http://hdl.handle.net/10027/7321 Wed, 19 Jun 2013 07:30:31 GMT 2013-06-19T07:30:31Z http://hdl.handle.net/10027/8593 Diagnostic accuracy of existing methods for identifying diabetic foot ulcers from inpatient and outpatient datasets Sohn, Min-Woong; Budiman-Mak, Elly; Stuck, Rodney M; Siddiqui, Farah; Lee, Todd A Background: As the number of persons with diabetes is projected to double in the next 25 years in the US, an accurate method of identifying diabetic foot ulcers in population-based data sources are ever more important for disease surveillance and public health purposes. The objectives of this study are to evaluate the accuracy of existing methods and to propose a new method. Methods: Four existing methods were used to identify all patients diagnosed with a foot ulcer in a Department of Veterans Affairs (VA) hospital from the inpatient and outpatient datasets for 2003. Their electronic medical records were reviewed to verify whether the medical records positively indicate presence of a diabetic foot ulcer in diagnoses, medical assessments, or consults. For each method, five measures of accuracy and agreement were evaluated using data from medical records as the gold standard. Results: Our medical record reviews show that all methods had sensitivity > 92% but their specificity varied substantially between 74% and 91%. A method used in Harrington et al. (2004) was the most accurate with 94% sensitivity and 91% specificity and produced an annual prevalence of 3.3% among VA users with diabetes nationwide. A new and simpler method consisting of two codes (707.1× and 707.9) shows an equally good accuracy with 93% sensitivity and 91% specificity and 3.1% prevalence. Conclusions: Our results indicate that the Harrington and New methods are highly comparable and accurate. We recommend the Harrington method for its accuracy and the New method for its simplicity and comparable accuracy. © 2010 Sohn et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. doi:10.1186/1757-1146-3-27 Wed, 24 Nov 2010 06:00:00 GMT http://hdl.handle.net/10027/8593 2010-11-24T06:00:00Z http://hdl.handle.net/10027/8507 The Association Between Physical Functioning and Self-rated General Health in Later Life: The Implications of Social Comparison Yin, Hongjun; Lin, Swu-Jane; Kong, Sheldon X.; Benzeroual, Kenza; Crawford, Stephanie Y.; Hedeker, Donald; Lambert, Bruce L.; Muramatsu, Naoko Self-rated general health has been used widely in health surveys as a single-item measurement of health-rated quality of life. Heterogeneity in self-evaluation of health has been well documented, yet the causes of this heterogeneity are poorly understood. This study evaluated the moderating effects of age, aging, gender, race, education and income on the relationship between physical functioning and self-rated general health using social comparison theory as a guiding framework. A longitudinal mixed-effects regression model was used to analyze a cohort enrolled into the Health and Retirement Study in 1993 that was interviewed at baseline and during four subsequent waves. The results revealed that the association between physical functioning and self-rated general health is weaker among subgroups that tend to have lower health status; i.e., older individuals, non-Caucasians and less educated individuals. These findings suggest the usefulness of social comparison theory in explaining self-rated general health and provide the basis for future research. Post print version of article may differ from published version. The original publication is available at springerlink.com; DOI: 10.1007/s11482-010-9109-3 Tue, 01 Mar 2011 06:00:00 GMT http://hdl.handle.net/10027/8507 2011-03-01T06:00:00Z http://hdl.handle.net/10027/8153 Measuring Efficiency under Fixed Proportion Technologies Barnum, Darold T.; Gleason, John M. Data Envelopment Analysis (DEA) applications frequently involve nonsubstitutable inputs and nonsubstitutable outputs (that is, fixed proportion technologies). However, DEA theory requires substitutability. In this paper, we illustrate the consequences of nonsubstitutability on DEA efficiency estimates, and we develop new efficiency indicators that are similar to those of conventional DEA models except that they require nonsubstitutability. Then, using simulated and real-world datasets that encompass fixed proportion technologies, we compare DEA efficiency estimates with those of the new indicators. The examples demonstrate that DEA efficiency estimates are biased when inputs and outputs are nonsubstitutable. The degree of bias varies considerably among Decision Making Units, resulting in substantial differences in efficiency rankings between DEA and the new measures. And, over 90 percent of the units that DEA identifies as efficient are, in truth, not efficient. We conclude that when inputs and outputs are not substituted for either technological or socio-economic/legal reasons, conventional DEA models should be replaced with models that account for nonsubstitutability. Post print version of article may differ from published version. The original publication is available at springerlink.com; DOI: 10.1007/s11123-010-0194-y. Wed, 01 Jun 2011 05:00:00 GMT http://hdl.handle.net/10027/8153 2011-06-01T05:00:00Z http://hdl.handle.net/10027/7669 Measuring Hospital Efficiency with Data Envelopment Analysis: Nonsubstitutable vs. Substitutable Inputs and Outputs Barnum, Darold T.; Walton, Surrey M.; Shields, Karen L.; Schumock, Glen T. There is a conflict between Data Envelopment Analysis (DEA) theory’s requirement that inputs (outputs) be substitutable, and the ubiquitous use of nonsubstitutable inputs and outputs in DEA applications to hospitals. This paper develops efficiency indicators valid for nonsubstitutable variables. Then, using a sample of 87 community hospitals, it compares the new measures’ efficiency estimates with those of conventional DEA measures. DEA substantially overestimated the hospitals’ efficiency on the average, and reported many inefficient hospitals to be efficient. Further, it greatly overestimated the efficiency of some hospitals but only slightly overestimated the efficiency of others, thus making any comparisons among hospitals questionable. These results suggest that conventional DEA models should not be used to estimate the efficiency of hospitals unless there is empirical evidence that the inputs (outputs) are substitutable. If inputs (outputs) are not substitutes, efficiency indicators valid for nonsubstitutability should be employed, or, before applying DEA, the nonsubstitutable variables should be combined using an appropriate weighting scheme or statistical methodology. The post print version of this article may differ from the published version. The original publication is available at www.springerlink.com at DOI: 10.1007/s10916-009-9416-0. Tue, 15 Dec 2009 06:00:00 GMT http://hdl.handle.net/10027/7669 2009-12-15T06:00:00Z