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Emissions of methane (CH4) from tropical ecosystems, and how they respond to changes in climate, represent one of the biggest uncertainties associated with the global CH4 budget. Historically, this has been due to the dearth of pan-tropical in situ measurements, which is particularly acute in Africa. By virtue of their superior spatial coverage, satellite observations of atmospheric CH4 columns can help to narrow down some of the uncertainties in the tropical CH4 emission budget. We use proxy column retrievals of atmospheric CH4 (XCH4) from the Japanese Greenhouse gases Observing Satellite (GOSAT) and the nested version of the GEOS-Chem atmospheric chemistry and transport model (0.5∘×0.625∘) to infer emissions from tropical Africa between 2010 and 2016. Proxy retrievals of XCH4 are less sensitive to scattering due to clouds and aerosol than full physics retrievals, but the method assumes that the global distribution of carbon dioxide (CO2) is known. We explore the sensitivity of inferred a posteriori emissions to this source of systematic error by using two different XCH4 data products that are determined using different model CO2 fields. We infer monthly emissions from GOSAT XCH4 data using a hierarchical Bayesian framework, allowing us to report seasonal cycles and trends in annual mean values. We find mean tropical African emissions between 2010 and 2016 range from 76 (74–78) to 80 (78–82) Tg yr−1, depending on the proxy XCH4 data used, with larger differences in Northern Hemisphere Africa than Southern Hemisphere Africa. We find a robust positive linear trend in tropical African CH4 emissions for our 7-year study period, with values of 1.5 (1.1–1.9) Tg yr−1 or 2.1 (1.7–2.5) Tg yr−1, depending on the CO2 data product used in the proxy retrieval. This linear emissions trend accounts for around a third of the global emissions growth rate during this period. A substantial portion of this increase is due to a short-term increase in emissions of 3 Tg yr−1 between 2011 and 2015 from the Sudd in South Sudan. Using satellite land surface temperature anomalies and altimetry data, we find this increase in CH4 emissions is consistent with an increase in wetland extent due to increased inflow from the White Nile, although the data indicate that the Sudd was anomalously dry at the start of our inversion period. We find a strong seasonality in emissions across Northern Hemisphere Africa, with the timing of the seasonal emissions peak coincident with the seasonal peak in ground water storage. In contrast, we find that a posteriori CH4 emissions from the wetland area of the Congo Basin are approximately constant throughout the year, consistent with less temporal variability in wetland extent, and significantly smaller than a priori estimates.

The global atmospheric methane growth rates reported by NOAA for 2020 and 2021 are the largest since systematic measurements began in 1983. To explore the underlying reasons for these anomalous growth rates, we use newly available methane data from the Japanese Greenhouse gases Observing SATellite (GOSAT) to estimate methane surface emissions. Relative to baseline values in 2019, we find that a significant global increase in methane emissions of 27.0 ± 11.3 and 20.8 ± 11.4 Tg is needed to reproduce observed atmospheric methane in 2020 and 2021, respectively, assuming fixed climatological values for OH. We see the largest annual increases in methane emissions during 2020 over Eastern Africa (14 ± 3 Tg), tropical Asia (3 ± 4 Tg), tropical South America (5 ± 4 Tg), and temperate Eurasia (3 ± 3 Tg), and the largest reductions are observed over China (−6 ± 3 Tg) and India (−2 ± 3 Tg). We find comparable emission changes in 2021, relative to 2019, except for tropical and temperate South America where emissions increased by 9 ± 4 and 4 ± 3 Tg, respectively, and for temperate North America where emissions increased by 5 ± 2 Tg. The elevated contributions we saw in 2020 over the western half of Africa (−5 ± 3 Tg) are substantially reduced in 2021, compared to our 2019 baseline. We find statistically significant positive correlations between anomalies of tropical methane emissions and groundwater, consistent with recent studies that have highlighted a growing role for microbial sources over the tropics. Emission reductions over India and China are expected in 2020 due to the Covid-19 lockdown but continued in 2021, which we do not currently understand. To investigate the role of reduced OH concentrations during the Covid-19 lockdown in 2020 on the elevated atmospheric methane growth in 2020–2021, we extended our inversion state vector to include monthly scaling factors for OH concentrations over six latitude bands. During 2020, we find that tropospheric OH is reduced by 1.4 ± 1.7 % relative to the corresponding 2019 baseline value. The corresponding revised global growth of a posteriori methane emissions in 2020 decreased by 34 % to 17.9 ± 13.2 Tg, relative to the a posteriori value that we inferred using fixed climatological OH values, consistent with sensitivity tests using the OH climatology inversion using reduced values for OH. The counter statement is that 66 % of the global increase in atmospheric methane during 2020 was due to increased emissions, particularly from tropical regions. Regional flux differences between the joint methane–OH inversion and the OH climatology inversion in 2020 are typically much smaller than 10 %. We find that OH is reduced by a much smaller amount during 2021 than in 2020, representing about 10 % of the growth of atmospheric methane in that year. Therefore, we conclude that most of the observed increase in atmospheric methane during 2020 and 2021 is due to increased emissions, with a significant contribution from reduced levels of OH.

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

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 CH₃CCl₃ 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.

Tropical wetlands are a significant source of atmospheric methane (CH 4 ), but their importance to the global CH 4 budget is uncertain due to a paucity of direct observations. Net wetland emissions result from complex interactions and co-variation between microbial production and oxidation in the soil, and transport to the atmosphere. Here we show that phenology is the overarching control of net CH 4 emissions to the atmosphere from a permanent, vegetated tropical swamp in the Okavango Delta, Botswana, and we find that vegetative processes modulate net CH 4 emissions at sub-daily to inter-annual timescales. Without considering the role played by papyrus on regulating the efflux of CH 4 to the atmosphere, the annual budget for the entire Okavango Delta, would be under- or over-estimated by a factor of two. Our measurements demonstrate the importance of including vegetative processes such as phenological cycles into wetlands emission budgets of CH 4 . Tropical wetlands are a significant but understudied source of methane. Here, methane emissions were measured over three years in a perennial tropical swamp in the Okavango Delta, Botswana, finding phenology was the overarching control of emissions.

Surface observations have recorded large and incompletely understood changes to atmospheric methane (CH 4 ) this century. However, their ability to reveal the responsible surface sources and sinks is limited by their geographical distribution, which is biased towards the northern midlatitudes. Data from Earth-orbiting satellites designed specifically to measure atmospheric CH 4 have been available since 2009 with the launch of the Japanese Greenhouse gases Observing SATellite (GOSAT). We assess the added value of GOSAT to data collected by the US National Oceanic and Atmospheric Administration (NOAA), which have been the lynchpin for knowledge about atmospheric CH 4 since the 1980s. To achieve that we use the GEOS-Chem atmospheric chemistry transport model and an inverse method to infer a posteriori flux estimates from the NOAA and GOSAT data using common a priori emission inventories. We find the main benefit of GOSAT data is from its additional coverage over the tropics where we report large increases since the 2014/2016 El Niño, driven by biomass burning, biogenic emissions and energy production. We use data from the European TROPOspheric Monitoring Instrument to show how better spatial coverage and resolution measurements allow us to quantify previously unattainable diffuse sources of CH 4 , thereby opening up a new research frontier. This article is part of a discussion meeting issue ‘Rising methane: is warming feeding warming? (part 1)’.

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.

ObjectivesBiologic disease-modifying antirheumatic drugs (bDMARDs) have revolutionised treatment and outcomes for rheumatoid arthritis (RA). The expanding repertoire allows the option of switching bDMARD if current treatment is not effective. For some patients, even after switching, disease control remains elusive. This analysis aims to quantify the frequency of, and identify factors associated with, bDMARD refractory disease.MethodsPatients with RA starting first-line tumour necrosis factor inhibitor in the British Society for Rheumatology Biologics Register for RA from 2001 to 2014 were included. We defined patients as bDMARD refractory on the date they started their third class of bDMARD. Follow-up was censored at last follow-up date, 30 November 2016, or death, whichever came first. Switching patterns and stop reasons of bDMARDs were investigated. Cox regression identified baseline clinical factors associated with refractory disease. Multiple imputation of missing baseline data was used.Results867 of 13 502 (6%) patients were bDMARD refractory; median time to third bDMARD class of 8 years. In the multivariable analysis, baseline factors associated with bDMARD refractory disease included patients registered more recently, women, younger age, shorter disease duration, higher patient global assessment, higher Health Assessment Questionnaire score, current smokers, obesity and greater social deprivation.ConclusionsThis first national study has identified the frequency of bDMARD refractory disease to be at least 6% of patients who have ever received bDMARDs. As the choice of bDMARDs increases, patients are cycling through bDMARDs quicker. The aetiopathogenesis of bDMARD refractory disease requires further investigation. Focusing resources, such as nursing support, on these patients may help them achieve more stable, controlled disease.

East Africa is a key location for wetland emissions of methane (CH4), driven by variations in rainfall that are in turn influenced by sea-surface temperature gradients over the Indian Ocean. Using satellite observations of CH4 and an atmospheric chemistry-transport model, we quantified East African CH4 emissions during 2018 and 2019 when there was 3-σ anomalous rainfall during the long rains (March-May) in 2018 and the short rains (October-December) in 2019. These rainfall anomalies resulted in CH4 emissions of 6.2 ± 0.3 Tg CH4 and 8.6 ± 0.3 Tg CH4, in each three month period, respectively, and represent a 10% and 37% increase compared to the equivalent season in the opposite year, when rainfall was close to the long-term seasonal mean. We find the additional short rains emissions were equivalent to over a quarter of the growth in global emissions in 2019, highlighting the disproportionate role of East Africa in the global CH4 budget.

Background Propensity scores are widely used to deal with confounding bias in medical research. An incorrectly specified propensity score model may lead to residual confounding bias; therefore it is essential to use diagnostics to assess propensity scores in a propensity score analysis. The current use of propensity score diagnostics in the medical literature is unknown. The objectives of this study are to (1) assess the use of propensity score diagnostics in medical studies published in high-ranking journals, and (2) assess whether the use of propensity score diagnostics differs between studies (a) in different research areas and (b) using different propensity score methods. Methods A PubMed search identified studies published in high-impact journals between Jan 1st 2014 and Dec 31st 2016 using propensity scores to answer an applied medical question. From each study we extracted information regarding how propensity scores were assessed and which propensity score method was used. Research area was defined using the journal categories from the Journal Citations Report. Results A total of 894 papers were included in the review. Of these, 187 (20.9%) failed to report whether the propensity score had been assessed. Commonly reported diagnostics were p -values from hypothesis tests (36.6%) and the standardised mean difference (34.6%). Statistical tests provided marginally stronger evidence for a difference in diagnostic use between studies in different research areas ( p  = 0.033) than studies using different propensity score methods ( p  = 0.061). Conclusions The use of diagnostics in the propensity score medical literature is far from optimal, with different diagnostics preferred in different areas of medicine. The propensity score literature may improve with focused efforts to change practice in areas where suboptimal practice is most common.