Estimating a population cumulative incidence under calendar time trends

Abstract Background The risk of a disease or psychiatric disorder is frequently measured by the age-specific cumulative incidence. Cumulative incidence estimates are often derived in cohort studies with individuals recruited over calendar time and with the end of follow-up governed by a specific date. It is common practice to apply the Kaplan\u2013Meier or Aalen\u2013Johansen estimator to the total sample and report either the estimated cumulative incidence curve or just a single point on the curve as a description of the disease risk. Methods We argue that, whenever the disease or disorder of interest is influenced by calendar time trends, the total sample Kaplan\u2013Meier and Aalen\u2013Johansen estimators do not provide useful estimates of the general risk in the target population. We present some alternatives to this type of analysis. Results We show how a proportional hazards model may be used to extrapolate disease risk estimates if proportionality is a reasonable assumption. If not reasonable, we instead advocate that a more useful description of the disease risk lies in the age-specific cumulative incidence curves across strata given by time of entry or perhaps just the end of follow-up estimates across all strata. Finally, we argue that a weighted average of these end of follow-up estimates may be a useful summary measure of the disease risk within the study period. Conclusions Time trends in a disease risk will render total sample estimators less useful in observational studies with staggered entry and administrative censoring. An analysis based on proportional hazards or a stratified analysis may be better alternatives

Development of a Novel Object Detection System Based on Synthetic Data Generated from Unreal Game Engine

This paper presents a novel approach to training a real-world object detection system based on synthetic data utilizing state-of-the-art technologies. Training an object detection system can be challenging and time-consuming as machine learning requires substantial volumes of training data with associated metadata. Synthetic data can solve this by providing unlimited desired training data with automatic generation. However, the main challenge is creating a balanced dataset that closes the reality gap and generalizes well when deployed in the real world. A state-of-the-art game engine, Unreal Engine 4, was used to approach the challenge of generating a photorealistic dataset for deep learning model training. In addition, a comprehensive domain randomized environment was implemented to create a robust dataset that generalizes the training data well. The randomized environment was reinforced by adding high-dynamic-range image scenes. Finally, a modern neural network was used to train the object detection system, providing a robust framework for an adaptive and self-learning model. The final models were deployed in simulation and in the real world to evaluate the training. The results of this study show that it is possible to train a real-world object detection system on synthetic data. However, the models showcase a lot of potential for improvements regarding the stability and confidence of the inference results. In addition, the paper provides valuable insight into how the number of assets and training data influence the resulting model.publishedVersio

Integrated modelling of crop production and nitrate leaching with the Daisy model

An integrated modelling strategy was designed and applied to the Soil-Vegetation-Atmosphere Transfer model Daisy for simulation of crop production and nitrate leaching under pedo-climatic and agronomic environment different than that of model original parameterisation. The points of significance and caution in the strategy are: • Model preparation should include field data in detail due to the high complexity of the soil and the crop processes simulated with process-based model, and should reflect the study objectives. Inclusion of interactions between parameters in a sensitivity analysis results in better account for impacts on outputs of measured variables. • Model evaluation on several independent data sets increases robustness, at least on coarser time scales such as month or year. It produces a valuable platform for adaptation of the model to new crops or for the improvement of the existing parameters set. On daily time scale, validation for highly dynamic variables such as soil water transport remains challenging. • Model application is demonstrated with relevance for scientists and regional managers. The integrated modelling strategy is applicable for other process-based models similar to Daisy. It is envisaged that the strategy establishes model capability as a useful research/decision-making, and it increases knowledge transferability, reproducibility and traceability

New‐Onset Atrial Fibrillation is Associated With Cardiovascular Events Leading to Death in a First Time Myocardial Infarction Population of 89 703 Patients With Long‐Term Follow‐Up:A Nationwide Study

BACKGROUND: New‐onset atrial fibrillation (AF) is reported to increase the risk of death in myocardial infarction (MI) patients. However, previous studies have reported conflicting results and no data exist to explain the underlying cause of higher death rates in these patients. METHODS AND RESULTS: All patients with first acute MI between 1997 and 2009 in Denmark, without prior AF, were identified from Danish nationwide administrative registers. The impact of new‐onset AF on all‐cause mortality, cardiovascular death, fatal/nonfatal stroke, fatal/nonfatal re‐infarction and noncardiovascular death, were analyzed by multiple time‐dependent Cox models and additionally in propensity score matched analysis. In 89 703 patients with an average follow‐up of 5.0±3.5 years event rates were higher in patients developing AF (n=10 708) versus those staying in sinus‐rhythm (n=78 992): all‐cause mortality 173.9 versus 69.4 per 1000 person‐years, cardiovascular death 137.2 versus 50.0 per 1000 person‐years, fatal/nonfatal stroke 19.6/19.9 versus 6.2/5.6 per 1000 person‐years, fatal/nonfatal re‐infarction 29.0/60.7 versus 14.2/37.9 per 1000 person‐years. In time‐dependent multiple Cox analyses, new‐onset AF remained predictive of increased all‐cause mortality (HR: 1.9 [95% CI: 1.8 to 2.0]), cardiovascular death (HR: 2.1 [2.0 to 2.2]), fatal/nonfatal stroke (HR: 2.3 [2.1 to 2.6]/HR: 2.5 [2.2 to 2.7]), fatal/nonfatal re‐infarction (HR: 1.7 [1.6 to 1.8]/HR: 1.8 [1.7 to 1.9]), and non‐ cardiovascular death (HR: 1.4 [1.3 to 1.5]) all P<0.001). Propensity‐score matched analyses yielded nearly identical results (all P<0.001). CONCLUSIONS: New‐onset AF after first‐time MI is associated with increased mortality, which is largely explained by more cardiovascular deaths. Focus on the prognostic impact of post‐infarct AF is warranted

Effects of music on agitation in dementia: A meta-analysis

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Lithium in drinking water and incidence of suicide:A nationwide individual-level cohort study with 22 years of follow-up

Suicide is a major public health concern. High-dose lithium is used to stabilize mood and prevent suicide in patients with affective disorders. Lithium occurs naturally in drinking water worldwide in much lower doses, but with large geographical variation. Several studies conducted at an aggregate level have suggested an association between lithium in drinking water and a reduced risk of suicide; however, a causal relation is uncertain. Individual-level register-based data on the entire Danish adult population (3.7 million individuals) from 1991 to 2012 were linked with a moving five-year time-weighted average (TWA) lithium exposure level from drinking water hypothesizing an inverse relationship. The mean lithium level was 11.6 μg/L ranging from 0.6 to 30.7 μg/L. The suicide rate decreased from 29.7 per 100,000 person-years at risk in 1991 to 18.4 per 100,000 person-years in 2012. We found no significant indication of an association between increasing five-year TWA lithium exposure level and decreasing suicide rate. The comprehensiveness of using individual-level data and spatial analyses with 22 years of follow-up makes a pronounced contribution to previous findings. Our findings demonstrate that there does not seem to be a protective effect of exposure to lithium on the incidence of suicide with levels below 31 μg/L in drinking water

Steps and catalytic reactions: CO oxidation with preadsorbed O on Rh(553)

Industrial catalysts are often comprised of nanoparticles supported on high-surface-area oxides, in order to maximise the catalytically active surface area and thereby utilise the active material better. These nanoparticles expose steps and corners that, due to low coordination to neighboring atoms, are more reactive and, as a consequence, are often assumed to have higher catalytic activity. We have investigated the reaction between CO and preadsorbed O on a stepped Rh(553) surface, and show that CO oxidation indeed occurs faster than on the flat Rh(111) surface at the same temperature. However, we do find that this is not a result of reactions at the step sites but rather at the terrace sites close to the steps, due to in-plane relaxation enabled by the step. This insight can provide ways to optimize the shape of the nanoparticles to further improve the activity of certain reactions