Association of city-level walkability, accessibility to biking and public transportation and socio-economic features with COVID-19 infection in Massachusetts, USA: An ecological study

With people restricted to their residences, neighbourhood characteristics may affect behaviour and risk of coronavirus disease 2019 (COVID-19) infection. We aimed to analyse whether neighbourhoods with higher walkability, public transit, biking services and higher socio-economic status were associated with lower COVID-19 infection during the peak of the COVID-19 pandemic in Massachusetts. We used Walk Score®, Bike Score®, and Transit Score® indices to assess the walkability and transportation of 72 cities in Massachusetts, USA based on availability of data and collected the total COVID-19 case numbers of each city up to 10 April 2021. We used univariate and multivariate linear models to analyse the effects of these scores on COVID-19 cases per 100,000 in each city, adjusting for demographic covariates and all covariates, respectively. In the 72 cities studied, the average Walk Score, Transit Score and Bike Score was 48.7, 36.5 and 44.1, respectively, with a total of 426,182 COVID-19 cases. Higher Walk Score, Transit Score, and Bike Score rankings were negatively associated with COVID-19 cases per 100,000 persons (<0.05). Cities with a higher proportion of Hispanic population and a lower median household income were associated with more COVID-19 cases per 100,000 (P<0.05). Higher Walk Score, Transit Score and Bike Score were shown to be protective against COVID-19 transmission, while socio-demographic factors were associated with COVID-19 infection. Understanding the complex relationship of how the structure of the urban environment may constrain commuting patterns for residents and essential workers during COVID-19 would offer potential insights on future pandemic preparedness and response.

A visual atlas of genes tissue-specific pathological roles

Dysregulation of a gene′s function, either due to mutations or impairments in regulatory networks, often triggers pathological states in the affected tissue. Comprehensive mapping of these apparent gene–pathology relationships is an ever daunting task, primarily due to genetic pleiotropy and lack of suitable computational approaches. With the advent of high throughput genomics platforms and community scale initiatives such as the Human Cell Landscape (HCL) project [1], researchers have been able to create gene expression portraits of healthy tissues resolved at the level of single cells. However, a similar wealth of knowledge is currently not at our finger–tip when it comes to diseases. This is because the genetic manifestation of a disease is often quite heterogeneous and is confounded by several clinical and demographic covariates. To circumvent this, we mined ≈18 million PubMed abstracts published till May 2019 and selected ≈6.1 million of them that describe the pathological role of genes in different diseases. Further, we employed a word embedding technique from the domain of Natural Language Processing (NLP) to learn vector representation of entities such as genes, diseases, tissues, etc., in a way such that their relationship is preserved in a vector space. Notably, Pathomap, by the virtue of its underpinning theory, also learns transitive relationships. Pathomap provided a vector representation of words indicating a possible association between DNMT3A/BCOR with CYLD cutaneous syndrome (CCS). The first manuscript reporting this finding was not part of our training data.

The Mediating Effect of Depression on the Relationship between Cognitive Function and the Activities of Daily Living in Post-stroke Patient

Purpose: This study aimed to identify the relationship between cognitive function and activities of daily living (ADL) in addition to the mediating effect exerted by depression on this relationship in post-stroke patients.Methods: A cross-sectional study was performed. A total of 182 patients were recruited from two general and three geriatric hospitals in South Korea between July 2017 and June 2018. Cognitive function, depression, and ADL measures were assessed after informed consent was obtained. Data obtained were analyzed using multiple regression and a simple mediation model that applies the PROCESS macro with a 95% bias-corrected bootstrap confidence interval (5,000 bootstrap resampling).Results: The covariates were sex, age, educational level, types of paralysis, and type of hospital. After controlling for the demographic covariates, cognitive function significantly accounted for the variance of ADL. It was also demonstrated that depression partially mediated the relationship between cognitive function and ADL in post-stroke patients.Conclusion: Cognitive function directly influences the ADL in post-stroke patients and indirectly influences it through depression. This suggests that strategies for improving depression in post-stroke patients should be considered while managing cognitive functioning for improving the ADL.

Adherence to Recommended Preventive Behaviors During the COVID-19 Pandemic: The Role of Empathy and Perceived Health Threat

Background Coping via empathic responding may play a role in preventive behavior engagement during the COVID-19 pandemic, and unlike trait empathy, is a potentially alterable target for changing health behavior. Purpose Our goal was to examine the role of empathic responding in preventive behavior engagement during the COVID-19 pandemic, independent of trait empathy and perceived threat of COVID-19. Methods Participants (N = 2,841) completed a baseline survey early in the pandemic, and a follow-up survey approximately 2 weeks later (M = 13.50 days, SD = 5.61). Preventive health behaviors, including physical distancing and hygiene practices, were assessed at both timepoints. Hierarchical linear regression examined the contributions of trait empathy, perceived threat of COVID-19, and empathic responding at baseline to preventive behaviors at follow-up. Results Controlling for baseline levels of preventive behaviors and demographic covariates, trait empathy and threat of COVID-19 at baseline were each independently associated with preventive behaviors at follow-up. An interaction between perceived threat and empathic responding indicated that those perceiving high threat of COVID-19 at baseline tended to report engaging in preventive behaviors at follow-up regardless of their levels of empathic responding, whereas for those reporting low levels of perceived threat, higher levels of empathic responding were associated with higher engagement in preventive behavior. Conclusions When perceived threat of COVID-19 was low, higher empathic responding was associated with increased engagement in preventive behaviors regardless of trait empathy, suggesting that empathic responding can serve as an actionable target for intervention to promote preventive behavior during the pandemic.

Public Awareness and Perceptions of Invasive Alien Species in Small Towns

Invasive alien species (IAS) are a growing threat globally and cause a variety of ecological, economic, and social impacts. People can introduce IAS and facilitate their spread, and can also implement, support, or oppose their management. Understanding local knowledge, awareness, and perceptions are therefore crucial if management and policy are to be effective. We administered questionnaires to members of the public in eight small towns along the Berg River Catchment in the biodiverse fynbos biome of South Africa. We aimed to assess: (1) awareness of IAS by the general public, (2) local perceptions of the impacts associated with IAS, (3) whether awareness of IAS is correlated with demographic covariates and IAS density, and (4) people’s willingness to detect, report, and support IAS management. Overall, 262 respondents participated in the survey. Most respondents (65%) did not know what IAS are, and 10% were unsure. Many respondents also perceived IAS as beneficial. Using a logistic regression, we found that IAS density, educational level, and gender influenced people’s knowledge and perceptions about IAS in the region. There were a small number (4%) of respondents currently detecting and reporting IAS, but many respondents were interested to learn more. We concluded that people living in small towns in the Western Cape of South Africa remain largely unaware of IAS and their impacts. It is crucial to increase awareness-raising initiatives, and build support and engagement in management of IAS in small towns.

The impact of screen time and green time on mental health in children and adolescents during the COVID-19 pandemic

The COVID-19 pandemic has affected the life of children and adolescents in an unpredecented way, limiting, among others, everyday activities with direct social contacts to mitigate the spread of the virus. These limitations have been associated with worse mental health. Yet, little is known about the underlying mechanisms. In the present study, we focused on two activities that have been likely affected by mitigation measures: screen time and green time. We investigated how screen time and green time influenced each during the pandemic, how they affected children’s and adolescents' mental health, and which role socio-demographic characteristics have in predicting screen time, green time, and mental health. We used data collected over between autumn 2020 and spring 2021 from 844 participants aged 5 to 19 of a population-based, prospective cohort study in Ticino, Switzerland. We analyzed the data using an extended version of the Random Intercept Cross-Lagged Panel Model with time-invariant socio-demographic covariates and mental health as outcome. Results showed that, at the between-person level, screen time was a risk factor and green time a protective factor of mental health. However, within-person deviations of screen time and green time during the pandemic did not consistently predict mental health. Furthermore, they did not influence each other over time. Gender, age, socio-economic background, Body Mass Index and the availability of green space nearby all influenced stable measures of green time and screen time (i.e., random intercepts). Our results highlight the need for targeted actions to promote green time and raise awareness about the detrimental effect of screen time on children’s and adolescents’ mental health.

Concentrated Thermomics for Early Diagnosis of Breast Cancer

Thermography has been employed broadly as a corresponding diagnostic instrument in breast cancer diagnosis. Among thermographic techniques, deep neural networks show an unequivocal potential to detect heterogeneous thermal patterns related to vasodilation in breast cancer cases. Such methods are used to extract high-dimensional thermal features, known as deep thermomics. In this study, we applied convex non-negative matrix factorization (convex NMF) to extract three predominant bases of thermal sequences. Then, the data were fed into a sparse autoencoder model, known as SPAER, to extract low-dimensional deep thermomics, which were then used to assist the clinical breast exam (CBE) in breast cancer screening. The application of convex NMF-SPAER, combining clinical and demographic covariates, yielded a result of 79.3% (73.5%, 86.9%); the highest result belonged to NMF-SPAER at 84.9% (79.3%, 88.7%). The proposed approach preserved thermal heterogeneity and led to early detection of breast cancer. It can be used as a noninvasive tool aiding CBE.

Prevalence, Comorbidity, and Sociodemographic Correlates of Psychiatric Disorders in the All Of Us Biobank

ABSTRACTImportanceAll of Us is a landmark initiative for population-scale research into the etiology of psychiatric disorders and disparities across various sociodemographic categories.ObjectiveTo estimate the prevalence, comorbidity, and demographic covariates of psychiatric and substance use disorders in the All of Us biobank.Design, Setting, and ParticipantsWe estimated prevalence, overlap, and demographic correlates for psychiatric disorders derived from electronic health records in the All of Us biobank (release 5; N = 331,380)ExposuresSocial and demographic covariates.Main Outcome and MeasuresPsychiatric disorders derived from ICD10CM codes and grouped into phecodes across six broad domains: mood disorders, anxiety disorders, substance use disorders, stress-related disorders, schizophrenia, and personality disorders.ResultsThe prevalence of various disorders ranges from approximately 15% to less than 1%, with mood and anxiety disorders being the most common, followed by substance use disorders, stress-related disorders, schizophrenia, and personality disorders. There is substantial overlap among disorders, with a large portion of those with a disorder (~57%) having two or more registered diagnoses and tetrachoric correlations ranging from 0.43 – 0.74. The prevalence of disorders across demographic categories demonstrates that non-Hispanic whites, those of low socioeconomic status, women and those assigned female at birth, and sexual minorities are at greatest risk for most disorders.Conclusions and RelevanceAlthough the rates of disorders in All of Us are lower than rates for disorders in the general population, there is considerable variation, comorbidity, and differences across social groups. Large-scale resources like All of Us will prove to be invaluable for understanding the causes and consequences of psychiatric conditions. As we move towards an era of precision medicine, we must work to ensure it is delivered in an equitable manner.

How Well do Covariates Perform When Adjusting for Sampling Bias in COVID-19 Research? Insights from Multiverse Analyses

Background. COVID-19 research has relied heavily on convenience-based observational samples, which—though often necessary—are susceptible to important sampling biases. We begin with a theoretical overview and introduction to the dynamics that underlie sampling bias. We then empirically examine sampling bias in COVID-19 surveys and evaluate the degree to which common statistical adjustments for demographic covariates successfully attenuate such bias. Methods. We analysed responses to identical questions from three convenience and three largely representative samples (total N = 13,731) collected in Canada within the International COVID-19 Awareness and Responses Evaluation Study (www.icarestudy.com). We compared samples on 11 behavioural and psychological outcomes (e.g., adherence to COVID-19 prevention measures, vaccine intentions) across three time points and employed multiverse-style analyses to examine how 512 combinations of demographic covariates (e.g., sex, age, education, income, ethnicity) impact sampling discrepancies on these outcomes. Results. Significant discrepancies emerged between samples on 73% of outcomes, such that participants in the convenience samples held more positive thoughts towards and engaged in more COVID-19 prevention behaviours. Covariates attenuated sampling differences in only 55% of cases and increased differences in 45%. No covariate performed reliably well. Conclusion. Our results suggest that convenience samples may hold more positive dispositions towards COVID-19 prevention behaviours being studied than would samples drawn using more representative means. Adjusting results for demographic covariates frequently increased rather than decreased bias, suggesting that researchers should be cautious when interpreting adjusted findings. Using multiverse-style analyses as extended sensitivity analyses is recommended.

Pathological Gait Signatures of Post-stroke Dementia With Toe-Off and Heel-to-Ground Angles Discriminate From Alzheimer’s Disease

Given the limited power of neuropsychological tests, there is a need for a simple, reliable means, such as gait, to identify mild dementia and its subtypes. However, gait characteristics of patients with post-stroke dementia (PSD) and Alzheimer’s disease (AD) are unclear. We sought to describe their gait signatures and to explore gait parameters distinguishing PSD from post-stroke non-dementia (PSND) and patients with AD. We divided 3-month post-stroke patients into PSND and PSD groups based on the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and the activity of daily living (ADL). Thirty-one patients with AD and thirty-two healthy controls (HCs) were also recruited. Ten gait parameters in one single and two dual-task gait tests (counting-backward or naming-animals while walking) were compared among the groups, with adjustment for baseline demographic covariates and the MMSE score. The area under the receiver operating characteristic curve (AUC) was used to identify parameters discriminating PSD from individuals with PSND and AD. Patients with PSD and patients with AD showed impaired stride length, velocity, stride time, and cadence while patients with PSD had altered stance and swing phase proportions (all p ≤ 0.01, post hoc). Patients with AD had smaller toe-off (ToA) and heel-to-ground angles (HtA) (p ≤ 0.01) than HCs in dual-task gait tests. Individuals with PSD had a shorter stride length, slower velocity, and altered stance and swing phase percentages in all tests (p ≤ 0.01), but a higher coefficient of variation of stride length (CoVSL) and time (CoVST) only in the naming animals-task gait test (p ≤ 0.001) than individuals with PSND. ToA and HtA in the naming animals-task gait test were smaller in individuals with AD than those with PSD (p ≤ 0.01). Statistical significance persisted after adjusting for demographic covariates, but not for MMSE. The pace and the percentage of stance or swing phase in all tests, CoVST in the dual-task paradigm, and CoVSL only in the naming animals-task gait test (moderate accuracy, AUC &gt; 0.700, p ≤ 0.01) could distinguish PSD from PSND. Furthermore, the ToA and HtA in the naming animals-task gait paradigm discriminated AD from PSD (moderate accuracy, AUC &gt; 0.700, p ≤ 0.01). Thus, specific gait characteristics could allow early identification of PSD and may allow non-invasive discrimination between PSD and AD, or even other subtypes of dementia.