Hasil untuk "Costs"

D. Bonte, H. Van Dyck, J. Bullock et al.

Sanford J. Grossman, Oliver D. Hart, Peter Diamond et al.

T. Seuring, O. Archangelidi, M. Suhrcke

BackgroundThere has been a widely documented and recognized increase in diabetes prevalence, not only in high-income countries (HICs) but also in low- and middle-income countries (LMICs), over recent decades. The economic burden associated with diabetes, especially in LMICs, is less clear.ObjectiveWe provide a systematic review of the global evidence on the costs of type 2 diabetes. Our review seeks to update and considerably expand the previous major review of the costs of diabetes by capturing the evidence on overall, direct and indirect costs of type 2 diabetes worldwide that has been published since 2001. In addition, we include a body of economic evidence that has hitherto been distinct from the cost-of-illness (COI) work, i.e. studies on the labour market impact of diabetes.MethodsWe searched PubMed, EMBASE, EconLit and IBSS (without language restrictions) for studies assessing the economic burden of type 2 diabetes published from January 2001 to October 2014. Costs reported in the included studies were converted to international dollars ($) adjusted for 2011 values. Alongside the narrative synthesis and methodological review of the studies, we conduct an exploratory linear regression analysis, examining the factors behind the considerable heterogeneity in existing cost estimates between and within countries.ResultsWe identified 86 COI and 23 labour market studies. COI studies varied considerably both in methods and in cost estimates, with most studies not using a control group, though the use of either regression analysis or matching has increased. Direct costs were generally found to be higher than indirect costs. Direct costs ranged from $242 for a study on out-of-pocket expenditures in Mexico to $11,917 for a study on the cost of diabetes in the USA, while indirect costs ranged from $45 for Pakistan to $16,914 for the Bahamas. In LMICs—in stark contrast to HICs—a substantial part of the cost burden was attributed to patients via out-of-pocket treatment costs. Our regression analysis revealed that direct diabetes costs are closely and positively associated with a country’s gross domestic product (GDP) per capita, and that the USA stood out as having particularly high costs, even after controlling for GDP per capita. Studies on the labour market impact of diabetes were almost exclusively confined to HICs and found strong adverse effects, particularly for male employment chances. Many of these studies also took into account the possible endogeneity of diabetes, which was not the case for COI studies.ConclusionsThe reviewed studies indicate a large economic burden of diabetes, most directly affecting patients in LMICs. The magnitude of the cost estimates differs considerably between and within countries, calling for the contextualization of the study results. Scope remains large for adding to the evidence base on labour market effects of diabetes in LMICs. Further, there is a need for future COI studies to incorporate more advanced statistical methods in their analysis to account for possible biases in the estimated costs.

A. Allahverdi, C. T. Ng, T. Cheng et al.

David Hummels

D. Abegunde, C. Mathers, T. Adam et al.

Russell Cooper, J. Haltiwanger

M. S. Rozeff

K. Kirkland, Jane P. Briggs, Sharon L. Trivette et al.

Mark J. Zbaracki, M. Ritson, Daniel Levy et al.

J. Stevens, P. Corso, E. Finkelstein et al.

P. Klemperer

J. Obeso

Jean-françois Hennart

D. Baron, R. Myerson

Joseph Farrell, P. Klemperer, P. Klemperer

Laurie A. Rudman

S. Grosse, R. Nelson, Kwame Nyarko et al.

Qian Xie, Linda Cai, Alexander Terenin et al.

In automated machine learning, scientific discovery, and other applications of Bayesian optimization, deciding when to stop evaluating expensive black-box functions in a cost-aware manner is an important but underexplored practical consideration. A natural performance metric for this purpose is the cost-adjusted simple regret, which captures the trade-off between solution quality and cumulative evaluation cost. While several heuristic or adaptive stopping rules have been proposed, they lack guarantees ensuring stopping before incurring excessive function evaluation costs. We propose a principled cost-aware stopping rule for Bayesian optimization that adapts to varying evaluation costs without heuristic tuning. Our rule is grounded in a theoretical connection to state-of-the-art cost-aware acquisition functions, namely the Pandora's Box Gittins Index (PBGI) and log expected improvement per cost (LogEIPC). We prove a theoretical guarantee bounding the expected cost-adjusted simple regret incurred by our stopping rule when paired with either acquisition function. Across synthetic and empirical tasks, including hyperparameter optimization and neural architecture size search, pairing our stopping rule with PBGI or LogEIPC usually matches or outperforms other acquisition-function--stopping-rule pairs in terms of cost-adjusted simple regret.

Mehmet Hamza Erol, Batu El, Mirac Suzgun et al.

Widespread adoption of AI systems hinges on their ability to generate economic value that outweighs their inference costs. Evaluating this tradeoff requires metrics accounting for both performance and costs. Building on production theory, we develop an economically grounded framework to evaluate language models' productivity by combining accuracy and inference cost. We formalize cost-of-pass: the expected monetary cost of generating a correct solution. We then define the frontier cost-of-pass: the minimum cost-of-pass achievable across available models or the human-expert(s), using the approx. cost of hiring an expert. Our analysis reveals distinct economic insights. First, lightweight models are most cost-effective for basic quantitative tasks, large models for knowledge-intensive ones, and reasoning models for complex quantitative problems, despite higher per-token costs. Second, tracking the frontier cost-of-pass over the past year reveals significant progress, particularly for complex quant. tasks where the cost roughly halved every few months. Third, to trace key innovations driving this progress, we examine counterfactual frontiers -- estimates of cost-efficiency without specific model classes. We find that innovations in lightweight, large, and reasoning models have been essential for pushing the frontier in basic quant., knowledge-intensive, and complex quant. tasks, respectively. Finally, we assess the cost-reductions from common inference-time techniques (majority voting and self-refinement), and a budget-aware technique (TALE-EP). We find that performance-oriented methods with marginal performance gains rarely justify the costs, while TALE-EP shows some promise. Overall, our findings underscore that complementary model-level innovations are the primary drivers of cost-efficiency and our framework provides a principled tool for measuring this progress and guiding deployment.