Application of change point analysis to daily influenza-like illness emergency department visits

Background: The utility of healthcare utilization data from US emergency departments (EDs) for rapid monitoring of changes in influenza-like illness (ILI) activity was highlighted during the recent influenza A (H1N1) pandemic. Monitoring has tended to rely on detection algorithms, such as the Early Aberration Reporting System (EARS), which are limited in their ability to detect subtle changes and identify disease trends. Objective: To evaluate a complementary approach, change point analysis (CPA), for detecting changes in the incidence of ED visits due to ILI. Methodology and principal findings Data collected through the Distribute project (isdsdistribute.org), which aggregates data on ED visits for ILI from over 50 syndromic surveillance systems operated by state or local public health departments were used. The performance was compared of the cumulative sum (CUSUM) CPA method in combination with EARS and the performance of three CPA methods (CUSUM, structural change model and Bayesian) in detecting change points in daily time-series data from four contiguous US states participating in the Distribute network. Simulation data were generated to assess the impact of autocorrelation inherent in these time-series data on CPA performance. The CUSUM CPA method was robust in detecting change points with respect to autocorrelation in time-series data (coverage rates at 90% when −0.2≤ρ≤0.2 and 80% when −0.5≤ρ≤0.5). During the 2008–9 season, 21 change points were detected and ILI trends increased significantly after 12 of these change points and decreased nine times. In the 2009–10 flu season, we detected 11 change points and ILI trends increased significantly after two of these change points and decreased nine times. Using CPA combined with EARS to analyze automatically daily ED-based ILI data, a significant increase was detected of 3% in ILI on April 27, 2009, followed by multiple anomalies in the ensuing days, suggesting the onset of the H1N1 pandemic in the four contiguous states. Conclusions and significance As a complementary approach to EARS and other aberration detection methods, the CPA method can be used as a tool to detect subtle changes in time-series data more effectively and determine the moving direction (ie, up, down, or stable) in ILI trends between change points. The combined use of EARS and CPA might greatly improve the accuracy of outbreak detection in syndromic surveillance systems

PHSkb: A knowledgebase to support notifiable disease surveillance

BACKGROUND: Notifiable disease surveillance in the United States is predominantly a passive process that is often limited by poor timeliness and low sensitivity. Interoperable tools are needed that interact more seamlessly with existing clinical and laboratory data to improve notifiable disease surveillance. DESCRIPTION: The Public Health Surveillance Knowledgebase (PHSkb™) is a computer database designed to provide quick, easy access to domain knowledge regarding notifiable diseases and conditions in the United States. The database was developed using Protégé ontology and knowledgebase editing software. Data regarding the notifiable disease domain were collected via a comprehensive review of state health department websites and integrated with other information used to support the National Notifiable Diseases Surveillance System (NNDSS). Domain concepts were harmonized, wherever possible, to existing vocabulary standards. The knowledgebase can be used: 1) as the basis for a controlled vocabulary of reportable conditions needed for data aggregation in public health surveillance systems; 2) to provide queriable domain knowledge for public health surveillance partners; 3) to facilitate more automated case detection and surveillance decision support as a reusable component in an architecture for intelligent clinical, laboratory, and public health surveillance information systems. CONCLUSIONS: The PHSkb provides an extensible, interoperable system architecture component to support notifiable disease surveillance. Further development and testing of this resource is needed

Imaging Mass Spectrometry: Hype or Hope?

Imaging mass spectrometry is currently receiving a significant amount of attention in the mass spectrometric community. It offers the potential of direct examination of biomolecular patterns from cells and tissue. This makes it a seemingly ideal tool for biomedical diagnostics and molecular histology. It is able to generate beautiful molecular images from a large variety of surfaces, ranging from cancer tissue sections to polished cross sections from old-master paintings. What are the parameters that define and control the implications, challenges, opportunities, and (im)possibilities associated with the application of imaging MS to biomedical tissue studies. Is this just another technological hype or does it really offer the hope to gain new insights in molecular processes in living tissue? In this critical insight this question is addressed through the discussion of a number of aspects of MS imaging technology and sample preparation that strongly determine the outcome of imaging MS experiments

Contributions of scale: What we stand to gain from Indigenous and local inclusion in climate-health monitoring and surveillance systems

Understanding how climate change will affect global health is a defining challenge this century. This is predicated, however, on our ability to combine climate and health data to investigate the ways in which variations in climate, weather, and health outcomes interact. There is growing evidence to support the value of place- and community-based monitoring and surveillance efforts, which can contribute to improving both the quality and equity of data collection needed to investigate and understand the impacts of climate change on health. The inclusion of multiple and diverse knowledge systems in climate-health surveillance presents many benefits, as well as challenges. We conducted a systematic review, synthesis, and confidence assessment of the published literature on integrated monitoring and surveillance systems for climate change and public health. We examined the inclusion of diverse knowledge systems in climate-health literature, focusing on: 1) analytical framing of integrated monitoring and surveillance system processes 2) key contributions of Indigenous knowledge and local knowledge systems to integrated monitoring and surveillance systems processes; and 3) patterns of inclusion within these processes. In total, 24 studies met the inclusion criteria and were included for data extraction, appraisal, and analysis. Our findings indicate that the inclusion of diverse knowledge systems contributes to integrated climate-health monitoring and surveillance systems across multiple processes of detection, attribution, and action. These contributions include: the definition of meaningful problems; the collection of more responsive data; the reduction of selection and source biases; the processing and interpretation of more comprehensive datasets; the reduction of scale dependent biases; the development of multi-scale policy; long-term future planning; immediate decision making and prioritization of key issues; as well as creating effective knowledge-information-action pathways. The value of our findings and this review is to demonstrate how neither scientific, Indigenous, nor local knowledge systems alone will be able to contribute the breadth and depth of information necessary to detect, attribute, and inform action along these pathways of climate-health impact. Rather, it is the divergence or discordance between the methodologies and evidences of different knowledge systems that can contribute uniquely to this understanding. We critically discuss the possibility of what we, mainly local communities and experts, stand to lose if these processes of inclusion are not equitable. We explore how to shift the existing patterns of inclusion into balance by ensuring the equity of contributions and justice of inclusion in these integrated monitoring and surveillance system processes

Semi-quantitative analyses of metabolic systems of human colon cancer metastatic xenografts in livers of superimmunodeficient NOG mice

Analyses of energy metabolism in human cancer have been difficult because of rapid turnover of the metabolites and difficulties in reducing time for collecting clinical samples under surgical procedures. Utilization of xenograft transplantation of human-derived colon cancer HCT116 cells in spleens of superimmunodeficient NOD/SCID/IL-2Rγnull (NOG) mice led us to establish an experimental model of hepatic micrometastasis of the solid tumor, whereby analyses of the tissue sections collected by snap-frozen procedures through newly developed microscopic imaging mass spectrometry (MIMS) revealed distinct spatial distribution of a variety of metabolites. To perform intergroup comparison of the signal intensities of metabolites among different tissue sections collected from mice in fed states, we combined matrix-assisted laser desorption/ionization time-of-flight imaging mass spectrometry (MALDI–TOF-IMS) and capillary electrophoresis–mass spectrometry (CE–MS), to determine the apparent contents of individual metabolites in serial tissue sections. The results indicated significant elevation of ATP and energy charge in both metastases and the parenchyma of the tumor-bearing livers. To note were significant increases in UDP-N-acetyl hexosamines, and reduced and oxidized forms of glutathione in the metastatic foci versus the liver parenchyma. These findings thus provided a potentially important method for characterizing the properties of metabolic systems of human-derived cancer and the host tissues in vivo

Illegal immigration and media exposure: evidence on individual attitudes

Illegal immigration has been the focus of much debate in receiving countries, but little is known about the drivers of individual attitudes towards illegal immigrants. To study this question, we use the CCES survey, which was carried out in 2006 in the USA. We find evidence that—in addition to standard labor market and welfare state considerations—media exposure is significantly correlated with public opinion on illegal immigration. Controlling for education, income, ideology, and other socio-demographic characteristics, individuals watching Fox News are 9 percentage points more likely than CBS viewers to oppose the legalization of undocumented immigrants. We find an effect of the same size and direction for CNN viewers, whereas individuals watching PBS are instead more likely to support legalization. Ideological self-selection into different news programs plays an important role, but cannot entirely explain the correlation between media exposure and attitudes about illegal immigration