Background
Implementation
The OCEAN tool
Calibration process
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Number of simulations: Number of simulated cohorts.
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Number of simulations to keep: Number of simulated cohorts that will be kept and used in the cost-effectiveness analyses. Among all simulated cohorts, the best ones (the matrices producing the outcomes that minimize the differences with respect to target HPV infection prevalence and CC incidence). All these simulated cohorts will be used to obtain the cost-effectiveness outputs if the Uncertainty Level (see the inputs for the cost-effectiveness analysis in the next subsection) is not zero, and stored each in one sheet on the same file. This allows the tool to generate several estimates for each cost-effectiveness output (one per matrix) and therefore to generate and display confidence intervals.
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Percentage of change: Maximum allowed difference between two equivalent transition probability matrices.
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Mortality: Checkbox that should be ticked if mortality data are available and it is wanted to be used in the calibration process.
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Transition probability matrix: Starting values for the transition probability matrix. A general transition probability matrix will be used if no user-specific matrix is provided.
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Incidence file: Registered cervical cancer incidence for the specific setting, without any medical intervention (rate × 100,000 women). It is uploaded in a spreadsheet file and will be used as a target in the calibration process.
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Prevalence file: Registered HPV16/18 infection prevalence for the specific setting. It is uploaded in an Excel file and will be used as a target in the calibration process.
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Mortality file: Registered mortality due to cervical cancer for the specific setting (rate × 100,000 women). It is uploaded in an Excel file and will be used as a target in the calibration process if mortality is checked.
Cost-effectiveness analysis
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Number of different scenarios: The number of scenarios to be analyzed in the current session. Note that each scenario may include none (no intervention or natural history), one or more prevention strategies (screening alone, vaccination alone or vaccination followed by screening).
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Matrix of transition probabilities between different health states without medical intervention: Transition probability matrix obtained in the previous panel or uploaded by the user. Additionally, the included default file can be used.
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Medical costs and utility coefficients: An Excel file containing the treatment direct medical and nonmedical costs, indirect costs and utility coefficients. An example can be downloaded from the tool.
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Discount rate: Discount rate to be applied to health and costs (undiscounted results can be obtained by setting the discount rate to 0).
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Uncertainty level: It is known that the results of cost-effectiveness analyses are affected by a certain degree of uncertainty at different levels. They can be reflected in the OCEAN tool by using more than one transition probability matrix to feed the Markov model. If a file with several sheets is used and the uncertainty level is set to 0, an averaged matrix will be used, and only point estimates will be reported. If the uncertainty level is set to a value α between 0 and 50, all the matrices are used, and the outcomes obtained from each one are recorded. Then, the mean and percentiles \( \frac{\alpha }{2} \) and \( 1-\frac{\alpha }{2} \) for each outcome are calculated and reported.
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Natural history: No prevention strategy is considered in this scenario, which reproduces the natural history of HPV infection and cervical cancer. If this option is chosen, the other options disappear.
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Screening: Several screening scenarios can be defined, depending on test (cytology, HPV DNA test or visual inspection), frequency (every 1–10 years), targeted ages, and switch age from cytology to HPV testing. The screening coverage, positive predictive value, sensitivity and costs are read from the Excel file loaded as “Scenario-specific values”. The structure of this file can be explored by downloading the example file from the tool. Screening may be organized (all women are screened with the selected frequency) or opportunistic (the screening period is variable). To set an opportunistic screening scenario, an additional Excel file specifying the proportion of women screened each period is required.
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Vaccination: Preadolescent girls are successfully vaccinated at the age of 11 years with one, two or three doses of the vaccine against HPV types 16 and 18. Efficacy and coverage are set by the user and vaccination costs are read from the Excel file loaded as “Scenario-specific values”. The structure of this file can be explored by downloading the example file from the tool. Currently, only bivalent vaccine is considered but it is planned that quadrivalent and nonavalent vaccines will be available soon as well.
Results
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Natural history: The first scenario considers no prevention strategy.
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Vaccination alone: In this scenario, preadolescent girls are successfully vaccinated at the age of 11 years with three doses of the vaccine against HPV types 16 and 18. The analysis was carried out assuming favorable vaccine with 70% efficacy and a lifelong duration of vaccine immunity to prevent cervical lesions caused by HPV 16 and 18 among uninfected women. No cross-protection against other high-risk HPV types was assumed. The uptake is set to 70%.
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Screening alone: Assuming that screening with conventional cytology starts at 25 years of age and continues until age 65, this strategy considers that primary organized HPV DNA testing is performed in women older than 30 years of age with cytology triage for positive women. Women are screened in a 5-year period. For women younger than 30 years of age, cytology is the reference test. The sensitivity and specificity of HPV DNA testing to detect CIN2+ are 90.5 and 93.0%, respectively, and 90.5 and 91.9% for cytology after a positive HPV test [15]. Screening coverage is 70%. On the basis of a study carried out in Spain, we assume that the sensitivity and specificity of cytology to detect CIN2+ are 38.2 and 97.8%, respectively [15].
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Combined vaccination and screening: In this scenario, we implement vaccination in girls aged 11 years, followed by screening according to the parameters and assumptions described previously for vaccination and screening alone.