The age and sex dynamics of heterosexual HIV transmission in Zambia: an HPTN 071 (PopART) phylogenetic and modelling study
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eLife Assessment
This important study provides evidence for our understanding of HIV transmission dynamics by age and sex in Zambia during the PopART trial; by combining phylogenetic and individual-based mathematical modelling (IBM), it adds depth to the epidemiological literature and may inform more strategic allocation of HIV prevention resources in sub-Saharan Africa. The authors employ two complementary and well-established methodologies (phylogenetics and IBM), and this dual approach is a notable strength. However, the evidence supporting key conclusions is incomplete, with several claims insufficiently substantiated by the data presented. Improvements in data presentation (e.g., quantification of qualitative statements, statistical estimates, and clearer description of results) would substantially strengthen the paper.
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Abstract
Abstract
While much progress has been made in reducing the incidence of HIV-1 infection in sub-Saharan Africa in recent years, bringing the epidemic to an end will require identification of the demographic groups that continue to contribute to transmission. Pathogen phylogenetics and individual-based mathematical models (IBMs) of transmission are approaches that enable researchers to explore such questions. Here, we used both methods to characterise the ages and sexes of the individuals involved in heterosexual transmission in the context of the HPTN 071 (PopART) trial in Zambia. The results were concordant, and show that the male partner was on average older than the female by less than seven years, with larger age gaps in male-to-female than female-to-male transmissions. We found that the largest gaps for female recipients were amongst the youngest of those recipients. Conversely, the youngest male recipients saw the smallest gaps. We further used the IBM to demonstrate that transmission to new age cohorts first entering into sexual activity is driven predominantly by male-to-female transmission. We also simulated the PopART universal testing and treatment intervention into the future to show that effective treatment of under-35-year-olds accounts for 93.8% of the reduction in incidence by 2039, while effective treatment of under-35-year-old men accounts for 62.1%. Finally, we simulated a one-year cessation of ART treatment for the whole population, which resulted in an immediate increase in the average age at transmission of both sources and recipients. With it becoming ever more expensive and difficult to find treatment-naive individuals and link them to care, targeted interventions for demographic groups such as under-35 men may be the key to finally ending HIV.
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eLife Assessment
This important study provides evidence for our understanding of HIV transmission dynamics by age and sex in Zambia during the PopART trial; by combining phylogenetic and individual-based mathematical modelling (IBM), it adds depth to the epidemiological literature and may inform more strategic allocation of HIV prevention resources in sub-Saharan Africa. The authors employ two complementary and well-established methodologies (phylogenetics and IBM), and this dual approach is a notable strength. However, the evidence supporting key conclusions is incomplete, with several claims insufficiently substantiated by the data presented. Improvements in data presentation (e.g., quantification of qualitative statements, statistical estimates, and clearer description of results) would substantially strengthen the paper.
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Reviewer #1 (Public review):
Summary:
This manuscript describes the results of phylogenetic and epidemiological modeling of the PopART community cohorts in Zambia. The current manuscript draft is methodologically strong, but needs revision to strengthen the take-home messages. As written, there are many possible take-away conclusions. For example, the agreement between IBM and phylogenetic analysis is noteworthy and provides a methodological focus. The revealed age patterns of transmission could be a focus. The effects of the PopART intervention and the consequences of a 1-year disruption could be a focus. It is important, though, that any main messages summarized by the authors are substantiated by the evidence provided and do not extrapolate beyond the data that have been generated. I recommend that the authors think deeply about what …
Reviewer #1 (Public review):
Summary:
This manuscript describes the results of phylogenetic and epidemiological modeling of the PopART community cohorts in Zambia. The current manuscript draft is methodologically strong, but needs revision to strengthen the take-home messages. As written, there are many possible take-away conclusions. For example, the agreement between IBM and phylogenetic analysis is noteworthy and provides a methodological focus. The revealed age patterns of transmission could be a focus. The effects of the PopART intervention and the consequences of a 1-year disruption could be a focus. It is important, though, that any main messages summarized by the authors are substantiated by the evidence provided and do not extrapolate beyond the data that have been generated. I recommend that the authors think deeply about what the most important, well-supported messages are and reframe the discussion and abstract accordingly.
Strengths/weaknesses by section:
(1) ABSTRACT
The Abstract summarizes qualitative findings nicely, but the authors should incorporate quantitative results for all of the qualitative findings statements.
The ending claim is not substantiated by the modeling scenarios that have been run: "targeted interventions for demographic groups such as under-35 men may be the key to finally ending HIV." It is straightforward to run this specific scenario in the model to determine whether or not this is true.
The authors should add confidence intervals to the quantitative metrics, such as the 93.8% and 62.1% incidence reduction.
(2) RESULTS
The authors should check the Results section for any qualitative claims not substantiated by the analyses performed, and ensure the corresponding analyses are presented to support the claims.
The Results and Methods describe the model's implementation of the PopART intervention differently. The Methods describes it as including VMMC, TB, and STI services, while the Results only mentions intensified HIV testing and linkage.
A limitation of the model is that HIV disease progression is based on the ATHENA cohort in the Netherlands, which is a different HIV subtype (B) than the one in the research setting (C). The model should be configured using subtype C progression data, which have been published, or at least a sensitivity analysis should be conducted with respect to disease progression assumptions.
In Table 2, the authors should consider adding a p-value to establish whether or not IBM and phylogenetics estimates are different.
(3) DISCUSSION
The literature review and comparison of study results to previously published phylogenetic studies is very nice. The authors could strengthen this by providing quantitative estimates with CIs for a more scientific comparison of the study results vs. prior studies, perhaps as a table or figure.
The authors state that due to "the narrow geographical catchment area... The results should not be automatically extrapolated to apply to other SSA settings." The authors should exercise this caution when comparing the results to studies in South Africa and elsewhere.
There are many other limitations to the analysis, including some mentioned above, that are not acknowledged. The authors should think carefully about what the most important limitations are and acknowledge them honestly at the end of the Discussion section.
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Reviewer #2 (Public review):
Summary:
The authors analyzed PopART data to better characterize the age and sex-specific heterosexual HIV transmission dynamics in Zambia, with the goal of allocating resources.
Strengths:
Important analysis to hone in on the key driver of HIV transmission in Zambia, which hopefully can be used to tune prevention efforts to maximize effect while limiting required resources. Two analytic approaches were used, and while the phylogenetic data were markedly more limited, they mirrored the simulated epidemic. The authors did a nice job reviewing the limitations of the data and the analyses. The authors did a nice job of providing analyses to support their goals and hypothesis, and this work may have more impact now that resources in SSA for HIV prevention and treatment may become more scarce
Weaknesses:
To …
Reviewer #2 (Public review):
Summary:
The authors analyzed PopART data to better characterize the age and sex-specific heterosexual HIV transmission dynamics in Zambia, with the goal of allocating resources.
Strengths:
Important analysis to hone in on the key driver of HIV transmission in Zambia, which hopefully can be used to tune prevention efforts to maximize effect while limiting required resources. Two analytic approaches were used, and while the phylogenetic data were markedly more limited, they mirrored the simulated epidemic. The authors did a nice job reviewing the limitations of the data and the analyses. The authors did a nice job of providing analyses to support their goals and hypothesis, and this work may have more impact now that resources in SSA for HIV prevention and treatment may become more scarce
Weaknesses:
To increase the impact and utility of this work, it would be helpful to parse the analysis just a bit further to estimate the roles of undiagnosed vs diagnosed and untreated subpopulations on this transmission. PopART is a multifaceted intervention, but the cost, effort, and approach to reengagement in care vs testing/treatment can be quite different.
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Author response:
We thank the editors and reviewers for their positive and constructive comments. The three most substantial points raised by the public review are the following:
No explicit modelling of targeting of young men as a course to ending HIV.
We did not intend to imply that the epidemic could be ended by this alone, or even that targeting young men was the optimum strategy if resources were available for more general preventative interventions. The “last mile” for HIV will be a very complex scenario in which key populations will start to play an outsize role, and our modelling framework was not developed to consider it. As a result, we would not have confidence in modelling the decline of the viral population to zero. We shall be qualifying the existing language in the paper in order to make this clear.
Subtype-specific …
Author response:
We thank the editors and reviewers for their positive and constructive comments. The three most substantial points raised by the public review are the following:
No explicit modelling of targeting of young men as a course to ending HIV.
We did not intend to imply that the epidemic could be ended by this alone, or even that targeting young men was the optimum strategy if resources were available for more general preventative interventions. The “last mile” for HIV will be a very complex scenario in which key populations will start to play an outsize role, and our modelling framework was not developed to consider it. As a result, we would not have confidence in modelling the decline of the viral population to zero. We shall be qualifying the existing language in the paper in order to make this clear.
Subtype-specific disease progression data.
The criticism is that our modelling of disease progression was based on subtype B, while the HIV viral population in Zambia is overwhelmingly subtype C. Sensitivity to subtype has not been looked at in detail in this analysis as the literature suggests that the rate of CD4 decline does not differ between subtypes B and C.
While some studies have shown differences in CD4 cell decline between subtypes, they have generally highlighted that subtype D progresses faster than other subtypes. Little evidence has been published on the differences between subtype B and C, and studies that do include both subtypes concluded that there was no significant difference in rates of CD4 decline between subtypes.
No significant difference between rate of CD4 progression by subtype is evidenced in the following publications:
- Klein et al. (2014) (N=9772)
- Bouman et al. (2023) (although no subtype B)
- Easterbrook et al. (2010) (N=861)While some studies have illustrated that "progression changes with HIV subtype", an interrogation of the underlying data highlights that subtype B is not included, e.g.
- Kanki et al. (1999) looked at A versus "non-A subtype" but included no subtype B data.
- Vasan et al. (2006) claims differences in rate of CD4 decline by subtype when compared to subtype D but includes no subtype B data.
- Baeten et al. (2007) claims subtype D has faster progression that subtype A but includes no subtype B data.
- Kiwanuka et al. (2008) claims differences in rate of CD4 decline but includes no subtype B data.
- Amornkul et al. (2013) has no subtype B data.Furthermore, to explain why we used subtype B data to parameterise the model: usually, statistical analyses of CD4 count progression do not report parameters in a form that can be directly imported into models. Analysing summary statistics to include in models results in under-specified models of disease progression in simulations. For this reason we use the estimates from Cori et al. (2015); where the statistical analysis was specifically tailored to generate modelling parameters. The trade-off is therefore to use subtype C data with model misspecification, or subtype B data without; neither choice is perfect, and we chose the subtype B correctly specified estimates.
The role of undiagnosed versus diagnosed and untreated subpopulations.
We will add an additional analysis us to compare age differences in sources and recipients according to the diagnostic status of the source.
The rest of the comments in the public review ask for improvements in data presentation (including some additional statistical analyses) and to make sure qualitative claims are fully justified. We are happy to oblige with these, and will make our thinking clear on all points in the full response.
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