3 research outputs found

    Are We Missing an Opportunity? Prediabetes in the U.S. Military

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    INTRODUCTION: The prevalence of prediabetes is estimated to be one-third of Americans with approximately 80% of these individuals unaware of the diagnosis. In the active duty military population, the prevalence of prediabetes is largely unexplored. The purpose of this study was to investigate the prevalence of prediabetes in military service members by quantifying those meeting prediabetes screening criteria, those actually being screened, and those being appropriately diagnosed. MATERIALS AND METHODS: Data were analyzed from calendar years 2014 to 2018 for active duty service members 18 years of age or older. Vitals records were collected to obtain body mass index values. Composite Health Care System laboratory data were queried for hemoglobin A1c (HbA1c) results as well as fasting plasma glucose (FPG) and oral glucose tolerance test (OGTT) results. The percentage of active duty service members meeting criteria for prediabetes screening was determined by totaling members age 45 and older with members age 18- to 44-year old with a body mass index ≥25.0 kg/m2, then dividing by the total number of members for each respective military branch. The percentage of active duty service members actually screened for prediabetes was determined based on members meeting prediabetes screening criteria who in fact had FPG, OGTT, or HbA1c labs. The total number of labs meeting prediabetes criteria was determined based on those aforementioned labs with results in the prediabetes range (FPG between 100 and 125 mg/dL, OGTT between 140 and 199 mg/dL, or HbA1c range of 5.7%-6.4%). The total number of service members with appropriate prediabetes International Classification of Disease (ICD) code was determined by identifying members with ICD-9 and ICD-10 codes 790.21, 790.22, and R73.01-R73.03 in their medical record. RESULTS: From 2014 to 2018, 53.9% of 332,502, 56% of 543,081, and 47.3% of 531,313 active duty service members in the Air Force, Army and Navy, respectively, met criteria for prediabetes screening. The rates of actually screening for prediabetes were similar across the Air Force (4.8%), Army (6.7%), and Navy (5.5%). The percentage with labs meeting prediabetes criteria ranged from 17.9% to 28.4% in the Air Force, 24.2% to 30.3% in the Army, and 24.2% to 30.9% in the Navy. The rate of ICD coding for prediabetes increased from 2014 to 2018 across all branches (29.8%-65.3% for the Air Force, 24.6%-46.8% for the Army, and 40.0%-45.5% for the Navy). CONCLUSION: Screening for prediabetes in the active duty military population is grossly inadequate, and even of those screened, diagnosing those meeting prediabetes criteria is similarly inadequate. Although this scenario is not unique to the Military Health System, but reflective of a larger national problem, efforts should be made within the Military Health System to increase the screening for this common disorder. Identifying service members with prediabetes enables opportunities for targeted interventions to delay or prevent the progression to diabetes mellitus

    A history of AI and Law in 50 papers: 25 years of the international conference on AI and Law

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    Short-term leprosy forecasting from an expert opinion survey

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    We conducted an expert survey of leprosy (Hansen's Disease) and neglected tropical disease experts in February 2016. Experts were asked to forecast the next year of reported cases for the world, for the top three countries, and for selected states and territories of India. A total of 103 respondents answered at least one forecasting question. We elicited lower and upper confidence bounds. Comparing these results to regression and exponential smoothing, we found no evidence that any forecasting method outperformed the others. We found evidence that experts who believed it was more likely to achieve global interruption of transmission goals and disability reduction goals had higher error scores for India and Indonesia, but lower for Brazil. Even for a disease whose epidemiology changes on a slow time scale, forecasting exercises such as we conducted are simple and practical. We believe they can be used on a routine basis in public health
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