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Aim: This study investigates the effects of degree of multilingualism on cognitive functions in adulthood, with focus on episodic memory recall and including measures of verbal fluency as well as global cognition.

Design: We studied a large population-based cohort cross-sectionally, and we also assessed changes over time through longitudinal measurements on four time-points over a 15 year period. Participants were drawn from the Betula prospective cohort study in Umeå, Sweden. The participants included in this study at baseline (n = 894, mean age = 51.44, 59.4% females) were divided according to number of languages into bilinguals (n = 395), trilinguals (n = 284), quadrilinguals (n = 169), and pentalinguals (n = 46). 

Data and analysis: We analysed performance on tasks of episodic memory recall, verbal fluency (letter and category) and global cognition (Minimental State Examination, MMSE) both cross-sectionally and longitudinally. The control background variables were baseline age, gender, years of education, general fluid ability Gf (Wechsler Block Design Test), and socioeconomic status. We employed a linear mixed modelling approach with entropy balancing weights to assess effects of degree of multilingualism on cognitive functions.

Findings and conclusions: Using bilinguals as the reference group, our results indicated that all the other multilingual groups exhibited superior performance on episodic memory recall than bilinguals at baseline. The rate of change over time did not differ for trilinguals and pentalinguals compared to bilinguals. While quadrilinguals declined more over time than bilinguals, they still scored significantly higher than bilinguals at the last test wave. For letter fluency, similarly, all language groups scored higher than bilinguals at baseline, and none of the groups differed from bilinguals in rate of change over time. With regard to category fluency, quadrilinguals scored higher than bilinguals at baseline, but trilinguals and pentalinguals did not differ from bilinguals and none of the groups differed in change over time compared to bilinguals. Finally, for global cognition (MMSE), trilinguals and quadrilinguals scored significantly higher than bilinguals at baseline with no differences in change over time for any of the groups relative to bilinguals. Our study contributes to the understanding of multilingual cognition and sheds light into an under-researched cognitive domain known to decline in normal ageing, namely episodic memory recall.

Significance: Our study emphasizes the importance of researching less explored aspects of multilingualism on cognition, in particular on episodic memory recall, to aid our understanding of factors that could potentially aid cognitive decline in later adulthood.

This article presents methods to study the causal effect of a binary treatment on a functional outcome with observational data. We define a Functional Average Treatment Effect (FATE) and develop an outcome regression estimator. We show how to obtain valid inference on the FATE using simultaneous confidence bands, which cover the FATE with a given probability over the entire domain. Simulation experiments illustrate how the simultaneous confidence bands take the multiple comparison problem into account. Finally, we use the methods to infer the effect of early adult location on subsequent income development for one Swedish birth cohort.

Background: Macrophages are involved in kidney transplants. The aim of the study was to investigate if changes exist in the levels of glomerular macrophage index (GMI) between two consecutive kidney transplant biopsies, and if so to determine their potential impact on graft survival.

Methods: Two consecutive biopsies were performed on the same renal graft in 623 patients. GMI was categorized into three GMI classes: ≤1.8 Low, 1.9–4.5 Medium, and ≥4.6 High. This division yielded nine possible switches between the first and second biopsies (Low-Low, Low-Medium, etc.). Cox-regressions were used and hazard ratios (HR) with 95% confidence interval (CI) are presented.

Results: The worst graft survival was observed in the High-High group, and the best graft survival was observed in the Low-Low and High-Low groups. Compared to the High-High group, a reduction of risk was observed in nearly all other decreasing groups (reductions between 65% and 80% of graft loss). After adjustment for covariates, the risk for graft-loss was lower in the Low-Low (HR = 0.24, CI 0.13–0.46), Low-Medium (HR = 0.25, CI 0.11–0.55), Medium-Low (HR = 0.29, CI 0.11–0.77), and the High-Low GMI (HR = 0.31, CI 0.10–0.98) groups compared to the High-High group as the reference.

Conclusions: GMI may change dynamically, and the latest finding is of most prognostic importance. GMI should be considered in all evaluations of biopsy findings since high or increasing GMI levels are associated with shorter graft survival. Future studies need to consider therapeutic strategies to lower or maintain a low GMI. A high GMI besides a vague histological finding should be considered as a warning sign requiring more frequent clinical follow up.

Convolutional neural networks (CNN) have been successful in machine learning applications including image classification. When it comes to images, their success relies on their ability to consider the space invariant local features in the data. Here, we consider the use of CNN to fit nuisance models in semiparametric estimation of a one dimensional causal parameter: the average causal effect of a binary treatment. In this setting, nuisance models are functions of pre-treatment covariates that need to be controlled for. In an application where we want to estimate the effect of early retirement on a health outcome, we propose to use CNN to control for time-structured covariates. Thus, CNN is used when fitting nuisance models explaining the treatment assignment and the outcome. These fits are then combined into an augmented inverse probability weighting estimator yielding efficient and uniformly valid inference. Theoretically, we contribute by providing rates of convergence for CNN equipped with the rectified linear unit activation function and compare it to an existing result for feedforward neural networks. We also show when those rates guarantee uniformly valid inference for the proposed estimator. A Monte Carlo study is provided where the performance of the proposed estimator is evaluated and compared with other strategies. Finally, we give results on a study of the effect of early retirement on later hospitalization using a database covering the whole Swedish population.

Globally, stroke is the third-leading cause of mortality and disability combined, and one of the costliest diseases in society. More accurate predictions of stroke outcomes can guide healthcare organizations in allocating appropriate resources to improve care and reduce both the economic and social burden of the disease. We aim to develop and evaluate the performance and explainability of three supervised machine learning models and the traditional multinomial logistic regression (mLR) in predicting functional dependence and death three months after stroke, using routinely-collected data. This prognostic study included adult patients, registered in the Swedish Stroke Registry (Riksstroke) from 2015 to 2020. Riksstroke contains information on stroke care and outcomes among patients treated in hospitals in Sweden. Prognostic factors (features) included demographic characteristics, pre-stroke functional status, cardiovascular risk factors, medications, acute care, stroke type, and severity. The outcome was measured using the modified Rankin Scale at three months after stroke (a scale of 0-2 indicates independent, 3-5 dependent, and 6 dead). Outcome prediction models included support vector machines, artificial neural networks (ANN), eXtreme Gradient Boosting (XGBoost), and mLR. The models were trained and evaluated on 75% and 25% of the dataset, respectively. Model predictions were explained using SHAP values. The study included 102,135 patients (85.8% ischemic stroke, 53.3% male, mean age 75.8 years, and median NIHSS of 3). All models demonstrated similar overall accuracy (69%-70%). The ANN and XGBoost models performed significantly better than the mLR in classifying dependence with F1-scores of 0.603 (95% CI; 0.594-0.611) and 0.577 (95% CI; 0.568-0.586), versus 0.544 (95% CI; 0.545-0.563) for the mLR model. The factors that contributed most to the predictions were expectedly similar in the models, based on clinical knowledge. Our ANN and XGBoost models showed a modest improvement in prediction performance and explainability compared to mLR using routinely-collected data. Their improved ability to predict functional dependence may be of particular importance for the planning and organization of acute stroke care and rehabilitation.

Objective: Previous research has shown that daily activities are crucial for mental health among older people, and that such activities declined during the COVID-19 pandemic. While previous studies have confirmed a link between stringent restrictions and an increase in mental ill-health, the role of daily activities as a mediator in this relationship remains underexplored. We analyzed whether reductions in daily activities mediated the impact of these COVID-19 restrictions on mental ill-health during the pandemic’s initial phase.

Methods: We used data from Wave 8 SHARE Corona Survey covering 41,409 respondents from 25 European countries and Israel as well as data on COVID-19 restrictions from the Oxford Government Response  Tracker  (OxCGRT).  Multilevel  regression  and  multilevel-mediation  analysis  were  used  to  examine the relationships between restrictions, daily activities and mental ill-health.

Results: Reductions in walking and shopping showed a notably stronger association with increases in mental ill-health compared to social activities. Furthermore, declines in walking could account for about  a  quarter  of  the  relationship  between  restrictions  and  increased  mental  ill-health,  but  the  mediating effects of the other activates were negligible.

Conclusions: The study highlights the essential role of maintaining daily activities, particularly walking, to  mitigate  the  negative  psychological  effects  of  pandemic-related  restrictions  among  older  populations in Europe.

Aim: Speech difficulties are common in children with cleft palate, but research on foreign-born children is limited. This study aimed to compare speech outcomes, surgery and speech intervention in 5-year-old foreign-born and Swedish-born children with cleft palate with or without cleft lip.

Methods: This retrospective study analysed data from the Swedish cleft lip and palate registry for children born between 2009 and 2016 using Pearson's Chi-squared test and binary logistic regression.

Results: Among 160 foreign-born (106 boys, 54 girls) and 847 Swedish-born (479 boys, 368 girls) 5-year-olds, foreign-born children had significantly lower rates of sufficient velopharyngeal competence (77% vs. 86%), age-appropriate consonant production (28% vs. 60%), and speech without non-oral speech errors (70% vs. 86%). Differences remained after adjustment for cleft type, gender and additional diagnosed conditions. After further adjustments for age at completed primary palatal surgery, differences in age-appropriate consonant production and speech without non-oral speech errors remained significant. Foreign-born children underwent completed primary palatal surgery at older ages and received more secondary palatal surgery and speech intervention than Swedish-born peers.

Conclusion: Foreign-born children showed poorer speech outcomes than Swedish-born peers, despite more secondary palatal surgery and speech intervention. Age at completed primary palatal surgery could partly explain these differences.