At the population level, analysis of genetic data can potentially supplement standard approaches to evaluate the impact of an intervention. Comparative phylogenetic analyses of a baseline trial population and the population over the course of a trial can reveal the emergence or disappearance of clusters associated with particular traits. This approach was used to assess the impact of targeted hepatitis B vaccination in the Netherlands and showed that resulting decreases in hepatitis B virus incidence were due largely to declines in intravenous drug or heterosexual (but not MSM) risk groups.118 However, a follow-up study with increased sample size (n = 894 versus n = 85) suggested that reduced hepatitis B virus transmission was in fact because of reduced incidence in MSM, highlighting the importance of sample size and extended sampling periods for studies of this type.119 Alternatively, gene sequence data can be used to reconstruct HIV transmission networks rather than phylogenies, and cluster size distributions (CSDs) can be compared over the course of a prevention trial or intervention.88,120,121 In theory, CSDs will be dominated by larger clusters in populations where epidemic drivers (individuals with relatively high infectiousness) persist. Changes in CSD can reflect an intervention's impact on particular subgroups or the overall transmission patterns. These population-level approaches may be most suitable for trials in which clear incidence outcomes are equivocal or in which there are clear decreases in incidence but the underlying cause is unknown. Although the methods can be applied to a trial with any targeting strategy, clinical and demographic data from sequenced individuals are required.
Phylodynamics is the use of pathogen sequences to reconstruct epidemic history using viral genealogies and explicit population genetic models.120,122,123 Despite great methodological potential and scientific interest, phylodynamics has to date been rarely used for impact evaluation. This is likely related to a lack of consensus about the interpretation of the estimated parameter N e, [nominally the effective population size, a quantity proportional to the number of infected individuals, and estimated by coalescent approaches within the product N e × τ, where τ is the mean (viral) generation time]. Estimates of both N e and τ can be strongly affected by epidemic stage, transmission dynamics, or population sampling,11,124,125 and come with large variances. There is poor resolution of population size changes in the recent past (∼5 years).125 Additionally, simulation studies have shown that it may be difficult to disentangle the effects of changing incidence and changing transmission networks on phylodynamic parameters125,126; information on transmission network structure might be required for accurate parameter estimation. On a positive note, there is a growing base of modeling approaches to understand the relationships between epidemic models, phylogenetics, and transmission networks, which could be used to better understand how transmission network structure affects phylogenetic trees, and to model outcomes of specific prevention trial designs.89,120,127–131 Of particular interest is the incorporation of stochastic birth–death processes into phylodynamic estimation of epidemic parameters in lieu of standard coalescent models, allowing for more realistic assumptions about changes in epidemic size through time.132–136
Population sampling that is biased toward sequencing of incident cases can both decrease the required sample fraction and increase the probability of identifying transmission linkages. This “targeted” sampling strategy contrasts with the opportunistic sampling strategy generally found in standard phylogenetic studies. The approach is suited for studies that seek to identify HIV transmissions rather than reconstruct viral evolutionary history. As such, phylogenetic studies of transmission will be most informative and efficient when epidemiological questions are not simply overlaid onto ad hoc phylogenies reconstructed from randomly sampled individuals in a population (the standard approach for phylogenetic studies in, eg, systematics, biogeography, and phylodynamics).
Population sampling conducted over multiple periods, with the initial sample completed before the intervention, increases the probability of identifying transmission pairs or clusters with phylogenetic analysis. Transmission studies in concentrated epidemics generally involve post hoc use of sequence datasets that allows for retrospective analyses of epidemic history or transmission dynamics. The utility of HIV phylogenetics to inform prevention trials cannot be fully realized, however, based solely on retrospective analyses. The sampling frequency required to improve transmission cluster detection is unclear and will likely vary according to local incidence rates.
The development of a pan-African sequence database (analogous to the LANL HIV Sequence Database) will provide an important resource for future studies. The few phylogenetic studies of HIV transmission in Africa to date33,138 suggest that extra-community HIV transmission sources are common. The potential role of a large pan-African database would be to provide sets of African outgroup sequences to identify extra-community sources and to clarify their impact versus undiagnosed local sources. The database would also be useful for studying the spread of intersubtype recombinants and for characterizing the diversity of regional epidemics in Africa, that is, for phylodynamics and phylogeography of the African HIV epidemic.
A preferable approach would be to integrate the estimation of transmission risk factors (and other statistics of molecular epidemiology) directly into a phylodynamic inference framework, so that all the data available could be used and arbitrary clustering would not be a prerequisite step. Some authors have begun this process,139 but further methodological development and validation is needed. Additionally, the development of consistent quantitative definitions of transmission clusters, for example, based on tree shape characteristics, such as average branch lengths or nodal support, can make the identification of clusters more rigorous140; this includes assessing the statistical significance of trait clusters by simulation procedures similar to those used to examine gene flow among populations.141
Ethical challenges in studies involving transmission dynamics in HIV epidemics extend beyond those faced by randomized HIV control trials142 and apply to both concentrated and generalized epidemics. Phylogenetic studies conducted at the village or small community level may involve collection of HIV sequences and individual clinical and demographic data and in some studies may include geographic location data. The risk of individual identification could result in the loss of privacy and, in locations where HIV transmission or reckless exposure is a criminal offense, prosecution. Additionally, an important goal in phylogenetic studies of transmission is to identify traits associated with individuals and groups responsible for onward HIV transmission. Thus, there is the potential for stigmatization of individuals linked with or have common features of transmission network members, either underlying (eg, socioeconomic or demographic group) or proximate (eg, injection–drug use or sexual practice).63,64 In contrast to the stigma related to HIV infection, this challenge will include HIV-negative individuals as well.
Although sampling from generalized epidemics in regions of high prevalence might make such identifications unlikely, principles and governance on patient identifiable data will be necessary. Data from phylogenetic studies of transmission should be reported in ways where individuals cannot be identified. For example, the UKRDB and the Swiss HIV Cohort Study have adopted the strategy where only a minority (eg, 10%) of the sequences collected will be released, with these sequences chosen at random. This includes location data; one approach is to reduce the resolution of location data by including a set number of individuals (eg, 200) in the location set, such that individual identification by location is not possible. These data security strategies will also be addressed in the patient consent process and the tight restriction needed on data release must be recognized by funding agencies and scientific journals.
The authors thank Ward Cates and Nancy Padian for their careful review of earlier versions of the manuscript and Vladimir Novitsky and Mary Kate Grabowski for their helpful discussions. This work stemmed from discussions held during meetings sponsored by the National Institutes of Health (NIH) HIV Prevention Trials Network (February 21–22, 2012) and the Bill and Melinda Gates Foundation (BMGF) (November 1–2, 2012). The authors thank participants of these meetings and specifically David Burns at the NIH and Gina Dallabeta at the BMGF. The authors would also like to acknowledge members of the Phylogenetic and Networks for Generalized HIV Epidemics in Africa (PANGEA), a consortium sponsored by the BMGF.
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