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.

Identification of COVID-19 Waves: Considerations for Research and Policy

The identification of COVID-19 waves is a matter of the utmost importance, both for research and decision making. This study uses COVID-19 information from the 52 municipalities of the Metropolitan Region, Chile, and presents a quantitative method—based on weekly accumulated incidence rates—to define COVID-19 waves. We explore three different criteria to define the duration of a wave, and performed a sensitivity analysis using multivariate linear models to show their commonalities and differences. The results show that, compared to a benchmark definition (a 100-day wave), the estimations using longer periods of study are worse in terms of the model’s overall fit (adjusted R2). The article shows that defining a COVID-19 wave is not necessarily simple, and has consequences when performing data analysis. The results highlight the need to adopt well-defined and well-justified definitions for COVID-19 waves, since these methodological choices can have an impact in research and policy making.

Academic performance in adolescents born to mothers with gestational diabetes – a national Danish cohort study

Context The prevalence of Gestational Diabetes Mellitus (GDM) is increasing, and intrauterine hyperglycemia is suspected to affect offspring cognitive function. Objective We assessed academic performance by grade point average (GPA) in children aged 15–16 years at compulsory school graduation, comparing offspring exposed to GDM (O-GDM) with offspring from the background population (O-BP). Design Cohort study. Setting Register-based. Participants All singletons born in Denmark between 1994 and 2001 (O-GDM: n=4,286; O-BP: n=501,045). Standardized and internationally comparable GPAs were compared in univariate- and multivariate linear models. Main outcome measures Adjusted mean difference in GPA. We additionally analyzed the probability of having a high GPA, a GPA below passing, and no GPA registered. Results O-GDM had a GPA of 6.29 (SD 2.52), while O-BP had a GPA of 6.78 (SD 2.50). The adjusted mean difference was -0.36 [95% confidence interval (CI) -0.44; -0.29], corresponding to a Cohens D of 0.14. O-GDM had a lower probability of obtaining a high GPA (adjusted odds ratio (aOR) 0.68 [95 CI 0.59; 0.79]), while their risk of obtaining a GPA below passing was similar to O-BP (aOR 1.20 [95 CI 0.96; 1.50]). O-GDM had a higher risk of not having a GPA registered (aOR of 1.38 [95% CI 1.24; 1.53]). Conclusion Academic performance in O-GDM was marginally lower than in O-BP. However, this difference is unlikely to be of clinical importance.

Comparison of Woven EndoBridge device sizing with conventional measurements and virtual simulation using the Sim&Size software: a multicenter experience

BackgroundSelection of the appropriate device size mandatory during aneurysm treatment with a Woven EndoBridge (WEB). We aimed to investigate if virtual simulation with Sim&Size software may have an impact on technical, angiographic, and clinical outcomes after WEB treatment.MethodsData from two large-volume centers were collected and compared (January 2017–January 2020). Virtual simulation was systematically adopted in one center, while conventional sizing was used in the other one. Outcomes were the duration of intervention, the radiation dose (in milligrays, the number of corrective interventions for inappropriate WEB size, the number of WEBs not deployed, angiographic occlusion, and complications. Univariate and multivariate linear models were adopted.ResultsA total of 186 aneurysms were treated with WEB (109 with and 77 without virtual simulation). Patient characteristics and aneurysm features were comparable among virtual and conventional sizing, except for mean age (62.2±11.8 years and 56.2±10.1 years, P=0.0004) and median aspect ratio (1.6, IQR=1.2–2 and 1.2, IQR=1–1.6, P=0.0001). Years of operator experience were comparable. Virtual simulation was independently associated with shorter intervention time (45 min, IQR=33–63.5 min vs 63.5 min, IQR=41–84.7 min, P=0.0001), lower radiation dose (1051 mGy, IQR=815–1399 mGy vs 1207 mGy, IQR=898–2084 mGy, P=0.0001), and lower number of WEBs not deployed (26/77=33.7% vs 8/109=7.3%, P=0.0001). The need for additional maneuvers was significantly lower in the virtual simulation group (5/109=4.6% vs 12/77=15.6%, P=0.021). Angiographic outcomes and complications were comparable.ConclusionsIn this multicenter experience, virtual simulation with Sim&Size software seems to facilitate the selection of the appropriate WEB device for aneurysm treatment, reducing the time of intervention, the radiation dose, the number of devices not deployed, and the need for corrective interventions.Trial registration numberclinicaltrials.gov Identifier: NCT04621552.

Blood neurofilament light concentration at admittance: a potential prognostic marker in COVID-19

Objective To test the hypotheses that serum concentrations of neurofilament light chain protein (NfL) and glial fibrillary acidic protein (GFAp) can serve as biomarkers for disease severity in COVID-19 patients. Methods Forty-seven inpatients with confirmed COVID-19 had blood samples drawn on admission for assessing serum biomarkers of CNS injury by Single molecule array (Simoa), NfL and GFAp. Concentrations of NfL and GFAp were analyzed in relation to symptoms, clinical signs, inflammatory biomarkers and clinical outcomes. We used multivariate linear models to test for differences in biomarker concentrations in the subgroups, accounting for confounding effects. Results In total, 21 % (n=10) of the patients were admitted to an intensive care unit, whereas the overall mortality rate was 13 % (n=6). Non-survivors had higher serum concentrations of NfL (p<0.001) than patients who were discharged alive both in adjusted analyses (p=2.6 x 10-7) and unadjusted analyses (p=0.001). The concentrations of NfL in non-survivors increased over repeated measurements whereas the concentrations in survivors were stable. Significantly higher concentrations of NfL were found in patients reporting fatigue, while reduced concentrations were found in patients experiencing cough, myalgia and joint pain. The GFAp concentration was also significantly higher in non-survivors than survivors (p=0.02). Conclusion Increased concentrations of NfL and GFAp in COVID-19 patients on admission may indicate increased mortality risk. Measurement of blood biomarkers for nervous system injury can be useful to detect and monitor CNS injury in COVID-19.

Envelopes in multivariate regression models with nonlinearity and heteroscedasticity

Summary Envelopes have been proposed in recent years as a nascent methodology for sufficient dimension reduction and efficient parameter estimation in multivariate linear models. We extend the classical definition of envelopes in Cook et al. (2010) to incorporate a nonlinear conditional mean function and a heteroscedastic error. Given any two random vectors ${X}\in\mathbb{R}^{p}$ and ${Y}\in\mathbb{R}^{r}$, we propose two new model-free envelopes, called the martingale difference divergence envelope and the central mean envelope, and study their relationships to the standard envelope in the context of response reduction in multivariate linear models. The martingale difference divergence envelope effectively captures the nonlinearity in the conditional mean without imposing any parametric structure or requiring any tuning in estimation. Heteroscedasticity, or nonconstant conditional covariance of ${Y}\mid{X}$, is further detected by the central mean envelope based on a slicing scheme for the data. We reveal the nested structure of different envelopes: (i) the central mean envelope contains the martingale difference divergence envelope, with equality when ${Y}\mid{X}$ has a constant conditional covariance; and (ii) the martingale difference divergence envelope contains the standard envelope, with equality when ${Y}\mid{X}$ has a linear conditional mean. We develop an estimation procedure that first obtains the martingale difference divergence envelope and then estimates the additional envelope components in the central mean envelope. We establish consistency in envelope estimation of the martingale difference divergence envelope and central mean envelope without stringent model assumptions. Simulations and real-data analysis demonstrate the advantages of the martingale difference divergence envelope and the central mean envelope over the standard envelope in dimension reduction.