Publications (4)21.02 Total impact
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Article: Predicting waist circumference from body mass index.
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ABSTRACT: Being overweight or obese increases risk for cardiometabolic disorders. Although both body mass index (BMI) and waist circumference (WC) measure the level of overweight and obesity, WC may be more important because of its closer relationship to total body fat. Because WC is typically not assessed in clinical practice, this study sought to develop and verify a model to predict WC from BMI and demographic data, and to use the predicted WC to assess cardiometabolic risk. Data were obtained from the Third National Health and Nutrition Examination Survey (NHANES) and the Atherosclerosis Risk in Communities Study (ARIC). We developed linear regression models for men and women using NHANES data, fitting waist circumference as a function of BMI. For validation, those regressions were applied to ARIC data, assigning a predicted WC to each individual. We used the predicted WC to assess abdominal obesity and cardiometabolic risk. The model correctly classified 88.4% of NHANES subjects with respect to abdominal obesity. Median differences between actual and predicted WC were - 0.07 cm for men and 0.11 cm for women. In ARIC, the model closely estimated the observed WC (median difference: - 0.34 cm for men, +3.94 cm for women), correctly classifying 86.1% of ARIC subjects with respect to abdominal obesity and 91.5% to 99.5% as to cardiometabolic risk.The model is generalizable to Caucasian and African-American adult populations because it was constructed from data on a large, population-based sample of men and women in the United States, and then validated in a population with a larger representation of African-Americans. The model accurately estimates WC and identifies cardiometabolic risk. It should be useful for health care practitioners and public health officials who wish to identify individuals and populations at risk for cardiometabolic disease when WC data are unavailable.BMC Medical Research Methodology 08/2012; 12:115. · 2.67 Impact Factor -
Article: Medicare payments, healthcare service use, and telemedicine implementation costs in a randomized trial comparing telemedicine case management with usual care in medically underserved participants with diabetes mellitus (IDEATel).
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ABSTRACT: Objective To determine whether a diabetes case management telemedicine intervention reduced healthcare expenditures, as measured by Medicare claims, and to assess the costs of developing and implementing the telemedicine intervention. Design We studied 1665 participants in the Informatics for Diabetes Education and Telemedicine (IDEATel), a randomized controlled trial comparing telemedicine case management of diabetes to usual care. Participants were aged 55 years or older, and resided in federally designated medically underserved areas of New York State. Measurements We analyzed Medicare claims payments for each participant for up to 60 study months from date of randomization, until their death, or until December 31, 2006 (whichever happened first). We also analyzed study expenditures for the telemedicine intervention over six budget years (February 28, 2000- February 27, 2006). Results Mean annual Medicare payments (SE) were similar in the usual care and telemedicine groups, $9040 ($386) and $9669 ($443) per participant, respectively (p>0.05). Sensitivity analyses, including stratification by censored status, adjustment by enrollment site, and semi-parametric weighting by probability of dropping-out, rendered similar results. Over six budget years 28 821 participant/months of telemedicine intervention were delivered, at an estimated cost of $622 per participant/month. Conclusion Telemedicine case management was not associated with a reduction in Medicare claims in this medically underserved population. The cost of implementing the telemedicine intervention was high, largely representing special purpose hardware and software costs required at the time. Lower implementation costs will need to be achieved using lower cost technology in order for telemedicine case management to be more widely used.Journal of the American Medical Informatics Association 03/2010; 17(2):196-202. · 3.61 Impact Factor -
Article: Medicare payments, healthcare service use, and telemedicine implementation costs in a randomized trial comparing telemedicine case management with usual care in medically underserved participants with diabetes mellitus (IDEATel).
JAMIA. 01/2010; 17:196-202. -
Article: Prediction of first events of coronary heart disease and stroke with consideration of adiposity.
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ABSTRACT: Prediction of coronary heart disease (CHD) and cerebrovascular disease (CeVD) can aid healthcare providers and prevention programs. Previous reports have focused on traditional cardiovascular risk factors; less information has been available on the role of overweight and obesity. Baseline data from 4780 Framingham Offspring Study adults with up to 24 years of follow-up were used to assess risk for a first CHD event (angina pectoris, myocardial infarction, or cardiac death) alone, first CeVD event (acute brain infarction, transient ischemic attack, and stroke-related death) alone, and CHD and CeVD events combined. Accelerated failure time models were developed for the time of first event to age, sex, cholesterol, high-density lipoprotein cholesterol, diabetes mellitus (DM), systolic blood pressure, smoking status, and body mass index (BMI). Likelihood-ratio tests of statistical significance were used to identify the best-fitting predictive functions. Age, sex, smoking status, systolic blood pressure, ratio of cholesterol to high-density lipoprotein cholesterol, and presence of DM were highly related (P<0.01 for all) to the development of first CHD events, and all of the above except sex and DM were highly related to the first CeVD event. BMI also significantly predicted the occurrence of CHD (P=0.05) and CeVD (P=0.03) in multivariable models adjusting for traditional risk factors. The magnitude of the BMI effect was reduced but remained statistically significant when traditional variables were included in the prediction models. Greater BMI, higher systolic blood pressure, higher ratio of cholesterol to high-density lipoprotein cholesterol, and presence of DM were all predictive of first CHD events, and all but the presence of DM were predictive of first CeVD events. These results suggest that common pathophysiological mechanisms underlie the roles of BMI, DM, and systolic blood pressure as predictors for first CHD and CeVD events.Circulation 07/2008; 118(2):124-30. · 14.74 Impact Factor
