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      Potentially high-risk medication categories and unplanned hospitalizations: a case–time–control study

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          Abstract

          Empirical data of medication-related hospitalization are very limited. We aimed to investigate the associations between 12 high risk medication categories (diabetic agents, diuretics, nonsteroidal anti-inflammatory drugs (NSAIDs), anticoagulants, antiplatelets, antihypertensives, antiarrhythmics, anticonvulsants, antipsychotics, antidepressants, benzodiazepine (BZD)/Z-hypnotics, and narcotics) and unplanned hospitalizations. A population-based case–time–control study was performed using Taiwan’s National Health Insurance Research Database. Patients who experienced an unplanned hospitalization (index visit) were identified as index subjects and matched to a randomly selected reference visit within users of a specific category of high-risk medication. An unplanned hospitalization was defined as a hospital admission immediately after an emergency department visit. Discordant exposures to the high-risk medication during the case period (1–14 days before the visit) and the control period (366–379 days before the visit) were examined in both index and reference visits. Antipsychotics was associated with the highest risk of unplanned hospitalizations (adjusted OR: 1.54, 95% CI [1.37–1.73]), followed by NSAIDs (1.50, [1.44–1.56]), anticonvulsants (1.34, [1.10–1.64]), diuretics (1.24, [1.15–1.33]), BZD/Z-hypnotics (1.23, [1.16–1.31]), antidepressants (1.17, [1.05–1.31]) and antiplatelets (1.16, [1.07–1.26]). NSAIDs and narcotics were associated with the highest risks of unplanned hospitalizations with a length of stay ≥10 days. These medication categories should be targeted for clinical and policy interventions.

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          Most cited references27

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          National surveillance of emergency department visits for outpatient adverse drug events.

          Adverse drug events are common and often preventable causes of medical injuries. However, timely, nationally representative information on outpatient adverse drug events is limited. To describe the frequency and characteristics of adverse drug events that lead to emergency department visits in the United States. Active surveillance from January 1, 2004, through December 31, 2005, through the National Electronic Injury Surveillance System-Cooperative Adverse Drug Event Surveillance project. National estimates of the numbers, population rates, and severity (measured by hospitalization) of individuals with adverse drug events treated in emergency departments. Over the 2-year study period, 21,298 adverse drug event cases were reported, producing weighted annual estimates of 701,547 individuals (95% confidence interval [CI], 509,642-893,452) or 2.4 individuals per 1000 population (95% CI, 1.7-3.0) treated in emergency departments. Of these cases, 3487 individuals required hospitalization (annual estimate, 117,318 [16.7%]; 95% CI, 13.1%-20.3%). Adverse drug events accounted for 2.5% (95% CI, 2.0%-3.1%) of estimated emergency department visits for all unintentional injuries and 6.7% (95% CI, 4.7%-8.7%) of those leading to hospitalization and accounted for 0.6% of estimated emergency department visits for all causes. Individuals aged 65 years or older were more likely than younger individuals to sustain adverse drug events (annual estimate, 4.9 vs 2.0 per 1000; rate ratio [RR], 2.4; 95% CI, 1.8-3.0) and more likely to require hospitalization (annual estimate, 1.6 vs 0.23 per 1000; RR, 6.8; 95% CI, 4.3-9.2). Drugs for which regular outpatient monitoring is used to prevent acute toxicity accounted for 41.5% of estimated hospitalizations overall (1381 cases; 95% CI, 30.9%-52.1%) and 54.4% of estimated hospitalizations among individuals aged 65 years or older (829 cases; 95% CI, 45.0%-63.7%). Adverse drug events among outpatients that lead to emergency department visits are an important cause of morbidity in the United States, particularly among individuals aged 65 years or older. Ongoing, population-based surveillance can help monitor these events and target prevention strategies.
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            The case-time-control design.

            S Suissa (1995)
            Assessing the known or intended effects of a drug using non-experimental epidemiologic designs is often infeasible because of the absence of accurate data on a major confounder, the severity of the disease treated by this drug. To circumvent this problem of confounding by indication, I propose the case-time-control design, which does not require a measure of this confounder. Instead, the design uses subjects from a conventional case-control design as their own controls and thus requires that exposure be measurable at two or more points in time. I present a logistic model to estimate relative risks under this design and illustrate the method with data from a case-control study of 129 cases of fatal or near-fatal asthma and 655 controls. The exposure of interest was quantity of use of inhaled beta-agonists, drugs prescribed for the treatment of asthma. I found that the "best" estimate of relative risk for high vs low beta-agonist use using the conventional case-control approach is 3.1 [95% confidence interval (CI) = 1.8-5.4], which inherently includes the confounding effect of unmeasured severity. The corresponding estimate of drug effect using the proposed case-time-control approach is 1.2 (95% CI = 0.5-3.0), which excludes the confounding effect of unmeasured severity. This example indicates that the class of beta-agonists may not play the leading role attributed to it in the risk of fatal or near-fatal asthma, as had been previously suspected, except perhaps at excessive doses, as indicated by the dose-response analyses.
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              A systematic review of hospitalization resulting from medicine-related problems in adult patients.

              Medicine-related problems (MRPs) represent a major issue leading to hospitalization, especially in adult and elderly patients. The aims of this review are to investigate the prevalence, causes and major risk factors for MRPs leading to hospitalization in adult patients and to identify the main medicine classes involved. Studies were identified through electronic searches of Medline, Embase, Scopus and International Pharmaceutical Abstracts between January 2000 and May 2013. A systematic review was conducted of both retrospective and prospective studies. Studies included were those involving hospitalization resulting from MRPs in adults (≥18 years old), whereas studies excluded were those investigating drug misuse and abuse and studies investigating MRPs in hospitalized patients. Data analysis was performed using SPSS version 20. Forty-five studies were identified, including 21 that investigated hospitalization resulting from adverse drug reactions, six studies that investigated hospitalization due to adverse drug events and 18 studies that investigated hospitalization due to MRPs. The median prevalence rates of hospitalization resulting from adverse drug reactions, adverse drug events and MRPs were 7% (interquartile range, 2.4-14.9%), 4.6% (interquartile range, 2.85-16.6%) and 12.1% (interquartile range, 6.43-22.2%), respectively. The major causes contributing to MRPs were adverse drug reactions and noncompliance. In addition, the major risk factors associated with MRPs were old age, polypharmacy and comorbidities. Moreover, the main classes of medicines implicated were medicines used to treat cardiovascular diseases and diabetes. Hospitalization due to MRPs had a high prevalence, in the range of 4.6-12.1%. Most MRPs encountered were prevalent among adult patients taking medicines for cardiovascular diseases and diabetes. © 2013 The British Pharmacological Society.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                23 January 2017
                2017
                : 7
                : 41035
                Affiliations
                [1 ]Graduate Institute of Clinical Pharmacy, College of Medicine, National Taiwan University , Taipei, Taiwan
                [2 ]Clinical Informatics and Medical Statistics Research Center, Chang Gung University , Taoyuan, Taiwan
                [3 ]Aging and Health Research Center, National Yang Ming University , Taipei, Taiwan
                [4 ]Center for Geriatrics and Gerontology, Taipei Veterans General Hospital , Taipei, Taiwan
                [5 ]School of Pharmacy, College of Medicine, National Taiwan University , Taipei, Taiwan
                [6 ]Department of Pharmacy, National Taiwan University Hospital , Taipei, Taiwan
                Author notes
                Article
                srep41035
                10.1038/srep41035
                5253626
                28112193
                c9bfb07f-732d-404c-a378-6befa261316c
                Copyright © 2017, The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 09 February 2016
                : 15 December 2016
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