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Introduction: Kenya plans to focus on integrating services for non-communicable diseases (NCDs) into existing care platforms as a way of strengthening its health system, reducing redundancies and leveraging existing systems. Mathematical modelling provides a powerful tool to address questions around priorities, optimization and implementation. In this paper we will examine the case for integration of NCDs into HIV care platforms, review examples of how mathematical models have supported policy formulation in Kenya and provide a set of recommendations on the use of modelling in policy development on integration of NCD-HIV services in Kenya. Discussion: In Kenya, NCDs are the second leading cause of morbidity and mortality after HIV/AIDS and has been shown to be higher in people living with HIV. Integration of care services has shown to have generated advantages for both provider and user, be cost-effective, practical and achieve rapid coverage scale-up. The National Strategy for Prevention and Control of Non-Communicable Diseases 2015-2020 emphasizes integration of NCD with HIV care; their shared chronic nature means a majority of the programmatic and operational approaches and infrastructure developed for HIV programs could be used for NCDs, especially in resource-constrained settings. However, the vertical nature of current disease programs, policy financing and operations operate as barriers to NCD integration in Kenya. Modelling has successfully been used to inform health policy in Kenya across a number of disease areas and in a number of ways, including i) estimating current and future disease burden to set priorities for public health policy interventions, ii) forecast the requisite investments by government, iii) comparing the impact of different integration approaches, iv) performing cost-benefit analysis for integration, and v) evaluating health system capacity needs. Conclusions Modelling can and should play an integral part in the decision-making processes surrounding the scale-up of NCD-HIV integration in Kenya. Successful use of modelling to inform policy will depend on several factors including policy makers' comfort with and understanding of models and their uncertainties, modelers understanding the policy questions, funding opportunities and building local modelling capacity to ensure sustainability.