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BackgroundSolriamfetol, Pitolisant, and Sodium Oxybate are relatively new, high-cost, treatments for Narcolepsy, prescribed initially in secondary care. There is significant variation in the pathways for funding these treatments, from NICE approval for Solriamfetol, to shared care protocols (SCPs) and individual funding requests for some or all medications in certain trusts.Secondary care medicines data (SCMD) is open source data providing information on the quantities and types of medicines prescribed in secondary care. Through analysing SCMD, we review and describe the current regional prescribing trends for narcolepsy management.MethodsSCMD for high-cost drugs prescribed for Narcolepsy (January 2019- December 2021) was retrieved from the NHS Service Business Authority website, from 219 different hospital trusts.ResultsAnalysis of >30,000 prescribing months, revealed significant variation in prescribing of high cost drugs to treat narcolepsy. The volume of high-cost drugs prescribed in SE London & Cheshire/Merseyside exceeded the combined total of all other trusts, comparatively there was minimal prescribing in the East and South-west regions.ConclusionThe significant regional variation in the prescribing of narcolepsy treatments may represent inequalities in access to Narcolepsy care. Furthermore, this study highlights the potential benefits of SCMD in reviewing specialist services and the prescribing of high-cost drugs.

There is concern about medium to long-term adverse outcomes following acute Coronavirus Disease 2019 (COVID-19), but little relevant evidence exists. We aimed to investigate whether risks of hospital admission and death, overall and by specific cause, are raised following discharge from a COVID-19 hospitalisation. With the approval of NHS-England, we conducted a cohort study, using linked primary care and hospital data in OpenSAFELY to compare risks of hospital admission and death, overall and by specific cause, between people discharged from COVID-19 hospitalisation (February to December 2020) and surviving at least 1 week, and (i) demographically matched controls from the 2019 general population; and (ii) people discharged from influenza hospitalisation in 2017 to 2019. We used Cox regression adjusted for age, sex, ethnicity, obesity, smoking status, deprivation, and comorbidities considered potential risk factors for severe COVID-19 outcomes. We included 24,673 postdischarge COVID-19 patients, 123,362 general population controls, and 16,058 influenza controls, followed for ≤315 days. COVID-19 patients had median age of 66 years, 13,733 (56%) were male, and 19,061 (77%) were of white ethnicity. Overall risk of hospitalisation or death (30,968 events) was higher in the COVID-19 group than general population controls (fully adjusted hazard ratio [aHR] 2.22, 2.14 to 2.30, p < 0.001) but slightly lower than the influenza group (aHR 0.95, 0.91 to 0.98, p = 0.004). All-cause mortality (7,439 events) was highest in the COVID-19 group (aHR 4.82, 4.48 to 5.19 versus general population controls [p < 0.001] and 1.74, 1.61 to 1.88 versus influenza controls [p < 0.001]). Risks for cause-specific outcomes were higher in COVID-19 survivors than in general population controls and largely similar or lower in COVID-19 compared with influenza patients. However, COVID-19 patients were more likely than influenza patients to be readmitted or die due to their initial infection or other lower respiratory tract infection (aHR 1.37, 1.22 to 1.54, p < 0.001) and to experience mental health or cognitive-related admission or death (aHR 1.37, 1.02 to 1.84, p = 0.039); in particular, COVID-19 survivors with preexisting dementia had higher risk of dementia hospitalisation or death (age- and sex-adjusted HR 2.47, 1.37 to 4.44, p = 0.002). Limitations of our study were that reasons for hospitalisation or death may have been misclassified in some cases due to inconsistent use of codes, and we did not have data to distinguish COVID-19 variants. In this study, we observed that people discharged from a COVID-19 hospital admission had markedly higher risks for rehospitalisation and death than the general population, suggesting a substantial extra burden on healthcare. Most risks were similar to those observed after influenza hospitalisations, but COVID-19 patients had higher risks of all-cause mortality, readmission or death due to the initial infection, and dementia death, highlighting the importance of postdischarge monitoring.

BackgroundTraditionally, intramuscular vitamin B12 injections were considered by patients and clinicians the most effective treatment option for B12 deficiency. The improving understanding of the condition paired with the restricted National Health Service (NHS) resources, resulted in a shift from injections towards tablets. The COVID-19 pandemic accelerated this change, while healthcare services were adapted to reduce COVID-19 transmission. This included new guidelines on vitamin B12 prescribing where injections were substituted by tablets.ObjectiveWe investigated changes between 2015 and 2024 in prescribing B12 injections and tablets including the effect of the COVID-19 pandemic.Design, population and settingThis was an observational study of general practice in England covering 100% of the population.MethodsWe used prescribing data published by the NHS Business Service Authority. Monthly prescription counts and rates per 100 000 patients were visualised in longitudinal plots from 1 January 2015 to 30 September 2024. We stratified the analysis by regions in England. Changes in yearly counts and rates were summarised using descriptive statistics. Linear regression and data from before the COVID-19 pandemic were used to model trends from 2020 to 2024 as if the pandemic had not occurred. The predicted values and their 95% CI were used to assess the effect of the pandemic.ResultsThe number of prescriptions for B12 formulations doubled in the last 10 years from 2.5 million to 5 million per year. The prescriptions for tablets increased from half a million in 2015 to 2 million in 2024. While the prescriptions for injections increased from 2 million to 3 million. In 2020, there was a sharp drop in prescriptions for injections and a simultaneous rapid increase in prescriptions for tablets coinciding with the onset of the pandemic. There were 806 031 (27%) less than expected prescriptions for injections (2 171 924 observed vs 2 977 956 predicted, 95% CI 2 905 348 to 3 050 565) and 299 834 (27%) more prescriptions for tablets (1 415 315 observed vs 1 115 481 predicted, 95% CI 1 094 350 to 1 136 612). After the 2020 drop, by 2024, injections returned to the prepandemic levels of 3 million prescriptions per year and tablets doubled from 1 million in 2019 to 2 million prescriptions in 2024.ConclusionsIn this study, we document important changes to vitamin B12 prescribing in England over the last 10 years. Before the pandemic, injections were the medication of choice for B12 deficiency but there had been an ongoing debate about the benefits and cost of injections over tablets. The pandemic accelerated the switch from injections to tablets. However, these changes in the pandemic were driven by the availability of resources and not necessarily clinical evidence or patient preference. To establish best practices, more evidence is needed comparing the safety and effectiveness of injections and tablets specific to the condition being treated.

Background NHS England issued commissioning guidance on 18 low-priority treatments which should not be routinely prescribed in primary care. We aimed to monitor the impact of an educational intervention delivered to regional prescribing advisors by senior pharmacists from NHS England on the primary care spend on low-priority items. Methods An opportunistic randomised, controlled parallel-group trial. Participants (clinical commissioning groups, CCGs) were randomised to intervention or control in a 1:1 ratio. The intervention group were invited to participate. The intervention was a one-off educational session. Our primary outcomes concerned the total prescribing of low-priority items in primary care. Secondary outcomes concerned the prescribing of specific low-priority items. We also measured the impact on information-seeking behaviour. Results 40 CCGs were randomised, 20 allocated to intervention, with 11 receiving the intervention. There was no significant impact on any prescribing outcomes. There was some possible evidence of increased engagement with data, in the form of CCG email alert sign-ups ( p  = 0.077). No harms were detected. Conclusions A one-off intervention delivered to CCGs by NHS England did not significantly influence low-priority prescribing. This trial demonstrates how routine interventions planned to improve uptake or adherence to healthcare guidance can be delivered as low-cost randomised trials and how to robustly assess their effectiveness. Trial registration ISRCTN31218900, October 01 2018.

[This corrects the article DOI: 10.1371/journal.pone.0311515.].

Antimicrobial resistance (AMR) is a multifaceted global challenge, partly driven by inappropriate antibiotic prescribing. The objectives of this study were to evaluate the impact of the COVID-19 pandemic on treatment of common infections, develop risk prediction models and examine the effects of antibiotics on infection-related hospital admissions. With the approval of NHS England, we accessed electronic health records from The Phoenix Partnership (TPP) through OpenSAFELY platform. We included adult patients with primary care diagnosis of common infections, including lower respiratory tract infection (LRTI), upper respiratory tract infections (URTI), and lower urinary tract infection (UTI), from 1 January 2019 to 31 August 2022. We excluded patients with a COVID-19 record in the 90 days before to 30 days after the infection diagnosis. Risk prediction models using Cox proportional-hazard regression were developed for infection-related hospital admission in the 30 days after the common infection diagnosis. We found 12,745,165 infection diagnoses from 1 January 2019 to 31 August 2022. Of them, 80,395 (2.05%) cases were admitted to the hospital during follow-up. Counts of hospital admission for infections dropped during COVID-19, for example LRTI from 3,950 in December 2019 to 520 in April 2020. Comparing those prescribed an antibiotic to those without, reduction in risk of hospital admission were largest with LRTI (adjusted hazard ratio (aHR) of 0.35; 95% confidence interval (CI), 0.35-0.36) and UTI (aHR 0.45; 95% CI, 0.44-0.46), compared to URTI (aHR 1.04; 95% CI, 1.03-1.06). A substantial variation in hospital admission risks between infections and patient groups was found. Antibiotics appeared more effective in preventing infection-related complications with LRTI and UTI, but not URTI. While this study has several limitations, the results indicate that a focus on risk-based antibiotic prescribing could help tackle AMR in primary care.

Background Long COVID potentially increases healthcare utilisation and costs. However, its impact on the NHS remains to be determined. Methods This study aims to assess the healthcare utilisation of individuals with long COVID. With the approval of NHS England, we conducted a matched cohort study using primary and secondary care data via OpenSAFELY, a platform for analysing anonymous electronic health records. The long COVID exposure group, defined by diagnostic codes, was matched with five comparators without long COVID between Nov 2020 and Jan 2023. We compared their total healthcare utilisation from GP consultations, prescriptions, hospital admissions, A&E visits, and outpatient appointments. Healthcare utilisation and costs were evaluated using a two-part model adjusting for covariates. Using a difference-in-difference model, we also compared healthcare utilisation after long COVID with pre-pandemic records. Results We identified 52,988 individuals with a long COVID diagnosis, matched to 264,867 comparators without a diagnosis. In the 12 months post-diagnosis, there was strong evidence that those with long COVID were more likely to use healthcare resources (OR: 8.29, 95% CI: 7.74–8.87), and have 49% more healthcare utilisation (RR: 1.49, 95% CI: 1.48–1.51). Our model estimated that the long COVID group had 30 healthcare visits per year (predicted mean: 29.23, 95% CI: 28.58–29.92), compared to 16 in the comparator group (predicted mean visits: 16.04, 95% CI: 15.73–16.36). Individuals with long COVID were more likely to have non-zero healthcare expenditures (OR = 7.66, 95% CI = 7.20–8.15), with costs being 44% higher than the comparator group (cost ratio = 1.44, 95% CI: 1.39–1.50). The long COVID group costs approximately £2500 per person per year (predicted mean cost: £2562.50, 95% CI: £2335.60–£2819.22), and the comparator group costs £1500 (predicted mean cost: £1527.43, 95% CI: £1404.33–1664.45). Historically, individuals with long COVID utilised healthcare resources more frequently, but their average healthcare utilisation increased more after being diagnosed with long COVID, compared to the comparator group. Conclusions Long COVID increases healthcare utilisation and costs. Public health policies should allocate more resources towards preventing, treating, and supporting individuals with long COVID.

Obesity and rapid weight gain are established risk factors for noncommunicable diseases and have emerged as independent risk factors for severe disease following Coronavirus Disease 2019 (COVID-19) infection. Restrictions imposed to reduce COVID-19 transmission resulted in profound societal changes that impacted many health behaviours, including physical activity and nutrition, associated with rate of weight gain. We investigated which clinical and sociodemographic characteristics were associated with rapid weight gain and the greatest acceleration in rate of weight gain during the pandemic among adults registered with an English National Health Service (NHS) general practitioner (GP) during the COVID-19 pandemic. With the approval of NHS England, we used the OpenSAFELY platform inside TPP to conduct an observational cohort study of routinely collected electronic healthcare records. We investigated changes in body mass index (BMI) values recorded in English primary care between March 2015 and March 2022. We extracted data on 17,742,365 adults aged 18 to 90 years old (50.1% female, 76.1% white British) registered with an English primary care practice. We estimated individual rates of weight gain before (δ-prepandemic) and during (δ-pandemic) the pandemic and identified individuals with rapid weight gain (>0.5 kg/m2/year) in each period. We also estimated the change in rate of weight gain between the prepandemic and pandemic period (δ-change = δ-pandemic-δ-prepandemic) and defined extreme accelerators as the 10% of individuals with the greatest increase in their rate of weight gain (δ-change ≥1.84 kg/m2/year) between these periods. We estimated associations with these outcomes using multivariable logistic regression adjusted for age, sex, index of multiple deprivation (IMD), and ethnicity. P-values were generated in regression models. The median BMI of our study population was 27.8 kg/m2, interquartile range (IQR) [24.3, 32.1] in 2019 (March 2019 to February 2020) and 28.0 kg/m2, IQR [24.4, 32.6] in 2021. Rapid pandemic weight gain was associated with sex, age, and IMD. Male sex (male versus female: adjusted odds ratio (aOR) 0.76, 95% confidence interval (95% CI) [0.76, 0.76], p < 0.001), older age (e.g., 50 to 59 years versus 18 to 29 years: aOR 0.60, 95% CI [0.60, 0.61], p < 0.001]); and living in less deprived areas (least-deprived-IMD-quintile versus most-deprived: aOR 0.77, 95% CI [0.77, 0.78] p < 0.001) reduced the odds of rapid weight gain. Compared to white British individuals, all other ethnicities had lower odds of rapid pandemic weight gain (e.g., Indian versus white British: aOR 0.69, 95% CI [0.68, 0.70], p < 0.001). Long-term conditions (LTCs) increased the odds, with mental health conditions having the greatest effect (e.g., depression (aOR 1.18, 95% CI [1.17, 1.18], p < 0.001)). Similar characteristics increased odds of extreme acceleration in the rate of weight gain between the prepandemic and pandemic periods. However, changes in healthcare activity during the pandemic may have introduced new bias to the data. We found female sex, younger age, deprivation, white British ethnicity, and mental health conditions were associated with rapid pandemic weight gain and extreme acceleration in rate of weight gain between the prepandemic and pandemic periods. Our findings highlight the need to incorporate sociodemographic, physical, and mental health characteristics when formulating research, policies, and interventions targeting BMI in the period of post pandemic service restoration and in future pandemic planning.

Background Clinical guidance in England currently recommends that women undergo a postnatal health and wellbeing examination with a general practitioner 6-8 weeks after giving birth. The current study aimed to describe the impact of the COVID-19 pandemic on the rate of maternal postnatal examinations in England over time and its predictors, including socioeconomic deprivation and ethnicity. Methods With the approval of NHS England, the OpenSAFELY platform was used to access the TPP SystmOne electronic health record (EHR) system for primary care. All records for registered female patients aged 14 to 49 years with a recorded birth coded between January 2019 and August 2023. Monthly rates of postnatal examinations were estimated based on the number of patients with a postnatal-related code within 6, 8 or 12 weeks of birth. Interrupted time-series analysis modelled the impact of the COVID-19 pandemic on the rate of examinations. Characteristics that may influence the likelihood of not having a postnatal examination were estimated using logistic regression. Results For 626,180 patients with births coded, the rate of postnatal examinations increased with length of time after birth. Rates within 8 weeks fell from 368 to 279 per 1000 (↓24.1%) between January and March 2020, recovering to 402 per 1000 by January 2023. The incident rate ratio for the first national lockdown was 0.87 (95%CI 0.81–0.91) for 6 weeks, 0.84 (0.81–0.87) for 8 and 1.06 (1.04–1.08) for 12 weeks. The odds of no examination were affected by ethnicity (Asian or Asian British (OR 1.11, 1.09–1.14)), region (North East (1.39, 1.28–1.37) and West Midlands (1.33, 1.27–1.39)) and deprivation (most deprived (1.43, 1.31–1.37)). Conclusions Maternal postnatal examinations within the recommended time were negatively affected by the onset of the pandemic. Despite rates improving over time, most failed to occur within the recommended 6–8 weeks. Significant variation in rates exists across NHS regions: rates were lower in ethnic minority groups and in more deprived populations. Addressing these disparities will require a combination of policy changes, financial incentives and targeted, culturally appropriate interventions to ensure equitable access to care for all mothers and infants.

Background With the global challenge of antimicrobial resistance intensified during the COVID-19 pandemic, evaluating adverse events (AEs) post-antibiotic treatment for common infections is crucial. This study aims to examines the changes in incidence rates of AEs during the COVID-19 pandemic and predict AE risk following antibiotic prescriptions for common infections, considering their previous antibiotic exposure and other long-term clinical conditions. Methods With the approval of NHS England, we used OpenSAFELY platform and analysed electronic health records from patients aged 18–110, prescribed antibiotics for urinary tract infection (UTI), lower respiratory tract infections (LRTI), upper respiratory tract infections (URTI), sinusitis, otitis externa, and otitis media between January 2019 and June 2023. We evaluated the temporal trends in the incidence rate of AEs for each infection, analysing monthly changes over time. The survival probability of emergency AE hospitalisation was estimated in each COVID-19 period (period 1: 1 January 2019 to 25 March 2020, period 2: 26 March 2020 to 8 March 2021, period 3: 9 March 2021 to 30 June 2023) using the Kaplan–Meier approach. Prognostic models, using Cox proportional hazards regression, were developed and validated to predict AE risk within 30 days post-prescription using the records in Period 1. Results Out of 9.4 million patients who received antibiotics, 0.6% of UTI, 0.3% of URTI, and 0.5% of LRTI patients experienced AEs. UTI and LRTI patients demonstrated a higher risk of AEs, with a noted increase in AE incidence during the COVID-19 pandemic. Higher comorbidity and recent antibiotic use emerged as significant AE predictors. The developed models exhibited good calibration and discrimination, especially for UTIs and LRTIs, with a C-statistic above 0.70. Conclusions The study reveals a variable incidence of AEs post-antibiotic treatment for common infections, with UTI and LRTI patients facing higher risks. AE risks varied between infections and COVID-19 periods. These findings underscore the necessity for cautious antibiotic prescribing and call for further exploration into the intricate dynamics between antibiotic use, AEs, and the pandemic.