Consistency, completeness and external validity of ethnicity recording in NHS primary care records: a cohort study in 25 million patients' records at source using OpenSAFELY.

BACKGROUND: Ethnicity is known to be an important correlate of health outcomes, particularly during the COVID-19 pandemic, where some ethnic groups were shown to be at higher risk of infection and adverse outcomes. The recording of patients' ethnic groups in primary care can support research and efforts to achieve equity in service provision and outcomes; however, the coding of ethnicity is known to present complex challenges. We therefore set out to describe ethnicity coding in detail with a view to supporting the use of this data in a wide range of settings, as part of wider efforts to robustly describe and define methods of using administrative data. METHODS: We describe the completeness and consistency of primary care ethnicity recording in the OpenSAFELY-TPP database, containing linked primary care and hospital records in > 25 million patients in England. We also compared the ethnic breakdown in OpenSAFELY-TPP with that of the 2021 UK census. RESULTS: 78.2% of patients registered in OpenSAFELY-TPP on 1 January 2022 had their ethnicity recorded in primary care records, rising to 92.5% when supplemented with hospital data. The completeness of ethnicity recording was higher for women than for men. The rate of primary care ethnicity recording ranged from 77% in the South East of England to 82.2% in the West Midlands. Ethnicity recording rates were higher in patients with chronic or other serious health conditions. For each of the five broad ethnicity groups, primary care recorded ethnicity was within 2.9 percentage points of the population rate as recorded in the 2021 Census for England as a whole. For patients with multiple ethnicity records, 98.7% of the latest recorded ethnicities matched the most frequently coded ethnicity. Patients whose latest recorded ethnicity was categorised as Other were most likely to have a discordant ethnicity recording (32.2%). CONCLUSIONS: Primary care ethnicity data in OpenSAFELY is present for over three quarters of all patients, and combined with data from other sources can achieve a high level of completeness. The overall distribution of ethnicities across all English OpenSAFELY-TPP practices was similar to the 2021 Census, with some regional variation. This report identifies the best available codelist for use in OpenSAFELY and similar electronic health record data.

Changes in sick notes associated with COVID-19 from 2020 to 2022: a cohort study in 24 million primary care patients in OpenSAFELY-TPP.

OBJECTIVES: Long-term sickness absence from employment has negative consequences for the economy and can lead to widened health inequalities. Sick notes (also called 'fit notes') are issued by general practitioners when a person cannot work for health reasons for more than 7 days. We quantified the sick note rate in people with evidence of COVID-19 in 2020, 2021 and 2022, as an indication of the burden for people recovering from COVID-19. DESIGN: Cohort study. SETTING: With National Health Service (NHS) England approval, we used routine clinical data (primary care, hospital and COVID-19 testing records) within the OpenSAFELY-TPP database. PARTICIPANTS: People 18-64 years with a recorded positive test or diagnosis of COVID-19 in 2020 (n=365 421), 2021 (n=1 206 555) or 2022 (n=1 321 313); general population matched in age, sex and region in 2019 (n=3 140 326), 2020 (n=3 439 534), 2021 (n=4 571 469) and 2022 (n=4 818 870); people hospitalised with pneumonia in 2019 (n=29 673). PRIMARY OUTCOME MEASURE: Receipt of a sick note in primary care. RESULTS: Among people with a positive SARS-CoV-2 test or COVID-19 diagnosis, the sick note rate was 4.88 per 100 person-months (95% CI 4.83 to 4.93) in 2020, 2.66 (95% CI 2.64 to 2.67) in 2021 and 1.73 (95% CI 1.72 to 1.73) in 2022. Compared with the age, sex and region-matched general population, the adjusted HR for receipt of a sick note over the entire follow-up period (up to 10 months) was 4.07 (95% CI 4.02 to 4.12) in 2020 decreasing to 1.57 (95% CI 1.56 to 1.58) in 2022. The HR was highest in the first 30 days postdiagnosis in all years. Among people hospitalised with COVID-19, after adjustment, the sick note rate was lower than in people hospitalised with pneumonia. CONCLUSIONS: Given the under-recording of postacute COVID-19-related symptoms, these findings contribute a valuable perspective on the long-term effects of COVID-19. Despite likely underestimation of the sick note rate, sick notes were issued more frequently to people with COVID-19 compared with those without, even in an era when most people are vaccinated. Most sick notes occurred in the first 30 days postdiagnosis, but the increased risk several months postdiagnosis may provide further evidence of the long-term impact.

Data-Driven Identification of Potentially Successful Intervention Implementations Using 5 Years of Opioid Prescribing Data: Retrospective Database Study.

BACKGROUND: We have previously demonstrated that opioid prescribing increased by 127% between 1998 and 2016. New policies aimed at tackling this increasing trend have been recommended by public health bodies, and there is some evidence that progress is being made. OBJECTIVE: We sought to extend our previous work and develop a data-driven approach to identify general practices and clinical commissioning groups (CCGs) whose prescribing data suggest that interventions to reduce the prescribing of opioids may have been successfully implemented. METHODS: We analyzed 5 years of prescribing data (December 2014 to November 2019) for 3 opioid prescribing measures-total opioid prescribing as oral morphine equivalent per 1000 registered population, the number of high-dose opioids prescribed per 1000 registered population, and the number of high-dose opioids as a percentage of total opioids prescribed. Using a data-driven approach, we applied a modified version of our change detection Python library to identify reductions in these measures over time, which may be consistent with the successful implementation of an intervention to reduce opioid prescribing. This analysis was carried out for general practices and CCGs, and organizations were ranked according to the change in prescribing rate. RESULTS: We identified a reduction in total opioid prescribing in 94 (49.2%) out of 191 CCGs, with a median reduction of 15.1 (IQR 11.8-18.7; range 9.0-32.8) in total oral morphine equivalence per 1000 patients. We present data for the 3 CCGs and practices demonstrating the biggest reduction in opioid prescribing for each of the 3 opioid prescribing measures. We observed a 40% proportional drop (8.9% absolute reduction) in the regular prescribing of high-dose opioids (measured as a percentage of regular opioids) in the highest-ranked CCG (North Tyneside); a 99% drop in this same measure was found in several practices (44%-95% absolute reduction). Decile plots demonstrate that CCGs exhibiting large reductions in opioid prescribing do so via slow and gradual reductions over a long period of time (typically over a period of 2 years); in contrast, practices exhibiting large reductions do so rapidly over a much shorter period of time. CONCLUSIONS: By applying 1 of our existing analysis tools to a national data set, we were able to identify rapid and maintained changes in opioid prescribing within practices and CCGs and rank organizations by the magnitude of reduction. Highly ranked organizations are candidates for further qualitative research into intervention design and implementation.

Effectiveness of mRNA COVID-19 Vaccines as First Booster Doses in England: An Observational Study in OpenSAFELY-TPP.

BACKGROUND: The UK delivered its first "booster" COVID-19 vaccine doses in September 2021, initially to individuals at high risk of severe disease, then to all adults. The BNT162b2 Pfizer-BioNTech vaccine was used initially, then also Moderna mRNA-1273. METHODS: With the approval of the National Health Service England, we used routine clinical data to estimate the effectiveness of boosting with BNT162b2 or mRNA-1273 compared with no boosting in eligible adults who had received two primary course vaccine doses. We matched each booster recipient with an unboosted control on factors relating to booster priority status and prior COVID-19 immunization. We adjusted for additional factors in Cox models, estimating hazard ratios up to 182 days (6 months) following booster dose. We estimated hazard ratios overall and within the following periods: 1-14, 15-42, 43-69, 70-97, 98-126, 127-152, and 155-182 days. Outcomes included a positive SARS-CoV-2 test, COVID-19 hospitalization, COVID-19 death, non-COVID-19 death, and fracture. RESULTS: We matched 8,198,643 booster recipients with unboosted controls. Adjusted hazard ratios over 6-month follow-up were: positive SARS-CoV-2 test 0.75 (0.74, 0.75); COVID-19 hospitalization 0.30 (0.29, 0.31); COVID-19 death 0.11 (0.10, 0.14); non-COVID-19 death 0.22 (0.21, 0.23); and fracture 0.77 (0.75, 0.78). Estimated effectiveness of booster vaccines against severe COVID-19-related outcomes peaked during the first 3 months following the booster dose. By 6 months, the cumulative incidence of positive SARS-CoV-2 test was higher in boosted than unboosted individuals. CONCLUSIONS: We estimate that COVID-19 booster vaccination, compared with no booster vaccination, provided substantial protection against COVID-19 hospitalization and COVID-19 death but only limited protection against positive SARS-CoV-2 test. Lower rates of fracture in boosted than unboosted individuals may suggest unmeasured confounding. Observational studies should report estimated vaccine effectiveness against nontarget and negative control outcomes.

Cellular endosomal potassium ion flux regulates arenavirus uncoating during virus entry.

Lymphocytic choriomeningitis virus (LCMV) is an enveloped and segmented negative-sense RNA virus classified within the Arenaviridae family of the Bunyavirales order. LCMV is associated with fatal disease in immunocompromised populations and, as the prototypical arenavirus member, acts as a model for the many highly pathogenic members of the Arenaviridae family, such as Junín, Lassa, and Lujo viruses, all of which are associated with devastating hemorrhagic fevers. To enter cells, the LCMV envelope fuses with late endosomal membranes, for which two established requirements are low pH and interaction between the LCMV glycoprotein (GP) spike and secondary receptor CD164. LCMV subsequently uncoats, where the RNA genome-associated nucleoprotein (NP) separates from the Z protein matrix layer, releasing the viral genome into the cytosol. To further examine LCMV endosome escape, we performed an siRNA screen which identified host cell potassium ion (K+) channels as important for LCMV infection, with pharmacological inhibition confirming K+ channel involvement during the LCMV entry phase completely abrogating productive infection. To better understand the K+-mediated block in infection, we tracked incoming virions along their entry pathway under physiological conditions, where uncoating was signified by separation of NP and Z proteins. In contrast, K+ channel blockade prevented uncoating, trapping virions within Rab7 and CD164-positive endosomes, identifying K+ as a third LCMV entry requirement. K+ did not increase GP-CD164 binding or alter GP-CD164-dependent fusion. Thus, we propose that K+ mediates uncoating by modulating NP-Z interactions within the virion interior. These results suggest K+ channels represent a potential anti-arenaviral target.IMPORTANCEArenaviruses can cause fatal human disease for which approved preventative or therapeutic options are not available. Here, using the prototypical LCMV, we identified K+ channels as critical for arenavirus infection, playing a vital role during the entry phase of the infection cycle. We showed that blocking K+ channel function resulted in entrapment of LCMV particles within late endosomal compartments, thus preventing productive replication. Our data suggest K+ is required for LCMV uncoating and genome release by modulating interactions between the viral nucleoprotein and the matrix protein layer inside the virus particle.

Changes in opioid prescribing during the COVID-19 pandemic in England: an interrupted time-series analysis in the OpenSAFELY-TTP cohort.

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.

Impact of long COVID on health-related quality-of-life: an OpenSAFELY population cohort study using patient-reported outcome measures (OpenPROMPT).

Background: Long COVID is a major problem affecting patient health, the health service, and the workforce. To optimise the design of future interventions against COVID-19, and to better plan and allocate health resources, it is critical to quantify the health and economic burden of this novel condition. We aimed to evaluate and estimate the differences in health impacts of long COVID across sociodemographic categories and quantify this in Quality-Adjusted Life-Years (QALYs), widely used measures across health systems. Methods: With the approval of NHS England, we utilised OpenPROMPT, a UK cohort study measuring the impact of long COVID on health-related quality-of-life (HRQoL). OpenPROMPT invited responses to Patient Reported Outcome Measures (PROMs) using a smartphone application and recruited between November 2022 and October 2023. We used the validated EuroQol EQ-5D questionnaire with the UK Value Set to develop disutility scores (1-utility) for respondents with and without Long COVID using linear mixed models, and we calculated subsequent Quality-Adjusted Life-Months (QALMs) for long COVID. Findings: The total OpenPROMPT cohort consisted of 7575 individuals who consented to data collection, with which we used data from 6070 participants who completed a baseline research questionnaire where 24.6% self-reported long COVID. In multivariable regressions, long COVID had a consistent impact on HRQoL, showing a higher likelihood or odds of reporting loss in quality-of-life (Odds Ratio (OR): 4.7, 95% CI: 3.72-5.93) compared with people who did not report long COVID. Reporting a disability was the largest predictor of losses of HRQoL (OR: 17.7, 95% CI: 10.37-30.33) across survey responses. Self-reported long COVID was associated with an 0.37 QALM loss. Interpretation: We found substantial impacts on quality-of-life due to long COVID, representing a major burden on patients and the health service. We highlight the need for continued support and research for long COVID, as HRQoL scores compared unfavourably to patients with conditions such as multiple sclerosis, heart failure, and renal disease. Funding: This research was supported by the National Institute for Health and Care Research (NIHR) (OpenPROMPT: COV-LT2-0073).

Clinical coding of long COVID in primary care 2020-2023 in a cohort of 19 million adults: an OpenSAFELY analysis.

Background: Long COVID is the patient-coined term for the persistent symptoms of COVID-19 illness for weeks, months or years following the acute infection. There is a large burden of long COVID globally from self-reported data, but the epidemiology, causes and treatments remain poorly understood. Primary care is used to help identify and treat patients with long COVID and therefore Electronic Health Records (EHRs) of past COVID-19 patients could be used to help fill these knowledge gaps. We aimed to describe the incidence and differences in demographic and clinical characteristics in recorded long COVID in primary care records in England. Methods: With the approval of NHS England we used routine clinical data from over 19 million adults in England linked to SARS-COV-2 test result, hospitalisation and vaccination data to describe trends in the recording of 16 clinical codes related to long COVID between November 2020 and January 2023. Using OpenSAFELY, we calculated rates per 100,000 person-years and plotted how these changed over time. We compared crude and adjusted (for age, sex, 9 NHS regions of England, and the dominant variant circulating) rates of recorded long COVID in patient records between different key demographic and vaccination characteristics using negative binomial models. Findings: We identified a total of 55,465 people recorded to have long COVID over the study period, which included 20,025 diagnoses codes and 35,440 codes for further assessment. The incidence of new long COVID records increased steadily over 2021, and declined over 2022. The overall rate per 100,000 person-years was 177.5 cases in women (95% CI: 175.5-179) and 100.5 in men (99.5-102). The majority of those with a long COVID record did not have a recorded positive SARS-COV-2 test 12 or more weeks before the long COVID record. Interpretation: In this descriptive study, EHR recorded long COVID was very low between 2020 and 2023, and incident records of long COVID declined over 2022. Using EHR diagnostic or referral codes unfortunately has major limitations in identifying and ascertaining true cases and timing of long COVID. Funding: This research was supported by the National Institute for Health and Care Research (NIHR) (OpenPROMPT: COV-LT2-0073).

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: The impact of COVID-19 on azathioprine, leflunomide and methotrexate monitoring, and factors associated with change in monitoring rate.

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.

The impact of COVID-19 on medication reviews in English primary care. An OpenSAFELY-TPP analysis of 20 million adult electronic health records.

AIMS: The COVID-19 pandemic caused significant disruption to routine activity in primary care. Medication reviews are an important primary care activity ensuring safety and appropriateness of prescribing. A disruption could have significant negative implications for patient care. Using routinely collected data, our aim was first to describe codes used to record medication review activity and then to report the impact of COVID-19 on the rates of medication reviews. METHODS: With the approval of NHS England, we conducted a cohort study of 20 million adult patient records in general practice, in-situ using the OpenSAFELY platform. For each month, between April 2019 and March 2022, we report the percentage of patients with a medication review coded monthly and in the previous 12 months with breakdowns by regional, clinical and demographic subgroups and those prescribed high-risk medications. RESULTS: In April 2019, 32.3% of patients had a medication review coded in the previous 12 months. During the first COVID-19 lockdown, monthly activity decreased (-21.1% April 2020), but the 12-month rate was not substantially impacted (-10.5% March 2021). The rate of structured medication review in the last 12 months reached 2.9% by March 2022, with higher percentages in high-risk groups (care home residents 34.1%, age 90+ years 13.1%, high-risk medications 10.2%). The most used medication review code was Medication review done 314530002 (59.5%). CONCLUSIONS: There was a substantial reduction in the monthly rate of medication reviews during the pandemic but rates recovered by the end of the study period. Structured medication reviews were prioritized for high-risk patients.

Impact of vaccination on the association of COVID-19 with cardiovascular diseases: An OpenSAFELY cohort study.

Infection with SARS-CoV-2 is associated with an increased risk of arterial and venous thrombotic events, but the implications of vaccination for this increased risk are uncertain. With the approval of NHS England, we quantified associations between COVID-19 diagnosis and cardiovascular diseases in different vaccination and variant eras using linked electronic health records for ~40% of the English population. We defined a 'pre-vaccination' cohort (18,210,937 people) in the wild-type/Alpha variant eras (January 2020-June 2021), and 'vaccinated' and 'unvaccinated' cohorts (13,572,399 and 3,161,485 people respectively) in the Delta variant era (June-December 2021). We showed that the incidence of each arterial thrombotic, venous thrombotic and other cardiovascular outcomes was substantially elevated during weeks 1-4 after COVID-19, compared with before or without COVID-19, but less markedly elevated in time periods beyond week 4. Hazard ratios were higher after hospitalised than non-hospitalised COVID-19 and higher in the pre-vaccination and unvaccinated cohorts than the vaccinated cohort. COVID-19 vaccination reduces the risk of cardiovascular events after COVID-19 infection. People who had COVID-19 before or without being vaccinated are at higher risk of cardiovascular events for at least two years.

During the COVID-19 pandemic 20 000 prostate cancer diagnoses were missed in England.

OBJECTIVES: To investigate the effect of the COVID-19 pandemic on prostate cancer incidence, prevalence, and mortality in England. PATIENTS AND METHODS: With the approval of NHS England and using the OpenSAFELY-TPP dataset of 24 million patients, we undertook a cohort study of men diagnosed with prostate cancer. We visualised monthly rates in prostate cancer incidence, prevalence, and mortality per 100 000 adult men from January 2015 to July 2023. To assess the effect of the pandemic, we used generalised linear models and the pre-pandemic data to predict the expected rates from March 2020 as if the pandemic had not occurred. The 95% confidence intervals (CIs) of the predicted values were used to estimate the significance of the difference between the predicted and observed rates. RESULTS: In 2020, there was a drop in recorded incidence by 4772 (31%) cases (15 550 vs 20 322; 95% CI 19 241-21 403). In 2021, the incidence started to recover, and the drop was 3148 cases (18%, 17 950 vs 21 098; 95% CI 19 740-22 456). By 2022, the incidence returned to the levels that would be expected. During the pandemic, the age at diagnosis shifted towards older men. In 2020, the average age was 71.6 (95% CI 71.5-71.8) years, in 2021 it was 71.8 (95% CI 71.7-72.0) years as compared to 71.3 (95% CI 71.1-71.4) years in 2019. CONCLUSIONS: Given that our dataset represents 40% of the population, we estimate that proportionally the pandemic led to 20 000 missed prostate cancer diagnoses in England alone. The increase in incidence recorded in 2023 was not enough to account for the missed cases. The prevalence of prostate cancer remained lower throughout the pandemic than expected. As the recovery efforts continue, healthcare should focus on finding the men who were affected. The research should focus on investigating the potential harms to men diagnosed at older age.

High concordance of blood glucose measurement in cats between a beta prototype glucometer device and a reference laboratory standard in a clinical setting.

OBJECTIVE: The objective of this study was to evaluate the accuracy of a beta prototype version of a new portable blood glucose meter in feline patients. ANIMALS: 60 client-owned cats. METHODS: In this prospective study, 3-mL blood samples were collected from each cat and analyzed in triplicate using a beta prototype device (AlphaTRAK 3 [AT3]) and by a reference lab standard immediately after collection. Accuracy of the AT3 device was determined in accordance with the International Organization of Standardization (ISO) 15197:2013 criteria, including Bland-Altman plotting and consensus error grid analysis. A Passing-Bablok regression analysis was also performed. RESULTS: 96% of feline measurements fell within the ISO accuracy threshold, and 100% of measurements fell within zones A and B of the consensus error grid, meeting the ISO accuracy requirements. There was no significant bias in the data according to the Bland-Altman analysis. Within the full range of glucose concentrations (20 to 750 mg/dL) the correlation coefficient between the AT3 and the reference lab standard was 0.99. There was no significant constant or proportional bias present in the data. CLINICAL RELEVANCE: The AT3 device met the ISO requirements and is accurate for measurement of blood glucose concentrations in cats.

Top-down control of flight by a non-canonical cortico-amygdala pathway.

Survival requires the selection of appropriate behaviour in response to threats, and dysregulated defensive reactions are associated with psychiatric illnesses such as post-traumatic stress and panic disorder1. Threat-induced behaviours, including freezing and flight, are controlled by neuronal circuits in the central amygdala (CeA)2; however, the source of neuronal excitation of the CeA that contributes to high-intensity defensive responses is unknown. Here we used a combination of neuroanatomical mapping, in vivo calcium imaging, functional manipulations and electrophysiology to characterize a previously unknown projection from the dorsal peduncular (DP) prefrontal cortex to the CeA. DP-to-CeA neurons are glutamatergic and specifically target the medial CeA, the main amygdalar output nucleus mediating conditioned responses to threat. Using a behavioural paradigm that elicits both conditioned freezing and flight, we found that CeA-projecting DP neurons are activated by high-intensity threats in a context-dependent manner. Functional manipulations revealed that the DP-to-CeA pathway is necessary and sufficient for both avoidance behaviour and flight. Furthermore, we found that DP neurons synapse onto neurons within the medial CeA that project to midbrain flight centres. These results elucidate a non-canonical top-down pathway regulating defensive responses.

A Linguistic Analysis of Instagram Captions Between Adolescent Suicide Decedents and Living Controls.

Background: Suicide rates continue to rise for adolescents in the United States. 62% of teenagers use Instagram, and as technology and research in this domain advance, social media posts could provide insights into near-term adolescent risk states and could inform new strategies for suicide prevention. This study analyzed language in captions of teenagers' Instagram accounts in the 3 months before suicide and compared caption language to matched living controls. Method: The study identified 89 teenagers who died by suicide using obituaries and news reports and 89 matched living control teenagers. Linguistic Inquiry and Word Count (LIWC) software was used to test for differences in specific language categories across linguistic, psychological, and topical categories (e.g., word count, tone, grammar, affective, cognitive, social, punctuation marks, etc.). Results: Significant differences between suicide decedents and living controls were found. Adolescent suicide decedents used more words per sentence, more references to sadness, male individuals, drives, and leisure and fewer verbs and references to they, affiliation, achievement, and power. Limitations: Methodological limitations include the use of only public accounts, small sample size, occasional short posts, and lack of adjustment for multiple testing. Conclusion: Although the sample size is relatively small and only included youth with public accounts, we identified differences in Instagram caption language between adolescents who died by suicide as compared to living controls.

Digital Phenotyping for Mood Disorders: Methodology-Oriented Pilot Feasibility Study.

BACKGROUND: In the burgeoning area of clinical digital phenotyping research, there is a dearth of literature that details methodology, including the key challenges and dilemmas in developing and implementing a successful architecture for technological infrastructure, patient engagement, longitudinal study participation, and successful reporting and analysis of diverse passive and active digital data streams. OBJECTIVE: This article provides a narrative rationale for our study design in the context of the current evidence base and best practices, with an emphasis on our initial lessons learned from the implementation challenges and successes of this digital phenotyping study. METHODS: We describe the design and implementation approach for a digital phenotyping pilot feasibility study with attention to synthesizing key literature and the reasoning for pragmatic adaptations in implementing a multisite study encompassing distinct geographic and population settings. This methodology was used to recruit patients as study participants with a clinician-validated diagnostic history of unipolar depression, bipolar I disorder, or bipolar II disorder, or healthy controls in 2 geographically distinct health care systems for a longitudinal digital phenotyping study of mood disorders. RESULTS: We describe the feasibility of a multisite digital phenotyping pilot study for patients with mood disorders in terms of passively and actively collected phenotyping data quality and enrollment of patients. Overall data quality (assessed as the amount of sensor data obtained vs expected) was high compared to that in related studies. Results were reported on the relevant demographic features of study participants, revealing recruitment properties of age (mean subgroup age ranged from 31 years in the healthy control subgroup to 38 years in the bipolar I disorder subgroup), sex (predominance of female participants, with 7/11, 64% females in the bipolar II disorder subgroup), and smartphone operating system (iOS vs Android; iOS ranged from 7/11, 64% in the bipolar II disorder subgroup to 29/32, 91% in the healthy control subgroup). We also described implementation considerations around digital phenotyping research for mood disorders and other psychiatric conditions. CONCLUSIONS: Digital phenotyping in affective disorders is feasible on both Android and iOS smartphones, and the resulting data quality using an open-source platform is higher than that in comparable studies. While the digital phenotyping data quality was independent of gender and race, the reported demographic features of study participants revealed important information on possible selection biases that may result from naturalistic research in this domain. We believe that the methodology described will be readily reproducible and generalizable to other study settings and patient populations given our data on deployment at 2 unique sites.

Classifying and clustering mood disorder patients using smartphone data from a feasibility study.

Differentiating between bipolar disorder and major depressive disorder can be challenging for clinicians. The diagnostic process might benefit from new ways of monitoring the phenotypes of these disorders. Smartphone data might offer insight in this regard. Today, smartphones collect dense, multimodal data from which behavioral metrics can be derived. Distinct patterns in these metrics have the potential to differentiate the two conditions. To examine the feasibility of smartphone-based phenotyping, two study sites (Mayo Clinic, Johns Hopkins University) recruited patients with bipolar I disorder (BPI), bipolar II disorder (BPII), major depressive disorder (MDD), and undiagnosed controls for a 12-week observational study. On their smartphones, study participants used a digital phenotyping app (mindLAMP) for data collection. While in use, mindLAMP gathered real-time geolocation, accelerometer, and screen-state (on/off) data. mindLAMP was also used for EMA delivery. MindLAMP data was then used as input variables in binary classification, three-group k-nearest neighbors (KNN) classification, and k-means clustering. The best-performing binary classification model was able to classify patients as control or non-control with an AUC of 0.91 (random forest). The model that performed best at classifying patients as having MDD or bipolar I/II had an AUC of 0.62 (logistic regression). The k-means clustering model had a silhouette score of 0.46 and an ARI of 0.27. Results support the potential for digital phenotyping methods to cluster depression, bipolar disorder, and healthy controls. However, due to inconsistencies in accuracy, more data streams are required before these methods can be applied to clinical practice.

Impact of the COVID-19 pandemic on antipsychotic prescribing in individuals with autism, dementia, learning disability, serious mental illness or living in a care home: a federated analysis of 59 million patients' primary care records in situ using OpenSAFELY.

BACKGROUND: The COVID-19 pandemic affected how care was delivered to vulnerable patients, such as those with dementia or learning disability. OBJECTIVE: To explore whether this affected antipsychotic prescribing in at-risk populations. METHODS: With the approval of NHS England, we completed a retrospective cohort study, using the OpenSAFELY platform to explore primary care data of 59 million patients. We identified patients in five at-risk groups: autism, dementia, learning disability, serious mental illness and care home residents. We calculated the monthly prevalence of antipsychotic prescribing in these groups, as well as the incidence of new prescriptions in each month. FINDINGS: The average monthly rate of antipsychotic prescribing increased in dementia from 82.75 patients prescribed an antipsychotic per 1000 patients (95% CI 82.30 to 83.19) in January-March 2019 to 90.1 (95% CI 89.68 to 90.60) in October-December 2021 and from 154.61 (95% CI 153.79 to 155.43) to 166.95 (95% CI 166.23 to 167.67) in care homes. There were notable spikes in the rate of new prescriptions issued to patients with dementia and in care homes. In learning disability and autism groups, the rate of prescribing per 1000 decreased from 122.97 (95% CI 122.29 to 123.66) to 119.29 (95% CI 118.68 to 119.91) and from 54.91 (95% CI 54.52 to 55.29) to 51.04 (95% CI 50.74 to 51.35), respectively. CONCLUSION AND IMPLICATIONS: We observed a spike in antipsychotic prescribing in the dementia and care home groups, which correlated with lockdowns and was likely due to prescribing of antipsychotics for palliative care. We observed gradual increases in antipsychotic use in dementia and care home patients and decreases in their use in patients with learning disability or autism.

Healthcare in England was affected by the COVID-19 pandemic across the pancreatic cancer pathway: A cohort study using OpenSAFELY-TPP.

Background: Healthcare across all sectors, in the UK and globally, was negatively affected by the COVID-19 pandemic. We analysed healthcare services delivered to people with pancreatic cancer from January 2015 to March 2023 to investigate the effect of the COVID-19 pandemic. Methods: With the approval of NHS England, and drawing from a nationally representative OpenSAFELY-TPP dataset of 24 million patients (over 40% of the English population), we undertook a cohort study of people diagnosed with pancreatic cancer. We queried electronic healthcare records for information on the provision of healthcare services across the pancreatic cancer pathway. To estimate the effect of the COVID-19 pandemic, we predicted the rates of healthcare services if the pandemic had not happened. We used generalised linear models and the pre-pandemic data from January 2015 to February 2020 to predict rates in March 2020 to March 2023. The 95% confidence intervals of the predicted values were used to estimate the significance of the difference between the predicted and observed rates. Results: The rate of pancreatic cancer and diabetes diagnoses in the cohort was not affected by the pandemic. There were 26,840 people diagnosed with pancreatic cancer from January 2015 to March 2023. The mean age at diagnosis was 72 (±11 SD), 48% of people were female, 95% were of White ethnicity, and 40% were diagnosed with diabetes. We found a reduction in surgical resections by 25-28% during the pandemic. In addition, 20%, 10%, and 4% fewer people received body mass index, glycated haemoglobin, and liver function tests, respectively, before they were diagnosed with pancreatic cancer. There was no impact of the pandemic on the number of people making contact with primary care, but the number of contacts increased on average by 1-2 per person amongst those who made contact. Reporting of jaundice decreased by 28%, but recovered within 12 months into the pandemic. Emergency department visits, hospital admissions, and deaths were not affected. Conclusions: The pandemic affected healthcare in England across the pancreatic cancer pathway. Positive lessons could be learnt from the services that were resilient and those that recovered quickly. The reductions in healthcare experienced by people with cancer have the potential to lead to worse outcomes. Current efforts should focus on addressing the unmet needs of people with cancer. Funding: This work was jointly funded by 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). This work was funded by Medical Research Council (MRC) grant reference MR/W021390/1 as part of the postdoctoral fellowship awarded to AL and undertaken at the Bennett Institute, University of Oxford. The views expressed are those of the authors and not necessarily those of the NIHR, NHS England, UK Health Security Agency (UKHSA), or the Department of Health and Social Care. Funders had no role in the study design, collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.