Global Health Monitoring Research Articles

Global health monitoring and evaluation partnerships as contested spaces in Zimbabwe

Background: Global health partnerships (GHPs) have flourished across Africa as alternative governance mechanisms seeking to strengthen local health systems for effective national planning, implementation, monitoring, and evaluation. Mutual and trust-based relationships anticipate fostering relations that build weak systems for improved availability of data and information for local informed decision-making and programme learning.Objectives: This article aims to explore and demonstrate how global health monitoring and evaluation partnerships (GHMEPs) are contested spaces contrary to the pervasive collaborative discourse in official government policies.Method: Data for this study were collected using content analysis of existing documents and key informant interviews for a qualitative case study. Furthermore, monitoring and evaluation (ME) policy documents and key informant interviews with the ME staff from the Ministry of Health and Child Care, Zimbabwe, were purposively selected. Ethics clearance was sought from the University of KwaZulu-Natal, HSREC/00002455/2021.Results: The results show that GHMEPs are contested spaces despite the expectation to foster mutual trust and improved availability of quality data and information for informed decision-making and learning. Evidence shows partner contests through unspectacular soft power strategies to counterbalance resource and power imbalances in partnerships.Conclusion: The evidence of unspectacular soft power strategies suggests that collaboration for ME conceals and prolongs opportunities for addressing practical and contested challenges, hence failing the test for ideal partnerships.Contribution: The article contributes to a critical understanding of the limitations of the current theorisation of partnerships, which erroneously assumes trust, mutuality, and equality between resourced and under-resourced partners.

The future of global river health monitoring

Rivers are the arteries of human civilisation and culture, providing essential goods and services that underpin water and food security, socio-economic development and climate resilience. They also support an extraordinary diversity of biological life. Human appropriation of land and water together with changes in climate have jointly driven rapid declines in river health and biodiversity worldwide, stimulating calls for an Emergency Recovery Plan for freshwater ecosystems. Yet freshwater ecosystems like rivers have been consistently under-represented within global agreements such as the UN Sustainable Development Goals and the UN Convention on Biological Diversity. Even where such agreements acknowledge that river health is important, implementation is hampered by inadequate global-scale indicators and a lack of coherent monitoring efforts. Consequently, there is no reliable basis for tracking global trends in river health, assessing the impacts of international agreements on river ecosystems and guiding global investments in river management to priority issues or regions. We reviewed national and regional approaches for river health monitoring to develop a comprehensive set of scalable indicators that can support “top-down” global surveillance while also facilitating standardised “bottom-up” local monitoring efforts. We evaluate readiness of these indicators for implementation at a global scale, based on their current status and emerging improvements in underlying data sources and methodologies. We chart a road map that identifies data and technical priorities and opportunities to advance global river health monitoring such that an adequate monitoring framework could be in place and implemented by 2030, with the potential for substantial enhancement by 2050. Lastly, we present recommendations for coordinated action and investment by policy makers, research funders and scientists to develop and implement the framework to support conservation and restoration of river health globally.

One Digital Health Intervention for Monitoring Human and Animal Welfare in Smart Cities: Viewpoint and Use Case.

Smart cities and digital public health are closely related. Managing digital transformation in urbanization and living spaces is challenging. It is critical to prioritize the emotional and physical health and well-being of humans and their animals in the dynamic and ever-changing environment they share. Human-animal bonds are continuous as they live together or share urban spaces and have a mutual impact on each other's health as well as the surrounding environment. In addition, sensors embedded in the Internet of Things are everywhere in smart cities. They monitor events and provide appropriate responses. In this regard, accident and emergency informatics (A&EI) offers tools to identify and manage overtime hazards and disruptive events. Such manifold focuses fit with One Digital Health (ODH), which aims to transform health ecosystems with digital technology by proposing a comprehensive framework to manage data and support health-oriented policies. We showed and discussed how, by developing the concept of ODH intervention, the ODH framework can support the comprehensive monitoring and analysis of daily life events of humans and animals in technologically integrated environments such as smart homes and smart cities. We developed an ODH intervention use case in which A&EI mechanisms run in the background. The ODH framework structures the related data collection and analysis to enhance the understanding of human, animal, and environment interactions and associated outcomes. The use case looks at the daily journey of Tracy, a healthy woman aged 27 years, and her dog Mego. Using medical Internet of Things, their activities are continuously monitored and analyzed to prevent or manage any kind of health-related abnormality. We reported and commented on an ODH intervention as an example of a real-life ODH implementation. We gave the reader examples of a "how-to" analysis of Tracy and Mego's daily life activities as part of a timely implementation of the ODH framework. For each activity, relationships to the ODH dimensions were scored, and relevant technical fields were evaluated in light of the Findable, Accessible, Interoperable, and Reusable principles. This "how-to" can be used as a template for further analyses. An ODH intervention is based on Findable, Accessible, Interoperable, and Reusable data and real-time processing for global health monitoring, emergency management, and research. The data should be collected and analyzed continuously in a spatial-temporal domain to detect changes in behavior, trends, and emergencies. The information periodically gathered should serve human, animal, and environmental health interventions by providing professionals and caregivers with inputs and "how-to's" to improve health, welfare, and risk prevention at the individual and population levels. Thus, ODH complementarily combined with A&EI is meant to enhance policies and systems and modernize emergency management.

Nonlinearity detection using new signal analysis methods for global health monitoring

Nonlinearity detection using new signal analysis methods for global health monitoring

Distributed Full Synchronized System for Global Health Monitoring Based on FLSA

In modern medicine, smart wireless connected devices are gaining an increasingly important role in aiding doctors' job of monitoring patients. More and more complex systems, with a high density of sensors capable of monitoring many biological signals, are arising. Merging the data offers a great opportunity for increasing the reliability of diagnosis. However, a huge problem is constituted by synchronization. Multi-board wireless-connected monitoring systems are a typical example of distributed systems and synchronization has always been a challenging issue. In this paper, we present a distributed full synchronized system for monitoring patients' health capable of heartbeat rate, oxygen saturation, gait and posture analysis, and muscle activity measurements. The time synchronization is guaranteed thanks to the Fractional Low-power Synchronization Algorithm (FLSA).

Contextual factors associated with contraceptive utilization and unmet need among sexually active unmarried women in Kenya: A multilevel regression analysis.

Unmarried women who report less recent sexual intercourse (>30 days from survey enumeration) are largely excluded from global health monitoring and evaluation efforts. This study investigated level and contextual factors in modern contraceptive utilization and unmet need within this overlooked female subpopulation in Kenya from 2014 to 2019. This study analyzed data from the Performance Monitoring and Accountability (PMA) survey in Kenya, a nationally representative survey of female respondents, to understand the level and contextual factors for family planning utilization and unmet need within female subgroups including married, unmarried sexually active (defined as sexual intercourse within 30 days of survey enumeration), and unmarried with less recent sexual intercourse (defined as sexual intercourse 1-12 months prior to survey enumeration). The analysis included multilevel regression modeling to assess correlates on outcomes of modern contraceptive prevalence rate (mCPR), unmet need, and recent emergency contractive pill (ECP) use, which is a unique PMA question: "Have you used emergency contraception at any time in the last 12 months?". Cumulatively, the surveys enumerated 19,161 women and this weighted analysis included 12,574 women aged 15-49 from three female subgroups: 9,860 married women (78.4%), 1,020 unmarried sexually active women (8.1%), and 1,694 unmarried women with less recent sexual intercourse (13.5%). In 2019, while controlling for covariates, unmarried women with less recent sexual intercourse exhibited statistically significant differences (p-value<0.02) in current mCPR, mCPR at last sexual intercourse, unmet need for modern contraceptives, and recent ECP use. As compared to an unmarried woman with less recent sexual intercourse (i.e., reported sex 1-12 months prior to survey), the odds of an unmarried sexually active woman (i.e., reported sex within last 30 days of survey) currently using modern contraceptives was 2.28 (95% CI: 1.64, 3.18), using modern contraceptives at last sexual intercourse was 1.44 (95% CI: 1.06, 1.95), and having an unmet need for modern contraceptives was 2.01 (95% CI: 1.29, 3.13) while controlling for covariates. The odds of a married woman using ECP during the last 12 months was 0.60 (95% CI: 0.44, 0.82) as compared to an unmarried woman with less recent sexual intercourse. In 2019, unmarried women with less recent sexual intercourse reported the highest rate of ECP use during the last 12 months at 13.5%, which was similar for unmarried sexually active women at 13.3%. Since 2014, summary measures of unmet need and total demand for modern contraceptives increased for unmarried women with less recent sexual intercourse, but declined for the other female subgroups. In Kenya, unmarried women with less recent sexual intercourse exhibited significantly different contraceptive utilization, unmet need, and recent emergency contraceptive use. Moreover, changes over time in key family planning indicators were asymmetrical by female subgroup. This study identifies an important monitoring gap regarding unmarried women with less recent sexual intercourse. Evidence dissemination by the global measurement community for these unmarried women is exceedingly scarce; therefore, developing an inclusive research agenda and actionable information about these marginalized women is needed to enable targeted planning and equitable service delivery.

Recent progress in 0D optical nanoprobes for applications in the sensing of (bio)analytes with the prospect of global health monitoring and detailed mechanistic insights

In the present review, the current trends of the research endeavours (2017–2022) based on emerging optical biosensing applications by various 0D nanomaterials have been comprehensively described towards the detection of targeted bio-analytes.

The World after COVID-19: Reflections on Global Health and Policy.

COVID-19 has infected hundreds of millions of people across the globe. The pandemic has also inflicted serious damages on global and regional governing political structures to a degree meriting a revisit of their own raison d’etre. The global economic fallout is also unprecedented as the flows of goods and people got severely disrupted while lockdowns hit the transport, services and retail industries, among others. We argue that three realities need to be genuinely addressed for building a post COVID-19 order that has to be amply equipped to deal with the next global crisis, as well as the ones on-going for decades. First, there is need to shelf-away the hitherto practiced doctrine that global crises and problems are confronted through local responses. Second, the COVID-19 pandemic has cautioned us on the need to (re)invest in basic, many may consider naïve and simple, public health functions such as sanitation as well as transparent national and global health monitoring. Third, the pandemic is a clear reprimand to discard the mantra that privatization of healthcare delivery system is the solution in favor of viewing health as a public good that needs to be managed and executed by the state and its public sector, be it national, sub-regional or local. It is critical that we learn from such pandemic and advance our societies to become stronger.

Variance estimation for effective coverage measures: A simulation study.

Effective coverage research is increasing rapidly in global health and development, as researchers use a range of measures and combine data sources to adjust coverage for the quality of services received. However, most estimates of effective coverage that combine data sources are reported only as point estimates, which may be due to the challenge of calculating the variance for a composite measure. In this paper, we evaluate three methods to quantify the uncertainty in the estimation of effective coverage. We conducted a simulation study to evaluate the performance of the exact, delta, and parametric bootstrap methods for constructing confidence intervals around point estimates that are calculated from combined data on coverage and quality. We assessed performance by computing the number of nominally 95% confidence intervals that contain the truth for a range of coverage and quality values and data source sample sizes. To illustrate these approaches, we applied the delta and exact methods to estimates of adjusted coverage of antenatal care (ANC) in Senegal. We used household survey data for coverage and health facility assessments for readiness to provide services. With small sample sizes, when the true effective coverage value was close to the boundaries 0 or 1, the exact and parametric bootstrap methods resulted in substantial over or undercoverage and, for the exact method, a high proportion of invalid confidence intervals, while the delta method yielded modest overcoverage. The proportion of confidence intervals containing the truth in all three methods approached the intended 95% with larger sample sizes and as the true effective coverage value moved away from the 0 or 1 boundary. Confidence intervals for adjusted ANC in Senegal were largely overlapping across the delta and exact methods, although at the sub-national level, the exact method produced invalid confidence intervals for estimates near 0 or 1. We provide the code to implement these methods. The uncertainty around an effective coverage estimate can be characterized; this should become standard practice if effective coverage estimates are to become part of national and global health monitoring. The delta method approach outperformed the other methods in this study; we recommend its use for appropriate inference from effective coverage estimates that combine data sources, particularly when either sample size is small. When used for estimates created from facility type or regional strata, these methods require assumptions of independence that must be considered in each example.

Variance estimation for effective coverage measures: A simulation study.

BackgroundEffective coverage research is increasing rapidly in global health and development, as researchers use a range of measures and combine data sources to adjust coverage for the quality of services received. However, most estimates of effective coverage that combine data sources are reported only as point estimates, which may be due to the challenge of calculating the variance for a composite measure. In this paper, we evaluate three methods to quantify the uncertainty in the estimation of effective coverage.MethodsWe conducted a simulation study to evaluate the performance of the exact, delta, and parametric bootstrap methods for constructing confidence intervals around point estimates that are calculated from combined data on coverage and quality. We assessed performance by computing the number of nominally 95% confidence intervals that contain the truth for a range of coverage and quality values and data source sample sizes. To illustrate these approaches, we applied the delta and exact methods to estimates of adjusted coverage of antenatal care (ANC) in Senegal. We used household survey data for coverage and health facility assessments for readiness to provide services.ResultsWith small sample sizes, when the true effective coverage value was close to the boundaries 0 or 1, the exact and parametric bootstrap methods resulted in substantial over or undercoverage and, for the exact method, a high proportion of invalid confidence intervals, while the delta method yielded modest overcoverage. The proportion of confidence intervals containing the truth in all three methods approached the intended 95% with larger sample sizes and as the true effective coverage value moved away from the 0 or 1 boundary. Confidence intervals for adjusted ANC in Senegal were largely overlapping across the delta and exact methods, although at the sub-national level, the exact method produced invalid confidence intervals for estimates near 0 or 1. We provide the code to implement these methods.ConclusionsThe uncertainty around an effective coverage estimate can be characterized; this should become standard practice if effective coverage estimates are to become part of national and global health monitoring. The delta method approach outperformed the other methods in this study; we recommend its use for appropriate inference from effective coverage estimates that combine data sources, particularly when either sample size is small. When used for estimates created from facility type or regional strata, these methods require assumptions of independence that must be considered in each example.

Surveillance and monitoring of oral health in elderly people

Continued ageing of the global population is expected to pose significant challenges to maintaining optimum lifelong health in individuals and populations. Oral health is an essential element of general health and quality of life throughout an individual's life course, yet it is often neglected in integrated approaches to general health promotion. Surveillance and monitoring systems are essential for developing oral health policy and strategy at both national and community levels. As major oral diseases, dental caries and periodontal diseases are core indicators for surveillance at every stage of life. In addition, oral mucosal lesions and masticatory function are essential indicators, especially in an ageing population. The assessment of risk factors such as tobacco use, alcohol consumption or dietary habits (e.g., sugar intake) is also important for oral disease prevention. Although surveillance is conducted through clinical examinations (normative assessments), this method is becoming more difficult to use because of high costs and human resource shortages, even in high-income countries. Alternative and less resource-demanding approaches, such as self-reported protocols, are therefore needed at the global level. The World Health Organization's (WHO) oral health surveillance and monitoring activities have evolved into the Oral Health STEPwise approach, which includes questionnaire surveys (Step 1) and clinical examinations (Step 2). Collaboration between international organisations such as the World Dental Federation (FDI), WHO and the International Association for Dental Research (IADR) is now needed to coordinate global oral health surveillance and monitoring systems.

A novel machine learning‐based algorithm to detect damage in high‐rise building structures

SummaryA novel model is presented for global health monitoring of large structures such as high‐rise building structures through adroit integration of 2 signal processing techniques, synchrosqueezed wavelet transform and fast Fourier transform, an unsupervised machine learning technique, the restricted Boltzmann machine, and a recently developed supervised classification algorithm called neural dynamics classification (NDC) algorithm. The model extracts hidden features in the frequency domain of the denoised measured response signals recorded by sensors on different elevations or floors of a structure. The extracted features are used as an input of the NDC to detect and classify the global health of the structure into categories such as healthy, light damage, moderate damage, severe damage, and near collapse. The proposed model is validated using the data obtained from a 3D 1:20 scaled 38‐story reinforced concrete building structure. The results are compared with 3 other supervised classification algorithms: k‐nearest neighbor (KNN), probabilistic neural networks (PNN), and enhanced PNN (EPNN). NDC, EPNN, PNN, and KNN yield maximum average accuracies of 96%, 94%, 92%, and 82%, respectively.

Offline: A unique opportunity for WHO

2017 marks the tenth anniversary of the Institute for Health Metrics and Evaluation (IHME) and the 20th anniversary of the Global Burden of Disease. The success of IHME was not assured. An attempt to launch a centre for generating global health data in 2005 failed when the donor, Larry Ellison, quixotically withdrew his US$115 million gift to Harvard University. The Bill & Melinda Gates Foundation stepped in to fill the void, and IHME, led by Chris Murray, was established as a self-governing research organisation within the University of Washington in 2007. Last week, IHME's Board—including Julio Frenk (President, University of Miami), Sally Davies (Chief Medical Officer for England), and Victor Dzau (President, US National Academy of Medicine)—met to review the Institute's achievements and challenges. They did so in the wake of the Gates Foundation's recent award of a further $279 million to IHME. The success of the Institute has depended on its mission-driven ethos, ownership and critical engagement by over 2400 collaborators, scientific innovation and rigour, and an industrial capacity for data gathering and processing. Nature recently noted that IHME is a GPS for global health, developing “cutting-edge approaches to complex modelling”, and becoming “regarded as a go-to source for health information”. WHO is the world's leading global health agency and is the only legitimate multilateral mechanism for advancing the health of nations. But in recent years WHO has struggled to find resources to invest in the production of global health data. It has found the retention and recruitment of a new generation of health metricians difficult. And the ability of WHO to maintain its independence has been compromised. WHO's requirement to have country data pre-approved by member states before release has led to political interference that has undermined the agency's reputation for impartiality. The scale of what it now takes to produce comprehensive global health information has surpassed WHO's capabilities. By 2018, IHME will have 361 staff, over half of whom will be dedicated to producing the Global Burden of Disease. WHO is unable to compete with IHME's critical mass of health metrics scientists, let alone the massive computational capital needed to produce data for mortality, morbidity, and risk factors across 194 nations. Add to that the work of IHME on financing health, measuring health system performance, assessing progress on meeting the Sustainable Development Goals, evaluating the impact of global health initiatives (such as GAVI and the Global Fund), and monitoring specific concerns around, for example, air pollution and obesity. In 2016, IHME published 150 papers in 77 scientific journals, and its data were used by 156 governments. IHME has achieved its objective of producing trusted, timely, reliable, and actionable global health data. Here is the unique opportunity that is now available to WHO. With a new Director-General of WHO—Tedros Adhanom Ghebreyesus—to take office on July 1, 2017, and with an entirely new leadership team to be appointed across the agency in the coming months, now is a good moment to reassess the relationship between WHO and IHME. For much of the past decade, WHO officials have been understandably suspicious of IHME's work and intentions. They have seen IHME as a competitor to WHO. They have frequently been reluctant to use data published by IHME scientists. But WHO has misread IHME's goal. IHME seeks to produce information that is a global public good for nations. WHO's new leadership should see IHME not as a rival but as a partner in its mission to deliver healthy lives for all. The advantage of such a positive reset in their relationship is clear: adopting IHME global health data as WHO's official world health statistics would propel WHO into a position of leadership in global health monitoring. Some critics may say that the sacrifice is too great. Would accepting that WHO cannot keep pace with the science of health metrics further damage the agency's reputation? But IHME does not seek to harm WHO. On the contrary, IHME seeks to strengthen the agency's mandate and ability to advance health. Partnering with IHME would not be a capitulation by WHO. Instead, an alliance between the two organisations would secure and strengthen the agency's capacity and ability to speak with authority on global health. Such an alliance is an opportunity to be seized by WHO's new Director-General.

IRI measurement using dynamic tire pressure sensor with an axle accelerometer

This paper develops a new method to estimate IRI using a dynamic tire pressure sensor (DTPS) inside the tire with an axle accelerometer. This method is based on a physical 2-D dynamic tire model. This paper verifies that speeds above 40 km/h (25 mph) are suitable for both highways and urban roads. Feasibility and repeatability tests were hereby conducted and reported on an airport runway through a certification test that was administered by the Massachusetts Department of Transportation (MassDOT), highways and urban roads. During testing, the accuracy of the new method was equivalent to IRI values measured using the laser profilometers and accelerometers. The newly developed method has the potential to be integrated into vehicle computer systems to form a network wide global health monitoring system of roadways to make the right repair, at the right time, in the right place.

Semiparametric Bayesian Density Estimation With Disparate Data Sources: A Meta-Analysis of Global Childhood Undernutrition

Undernutrition, resulting in restricted growth, and quantified here using height-for-age z-scores, is an important contributor to childhood morbidity and mortality. Since all levels of mild, moderate, and severe undernutrition are of clinical and public health importance, it is of interest to estimate the shape of the z-scores’ distributions. We present a finite normal mixture model that uses data on 4.3 million children to make annual country-specific estimates of these distributions for under-5-year-old children in the world’s 141 low- and middle-income countries between 1985 and 2011. We incorporate both individual-level data when available, as well as aggregated summary statistics from studies whose individual-level data could not be obtained. We place a hierarchical Bayesian probit stick-breaking model on the mixture weights. The model allows for nonlinear changes in time, and it borrows strength in time, in covariates, and within and across regional country clusters to make estimates where data are uncertain, sparse, or missing. This work addresses three important problems that often arise in the fields of public health surveillance and global health monitoring. First, data are always incomplete. Second, different data sources commonly use different reporting metrics. Last, distributions, and especially their tails, are often of substantive interest.

Damage Detection Using Mode Shape of Beam Structures with Multiple Damages

Health monitoring is important technique in aerospace and other mechanical engineering structures to prevent structures from breaking down by hidden damages. Damages in a structure change its dynamic behavior such as natural frequencies, damping and mode shapes. Use of this phenomenon is effective for global health monitoring of structures. Flexural vibration analysis of damaged beams is made in Fourier transform to discuss a method of damage detection of beam structures with multiple damages. To analyze an inhomogeneous beam due to existing damages, a damaged beam is modeled as a piecewise continuous beam. Solution of dynamic response of the beam is obtained by taking account of continuity of piecewise connected boundaries. The mode shape of the beam is estimated using value of the peak at first response frequency in Fourier transform under a set of positions of impulse load. It is found that positions of damages can be predicted using wavelet transform of difference between mode shapes for damaged beam and undamaged beam. Validity of the method on the damage detection is also illustrated with experimental result.

Experimental study of the substructure isolation method for local health monitoring

SUMMARY This paper extends and studies experimentally the substructure isolation method. Local health monitoring is significant for large and complex structures, since it costs less and can be easily implemented compared with global analysis. In contrast to other substructuring methods, in which the substructure is separated from the global structure, but coupled to it via the interface forces, the substructure isolation method isolates the substructure into an independent structure by placing virtual fixed supports on the interface. Model updating or damage identification can be then performed locally and precisely using the constructed responses of the isolated substructure and any of the existing methods aimed originally at global identification. This paper discusses and further extends the approach to improve its performance in real applications. A new type of virtual interface support (free support) is proposed for isolation. Relaxation of the original requirements concerning the type and placement of the isolating excitations is discussed. Previously, the method relied on the linearity of the global structure; here, only the substructure is required to be linear, the global structure besides the substructure can be non-linear, yielding, changing or unknown. A damaged cantilever beam is used in the experimental study. Up to three modified global structures with the same substructure are used to test the robustness of the isolation with respect to unknown modifications and non-linearities of the outside structure. Two typical global health monitoring methods are applied at the substructural level. A comparison with the results obtained from a generic substructure separation method is offered. Copyright © 2011 John Wiley & Sons, Ltd.

Study on Electrical Resistance Tomography Simulation of Carbon Fiber Smart Layers on Two-Dimensional Curved Surface

Based on the piezoresistive function character and the conductivity of carbon fiber smart layer (CFSL), a novel electrical resistance tomography (ERT) system of spherical and cylindrical CFSL has been developed for global health monitoring. Based on the background of the third generation nuclear containment vessel, CFSL is stuck on the surface of the structure, and the effectiveness of the ERT system is verified by virtual experiment. The image reconstruction results show that CFSL on two-dimensional curved surface has good sensing characteristics in the complex strain state. Compared with structural volumetric strain in the monitoring area, it indicates that the high electrical resistance of CFSL is distributed in the high strain area of structures, and the low electrical resistance of CFSL is just distributed in the low strain area of structures. Therefore the resistivity’s distribution of CFSL can reflect qualitatively the structural volumetric strain field.

WHO and Global Health Monitoring: The Way Forward

While working at WHO, CAZ was involved in the development of global estimates. Disclaimer: The authors are staff members of the World Health Organization. The authors alone are responsible for the views expressed in this publication, and they do not necessarily represent the decisions or policies of the World Health Organization. No specific funding was received for this article.

Networked Biomedical System for Ubiquitous Health Monitoring

We propose a distributed system that enables global and ubiquitous health monitoring of patients. The biomedical data will be collected by wearable health diagnostic devices, which will include various types of sensors and will be transmitted towards the corresponding Health Monitoring Centers. The permanent medical data of patients will be kept in the corresponding Home Data Bases, while the measured biomedical data will be sent to the Visitor Health Monitor Center and Visitor Data Base that serves the area of present location of the patient. By combining the measured biomedical data and the permanent medical data, Health Medical Centers will be able to coordinate the needed actions and help the local medical teams to make quickly the best decisions that could be crucial for the patient health, and that can reduce the cost of health service.