The ability of management strategies to achieve the fishery management goals are impacted by environmental variation and, therefore, also by global climate change. Management strategies can be modified to use environmental data using the “dynamic B0” concept, and changing the set of years used to define biomass reference points. Two approaches have been developed to apply management strategy evaluation to evaluate the impact of environmental variation on the performance of management strategies. The “mechanistic approach” estimates the relationship between the environment and elements of the population dynamics of the fished species and makes predictions for population trends using the outputs from global climate models. In contrast, the “empirical approach” examines possible broad scenarios without explicitly identifying mechanisms. Many reviewed studies have found that modifying management strategies to include environmental factors does not improve the ability to achieve management goals much, if at all, and only if the manner in which these factors drive the system is well known. As such, until the skill of stock projection models improves, it seems more appropriate to consider the implications of plausible broad forecasts related to how biological parameters may change in the future as a way to assess the robustness of management strategies, rather than attempting specific predictions per se.

Background Several prostate cancer (PCa) early-detection biomarkers are available for reflex testing in men with intermediate prostate-specific antigen (PSA) levels. Studies of these biomarkers typically provide information about diagnostic performance but not about overdiagnosis and lives saved, the primary drivers of associated harm and benefit. Methods We projected overdiagnoses and lives saved using an established microsimulation model of PCa incidence and mortality with screening and treatment efficacy based on randomized trials. We used this framework to evaluate four urinary reflex biomarkers (measured in 1112 men presenting for prostate biopsy at 10 US academic or community clinics) and two hypothetical ideal biomarkers (with 100% sensitivity or specificity for any or for high-grade PCa) at one-time screening tests at ages 55 and 65 years. Results Compared with biopsying all men with elevated PSA, reflex testing reduced overdiagnoses (range across ages and biomarkers = 8.8–60.6%) but also reduced lives saved (by 7.3–64.9%), producing similar overdiagnoses per life saved. The ideal biomarker for high-grade disease improved this ratio (by 35.2% at age 55 years and 42.0% at age 65 years). Results were similar under continued screening for men not diagnosed at age 55 years, but the ideal biomarker for high-grade disease produced smaller incremental improvement. Conclusions Modeling is a useful tool for projecting the implications of using reflex biomarkers for long-term PCa outcomes. Under simplified conditions, reflex testing with urinary biomarkers is expected to reduce overdiagnoses but also produce commensurate reductions in lives saved. Reflex testing that accurately identifies high-grade PCa could improve the net benefit of screening.

Background There is a need for automated approaches to incorporate information on cancer recurrence events into population-based cancer registries. Objective The aim of this study is to determine the accuracy of a novel data mining algorithm to extract information from linked registry and medical claims data on the occurrence and timing of second breast cancer events (SBCE). Methods We used supervised data from 3092 stage I and II breast cancer cases (with 394 recurrences), diagnosed between 1993 and 2006 inclusive, of patients at Kaiser Permanente Washington and cases in the Puget Sound Cancer Surveillance System. Our goal was to classify each month after primary treatment as pre- versus post-SBCE. The prediction feature set for a given month consisted of registry variables on disease and patient characteristics related to the primary breast cancer event, as well as features based on monthly counts of diagnosis and procedure codes for the current, prior, and future months. A month was classified as post-SBCE if the predicted probability exceeded a probability threshold (PT); the predicted time of the SBCE was taken to be the month of maximum increase in the predicted probability between adjacent months. Results The Kaplan-Meier net probability of SBCE was 0.25 at 14 years. The month-level receiver operating characteristic curve on test data (20% of the data set) had an area under the curve of 0.986. The person-level predictions (at a monthly PT of 0.5) had a sensitivity of 0.89, a specificity of 0.98, a positive predictive value of 0.85, and a negative predictive value of 0.98. The corresponding median difference between the observed and predicted months of recurrence was 0 and the mean difference was 0.04 months. Conclusions Data mining of medical claims holds promise for the streamlining of cancer registry operations to feasibly collect information about second breast cancer events.