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Online retail is currently profoundly restructuring the working conditions in the retail sector. Existing studies generally describe the working conditions in the large warehouses of online retail as 'digital Taylorism'. This article broadens this perspective and draws on the theoretical concept of the local labour control regime. It explores how managements' technological and social labour control strategies vary spatially between online retail warehouses located in inner and outer metropolitan areas of the four largest German cities. The study uses qualitative methods and is mainly based on expert interviews with executives, representatives of trade unions and works councils, representatives of associations, and further experts. This study gains insights on the spatial variety of labour control and thus is relevant for international research on labour control and for practitioners' ability to create decent and humane work. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic disrupted health-care delivery, including difficulty accessing in-person care, which could have increased the need for strong pharmacological pain relief. Due to the risks associated with overprescribing of opioids, especially to vulnerable populations, we aimed to quantify changes to measures during the COVID-19 pandemic, overall, and by key subgroups., Methods: For this interrupted time-series analysis study conducted in England, with National Health Service England approval, we used routine clinical data from more than 20 million general practice adult patients in OpenSAFELY-TPP, which is a a secure software platform for analysis of electronic health records. We included all adults registered with a primary care practice using TPP-SystmOne software. Using interrupted time-series analysis, we quantified prevalent and new opioid prescribing before the COVID-19 pandemic (January, 2018-February, 2020), during the lockdown (March, 2020-March, 2021), and recovery periods (April, 2021-June, 2022), overall and stratified by demographics (age, sex, deprivation, ethnicity, and geographical region) and in people in care homes identified via an address-matching algorithm., Findings: There was little change in prevalent prescribing during the pandemic, except for a temporary increase in March, 2020. We observed a 9·8% (95% CI -14·5 to -6·5) reduction in new opioid prescribing from March, 2020, with a levelling of the downward trend, and rebounding slightly after April, 2021 (4·1%, 95% CI -0·9 to 9·4). Opioid prescribing rates varied by demographics, but we found a reduction in new prescribing for all subgroups except people aged 80 years or older. Among care home residents, in April, 2020, parenteral opioid prescribing increased by 186·3% (153·1 to 223·9)., Interpretation: Opioid prescribing increased temporarily among older people and care home residents, likely reflecting use to treat end-of-life COVID-19 symptoms. Despite vulnerable populations being more affected by health-care disruptions, disparities in opioid prescribing by most demographic subgroups did not widen during the pandemic. Further research is needed to understand what is driving the changes in new opioid prescribing and its relation to changes to health-care provision during the pandemic., Funding: The Wellcome Trust, Medical Research Council, The National Institute for Health and Care Research, UK Research and Innovation, and Health Data Research UK., Competing Interests: Declaration of interests BG has received research funding from the Bennett Foundation, the Laura and John Arnold Foundation, the National Health Service (NHS), The National Institute for Health and Care Research (NIHR), the NIHR School of Primary Care Research, NHS England, the NIHR Oxford Biomedical Research Centre, the Mohn–Westlake Foundation, NIHR Applied Research Collaboration Oxford and Thames Valley, the Wellcome Trust, the Good Thinking Foundation, Health Data Research UK, the Health Foundation, WHO, UK Research and Innovation Medical Research Council, Asthma UK, the British Lung Foundation, and the Longitudinal Health and Wellbeing strand of the National Core Studies programme; has previously been a Non-Executive Director at NHS Digital; and has received personal income from speaking and writing for lay audiences on the misuse of science. BM is employed by NHS England working on medicines policy and is clinical lead for primary care medicines data. AM has represented the Royal College of General Practitioners in the health informatics group and the Profession Advisory Group that advises on access to GP Data for Pandemic Planning and Research, of which the latter was a paid role. AM is a former employee and interim Chief Medical Officer of NHS Digital; and has consulted for health-care vendors, the last time being in 2022. The companies consulted in the last 3 years have no relationship to OpenSAFELY., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Electronic health records (EHRs) and other administrative health data are increasingly used in research to generate evidence on the effectiveness, safety, and utilisation of medical products and services, and to inform public health guidance and policy. Reproducibility is a fundamental step for research credibility and promotes trust in evidence generated from EHRs. At present, ensuring research using EHRs is reproducible can be challenging for researchers. Research software platforms can provide technical solutions to enhance the reproducibility of research conducted using EHRs. In response to the COVID-19 pandemic, we developed the secure, transparent, analytic open-source software platform OpenSAFELY designed with reproducible research in mind. OpenSAFELY mitigates common barriers to reproducible research by: standardising key workflows around data preparation; removing barriers to code-sharing in secure analysis environments; enforcing public sharing of programming code and codelists; ensuring the same computational environment is used everywhere; integrating new and existing tools that encourage and enable the use of reproducible working practices; and providing an audit trail for all code that is run against the real data to increase transparency. This paper describes OpenSAFELY's reproducibility-by-design approach in detail., (© 2024 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons Ltd.)

Aims: The COVID-19 pandemic created unprecedented pressure on healthcare services. This study investigates whether disease-modifying antirheumatic drug (DMARD) safety monitoring was affected during the COVID-19 pandemic., Methods: A population-based cohort study was conducted using the OpenSAFELY platform to access electronic health record data from 24.2 million patients registered at general practices using TPP's SystmOne software. Patients were included for further analysis if prescribed azathioprine, leflunomide or methotrexate between November 2019 and July 2022. Outcomes were assessed as monthly trends and variation between various sociodemographic and clinical groups for adherence with standard safety monitoring recommendations., Results: An acute increase in the rate of missed monitoring occurred across the study population (+12.4 percentage points) when lockdown measures were implemented in March 2020. This increase was more pronounced for some patient groups (70-79 year-olds: +13.7 percentage points; females: +12.8 percentage points), regions (North West: +17.0 percentage points), medications (leflunomide: +20.7 percentage points) and monitoring tests (blood pressure: +24.5 percentage points). Missed monitoring rates decreased substantially for all groups by July 2022. Consistent differences were observed in overall missed monitoring rates between several groups throughout the study., Conclusion: DMARD monitoring rates temporarily deteriorated during the COVID-19 pandemic. Deterioration coincided with the onset of lockdown measures, with monitoring rates recovering rapidly as lockdown measures were eased. Differences observed in monitoring rates between medications, tests, regions and patient groups highlight opportunities to tackle potential inequalities in the provision or uptake of monitoring services. Further research should evaluate the causes of the differences identified between groups., (© 2024 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

Theories of posttraumatic stress disorder (PTSD) highlight the role of cognitive and behavioral factors in its development, maintenance, and treatment. This study investigated the relationship between changes in factors specified in Ehlers and Clark's (2000) model of PTSD and PTSD symptom change in 217 patients with PTSD who were treated with cognitive therapy for PTSD (CT-PTSD) in routine clinical care. Bivariate latent change score models (LCSM) of session-by-session changes in self-report measures showed that changes in PTSD symptoms were preceded by changes in negative appraisals, flashback characteristics of unwanted memories, safety behaviours, and unhelpful responses to intrusions, but not vice versa. For changes in trauma memory disorganization and PTSD symptoms we found a bidirectional association. This study provides evidence that cognitive and behavioral processes proposed in theoretical models of PTSD play a key role in driving symptom improvement during CT-PTSD., Competing Interests: Declaration of competing interest No potential conflict of interest was reported by the authors., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: The COVID-19 pandemic has had a significant impact on delivery of NHS care. We have developed the OpenSAFELY Service Restoration Observatory (SRO) to develop key measures of primary care activity and describe the trends in these measures throughout the COVID-19 pandemic., Methods: With the approval of NHS England, we developed an open source software framework for data management and analysis to describe trends and variation in clinical activity across primary care electronic health record (EHR) data on 48 million adults.We developed SNOMED-CT codelists for key measures of primary care clinical activity such as blood pressure monitoring and asthma reviews, selected by an expert clinical advisory group and conducted a population cohort-based study to describe trends and variation in these measures January 2019-December 2021, and pragmatically classified their level of recovery one year into the pandemic using the percentage change in the median practice level rate., Results: We produced 11 measures reflective of clinical activity in general practice. A substantial drop in activity was observed in all measures at the outset of the COVID-19 pandemic. By April 2021, the median rate had recovered to within 15% of the median rate in April 2019 in six measures. The remaining measures showed a sustained drop, ranging from a 18.5% reduction in medication reviews to a 42.0% reduction in blood pressure monitoring. Three measures continued to show a sustained drop by December 2021., Conclusions: The COVID-19 pandemic was associated with a substantial change in primary care activity across the measures we developed, with recovery in most measures. We delivered an open source software framework to describe trends and variation in clinical activity across an unprecedented scale of primary care data. We will continue to expand the set of key measures to be routinely monitored using our publicly available NHS OpenSAFELY SRO dashboards with near real-time data., Funding: This research used data assets made available as part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref MC_PC_20058).The OpenSAFELY Platform is supported by grants from the Wellcome Trust (222097/Z/20/Z); MRC (MR/V015757/1, MC_PC-20059, MR/W016729/1); NIHR (NIHR135559, COV-LT2-0073), and Health Data Research UK (HDRUK2021.000, 2021.0157)., Competing Interests: LF, HC, RC, MW, VS, CW, AB, LH, RH, JM, PI, CM, AW, JM, SB, GH, TL, MW, WB, HS, CD, KC, DE, ID, TW, SD, RS, WH, AG No competing interests declared, AM Former employee and interim CMO of NHS Digital. Member of the RCGP health informatics group and the NHS digital GP data Professional Advisory Group, OM Received payment for:- Presenter at Psych 1 courses (College of Mental Health Pharmacy)- External assessor for Aston University modules on the PG Dip Psych Pharm course.Member of the College of Mental Health Pharmacy Council and co-lead the Education portfolio for this council. This is a charitable organisation and my work for the council is not paid, however, expenses for travel are reimbursed, MM Chair of MHRA IVD Expert Advisory Group. Unpaid, MS Between August 2018 and September 2022, employed as an NIHR funded Academic Clinical Fellow in Primary Care based at Queen Mary University London.Since September 2022, employed as a clinical research fellow on the NIHR funded AI MULTIPLY grant. All funding is awarded through Queen Mary University London, RC Honoraria for charing/presenting on webinars with Primary Care Pharmacy Association, CB Chris Bates is affiliated with TPP. The author has no financial interests to declare, JP John Parry is affiliated with TPP. The author has no financial interests to declare, FH Frank Hester is affiliated with TPP. The author has no financial interests to declare, SH Sam Harper is affiliated with TPP. The author has no financial interests to declare, JC Jonathan Cockburn is affiliated with TPP. The author has no financial interests to declare, SO Shaun O'Hanlon is affiliated with EMIS. The author has no financial interests to declare, AE Alex Eavis is affiliated with EMIS. Sits on the advisory board for OpenSAFELY and QResearch; both unpaid, RJ Richard Jarvis is affiliated with EMIS. The author has no financial interests to declare, DA Dima Avramov is affiliated with EMIS. The author has no financial interests to declare, PG Paul Griffiths is affiliated with EMIS. The author has no financial interests to declare, AF Aaron Fowles is affiliated with EMIS. The author has no financial interests to declare, NP Nasreen Parkes is affiliated with EMIS. The author has no financial interests to declare, BM Seconded to the Bennett Institute.Trustee of ICAP, a charity delivering counselling, BG BG has received research funding from the Laura and John Arnold Foundation, the NHS National Institute for Health Research (NIHR), the NIHR School of Primary Care Research, NHS England, the NIHR Oxford Biomedical Research Centre, the Mohn-Westlake Foundation, NIHR Applied Research Collaboration Oxford and Thames Valley, the Wellcome Trust, the Good Thinking Foundation, Health Data Research UK, the Health Foundation, the World Health Organisation, UKRI MRC, Asthma UK, the British Lung Foundation, and the Longitudinal Health and Wellbeing strand of the National Core Studies programme; he is a Non-Executive Director at NHS Digital; he also receives personal income from speaking and writing for lay audiences on the misuse of science, (© 2023, Fisher et al.)

Background: In the event of emergency response to large-scale incidents, such as a mass casualty incident (MCI) or a mass casualty incident-infectious disease (MCI-ID), regular training is essential in order to have experienced emergency personnel available in the event of an incident. Due to the pandemic drills often had to be cancelled or were only possible with small groups of people. It was often not possible to simulate a large-scale emergency with actors, so that the trainees could not be offered realistic scenarios of a mass casualty or disease incident. As part of two research projects, a digital platform for conducting training was used to avoid the risk of infection between participants during the exercises, so that on-site personnel deployment could be reduced to a minimum. The goal of this work was to evaluate end-user acceptance of the digital solution approaches., Methods: Within the framework of the project "Adaptive Resilience Management in Ports" (ARMIHN), a digital exercise platform was applied and evaluated with the help of participant surveys according to the focal points "implementation", "alternative possibilities", "learning effect" and "usability". The participants used the digital platform to exchange information and to communicate. For this purpose, various collaboration tools were embedded in the platform, which enabled simultaneous exchange of information in real time. Constant video communication with in-house and external authorities/teams was also established., Results: The potential of the digital platform as an alternative to on-site exercises was confirmed by the participating end users in the ARMIHN project with 90% agreement. The increase in subjective skills and knowledge gained during a MCI-ID was also predominantly rated approvingly (up to 70%). Participants who rated the implementation of the online format as well performed were significantly more likely to state that subjectively their ability to handle a MCI-ID had improved ( p  = 0.016). In contrast, virtual staff teamwork in real crisis situations was viewed critically by about half of respondents., Conclusion: Overall, the evaluation results point to the high end-user acceptance of the developed concept. Even though the aim is to evaluate the system over a longer period with a larger number of participants, the studies already conducted confirm the positive experiences in the respective projects., (© The Author(s) 2023.)

Background: The COVID-19 pandemic has disrupted healthcare activity across a broad range of clinical services. The NHS stopped non-urgent work in March 2020, later recommending services be restored to near-normal levels before winter where possible., Aim: To describe changes in the volume and variation of coded clinical activity in general practice across six clinical areas: cardiovascular disease, diabetes, mental health, female and reproductive health, screening and related procedures, and processes related to medication., Design and Setting: With the approval of NHS England, a cohort study was conducted of 23.8 million patient records in general practice, in situ using OpenSAFELY., Method: Common primary care activities were analysed using Clinical Terms Version 3 codes and keyword searches from January 2019 to December 2020, presenting median and deciles of code usage across practices per month., Results: Substantial and widespread changes in clinical activity in primary care were identified since the onset of the COVID-19 pandemic, with generally good recovery by December 2020. A few exceptions showed poor recovery and warrant further investigation, such as mental health (for example, for 'Depression interim review' the median occurrences across practices in December 2020 was down by 41.6% compared with December 2019)., Conclusion: Granular NHS general practice data at population-scale can be used to monitor disruptions to healthcare services and guide the development of mitigation strategies. The authors are now developing real-time monitoring dashboards for the key measures identified in this study, as well as further studies using primary care data to monitor and mitigate the indirect health impacts of COVID-19 on the NHS., (© The Authors.)

Objective: To ascertain patient eligibility status and describe coverage of antiviral drugs and neutralising monoclonal antibodies (nMAB) as treatment for covid-19 in community settings in England., Design: Retrospective, descriptive cohort study, approved by NHS England., Setting: Routine clinical data from 23.4 million people linked to data on covid-19 infection and treatment, within the OpenSAFELY-TPP database., Participants: Outpatients with covid-19 at high risk of severe outcomes., Interventions: Nirmatrelvir/ritonavir (paxlovid), sotrovimab, molnupiravir, casirivimab/imdevimab, or remdesivir, used in the community by covid-19 medicine delivery units., Results: 93 870 outpatients with covid-19 were identified between 11 December 2021 and 28 April 2022 to be at high risk of severe outcomes and therefore potentially eligible for antiviral or nMAB treatment (or both). Of these patients, 19 040 (20%) received treatment (sotrovimab, 9660 (51%); molnupiravir, 4620 (24%); paxlovid, 4680 (25%); casirivimab/imdevimab, 50 (<1%); and remdesivir, 30 (<1%)). The proportion of patients treated increased from 9% (190/2220) in the first week of treatment availability to 29% (460/1600) in the latest week. The proportion treated varied by high risk group, being lowest in those with liver disease (16%; 95% confidence interval 15% to 17%); by treatment type, with sotrovimab favoured over molnupiravir and paxlovid in all but three high risk groups (Down's syndrome (35%; 30% to 39%), rare neurological conditions (45%; 43% to 47%), and immune deficiencies (48%; 47% to 50%)); by age, ranging from ≥80 years (13%; 12% to 14%) to 50-59 years (23%; 22% to 23%); by ethnic group, ranging from black (11%; 10% to 12%) to white (21%; 21% to 21%); by NHS region, ranging from 13% (12% to 14%) in Yorkshire and the Humber to 25% (24% to 25%) in the East of England); and by deprivation level, ranging from 15% (14% to 15%) in the most deprived areas to 23% (23% to 24%) in the least deprived areas. Groups that also had lower coverage included unvaccinated patients (7%; 6% to 9%), those with dementia (6%; 5% to 7%), and care home residents (6%; 6% to 7%)., Conclusions: Using the OpenSAFELY platform, we were able to identify patients with covid-19 at high risk of severe outcomes who were potentially eligible to receive treatment and assess the coverage of these new treatments among these patients. In the context of a rapid deployment of a new service, the NHS analytical code used to determine eligibility could have been over-inclusive and some of the eligibility criteria not fully captured in healthcare data. However targeted activity might be needed to resolve apparent lower treatment coverage observed among certain groups, in particular (at present): different NHS regions, ethnic groups, people aged ≥80 years, those living in socioeconomically deprived areas, and care home residents., Competing Interests: Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from the Wellcome Trust, MRC, NIHR, and Health Data Research UK for the submitted work. BG has received research funding from the Laura and John Arnold Foundation, NHS NIHR, NIHR School of Primary Care Research, NIHR Oxford Biomedical Research Centre, Mohn-Westlake Foundation, NIHR Applied Research Collaboration Oxford and Thames Valley, Wellcome Trust, Good Thinking Foundation, Health Data Research UK, Health Foundation, World Health Organization, UK Research and Innovation, Asthma UK, British Lung Foundation, and Longitudinal Health and Wellbeing strand of the National Core Studies programme; he also receives personal income from speaking and writing for lay audiences on the misuse of science. IJD has received unrestricted research grants and holds shares in GlaxoSmithKline (GSK). JT is employed by the London School of Hygiene and Tropical Medicine (LSHTM) on a fellowship sponsored by an unrestricted GSK grant. NJD received research funding related to the COVID-19 pandemic from the Federal Ministry of Education and Research (BMBF, Germany)., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Objective: Controlled and randomized study to analyze longitudinal voice data of boys and girls aged 8-12.5, to describe their physiological development and to evaluate the influence of a one-year music pedagogical interventional program focused on the singing voice., Methods: Singing voice profiles of 116 children (52 boys, 64 girls) aged 8-12.5 years were collected longitudinally at the beginning and the end of the third school year and the end of the fourth school year. 64 of the 116 children received a music pedagogical program during their third school year (interventional group). Maximum and minimum voice intensity, highest and lowest frequency, maximum phonation time (MPT) and Jitter were investigated., Results: In two years' time highest frequency increased on average by 100.23 Hz from G1 up to A1 for boys and for girls. Lowest frequency decreased by 18.36Hz from Gis-1 to G-1 (boys: Gis-1-Fis-1; girls: A-1-G-1). There was no clinically relevant development of the intensity parameters for both sexes. However, after the interventional year, minimum voice intensity significantly decreased in the interventional group compared to the control group. The MPT increased by 2.41 seconds from 10.67 seconds up to 13.09 seconds in two years. Here the increase was higher for boys (+3.2 seconds) than for girls (+1.77 seconds). Jitter was found to be 0.84%-1.11%, showing no clinically relevant changes in two years., Conclusion: To the best of our knowledge, for the first time this study presents longitudinal data on singing voice parameters of the voice range profile of boys and girls aged 8-12.5 years. While frequency and intensity parameters develop equally for boys and girls, the MPT is found to develop more distinctly within boys. A music pedagogical intervention of a small extent has a positive effect on the ability to sing as soft as possible., (Copyright © 2020 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)

Latent change score models (LCSMs) are used across disciplines in behavioural sciences to study how constructs change over time. LCSMs can be used to estimate the trajectory of one construct (univariate) and allow the investigation of how changes between two constructs (bivariate) are associated with each other over time. This paper introduces the R package lcsm, a tool that aims to help users understand, analyse, and visualise different latent change score models. The lcsm package provides functions to generate model syntax for basic univariate and bivariate latent change score models with different model specifications. It is also possible to visualise different model specifications in simplified path diagrams. An interactive application illustrates the main functions of the package and demonstrates how the model syntax and path diagrams change based on different model specifications. This R package aims to increase the transparency of reporting analyses and to provide an additional resource to learn latent change score modelling., Competing Interests: No competing interests were disclosed., (Copyright: © 2022 Wiedemann M et al.)