ADHD medicine consumption in Europe after COVID-19: catch-up or trend change?

BACKGROUND

Although the COVID-19 pandemic and its implications have been associated with mental health services utilization and medication consumption, there is no longitudinal study on the long-term impact on ADHD medication use trends.

METHODS

This study examines the European ADHD medication consumption in 2020 to 2022 compared to the predicted consumption assuming the persistence of pre-pandemic trends. Predictions are calculated using Seasonal Autoregressive Integrated Moving Average (SARIMA) models.

RESULTS

While European ADHD medication sales recorded a drop in 2020, they returned to the predicted level in 2021, even slightly exceeding it. In 2022, we found a clear exceedance of the predicted level by 16.4% on average at country level. Furthermore, the increase in consumption growth in the post-pandemic period (2021-2022) compared to the pre-pandemic period (2014-2019) was significant in 26 of the 28 European countries under consideration.

CONCLUSION

There is strong evidence of a trend change in the ADHD medicine consumption growth throughout Europe after the COVID-19 pandemic.

The impact of the COVID-19 pandemic on ADHD medicine consumption in 47 countries and regions

The objective of this study is to quantify the impact of the COVID-19 pandemic on attention deficit hyperactivity disorder (ADHD) medication consumption globally and nationally using pharmaceutical sales data from 2014 to 2021 across 47 countries and regions. A seasonal autoregressive integrated moving average model (SARIMA) was applied to the time series until the end of 2019 at country level and used for the prediction of the ADHD medication consumption in 2020 and 2021. The deviations from the actual to the forecasted sales, which simulate the development without the emergence of COVID-19, yield estimates for the pandemic's impact. In 36 of the 47 countries and regions, the actual sales in 2020 were lower than predicted, with an average relative drop of 6.2% in defined daily doses (DDD) per 1000 inhabitants per day at country-level. In 2021, most countries recorded actually higher ADHD medication use than predicted at the end of 2019. On average, the consumption increased per country by 1.60%. The deviations strongly correlate with the stringency of anti-pandemic government policies. The findings suggest that the pandemic led to a substantially lower consumption of ADHD medication in 2020. However, in 2021 the pandemic had an accelerating effect as the increasing consumption trends are more pronounced than before the pandemic.

Stroke-derived neutrophils demonstrate higher formation potential and impaired resolution of CD66b + driven neutrophil extracellular traps

Background

Recent evidence suggests a merging role of immunothrombosis in the formation of arterial thrombosis. Our study aims to investigate its relevance in stroke patients.

Methods

We compared the peripheral immunological profile of stroke patients vs. healthy controls. Serum samples were functionally analyzed for their formation and clearance of Neutrophil-Extracellular-Traps. The composition of retrieved thrombi has been immunologically analyzed.

Results

Peripheral blood of stroke patients showed significantly elevated levels of DNAse-I (p < 0.001), LDG (p = 0.003), CD4 (p = 0.005) as well as the pro-inflammatory cytokines IL-17 (p < 0.001), INF-γ (p < 0.001) and IL-22 (p < 0.001) compared to controls, reflecting a T_H1/T_H17 response. Increased counts of DNAse-I in sera (p = 0.045) and Neutrophil-Extracellular-Traps in thrombi (p = 0.032) have been observed in patients with onset time of symptoms longer than 4,5 h. Lower values of CD66b in thrombi were independently associated with greater improvement of NIHSS after mechanical thrombectomy (p = 0.045). Stroke-derived neutrophils show higher potential for Neutrophil-Extracellular-Traps formation after stimulation and worse resolution under DNAse-I treatment compared to neutrophils derived from healthy individuals.

Conclusions

Our data provide new insight in the role of activated neutrophils and Neutrophil-Extracellular-Traps in ischemic stroke. Future larger studies are warranted to further investigate the role of immunothrombosis in the cascades of stroke.

Trial registration

DRKS, DRKS00013278, Registered 15 November 2017, https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00013278

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-022-02707-0.

TEAD-YAP Interaction Inhibitors and MDM2 Binders from DNA-Encoded Indole-Focused Ugi Peptidomimetics

DNA-encoded combinatorial synthesis provides efficient and dense coverage of chemical space around privileged molecular structures. The indole side chain of tryptophan plays a prominent role in key, or "hot spot", regions of protein-protein interactions. A DNA-encoded combinatorial peptoid library was designed based on the Ugi four-component reaction by employing tryptophan-mimetic indole side chains to probe the surface of target proteins. Several peptoids were synthesized on a chemically stable hexathymidine adapter oligonucleotide "hexT", encoded by DNA sequences, and substituted by azide-alkyne cycloaddition to yield a library of 8112 molecules. Selection experiments for the tumor-relevant proteins MDM2 and TEAD4 yielded MDM2 binders and a novel class of TEAD-YAP interaction inhibitors that perturbed the expression of a gene under the control of these Hippo pathway effectors.

Robust Test for Detecting Changes in the Autocovariance Function of a Time Series

We propose a new robust test to detect changes in the autocovariance function of a time series. The test is based on empirical autocovariances of a robust transformation of the original time series. Because of the transformation, we do not require any finite moments of the original time series, making the test especially suitable for heavy tailed time series. We furthermore propose a lag weighting scheme, which puts emphasis on changes of the autocovariance at smaller lags. Our approach is compared to existing ones in some simulations.

A robust method for shift detection in time series

We present a robust and nonparametric test for the presence of a changepoint in a time series, based on the two-sample Hodges–Lehmann estimator. We develop new limit theory for a class of statistics based on two-sample U-quantile processes in the case of short-range dependent observations. Using this theory, we derive the asymptotic distribution of our test statistic under the null hypothesis of a constant level. The proposed test shows better overall performance under normal, heavy-tailed and skewed distributions than several other modifications of the popular cumulative sums test based on U-statistics, one-sample U-quantiles or M-estimation. The new theory does not involve moment conditions, so any transform of the observed process can be used to test the stability of higher-order characteristics such as variability, skewness and kurtosis.

Variable selection for disease progression models: methods for oncogenetic trees and application to cancer and HIV

Background

Disease progression models are important for understanding the critical steps during the development of diseases. The models are imbedded in a statistical framework to deal with random variations due to biology and the sampling process when observing only a finite population. Conditional probabilities are used to describe dependencies between events that characterise the critical steps in the disease process.

Many different model classes have been proposed in the literature, from simple path models to complex Bayesian networks. A popular and easy to understand but yet flexible model class are oncogenetic trees. These have been applied to describe the accumulation of genetic aberrations in cancer and HIV data. However, the number of potentially relevant aberrations is often by far larger than the maximal number of events that can be used for reliably estimating the progression models. Still, there are only a few approaches to variable selection, which have not yet been investigated in detail.

Results

We fill this gap and propose specifically for oncogenetic trees ten variable selection methods, some of these being completely new. We compare them in an extensive simulation study and on real data from cancer and HIV. It turns out that the preselection of events by clique identification algorithms performs best. Here, events are selected if they belong to the largest or the maximum weight subgraph in which all pairs of vertices are connected.

Conclusions

The variable selection method of identifying cliques finds both the important frequent events and those related to disease pathways.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1762-1) contains supplementary material, which is available to authorized users.

The Spatial Sign Covariance Matrix and Its Application for Robust Correlation Estimation

We summarize properties of the spatial sign covariance matrix and especially consider the relationship between its eigenvalues and those of the shape matrix of an elliptical distribution. The explicit relationship known in the bivariate case was used to construct the spatial sign correlation coefficient, which is a non-parametric and robust estimator for the correlation coefficient within the elliptical model. We consider a multivariate generalization, which we call the multivariate spatial sign correlation matrix. A small simulation study indicates that the new estimator is very efficient under various elliptical distributions if the dimension is large. We furthermore derive its influence function under certain conditions which indicates that the multivariate spatial sign correlation becomes more sensitive to outliers as the dimension increases.

Robust Shift Detection in Time-Varying Autoregressive Processes

Tests for shift detection in locally-stationary autoregressive time series are constructed which resist contamination by a substantial amount of outliers. Tests based on a comparison of local medians standardized by a highly robust estimate of the variability show reliable performance in a broad variety of situations if the thresholds are adjusted for possible autocorrelations.

Robust Trend Estimation for AR(1) Disturbances

We discuss the robust estimation of a linear trend if the noise follows an autoregressive process of first order. We find the ordinary repeated median to perform well except for negative correlations. In this case it can be improved by a Prais-Winsten transformation using a robust autocorrelation estimator.

On Outliers and Interventions in Count Time Series following GLMs

We discuss the analysis of count time series following generalised linear models in the presence of outliers and intervention effects. Different modifications of such models are formulated which allow to incorporate, detect and to a certain degree distinguish extraordinary events (interventions) of different types in count time series retrospectively. An outlook on extensions to the problem of robust parameter estimation, identification of the model orders by robust estimation of autocorrelations and partial autocorrelations, and online surveillance by sequential testing for outlyingness is provided. 

Cumulative disease progression models for cross-sectional data: a review and comparison

A better understanding of disease progression is beneficial for early diagnosis and appropriate individual therapy. Many different approaches for statistical modelling of cumulative disease progression have been proposed in the literature, including simple path models up to complex restricted Bayesian networks. Important fields of application are diseases such as cancer and HIV. Tumour progression is measured by means of chromosome aberrations, whereas people infected with HIV develop drug resistances because of genetic changes of the HI-virus. These two very different diseases have typical courses of disease progression, which can be modelled partly by consecutive and partly by independent steps. This paper gives an overview of the different progression models and points out their advantages and drawbacks. Different models are compared via simulations to analyse how they work if some of their assumptions are violated. In a simulation study, we evaluate how models perform in terms of fitting induced multivariate probability distributions and topological relationships. We often find that the true model class used for generating data is outperformed by either a less or a more complex model class. The more flexible conjunctive Bayesian networks can be used to fit oncogenetic trees, whereas mixtures of oncogenetic trees with three tree components can be well fitted by mixture models with only two tree components.

Interventions in log-linear Poisson autoregression

We consider the problem of estimating and detecting outliers in count time series data following a log-linear observation driven model. Log-linear models for count time series arise naturally because they correspond to the canonical link function of the Poisson distribution. They yield both positive and negative dependence, and covariate information can be conveniently incorporated. Within this framework, we establish test procedures for detection of unusual events (‘interventions’) leading to different kinds of outliers, we implement joint maximum likelihood estimation of model parameters and outlier sizes and we derive formulae for correcting the data for detected interventions. The effectiveness of the proposed methodology is illustrated with two real data examples. The first example offers a fresh data analytic point of view towards the polio data. Our methodology identifies different forms of outliers in these data by an observation-driven model. The second example deals with some campylobacterosis data which we analyzed in a previous communication, by a different model. The results are reconfirmed by the new model that we put forward in this communication. The reliability of the procedure is verified using artificial data examples.

Medical device alarms

The high number of false positive alarms has long been known to be a serious problem in critical care medicine - yet it remains unresolved. At the same time, threats to patient safety due to missing or suppressed alarms are being reported. The purpose of this paper is to present results from a workshop titled "Too many alarms? Too few alarms?" organized by the Section Patient Monitoring and the Workgroup Alarms of the German Association of Biomedical Engineering of the Association for Electrical, Electronic and Information Technologies. The current situation regarding alarms and their problems in intensive care, such as lack of clinical relevance, alarm fatigue, workload increases due to clinically irrelevant alarms, usability problems in alarm systems, problems with manuals and training, and missing alarms due to operator error are outlined, followed by a discussion of solutions and strategies to improve the current situation. Finally, the need for more research and development, focusing on signal quality considerations, networking of medical devices at the bedside, diagnostic alarms and predictive warnings, usability of alarm systems, education of healthcare providers, creation of annotated clinical databases for testing, standardization efforts, and patient monitoring in the regular ward, are called for.

Recursive filtering for zero offset correction of diving depth time series with GNU R package diveMove

Zero offset correction of diving depth measured by time-depth recorders is required to remove artifacts arising from temporal changes in accuracy of pressure transducers. Currently used methods for this procedure are in the proprietary software domain, where researchers cannot study it in sufficient detail, so they have little or no control over how their data were changed. GNU R package diveMove implements a procedure in the Free Software domain that consists of recursively smoothing and filtering the input time series using moving quantiles. This paper describes, demonstrates, and evaluates the proposed method by using a “perfect” data set, which is subsequently corrupted to provide input for the proposed procedure. The method is evaluated by comparing the corrected time series to the original, uncorrupted, data set from an Antarctic fur seal (Arctocephalus gazella Peters, 1875). The Root Mean Square Error of the corrected data set, relative to the “perfect” data set, was nearly identical to the magnitude of noise introduced into the latter. The method, thus, provides a flexible, reliable, and efficient mechanism to perform zero offset correction for analyses of diving behaviour. We illustrate applications of the method to data sets from four species with large differences in diving behaviour, measured using different sampling protocols and instrument characteristics.

Executive functioning in high-IQ adults with ADHD

BACKGROUND

To examine the association between psychological tests of executive functioning and functional outcomes among high-IQ adults with attention deficit hyperactivity disorder (ADHD).

METHOD

Subjects were high-IQ adults with (n=64) and without ADHD (n=53). Subjects were administered a battery of neuropsychological tests assessing executive functioning.

RESULTS

High-IQ adults with ADHD performed less well than those without ADHD on several psychological tests of executive functioning, including the Wisconsin Card Sorting Test (WCST), Stroop Color and Word Test, Rey-Osterrieth Complex Figure Test (ROCF), California Verbal Learning Test (CVLT) and an auditory continuous performance test (CPT). Test performance in the high-IQ adult ADHD group, however, was average. In the entire sample, performance on several tests of executive functioning including the ROCF and the CVLT were significant predictors of real-world functioning.

CONCLUSIONS

High-IQ adults with ADHD perform less well on tests of executive functioning relative to high-IQ control participants. Performance on several tests of executive functioning was a significant predictor of functioning.

Is adult attention deficit hyperactivity disorder a valid diagnosis in the presence of high IQ?

BACKGROUND

Because the diagnosis of attention deficit hyperactivity disorder (ADHD) in higher education settings is rapidly becoming a contentious issue, particularly among patients with high IQs, we sought to assess the validity of diagnosing ADHD in high-IQ adults and to further characterize the clinical features associated with their ADHD.

METHOD

We operationalized high IQ as having a full-scale IQ120. We identified 53 adults with a high IQ who did not have ADHD and 64 adults with a high IQ who met diagnostic criteria for ADHD. Groups did not differ on IQ, socio-economic status or gender.

RESULTS

High-IQ adults with ADHD reported a lower quality of life, had poorer familial and occupational functioning, and had more functional impairments, including more speeding tickets, accidents and arrests. Major depressive disorder, obsessive-compulsive disorder and generalized anxiety disorder diagnoses were higher in high-IQ adults with ADHD. All other psychiatric co-morbidities, including antisocial personality disorder and substance abuse, did not differ between the two high-IQ groups. ADHD was more prevalent in first-degree relatives of adults with ADHD relative to controls.

CONCLUSIONS

Our data suggest that adults with ADHD and a high IQ display patterns of functional impairments, familiality and psychiatric co-morbidities that parallel those found in the average-IQ adult ADHD population.

Neurocognitive impairment in unaffected siblings of youth with bipolar disorder

BACKGROUND

There is growing evidence for the familiality of pediatric bipolar disorder (BPD) and its association with impairments on measures of processing speed, verbal learning and 'executive' functions. The current study investigated whether these neurocognitive impairments index the familial risk underlying the diagnosis.

METHOD

Subjects were 170 youth with BPD (mean age 12.3 years), their 118 non-mood-disordered siblings and 79 non-mood-disordered controls. Groups were compared on a battery of neuropsychological tests from the Wechsler Intelligence Scales, the Stroop Color Word Test, the Wisconsin Card Sorting Test (WCST), the Rey-Osterrieth Complex Figure (ROCF), an auditory working memory Continuous Performance Test (CPT) and the California Verbal Learning Test-Children's Version (CVLT-C). Measures were factor analyzed for data reduction purposes. All analyses controlled for age, sex and attention-deficit/hyperactivity disorder (ADHD).

RESULTS

Principal components analyses with a promax rotation yielded three factors reflecting: (1) processing speed/verbal learning, (2) working memory/interference control and (3) abstract problem solving. The CPT working memory measure with interference filtering demands (WM INT) was only administered to subjects aged > or =12 years and was therefore analyzed separately. BPD youth showed impairments versus controls and unaffected relatives on all three factors and on the WM INT. Unaffected relatives exhibited impairments versus controls on the abstract problem-solving factor and the WM INT. They also showed a statistical trend (p=0.07) towards worse performance on the working memory/interference control factor.

CONCLUSIONS

Neurocognitive impairments in executive functions may reflect the familial neurobiological risk mechanisms underlying pediatric BPD and may have utility as endophenotypes in molecular genetic studies of the condition.

Smart alarms from medical devices in the OR and ICU

Alarms in medical devices are a matter of concern in critical and perioperative care. The high rate of false alarms is not only a nuisance for patients and caregivers, but can also compromise patient safety and effectiveness of care. The development of alarm systems has lagged behind the technological advances of medical devices over the last 20 years. From a clinical perspective, major improvements in alarm algorithms are urgently needed. This review gives an overview of the current clinical situation and the underlying problems, and discusses different methods from statistics and computational science and their potential for clinical application. Some examples of the application of new alarm algorithms to clinical data are presented.

Stability of executive function deficits into young adult years: a prospective longitudinal follow-up study of grown up males with ADHD

OBJECTIVE

Although individuals with attention deficit-hyperactivity disorder (ADHD) commonly exhibit deficits in executive functions that greatly increase the morbidity of the disorder, all available information on the subject is cross sectional.

METHOD

Males (n = 85) 9-22 years with ADHD followed over 7 years into young adulthood were assessed on measures of sustained attention/vigilance, planning and organization, response inhibition, set shifting and categorization, selective attention and visual scanning, verbal and visual learning, and memory. A binary definition of executive function deficits (EFDs) was defined based on a subject manifesting at least two abnormal tests 1.5 standard deviations from controls.

RESULTS

The majority of subjects maintained EFDs over time (kappa: 0.41, P < 0.001; sensitivity: 55%, specificity: 85%, positive predictive value: 69%, and negative predictive value: 75%).

CONCLUSION

Considering the morbidity of EFDs, these findings stress the importance of their early recognition for prevention and early intervention strategies. EFDs are stable over time.