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Combining modelling tools to evaluate a goose management scheme.

ABSTRACT: Many goose species feed on agricultural land, and with growing goose numbers, conflicts with agriculture are increasing. One possible solution is to designate refuge areas where farmers are paid to leave geese undisturbed. Here, we present a generic modelling tool that can be used to designate the best locations for refuges and to gauge the area needed to accommodate the geese. With a species distribution model, locations are ranked according to goose suitability. The size of the area to be designated as refuge can be chosen by including more or less suitable locations. A resource depletion model is then used to estimate whether enough resources are available within the designated refuge to accommodate all geese, taking into account the dynamics of food resources, including depletion by geese. We illustrate this with the management scheme for pink-footed goose Anser brachyrhynchus implemented in Norway. Here, all geese can be accommodated, but damage levels appear to depend on weather, land use and refuge size.

SUBMITTER: Baveco JM

PROVIDER: S-EPMC5316330 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Combining modelling tools to evaluate a goose management scheme.

Baveco Johannes M JM Bergjord Anne-Kari AK Bjerke Jarle W JW Chudzińska Magda E ME Pellissier Loïc L Simonsen Caroline E CE Madsen Jesper J Tombre Ingunn M IM Nolet Bart A BA

Ambio 20170301 Suppl 2

Many goose species feed on agricultural land, and with growing goose numbers, conflicts with agriculture are increasing. One possible solution is to designate refuge areas where farmers are paid to leave geese undisturbed. Here, we present a generic modelling tool that can be used to designate the best locations for refuges and to gauge the area needed to accommodate the geese. With a species distribution model, locations are ranked according to goose suitability. The size of the area to be desi ...[more]

PMID: 28215007

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Project description:BACKGROUND:Traditional budget impact models predict the financial consequences of a new drug entering the market. This study provides an example of applying the budget impact framework to a new research question of interest to managed care organizations-what is the budget impact of our formulary and utilization management (UM) policy changes? OBJECTIVE:To predict the 3-year annual budgetary impact of TRICARE's antidiabetic formulary and UM policy changes using TRICARE claims data. METHODS:A budget impact model was built in Microsoft Excel using health plan claims data for a 3-year time horizon. Model outcomes included spending on antidiabetic medications and medications used for side effect treatment. In sensitivity analyses, medical costs from inpatient, outpatient, and emergency room visits were also estimated. Model inputs included health plan antidiabetic medication utilization, as well as publicly available drug cost, rebate, dispensing fee, and patient cost-sharing estimates. Type of enrollee and pharmacy were also incorporated into the model. Sensitivity analyses varied estimates for utilization switch rates between preferred and nonpreferred agents, drug costs, rebates, and dispensing fees, as well as predicted impact from implementation delays. RESULTS:For the 623,827 affected by the formulary and UM policy changes, the model predicted annual savings that increased from $24 million in the first year to $43 million in the third year after the changes. The majority of savings came from drug acquisition costs, as opposed to rebates, copays, and dispensing fees. Sensitivity analyses found savings across all varied parameters and scenarios except an unlikely scenario when 0% of utilization switched from nonpreferred to preferred agents. The model also predicted that the formulary and UM policy changes would lead to $529,439 in savings from medical visit costs in Year 3. CONCLUSIONS:This budget impact model predicted cost savings from the payer's formulary and UM policy changes. DISCLOSURES:This project was supported by grant number F32HS024857 from the Agency for Healthcare Research and Quality (AHRQ), which contracted with the University of Maryland School of Pharmacy to conduct this study. The content is solely the responsibility of the authors and does not necessarily represent the official views of the AHRQ, which had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or design to submit the manuscript for publication. The findings discussed in this manuscript represent the views of the authors and do not necessarily reflect the views of the Department of Defense, the Defense Health Agency, nor the Departments of the Army, Navy, and Air Force. Hung reports a grant from the AHRQ, during the conduct of the study, and personal fees from CVS Health and BlueCross BlueShield Association, outside the submitted work. Mullins reports grants and personal fees from Bayer and Pfizer and personal fees from Boehringer-Ingelheim, Janssen/J&J, Regeneron, and Sanofi, outside the submitted work. Mullins, Slejko, and Shaya are employed by the University of Maryland School of Pharmacy. Haines and Lugo have nothing to disclose. Part of this content was previously presented as a poster at the 2017 AMCP Managed Care & Specialty Pharmacy Annual Meeting; March 27-30, 2017; Denver, CO, and as poster and oral presentations at the 2017 AMCP Nexus Meeting; October 16-19, 2017; Dallas, TX. Part of this content was published as Hung's PhD dissertation.

Dataset Information

Combining modelling tools to evaluate a goose management scheme.

Publications

Combining modelling tools to evaluate a goose management scheme.

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