Search

Your search keyword '"Laura E. Henderson"' showing total 3 results
Start Over Author "Laura E. Henderson" Language english
3 results on '"Laura E. Henderson"'

1. An index of geospatial disadvantage predicts both obesity and unmeasured body weight

Author

Lincoln R. Sheets, Laura E. Henderson Kelley, Kristen Scheitler-Ring, Gregory F. Petroski, Yan Barnett, Chris Barnett, Amy J.H. Kind, and Jerry C. Parker

Subjects
Risk prediction, Obesity risk, Elderly, Social determinants, Geospatial analysis, Data quality, Medicine
Abstract

Neighborhood context impacts health. Using an index of geospatial disadvantage measures to predict neighborhood socioeconomic disparities would support area-based allocation of preventative resources, as well as the use of location as a clinical risk factor in care of individual patients. This study tested the association of the Area Deprivation Index (ADI), a neighborhood-based index of socioeconomic contextual disadvantage, with elderly obesity risk. We sampled 5066 Medicare beneficiaries at the University of Missouri between September 1, 2013 and September 1, 2014. We excluded patients with unknown street addresses, excluded body mass index (BMI) lower than 18 or higher than 62 as probable errors, and excluded patients with missing BMI data. We used a plot of simple proportions to examine the association between ADI and prevalence of obesity, defined as BMI of 30 and over. We found that obesity was significantly less prevalent in the least-disadvantaged ADI decile (decile 1) than in all other deciles (p

Published
2020
Full Text
View/download PDF

2. Response to Prakash, et al

Author

Daniel D. Dressler, Sunil Kripalani, Anna Kho, and Laura E. Henderson

Subjects
medicine.medical_specialty, Medical education, business.industry, education, Alternative medicine, Intervention group, Controlled studies, Clinical decision support system, law.invention, Randomized controlled trial, law, Internal Medicine, medicine, Observational study, Knowledge test, business, Letters to the Editor
Abstract

To the Editor:—We greatly appreciate the letter from Prakash and colleagues, which brings to light recent reports on the use of personal digital assistants (PDAs) in medical education published since the completion of the literature search for our systematic review.1 These reports confirm that PDAs remain useful to trainees in a variety of ways—e.g., in logging work hours2 and examination skills,3 and in learning about disease processes.4 Most of the reports cited by Prakash and colleagues were observational studies, while only 1 was a controlled trial. This underscores the continued lack of, and need for, more rigorously designed investigations. The controlled trial, by Grad et al.,5 warrants comment. It found no effect on knowledge test scores after first-year family medicine residents used a PDA-assisted evidence-based medicine (EBM) course. These findings differ from results of a randomized control trial by Leung et al.,6 which demonstrated that third-year medical students using PDAs with clinical decision support software increased their learning and application of EBM. Several factors may have led to these conflicting results, including differences in the study design and populations. In addition, more technical problems with the handheld device occurred in Grad's5 study, where 65% of the residents in the intervention group had technical problems with their handheld device on an average of 28.5 days over the 208 investigational days. The recent reports highlighted by Prakash are noteworthy. However, more rigorous controlled studies are still needed to examine how handheld devices impact medical learning and patient outcomes.

Published
2006

3. The Effect of Neighborhood Disadvantage on Diabetes Prevalence.

Author

Sheets L, Petroski GF, Jaddoo J, Barnett Y, Barnett C, Kelley LEH, Raman V, Kind AJH, and Parker JC

Subjects
Adult, Aged, Diabetes Mellitus ethnology, Female, Humans, Logistic Models, Male, Medicare, Middle Aged, Poverty Areas, Prevalence, Racial Groups, Residence Characteristics, United States epidemiology, Diabetes Mellitus epidemiology, Health Status Disparities, Socioeconomic Factors
Abstract

Patient socioeconomic data is not usually included in medical records nor easily accessible to clinicians, yet socioeconomic disadvantage can be an important guide to disease management. This study evaluated the neighborhood-level Area Deprivation Index (ADI), a measure of neighborhood socioeconomic disadvantage, as a factor in diabetes mellitus prevalence. Electronic health records at an academic hospital system identified 4,770 Medicare beneficiaries. Logistic regression of diabetes diagnosis (ICD9=250.x) against ADI quintile, age, gender, and race/ethnicity found all these patient characteristics to be significantly associated. Diabetes prevalence was lowest in the least disadvantaged quintile of neighborhoods after adjusting for age, gender, and race/ethnicity. The positive non-linear association of diabetes prevalence with ADI demonstrates the power of this index to practically quantify socioeconomic disadvantage. The ADI may be suitable for clinical decision support, and for informing the policy changes which are needed to reduce socioeconomic disparities in diabetes prevalence and other health outcomes.

Published
2018

Catalog

Books, media, physical & digital resources
See catalog results