Cloud-based genomics pipelines for ophthalmology: Reviewed from research to clinical practice

Aim: To familiarize clinicians with clinical genomics, and to describe the potential of cloud computing for enabling the future routine use of genomics in eye hospital settings. Design: Review article exploring the potential for cloud-based genomic pipelines in eye hospitals. Methods: Narrative review of the literature relevant to clinical genomics and cloud computing, using PubMed and Google Scholar. A broad overview of these fields is provided, followed by key examples of their integration. Results: Cloud computing could benefit clinical genomics due to scalability of resources, potentially lower costs, and ease of data sharing between multiple institutions. Challenges include complex pricing of services, costs from mistakes or experimentation, data security, and privacy concerns. Conclusions and future perspectives: Clinical genomics is likely to become more routinely used in clinical practice. Currently this is delivered in highly specialist centers. In the future, cloud computing could enable delivery of clinical genomics services in non-specialist hospital settings, in a fast, cost-effective way, whilst enhancing collaboration between clinical and research teams

Big Data Analytics Framework for Childhood Infectious Disease Surveillance and Response System using Modified MapReduce Algorithm

This research article published by International Journal of Advanced Computer Science and Applications,Vol. 12, No. 3, 2021Tanzania, like most East African countries, faces a great burden from the spread of preventable infectious childhood diseases. Diarrhea, acute respiratory infections (ARI), pneumonia, malnutrition, hepatitis, and measles are responsible for the majority of deaths amongst children aged 0-5 years. Infectious disease surveillance and response is the foundation of public healthcare practices, and it is increasingly being undertaken using information technology. Tanzania however, due to challenges in information technology infrastructure and public health resources, still relies on paper-based disease surveillance. Thus, only traditional clinical patient data is used. Nontraditional and pre-diagnostic infectious disease report case data are excluded. In this paper, the development of the Big Data Analytics Framework for Childhood Infectious Disease Surveillance and Response System is presented. The framework was designed to guide healthcare professionals to track, monitor, and analyze infectious disease report cases from sources such as social media for prevention and control of infectious diseases affecting children. The proposed framework was validated through use-cases scenario and performance-based comparison

Accessibility of HIV Testing in Baton Rouge Metropolitan Statistical Area

This study examines HIV testing accessibility in the Baton Rouge Metropolitan Statistical Area (BR MSA) using the two-step floating catchment area (2SFCA) method to calculate accessibility scores for free, low-cost and all other HIV testing facilities. The two goals of this research are to apply accessibility estimation methods to HIV testing facilities, and to examine the accessibility of HIV testing facilities in the BR MSA. To achieve these goals, this study uses several research methods. The data about HIV testing providers and their locations were collected through Internet searches. By means of a fieldwork, the data were checked, revealing that only 20% of the free HIV testing providers found online are active and free. Almost all free testing providers are clustered in the largest cities, many facilities claimed as “free” turned out to be “low-cost” instead. A disaggregation technique with a linear regression was used to acquire the HIV prevalence rate at the census tract level, because it is only available at the parish/county level. To address accessibility questions, geographical methods, including mapping, the 2SFCA method, and the hot spot analysis were used. The low-cost testing providers are allocated equally throughout the study area and partly compensate the lack of free HIV testing providers for people outside of the largest cities. Almost all population of the BR MSA has access to HIV testing facilities, low-cost and fully charged, within a 30-minute driving time threshold. However, people living in the outskirts of the BR MSA have no access to free HIV testing providers even within a 40-minute driving time threshold

Big data analytics framework for childhood infectious disease surveillance system using modified mapreduce algorithm: a case study of Tanzania

A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Master’s in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyTanzania has been affected with a potential emerging and re-emerging of infectious diseases such as diarrhea, acute respiratory infections, pneumonia, hepatitis, and measles. There is an increasing trend for the occurrences of new emerging pandemic diseases such as the coronavirus (Covid-19) in 2020 as well as re-occurrence of old infectious diseases such as cholera epidemic in 2015-2017, chikungunya and dengue fever outbreak in 2010, 2012, 2014, 2018, and 2019 which affected different regions in Tanzania. These diseases by far are the main causes of the high mortality rate for women and children of 0-5 years of age. The traditional disease surveillance system as the foundation of the public healthcare practices has been facing challenges in data collection and analysis using health big data sources to prevent and control infectious diseases. Health big data sources on infectious diseases have been recognized as the potential supplement for the provision of evidence-based decision-making worldwide. Tanzania as one of the resource-limited setting countries has lagged because of the challenges in information technology infrastructure and public healthcare resources. The traditional disease surveillance system is still paper-based, semi-automated, and limited in scope which relies on clinical-oriented patient data sources and leaving out nontraditional and pre-diagnostic unstructured big data sources. This research study aimed to improve the traditional infectious disease surveillance system to employ big data analytics technology in healthcare data collection and analysis to improve decision-making. Big data analytics framework for the childhood infectious disease surveillance system was developed which guides healthcare professionals to streamline the collection and analysis of health big data for infectious disease surveillance. The framework was then fairly compared with the existing framework in its performance using infrastructures, data size and transformation, and running-time execution of the systems. The experimental results indicate the efficiency of the framework system performance with the highest running time execution of about 56% quicker over the traditional system. Also, it has the best performance in processing multiple data structures using additional processing units. In particular, the proposed framework can be adopted to improve the prenatal and postnatal healthcare system in Tanzania

SIREPH: an Integrated Approach to the Pre-Hospital Emergency Framework

Pre-hospital emergency care is one of the most important phases in the healthcare system framework, as it is the first point of contact between patients and healthcare professionals. In Portugal, there is still a considerable amount of human dependent processes that could be automated to avoid unnecessary miscommunication and to improve the quality of the service provided. The main areas of challenge lie on the communication between emergency centrals, between emergency centrals and the technician’s on the field and in the patient pre-hospital emergency information handover phase to the target hospital. As these interactions take place in environments where time is essential and the pos sibility of committing errors is considerable, relying exclusively on human processes may hinder the entire framework. This thesis presents the analysis, design and implemen tation of a proof of concept solution that tackles the deficiencies identified in process automation in the pre-hospital emergency framework, and aims to improve the service quality provided by the professionals in this field. The system follows a service-oriented architecture, were a REST API supports several user interfaces. All web-based interfaces are implemented with a low code platform. This thesis is focused on 3 main distinguishable actors: administrative emergency centrals (emergency dispatch centers at national level); non-administrative emergency centrals (firefighter and red cross stations); and hospitals. We conducted trials with 25 users to validate the reliability of the system, particularly of the API, and the suitability of the user interfaces. Results show that the system accu rately captures the pre-hospital emergency information workflow, and the several user interfaces were positively reviewed by all users.Os serviços de urgência pré-hospitalares são uma das fases mais importantes no âmbito do sistema de saúde, uma vez que são o primeiro ponto de contacto entre os doentes e os profissionais de saúde. Em Portugal, existe ainda uma quantidade considerável de pro cessos dependentes do ser humano que poderiam ser automatizados para evitar erros de comunicação indesejados e para melhorar a qualidade do serviço prestado. As principais áreas de desafio residem na comunicação entre os centros de emergência, entre os centros de emergência e os técnicos no terreno e na fase de entrega de informação de emergência pré-hospitalar do paciente ao hospital de destino. Como estas interações têm lugar em ambientes onde o tempo é essencial e a possibi lidade de cometer erros é considerável, confiar exclusivamente em processos humanos pode dificultar todo o funcionamento do serviço. Esta tese apresenta a análise, concepção e implementação de uma solução de prova de conceito que aborda as deficiências iden tificadas na automatização de processos no quadro de emergência pré-hospitalar, e visa melhorar a qualidade do serviço prestado pelos profissionais nesta área. O sistema segue uma arquitectura orientada para serviços, onde uma API REST su porta várias interfaces de utilizador. Todas as interface web são implementadas com uma plataforma de low-code. Esta tese centra-se em 3 intervenientes principais distintos: cen trais administrativas de emergência (centros de despacho de emergência a nível nacional); centrais não administrativas de emergência (estações de bombeiros e de cruz vermelha); e hospitais. Realizaram-se testes com 25 utilizadores de modo a validar a fiabilidade do sistema, particularmente da API, e a adequação das interfaces de utilizador. Os resultados mos tram que o sistema capta com precisão o fluxo de trabalho de informação de emergência pré-hospitalar, e as várias interfaces de utilizador foram avaliadas positivamente por todos os utilizadores

Am Heart J

BackgroundIndividuals with congenital heart defects (CHDs) are recommended to receive all inpatient cardiac and noncardiac care at facilities that can offer specialized care. We describe geographic accessibility to such centers in New York State and determine several factors associated with receiving care there.MethodsWe used inpatient hospitalization data from the Statewide Planning and Research Cooperative System (SPARCS) in New York State 2008\u20132013. In the absence of specific adult CHD care center designations during our study period, we identified pediatric/adult and adult-only cardiac surgery centers through the Cardiac Surgery Reporting System to estimate age-based specialized care. We calculated one-way drive and public transit time (in minutes) from residential address to centers using R gmapsdistance package and the Google Maps Distance Application Programming Interface (API). We calculated prevalence ratios using modified Poisson regression with model-based standard errors, fit with generalized estimating equations clustered at the hospital level and subclustered at the individual level.ResultsIndividuals with CHDs were more likely to seek care at pediatric/adult or adult-only cardiac surgery centers if they had severe CHDs, private health insurance, higher severity of illness at encounter, a surgical procedure, cardiac encounter, and shorter drive time. These findings can be used to increase care receipt (especially for noncardiac care) at pediatric/adult or adult-only cardiac surgery centers, identify areas with limited access, and reduce disparities in access to specialized care among this high-risk population.20212022-06-01T00:00:00ZCC999999/ImCDC/Intramural CDC HHSUnited States/33636136PMC80976611170

FOSS4G 2016 Proceedings: Academic Program - selected papers and posters

This Conference Proceedings is a collection of selected papers and posters submitted to the Academic Program of the International Conference for Free and Open Source Software for Geospatial (FOSS4G 2016), 24th to 26th August 2016 in Bonn, Germany. Like in previous FOSS4G conferences on national and international level the academic papers and posters cover an extensive wide range of topics reflecting the contribution of the academia to this field by the development of open source software components, in the design of open standards, in the proliferation of web-based solutions, in the dissemination of the open principles important in science and education, or in the collection and the hosting of freely available geo-data

Measuring and optimizing accessibility to emergency medical services

Emergency medical services (EMSs) undertake the responsibility of providing rapid medical care to patients suffering from unexpected illnesses or injuries and transferring them to definitive care facilities. This research concerns several research gaps that are associated with different EMS trips, real-time traffic conditions, improving EMS efficiency and equalities. This research aims to develop GIS-based spatial optimization methods to improve service efficiency and equality in EMS systems. Specifically, the research intends to achieve the following goals: (1) to measure spatiotemporal accessibility to EMS; (2) to improve EMS efficiency and provision through spatial optimization approaches; (3) to reduce urban-rural inequalities in EMS accessibility and coverage using spatial optimization approaches. The proposed approaches are applied in three empirical studies in Wuhan, China. To achieve the first objective, the proximity and the enhanced two-step floating catchment method (E-2SFCA) are adopted to evaluate spatiotemporal accessibility. First, the EMS travel time is estimated for the two related trips as an overall EMS journey: one is from the nearest EMS station to the scene (Trip 1), and the other is from the scene to the nearest emergency hospital (Trip 2). Then, the E-2SFCA method is employed to calculate the accessibility score that integrates both geographic accessibility and availability of EMS. Travel time is estimated by using both static road network with standard speed limits and online map service considering real-time traffic. To achieve the second objective, two facility location models are proposed to improve EMS service coverages for two-related trips (Trips 1 and 2). The first model maximizes the amount of demand covered by both ambulance coverage (EMS station – demand) and hospital coverage (demand – hospital). The second model maximizes the amount of demand that can be served by both ambulance coverage and overall coverage (EMS station – demand – hospital). To achieve the third objective, two bi-objective optimization models are developed. The two models have the same primary objective to maximize the total covered demand by ambulance. The second objective is to minimize one of the two inequality measures: one focuses on accessibility of uncovered rural people, and the other concerns the urban-rural inequality in service coverage. For the first empirical study with respect to spatiotemporal access to EMS, different spatial patterns are found for the three trips (two partial trips and the overall trip). Good accessibility to one trip cannot guarantee good accessibility to another trip. In addition, urban-rural inequalities in EMS accessibility and coverage are observed. Finally, it is observed that real-time traffic conditions greatly affect EMS accessibility, particularly in urban districts. Specifically, the accessibility of EMS becomes poor during the morning (7-9 am) and evening peak periods (5-7 pm). For the second empirical study in relation to EMS optimization involving two related trips, the results find that the first proposed model can guarantee that more demand to be covered by both ambulance and hospital coverages than the Maximum Coverage Location Problem (MCLP). The second proposed model can ensure that as many people as possible to be served by both ambulance and overall coverage than the work by ReVelle et al. (1976). For the third empirical study attempting to reduce urban-rural inequality in EMS, the results show that the first bi-objective model can improve EMS accessibility of uncovered rural demand, and the second model can reduce EMS service coverages between urban and rural areas. However, the improvement EMS inequalities between urban and rural areas leads to a cost of a decrease in the total covered population, especially in urban areas. Regarding policy implications, this research suggests that different EMS trips and traffic conditions should be considered when measuring spatial accessibility to EMS. Spatial optimization research can help improving service efficiency and reduce regional equalities in EMS systems. The work presented in this thesis can aid the planning practice of public services like EMS and provide decision support for policymakers

An appraisal of health datasets to enhance the surveillance of Lyme disease in the United Kingdom

Lyme disease is a tick-borne disease of increasing global public health interest. Clinical presentation is varied, posing challenges for case definition. Currently national incidence figures for the United Kingdom (UK) are derived from two-tier confirmatory laboratory diagnostic results. These figures have the potential to underestimate incidence as clinical cases managed without diagnostic investigation are unrecorded. This thesis aimed to identify and evaluate a variety of datasets for their ability to describe the incidence and sociodemographics of Lyme disease cases in the UK, and to assess whether they could be utilised in future national surveillance programmes. The datasets analysed were: Public Health England (PHE)’s Lyme disease diagnostic laboratory, PHE’s laboratory surveillance system, hospital episode statistics data for England and Wales, an electronic health records database of primary care in the UK, Twitter, and the Small Animals Veterinary Surveillance Network (SAVSNET). A generalised Lyme disease population could be described from these data. This population had a bimodal age distribution, was predominately white, was from rural areas, and increasingly from areas with lower societal deprivation. Geographic distribution of cases could be described for England and Wales and showed the highest incidence of disease in southern central to south western England. These data showed an increasing incidence of Lyme disease. The relative incidence of Lyme disease cases varied between datasets, with the primary care data having the largest incidence of 4.42 per 100,000 person-years (95% CI 4.23-4.67). Multiplication factors were described between the three datasets of routinely collected health care data. The most important being a multiplication factor of 2.35 (95% CI 1.81-2.88) between laboratory-confirmed incidence and primary care incidence in England and Wales. The results from this thesis start to describe the epidemiological picture of Lyme disease in the UK; specifically identified as a research gap by the NICE guidelines. They will provide a platform for the many unanswered questions about the changing landscape of Lyme disease in the UK. It was concluded that a combination of health datasets could be used for future Lyme disease surveillance systems in the UK. Ideally this would include laboratory and primary care data. Until this is in place, the multiplication factor can be used to estimate the national incidence of Lyme disease and the potential burden it places on the National Health Service and the patients it afflicts