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Published: 4 June 2021

Last updated: 20 February 2023

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1

Darvishian, Maryam, Zahid A. Butt, Stanley Wong, Eric M. Yoshida, Jaskaran Khinda, Michael Otterstatter, Amanda Yu, et al. "Elevated risk of colorectal, liver, and pancreatic cancers among HCV, HBV and/or HIV (co)infected individuals in a population based cohort in Canada." Therapeutic Advances in Medical Oncology 13 (January 2021): 175883592199298. http://dx.doi.org/10.1177/1758835921992987.

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Introduction:Studies of the impact of hepatitis C virus (HCV), hepatitis B virus (HBV) and HIV mono and co-infections on the risk of cancer, particularly extra-hepatic cancer, have been limited and inconsistent in their findings.Methods:In the British Columbia Hepatitis Testers Cohort, we assessed the risk of colorectal, liver, and pancreatic cancers in association with HCV, HBV and HIV infection status. Using Fine and Gray adjusted proportional subdistribution hazards models, we assessed the impact of infection status on each cancer, accounting for competing mortality risk. Cancer occurrence was ascertained from the BC Cancer Registry.Results:Among 658,697 individuals tested for the occurrence of all three infections, 1407 colorectal, 1294 liver, and 489 pancreatic cancers were identified. Compared to uninfected individuals, the risk of colorectal cancer was significantly elevated among those with HCV (Hazard ration [HR] 2.99; 95% confidence interval [CI] 2.55–3.51), HBV (HR 2.47; 95% CI 1.85–3.28), and HIV mono-infection (HR 2.30; 95% CI 1.47–3.59), and HCV/HIV co-infection. The risk of liver cancer was significantly elevated among HCV and HBV mono-infected and all co-infected individuals. The risk of pancreatic cancer was significantly elevated among individuals with HCV (HR 2.79; 95% CI 2.01–3.70) and HIV mono-infection (HR 2.82; 95% CI 1.39–5.71), and HCV/HBV co-infection.Discussion/Conclusion:Compared to uninfected individuals, the risk of colorectal, pancreatic and liver cancers was elevated among those with HCV, HBV and/or HIV infection. These findings highlight the need for targeted cancer prevention and diligent clinical monitoring for hepatic and extrahepatic cancers in infected populations.

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Wei, Yu, Wei Li, Tengfei Du, Zhangyong Hong, and Jianping Lin. "Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method." International Journal of Molecular Sciences 20, no. 14 (July 22, 2019): 3572. http://dx.doi.org/10.3390/ijms20143572.

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Human immunodeficiency virus type-1 and hepatitis C virus (HIV/HCV) coinfection occurs when a patient is simultaneously infected with both human immunodeficiency virus type-1 (HIV-1) and hepatitis C virus (HCV), which is common today in certain populations. However, the treatment of coinfection is a challenge because of the special considerations needed to ensure hepatic safety and avoid drug–drug interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for HIV/HCV coinfection. However, the identification of one molecule that acts on multiple targets simultaneously by experimental evaluation is costly and time-consuming. In silico target prediction tools provide more opportunities for the development of multitarget inhibitors. In this study, by combining Naïve Bayes (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints, MACCS and extended connectivity fingerprints 6 (ECFP6), 60 classification models were constructed to predict compounds that were active against 11 HIV-1 targets and four HCV targets based on a multiple quantitative structure–activity relationships (multiple QSAR) method. Five-fold cross-validation and test set validation were performed to measure the performance of the 60 classification models. Our results show that the 60 multiple QSAR models appeared to have high classification accuracy in terms of the area under the ROC curve (AUC) values, which ranged from 0.83 to 1 with a mean value of 0.97 for the HIV-1 models and from 0.84 to 1 with a mean value of 0.96 for the HCV models. Furthermore, the 60 models were used to comprehensively predict the potential targets of an additional 46 compounds, including 27 approved HIV-1 drugs, 10 approved HCV drugs and nine selected compounds known to be active against one or more targets of HIV-1 or HCV. Finally, 20 hits, including seven approved HIV-1 drugs, four approved HCV drugs, and nine other compounds, were predicted to be HIV/HCV coinfection multitarget inhibitors. The reported bioactivity data confirmed that seven out of nine compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding affinities. The remaining predicted hits and chemical-protein interaction pairs with the potential ability to suppress HIV/HCV coinfection are worthy of further experimental investigation. This investigation shows that the multiple QSAR method is useful in predicting chemical-protein interactions for the discovery of multitarget inhibitors and provides a unique strategy for the treatment of HIV/HCV coinfection.

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3

Abiodun, Oluwakemi E., Olukayode Adebimpe, James A. Ndako, Olajumoke Oludoun, Benedicta Aladeitan, and Michael Adeniyi. "Mathematical modeling of HIV-HCV co-infection model: Impact of parameters on reproduction number." F1000Research 11 (October 10, 2022): 1153. http://dx.doi.org/10.12688/f1000research.124555.1.

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Background: Hepatitis C Virus (HCV) and Human Immunodeficiency Virus (HIV) are both as classified blood-borne viruses since they are transmitted through contact with contaminated blood. Approximately 1.3 million of the 2.75 million global HIV/HCV carriers inject drugs (PWID). HIV co-infection has a harmful effect on the progression of HCV, resulting in greater rates of HCV persistence after acute infection, higher viral levels, and accelerated progression of liver fibrosis and end-stage liver disease. In this study, we developed and investigated a mathematical model for the dynamical behavior of HIV/AIDS and HCV co-infection, which includes therapy for both diseases, vertical transmission in HIV cases, unawareness and awareness of HIV infection, inefficient HIV treatment follow-up, and efficient condom use. Methods: Positivity and boundedness of the model under investigation were established using well-known theorems. The equilibria were demonstrated by bringing all differential equations to zero. The associative reproduction numbers for mono-infected and dual-infected models were calculated using the next-generation matrix approach. The local and global stabilities of the models were validated using the linearization and comparison theorem and the negative criterion techniques of bendixson and dulac, respectively. Results: The growing prevalence of HIV treatment dropout in each compartment of the HIV model led to a reduction in HIV on treatment compartments while other compartments exhibited an increase in populations. In dually infected patients, treating HCV first reduces co-infection reproduction number Rech, which reduces liver cancer risk. Conclusions: From the model's results, we infer various steps that policymakers could take to reduce the number of mono-infected and co-infected individuals.

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4

Abiodun, Oluwakemi E., Olukayode Adebimpe, James A. Ndako, Olajumoke Oludoun, Benedicta Aladeitan, and Michael Adeniyi. "Mathematical modeling of HIV-HCV co-infection model: Impact of parameters on reproduction number." F1000Research 11 (December 19, 2022): 1153. http://dx.doi.org/10.12688/f1000research.124555.2.

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Background: Hepatitis C Virus (HCV) and Human Immunodeficiency Virus (HIV) are both classified as blood-borne viruses since they are transmitted through contact with contaminated blood. Approximately 1.3 million of the 2.75 million global HIV/HCV carriers are people who inject drugs (PWID). HIV co-infection has a harmful effect on the progression of HCV, resulting in greater rates of HCV persistence after acute infection, higher viral levels, and accelerated progression of liver fibrosis and end-stage liver disease. In this study, we developed and investigated a mathematical model for the dynamical behavior of HIV/AIDS and HCV co-infection, which includes therapy for both diseases, vertical transmission in HIV cases, unawareness and awareness of HIV infection, inefficient HIV treatment follow-up, and efficient condom use. Methods: Positivity and boundedness of the model under investigation were established using well-known theorems. The equilibria were demonstrated by bringing all differential equations to zero. The associative reproduction numbers for mono-infected and dual-infected models were calculated using the next-generation matrix approach. The local and global stabilities of the models were validated using the linearization and comparison theorem and the negative criterion techniques of bendixson and dulac, respectively. Results: The growing prevalence of HIV treatment dropout in each compartment of the HIV model led to a reduction in HIV on treatment compartments while other compartments exhibited an increase in populations. In dually infected patients, treating HCV first reduces co-infection reproduction number Rech, which reduces liver cancer risk. Conclusions: From the model's results, we infer various steps (such as: campaigns to warn individuals about the consequences of having multiple sexual partners; distributing more condoms to individuals; continuing treatment for chronic HCV and AIDS) that policymakers could take to reduce the number of mono-infected and co-infected individuals.

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5

Oliveira, Dinamene, Maria do Rosário Martins, Rita Castro, Lemuel Cordeiro, Maria Rosalina Barroso, Maria Antónia Nazaré, and Filomena Pereira. "Seropositivity rate and sociodemographic factors associated to HIV, HBV, HCV and syphilis among parturients from Irene Neto Maternity of Lubango city, Angola." Sexually Transmitted Infections 96, no. 8 (May 18, 2020): 587–89. http://dx.doi.org/10.1136/sextrans-2019-054249.

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ObjectivesTo characterise infections by HIV, Treponema pallidum, hepatitis B (HBV) and C virus (HCV) in parturients admitted to Irene Neto Maternity, Lubango city, Huíla province, Angola, namely its seropositivity rate and its association with sociodemographic factors.MethodsAn observational, cross-sectional and analytical facility-based survey was conducted among 500 parturients at Irene Neto Maternity, from October 2016 to September 2017. Women in labour were screened for antibodies against HIV-1/2, T. pallidum and HCV. Antigen detection was used to diagnose HBV infections. Sociodemographic data were also collected. The seropositivity rate and respective CIs were estimated at a level of 95%. Multivariable logistic regression models were performed to explore the association between the studied infections and sociodemographic factors.ResultsIn 11.8% of the parturients (95% CI 9.3 to 14.9), at least one infection was detected. HBV infection was the most common (8.6%), followed by HIV infection (3.0%) and syphilis (1.0%). Coinfection with HBV and HIV was observed in two parturients (0.4%) and HBV, HIV and T. pallidum were all detected in one parturient (0.2%). No HCV infection was detected. For each additional year of formal education, pregnant women had a 10.0% lower chance of being infected with HBV (adjusted OR=0.900, 95% CI 0.816 to 0.992).ConclusionsThis study is one of the few reports contributing for the knowledge of some sexually transmitted infections epidemiology in Angola. The seropositivity rate of the studied infections is of concern, especially the high endemicity of HBV. There is a need for a stronger commitment and further research to design cost-effective public health and clinical interventions to improve the situation.

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6

Anderson, Russell W. "Mathematical models of HIV pathogenesis." Nature Medicine 3, no. 9 (September 1997): 936. http://dx.doi.org/10.1038/nm0997-936a.

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7

Grossman, Zvi, and Ronald B. Herberman. "Mathematical models of HIV pathogenesis." Nature Medicine 3, no. 9 (September 1997): 936–37. http://dx.doi.org/10.1038/nm0997-936b.

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8

Gore, S. M. "HIV Epidemiology--models and methods." Sexually Transmitted Infections 70, no. 5 (October 1, 1994): 364–65. http://dx.doi.org/10.1136/sti.70.5.364-b.

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9

Pace, Matthew J., Luis Agosto, Erin H. Graf, and Una O'Doherty. "HIV reservoirs and latency models." Virology 411, no. 2 (March 2011): 344–54. http://dx.doi.org/10.1016/j.virol.2010.12.041.

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10

Klotman, Paul E., Jay Rappaport, Patricio Ray, Jeffrey B. Kopp, Roberta Franks, Leslie A. Bruggeman, and Abner L. Notkins. "Transgenic models of HIV-1." AIDS 9, no. 4 (April 1995): 313–24. http://dx.doi.org/10.1097/00002030-199504000-00001.

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Klotman, Paul E., Jay Rappaport, Patricio Ray, Jeffrey B. Kopp, Roberta Franks, Leslie A. Bruggeman, and Abner L. Notkins. "Transgenic models of HIV-1." AIDS 9, no. 4 (April 1995): 313–24. http://dx.doi.org/10.1097/00002030-199509040-00001.

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12

Schultz, Alan M., and Shiu-Iok Hu. "Primate models for HIV vaccines." AIDS 7 (January 1992): S161—S170. http://dx.doi.org/10.1097/00002030-199201001-00021.

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13

Schultz, Alan M., and Shiu-Iok Hu. "Primate models for HIV vaccines." AIDS 7 (January 1993): S161—S170. http://dx.doi.org/10.1097/00002030-199301001-00021.

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14

GIDDING, H. F., J. AMIN, G. J. DORE, and M. G. LAW. "Hospitalization rates associated with hepatitis B and HIV co-infection, age and sex in a population-based cohort of people diagnosed with hepatitis C." Epidemiology and Infection 139, no. 8 (November 19, 2010): 1151–58. http://dx.doi.org/10.1017/s095026881000258x.

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SUMMARYTo determine the extent age, sex and co-infection affect morbidity in people infected with hepatitis C virus (HCV), we performed a population-based study linking HCV notifications in New South Wales, Australia with their hospital (July 2000 to June 2006), hepatitis B virus (HBV) and HIV notification, and death records. Poisson models were used to calculate hospitalization rate ratios (RRs) for all-cause, illicit drug and liver-related admissions. Co-infection RRs were used to estimate attributable risk (AR). The 86 501 people notified with HCV contributed 422 761 person-years of observation; 0·8% had HIV, 3·7% HBV, and 0·04% had both. RRs for males were equal to or lower than for females in younger ages, but higher in older ages (Pfor interaction ⩽0·013). HBV/HIV co-infection resulted in ARs of over 70% for liver disease and 30–60% otherwise. However, at the cohort level the impact was minimal (population ARs 1·3–8·7%). Our findings highlight the importance and success of public health measures, such as needle and syringe exchange programmes, which have helped to minimize the prevalence of co-infection in Australia. The findings also suggest that the age of study participants needs to be considered whenever the burden of HCV-related morbidity is reported by sex. The results are likely to be representative of patterns in hospital-related morbidity for the entire HCV-infected population in Australia and the ARs generalizable to other developed countries.

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15

Burdo, Tricia H., and Andrew D. Miller. "Animal models of HIV peripheral neuropathy." Future Virology 9, no. 5 (May 2014): 465–74. http://dx.doi.org/10.2217/fvl.14.28.

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16

Patel, Mahesh, and Harris Goldstein. "Animal models of cancer and HIV." Current Opinion in Oncology 16, no. 5 (September 2004): 463–67. http://dx.doi.org/10.1097/00001622-200409000-00009.

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17

Hatziioannou, Theodora, and David T. Evans. "Animal models for HIV/AIDS research." Nature Reviews Microbiology 10, no. 12 (November 16, 2012): 852–67. http://dx.doi.org/10.1038/nrmicro2911.

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18

Smith, Mike U. "HIV Epidemiology: Models and Methods.Alfredo Nicolosi." Quarterly Review of Biology 70, no. 1 (March 1995): 115. http://dx.doi.org/10.1086/418954.

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19

Borkow, Gadi. "Mouse models for HIV-1 infection." IUBMB Life (International Union of Biochemistry and Molecular Biology: Life) 57, no. 12 (December 1, 2005): 819–23. http://dx.doi.org/10.1080/15216540500459642.

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20

Yang, Hung-Chih. "Primary cell models of HIV latency." Current Opinion in HIV and AIDS 6, no. 1 (January 2011): 62–67. http://dx.doi.org/10.1097/coh.0b013e3283412568.

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21

Stover, John. "HIV models to inform health policy." Current Opinion in HIV and AIDS 6, no. 2 (March 2011): 108–13. http://dx.doi.org/10.1097/coh.0b013e328343ac05.

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22

Micci, Luca, Colleen S. McGary, and Mirko Paiardini. "Animal models in HIV cure research." Journal of Virus Eradication 1, no. 1 (January 2015): 17–22. http://dx.doi.org/10.1016/s2055-6640(20)31149-3.

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23

Apetrei, Cristian. "Models of protection against HIV/SIV." Lancet Infectious Diseases 12, no. 7 (July 2012): 520. http://dx.doi.org/10.1016/s1473-3099(12)70165-2.

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24

Bachar, Mostafa, and Anita Dorfmayr. "HIV treatment models with time delay." Comptes Rendus Biologies 327, no. 11 (November 2004): 983–94. http://dx.doi.org/10.1016/j.crvi.2004.08.007.

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25

Cassels, Susan, Samuel J. Clark, and Martina Morris. "Mathematical Models for HIV Transmission Dynamics." JAIDS Journal of Acquired Immune Deficiency Syndromes 47, Supplement 1 (March 2008): S34—S39. http://dx.doi.org/10.1097/qai.0b013e3181605da3.

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26

PENG, Wen, and Kai DENG. "Cell models of HIV-1 latency." SCIENTIA SINICA Vitae 50, no. 10 (September 22, 2020): 1025–31. http://dx.doi.org/10.1360/ssv-2020-0030.

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Lu, Ting-chi, John Cijiang He, and Paul Klotman. "Animal models of HIV-associated nephropathy." Current Opinion in Nephrology and Hypertension 15, no. 3 (May 2006): 233–37. http://dx.doi.org/10.1097/01.mnh.0000222688.69217.8e.

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Guedj, J., R. Thiébaut, and D. Commenges. "Practical Identifiability of HIV Dynamics Models." Bulletin of Mathematical Biology 69, no. 8 (June 8, 2007): 2493–513. http://dx.doi.org/10.1007/s11538-007-9228-7.

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29

Endashaw, Engida Endriyas, Dawit Melese Gebru, and Haileyesus Tessema Alemneh. "Coinfection Dynamics of HBV-HIV/AIDS with Mother-to-Child Transmission and Medical Interventions." Computational and Mathematical Methods in Medicine 2022 (December 21, 2022): 1–21. http://dx.doi.org/10.1155/2022/4563577.

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In this study, we analyzed the effect of mother-to-child transmission (MTCT) of hepatitis B virus (HBV) and human immunodeficiency virus (HIV) on the transmission dynamics of their coinfection to make a recommendation based on reasons to public health sector, policy makers, and programme implementers. We proved that the solutions of the sub and full models are positive and bounded. The effective reproduction numbers of the models are derived using the next generation matrix method. The disease-free and endemic equilibria of the submodels and the coinfection model are computed, and the stability of those equilibria is analyzed using Routh-Hurwitz criteria after computing the associated effective reproduction numbers. We performed a sensitivity analysis to show the influence of different parameters on the effective reproduction number of HBV-HIV/AIDS coinfection model, and we identified the most sensitive parameters are τ 2 and α 1 , which are the rate of MTCT of HIV and treatment rate for HBV infected class, respectively. The numerical simulation of the model is done using MATLAB and the findings from the simulations are discussed. From the results of numerical simulations, we observed that an increase in the rates of MTCT of HBV and HIV exacerbated HBV-HIV/AIDS coinfection, while a decrease in the rates of MTCT of these infections would decline the number of cases, minimize the spread, and help to eliminate HBV-HIV/AIDS coinfection from the society gradually.

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Endashaw, Engida Endriyas, and Temesgen Tibebu Mekonnen. "Modeling the Effect of Vaccination and Treatment on the Transmission Dynamics of Hepatitis B Virus and HIV/AIDS Coinfection." Journal of Applied Mathematics 2022 (May 5, 2022): 1–27. http://dx.doi.org/10.1155/2022/5246762.

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Hepatitis B and HIV/AIDS coinfections are common globally due to their similar mode of transmission. Since HIV infection modifies the course of HBV infection by increasing the rate of chronicity, prolonging HBV viremia, and increasing liver disease-associated deaths, individuals with coinfection of both diseases have a higher tendency of developing cirrhosis of the liver, higher levels of HBV DNA, reduced rate of clearance of the hepatitis B e antigen (HBeAg), and more likely to die than an individual with a single infection. This nature of HBV-HIV/AIDS coinfection motivated us to conduct this study. In this paper, we proposed and rigorously analyzed a deterministic mathematical model with the aim of investigating the effect of vaccination against hepatitis B virus and treatment for all infections on the transmission dynamics of HBV-HIV/AIDS coinfection in a population. We proved that the solutions of the submodels and the coinfection model are positive and bounded. The models are studied qualitatively using the stability theory of differential equations, and the effective reproduction numbers of the models are derived using the next generation matrix method. Stability of the equilibria of the submodels and the coinfection model is analyzed using Routh-Hurwitz criteria. The disease-free and endemic equilibria of the submodels and the coinfection model are computed, and both local and global asymptotic stability conditions for those equilibria are discussed. We performed a sensitivity analysis to illustrate the influence of different parameters on the effective reproduction number of HBV-HIV/AIDS coinfection model, and we identified the most sensitive parameters are ω B and ω H , which are the effective contact rates for HBV and HIV transmission, respectively. The numerical simulation of the model is done using MATLAB, and the findings from the simulations are discussed. It is observed that if the vaccination and treatment rates are increased, then the number of individuals susceptible to both infections and HBV-HIV/AIDS coinfection decreases and even falls to zero over time. Hence, it is concluded that vaccination against hepatitis B virus infection, treatment of hepatitis B and HIV/AIDS infections, and HBV-HIV/AIDS infection at the highest possible rate is very essential to control the spread of HBV-HIV/AIDS coinfection as an important public health problem.

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31

Rana, Urvi, Matt Driedger, Paul Sereda, Shenyi Pan, Erin Ding, Alex Wong, Sharon Walmsley, et al. "Clinical and demographic predictors of antiretroviral efficacy in HIV–HBV co-infected patients." Official Journal of the Association of Medical Microbiology and Infectious Disease Canada 6, no. 2 (July 2021): 137–48. http://dx.doi.org/10.3138/jammi-2020-0011.

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Background: The clinical and demographic characteristics that predict antiretroviral efficacy among patients co-infected with HIV and hepatitis B virus (HBV) remain poorly defined. We evaluated HIV virological suppression and rebound in a cohort of HIV–HBV co-infected patients initiated on antiretroviral therapy. Methods: A retrospective cohort analysis was performed with Canadian Observation Cohort Collaboration data. Cox proportional hazards models were used to determine the factors associated with time to virological suppression and time to virological rebound. Results: HBV status was available for 2,419 participants. A total of 8% were HBV co-infected, of whom 95% achieved virological suppression. After virological suppression, 29% of HIV–HBV co-infected participants experienced HIV virological rebound. HBV co-infection itself did not predict virological suppression or rebound risk. The rate of virological suppression was lower among patients with a history of injection drug use or baseline CD4 cell counts of <199 cells per cubic millimetre. Low baseline HIV RNA and men-who-have-sex-with-men status were significantly associated with a higher rate of virological suppression. Injection drug use and non-White race predicted viral rebound. Conclusions: HBV co-infected HIV patients achieve similar antiretroviral outcomes as those living with HIV mono-infection. Equitable treatment outcomes may be approached by targeting resources to key subpopulations living with HIV–HBV co-infection.

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WANG, LIANCHENG. "GLOBAL DYNAMICAL ANALYSIS OF HIV MODELS WITH TREATMENTS." International Journal of Bifurcation and Chaos 22, no. 09 (September 2012): 1250227. http://dx.doi.org/10.1142/s0218127412502276.

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In this paper, we study HIV mathematical models with treatments. Two models with RT inhibitor and HIV protease inhibitor are studied. Local and global analysis is carried out. By identifying a critical number [Formula: see text] for both treatments, we show that if the treatment is at least [Formula: see text] effective, then the uninfected steady state P0 is the only equilibrium in the feasible region, and P0 is globally asymptotically stable. Therefore, no HIV infection persists and infected T cells and HIV virus are cleared over time. However, if the treatment is not effective enough, i.e. less than [Formula: see text], then a unique infected steady state P* emerges in the interior of the feasible region. P0 becomes unstable and the system is uniformly persistent. Therefore, HIV infection persists. In this case, the unique infected steady state can be either stable or unstable. We show that it is locally stable only for r (the proliferation rate of T cells) small or large and unstable for some intermediate values. Global stability result is established for small values of r. Numerical simulation shows that once P* becomes unstable, periodic solution appears.

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33

McGinnis, Kathleen A., Shawn L. Fultz, Melissa Skanderson, Joseph Conigliaro, Kendall Bryant, and Amy C. Justice. "Hepatocellular Carcinoma and Non-Hodgkin's Lymphoma: The Roles of HIV, Hepatitis C Infection, and Alcohol Abuse." Journal of Clinical Oncology 24, no. 31 (November 1, 2006): 5005–9. http://dx.doi.org/10.1200/jco.2006.05.7984.

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Purpose To explore the relationship of HIV, hepatitis C (HCV), and alcohol abuse/dependence to risk for hepatocellular carcinoma and non-Hodgkin's lymphoma (NHL). Patients and Methods Male veterans (n = 14,018) with a first HIV diagnosis in the Veterans Affairs Healthcare System from October 1997 to September 2004; and 28,036 age-, race-, sex-, and location-matched HIV-negative veterans were identified. We examined the incidence of hepatocellular carcinoma and NHL and presence of HCV and alcohol abuse/dependence using International Classification of Diseases, ninth revision (ICD-9-CM) codes. HIV-positive to HIV-negative incident rate ratios (IRRs) and 95% CIs for the occurrence of hepatocellular carcinoma and NHL were calculated using Poisson regression models. Results HIV-positive veterans were at greater risk for hepatocellular carcinoma than HIV-negative veterans (IRR = 1.68; 95% CI, 1.02 to 2.77). After adjusting for HCV infection and alcohol abuse/dependence, HIV status was not independently associated with hepatocellular cancer (IRR = 0.96; 95% CI, 0.56 to 1.63). HIV-positive veterans had 9.71 times (95% CI, 6.99 to 13.49) greater risk of NHL than HIV-negative veterans. After adjusting for HCV and alcohol abuse/dependence, the IRR for NHL comparing HIV-positive with HIV-negative veterans is similar (IRR = 10.03, 95% CI, 7.19 to 13.97). Conclusion HIV-positive veterans have a higher relative incidence of hepatocellular carcinoma and NHL than HIV-negative veterans. For hepatocellular carcinoma, this association appears to be largely explained by the higher prevalence of HCV and alcohol abuse/dependence. Efforts to decrease hepatocellular carcinoma among persons with HIV should focus primarily on detecting and treating HCV and reducing heavy alcohol use.

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Hunt, Kelsey, Prosanta Mondal, Stephanie Konrad, Stuart Skinner, Kali Gartner, and Hyun J. Lim. "Identifying Factors Associated with Changes in CD4+Count in HIV-Infected Adults in Saskatoon, Saskatchewan." Canadian Journal of Infectious Diseases and Medical Microbiology 26, no. 4 (2015): 207–11. http://dx.doi.org/10.1155/2015/136568.

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OBJECTIVE: To assess the impact of clinical and social factors unique to HIV-infected adults in Saskatoon, Saskatchewan, regarding the rate of CD4+count change, and to identify factors associated with a risk of CD4+count decline.METHODS: A retrospective longitudinal cohort study from medical chart reviews at two clinics was conducted in Saskatoon. Univariate and multivariate linear mixed effects models were used to assess the impact of selected factors on CD4+count change.RESULTS: Four hundred eleven HIV-infected patients were identified from January 1, 2003 to November 30, 2011. Two hundred eighteen (53%) were male, mean (± SD) age was 35.6 ±10.1 years, 257 (70.8%) were First Nations or Métis, 312 (80.2%) were hepatitis C virus (HCV) coinfected and 300 (73.3%) had a history of injection drug use (IDU). In univariate models, age, ethnicity, HCV, IDU, antiretroviral therapy and social assistance were significant. Using ethnicity, HCV and IDU, three multivariate models (models 1, 2, 3) were built due to high correlation. First Nations or Métis ethnicity, HCV coinfection and a history of IDU were associated with significantly lower CD4+counts in multivariate models. Older age and social assistance were associated with significantly lower CD4+counts in models 1 and 3. Age was marginally significant in model 2 (P=0.055). Not prescribed antiretroviral therapy was associated with a significantly negative CD4+count slope in all multivariate models.CONCLUSION: The unique epidemiology of this HIV-infected population may be contributing to CD4+count change. Increased attention and resources focused on this high-risk population are needed to prevent disease progression and to improve overall health and quality of life.

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35

Hawkins, Jane, and Donna Molinek. "Markov cellular automata models for chronic disease progression." International Journal of Biomathematics 08, no. 06 (October 15, 2015): 1550085. http://dx.doi.org/10.1142/s1793524515500850.

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We analyze a Markov cellular automaton that models the spread of viruses that often progress to a chronic condition, such as human immunodeficiency virus (HIV) or hepatitis C virus (HCV). We show that the complex dynamical system produces a Markov process at the later stages, whose eigenvectors corresponding to the eigenvalue 1 have physical significance for the long-term prognosis of the virus. Moreover we show that drug treatment leads to chronic conditions that can be modeled by Markov shifts with more optimal eigenvectors.

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Ayalew, Kassahun Abere, Samuel Manda, and Bo Cai. "A Comparison of Bayesian Spatial Models for HIV Mapping in South Africa." International Journal of Environmental Research and Public Health 18, no. 21 (October 26, 2021): 11215. http://dx.doi.org/10.3390/ijerph182111215.

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Despite making significant progress in tackling its HIV epidemic, South Africa, with 7.7 million people living with HIV, still has the biggest HIV epidemic in the world. The Government, in collaboration with developmental partners and agencies, has been strengthening its responses to the HIV epidemic to better target the delivery of HIV care, treatment strategies and prevention services. Population-based household HIV surveys have, over time, contributed to the country’s efforts in monitoring and understanding the magnitude and heterogeneity of the HIV epidemic. Local-level monitoring of progress made against HIV and AIDS is increasingly needed for decision making. Previous studies have provided evidence of substantial subnational variation in the HIV epidemic. Using HIV prevalence data from the 2016 South African Demographic and Health Survey, we compare three spatial smoothing models, namely, the intrinsically conditionally autoregressive normal, Laplace and skew-t (ICAR-normal, ICAR-Laplace and ICAR-skew-t) in the estimation of the HIV prevalence across 52 districts in South Africa. The parameters of the resulting models are estimated using Bayesian approaches. The skewness parameter for the ICAR-skew-t model was not statistically significant, suggesting the absence of skewness in the HIV prevalence data. Based on the deviance information criterion (DIC) model selection, the ICAR-normal and ICAR-Laplace had DIC values of 291.3 and 315, respectively, which were lower than that of the ICAR-skewed t (348.1). However, based on the model adequacy criterion using the conditional predictive ordinates (CPO), the ICAR-skew-t distribution had the lowest CPO value. Thus, the ICAR-skew-t was the best spatial smoothing model for the estimation of HIV prevalence in our study.

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Siconolfi, Daniel E., Perry N. Halkitis, and Meighan E. Rogers. "Hepatitis Vaccination and Infection Among Gay, Bisexual, and Other Men Who Have Sex with Men who Attend Gyms in New York City." American Journal of Men's Health 3, no. 2 (March 31, 2008): 141–49. http://dx.doi.org/10.1177/1557988308315151.

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The authors examined hepatitis A virus (HAV) and hepatitis B virus (HBV) vaccination rates, hepatitis infection, and health care access in a gym-attending sample of 311 gay, bisexual, and other men who have sex with men (MSM) in New York City. Overall, 69% reported having been vaccinated for HAV and 70% reported having been vaccinated for HBV. Multivariate models were used to identify predictors of HAV and HBV vaccination, and younger men, HIV-positive men, and men who had access to a doctor or clinic were more likely to be vaccinated than older men, HIVnegative men, and men without access to a doctor or clinic. Men with health insurance coverage were more likely to have received HBV vaccination than men without coverage. Findings indicate that there is still a significant proportion of unvaccinated men in our sample.

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Drylewicz, Julia, Daniel Commenges, and Rodolphe Thiébaut. "Score Tests for Exploring Complex Models: Application to HIV Dynamics Models." Biometrical Journal 52, no. 1 (February 2010): 10–21. http://dx.doi.org/10.1002/bimj.200900030.

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Bull-Otterson, Lara, Ya-Lin A. Huang, Weiming Zhu, Hope King, Brian R. Edlin, and Karen W. Hoover. "Human Immunodeficiency Virus and Hepatitis C Virus Infection Testing Among Commercially Insured Persons Who Inject Drugs, United States, 2010–2017." Journal of Infectious Diseases 222, no. 6 (January 30, 2020): 940–47. http://dx.doi.org/10.1093/infdis/jiaa017.

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Abstract Background We assessed prevalence of testing for human immunodeficiency virus (HIV) and hepatitis C virus (HCV) infection among persons who inject drugs (PWID). Methods Using a nationwide health insurance database for claims paid during 2010–2017, we identified PWID by using codes from the International Classification of Diseases, Current Procedural Terminology, and National Drug Codes directory. We then estimated the percentage of PWIDs tested for HIV or HCV within 1 year of an index encounter, and we used multivariate logistic regression models to assess demographic and clinical factors associated with testing. Results Of 844 242 PWIDs, 71 938 (8.5%) were tested for HIV and 65 188 (7.7%) were tested for HCV infections. Missed opportunities were independently associated with being male (odds ratios [ORs]: HIV, 0.50 [95% confidence interval {CI}, 0.49–0.50], P < .001; HCV, 0.66 [95% CI, 0.65–0.72], P < .001), rural residence (ORs: HIV, 0.67 [95% CI, 0.65–0.69], P < .001; HCV, 0.75 [95% CI, 0.73–0.77], P < .001), and receiving services for skin infections or endocarditis (adjusted ORs: HIV, 0.91 [95% CI, 0.87–0.95], P < .001; HCV, 0.90 [95% CI, 0.86–0.95], P < .001). Conclusions Approximately 90% of presumed PWIDs missed opportunities for HIV or HCV testing, especially male rural residents with claims for skin infections or endocarditis, commonly associated with injection drug use.

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Xie, Xinqi, Junling Ma, and P. van den Driessche. "Backward bifurcation in within-host HIV models." Mathematical Biosciences 335 (May 2021): 108569. http://dx.doi.org/10.1016/j.mbs.2021.108569.

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V. Duyne, Rachel, Aarthi Narayanan, Kylene K.-Hall, Mohammed Saifuddin, Leonard Shultz, and Fatah Kashanchi. "Humanized Mouse Models of HIV-1 Latency." Current HIV Research 9, no. 8 (December 1, 2011): 595–605. http://dx.doi.org/10.2174/157016211798998781.

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Zhang, Jiangfeng, and Xiaohua Xia. "Identifiability Problems of Time-delay HIV Models." IFAC Proceedings Volumes 41, no. 2 (2008): 283–88. http://dx.doi.org/10.3182/20080706-5-kr-1001.00048.

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Boberg, Andreas, Andreas Bråve, Susanne Johansson, Britta Wahren, Jorma Hinkula, and Erik Rollman. "Murine models for HIV vaccination and challenge." Expert Review of Vaccines 7, no. 1 (February 2008): 117–30. http://dx.doi.org/10.1586/14760584.7.1.117.

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Moulton, Lawrence H., Frank C. Curriero, and Paulo F. Barroso. "Mixture models for quantitative HIV RNA data." Statistical Methods in Medical Research 11, no. 4 (August 2002): 317–25. http://dx.doi.org/10.1191/0962280202sm292ra.

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Pullen, Sara D. "Multidisciplinary Care Models in HIV/AIDS Care." Rehabilitation Oncology 37, no. 1 (January 2019): E1—E2. http://dx.doi.org/10.1097/01.reo.0000000000000157.

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Zhang, Liguo, and Lishan Su. "HIV-1 immunopathogenesis in humanized mouse models." Cellular & Molecular Immunology 9, no. 3 (April 16, 2012): 237–44. http://dx.doi.org/10.1038/cmi.2012.7.

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Kumar, Nitasha, Ann Chahroudi, and Guido Silvestri. "Animal models to achieve an HIV cure." Current Opinion in HIV and AIDS 11, no. 4 (July 2016): 432–41. http://dx.doi.org/10.1097/coh.0000000000000290.

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Apetrei, Cristian, Ivona Pandrea, and John W. Mellors. "Nonhuman Primate Models for HIV Cure Research." PLoS Pathogens 8, no. 8 (August 30, 2012): e1002892. http://dx.doi.org/10.1371/journal.ppat.1002892.

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Roan, Nadia R., and Jan Münch. "Improving preclinical models of HIV microbicide efficacy." Trends in Microbiology 23, no. 8 (August 2015): 445–47. http://dx.doi.org/10.1016/j.tim.2015.05.001.

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Gorantla, Santhi, Larisa Poluektova, and Howard E. Gendelman. "Rodent models for HIV-associated neurocognitive disorders." Trends in Neurosciences 35, no. 3 (March 2012): 197–208. http://dx.doi.org/10.1016/j.tins.2011.12.006.

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