Selecting a Mix of Prevention Strategies against Cervical Cancer for Maximum Efficiency with an Optimization Program
AbstractBackground:Background: Screening and vaccination against human papillomavirus (HPV) can protect against cervical cancer. Neither alone can provide 100% protection. Consequently it raises the important question about the most efficient combination of screening at specified time intervals and vaccination to prevent cervical cancer. Abstract: Objective:Objective: Our objective was to identify the mix of cervical cancer prevention strategies (screening and/or vaccination against HPV) that achieves maximum reduction in cancer cases within a fixed budget. Abstract: Methods:Methods: We assessed the optimal mix of strategies for the prevention of cervical cancer using an optimization program. The evaluation used two models. One was a Markov cohort model used as the evaluation model to estimate the costs and outcomes of 52 different prevention strategies. The other was an optimization model in which the results of each prevention strategy of the previous model were entered as input data. The latter model determined the combination of the different prevention options to minimize cervical cancer under budget, screening coverage and vaccination coverage constraints. We applied the model in two countries with different healthcare organizations, epidemiology, screening practices, resource settings and treatment costs: the UK and Brazil. 100 000 women aged 12 years and above across the whole population over a 1-year period at steady state were included. The intervention was papanicolaou (Pap) smear screening programmes and/or vaccination against HPV with the bivalent HPV 16/18 vaccine (Cervarix [Cervarix is a registered trademark of the GlaxoSmithKline group of companies]). The main outcome measures were optimal distribution of the population between different interventions (screening, vaccination, screening plus vaccination and no screening or vaccination) with the resulting number of cervical cancer and associated costs. Abstract: Results:Results: In the base-case analysis (- same budget as today), the optimal prevention strategy would be, after introducing vaccination with a coverage rate of 80% in girls aged 12 years and retaining screening coverage at pre-vaccination levels (65% in the UK, 50% in Brazil), to increase the screening interval to 6 years (from 3) in the UK and to 5 years (from 3) in Brazil. This would result in a reduction of cervical cancer by 41% in the UK and by 54% in Brazil from pre-vaccination levels with no budget increase. Sensitivity analysis shows that vaccination alone at 80% coverage with no screening would achieve a cervical cancer reduction rate of 20% in the UK and 43% in Brazil compared with the pre-vaccination situation with a budget reduction of 30% and 14%, respectively. In both countries, the sharp reduction in cervical cancer is seen when the vaccine coverage rate exceeds the maximum screening coverage rate, or when screening coverage rate exceeds the maximum vaccine coverage rate, while maintaining the budget. As with any model, there are limitations to the value of predictions depending upon the assumptions made in each model. Abstract: Conclusions:Conclusions: Spending the same budget that was used for screening and treatment of cervical cancer in the pre-vaccination era, results of the optimization program show that it would be possible to substantially reduce the number of cases by implementing an optimal combination of HPV vaccination (80% coverage) and screening at pre-vaccination coverage (65% UK, 50% Brazil) while extending the screening interval to every 6 years in the UK and 5 years in Brazil.
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Bibliographic InfoArticle provided by Springer Healthcare | Adis in its journal PharmacoEconomics.
Volume (Year): 30 (2012)
Issue (Month): 4 ()
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Web page: http://pharmacoeconomics.adisonline.com/
Budget-impact-analysis; Cervical-cancer; Cervical-smear; Cost-effectiveness; Human-papillomavirus-infections; Modelling; Papillomavirus-vaccines Screening.;
Find related papers by JEL classification:
- C - Mathematical and Quantitative Methods
- D - Microeconomics
- I - Health, Education, and Welfare
- Z - Other Special Topics
- I1 - Health, Education, and Welfare - - Health
- I19 - Health, Education, and Welfare - - Health - - - Other
- I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
- I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
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