Comparing the Scandinavian automobile taxation systems and their CO2 mitigation effects

Despite their similarities, Scandinavian countries have adopted starkly different automobile tax regimes. The Danish system entails very high and convex tax rates with moderate CO2 differentiation. In Norway, tax rates are high and convex with strong CO2 differentiation and total exemptions for zero emission vehicles, even from value added tax. Sweden practices feebates – CO2 dependent subsidization along with moderate taxation. Relying on a disaggregate discrete choice model of automobile purchase, we simulate the demand for passenger cars as of 2016 in Norway under a set of conditions resembling, respectively, the Danish, Norwegian or Swedish fiscal incentives before and after recent reforms. In all cases, implications are derived in terms of energy technology market shares, average type approval CO2 emission rates, and aggregate fiscal revenue. The automobile taxation system is seen to have remarkable impacts on all three accounts. In essence, among the three jurisdictions examined, the Norwegian fiscal regime has by far the strongest CO2 abatement effect. The Danish system is less effective in terms of CO2 abatement, but provides twice as much government revenue. The Swedish feebate strategy is by far the least effective in terms of both CO2 mitigation and revenue collection.publishedVersio

Analyzing repeated data collected by mobile phones and frequent text messages. An example of Low back pain measured weekly for 18 weeks

BACKGROUND: Repeated data collection is desirable when monitoring fluctuating conditions. Mobile phones can be used to gather such data from large groups of respondents by sending and receiving frequently repeated short questions and answers as text messages. The analysis of repeated data involves some challenges. Vital issues to consider are the within-subject correlation, the between measurement occasion correlation and the presence of missing values. The overall aim of this commentary is to describe different methods of analyzing repeated data. It is meant to give an overview for the clinical researcher in order for complex outcome measures to be interpreted in a clinically meaningful way. METHODS: A model data set was formed using data from two clinical studies, where patients with low back pain were followed with weekly text messages for 18 weeks. Different research questions and analytic approaches were illustrated and discussed, as well as the handling of missing data. In the applications the weekly outcome “number of days with pain” was analyzed in relation to the patients’ “previous duration of pain” (categorized as more or less than 30 days in the previous year). Research questions with appropriate analytical methods 1: How many days with pain do patients experience? This question was answered with data summaries. 2: What is the proportion of participants “recovered” at a specific time point? This question was answered using logistic regression analysis. 3: What is the time to recovery? This question was answered using survival analysis, illustrated in Kaplan-Meier curves, Proportional Hazard regression analyses and spline regression analyses. 4: How is the repeatedly measured data associated with baseline (predictor) variables? This question was answered using generalized Estimating Equations, Poisson regression and Mixed linear models analyses. 5: Are there subgroups of patients with similar courses of pain within the studied population? A visual approach and hierarchical cluster analyses revealed different subgroups using subsets of the model data. CONCLUSIONS: We have illustrated several ways of analysing repeated measures with both traditional analytic approaches using standard statistical packages, as well as recently developed statistical methods that will utilize all the vital features inherent in the data

Association between Pregnancy Loss and Urinary Phthalate Levels around the Time of Conception

Background: Animal studies indicate that some phthalate metabolites may harm female reproductive function

Sea Ice Melt Pond Fraction Derived From Sentinel‐2 Data: Along the MOSAiC Drift and Arctic‐Wide

Melt ponds forming on Arctic sea ice in summer significantly reduce the surface albedo and impact the heat and mass balance of the sea ice. Therefore, their areal coverage, which can undergo rapid change, is crucial to monitor. We present a revised method to extract melt pond fraction (MPF) from Sentinel-2 satellite imagery, which is evaluated by MPF products from higher-resolution satellite and helicopter-borne imagery. The analysis of melt pond evolution during the MOSAiC campaign in summer 2020, shows a split of the Central Observatory (CO) into a level ice and a highly deformed ice part, the latter of which exhibits exceptional early melt pond formation compared to the vicinity. Average CO MPFs are 17% before and 23% after the major drainage. Arctic-wide analysis of MPF for years 2017–2021 shows a consistent seasonal cycle in all regions and years

Proposal for the use of echocardiography in bloodstream infections due to different streptococcal species

BACKGROUND: Infective endocarditis (IE) is diagnosed in 7–8% of streptococcal bloodstream infections (BSIs), yet it is unclear when to perform transthoracic (TTE) and transoesophageal echocardiography (TOE) according to different streptococcal species. The aim of this sub-study was to propose a flowchart for the use of echocardiography in streptococcal BSIs. METHODS: In a population-based setup, we investigated all patients admitted with streptococcal BSIs and crosslinked data with nationwide registries to identify comorbidities and concomitant hospitalization with IE. Streptococcal species were divided in four groups based on the crude risk of being diagnosed with IE (low-risk  30%). Based on number of positive blood culture (BC) bottles and IE risk factors (prosthetic valve, previous IE, native valve disease, and cardiac device), we further stratified cases according to probability of concomitant IE diagnosis to create a flowchart suggesting TTE plus TOE (IE > 10%), TTE (IE 3–10%), or “wait & see” (IE < 3%). RESULTS: We included 6393 cases with streptococcal BSIs (mean age 68.1 years [SD 16.2], 52.8% men). BSIs with low-risk streptococci (S. pneumoniae, S. pyogenes, S. intermedius) are not initially recommended echocardiography, unless they have ≥3 positive BC bottles and an IE risk factor. Moderate-risk streptococci (S. agalactiae, S. anginosus, S. constellatus, S. dysgalactiae, S. salivarius, S. thermophilus) are guided to “wait & see” strategy if they neither have a risk factor nor ≥3 positive BC bottles, while a TTE is recommended if they have either ≥3 positive BC bottles or a risk factor. Further, a TTE and TOE are recommended if they present with both. High-risk streptococci (S. mitis/oralis, S. parasanguinis, G. adiacens) are directed to a TTE if they neither have a risk factor nor ≥3 positive BC bottles, but to TTE and TOE if they have either ≥3 positive BC bottles or a risk factor. Very high-risk streptococci (S. gordonii, S. gallolyticus, S. mutans, S. sanguinis) are guided directly to TTE and TOE due to a high baseline IE prevalence. CONCLUSION: In addition to the clinical picture, this flowchart based on streptococcal species, number of positive blood culture bottles, and risk factors, can help guide the use of echocardiography in streptococcal bloodstream infections. Since echocardiography results are not available the findings should be confirmed prospectively with the use of systematic echocardiography. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-021-06391-2