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Chloroform contributes to the depletion of the stratospheric ozone layer. However, due to its short lifetime and predominantly natural sources, it is not included in the Montreal Protocol that regulates the production and uses of ozone-depleting substances. Atmospheric chloroform mole fractions were relatively stable or slowly decreased during 1990–2010. Here we show that global chloroform mole fractions increased after 2010, based on in situ chloroform measurements at seven stations around the world. We estimate that the global chloroform emissions grew at the rate of 3.5% yr−1 between 2010 and 2015 based on atmospheric model simulations. We used two regional inverse modelling approaches, combined with observations from East Asia, to show that emissions from eastern China grew by 49 (41–59) Gg between 2010 and 2015, a change that could explain the entire increase in global emissions. We suggest that if chloroform emissions continuously grow at the current rate, the recovery of the stratospheric ozone layer above Antarctica could be delayed by several years. Atmospheric levels of chloroform increased after 2010, as a result of emissions in eastern China, according to analyses of measurements and inverse modelling.

Objectives To characterize the incidence of clinically diagnosed Paget’s disease of bone in the UK during 1999–2015 and to determine variations in the incidence of disease by age, sex, geography and level of deprivation. Methods Incident cases of Paget’s disease occurring between 1999 and 2015 were identified from primary care records. Overall crude incidence and incidence stratified by age and sex was calculated each year from 1999 to 2015. Direct age- and sex-standardized incidence was also calculated. We used Poisson regression to look at variations in incidence by deprivation and UK region. Results A total of 3592 incident cases of Paget’s disease were identified between 1999 and 2015. Incidence increased with age and at all ages was greater in men than women. In women and men, respectively, crude incidence increased from 0.037 and 0.074/10 000 population per year among those 45–49 years of age to 3.7 and 6.3/10 000 population per year among those ≥85 years. The overall standardized incidence decreased from 0.75/10 000 person-years in 1999 to 0.20/10 000 person-years in 2015. After adjustment for age and sex, incidence was >30% higher in the most- compared with least-deprived quintile of deprivation. There was evidence of geographic variation, with the highest incidence in the North West of England, which persisted after adjustment for age, sex and level of deprivation. Conclusion The incidence of clinically diagnosed Paget’s disease has continued to decrease since 1999. The reason for the decline in incidence remains unknown, although the rapidity of change points to an alteration in one or more environmental determinants.

Background/Aims In the real-world, tocilizumab is prescribed to a population of patients different from those prescribed TNF-inhibitors, often older with longer disease duration, worse functional status and more previous b- or tsDMARDs. The aim of this study was to evaluate if and how the risk of serious infection on tocilizumab and other bDMARDs differs when stratifying by line of therapy in a real-world population of rheumatoid arthritis patients. Methods We included patients registered in the BSRBR-RA treated with tocilizumab, etanercept, adalimumab, infliximab, certolizumab, abatacept or rituximab, including biosimilars. Primary outcome was the occurrence of a serious infection (defined as infection requiring hospitalisation, intravenous antibiotics or resulting in death). Primary covariate of interest was line of therapy (from first to fifth line of therapy). Every change to another b- or tsDMARD was considered a new line of therapy, but not a change between a bio-original and a biosimilar. Hazard ratios (HR) of serious infections were estimated using an inverse probability weighted Cox regression, based on a propensity score including baseline patient and disease characteristics, and adjusting for time in study (see table). The reference group was etanercept, which included the highest number of patients. Treatment exposure was analysed without and with stratification by line of therapy. Results A total of 33,916 treatment courses were included (Table) contributing to 62,532 years of follow-up. Compared to etanercept, participants starting abatacept, tocilizumab and rituximab were older, had more previous bDMARDs, longer disease duration and more comorbidities. The crude HR of serious infections were higher with infliximab and adalimumab, lower with certolizumab and rituximab, and not significantly different for abatacept and tocilizumab compared to etanercept. After adjustment, HR of serious infections were higher with tocilizumab, adalimumab and infliximab. However, when stratified by line of therapy, HR were no longer significantly different compared to etanercept for tocilizumab, adalimumab and infliximab for most lines of therapy. Conclusion Whilst initially there appears to be a difference in rates of serious infections between biologic therapies, line of therapy may be a confounding factor when comparing the risk of serious infections between bDMARDs. Disclosure K. Lauper: Honoraria; Gilead-Galapagos. Grants/research support; AbbVie. Other; AbbVie, Pfizer. L. Kearsley-Fleet: None. R. Davies: None. K. Watson: None. M. Lunt: None. K.L. Hyrich: Honoraria; AbbVie. Grants/research support; Pfizer, BMS.

Background/Aims People with osteoarthritis (OA) and rheumatoid arthritis (RA) are at increased risk of frailty, though the role of co-morbidities in contributing to frailty risk in these arthritides is uncertain. The aim of this study was i) to determine the association between common co-morbidities and frailty in OA and RA; and ii) to determine whether co-morbidities interact synergistically with OA and RA to increase the likelihood of frailty. Methods A cross-sectional analysis of the UK Biobank data was carried out. In this national cohort of participants aged 40-69 years, self-reported physician-diagnosed diseases including OA, RA, and common co-morbidities (including hypertension, coronary heart disease, diabetes, stroke/TIA, chronic obstructive pulmonary disease (COPD), and depression) were recorded. Frailty was assessed using a modification of the frailty phenotype (FP), comprising five components: self-reported weight loss, exhaustion, low physical activity (international activity questionnaire), slow walking speed, and low hand-grip strength. Pre-frailty and frailty, was identified based on the presence of 1-2 and ≥3 of these components, respectively. Among participants with OA and RA at baseline, the likelihood of being pre-frail or frail (vs not frail) at baseline among people with (vs without) each of the co-morbidities was assessed using multinomial logistic regression. To determine whether co-morbidities interact synergistically with OA and RA to increase the likelihood of frailty, deviation from additivity of risk was estimated by calculating the attributable proportion (AP) of risk due to biological interaction. An AP > 0 indicates positive (synergistic) biological interaction. Analyses were adjusted for age, sex, smoking status, BMI, and deprivation. Results In total, 457,561 people were included in the analysis; 35,884 (7.8%) had OA and 4,894 (1.1%) had RA. Overall, the mean (SD) age was 56.5 (8.1) years and 54% were female. The overall prevalence of frailty was 3.4%. The prevalence of frailty was higher among those with OA (10.0%) and RA (18.6%). Each of the co-morbidities considered was associated with increased relative risk of pre-frailty and frailty particularly COPD in OA, relative risk ratio (95%CI): pre-frailty, 2.09 (1.80, 2.42) and frailty, 4.45 (3.71, 5.33) and stroke/TIA in RA: pre-frailty, 1.97 (1.18, 3.29) and frailty, 3.64 (2.11, 6.29). Most of the co-morbidities considered interacted synergistically with OA and RA to increase the risk of pre-frailty and frailty, particularly diabetes in OA, AP (95% CI): pre-frailty, 0.13 (0.04, 0.23) and frailty, 0.49 (0.42, 0.55) and stroke/TIA in RA: pre-frailty, 0.34 (0.003, 0.68) and frailty 0.60 (0.38, 0.82). Conclusion Co-morbidity is associated with an increased occurrence of pre-frailty and frailty among people with OA and RA and interacts synergistically with OA and RA to increase the likelihood of pre-frailty and frailty. Disclosure M.J. Cook: None. M. Lunt: None. S.M.M. Verstappen: None. T.W. O'Neill: None.

There are concerns that immunomodulatory therapies may increase the risks of developing basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (SCC) in patients with moderate-severe psoriasis. A systematic review showed that psoriasis patients were significantly more likely to develop SCC than the general population, though included studies lacked adequate comparator cohorts and adjustment for relevant confounding factors including heterogeneity between BCC and SCC in association with phototherapy and immunomodulatory therapies and lacked data on newer biologic therapies. We therefore followed-up a large prospective patient cohort to determine whether the risks of developing BCC and SCC are increased in patients with psoriasis receiving biologic therapy compared with those treated with non-biologic systemic therapies only.

The natural ecosystems of tropical Africa represent a significant store of carbon, and play an important but uncertain role in the atmospheric budgets of carbon dioxide and methane. Recent studies using satellite data have concluded that methane emissions from this geographical region have increased since 2010 as a result of increased wetland extent, accounting for a third of global methane growth (Lunt et al 2019), and that the tropical Africa region dominates net carbon emission across the tropics (Palmer et al 2019). The conclusions of such studies are based on the accuracy of various satellite datasets and atmospheric transport models, over a geographical region where there are few independent observations available to check the robustness and validity of these datasets.Here we present the first ground-based observations of greenhouse gas (GHG) column concentrations over tropical East Africa, obtained using the University of Leicester EM27/SUN spectrometer during its deployment at the National Fisheries Resources Research Institute (NaFIRRI) in Jinja, Uganda. During the deployment we were able to operate the instrument remotely, using an automated weatherproof enclosure designed by the Technical University of Munich (Heinle and Chen 2018, Dietrich et al 2020). The instrument ran near-continuously for a three month period in early 2020, observing total atmospheric column concentrations of carbon dioxide and methane, along with other gases of interest including water vapour and carbon monoxide. We describe the data obtained during this period, processed using tools developed under the COCCON project (COllaborative Carbon Column Observing Network, Frey et al 2019), and demonstrate the value of performing GHG column measurements over tropical East Africa. We then evaluate the performance of CO2 observations from OCO-2 and CH4 from Sentinel 5P TROPOMI - datasets previously used in the studies of Palmer et al 2019 and Lunt et al 2019 respectively - and interpret the comparison with the ground-based observations in the light of data from the GEOS-Chem atmospheric chemistry transport model and the CAMS (Copernicus Atmospheric Monitoring Service) reanalyses.REFERENCES: Lunt, M. F., Palmer, P. I., Feng, L., Taylor, C. M., Boesch, H., and Parker, R. J.: An increase in methane emissions from tropical Africa between 2010 and 2016 inferred from satellite data, Atmos. Chem. Phys., 19, 14721–14740, https://doi.org/10.5194/acp-19-14721-2019, 2019.Palmer, P.I., Feng, L., Baker, D., Chevallier, F., Boesch, H., and Somkuti, P.: Net carbon emissions from African biosphere dominate pan-tropical atmospheric CO2 signal. Nat Commun 10, 3344, https://doi.org/10.1038/s41467-019-11097-w, 2019.Heinle, L. and Chen, J.: Automated enclosure and protection system for compact solar-tracking spectrometers, Atmos. Meas. Tech., 11, 2173–2185, https://doi.org/10.5194/amt-11-2173-2018, 2018.Dietrich, F., Chen, J., Voggenreiter, B., Aigner, P., Nachtigall, N., and Reger, B.: Munich permanent urban greenhouse gas column observing network, Atmos. Meas. Tech. Discussions, 2020, 1–24, https://doi.org/10.5194/amt-2020-300, 2020.Frey, M. et al.: Building the COllaborative Carbon Column Observing Network (COCCON): long-term stabilityand ensemble performance of the EM27/SUN Fourier transform spectrometer, Atmos. Meas. Tech., 12, 1513–1530, https://doi.org/10.5194/amt-12-1513-2019, 2019

Airborne measurements of methane (CH4) were recorded over three major wetland areas in Zambia in February 2019 during the MOYA (Methane Observations and Yearly Assessments) ZWAMPS field campaign. Enhancements of up to 600 ppb CH4 were measured over the Bangweulu (11°36’ S, 30°05’ E), Kafue (15°43’ S, 27°17’ E), and Lukanga (14°29’ S, 27°47’ E) wetlands. Three independent methods were used to quantify methane emission fluxes; aircraft mass balance, aircraft eddy covariance, and atmospheric inversion modelling. Results yielded methane emission fluxes of 10-20 mg CH4 m-2 hr-1, which were up to an order of magnitude greater than the emission fluxes simulated by various wetland process models (WetCHARTs ensemble and LPX-Bern). Independent column CH4 observations from the TROPOMI instrument were used to verify these fluxes, and investigate their applicability for timescales longer than the duration of the MOYA flight campaign.

To extend previous simulations on the performance of propensity score (PS) weighting and trimming methods to settings without and with unmeasured confounding, Poisson outcomes, and various strengths of treatment prediction (PS c statistic), we simulated studies with a binary intended treatment T as a function of 4 measured covariates. We mimicked treatment withheld and last-resort treatment by adding 2 “unmeasured” dichotomous factors that directed treatment to change for some patients in both tails of the PS distribution. The number of outcomes Y was simulated as a Poisson function of T and confounders. We estimated the PS as a function of measured covariates and trimmed the tails of the PS distribution using 3 strategies (“Crump,” “Stürmer,” and “Walker”). After trimming and reestimation, we used alternative PS weights to estimate the treatment effect (rate ratio): inverse probability of treatment weighting, standardized mortality ratio (SMR)-treated, SMR-untreated, the average treatment effect in the overlap population (ATO), matching, and entropy. With no unmeasured confounding, the ATO (123%) and “Crump” trimming (112%) improved relative efficiency compared with untrimmed inverse probability of treatment weighting. With unmeasured confounding, untrimmed estimates were biased irrespective of weighting method, and only Stürmer and Walker trimming consistently reduced bias. In settings where unmeasured confounding (e.g., frailty) may lead physicians to withhold treatment, Stürmer and Walker trimming should be considered before primary analysis.

Importance Treatments for psoriasis may be less effective in everyday practice than in clinical trials. Emulating a target trial using data from the British Association of Dermatologists Biologics and Immunomodulators Register (BADBIR) can provide treatment effect estimates that are robust and can inform both clinicians and regulatory bodies. Objectives To assess the comparative effectiveness of ustekinumab and secukinumab in patients with psoriasis, and to test whether the relative effectiveness estimate of the CLEAR trial, a randomized clinical trial that compared secukinumab with ustekinumab for psoriasis, can be replicated. Design, Setting, and Participants This comparative effectiveness research study used a target trial emulation approach and was performed between November 2007 and August 2019. Data were obtained from BADBIR, a multicenter longitudinal pharmacovigilance register of patients with moderate to severe psoriasis in the United Kingdom and Republic of Ireland. Participants had chronic plaque psoriasis, were 18 years or older, and had at least 1 record of a Psoriasis Area and Severity Index (PASI) of 12 or higher before their initiation to secukinumab or ustekinumab. Propensity score (PS) 1:1 matched analysis and inverse probability treatment weighted analysis were performed. Main Outcomes and Measures The primary outcomes were the risk ratio (RR) and the risk difference (RD) for achieving PASI of 2 or lower after 12 months of therapy for secukinumab compared with ustekinumab. Methods to account for missing outcome data were complete case analysis, nonresponder imputation, last observation carried forward, inverse probability of censoring weighting, and multiple imputation. Regulatory and estimate agreement metrics were used to benchmark the effect estimates in this study against those in the CLEAR trial. Results A total of 1231 patients were included in the analysis, with 917 receiving ustekinumab and 314 receiving secukinumab. Secukinumab was superior to ustekinumab in all analyses, except under the nonresponder imputation method, in the proportion of participants achieving a PASI of 2 or lower (PS-weighted complete case analysis: RR, 1.28 [95% CI, 1.06-1.55]; RD, 11.9% [1.6-22.1]). All analyses, except for nonresponder imputation, reached regulatory agreement in both PS-matching and PS-weighted analyses. Conclusions and Relevance This comparative effectiveness study found that secukinumab resulted in more patients achieving a PASI of 2 or lower after 12 months of therapy compared with ustekinumab in patients with psoriasis. Target trial emulation in this study resulted in regulatory and estimate agreement with the CLEAR randomized clinical trial; further such studies may help fill the evidence gap when comparing other systemic therapies for psoriasis.

With sustained growth of diabetes numbers, sustained patient engagement is essential. Using nationally available data, we have shown that the higher mortality associated with a diagnosis of T1DM/T2DM could produces loss of 6.4 million future life years in the current UK population. In the model, the 'average' person with T1DM (age 42.8 years) has a life expectancy from now of 32.6 years, compared to 40.2 years in the equivalent age non diabetes mellitus population, corresponding to lost life years (LLYs) of 7.6 years/average person. The 'average' person with T2DM (age 65.4 years) has a life expectancy from now of 18.6 years compared to the 20.3 years for the equivalent non diabetes mellitus population, corresponding to LLY of 1.7 years/average person. We estimate that for both T1DM and T2DM, one year with HbA1c >58 mmol/mol loses around 100 life days. Linking glycaemic control to mortality has the potential to focus minds on effective engagement with therapy and lifestyle recommendation adherence.

Background 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. Methods and findings 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, In this analysis of electronic health records, Meghna Jani and colleagues investigate how opioids are prescribed in UK primary care patients., Author summary Why was this study done? Whilst opioid prescribing for non-cancer pain has risen in the US and Canada, trends over time are less clear in the UK. No studies to our knowledge have evaluated how opioid dose/potency changes over time in UK patients started an opioid for the first time for non-cancer pain, to assess escalation, tapering, and long-term use. Physician prescribing behaviour has been implicated as a key driver of rising opioid prescriptions and long-term opioid use; however, this needs to be interpreted in the context of regional and individual patient differences. The association of region, practice, prescriber, and individual factors with long-term opioid use is highly important, as this has implications for policy and future targeted public health interventions. What did the researchers do and find? We conducted a study of 1,968,742 new opioid users without cancer from primary care electronic health records across the UK. We found that between 2006 and 2017 codeine was the most commonly prescribed opioid: There was a 5-fold increase in codeine prescriptions, a 7-fold increase in tramadol prescriptions, and a 30-fold increase in oxycodone prescriptions for non-cancer pain. Prescribing factors (e.g., high dose/potency of opioid or concurrent gabapentinoid use), older age, higher socioeconomic deprivation score, and other conditions including fibromyalgia, rheumatological conditions, history of substance abuse, suicide/self-harm, alcohol abuse, and major surgery were associated with long-term opioid use. After accounting for individual patient factors, the North West, Yorkshire and the Humber, and South West regions of England were associated with a higher risk of long-term opioid use. Whilst there were only 3.5% of prescribers who had significantly higher prescribing practices leading to long-term use after adjustment of patient factors, where they did, rates were up to 3.5 times higher than the population average. What do these findings mean? Clinicians should take care in prescribing high dose opioids at initiation and closely monitor those with the above individual factors for long-term use. The variation observed across regions, practices, and prescribers after adjustment for patient factors supports calls for action to reduce such variation and harmonise prescribing practices.

IntroductionEcological studies show association between antimicrobial resistance (AMR), and inappropriate oral antibiotics use. Moderating antibiotic prescribing requires an understanding of all drivers of local prescribing. The aim was to quantify how much is determined by external factors compared with discretionary clinical choices.MethodsOral antibiotic usage taken from England General Practitioner/Family Doctor practice prescribing data was aggregated using WHO/ATC defined daily doses (DDDs). The average annual antibiotic daily prescribing rate (AAADPR) in each practice was the total DDD of oral antibiotics divided by registered population and 365. The AAADPR of English practices in 2017_18 was linked by regression to factors including demographics, geography, medical comorbidities, clinical performance, patient satisfaction, medical workforce characteristics and prescribing selection. The regression coefficients for modifiable prescribing selection factors were applied to the difference between the median and top decile practice values to establish overall reduction opportunities through changing prescribing behaviour.ResultsTwenty five factors accounted for 58% of the AAADPR variation in 5889 practices supporting 49.8 million patients. Non‐modifiable factors linked increased AAADPR to more northerly location, higher prevalence of diabetes, COPD, CHD, and asthma; higher white ethnicity; higher patient satisfaction and lower population density. Modifiable behaviour accounted for 11% of the variation in AAADPR, with increases associated with a wider range of antibiotics, higher proportion taken as liquids, higher doses in each prescription, lower guideline compliance, lower targeted antibiotics, lower spend/dose, and less seasonal variation. If all practices achieved the level of modifiable factors of the top decile, this model suggests that overall AAADPR could reduce by 31%.ConclusionSuch analysis is associative and does not infer causation. However, demographics, location, medical condition of the population, and prescribing selection are drivers of overall antibiotic prescribing. This analysis provides benchmarks for both non‐modifiable and modifiable factors against which practices could evaluate their opportunities to reduce antibiotic prescribing.

Erectile dysfunction (ED) is common in older age and in diabetes mellitus (DM). Phosphodiesterase type 5-inhibitors (PDE5-is) are the first-line for ED. We investigated how the type of diabetes and age of males affect the PDE5-i use in the primary care setting.From 2018 to 2019, the general practice level quantity of all PDE5-i agents was taken from the general practice (GP) Prescribing Dataset in England. The variation in outcomes across practices was examined across one year, and for the same practice against the previous year.We included 5761 larger practices supporting 25.8 million men of whom 4.2 million ≥65 years old. Of these, 1.4 million had T2DM, with 0.8 million of these65. About 137 000 people had T1DM. About 28.8 million tablets of PDE5-i were prescribed within the 12 months (2018-2019) period in 3.7 million prescriptions (7.7 tablets/prescription), at total costs of £15.8 million (£0.55/tablet). The NHS ED limit of one tablet/user/wk suggests that 540 000 males are being prescribed a PDE5-i at a cost of £29/y each. With approximately 30 000 GPs practising, this is equivalent to one GP providing 2.5 prescriptions/wk to overall 18 males. There was a 3x variation between the highest decile of practices (2.6 tablets/male/y) and lowest decile (0.96 tablets/male/y). The statistical model captured 14% of this variation and showed that T1DM males were the largest users, while men age65 with T2DM were being prescribed four times as much as non-DM. Those T2DM65 were prescribed 80% of the non-DM amount.There is a wide variation in the use of PDE5-is. With only 14% variance capture, other factors including wide variation in patient awareness, prescribing rules of local health providers, and recognition of the importance of male sexual health by GP prescribers might have a significant impact.

Introduction: Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is the name given to the 2019 novel coronavirus. COVID-19 is the name given to the disease associated with the virus. SARS-CoV-2 is a new strain of coronavirus not been previously identified in humans. Methods: Two key factors, case incidence and case morbidity, were analysed for England. When taken together they give an estimate of relative demand on healthcare utilisation. To analyse case incidence, the latest values for indicators that could be associated with infection transmission rates were collected from the Office of National Statistics (ONS) and Quality Outcome Framework (QOF) sources. These included population density, %age >16, at fulltime work/education, %age over 60, %BME ethnicity, social deprivation as IMD2019, location as latitude/longitude, and patient engagement as %self-confident in their own long-term condition management. Average case morbidity was calculated. To provide a comparative measure of overall healthcare resource impact, individual GP practice impact scores were compared against the median practice. Results: The case incidence regression is a dynamic situation but it currently shows that Urban, %Working, and age >60 were the strongest determinants of case incidence. The local population comorbidity remains unchanged. The range of relative healthcare impact was wide with 80% of practices falling at 20%-250% of the national median. Once practice population numbers were included we found that the top 33% of GP practices supporting 45% of the patient population would require 68% of COVID-19 healthcare resources. The model provides useful information about the relative impact of Covid-19 on healthcare workload at GP practice granularity in all parts of England. Conclusion: Covid-19 is impacting on the utilisation of health/social care resources across the world. This model provides a way of predicting relative local levels of disease burden based on defined criteria, thereby providing a method for targeting limited care resources to optimise national/regional/local responses to the COVID-19 outbreak.

Introduction Hypogonadism is associated with poorer glycaemic outcomes/increased all-cause and cardiovascular morbidity/mortality in type 2 diabetes mellitus (T2DM). Increasing CAG repeat number within exon-1 of the androgen receptor (AR) gene is associated with increased AR resistance/insulin resistance. Methods We determined in a long-term 14-year follow-up cohort of 423 T2DM Caucasian men, the association between baseline androgen status/CAG repeat number (by PCR then Sequenom sequencing) and metabolic/cardiovascular outcomes. Results Metabolic outcomes: Lower total testosterone was associated with higher BMI (kg/m2) at 14-year-follow-up: regression coefficient −0.30 (95% confidence interval −0.445 to −0.157), P = 0.0001. The range of CAG repeat number was 9-29 repeats. Higher CAG repeat number in exon-1 of the AR gene was associated with higher follow-up HbA1c2016 – each unit increase in CAG repeat-associated with an increment of 0.1% in HbA1C2016 (P = 0.04), independent of baseline testosterone. Cardiovascular outcomes and mortality: At an average of 14-year-follow-up, 55.8% of hypogonadal men had died vs 36.1% of eugonadal men (P = 0.001). There was a ‘u’ shaped relation between number of CAG repeats and mortality. Twenty-one CAG repeats were associated with an up to nearly 50% lower mortality rate than 21 CAG repeats – independent of baseline testosterone level. Conclusion A higher number of CAG repeats at the AR gene associates with higher future HbA1c. There was a ‘u’ shaped relation between CAG repeat number and mortality rate. Determination of CAG repeat number may become part of assessment of androgen status/its consequences for men with T2DM.

Eastern Boundary Upwelling Systems (EBUSs) are coastal hotspots of the potent greenhouse gas nitrous oxide (N2O). However, estimates of their emissions suffer from large uncertainties due to their significant spatial and temporal heterogeneity. Here, we derive the first multiyear, monthly resolution N2O emissions from three of the four major EBUSs using high-frequency coastal atmospheric measurements and an inverse method. We find average combined N2O emissions from the northern California, Benguela, and southern Canary upwelling systems to be 57.7 (51.4-63.9) Gg-N yr(-1). We also find an offshore region near the Benguela EBUS that exhibits large pulses of emissions with emissions that reach 677 Gg-N yr(-1) in 1 month. Our findings highlight that atmospheric measurements coupled with inverse modeling can capture the large variability in EBUS emissions by quantifying emissions over large spatial distances and over long time periods compared to previous methods using traditional oceanographic measurements. Plain Language Summary Eastern Boundary Upwelling Systems (EBUSs) are important emissions hotspots of marine nitrous oxide to the atmosphere, where it acts as a greenhouse gas and ozone depleting substance. Emissions from the EBUSs are highly episodic, and most previous estimates are snapshots derived from ship-based measurements. The variability in emissions combined with the sparsity of measurements makes EBUS emission estimates highly uncertain. Here, we use multiyear, near-continuous atmospheric measurements from coastal stations and an inverse modeling framework to derive emissions from three of the four major EBUSs. Our results quantify the significant spatial and temporal variability in emissions, which is not well-represented in global studies of marine nitrous oxide emissions.

The Facts COVID-19 is the disease associated with the 2019 novel coronavirus Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). The pandemic related to this virus has transformed life for billions of people across the globe. Using population based data from England, we recently showed a strong independent relation over time between our calculated R value for COVID-19 transmission and the number of cases identified as definitely positive in the population. We determined that 26.8% of the population of the United Kingdom (UK) possibly had been infected by late April 2020 approximating to 18.2 million people. Reflection Different modelling approaches give different answers to the question of how many people in the UK and elsewhere have actually been infected by COVID-19, which all would agree in epidemiological terms is behaving very differently from other Coronaviruses that we have seen in the past. We are learning more and more about this virus. National policy is changing rapidly and COIVD-19 antibody testing is increasingly being applied. This will facilitate better datasets from population based studies. The picture will become clearer as more data accrues over time.

Background: The COVID-19 pandemic has led to radical political control of social behaviour. The purpose of this paper is to explore data trends from the pandemic regarding infection rates/policy impact, and draw learning points for informing the unlocking process. Methods: The daily published cases in England in each of 149 Upper Tier Local Authority (UTLA) areas were converted to Average Daily Infection Rate (ADIR), an R-value - the number of further people infected by one infected person during their infectious phase with Rate of Change of Infection Rate (RCIR) also calculated. Stepwise regression was carried out to see what local factors could be linked to differences in local infection rates. Findings: By the 19th April 2020 the infection R has fallen from 2.8 on 23rd March before the lockdown and has stabilised at about 0.8, sufficient for suppression. However there remain significant variations between England regions. Regression analysis across UTLAs found that the only factor relating to reduction in ADIR was the historic number of confirmed number infection/000 population, There is however wide variation between Upper Tier Local Authorities (UTLA) areas. Extrapolation of these results showed that unreported community infection may be 150 times higher than reported cases, providing evidence that by the end of the second week in April, 26.8% of the population may already have had the disease and so have increased immunity. Extrapolation of these results showed that unreported community infection may be 150 times higher than reported cases, providing evidence that by the end of the second week in April, 26.8% of the population may already have had the disease and so have increased immunity.Interpretation: Analysis of current case data using infectious ratio has provided novel insight into the current national state and can be used to make better-informed decisions about future management of restricted social behaviour and movement.

ObjectivesOther than age, diabetes is the largest contributor to overall healthcare costs and reduced life expectancy in Europe. This paper aims to more exactly quantify the net impact of diabetes on different aspects of healthcare provision in hospitals in England, building on previous work that looked at the determinants of outcome in type 1 diabetes (T1DM) and type 2 diabetes (T2DM).SettingNHS Digital Hospital Episode Statistics (HES) in England was combined with the National Diabetes Audit (NDA) to provide the total number in practice of people with T1DM/T2DM.Outcome measuresWe compared differences between T1DM/T2DM and non-diabetes individuals in relation to hospital activity and associated cost.ResultsThe study captured 90% of hospital activity and £36 billion/year of hospital spend. The NDA Register showed that out of a total reported population of 58 million, 2.9 million (6.5%) had T2DM and 240 000 (0.6%) had T1DM. Bed-day analysis showed 17% of beds are occupied by T2DM and 3% by T1DM. The overall cost of hospital care for people with diabetes is £5.5 billion/year. Once the normally expected costs including the older age of T2DM hospital attenders are allowed for this fell to £3.0 billion/year or 8% of the total captured secondary care costs. This equates to £560/non-diabetes person compared with £3280/person with T1DM and £1686/person with T2DM. For people with diabetes, the net excess impact on non-elective/emergency work is £1.2 billion with additional estimated diabetes-related accident & emergency attendances at 440 000 costing the NHS £70 million/year. T1DM individuals required five times more secondary care support than non-diabetes individuals. T2DM individuals, even allowing for the age, require twice as much support as non-diabetes individuals.ConclusionsThis analysis shows that additional cost of provision of hospital services due to their diabetes comorbidities is £3 billion above that for non-diabetes, and that within this, T1DM has three times as much cost impact as T2DM. We suggest that supporting patients in diabetes management may significantly reduce hospital activity.

With the COVID-19 pandemic leading to radical political control of social behaviour, including restricted movement outsides homes. Can more detailed analysis of the published confirmed local case data from the pandemic in England using infection ratio and comparing local level data provide a deeper understanding of the wider community infection and inform the future unlocking process.The historic daily published 78,842 confirmed cases in England up to 13/4/2020 in each of 149 Upper Tier Local Authority (UTLA) were converted to Average Daily Infection Rate (RADIR), an R-value - the number of further people infected by one infected person after their 5-day incubation and during their 5-day infectious phase, and the associated Rate of Change of Infection Rate (ΔIR) also calculated. Results compared to look for significant variances between regions. Stepwise regression was carried out to see what local factors could be linked to the difference in local infection rates.The peak of COVID-19 infection has passed. The current RADIR is now below 1. The rate of decline is such that within 14 days it may be below 0.5. There are significant variations in the current RADIR and ΔIR between the UTLAs, suggesting that the disease locally may be at different stages.Regression analysis across UTLAs found that the only factor that could be related to the fall in RADIR was an increase in the number of confirmed infection/1,000 population. Extrapolation of these results showed that based on assuming a link to increased immunity, unreported community infection may be over 200 times higher than the reported confirmed cases providing evidence that by the end of the second week in April 26% of the population may already have had the disease and so now have increased immunity. Linking these increased estimated infected numbers to recorded deaths indicates a possible mortality rate of 0.14%.Analysis of the current reported local case data using the infectious ratio does provide greater insight into the current levels of community infection and can be used to make better-informed decisions about the future management of restricted social behaviour and movement

Background Tocilizumab is an IL-6 receptor humanised monoclonal antibody treatment option in rheumatoid arthritis (RA) who have not responded or are intolerant of disease modifying anti-rheumatic drugs (DMARDs) or other biologics. Tocilizumab was available initially as an intravenous preparation, dosed according to weight, and more recently as a subcutaneous (SC) preparation given at 162mg/weekly irrespective of body weight. Obesity is highly prevalent in RA and there has been concern that starting or switching patients to SC tocilizumab could reduce its effectiveness in those patients with a higher body weight. The objective of the current study is to investigate the relationship between body weight and DAS28 response at 6 months in tocilizumab naïve RA patients starting SC tocilizumab. Methods The study population comprised RA subjects recruited to the BSRBR-RA up to 30/11/2018 commencing SC tocilizumab for the first time. Patients had to be tocilizumab naïve and have at least one six monthly study follow-ups recorded after starting tocilizumab. Baseline characteristics at point of starting tocilizumab are described. Linear regression, fully adjusted for relevant confounders, was used to investigate the relationship between change in DAS28 score from baseline to six months and body weight per ten kilograms (kg), and in a separate analysis, as BMI category. Multiple imputation was used to handle missing data. Results Three hundred and fifteen patients starting SC tocilizumab were eligible for analysis. The median age was 60 years, majority were female, and had median disease duration of 11 years at baseline. Seventy percent had prior biologic exposure. Median weight was 76kg, and the majority of patients were categorised as normal weight (37%) or pre-obesity (31%) according to BMI. Median DAS28 score was 5.5 at start of treatment with median improvement after 6-months of 2.0 units. The fully adjusted linear regression model showed no association between body weight or BMI and change in DAS28 score at six months (Table 1). Conclusion Data from this study show that body weight does not appear to affect initial response to SC tocilizumab. This is reassuring given that patients are likely to be given subcutaneous tocilizumab due to ease of administration and reduced hospital costs. Disclosures R. Davies None. A. Vivekanantham None. M. Lunt None. K. Watson None. K.L. Hyrich None. J. Bluett None.

Background In 2017, a new class of oral disease modifying anti-rheumatic drugs (DMARDs), janus kinase inhibitors (JAKi), were licensed for rheumatoid arthritis (RA): baricitinib and tofacitinib. In the UK, they are approved for use in patients with high disease activity with or without methotrexate, following failure of conventional synthetic (cs) DMARDs or biologic DMARDs, the latter when rituximab is contraindicated. As a new therapy option, it is currently unclear how and when these drugs are being prescribed in patients with RA. This analysis aims to describe the characteristics of patients starting JAKi and registered with the BSRBR-RA. Methods The BSRBR-RA aims to capture exposure and outcome data in patients with RA receiving biologics, biosimilars and targeted synthetic DMARDs. At the start of therapy, demographic and clinical data, including past treatment data, are collected. Characteristics of all patients receiving a JAKi for the first time with data recorded in the BSRBR-RA up to 31/03/2019 are described. Results To 31/03/2019, 698 patients in the BSRBR-RA have been treated with a JAKi; 596 patients baricitinib and 110 tofacitinib (Table 1). A quarter of patients received a JAKi with no prior biologic exposure; 148 (25%) baricitinib and 16 (15%) tofacitinib. Of these, 15% had a prior malignancy history. Of those with prior biologic exposure, the median number of previous biologics was 3 (IQR 2-4), the majority had prior TNFi (91%) or rituximab (51%), and 50% were receiving concurrent methotrexate. Conclusion To date, more patients have been recruited starting baricitinib than tofacitinib, likely owing to the later licensing of tofacitinib. Two groups are emerging with a quarter of patients receiving JAKi immediately after csDMARDs and a majority as a later stage alternative following multiple biologics. Further recruitment and follow-up patients will allow for analysis of real-world safety and effectiveness, but differences in patient characteristics will need to be considered in any comparative effectiveness analyses. Disclosures L. Kearsley-Fleet None. R. Davies None. K. Watson None. M. Lunt None. K.L. Hyrich Honoraria; AbbVie. Grants/research support; UCB, Pfizer, BMS.

Background Physician prescribing behaviour has been described as a key driver of rising opioid prescriptions and long-term opioid use. However, the effect of prescribers requires interpretation within context. No studies have investigated the extent to which regions, practices, prescribers, vary in opioid prescribing by considering this hierarchy together, whilst accounting for case-mix. Objectives: (i)quantify and identify risk factors for the transition from new-users to long-term opioid users (ii) quantify variation of long-term use attributed to region, practice, prescriber, accounting for patient mix and chance variation. Methods We conducted a retrospective observational study between 2006-2017 using Clinical Practice Research Datalink. New users of opioids, ≥18 years, without cancer were identified. Long-term opioid use was defined as ≥ 3 opioid prescriptions within a 90-day period from index date, or ≥ 1 opioid prescription lasting at least 90-days in the first year. A multi-level random-effects logistic regression model was used to examine the association of patient characteristics with the odds of becoming a long-term opioid-user. To examine variation in opioid use among prescribers, GP-practices and region after adjusting for case-mix, we used a nested random-effect structure. A ‘high-risk’ region, prescriber or practice was defined as those where the entire adjusted 95% CI lay above population average. Results 1,968,742 new opioid users were included; 14.6% transitioned to long-term use. In the fully-adjusted model, factors associated with higher-odds of long-term use included high morphine-milligram equivalents (MME)/day at first prescription, older-age, deprivation, fibromyalgia, rheumatological conditions and prior surgery (Table 1). After adjustment for case-mix, the North-West, Yorkshire and South-West were found to be high-risk regions for long-term use. 103 practices (25.6%) and 540 prescribers (3.5%) were associated with a significantly higher risk of long-term use. The odds of becoming a long-term user for patients belonging to these prescribers reached up to > 3.5 times than the population average. Conclusion Prescribing factors, age, deprivation and conditions including fibromyalgia and rheumatological conditions were associated with higher odds of long-term opioid use. In the first UK study evaluating long-term opioid prescribing with patient-level characteristics adjustment, variation in regions, especially practices and prescribers were observed. Our findings support greater calls for action to reduce practice/prescriber variation by promoting safe practice in opioid-prescribing. Disclosures M. Jani: None. B. Birlie-Yimer: None. T. Sheppard: None. M. Lunt: None. W.G. Dixon: None.

Tropical wetlands have been proposed as a potential driver for the recent rise in global atmospheric methane. However, direct access and quantification of emissions is difficult. In March 2019, a pilot study was given permission to overfly the Bolivian Llanos de Moxos wetlands to measure atmospheric mixing ratios of methane and collect spot samples for isotopic analysis. Combined with this was a short ground campaign to collect isotopic samples directly above the wetland edge to compare with the integrated atmospheric signature.Atmospheric mixing ratios of methane reached a maximum of 2400 ppb (500 ppb above baseline concentrations) in the well mixed boundary layer flying at 400m above the wetland. Upwind and downwind transects were a maximum of 300 km, and methane mixing ratios increased roughly linearly with distance downwind. The isotopic data from the airborne surveys and ground surveys give a bulk isotopic signature for δ13CCH4 of ~-59 ‰ ± 4, which is less negative than Amazon floodplain work focusing on emission of methane through trees, but match well with bulk isotopic values from the Amazon Basin. Ground based wetland samples taken concurrently near Trinidad, Bolivia, gave a source signature of -56 ‰ ± 4 re-enforcing the likelihood that the atmospheric enhancements measured are related to the wetland emissions. For comparison, tropical wetlands measured at ground level during a recent Ugandan and Zambian campaign gave heavier δ13CCH4 isotopic source signatures of -50 to -54 ‰. Based on this snap shot study, flux estimations suggest that the Bolivian wetlands could be emitting ~10mg CH4 m-2 h-1. The observed mole fractions will be compared to model simulations to determine how well the Bolivian wetland methane fluxes are represented.

IntroductionSevere Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is the name given to the 2019 novel coronavirus. COVID-19 is the name given to the disease associated with the virus. SARS-CoV-2 is a new strain of coronavirus that has not been previously identified in humans.MethodsTwo key factors were analysed which when multiplied together would give an estimate of relative demand on healthcare utilisation. These factors were case incidence and case morbidity.GP Practice data was used as this provided the most geographically granular source of published public population data. To analyse case incidence, the latest values for indicators that could be associated with infection transmission rates were collected from the Office of National Statistics (ONS) and Quality Outcome Framework (QOF) sources. These included population density, % age >16 at fulltime work/education, % age over 60, % BME ethnicity, social deprivation as IMD 2019, Location as latitude/longitude, and patient engagement as % self-confident in their own long term condition management.Average case morbidity was calculated by applying the international mortality Odds Ratio to the local population relevant age and disease prevalences and then summing and dividing by the equivalent national figure. To provide a comparative measure of overall healthcare resource impact, individual GP practice impact scores were compared against the median practice.ResultsThe case incidence regression is a dynamic situation with the significance of specific factors moderating over time as the balance between external infection, community transmission and impact of mitigation measures feeds through to the number of cases. It showed that currently Urban, % Working and age >60 were the strongest determinants of case incidence.The local population comorbidity remains unchanged. The range of relative HC impact was wide with 80% of practices falling between 20%-250% of the national median.Once practice population numbers were included it showed that the top 33% of GP practices supporting 45% of the patient population would require 68% of COVID-19 healthcare resources. The model provides useful information about the relative impact of Covid-19 on healthcare workload at GP practice granularity in all parts of England.ConclusionCovid-19 is impacting on the utilisation of health and social care resources across the country. This model provides a method for predicting relative local levels of disease burden based on defined criteria and thereby providing a method for targeting limited (and perhaps soon to be scarce) care resources to optimise national, regional and local responses to the COVID-19 outbreak..

The TROPOspheric Monitoring Instrument (TROPOMI) aboard of the Sentinel 5 Precursor (S5P) has provided methane measurements for more than two years. The high accuracy together with the exceptional spatial resolution (7 x 7 km2, 7 x 5.2 km2 since August 2019) and temporal coverage (daily) of TROPOMI provides a unique perspective on local to regional methane enhancements. In this contribution, we discuss observations of enhanced methane concentrations over the United States. We analyse in detail temporal and spatial variability of methane over wetlands and agricultural areas along the Mississippi river and in Florida. To understand the observed CH4 anomalies regarding both natural and anthropogenic sources and transport at regional scales, we support our analysis with simulations from the GEOS-Chem atmospheric chemistry and transport model. We also investigate the possibility to use other datasets as a proxy for CH4 emissions (e.g. NO2 for agricultural areas, land surface temperature for wetlands). These results are based on an improved TROPOMI methane product that features among others a new bias correction that is fully independent of any reference measurements. The verification of the TROPOMI XCH4 data with ground-based measurements by the TCCON network yields a station-to-station variability of the XCH4 error below 10 ppb, in agreement with the comparison with the proxy methane product from the Japanese GOSAT and GOSAT-2 missions. The improved TROPOMI methane product is planned as a future update of the operational TROPOMI processor.

BackgroundPsoriasis is associated with risk factors for serious infections, but the independent relationship between psoriasis and serious infection is as yet unclear.ObjectivesTo determine whether people with psoriasis have a higher risk of hospitalization due to any infection, respiratory infections, soft‐tissue and skin infections, or a higher risk of death due to infection.MethodsWe conducted a cohort study of people (≥ 18 years) with psoriasis using the UK Clinical Practice Research Datalink (CPRD GOLD) linked to Hospital Episode Statistics (HES) and Office for National Statistics (ONS) mortality records between 1 April 2003 and 31 December 2016, and matched with up to six comparators on age, sex and general practice. Hospitalization was ascertained from HES records; death was ascertained from ONS mortality records. Stratified Cox proportional hazard models were estimated, with stepwise adjustment in different models for potential confounders or mediators between psoriasis and serious infection.ResultsThere were 69 315 people with psoriasis and 338 620 comparators who were followed up for a median (interquartile range) of 4·9 (5·9) and 5·1 (6·3) years, respectively. People with psoriasis had a higher incidence rate of serious infection [20·5 per 1000 person‐years, 95% confidence interval (CI) 20·0–21·0, n = 7631] compared with those without psoriasis (16·1 per 1000 person‐years, 95% CI 15·9–16·3, n = 30 761). The fully adjusted hazard ratio for the association between psoriasis and serious infection was 1·36 (95% CI 1·31–1·40), with similar results across the other outcomes.ConclusionsPsoriasis is associated with a small increase in the risk of serious infection. Further research is needed to understand how psoriasis predisposes to a higher risk of infection.

BACKGROUND: Real-world biologic drug survival is an important proxy measure for effectiveness. Predictors of drug survival may help patients with psoriasis choose between biologic therapies.\ud \ud OBJECTIVES: (i) To assess the relative drug survival of adalimumab, ustekinumab and secukinumab in patients with psoriasis. (ii) To investigate predictors of biologic drug survival. \ud \ud METHODS: A prospective cohort study was performed in the British Association of Dermatologists Biologics and Immunomodulators Register (BADBIR) between November 2007 and August 2019. We performed survival analysis and fitted a flexible parametric survival model for biologic discontinuation due to ineffectiveness. \ud \ud RESULTS: In total 9652 patients were included: 5543 starting on adalimumab (57·4%), 991 on secukinumab (10·3%) and 3118 on ustekinumab (32·3%). The overall drug survivals of adalimumab, secukinumab and ustekinumab in year 1 were 0·78 [95% confidence interval (CI) 0·77-0·79], 0·88 (95% CI 0·86-0·91) and 0·88 (95% CI 0·87-0·89), respectively. The adjusted hazard ratios (adjHRs) for discontinuation of adalimumab and secukinumab compared with ustekinumab were 2·11 (95% CI 1·76-2·54) and 0·67 (95% CI 0·40-1·11), respectively. The presence of psoriatic arthritis predicted for survival in the adalimumab and secukinumab cohorts (adjHR 0·67, 95% CI 0·51-0·88 and 0·70, 95% CI 0·40-1·24, respectively), but for discontinuation in the ustekinumab cohort (adjHR 1·42, 95% CI 1·12-1·81). Previous exposure to biologic therapies predicted for discontinuation in the ustekinumab and secukinumab cohorts (adjHR 1·54, 95% CI 1·26-1·89 and 1·49, 95% CI 0·91-2·45, respectively) and for survival in the adalimumab cohort (adjHR 0·71, 95% CI 0·55-0·92). \ud \ud CONCLUSIONS: Secukinumab and ustekinumab have similar sustained drug survival, while adalimumab has a lower drug survival in patients with psoriasis. Psoriatic arthritis and previous biologic experience were predictors with differential effects between the biologic therapies. What is already known about this topic? There is conflicting evidence over the real-world drug survival of secukinumab in patients with psoriasis. Data from registries to date suggest that secukinumab has a lower drug survival than that reported from clinical trials. What does this study add? This study found that secukinumab and ustekinumab had similar sustained drug survival in the real world, while the drug survival of adalimumab was lower, suggesting that the real-world drug survival of secukinumab is higher than previously reported. We found that psoriatic arthritis and previous biologic experience had differential effects on drug discontinuation in the three biologic cohorts. These predictors may help patients and clinicians choose the most appropriate biologic therapy.

Background Patients with rheumatoid arthritis (RA) have increased cardiovascular (CV) and mortality risk. Patients with RA are also frequently prescribed glucocorticoids (GCs) which have been associated with increased risk of mortality. In addition, for patients who have concomitant diabetes mellitus (DM), GCs are known to worsen glycaemic control and hence may further increase CV and mortality risk. This study aimed to understand the relationship between GCs, DM and mortality in patients with RA. Methods This was a retrospective cohort study of patients with incident RA identified from UK primary care electronic medical records. Patients with linkage to Office for National Statistics (ONS) for mortality data (N = 9085) were included. DM was identified through Read codes, prescriptions and blood tests, and GC use was identified through prescriptions. Mortality rate ratios (RR) and rate differences (RD) were calculated across the different exposure groups. Cox proportional hazards regression models were used to estimate interaction on the multiplicative and additive scales. Results In those without DM GC use had a 4.4-fold increased all-cause mortality RR (95% confidence interval (CI): 3.77 to 5.07) compared to non-use, whilst those with DM had a lower RR for GC use (2.99 (95% CI: 2.32, 3.87)). However, those with DM had a higher RD associated with GC use because of their higher baseline risk. In those with DM, GC use was associated with an additional 44.9 deaths/1000 person-years (pyrs) (95% CI: 32.9 to 56.8) compared to non-use, while in those without DM GC use was associated with an additional 34.4 deaths/1000 pyrs (95% CI: 30.1 to 38.7) compared to non-use, while in those without DM GC use was associated with an additional 36.2 deaths/1000 pyrs (95% CI: 31.6 to 40.8). A similar pattern was seen for CV mortality. The adjusted Cox proportional hazards model showed no evidence of multiplicative interaction, but additive interaction indicated a non-significant increased risk. For CV mortality there was no interaction on either scale. Conclusions GC use was associated with higher mortality rates in people with comorbid DM compared to people without DM, despite apparently reassuring similar relative risks. Clinicians need to be aware of the higher baseline risk in patients with DM, and consider this when prescribing GCs in patients with RA and comorbid DM.

Background: Hypogonadism associates with increased cardiovascular morbidity/mortality in type 2 diabetes mellitus (T2DM)[1]. Increasing CAG repeat number within exon 1 of the androgen receptor gene associates with increased androgen receptor resistance/insulin resistance [2]. We here investigated the link between CAG repeat number and metabolic/cardiovascular outcomes in T2DM men. Methods: We determined in a 14-year follow-up cohort of 274 T2DM Caucasian men in Salford UK, the association between baseline androgen status/CAG repeat number (determined by PCR followed by Sequenom sequencing) and metabolic trajectory plus mortality. Results: Lower baseline testosterone was associated with higher Body Mass Index (BMI) (kg/m2) at 14-year follow-up: regression coefficient −0.30 (95% CI: −0.445 to −0.157), p = 0.0001 (total testosterone data) and higher HbA1c 2016. Higher baseline CAG repeat number associated with higher follow-up BMI in 2016 - each unit increase in CAG repeat associated with 0.43 increment in BMI 2016. At an average 14 year follow-up 55.8% of hypogonadal men had died vs 36.1% of eugonadal men (p = 0.001). 72% of deaths were from cardiovascular causes. There was a ‘u’ shaped relation between the number of CAG repeats and mortality such that 21–23 CAG repeats was associated with an up to 58% lower mortality rate than 23 CAG repeats (Figure 1). This was independent of baseline testosterone. Conclusion: A higher number of CAG repeats at the testosterone receptor gene associates with higher future BMI/increased HbA1c. There was a ‘u’ shaped relation between CAG repeat number and mortality rate. CAG repeat number may become part of cardiovascular risk assessment in T2DM men. Figure 1 CAG_cat Mortality Proportional mortality rate Lower bound Upper bound ≤20 40 43 32 53 21 12 24 13 39 22–23 28 50 36 64 >23 34 45 34 57

Overcoming bias due to confounding and missing data is challenging when analyzing observational data. Propensity scores are commonly used to account for the first problem and multiple imputation for the latter. Unfortunately, it is not known how best to proceed when both techniques are required. We investigate whether two different approaches to combining propensity scores and multiple imputation (Across and Within) lead to differences in the accuracy or precision of exposure effect estimates. Both approaches start by imputing missing values multiple times. Propensity scores are then estimated for each resulting dataset. Using the Across approach, the mean propensity score across imputations for each subject is used in a single subsequent analysis. Alternatively, the Within approach uses propensity scores individually to obtain exposure effect estimates in each imputation, which are combined to produce an overall estimate. These approaches were compared in a series of Monte Carlo simulations and applied to data from the British Society for Rheumatology Biologics Register. Results indicated that the Within approach produced unbiased estimates with appropriate confidence intervals, whereas the Across approach produced biased results and unrealistic confidence intervals. Researchers are encouraged to implement the Within approach when conducting propensity score analyses with incomplete data.

Aim To use general practice-level data for England, available through the National Diabetes Audit, and primary care prescribing data to identify prescription treatment factors associated with variations in achieved glucose control (HbA1c). Methods General practice-level National Diabetes Audit data on Type 1 diabetes, including details of population characteristics, services, proportion of people achieving target glycaemic control [HbA1c ≤58 mmol/mol (7.5%)] and proportion of people at high glycaemic risk [HbA1c >86 mmol/ml (10%)], were linked to 2013–2016 primary care diabetes prescribing data on insulin types and blood glucose monitoring for all people with diabetes. Results A wide variation was found between the 10th percentile and the 90th percentile of general practices in both target glycaemic control (15.6% to 44.8%, respectively) and high glycaemic risk (4.8% to 28.6%, respectively). Our analysis suggests that, given the extrapolated total of 280 000 people with Type 1 diabetes in the UK, there may be the potential to increase the number of those within target glycaemic control from 80 000 to 101 000; 53% of this increase (11 000 people) would result from service improvements and 47% (10 000 people) from medication and technology changes. The same improvements would also provide the opportunity to reduce the number of people at high glycaemic risk from 42 000 to 26 500. A key factor associated with practice-level target HbA1c achievement would be greater use of insulin pumps for up to an additional 56 000 people. Conclusion If the HbA1c achievement rates in service provision, medication and use of technology currently seen in practices in the 90th percentile were to be matched with regard to HbA1c achievement rates in all general practices, glycaemic control might be improved for 36 500 people, with all the attendant health benefits.

Background:Long-term studies in rheumatoid arthritis (RA) have reported low inflammation yet high disability over time. It is important to determine which factors are driving this disparity, so appropriate interventions can be used to reduce this gap.Objectives:To identify a subgroup of people with RA with low inflammation yet high disability over 10 years, and describe their characteristics.Methods:Data came from two cohorts of inflammatory arthritis with regular assessments over 10 years: the Norfolk Arthritis Register (NOAR, inclusion: ≥2 swollen joints for ≥4 weeks) from the UK and the Etude et Suivi des Polyarthrites Indifférenciées Récentes study (ESPOIR, inclusion: early RA) from France. Participants provided demographic data and completed patient reported outcomes (PROs, including the Health Assessment Questionnaire [HAQ]). The 2-component Disease Activity Score (DAS28-2C)1, a measure of inflammation, was calculated from swollen joint counts and C-reactive protein level. Inclusion criteria for this analyis: Results:1001 NOAR and 767 ESPOIR participants were included. In both cohorts, a four group trajectory model had the best fit (Figure). Two subgroups were identified in each cohort that demonstrated the hypothesised relationship: similar DAS28-2C but differing HAQ scores (red trajectories in Figure), titled “High HAQ” and “Low HAQ” (mean difference in HAQ over follow-up [95% confidence interval (CI)]: NOAR 0.76 [0.73, 0.80]; ESPOIR 0.89 [0.82, 0.96]). At baseline, the High HAQ groups in both NOAR and ESPOIR were older, had a higher proportion of women, and had higher levels of fatigue (NOAR: odds ratio [OR] 1.16 [95% CI 1.06, 1.28]; ESPOIR: OR 1.20 [95% CI 1.05, 1.36] [Table]) and pain (NOAR only).Table 1.Baseline characteristics / logistic regression analysisNOARESPOIRVariableLow HAQ trajectory, mean (SD)High HAQ trajectory, mean (SD)OR (95% CI)Low HAQ trajectory, mean (SD)High HAQ trajectory, mean (SD)OR (95% CI)N (%)343 (59%)239 (41%)-131 (55%)108 (45%)-Age, years54.9 (14.2)62.1 (13.8)1.07 (1.05, 1.08)47.8 (13.3)51.8 (11.2)1.04 (1.01, 1.06)Women, N (%)224 (65.3%)176 (73.6%)1.82 (1.12, 2.78)100 (76.3%)95 (88.0%)2.73 (1.20, 6.23)Symptom duration, months7.8 (5.1)8 (5.4)1.10 (0.98, 1.05)3.4 (1.8)3.6 (1.8)1.16 (0.98, 1.37)Current smoker, N (%)77 (22.4%)50 (20.9%)1.19 (0.71, 2.00)61 (46.6%)52 (48.1%)1.52 (0.82, 2.83)DAS28-2C3.14 (1.46)3.21 (1.56)-4.65 (1.31)4.41 (1.35)-HAQ0.8 (0.6)1.4 (0.5)-1.1 (0.6)1.6 (0.6)-Pain (0-10)3.7 (2.4)4.6 (2.5)1.16 (1.07, 1.26)4.1 (2.8)5.1 (2.6)1.07 (0.95, 1.20)Fatigue (0-10)4.3 (2.8)5.3 (2.5)1.16 (1.06, 1.28)5.0 (2.6)6.5 (2.5)1.20 (1.05, 1.36)AIMS anxiety3.99 (1.96)4.42 (1.99)1.06 (0.88, 1.29)4.9 (2.26)5.98 (2.25)1.10 (0.94, 1.29)AIMS depression2.85 (1.87)3.38 (1.87)1.10 (0.94, 1.29)3.96 (1.99)5.08 (2.32)1.12 (0.94, 1.33)RF+, N (%)142 (41.4%)106 (44.4%)0.94 (0.60, 1.46)79 (60.3%)50 (46.3%)0.77 (0.34, 1.75)Anti-CCP+, N (%)113 (32.9%)86 (36.0%)1.35 (0.84, 2.17)76 (58.0%)45 (41.7%)0.89 (0.39, 2.05)Conclusion:There is a group of people with RA with high levels of disability, despite low inflammation. These results underline the potential need for pain and fatigue management in people with RA, even when inflammation is low.References:[1]Hensor et al (2019). Rheumatology (Oxford) 58(8)Acknowledgements:Thanks to the participants of NOAR and ESPOIR and those working in the recruiting centresESPOIR Funding:An unrestricted grant from Merck Sharp and Dohme (MSD) was allocated for the first 5 years of the cohort study. Two additional grants from INSERM supported part of the biological database. The French Society of Rheumatology, Abbvie, Pfizer, Lilly and more recently Fresenius and Biogen supported the ESPOIR cohort study.Disclosure of Interests:James Gwinnutt Grant/research support from: Research grant from Bristol Myers Squibb unrelated to this project, Sam Norton Consultant of: Pfizer and AstraZeneca, Kimme Hyrich Consultant of: Abbvie, Grant/research support from: Pfizer and BMS, Mark Lunt: None declared, Bernard Combe: None declared, Nathalie Rincheval: None declared, Adeline Ruyssen-Witrand: None declared, Bruno Fautrel: None declared, Jacqueline Chipping: None declared, Alex MacGregor: None declared, Suzanne Verstappen: None declared

Summary Background Evidence of the comparative effectiveness of biological therapies for psoriasis on health‐related quality of life (HRQoL) in routine clinical practice is limited. Objectives To examine the comparative effectiveness of adalimumab, etanercept and ustekinumab on HRQoL in patients with psoriasis, and to identify potential predictors for improved HRQoL. Methods This was a prospective cohort study in which changes in HRQoL were assessed using the Dermatology Life Quality Index (DLQI) and EuroQoL‐5D (EQ‐5D) at 6 and 12 months. Multivariable regression models were developed to identify factors associated with achieving a DLQI of 0/1 and improvements in the EQ‐5D utility score. Results In total, 2152 patients with psoriasis were included, with 1239 patients on adalimumab, 517 on etanercept and 396 on ustekinumab; 81% were biologic naïve. For the entire cohort, the median (interquartile range) DLQI and EQ‐5D improved from 18 (13–24) and 0·73 (0·69–0·80) at baseline to 2 (0–7) and 0·85 (0·69–1·00) at 6 months, respectively (P < 0·001). Similar improvements were achieved at 12 months. At 12 months, multivariable regression modelling showed that female sex, multiple comorbidities, smoking and a higher DLQI or a lower EQ‐5D utility score at baseline predicted a lower likelihood of achieving a DLQI of 0/1 or improvement in the EQ‐5D. Compared with adalimumab, patients receiving etanercept, but not ustekinumab, were less likely to achieve a DLQI of 0/1. There was no significant difference between the biological therapies in EQ‐5D improvement. Conclusions In routine clinical practice biological therapies produce marked improvement in HRQoL, which is influenced by the choice of biological therapy, baseline impairment in HRQoL, lifestyle characteristics and comorbidities. These findings should help inform selection of optimal biological therapy for patients related to improvements in HRQoL., What's already known about this topic? Evidence of the comparative effectiveness of biological therapies for psoriasis on health‐related quality of life (HRQoL) in routine clinical practice is limited.Earlier observational studies were either cross‐sectional, thereby limiting the ability to compare changes in HRQoL, or cohort studies that have not taken into account important clinical factors that could influence treatment response, such as alterations in dosing regimens of biological therapies and the concomitant use of conventional systemic treatments for psoriasis. What does this study add? This large prospective cohort study found that in routine clinical practice, the use of biological therapies for psoriasis was associated with marked improvements in HRQoL over 12 months.These improvements were influenced by the choice of biological therapy, baseline impairment in HRQoL, lifestyle characteristics and comorbidities.Compared with adalimumab, patients receiving etanercept were less likely to achieve a DLQI of 0/1, but there was no significant difference between ustekinumab and adalimumab in the proportion of patients achieving a DLQI of 0/1.There was no significant difference between the three biological therapies in level of improvement in the EQ‐5D. Linked Comment: Finlay. Br J Dermatol 2017; 177:1164–1165.

The growth in global methane (CH4) concentration, which had been ongoing since the industrial revolution, stalled around the year 2000 before resuming globally in 2007. We evaluate the role of the hydroxyl radical (OH), the major CH4 sink, in the recent CH4 growth. We also examine the influence of systematic uncertainties in OH concentrations on CH4 emissions inferred from atmospheric observations. We use observations of 1,1,1-trichloroethane (CH3CCl3), which is lost primarily through reaction with OH, to estimate OH levels as well as CH3CC3 emissions, which have uncertainty that previously limited the accuracy of OH estimates. We find a 64–70% probability that a decline in OH has contributed to the post-2007 methane rise. Our median solution suggests that CH4 emissions increased relatively steadily during the late 1990s and early 2000s, after which growth was more modest. This solution obviates the need for a sudden statistically significant change in total CH4 emissions around the year 2007 to explain the atmospheric observations and can explain some of the decline in the atmospheric 13CH4/12CH4 ratio and the recent growth in C2H6. Our approach indicates that significant OH-related uncertainties in the CH4 budget remain, and we find that it is not possible to implicate, with a high degree of confidence, rapid global CH4 emissions changes as the primary driver of recent trends when our inferred OH trends and these uncertainties are considered.

Antimicrobial resistance (AMR) is a critical and worsening public health threat driven by high volumes of use and inappropriate clinical targeting. Ecological studies show an association between antibiotic use and resistance rates. Moderation of antibiotic prescribing requires a deeper understanding of differences in local prescribing behaviours. The purpose of this study was to understand the factors in antibiotic prescribing determined by discretionary choice behaviour that may provide insights into enhanced methods to modify antibiotic usage and control AMR. Oral antibiotic prescribing information extracted from general practitioner (GP) prescribing data were aggregated by applying WHO Anatomical …

The aims of the present study are to describe the characteristics of rheumatoid arthritis (RA) patients selected for tocilizumab (TCZ), compare the “real-world” effectiveness of TCZ and tumour necrosis factor inhibitors (TNFi) when used as a first biologic and assess the influence of past biologic exposure/concurrent methotrexate (MTX) therapy on post-TCZ treatment outcomes. The British Society for Rheumatology Biologics Register (BSRBR-RA) is a prospective cohort study following RA patients starting biologics in the UK. This includes patients starting TCZ as first or subsequent biologic, alongside biologic-naïve patients starting TNFi. Six-month disease activity and 1-year drug survival were compared between biologic-naïve patients starting TCZ versus TNFi and first-line versus subsequent TCZ users and TCZ users with MTX versus without using regression models adjusted by propensity score. Two hundred seventeen patients started TCZ, and 2419 started TNFi as first biologic. Seven hundred seventy-seven started TCZ after other biologics. First-line TCZ users had a higher prevalence of pulmonary fibrosis and cancer history than TNFi users. The first-line TCZ users were more likely to achieve DAS28 remission at 6 months than first-line TNFi, but other improvement markers were similar. The treatment response at 6 months was similar between subsequent-line TCZ users and first-line users after adjusting for baseline patient differences. Concurrent MTX use was not associated with treatment response in either first- or subsequent-line TCZ users. TCZ has been primarily used as subsequent-line biologic in the UK. When used as first line, the response appears similar to that observed in patients starting TNFi, suggesting that clinical response alone should not decide between initial biologic therapies. Electronic supplementary material The online version of this article (doi:10.1007/s10067-016-3485-5) contains supplementary material, which is available to authorized users.

Patients with chronic pain commonly believe their pain is related to the weather. Scientific evidence to support their beliefs is inconclusive, in part due to difficulties in getting a large dataset of patients frequently recording their pain symptoms during a variety of weather conditions. Smartphones allow the opportunity to collect data to overcome these difficulties. Our study Cloudy with a Chance of Pain analysed daily data from 2658 patients collected over a 15-month period. The analysis demonstrated significant yet modest relationships between pain and relative humidity, pressure and wind speed, with correlations remaining even when accounting for mood and physical activity. This research highlights how citizen-science experiments can collect large datasets on real-world populations to address long-standing health questions. These results will act as a starting point for a future system for patients to better manage their health through pain forecasts.

ObjectiveEvaluate relative clinical effectiveness of treatment options for type 2 diabetes mellitus (T2DM) using a statistical model of real-world evidence within UK general practitioner practices (GPP), to quantify the opportunities for diabetes care performance improvement.MethodFrom the National Diabetes Audit in 2015–2016 and 2016–2017, GPP target glycaemic control (TGC—%HbA1c ≤58 mmol/mol) and higher glycaemic risk (HGR —%HbA1c results >86 mmol/mol) outcomes were linked using multivariate linear regression to prescribing, demographics and practice service indicators. This was carried out both cross-sectionally (XS) (within year) and longitudinally (Lo) (across years) on 35 indicators. Standardised β coefficients were used to show relative level of impact of each factor. Improvement opportunity was calculated as impact on TGC & HGR numbers.ResultsValues from 6525 GPP with 2.7 million T2DM individuals were included. The cross-sectional model accounted for up to 28% TGC variance and 35% HGR variance, and the longitudinal model accounted for up to 9% TGC and 17% HGR variance. Practice service indicators including % achieving routine checks/blood pressure/cholesterol control targets were positively correlated, while demographic indicators including % younger age/social deprivation/white ethnicity were negatively correlated. The β values for selected molecules are shown as (increased TGC; decreased HGR), canagliflozin (XS 0.07;0.145/Lo 0.04;0.07), metformin (XS 0.12;0.04/Lo –;–), sitagliptin (XS 0.06;0.02/Lo 0.10;0.06), empagliflozin (XS–;0.07/Lo 0.09;0.07), dapagliflozin (XS –;0.04/Lo –;0.4), sulphonylurea (XS −0.18;−0.12/Lo–;–) and insulin (XS−0.14;0.02/ Lo−0.09;–). Moving all GPP prescribing and interventions to the equivalent of the top performing decile of GPP could result in total patients in TGC increasing from 1.90 million to 2.14 million, and total HGR falling from 191 000 to 123 000.ConclusionsGPP using more legacy therapies such as sulphonylurea/insulin demonstrate poorer outcomes, while those applying holistic patient management/use of newer molecules demonstrate improved glycaemic outcomes. If all GPP moved service levels/prescribing to those of the top decile, both TGC/HGR could be substantially improved.

One hydrochlorofluorocarbon and two hydrofluorocarbons (HCFC-22, HFC-125, and HFC-152a) were measured in air samples at the Cape Point observatory (CPT), South Africa, during 2017. These data represent the first such atmospheric measurements of these compounds from southwestern South Africa (SWSA). Baseline atmospheric growth rates were estimated to be 8.36, 4.10, and 0.71 ppt year-1 for HCFC-22, HFC-125, and HFC-152a, respectively. The CPT measurements were combined with an inverse model to investigate emissions from SWSA. For all three halocarbons, Cape Town was found to be the dominant source within SWSA. These estimates were extrapolated, based on population statistics, to estimate emissions for the whole of South Africa. We estimate South Africa's 2017 emissions to be 3.0 (1.6-4.4), 0.8 (0.5-1.2), and 1.1 (0.6-1.6) Gg year-1 for HCFC-22, HFC-125, and HFC-152a, respectively. For all three halocarbons, South Africa's contribution to global emissions is small (

Objectives To compare cervical screening attendance and cytology (high- and low-grade cervical dysplasia [HGCD and LGCD]) between women with RA and the English general population and between biologic DMARD (bDMARD)-naïve and exposed women. Methods The British Society for Rheumatology Biologics Register for RA (BSRBR-RA), a national prospective study of RA treatment outcomes, was linked to the National Health Service Cervical Screening Programme, providing data for 12 785 women to compare with national screening data. Rates of HGCD/LGCD were compared with rates of negative smears using risk difference calculations between BSRBR-RA and national statistics. Within the BSRBR-RA, coverage was compared between those with low and high physical disability scores, while coverage and cytology results were compared between bDMARD-naïve and -exposed RA patients. Results The mean 5 year screening coverage was significantly higher in BSRBR-RA (83%) compared with the general population (79%), but lower in women with high disability (78%) compared with lesser disability (85%). Risk differences for HGCD were lower in the BSRBR-RA compared with national statistics, whereas risk differences for LGCD were higher. There was no statistically significant difference in the rates of HGCD or LGCD between bDMARD-exposed and -naïve women. Conclusion This first-ever British analysis of cervical screening rates in RA has shown that women with RA have higher screening rates than the general population. Disability negatively impacts attendance, but treatment type does not. Women with RA did not have an increased risk of HGCD compared with national statistics, which was also not influenced by bDMARD exposure.

Summary Introduction Hypogonadism is more prevalent in men with type 2 diabetes (T2DM) (25%‐40%) than in men without T2DM. Hypogonadism has been associated with poorer glycaemic outcomes and increased cardiovascular morbidity/mortality. We report a 14‐year follow‐up study to evaluate the influence of baseline testosterone level on T2DM outcomes. Research design and methods A total of 550 men with T2DM underwent baseline total testosterone and dihydrotestosterone measurement by tandem mass spectrometry. Mean age of the men was 59.7 ± 12 (mean ± SD) years. Sex hormone‐binding globulin (SHBG) was measured and free testosterone estimated. Patients were followed up between 2002 and 2016. Mean follow‐up period was 12.2 ± 4 years using the Salford (UK) Integrated Health Records system. Results Mean baseline total testosterone was 13.7 ± 5.8 nmol/L, and mean free testosterone was 245.7 ± 88.0 pmol/L. Mean for low total testosterone (

Background Joint erosions and autoantibody positivity (rheumatoid factor [RF], anti-cyclic citrullinated peptide antibody [anti-CCP]) predict poor outcomes in patients with rheumatoid arthritis (RA). There are limited data on the combination of these factors and clinical and patient reported outcomes over time. Objectives To compare the disease activity and disability over 1 year of those with poor prognostic factors at treatment initiation (methotrexate [MTX] or biologics) to those without. Methods Patients were recruited to 1 of 2 UK-based multi-centre prospective cohort studies: MTX-starters: Rheumatoid Arthritis Medication Study (RAMS); biologic-starters: Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate (BRAGGSS). Anti-CCP (Axis-Shield Anti-CCP test; U/ml; anti-CCP titre >5 U/ml = anti-CCP+)/RF (Beckman Coulter AU5400, RF latex assay; IU/ml; RF titre >14 IU/ml = RF+) status were determined from baseline (BL) blood samples at the co-ordinating centre; erosions (yes/no) were recorded from medical notes. Missing data resulting from anti-CCP assay failure were imputed using multiple imputation. Patients who were anti-CCP+ and/or RF+ and had erosions were classified as having poor prognosis (PP); all other patients were classified as not having poor prognosis (NPP). Patients completed the Health Assessment Questionnaire (HAQ) and the Disease Activity Score (DAS28) was calculated at BL, 6 months and 12 months. The DAS28 and HAQ scores of the prognostic groups were compared at each assessment using linear regression, adjusted for age, gender and disease duration. Results In total, 1179 (PP = 182 [15.4%]) MTX-starters and 1152 (PP = 467 [40.5%]) biologic-starters were included (BL characteristics in Table). For MTX-starters, PP and NPP patients had similar DAS28, whereas for biologic-starters, PP patients had lower DAS28 compared to NPP patients after baseline (adjusted mean difference [95% CI]: MTX-starters BL = 0.1 [-0.1, 0.3]; 6 months = -0.1 [-0.3, 0.2]; 12 months = -0.1 [-0.4, 0.2]; biologic-starters BL = -0.1 [-0.2, 0.0]; 6 months = -0.2 [-0.4, 0.0]; 12 months = -0.4 [-0.6, -0.1]). HAQ scores were similar between PP and NPP patients in both cohorts (adjusted mean difference [95% CI]: MTX-starters BL = 0.08 [-0.04, 0.20]; 6 months = 0.02 [-0.11, 0.15]; 12 months = -0.03 [-0.17, 0.10]; biologic-starters BL = 0.00 [-0.09, 0.08]; 6 months = 0.00 [-0.12, 0.13]; 12 months = 0.01 [-0.14, 0.16]). Conclusion PP and NPP MTX-starters had similar outcomes. For biologic-starters, PP patients had lower disease activity after baseline; knowledge of these prognostic factors may have prompted more intensive assessment of PP patients after starting treatment. Disclosure of Interests James Gwinnutt: None declared, Kimme Hyrich Grant/research support from: Grants to institution: BMS, Pfizer, UCB, Mark Lunt: None declared, Darren Plant: None declared, Nisha Nair: None declared, Anne Barton: None declared, Suzanne Verstappen: None declared

Background: Whilst anti-cyclic citrullinated peptide antibody (anti-CCP) positivity in associated with poor outcomes in patients with rheumatoid arthritis (RA), it is unclear whether titre level over time is associated with outcome. Objectives: Whilst anti-cyclic citrullinated peptide antibody (anti-CCP) positivity in associated with poor outcomes in patients with rheumatoid arthritis (RA), it is unclear whether titre level over time is associated with outcome. Methods: Patients were recruited to one of two, UK-based, multi-centre prospective cohort studies: MTX-starters = Rheumatoid Arthritis Medication Study (RAMS); biologic-starters = Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate (BRAGGSS). Anti-CCP titre (Axis-Shield Anti-CCP test; U/ml; anti-CCP titre >5 U/ml = anti-CCP+) was measured at the co-ordinating centre from blood samples taken at baseline, 6 months and 12 months. Patients who were anti-CCP+ at baseline and had titre measured at one other assessment were included in the analysis. Patients completed the Health Assessment Questionnaire (HAQ) and the Disease Activity Score (DAS28) was calculated at each assessment. The association between anti-CCP titre and DAS28 and HAQ was assessed using linear random effects models, controlling for age, gender and disease duration (months). Anti-CCP titre was natural log-transformed. Missing data resulting from anti-CCP assay failure were imputed using multiple imputation. Results: In total, 686 MTX-starters and 346 biologic-starters were included. Biologic-starters had worse disease at baseline compared to MTX-starter (Table). Median [IQR] titre decreased slightly over follow-up for MTX-starters (baseline = 147 [48, 416]; 6 months = 133 [47, 454]; 12 months = 124 [44, 421]; p=0.63) and biologic-starters (baseline = 138 [37, 492]; 6 months = 112 [30, 468]; 12 months = 103 [30, 451]; p=0.36) but this was non-significant. For MTX-starters, anti-CCP titre was statistically significantly associated with DAS28 and HAQ, but the effect sizes were small (mean difference [95% CI] per unit increase in log anti-CCP: DAS28 = 0.07 [0.02, 0.11]; HAQ = 0.06 [0.03, 0.08]). There was no significant association between anti-CCP titre and DAS28 and HAQ for biologic-starters (mean difference [95% CI] per unit increase in log anti-CCP: DAS28 = 0.03 [-0.05, 0.10]; HAQ = 0.02 [-0.01, 0.05]). Conclusion: Time-varying anti-CCP titre level was not strongly associated with disease activity or disability over one year in either MTX-starters or biologic-starters who were anti-CCP+ at baseline, indicating repeat testing of anti-CCP level may not be necessary. Disclosure of Interests: James Gwinnutt: None declared, Kimme Hyrich Grant/research support from: Grants to institution: BMS, Pfizer, UCB, Mark Lunt: None declared, Darren Plant: None declared, Nisha Nair: None declared, Anne Barton: None declared, Suzanne Verstappen: None declared

Background: In 2017 a new class of oral disease modifying anti-rheumatic drugs (DMARDs), janus kinase inhibitors (JAKi), were licensed for rheumatoid arthritis (RA): baricitinib and tofacitinib. In the UK, they are approved for use in patients with high disease activity with or without methotrexate, following failure of conventional synthetic (cs) DMARDs or biologic DMARDs, the latter when rituximab is contraindicated. As a new therapy option, it is currently unclear how and when these drugs are being prescribed in patients with RA. Objectives: This analysis aims to describe the characteristics of patients starting JAKi and registered with the BSRBR-RA. Methods: The BSRBR-RA aims to capture exposure and outcome data in patients with RA receiving biologics, biosimilars and targeted synthetic DMARDs. At the start of therapy, demographic and clinical data, including past treatment data, are collected. Characteristics of all patients receiving a JAKi for the first time with data recorded in the BSRBR-RA up to 30/11/2018 are described. Results: To 30/11/2018, 443 patients in the BSRBR-RA have been treated with a JAKi; 374 patients baricitinib and 69 tofacitinib (Table). Twenty-eight percent of baricitinib and 13% of tofacitinib patients received a JAKi following csDMARDs, with no prior biologic exposure. Of these, 15% had a prior malignancy history. Of those with prior exposure, the median number of previous biologics was 3 (IQR 2-4) and 52% had received rituximab. Forty-three percent were receiving concurrent methotrexate. Conclusion: To date, more patients have been recruited starting baricitinib than tofacitinib, likely owing to the later licensing of tofacitinib. Two groups are emerging with a quarter of patients receiving JAKi immediately after csDMARDs and a majority as a later stage alternative following multiple biologics. Further recruitment and follow-up patients will allow for analysis of real-world safety and effectiveness, but differences in patient characteristics will need to be considered in any comparative effectiveness analyses. Disclosure of Interests: Jennifer Page: None declared, Lianne Kearsley-Fleet: None declared, Rebecca Davies: None declared, Kath Watson: None declared, Kimme Hyrich Grant/research support from: Grants to institution: BMS, Pfizer, UCB, Mark Lunt: None declared

Background: Since February 2015, a number of biosimilar drugs have been introduced for treatment of rheumatoid arthritis (RA) in the UK. Biosimilars are required to have no clinically meaningful differences in efficacy or safety from the originator product. There are clear cost-saving implications when switching patients to a biosimilar; however, the longer term implications of switching have yet to be studied. Objectives: To describe the frequency of switching back to biologic-originator (BIO-O) in patients who initially switch from BIO-O to biologic biosimilar (BIO-B), focussing on etanercept (ETA) and infliximab (INF). Methods: The study population comprised RA subjects recruited to BSRBR-RA to 30/11/2018 who had switched from BIO-O to BIO-B (INF and ETA) and had completed at least one further study follow-up following switch. Baseline characteristics at point of switch and the proportion of patients who were recorded as switching back to BIO-O or discontinuing BIO completely following switch with reasons are described. Results: Nine hundred and sixty six patients were included (760 switching from ETA-O; 206 switching from INF-O). After a median follow-up of 22.5 months post-switch, the majority of patients remained on BIO-B (ETA-B = 619 (81%), INF-B =166 (81%)). Seven percent of patients switched back to originator, with a majority following reports of ineffectiveness or feeling generally unwell (Table). Eleven percent stopped BIO completely, with a majority stopping for serious adverse events such as malignancy or infection. Similar results were seen for both ETA and INF. Six ETA and 3 INF reported injection or infusion related reactions following switch (0.9% overall) leading to switch back (4 ETA) or biologic discontinuation (2 ETA/3 INF). Conclusion: Data from this study show that the majority of patients who switched from BIO-O to BIO-B remain on the biosimilar over the short term. A small proportion of patients have switched back to the originator drug with the majority doing so for reports of ineffectiveness rather than safety concerns. Hypersensitivity reactions, such as infusion reactions were rare reasons to stop either drug completely or switch back to originator. Disclosure of Interests: Rebecca Davies: None declared, Lianne Kearsley-Fleet: None declared, Mark Lunt: None declared, Kath Watson: None declared, Kimme Hyrich Grant/research support from: Grants to institution: BMS, Pfizer, UCB

Background: The opioid epidemic in the U.S. has led to similar concerns about prescribed opioids in countries within Europe. In new users, the rate of escalation to more potent opioids is likely to contribute to long-term prescriptions, which in turn may be associated with opioid dependency, addiction and overdose. The scale of such escalation however is unclear in the U.K for non-cancer pain. Objectives: We sought to: (i) describe trends of prescribed opioids for non-cancer pain in the UK primary care setting over a 10-year period (ii) assess the sequential transition of opioid strength from index date over a 2-year period. Methods: We conducted a retrospective observational study over a 10-year period from 1/1/2006 to 31/12/2015 using the Clinical Practice Research Datalink (CPRD). CPRD collects de-identified patient data from a network of GP practices across the UK. New users of opioids, 18 years or over, without cancer in the 2 years prior to index date were included. The number of prescriptions for each drug were calculated by each calendar year accounting for the number of eligible patients registered in CPRD for that year. Sunburst plots were created to evaluate the sequential transition of opioids over time. A 4-state hidden Markov model was used to estimate the transition probability for individuals escalating to more potent opioids over a 2 year period. States were defined as (i) no drug (ii) weak opioid (codeine, dihydrocodeine) (iii) moderate opioid (tramadol) (iii) strong opioid (all others in CPRD). Methadone prescriptions were excluded for the purposes of this analysis. Results: 1,026,955 opioid users were included: mean age (SD) was 55(18) years; 58% being female. New users of opioids were most commonly prescribed codeine (n=723,102; 70.8%), followed by dihydrocodeine (n=179,831; 17.6%), tramadol (n=93,338, 9.1%) with n=24,808 (2.4%) strong opioid prescriptions. The rate of prescribing strong opioids/10,000 population increased 12 fold from 2006-2013, followed by a gradual decline till 2015 (Figure 1). This trend was most marked with certain opioids: morphine, oxycodone, buprenorphine and fentanyl (28.5 prescriptions per 10,000 population to a peak of 353.0 prescriptions per 10,000 population in 2013 and 303.1 prescriptions per 10,000 population in 2015). Using sunburst plots, of the new users prescribed weak opioids as their first prescription at index date, 5.5% transitioned to moderate opioids, 4.1% to strong opioids over 2 years (Figure 2). Transition probability of moving from weak to strong opioid at a given time point over 2 years was 0.001, whilst staying on a strong opioid (if first prescription at index date) was 0.97. Conclusion: Strong opioid prescribing increased till 2013-14 gradually decreasing following UK initiatives to improve monitoring and use of controlled drugs. Although less potent codeine prescriptions made up the majority of first prescriptions, the transition probability of staying on a strong opioid at 2 years remained high if prescribed first as a new user. Disclosure of Interests: None declared