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P279 Development and validation of sexually transmitted infections decision modelling software in cooperation with policy makers
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  1. Fabian Sailer1,
  2. John Saunders2,
  3. Greta Rait3,
  4. Rachael Hunter1
  1. 1Research Department of Primary Care and Population Health, London, UK
  2. 2University College London, NIHR Health Protection Research Unit in Blood Borne and Sexually Transmitted Infections, London, UK
  3. 3University College London, Research Department of Primary Care and Population Health, London, UK

Abstract

Background HIV and other sexually transmitted infections (STIs) do not operate in isolation, particularly as people with risk-taking sexual behaviour may be co-infected. In this complex landscape, policy makers are limited by resource constraints while trying to find optimal coverage solutions. Disease modelling could help in this context. We aim to develop a user-friendly modelling software examining several STIs and HIV simultaneously, as we are unaware of any multi-STI decision support tools currently available.

Methods We developed STI modelling software using the programming language Java, consisting of several models and a graphical user interface (UI). The models were drafted based on literature reviews and subsequently refined by experts, e.g. STI clinicians and policy makers. All models were internally and externally validated. The UI was developed with UI development experts and policy makers.

Results Separate disease models, which describe the progression of chlamydia, gonorrhoea, HIV, syphilis, and their sequelae are included in the software. Sexual network models are used to describe the formation and dissolution of partnerships and thereby the occurrence of sexual contacts. Four different network models are included in the software. The clinical pathway models describe interventions, for example screening or STI treatment and reflect the current UK setting. All the models are interacting, individual-based discrete event simulations and have been validated using sensitivity analyses and publicly available data sources. The UI has been validated by policy makers.

Conclusion With this modelling software policy makers can compare both existing and hypothetical intervention options in regards to their costs and consequences. All the parameters, formulas, model structures, and clinical pathways are editable. The software is flexible and usable in different settings and contexts. It can be and updated if needed, e.g. if medical knowledge changes. By adapting parameters which describe treatment pathways the software could be used in non-UK settings.

Disclosure No significant relationships.

  • policy & community engagement

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