China
Africa
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Personalizing Environmental Health for the Military—Striving for Precision
Published in Kirk A. Phillips, Dirk P. Yamamoto, LeeAnn Racz, Total Exposure Health, 2020
The second way to build actionable information for exposures is to develop quantitative risk measurements. In genomic medicine and exposure science, there are some basic statistical tools that can be used to calculate risk. Odds ratios are used to measure the effectiveness of a particular diagnostic test (e.g., a genomic assay or a serum biomarker test). It is the probability of a test being positive if a patient has a disease, versus the odds of a test being positive if a patient does not have a disease. Absolute risk is the probability of a health effect occurring under specific conditions, while relative risk is the likelihood of a health effect occurring in a group of people compared to a separate group of people with different backgrounds or in different environments. The GBD group utilizes relative risk as the primary method for calculation of global disease burden. The quantitative measurement utilized is the summary exposure value (SEV), which represents the continuous relative risk accumulated over time.
Environmental Hazards and Their Management
Published in Danny D. Reible, Fundamentals of Environmental Engineering, 2017
The deficiencies of the approach are most apparent when attempting to estimate an absolute risk associated with a particular activity. Using the predicted absolute risk also requires definition of an acceptable level of risk. This has proven exceedingly difficult since the risk that a community is willing to accept tends to vary depending on whether (1) they or someone else receives the potential benefits associated with the activity causing the risk, and whether (2) they control exposure to that risk.
A Self-Learning Fuzzy Rule-based System for Risk-Level Assessment of Coronary Heart Disease
Published in IETE Journal of Research, 2019
R. Priyatharshini, S. Chitrakala
Cardiovascular diseases, the leading reason behind premature death in the world, include heart attacks, strokes, and different vascular diseases. Series of environmental, social, and structural changes that occur over time lead to exposure to risk factors for chronic diseases. Cardiovascular disease (CVD) is common within the general public, affecting the greater part of adults past the age of 60 years. Whereas a general assessment of the relative risk for CVD is approximated by investigating the number of major risk factors present in a patient, additional precise estimation of the absolute risk for a primary CVD event is essential when designing treatment recommendations for a selected individual. Sources of multimodal health data include clinical data in the form of electronic health records (EHRs), medical images, genomic data in the form of DNA (deoxyribonucleic acid) sequences, and behavioural data comprising mobile sensor and social network data. Clinical data includes lab results (structured EHRs), clinical notes (unstructured EHRs), and medical images. EHRs contain patients’ medical histories. Early detection of diagnostic biomarkers from multimodal clinical data is an active research area where the challenge to procure more accurate diagnostic systems is still an ongoing process for disease control and prediction.