Search

Objectives The objectives of the study are to investigate infection risk in offspring born to women with systemic lupus erythematosus (SLE) compared with offspring born to women without SLE and examine the mediating role of preterm birth.Design This is a register-based cohort study.Setting Liveborn singletons born in Sweden, 2006–2021, were included in the study.Participants 1248 infants born to mothers with SLE (≥2 International Classification of Diseases-coded visits in the National Patient Register (NPR) and Medical Birth Register, with ≥1 visit before pregnancy) and 34 886 infants born to women without SLE from the general population were included.Primary and secondary outcome measures The primary outcome was any visit for infection in the NPR or anti-infectives in the Prescribed Drug Register. The secondary outcome was hospitalised infection. Infection risks within 72 hours, within 1 month and within 1 year were estimated.Results SLE offspring had a higher risk of infection in the first 72 hours compared with non-SLE (2.1% vs 1.2%; risk ratios (RR) (95% CI) 1.62 (1.09 to 2.42)), the first month (5.2% vs 4.5%; RR 1.12 (0.88 to 1.43)) and first year of life (38.2% vs 37.2%; RR 1.09 (1.01 to 1.17)). The hospitalised infection risk for SLE offspring was similar to that of non-SLE (5.8% vs 5.5%, first year of life). The percentage of the total effect of maternal SLE on infant infection mediated through preterm birth was 86% for infection in the first 72 hours and 27% in the first year of life.Conclusions The risk of infection in SLE offspring is most increased in the first 3 days after birth, and a proportion of this association can be explained by preterm birth. To prevent early neonatal infections, maternal SLE could be considered as a risk factor before allowing early discharge from postnatal care.

Objective: The estimand framework offers a structured approach to define the treatment effect to be estimated in a clinical study. Defining the estimand upfront helps formulating the research question and informs study design, data collection and statistical analysis methods. Since the Trials within Cohorts (TwiCs) design has unique characteristics, the objective of this study is to describe considerations and provide guidance for formulating estimands for TwiCs studies.Methods: The key attributes of an estimand are the target population, treatments that are compared, the endpoint, intercurrent events and their handling, and the population-level summary measure. The estimand framework was applied retrospectively to two TwiCs studies: the SPONGE and UMBRELLA Fit trial. The aim is to demonstrate how the estimand framework can be implemented in TwiCs studies, thereby focusing on considerations relevant for defining the estimand. Three estimands were defined for both studies. For the SPONGE trial, estimators were derived.Results: Intercurrent events considered to occur exclusively or more frequently in TwiCs studies compared to conventional randomized trials included intervention refusal after randomization, misalignment of timing of routine cohort measurements and the intervention period, and participants in the control arm initiating treatments similar to the studied intervention. Considerations for handling refusal after randomization related to decisions on whether the target population should include all eligible participants or the subpopulation that would accept (or undergo) the intervention when offered. Considerations for handling treatment initiation in the control arm and misalignments of timing related to decisions on whether such events should be considered part of treatment policy or whether interest is in a hypothetical scenario where such events do not occur.Conclusion: The TwiCs study design has unique features that pose specific considerations when formulating an estimand. The examples in this study can provide guidance in the definition of estimands in future TwiCs studies.

Abstract Background Planning for return to work (RTW) is relevant among sub‐groups of metastatic breast cancer (mBC) survivors. RTW and protective factors for RTW in patients with mBC were determined. Methods Patients with mBC, ages 18–63 years, were identified in Swedish registers, and data were collected starting 1 year before their mBC diagnosis. The prevalence of working net days (WNDs) (>90 and >180) during the year after mBC diagnosis (y1) was determined. Factors associated with RTW were assessed using regression analysis. The impact of contemporary oncological treatment of mBC on RTW and 5‐year mBC‐specific survival was compared between those diagnosed in 1997–2002 and 2003–2011. Results Of 490 patients, 239 (48.8%) and 189 (36.8%) had >90 and >180 WNDs, respectively, during y1. Adjusted odds ratios (AORs) of WNDs >90 or >180 during y1 were significantly higher for patients with age ≤50 years (AOR180 = 1.54), synchronous metastasis (AOR90 = 1.68, AOR180 = 1.67), metastasis within 24 months (AOR180 = 1.51), soft tissue, visceral, brain as first metastatic site (AOR90 = 1.47) and sickness absence

Abstract Cyclophosphamide (CPA) dosing by body surface area (BSA, m2) has been questioned as a predictor for individual drug exposure. This study investigated phosphoramide mustard-hemoglobin (PAM-Hb, pmol g−1 Hb) as a biomarker of CPA exposure in 135 female breast cancer patients receiving CPA during three courses based on BSA: 500 mg/m2 (C500 group, n = 67) or 600 mg/m2 (C600 group, n = 68). The inter-individual difference was calculated for both groups by dividing the highest through the lowest PAM-Hb value of each course. The inter-occasion difference was calculated in percentage for each individual by dividing their PAM-Hb value through the group mean per course, and subsequently dividing this ratio of the latter through the previous course. A multivariable linear regression (MLR) was performed to identify factors that explained the variation of PAM-Hb. During the three courses, the inter-individual difference changed from 3.5 to 2.1 and the inter-occasion difference ranged between 13.3% and 11.9% in the C500 group. In the C600 group, the inter-individual difference changed from 2.7 to 2.9 and the inter-occasion difference ranged between 14.1% and 11.7%. The MLR including BSA, age, GFR, and albumin explained 17.1% of the variation of PAM-Hb and was significantly better then the model including only BSA. These factors should be considered when calculating the first dose of CPA for breast cancer patients.

Abstract Further reservoir-based hydropower development can contribute to the United Nations’ sustainable development goals (SDGs) on affordable and clean energy, and climate action. However, hydropower reservoir operation can lead to biodiversity impacts, thus interfering with the SDGs on clean water and life on land. We combine a high-resolution, location-specific, technical assessment with newly developed life cycle impact assessment models, to assess potential biodiversity impacts of possible future hydropower reservoirs, resulting from land occupation, water consumption and methane emissions. We show that careful selection of hydropower reservoirs has a large potential to limit biodiversity impacts, as for example, 0.3% of the global hydropower potential accounts for 25% of the terrestrial biodiversity impact. Local variations, e.g. species richness, are the dominant explanatory factors of the variance in the quantified biodiversity impact and not the mere amount of water consumed, or land occupied per kWh. The biodiversity impacts are mainly caused by land occupation and water consumption, with methane emissions being much less important. Further, we indicate a trade-off risk between terrestrial and aquatic biodiversity impacts, as due to the weak correlation between terrestrial and aquatic impacts, reservoirs with small aquatic biodiversity impacts tend to have larger terrestrial impacts and vice versa.

The potential of seasonal pumped hydropower storage (SPHS) plant to fulfil future energy storage requirements is vast in mountainous regions. Here the authors show that SPHS costs vary from 0.007 to 0.2 US$ m−3 of water stored, 1.8 to 50 US$ MWh−1 of energy stored and 0.37 to 0.6 US$ GW−1 of installed power generation capacity.

Photovoltaic power (PV) is the fastest-growing source of renewable electricity. Making reliable scenarios of PV deployment requires information on what drives the spatial distribution of PV facilities. Here we empirically derive the determinants of the distribution of utility-scale PV facilities across six continents, using a mixed effects logistic regression modelling approach relating the occurrence of over 10 000 PV facilities to a set of potential determinants as well as accounting for country and spatially correlated random effects. Our regression models explain the distribution of PV facilities with high accuracy, with travel times to settlements and irradiation as the main determinants. In contrast, our results suggest that land cover types are not strong determinants of the PV distribution, except for Asia and Africa where the PV distribution is related to the presence of agriculture, short natural vegetation and bare land. For Europe and Asia a considerable part of the variance in PV distribution is explained by inter-country differences in factors not included in our fixed determinants. Relevant determinants identified in our study are in line with the main assumptions made in cost of electricity (COE) maps used in the IMAGE integrated assessment model (IAM). However, we found correlations (Spearman ρ ) of −0.18–0.54 between our PV probability maps and IMAGE’s COE maps. These may partly be explained by conceptual differences between our empirically-derived probability maps and the COE maps, but we also recommend using higher-resolution maps of PV potential and COE computations such as used in IAMs.

Increasing renewable energy use is an essential strategy for mitigating climate change. Nevertheless, the sensitivity of renewable energy to climatic conditions means that the energy system’s vulnerability to climate change can also become larger. In this research, we used two integrated assessment models and data from four climate models to analyse climate change impacts on primary energy use at a global and regional scale under a low-level (RCP2.6) and a medium-level (RCP6.0) climate change scenario. The impacts are analysed on the energy system focusing on four renewable sources (wind, solar, hydropower, and biomass). Globally, small climate impacts on renewable primary energy use are found in both models (5% for RCP2.6 and 6% for RCP6.0). These impacts lead to a decrease in the use of fossil sources for most regions, especially for North America and Europe under the RCP60 scenario. Overall, IMAGE and GCAM provide a similar signal impact response for most regions. E.g. in Asia (excluding China and India), climate change induces an increase in wind and hydropower use under the RCP6.0 scenarios; however, for India, a decrease in solar energy use can be expected under both scenarios and models.

Abstract Breast cancer incidence and survival is high in Southeast Asia. As such, many women diagnosed with breast cancer are at risk of dying of other causes. Given the increased risk of cardiotoxicity induced by breast cancer treatments, it is important to identify patients at high risk of cardiovascular disease (CVD) mortality. The aim of this study was to investigate if this risk varies by age and ethnicity. Patient details were obtained from 5,868 Chinese, Malay, and Indian women diagnosed with in situ or non-metastasized invasive breast cancer at the National University Hospital of Singapore and KK Women’s and Children’s Hospital in Singapore. Death causes were obtained from the National Registry of Births and Deaths. Flexible parametric survival models estimated CVD mortality rates and hazard ratios. During a median follow-up of six years, 1,010 deaths occurred of which 6.8% were due to CVD. CVD mortality rates of older women peaked within the first year following diagnosis and increased over time since diagnosis. Indian had more than double the risk of CVD mortality than Chinese, independent of age at diagnosis and stage. Taking ethnicity and age into account may promote CVD risk stratification and management in (Southeast Asian) women with breast cancer.

Introduction Cardiovascular disease (CVD) is an important cause of death in breast cancer survivors. Some breast cancer treatments including anthracyclines, trastuzumab and radiotherapy can increase the risk of CVD, especially for patients with pre-existing CVD risk factors. Early identification of patients at increased CVD risk may allow switching to less cardiotoxic treatments, active surveillance or treatment of CVD risk factors. One of the strongest independent CVD risk factors is the presence and extent of coronary artery calcifications (CAC). In clinical practice, CAC are generally quantified on ECG-triggered cardiac CT scans. Patients with breast cancer treated with radiotherapy routinely undergo radiotherapy planning CT scans of the chest, and those scans could provide the opportunity to routinely assess CAC before a potentially cardiotoxic treatment. The Bragatston study aims to investigate the association between calcifications in the coronary arteries, aorta and heart valves (hereinafter called ‘cardiovascular calcifications’) measured automatically on planning CT scans of patients with breast cancer and CVD risk.Methods and analysis In a first step, we will optimise and validate a deep learning algorithm for automated quantification of cardiovascular calcifications on planning CT scans of patients with breast cancer. Then, in a multicentre cohort study (University Medical Center Utrecht, Utrecht, Erasmus MC Cancer Institute, Rotterdam and Radboudumc, Nijmegen, The Netherlands), the association between cardiovascular calcifications measured on planning CT scans of patients with breast cancer (n≈16 000) and incident (non-)fatal CVD events will be evaluated. To assess the added predictive value of these calcifications over traditional CVD risk factors and treatment characteristics, a case-cohort analysis will be performed among all cohort members diagnosed with a CVD event during follow-up (n≈200) and a random sample of the baseline cohort (n≈600).Ethics and dissemination The Institutional Review Boards of the participating hospitals decided that the Medical Research Involving Human Subjects Act does not apply. Findings will be published in peer-reviewed journals and presented at conferences.Trial registration number NCT03206333.

Author(s): Jessica Jewell (corresponding author) [1, 2]; David McCollum [1, 3]; Johannes Emmerling [4, 5]; Christoph Bertram [6]; David E. H. J. Gernaat [7, 8]; Volker Krey [1]; Leonidas Paroussos [...]

Integrated assessment models (IAMs) form a prime tool in informing about climate mitigation strategies. Diagnostic indicators that allow comparison across these models can help describe and explain differences in model projections. This increases transparency and comparability. Earlier, the IAM community has developed an approach to diagnose models (Kriegler (2015 Technol. Forecast. Soc. Change 90 45–61)). Here we build on this, by proposing a selected set of well-defined indicators as a community standard, to systematically and routinely assess IAM behaviour, similar to metrics used for other modeling communities such as climate models. These indicators are the relative abatement index, emission reduction type index, inertia timescale, fossil fuel reduction, transformation index and cost per abatement value. We apply the approach to 17 IAMs, assessing both older as well as their latest versions, as applied in the IPCC 6th Assessment Report. The study shows that the approach can be easily applied and used to indentify key differences between models and model versions. Moreover, we demonstrate that this comparison helps to link model behavior to model characteristics and assumptions. We show that together, the set of six indicators can provide useful indication of the main traits of the model and can roughly indicate the general model behavior. The results also show that there is often a considerable spread across the models. Interestingly, the diagnostic values often change for different model versions, but there does not seem to be a distinct trend.