The prevalence of hepatitis A virus antibodies was studied using a commercial ELISA method. 2,214 subjects were included, 1,211 in 1992 and 1,003 during 1986-87. In 1992 the seroprevalence rates among subjects 1-9, 10-19, 20-29 and 30-39 years old were 2.4%, 21%, 57.6% and 87.5% respectively, as compared with 7.7%, 37.9%, 80.6% and 98.1% respectively, in a similar group of subjects studied 5 years earlier (p < or = 0.001). The reported number viral hepatitis cases declined from 35.0 per 100,000 people in 1984 to 8.9 per 100,000 in 1992. Concurrently, the age when contracting the disease rose. The mean age for patients acquiring hepatitis A was 15.5 in 1986-88 and 20.1 in 1991-92. The decline in incidence and prevalence of HAV infection indicates a progressive and continuous decrease in HAV circulation in this geographical area.

BackgroundAdverse drug reactions (ADRs) in children are a significant cause of hospitalisation. A systematic review published in 2013 estimates this incidence in the range from 0.16–4.3%.PurposeThe main objective was to describe the incidence of ADRs leading to admission in a paediatric hospital. Secondary objectives were to determine the drug classes causing ADRs, duration of hospitalisation and to compare the incidence obtained with the current literature.Material and methodsA retrospective study of all ADRs codes in the medical records of paediatric patients. ADRs were coded by a medical archivist for an 11-year period in a database.ResultsA total of 73,864 hospitalizations of children were evaluated. We detected 520 ADRs resulting in hospital admission. We calculated on average 47.4 ADRs coded per year for an annual average incidence of 0.7%. ADRs coded occurred amongst 0–5 year-olds and 12–17 year-olds in 53.7% and 18.2%, respectively. 49.3% were females. Mean hospitalisation time due to ADRs was 6.3 days.The organ systems most commonly involved were the haematopoietic system (63.4%), central nervous system (10.6%), digestive system (8.8%) and skin (6.1%). The classes of drugs most frequently involved were antineoplastic drugs (65.0%), drugs active on the central nervous system (8.6%) and anti-infective agents (5.8%).ConclusionThe incidence of ADRs as a cause of hospital admission in this study (0.7%) falls within the range of incidences in the current literature. The organ system most commonly involved is the haematopoietic system and the class of drug most frequently involved is antineoplastic drugs. Drug surveillance studies are necessary to characterise risk factors within this population and to test prevention strategies to effectively promote the safer use of drugs in children.ReferencesZed PJ, Haughn C, Black KJ, et al. Medication-related emergency department visits and hospital admissions in pediatric patients: a qualitative systematic review. J Pediatr 2013;163:477–83Gallagher RM, Mason JR, Bird KA, et al. Adverse drug reactions causing admission to a paediatric hospital. PLoS One 2012;7(12):e50127No conflict of interest.

BackgroundNew technologies have improved efficiency and safety of drug management in hospitals. From 2006 to 2009, six automated dispensing systems (ADS) (Pyxis) were implemented in five units at a tertiary hospital, and nurses were instructed on its use. The correct management of these systems is essential for the proper performance and availability of drugs.PurposeTo assess indicators related to ADS, focused on discrepancies in stock.Material and methodsDuring 2013 and 2014, the number of dispensations (ND), inventories (NI) and resupplies (NR) in six ADS were collected using Web-Reporting software, as well as the number of discrepancies. Two indicators were defined and associated with ward dispensing mistakes:Inventory discrepancies (ID), percentage of the discrepancies detected during the inventory divided by NI. These are performed by nurses in each unit.Resupply discrepancies (RD), percentage of the discrepancies detected during the resupply divided by NR. These are corrected by pharmacy assistants.ResultsIn each of these five units, the following results were obtained:Emergency department:2013: ND: 84 529; NI: 1778; NR: 8816; ID: 54.2%; RD: 29.8%.2014: ND: 92 010; NI: 3378; NR: 9400; ID: 30.0%; RD: 28.1%.Postoperative care unit (two ADS):2013: ND: 52 824 and 30 071; NI: 2022 and 1546; NR: 7693 and 4931; ID: 50.1% and 34.7%; RD: 17.7% and 17.8%.2014: ND: 51 999 and 20 199; NI: 2774 and 1921; NR: 8089 and 3802; ID: 33.2% and 18.3%; RD: 17.3% and 16.2%.Pre-hospitalisation unit:2013: ND: 21 741; NI: 733; NR: 2323; ID: 49.4%; RD: 24.7%.2014: ND: 25 845; NI: 2568; NR: 2727; ID: 19.6%; RD: 23.7%.Short stay unit:2013: ND: 35 230; NI: 1262; NR: 3180; ID: 37.1%; RD: 21.6%.2014: ND: 34 521; NI: 1833; NR: 3235; ID: 18.3%; RD: 18.6%.Neonatal intensive care unit:2013: ND: 18 040; NI: 1112; NR: 2267; ID: 29.9%; RD: 29.9%.2014: ND: 17 548; NI: 1192; NR: 2370; ID: 14.4%; RD: 26.3%.ConclusionA high rate of discrepancies in the stock of medicines was found, with important differences among units. These indicators have shown the effectiveness of monitoring these processes. We need to establish a training programme for nurses to improve the management of ADS.No conflict of interest.

Background Primary care pharmacists found an increase in osteoporosis drugs use and expenditure. This concern was forwarded to the hospital management team, which approved the implementation of a multidisciplinary strategy involving Pharmacy and Rheumatology Department. Purpose To improve osteoporosis treatment and assess the impact of a cost-saving strategy in a health care area. Materials and methods To carry out the project we made evidence-based abstracts about osteoporosis drugs of concern (teriparatide, parathyroid hormone and strontium ranelate) that were sent to physicians and we also called patients to attend an appointment with the rheumatologist. After that, we analysed the number of prescriptions and expenditure coming from general practitioners and hospital physicians 6 months after the beginning of the project and compared with data from the previous year when no intervention was made. Results From July to December 2012 the number of drug prescriptions and expenditure decreased compared to the previous year as follows: Teriparatide, 41% fewer prescriptions and 6,159 € saving (-37%); parathyroid hormone, 78.4% fewer prescriptions and 88,272 € saving (-80%); strontium ranelate, 22.2% fewer prescriptions and 43,988 € saving (-30%). Overall, we estimate global savings of 192,419 € (-46%) compared to the previous year. We find some limitations with these conclusions as the introduction of law 16/2012 could have contributed to the decrease in prescriptions as well as cost savings due to a greater patient contribution. Nevertheless, the overall reduction in number of prescriptions and pharmaceutical spending were 16% and 23%, less than the results we achieved with these three drugs. Conclusions Simple actions like promotion of cost-effective use of medicines, providing evidence-based information to physicians, as well as the creation of a specialised osteoporosis consultation, were implemented in our hospital with positive initial results. No conflict of interest.

Background Little agreement exits between different drug interaction databases. Purpose To compare the frequency and severity of potential drug-drug interactions (DDIs) occurring in a haematological unit and detected by two drug interactions databases. Materials and methods A prospective, observational and descriptive study was carried out from November 2012 to February 2013. Twice a week, every patient’s treatment sheet was collected and screened through two drug interactions databases: Thomson Micromedex and Drug Interaction Facts. All potential DDIs identified were recorded and graded by their level of severity. Results Among 317 analysed treatment sheets, a total of 2373 potential DDIs were detected by the two databases. According to Micromedex, 1348 potential DDIs were found, counting 176 different pairs of drugs; of these DDIs, 64 were classified as contraindicated, 538 as major, 718 as moderate and 28 as minor. Regarding Drug Interaction Facts, 1025 potential DDIs were found, counting 124 different pairs of drugs; of these DDIs, 203 were classified as major, 537 as moderate and 285 as minor. There was a pool of 225 different pairs of drugs detected by both databases, irrespective of how many times these interactions appeared. Upon assessing the total number of pairs of drugs identified by the two databases, Micromedex identified 78.2% (176/225) and Drug Interaction Facts, 55.1% (124/225) of the potential interactions. Upon evaluation of the congruence of severity ratings between both databases, there was an agreement in 16.4% of the 225 pairs of drugs identified (37/225). Conclusions The lack of agreement between different databases shows how complicated it is to detect potentially significant drug interactions in clinical practice. No conflict of interest.