Assessment of MERRA-2 Land Surface Energy Flux Estimates

In the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) system the land is forced by replacing the model-generated precipitation with observed precipitation before it reaches the surface. This approach is motivated by the expectation that the resultant improvements in soil moisture will lead to improved land surface latent heating (LH). Here we assess aspects of the MERRA-2 land surface energy budget and 2 m air temperatures (T(sup 2m)). For global land annual averages, MERRA-2 appears to overestimate the LH (by 5 W/sq m), the sensible heating (by 6 W/sq m), and the downwelling shortwave radiation (by 14 W/sq m), while underestimating the downwelling and upwelling (absolute) longwave radiation (by 10-15 W/sq m each). These results differ only slightly from those for NASA's previous reanalysis, MERRA. Comparison to various gridded reference data sets over Boreal summer (June-July-August) suggests that MERRA-2 has particularly large positive biases (>20 W/sq m) where LH is energy-limited, and that these biases are associated with evaporative fraction biases rather than radiation biases. For time series of monthly means during Boreal summer, the globally averaged anomaly correlations (R(sub anom)) with reference data were improved from MERRA to MERRA-2, for LH (from 0.39 to 0.48 vs. GLEAM data) and the daily maximum T(sup 2m) (from 0.69 to 0.75 vs. CRU data). In regions where T(sup 2m) is particularly sensitive to the precipitation corrections (including the central US, the Sahel, and parts of south Asia), the changes in the T(sup 2m) R(sub anom) are relatively large, suggesting that the observed precipitation influenced the T(sup 2m) performance

Estimating Root Mean Square Errors in Remotely Sensed Soil Moisture over Continental Scale Domains

Root Mean Square Errors (RMSE) in the soil moisture anomaly time series obtained from the Advanced Scatterometer (ASCAT) and the Advanced Microwave Scanning Radiometer (AMSR-E; using the Land Parameter Retrieval Model) are estimated over a continental scale domain centered on North America, using two methods: triple colocation (RMSETC ) and error propagation through the soil moisture retrieval models (RMSEEP ). In the absence of an established consensus for the climatology of soil moisture over large domains, presenting a RMSE in soil moisture units requires that it be specified relative to a selected reference data set. To avoid the complications that arise from the use of a reference, the RMSE is presented as a fraction of the time series standard deviation (fRMSE). For both sensors, the fRMSETC and fRMSEEP show similar spatial patterns of relatively highlow errors, and the mean fRMSE for each land cover class is consistent with expectations. Triple colocation is also shown to be surprisingly robust to representativity differences between the soil moisture data sets used, and it is believed to accurately estimate the fRMSE in the remotely sensed soil moisture anomaly time series. Comparing the ASCAT and AMSR-E fRMSETC shows that both data sets have very similar accuracy across a range of land cover classes, although the AMSR-E accuracy is more directly related to vegetation cover. In general, both data sets have good skill up to moderate vegetation conditions

State of the Art in Large-Scale Soil Moisture Monitoring

Soil moisture is an essential climate variable influencing land atmosphere interactions, an essential hydrologic variable impacting rainfall runoff processes, an essential ecological variable regulating net ecosystem exchange, and an essential agricultural variable constraining food security. Large-scale soil moisture monitoring has advanced in recent years creating opportunities to transform scientific understanding of soil moisture and related processes. These advances are being driven by researchers from a broad range of disciplines, but this complicates collaboration and communication. For some applications, the science required to utilize large-scale soil moisture data is poorly developed. In this review, we describe the state of the art in large-scale soil moisture monitoring and identify some critical needs for research to optimize the use of increasingly available soil moisture data. We review representative examples of 1) emerging in situ and proximal sensing techniques, 2) dedicated soil moisture remote sensing missions, 3) soil moisture monitoring networks, and 4) applications of large-scale soil moisture measurements. Significant near-term progress seems possible in the use of large-scale soil moisture data for drought monitoring. Assimilation of soil moisture data for meteorological or hydrologic forecasting also shows promise, but significant challenges related to model structures and model errors remain. Little progress has been made yet in the use of large-scale soil moisture observations within the context of ecological or agricultural modeling. Opportunities abound to advance the science and practice of large-scale soil moisture monitoring for the sake of improved Earth system monitoring, modeling, and forecasting

A prognostic model of all-cause mortality at 30 days in patients with cancer and COVID-19

Background: Patients with cancer are at higher risk of dying of COVID-19. Known risk factors for 30-day all-cause mortality (ACM-30) in patients with cancer are older age, sex, smoking status, performance status, obesity, and co-morbidities. We hypothesized that common clinical and laboratory parameters would be predictive of a higher risk of 30-day ACM, and that a machine learning approach (random forest) could produce high accuracy. Methods: In this multi-institutional COVID-19 and Cancer Consortium (CCC19) registry study, 12,661 patients enrolled between March 17, 2020 and December 31, 2021 were utilized to develop and validate a model of ACM-30. ACM-30 was defined as death from any cause within 30 days of COVID-19 diagnosis. Pre-specified variables were: age, sex, race, smoking status, ECOG performance status (PS), timing of cancer treatment relative to COVID19 diagnosis, severity of COVID19, type of cancer, and other laboratory measurements. Missing variables were imputed using random forest proximity. Random forest was utilized to model ACM-30. The area under the curve (AUC) was computed as a measure of predictive accuracy with out-of-bag prediction. One hundred bootstrapped samples were used to obtain the standard error of the AUC. Results: The median age at COVID-19 diagnosis was 65 years, 53% were female, 18% were Hispanic, and 16.7% were Black. Over half were never smokers and the median body mass index was 28.2. Random forest with under sampling selected 20 factors prognostic of ACM-30. The AUC was 88.9 (95% CI 88.5-89.2). Highly informative parameters included: COVID-19 severity at presentation, cancer status, age, troponin level, ECOG PS and body mass index. Conclusions: This prognostic model based on readily available clinical and laboratory values can be used to estimate individual survival probability within 30-days for COVID-19. In addition, this model can be used to select or classify patients with cancer and COVID-19 into risk groups based on validated cut points, for treatment selection, prophylaxis prioritization, and/or enrollment in clinical trials. Future work includes external validation using other large datasets of patients with COVID-19 and cancer

Association of immunotherapy and immunosuppression with severe COVID-19 disease in patients with cancer

Background: Cytokine storm due to COVID-19 can cause high morbidity and mortality. Patients with cancer treated with immunotherapy (IO) and those with immunosuppression may have higher rates of cytokine storm due to immune dysregulation. We sought to evaluate the association of IO and immunosuppression with COVID-19 outcomes and cytokine storm occurrence among patients with cancer and COVID-19, based on data from the COVID-19 and Cancer Consortium (CCC19). Methods: A registry-based retrospective cohort study was conducted on patients reported to the CCC19 registry from March 2020 to September 2021. The primary outcome was defined as an ordinal scale of COVID-19 severity. The secondary outcome was the occurrence of a cytokine storm using CCC19 variables, defined as biological and clinical evidence of severe inflammation, with end-organ dysfunction (Fajgenbaum D.C. et al., N Engl J Med., 2020). The association of IO or immunosuppression with the outcomes of interest were evaluated using a multivariable logistic regression balanced for covariate distributions through inverse probability of treatment weighting (IPTW). Results: A total of 10,214 patients were included, among which 482 (4.7%) received IO, 3,715 (36.4%) received non-IO systemic therapies, and 6,017 (58.9%) were untreated in the 3 months prior to COVID-19 diagnosis. No difference in COVID-19 severity or the development of a cytokine storm was found in the IO group compared to the untreated group (aOR: 0.77; 95%CI:0.45-1.32, and aOR: 1.06; 95%CI:0.42-2.67, respectively). On multivariable analysis, baseline immunosuppression was associated with worse outcomes both in relation to COVID-19 severity (aOR: 1.89; 95%CI:1.51-2.35) and the presence of a cytokine storm (aOR: 1.75; 95%CI:1.30-2.35). Conclusions: Administration of IO was not associated with severe outcomes in patients with cancer and COVID-19, whereas pre-existing baseline immunosuppression appears to be independently associated with worse clinical outcomes including cytokine storm

Multilevel analyses of related public health indicators: The European Surveillance of Congenital Anomalies (EUROCAT) Public Health Indicators

BACKGROUND:Public health organisations use public health indicators to guide health policy. Joint analysis of multiple public health indicators can provide a more comprehensive understanding of what they are intended to evaluate. OBJECTIVE:To analyse variaitons in the prevalence of congenital anomaly-related perinatal mortality attributable to termination of pregnancy for foetal anomaly (TOPFA) and prenatal diagnosis of congenital anomaly prevalence. METHODS:We included 55 363 cases of congenital anomalies notified to 18 EUROCAT registers in 10 countries during 2008-12. Incidence rate ratios (IRR) representing the risk of congenital anomaly-related perinatal mortality according to TOPFA and prenatal diagnosis prevalence were estimated using multilevel Poisson regression with country as a random effect. Between-country variation in congenital anomaly-related perinatal mortality was measured using random effects and compared between the null and adjusted models to estimate the percentage of variation in congenital anomaly-related perinatal mortality accounted for by TOPFA and prenatal diagnosis. RESULTS:The risk of congenital anomaly-related perinatal mortality decreased as TOPFA and prenatal diagnosis prevalence increased (IRR 0.79, 95% confidence interval [CI] 0.72, 0.86; and IRR 0.88, 95% CI 0.79, 0.97). Modelling TOPFA and prenatal diagnosis together, the association between congenital anomaly-related perinatal mortality and TOPFA prevalence became stronger (RR 0.70, 95% CI 0.61, 0.81). The prevalence of TOPFA and prenatal diagnosis accounted for 75.5% and 37.7% of the between-country variation in perinatal mortality, respectively. CONCLUSION:We demonstrated an approach for analysing inter-linked public health indicators. In this example, as TOPFA and prenatal diagnosis of congenital anomaly prevalence decreased, the risk of congenital anomaly-related perinatal mortality increased. Much of the between-country variation in congenital anomaly-related perinatal mortality was accounted for by TOPFA, with a smaller proportion accounted for by prenatal diagnosis

Amniotic band syndrome and limb body wall complex in Europe 1980–2019

Amniotic band syndrome (ABS) and limb body wall complex (LBWC) have an overlapping phenotype of multiple congenital anomalies and their etiology is unknown. We aimed to determine the prevalence of ABS and LBWC in Europe from 1980 to 2019 and to describe the spectrum of congenital anomalies. In addition, we investigated maternal age and multiple birth as possible risk factors for the occurrence of ABS and LBWC. We used data from the European surveillance of congenital anomalies (EUROCAT) network including data from 30 registries over 1980–2019. We included all pregnancy outcomes, including live births, stillbirths, and terminations of pregnancy for fetal anomalies. ABS and LBWC cases were extracted from the central EUROCAT database using coding information responses from the registries. In total, 866 ABS cases and 451 LBWC cases were included in this study. The mean prevalence was 0.53/10,000 births for ABS and 0.34/10,000 births for LBWC during the 40 years. Prevalence of both ABS and LBWC was lower in the 1980s and higher in the United Kingdom. Limb anomalies and neural tube defects were commonly seen in ABS, whereas in LBWC abdominal and thoracic wall defects and limb anomalies were most prevalent. Twinning was confirmed as a risk factor for both ABS and LBWC. This study includes the largest cohort of ABS and LBWC cases ever reported over a large time period using standardized EUROCAT data. Prevalence, clinical characteristics, and the phenotypic spectrum are described, and twinning is confirmed as a risk factor.publishedVersio

Maternal risk factors for the VACTERL association: a EUROCAT case-control study

International audienc

Spectrum of congenital anomalies among VACTERL cases: a EUROCAT population-based study

Background: The VACTERL (Vertebral anomalies, Anal atresia, Cardiac malformations, Tracheo-Esophageal fistula, Renal anomalies, Limb abnormalities) association is the non-random occurrence of at least three of these congenital anomalies: vertebral, anal, cardiac, tracheo-esophageal, renal, and limb anomalies. Diagnosing VACTERL patients is difficult, as many disorders have multiple features in common with VACTERL. The aims of this study were to clearly outline component features, describe the phenotypic spectrum among the largest group of VACTERL patients thus far reported, and to identify phenotypically similar subtypes. Methods: A case-only study was performed assessing data on 501 cases recorded with VACTERL in the JRC-EUROCAT (Joint Research Centre-European Surveillance of Congenital Anomalies) central database (birth years: 1980–2015). We differentiated between major and minor VACTERL features and anomalies outside the VACTERL spectrum to create a clear definition of VACTERL. Results: In total, 397 cases (79%) fulfilled our VACTERL diagnostic criteria. The most commonly observed major VACTERL features were anorectal malformations and esophageal atresia/tracheo-esophageal fistula (both occurring in 62% of VACTERL cases), followed by cardiac (57%), renal (51%), vertebral (33%), and limb anomalies (25%), in every possible combination. Three VACTERL subtypes were defined: STRICT-VACTERL, VACTERL-LIKE, and VACTERL-PLUS, based on severity and presence of additional congenital anomalies. Conclusion: The clearly defined VACTERL component features and the VACTERL subtypes introduced will improve both clinical practice and etiologic research.acceptedVersio

Systemic Anticancer Therapy and Thromboembolic Outcomes in Hospitalized Patients With Cancer and COVID-19

IMPORTANCE: Systematic data on the association between anticancer therapies and thromboembolic events (TEEs) in patients with COVID-19 are lacking. OBJECTIVE: To assess the association between anticancer therapy exposure within 3 months prior to COVID-19 and TEEs following COVID-19 diagnosis in patients with cancer. DESIGN, SETTING, AND PARTICIPANTS: This registry-based retrospective cohort study included patients who were hospitalized and had active cancer and laboratory-confirmed SARS-CoV-2 infection. Data were accrued from March 2020 to December 2021 and analyzed from December 2021 to October 2022. EXPOSURE: Treatments of interest (TOIs) (endocrine therapy, vascular endothelial growth factor inhibitors/tyrosine kinase inhibitors [VEGFis/TKIs], immunomodulators [IMiDs], immune checkpoint inhibitors [ICIs], chemotherapy) vs reference (no systemic therapy) in 3 months prior to COVID-19. MAIN OUTCOMES AND MEASURES: Main outcomes were (1) venous thromboembolism (VTE) and (2) arterial thromboembolism (ATE). Secondary outcome was severity of COVID-19 (rates of intensive care unit admission, mechanical ventilation, 30-day all-cause mortality following TEEs in TOI vs reference group) at 30-day follow-up. RESULTS: Of 4988 hospitalized patients with cancer (median [IQR] age, 69 [59-78] years; 2608 [52%] male), 1869 had received 1 or more TOIs. Incidence of VTE was higher in all TOI groups: endocrine therapy, 7%; VEGFis/TKIs, 10%; IMiDs, 8%; ICIs, 12%; and chemotherapy, 10%, compared with patients not receiving systemic therapies (6%). In multivariable log-binomial regression analyses, relative risk of VTE (adjusted risk ratio [aRR], 1.33; 95% CI, 1.04-1.69) but not ATE (aRR, 0.81; 95% CI, 0.56-1.16) was significantly higher in those exposed to all TOIs pooled together vs those with no exposure. Among individual drugs, ICIs were significantly associated with VTE (aRR, 1.45; 95% CI, 1.01-2.07). Also noted were significant associations between VTE and active and progressing cancer (aRR, 1.43; 95% CI, 1.01-2.03), history of VTE (aRR, 3.10; 95% CI, 2.38-4.04), and high-risk site of cancer (aRR, 1.42; 95% CI, 1.14-1.75). Black patients had a higher risk of TEEs (aRR, 1.24; 95% CI, 1.03-1.50) than White patients. Patients with TEEs had high intensive care unit admission (46%) and mechanical ventilation (31%) rates. Relative risk of death in patients with TEEs was higher in those exposed to TOIs vs not (aRR, 1.12; 95% CI, 0.91-1.38) and was significantly associated with poor performance status (aRR, 1.77; 95% CI, 1.30-2.40) and active/progressing cancer (aRR, 1.55; 95% CI, 1.13-2.13). CONCLUSIONS AND RELEVANCE: In this cohort study, relative risk of developing VTE was high among patients receiving TOIs and varied by the type of therapy, underlying risk factors, and demographics, such as race and ethnicity. These findings highlight the need for close monitoring and perhaps personalized thromboprophylaxis to prevent morbidity and mortality associated with COVID-19-related thromboembolism in patients with cancer