Systematic review of model-based cervical screening evaluations.
BACKGROUND
Optimising population-based cervical screening policies is becoming more complex due to the expanding range of screening technologies available and the interplay with vaccine-induced changes in epidemiology. Mathematical models are increasingly being applied to assess the impact of cervical cancer screening strategies.


METHODS
We systematically reviewed MEDLINE®, Embase, Web of Science®, EconLit, Health Economic Evaluation Database, and The Cochrane Library databases in order to identify the mathematical models of human papillomavirus (HPV) infection and cervical cancer progression used to assess the effectiveness and/or cost-effectiveness of cervical cancer screening strategies. Key model features and conclusions relevant to decision-making were extracted.


RESULTS
We found 153 articles meeting our eligibility criteria published up to May 2013. Most studies (72/153) evaluated the introduction of a new screening technology, with particular focus on the comparison of HPV DNA testing and cytology (n = 58). Twenty-eight in forty of these analyses supported HPV DNA primary screening implementation. A few studies analysed more recent technologies - rapid HPV DNA testing (n = 3), HPV DNA self-sampling (n = 4), and genotyping (n = 1) - and were also supportive of their introduction. However, no study was found on emerging molecular markers and their potential utility in future screening programmes. Most evaluations (113/153) were based on models simulating aggregate groups of women at risk of cervical cancer over time without accounting for HPV infection transmission. Calibration to country-specific outcome data is becoming more common, but has not yet become standard practice.


CONCLUSIONS
Models of cervical screening are increasingly used, and allow extrapolation of trial data to project the population-level health and economic impact of different screening policy. However, post-vaccination analyses have rarely incorporated transmission dynamics. Model calibration to country-specific data is increasingly common in recent studies.
Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, Bloomsbury, London, WC1E 7HT, UK. diana.mendes@lshtm.ac.uk.|Modelling and Economics Unit, Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK. diana.mendes@lshtm.ac.uk.
Diana
D
Mendes
Modelling and Economics Unit, Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK. iren.bains@phe.gov.uk.
Iren
I
Bains
Brazilian Ministry of Health, Esplanada dos Ministérios Bloco G, Brasília-DF, CEP: 70058-900, Brasil. tazio.vanni@saude.gov.br.
Tazio
T
Vanni
Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, Bloomsbury, London, WC1E 7HT, UK. mark.jit@phe.gov.uk.|Modelling and Economics Unit, Public Health England, 61 Colindale Avenue, London, NW9 5EQ, UK. mark.jit@phe.gov.uk.
Mark
M
Jit
D003362:Cost-Benefit Analysis / Q000379:methods*; D055088:Early Detection of Cancer / Q000191:economics* / Q000379:methods*; D005260:Female; D006801:Humans; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis* / Q000191:economics*