Background-Clinical trial registries are in widespread use to promote transparency around trials and their results.

Purpose-Adverse drug events (ADEs) are a common complication of medical care resulting in high morbidity and medical expenditure. Population level estimates of outpatient ADEs are limited. Our objective was to provide national estimates and characterizations of outpatient ADEs and determine risk factors associated with these events.Methods-Data are from the National Center for Health Statistics which collects information on patient visits to outpatient clinics and emergency departments throughout the United States. We examined visits between 1995 and 2005 and measured the national annual estimates of and risk factors for outpatient ADEs requiring medical treatment.Results-The national annual number of ADE-related visits was 4,335,990 (95%CI,4,326,345,108). Visits for ADEs to outpatient clinics increased over the study period from 9.0 to 17.0 per 1000 persons (P value for trend<0.001). In multivariate analyses, factors associated with ADE visits included patient age (OR 2.13; 95%CI 1.63-2.79 for 65 years and older), number of medications taken by patient (OR, 1.88; 95%CI, 1.58-2.25 for five medications or more), and female gender (OR, 1.51; 95%CI, 1.34-1.71). Overall, outpatient ADEs resulted in 107,468 (95%CI, 89,925) hospital admissions annually, with older patients at highest risk for hospitalization (P value for trend<0.001).Conclusions-Both patient age and polypharmacy use are risk factors for ADE-related healthcare visits, which have substantially increased in outpatient clinics between 1995 and 2005. The incidence of ADEs has particularly increased among patients 65 years and older with as many as 1 in 20 persons seeking medical care for an ADE.

Abstract

A quantitative trait locus (QTL) analysis of growth and fatness data from a three-generation experimental cross between Meishan (MS) and Large White (LW) pig breeds is presented. Six boars and 23 F1 sows, the progeny of six LW boars and six MS sows, produced 530 F2 males and 573 F2 females. Nine growth traits, i.e. body weight at birth and at 3, 10, 13, 17 and 22 weeks of age, average daily gain from birth to 3 weeks, from 3 to 10 weeks and from 10 to 22 weeks of age, as well as backfat thickness at 13, 17 and 22 weeks of age and at 40 and 60 kg live weight were analysed. Animals were typed for a total of 137 markers covering the entire porcine genome. Analyses were performed using two interval mapping methods: a line-cross (LC) regression method where founder lines were assumed to be fixed for different QTL alleles and a half-/full-sib (HFS) maximum likelihood method where allele substitution effects were estimated within each half-/full-sib family. Both methods revealed highly significant gene effects for growth on chromosomes 1, 4 and 7 and for backfat thickness on chromosomes 1, 4, 5, 7 and X, and significant gene effects on chromosome 6 for growth and backfat thickness. Suggestive QTLs were also revealed by both methods on chromosomes 2 and 3 for growth and 2 for backfat thickness. Significant gene effects were detected for growth on chromosomes 11, 13, 14, 16 and 18 and for backfat thickness on chromosome 8, 10, 13 and 14. LW alleles were associated with high growth rate and low backfat thickness, except for those of chromosome 7 and to a lesser extent early-growth alleles on chromosomes 1 and 2 and backfat thickness alleles on chromosome 6.

BackgroundPatients routinely use Twitter to share feedback about their experience receiving healthcare. Identifying and analysing the content of posts sent to hospitals may provide a novel real-time measure of quality, supplementing traditional, survey-based approaches.ObjectiveTo assess the use of Twitter as a supplemental data stream for measuring patient-perceived quality of care in US hospitals and compare patient sentiments about hospitals with established quality measures.Design404 065 tweets directed to 2349 US hospitals over a 1-year period were classified as having to do with patient experience using a machine learning approach. Sentiment was calculated for these tweets using natural language processing. 11 602 tweets were manually categorised into patient experience topics. Finally, hospitals with ≥50 patient experience tweets were surveyed to understand how they use Twitter to interact with patients.Key resultsRoughly half of the hospitals in the US have a presence on Twitter. Of the tweets directed toward these hospitals, 34 725 (9.4%) were related to patient experience and covered diverse topics. Analyses limited to hospitals with ≥50 patient experience tweets revealed that they were more active on Twitter, more likely to be below the national median of Medicare patients (p<0.001) and above the national median for nurse/patient ratio (p=0.006), and to be a nonprofit hospital (p<0.001). After adjusting for hospital characteristics, we found that Twitter sentiment was not associated with Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) ratings (but having a Twitter account was), although there was a weak association with 30-day hospital readmission rates (p=0.003).ConclusionsTweets describing patient experiences in hospitals cover a wide range of patient care aspects and can be identified using automated approaches. These tweets represent a potentially untapped indicator of quality and may be valuable to patients, researchers, policy makers and hospital administrators.