Dynamic Assessment of Personal Exposure to Air Pollution for Everyone: a Smartphone-Based Approach

Abstract. In Epidemiology, exposure assessment is the process of measuring or estimating the intensity of human exposures to an environmental agent such as air pollution. Healthcare agencies typically take into consideration yearly averaged pollution values and apply them to all citizens, in risk models. However distinct parts of cities can have significantly different levels of pollution and individual habits can influence exposure, too. Consequently, in epidemiology and public health, there is an increasing interest for personal exposure assessment, i.e. the capability of measuring the exposure of individuals. Within the EU H2020 PULSE project, an innovative mechanism for the individual and dynamic assessment of exposure to air pollution has been implemented. The present paper illustrates its technological and scientific components. The system has already been deployed to several pilot cities of the project and Pavia, Italy, has been the first one. In that city several hundreds of tracks have already been acquired and processed. Therefore, the paper thoroughly illustrates the assessment procedure with examples

CloudSat-based assessment of GPM Microwave Imager snowfall observation capabilities

The sensitivity of Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channels to snowfall at higher latitudes (around 60◦N/S) is investigated using coincident CloudSat observations. The 166 GHz channel is highlighted throughout the study due to its ice scattering sensitivity and polarization information. The analysis of three case studies evidences the important combined role of total precipitable water (TPW), supercooled cloud water,and background surface composition on the brightness temperature (TB) behavior for different snow-producing clouds. A regression tree statistical analysis applied to the entire GMI-CloudSat snowfall dataset indicates which variables influence the 166 GHz polarization difference (166∆TB)and its relation to snowfall. Critical thresholds of various parameters (sea ice concentration (SIC), TPW, ice water path (IWP)) are established for optimal snowfall detection capabilities. The 166∆TB can identify snowfall events over land and sea when critical thresholds are exceeded (TPW \u3e 3.6 kg·m−2, IWP \u3e 0.24 kg·m−2 over land, and SIC \u3e 57%, TPW \u3e 5.1 kg·m−2 over sea). The complex combined 166∆TB-TB relationship at higher latitudes and the impact of supercooled water vertical distribution are also investigated. The findings presented in this study can be exploited to improve passive microwave snowfall detection algorithms

Transfer Learning for Urban Landscape Clustering and Correlation with Health Indexes

Within the EU-funded Pulse project, we are implementing a data analytic platform designed to provide public health decision makers with advanced approaches to jointly analyze maps and geospatial information with health care data and air pollution measurements. In this paper we describe a component of such platform, designed to couple deep learning analysis of geospatial images of cities and some healthcare and behavioral indexes collected by the 500 cities US project, showing that, in New York City, urban landscape significantly correlates with the access to healthcare services

Spatial Enablement to Support Environmental, Demographic, Socioeconomics, and Health Data Integration and Analysis for Big Cities: A Case Study With Asthma Hospitalizations in New York City

The percentage of the world's population living in urban areas is projected to increase in the next decades. Big cities are heterogeneous environments in which socioeconomic and environmental differences among the neighborhoods are often very pronounced. Each individual, during his/her life, is constantly subject to a mix of exposures that have an effect on their phenotype but are frequently difficult to identify, especially in an urban environment. Studying how the combination of environmental and socioeconomic factors which the population is exposed to influences pathological outcomes can help transforming public health from a reactive to a predictive system. Thanks to the application of state-of-the-art spatially enabled methods, patients can be stratified according to their characteristics and the geographical context they live in, optimizing healthcare processes and the reducing its costs. Some public health studies focusing specifically on urban areas have been conducted, but they usually consider a coarse spatial subdivision, as a consequence of scarce availability of well-integrated data regarding health and environmental exposure at a sufficient level of granularity to enable meaningful statistical analyses. In this paper, we present an application of highly fine-grained spatial resolution methods to New York City data. We investigated the link between asthma hospitalizations and a combination of air pollution and other environmental and socioeconomic factors. We first performed an explorative analysis using spatial clustering methods that shows that asthma is related to numerous factors whose level of influence varies considerably among neighborhoods. We then performed a Geographically Weighted Regression with different covariates and determined which environmental and socioeconomic factors can predict hospitalizations and how they vary throughout the city. These methods showed to be promising both for visualization and analysis of demographic and epidemiological urban dynamics, that can be used to organize targeted intervention and treatment policies to address the single citizens considering the factors he/she is exposed to. We found a link between asthma and several factors such as PM2.5, age, health insurance coverage, race, poverty, obesity, industrial areas, and recycling. This study has been conducted within the PULSE project, funded by the European Commission, briefly presented in this paper

Array-Aware Matching: Taming the Complexity of Large-Scale Simulation Models

Equation-based modelling is a powerful approach to tame the complexity of large-scale simulation problems. Equation-based tools automatically translate models into imperative languages. When confronted with nowadays' problems, however, well assessed model translation techniques exhibit scalability issues, that are particularly severe when models contain very large arrays. In fact, such models can be made very compact by enclosing equations into looping constructs, but reflecting the same compactness into the translated imperative code is not trivial. In this paper, we face this issue by concentrating on a key step of equations-to-code translation, the equation/variable matching. We first show that an efficient translation of models with (large) arrays needs awareness of their presence, by defining a figure of merit to measure how much the looping constructs are preserved along the translation. We then show that the said figure of merit allows to define an optimal array-aware matching, and as our main result, that the so stated optimal array-aware matching problem is NP-complete. As an additional result, we propose a heuristic algorithm capable of performing array-aware matching in polynomial time. The proposed algorithm can be proficiently used by model translator developers in the implementation of efficient tools for large-scale system simulation

Biomarkers of myocardial injury with different energy sources for atrial fibrillation catheter ablation

Background: Our study aims to compare acute myocardial injury biomarker rise after atrial fibrillation ablation performed with different technologies.Methods and Results: One hundred and ten patients were treated with pulmonary vein isolation with 4 different technologies: open-irrigated tip radiofrequency (RF) catheter in35 patients (Group A), cryoballoon in 35 patients (Group B), visually guided laser balloon in 20 patients (Group C), open-irrigated tip RF catheter with contact-force-sensing technology in 20 patients (Group D). Post-procedure samples of cardiac troponin I (cTnI) and creatinine kinase-MB (CK-MB) were collected at 19 ± 3 h and 43 ± 3 h after ablation. At the first postprocedural sample, cTnI and CK-MB levels were found elevated in all 110 patients with a median value of 2.11 ng/mL and 8.95 ng/mL, respectively. Group B showed cTnI levels increased (median 5.96 ng/mL) compared to other groups (median Group A: 1.72 ng/mL, Group C: 1.54 ng/mL, Group D: 2.0 ng/mL; p < 0.001). Also CK-MB levels resulted higher in cryoablation (median 26.4 ng/mL) compared to other groups (median Group A: 6.40 ng/mL, Group C: 7.15 ng/mL, Group D: 6.50 ng/mL; p < 0.001). No significant association was observed between biomarker levels and recurrences of atrial fibrillation after a mean follow-up of 369 ± 196 days.Conclusions: Highest markers for myocardial injury were observed in the cryoballoon group. It is possible that a longer delivery energy duration and other factors affecting lesion size resulted in higher amount of cardiac injury in cryoablation. The higher levels of cardiac biomarkers did not translate into a better outcome and its physiologic significance is unknown.

Clinical governance of patients with acute coronary syndromes

Aims Using the principles of clinical governance, a patient-centred approach intended to promote holistic quality improvement, we designed a prospective, multicentre study in patients with acute coronary syndrome (ACS). We aimed to verify and quantify consecutive inclusion and describe relative and absolute effects of indicators of quality for diagnosis and therapy. Methods and results Administrative codes for invasive coronary angiography and acute myocardial infarction were used to estimate the ACS universe. The ratio between the number of patients included and the estimated ACS universe was the consecutive index. Co-primary quality indicators were timely reperfusion in patients admitted with ST-elevation ACS and optimal medical therapy at discharge. Cox-proportional hazard models for 1-year death with admission and discharge-specific covariates quantified relative risk reductions and adjusted number needed to treat (NNT) absolute risk reductions. Hospital codes tested had a 99.5% sensitivity to identify ACS universe. We estimated that 7344 (95% CI: 6852-7867) ACS patients were admitted and 5107 were enrolled-i.e. a consecutive index of 69.6% (95% CI 64.9-74.5%), which varied from 30.7 to 79.2% across sites. Timely reperfusion was achieved in 22.4% (95% CI: 20.7-24.1%) of patients, was associated with an adjusted hazard ratio (HR) for 1-year death of 0.60 (95% CI: 0.40-0.89) and an adjusted NNT of 65 (95% CI: 44-250). Corresponding values for optimal medical therapy were 70.1% (95% CI: 68.7-71.4%), HR of 0.50 (95% CI: 0.38-0.66), and NNT of 98 (95% CI: 79-145). Conclusion A comprehensive approach to quality for patients with ACS may promote equitable access of care and inform implementation of health care delivery. Registration ClinicalTrials.Gov ID NCT0425553

Performance of Prognostic Scoring Systems in MINOCA: A Comparison among GRACE, TIMI, HEART, and ACEF Scores

Background: the prognosis of patients with myocardial infarction with non-obstructive coronary arteries (MINOCA) is not benign; thus, prompting the need to validate prognostic scoring systems for this population. Aim: to evaluate and compare the prognostic performance of GRACE, TIMI, HEART, and ACEF scores in MINOCA patients. Methods: A total of 250 MINOCA patients from January 2017 to September 2021 were included. For each patient, the four scores at admission were retrospectively calculated. The primary outcome was a composite of all-cause death and acute myocardial infarction (AMI) at 1-year follow-up. The ability to predict 1-year all-cause death was also tested. Results: Overall, the tested scores presented a sub-optimal performance in predicting the composite major adverse event in MINOCA patients, showing an AUC ranging between 0.7 and 0.8. Among them, the GRACE score appeared to be the best in predicting all-cause death, reaching high specificity with low sensitivity. The best cut-off identified for the GRACE score was 171, higher compared to the cut-off of 140 generally applied to identify high-risk patients with obstructive AMI. When the scores were tested for prediction of 1-year all-cause death, the GRACE and the ACEF score showed very good accuracy (AUC = 0.932 and 0.828, respectively). Conclusion: the prognostic scoring tools, validated in AMI cohorts, could be useful even in MINOCA patients, although their performance appeared sub-optimal, prompting the need for risk assessment tools specific to MINOCA patients