Emergency Department Visits Involving Nonmedical Use of Selected Prescription Drugs in the United States, 2004-2008

Office of Applied Studies, Substance Abuse and Mental Health Services Administration, USA.
Journal of Pain & Palliative Care Pharmacotherapy 09/2010; 24(3):293-7. DOI: 10.3109/15360288.2010.503730
Source: PubMed

ABSTRACT This report, adapted from the lead article in the June 10, 2010, issue of Morbidity and Mortality Weekly Reports, describes the alarming increase in overdose deaths involving prescription drugs. Oxycodone, hydrocodone, and methadone were the drugs most highly implicated. Data were derived from the federal Drug Abuse Warning Network (Dawn). Other drugs commonly used in managing pain patients, including benzodiazepines and muscle relaxants, also were implicated.

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    • "Our results indicate, as expected from earlier work, that oxycodone and hydrocodone products, the opioids most commonly used to control pain in the medical and dental fields [19] [37] [41], are likewise the most highly abused opioids [2] [7] [12] [21] [27] [37] [42] [45]. Given that it has been shown that there is a direct relationship between the number of opioid prescriptions by health care providers and the magnitude of diversion to the illicit marketplace [6] [13] [29], accessibility is certainly a major feature attracting non-therapeutic users to these drugs. "
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    ABSTRACT: Linear prediction method is one of the most frequently used analysis methods of speech. Covariance method and auto-correlation method of linear prediction often fail to make a precise analysis of speech because of the excitation source or fundamental frequency. In order to decrease the affect of the excitation source, various kinds of difference operations are usually employed for preprocessing. However, such preprocessings do not always work satisfactorily. Here proposed is a new approach to LPC analysis based on selective use of speech data to reject the data disturbed by the excitation source, and is called selective linear prediction method. The method is constructed aiming to improve the accuracy of analysis. First, the formulation of linear prediction is presented using generalized inverse matrices. Then, a successive computation is described based on Givens' reduction. The selective computation, which plays an essential role in our method, owes its efficiency to Givens' reduction. Finally the advantage of the proposed method is demonstrated by computer simulation using both synthetic and natural speech.
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