ABSTRACT
In order to address the loss of crystallographic training opportunities resulting from the cancelation of conventional schools around the world due to the COVID-19 pandemic, we have started an online crystallography school with live lectures and live Q&A using Zoom Webinar. Since we were trying to reach a large audience in a relatively short period, we have limited the school to ten 1 h lectures covering practical aspects of small molecule crystallography including data collection, data processing, and structure solution. In the school, we also covered some advanced topics that students commonly see in their work: absolute structure determination, twinning, and disorder. To round out the education, we provided lectures on macromolecular crystallography and powder diffraction. For students to practice on their own, we used freely available data reduction and structure solution software, as well as datasets with which to practice. To give students credit for course completion, we provided an online exam and an electronic certificate of completion. In this editorial, we will provide some insight into the issues of holding lectures with up to 750 students of very diverse backgrounds and review the efficacy of the school in teaching crystallography for the two cohorts of students.
ABSTRACT
BACKGROUND: The science of complex systems has been proposed as a way of understanding health services and the demand for them, but there is little quantitative evidence to support this. We analysed patterns of healthcare use in different urgent care settings to see if they showed two characteristic statistical features of complex systems: heavy-tailed distributions (including the inverse power law) and generative burst patterns. METHODS: We conducted three linked studies. In study 1 we analysed the distribution of number of contacts per patient with an urgent care service in two settings: emergency department (ED) and primary care out-of-hours (PCOOH) services. We hypothesised that these distributions should be heavy-tailed (inverse power law or log-normal) in keeping with typical complex systems. In study 2 we analysed the distribution of bursts of contact with urgent care services by individuals: correlated bursts of activity occur in complex systems and represent a mechanism by which overall heavy-tailed distributions arise. In study 3 we replicated the approach of study 1 using data systematically identified from published sources. RESULTS: Study 1 involved data from a PCOOH service in Scotland (725,000) adults, 1.1 million contacts) and an ED in New Zealand (60,000 adults, 98,000 contacts). The total number of contacts per individual in each dataset was statistically indistinguishable from an inverse power law (p > 0.05) above 4 contacts for the PCOOH data and 3 contacts for the ED data. Study 2 found the distribution of contact bursts closely followed a heavy-tailed distribution (p < 0.008), indicating the presence of correlated bursts. Study 3 identified data from 17 studies across 8 countries and found distributions similar to study 1 in all of them. CONCLUSIONS: Urgent healthcare use displays characteristic statistical features of large complex systems. These studies provide strong quantitative evidence that healthcare services behave as complex systems and have important implications for urgent care. Interventions to manage demand must address drivers for consultation across the whole system: focusing on only the highest users (in the tail of the distribution) will have limited impact on efficiency. Bursts of attendance - and ways to shorten them - represent promising targets for managing demand.
Subject(s)
Delivery of Health Care/standards , Primary Health Care/methods , Adult , Female , Humans , Middle AgedABSTRACT
BACKGROUND: Many patients in primary care stop antidepressant treatment after only one prescription, so do not benefit from treatment. Some patients who stop initial antidepressant treatment go on to restart it, but neither the incidence of restarting nor the probability that patients who restart treatment subsequently complete an adequate course of treatment is known. OBJECTIVE: To examine subsequent antidepressant use in patients who discontinued treatment after only one antidepressant prescription. METHODS: We used a primary care database (over 1.2 million records) to study patients who commenced treatment with an eligible antidepressant between April 2007 and March 2008 and who stopped treatment for at least 1 month after the first prescription. We examined their subsequent antidepressant prescriptions to estimate the probability of restarting antidepressant treatment, the likelihood of continuing subsequent treatment and the patient characteristics associated with these. RESULTS: Out of 24817 patients, 6952 (28%) patients discontinued antidepressant treatment after the first prescription. The cumulative probability of restarting treatment after early discontinuation was 8.6% (95% confidence interval [CI] 8.0-9.3) after 1 month off-treatment, and 24.1% (22.9-25.2) after 9 months off-treatment. The probability of those who restarted treatment continuing for 6 months or more was 29.3% (26.5-32.5). CONCLUSIONS: Few patients who stop antidepressant treatment after the first prescription subsequently complete an adequate treatment course within the next year. Initiatives to promote adherence to appropriate antidepressant treatment should begin during the first prescription.
