Tag: statistics

Int J Health Plann Manage. 2022 Apr 7. doi: 10.1002/hpm.3469. Online ahead of print.

ABSTRACT

INTRODUCTION: The relationships among health expenditure, health outcome, and economic growth have been given significant consideration in the current literature. Nevertheless, there are potential gaps in the nature of health-growth nexus that current empirical studies have not thoroughly considered.

METHODOLOGY: This study explores Granger causality and cointegration relationships in a trivariate framework among, health expenditure, health outcome, and economic growth. We used three health outcome measures and a panel vector autoregressive model to study 45 countries in Sub-Saharan Africa between 1990 and 2018. Our innovative panel data evaluation technique allows to ascertain significant causal relationships among the studied variables in the short and long run.

RESULTS: Findings from the study include (1) health expenditure and health outcome Granger-cause economic growth in the long run; (2) economic growth Granger-cause health expenditure in the short run; (3) no causal relationship was found running from health expenditure and health outcome to economic growth in the short-run. The former result (1) may not be surprising, given that the countries considered in this study are relatively less developed countries from Sub-Saharan Africa. Hence, further health improvement may play a statistically significant role in spurring further economic growth.

CONCLUSION: Based on the results, the study presents interesting and possible effective policy perspectives for health improvement in the studied countries. Policies that stimulate health spending are needed to create a better and more industrious society that can support SSA’s economic progress. This is because a healthy person may be more productive than someone who is sick, allowing them to produce greater output.

PMID:35393680 | DOI:10.1002/hpm.3469

Dev Med Child Neurol. 2022 Apr 8. doi: 10.1111/dmcn.15237. Online ahead of print.

ABSTRACT

AIM: This study aimed to explore eye movements and stress during eye-tracking gaming performance in children with dyskinetic cerebral palsy (CP) compared with typically developing children, and associations between eye-tracking performance, eye movements, stress, and participants’ characteristics.

METHOD: This cohort study included 12 children with dyskinetic CP aged 5 to 12 years (mean age 8 years 7 months, standard deviation [SD] 2 years 3 months) and 23 typically developing children aged 5 to 13 years (mean age 9 years 0 months, SD 2 years 7 months). Participants played 10 eye-tracking games. Tobii X3-120 and Tobii Pro Lab were used to record and analyse eye movements. Stress was assessed through heart rate variability (HRV), recorded during rest, and eye-tracking performance using the Bittium Faros360° ECG Holter device. Eye-tracking performance was measured using gaming completion time. Fixation and saccade variables were used to quantify eye movements, and time- and frequency-domain variables to quantify HRV. Non-parametric statistics were used.

RESULTS: Gaming completion time was significantly different (p < 0.001) between groups, and it was negatively correlated with experience (r_s = -0.63, p = 0.029). No significant differences were found between groups in fixation and saccade variables. HRV significantly changed from rest to eye-tracking performance only in typically developing children and not in children with dyskinetic CP.

INTERPRETATION: Children with dyskinetic CP took longer to perform the 10 games, especially the inexperienced users, indicating the importance of the early provision of eye-tracking training opportunities. It seems that eye-tracking tasks are not a source of increased stress and effort in children with dyskinetic CP.

PMID:35393636 | DOI:10.1111/dmcn.15237

Nat Genet. 2022 Apr 7. doi: 10.1038/s41588-022-01036-9. Online ahead of print.

ABSTRACT

Polygenic risk scores suffer reduced accuracy in non-European populations, exacerbating health disparities. We propose PolyPred, a method that improves cross-population polygenic risk scores by combining two predictors: a new predictor that leverages functionally informed fine-mapping to estimate causal effects (instead of tagging effects), addressing linkage disequilibrium differences, and BOLT-LMM, a published predictor. When a large training sample is available in the non-European target population, we propose PolyPred⁺, which further incorporates the non-European training data. We applied PolyPred to 49 diseases/traits in four UK Biobank populations using UK Biobank British training data, and observed relative improvements versus BOLT-LMM ranging from +7% in south Asians to +32% in Africans, consistent with simulations. We applied PolyPred⁺ to 23 diseases/traits in UK Biobank east Asians using both UK Biobank British and Biobank Japan training data, and observed improvements of +24% versus BOLT-LMM and +12% versus PolyPred. Summary statistics-based analogs of PolyPred and PolyPred⁺ attained similar improvements.

PMID:35393596 | DOI:10.1038/s41588-022-01036-9

Eur Heart J. 2022 Apr 8:ehac179. doi: 10.1093/eurheartj/ehac179. Online ahead of print.

ABSTRACT

AIMS: Aspirin is widely used in cardiovascular disease prevention but is also associated with an increased risk of bleeding. The net effect of aspirin on dementia and cognitive impairment is uncertain.

METHODS AND RESULTS: In the ASCEND trial, 15 480 people from the UK with diabetes and no history of cardiovascular disease were randomized to aspirin 100 mg daily or matching placebo for a mean of 7.4 years. The 15 427 ASCEND participants with no recorded dementia prior to baseline were included in this cognitive study with a primary pre-specified outcome of ‘broad dementia’, comprising dementia, cognitive impairment, or confusion. This was ascertained through participant, carer, or general practitioner report or hospital admission diagnosis, by 31 March 2019 (∼2 years beyond the scheduled treatment period). The broad dementia outcome occurred in a similar percentage of participants in the aspirin group and placebo group: 548 participants (7.1%) vs. 598 (7.8%), rate ratio 0.91 [95% confidence interval (CI), 0.81-1.02]. Thus, the CI excluded proportional hazards of >2% and proportional benefits of >19%.

CONCLUSION: Aspirin does not have a large proportional effect on the risk of dementia. Trials or meta-analyses with larger total numbers of incident dementia cases to increase statistical power are needed to assess whether any modest proportional 10-15% benefits of 5-7 years of aspirin use on dementia exist.

CLINICAL TRIAL REGISTRATION: Current Controlled Trials number, ISRCTN60635500; ClinicalTrials.gov number: NCT00135226.

PMID:35393614 | DOI:10.1093/eurheartj/ehac179

Nat Genet. 2022 Apr 7. doi: 10.1038/s41588-022-01039-6. Online ahead of print.

ABSTRACT

The human brain forms functional networks of correlated activity, which have been linked with both cognitive and clinical outcomes. However, the genetic variants affecting brain function are largely unknown. Here, we used resting-state functional magnetic resonance images from 47,276 individuals to discover and validate common genetic variants influencing intrinsic brain activity. We identified 45 new genetic regions associated with brain functional signatures (P < 2.8 × 10^-11), including associations to the central executive, default mode, and salience networks involved in the triple-network model of psychopathology. A number of brain activity-associated loci colocalized with brain disorders (e.g., the APOE ε4 locus with Alzheimer’s disease). Variation in brain function was genetically correlated with brain disorders, such as major depressive disorder and schizophrenia. Together, our study provides a step forward in understanding the genetic architecture of brain functional networks and their genetic links to brain-related complex traits and disorders.

PMID:35393594 | DOI:10.1038/s41588-022-01039-6

Bone Marrow Transplant. 2022 Apr 7. doi: 10.1038/s41409-022-01611-y. Online ahead of print.

ABSTRACT

Comparative data of fludarabine, cytarabine and amsacrine (FLAMSA) chemotherapy followed by busulfan (Bu)-based reduced-intensity conditioning (RIC) (FLAMSA-Bu) versus RIC regimens are lacking in patients with active relapsed/refractory (R/R) acute myeloid leukemia (AML) at the time of allogeneic hematopoietic stem cell transplantation (alloSCT). Here, we retrospectively analyzed outcomes after FLAMSA-Bu versus fludarabine/busulfan (FluBu2) conditioning in this patient population. A total of 476 patients fulfilled the inclusion criteria, of whom 257 received FluBu2 and 219 FLAMSA-Bu. Median follow-up was 41 months. Two-year non-relapse mortality (21%), graft-versus-host disease-free, relapse-free survival (24%) and chronic graft-versus-host disease (GVHD) (29%) were not statistically different between cohorts. FLAMSA-Bu was associated with lower 2-year relapse incidence (RI) (38 vs 49% after FluBu2, p = 0.004), and increased leukemia-free survival (LFS) (42 vs 29%, p = 0.001), overall survival (47 vs 39%, p = 0.008) and grades II-IV acute GVHD (36 vs 20%, p = 0.001). In the multivariate analysis, FLAMSA-Bu remained associated with lower RI (HR 0.69, p = 0.042), increased LFS (HR 0.74, p = 0.048) and a higher risk of acute GVHD (HR 2.06, p = 0.005). Notwithstanding the limitations inherent in this analysis, our data indicate that FLAMSA-Bu constitutes a tolerable conditioning strategy, resulting in a long-term benefit in a subset of patients reaching alloSCT with active disease.

PMID:35393528 | DOI:10.1038/s41409-022-01611-y

Sci Rep. 2022 Apr 7;12(1):5900. doi: 10.1038/s41598-022-09938-8.

ABSTRACT

Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.

PMID:35393511 | DOI:10.1038/s41598-022-09938-8

Sci Rep. 2022 Apr 7;12(1):5905. doi: 10.1038/s41598-022-09994-0.

ABSTRACT

Hearing is one of the earliest senses to develop and is quite mature by birth. Contemporary theories assume that regularities in sound are exploited by the brain to create internal models of the environment. Through statistical learning, internal models extrapolate from patterns to predictions about subsequent experience. In adults, altered brain responses to sound enable us to infer the existence and properties of these models. In this study, brain potentials were used to determine whether newborns exhibit context-dependent modulations of a brain response that can be used to infer the existence and properties of internal models. Results are indicative of significant context-dependence in the responsivity to sound in newborns. When common and rare sounds continue in stable probabilities over a very long period, neonates respond to all sounds equivalently (no differentiation). However, when the same common and rare sounds at the same probabilities alternate over time, the neonate responses show clear differentiations. The context-dependence is consistent with the possibility that the neonate brain produces more precise internal models that discriminate between contexts when there is an emergent structure to be discovered but appears to adopt broader models when discrimination delivers little or no additional information about the environment.

PMID:35393525 | DOI:10.1038/s41598-022-09994-0

J Phys Condens Matter. 2022 Apr 7. doi: 10.1088/1361-648X/ac655b. Online ahead of print.

ABSTRACT

Complex band structure emerges when translational symmetry is broken and material states with complex wavevectors become admissible. The resulting complex bands continuously connect conventional bands and their shapes are directly related to measurable physical quantities. To date, interpretations of complex bands usually assume they are semielliptical because this is the shape produced by the Su-Schrieffer-Heeger model. However, numerous studies have reported complex band structures with distinctly non-semielliptical shapes, including loops (essentially deformed, asymmetric semiellipses), spikes, and vertical lines. The primary goal of this work is to explore the phenomenology of these shapes such that deeper physical insight can be obtained from a qualitative inspection of a material’s complex band structure. By using several variations on the Su-Schrieffer-Heeger model, we find that (i) vertical lines are unphysical numerical artifacts, (ii) spikes indicate perfectly evanescent states in the material that couple adjacent layers but do not transfer amplitude, and (iii) asymmetric loops result from hybridization. Secondarily, we also develop a strategy for eliminating any unphysical vertical lines from calculations, thereby improving computational techniques for complex band structure.

PMID:35390781 | DOI:10.1088/1361-648X/ac655b

Epilepsy Res. 2022 Mar 31;182:106917. doi: 10.1016/j.eplepsyres.2022.106917. Online ahead of print.

ABSTRACT

OBJECTIVE: Epilepsies are severe chronic neurological diseases that impair several domains in life and are often accompanied by various somatic and psychiatric comorbidities. Associations between epilepsy and its comorbidities remain poorly understood. As epidemiological research mainly relies on cross-sectional designs and descriptive results, homogeneities regarding comorbidities in individuals suffering from epilepsy remain uncovered. Therefore, we aimed to identify clusters of individuals based on selected seizure-related variables and somatic comorbidities, and their respective risk of experiencing affective disorders, using a Latent Class Analysis (LCA).

METHODS: Latent class analysis, is a model-driven statistical approach, which aims at latent, unobservable clusters on selected disease features. LCA has therefore the potential for uncovering previously unobservable groups or classes with similar comorbidity patterns. It allows for comparisons between those classes regarding risk or promotive factors – such as affective disorders. Our data derives from the Austrian cohort of the European Study on Burden and Care of Epilepsy (ESBACE; http://www.esbace.eu/). In ESBACE, multiple factors were collected to get a detailed picture on prevalence, epilepsy-related variables and comorbidities in a population-based cohort from the region of Salzburg, Austria. We used LCA to identify epilepsy-somatic-comorbidity-clusters and further, compared them to the observed the risk of suffering from affective disorders.

RESULTS: The prevalence of epilepsy in the study region was 9.14/1000 inhabitants. LCA unveiled a three-cluster solution, of which one cluster, mainly consisting of individuals with mixed seizure types, higher age, and discrete somatic comorbidities (stroke, cardiovascular – and respiratory/pulmonary diseases) had a higher risk of experiencing affective disorders.

SIGNIFICANCE: To our knowledge, this is the first large scale study that uses LCA to identify epilepsy-related comorbidity phenotypes, and therefore it might open a new way for epidemiological research.

PMID:35390702 | DOI:10.1016/j.eplepsyres.2022.106917