Molecular epidemiology of human papillomavirus genotypes among HIV-positive and HIV-negative women with cervical cancer in Nigeria

Molecular epidemiology of human papillomavirus genotypes among HIV-positive and HIV-negative women with cervical cancer in Nigeria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Molecular epidemiology of human papillomavirus genotypes among HIV-positive and HIV-negative women with cervical cancer in Nigeria Chuwang J. Nyam, Jonah Musa, Brian T. Joyce, Kyeezu Kim, Jun Wang, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5160011/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The prevalence of invasive cervical cancer (ICC) is high in Nigeria, with over 12,000 new cases and 8,000 deaths annually. Differences in diagnostic methods for human papillomavirus (HPV) genotypes have generated varied prevalence rates across populations. Methods We examined the prevalence and distribution of HPV genotypes among HIV-negative women with ICC, HIV-positive women with ICC, and HIV-positive women without ICC. We utilized baseline data and DNA samples from cervical tissue obtained from a prospective cohort study between March 2018 and September 2022. High-throughput next-generation amplicon sequencing of the HPV L-1 gene was used to identify and classify the HPV genotypes. Modified Poisson regression models estimated associations between HIV and HPV status, adjusting for other variables of interest. Results Among 286 women tested for HPV, 48.9% were HIV-negative with ICC, 17.2% were HIV-positive with ICC, and 33.9% were HIV-positive without ICC. The prevalence of high-risk HPV (HR-HPV) was 77.6% among HIV-positive women with ICC, whereas it was 60.0% among HIV-negative women with ICC (p < 0.001). HIV-positive women more frequently had multiple HPV genotypes (8.2% versus 1.4% among HIV-negative women with ICC and 2.1% among HIV-negative women without ICC) (p < 0.001). HPV16 or HPV18 accounted for 29.4% of all HPV cases. The most frequently detected HR-HPV genotypes included HPV16 (20.6%), HPV18 (8.7%), HPV45 (4.2%), and HPV35 (2.8%). In multivariable models adjusted for age, BMI, parity, and study site, HIV-positive women had an increased risk of HR-HPV (aPRR = 1.46, 95% CI: 1.17, 1.82) and any HPV infection (aPRR = 2.29, 95% CI: 1.83, 2.74) compared to HIV-negative women. Conclusion Our NGS approach to HPV typing in Nigerian women, including those with cervical cancer and HIV, revealed the presence of HPV types not covered by the Gardasil-4 vaccine. This highlights the need for broader coverage of vaccines to protect against most HR-HPV types, irrespective of HIV status. ICC HIV HPV Genotypes Nigeria Figures Figure 1 Introduction Persistent infection with a high-risk oncogenic human papillomavirus (HR-HPV) genotype has been well established as the cause of nearly all cervical cancer cases (95%), although infection alone is not sufficient [ 1 – 3 ]. Invasive cervical cancer (ICC) is highly preventable through vaccination and can be cured with early detection and effective management of precancer conditions [ 4 ]. It ranks fourth among the most common cancers in women globally, with an estimated 660,000 women diagnosed annually. An estimated 350,000 women died from the disease in 2022, and nearly 90% of these deaths occurred in low- and middle-income countries (LMICs) [ 4 ]. This is an enormous burden to Sub-Saharan African countries, especially Nigeria, which is due to limited access to public health services and inadequate implementation of screening and treatment for the disease [ 5 ]. Another contributor to the high prevalence and incidence of cervical cancer is the large number of HIV-positive women, with Nigeria ranking fourth in the world in terms of HIV burden [ 6 – 8 ]. Nigeria lacks a comprehensive national screening program for cervical cancer, and the existing screening services are mostly opportunistic [ 9 ]. As a result, fewer than 9% of eligible Nigerian women have accessed these services, which are sparsely distributed and rely primarily on opportunistic methods [ 9 ]. The most commonly used methods for cervical cancer screening (CCS) are conventional Pap cytology (Pap test or Pap smear) and visual inspection with acetic acid (VIA) [ 10 , 11 ]. However, several studies [ 12 – 14 ] have reported the use of HPV-DNA detection as a screening method. In Nigeria, these studies revealed variations in the prevalence rates of HPV16 and HPV18 among HIV-positive women [ 12 – 14 ]. The varying prevalence of HPV16 and HPV18 may be due to different sample types, sampling bias, and HPV detection methods. For example, Emeribe et al. conducted a systematic review of 18 epidemiological studies that investigated the prevalence of HPV infection and genotypes among Nigerian women over a decade from 1999 to 2019 [ 12 ]. Based on studies using hybrid capture 2 technology (HC2) or specific primers (GP5+/6+) polymerase chain reaction (PCR) methods, they reported a pooled HPV prevalence of 20.6%, and the most common HR-HPV genotypes in circulation were HPV31 (70.8%), HPV35 (69.9%), and HPV16 (52.9%). Another recent systematic review and meta-analysis conducted by Kabuga et al. in Nigerian women revealed that among HIV-positive women, the prevalence of HPV was 37% (95% CI: 25–50%), and the most prevalent genotypes detected were HPV16, 18, 31, 35, 52, 58, and 45 [ 13 ]. Our recently published study by Musa et al. employed the Anyplex™ II HPV28 PCR detection method. Our findings indicated that 45.2% of the 138 women tested had either HPV 16 or 18 along with another HR-HPV type, whereas 12.9% had HPV 35 along with other HR-HPV types [ 14 ]. Hence, to reconcile these inconsistencies and accurately estimate the prevalence and distribution of HPV types, adopting a robust typing methodology such as next-generation sequencing (NGS) is imperative. NGS is more comprehensive in detecting HPV types, overcomes the limitations associated with PCR-based techniques, and offers possibilities for accurately identifying circulating HPV-specific types in Nigeria [ 15 ]. Two studies identified from our search documented the use of targeted PCR amplification and sequencing for HPV genotype detection among women in Nigeria. Nejo et al. utilized PCR with consensus primers targeting viral E6/E7 genes, followed by Sanger sequencing for genotyping. These findings revealed that HPV31 (32.8%), HPV35 (17.2%), and HPV16 (15.5%) were the predominant strains [ 16 ]. While effective in many instances, Sanger sequencing of PCR amplicons has limitations in detecting polyviral infections with multiple genotypes. Next-generation sequencing workflows offer the advantages of sensitive detection of multiple viral genotypes and simultaneous sequencing of large samples. Notably, the only study that utilized both NGS and type-specific PCR for HPV typing, conducted by Dom-Chima N et al. [ 17 ], included 90 cervical samples. DNA from these samples was analyzed via next-generation sequencing (NGS) and type-specific PCR (tsPCR). The top five prevalent types found in their study were HPV71 (17%), HPV82 (15%), HPV16 (16%), HPV6 (10%), and HPV20 (7%) [ 17 ]. The difference in HPV prevalence between the two studies may be due to variations in the source population and HPV detection methods. Their study included women with and without cytological abnormalities or symptoms of STIs who attended routine clinics during the study period [ 16 , 17 ]. Accurately identifying the spectrum of HPV types prevalent in areas with high cervical cancer rates and HIV, such as Nigeria, is crucial. Currently, several HPV vaccines, including Cervarix-2, Gardasil-4, and Gardasil-9, which only protect against the HPV6, 11, 16, 18, 31, 33, 45, 52, and 58 types, are available [ 18 ]. As of October 2023, the Nigerian government added Gardasil-4 to its routine immunization program to protect 7.7 million girls aged 9–14 against HPV6, 11, 16, and 18 [ 19 ]. However, there may be a need for an HPV vaccine that provides wider coverage, protecting against most HR-HPV types in this region, regardless of an individual's HIV status. The objective of this study was to compare the distribution of HR-HPV genotypes between HIV-positive and negative women with cervical cancer, as no prior research has directly compared these two groups via this method. Such data may contribute to resolving inconsistencies in previously published studies concerning circulating HPV in Nigeria, and this knowledge can aid in the development of more targeted and effective strategies for preventing and controlling cervical cancer. Methods and materials Study design, participants, and data collection procedures This cross-sectional analysis utilized baseline data from a prospective cohort of 286 women from the U54CA221205 project. The details of recruitment and enrollment for this study have been described previously [20]. Participants eligible for the study were recruited from Jos University Teaching Hospital (JUTH) and Lagos University Teaching Hospital (LUTH) between March 2018 and September 2022. The eligibility criteria included women aged 18 years or older who were not pregnant, had no history of hysterectomy, and were not receiving cervical cancer treatment at the time of recruitment. Eligible and enrolled participants completed an interview-administered survey to assess their clinical and sociodemographic data, personal behaviors, and practices in the participants’ language of choice (English or Hausa). HIV diagnosis and care information For this study, the HIV status of participants who received care and treatment at the Presidential Emergency Plan for AIDS Relief (PEPFAR) program of the two participating institutions was obtained from the adult HIV treatment and care database, as previously described [21, 22]. HIV testing followed the national serial algorithm, which involves the use of Rapid Determine Test (Abbott, California, USA), Unigold (Trinity Biotech Plc., Ireland), and STAT Pack (Chembio Diagnostic Systems, Inc., New York, USA) quick HIV diagnostic test kits. All HIV-positive women who were receiving care in the PEPFAR program at both study sites were on antiretroviral therapy (ART) at the time of study enrollment. For those whose HIV infection was diagnosed during enrollment, HIV counseling was provided, and they were linked to care and initiated on ART in the PEPFAR program of the participating institutions. Specimen collection and processing Suspected cases of cervical cancer seen at the gynecologic oncology units of JUTH and LUTH were evaluated by the oncology team of investigators at both institutions. The evaluation followed standard care of the diagnostic assessment of suspected cases of cervical cancer at both institutions, as previously described [21, 22]. This included examination under anesthesia (EUA), colposcopy, clinical staging, and cervical tissue biopsy for histopathological diagnosis. The consent form for this project provided details of these evaluations and procedures, and only those who provided written informed consent to participate were enrolled. Women suspected of having cervical cancer and presenting at the gynecologic oncology unit underwent colposcopy. Tissue biopsy forceps were used to obtain three punches of specimens. Two pieces of cervical tissue were immediately placed in transport medium and sent to the genomic laboratory at JUTH and LUTH, where they were stored at -80°C until DNA extraction. The third biopsy specimen was fixed in formalin and transported to the histopathology laboratory for processing and histologic examination by a trained pathologist. Histopathological diagnosis, clinical staging, and tumor grading diagnostic evaluation of cervical tissue were subsequently performed by expert pathologists at the two enrollment institutions with quality control through telepathology review by Northwestern University’s Pathology core [23]. Cervical tissue DNA extraction and quantification DNA was extracted from approximately 25–30 mg of tumor and normal cervical tissue biopsies following our previous method [20] using QIAGEN QIAamp DNA Mini Kit (Qiagen, Hilden, Germany). DNA was quantified using a Qubit 4.0 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) with a dsDNA BR Assay (Life Technologies, Grand Island, NY). The DNA samples were stored at −80°C until shipment. All the DNA samples were shipped on dry ice to the Pathogenomic Core facility at Northwestern University and stored at -20°C. This study transferred 10 µL of 5 ng/µL concentration from all samples into 96-well microplates (Thermo Fisher Scientific, Waltham, MA, USA). The samples in the 96-well microplates were subsequently transferred on dry ice to the Genomics and Microbiome Core Facility at Rush University for the detection and genotyping of HPV. Detection of human papillomavirus using next-generation sequencing The cervical tissue DNA was processed by next-generation sequencing (NGS) using a two-stage PCR protocol, as previously reported [24]. The DNA was amplified with pooled PGMY primers (Integrated DNA Technologies, Coralville, IA, USA) targeting the 450 bp L1 gene fragment. These primer sequences were originally published by Dube et al. (Additional File 1: Table S1) [25]. The pools consisted of five PGMY11 and 14 PGMY09 primers, as described previously [25], but were modified with Fluidigm CS1 (PGMY11) and CS2 (PGMY09) linkers [24]. The PGMY amplicons were generated using Tough Mix PCR Master Mix (Quantabio, Beverly, MA, USA) with the following thermocycling conditions: initial denaturation at 98°C for 120 s; 32 or 40 cycles of 98°C for 10 s, 50°C for 1 s, and 68°C for 1 s. Samples generating no amplification at 32 cycles were re-amplified with 40 cycles of PCR. A negative control was generated using 1 µL of DNA-free water as the template. The amplicons generated during the first stage of PCR were subsequently used as a template for the second stage of PCR amplification (8 cycles) with Fluidigm primers containing sequencing adapters and sample-specific barcode sequences using the same master mix conditions described above [24]. The thermocycling conditions were the same as those described above, except that the annealing temperature was 60°C, and only 8 cycles were performed. The final libraries containing the PGMY amplicons from the HPV L1 region were sequenced on an Illumina MiSeq sequencer (Illumina, Inc., San Diego, CA, USA) using V3 chemistry and 2 × 300 base reads. The mean and median depth of sequencing were approximately 16,500 clusters/sample (range 834–29,571). To verify that the samples contained amplifiable DNA, PCR reactions were also performed with primers targeting human beta-actin (GH2O_FP and PC04_RP) [24]. The PCR conditions were the same as those for the PGMY amplicons, with the exception that only 28 cycles were performed. Amplicons were evaluated using agarose gel electrophoresis. Bioinformatics We counted the number of HPV sequences per genotype for each sample using a data analysis pipeline implemented within the software package CLC Genomics Workbench (v22). Briefly, raw reads were imported and trimmed at the Q20 level. Forward and reverse reads were merged using the read merging function with default settings. Subsequently, sequences without both forward and reverse primer sequences in the proper orientation were removed from the dataset. Merged, primer, and quality trimmed sequence data were mapped against a reference database of 34 reference HPV sequences (Additional file 2: Table S2) to identify the HPV genotypes within each sample. Data management All clinical and survey data were retrieved from REDCap (Research Electronic Data Capture) and analyzed using Stata/SE version 17 for Windows (Statacorp LLC, College Station, TX, USA). We have previously reported on the details of our experience using REDCap to manage research data for this study cohort [20]. Statistical analysis The study participants were categorized into three groups based on their HIV and ICC status: HIV-negative women with ICC, HIV-positive women with ICC, and HIV-positive women without ICC. We compared the baseline sociodemographics, personal behaviors, and practices of the participants across the three groups. ANOVA, or the Kruskal‒Wallis test, was used to compare the means of continuous variables across groups. Pearson's chi-square tests were used to evaluate categorical datasets, or Fisher’s exact tests were used for categorical variables with small cell sizes. Our primary outcome was HR-HPV infection, which was defined (yes vs. no) according to the recommendations of the International Agency for Research on Cancer (IARC) [26]. The primary exposure/covariate of interest was HIV status (positive vs. negative), and all other covariates were selected a priori as possible conceptual confounders on the basis of their demonstrated relationships with HR-HPV, HIV, and cervical cancer [1-3]. These covariates included age, body mass index (BMI), marital status, socioeconomic status (employment, educational attainment, and income), age at sexual initiation, smoking history, self-reported history of treatment for any sexually transmitted infections (STIs), parity, and total number of lifetime sex partners. We categorized parity as ≤ 3, 4-5, 6-7, or >7 term pregnancies based on the literature supporting the importance of these cutoff points in relation to cervical cancer [27]. The total lifetime number of sex partners was categorized as 1, 2--3, or >4. The CD4+ T-cell count was dichotomized (< 350 cells/μl and CD4 ≥ 350 cells/μl) following WHO recommendations [28]. Income was dichotomized as earning <N100,000 (N1,000,000 (> $ 250) per month using the Nigerian Central Bank exchange rate for dollars [29]. We first performed bivariate analysis and identified variables significant at the p<0.10 level for inclusion in our multivariable regression models. Additionally, we utilized a single stratified analysis to determine what should be included in the multivariable models. A robust (modified) Poisson regression model was used to estimate prevalence rate ratios (PRRs) and identify factors potentially associated with HR-HPV [30]. A backward selection procedure was used to select a parsimonious model, and those with significance at p < 0.05 were retained in multivariable models. Model evaluation was conducted using the Akaike information criterion (AIC), where a minimized AIC indicates a better-fitting and more parsimonious model [31]. The crude and adjusted PRRs and their corresponding 95% confidence intervals (95% CIs) are reported. Results Table 1 shows the distribution of the sociodemographic and behavioral practices of the study participants based on their HIV and ICC status. At baseline, HIV-positive women without ICC tended to be younger (mean age 47.0 years) than HIV-positive women with ICC (mean age 49.5 years) and HIV-negative women with ICC (mean age 58.8 years), p <0.001. Employment, education, behavioral practices, and personal practices differed between groups. Compared with HIV-negative women, HIV-positive women had a lower parity. (Insert Table 1 here). TABLE 1. The distribution of participants’ characteristics based on their HIV and invasive cervical cancer tatus (N=286) Variables HIV- with ICC, N=140 n (%) HIV+ with ICC, N=49 n (%) HIV+ without ICC, N=97 n (%) p value* Age, Mean Years (SD) 58.8 (12.9) 48.3 (9.9) 47.3 (8.8) <0.001 Age, category 25-49 35 (25.2) 28 (57.2) 57 (58.8) <0.001 50-59 30 (21.6) 15 (30.6) 33 (34.0) 60-69 38 (27.3) 5 (10.2) 6 (6.2) 70-93 36 (25.9) 1 (2.0) 1 (1.0) Missing 1 0 0 BMI, Mean kg/m2 (SD) 26.8 (6.7) 25.4 (6.2) 27.1 (6.6) 0.720 BMI category 0.578 Underweight (BMI < 18.5) 4 (3.1) 4 (8.5) 5 (5.4) Normal weight (BMI 18.5 to <25) 56 (43.1) 22 (46.8) 33 (36.3) Overweight (BMI 25.0 to <30) 32 (24.6) 11 (23.4) 27 (29.7) Obesity (30+) 38 (29.2) 10 (21.3) 26 (28.6) Missing 10 2 6 Marital status 0.626 Married 85 (61.6) 29 (59.2) 53 (56.4) Not married 53 (38.4) 20 (40.8) 41 (43.6) Missing 2 0 3 Smoking history 0.312 Never 129 (100.0) 46 (97.9) 83 (98.8) Past/current 0 (0.0) 1 (2.1) 1 (1.2) Missing 11 2 13 Income 0.788 <N100,000 pa (N1,000,000 pa (> $ 250) 34 (58.6) 20 (55.6) 33 (52.4) Missing 82 13 34 Employment 0.081 Employed 99 (72.3) 41 (83.7) 75 (83.3) Unemployed 38 (27.7) 8 (16.3) 15 (16.7) Missing 3 0 7 Education <0.001 Less than primary 79 (57.6) 18 (36.7) 24 (25.0) Secondary 29 (21.2) 20 (40.8) 45 (46.9) Tertiary 29 (21.2) 11 (22.5) 27 (28.1) Missing 3 0 1 Age at sexual initiation, Mean years (SD) 18.5 (3.2) 18.3 (3.6) 19.5 (3.9) 0.013 Age at sexual initiation category 0.047 Early Coitarche (<17 years) 36 (28.1) 14 (28.6) 14 (15.6) Not Early Coitarche (≥17 years) 92 (71.9) 35 (71.4) 76 (84.4) Missing 12 0 7 Total lifetime sex partners, Mean (SD) 2.3 (1.8) 2.9 (2.4) 2.8 (1.9) 0.016 Category of total lifetime sex partners 0.026 1 52 (43.7) 18 (37.5) 24 (25.3) 2-3 45 (37.8) 14 (29.2) 51 (53.7) >4 22 (18.5) 16 (33.3) 20 (21.0) Missing 21 1 2 Parity 7 34 (25.2) 6 (12.5) 1 (1.1) Missing 5 1 6 Testing positive for any HPV <0.001 Yes 84 (60.0) 38 (77.6) 7 (7.2) No 56 (40.0) 11(22.5) 90 (92.8) Testing positive for high-risk HPV <0.001 Yes 84 (60.0) 38 (77.6) 6 (6.2) No 56 (40.0) 11(22.4) 91(93.8) History of treatment for any (STIs) 0.001 No 45 (32.1) 27 (55.1) 47 (49.0) Yes 56 (40.0) 12 (24.5) 43 (44.8) Unknown 32 (22.9) 9 (18.4) 6 (6.2) Missing 7 1 1 CD4+ cell count category 0.942 <350/mm 3 NA 8 (80.0) 30 (79.0) ≥350/mm 3 NA 2 (20.0) 8 (21.0) Missing NA 39 59 Histology type 0.445 Squamous Cell Carcinoma 112 (89.6) 38 (84.5) NA Adenocarcinoma 9 (7.2) 6 (13.3) NA Other 4 (3.2) 1 (2.2) NA Missing 15 4 NA Study site 0.001 Jos 81 (57.9) 40 (81.6.7) 49 (50.5) Lagos 59 (42.1) 9 (18.3) 48 (49.5) * Pearson’s chi-square test was used to compare categorical variables, and Fisher’s exact test was used when n < 5 in any cell. ANOVA, or the Kruskal‒Wallis test, was used to compare the means of continuous variables across groups. Continuous variables are presented as the mean plus/minus standard deviation (SD). In this analysis, which included 286 participants, the HPV genotyping results indicated an overall prevalence of 45.0% for the detection of any HPV infection. HPV infection was detected most frequently among HIV-positive women with ICC (77.6%). In contrast, it was detected in 60.0% of the HIV-negative women with ICC and 6.2% of the HIV-positive women without ICC (p < 0.001). Compared with HIV-negative women with ICC and HIV-positive women without ICC, HIV-positive women with ICC had a higher prevalence of HR-HPV infections, multiple HPV infections, and low-risk HPV (LR-HPV) infections (Fig. 1). Table 2 shows the prevalence of HPV types, and overall, HPV16 or HPV18 accounted for the highest prevalence of HR-HPV, representing 29.4% of all HPV infections. HPV16 was the most prevalent, accounting for 20.6%, followed by HPV18 at 8.7%. The prevalence of other HR-HPV types in the study population was less than 5%: HPV45 (4.2%), HPV35 (2.8%), HPV52 (2.5%), and HPV59 (1.8%). We also identified LR-HPV genotypes, with HPV11, HPV61, and HPV81 being the most common, each representing proportions of 1.1%, 0.7%, and 0.7%, respectively. TABLE 2. Prevalence of specific HPV types in the study population Variables n (%) 95% CI Any HR-HPV 128 (44.8) (39.1, 50.6) HPV16 or18 84 (29.4) (24.3, 34.9) HPV16 59 (20.6) (16.3, 25.7) HPV18 25 (8.7) (6.0, 12.6) HPV45 12 (4.2) (2.4, 7.3) HPV35 8 (2.8) (1.4, 5.5) HPV52 7 (2.5) (1.2, 5.0) HPV59 5 (1.8) (0.7, 4.1) HPV58 4 (1.4) (0.5, 3.7) HPV31 2 (0.7) (0.2, 2.8) HPV39 2 (0.7) (0.2, 2.8) HPV73 2 (0.7) (0.2, 2.8) Multiple HR-HPV* 8 (2.8) (1.4, 5.5) Any LR-HPV 3 (1.1) (0.0, 0.3) HPV11 3 (1.1) (0.0, 0.3) HPV61 2 (0.7) (0.2, 2.8) HPV81 2 (0.7) (0.2, 2.8) Multiple LR-HPV* 2 (0.7) (0.2, 2.8) Total Any HR-HPV and/or LR-HPV 129 (45.1) (3.9, 50.9) *We used a threshold of 1% reads to identify a multistrain genotype (Multiple HR-HPV and Multiple LR-HPV). Table 3 shows the results of modified Poisson regression models for the associations between HR-HPV and HIV status in women with cervical cancer, adjusting for age, BMI, parity, and study site. Model 1 shows that HIV-positive women are 1.35 times more likely to have HR-HPV than HIV-negative women (aPRR = 1.35, 95% CI: 1.06, 1.72) after adjusting for all covariates. Similarly, in Model 2 and Model 3, the associations between HIV positivity and HR-HPV remained significant after performing backward selection and adjusting for age and study site (aPRR = 1.36, 95% CI: 1.07, 1.74 and aPRR = 1.46, 95% CI: 1.17, 1.82, respectively). There are significant differences between the study sites (Jos vs. Lagos) in all three models. Compared with those from Jos, women from Lagos have higher prevalence rates of HR-HPV. This difference persisted in the final multivariable model (aPRR = 1.33, 95% CI: 1.06, 1.67). Age, BMI, and parity did not show significant associations with HR-HPV in any of the models. TABLE 3. Results of robust (modified) Poisson regression model: Association of HIV status with high-risk HPV positivity among women with cervical cancer (N=170) Variables PRR (95% CI) p value PRR (95% CI) p value PRR (95% CI) p value HIV positivity 1.35 (1.06, 172) 0.017 1.36 (1.07, 1.74) 0.012 1.46 (1.17, 1.82) 0.001 Age (continuous) 0.90 (0.79, 1.02) 0.098 0.93 (0.83, 1.04) 0.222 BMI c (continuous) 0.92 (0.82, 1.05) 0.215 Parity (continuous) 1.06 (0.94, 1.19) 0.346 Study site Jos vs. Lagos 1.34 (1.07, 1.68) 0.012 1.31 (1.04, 1.65) 0.001 1.33 (1.06, 1.67) 0.015 AIC c 319.48 316.35 314.88 BIC c 338.30 328.89 324.29 a Model 1 represents the full model adjusted for all covariates presented, and Model 2 shows values after performing backward selection and adjusting for age and study site. b Model 3 represents the final model, including the study site, at a significance level of p<0.05. c Abbreviations: BMI, Body Mass Index; AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; PRR = Prevalence Rate Ratio Table 4 shows the results of modified Poisson regression models for the associations between cervical cancer status and any HPV infection among HIV-positive women, adjusting for history of treatment for any STI, early coitarche (<17 years), and parity. Model 1 shows that HIV-positive women with cervical cancer (ICC) are 2.27 times more likely to have any HPV infection than those without ICC (aPRR = 2.27, 95% CI: 1.70, 2.55). HIV positivity remained significantly associated with HPV infection in Model 2 (aPRR = 2.29, 95% CI: 1.83, 2.74) when adjusted for only the variables significant at the p<0.05 level. Histories of treatment for any STI, early coitarche (<17 years), and parity did not show significant associations with cervical cancer in either Model 1 or Model 2. TABLE 4. Results of robust (modified) Poisson regression model: Association of HPV infection with cervical cancer among HIV-positive women (N=132) Variables PRR (95% CI) p value PRR (95% CI) p value Any HPV (positive vs negative) 2.27 (1.70, 2.55) <0.001 2.29 (1.83, 2.74) <0.001 History of treatment for any STI 1.14 (0.92, 1.40) 0.228 Early Coitarche (<17 years) 1.03 (0.71, 1.45) 0.881 Parity (continuous) 0.91 (0.76, 1.08) 0.280 AIC 149.29 147.02 BIC 163.71 152.87 a Model 1 represents the full model adjusted for all covariates presented. b Model 2 represents the final model (excluding all other non-significant variables) at a significant level of 0.05. PRR = Prevalence rate ratio Discussion This study reports the prevalence and distribution of HPV infection among Nigerian women with cervical cancer, comparing those who are HIV-positive with those who are HIV-negative. Our results confirmed that the most frequently detected HR-HPV genotypes were HPV16, HPV18, HPV45, and HPV35. The study also revealed that HIV-positive women tended to develop cervical cancer approximately 10 years earlier than HIV-negative women. Compared with HIV-negative women, HIV-positive women also had a higher prevalence of HR-HPV and multiple HPV infections. Age and ICC status Our findings of a younger age at ICC diagnosis for HIV-positive women are in accordance with the literature. Previous studies conducted in sub-Saharan Africa have shown that HIV-positive women are at a higher risk of developing cervical precancer and cancer at a younger age [ 32 – 34 ]. A study in Jos revealed that ICC occurs at a lower median age of 35 years in HIV-positive women than it does at a median age of 40 years in HIV-negative women [ 32 ]. In Kenya and South Africa, HIV-positive women with ICC were, on average, approximately 10 years younger than their HIV-negative counterparts [ 33 , 34 ]. One plausible explanation is that HIV-positive women may have engaged in sexual activity at a younger age, leading to a greater number of lifetime sexual partners and an elevated risk of acquiring and persisting with HPV infections [ 33 ]. This highlights the importance of targeted interventions and early detection in reducing the burden of cervical cancer, particularly among HIV-positive women. Prevalence and distribution of type-specific human papillomaviruses in the study population Our results indicated that HIV-positive women with ICC had a higher prevalence of HR-HPV infection. HPV16 or HPV18 were the most prevalent types, accounting for 29.4% of the observed cases. Previous studies conducted in Nigerian populations have shown inconsistent results regarding the distribution and prevalence of HR-HPV-specific types [ 12 – 14 , 16 , 17 ]. However, our study results agree with some of those reports and the prevalence observed worldwide [ 14 , 35 – 37 ]. Our recent study by Musa et al. revealed that the HPV16 and 18 genotypes were the most prevalent in North Central Nigeria [ 14 ]. The prevalence of HPV16 and HPV18 among the HIV-positive women in our study was notably higher, which could be due to differences in the study timelines, regions involved, methods of detection used, and ICC statuses. The eight most frequently occurring HR-HPV genotypes identified in our study (HPV16, HPV18, HPV31, HPV33, HPV35, HPV45, HPV52, and HPV58) have been shown to be responsible for 90% of all cervical cancer cases worldwide [ 35 , 36 ]. Our study included women seeking care in two tertiary hospitals (Jos and Lagos) in Nigeria's Northern and Southern regions. As a result, these findings may not be fully representative of the broader population of women in Nigeria due to several factors. Nigeria is home to a wide range of ethnic groups, and the majority of the population lives in rural areas where access to tertiary healthcare facilities is often limited [ 12 , 13 ]. Furthermore, socioeconomic disparities, low educational attainment for girls, early marriage, and a high prevalence of HIV infections among women in certain regions of the country may not be fully represented in our cohort [ 5 , 13 , 38 , 39 ]. Our results and those of other studies demonstrate that the prevalence of multiple HPV infections was higher among HIV-positive women, stemming from a higher risk of acquisition and a reduced ability to clear the HPV infection [ 6 , 7 , 37 , 40 ]. We detected HPV52, HPV73, HPV11, and HPV61 among the multiple HPV infections in these women, albeit in a relatively low proportion. This increased susceptibility is attributed to a combination of factors, such as a compromised immune system and behavioral factors, that have been found to be associated with both HIV and HPV infections [ 6 , 7 , 37 , 40 ]. HIV infection compromises the immune system, particularly affecting CD4 + T cells. As a result, the impaired immune response in HIV-positive individuals reduces their ability to effectively clear HPV infections and control viral replication [ 6 ]. This persistence of HPV infection increases the likelihood of acquiring multiple HPV genotypes [ 37 , 40 ]. Furthermore, studies have shown that HIV-positive women demonstrate altered cytokine profiles, leading to chronic inflammation, which may enhance their susceptibility to multiple HPV infections [ 40 , 41 ]. The confirmation of prevalent HPV genotypes among Nigerian women using highly sensitive techniques such as PCR-NGS is of public health significance, particularly following the official rollout of the HPV vaccination program in Nigeria in October 2023 [ 19 ]. Importantly, we identified HPV35 as the fourth most common HR-HPV genotype in our study population, as recent studies have suggested a strong association between HPV35 and cervical carcinogenesis, particularly in women of African ancestry [ 42 – 44 ]. Several studies in Nigeria have reported HPV35 as one of the most common HPV types in women with cervical cancer, especially among those who are also HIV positive [ 12 , 39 , 44 ]. Mcharo et al. reported HPV35 as the predominant HPV type in women with high-grade squamous intraepithelial lesions (HSILs), particularly those living with HIV. However, the authors noted that HPV35 is rarely detected as a single-type infection in HSIL and cervical cancer cases. Instead, it commonly co-occurs with other HR-HPV types, such as HPV16, 18, and 45, in both HIV-positive and HIV-negative women [ 45 ]. We conducted further analysis to investigate the co-occurrence of HPV35 with other types, especially in ICC cases, within the framework of our study. Interestingly, we observed that HPV35 was present alone in 6 out of 8 cases (75.0%), whereas in 2 out of 8 cases (25.0%), it was detected together with HPV18 and HPV81. Our study has a small sample size with a low prevalence of HPV35 (2.8%), limiting our conclusions regarding the prevalence of HPV35 in Nigeria. The Gardasil 9 vaccine does not cover the HPV35 strain. Therefore, it is necessary to conduct further research to determine whether including HPV35 in the already highly effective Gardasil vaccine would increase its protective benefits for women in Africa or those of African heritage, especially HIV-positive women. Associations between human immunodeficiency virus status and high-risk human papillomavirus status among women with cervical cancer Compared with HIV-negative women with cervical cancer, HIV-positive women with cervical cancer were 1.46 times more likely to have HR-HPV. Our findings are consistent with several epidemiological studies that reported higher rates of HR-HPV in HIV-positive women [ 6 , 40 , 46 ]. A comprehensive meta-analysis of 38 epidemiological studies revealed that HIV-positive women are more than twice as likely to be infected with HPV than their HIV-negative counterparts are, with a relative risk of 2.6 (95% CI: 2.0–3.4) [ 47 ]. Sally et al. reported a prevalence ratio of 4.18 (95% CI: 2.1–8.5) for any HR-HPV infection in HIV-positive women compared with HIV-negative women in Nigeria after adjusting for age and educational attainment [ 39 ]. The mechanisms behind these associations have been elucidated in earlier sections of this paper. Our analysis revealed no significant associations between age, body mass index (BMI), or parity and HR-HPV infection among HIV-positive women with cervical cancer, contrary to the findings of several other studies [ 1 , 2 , 48 , 49 ]. This lack of association may be due to the limited statistical power to detect significant associations with these variables, the cross-sectional design, or the referral patterns in these tertiary care hospitals. We also found that women in Lagos were 1.36 times more likely to have HR-HPV infection. These findings suggest that factors specific to the Lagos region, such as differences in healthcare access and practices, may affect the likelihood of detecting HR-HPV infection in women with cervical cancer. However, these results should be interpreted with caution due to several factors. Specifically, the relatively small sample size and the fact that the participants were urban women seeking routine care may limit the generalizability of the findings to the broader population of women in Nigeria. Moreover, our results demonstrated significant disparities between the study sites in terms of participant characteristics, including the presence of missing data. Associations between cervical cancer status and any human papillomavirus infection among women with human immunodeficiency virus infection Our multivariable Poisson regression analysis indicated that HIV-positive women with ICC are 2.29 times more likely to have any HPV infection than HIV-positive women without ICC after controlling for covariates. The study recruited and assessed participants cross-sectionally and had no data on prior screening and treatment history for the control group (HIV-positive women without invasive cervical cancer). The potential presence of "prior cases" among the controls could attenuate the contrast between study groups, diminishing the study's power to detect significant differences. Furthermore, we note that it is important to consider both the CD4 count and viremia when studying the distribution of HPV in HIV-positive women. This can help us better understand the relationship between HIV parameters and HPV infection. Even though all the HIV-positive women in this study were receiving ART, data concerning the effects of biological markers of ART effects on the viral load and CD4 counts are needed. Owing to this lack of information, we could not account for this potential confounding factor in our study. Nevertheless, our findings further substantiate the understanding that HIV-positive women have an elevated risk of developing cervical cancer when infected with HPV [ 6 , 40 , 41 , 45 , 46 ]. Studies with larger sample sizes and diverse populations utilizing the NGS approach are essential to strengthen these findings. Although our recent study utilized the Anyplex™ II HPV28 PCR detection method, we opted for next-generation sequencing (NGS) to address the discrepancies noted in previous research. While our study did not directly compare these methodologies, recent findings by Latsuzbaia et al. revealed that NGS not only detected the 25 genotypes covered by Anyplex but also identified an additional 41 genotypes [ 50 ]. Our study employed pooled PGMY primers that specifically target the 450 bp L1 gene fragment because of their enhanced sensitivity in amplifying HPV types [ 25 ]. This approach enables HPV typing across a broader section of the L1 gene, thereby bolstering result reliability [ 25 , 51 , 52 ]. Unlike the Anyplex II HPV28 method, which uses different primer sets yielding shorter amplicons targeting 100 to 200 bp fragments in the L1 region of 28 distinct HPV genotypes (comprising 19 HR-HPV and 9 LR-HPV types), our method offers broader coverage [ 53 ]. Additionally, we verified that the NGS assay effectively detected genotype sequences occurring at a frequency of 1% in cases of multiple HPV infections. The PCR-NGS method described herein can be easily updated to include primers targeting novel variants and can be heavily multiplexed. Strategies allowing for multiplexing up to 1536 samples per sequencing run are currently available, and this level of multiplexing can reduce the sequencing cost per sample and allow the method to be deployed in resource-limited regions such as Africa [ 54 ]. Despite cost constraints, NGS can play a crucial role in evaluating and monitoring HPV vaccines, serving as a second-line test in cervical cancer screening and supporting epidemiological surveys. These advantages hold particular significance in Nigeria, where the burden of ICC is high and where an HPV vaccine program has recently been initiated. Strengths and limitations The strengths of our study include that it is the second in Nigeria to utilize a PCR‒NGS workflow for HPV genotyping. Our method is highly robust, easy to deploy, and adaptable to additional primers or primer sets. Our results align with findings from international studies on HPV genotypes in Africa and several studies in Nigeria that identified HPV16 and HPV18 as the most prevalent HR-HPV types in Nigeria. We also identified HPV35 as the fourth most common HR-HPV type, which has been documented to be prevalent in sub-Saharan Africa. This contribution helps address the discrepancies found in previously published research. One key strength of our study is the histopathological diagnosis of invasive cervical cancer, which was conducted independently by certified pathologists at two institutions (Jos and Lagos). To ensure quality control, Northwestern's Pathology Core reviewed the histopathology slides and paraffin-fixed blocks via telepathology. To increase the reliability and validity of the histopathological assessments, the histopathology slides and paraffin-fixed blocks were shared with the Pathology Core team at Northwestern University Cancer Center for verification [ 23 ]. One major limitation of this study is missing data for several key variables, such as obesity, total lifetime sex partners, age at sexual initiation, and parity, which introduces potential bias. We performed a sensitivity analysis to assess whether the inclusion or exclusion of these covariates had any significant effect on the strength or direction of the relationship between HR-HPV and HIV positivity. As mentioned earlier in the methods section, our findings indicated that removing covariates with missing data did not result in any notable effect on the relationship between HR-HPV and HIV positivity. As noted above, we were also missing data on viral load and CD4 count for HIV-positive women. The cross-sectional design, relatively small sample size, and generalizability of the findings are limited to urban populations/tertiary care facilities, highlighting the need for a larger multicenter longitudinal cohort study to more precisely measure the time-varying occurrence of HPV clearance or progression to cervical intraepithelial neoplasia (CIN) and subsequent cancer. Future studies should also assess the impact of cofactors that can contribute to carcinogenesis, such as bacterial vaginosis (BV), Trichomonas vaginalis (TV) infection, Chlamydia trachomatis (CT) infection, herpes simplex virus (HSV) infection, the viral load, and the CD4 count. Conclusion Our study provides valuable insights into the prevalence and distribution of HPV infection among women in Nigeria, particularly those with cervical cancer and HIV-positive women. The high prevalence of HPV16 and HPV18 among women with ICC highlights their critical role in the development of the disease. Additionally, the presence of multiple high-risk HPV genotypes and the high prevalence of multiple HPV infections among HIV-positive women emphasize the need for targeted interventions to reduce the burden of cervical cancer in this population. These findings can guide public health practitioners and policymakers in establishing effective prevention and control strategies tailored to the Nigerian population, including targeted vaccination programs, improved healthcare access, and comprehensive reproductive health services. Declarations Ethics approval and consent to participate This study involves a secondary analysis of data obtained from the Northwestern University REDCap database, following a formal request to the Northwestern University Institutional Review Board (IRB) (STU00218862). Given that the dataset was de-identified, the study was classified as not human subject research and was exempt from the human subject research approval process. The data were accessed for research purposes on July 15, 2023. The dataset was originally collected in a prospective cohort study, which received IRB approval from the University of Jos (JUTH/DCS/ADM/127/XXVII/630) and the University of Lagos (CMUL/HREC/02/22/327/V4) in Nigeria, as well as Northwestern University (STU00207051) in the United States. The original study was part of the National Cancer Institute's project on Epigenomic Biomarkers of HIV-associated Cancers in Nigeria (U54CA221205). All participants in the original study provided informed consent and were fully briefed on the study's purpose and procedures before enrollment. Consent for publication 'Not applicable' Availability of data and materials The raw HPV L1 DNA sequence data (i.e., FASTQ files) have been submitted to the NCBI Sequence Read Archive (SRA) with the BioProject Accession Number PRJNA1023898. Competing interests The author(s) declare(s) that they have no competing interests. Funding The research findings reported in this manuscript were supported by the National Cancer Institute of the National Institutes of Health under award number U54CA221205. CJN received funding for training through an NIH/FIC/D43TW009575 titled “The Northwestern Nigerian Research Training Program in HIV and Malignancies (NN-HAM)” and a Seed Award grant from the U54CA221205 project. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Authors' contributions CN, LH, and SM conceptualized and developed the study design and methodology. CN had full access to the data and led the writing of the manuscript. CN performed the statistical analysis and interpreted the results with support from SM, KK, and BJ. SJ and CN performed the laboratory process for HPV genotyping and data curation. LH, RM, and JM acquired the financial support for the project leading to this publication. JM, OS, and GI provided additional support, and GI provided additional support for data collection and interpretation of the results. All the listed authors contributed to editing the draft manuscript and approved the final version of the manuscript for submission. Acknowledgments I would like to recognize the contributions of the U54 participants and staff from Jos University Teaching Hospital (JUTH) and Lagos University Teaching Hospital (LUTH). Special thanks go to the dedicated laboratory staff, Ms. Cecilia S. Chau and Ashley Wu, at the Genomics and Microbiome Core Facility, Rush University in Chicago, IL, USA. I am also grateful to the University of Jos for enabling me to pursue training in the USA. Authors' information (optional) 'Not applicable' References Bosch FX, Lorincz A, Muñoz N, Meijer CJ, Shah KV. The causal relation between human papillomavirus and cervical cancer. 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Targeted Next-Generation Sequencing of Acute Leukemia. Methods Mol Biol. 2017;1633:163-184. doi: 10.1007/978-1-4939-7142-8_11. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Additional file 1. List of PGMY primers used for Illumina next-generation sequencing. This table provides information about the list of PGMY primers, which are a set of degenerate primers specifically designed to amplify a wide range of HPV types by targeting the L1 region of the HPV genome. These primers were utilized in PCR (polymerase chain reaction) to produce DNA fragments, which were subsequently sequenced. Additionalfile2.docx Additional file 2. Reference sequences utilized for HPV genotyping. 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Nyam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYLACxgYI9QBI8PCRooXZAKSFjRQtbBJgkpBqc4nkpxt+7jic2D+7/Vrl1xw7GTYG5oePbuDRYjkjzexm75nDiTPunCm7LbstGegwNmPjHDxaDG4kmN1mbDuc23AjJ+225DZmoBYeNmn8WtK/gbXMB2opltxWT4yWHIgtG26kH2P8uO0wEVrOvCm72duWXr/xRg6zNOO24zxszIT8cjx9242fbdbGcjfSH378ua3anp+9+eFjfFoYBBJgLB4DZh4QzYxPOQjwH4Cx2B8w/iCkehSMglEwCkYkAABb0lCR+lW8cQAAAABJRU5ErkJggg==","orcid":"","institution":"Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, IL","correspondingAuthor":true,"prefix":"","firstName":"Chuwang","middleName":"J.","lastName":"Nyam","suffix":""},{"id":363775337,"identity":"babac6e0-7107-481c-8a64-1c5574ac8e0b","order_by":1,"name":"Jonah Musa","email":"","orcid":"","institution":"Department of Obstetrics and Gynecology, College of Health Sciences, University of Jos, Plateau State","correspondingAuthor":false,"prefix":"","firstName":"Jonah","middleName":"","lastName":"Musa","suffix":""},{"id":363775338,"identity":"6e941f54-e522-4f32-a993-ab76e72bdeaf","order_by":2,"name":"Brian T. Joyce","email":"","orcid":"","institution":"Department of Preventive Medicine, Division of Cancer Epidemiology and Prevention, Feinberg School of Medicine, Northwestern University, Chicago, IL","correspondingAuthor":false,"prefix":"","firstName":"Brian","middleName":"T.","lastName":"Joyce","suffix":""},{"id":363775339,"identity":"3a359f89-ef78-4e93-bf4c-2c0832d269f9","order_by":3,"name":"Kyeezu Kim","email":"","orcid":"","institution":"Department of Social and Preventive Medicine, Sungkyunkwan University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kyeezu","middleName":"","lastName":"Kim","suffix":""},{"id":363775340,"identity":"cf9889fe-ef50-4f32-ba7d-02a2c31bd9a0","order_by":4,"name":"Jun Wang","email":"","orcid":"","institution":"Department of Preventive Medicine, Division of Cancer Epidemiology and Prevention, Feinberg School of Medicine, Northwestern University, Chicago, IL","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Wang","suffix":""},{"id":363775341,"identity":"90feb6fb-d1b0-4258-b054-3998b32ca033","order_by":5,"name":"Stefan J. Green","email":"","orcid":"","institution":"Genomics and Microbiome Core Facility, Rush University, Chicago, IL","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"J.","lastName":"Green","suffix":""},{"id":363775342,"identity":"058560cf-b623-420a-a71b-a35ea059c255","order_by":6,"name":"Demirkan B. Gursel","email":"","orcid":"","institution":"Department of Pathology, Feinberg School of Medicine, Northwestern University, Chicago, IL","correspondingAuthor":false,"prefix":"","firstName":"Demirkan","middleName":"B.","lastName":"Gursel","suffix":""},{"id":363775343,"identity":"0ffcf053-1ae6-4f24-a24d-4792907170b3","order_by":7,"name":"Fatimah Abdulkareem","email":"","orcid":"","institution":"Department of Anatomic and Forensic Pathology, College of Medicine, University of Lagos","correspondingAuthor":false,"prefix":"","firstName":"Fatimah","middleName":"","lastName":"Abdulkareem","suffix":""},{"id":363775344,"identity":"6f86057a-ed5f-4901-9341-8415a93cdfd2","order_by":8,"name":"Alani S. Akanmu","email":"","orcid":"","institution":"Department of Hematology and Blood Transfusion, College of Health Sciences, University of Lagos","correspondingAuthor":false,"prefix":"","firstName":"Alani","middleName":"S.","lastName":"Akanmu","suffix":""},{"id":363775345,"identity":"7b1b0fc7-eaec-4f81-a366-8baa937cdbdd","order_by":9,"name":"Olugbenga A. Silas","email":"","orcid":"","institution":"Department of Anatomic and Forensic Pathology, College of Health Sciences, University of Jos, Plateau State","correspondingAuthor":false,"prefix":"","firstName":"Olugbenga","middleName":"A.","lastName":"Silas","suffix":""},{"id":363775346,"identity":"00075555-a1c6-4a74-8a8f-796f2e07d8a9","order_by":10,"name":"Godwin E. Imade","email":"","orcid":"","institution":"Department of Obstetrics and Gynecology, College of Health Sciences, University of Jos, Plateau State","correspondingAuthor":false,"prefix":"","firstName":"Godwin","middleName":"E.","lastName":"Imade","suffix":""},{"id":363775347,"identity":"03094ef3-5cd6-4bf8-9df3-617b317f8fa0","order_by":11,"name":"Rose Anorlu","email":"","orcid":"","institution":"Department of Obstetrics and Gynecology, College of Medicine, University of Lagos","correspondingAuthor":false,"prefix":"","firstName":"Rose","middleName":"","lastName":"Anorlu","suffix":""},{"id":363775348,"identity":"922a0cbb-6428-4ac0-9584-f68ae73c5f5d","order_by":12,"name":"Folasade Ogunsola","email":"","orcid":"","institution":"Department of Medical Microbiology, College of Medicine, University of Lagos","correspondingAuthor":false,"prefix":"","firstName":"Folasade","middleName":"","lastName":"Ogunsola","suffix":""},{"id":363775349,"identity":"8a798228-174f-4463-8ed2-84426215c541","order_by":13,"name":"Atiene S. Sagay","email":"","orcid":"","institution":"Department of Obstetrics and Gynecology, College of Health Sciences, University of Jos, Plateau State","correspondingAuthor":false,"prefix":"","firstName":"Atiene","middleName":"S.","lastName":"Sagay","suffix":""},{"id":363775350,"identity":"378f4111-add4-4791-a0a4-3bedb9f1d6fa","order_by":14,"name":"Robert L. Murphy","email":"","orcid":"","institution":"Division of Infectious Diseases, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"L.","lastName":"Murphy","suffix":""},{"id":363775351,"identity":"524e3e04-d6d2-4ac1-b41b-44df23203171","order_by":15,"name":"Lifang Hou","email":"","orcid":"","institution":"Department of Preventive Medicine, Division of Cancer Epidemiology and Prevention, Feinberg School of Medicine, Northwestern University, Chicago, IL","correspondingAuthor":false,"prefix":"","firstName":"Lifang","middleName":"","lastName":"Hou","suffix":""},{"id":363775352,"identity":"b46afe6b-0851-43b0-b33f-2b68ac3c4dc9","order_by":16,"name":"Supriya D. Mehta","email":"","orcid":"","institution":"Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, IL","correspondingAuthor":false,"prefix":"","firstName":"Supriya","middleName":"D.","lastName":"Mehta","suffix":""}],"badges":[],"createdAt":"2024-09-26 16:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5160011/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5160011/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67260945,"identity":"1be3bbe3-fbd7-4194-874e-95cee8ca4bf9","added_by":"auto","created_at":"2024-10-23 06:06:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":120943,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency distribution of type-specific HPV genotypes at enrollment according to HIV and ICC status. The multiple HPV infections in the figure above refer to the presence of multiple types or strains of any HPV infection in the study participants.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5160011/v1/f173714c6a792d4351c2082b.png"},{"id":69906493,"identity":"c9717938-a25e-4d0e-b281-6f5e16200bb2","added_by":"auto","created_at":"2024-11-26 13:01:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1086745,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5160011/v1/714c8590-a155-43c0-ab79-61f8acb79bd4.pdf"},{"id":67260946,"identity":"f57b86cd-065b-4efd-b2aa-2d042615442e","added_by":"auto","created_at":"2024-10-23 06:06:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":311998,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 1\u003c/strong\u003e. List of PGMY primers used for Illumina next-generation sequencing. This table provides information about the list of PGMY primers, which are a set of degenerate primers specifically designed to amplify a wide range of HPV types by targeting the L1 region of the HPV genome. These primers were utilized in PCR (polymerase chain reaction) to produce DNA fragments, which were subsequently sequenced.\u003c/p\u003e","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5160011/v1/363c6ccd6fa5d708bcf00ba5.docx"},{"id":67260947,"identity":"07b6188b-11b1-47c7-9c45-524c537f373d","added_by":"auto","created_at":"2024-10-23 06:06:15","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19486,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 2\u003c/strong\u003e. Reference sequences utilized for HPV genotyping. Accessed at: \u003ca href=\"https://infectagentscancer.biomedcentral.com/articles/10.1186/s13027-022-00456-w#Sec22\"\u003ehttps://infectagentscancer.biomedcentral.com/articles/10.1186/s13027-022-00456-w#Sec22\u003c/a\u003e\u003c/p\u003e","description":"","filename":"Additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5160011/v1/14babdf13118fe7aa7c4a400.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Molecular epidemiology of human papillomavirus genotypes among HIV-positive and HIV-negative women with cervical cancer in Nigeria","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePersistent infection with a high-risk oncogenic human papillomavirus (HR-HPV) genotype has been well established as the cause of nearly all cervical cancer cases (95%), although infection alone is not sufficient [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Invasive cervical cancer (ICC) is highly preventable through vaccination and can be cured with early detection and effective management of precancer conditions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It ranks fourth among the most common cancers in women globally, with an estimated 660,000 women diagnosed annually. An estimated 350,000 women died from the disease in 2022, and nearly 90% of these deaths occurred in low- and middle-income countries (LMICs) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This is an enormous burden to Sub-Saharan African countries, especially Nigeria, which is due to limited access to public health services and inadequate implementation of screening and treatment for the disease [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Another contributor to the high prevalence and incidence of cervical cancer is the large number of HIV-positive women, with Nigeria ranking fourth in the world in terms of HIV burden [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNigeria lacks a comprehensive national screening program for cervical cancer, and the existing screening services are mostly opportunistic [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. As a result, fewer than 9% of eligible Nigerian women have accessed these services, which are sparsely distributed and rely primarily on opportunistic methods [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The most commonly used methods for cervical cancer screening (CCS) are conventional Pap cytology (Pap test or Pap smear) and visual inspection with acetic acid (VIA) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, several studies [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] have reported the use of HPV-DNA detection as a screening method. In Nigeria, these studies revealed variations in the prevalence rates of HPV16 and HPV18 among HIV-positive women [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The varying prevalence of HPV16 and HPV18 may be due to different sample types, sampling bias, and HPV detection methods. For example, Emeribe et al. conducted a systematic review of 18 epidemiological studies that investigated the prevalence of HPV infection and genotypes among Nigerian women over a decade from 1999 to 2019 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Based on studies using hybrid capture 2 technology (HC2) or specific primers (GP5+/6+) polymerase chain reaction (PCR) methods, they reported a pooled HPV prevalence of 20.6%, and the most common HR-HPV genotypes in circulation were HPV31 (70.8%), HPV35 (69.9%), and HPV16 (52.9%). Another recent systematic review and meta-analysis conducted by Kabuga et al. in Nigerian women revealed that among HIV-positive women, the prevalence of HPV was 37% (95% CI: 25\u0026ndash;50%), and the most prevalent genotypes detected were HPV16, 18, 31, 35, 52, 58, and 45 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Our recently published study by Musa et al. employed the Anyplex\u0026trade; II HPV28 PCR detection method. Our findings indicated that 45.2% of the 138 women tested had either HPV 16 or 18 along with another HR-HPV type, whereas 12.9% had HPV 35 along with other HR-HPV types [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Hence, to reconcile these inconsistencies and accurately estimate the prevalence and distribution of HPV types, adopting a robust typing methodology such as next-generation sequencing (NGS) is imperative. NGS is more comprehensive in detecting HPV types, overcomes the limitations associated with PCR-based techniques, and offers possibilities for accurately identifying circulating HPV-specific types in Nigeria [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTwo studies identified from our search documented the use of targeted PCR amplification and sequencing for HPV genotype detection among women in Nigeria. Nejo et al. utilized PCR with consensus primers targeting viral E6/E7 genes, followed by Sanger sequencing for genotyping. These findings revealed that HPV31 (32.8%), HPV35 (17.2%), and HPV16 (15.5%) were the predominant strains [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. While effective in many instances, Sanger sequencing of PCR amplicons has limitations in detecting polyviral infections with multiple genotypes. Next-generation sequencing workflows offer the advantages of sensitive detection of multiple viral genotypes and simultaneous sequencing of large samples. Notably, the only study that utilized both NGS and type-specific PCR for HPV typing, conducted by Dom-Chima N et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], included 90 cervical samples. DNA from these samples was analyzed via next-generation sequencing (NGS) and type-specific PCR (tsPCR). The top five prevalent types found in their study were HPV71 (17%), HPV82 (15%), HPV16 (16%), HPV6 (10%), and HPV20 (7%) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The difference in HPV prevalence between the two studies may be due to variations in the source population and HPV detection methods. Their study included women with and without cytological abnormalities or symptoms of STIs who attended routine clinics during the study period [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccurately identifying the spectrum of HPV types prevalent in areas with high cervical cancer rates and HIV, such as Nigeria, is crucial. Currently, several HPV vaccines, including Cervarix-2, Gardasil-4, and Gardasil-9, which only protect against the HPV6, 11, 16, 18, 31, 33, 45, 52, and 58 types, are available [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. As of October 2023, the Nigerian government added Gardasil-4 to its routine immunization program to protect 7.7\u0026nbsp;million girls aged 9\u0026ndash;14 against HPV6, 11, 16, and 18 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, there may be a need for an HPV vaccine that provides wider coverage, protecting against most HR-HPV types in this region, regardless of an individual's HIV status. The objective of this study was to compare the distribution of HR-HPV genotypes between HIV-positive and negative women with cervical cancer, as no prior research has directly compared these two groups via this method. Such data may contribute to resolving inconsistencies in previously published studies concerning circulating HPV in Nigeria, and this knowledge can aid in the development of more targeted and effective strategies for preventing and controlling cervical cancer.\u003c/p\u003e"},{"header":"Methods and materials","content":"\u003cp\u003e\u003cstrong\u003eStudy design, participants, and data collection procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis cross-sectional analysis utilized baseline data from a prospective cohort of 286 women from the U54CA221205 project. The details of recruitment and enrollment for this study have been described previously [20]. Participants eligible for the study were recruited from Jos University Teaching Hospital (JUTH) and Lagos University Teaching Hospital (LUTH) between March 2018 and September 2022. The eligibility criteria included women aged 18 years or older who were not pregnant, had no history of hysterectomy, and were not receiving cervical cancer treatment at the time of recruitment. Eligible and enrolled participants completed an interview-administered survey to assess their clinical and sociodemographic data, personal behaviors, and practices in the participants\u0026rsquo; language of choice (English or Hausa).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHIV diagnosis and care information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor this study, the HIV status of participants who received care and treatment at the Presidential Emergency Plan for AIDS Relief (PEPFAR) program of the two participating institutions was obtained from the adult HIV treatment and care database, as previously described [21, 22]. HIV testing followed the national serial algorithm, which involves the use of Rapid Determine Test (Abbott, California, USA), Unigold (Trinity Biotech Plc., Ireland), and STAT Pack (Chembio Diagnostic Systems, Inc., New York, USA) quick HIV diagnostic test kits. All HIV-positive women who were receiving care in the PEPFAR program at both study sites were on antiretroviral therapy (ART) at the time of study enrollment. For those whose HIV infection was diagnosed during enrollment, HIV counseling was provided, and they were linked to care and initiated on ART in the PEPFAR program of the participating institutions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpecimen collection and processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSuspected cases of cervical cancer seen at the gynecologic oncology units of JUTH and LUTH were evaluated by the oncology team of investigators at both institutions. The evaluation followed standard care of the diagnostic assessment of suspected cases of cervical cancer at both institutions, as previously described [21, 22]. This included examination under anesthesia (EUA), colposcopy, clinical staging, and cervical tissue biopsy for histopathological diagnosis. The consent form for this project provided details of these evaluations and procedures, and only those who provided written informed consent to participate were enrolled. Women suspected of having cervical cancer and presenting at the gynecologic oncology unit underwent colposcopy. Tissue biopsy forceps were used to obtain three punches of specimens. Two pieces of cervical tissue were immediately placed in transport medium and sent to the genomic laboratory at JUTH and LUTH, where they were stored at -80\u0026deg;C until DNA extraction. The third biopsy specimen was fixed in formalin and transported to the histopathology laboratory for processing and histologic examination by a trained pathologist. Histopathological diagnosis, clinical staging, and tumor grading diagnostic evaluation of cervical tissue were subsequently performed by expert pathologists at the two enrollment institutions with quality control through telepathology review by Northwestern University\u0026rsquo;s Pathology core [23].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCervical tissue DNA extraction and quantification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDNA was extracted from approximately 25\u0026ndash;30 mg of tumor and normal cervical tissue biopsies following our previous method [20] using QIAGEN QIAamp DNA Mini Kit (Qiagen, Hilden, Germany). DNA was quantified using a Qubit 4.0 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) with a dsDNA BR Assay (Life Technologies, Grand Island, NY). The DNA samples were stored at \u0026minus;80\u0026deg;C until shipment. All the DNA samples were shipped on dry ice to the Pathogenomic Core facility at Northwestern University and stored at -20\u0026deg;C. This study transferred 10 \u0026micro;L of 5 ng/\u0026micro;L concentration from all samples into 96-well microplates (Thermo Fisher Scientific, Waltham, MA, USA). The samples in the 96-well microplates were subsequently transferred on dry ice to the Genomics and Microbiome Core Facility at Rush University for the detection and genotyping of HPV.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetection of human papillomavirus using next-generation sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cervical tissue DNA was processed by next-generation sequencing (NGS) using a two-stage PCR protocol, as previously reported [24]. The DNA was amplified with pooled PGMY primers (Integrated DNA Technologies, Coralville, IA, USA) targeting the 450 bp L1 gene fragment. These primer sequences were originally published by Dube et al. (Additional File 1: Table S1) [25]. The pools consisted of five PGMY11 and 14 PGMY09 primers, as described previously [25], but were modified with Fluidigm CS1 (PGMY11) and CS2 (PGMY09) linkers [24]. The PGMY amplicons were generated using Tough Mix PCR Master Mix (Quantabio, Beverly, MA, USA) with the following thermocycling conditions: initial denaturation at 98\u0026deg;C for 120 s; 32 or 40 cycles of 98\u0026deg;C for 10 s, 50\u0026deg;C for 1 s, and 68\u0026deg;C for 1 s. Samples generating no amplification at 32 cycles were re-amplified with 40 cycles of PCR. A negative control was generated using 1 \u0026micro;L of DNA-free water as the template. The amplicons generated during the first stage of PCR were subsequently used as a template for the second stage of PCR amplification (8 cycles) with Fluidigm primers containing sequencing adapters and sample-specific barcode sequences using the same master mix conditions described above [24]. The thermocycling conditions were the same as those described above, except that the annealing temperature was 60\u0026deg;C, and only 8 cycles were performed. The final libraries containing the PGMY amplicons from the HPV L1 region were sequenced on an Illumina MiSeq sequencer (Illumina, Inc., San Diego, CA, USA) using V3 chemistry and 2 \u0026times; 300 base reads. The mean and median depth of sequencing were approximately 16,500 clusters/sample (range 834\u0026ndash;29,571).\u003c/p\u003e\n\u003cp\u003eTo verify that the samples contained amplifiable DNA, PCR reactions were also performed with primers targeting human beta-actin (GH2O_FP and PC04_RP) [24]. The PCR conditions were the same as those for the PGMY amplicons, with the exception that only 28 cycles were performed. Amplicons were evaluated using agarose gel electrophoresis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBioinformatics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe counted the number of HPV sequences per genotype for each sample using a data analysis pipeline implemented within the software package CLC Genomics Workbench (v22). Briefly, raw reads were imported and trimmed at the Q20 level. Forward and reverse reads were merged using the read merging function with default settings. Subsequently, sequences without both forward and reverse primer sequences in the proper orientation were removed from the dataset. Merged, primer, and quality trimmed sequence data were mapped against a reference database of 34 reference HPV sequences (Additional file 2: Table S2) to identify the HPV genotypes within each sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData management\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll clinical and survey data were retrieved from REDCap (Research Electronic Data Capture) and analyzed using Stata/SE version 17 for Windows (Statacorp LLC, College Station, TX, USA). We have previously reported on the details of our experience using REDCap to manage research data for this study cohort [20].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study participants were categorized into three groups based on their HIV and ICC status: HIV-negative women with ICC, HIV-positive women with ICC, and HIV-positive women without ICC. We compared the baseline sociodemographics, personal behaviors, and practices of the participants across the three groups. ANOVA, or the Kruskal‒Wallis test, was used to compare the means of continuous variables across groups. Pearson\u0026apos;s chi-square tests were used to evaluate categorical datasets, or Fisher\u0026rsquo;s exact tests were used for categorical variables with small cell sizes.\u003c/p\u003e\n\u003cp\u003eOur primary outcome was HR-HPV infection, which was defined (yes vs. no) according to the recommendations of the International Agency for Research on Cancer (IARC) [26]. The primary exposure/covariate of interest was HIV status (positive vs. negative), and all other covariates were selected a priori as possible conceptual confounders on the basis of their demonstrated relationships with HR-HPV, HIV, and cervical cancer [1-3]. These covariates included age, body mass index (BMI), marital status, socioeconomic status (employment, educational attainment, and income), age at sexual initiation, smoking history, self-reported history of treatment for any sexually transmitted infections (STIs), parity, and total number of lifetime sex partners.\u003c/p\u003e\n\u003cp\u003eWe categorized parity as \u0026le; 3, 4-5, 6-7, or \u0026gt;7 term pregnancies based on the literature supporting the importance of these cutoff points in relation to cervical cancer [27]. The total lifetime number of sex partners was categorized as 1, 2--3, or \u0026gt;4. The CD4+ T-cell count was dichotomized (\u0026lt; 350 cells/\u0026mu;l and CD4 \u0026ge; 350 cells/\u0026mu;l) following WHO recommendations [28]. Income was dichotomized as earning \u0026lt;N100,000 (\u0026lt; $ 250) per month and \u0026gt;N1,000,000 (\u0026gt; $ 250) per month using the Nigerian Central Bank exchange rate for dollars [29].\u003c/p\u003e\n\u003cp\u003eWe first performed bivariate analysis and identified variables significant at the p\u0026lt;0.10 level for inclusion in our multivariable regression models. Additionally, we utilized a single stratified analysis to determine what should be included in the multivariable models. A robust (modified) Poisson regression model was used to estimate prevalence rate ratios (PRRs) and identify factors potentially associated with HR-HPV [30]. A backward selection procedure was used to select a parsimonious model, and those with significance at p \u0026lt; 0.05 were retained in multivariable models. Model evaluation was conducted using the Akaike information criterion (AIC), where a minimized AIC indicates a better-fitting and more parsimonious model [31]. The crude and adjusted PRRs and their corresponding 95% confidence intervals (95% CIs) are reported.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e shows the distribution of the sociodemographic and behavioral practices of the study participants based on their HIV and ICC status. At baseline, HIV-positive women without ICC tended to be younger (mean age 47.0 years) than HIV-positive women with ICC (mean age 49.5 years) and HIV-negative women with ICC (mean age 58.8 years), p \u0026lt;0.001. Employment, education, behavioral practices, and personal practices differed between groups. Compared with HIV-negative women, HIV-positive women had a lower parity. (Insert Table 1 here).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003eTABLE 1. The distribution of participants\u0026rsquo; characteristics based on their HIV and invasive cervical cancer tatus (N=286)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003eHIV- with\u003c/p\u003e\n \u003cp\u003eICC, N=140\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003eHIV+ with\u003c/p\u003e\n \u003cp\u003eICC, N=49\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003eHIV+ without\u003c/p\u003e\n \u003cp\u003eICC, N=97\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ep value*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eAge, Mean Years (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e58.8 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e48.3 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e47.3 (8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eAge, category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003e25-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e35 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e28 (57.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e57 (58.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003e50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e30 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e15 (30.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e33 (34.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003e60-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e38 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e5 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e6 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003e70-93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e36 (25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e1 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e1 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eBMI, Mean kg/m2 (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e26.8 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e25.4 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e27.1 (6.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eBMI category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eUnderweight (BMI \u0026lt; 18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e4 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e4 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e5 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eNormal weight (BMI 18.5 to \u0026lt;25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e56 (43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e22 (46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e33 (36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eOverweight (BMI 25.0 to \u0026lt;30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e32 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e11 (23.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e27 (29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eObesity (30+)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e38 (29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e10 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e26 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.626\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e85 (61.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e29 (59.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e53 (56.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eNot married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e53 (38.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e20 (40.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e41 (43.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eSmoking history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e129 (100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e46 (97.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e83 (98.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003ePast/current\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e1 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e1 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003e\u0026lt;N100,000 pa (\u0026lt; $ 250)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e24 (41.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e16 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e30 (47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003e\u0026gt;N1,000,000 pa (\u0026gt; $ 250)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e34 (58.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e20 (55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e33 (52.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eEmployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eEmployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e99 (72.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e41 (83.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e75 (83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e38 (27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e8 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e15 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eLess than primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e79 (57.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e18 (36.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e24 (25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e29 (21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e20 (40.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e45 (46.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eTertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e29 (21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e11 (22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e27 (28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eAge at sexual initiation, Mean years (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e18.5 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e18.3 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e19.5 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eAge at sexual initiation category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eEarly Coitarche (\u0026lt;17 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e36 (28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e14 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e14 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eNot Early Coitarche (\u0026ge;17 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e92 (71.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e35 (71.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e76 (84.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eTotal lifetime sex partners, Mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e2.3 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e2.9 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e2.8 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eCategory of total lifetime sex partners\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e52 (43.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e18 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e24 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003e2-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e45 (37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e14 (29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e51 (53.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003e\u0026gt;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e22 (18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e16 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e20 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eParity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003e\u0026le; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e29 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e19 (39.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e58 (63.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003e4-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e39 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e16 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e27 (29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003e6-7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e33 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e7 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e5 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003e\u0026gt;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e34 (25.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e6 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e1 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eTesting positive for any HPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e84 (60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e38 (77.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e7 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e56 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e11(22.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e90 (92.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eTesting positive for high-risk HPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e84 (60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e38 (77.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e6 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e56 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e11(22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e91(93.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eHistory of treatment for any (STIs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e45 (32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e27 (55.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e47 (49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e56 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e12 (24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e43 (44.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e32 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e9 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e6 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eCD4+ cell count category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003e\u0026lt;350/mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e8 (80.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e30 (79.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003e\u0026ge;350/mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e2 (20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e8 (21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eHistology type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eSquamous Cell Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e112 (89.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e38 (84.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e9 (7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e6 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e4 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e1 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eStudy site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eJos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e81 (57.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e40 (81.6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e49 (50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.3077%;\"\u003e\n \u003cp\u003eLagos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e59 (42.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.4231%;\"\u003e\n \u003cp\u003e9 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.3462%;\"\u003e\n \u003cp\u003e48 (49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5769%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e Pearson\u0026rsquo;s chi-square test was used to compare categorical variables, and Fisher\u0026rsquo;s exact test was used when n\u0026thinsp;\u0026lt;\u0026thinsp;5 in any cell. ANOVA, or the Kruskal‒Wallis test, was used to compare the means of continuous variables across groups. Continuous variables are presented as the mean plus/minus standard deviation (SD).\u003c/p\u003e\n\u003cp\u003eIn this analysis, which included 286 participants, the HPV genotyping results indicated an overall prevalence of 45.0% for the detection of any HPV infection. HPV infection was detected most frequently among HIV-positive women with ICC (77.6%). In contrast, it was detected in 60.0% of the HIV-negative women with ICC and 6.2% of the HIV-positive women without ICC (p \u0026lt; 0.001). Compared with HIV-negative women with ICC and HIV-positive women without ICC, HIV-positive women with ICC had a higher prevalence of HR-HPV infections, multiple HPV infections, and low-risk HPV (LR-HPV) infections (Fig. 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e shows the prevalence of HPV types, and overall, HPV16 or HPV18 accounted for the highest prevalence of HR-HPV, representing 29.4% of all HPV infections. HPV16 was the most prevalent, accounting for 20.6%, followed by HPV18 at 8.7%. The prevalence of other HR-HPV types in the study population was less than 5%: HPV45 (4.2%), HPV35 (2.8%), HPV52 (2.5%), and HPV59 (1.8%). We also identified LR-HPV genotypes, with HPV11, HPV61, and HPV81 being the most common, each representing proportions of 1.1%, 0.7%, and 0.7%, respectively.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003eTABLE 2. Prevalence of specific HPV types in the study population\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57.1429%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8095%;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57.1429%;\"\u003e\n \u003cp\u003eAny HR-HPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e128 (44.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8095%;\"\u003e\n \u003cp\u003e(39.1, 50.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57.1429%;\"\u003e\n \u003cp\u003eHPV16 or18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e84 (29.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8095%;\"\u003e\n \u003cp\u003e(24.3, 34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57.1429%;\"\u003e\n \u003cp\u003eHPV16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e59 (20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8095%;\"\u003e\n \u003cp\u003e(16.3, 25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57.1429%;\"\u003e\n \u003cp\u003eHPV18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e25 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8095%;\"\u003e\n \u003cp\u003e(6.0, 12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57.1429%;\"\u003e\n \u003cp\u003eHPV45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e12 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8095%;\"\u003e\n \u003cp\u003e(2.4, 7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57.1429%;\"\u003e\n \u003cp\u003eHPV35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e8 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8095%;\"\u003e\n \u003cp\u003e(1.4, 5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57.1429%;\"\u003e\n \u003cp\u003eHPV52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e7 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8095%;\"\u003e\n \u003cp\u003e(1.2, 5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57.1429%;\"\u003e\n \u003cp\u003eHPV59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e5 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8095%;\"\u003e\n \u003cp\u003e(0.7, 4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57.1429%;\"\u003e\n \u003cp\u003eHPV58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e4 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8095%;\"\u003e\n \u003cp\u003e(0.5, 3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57.1429%;\"\u003e\n \u003cp\u003eHPV31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e2 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8095%;\"\u003e\n \u003cp\u003e(0.2, 2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57.1429%;\"\u003e\n \u003cp\u003eHPV39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e2 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8095%;\"\u003e\n \u003cp\u003e(0.2, 2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57.1429%;\"\u003e\n \u003cp\u003eHPV73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e2 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8095%;\"\u003e\n \u003cp\u003e(0.2, 2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57.1429%;\"\u003e\n \u003cp\u003eMultiple HR-HPV*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e8 (2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8095%;\"\u003e\n \u003cp\u003e(1.4, 5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57.1429%;\"\u003e\n \u003cp\u003eAny LR-HPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e3 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8095%;\"\u003e\n \u003cp\u003e(0.0, 0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57.1429%;\"\u003e\n \u003cp\u003eHPV11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e3 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8095%;\"\u003e\n \u003cp\u003e(0.0, 0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57.1429%;\"\u003e\n \u003cp\u003eHPV61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e2 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8095%;\"\u003e\n \u003cp\u003e(0.2, 2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57.1429%;\"\u003e\n \u003cp\u003eHPV81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e2 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8095%;\"\u003e\n \u003cp\u003e(0.2, 2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57.1429%;\"\u003e\n \u003cp\u003eMultiple LR-HPV*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e2 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8095%;\"\u003e\n \u003cp\u003e(0.2, 2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 57.1429%;\"\u003e\n \u003cp\u003eTotal Any HR-HPV and/or LR-HPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e129 (45.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8095%;\"\u003e\n \u003cp\u003e(3.9, 50.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*We used a threshold of 1% reads to identify a multistrain genotype (Multiple HR-HPV and Multiple LR-HPV).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e shows the results of modified Poisson regression models for the associations between HR-HPV and HIV status in women with cervical cancer, adjusting for age, BMI, parity, and study site. Model 1 shows that HIV-positive women are 1.35 times more likely to have HR-HPV than HIV-negative women (aPRR = 1.35, 95% CI: 1.06, 1.72) after adjusting for all covariates. Similarly, in Model 2 and Model 3, the associations between HIV positivity and HR-HPV remained significant after performing backward selection and adjusting for age and study site (aPRR = 1.36, 95% CI: 1.07, 1.74 and aPRR = 1.46, 95% CI: 1.17, 1.82, respectively). There are significant differences between the study sites (Jos vs. Lagos) in all three models. Compared with those from Jos, women from Lagos have higher prevalence rates of HR-HPV. This difference persisted in the final multivariable model (aPRR = 1.33, 95% CI: 1.06, 1.67). Age, BMI, and parity did not show significant associations with HR-HPV in any of the models.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"647\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 647px;\"\u003e\n \u003cp\u003eTABLE 3. Results of robust (modified) Poisson regression model: Association of HIV status with high-risk HPV positivity among women with cervical cancer (N=170)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003ePRR \u0026nbsp;(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ePRR \u0026nbsp;(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003ePRR \u0026nbsp;(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eHIV positivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1.35 (1.06, 172)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.36 (1.07, 1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1.46 (1.17, 1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAge (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e0.90 (0.79, 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0.93 (0.83, 1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eBMI\u003csup\u003ec\u003c/sup\u003e (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e0.92 (0.82, 1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eParity (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1.06 (0.94, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eStudy site Jos vs. Lagos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1.34 (1.07, 1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e1.31 (1.04, 1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1.33 (1.06, 1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eAIC\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e319.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e316.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e314.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eBIC\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e338.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003e328.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 51px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e324.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 647px;\"\u003e\n \u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eModel 1 represents the full model adjusted for all covariates presented, and Model 2 shows values after performing backward selection and adjusting for age and study site.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003e Model 3 represents the final model, including the study site, at a significance level of p\u0026lt;0.05.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Abbreviations: BMI, Body Mass Index; AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion; PRR = Prevalence Rate Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e shows the results of modified Poisson regression models for the associations between cervical cancer status and any HPV infection among HIV-positive women, adjusting for history of treatment for any STI, early coitarche (\u0026lt;17 years), and parity. Model 1 shows that HIV-positive women with cervical cancer (ICC) are 2.27 times more likely to have any HPV infection than those without ICC (aPRR = 2.27, 95% CI: 1.70, 2.55). HIV positivity remained significantly associated with HPV infection in Model 2 (aPRR = 2.29, 95% CI: 1.83, 2.74) when adjusted for only the variables significant at the p\u0026lt;0.05 level. Histories of treatment for any STI, early coitarche (\u0026lt;17 years), and parity did not show significant associations with cervical cancer in either Model 1 or Model 2.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 642px;\"\u003e\n \u003cp\u003eTABLE 4. Results of robust (modified) Poisson regression model: Association of HPV infection with cervical cancer among HIV-positive women (N=132)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 231px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003ePRR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003ePRR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 231px;\"\u003e\n \u003cp\u003eAny HPV (positive vs negative)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e2.27 (1.70, 2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e2.29 (1.83, 2.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 231px;\"\u003e\n \u003cp\u003eHistory of treatment for any STI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e1.14 (0.92, 1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 231px;\"\u003e\n \u003cp\u003eEarly Coitarche (\u0026lt;17 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e1.03 (0.71, 1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 231px;\"\u003e\n \u003cp\u003eParity (continuous)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.91 (0.76, 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 231px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e149.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e147.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 231px;\"\u003e\n \u003cp\u003eBIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e163.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e152.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 642px;\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Model 1 represents the full model adjusted for all covariates presented.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eModel 2 represents the final model (excluding all other non-significant variables) at a significant level of 0.05.\u003c/p\u003e\n \u003cp\u003ePRR = Prevalence rate ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study reports the prevalence and distribution of HPV infection among Nigerian women with cervical cancer, comparing those who are HIV-positive with those who are HIV-negative. Our results confirmed that the most frequently detected HR-HPV genotypes were HPV16, HPV18, HPV45, and HPV35. The study also revealed that HIV-positive women tended to develop cervical cancer approximately 10 years earlier than HIV-negative women. Compared with HIV-negative women, HIV-positive women also had a higher prevalence of HR-HPV and multiple HPV infections.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAge and ICC status\u003c/h2\u003e \u003cp\u003eOur findings of a younger age at ICC diagnosis for HIV-positive women are in accordance with the literature. Previous studies conducted in sub-Saharan Africa have shown that HIV-positive women are at a higher risk of developing cervical precancer and cancer at a younger age [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. A study in Jos revealed that ICC occurs at a lower median age of 35 years in HIV-positive women than it does at a median age of 40 years in HIV-negative women [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In Kenya and South Africa, HIV-positive women with ICC were, on average, approximately 10 years younger than their HIV-negative counterparts [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. One plausible explanation is that HIV-positive women may have engaged in sexual activity at a younger age, leading to a greater number of lifetime sexual partners and an elevated risk of acquiring and persisting with HPV infections [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This highlights the importance of targeted interventions and early detection in reducing the burden of cervical cancer, particularly among HIV-positive women.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence and distribution of type-specific human papillomaviruses in the study population\u003c/h2\u003e \u003cp\u003eOur results indicated that HIV-positive women with ICC had a higher prevalence of HR-HPV infection. HPV16 or HPV18 were the most prevalent types, accounting for 29.4% of the observed cases. Previous studies conducted in Nigerian populations have shown inconsistent results regarding the distribution and prevalence of HR-HPV-specific types [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, our study results agree with some of those reports and the prevalence observed worldwide [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Our recent study by Musa et al. revealed that the HPV16 and 18 genotypes were the most prevalent in North Central Nigeria [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The prevalence of HPV16 and HPV18 among the HIV-positive women in our study was notably higher, which could be due to differences in the study timelines, regions involved, methods of detection used, and ICC statuses. The eight most frequently occurring HR-HPV genotypes identified in our study (HPV16, HPV18, HPV31, HPV33, HPV35, HPV45, HPV52, and HPV58) have been shown to be responsible for 90% of all cervical cancer cases worldwide [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Our study included women seeking care in two tertiary hospitals (Jos and Lagos) in Nigeria's Northern and Southern regions. As a result, these findings may not be fully representative of the broader population of women in Nigeria due to several factors. Nigeria is home to a wide range of ethnic groups, and the majority of the population lives in rural areas where access to tertiary healthcare facilities is often limited [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Furthermore, socioeconomic disparities, low educational attainment for girls, early marriage, and a high prevalence of HIV infections among women in certain regions of the country may not be fully represented in our cohort [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur results and those of other studies demonstrate that the prevalence of multiple HPV infections was higher among HIV-positive women, stemming from a higher risk of acquisition and a reduced ability to clear the HPV infection [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. We detected HPV52, HPV73, HPV11, and HPV61 among the multiple HPV infections in these women, albeit in a relatively low proportion. This increased susceptibility is attributed to a combination of factors, such as a compromised immune system and behavioral factors, that have been found to be associated with both HIV and HPV infections [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. HIV infection compromises the immune system, particularly affecting CD4\u0026thinsp;+\u0026thinsp;T cells. As a result, the impaired immune response in HIV-positive individuals reduces their ability to effectively clear HPV infections and control viral replication [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This persistence of HPV infection increases the likelihood of acquiring multiple HPV genotypes [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Furthermore, studies have shown that HIV-positive women demonstrate altered cytokine profiles, leading to chronic inflammation, which may enhance their susceptibility to multiple HPV infections [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The confirmation of prevalent HPV genotypes among Nigerian women using highly sensitive techniques such as PCR-NGS is of public health significance, particularly following the official rollout of the HPV vaccination program in Nigeria in October 2023 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eImportantly, we identified HPV35 as the fourth most common HR-HPV genotype in our study population, as recent studies have suggested a strong association between HPV35 and cervical carcinogenesis, particularly in women of African ancestry [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Several studies in Nigeria have reported HPV35 as one of the most common HPV types in women with cervical cancer, especially among those who are also HIV positive [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Mcharo et al. reported HPV35 as the predominant HPV type in women with high-grade squamous intraepithelial lesions (HSILs), particularly those living with HIV. However, the authors noted that HPV35 is rarely detected as a single-type infection in HSIL and cervical cancer cases. Instead, it commonly co-occurs with other HR-HPV types, such as HPV16, 18, and 45, in both HIV-positive and HIV-negative women [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. We conducted further analysis to investigate the co-occurrence of HPV35 with other types, especially in ICC cases, within the framework of our study. Interestingly, we observed that HPV35 was present alone in 6 out of 8 cases (75.0%), whereas in 2 out of 8 cases (25.0%), it was detected together with HPV18 and HPV81. Our study has a small sample size with a low prevalence of HPV35 (2.8%), limiting our conclusions regarding the prevalence of HPV35 in Nigeria. The Gardasil 9 vaccine does not cover the HPV35 strain. Therefore, it is necessary to conduct further research to determine whether including HPV35 in the already highly effective Gardasil vaccine would increase its protective benefits for women in Africa or those of African heritage, especially HIV-positive women.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociations between human immunodeficiency virus status and high-risk human papillomavirus status among women with cervical cancer\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCompared with HIV-negative women with cervical cancer, HIV-positive women with cervical cancer were 1.46 times more likely to have HR-HPV. Our findings are consistent with several epidemiological studies that reported higher rates of HR-HPV in HIV-positive women [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. A comprehensive meta-analysis of 38 epidemiological studies revealed that HIV-positive women are more than twice as likely to be infected with HPV than their HIV-negative counterparts are, with a relative risk of 2.6 (95% CI: 2.0\u0026ndash;3.4) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Sally et al. reported a prevalence ratio of 4.18 (95% CI: 2.1\u0026ndash;8.5) for any HR-HPV infection in HIV-positive women compared with HIV-negative women in Nigeria after adjusting for age and educational attainment [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The mechanisms behind these associations have been elucidated in earlier sections of this paper. Our analysis revealed no significant associations between age, body mass index (BMI), or parity and HR-HPV infection among HIV-positive women with cervical cancer, contrary to the findings of several other studies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. This lack of association may be due to the limited statistical power to detect significant associations with these variables, the cross-sectional design, or the referral patterns in these tertiary care hospitals. We also found that women in Lagos were 1.36 times more likely to have HR-HPV infection. These findings suggest that factors specific to the Lagos region, such as differences in healthcare access and practices, may affect the likelihood of detecting HR-HPV infection in women with cervical cancer. However, these results should be interpreted with caution due to several factors. Specifically, the relatively small sample size and the fact that the participants were urban women seeking routine care may limit the generalizability of the findings to the broader population of women in Nigeria. Moreover, our results demonstrated significant disparities between the study sites in terms of participant characteristics, including the presence of missing data.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociations between cervical cancer status and any human papillomavirus infection among women with human immunodeficiency virus infection\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOur multivariable Poisson regression analysis indicated that HIV-positive women with ICC are 2.29 times more likely to have any HPV infection than HIV-positive women without ICC after controlling for covariates. The study recruited and assessed participants cross-sectionally and had no data on prior screening and treatment history for the control group (HIV-positive women without invasive cervical cancer). The potential presence of \"prior cases\" among the controls could attenuate the contrast between study groups, diminishing the study's power to detect significant differences. Furthermore, we note that it is important to consider both the CD4 count and viremia when studying the distribution of HPV in HIV-positive women. This can help us better understand the relationship between HIV parameters and HPV infection. Even though all the HIV-positive women in this study were receiving ART, data concerning the effects of biological markers of ART effects on the viral load and CD4 counts are needed. Owing to this lack of information, we could not account for this potential confounding factor in our study. Nevertheless, our findings further substantiate the understanding that HIV-positive women have an elevated risk of developing cervical cancer when infected with HPV [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies with larger sample sizes and diverse populations utilizing the NGS approach are essential to strengthen these findings. Although our recent study utilized the Anyplex\u0026trade; II HPV28 PCR detection method, we opted for next-generation sequencing (NGS) to address the discrepancies noted in previous research. While our study did not directly compare these methodologies, recent findings by Latsuzbaia et al. revealed that NGS not only detected the 25 genotypes covered by Anyplex but also identified an additional 41 genotypes [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Our study employed pooled PGMY primers that specifically target the 450 bp L1 gene fragment because of their enhanced sensitivity in amplifying HPV types [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This approach enables HPV typing across a broader section of the L1 gene, thereby bolstering result reliability [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Unlike the Anyplex II HPV28 method, which uses different primer sets yielding shorter amplicons targeting 100 to 200 bp fragments in the L1 region of 28 distinct HPV genotypes (comprising 19 HR-HPV and 9 LR-HPV types), our method offers broader coverage [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Additionally, we verified that the NGS assay effectively detected genotype sequences occurring at a frequency of 1% in cases of multiple HPV infections. The PCR-NGS method described herein can be easily updated to include primers targeting novel variants and can be heavily multiplexed. Strategies allowing for multiplexing up to 1536 samples per sequencing run are currently available, and this level of multiplexing can reduce the sequencing cost per sample and allow the method to be deployed in resource-limited regions such as Africa [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Despite cost constraints, NGS can play a crucial role in evaluating and monitoring HPV vaccines, serving as a second-line test in cervical cancer screening and supporting epidemiological surveys. These advantages hold particular significance in Nigeria, where the burden of ICC is high and where an HPV vaccine program has recently been initiated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThe strengths of our study include that it is the second in Nigeria to utilize a PCR‒NGS workflow for HPV genotyping. Our method is highly robust, easy to deploy, and adaptable to additional primers or primer sets. Our results align with findings from international studies on HPV genotypes in Africa and several studies in Nigeria that identified HPV16 and HPV18 as the most prevalent HR-HPV types in Nigeria. We also identified HPV35 as the fourth most common HR-HPV type, which has been documented to be prevalent in sub-Saharan Africa. This contribution helps address the discrepancies found in previously published research.\u003c/p\u003e \u003cp\u003eOne key strength of our study is the histopathological diagnosis of invasive cervical cancer, which was conducted independently by certified pathologists at two institutions (Jos and Lagos). To ensure quality control, Northwestern's Pathology Core reviewed the histopathology slides and paraffin-fixed blocks via telepathology. To increase the reliability and validity of the histopathological assessments, the histopathology slides and paraffin-fixed blocks were shared with the Pathology Core team at Northwestern University Cancer Center for verification [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOne major limitation of this study is missing data for several key variables, such as obesity, total lifetime sex partners, age at sexual initiation, and parity, which introduces potential bias. We performed a sensitivity analysis to assess whether the inclusion or exclusion of these covariates had any significant effect on the strength or direction of the relationship between HR-HPV and HIV positivity. As mentioned earlier in the methods section, our findings indicated that removing covariates with missing data did not result in any notable effect on the relationship between HR-HPV and HIV positivity. As noted above, we were also missing data on viral load and CD4 count for HIV-positive women. The cross-sectional design, relatively small sample size, and generalizability of the findings are limited to urban populations/tertiary care facilities, highlighting the need for a larger multicenter longitudinal cohort study to more precisely measure the time-varying occurrence of HPV clearance or progression to cervical intraepithelial neoplasia (CIN) and subsequent cancer. Future studies should also assess the impact of cofactors that can contribute to carcinogenesis, such as bacterial vaginosis (BV), \u003cem\u003eTrichomonas vaginalis\u003c/em\u003e (TV) infection, \u003cem\u003eChlamydia trachomatis\u003c/em\u003e (CT) infection, herpes simplex virus (HSV) infection, the viral load, and the CD4 count.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study provides valuable insights into the prevalence and distribution of HPV infection among women in Nigeria, particularly those with cervical cancer and HIV-positive women. The high prevalence of HPV16 and HPV18 among women with ICC highlights their critical role in the development of the disease. Additionally, the presence of multiple high-risk HPV genotypes and the high prevalence of multiple HPV infections among HIV-positive women emphasize the need for targeted interventions to reduce the burden of cervical cancer in this population. These findings can guide public health practitioners and policymakers in establishing effective prevention and control strategies tailored to the Nigerian population, including targeted vaccination programs, improved healthcare access, and comprehensive reproductive health services.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involves a secondary analysis of data obtained from the Northwestern University REDCap database, following a formal request to the Northwestern University Institutional Review Board (IRB) (STU00218862). Given that the dataset was de-identified, the study was classified as not human subject research and was exempt from the human subject research approval process. The data were accessed for research purposes on July 15, 2023. The dataset was originally collected in a prospective cohort study, which received IRB approval from the University of Jos (JUTH/DCS/ADM/127/XXVII/630) and the University of Lagos (CMUL/HREC/02/22/327/V4) in Nigeria, as well as Northwestern University (STU00207051) in the United States. The original study was part of the National Cancer Institute\u0026apos;s project on Epigenomic Biomarkers of HIV-associated Cancers in Nigeria (U54CA221205). All participants in the original study provided informed consent and were fully briefed on the study\u0026apos;s purpose and procedures before enrollment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026apos;Not applicable\u0026apos;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw HPV L1 DNA sequence data (i.e., FASTQ files) have been submitted to the NCBI Sequence Read Archive (SRA) with the BioProject Accession Number PRJNA1023898.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare(s) that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research findings reported in this manuscript were supported by the National Cancer Institute of the National Institutes of Health under award number U54CA221205. CJN received funding for training through an NIH/FIC/D43TW009575 titled \u0026ldquo;The Northwestern Nigerian Research Training Program in HIV and Malignancies (NN-HAM)\u0026rdquo; and a Seed Award grant from the U54CA221205 project. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCN, LH, and SM conceptualized and developed the study design and methodology. CN had full access to the data and led the writing of the manuscript. CN performed the statistical analysis and interpreted the results with support from SM, KK, and BJ. SJ and CN performed the laboratory process for HPV genotyping and data curation. LH, RM, and JM acquired the financial support for the project leading to this publication. JM, OS, and GI provided additional support, and GI provided additional support for data collection and interpretation of the results. All the listed authors contributed to editing the draft manuscript and approved the final version of the manuscript for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI would like to recognize the contributions of the U54 participants and staff from Jos University Teaching Hospital (JUTH) and Lagos University Teaching Hospital (LUTH). Special thanks go to the dedicated laboratory staff, Ms. Cecilia S. Chau and Ashley Wu, at the Genomics and Microbiome Core Facility, Rush University in Chicago, IL, USA. I am also grateful to the University of Jos for enabling me to pursue training in the USA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information (optional)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026apos;Not applicable\u0026apos;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBosch FX, Lorincz A, Mu\u0026ntilde;oz N, Meijer CJ, Shah KV. The causal relation between human papillomavirus and cervical cancer. J Clin Pathol. 2002;55(4):244-65. doi: 10.1136/jcp.55.4.244.\u003c/li\u003e\n\u003cli\u003ede Sanjose S, Quint WG, Alemany L, Geraets DT, Klaustermeier JE, Lloveras B, et al. Retrospective International Survey and HPV Time Trends Study Group. Human papillomavirus genotype attribution in invasive cervical cancer: a retrospective cross-sectional worldwide study. Lancet Oncol. 2010;11(11):1048-56.\u003c/li\u003e\n\u003cli\u003eSchiffman M, Castle PE, Jeronimo J, Rodriguez AC, Wacholder S. 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High parity is associated with increased risk of cervical cancer: Systematic review and meta-analysis of case‒control studies. Womens Health (Lond). 2022:17455065221075904. doi: 10.1177/17455065221075904.\u003c/li\u003e\n\u003cli\u003eLatsuzbaia A, Wienecke-Baldacchino A, Tapp J, Arbyn M, Karabegović I, Chen Z, et al. Characterization and Diversity of 243 Complete Human Papillomavirus Genomes in Cervical Swabs Using Next Generation Sequencing. Viruses. 2020;12(12):1437. doi: 10.3390/v12121437.\u003c/li\u003e\n\u003cli\u003eGravitt PE, Peyton CL, Alessi TQ, Wheeler CM, Coutl\u0026eacute;e F, Hildesheim A, et al. Improved amplification of genital human papillomaviruses. J Clin Microbiol. 2000;38(1):357-61. doi: 10.1128/JCM.38.1.357-361.2000.\u003c/li\u003e\n\u003cli\u003eBarzon L, Militello V, Lavezzo E, Franchin E, Peta E, Squarzon L, et al. Human papillomavirus genotyping by 454 next generation sequencing technology. J Clin Virol. 2011;52(2):93-7. doi: 10.1016/j.jcv.2011.07.006.\u003c/li\u003e\n\u003cli\u003eRollo F, Don\u0026agrave; MG, Pichi B, Pellini R, Covello R, Benevolo M. Evaluation of the Anyplex II HPV28 Assay in the Detection of Human Papillomavirus in Archival Samples of Oropharyngeal Carcinomas. Arch Pathol Lab Med. 2020;144(5):620-625. doi: 10.5858/arpa.2019-0199-OA.\u003c/li\u003e\n\u003cli\u003eKonnick E, Lockwood CM, Wu D. Targeted Next-Generation Sequencing of Acute Leukemia. Methods Mol Biol. 2017;1633:163-184. doi: 10.1007/978-1-4939-7142-8_11.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ICC, HIV, HPV, Genotypes, Nigeria","lastPublishedDoi":"10.21203/rs.3.rs-5160011/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5160011/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe prevalence of invasive cervical cancer (ICC) is high in Nigeria, with over 12,000 new cases and 8,000 deaths annually. Differences in diagnostic methods for human papillomavirus (HPV) genotypes have generated varied prevalence rates across populations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe examined the prevalence and distribution of HPV genotypes among HIV-negative women with ICC, HIV-positive women with ICC, and HIV-positive women without ICC. We utilized baseline data and DNA samples from cervical tissue obtained from a prospective cohort study between March 2018 and September 2022. High-throughput next-generation amplicon sequencing of the HPV L-1 gene was used to identify and classify the HPV genotypes. Modified Poisson regression models estimated associations between HIV and HPV status, adjusting for other variables of interest.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 286 women tested for HPV, 48.9% were HIV-negative with ICC, 17.2% were HIV-positive with ICC, and 33.9% were HIV-positive without ICC. The prevalence of high-risk HPV (HR-HPV) was 77.6% among HIV-positive women with ICC, whereas it was 60.0% among HIV-negative women with ICC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). HIV-positive women more frequently had multiple HPV genotypes (8.2% versus 1.4% among HIV-negative women with ICC and 2.1% among HIV-negative women without ICC) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). HPV16 or HPV18 accounted for 29.4% of all HPV cases. The most frequently detected HR-HPV genotypes included HPV16 (20.6%), HPV18 (8.7%), HPV45 (4.2%), and HPV35 (2.8%). In multivariable models adjusted for age, BMI, parity, and study site, HIV-positive women had an increased risk of HR-HPV (aPRR\u0026thinsp;=\u0026thinsp;1.46, 95% CI: 1.17, 1.82) and any HPV infection (aPRR\u0026thinsp;=\u0026thinsp;2.29, 95% CI: 1.83, 2.74) compared to HIV-negative women.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur NGS approach to HPV typing in Nigerian women, including those with cervical cancer and HIV, revealed the presence of HPV types not covered by the Gardasil-4 vaccine. This highlights the need for broader coverage of vaccines to protect against most HR-HPV types, irrespective of HIV status.\u003c/p\u003e","manuscriptTitle":"Molecular epidemiology of human papillomavirus genotypes among HIV-positive and HIV-negative women with cervical cancer in Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-23 06:06:09","doi":"10.21203/rs.3.rs-5160011/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6b5ad111-0c3a-4874-8870-9baf83b8ad49","owner":[],"postedDate":"October 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-26T12:53:38+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-23 06:06:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5160011","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5160011","identity":"rs-5160011","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}