Rose Higgins

Background: The Secondary Care Medicines Data (SCMD) makes detailed data on medicines issued by National Health Service (NHS) hospital trusts in England openly available for the first time. These data provide an opportunity to understand medicine use patterns, highlight variation, and drive improvements in evidence-based practice, prescribing safety, and cost-effectiveness. However, this dataset requires extensive domain and technical knowledge to access, clean, transform, and link to other external sources before it can be used effectively. Objective: This study aimed to describe the development, implementation, and functionality of OpenPrescribing Hospitals and showcases example use cases. OpenPrescribing Hospitals is an open-access platform for analyzing hospital medicines use in England using the SCMD. Methods: We developed a data pipeline that imports the SCMD and links further medicinal product information from the dictionary of medicines and devices, organization information from the Organisation Reference Data, and medicines classifications and measurement information from the Anatomical Therapeutic Chemical and Defined Daily Dose index. Using these linked data, we calculated alternative measures of quantity to support appropriate comparison across the products reported. These data are presented through an open-access web platform with 3 main features: Submission History, Measures, and Analyse. We describe these features through illustrative use cases. We also summarize the web analytics collected between the platform launch in February 2025 and September 2026. Results: The platform supports open, accessible use of preprocessed hospital medicines data and supports analysis and comparisons across NHS trusts, Integrated Care Boards, NHS regions, ingredients, and therapeutic groups. The Submission History feature displays patterns that can indicate changes in the completeness and consistency of data submissions by NHS trusts. The Measures feature presents predefined measures and benchmarking based on national medicines recommendations. The Analyse feature supports detailed user-defined analyses. Illustrative examples show how incomplete submissions can affect apparent medicines use and how the platform can surface trends and variation in the issuing of selected medicines. As of September 2026, the platform attracts approximately 1000 unique visitors monthly and has been used to run more than 20,000 custom analyses. Conclusions: OpenPrescribing Hospitals reduces the technical barriers to analyzing hospital medicines data in England. The platform is openly accessible and accompanied by extensive documentation to support transparent and reproducible analyses. We encourage adoption of the platform by clinicians, academics, and policy advisors to support rapid local feedback against national medicines standards and to monitor the extent of and variation in the uptake of new medicines. This early report does not assess usability, clinical outcomes, prescribing changes, or cost savings. Further research is needed to evaluate data validity, user experience, and effects on medicine optimization.

We discovered that the official list of clinical codes for pregnancy during the COVID-19 pandemic identified some unlikely pregnancies (for example, in older men), principally due to a code describing a specific fetal position (“knee presentation”), which notably lacks “fetal” in the code description. This is an informative example of commonly overlooked problems in creating and using clinical data.

BACKGROUND The Secondary Care Medicines Data (SCMD) makes detailed data on medicines issued by National Health Service (NHS) hospital trusts in England openly available for the first time. These data provide an opportunity to understand medicine use patterns, highlight variation, and drive improvements in evidence-based practice, prescribing safety, and cost-effectiveness. However, this dataset requires extensive domain and technical knowledge to access, clean, transform, and link to other external sources before it can be used effectively. OBJECTIVE This study aimed to describe the development, implementation, and functionality of OpenPrescribing Hospitals and showcases example use cases. OpenPrescribing Hospitals is an open-access platform for analyzing hospital medicines use in England using the SCMD. METHODS We developed a data pipeline that imports the SCMD and links further medicinal product information from the dictionary of medicines and devices, organization information from the Organisation Reference Data, and medicines classifications and measurement information from the Anatomical Therapeutic Chemical and Defined Daily Dose index. Using these linked data, we calculated alternative measures of quantity to support appropriate comparison across the products reported. These data are presented through an open-access web platform with 3 main features: Submission History, Measures, and Analyse. We describe these features through illustrative use cases. We also summarize the web analytics collected between the platform launch in February 2025 and September 2026. RESULTS The platform supports open, accessible use of preprocessed hospital medicines data and supports analysis and comparisons across NHS trusts, Integrated Care Boards, NHS regions, ingredients, and therapeutic groups. The Submission History feature displays patterns that can indicate changes in the completeness and consistency of data submissions by NHS trusts. The Measures feature presents predefined measures and benchmarking based on national medicines recommendations. The Analyse feature supports detailed user-defined analyses. Illustrative examples show how incomplete submissions can affect apparent medicines use and how the platform can surface trends and variation in the issuing of selected medicines. As of September 2026, the platform attracts approximately 1000 unique visitors monthly and has been used to run more than 20,000 custom analyses. CONCLUSIONS OpenPrescribing Hospitals reduces the technical barriers to analyzing hospital medicines data in England. The platform is openly accessible and accompanied by extensive documentation to support transparent and reproducible analyses. We encourage adoption of the platform by clinicians, academics, and policy advisors to support rapid local feedback against national medicines standards and to monitor the extent of and variation in the uptake of new medicines. This early report does not assess usability, clinical outcomes, prescribing changes, or cost savings. Further research is needed to evaluate data validity, user experience, and effects on medicine optimization. CLINICALTRIAL

Objective: To quantify the changes in opioid prescribing over time to a population with high rates of opioid use to understand the impact of longer elective wait times during the covid-19 pandemic. Design: With the approval of NHS England, a retrospective cohort study using linked electronic health record data in OpenSAFELY-TPP. Setting: Primary and secondary care electronic health records of people registered at general practices in England that use TPP SystmOne software, covering about 43% of the total registered population in England, linked to data from the Waiting List Minimum Dataset (WLMDS) within the OpenSAFELY-TPP platform, which is part of the NHS England OpenSAFELY covid-19 service. Participants: 63 850 eligible patients on the waiting list for elective trauma procedures or orthopaedic procedures whose wait ended in admission between May 2021 and April 2022. Main outcome measures: Opioid prescribing to eligible patients before referral to the waiting list, while waiting for treatment, and after discharge from treatment. Opioids were classified based on their strength (weak, moderate, or strong opioids) and duration of action (immediate release v modified release opioids). Results: Of 63 850 people on elective trauma or orthopaedic waiting lists whose wait ended during the study period (median age 61 years, 54.6% female), 20.5% waited for more than 52 weeks to be admitted. In the three months before their waiting list referral date, 9890 (15.5%) participants had three or more opioid prescriptions, and 3790 (5.9%) were prescribed a strong opioid. Weekly opioid prescribing rates per 100 people on the waiting list were stable over time, with prescription rates peaking immediately after treatment and plateauing about three months after treatment. Comparing the three month period before the waiting list referral date to the period four to six months after the waiting list end date, changes in the proportion of people with three or more prescriptions for an opioid during that period were −1.6% (95% confidence interval −2.2% to −1.0%) for people on the waiting list for 18 weeks or less, −1.1% (−1.7% to −0.5%) for people waiting for 19-52 weeks, and −0.5% (−1.4% to 0.4%) for people waiting for more than 52 weeks. Conclusions: In this study, one in five people who received treatment for an elective orthopaedic procedure between May 2021 and April 2022 waited for more than one year. Nearly one in seven people were prescribed opioids long term before their referral date to the waiting list, and only small reductions in long term opioid prescribing were observed after a patient’s procedure, regardless of length of time spent on the waiting list.

Primary care prescribing data for general practices in England has been openly published since 2011. The OpenPrescribing platform provides an open, transparent and easy-to-use interface to this data. OpenPrescribing is widely used by NHS professionals, the public and by industry and has supported analyses to identify cost-saving opportunities and unsafe prescribing in primary care in England. The Secondary Care Medicines Data, published since July 2021 makes analogous data for medicines issued in NHS trusts in England available for the first time. As with primary care prescribing data, this dataset requires extensive domain and technical knowledge to access, clean, transform and link to other external sources before it can be used effectively. We have developed the OpenPrescribing Hospitals platform to provide an intuitive interface to the dataset, built on top of a robust data pipeline that removes the barriers to the dataset’s use. This platform is openly accessible, with transparent methodology and aims to deliver the same benefits as OpenPrescribing. Here we describe why the platform is needed, its key features, and highlight potential use cases. We encourage adoption of the platform by clinicians, academics and policy advisors to support rapid local feedback against national medicines standards and to monitor the extent and variation of uptake of new medicines.

Background Waiting times for elective procedures increased dramatically during the COVID-19 pandemic. People waiting for orthopaedic procedures often require opioids to manage pain, and longer waiting times may result in a need for more and stronger opioids to treat symptoms. Methods With NHS England approval, we used routine clinical data from general practice adult patients in OpenSAFELY-TPP linked with the National Minimum Waiting List Dataset. We identified people on elective waiting lists for inpatient trauma or orthopaedic procedures (May 2021-Apr 2022). We quantified changes in weekly opioid prescribing from 6 months pre-waiting list start date to 1 year after the waiting list end date. We also compared long-term opioid prescribing rates in the 3 months before the waiting list start date and months 4-6 after the waiting list end date. We also stratified analyses by time spent on the waiting list (<=18 weeks, 19-52 weeks, >52 weeks). Results Among 63,850 people on elective trauma or orthopaedic waiting lists (median age = 61 years, 54.6% female), 20.5% waited more than 52 weeks. Weekly opioid prescribing rates per 100 waiting list population were relatively stable over time, with peaks immediately post-treatment, and plateauing again after approximately 3 months. Comparing the 3 months before the waiting list start date to months 4-6 after the waiting list end date, changes in the proportion of people with >=3 opioid prescriptions were -1.6% (95%CI -2.2%, -1.0%) for people on the waiting list <=18 weeks, -1.1% (95%CI -1.7%, -0.5%) among people waiting 19-52 weeks, and -0.5% (95%CI -1.4%, 0.4%) among people waiting >52 weeks. Conclusion During the COVID-19 pandemic, one in five people waiting for elective orthopaedic procedures waited more than one year. Nearly one in seven were prescribed opioids long-term prior to their referral date, and only small reductions in long-term opioid prescribing was observed post-procedure, regardless of time spent on the waiting list. However, people on waiting lists experienced much longer wait times during the COVID-19 pandemic which also means greater exposure to opioids while awaiting treatment.

Background: COVID-19 restrictions led to increased reports of depressive symptoms in the general population and impacted health and social care services. We explored whether these changes affected antidepressant prescribing trends in the general population and those with learning disability or autism. Methods: With the approval of NHS England, we used >24 million patients’ primary care data from the OpenSAFELY-TPP platform. We used interrupted time series analysis to quantify trends in those prescribed and newly prescribed an antidepressant across key demographic and clinical subgroups, comparing pre-COVID-19 (January 2018–February 2020), COVID-19 restrictions (March 2020–February 2021) and recovery (March 2021–December 2022) periods. Results: Prior to COVID-19 restrictions, antidepressant prescribing was increasing in the general population and in those with learning disability or autism. We did not find evidence that the pandemic was associated with a change in antidepressant prescribing trend in the general population (relative risk (RR) 1.00 (95% CI 0.97 to 1.02)), in those with autism (RR 0.99 (95% CI 0.97 to 1.01)) or in those with learning disability (RR 0.98 (95% CI 0.96 to 1.00)). New prescribing post restrictions was 13% and 12% below expected had COVID-19 not happened in both the general population and those with autism (RR 0.87 (95% CI 0.83 to 0.93), RR 0.88 (95% CI 0.83 to 0.92)), but not learning disability (RR 0.96 (95% CI 0.87 to 1.05)). Conclusions and implications: In this England study, we did not see an impact of COVID-19 on overall antidepressant prescribing, although unique trends were noted, such as trends in new antidepressant prescriptions which increased in care homes over the pandemic and decreased in the general population and those with autism since recovery.

Background COVID-19 lockdowns led to increased reports of depressive symptoms in the general population and impacted the health and social care services of people with learning disability and autism. We explored whether the COVID-19 pandemic had an impact on antidepressant prescribing trends within these and the general population. Methods With the approval of NHS England, we used >24 million patients’ primary care data from the OpenSAFELY-TPP platform. We identified patients with learning disability or autism and used an interrupted time series analysis to quantify trends in those prescribed and newly prescribed an antidepressant across key demographic and clinical subgroups, comparing pre-COVID-19 (January 2018-February 2020), COVID-19 lockdown (March 2020-February 2021) and the recovery period (March 2021-December 2022). Results Prior to COVID-19 lockdown, antidepressant prescribing was increasing at 0.3% (95% CI 0.2% to 0.3%) patients per month, in the general population and in those with learning disability, and 0.3% (95% CI 0.2% to 0.4%) in those with autism. We did not find evidence that the pandemic was associated with a change in trend of antidepressant prescribing in the general population (RR 1.00 (95% CI 0.97 to 1.02)), in those with autism (RR 0.99 (95% CI 0.97 to 1.01)), or in those with learning disability (RR 0.98 (95% CI 0.96 to 1.00)). New prescribing post lockdown was 13% and 12% below expected if COVID-19 had not happened in both the general population and those with autism (RR 0.87 (95% CI 0.83 to 0.93), RR 0.88 (95% CI 0.83 to 0.92))), but not learning disability (RR 0.96 (95% CI 0.87 to 1.05)). Conclusions and Implications Pre-COVID-19, antidepressant prescribing was increasing at 0.3% per month. While we did not see an impact of COVID-19 on overall prescribing in the general population, prescriptions to those aged 0-19, 20-29, and new prescriptions were lower than pre-COVID-19 trends would have predicted, but tricyclics and new prescriptions in care homes were higher than expected. What is already known on this topic ⇒ The prescribing of antidepressants in the UK has been increasing for more than a decade. ⇒ Studies globally have found differing impacts of COVID-19 on mental health outcomes in the general population, by age, sex, socio-economic status, and care home status. What this study adds ⇒ This study describes the impact of COVID-19 on antidepressant prescribing in England with additional follow-up through December 2022, with a focus on people with a learning disability or autism. How this study might affect research, practice, or policy ⇒ This study demonstrates how the pandemic did not lead to an increase in antidepressant prescriptions in the general population, but more is needed to ensure that antidepressants are used appropriately within vulnerable populations. ⇒ Improvements are needed in the documentation of diagnosis when prescribing medicines.

Objective To investigate the impact of the COVID-19 pandemic on Group A streptococcal (GAS) cases and related antibiotic prescriptions. Design A retrospective cohort study with supporting dashboards with the approval of NHS England. Setting Primary care practices in England using TPP SystmOne software from January 2018 through March 2023. Participants Patients included were those registered at a TPP practice for each month of the study period. Patients with missing sex or age were excluded, resulting in a population of 23,816,470 in January 2018, increasing to 25,541,940 by March 2023. Main outcome measures We calculated monthly counts and crude rates of GAS cases (sore throat/tonsillitis, scarlet fever, invasive group A strep) and prescriptions linked with a GAS case, before (pre-April 2020), during and after (post-April 2021) COVID-19 restrictions. We calculated the maximum and minimum count and rate for each season (years running September-August), and the rate ratio (RR) of the 2022/23 season to the last comparably high season (2017/18). Results Recording of GAS cases and antibiotic prescription linked with a GAS case peaked in December 2022, higher than the 2017/2018 peak. The peak rate of monthly sore throat/tonsillitis (possible group A strep throat) recording was 5.33 per 1,000 (RR 2022/23 versus 2017/18 1.39 (CI: 1.38 to 1.40)). Scarlet fever recording peaked at 0.51 per 1,000 (RR 2.68 (CI: 2.59 to 2.77)), and invasive group A streptococcal infection (iGAS) at 0.01 per 1,000 (RR 4.37 (CI: 2.94 to 6.48)). First line antibiotics with a record of a GAS infection peaked at 2.80 per 1,000 (RR 1.37 (CI:1.35 to 1.38)), alternative antibiotics at 2.03 per 1,000 (RR 2.30 (CI:2.26 to 2.34)), and reserved antibiotics at 0.09 per 1,000 (RR 2.42 (CI:2.24 to 2.61). For individual antibiotics, azithromycin with GAS indication showed the greatest relative increase (RR 7.37 (CI:6.22 to 8.74)).This followed a sharp drop in recording of cases and associated prescriptions during the period of COVID-19 restrictions where the maximum count and rates were lower than any pre COVID-19 minimum. More detailed demographic breakdowns can be found in our regularly updated dashboard report. Conclusions Rates of scarlet fever, sore throat/tonsillitis and iGAS recording and associated antibiotic prescribing peaked in December 2022. Primary care data can supplement existing infectious disease surveillance through linkages with relevant prescribing data and detailed clinical and demographic subgroups. What is known During the COVID-19 pandemic there has been a substantial change to the pattern of circulating viruses and bacteria that cause illnesses. A spike in group A streptococcal infections in England starting December 2022 was associated with 426 deaths, including 48 children as of 7th May 2023. Increased demand for antibiotics in this period led to medicines shortages and the introduction of Serious Shortage Protocols (SSPs). Existing surveillance systems such as notifiable disease reports and GP in-hours surveillance bulletins describe clinical events, but they do not link to relevant prescribing data. What this study adds - This study supports the findings of routine surveillance reports which indicated a drop in GAS infections during the COVID-19 restrictions, followed by a spike in December 2022, demonstrating that the OpenSAFELY platform and primary care data can be used to rapidly describe not only clinical events but also relevant prescribing in the case of future outbreaks. - Antibiotic prescribing with a GAS indication, particularly for phenoxymethylpenicillin alternatives and reserved antibiotics, was higher in the December 2022 peak than in the 2017/2018 peak.

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).