DNA), Overall 245 82.0 30 30.0 <0.001
Human herpes virus 8
Li (2014)113 Cross-sectional None Saliva, Overall 245 14.3 30 0.0 <0.001
Zhang (2011)116 Cross-sectional None Sera, Overall 305 16.4 315 4.8 <0.001
Lee (2014)114 Cross-sectional None Sera, MSM 127 32.3 104 15.4 <0.001
Lee (2014)114 Cross-sectional None Sera, IDU 377 6.6 176 1.1 <0.001
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aDemographic versus between HIV infected and uninfected group: Overall VS General, Female VS General female, MSM VS MSM, and IDU VS IDU.

bHIV uninfected controls were enrolled from non-IDU group.

Smoking

A total of twenty-six articles reported prevalence of smoking among PLWHA. Overall, the sPrev for current smoking was 41.1% (95% CI: 35.3–46.9%, I 2 = 96.0%) in PLWHA based on the meta-analysis of 21 studies. The sPrev of current smoking was 63.2% (95% CI: 39.7–86.8%) for male PLWHA and was 3.4% (95% CI: 2.2–4.6%) for females, based on the 5 studies with gender-specific prevalence. Overall, the sPrev of ever and former smoking was 51.9% (95% CI: 42.8–61.1%) based on 5 studies and 13.9% (95% CI: 0.3–27.6%) based on 3 studies, respectively (Table 3). Significantly higher smoking prevalence was observed in PLWHA than in HIV uninfected subjects when compared in the same studies (Table 4).

Alcohol consumption

Thirty-two publications reported prevalence of alcohol consumption among PLWHA. Overall, the sPrev for current alcohol consumption was 30.3% (95% CI: 23.3–37.4%, I 2 = 98.6%) based on the meta-analysis of 25 studies. Gender-specific estimates showed that the sPrev of current alcohol consumption was 26.9% (95% CI: 18.5–35.4%, I 2 = 91.8%) in male PLWHA based on 5 studies and was 3.3% (95% CI: 1.2–5.4%, I 2 = 67.8%) in females based on 3 studies. MSM presented a current drinking sPrev of 35.7% (95% CI: 18.9–52.5%) based on 3 studies while IDUs presented a current drinking sPrev of 13.1% (95% CI: 8.3–17.9%) based on 2 studies. Overall, the sPrev of ever and former alcohol drinking was 64.3% (95% CI: 60.8–67.7%) and 17.8% (95% CI: 3.6–32.0%), respectively (Table 3). Six studies compared the prevalence of alcohol consumption between PLWHA and HIV negative controls, 5 of which reported no significant difference while 1 reported a lower prevalence in HIV positive MSM than in HIV negatives (33.8% vs. 50.0%, P = 0.004) (Table 4).

Overweight and obesity

Six studies reported the prevalence of overweight and obesity (BMI ≧ 24 kg/m2) among PLWHA, with a sPrev of 24.4% (95% CI: 14.7–30.2%, I 2 = 94.3%) (Table 3).

Hepatitis B virus infection

Twenty-nine articles reported the prevalence of HBV infection defined as HBsAg seropositivity in PLWHA. The sPrev of HBsAg was 12.5% (95% CI: 10.6–14.3%, I 2 = 96.1%), 10.4% (95% CI: 8.1–12.8%, I 2 = 66.2%) and 13.4% (95% CI: 11.0–15.8%, I 2 = 89.0%) in overall, female and male groups, respectively. The highest sPrev of HBsAg was among IDUs (32.8%, 95% CI: 0.0–70.7%, I 2 = 99.7%) (Table 3). In a study reporting comparisons with HIV uninfected group, HBsAg prevalence in PLWHA was also higher in IDU group (18.9% vs. 11.3%, P = 0.013) (Table 4).

Hepatitis C virus infection

Forty-two publications reported the prevalence of HCV infection defined as HCV antibody seropositivity. Based on the meta-analysis of 31 studies, the overall HCV sPrev was 29.1% (95% CI: 23.6–34.5%, I 2 = 99.5%). Male PLWHA showed a sPrev of 47.1% (95% CI: 32.2–62.0%, I 2 = 99.8%) based on 15 studies, while female PLWHA had a sPrev of 29.9% (95% CI: 23.2–36.6%, I 2 = 98.4%) based on 16 studies. The highest HCV prevalence was found in IDU group with a sPrev of 83.4% (95% CI: 63.8–100.0%, I 2 = 99.8%) (Table 3). Higher prevalence was found in HIV infected IDUs comparing to HIV-negative participants in the same study (Table 4).

Human papillomavirus infection

Nine studies reported prevalence of HPV infection in PLWHA. The number of studies for each group-specific meta-analysis ranged from 2 to 4. For cervical samples, the HPV sPrev of both any type and high-risk type was 42.0% (95% CI: 37.7–46.3%, I 2 = 34.3%) and 33.9% (95% CI: 24.3–43.5%, I 2 = 90.2%), respectively. For anal samples, the sPrev among male PLWHA of any type of HPV was 78.6% (95% CI: 69.4–87.7%, I 2 = 81.5%), and the sPrev of high-risk type was 52.1% (95% CI: 27.9–76.2%, I 2 = 96.3%). Similar sPrev was observed among HIV positive MSM (Table 3). All comparisons between PLWHA and HIV uninfected group reported significantly higher prevalence of HPV in PLWHA from the same studies (Table 4).

Epstein-Barr virus infection

Nine publications presented the prevalence of Epstein-Barr virus infection. The number of studies for meta-analysis ranged from 1 to 4 when stratified by EBV testing method and demographic groups. Immunoglobulin M (IgM) was tested in 4 studies and the sPrev was 2.7% (95% CI: 0.7–4.7%, I 2 = 89.7%). IgG was tested in 2 studies, and the sPrev in overall, female and male groups was 94.7% (95% CI: 90.7–98.8%, I 2 = 85.5%), 92.5% (95% CI: 88.3–96.7%, I 2 = 61.1%) and 96.8% (95% CI: 93.6–100.0%, I 2 = 71.9%), respectively. DNA from peripheral blood was tested in 2 studies, and the overall sPrev was 25.6% (95% CI: 12.4–38.8%, I 2 = 90.4%). One study reported the prevalence of 27.1% (95% CI: 16.7–37.6%) in female and 34.2% (95% CI: 27.4–41.0%) in male PLWHA for EBV DNA positivity (Table 3). One study tested DNA in saliva from PLWHA and the prevalence was 82.0% (95% CI: 77.2–86.8%), which was higher than the prevalence in uninfected comparison group (30.0%) (Table 4).

Human herpes virus 8 infection

Four articles reported the prevalence of HHV8 infection in PLWHA, of which 3 studies had serum antibody and one had saliva DNA tested. The prevalence of saliva DNA positivity was 14.3% (95% CI: 12.4–17.4%). For serum antibody, the sPrev in overall, female, male and IDU groups was 14.9% (95% CI: 12.4–17.4%, I 2 = 0.0%), 12.1% (95% CI: 0.0–25.3%, I 2 = 92.9%), 17.7% (95% CI: 8.3–27.2%, I 2 = 85.0%) and 16.3% (95% CI: 0.0–35.7%, I 2 = 97.0%), respectively. The highest serum HHV8 prevalence was among MSM (32.3%, 95% CI: 24.2–40.4%) (Table 3). Significantly higher prevalence was consistently found in PLWHA than in the HIV-negative comparison groups from the same studies (Table 4).

Sensitivity analyses and publication bias

We recalculated each sPrevomi for all studies except the “omitted” one in turn, with the results of full meta-analyses named as sPrevall. The influence was evaluated by |sPrevomi - sPrevall|/sPrevall. All observed influences were less than 15% in our sensitivity analyses. Influences greater than 10% could be found in smoking among females (3.4% for all studies versus 3.1% for studies with higher potential for bias omitted) and males (63.2% vs. 55.4%), alcohol consumption among males (26.9% vs. 30.2%), overweight and obesity (22.4% vs. 25.8%), and HCV infection among IDUs (83.4% vs. 93.0%).

In our meta-analysis, substantial publication bias was significantly observed by the Egger test and/or the Begg test in estimating the overall sPrev of smoking (P-value for Egger test was 0.028, P-value for Begg test was 0.057), the overall sPrev of alcohol consumption (P Egger = 0.002, P Begg = 0.007), the overall sPrev of HBV infection (P Egger = 0.791, P Begg = 0.018), and the sPrev of HCV infection overall (P Egger = 0.141, P Begg = 0.014), in females (P Egger = 0.015, P Begg = 0.344), and in males (P Egger = 0.095, P Begg = 0.621).

Discussion

To our knowledge, this is the first comprehensive meta-analysis to report the summary prevalence of cancer risk factors among PLWHA in China. We found that Chinese PLWHA had higher prevalence of smoking, alcohol consumption, and virus infections including HBV, HCV, HPV, EBV and HHV8, but had lower prevalence of overweight and obesity when compared with the HIV-negative participants from the same studies and the Chinese general population.

For such two modifiable behavioral risk factors as tobacco and alcohol use, Chinese PLWHA showed high prevalence of tobacco smoking and alcohol drinking overall and especially among males. For example, the smoking prevalence is 53.3% for general Chinese men117, while the sPrev is even higher, reaching 63.2% for male PLWHA. Such elevated smoking prevalence in HIV infected people was also found in independent Chinese studies comparing with HIV-negative participants24, 35, and in a meta-analysis in western countries with lower smoking prevalence in general population118. The sPrevs also varied by group-specific analyses, with certain subgroups at higher risk in tobacco and alcohol use. The overall sPrev of alcohol drinking is close to the prevalence from Chinese general population (30.3% vs. 28.8%)119 while MSM showed slightly higher sPrev (35.7%) and IDUs showed significantly lower estimate (13.1%). As group I carcinogens defined by IARC, tobacco smoking and alcohol consumption are not only associated with a series of human cancers, but also with other important chronic diseases. Based on our summarized estimates, the high prevalence of tobacco and alcohol use especially among male PLWHA suggested the need for an effective and more targeted intervention in smoking and drinking behaviors as well as a close observation of their roles in HIV and cancer disease progression to reduce morbidity and to improve quality of life in this aging population with prolonged life expectancy.

The prevalence of cancer-related viral infections, including HBV, HCV, HPV, EBV and HHV8 infection, are significantly higher in PLWHA than in general Chinese population120–126, shown by the results from both meta-analyses and independent studies. By demographic group, 10.4–13.4% of PLWHA were HBsAg positive, and 29.1–47.1% were HCV antibody positive. Moreover, the highest prevalence of HBV (32.8%) and HCV (83.4%) infection were both found in IDUs, probably due to the shared transmission route with HIV. For female PLWHA, the sPrev of both any type of HPV (42.0%) and high-risk type of HPV (33.9%) from cervical samples were higher than the prevalence in Chinese general population aged 15–60 years122, 123. Moreover, we found that in anal samples collected from male PLWHA, the HPV prevalence was very high no matter whether they were MSM or not. These observations call attention since HPV infection is also a risk factor for anal cancer. The high seropositivity of EBV-IgG was close to the prevalence reported earlier in the Chinese general population (97.6%)125. Although the latter statistic might be limited to experimental conditions and in need of update, it is believed that EBV infection is very common among adults127. Moreover, several studies suggested higher detection rate of EBV DNA among HIV-infected subjects compared to HIV-negatives, suggesting potentially more active viral application in immune-suppressed population110, 128, 129. Last but not least, the overall seroprevalence of HHV8 infection was 14.9%, higher than the prevalence in the Chinese general population126, with the highest prevalence found in MSM (32.3%, 95% CI: 24.2–40.4%).

The overall prevalence of overweight and obesity among PLWHA was 22.4% (95% CI: 14.7–30.2%), lower than the prevalence of 42.6% in Chinese general population119. This is similar with the observations in western countries that the obesity prevalence was lower in HIV population118. We need to further observe the change in weights in PLWHA to have a better understanding and balance in control of both the HIV wasting syndrome and risk for obesity-related cancers and chronic diseases.

A meta-analysis was recently published on prevalence of risk factors for NADCs among PLWHA in western high-income countries118, which, consistent with our analysis, also observed higher prevalence of cancer risk factors among PLWHA than the general population. However, the prevalence level seems to be different between PLWHA in western countries and in China. Compared with PLWHA in western countries, PLWHA in China seemed to have higher prevalence in alcohol consumption (30.3% versus 24.0%), HCV (29.1–47.1% versus 23.0–28.0%) and HBV (10.4–13.4% versus 4.0–5.0%) infection, but lower prevalence in smoking (41.1% versus 54.0%), overweight and obesity (22.4% versus 53.0%) and HPV infection (cervical high-risk types: 33.9% versus 46.0%, anal high-risk types: 52.1% versus 66.0%). Thus, the spectrum of cancers among PLWHA in China might be different from that in western countries and further investigations and interventions might be targeted to different high-risk groups.

Substantial heterogeneity across subgroups was significantly observed with I 2 values larger than 90%. This was expected since heterogeneity could be found in almost all ‘overall’ groups, because we could not know the proportion of genders or behavioral characteristics in these study populations across publications. So was the case in ‘male’ groups, which could have different proportions of heterosexual male, MSM and IDU. Meanwhile, extensive heterogeneity still existed in specific demographic groups such as female, MSMs and IDUs. This could be attributable to many potential factors including study design, sampling method, geographical regions, study period, age, ethnicity, the definition or method of measurement for risk factors, differences in CD4+ and the length of time in ART, etc118, 130. Insufficient number of studies has limited our further stratification analysis. However, random effects models were used to minimize the impact of heterogeneity on precision and provided rational approximations of pooled prevalence estimates in our meta-analyses. Sources of heterogeneity should be further explored in the future with more research for specific subgroups.

Several limitations in our study should be noted. First, there might be some information bias since the methods for measuring these risk factors might vary across studies. Most (69.2%) studies did not give a clear definition of smoking habits (e.g., ever smoking: have smoked at least 100 cigarettes in life; Current smoking: smoked within the past 30 days) or did not have a precise measurement for smoking dose or frequency, while only 50% of studies presented the range of time (past 30 days or a day) for current alcohol drinking. Although we tried to extract information from reference studies using standardized definitions, misclassification might still exist since not every study gave a clear definition for each risk factor. Also, the testable HPV subtypes varied across studies, and therefore, underestimation of HPV prevalence might have occurred in studies with fewer testable HPV subtypes. Second, the representativeness of our meta-analyses is compromised since it covered 24 but not all 31 provinces or municipalities in China, underscoring the need for expanded research and surveillance efforts on cancer risk factors among PLWHA all over the country. Third, comparability was not considered across different sources of data due to time lags among studies. Some risk factors may present a temporal trend, some may not. For those with an increasing or decreasing trend, it may have compromised the comparison. Fourth, as prevalence estimates of cancer risk factors can vary with characteristics including age, lack of matching for age and other factors also limited the validity of the comparisons between PLWHA and the general population. However, consistent results were still observed when stratified roughly by age where possible (Supplemental Table S1). Fifth, substantial publication bias was observed according to rigorous publication bias tests, and might be attributable to several factors, including the presence of extensive heterogeneity, which we have discussed above131, 132. The association between behavioral changes such as smoking and alcohol consumption and severity of HIV patients might lead to underestimation of prevalence of cancer risk factors among PLWHA.

Despite these limitations, the present study provides a broad overview of prevalence of cancer risk factors among PLWHA in China. Both in comparison with the Chinese general population and with the uninfected comparison groups, the studies reach a wide consensus on higher prevalence of cancer risk factors among PLWHA. Along with the management of HIV individuals and ART program, interventions to reduce cancer risk factors should also be implemented in this population. Cancer prevention measures in PLWHA should include smoking cessation, HBV and HCV treatment, vaccination against HBV and HPV, annual cervical and anal Pap tests and cancer screening according to relevant guidelines4, 133–138. Furthermore, a better understanding of cancer risk attributable to specific factors in different HIV infected groups is needed to develop strategies in preventing cancer.

Methods

Search strategy

We searched for peer-reviewed research articles published in English and Chinese from the following databases: PubMed, China National Knowledge Infrastructure (CNKI), Wanfang Data and Chinese Scientific Journals Fulltext Database (CQVIP). Using the Medical Subject Headings (MeSH) terms ‘China’ or ‘Taiwan’ with ‘HIV,’ ‘HIV Infections’ or ‘Acquired Immunodeficiency Syndrome’, the search also included MeSH terms for the specific cancer risk factors as follows: smoking (‘Smoking’ or ‘Tobacco Products’ or ‘Tobacco’ or ‘Tobacco Use’), alcohol consumption (‘Alcohol Drinking’ or ‘Alcoholism’ or ‘Alcoholic Intoxication’), overweight or obesity (‘Overweight’ or ‘Obesity’ or ‘Body Mass Index’ or ‘Body Weight’), HBV (‘Hepatitis B’), HCV (‘Hepatitis C’), HPV (‘Papillomavirus Infections’ or ‘Papillomaviridae’), EBV (‘Epstein-Barr Virus Infections’), and HHV8 (‘Herpesvirus 8, Human’). We also searched those MeSH terms and their abbreviations of each risk factor under the condition of ‘all fields’ in each database. Due to the large number of studies published on HBV and HCV infections among PLWHA in China, we restricted published data from January 1, 2013 to February 29, 2016 in order to use the latest published prevalence of HBV and HCV infections. For other cancer risk factors, data published from database inception to February 29, 2016 were included in the analyses.

Study selection

One author (Jin Z-Y) identified relevant articles, excluding those with unrelated titles and abstracts before obtaining full-text references. Two independent authors (Jin Z-Y and Liu X) conducted full-text review. Cross-sectional, case-control, prospective or retrospective cohort and intervention trial studies were included. Studies with sample size greater than 100 were considered to have a sufficiently stable prevalence estimate to be qualified for inclusion in the study. Studies lacking original data for the numerator or denominator of the prevalence estimate were excluded. If information from one study had been published for more than once, we chose the publication with more detailed information such as prevalence estimates stratified by specific demographic groups, studies with a larger sample size, or those most recently updated.

Data extraction

Data were extracted and organized using Microsoft Excel (Version 2010, Microsoft Corp., Redmond, WA, USA). For each risk factor, the number of PLWHA with known status was recorded as the denominator and the number of positive subjects was recorded as the numerator. The participants with unknown status were excluded from the calculation. If an article contained more than one of the mentioned risk factors, we kept all of them. If the PLWHA were stratified into different groups such as females, males, men who have sex with men (MSM) or injection drug users (IDU), we included data from each subgroup for prevalence estimate. The definitions of smoking and drinking status including “ever/never”, “former/current” were based on the descriptions in the original studies when a clear definition is not available. We also extracted prevalence estimates for HIV uninfected comparison groups if data were available from the same study. Other information including the first author’s name, year of publication, study site, original study design, sampling frame, calendar years of research, major information for participants including the mean or median age, percentage of males, MSM, IDU, treatment with antiretroviral therapy (ART), and the measurement of risk factors were also extracted.

We also extracted prevalence estimates for cancer risk factors among the general population from the most representative surveys. For tobacco smoking, alcohol consumption, overweight and obesity, prevalence estimates were extracted from a national survey, which was carried out in Chinese adults (aged 18 years and over with the median age in the range from 45 to 49 years) at 162 disease surveillance points (DSPs) from 31 provinces in 2010117, 119. For HBV and HCV infections, prevalence estimates were obtained from a national serosurvey in 2006, of which participants were local residents aged 1–59 years (the median age was in the range from 15 to 59 years for HBV and in the range from 15 to 20 years for HCV) living in 31 provinces with 160 DSPs120, 121. For HPV infection, prevalence estimates were acquired from a nationwide investigation of female subjects aged 15–60 years in 37 Chinese cities in 2012, a review (age ranging from 15 to 59 years) and a population-based study among men aged 25–65 years (the median age was 43 years)122–124. For EBV infection, the prevalence estimate was extracted from a 1989 study by Liu125. For HHV8 infection, the prevalence estimate was from a systematic review and meta-analysis published in 2012126.

Statistical analysis

PLWHA were stratified into overall, female, male, MSM, and IDU demographic groups when such subgroups were available. The ‘Overall’ group represents PLWHA not restricted by gender or HIV transmission category (i.e., did not include a research on specific group by gender or HIV transmission category). Similarly, the ‘male’ group implies that PLWHA were not restricted by HIV transmission category. A meta-analysis of prevalence was performed for each risk factor by demographic group if there was more than one study included. Heterogeneity tests across subgroups were examined by Q-test (P < 0.1 refers to statistically significant heterogeneity) and I 2 values139, 140. Random effects models were used because of significant heterogeneity across subgroups141, 142. DerSimonian and Laird method (D-L) was applied to estimate the summary prevalence estimate (sPrev) and its 95% confidence interval (CI) for each risk factor143. If only one study was presented for the specific subgroup, we showed the single prevalence and its 95% CI. In sensitivity analyses, each sPrev was recalculated excluding studies evaluated as having higher potential for bias. Lastly, we performed a publication bias test when the meta-analysis included more than 10 studies144, 145. Publication bias was assessed through the Egger weighted regression and Begg rank correlation method (P < 0.1 represents statistically significant publication bias)146, 147. All the analyses were conducted by Stata 12.0 (Stata Corp LP).

Electronic supplementary material

Supplementary Information (335KB, pdf)

Acknowledgements

This study was funded by Natural Science Foundation of China (NSFC) (grant no: 81361120385; 81373062) and Shanghai Municipal Health and Family Planning Commission (grant no: GWTD2015S05; 15GWZK0101; 15GWZK0801).

Author Contributions

Z.Y.J. and N.H. designed the study. Z.Y.J. performed the statistical analysis. Z.Y.J. and X.L. made the data extraction and wrote the first draft of the manuscript. All authors contributed to the interpretation of the results and to the final version of the manuscript to be published.

Competing Interests

The authors declare that they have no competing interests.

Footnotes

Zi-Yi Jin and Xing Liu contributed equally to this work.

Electronic supplementary material

Supplementary information accompanies this paper at doi:10.1038/s41598-017-05138-x

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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