# Building better HIV models: a framework for incorporating evidence on structural determinants and interventions to estimate their impacts on HIV epidemics

James Stannah, Jorge Luis Flores Anato, Michael Pickles, Joseph

Larmarange, Kate M. Mitchell, Adelina Artenie, Kostyantyn Dumchev, Serge

Niangoran, Lucy Platt, Fern Terris-Prestholt, et al.

► To cite this version:

James Stannah, Jorge Luis Flores Anato, Michael Pickles, Joseph Larmarange, Kate M. Mitchell, et al. Building better HIV models: a framework for incorporating evidence on structural determinants and interventions to estimate their impacts on HIV epidemics. HIVR4P 2024, the 5th HIV Research for Prevention Conference, Oct 2024, Lima, Peru. hal-04971981

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Submitted on 28 Feb 2025

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# Building better HIV models: A framework for incorporating evidence on structural determinants and interventions to estimate their impacts on HIV epidemics

J Stannah<fnref n="1" />*, JL Flores Anato<fnref n="1" />, M Pickles², J Larmarange³, KM Mitchell²,⁴, A Artenie⁵, K Dumchev⁶, S Niangoran⁷, L Platt⁸, F Terris-Prestholt⁹, A Singh¹⁰, J Stone⁵, P Vickerman⁵, A Phillips¹¹, LF Johnson¹², M Maheu-Giroux<fnref n="1" />, MC Boily²

*Email: james.stannah@mail.mcgill.ca, Twitter/X: @JamesStannah, LinkedIn: James Stannah

## Structural determinants of HIV:

The social, economic, political, cultural, organisational, and environmental factors that shape inequalities in HIV burden and access to prevention, treatment, and care. Examples: poverty, stigma, discrimination, violence.

### Background

Structural determinants present significant barriers to the HIV response, especially for key populations. To address these challenges, UNAIDS introduced the 10-10-10 targets (Fig. 1).

<figure><img src="image_2.png" /><figcaption>Fig. 1. UNAIDS 10-10-10 goals for 2025.</figcaption></figure>

Evidence-based models of structural determinants can:

* Evaluate progress towards these targets.

* Estimate the population-level impacts of structural determinants and interventions on HIV transmission and acquisition.

### Challenges to modelling structural determinants:

* Complex to model distal factors.

* They may impact HIV through multiple mechanisms (mediators).

* Limited empirical estimates of impacts.

<figure><img src="image_3.png" /></figure>

### Methods

**Scoping review** of HIV transmission dynamic models addressing structural determinants, of studies up to August 28, 2023 (Fig. 2).

* Databases: Ovid Embase and Medline

To develop our framework, we evaluated:

* Representations of structural determinants in models.

* Causal pathways and impacts of structural determinants/interventions in models.

* Types of data on structural determinants used in models.

### Scoping Review Results

<figure><img src="image_4.png" /><figcaption>Fig. 2. Search results of scoping review.</figcaption></figure>

### Exposure representations:

* Most studies modelled proximate structural determinants (Table 1).

* Many representations were static (Fig. 3).

* Few models (except of incarceration) considered long-term effects of exposure.

<figure><img src="image_5.png" /></figure>

* **Static:** fixed rates or probabilities were applied to parameters to account for the influence of structural determinants on mediators/HIV.

* **Dynamic:** the population was divided into compartments or states representing different exposure histories to structural determinants.

<caption>Fig. 3. Representations of exposure to structural determinants in models.</caption>

### Causal pathways:

* Most effects of exposure were indirect.

* Pathways modelled had few mediating variables - mostly proximate (i.e., directly influenced HIV).

* Common mediators: contact patterns (n=8) and frequency (n=9), condoms (n=6), ART (n=5).

### Model parameterisation:

* Most studies used proportions exposed (n=9) and effect sizes of exposure (n=10) to structural determinants on mediators/HIV.

* Effect sizes largely cross-sectional/non-causal.

* Structural determinants parameters were not always from the settings/populations modelled.

<table><caption>Table 1. Models identified in our scoping review.</caption><thead><tr><th>Study</th><th>Main structural determinant (and population) modelled</th></tr></thead><tbody><tr><td colspan="2"><strong>a) Static</strong></td></tr><tr><td>Stover 2021</td><td>UNAIDS 10-10-10 societal enablers: criminalisation, HIV stigma, gender-based violence (PWID, PLHIV, FSW)</td></tr><tr><td>Levy 2021</td><td>HIV stigma (general population)</td></tr><tr><td>Ronoh 2020</td><td>Positive and negative attitudes (general population aged 15-24)</td></tr><tr><td>Vassall 2014</td><td>Community mobilization and empowerment (FSW)</td></tr><tr><td>Wirtz 2014</td><td>Community empowerment (FSW)</td></tr><tr><td>Decker 2013</td><td>Violence (FSW)</td></tr><tr><td>Strathdee 2010</td><td>Police beatings (PWID)</td></tr><tr><td colspan="2"><strong>b) Stratification-based (one level)</strong></td></tr><tr><td>Rigby &amp; Johnson 2017</td><td>Intimate partner violence (general population)</td></tr><tr><td>Stone 2022</td><td>Housing instability (PWID)</td></tr><tr><td colspan="2"><strong>c) Stratification-based (multiple levels)</strong></td></tr><tr><td>Shannon 2015</td><td>Violence (FSW)</td></tr><tr><td>Ward 2022</td><td>Incarceration and drug law reform (PWID)</td></tr><tr><td>Adams 2021</td><td>Incarceration (African-American men)</td></tr><tr><td>Bernard 2020</td><td>Incarceration and drug law reform (PWID)</td></tr><tr><td>Adams 2018</td><td>Incarceration (African-American men)</td></tr><tr><td>Borquez 2018</td><td>Incarceration, syringe confiscation, and drug law reform (PWID)</td></tr><tr><td>Altice 2016</td><td>Incarceration (PWID)</td></tr><tr><td>Dolan 2016</td><td>Incarceration (PWID)</td></tr></tbody></table>

FSW=female sex workers, PLHIV=people living with HIV, PWID=people who inject drugs

<figure><img src="image_6.png" /><figcaption>Fig. 5. Future empirical evidence needed that will help improve models of structural determinants.</figcaption></figure>

### Acknowledgements

James Stannah is supported by the Fonds de Recherche de Québec en Santé (FRQS). This work was supported by the Wellcome Trust [226619/Z/22/Z], the McGill Interdisciplinary Initiative in Infection and Immunity—McGill Global Health Programmes (Mi4-GHP), and the HIV Modelling Consortium via the Bill & Melinda Gates Foundation. Mathieu Maheu-Giroux is the Canada Research Chair in Population Health Modelling. MCB acknowledges funding from the MRC Centre for Global Infectious Disease Analysis [MR/X020258/1], funded by the UK Medical Research Council (MRC). This UK-funded award is carried out in the frame of the Global Health EDCTP3 Joint Undertaking

### Conclusions

* Estimating the impacts of structural determinants & interventions on HIV is crucial for the next phase of the global HIV response - models can help.

* Future models should dynamically model exposures and interventions using strong evidence of their effects on HIV risks.

<footnote n="1">¹McGill University, Montreal, Canada; ²Imperial College London, UK; ³Institut de Recherche pour le Développement, Paris, France; ⁴Glasgow Caledonian University, London, UK; ⁵University of Bristol, Bristol, UK; ⁶Ukrainian Institute on Public Health Policy, Kyiv, Ukraine; ⁷Programme PAC-CI, Abidjan, Côte d'Ivoire; ⁸LSHTM, London, UK; ⁹Warwick Medical School, Coventry, UK; ¹⁰Johns Hopkins University School of Medicine, Delhi, India; ¹¹University College London, London, UK; ¹²University of Cape Town, South Africa.</footnote>