Drug Safety
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Paracetamol (acetaminophen) is the most common drug taken in overdose in the UK, accounting for 48% of poisoning admissions to hospital and being involved in an estimated 100-200 deaths per year. In 1998, the UK government introduced legislation that reduced the maximum pack size of all non-effervescent tablets and capsules containing aspirin (acetylsalicylic acid) or paracetamol that can be sold or supplied from outlets other than registered pharmacies from 25 to 16 tablets or capsules. This article reviews the literature to determine the effectiveness of the legislation, focusing specifically on paracetamol poisoning. ⋯ Some studies do not clearly differentiate between the paracetamol preparations covered by the legislation and those not. The limited number of studies to date, combined with a variety of outcome measures, make it difficult to determine with accuracy whether or not the legislation has been a success. More long-term studies are needed to fully assess the impact of the legislation.
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Comparative Study
Potential use of data-mining algorithms for the detection of 'surprise' adverse drug reactions.
Various data mining algorithms (DMAs) that perform disporportionality analysis on spontaneous reporting system (SRS) data are being heavily promoted to improve drug safety surveillance. The incremental value of DMAs is ultimately related to their ability to detect truly unexpected associations that would have escaped traditional surveillance and/or their ability to identify the same associations as traditional methods but with greater scientific efficiency. As to the former potential benefit, in the course of evaluating DMAs, we have observed what we call 'surprise reactions'. These adverse reactions may be discounted in manual review of adverse drug reaction (ADR) lists because they are less clinically dramatic, less characteristic of drug effects in general, less serious than the classical type B hypersensitivity reactions or may have subtle pharmacological explanations. Thus these reactions may only become recognised when post hoc explanations are sought based on more refined pharmacological knowledge of the formulation. The objective of this study was to explore notions of 'unexpectedness' as relates to signal detection and data mining by introducing the concept of 'surprise reactions' and to determine if the latter associations, often first reported in the literature, represent a type of ADR amenable to detection with the assistance of adjunctive statistical calculations on SRS data. ⋯ Identification of surprise reactions may serve as an important niche for DMAs.
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The prescription drugs or drug classes that are most frequently associated with death in the US might be identifiable from death certificate data. ⋯ Deaths due to overdoses are the most prominent cause of drug-related mortality in death certificate data. Certain drugs and drug classes, especially the opioids (e.g. narcotics, methadone), psychoactive drugs (e.g. antidepressants, amfetamines), anticoagulants and antibacterials (which cause or contribute to C. difficile enterocolitis) are associated with large and increasing numbers of deaths and preventive strategies should be considered.