Health and economic effects of introducing single-dose or two-dose human papillomavirus vaccination in India.

Background: Cervical cancer is a major public health problem in India, where access to prevention programmes is low. The WHO-Strategic Advisory Group of Experts recently updated their recommendation for human papillomavirus (HPV) vaccination to include a single-dose option in addition to the two-dose option, which could make HPV vaccination programmes easier to implement and more affordable.

Methods: We combined projections from a type-specific HPV transmission model and a cancer progression model to assess the health and economic effects of HPV vaccination at national and state level in India.

Evidence-based impact projections of single-dose human papillomavirus vaccination in India: a modelling study.

Background: Despite the high burden of cervical cancer, access to preventive measures remains low in India. A single-dose immunisation schedule could facilitate the scale-up of human papillomavirus (HPV) vaccination, contributing to global elimination of cervical cancer. We projected the effect of single-dose quadrivalent HPV vaccination in India in comparison with no vaccination or to a two-dose schedule.

Choosing a Metamodel of a Simulation Model for Uncertainty Quantification.

Background: Metamodeling may substantially reduce the computational expense of individual-level state transition simulation models (IL-STM) for calibration, uncertainty quantification, and health policy evaluation. However, because of the lack of guidance and readily available computer code, metamodels are still not widely used in health economics and public health. In this study, we provide guidance on how to choose a metamodel for uncertainty quantification.

Evaluating Parameter Uncertainty in a Simulation Model of Cancer Using Emulators.

. Microsimulation models have been extensively used in the field of cancer modeling. However, there is substantial uncertainty regarding estimates from these models, for example, overdiagnosis in prostate cancer.

Comparative effectiveness of prostate cancer screening between the ages of 55 and 69 years followed by active surveillance.

Background: Because of the recent grade C draft recommendation by the US Preventive Services Task Force (USPSTF) for prostate cancer screening between the ages of 55 and 69 years, there is a need to determine whether this could be cost-effective in a US population setting.

Methods: This study used a microsimulation model of screening and active surveillance (AS), based on data from the European Randomized Study of Screening for Prostate Cancer and the Surveillance, Epidemiology, and End Results Program, for the natural history of prostate cancer and Johns Hopkins AS cohort data to inform the probabilities of referral to treatment during AS. A cohort of 10 million men, based on US life tables, was simulated.

The efficacy of prostate-specific antigen screening: Impact of key components in the ERSPC and PLCO trials.

Background: The European Randomized Study of Screening for Prostate Cancer (ERSPC) demonstrated that prostate-specific antigen (PSA) screening significantly reduced prostate cancer mortality (rate ratio, 0.79; 95% confidence interval, 0.69-0.

Summary statement on screening for prostate cancer in Europe.

The European Randomised Study of Screening for Prostate Cancer (ERSPC) showed that Prostate-Specific Antigen (PSA) based screening results in a significant prostate cancer mortality reduction. Although there are concerns on overdiagnosis and overtreatment, it has been shown that the benefits can outweigh the harms if screening is stopped in older ages to prevent overdiagnosis. A limited screening program (for example screening at ages 55-59 years), including active surveillance for men with low-risk tumors, can even be cost-saving, compared with testing in an opportunistic setting in the wrong ages, as currently in Europe.

Reconciling the Effects of Screening on Prostate Cancer Mortality in the ERSPC and PLCO Trials.

Background: The ERSPC (European Randomized Study of Screening for Prostate Cancer) found that screening reduced prostate cancer mortality, but the PLCO (Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial) found no reduction.

Objective: To evaluate whether effects of screening on prostate cancer mortality relative to no screening differed between the ERSPC and PLCO.

Design: Cox regression of prostate cancer death in each trial group, adjusted for age and trial.

When should active surveillance for prostate cancer stop if no progression is detected?

Background: A significant proportion of screen-detected men with prostate cancer may be overdiagnosed. Active Surveillance (AS) has emerged as a way to mitigate this problem, by delaying treatment of men, who are at low-risk until this becomes necessary. However, it is not known after how much time or biopsy rounds should patients stop AS and transition to conservative management (CM), if no progression is detected.

Estimating the harms and benefits of prostate cancer screening as used in common practice versus recommended good practice: A microsimulation screening analysis.

Background: Prostate-specific antigen (PSA) screening and concomitant treatment can be implemented in several ways. The authors investigated how the net benefit of PSA screening varies between common practice versus "good practice."

Methods: Microsimulation screening analysis (MISCAN) was used to evaluate the effect on quality-adjusted life-years (QALYs) if 4 recommendations were followed: limited screening in older men, selective biopsy in men with elevated PSA, active surveillance for low-risk tumors, and treatment preferentially delivered at high-volume centers.

Estimating the risks and benefits of active surveillance protocols for prostate cancer: a microsimulation study.

Objective: To estimate the increase in prostate cancer mortality (PCM) and the reduction in overtreatment resulting from different active surveillance (AS) protocols, compared with treating men immediately.

Patients And Methods: We used a microsimulation model (MISCAN-Prostate), with the natural history of prostate cancer based on European Randomized Study of Screening for Prostate Cancer data. We estimated the probabilities of referral to radical treatment while on AS, depending on disease stage, using data from the Johns Hopkins AS cohort.

Estimating the individual benefit of immediate treatment or active surveillance for prostate cancer after screen-detection in older (65+) men.

A significant proportion of screen-detected men with prostate cancer is likely to be overtreated, especially in older age groups. We aim to find which groups of screen-detected older men (65+) benefit the most from Immediate Radical Treatment or Active Surveillance (AS) for prostate cancer, depending on age, screening history, health status and prostate cancer stage at detection. We used a microsimulation model (MISCAN) of the natural history of prostate cancer based on ERSPC data.

Screening for prostate cancer in the US? Reduce the harms and keep the benefit.

While the benefit of prostate-specific antigen (PSA) based screening is uncertain, a significant proportion of screen-detected cases is overdiagnosed. In order to make screening worthwhile, it is necessary to find policies that minimize overdiagnosis, without significantly increasing prostate cancer mortality (PCM). Using a microsimulation model (MISCAN) we project the outcomes of 83 screening policies in the US population, with different start and stop ages, screening frequencies, strategies where the PSA value changes the screening frequency, and strategies in which the PSA threshold (PSAt) increases with age.

Personalizing age of cancer screening cessation based on comorbid conditions: model estimates of harms and benefits.

Background: Harms and benefits of cancer screening depend on age and comorbid conditions, but reliable estimates are lacking.

Objective: To estimate the harms and benefits of cancer screening by age and comorbid conditions to inform decisions about screening cessation.

Design: Collaborative modeling with 7 cancer simulation models and common data on average and comorbid condition level-specific life expectancy.