Biosignal and context monitoring: Distributed multimedia applications of body area networks in healthcare

We are investigating the use of Body Area Networks (BANs), wearable sensors and wireless communications for measuring, processing, transmission, interpretation and display of biosignals. The goal is to provide telemonitoring and teletreatment services for patients. The remote health professional can view a multimedia display which includes graphical and numerical representation of patients’ biosignals. Addition of feedback-control enables teletreatment services; teletreatment can be delivered to the patient via multiple modalities including tactile, text, auditory and visual. We describe the health BAN and a generic mobile health service platform and two context aware applications. The epilepsy application illustrates processing and interpretation of multi-source, multimedia BAN data. The chronic pain application illustrates multi-modal feedback and treatment, with patients able to view their own biosignals on their handheld device

Distribution of Respiratory Tracy Infectious Diseases in Relation to Particulate Matter (PM2.5) Concentration in Selected Urban Centres in Niger Delta Region of Nigeria

Due to the visibility of soot in the environment of the Niger Delta especially Rivers State that has led to the increase of  espiratory Tract Infections (RTIs) in the region, this study was undertaken to determine the relationship between Particulate Matter (PM2.5) concentration and the incident of Respiratory Tract Infections (RTIs) in selected urban centres of the Niger Delta. Data on RTIs were collected from the Hospital Management Boards of the Ministries of Health of Rivers, Bayelsa and Delta States and the data for PM2.5 were remotely sensed from 2016 to 2019, and subsequently analyzed with ANOVA and Spearman’s rank correlation statistics. The findings of this study revealed that there was significant variation in the occurrence of PM2.5 across the selected urban centres in the Niger Delta Region. The PM2.5 for the reviewed years was far above the World Health Organization (WHO) annual permissible limit of 10 µg/m3 thereby exacerbating Respiratory Tract Infections (RTIs).The epidemiology of the RTIs showed that there are basically four (4) prominent RTI diseases: Asthma, Tuberculosis, Pneumonia and Chronic Obstructive Pulmonary Disease (COPD). The result of this study showed that the concentration of PM2.5 varies in all the selected cities, and the mean monthly variation (2016-2019) showed that Port Harcourt had 47.27 µg/m3 for January while Yenagoa and Asaba had 46 µg/m3 and 47.51 µg/m3 respectively for January; while the lowest mean value in the cities were seen within the month of September and October, which also had a strong seasonal influence on the concentration of PM2.5. The concentration of PM2.5 and the numbers of RTIs also gradually increases in the study areas from 2016 to 2019. The study recommends that the necessary regulatory bodies should closely monitor the activities of the companies likely to cause such pollution; guild them through their operations and give prompt sanctions and heavy fines to defaulters of the accepted standards

Ambient particulate air pollution (PM2.5) is associated with the ratio of type 2 diabetes to obesity

JRS was supported by the 1000 talents program and a Wolfson merit award from the Royal Sociey. MM was supported by a TWAS studentship of the Chinese Academy of Sciences.Peer reviewedPublisher PD

The use of satellite data, meteorology and land use data to define high resolution temperature exposure for the estimation of health effects in Italy

Introduction. Despite the mounting evidence on heat-related health risks, there is limited evidence in suburban and rural areas. The limited spatial resolution of temperature data also hinders the evidence of the differential heat effect within cities due to individual and area-based characteristics. Methods. Satellite land surface temperature (LST), observed meteorological and spatial and spatio-temporal land use data were combined in mixed-effects regression models to estimate daily mean air temperature with a 1x1km resolution for the period 2000-2010. For each day, random intercepts and slopes for LST were estimated to capture the day-to-day temporal variability of the Ta–LST relationship. The models were also nested by climate zones to better capture local climates and daily weather patterns across Italy. The daily exposure data was used to estimate the effects and impacts of heat on cause-specific mortality and hospital admissions in the Lazio region at municipal level in a time series framework. Furthermore, to address the differential effect of heat within an urban area and account for potential effect modifiers a case cross-over study was conducted in Rome. Mean temperature was attributed at the individual level to the Rome Population Cohort and the urban heat island (UHI) intensity using air temperature data was calculated for Rome. Results. Exposure model performance was very good: in the stage 1 model (only on grid cells with both LST and observed data) a mean R2 value of 0.96 and RMSPE of 1.1°C and R2 of 0.89 and 0.97 for the spatial and temporal domains respectively. The model was also validated with regional weather forecasting model data and gave excellent results (R2=0.95 RMSPE=1.8°C. The time series study showed significant effects and impacts on cause-specific mortality in suburban and rural areas of the Lazio region, with risk estimates comparable to those found in urban areas. High temperatures also had an effect on respiratory hospital admissions. Age, gender, pre-existing cardiovascular disease, marital status, education and occupation were found to be effect modifiers of the temperature-mortality association. No risk gradient was found by socio-economic position (SEP) in Rome. Considering the urban heat island (UHI) and SEP combined, differential effects of heat were observed by UHI among same SEP groupings. Impervious surfaces and high urban development were also effect modifiers of the heat-related mortality risk. Finally, the study found that high resolution gridded data provided more accurate effect estimates especially for extreme temperature intervals. Conclusions. Results will help improve heat adaptation and response measures and can be used predict the future heat-related burden under different climate change scenarios.Open Acces

Identifying Population Hollowing Out Regions and Their Dynamic Characteristics across Central China

Continuous urbanization and industrialization lead to plenty of rural residents migrating to cities for a living, which seriously accelerated the population hollowing issues. This generated series of social issues, including residential estate idle and numerous vigorous laborers migrating from undeveloped rural areas to wealthy cities and towns. Quantitatively determining the population hollowing characteristic is the priority task of realizing rural revitalization. However, the traditional field investigation methods have obvious deficiencies in describing socio-economic phenomena, especially population hollowing, due to weak efficiency and low accuracy. Here, this paper conceives a novel scheme for representing population hollowing levels and exploring the spatiotemporal dynamic of population hollowing. The nighttime light images were introduced to identify the potential hollowing areas by using the nightlight decreasing trend analysis. In addition, the entropy weight approach was adopted to construct an index for evaluating the population hollowing level based on statistical datasets at the political boundary scale. Moreover, we comprehensively incorporated physical and anthropic factors to simulate the population hollowing level via random forest (RF) at a grid-scale, and the validation was conducted to evaluate the simulation results. Some findings were achieved. The population hollowing phenomenon decreasing gradually was mainly distributed in rural areas, especially in the north of the study area. The RF model demonstrated the best accuracy with relatively higher R2 (Mean = 0.615) compared with the multiple linear regression (MLR) and the geographically weighted regression (GWR). The population hollowing degree of the grid-scale was consistent with the results of the township scale. The population hollowing degree represented an obvious trend that decreased in the north but increased in the south during 2016–2020 and exhibited a significant reduction trend across the entire study area during 2019–2020. The present study supplies a novel perspective for detecting population hollowing and provides scientific support and a first-hand dataset for rural revitalization

Environ Monit Assess

In order to examine associations between asthma morbidity and local ambient air pollution in an area with relatively low levels of pollution, we conducted a time-series analysis of asthma hospital admissions and fine particulate matter pollution (PM|) in and around Jackson, MS, for the period 2003 to 2011. Daily patient-level records were obtained from the Mississippi State Department of Health (MSDH) Asthma Surveillance System. Patient geolocations were aggregated into a grid with 0.1\ub0\u2009 7\u20090.1\ub0 resolution within the Jackson Metropolitan Statistical Area. Daily PM| concentrations were estimated via machine-learning algorithms with remotely sensed aerosol optical depth and other associated parameters as inputs. Controlling for long-term temporal trends and meteorology, we estimated a 7.2% (95% confidence interval 1.7-13.1%) increase in daily all-age asthma emergency room admissions per 10\ua0\u3bcg/m| increase in the 3-day average of PM| levels (current day and two prior days). Stratified analyses reveal significant associations between asthma and 3-day average PM| for males and blacks. Our results contribute to the current epidemiologic evidence on the association between acute ambient air pollution exposure and asthma morbidity, even in an area characterized by relatively good air quality.R21 ES019713/ES/NIEHS NIH HHSUnited States/U59 EH000208/EH/NCEH CDC HHSUnited States/R21ES019713/ES/NIEHS NIH HHSUnited States

Digital Healthcare for Airway Diseases from Personal Environmental Exposure

Digital technologies have emerged in various dimensions of human life, ranging from education to professional services to well-being. In particular, health products and services have expanded by the use and development of artificial intelligence, mobile health applications, and wearable electronic devices. Such advancements have enabled accurate and updated tracking and modeling of health conditions. For instance, digital health technologies are capable of measuring environmental pollution and predicting its adverse health effects. Several health conditions, including chronic airway diseases such as asthma and chronic obstructive pulmonary disease, can be exacerbated by pollution. These diseases impose substantial health burdens with high morbidity and mortality. Recently, efforts have been made to develop digital technologies to alleviate such conditions. Moreover, the COVID-19 pandemic has facilitated the application of telemedicine and telemonitoring for patients with chronic airway diseases. This article reviews current trends and studies in digital technology utilization for investigating and managing environmental exposure and chronic airway diseases. First, we discussed the recent progression of digital technologies in general environmental healthcare. Then, we summarized the capacity of digital technologies in predicting exacerbation and self-management of airway diseases. Concluding these reviews, we provided suggestions to improve digital health technologies' abilities to reduce the adverse effects of environmental exposure in chronic airway diseases, based on personal exposure-response modeling.ope

Spatial and temporal analysis of dust storms in Saudi Arabia and associated impacts, using Geographic Information Systems and remote sensing

Dust storm events occur in arid and semi-arid areas around the world. These result from strong surface winds and blow dust and sand from loose, dry soil surfaces into the atmosphere. Such events can have damaging effects on human health, environment, infrastructure and transport. In the first section of this PhD dissertation, focus on the suitability of the existing of five different MODIS-based methods for detecting airborne dust over the Arabian Peninsula are examined. These are the: (a) Normalized Difference Dust Index (NDDI); (b) Brightness Temperature Difference (BTD) (Band 31–32); (c) BTD (Band 20–31); (d) Middle East Dust Index (MEDI) and (e) Reflective Solar Band (RSB). This work also develops dust detection thresholds for each index by comparing observed values for ‘dust-present’ versus ‘dust-free’ conditions, taking into account various land cover settings and analysing associated temporal trends. The results suggest the most suitable indices for identifying dust storms over different land cover types across the Arabian Peninsula are BTD31–32 and the RSB index. Methods such as NDDI and BTD20 – 31 have limitations in detecting dust over multiple land-cover types. In addition, MEDI was found to be an unsuccessful index for detecting dust storms over all types of land cover in the study area. Furthermore, this thesis explores the spatial and temporal variations of dust storms by using monthly meteorological data from 27 observation stations across Saudi Arabia during the period (2000–2016), considering the associations between dust storm frequency and temperature, precipitation and wind variables. In terms of the frequency of dust in Saudi Arabia, the results show significant spatial, seasonal and inter-annual. In the eastern part of the study area, for example, dust storm events have increased over time, especially in Al-Ahsa. There are evident relationships (p < 0.005) between dust storm occurrence and wind speed, wind direction and precipitation. This thesis also describes the impact of dust on health, and specifically on respiratory admissions to King Fahad Medical City (KFMC) for the period (February 2015 – January 2016).This study uses dust data from the World Meteorological Or-ganization (WMO) for comparing and analysing the daily weather conditions and hospital admissions. The findings indicate that the total number of emergency respiratory admissions during dust events was higher than background levels by 36% per day on average. Numbers of admissions during ‘widespread dust’ events were 19.62% per day higher than during periods of ‘blowing dust’ activity. The average number of hospital admissions for lower respiratory tract infections (LRTI) was 11.62 per day during widespread dust events and 10.36 per day during blowing dust. The average number of hospital admissions for upper respiratory tract infections (URTI) was 10.25 per day during widespread dust events and 7.87 per day during blowing dust ones. I found clear seasonal variability with a peak in the number of emergency admissions during the months of February to April. Furthermore, qualitative evidence suggests that there is a significant impact on hospital operations due to the increase in patients and pressure on staffing and hospital consumables in this period. Taken together, these findings suggest the (BTD 31–32) and (RSB) are the most suitable indices of the five different MODIS-based methods for detecting airborne dust over the Arabian Peninsula and over different land cover. There are important spatial and temporal pattern variations, as well as seasonal and inter-annual variability, in the occurrence of dust storms in Saudi Arabia. There is also a seasonal pat-tern to the number of hospital admissions during dust events. This is research in-tended to fill the knowledge gap in the dust detection filed. Here I address the knowledge gap by evaluating the identified dust methods over the whole Arabian Peninsula and by considering different land cover. To my knowledge, this is the first study analysed the temporal trends in indices values considering dust and dust-free conditions. Previous work has only focused on 13 stations for analysing dust storms over Saudi Arabia. Therefore, this study has analysed the seasonal and inter-annual and spatial variation by using data from 27 observations in Saudi Arabia. This study addresses the relationship between dust storm frequency and the three meteorological factors (i.e. temperature, precipitation and wind variables) which have not yet been clarified in previous studies. In addition, this research fills the gap in the literature by investigating the correlation between different types of dust events such as (wide-spread dust and blowing dust) and their effects on the hospital admissions for upper and lower respiratory tract issues for pediatric in Riyadh city

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial