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Approaches for estimating benefits and costs of interventions in plant biosecurity across invasion phases.

ABSTRACT: Nonnative plant pests cause billions of dollars in damages. It is critical to prevent or reduce these losses by intervening at various stages of the invasion process, including pathway risk management (to prevent pest arrival), surveillance and eradication (to counter establishment), and management of established pests (to limit damages). Quantifying benefits and costs of these interventions is important to justify and prioritize investments and to inform biosecurity policy. However, approaches for these estimations differ in (1) the assumed relationship between supply, demand, and prices, and (2) the ability to assess different types of direct and indirect costs at invasion stages, for a given arrival or establishment probability. Here we review economic approaches available to estimate benefits and costs of biosecurity interventions to inform the appropriate selection of approaches. In doing so, we complement previous studies and reviews on estimates of damages from invasive species by considering the influence of economic and methodological assumptions. Cost accounting is suitable for rapid decisions, specific impacts, and simple methodological assumptions but fails to account for feedbacks, such as market adjustments, and may overestimate long-term economic impacts. Partial equilibrium models consider changes in consumer and producer surplus due to pest impacts or interventions and can account for feedbacks in affected sectors but require specialized economic models, comprehensive data sets, and estimates of commodity supply and demand curves. More intensive computable general equilibrium models can account for feedbacks across entire economies, including capital and labor, and linkages among these. The two major considerations in choosing an approach are (1) the goals of the analysis (e.g., consideration of a single pest or intervention with a limited range of impacts vs. multiple interventions, pests or sectors), and (2) the resources available for analysis such as knowledge, budget and time.

SUBMITTER: Welsh MJ

PROVIDER: S-EPMC8365635 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIn Mexico, the lifetime risk of dying from maternal causes is 1 in 370 compared to 1 in 2,500 in the U.S. Although national efforts have been made to improve maternal services in the last decade, it is unclear if Millennium Development Goal 5--to reduce maternal mortality by three-quarters by 2015--will be met.Methodology/principal findingsWe developed an empirically calibrated model that simulates the natural history of pregnancy and pregnancy-related complications in a cohort of 15-year-old women followed over their lifetime. After synthesizing national and sub-national trends in maternal mortality, the model was calibrated to current intervention-specific coverage levels and validated by comparing model-projected life expectancy, total fertility rate, crude birth rate and maternal mortality ratio with Mexico-specific data. Using both published and primary data, we assessed the comparative health and economic outcomes of alternative strategies to reduce maternal morbidity and mortality. A dual approach that increased coverage of family planning by 15%, and assured access to safe abortion for all women desiring elective termination of pregnancy, reduced mortality by 43% and was cost saving compared to current practice. The most effective strategy added a third component, enhanced access to comprehensive emergency obstetric care for at least 90% of women requiring referral. At a national level, this strategy reduced mortality by 75%, cost less than current practice, and had an incremental cost-effectiveness ratio of $300 per DALY relative to the next best strategy. Analyses conducted at the state level yielded similar results.Conclusions/significanceIncreasing the provision of family planning and assuring access to safe abortion are feasible, complementary and cost-effective strategies that would provide the greatest benefit within a short-time frame. Incremental improvements in access to high-quality intrapartum and emergency obstetric care will further reduce maternal deaths and disability.

Project description:BACKGROUND: In genomic prediction, an important measure of accuracy is the correlation between the predicted and the true breeding values. Direct computation of this quantity for real datasets is not possible, because the true breeding value is unknown. Instead, the correlation between the predicted breeding values and the observed phenotypic values, called predictive ability, is often computed. In order to indirectly estimate predictive accuracy, this latter correlation is usually divided by an estimate of the square root of heritability. In this study we use simulation to evaluate estimates of predictive accuracy for seven methods, four (1 to 4) of which use an estimate of heritability to divide predictive ability computed by cross-validation. Between them the seven methods cover balanced and unbalanced datasets as well as correlated and uncorrelated genotypes. We propose one new indirect method (4) and two direct methods (5 and 6) for estimating predictive accuracy and compare their performances and those of four other existing approaches (three indirect (1 to 3) and one direct (7)) with simulated true predictive accuracy as the benchmark and with each other. RESULTS: The size of the estimated genetic variance and hence heritability exerted the strongest influence on the variation in the estimated predictive accuracy. Increasing the number of genotypes considerably increases the time required to compute predictive accuracy by all the seven methods, most notably for the five methods that require cross-validation (Methods 1, 2, 3, 4 and 6). A new method that we propose (Method 5) and an existing method (Method 7) used in animal breeding programs were the fastest and gave the least biased, most precise and stable estimates of predictive accuracy. Of the methods that use cross-validation Methods 4 and 6 were often the best. CONCLUSIONS: The estimated genetic variance and the number of genotypes had the greatest influence on predictive accuracy. Methods 5 and 7 were the fastest and produced the least biased, the most precise, robust and stable estimates of predictive accuracy. These properties argue for routinely using Methods 5 and 7 to assess predictive accuracy in genomic selection studies.

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Approaches for estimating benefits and costs of interventions in plant biosecurity across invasion phases.

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