Mathematical Models for Evaluating Effectiveness and Cost-Effectiveness of Cervical Cancer Control Policies in Populations Including Women Living With Human Immunodeficiency Virus: A Scoping Review



Mathematical modeling is increasingly used to inform cervical cancer control policies, and model-based evaluations of such policies in women living with human immunodeficiency virus (HIV) are an emerging research area. We did a scoping review of published literature to identify research gaps and inform future work in this field.


We systematically searched literature up to April 2022 and included mathematical modeling studies evaluating the effectiveness or cost-effectiveness of cervical cancer prevention strategies in populations including women living with HIV. We extracted information on prevention strategies and modeling approaches.


We screened 1504 records and included 22 studies, almost half of which focused on South Africa. We found substantial between-study heterogeneity in terms of strategies assessed and modeling approaches used. Fourteen studies evaluated cervical cancer screening strategies, 7 studies assessed human papillomavirus vaccination (with or without screening), and 1 study evaluated the impact of HIV control measures on cervical cancer incidence and mortality. Thirteen conducted cost-effectiveness analyses. Markov cohort state-transition models were used most commonly (n = 12). Most studies (n = 17) modeled the effect of HIV by creating HIV-related health states. Thirteen studies performed model calibration, but 11 did not report the calibration methods used. Only 1 study stated that model code was available upon request.


Few model-based evaluations of cervical cancer control strategies have specifically considered women living with HIV. Improvements in model transparency, by sharing information and making model code publicly available, could facilitate the utility of these evaluations for other high disease-burden countries, where they are needed for assisting policy makers.


Rowan Iskandar Katayoun Taghavi Nicola Low Wichor M. Bramer Matthias Egger Eliane Rohner

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