GeoMedStat: an integrated spatial surveillance system to track air pollution and associated healthcare events.

Air pollutants, such as particulate matter with a diameter ≤2.5 microns (PM2.5) and ozone (O3), are known to exacerbate asthma and other respiratory diseases. An integrated surveillance system that tracks such air pollutants and associated disease incidence can assist in risk assessment, healthcare preparedness and public awareness. However, the implementation of such an integrated environmental health surveillance system is a challenge due to the disparate sources of many types of data and the implementation becomes even more complicated for a spatial and real-time system due to lack of standardised technological components and data incompatibility. In addition, accessing and utilising health data that are considered as Protected Health Information (PHI) require maintaining stringent protocols, which have to be supported by the system. This paper aims to illustrate the development of a spatial surveillance system (GeoMedStat) that is capable of tracking daily environmental pollutants along with both daily and historical patient encounter data. It utilises satellite data and the groundmonitor data from the US National Aeronautics and Space Administration (NASA) and the US Environemental Protection Agenecy (EPA), rspectively as inputs estimating air pollutants and is linked to hospital information systems for accessing chief complaints and disease classification codes. The components, developmental methods, functionality of GeoMedStat and its use as a real-time environmental health surveillance system for asthma and other respiratory syndromes in connection with with PM2.5 and ozone are described. It is expected that the framework presented will serve as an example to others developing real-time spatial surveillance systems for pollutants and hospital visits.

Use of hyperspectral imaging to distinguish normal, precancerous, and cancerous cells.

BACKGROUND: The objective of the current study was to test the hypothesis that the cytologic diagnosis of cancer cells can be enhanced by the technique of hyperspectral imaging (HSI). METHODS: As a proof of principle, HSI was employed to obtain hyperspectrum from a normal human fibroblast, as well as its telomerase-immortalized and SV40-transformed derivatives. Novel algorithms were developed to differentiate among these cell models based on spectral and spatial differences. Using the same technique with modified algorithms, the authors were able to differentiate among normal and precancerous (low-grade [LG] and high-grade [HG]) cervical cells and squamous cell carcinoma (SCC) on liquid-based Papanicolaou (Pap) test slides. RESULTS: The specificity for identifying normal fibroblast cell type based on spatial and spectral algorithms was 74.2%. The sensitivity for identifying telomerase-immortalized and SV40-transformed cells was 100% and 90.3%, respectively. The system identified normal cervical cells with a specificity of 95.8%. With regard to LG precancerous cells and HG precancerous cells, the sensitivity was 66.7% and 93.5%, respectively. The sensitivity detected for SCC was 98.6%. CONCLUSIONS: HSI can be utilized in prescreening liquid-based Pap test slides to improve efficiency in Pap test diagnoses with the goal of ultimately reducing the mortality from cervical cancer while reducing health care costs.