Molecular Epidemiology and Transmission Dynamics of Recent and Long-Term HIV-1 Infections in Rural Western Kenya

Objective

To identify unique characteristics of recent versus established HIV infections and describe sexual transmission networks, we characterized circulating HIV-1 strains from two randomly selected populations of ART-naïve participants in rural western Kenya.

Methods

Recent HIV infections were identified by the HIV-1 subtype B, E and D, immunoglobulin G capture immunoassay (IgG BED-CEIA) and BioRad avidity assays. Genotypic and phylogenetic analyses were performed on the pol gene to identify transmitted drug resistance (TDR) mutations, characterize HIV subtypes and potential transmission clusters. Factors associated with recent infection and clustering were assessed by logistic regression.

Results

Of the 320 specimens, 40 (12.5%) were concordantly identified by the two assays as recent infections. Factors independently associated with being recently infected were age ≤19 years (P = 0.001) and history of sexually transmitted infections (STIs) in the past six months (P = 0.004). HIV subtype distribution differed in recently versus chronically infected participants, with subtype A observed among 53% recent vs. 68% chronic infections (p = 0.04) and subtype D among 26% recent vs. 12% chronic infections (p = 0.012). Overall, the prevalence of primary drug resistance was 1.16%. Of the 258 sequences, 11.2% were in monophyletic clusters of between 2–4 individuals. In multivariate analysis factors associated with clustering included having recent HIV infection P = 0.043 and being from Gem region P = 0.002.

Conclusions

Recent HIV-1 infection was more frequent among 13–19 year olds compared with older age groups, underscoring the ongoing risk and susceptibility of younger persons for acquiring HIV infection. Our findings also provide evidence of sexual networks. The association of recent infections with clustering suggests that early infections may be contributing significant proportions of onward transmission highlighting the need for early diagnosis and treatment as prevention for ongoing prevention. Larger studies are needed to better understand the structure of these networks and subsequently implement and evaluate targeted interventions.

Below: Distribution of HIV-1 subtypes among participants, by recency of infection, Western Kenya, October 2003-May 2005

Full article at:   http://goo.gl/B0sgNH

By:  

Clement Zeh, Pauli N. Amornkul, Lisa A. Mills

US Centers for Disease Control and Prevention, Division of HIV/AIDS Prevention (CDC), Kisumu, Kenya

Seth C. Inzaule, Lillian G. Nafisa, Alex Kasembeli, Fredrick Otieno, Lisa A. Mills

Kenya Medical Research Institute (KEMRI)/CDC Research and Public Health Collaboration, Kisumu Field Research Station, Kisumu, Kenya

Seth C. Inzaule, Pascale Ondoa

Amsterdam Institute of Global Health and Development (AIGHD), Department of Global Health of the Academic Medical Center, Amsterdam, The Netherlands

Hilde Vandenhoudt

Institute of Tropical Medicine (ITM), Antwerp, Belgium

John N. Nkengasong

Division of Global HIV and Tuberculosis, Center for Global Health, CDC Atlanta, Georgia, United States of America

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A Method to Estimate the Size and Characteristics of HIV-Positive Populations Using an Individual-based Stochastic Simulation Model

It is important not only to collect epidemiologic data on HIV but to also fully utilize such information to understand the epidemic over time and to help inform and monitor the impact of policies and interventions. We describe and apply a novel method to estimate the size and characteristics of HIV-positive populations. The method was applied to data on men who have sex with men living in the UK and to a pseudo dataset to assess performance for different data availability. The individual-based simulation model was calibrated using an approximate Bayesian computation-based approach. In 2013, 48,310 (90% plausibility range: 39,900–45,560) men who have sex with men were estimated to be living with HIV in the UK, of whom 10,400 (6,160–17,350) were undiagnosed. There were an estimated 3,210 (1,730–5,350) infections per year on average between 2010 and 2013. Sixty-two percent of the total HIV-positive population are thought to have viral load <500 copies/ml. In the pseudo-epidemic example, HIV estimates have narrower plausibility ranges and are closer to the true number, the greater the data availability to calibrate the model. We demonstrate that our method can be applied to settings with less data, however plausibility ranges for estimates will be wider to reflect greater uncertainty of the data used to fit the model.

Below:  Calibrating the model to data on MSM in the UK. A, Number of HIV diagnoses, (B) number of AIDS diagnoses, (C) number of deaths, (D) proportion of diagnoses which were recent infections (defined here as an infection which took place within six months of an HIV diagnosis), (E) total number seen for care, (F) Median CD4 count at diagnosis. Diamonds represent surveillance data until 2012 supplied by Public Health England (PHE). Filled diamonds show data used to calibrate the model; open diamonds show data not used to calibrate the model. Model median (solid line), model 90% plausibility range (dotted lines) and model range (light grey band) also shown. RITA indicates recent infection testing algorithm; SOPHID, survey of prevalent HIV infections diagnosed; CD4 SS, CD4 surveillance scheme.

Below:  A, Estimated incidence (number of new HIV infections in a year) and the (B) estimated diagnosis rate (probability of being diagnosed in any given 3-month period) among MSM in the UK.

Below:  Estimates of the (A) total number of MSM living with HIV in the UK and (B) total number of MSM living with undiagnosed HIV, by calendar year. Columns and error bars: Modeled median and 90% plausibility range.

Below:  Estimated (A) treatment cascade and (B) population characteristics of all MSM living with HIV in the UK in 2013. Columns and error bars: Modeled median and 90% plausibility range. ART indicates antiretroviral therapy. “Resistance” is defined as at least one resistance mutation in majority virus. “In need of ART” includes people who are on ART and those who are ART-naïve with CD4 count <500 cells/mm³. ART indicates antiretroviral therapy.

By:  Fumiyo Nakagawa,a Ard van Sighem,b Rodolphe Thiebaut,c Colette Smith,a Oliver Ratmann,d Valentina Cambiano,aJan Albert,e,f Andrew Amato-Gauci,g Daniela Bezemer,b Colin Campbell,h Daniel Commenges,c Martin Donoghoe,iDeborah Ford,j Roger Kouyos,k Rebecca Lodwick,l Jens Lundgren,m Nikos Pantazis,n Anastasia Pharris,g Chantal Quinten,g Claire Thorne,o Giota Touloumi,n Valerie Delpech,p and Andrew Phillipsa, on behalf of the SSOPHIE project working group in EuroCoord

From the ^aResearch Department of Infection and Population Health, UCL, London, United Kingdom; ^bStichting HIV Monitoring, Amsterdam, The Netherlands; ^cINSERM, Centre INSERM U897, Bordeaux, France; ^dDepartment of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom; ^eDepartment of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholm, Sweden; ^fDepartment of Clinical Microbiology, Karolinska University Hospital, Stockholm, Sweden; ^gEuropean Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden; ^hCEEISCAT, Generalitat de Catalunya, Barcelona, Spain; ⁱWHO Regional Office for Europe, Copenhagen, Denmark; ^jInstitute of Clinical Trials and Methodology, UCL, London, United Kingdom; ^kDivision of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland; ^lResearch Department of Primary Care and Population Health, UCL, London, United Kingdom; ^mCHIP @ Department of Infectious Diseases, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark; ⁿDepartment of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Athens, Greece; ^oUCL Institute of Child Health, UCL, London, United Kingdom; and^pPublic Health England, London, United Kingdom.

Corresponding author.

Correspondence: Fumiyo Nakagawa, Research Department of Infection and Population Health, UCL, Royal Free Hospital, Rowland Hill Street, London, NW3 2PF, UK. E-mail: ku.ca.lcu@awagakan.f.

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HIV Surveillance - Persons Who Inject Drugs

Via:   http://goo.gl/LkL7cC

Characterizing HIV Epidemiology in Stable Couples in Cambodia, the Dominican Republic, Haiti, and India

Using a set of statistical methods and HIV mathematical models applied on nationally representative Demographic and Health Survey data, we characterized HIV serodiscordancy patterns and HIV transmission dynamics in stable couples (SCs) in four countries: Cambodia, the Dominican Republic, Haiti, and India. The majority of SCs affected by HIV were serodiscordant, and about a third of HIV-infected persons had uninfected partners. 

Overall, nearly two-thirds of HIV infections occurred in individuals in SCs, but only about half of these infections were due to transmissions within serodiscordant couples. The majority of HIV incidence in the population occurred through extra-partner encounters in SCs. 

There is similarity in HIV epidemiology in SCs between these countries and countries in sub-Saharan Africa, despite the difference in scale of epidemics. It appears that HIV epidemiology in SCs may share similar patterns globally, possibly because it is a natural 'spillover' effect of HIV dynamics in high-risk populations.

Below:  Patterns of HIV serodiscordancy in India, Cambodia, the Dominican Republic, and Haiti compared to those in low and high HIV prevalence countries in sub-Saharan Africa. Countries are shown in order of increasing HIV prevalence

Below:  HIV incidence and its sources in stable couples. (a) Fraction of new HIV infections in stable HIV serodiscordant couples that are due to sources external vs. internal to the couple. Estimates are shown for HIV incidence arising in stable HIV serodiscordant couples due to sources external to the couple. (b–h) The mean country-specific contributions to HIV incidence in the population stratified by couples' serostatus and source of HIV infection. Results are displayed for India, Cambodia, the Dominican Republic, and Haiti, compared to those in low and high HIV prevalence countries in sub-Saharan African. Countries are shown in order of increasing HIV prevalence.

Full article at:   http://goo.gl/YmTqBv

By:  Chemaitelly H1, Abu-Raddad LJ1.

• 1Infectious Disease Epidemiology Group,Weill Cornell Medical College - Qatar,Cornell University,Qatar Foundation - Education City,Doha,Qatar. 

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