Methods for Characterizing Fine Particulate Matter Using Satellite Remote-Sensing Data and Ground Observations: Potential Use for Environmental Public Health Surveillance

This study describes and demonstrates different techniques for surfacing daily environmental / hazards data of particulate matter with aerodynamic diameter less than or equal to 2.5 micrometers (PM2.5) for the purpose of integrating respiratory health and environmental data for the Centers for Disease Control and Prevention (CDC s) pilot study of Health and Environment Linked for Information Exchange (HELIX)-Atlanta. It described a methodology for estimating ground-level continuous PM2.5 concentrations using B-Spline and inverse distance weighting (IDW) surfacing techniques and leveraging National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectrometer (MODIS) data to complement The Environmental Protection Agency (EPA) ground observation data. The study used measurements of ambient PM2.5 from the EPA database for the year 2003 as well as PM2.5 estimates derived from NASA s satellite data. Hazard data have been processed to derive the surrogate exposure PM2.5 estimates. The paper has shown that merging MODIS remote sensing data with surface observations of PM2.5 not only provides a more complete daily representation of PM2.5 than either data set alone would allow, but it also reduces the errors in the PM2.5 estimated surfaces. The results of this paper have shown that the daily IDW PM2.5 surfaces had smaller errors, with respect to observations, than those of the B-Spline surfaces in the year studied. However the IDW mean annual composite surface had more numerical artifacts, which could be due to the interpolating nature of the IDW that assumes that the maxima and minima can occur only at the observation points. Finally, the methods discussed in this paper improve temporal and spatial resolutions and establish a foundation for environmental public health linkage and association studies for which determining the concentrations of an environmental hazard such as PM2.5 with good accuracy levels is critical

Patients visiting the complementary medicine clinic for pain: a cross sectional study

<p>Abstract</p> <p>Background</p> <p>Pain is one of the most common reasons for seeking medical care. The purpose of this study was to characterize patients visiting the complementary medicine clinic for a pain complaint.</p> <p>Methods</p> <p>This is a cross-sectional study. The study took place at Clalit Health Services (CHS) complementary clinic in Beer-Sheva, Israel. Patients visiting the complementary clinic, aged 18 years old and older, Hebrew speakers, with a main complaint of pain were included. Patients were recruited consecutively on random days of the month during a period of six months. Main outcome measures were: pain levels, location of pain, and interference with daily activities. Once informed consent was signed patients were interviewed using a structured questionnaire by a qualified nurse. The questionnaire included socio-demographic data, and the Brief Pain Inventory (BPI).</p> <p>Results</p> <p>Three-hundred and ninety-five patients were seen at the complementary medicine clinic during the study period, 201 (50.8%) of them met the inclusion criteria. Of them, 163 (81.1%) agreed to participate in the study and were interviewed. Pain complaints included: 69 patients (46.6%) with back pain, 65 (43.9%) knee pain, and 28 (32.4%) other limbs pain. Eighty-two patients (50.3%) treated their pain with complementary medicine as a supplement for their conventional treatment, and 55 (33.7%) felt disappointed from the conventional medicine experience. Eighty-three patients (50.9%) claimed that complementary medicine can result in better physical strength, or better mental state 51 (31.3%). Thirty-seven patients (22.7%) were hoping that complementary medicine will prevent invasive procedures.</p> <p>Conclusion</p> <p>Given the high proportion of patients with unsatisfactory pain relief using complementary and alternative medicine (CAM), general practitioners should gain knowledge about CAM and CAM providers should gain training in pain topics to improve communication and counsel patients. More clinical research to evaluate safety and efficiency of CAM for pain is needed to provide evidence based counseling.</p

AB1047 Towards the Design of a Decision Support Tool for Precise Care for Arthritis

BackgroundDecision Support requires the ability to classify individuals into subpopulations that differ in their susceptibility to diseases or their response to a specific treatment. Preventive or therapeutic interventions can then be focused on those who will benefit, sparing expense and side effects for those who will not. Thus, it is the tailoring of medical treatment to the individual characteristics of each patient and their susceptibility to various chronic diseases.ObjectivesBig Data analytics will empower physicians at the point of care to diagnose early arthritis stages, choose treatment approaches, decide when to refer to a subspecialist, and mitigate co-morbidities.Co-morbidity refers to co-occurrence of more than one disease in a person at a time. Examples include Diabetes, Cardiovascular diseases, renal diseases, Arthritis, etc. These diseases can occur by chance or there can be complex pathological associations. These indirect causal factors are only partially understood. It has been observed that the number of hospital admissions, as well as the mortality rate of comorbid patients, is significantly high. Hence, there is a need for early detection of these diseases. The aim of this project is to develop a clinical decision support system to study the clinical and genomic factors responsible for causing these diseases. Based on these findings, educate clinicians about how certain clinical and genomic factors are responsible for causing these diseases.MethodsMost genetic variations among people is a result of single nucleotide polymorphisms (SNPs), which are differences in a single nucleotide within a stretch of DNA. SNPs can result in the production of different RNA molecules and proteins, thus altering the body's metabolism and physiology. With approximately 10 million SNPs in the human genome, “big data” analytical methods are the most efficient means for discovering which SNPs are associated with a particular disease. Candidate gene studies and genome-wide association studies (GWAS) serve a similar purpose on a much smaller scale, but are infeasible for analyzing large amounts of data.ResultsDesign and Methodology: a.From a large EMR database extract records of persons with arthritis.b.Obtain information about SNP known to be risk causing from SNPedia, dbSNP.c.Integrate clinical and genomic data to obtain a universal feature vector.d.Perform feature extraction to extract relevant attributes.e.Run data mining algorithms like simple k-means to obtain clusters of patients and study similarity between them.The application systems interconnection logic is depicted in the diagram.ConclusionsThe proposed framework will enable a decision support tool for precision medicine in treatment of persons with arthritis.AcknowledgementsThis research has been sponsored by the U.S. Arthritis Foundation.Disclosure of InterestNone declare