Understanding small-scale COVID-19 transmission dynamics with the Granger causality test.

Mobility patterns have been broadly studied and deeply altered due to the coronavirus disease (COVID-19). In this paper, we study small-scale COVID-19 transmission dynamics in the city of Valencia and the potential role of subway stations and healthcare facilities in this transmission. A total of 2,398 adult patients were included in the analysis. We study the temporal evolution of the pandemic during the first six months at a small-area level. Two Voronoi segmentations of the city (based on the location of subway stations and healthcare facilities) have been considered, and we have applied the Granger causality test at the Voronoi cell level, considering both divisions of the study area. Considering the output of this approach, the so-called 'donor stations' are subway stations that have sent more connections than they have received and are mainly located in interchanger stations. The transmission in primary healthcare facilities showed a heterogeneous pattern. Given that subway interchange stations receive many cases from other regions of the city, implementing isolation measures in these areas might be beneficial for the reduction of transmission.

A mechanistic spatio-temporal modeling of COVID-19 data.

Understanding the evolution of an epidemic is essential to implement timely and efficient preventive measures. The availability of epidemiological data at a fine spatio-temporal scale is both novel and highly useful in this regard. Indeed, having geocoded data at the case level opens the door to analyze the spread of the disease on an individual basis, allowing the detection of specific outbreaks or, in general, of some interactions between cases that are not observable if aggregated data are used. Point processes are the natural tool to perform such analyses. We analyze a spatio-temporal point pattern of Coronavirus disease 2019 (COVID-19) cases detected in Valencia (Spain) during the first 11 months (February 2020 to January 2021) of the pandemic. In particular, we propose a mechanistic spatio-temporal model for the first-order intensity function of the point process. This model includes separate estimates of the overall temporal and spatial intensities of the model and a spatio-temporal interaction term. For the latter, while similar studies have considered different forms of this term solely based on the physical distances between the events, we have also incorporated mobility data to better capture the characteristics of human populations. The results suggest that there has only been a mild level of spatio-temporal interaction between cases in the study area, which to a large extent corresponds to people living in the same residential location. Extending our proposed model to larger areas could help us gain knowledge on the propagation of COVID-19 across cities with high mobility levels.

A comparison of multiple neighborhood matrix specifications for spatio-temporal model fitting: a case study on COVID-19 data.

Establishing proper neighbor relations between a set of spatial units under analysis is essential when carrying out a spatial or spatio-temporal analysis. However, it is usual that researchers choose some of the most typical (and simple) neighborhood structures, such as the first-order contiguity matrix, without exploring other options. In this paper, we compare the performance of different neighborhood matrices in the context of modeling the weekly relative risk of COVID-19 over small areas located in or near Valencia, Spain. Specifically, we construct contiguity-based, distance-based, covariate-based (considering mobility flows and sociodemographic characteristics), and hybrid neighborhood matrices. We evaluate the goodness of fit, the overall predictive quality, the ability to detect high-risk spatio-temporal units, the capability to capture the spatio-temporal autocorrelation in the data, and the goodness of smoothing for a set of spatio-temporal models based on each of the neighborhood matrices. The results show that contiguity-based matrices, some of the distance-based matrices, and those based on sociodemographic characteristics perform better than the matrices based on k-nearest neighbors and those involving mobility flows. In addition, we test the linear combination of some of the constructed neighborhood matrices and the reweighting of these matrices after eliminating weak neighbor relations, without any model improvement.

Predictive Clinical Decision Support System Using Machine Learning and Imaging Biomarkers in Patients With Neurostimulation Therapy: A Pilot Study.

BACKGROUND: Chronic pain is correlated with alterations in brain structure and function. The selection process for the ideal candidate for spinal cord stimulation (SCS) therapy is based on functional variables analysis and pain evaluation scores. In addition to the difficulties involved in the initial selection of patients and the predictive analysis of the trial phase, the large rate of explants is one of the most important concerns in the analysis of the suitability of implanted candidates. OBJECTIVE: To investigate the usefulness of imaging biomarkers, functional connectivity (FC) and volumetry of the whole brain in patients with Failed back surgery syndrome (FBSS) and to create a clinical patient-based decision support system (CDSS) combining neuroimaging and clinical data for predicting the effectiveness of neurostimulation therapy after a trial phase. STUDY DESIGN: A prospective, consecutive, observational, single center study. SETTING: The Multidisciplinary Pain Management Department of the General University Hospital in Valencia, Spain. METHODS: A prospective, consecutive, and observational single-center study. Using Resting-state functional magnetic resonance imaging (rs-fMRI) and Region of interest (ROI) to ROI analysis, we compared the functional connectivity between regions to detect differences in FC and volume changes. Basal magnetic resonance images were obtained in a 1.5T system and clinical variables were collected twice, at the basal condition and at 6-months post-SCS implant. We also conducted a seed-to-voxel analysis with 9 items as seed-areas characterizing the functional connectivity networks. A decreased in 10 units in the Pain Detect Questionnaire (PD-Q) score was established to define the subgroup of Responders Group (R-G) to neurostimulation therapy. The clinical variables collected and the imaging biomarkers obtained (FC and volumes) were tested on a set of 6 machine learning approaches in an effort to find the best classifier system for predicting the effectiveness of the neurostimulator. RESULTS: Twenty-four patients were analyzed and only seven were classified in the R-G. Volumetric differences were found in the left putamen, F = 34.06, P = 0.02. Four pairwise brain areas showed statistical differences in the rs-fMRI including the right insular cortex. Linear Discriminant Analysis showed the best performance for building the CDSS combining clinical variables and significant imaging biomarkers, the prediction increased diagnostic accuracy in the R-G patients from 29% in current practice to 96% of long-term success. CONCLUSION: These findings confirm a major role of the left putamen and the four pairs of brain regions in FBBS patients and suggest that a CDSS would be able to select patients susceptible to benefitting from SCS therapy adding imaging biomarkers.

Trends in Incidence and Transmission Patterns of COVID-19 in Valencia, Spain.

Importance: Limited information on the transmission and dynamics of SARS-CoV-2 at the city scale is available. Objective: To describe the local spread of SARS-CoV-2 in Valencia, Spain. Design, Setting, and Participants: This single-center epidemiological cohort study of patients with SARS-CoV-2 was performed at University General Hospital in Valencia (population in the hospital catchment area, 364 000), a tertiary hospital. The study included all consecutive patients with COVID-19 isolated at home from the start of the COVID-19 pandemic on February 19 until August 31, 2020. Exposures: Cases of SARS-CoV-2 infection confirmed by the presence of IgM antibodies or a positive polymerase chain reaction test result on a nasopharyngeal swab were included. Cases in which patients with negative laboratory results met diagnostic and clinical criteria were also included. Main Outcomes and Measures: The primary outcome was the characterization of dissemination patterns and connections among the 20 neighborhoods of Valencia during the outbreak. To recreate the transmission network, the inbound and outbound connections were studied for each region, and the relative risk of infection was estimated. Results: In total, 2646 patients were included in the analysis. The mean (SD) age was 45.3 (22.5) years; 1203 (46%) were male and 1442 (54%) were female (data were missing for 1); and the overall mortality was 3.7%. The incidence of SARS-CoV-2 cases was higher in neighborhoods with higher household income (ß2 [for mean income per household] = 0.197; 95% CI, 0.057-0.351) and greater population density (ß1 [inhabitants per km2] = 0.228; 95% CI, 0.085-0.387). Correlations with meteorological variables were not statistically significant. Neighborhood 3, where the hospital and testing facility were located, had the most outbound connections (14). A large residential complex close to the city (neighborhood 20) had the fewest connections (0 outbound and 2 inbound). Five geographically unconnected neighborhoods were of strategic importance in disrupting the transmission network. Conclusions and Relevance: This study of local dissemination of SARS-COV-2 revealed nonevident transmission patterns between geographically unconnected areas. The results suggest that tailor-made containment measures could reduce transmission and that hospitals, including testing facilities, play a crucial role in disease transmission. Consequently, the local dynamics of SARS-CoV-2 spread might inform the strategic lockdown of specific neighborhoods to stop the contagion and avoid a citywide lockdown.

Nations with high smoking rate have low SARS-CoV-2 infection and low COVID-19 mortality rate.

Background and aim of the work The effect of tobacco smoking on COVID-19 disease is debated with common sense and experts suggesting a deleterious effect and manuscripts worldwide reporting a low prevalence of active tobacco smokers among intensive care unit patients. Methods We categorized countries worldwide into three groups with <25%; 25-45%; >45% of active male smokers with data expressed as median and interquartile range [IQR] and extracted data on SARS-CoV-2 infections and COVID-19 deaths per million inhabitants. We also applied multivariate regression techniques to adjust for several epidemiological factors. Results COVID-19 mortality was 13 (5-24) per million inhabitants in countries with male smokers >45% and 33 (4-133) in countries where male smokers were <25%. SARS-CoV-2 infection rates were 436 (217-954) and 1139 (302-4084) with data confirmed when dividing data for each continent and when controlling for confounding factors. Conclusions We found a counterintuitive low COVID-19 mortality and SARS-CoV-2 infection in countries with high prevalence of male smokers at the global level and within each continent, suggesting that active smoking habit is protective. Further research should urgently investigate which is the possible mechanism of action.

Medical Device Related Pressure Injury in the Treatment of Chronic Pain: An Early Sign of Explantation in Suspected Infection.

BACKGROUND: Chronic back pain is a prevalent disease and has a high impact in daily life. Implantable devices (IDs) for chronic pain management include spinal cord stimulation (SCS) systems and intrathecal drug delivery (ITDD) pumps. The number of ITDD implants have increased exponentially in the last decade. The number of complications, such as infections, are also more prevalent. Infection management guidelines are needed to standarize our clinical practice and define protocols of explantation. OBJECTIVES: The primary outcome is to define the likelihood of device explantation regarding some covariates related to the patient, antibiotic therapy or surgerical procedures. The secondary outcome is to evaluate performance compared to the results published in the literature. STUDY DESIGN: Retrospective study. SETTING: Hospital General of Valencia. Valencia. Spain. METHODS: A retrospective study of 288 implantable device surgeries was conducted at the Hospital General Universitary of Valencia (Spain) from 1994 to 2015. Demographical and infection data were collected. We have followed the "guidelines for the diagnosis, prevention and management of implantable electronic cardiac device infection" due to the lack of a specific guideline in our field. RESULTS: Forty-three out of 288 procedures were identified as suspected device-infected interventions. Half of the patients had microbiologically confirmed infection after wound, blood or lumbar fluid culture. The odds ratio (OR) for explantation of the device was 19 for the presence of decubitus, a sign of medical device related pressure injury (P < 0.0005) and 5 for positive wound culture (P < 0.0452). Medical indication leading to device implantation and the antibiotics on discharge also played a role in the decision of device explantation. LIMITATIONS: Lack of external validity and others. CONCLUSION: In this study, presence of decubitus is the defining variable for device explantation when a infection is suspected rather than waiting to culture results. Due to a high variability in infection rates, multidisciplinary guidelines are needed to provide an approach that focuses on accurate data monitoring, rigurous implantation technique and standardized protocols. KEY WORDS: Chronic pain, spinal cord stimulation infection, neurostimulator, intrathecal drug delivery pump, complication, infection, explantation.