Psychotropic-related hip fractures: Meta-analysis of first- generation and second-generation antidepressant and antipsychotic drugs [Fracturas de cadera relacionadas con agentes psicotrópicos: Un metaanálisis de fármacos antidepresores y antipsicóticos de primera generación y de segunda generación]
Oderda L.H.,University of Utah |
Young J.R.,University of Utahs |
Asche C.V.,Illinois College |
Pepper G.A.,University of Utah
Annals of Pharmacotherapy | Year: 2012
BACKGROUND: In 2007, more than 18,000 adults aged 65 or older died from injuries related to falls, with up to 30% experiencing severe injuries such as hip fracture or head trauma. The economic impact of falls and fractures among older people is substantial, with direct economic consequences totaling $19 billion in 2000. OBJECTIVE: To evaluate the association between antipsychotic and antidepressant agents and the risk of hip fracture in older adults, across multiple studies. METHODS: An English-language PubMed/MEDLINE search for studies from January 1966 to January 2011 was conducted, using key words including aged, hip fracture, fractures, antidepressive agents, and antipsychotic agents, as well as individual drug names. Criteria for study inclusion were mean subject age greater than or equal to 65 years, adjusted for age and sex, hip fracture-specific results provided, data specific to a drug class, subclass, or single agents, and cohort or case-controlled study design. Two authors reviewed all studies for inclusion/ exclusion. A random effects model was used to calculate summary odds ratios. RESULTS: A total of 166 studies were identified in the initial search. Ten antipsychotic- related and 14 antidepressant-related studies, representing more than 70,000 hip fracture cases and approximately 270,000 subjects from 4 continents, met the inclusion criteria. Summary odds ratios (95% CI) were first-generation (conventional) antipsychotics 1.68 (1.43 to 1.99), second-generation (atypical) antipsychotics 1.30 (1.14 to 1.49), first-generation (tricyclic) antidepressants 1.71 (1.43 to 2.04), and second-generation (selective serotonin reuptake inhibitors, serotonin-norepinephrine reuptake inhibitors, and unique agents such as bupropion, mirtazapine, and trazodone) antidepressants 1.94 (1.37 to 2.76). Clear evidence of heterogeneity was noted among all antidepressant study analyses (I2 > 87%; Q statistic p < 0.05). CONCLUSIONS: All drug classes studied-first- and second-generation antipsychotics and antidepressants-were associated with an increased risk of hip fracture in predominantly older adult populations.
Langerak F.,TU Eindhoven |
Griffin A.,University of Utahs |
Hultink E.J.,Technical University of Delft
Journal of Product Innovation Management | Year: 2010
Development teams often use mental models to simplify development time decision making because a comprehensive empirical assessment of the trade-offs across the metrics of development time, development costs, proficiency in market-entry timing, and new product sales is simply not feasible. Surprisingly, these mental models have not been studied in prior research on the trade-offs among the aforementioned metrics. These mental models are important to consider, however, because they define reality, specify what team members attend to, and guide their decision making. As such, these models influence how development teams make trade-offs across the four metrics to try to optimize new product profitability. Teams with such an objective should manage to a development time that minimizes development costs and to a proficient market-entry timing that maximizes new product sales. Yet many teams use mental models for development time decision making that focus either just on development costs or on proficiency in market-entry timing. This survey-based study uses data from 115 completed NPD projects, all product line additions from manufacturers in The Netherlands, to demonstrate that there is a cost to simplifying decision making. Making development time decisions without taking into account the contingency between development time and proficiency in market-entry timing can be misleading, and using either a sales-maximization or a cost-minimization simplified decision-making model may result in a cost penalty or a sales loss. The results from this study show that the development time that maximizes new product profitability is longer than the time that maximizes new product sales and is shorter than the development time that minimizes development costs. Furthermore, the results reveal that the cost penalty of sales maximization is smaller than the sales loss of development costs minimization. An important implication of the results is that, to determine the optimal development time, teams need to distinguish between cost and sales effects of development time reductions. To determine the relative impact of these effects this study also estimates the elasticities of development costs, new product sales, and new product profitability with regard to development time. Armed with this knowledge, development teams should be better equipped to make trade-offs among the four metrics of development time, development costs, proficiency in market-entry timing, and new product sales. © 2010 Product Development & Management Association.
Cankurtaran P.,Technical University of Delft |
Langerak F.,TU Eindhoven |
Griffin A.,University of Utahs
Journal of Product Innovation Management | Year: 2013
Five meta-analyses previously have been published on the topic of new product development involving the concept of new product development speed. Three of these studies have investigated antecedents to new product development success, of which just one was new product development speed. The other two studies used new product development speed as the dependent variable, and analyzed antecedents to achieving speed. This article extends previous empirical generalizations in this domain by using a meta-analytic methodology to understand the link between new product development speed and new product success at a more granular level. Specifically, it considers the relationship with different dimensions of success as measured overall or compositely, operationally (i.e., the process measures of decreasing development costs and proficiently managing market entry timing and the product measures of technical product performance and product competitive advantage), and relative to external success outcomes (i.e., customer based and financial success). While the results indicate that, in general, new product development speed is associated with improving success outcomes, those relationships may diminish or even disappear depending upon a number of methodological design decisions and research contexts. A subsequent meta-analysis of the antecedents of development speed provides a more holistic picture of development speed. These results are broadly consistent with those produced by another recent meta-analytic investigation of the issue. Together, these findings have important implications for academics pursuing further research in this domain, as well as for managers considering implementing a program to increase new product development speed. © 2013 Product Development & Management Association.
Livnat Y.,University of Utahs |
Rhyne T.-M.,University of Utahs |
Samore M.,University of Utahs
IEEE Computer Graphics and Applications | Year: 2012
Early detection and rapid response to infectious-disease outbreaks rely on effective decision making based on information from disparate sources. To improve decision-making in outbreak detection and response, it's important to understand how public health practitioners seek relevant information. Epinome, a user-centric visual-analytics system, supports research on decision-making in public health, particularly evaluation of information search strategies. Epinome facilitates investigation of scripted high-fidelity large-scale simulated disease outbreaks. Its dynamic environment seamlessly evolves and adapts as the user's tasks and focus change. This video shows how the Epinome system facilitates interactive simulations of disease outbreaks. © 2012 IEEE.