lcsm: an R package and tutorial on latent change score modelling

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

Changes in cognitive processes and coping strategies precede changes in symptoms during cognitive therapy for posttraumatic stress disorder

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

OpenSAFELY: A platform for analysing electronic health records designed for reproducible research

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

Trends, variation, and clinical characteristics of recipients of antiviral drugs and neutralising monoclonal antibodies for covid-19 in community settings: retrospective, descriptive cohort study of 23.4 million people in OpenSAFELY

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

OpenSAFELY: a platform for analysing electronic health records designed for reproducible research

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

OpenSAFELY NHS Service Restoration Observatory 2: changes in primary care clinical activity in England during the COVID-19 pandemic

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

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Analytic Code: Cognitive Processes Associated with Sudden Gains in Cognitive Therapy for Posttraumatic Stress Disorder in Routine Care

Analytic code for: Wiedemann, M., Stott, R., Nickless, A., Beierl, E. T., Wild, J., Warnock-Parkes, E., Grey, N., Clark, D. M., &amp; Ehlers, A. (2020). Cognitive processes associated with sudden gains in cognitive therapy for posttraumatic stress disorder in routine care. Journal of Consulting and Clinical Psychology. https://doi.org/10.1037/ccp000048

literature

https://www.zotero.org/groups/2280342/suddengain