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Adverse drug reactions are the fifth most frequent cause of deaths in developed countries, effectively a global epidemic. However, progress in ameliorating the problem has been slow. Pharmacovigilance currently operates without clear objectives in relation to individual decisions, with no protocol (although risk management plans are a great step forward), with obscure materials and methods used for making decisions, with very limited reasoning and discussion, and little or no follow up and audit of the results. Problems include under-reporting, poor quality reports, underuse of the latest communications technology and suboptimal individual feedback to reporters. Assessment of causality is poor, impeding decision-making. After signal detection, more active measures to assess the risk to public health are needed. Other essential factors include precision about the ways in which data are prepared and transformed into databases, the recognition of secondary effects, which may be more obvious than the primary effect, but not so easy to link causally, and cognisance of all kinds of interactions. Areas that should be developed include pharmacoepidemiology, knowledge finding (through data mining), and communication and systems technology. The general way forward seems clear: a rigorous way of documenting all the steps, from getting reports of harms into regulatory databases to assessing their effects on public health, is essential and should be publicly reviewed for weaknesses. In turn, matters would be much improved by input on benefit/harm perceptions from patient groups, influencing decisions about what should be the true targets for regulatory and pharmacovigilance activities, avoiding second guessing by regulators. [ABSTRACT FROM AUTHOR]

In courts case histories play a central part when a crime may have resulted from an effect of a prescribed drug; in civil cases where a person may have suffered damage from a drug; and in coroners' enquiries into the cause of unexplained deaths. The court must decide two important questions: 1. Can the suspected medication(s) cause this kind of effect? 2. Did it (or they) do so in this particular case? Many judges and coroners have not addressed these questions clearly and have not used expert witnesses consistently, on occasion disregarding scientific evidence. Courts need to appoint experts to explain and interpret the scientific evidence. Few judges are equipped to resolve contradictions between different experts. Brief accounts of five cases from four countries illustrate these points. The reluctance of legal processes to implicate drugs as a possible cause of violent behaviour leads to injustice. Courts must be required to obtain appropriate expert evidence, and be given independent data on which drugs can cause such behaviour. [ABSTRACT FROM AUTHOR]

A thorough analysis of a case that involves a medication that may have caused or contributed to an adverse outcome, or a comparison of two compounds in a patent dispute, requires consideration of many processes that affect the clinical effects of a medication. These include its chemical structure, its pharmacological actions (pharmacodynamics), the pharmaceutical formulation, and its absorption, distribution, metabolism, and excretion (pharmacokinetics). They also include analysis of clinical details, including the diagnosis, the quality of the prescribing decisions, the accuracy of the prescription, dispensing, and administration of medications, and how appropriately the case was managed, including monitoring. A causality assessment should be attempted for both the general case and the particular case. Knowledge of the systems that describe a medication's mechanisms of action (EIDOS) and the dose-relationships and time-courses of adverse outcomes and individual susceptibilities to them (DoTS) can inform several aspects of the analysis. Reports should be written in clear English and should not contain statements that rely on expertise that the expert does not possess. [ABSTRACT FROM AUTHOR]

Background In screening the Intercontinental Medical Statistics (IMS) Health Disease Analyzer database of GP records from the UK, an increased registration of pneumonia subsequent to the prescription of some antipsychotic medicines was identified. Aim To investigate the temporal pattern between antipsychotic prescriptions and pneumonia with respect to age, type of pneumonia and other chest infections, and antipsychotic class. Design of study Self-controlled cohort analysis. Setting Electronic health records from the UK IMS Health Disease Analyzer database. Method Three groups of pneumonia-related International Classification of Diseases (ICD)-10 terms and prescriptions of atypical and conventional antipsychotic medicines were studied. Separate analyses were carried out for patients aged ≥65 years. The observed rate of pneumonia terms registered in different time periods in connection to antipsychotic prescriptions was contrasted to the overall rate of pneumonia terms relative to prescriptions of other drugs in the same dataset. Results In patients aged z65 years, an increased registration of a group of terms defined as 'acute chest infections', after atypical antipsychotic prescriptions, was identified. The corresponding increase after conventional antipsychotic prescriptions was much smaller. Bronchopneumonia had a striking increase after both atypical and conventional antipsychotic prescriptions, and was commonly recorded with fatal outcome. Few registrations of hypostatic pneumonia were noted. Patients aged <65 years did not have a higher rate of acute chest infections after receiving antipsychotic prescriptions. Conclusion The consistent pattern of an increased rate of chest infections after atypical antipsychotic prescnptions in older people seen in this outpatient study, together with the higher risk shown in a previous study on hospitalised patients, suggests a causal relationship. This is of importance since bronchopneumonia seems highly linked to fatal outcome. In the absence of a mechanism, further investigation of the role of antipsychotics in older people is needed. [ABSTRACT FROM AUTHOR]

Large collections of electronic patient records provide a vast but still underutilised source of information on the real world use of medicines. They are maintained primarily for the purpose of patient administration, but contain a broad range of clinical information highly relevant for data analysis. While they are a standard resource for epidemiological confirmatory studies, their use in the context of exploratory data analysis is still limited. In this paper, we present a framework for open-ended pattern discovery in large patient records repositories. At the core is a graphical statistical approach to summarising and visualising the temporal association between the prescription of a drug and the occurrence of a medical event. The graphical overview contrasts the observed and expected number of occurrences of the medical event in different time periods both before and after the prescription of interest. In order to effectively screen for important temporal relationships, we introduce a new measure of temporal association, which contrasts the observed-to-expected ratio in a time period immediately after the prescription to the observed-to-expected ratio in a control period 2 years earlier. An important feature of both the observed-to-expected graph and the measure of temporal association is a statistical shrinkage towards the null hypothesis of no association, which provides protection against highlighting spurious associations. We demonstrate the usefulness of the proposed pattern discovery methodology by a set of examples from a collection of over two million patient records in the United Kingdom. The identified patterns include temporal relationships between drug prescriptions and medical events suggestive of persistent and transient risks of adverse events, possible beneficial effects of drugs, periodic co-occurrence, and systematic tendencies of patients to switch from one medication to another. [ABSTRACT FROM AUTHOR]