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The US opioid epidemic has led to similar concerns about prescribed opioids in the UK. In new users, initiation of or escalation to more potent and high dose opioids may contribute to long-term use. Additionally, physician prescribing behaviour has been described as a key driver of rising opioid prescriptions and long-term opioid use. No studies to our knowledge have investigated the extent to which regions, practices, and prescribers vary in opioid prescribing whilst accounting for case mix. This study sought to (i) describe prescribing trends between 2006 and 2017, (ii) evaluate the transition of opioid dose and potency in the first 2 years from initial prescription, (iii) quantify and identify risk factors for long-term opioid use, and (iv) quantify the variation of long-term use attributed to region, practice, and prescriber, accounting for case mix and chance variation. A retrospective cohort study using UK primary care electronic health records from the Clinical Practice Research Datalink was performed. Adult patients without cancer with a new prescription of an opioid were included; 1,968,742 new users of opioids were identified. Mean age was 51 ± 19 years, and 57% were female. Codeine was the most commonly prescribed opioid, with use increasing 5-fold from 2006 to 2017, reaching 2,456 prescriptions/10,000 people/year. Morphine, buprenorphine, and oxycodone prescribing rates continued to rise steadily throughout the study period. Of those who started on high dose (120-199 morphine milligram equivalents [MME]/day) or very high dose opioids (≥200 MME/day), 10.3% and 18.7% remained in the same MME/day category or higher at 2 years, respectively. Following opioid initiation, 14.6% became long-term opioid users in the first year. In the fully adjusted model, the following were associated with the highest adjusted odds ratios (aORs) for long-term use: older age (≥75 years, aOR 4.59, 95% CI 4.48-4.70, p < 0.001; 65-74 years, aOR 3.77, 95% CI 3.68-3.85, p < 0.001, compared to <35 years), social deprivation (Townsend score quintile 5/most deprived, aOR 1.56, 95% CI 1.52-1.59, p < 0.001, compared to quintile 1/least deprived), fibromyalgia (aOR 1.81, 95% CI 1.49-2.19, p < 0.001), substance abuse (aOR 1.72, 95% CI 1.65-1.79, p < 0.001), suicide/self-harm (aOR 1.56, 95% CI 1.52-1.61, p < 0.001), rheumatological conditions (aOR 1.53, 95% CI 1.48-1.58, p < 0.001), gabapentinoid use (aOR 2.52, 95% CI 2.43-2.61, p < 0.001), and MME/day at initiation (aOR 1.08, 95% CI 1.07-1.08, p < 0.001). After adjustment for case mix, 3 of the 10 UK regions (North West [16%], Yorkshire and the Humber [15%], and South West [15%]), 103 practices (25.6%), and 540 prescribers (3.5%) had a higher proportion of patients with long-term use compared to the population average. This study was limited to patients prescribed opioids in primary care and does not include opioids available over the counter or prescribed in hospitals or drug treatment centres. Of patients commencing opioids on very high MME/day (≥200), a high proportion stayed in the same category for a subsequent 2 years. Age, deprivation, prescribing factors, comorbidities such as fibromyalgia, rheumatological conditions, recent major surgery, and history of substance abuse, alcohol abuse, and self-harm/suicide were associated with long-term opioid use. Despite adjustment for case mix, variation across regions and especially practices and prescribers in high-risk prescribing was observed. Our findings support greater calls for action for reduction in practice and prescriber variation by promoting safe practice in opioid prescribing.

In pharmacoepidemiology, routinely collected data from electronic health records (including primary care databases, registries, and administrative healthcare claims) are a resource for research evaluating the real world effectiveness and safety of medicines. Currently available guidelines for the reporting of research using non-randomised, routinely collected data—specifically the REporting of studies Conducted using Observational Routinely collected health Data (RECORD) and the Strengthening the Reporting of OBservational studies in Epidemiology (STROBE) statements—do not capture the complexity of pharmacoepidemiological research. We have therefore extended the RECORD statement to include reporting guidelines specific to pharmacoepidemiological research (RECORD-PE). This article includes the RECORD-PE checklist (also available on www.record-statement.org) and explains each checklist item with examples of good reporting. We anticipate that increasing use of the RECORD-PE guidelines by researchers and endorsement and adherence by journal editors will improve the standards of reporting of pharmacoepidemiological research undertaken using routinely collected data. This improved transparency will benefit the research community, patient care, and ultimately improve public health.

The opioid epidemic in North America has been driven by an increase in the use and potency of prescription opioids, with ensuing excessive opioid-related deaths. Internationally, there are lower rates of opioid-related mortality, possibly because of differences in prescribing and health system policies. Our aim was to compare opioid prescribing rates in patients without cancer, across 5 centers in 4 countries. In addition, we evaluated differences in the type, strength, and starting dose of medication and whether these characteristics changed over time. We conducted a retrospective multicenter cohort study of adults who are new users of opioids without prior cancer. Electronic health records and administrative health records from Boston (United States), Quebec and Alberta (Canada), United Kingdom, and Taiwan were used to identify patients between 2006 and 2015. Standard dosages in morphine milligram equivalents (MMEs) were calculated according to The Centers for Disease Control and Prevention. Age- and sex-standardized opioid prescribing rates were calculated for each jurisdiction. Of the 2,542,890 patients included, 44,690 were from Boston (US), 1,420,136 Alberta, 26,871 Quebec (Canada), 1,012,939 UK, and 38,254 Taiwan. The highest standardized opioid prescribing rates in 2014 were observed in Alberta at 66/1,000 persons compared to 52, 51, and 18/1,000 in the UK, US, and Quebec, respectively. The median MME/day (IQR) at initiation was highest in Boston at 38 (20 to 45); followed by Quebec, 27 (18 to 43); Alberta, 23 (9 to 38); UK, 12 (7 to 20); and Taiwan, 8 (4 to 11). Oxycodone was the first prescribed opioid in 65% of patients in the US cohort compared to 14% in Quebec, 4% in Alberta, 0.1% in the UK, and none in Taiwan. One of the limitations was that data were not available from all centers for the entirety of the 10-year period. In this study, we observed substantial differences in opioid prescribing practices for non-cancer pain between jurisdictions. The preference to start patients on higher MME/day and more potent opioids in North America may be a contributing cause to the opioid epidemic.

Opioids administered in hospital during the immediate postoperative period are likely to influence post-surgical outcomes, but inpatient prescribing during the admission is challenging to access. Modified-release(MR) preparations have been especially associated with harm, whilst certain populations such as the elderly or those with renal impairment may be vulnerable to complications. This study aimed to assess postoperative opioid utilisation patterns during hospital stay for people admitted for major/orthopaedic surgery. Patients admitted to a teaching hospital in the North-West of England between 2010-2021 for major/orthopaedic surgery with an admission for ≥1 day were included. We examined opioid administrations in the first seven days post-surgery in hospital, and \"first 48 hours\" were defined as the initial period. Proportions of MR opioids, initial immediate-release(IR) oxycodone and initial morphine milligram equivalents (MME)/day were calculated and summarised by calendar year. We also assessed the proportion of patients prescribed an opioid at discharge. Among patients admitted for major/orthopaedic surgery, 71.1% of patients administered opioids during their hospitalisation. In total 50,496 patients with 60,167 hospital admissions were evaluated. Between 2010-2017 MR opioids increased from 8.7% to 16.1% and dropped to 11.6% in 2021. Initial use of oxycodone IR among younger patients (≤70 years) rose from 8.3% to 25.5% (2010-2017) and dropped to 17.2% in 2021. The proportion of patients on ≥50MME/day ranged from 13% (2021) to 22.9% (2010). Of the patients administered an opioid in hospital, 26,920 (53.3%) patients were discharged on an opioid. In patients hospitalised with major/orthopaedic surgery, 4 in 6 patients were administered an opioid. We observed a high frequency of administered MR opioids in adult patients and initial oxycodone IR in the ≤70 age group. Patients prescribed with ≥50MME/day ranged between 13-22.9%. This is the first published study evaluating UK inpatient opioid use, which highlights opportunities for improving safer prescribing in line with latest recommendations.

Our sleep timing preference, or chronotype, is a manifestation of our internal biological clock. Variation in chronotype has been linked to sleep disorders, cognitive and physical performance, and chronic disease. Here we perform a genome-wide association study of self-reported chronotype within the UK Biobank cohort ( n =100,420). We identify 12 new genetic loci that implicate known components of the circadian clock machinery and point to previously unstudied genetic variants and candidate genes that might modulate core circadian rhythms or light-sensing pathways. Pathway analyses highlight central nervous and ocular systems and fear-response-related processes. Genetic correlation analysis suggests chronotype shares underlying genetic pathways with schizophrenia, educational attainment and possibly BMI. Further, Mendelian randomization suggests that evening chronotype relates to higher educational attainment. These results not only expand our knowledge of the circadian system in humans but also expose the influence of circadian characteristics over human health and life-history variables such as educational attainment. Here, Richa Saxena and colleagues perform a genome-wide association study (GWAS) of self-reported morningness/eveningness preference in the UKBiobank cohort, and identify new genetic loci that contribute to a person's chronotype.

Population health studies increasingly collect longitudinal, patient-reported symptom data via mobile devices, offering unique insights into experiences outside clinical settings, such as pain, fatigue or mood. However, such data present challenges due to ordinal measurement scales, irregular sampling and temporal autocorrelation. This paper introduces two novel summary measures for analysing ordinal outcomes: (1) the mean absolute deviation from the median ( Madm ) for cross-sectional analyses and (2) the mean absolute deviation from expectation ( Made ) for longitudinal data. The latter is based on a latent cumulative model with penalized splines, enabling smooth transitions between irregular time points while accounting for the ordinal nature of the data. Unlike black-box machine learning approaches, this method is interpretable, computationally efficient and easy to implement in standard statistical software. Through simulations, we demonstrate that the proposed measures outperform standard methods when the assumptions of normality or stationarity are violated. Application to real-world data from a national smartphone study, Cloudy with a Chance of Pain , highlights the utility of these measures in characterising symptom variability and trends over time. The methods developed here provide intuitive tools for analysing patient-reported outcomes in longitudinal studies, with potential applications in prediction modelling, causal discovery and evaluation of interventions.

Background Opioid use for chronic non-cancer pain remains common in the UK, despite limited evidence of long-term effectiveness. Delirium, a serious acute confusional state associated with increased mortality, is a known adverse effect of opioid use. Pharmacological differences between opioids may influence delirium risk, but comparative evidence is scarce. This study evaluated the association of opioid type and dosage with the risk of in-hospital delirium in non-cancer patients. Methods We conducted a retrospective cohort study using electronic health records (EHRs) from a tertiary care hospital in northwest England (September 26, 2014–December 31, 2020). Adult (≥ 18 years) without cancer who were administered with opioids during admission were included. Delirium was identified using the 4 ‘A’s Test or through a combination of ICD-10 codes and new-onset confusion scores (= 3) on the National Early Warning Score. Daily opioid doses were converted to daily morphine milligram equivalents (MME/day) to assess the effect of dose across different opioid types. Incidence rates were calculated by opioid type and opioid dosage. Cox regression models, adjusted for confounders, were used to evaluate delirium risk. Results Among 50,586 opioid-exposed patients (mean [SD] age, 55 [20] years; 53% female), 867 patients (1.7%) experienced delirium during their first hospital admission (mean [SD] age, 75.1 [16.7] years). Compared to codeine, oxycodone (hazard ratio [HR] 3.52, 95% C I 2.77–4.46), fentanyl ( HR 2.45, 95% CI 1.71–3.51), buprenorphine ( HR 2.43, 95% CI 1.54–3.82), combination opioids ( HR 2.22, 95% CI 1.63–3.02), and morphine ( HR 2.15, 95% CI 1.65–2.79) were associated with significantly higher delirium risk. No clear dose–response association was observed: doses of 50–119 MME/day were not associated with a significant increase in risk compared to < 50 MME/day ( HR 0.96, 95% CI 0.66–1.39). Conclusions Using in-hospital medication administration records to capture opioid exposure, we found that oxycodone, fentanyl, buprenorphine, morphine, and combination opioids were associated with increased delirium risk compared with codeine. Oxycodone was associated with a higher risk of delirium compared with both codeine and morphine. These findings support personalised opioid prescribing in non-cancer pain and can inform shared clinical decision-making to prevent delirium in patients prescribed opioids.

Digital-health technologies support the collection of patient-generated health data that is frequent, longitudinal, and collected in participant’s own environments. Such high-frequency data could detect patterns of variation in disease and associated symptoms, but characterizing, interpreting, and understanding the reasons for this variability remain open questions. Here, we examine 2070 people living with chronic pain to quantify daily variability in pain severity across seven-day periods and identify factors associated with that variability. Data were collected via a smartphone application from a population-based mobile-health study, Cloudy with a Chance of Pain. Summary statistics and distributions of pain changes on consecutive days were calculated within 13,052 complete weeks of data, which had been assigned to one of four clusters via a previously published k-mediods clustering algorithm: no/low pain, mild pain, moderate pain, and severe pain. Cumulative-probit models were used to identify associations between changes in pain severity and changes in exposure data. Across the four clusters, the no/low-pain cluster had the highest proportion of weeks with no within-week changes (59%) in pain severity compared to the other clusters (48–53%). When pain did change, it changed one unit (out of five) about 20% of the time, but larger changes of two to four units also occurred. Changes in pain severity were associated most strongly with changes in pain interference (i.e., how pain impacts daily activities) and were also associated with changes in fatigue, morning stiffness, mood, and participant well-being. Thus, this study showed that data collected frequently through digital-health technologies can be used to explore variability in symptoms and their associations with other variables. That pain severity was associated with changes in modifiable variables (e.g., fatigue, mood) suggests opportunities for different treatment and self-management regimes for different patient subtypes within the four clusters.

Clinical coding is the process of extracting key information contained within clinical free-text and representing this information using standardised clinical terminologies. In doing so, unstructured text is transformed into structured data that can be retrieved and analysed more effectively. This process is essential to improving direct care, supporting communication between clinicians and enabling clinical research. However, manual clinical coding is difficult and time consuming, motivating the development and use of natural language processing for automated coding. This work evaluates the quality and consistency of both manual and automated coding of diagnoses from hospital outpatient letters. Using 100 randomly selected letters, two human clinicians performed coding of diagnosis lists to SNOMED CT. Automated coding was also performed using IMO’s Concept Tagger. A gold standard was constructed by a panel of clinicians from a subset of the annotated diagnoses. This was used to evaluate the quality and consistency of manual and automated coding via (1) a distance-based metric, treating SNOMED CT as a graph, and (2) a qualitative metric agreed upon by the panel of clinicians. Correlation between the two metrics was also evaluated. Comparing human and computer-generated codes to the gold standard, the results indicate that humans slightly out-performed automated coding, while both performed notably better when there was only a single diagnosis contained in the free-text description. Automated coding was considered acceptable by the panel of clinicians in approximately 90% of cases.

Guidelines for the management of rheumatoid arthritis (RA) recommend using influenza and pneumococcal vaccinations to mitigate infection risk. The level of adherence to these guidelines is not well known in the UK. The aims of this study were to describe the uptake of influenza and pneumococcal vaccinations in patients with RA in the UK, to compare the characteristics of those vaccinated to those not vaccinated and to compare vaccination rates across regions of the UK. A retrospective cohort study of adults diagnosed with incident RA and treated with non-biologic immunosuppressive therapy, using data from a large primary care database. For the influenza vaccination, patients were considered unvaccinated on 1st September each year and upon vaccination their status changed to vaccinated. For pneumococcal vaccination, patients were considered vaccinated after their first vaccination until the end of follow-up. Patients were stratified by age 65 at the start of follow-up, given differences in vaccination guidelines for the general population. Overall (N=15,724), 80% patients received at least one influenza vaccination, and 50% patients received a pneumococcal vaccination, during follow-up (mean 5.3 years). Of those aged below 65 years (N=9,969), 73% patients had received at least one influenza vaccination, and 43% patients received at least one pneumococcal vaccination. Of those aged over 65 years (N=5,755), 91% patients received at least one influenza vaccination, and 61% patients had received at least one pneumococcal vaccination. Those vaccinated were older, had more comorbidity and visited the GP more often. Regional differences in vaccination rates were seen with the highest rates in Northern Ireland, and the lowest rates in London. One in five patients received no influenza vaccinations and one in two patients received no pneumonia vaccine over five years of follow-up. There remains significant scope to improve uptake of vaccinations in patients with RA.