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Prevalence of non-HIV cancer risk factors in persons living with HIV/AIDS

a meta-analysis

Park, Lesley S.; Hernández-Ramírez, Raúl U.; Silverberg, Michael J.; Crothers, Kristina; Dubrow, Robert

doi: 10.1097/QAD.0000000000000922
Epidemiology and Social
Free
SDC

Objective: The burden of cancer among persons living with HIV/AIDS (PLWHA) is substantial and increasing. We assessed the prevalence of modifiable cancer risk factors among adult PLWHA in Western high-income countries since 2000.

Design: Meta-analysis.

Methods: We searched PubMed to identify articles published in 2011–2013 reporting prevalence of smoking, alcohol consumption, overweight/obesity, and infection with human papillomavirus (HPV), hepatitis C virus (HCV) and hepatitis B virus (HBV) among PLWHA. We conducted random effects meta-analyses of prevalence for each risk factor, including estimation of overall, sex-specific, and HIV-transmission-group-specific prevalence. We compared prevalence in PLWHA with published prevalence estimates in US adults.

Results: The meta-analysis included 113 publications. Overall summary prevalence estimates were current smoking, 54% [95% confidence interval (CI) 49–59%] versus 20–23% in US adults; cervical high-risk HPV infection, 46% (95% CI 34–58%) versus 29% in US females; oral high-risk HPV infection, 16% (95% CI 10–23%) versus 4% in US adults; anal high-risk HPV infection (men who have sex with men), 68% (95% CI 57–79%), with no comparison estimate available; chronic HCV infection, 26% (95% CI 21–30%) versus 0.9% in US adults; and HBV infection, 5% (95% CI 4–5%) versus 0.3% in US adults. Overweight/obesity prevalence (53%; 95% CI 46–59%) was below that of US adults (68%). Meta-analysis of alcohol consumption prevalence was impeded by varying assessment methods. Overall, we observed considerable study heterogeneity in prevalence estimates.

Conclusion: Prevalence of smoking and oncogenic virus infections continues to be extraordinarily high among PLWHA, indicating a vital need for risk factor reduction efforts.

aDivision of Endocrinology, Gerontology, and Metabolism, Department of Medicine and Division of Epidemiology, Department of Health Policy and Research, Stanford University School of Medicine, Stanford, California

bDepartment of Chronic Disease Epidemiology, Yale School of Public Health, Yale School of Medicine, New Haven, Connecticut

cDivision of Research, Kaiser Permanente, Oakland, California

dDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Washington School of Medicine, Seattle, Washington, USA.

*Lesley S. Park and Raúl U. Hernández-Ramírez contributed equally to this article.

Correspondence to Lesley S. Park, PhD, MPH, Stanford University Medical Center, 300 Pasteur Drive Room S025, Stanford, CA 94305-5103, USA. Tel: +1 703 835 1987; fax: +1 650 725 7085; e-mail: lesley.park@stanford.edu

Received 9 March, 2015

Revised 1 September, 2015

Accepted 28 September, 2015

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Website (http://www.AIDSonline.com).

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Introduction

Cancer is a leading cause of death among persons living with HIV/AIDS (PLWHA) in the United States and Europe [1–4]. Furthermore, incidence of both AIDS-defining and specific non-AIDS-defining cancers is elevated in PLWHA compared with the general population [5–13] (Table 1). Both impaired immune function and high prevalence of modifiable non-HIV cancer risk factors contribute to this substantial cancer burden [6,14–17]. In this meta-analysis, we estimated the prevalence of cancer risk factors [smoking, alcohol consumption, overweight/obesity, and infection with human papillomavirus (HPV), hepatitis C virus (HCV), and hepatitis B virus (HBV); Table 1] [18–37] among adult PLWHA in Western high-income countries (United States, Canada, Western Europe, Australia) in recent years (2000–2013) from cohort, cross-sectional, case-control, and experimental studies and compared these prevalence estimates with those among adults in the United States; we also compared prevalence estimates between PLWHA and uninfected comparison groups from the same study when such comparison groups were available.

Table 1

Table 1

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Methods

Study selection

We searched PubMed/MEDLINE to identify relevant references published in English during 2011–2013 and available in PubMed as of 17 April 2014. We cross-referenced Medical Subject Heading (MeSH) terms for each risk factor (Table 2) with MeSH terms ‘HIV infections’ or ‘acquired immunodeficiency syndrome’ and ‘adult.’ One author (R.U.H.R.) screened abstracts to remove clearly ineligible studies. Two authors (R.U.H.R. and R.D.) then independently performed full-text review of the remaining articles for eligibility, with discrepancies resolved by discussion.

Table 2

Table 2

We restricted prevalence estimates to those based on data collected during 2000–2013 [to reflect experience in the modern antiretroviral therapy (ART) era] with at least 100 adult PLWHA living in the United States, Canada, Western Europe, or Australia. We included prospective or retrospective cohort, cross-sectional, case-control, and experimental studies. We excluded publications for which severe selection bias could be anticipated (e.g., estimation of HBV prevalence among hepatocellular carcinoma patients). When eligibility was uncertain we queried authors for clarification.

If the prevalence of a risk factor from a given study was reported in more than one publication, in general we used the following hierarchy to decide which publication to include: availability of a comparison prevalence estimate from uninfected persons; availability of prevalence estimates by sex or high-risk behavior (i.e. MSM and IDU); and sample size. If more than one publication from a given study each presented unique information (e.g., sex-specific prevalence estimates in one publication, and an overall, unstratified prevalence estimate with a larger overall sample size in another publication), each publication contributed to the relevant meta-analysis (e.g., female, male, overall).

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Data extraction

Three authors conducted data extraction (R.U.H.R., R.D., and L.S.P.), with independent extraction by a pair of these authors for each data element and with discrepancies resolved by discussion. For prevalence estimates, our denominator was the number of persons with known status for the risk factor. If an article presented a prevalence estimate that included unknowns in the denominator, we re-calculated the prevalence estimate, excluding unknowns. We examined eligible publications identified from each specific risk factor search for the presence of prevalence estimates for other risk factors and included these estimates in our analyses (e.g. if a publication identified in the smoking search, but not in the HCV search, reported HCV prevalence, we included the HCV prevalence estimate).

We extracted prevalence estimates for study samples unrestricted by sex or HIV transmission category (henceforth called the ‘overall’ group), as well as estimates for the following demographic groups: female, male (unrestricted by HIV transmission category), MSM, and IDU. We extracted prevalence estimates for internal uninfected comparison groups when available. For eligible publications (meaning that the publication included at least 100 adult PLWHA), we imposed no sample size restriction for demographic sub-groups or uninfected comparison groups.

We extracted data on country(ies), study design, prevalence estimate calendar year(s), sampling frame (e.g., clinic/hospital-based; geography-based), sex, age, race, HIV risk group, CD4+ cell count, ART, and method of measurement/definition of risk factors. For each risk factor reported in each publication, we assessed four indicators of potential for bias: whether or not (1) the aim of the study was to measure the prevalence of the risk factor or the risk factor was a predictor, outcome, or covariate in the study; (2) exclusion criteria might cause selection bias; (3) patients were excluded due to missing information on the risk factor; and (4) there were included patients with missing information on the risk factor. For the latter three indicators, we calculated the proportion excluded/missing, if known. We then classified a prevalence estimate to have higher potential for bias if the study did not aim to measure the risk factor and the risk factor was not a predictor, outcome, or covariate (indicator 1); or if the total of the proportions excluded/missing across indicators 2–4 was known to be more than 10%, or if any of these proportions was unknown.

We extracted prevalence estimates for the adult civilian noninstitutionalized population of the United States (henceforth called ‘US adults’) from the National Health Interview Survey (NHIS) [38,39], National Health and Nutrition Examination Survey (NHANES) [40–49], and Behavioral Risk Factor Surveillance System (BRFSS) [50–52], each during a calendar period between 2000 and 2010 for which data were reported. In NHANES, HIV prevalence was 0.5% in the age group 18–49 years during 2003–2006 [53]; there were no data on HIV prevalence from NHIS or BRFSS.

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Statistical analysis

For each risk factor, we conducted a meta-analysis of prevalence for each demographic group with at least two individual study prevalence estimates. The summary prevalence estimate (sPrev) for each group was based solely on individual study prevalence estimates for that group. Thus, sPrev for ‘overall’ did not include individual study prevalence estimates from studies restricted according to sex or HIV transmission category. Using the Stata 12.1 metaprop module [54], we calculated sPrev and 95% confidence intervals (CI), as well as I2 values and Q statistic P-values to assess study heterogeneity [55]. To stabilize variances, we transformed individual study prevalence estimates using the Freeman–Tukey double arcsine transformation [56,57]. We used random-effects models [58] because we expected substantial study heterogeneity. Therefore, each sPrev estimate should be interpreted as an average prevalence across studies with true differences in target population prevalence, not a common prevalence across studies with the same target population prevalence [59]. If only one study reported a particular risk factor prevalence for a given group, we presented that individual estimate and Wilson score 95% CI. In sensitivity analyses, we calculated sPrev estimates excluding studies classified as having higher potential for bias. Finally, we assessed bias in study selection for each risk factor/group with at least 10 individual studies [60] through visual inspection of funnel plots and the Egger [61] and Begg [62] tests.

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Results

We identified 1573 unique references from the PubMed queries. After initial review of abstracts, we performed full-text review of 717 articles and found 113 eligible publications [63–175] (Table 2). Supplemental Table S1, http://links.lww.com/QAD/A858 presents characteristics of each eligible publication. The study design distribution was prospective cohort, 49; retrospective cohort, 10; cross-sectional, 46; case-control, 2; and experimental, 6. Most (87%) study samples were clinic/hospital-based; only one study sample was population-based. Among the 113 publications, the median number of patients was 388 [interquartile range (IQR), 192–905]. The geographic location distribution was United States, 59; Western Europe, 46; Canada, 4; and Australia, 4. Of 104 publications that reported sex distribution, the median percentage male was 75.4% (IQR, 65.0–85.4%). Of 91 publications that reported mean or median age, the median of the mean or median was 44.0 years (IQR, 41.6–46.5). Of 63 publications that reported mean or median CD4+ cell count, the median of the mean or median was 487.0 (IQR, 426.0–513.3). Of 75 publications that reported percentage on ART, the median was 84.0% (IQR, 73.0–93.9%). Fewer than 60% of publications reported race or HIV transmission group.

Table 3 presents results for each risk-factor-specific, group-specific meta-analysis. Forest plots for key meta-analyses are presented in Supplemental Figure S1, http://links.lww.com/QAD/A857. Individual study prevalence estimates are presented in Supplemental Tables S2–S7, http://links.lww.com/QAD/A857.

Table 3

Table 3

Table 3

Table 3

Table 3

Table 3

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Smoking

Forty-five publications reported smoking prevalence [63–107], but most (84%) presented frequencies for ‘current,’ ‘former,’ and/or ‘ever’ smoker without precisely defining these terms (e.g., ever smoker: at least 100 cigarettes lifetime) (Supplemental Table S2, http://links.lww.com/QAD/A857). Overall current smoking sPrev was 54% (95% CI 49–59%; I2 = 99%), about 2.5 times the prevalence among US adults (Table 3) [39,41,51]. The majority of studies with uninfected comparison groups found higher smoking prevalence in PLWHA (Table 4). The highest current smoking sPrev was among IDU (74%; 95% CI 60–85%; I2 = 89%) (Table 3).

Table 4

Table 4

Table 4

Table 4

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Alcohol consumption

Twenty-six publications reported hazardous alcohol consumption prevalence [63,65,66,71,73,76,84,91,94,101,103,104,108–121]. Meta-analysis of hazardous alcohol consumption prevalence was hampered by the wide range of definitions over varying time spans (e.g., past 30 days, past 6 months) (Supplemental Table S3, http://links.lww.com/QAD/A857). Definition-specific meta-analyses yielded small numbers of studies and sPrev estimates with wide 95% CIs. We therefore chose not to stratify but to estimate sPrev of ‘hazardous alcohol consumption’ regardless of definition. Overall hazardous alcohol consumption sPrev was 24% (95% CI 15–33%; I2 = 100%) (Table 3) compared with 5–15% prevalence among US adults, depending on the definition [39,42,52]. Hazardous alcohol consumption prevalence did not meaningfully differ between PLWHA and uninfected comparison groups (Table 4).

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Overweight/obesity

Eighteen publications reported overweight (BMI: 25.0–29.9 kg/m2) and/or obesity (BMI: ≥30.0 kg/m2) prevalence [68,69,72,73,75,77,78,90,101,104,105,118,122–127]. In all publications, weight and height were directly measured; we therefore restricted our comparison with US adults to NHANES, the only nationally representative survey with directly measured weight and height. Overall overweight sPrev (32%; 95% CI 29–35%; I2 = 88%) was similar to the prevalence in US adults (34%), but obesity sPrev (17%; 95% CI 14–21%; I2 = 98%) was lower than the prevalence in US adults (34%) [43], as was overweight/obesity sPrev (53%; 95% CI 46–59%; I2 = 98%, versus 68%) (Table 3). Prevalence of overweight/obesity was consistently higher in uninfected comparison groups compared with PLWHA (Table 4).

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Human papillomavirus infection

Eighteen publications reported HPV infection prevalence [91–93,95,96,98,128–139]. The number of HPV types tested varied from 15 to 47; the number of high-risk HPV types tested varied from 11 to 22 (Supplemental Table S5, http://links.lww.com/QAD/A857). Cervical HPV sPrev was 64% (95% CI 25–95%; I2 = 99%), compared with 43% prevalence among US females [44] (Table 3); cervical high-risk-type HPV sPrev was 46% (95% CI 34–58%; I2 = 96%) compared with 29% prevalence in US females [45]. Overall oral HPV sPrev was 34% (95% CI 26–42%; I2 = 68%), about five times the prevalence in US adults (7%) [46]; high-risk-type oral HPV sPrev was 16% (95% CI 10–23%; I2 = 71%), compared with 4% prevalence in US adults [46,47] (Table 3). Most studies of anal HPV prevalence were among MSM (sPrev = 91%; 95% CI 87–95%; I2 = 93% for any type and sPrev = 68%; 95% CI 57–79%; I2 = 98% for high-risk types) (Table 3); prevalence was similarly high in all groups. PLWHA generally had significantly higher oral and anal HPV prevalence than uninfected comparison groups (Table 4).

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Hepatitis C virus infection

Sixty-three studies reported HCV infection prevalence [63–65,69,72,73,75,77,79–84,88,90,102–104,107,109,110,114,117–119,123,127,133,140–173]. We calculated sPrev for HCV-exposed, defined in 69% of individual studies as positive by HCV antibody test, and for chronic HCV infection, defined in 80% of studies as positive by HCV antibody and HCV RNA tests (see Supplemental Table S6, http://links.lww.com/QAD/A857 for all definitions). Overall HCV-exposed sPrev was 28% (95% CI 23–33%; I2 = 100%), compared with just 1.3% prevalence in US adults [48] (Table 3). Overall chronic HCV infection sPrev (26%; 95% CI 21–30%; I2 = 99%) was also much higher than the prevalence in US adults (0.9%) [48]. HCV sPrev was relatively low among MSM (HCV-exposed sPrev = 8%; 95% CI 6–11%; I2 = 93% and chronic HCV sPrev = 5%; 95% CI 2–10%; I2 = 94%), but was extremely high among IDU (HCV-exposed sPrev = 80%; 95% CI 68–89%; I2 = 97% and chronic HCV sPrev = 71%; 95% CI 55–86%; I2 = 94%). HCV prevalence was consistently higher in PLWHA than uninfected comparison groups (Table 4).

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Hepatitis B virus infection

Twenty-six publications reported HBV infection prevalence [63,65,69,71,79–81,89,102,106,110,123,141,144,147,150,151,154,157,159,162,165,170,172,174,175]. HBV infection was defined in 72% of individual studies as positive by HBV surface antigen test (see Supplemental Table S7, http://links.lww.com/QAD/A857 for all definitions). Overall HBV sPrev was 5% (95% CI 4–5%; I2 = 87%) compared with just 0.3% in US adults [49] (Table 3).

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Potential for bias

Across all risk factors summed over all publications, we classified 39% as having higher potential for bias, including smoking, 30%; alcohol, 42%; overweight/obesity, 37%; HPV, 49%; HCV, 38%; and HBV, 42%. Furthermore, across all risk factors summed over all publications we found that the study did not aim to measure the risk factor and the risk factor was not a predictor, outcome, or covariate in 8% of cases; there were exclusion criteria that might cause selection bias in 17%; there were excluded patients due to missing information on the risk factor in 8%; and there were included patients with missing information on the risk factor in 33% (only 13% with more than 10% missing information).

In sensitivity analyses excluding studies with higher potential for bias, the change in sPrev was both meaningful (i.e., |sPrevexc – sPrevall|/sPrevall>15%) and statistically significant (i.e., P-value for difference between sPrevlowerpotentialforbias and sPrevhigherpotentialforbias<0.05) for former smoker among females (15% for all studies, 18% for studies with higher potential for bias excluded); hazardous alcohol consumption among females (15 versus 18%) and among MSM (25 versus 32%); overweight/obesity among females (64 versus 44%) and males (55 versus 44%); cervical HPV, any type (64 versus 44%); and oral HPV, any type, among males (27 versus 40%).

We observed some funnel plot asymmetry (Supplemental Figure S2, http://links.lww.com/QAD/A857) for hazardous alcohol consumption (overall), with a deficit of smaller studies with higher prevalence; and for overweight/obesity (overall) and chronic HCV infection (overall), each with a deficit of smaller studies with lower prevalence. The Egger test for chronic HCV infection was the only statistically significant test for bias in study selection (P = 0.008).

The PLWHA and uninfected comparison groups in Table 4 generally had similar demographic characteristics. Exceptions that might have influenced comparisons included Morano et al. (2013) for alcohol and HCV (IDU: 43% in PLWHA, 14% in uninfected; mean age: 43.8 years in PLWHA; 35.9 years in uninfected); Raymond et al. (2012) for HCV (IDU: 32% in PLWHA, 12% in uninfected); and Wieland et al. (2011) for smoking (100% MSM in PLWHA; uninfected group was males, not restricted to MSM).

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Discussion

To our knowledge, this is the first comprehensive meta-analysis of the prevalence of cancer risk factors among PLWHA. There is one meta-analysis published on cervical HPV prevalence in PLWHA [176]. Our analyses quantified the continuing high prevalence of smoking and HPV, HCV, and HBV infections among PLWHA. Prevalence of overweight/obesity was lower than in US adults. Assessment of alcohol consumption was hampered by variability in assessment methods.

Overall, about half of PLWHA were current smokers, a prevalence 2.5 times higher than in US adults. This high prevalence often reflected the high prevalence in demographically similar uninfected persons, where, for example, IDU smoking prevalence was high, regardless of HIV status (Table 4). The prevalence of high-risk HPV infections remained distressingly high, with cervical sPrev of 46% (1.6 times the prevalence in US adults); oral prevalence of 10–16% (2.5–4 times the prevalence in US adults); and anal sPrev around 70% (no nationally representative prevalence estimate in US adults available). More than one quarter of PLWHA were infected with HCV (12–40 times higher than in US adults) and about 5% were infected with HBV (10–25 times higher than in US adults). Furthermore, 70–80% of IDU were infected with HCV, accounting for the fact that in the United States, about half of liver cancer cases among PLWHA occur in IDU [177], who constitute 22% of PLWHA [178].

With the introduction of ART and greater control of HIV wasting syndrome, the prevalence of overweight/obesity among PLWHA has increased [179,180]. However, our results showed that PLWHA have not yet reached the overweight/obesity prevalence of US adults (53 versus 68%) or of demographically similar uninfected persons. Finally, results for alcohol consumption were difficult to interpret due to considerable heterogeneity in measurement. Although the overall prevalence of hazardous alcohol consumption (regardless of definition) was 24%, roughly 1.5–4 times higher than in US adults, direct comparisons of PLWHA with demographically similar uninfected comparison groups, using the same definition, showed no differences.

We observed considerable study heterogeneity in prevalence estimates, with the majority of I2 values greater than 90%. This result was not surprising in our ‘overall’ group, which could vary across studies by sex and MSM and IDU status distribution, or in our male group, which could vary by MSM and IDU status distribution. However, heterogeneity was generally high even within our more narrowly defined demographic groups (females, MSM, IDU). Potential sources of heterogeneity included differences in study design, geographic location, sex, age, race/ethnicity, prevalence estimate calendar year(s), and risk factor measurement method/definition. Differences in CD4+ cell count [181] or number of HPV types tested could be sources of heterogeneity for HPV prevalence, and differences in amount of time on ART could be a source of heterogeneity for overweight/obesity [182].

Our goal was to provide a broad overview of cancer risk factor prevalence in PLWHA. Our random effects models, which appropriately penalized the precision of our estimates in the context of high study heterogeneity, provided reasonable approximations of average prevalence estimates. Future research focused on identifying risk-factor-specific sources of heterogeneity could identify high prevalence sub-groups to target for risk factor reduction efforts.

Our study had limitations. First, the individual study PLWHA samples were primarily clinic/hospital-based samples that may not have been representative of the overall PLWHA population. However, samples from a broad range of well-established cohorts and HIV treatment centers were represented in this meta-analysis (Supplemental Table S1, http://links.lww.com/QAD/A858), suggesting that our results provide robust estimates of cancer risk factor prevalence among PLWHA receiving HIV care.

Second, risk factors often are included in descriptions of baseline characteristics or are used as covariates, without being central to the study and therefore without being indexed in PubMed. This phenomenon is illustrated by the fact that 42% of our publications reported the prevalence of one or more risk factors that were not identified in the searches for those risk factors. Recognizing that there most likely were other articles that reported the prevalence of one or more risk factors but were not identified in any of the searches, we aimed for a robust representation of publications indexed in PubMed, with the limitation that we would not identify all of them. Inspection of funnel plots provided limited evidence for bias in study selection for hazardous alcohol consumption, overweight/obesity, and chronic HCV infection; however, the only significant statistical test was the Egger test for chronic HCV infection.

Third, although our sensitivity analyses suggested that our sPrev estimates were not heavily distorted by bias, because most studies did not report participation rates, we were unable to include participation rates in our potential for bias measure.

Fourth, in our extraction of prevalence estimates we excluded unknowns from the denominator. This approach is valid if the prevalence of the risk factor is similar among the knowns and unknowns, but otherwise is biased. However, the alternative of including the unknowns in the denominator always underestimates prevalence unless the risk factor is absent in all of the unknowns. Fortunately, only 13% of risk factor-publication combinations had more than 10% of patients with missing values.

Finally, we need to consider the possibility of information bias in the individual studies. We know little about the smoking definitions. Determination of overweight/obesity should be accurate because weight and height were directly measured. There was variability in the number of HPV types tested; studies testing for fewer types might underestimate prevalence. Similarly, studies with narrower definitions of HCV or HBV infection (e.g. positive by HBV DNA test versus positive by DNA test or HBV surface antigen test) might underestimate prevalence.

Despite these potential sources of bias, the general consistency between comparisons of our sPrev estimates among PLWHA with prevalence estimates among US adults and comparisons of prevalence estimates among PLWHA and uninfected comparison groups from the same study (Table 4) provides validation for our essential findings.

Interventions to reduce the high prevalence of smoking and oncogenic virus infections among PLWHA can play a critical role in reducing the high cancer burden. Specific interventions include smoking cessation [183,184], HPV [185–188] and HBV vaccination [189,190], and HCV [191,192] and HBV [189,193] treatment. Research is needed to develop effective, tailored smoking cessation interventions, including for sub-populations (e.g., IDU, MSM), to effectively address the high prevalence of co-occurring risk factors, to identify potential adverse interactions between pharmacologic interventions and ART [184,189,194–196], and to overcome impaired immunogenicity [189,190,197,198] of or nonadherence [199] to vaccine regimens. Finally, epidemiologic studies to estimate the population attributable risk percent for various cancer types due to cancer risk factors among PLWHA would help guide both research and practice.

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Acknowledgements

Authors’ contributions: L.S.P. and R.D. designed the study and wrote the first draft of the manuscript. R.U.H.R. conducted the literature review and the initial review of abstracts. R.U.H.R. and R.D. performed the full-text article reviews for eligibility. R.U.H.R., R.D., and L.S.P. performed data extraction. R.U.H.R. performed the meta-analysis with supervision from L.S.P. and R.D. All authors contributed to the overall intellectual content of the manuscript, read and edited subsequent drafts, and approved the final version.

Sources of funding: This work was funded by grants from the National Institute of Mental Health (5T32-MH020031, P30-MH062294), the National Institute on Alcohol Abuse and Alcoholism (1U01-AA020790), the National Cancer Institute (R01-CA165937, F31-CA180775, R01-CA173754), the National Institute of Allergy and Infectious Diseases (K01-AI071725), and the National Institute of Diabetes and Digestive and Kidney Diseases (3T32-DK007217).

Disclaimers: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Department of Veterans Affairs.

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Conflicts of interest

No conflicts of interest declared.

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Keywords:

acquired immunodeficiency syndrome; cancer prevention; cancer risk factors; high-income countries; HIV infections; neoplasms

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