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Angela J. Jacques-Tiura Deborah A. Ellis April Idalski Carcone Sylvie Naar Kathryn Brogan Hartlieb Elizabeth K. Towner Thomas N. Templin K.-L. Catherine Jen 《The Journal of adolescent health》2019,64(3):355-361
Objective
Successful weight loss interventions for African-Americans adolescents are lacking. Cognitive-behavioral interventions seek to develop weight loss skills (e.g., counting calories, goal setting, managing one's environment). Little is known about how well adolescents implement such skills in their daily lives. Study aims were to (1) examine weight loss skills utilization at midpoint and end of a 6-month cognitive-behavioral/motivational interviewing weight loss sequential multiple assignment randomized trial (SMART), and (2) determine if greater skill utilization predicted weight loss at treatment end and 3 months post-treatment.Method
One hundred and eighty six African-Americans adolescents with obesity and their caregiver were first randomly assigned to complete 3 months of cognitive-behavioral and motivational interviewing family-based weight loss treatment in their home or in the research office (Phase 1). Nonresponders (i.e., those who lost < 3% of initial weight, n?=?161) were rerandomized to 3 months of continued skills training (n?=?83) or contingency management (n?=?78) for Phase 2; responders were allocated to 3 months of relapse prevention (n?=?20). Adolescents’ frequency of weight loss skills utilization was assessed via questionnaire at treatment midpoint and end.Results
Higher treatment attendance was associated with better skill utilization. Higher skill utilization was associated with more weight loss at treatment end, whereas higher baseline confidence was associated with more weight loss at follow-up.Conclusions
This study indicates the importance of attending weight loss intervention sessions to develop and strengthen weight loss skills in African-American adolescents with obesity, and strengthening confidence to use such skills for continued weight loss. 相似文献4.
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Julia Thornton Snider Jesse Sussell Mahlet Gizaw Tebeka Alicia Gonzalez Joshua T. Cohen Peter Neumann 《Value in health》2019,22(3):332-339
Background
Payers frequently rely on budget impact model (BIM) results to help determine drug coverage policy and its effect on their bottom line. It is unclear whether BIMs typically overestimate or underestimate real-world budget impact.Objective
We examined how different modeling assumptions influenced the results of 6 BIMs from the Institute for Clinical and Economic Review (ICER).Study Design
Retrospective analysis of pharmaceutical sales data.Methods
From ICER reports issued before 2016, we collected estimates of 3 BIM outputs: aggregate therapy cost (ie, cost to treat the patient population with a particular therapy), therapy uptake, and price. We compared these against real-world estimates that we generated using drug sales data. We considered 2 classes of BIM estimates: those forecasting future uptake of new agents, which assumed “unmanaged uptake,” and those describing the contemporaneous market state (ie, estimates of current, managed uptake and budget impact for compounds already on the market).Results
Differences between ICER's estimates and our own were largest for forecasted studies. Here, ICER's uptake estimates exceeded real-world estimates by factors ranging from 7.4 (sacubitril/valsartan) to 54 (hepatitis C treatments). The “unmanaged uptake” assumption (removed from ICER's approach in 2017) yields large deviations between BIM estimates and real-world consumption. Nevertheless, in some cases, ICER's BIMs that relied on current market estimates also deviated substantially from real-world sales data.Conclusions
This study highlights challenges with forecasting budget impact. In particular, assumptions about uptake and data source selection can greatly influence the accuracy of results. 相似文献7.
Alan. B. Franklin Sarah N. Bevins Jeremy W. Ellis Ryan S. Miller Susan A. Shriner J. Jeffrey Root Daniel P. Walsh Thomas J. Deliberto 《Transboundary and Emerging Diseases》2019,66(2):705-714
Using data on waterfowl band recoveries, we identified spatially explicit hotspots of concentrated waterfowl movement to predict occurrence and spatial spread of a novel influenza A virus (clade 2.3.4.4) introduced from Asia by waterfowl from an initial outbreak in North America in November 2014. In response to the outbreak, the hotspots of waterfowl movement were used to help guide sampling for clade 2.3.4.4 viruses in waterfowl as an early warning for the US poultry industry during the outbreak . After surveillance sampling of waterfowl, we tested whether there was greater detection of clade 2.3.4.4 viruses inside hotspots. We found that hotspots defined using kernel density estimates of waterfowl band recoveries worked well in predicting areas with higher prevalence of the viruses in waterfowl. This approach exemplifies the value of ecological knowledge in predicting risk to agricultural security. 相似文献
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