Global, regional, and national burden of disorders affecting the nervous system, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND

Disorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.

METHODS

We estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.

FINDINGS

Globally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378-521), affecting 3·40 billion (3·20-3·62) individuals (43·1%, 40·5-45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7-26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6-38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5-32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7-2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.

INTERPRETATION

As the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed.

FUNDING

Bill & Melinda Gates Foundation.

Usability and feasibility of PreventS-MD web app for stroke prevention

BACKGROUND

Most strokes and cardiovascular diseases (CVDs) are potentially preventable if their risk factors are identified and well controlled. Digital platforms, such as the PreventS-MD web app (PreventS-MD) may aid health care professionals (HCPs) in assessing and managing risk factors and promoting lifestyle changes for their patients.

METHODS

This is a mixed-methods cross-sectional two-phase survey using a largely positivist (quantitative and qualitative) framework. During Phase 1, a prototype of PreventS-MD was tested internationally by 59 of 69 consenting HCPs of different backgrounds, age, sex, working experience, and specialties using hypothetical data. Collected comments/suggestions from the study HCPs in Phase 1 were reviewed and implemented. In Phase 2, a near-final version of PreventS-MD was developed and tested by 58 of 72 consenting HCPs using both hypothetical and real patient (n = 10) data. Qualitative semi-structured interviews with real patients (n = 10) were conducted, and 1 month adherence to the preventive recommendations was assessed by self-reporting. The four System Usability Scale (SUS) groups of scores (0-50 unacceptable; 51-68 poor; 68-80.3 good; >80.3 excellent) were used to determine usability of PreventS-MD.

FINDINGS

Ninety-nine HCPs from 27 countries (45% from low- to middle-income countries) participated in the study, and out of them, 10 HCPs were involved in the development of PreventS before the study, and therefore were not involved in the survey. Of the remaining 89 HCPs, 69 consented to the first phase of the survey, and 59 of them completed the first phase of the survey (response rate 86%), and 58 completed the second phase of the survey (response rate 84%). The SUS scores supported good usability of the prototype (mean score = 80.2; 95% CI [77.0-84.0]) and excellent usability of the final version of PreventS-MD (mean score = 81.7; 95% CI [79.1-84.3]) in the field. Scores were not affected by the age, sex, working experience, or specialty of the HCPs. One-month follow-up of the patients confirmed the high level of satisfaction/acceptability of PreventS-MD and (100%) adherence to the recommendations.

INTERPRETATION

The PreventS-MD web app has a high level of usability, feasibility, and satisfaction by HCPs and individuals at risk of stroke/CVD. Individuals at risk of stroke/CVD demonstrated a high level of confidence and motivation in following and adhering to preventive recommendations generated by PreventS-MD.

Te Ōranga Ō Te Roro: kaumātua perspectives on the development of a mobile app for mate wareware (dementia) awareness

AIM

Mate wareware (dementia) presents a significant social and economic burden for Māori in Aotearoa New Zealand. Previous literature has highlighted the need to improve health literacy for Māori regarding the causes and management of mate wareware, yet there is a lack of Māori-centred educational resources. It was determined that a mobile phone application (app) could meet this need and that early consultation with Māori was required to ensure the digital solution would be culturally safe and relevant.

METHOD

This study explored the perspectives of kaumātua (Māori elders) regarding how to cater the mate wareware mobile app to Māori. Through a qualitative approach based on Kaupapa Māori principles, two focus groups were held with 15 kaumātua. Focus group data were thematically analysed.

RESULTS

The analysis identified four themes related to the content of the proposed app and its design features. "Information about mate wareware" and "Caregiver support" were prominent themes that kaumātua prioritised for inclusion in the proposed app. To ensure uptake, kaumātua emphasised that the "Access" and "Appeal" of the proposed app should be considered.

CONCLUSION

The findings have informed the design of the Mate Wareware app and should be considered when developing other digital health interventions for Māori.

Filter and Wrapper Stacking Ensemble (FWSE): a robust approach for reliable biomarker discovery in high-dimensional omics data

Selecting informative features, such as accurate biomarkers for disease diagnosis, prognosis and response to treatment, is an essential task in the field of bioinformatics. Medical data often contain thousands of features and identifying potential biomarkers is challenging due to small number of samples in the data, method dependence and non-reproducibility. This paper proposes a novel ensemble feature selection method, named Filter and Wrapper Stacking Ensemble (FWSE), to identify reproducible biomarkers from high-dimensional omics data. In FWSE, filter feature selection methods are run on numerous subsets of the data to eliminate irrelevant features, and then wrapper feature selection methods are applied to rank the top features. The method was validated on four high-dimensional medical datasets related to mental illnesses and cancer. The results indicate that the features selected by FWSE are stable and statistically more significant than the ones obtained by existing methods while also demonstrating biological relevance. Furthermore, FWSE is a generic method, applicable to various high-dimensional datasets in the fields of machine intelligence and bioinformatics.

A Pilot Study of Application of the Stroke Riskometer Mobile App for Assessment of the Course and Clinical Outcomes of COVID-19 among Hospitalized Patients

INTRODUCTION

Early determination of COVID-19 severity and health outcomes could facilitate better treatment of patients. Different methods and tools have been developed for predicting outcomes of COVID-19, but they are difficult to use in routine clinical practice.

METHODS

We conducted a prospective cohort study of inpatients aged 20-92 years, diagnosed with COVID-19 to determine whether their individual 5-year absolute risk of stroke at the time of hospital admission predicts the course of COVID-19 severity and mortality. The risk of stroke was determined by the Stroke Riskometer mobile application.

RESULTS

We examined 385 patients hospitalized with COVID-19 (median age 61 years). The participants were categorized based on COVID-19 severity: 271 (70.4%) to the "not severe" and 114 (29.6%) to the "severe" groups. The median risk of stroke the next day after hospitalization was significantly higher among patients in the severe group (2.83, 95% CI: 2.35-4.68) versus the not severe group (1.11, 95% CI: 1.00-1.29). The median risk of stroke and median systolic blood pressure (SBP) were significantly higher among non-survivors (12.04, 95% CI: 2.73-21.19) and (150, 95% CI: 140-170) versus survivors (1.31, 95% CI: 1.14-1.52) and (134, 95% CI: 130-135), respectively. Those who spent more than 2.5 h a week on physical activity were 3.1 times more likely to survive from COVID-19. Those who consumed more than one standard alcohol drink a day, or suffered with atrial fibrillation, or had poor memory were 2.5, 2.3, and 2.6 times more likely not to survive from COVID-19, respectively.

CONCLUSIONS

High risk of stroke, physical inactivity, alcohol intake, high SBP, and atrial fibrillation are associated with severity and mortality of COVID-19. Our findings suggest that the Stroke Riskometer app could be used as a simple predictive tool of COVID-19 severity and mortality.

Tobacco use among Kola Sámi, the indigenous people of the Murmansk region, Russia: A cross-sectional study

Russia is among the top ten nations in terms of smoking prevalence. Little is known about smoking rates among Indigenous Peoples in Russia. Our aim was to assess the prevalence of tobacco and nicotine product use among Kola peninsula Sámi. An exploratory cross-sectional survey was conducted to determine tobacco or nicotine product use among 505 Sámi people (about 30% of the whole Sámi population of Russia). Over 60% of participants had tried tobacco or nicotine products. Median age of first use was 15 years, with cigarettes being the most frequent first product tried and the most common type of product used currently. About a third of participants used a tobacco or nicotine product at least occasionally; 25% (predominantly males) smoked at least occasionally with 23.8% smoking daily. Of participants who smoked, 52.5% scored medium and 44.2% scored high on the Heaviness of Smoking Index. Seventeen percent of participants smoked formerly but not currently. Like some other Indigenous Peoples, Kola Sámi in Russia have a higher smoking prevalence than the average among the Russian population. Interest in a smoking cessation mobile app designed for the Sámi population suggests that such an intervention could help to reduce this inequity.

Machine learning, artificial intelligence and the prediction of dementia

PURPOSE OF REVIEW

Artificial intelligence and its division machine learning are emerging technologies that are increasingly applied in medicine. Artificial intelligence facilitates automatization of analytical modelling and contributes to prediction, diagnostics and treatment of diseases. This article presents an overview of the application of artificial intelligence in dementia research.

RECENT FINDINGS

Machine learning and its branch Deep Learning are widely used in research to support in diagnosis and prediction of dementia. Deep Learning models in certain tasks often result in better accuracy of detection and prediction of dementia than traditional machine learning methods, but they are more costly in terms of run times and hardware requirements. Both machine learning and Deep Learning models have their own strengths and limitations. Currently, there are few datasets with limited data available to train machine learning models. There are very few commercial applications of machine learning in medical practice to date, mostly represented by mobile applications, which include questionnaires and psychometric assessments with limited machine learning data processing.

SUMMARY

Application of machine learning technologies in detection and prediction of dementia may provide an advantage to psychiatry and neurology by promoting a better understanding of the nature of the disease and more accurate evidence-based processes that are reproducible and standardized.

General hospital specialists' attitudes toward psychiatry: a cross-sectional survey in seven countries

OBJECTIVE

Psychiatric comorbidities are common in physical illness and significantly affect health outcomes. Attitudes of general hospital doctors toward psychiatry are important as they influence referral patterns and quality of care. Little is known about these attitudes and their cultural correlates. The aim of this study was to identify attitudes toward psychiatry among general hospital specialists in relation to culture of the practice setting and other clinician factors (gender, age, seniority and specialty).

METHODS

A cross-sectional, descriptive study was carried out in seven countries (New Zealand, China, Sri Lanka, Russia, Israel, Brazil, the Netherlands). Data were collected from senior medical staff of various disciplines using an updated version of Mayou and Smith's (1986) self-administered questionnaire.

RESULTS

A total of 889 hospital doctors participated. While favourable attitudes toward both psychiatric consultation and management were endorsed by a majority, significant differences were also observed between countries. Subgroup differences were mostly confined to gender, acuity of practice setting and specialty. For example, female doctors in Russia (χ²=7.7, p=0.0056), China (χ²=9.2, p=0.0025) and the Netherlands (χ²=5.7, p=0.0174) endorsed more positive attitudes compared with their male counterparts, but this gender effect was not replicated in the total sample. Chronic care specialists were overall more inclined to manage patients' emotional problems compared with those working in acute care (χ²=70.8, p (adjusted)<0.0001), a significant finding seen also in individual countries (China, New Zealand, the Netherlands, Russia). Physicians were more favourably disposed toward psychiatry compared with other specialists, especially surgeons, in all countries except Israel.

CONCLUSIONS

This study adds to evidence for the association of medical attitudes with individual clinician factors and demonstrates that the influence of these factors varies by country. Understanding these issues may help to overcome barriers and improve quality of care provided to general hospital patients.

Trends in tobacco smoking and smoking cessation in Russia with a focus on Indigenous populations: A narrative review

Introduction

Tobacco smoking is one of the main preventable causes of illness and premature death. Globally, more than 7 million people die annually from diseases associated with smoking, and this number is projected to increase to 8 million per year by 2030. Wide disparities in smoking prevalence exist by gender, age, socioeconomic status, rurality and ethnicity. In several countries, smoking is disproportionately high among the Indigenous populations.

Objective

This review assesses the prevalence and harm of smoking and current trends in smoking cessation among the diverse multi-ethnic populations of Russia, with a particular emphasis on Indigenous populations.

Data sources

We systematically searched health, nursing, social science and grey literature databases and bibliographies for relevant studies. Search strings combined keywords related to smoking prevalence and smoking cessation with keywords related to Russia and the Indigenous populations of Russia.

Study selection

Studies were included if they were published between 1 January 2005 and 14 October 2020, and if they reported prevalence of tobacco smoking and/or activities and outcomes of a smoking cessation programme or ban in the Russian Federation.

Conclusions

Tobacco smoking is significant in the entire Russian population, a higher prevalence of smoking in Indigenous populations compared to the dominant Russian (Slavic) ethnic group is common. Smoking prevalence data for most of the Indigenous ethnic groups of Russia remains unclear. Tobacco control interventions for Indigenous groups are underdeveloped even though they have the potential to deliver proportionately greater reduction in smoking harm.

Personalised predictive modelling with brain-inspired spiking neural networks of longitudinal MRI neuroimaging data and the case study of dementia

BACKGROUND

Longitudinal neuroimaging provides spatiotemporal brain data (STBD) measurement that can be utilised to understand dynamic changes in brain structure and/or function underpinning cognitive activities. Making sense of such highly interactive information is challenging, given that the features manifest intricate temporal, causal relations between the spatially distributed neural sources in the brain.

METHODS

The current paper argues for the advancement of deep learning algorithms in brain-inspired spiking neural networks (SNN), capable of modelling structural data across time (longitudinal measurement) and space (anatomical components). The paper proposes a methodology and a computational architecture based on SNN for building personalised predictive models from longitudinal brain data to accurately detect, understand, and predict the dynamics of an individual's functional brain state. The methodology includes finding clusters of similar data to each individual, data interpolation, deep learning in a 3-dimensional brain-template structured SNN model, classification and prediction of individual outcome, visualisation of structural brain changes related to the predicted outcomes, interpretation of results, and individual and group predictive marker discovery.

RESULTS

To demonstrate the functionality of the proposed methodology, the paper presents experimental results on a longitudinal magnetic resonance imaging (MRI) dataset derived from 175 older adults of the internationally recognised community-based cohort Sydney Memory and Ageing Study (MAS) spanning 6 years of follow-up.

SIGNIFICANCE

The models were able to accurately classify and predict 2 years ahead of cognitive decline, such as mild cognitive impairment (MCI) and dementia with 95% and 91% accuracy, respectively. The proposed methodology also offers a 3-dimensional visualisation of the MRI models reflecting the dynamic patterns of regional changes in white matter hyperintensity (WMH) and brain volume over 6 years.

CONCLUSION

The method is efficient for personalised predictive modelling on a wide range of neuroimaging longitudinal data, including also demographic, genetic, and clinical data. As a case study, it resulted in finding predictive markers for MCI and dementia as dynamic brain patterns using MRI data.

Cross-cultural validation of the stroke riskometer using generalizability theory

The Stroke Riskometer mobile application is a novel, validated way to provide personalized stroke risk assessment for individuals and motivate them to reduce their risks. Although this app is being used worldwide, its reliability across different countries has not yet been rigorously investigated using appropriate methodology. The Generalizability Theory (G-Theory) is an advanced statistical method suitable for examining reliability and generalizability of assessment scores across different samples, cultural and other contexts and for evaluating sources of measurement errors. G-Theory was applied to the Stroke Riskometer data sampled from 1300 participants in 13 countries using two-facet nested observational design (person by item nested in the country). The Stroke Riskometer demonstrated strong reliability in measuring stroke risks across the countries with coefficients G relative and absolute of 0.84, 95%CI [0.79; 0.89] and 0.82, 95%CI [0.76; 0.88] respectively. D-study analyses revealed that the Stroke Riskometer has optimal reliability in its current form in measuring stroke risk for each country and no modifications are required. These results suggest that the Stroke Riskometer's scores are generalizable across sample population and countries permitting cross-cultural comparisons. Further studies investigating reliability of the Stroke Riskometer over time in longitudinal study design are warranted.

A precision measurement of the mass of the top quark

The standard model of particle physics contains parameters--such as particle masses--whose origins are still unknown and which cannot be predicted, but whose values are constrained through their interactions. In particular, the masses of the top quark (M(t)) and W boson (M(W)) constrain the mass of the long-hypothesized, but thus far not observed, Higgs boson. A precise measurement of M(t) can therefore indicate where to look for the Higgs, and indeed whether the hypothesis of a standard model Higgs is consistent with experimental data. As top quarks are produced in pairs and decay in only about 10(-24) s into various final states, reconstructing their masses from their decay products is very challenging. Here we report a technique that extracts more information from each top-quark event and yields a greatly improved precision (of +/- 5.3 GeV/c2) when compared to previous measurements. When our new result is combined with our published measurement in a complementary decay mode and with the only other measurements available, the new world average for M(t) becomes 178.0 +/- 4.3 GeV/c2. As a result, the most likely Higgs mass increases from the experimentally excluded value of 96 to 117 GeV/c2, which is beyond current experimental sensitivity. The upper limit on the Higgs mass at the 95% confidence level is raised from 219 to 251 GeV/c2.