Data Collection Capacity Research Articles

Experiences from the field: maternal, reproductive and child health data collection in humanitarian and emergency situations.

BackgroundHumanitarian emergencies can disproportionately affect women of reproductive age, and children. Good data on reproductive maternal, newborn and child health (RMNCH) are vital to plan and deliver programmes to address RMNCH needs. There is currently a lack of information regarding the availability, use and applicability of data collection tools.MethodsKey informant interviews (KII) were conducted with participants with experience of data collection in humanitarian settings, identified from relevant publications. Data were analysed using the thematic framework approach.ResultsAll participants reported challenges, especially in the acute phase of an emergency and when there is insufficient security. Four common themes were identified: the importance of a mixed methods approach, language both with regard to development of data collection tools and data collection, the need to modify existing tools and build local capacity for data collection. Qualitative data collection was noted to be time consuming but considered to be important to understand the local context. Both those who have experienced trauma (including sexual violence) and data collectors require debriefing after documenting these experiences.ConclusionsThere were numerous challenges associated with data collection assessing the health status of, and services available, to women and children in humanitarian settings, and researchers should be well prepared.

English

Big data concerns of large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, big data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. Data mining have challenges with big data. From all the challenges, existing systems focused on data privacy and data security. Data privacy can protect using K- Anonymity technique and data security is implemented using authentication method. K- Anonymity is the method that annonymized data fields such that sensitive information cannot be pinpointed to an individual record. But leakage of sensitive data are still there, so there must need a better privacy preserving technique. In this paper we are proposing an alternate method using Alpha K Anonymity in order to obtain better privacy

The California Health Policy Research Program - supporting policy making through evidence and responsive research.

This article explores the creation, design, and execution of a university-based collaboration to provide responsive research and evidence to a group of diverse health care, labor, and consumer stakeholders through convening a funded series of deliberative meetings, research briefs, peer-reviewed journal articles, ad hoc data analyses, and policy analyses. Funded by the California Endowment, the California Health Policy Research Program was created by researchers at the University of California, Berkeley Center for Labor Research and Education, and the UCLA Center for Health Policy Research. The collaboration not only allowed new research and analyses to be used by stakeholders and policy makers in decision making but also allowed university researchers to receive input on the important health policy issues of the day. The guidance of stakeholders in the research and policy analysis process was vital in driving meaningful results during an important time in health policy making in California. The manuscript discusses lessons learned in building relationships with stakeholders; meeting research and analytic needs; engaging stakeholders and policy makers; building capacity for quick-turnaround data collection and analysis, dissemination and publication; and maintaining the collaboration.

Data mining with big data

Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.

An economic analysis of money follows the patient

As part of the proposed changes to re-design the Irish health-care system, the Department of Health (money follows the patient-policy paper on hospital financing, 2013b) outlined a new funding model for Irish hospitals-money follows the patient (MFTP). This will replace the existing system which is predominately prospective with hospitals receiving a block grant per annum. MFTP will fund episodes of care rather than hospitals. Thus, hospital revenue will be directly linked to activity [activity-based funding (ABF)]. With ABF there is a fundamental shift to a system where hospitals generate their own income and this changes incentive structures. While some of these incentives are intended (reducing cost per case and increasing coding quality), others are less intended and less desirable. As a result, there may be reductions in quality, upcoding, cream skimming and increased pressure on other parts of the health system. In addition, MFTP may distort health system priorities. There are some feasibility concerns associated with the implementation of MFTP. Data collection, coding and classification capacity are crucial for its success. While MFTP can build on existing systems, significant investment is required for its success. This includes investment in coding and classification, infrastructure, skills, IT, contracting, commissioning, auditing and performance monitoring systems. Despite the challenges facing implementers, MFTP could greatly improve the transparency and accountability of the system. Thus if the downside risks are managed, there is potential for MFTP to confer significant benefits to Irish hospital care.

Distributed Data Collection in Large-Scale Asynchronous Wireless Sensor Networks Under the Generalized Physical Interference Model

Wireless sensor networks (WSNs) are more likely to be d-pistributed asynchronous systems. In this paper, we investigate the achievable data collection capacity of realistic distributed asynchronous WSNs. Our main contributions include five aspects. First, to avoid data transmission interference, we derive an ℜ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -proper carrier-sensing range (ℜ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -PCR) under the generalized physical interference model, where ℜ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> is the satisfied threshold of data receiving rate. Taking ℜ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -PCR as its carrier-sensing range, any sensor node can initiate a data transmission with a guaranteed data receiving rate. Second, based on ℜ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -PCR, we propose a Distributed Data Collection (DDC) algorithm with fairness consideration. Theoretical analysis of DDC surprisingly shows that its achievable network capacity is order-optimal and independent of network size. Thus, DDC is scalable. Third, we discuss how to apply ℜ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> -PCR to the distributed data aggregation problem and propose a Distributed Data Aggregation (DDA) algorithm. The delay performance of DDA is also analyzed. Fourth, to be more general, we study the delay and capacity of DDC and DDA under the Poisson node distribution model. The analysis demonstrates that DDC is also scalable and order-optimal under the Poisson distribution model. Finally, we conduct extensive simulations to validate the performance of DDC and DDA.

An energy-efficient clustered distributed coding for large-scale wireless sensor networks

A wireless sensor network (WSN) usually consists of a large number of battery-powered low-cost sensors with limited data collection and processing capacity. In order to prolong the lifetime of the WSN with a target error performance, a novel clustered distributed coding framework, referred to as distributed multiple-sensor cooperative turbo coding (DMSCTC), is developed for a large-scale WSN with sensor grouped in cooperative cluster. In the proposed DMSCTC scheme, a simple forward error correction is employed at each sensor and a simple multi-sensor joint coding is adopted at the cluster head, while complicated joint iterative decoding is implemented only at the data collector. The proposed DMSCTC scheme achieves extra distributed coding gain and cooperative spatial diversity without introducing extra complexity burden on the sensors by transferring the complicated joint decoding process to the data collector. With the proposed scheme, the WSN can achieve the target error performance with less power consumption, thus prolonging its lifetime. The error performance and energy efficiency of the proposed DMSCTC scheme are analyzed, and followed by Monte Carlo simulations. Both analytical and simulation results show that the DMSCTC can substantially improve the energy efficiency of the clustered WSN.

Risk Perception and Communication in Sub-Saharan Africa

In this narrative review, a brief summary of theoretical approaches to risk perception is followed by an analysis of some of the special factors influencing risk perception and risk communication in sub-Saharan Africa. Examples of recent and emergent local medicines and vaccine controversies in several countries are given along with evidence and analysis of how they were managed. These demonstrate, among other things, the extent to which ethnic, religious and cultural issues influence popular perception, and the power of rumour and anecdote in shaping public opinion and official responses to events. Where safety monitoring systems exist, they are in their infancy, with limited capacity for data collection, credible scientific review, effective public communication and robust crisis management. Although increasing democratic freedoms, including less restricted media, and evolving health systems are addressing the challenges and give hope for further progress, there are still deep and intractable issues that inhibit transparent and effective risk communication and stand in the way of African populations comprehending medicines and their risks in safer and more balanced ways. Some proposals for future change and action are offered, including the pursuit of a deeper understanding of local and national values, assumptions and beliefs that drive risk perception; tailoring public health planning and communications to specifically-targeted regions and populations; strengthening of safety surveillance and data-collection systems; giving higher priority to medicines safety issues in healthcare training and public education.

Data collection capacity of random-deployed wireless sensor networks under physical models

Data collection is one of the most important functions provided by wireless sensor networks. In this paper, we study the theoretical limitations of data collection in terms of capacity for a wireless sensor network wheren sensors are randomly deployed. We first consider data collection under physical model. We show that the capacity of data collection is in order of .W / where W is the fixed data-rate on individual links. Particularly, we give a simple collection method based on interference blocks and theoretically prove that the method can achieve the optimal capacity in order. If each sensor can aggregate its receiving packets into a single packet to send, the capacity of data collection increases to n logn W. We then derive a lower bound and an upper bound of data collection capacity under generalized physical model where the data rate depends on the signal to interference plus noise ratio.

Continuous Data Collection Capacity of Dual-Radio Multichannel Wireless Sensor Networks

The performance of data collection in Wireless Sensor Networks (WSNs) can be measured by network capacity. However, few existing works dedicatedly consider the Continuous Data Collection (CDC) capacity for WSNs under the protocol interference model. In this paper, we propose a multipath scheduling algorithm for SDC in single-radio multichannel WSNs and derive its network capacity which is a tighter lower bound compared with the previously best result [CHECK END OF SENTENCE]. We also propose a novel CDC method for dual-radio multichannel WSNs. It significantly speeds up the data collection process, and achieves a capacity of (nW/12M⌈(3.63ρ2+c3 ρ+c4)/H⌉) when Δe ≤ 12 or (nW/MΔe⌈(3.63ρ2+c3 ρ+c4)/H⌉) when Δe >;12, where n is the number of the sensors, M is a constant value and usually M ≪ n, Δe is the maximum number of the leaf nodes having a same parent in the data collection tree, W is the channel bandwidth, H is the number of available orthogonal channels, \rho is the ratio of the interference radius over the transmission radius, c3 = (8π/√(3)) + π + 2, and c4 = (8π/√(3)) + 2π + 6. Extensive simulation results indicate that the proposed algorithms improve network capacity significantly compared with existing works.

Health and demographic surveillance systems: a step towards full civil registration and vital statistics system in sub-Sahara Africa?

BackgroundIn the developed world, information on vital events is routinely collected nationally to inform population and health policies. However, in many low-and middle-income countries, especially those in sub-Saharan Africa (SSA), there is a lack of effective and comprehensive national civil registration and vital statistics system. In the past decades, the number of Health and Demographic Surveillance Systems (HDSSs) has increased throughout SSA. An HDSS monitors births, deaths, causes of death, migration, and other health and socio-economic indicators within a defined population over time. Currently, the International Network for the Continuous Demographic Evaluation of Populations and Their Health (INDEPTH) brings together 38 member research centers which run 44 HDSS sites from 20 countries in Africa, Asia and Oceana. Thirty two of these HDSS sites are in SSA.DiscussionThis paper argues that, in the absence of an adequate national CRVS, HDSSs should be more effectively utilised to generate relevant public health data, and also to create local capacity for longitudinal data collection and management systems in SSA. If HDSSs get strategically located to cover different geographical regions in a country, data from these sites could be used to provide a more complete national picture of the health of the population. They provide useful data that can be extrapolated for national estimates if their regional coverage is well planned. HDSSs are however resource-intensive. Efforts are being put towards getting them linked to local or national policy contexts and to reduce their dependence on external funding. Increasing their number in SSA to cover a critical proportion of the population, especially urban populations, must be carefully planned. Strategic planning is needed at national levels to geographically locate HDSS sites and to support these through national funding mechanisms.SummaryThe paper does not suggest that HDSSs should be seen as a replacement for civil registration systems. Rather, they should serve as a short- to medium-term measure to provide data for health and population planning at regional levels with possible extrapolation to national levels. HDSSs can also provide useful lessons for countries that intend to set up nationally representative sample vital registration systems in the long term.

Microinsurance and natural disasters: Challenges and options

Providing disaster microinsurance to low income individuals is far from easy. Designing and structuring products so that they can be sold at low cost raises a set of challenges, and even then the level of voluntary purchase can be disappointingly low. Recent innovations in providing agricultural insurance could have broad implications for other disaster microinsurance products. If a market is to be viable governments will have key roles to play, including: approving insurance policy small print and product designs; supporting or leading industry-wide investments in data collection, index design, and loss adjustment capacity; financing sustained public information campaigns; nudging or compelling purchase; and committing to limits on post-disaster financial assistance to the uninsured. If disaster microinsurance prices do not accurately reflect the risks faced, the presence of disaster microinsurance can distort incentives to migrate. However, in the presence of risk-based pricing, it seems plausible that disaster microinsurance could make it easier for people to stay where they are even as their environment becomes more fragile, yet increase post-disaster migration through increasing post-disaster wealth.

The Application and Comparison of Digital Ground Photogrammetry Technology for Bridge Safety Monitoring

For needs of transportation, it is more and more obvious of the bridge unsafe, and the study of bridge safety monitor will become more important at the area of civil engineering. In this paper we discuss the methods of digital ground photogrammetry for bridge safety monitoring and the application of those, we took digital cameras and 3-D laser scanners as the tools for close-range photogrammetry, and adopted 3D Time Baseline Parallax combined with Kalman Variance Filters as the theories for monitoring dynamic deformation, then developed software to deal with data, which can draw curves of dynamic deformation automatically, we can monitor the deformation regularity by these curves effectively, then we can monitor and analysis the safety of bridges by this way. The method we designed is very simple and economical, and we also did a lot of experiments to verify availability of this system, and we happy to say that the system has the characteristics of high automation degree, large data collection capacity and strong system reliability, etc. It will be practical value at the area of civil engineering.

New Technologies, Tools and Approaches for Improving Crop Breeding

New Technologies, Tools and Approaches for Improving Crop Breeding

Capacity of Data Collection in Arbitrary Wireless Sensor Networks

Data collection is a fundamental function provided by wireless sensor networks. How to efficiently collect sensing data from all sensor nodes is critical to the performance of sensor networks. In this paper, we aim to understand the theoretical limits of data collection in a TDMA-based sensor network in terms of possible and achievable maximum capacity. Previously, the study of data collection capacity has concentrated on large-scale random networks. However, in most of the practical sensor applications, the sensor network is not uniformly deployed and the number of sensors may not be as huge as in theory. Therefore, it is necessary to study the capacity of data collection in an arbitrary network. In this paper, we first derive the upper and lower bounds for data collection capacity in arbitrary networks under protocol interference and disk graph models. We show that a simple BFS tree-based method can lead to order-optimal performance for any arbitrary sensor networks. We then study the capacity bounds of data collection under a general graph model, where two nearby nodes may be unable to communicate due to barriers or path fading, and discuss performance implications. Finally, we provide discussions on the design of data collection under a physical interference model or a Gaussian channel model.

Comparing data collection and processing options for terrestrial acoustical monitoring addressing long durations and large spatial scales

For generations, skilled naturalists have listened attentively to expand the scope of their field searches and surveys. The rapid proliferation of digital audio recorders with large storage capacity and low power consumption offers numerous options to pursue standardized acoustical surveys of several weeks duration across large areas. This presentation will assess a representative sample of available equipment in relation to the parameters determining their suitability for these applications: price, capacity, fidelity, and ancillary features. Enormous data collection capacity is not useful unless there are adequate tools for processing the data to render the necessary results. The features and performance of several software packages will be compared in the context of different classes of potential application. In particular, the merits of highly selective detectors will be discussed in relation to the alternative of more permissive screening for potentially interesting sounds followed by a measurement an...

A Multi-function Public Health Surveillance System and the Lessons Learned in Its Development: The Alberta Real Time Syndromic Surveillance Net

Objective: We describe a centralized automated multi-function detection and reporting system for public health surveillance–the Alberta Real Time Syndromic Surveillance Net (ARTSSN). This improves upon traditional paper-based systems which are often fragmented, limited by incomplete data collection and inadequate analytical capacity, and incapable of providing timely information for public health action.Methods: ARTSSN concurrently analyzes multiple electronic data sources in real time to describe results in tables, charts and maps. Detected anomalies are immediately disseminated via alerts to decision-makers for action.Results: ARTSSN provides richly integrated information on a variety of health conditions for early detection of and prompt action on abnormal events such as clusters, outbreaks and trends. Examples of such health conditions include chronic and communicable disease, injury and environmentmediated adverse incidents.Discussion: Key advantages of ARTSSN over traditional paper-based methods are its timeliness, comprehensiveness and automation. Public health surveillance of communicable disease, injury, environmental hazard exposure and chronic disease now occurs in a single system in real time year round. Examples are given to demonstrate the public health value of this system, particularly during Pandemic (H1N1) 2009.

Analyzing Gene Expression Data from Microarray and Next‐Generation DNA Sequencing Transcriptome Profiling Assays Using GeneSifter Analysis Edition

Transcription profiling with microarrays has become a standard procedure for comparing the levels of gene expression between pairs of samples, or multiple samples following different experimental treatments. New technologies, collectively known as next-generation DNA sequencing methods, are also starting to be used for transcriptome analysis. These technologies, with their low background, large capacity for data collection, and dynamic range, provide a powerful and complementary tool to the assays that formerly relied on microarrays. In this chapter, we describe two protocols for working with microarray data from pairs of samples and samples treated with multiple conditions, and discuss alternative protocols for carrying out similar analyses with next-generation DNA sequencing data from two different instrument platforms (Illumina GA and Applied Biosystems SOLiD).

2007 Annual Consumer Bankruptcy Demographics Report

The deteriorating American economic situation in 2007 was first felt by individual consumers and families throughout the country experiencing personal financial crisis and an increase in home foreclosures. This personal level economic crisis was followed by failures and retractions in the financial services sector, which in turn was followed by retractions throughout the wider economy and in the stock market. Finally, the impact of these wider economic retractions caused financial aftershocks for individual consumers and families in the form of job loss, reduced income and reduced net worth driven by the plummeting values of their remaining real estate and retirement holdings. From this cycle one can draw a hypothesis that reducing the incidence of personal financial crisis should have a positive and stabilizing impact on the wider economy. One commonly proposed method for reducing the incidence of personal financial crisis is to modify consumer behavior through financial literacy education. The development of data identifying those Americans in personal financial crisis is necessary to create effective financial literacy education programs. In 2006 the Institute for Financial Literacy established a comprehensive, neutral research program designed to collect demographic information from individuals contemplating and eventually filing for bankruptcy protection, a population that is by definition in personal financial crisis. Utilizing the Institute's capacity for large scale data collection, information was collected for the period January 1, 2007 through December 31, 2007 in the following categories: gender, age, ethnicity, educational attainment, personal income level, employment status, marital status and cause(s) of financial distress. This data was then analyzed by the Institute's Center for Consumer Financial Research (CCFR) and indicates that the average American who is in financial distress and seeking bankruptcy related credit counseling and financial education is: Caucasian, married, employed, between the ages of 35-44 years old, has at least a high school education or some college, and makes no more than $30,000 per year. In light of its findings, this paper challenges the academic community to craft effective solutions for America's ever growing personal debt problem.

Aging and Bankruptcy: Are Older Americans Truly Afforded an Economic Fresh Start

The explosion of personal financial crisis, especially foreclosure, across large and diverse segments of the American population in 2007 and 2008 significantly impacted the American and global economies. This causal connection supports a hypothesis that reducing the incidence of personal financial crisis and accelerating recovery there from should have a positive macroeconomic impact. The first step in the development of effective educational programs that help Americans reduce the incidence of and/or recover from personal financial crisis is the clear identification of those Americans in personal financial crisis. In 2006, the Institute for Financial Literacy's Center for Consumer Financial Research (CCFR) established a comprehensive, neutral research program designed to collect demographic information from individuals contemplating and eventually filing for bankruptcy protection, a population that is by definition in personal financial crisis. Utilizing the Institute's capacity for large scale data collection, information was collected for the period January 1, 2006 through December 31, 2006 in the following categories: gender, age, ethnicity, educational attainment, personal income level, employment status, marital status and cause(s) of financial distress. Initial data analysis identified several demographic trends, including increased bankruptcy filing rates among older Americans. While overall findings were reported in Who Went Bankrupt in 2006? A Demographic Analysis of American Debtors in May 2007, this paper more specifically focuses on the categories of age, employment and causes of financial distress. In light of its findings, this paper challenges researchers and those developing financial education programs to address the unique challenges facing older Americans recovering from personal financial crisis given their shorter income horizons.