Destination Area: Data and Decisions (D&D)
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The DA Data and Decisions advances the human condition and society with better decisions through data. D&D integrates all DAs and SGAs with data analytics and decision sciences. Work in this area embraces equity in the human condition by seeking the equitable distribution and availability of physical safety and well-being, psychological well-being, respect for human dignity, and access to crucial material and social resources throughout the world’s diverse communities. D&D also addresses policymaking and policy analysis, collaborating at the intersection of scientific evidence, governance, and analyses to translate scholarship into practice.
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Browsing Destination Area: Data and Decisions (D&D) by Content Type "Article"
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- Are Ayurvedic herbs for diabetes effective?Shekelle, Paul G.; Hardy, Mary; Morton, Sally C.; Coulter, Ian; Venuturupalli, Swamy; Favreau, Joya; Hilton, Lara K. (Frontline Medical Communications Inc., 2005-10)Objective: To evaluate and synthesize the evidence on the effect of Ayurvedic therapies for diabetes mellitus. Design: Systematic review of trials. Measurements and main results: We found no study that assessed Ayurvedic as a system of care. Botanical therapy was by far the most commonly studied Ayurvedic treatment. Herbs were studied either singly or as formulas. In all, 993 titles in Western computerized databases and 318 titles identified by hand-searching journals in India were examined, yield ing 54 articles reporting the results of 62 studies. The most-studied herbs were G sylvestre, C indica, fenugreek, and Eugenia jambo/ana. A number of herbal formulas were tested, but Ayush- 82 and 0 -400 were most often studied. Thirty-five of the studies included came from the Western literature, 27 from the Indian. Seven were randomized controlled trials (RCTs) and 10 controlled clinical trials (CCTs) or natural experiments. Twenty-two studies went on to further analysis based on a set of criteria. Of these, 10 were RCTs, eCTs, or natural experiments, 12 were case series or cohort studies. There is evidence to suggest that the herbs C indica, holy basil, fenugreek, and G sylvestre, and the herbal formulas Ayush-82 and 0 -400 have a glucose-lowering effect and deserve further study. Evidence of effectiveness of several other herbs is less extensive (C tamala, E jambo/ana, and Momordica charantia). Conclusions: There is heterogeneity in the available literature on Ayurvedic treatment for diabetes. Most studies test herbal therapy. Heterogeneity exists in the herbs and formulas tested (more than 44 different interventions identified) and in the method of their preparation. Despite these limitations, there are sufficient data for several herbs or herbal formulas to warrant further studies.
- Can Administrative Housing Data Replace Survey Data?Molfino, Emily; Korkmaz, Gizem; Keller, Sallie A.; Schroeder, Aaron; Shipp, Stephanie; Weinberg, Daniel H. (HUD, 2017)This article examines the feasibility of using local administrative data sources for enhancing and supplementing federally collected survey data to describe housing in Arlington County, Virginia. Using real estate assessment data and the American Community Survey (ACS) from 2009 to 2013, we compare housing estimates for six characteristics: number of housing units, type of housing unit, year built, number of bedrooms, housing value, and real estate taxes paid. The findings show that housing administrative data can be repurposed to enhance and supplement the ACS, but limitations exist. We then discuss the challenges of repurposing housing administrative data for research.
- An Ensemble Approach to Predicting the Impact of Vaccination on Rotavirus Disease in NigerPark, J.; Goldstein, J.; Haran, M.; Ferrari, M. (2017-05-09)Recently developed vaccines provide a new way of controlling rotavirus in sub-Saharan Africa. Models for the transmission dynamics of rotavirus are critical both for estimating current burden from imperfect surveillance and for assessing potential effects of vaccine intervention strategies. We examine rotavirus infection in the Maradi area in southern Niger using hospital surveillance data provided by Epicentre collected over two years. Additionally, a cluster survey of households in the region allows us to estimate the proportion of children with diarrhea who consulted at a health structure. Model fit and future projections are necessarily particular to a given model; thus, where there are competing models for the underlying epidemiology an ensemble approach can account for that uncertainty. We compare our results across several variants of Susceptible-Infectious-Recovered (SIR) compartmental models to quantify the impact of modeling assumptions on our estimates. Model-specific parameters are estimated by Bayesian inference using Markov chain Monte Carlo. We then use Bayesian model averaging to generate ensemble estimates of the current dynamics, including estimates of $R_0$, the burden of infection in the region, as well as the impact of vaccination on both the short-term dynamics and the long-term reduction of rotavirus incidence under varying levels of coverage. The ensemble of models predicts that the current burden of severe rotavirus disease is 2.9 to 4.1% of the population each year and that a 2-dose vaccine schedule achieving 70% coverage could reduce burden by 37-43%.
- Predicting Complications in Critical Care Using Heterogeneous Clinical DataHuddar, Vijay; Desiraju, Bapu Koundinya; Rajan, Vaibhav; Bhattacharya, Sakyajit; Roy, Shourya; Reddy, Chandan K. (IEEE, 2016-10-19)Patients in hospitals, particularly in critical care, are susceptible to many complications affecting morbidity and mortality. Digitized clinical data in electronic medical records can be effectively used to develop machine learning models to identify patients at risk of complications early and provide prioritized care to prevent complications. However, clinical data from heterogeneous sources within hospitals pose signi ficant modeling challenges. In particular, unstructured clinical notes are a valuable source of information containing regular assessments of the patient's condition but contain inconsistent abbreviations and lack the structure of formal documents. Our contributions in this paper are twofold. First, we present a new preprocessing technique for extracting features from informal clinical notes that can be used in a classifi cation model to identify patients at risk of developing complications. Second, we explore the use of collective matrix factorization, a multi-view learning technique, to model heterogeneous clinical data text-based features in combination with other measurements, such as clinical investigations, comorbidities, and demographic data. We present a detailed case study on postoperative respiratory failure using more than 700 patient records from the MIMIC II database. Our experiments demonstrate the ef ficacy of our preprocessing technique in extracting discriminatory features from clinical notes as well as the bene fits of multi-view learning to combine clinical measurements with text data for predicting complications.