Publications by Year: 2014

D. J. Wallace, J. M. Kahn, D. C. Angus, C. Martin-Gill, C. W. Callaway, T. D. Rea, J. Chhatwal, K. Kurland, and C. W. Seymour. 2014. “Accuracy of prehospital transport time estimation.” Acad Emerg Med, 21, Pp. 9-16.Abstract
OBJECTIVES: Estimates of prehospital transport times are an important part of emergency care system research and planning; however, the accuracy of these estimates is unknown. The authors examined the accuracy of three estimation methods against observed transport times in a large cohort of prehospital patient transports. METHODS: This was a validation study using prehospital records in King County, Washington, and southwestern Pennsylvania from 2002 to 2006 and 2005 to 2011, respectively. Transport time estimates were generated using three methods: linear arc distance, Google Maps, and ArcGIS Network Analyst. Estimation error, defined as the absolute difference between observed and estimated transport time, was assessed, as well as the proportion of estimated times that were within specified error thresholds. Based on the primary results, a regression estimate was used that incorporated population density, time of day, and season to assess improved accuracy. Finally, hospital catchment areas were compared using each method with a fixed drive time. RESULTS: The authors analyzed 29,935 prehospital transports to 44 hospitals. The mean (+/- standard deviation [+/-SD]) absolute error was 4.8 (+/-7.3) minutes using linear arc, 3.5 (+/-5.4) minutes using Google Maps, and 4.4 (+/-5.7) minutes using ArcGIS. All pairwise comparisons were statistically significant (p < 0.01). Estimation accuracy was lower for each method among transports more than 20 minutes (mean [+/-SD] absolute error was 12.7 [+/-11.7] minutes for linear arc, 9.8 [+/-10.5] minutes for Google Maps, and 11.6 [+/-10.9] minutes for ArcGIS). Estimates were within 5 minutes of observed transport time for 79% of linear arc estimates, 86.6% of Google Maps estimates, and 81.3% of ArcGIS estimates. The regression-based approach did not substantially improve estimation. There were large differences in hospital catchment areas estimated by each method. CONCLUSIONS: Route-based transport time estimates demonstrate moderate accuracy. These methods can be valuable for informing a host of decisions related to the system organization and patient access to emergency medical care; however, they should be employed with sensitivity to their limitations.
M. Kabiri, A. B. Jazwinski, M. S. Roberts, A. J. Schaefer, and J. Chhatwal. 2014. “The changing burden of hepatitis C virus infection in the United States: model-based predictions.” Ann Intern Med, 161, Pp. 170-80.Abstract
BACKGROUND: Chronic hepatitis C virus (HCV) infection causes a substantial health and economic burden in the United States. With the availability of direct-acting antiviral agents, recently approved therapies and those under development, and 1-time birth-cohort screening, the burden of this disease is expected to decrease. OBJECTIVE: To predict the effect of new therapies and screening on chronic HCV infection and associated disease outcomes. DESIGN: Individual-level state-transition model. SETTING: Existing and anticipated therapies and screening for HCV infection in the United States. PATIENTS: Total HCV-infected population in the United States. MEASUREMENTS: The number of cases of chronic HCV infection and outcomes of advanced-stage HCV infection. RESULTS: The number of cases of chronic HCV infection decreased from 3.2 million in 2001 to 2.3 million in 2013. One-time birth-cohort screening beginning in 2013 is expected to identify 487,000 cases of HCV infection in the next 10 years. In contrast, 1-time universal screening could identify 933,700 cases. With the availability of highly effective therapies, HCV infection could become a rare disease in the next 22 years. Recently approved therapies for HCV infection and 1-time birth-cohort screening could prevent approximately 124,200 cases of decompensated cirrhosis, 78,800 cases of hepatocellular carcinoma, 126,500 liver-related deaths, and 9900 liver transplantations by 2050. Increasing the treatment capacity would further reduce the burden of HCV disease. LIMITATION: Institutionalized patients with HCV infection were excluded, and empirical data on the effectiveness of future therapies and on the future annual incidence and treatment capacity of HCV infection are lacking. CONCLUSION: New therapies for HCV infection and widespread implementation of screening and treatment will play an important role in reducing the burden of HCV disease. More aggressive screening recommendations are needed to identify a large pool of infected patients. PRIMARY FUNDING SOURCE: National Institutes of Health.
J Chhatwal, S Jayasuriya, and E. H. Elbasha. 2014. “Changing Cycle Lengths in State-Transition Models: Doing it the Right Way.” ISPOR Connections, 20, Pp. 12-14.
M. U. Ayvaci, O. Alagoz, J. Chhatwal, A. Munoz del Rio, E. A. Sickles, H. Nassif, K. Kerlikowske, and E.S. Burnside. 2014. “Predicting invasive breast cancer versus DCIS in different age groups.” BMC Cancer, 14, Pp. 584.Abstract
BACKGROUND: Increasing focus on potentially unnecessary diagnosis and treatment of certain breast cancers prompted our investigation of whether clinical and mammographic features predictive of invasive breast cancer versus ductal carcinoma in situ (DCIS) differ by age. METHODS: We analyzed 1,475 malignant breast biopsies, 1,063 invasive and 412 DCIS, from 35,871 prospectively collected consecutive diagnostic mammograms interpreted at University of California, San Francisco between 1/6/1997 and 6/29/2007. We constructed three logistic regression models to predict the probability of invasive cancer versus DCIS for the following groups: women >/= 65 (older group), women 50-64 (middle age group), and women < 50 (younger group). We identified significant predictors and measured the performance in all models using area under the receiver operating characteristic curve (AUC). RESULTS: The models for older and the middle age groups performed significantly better than the model for younger group (AUC = 0.848 vs, 0.778; p = 0.049 and AUC = 0.851 vs, 0.778; p = 0.022, respectively). Palpability and principal mammographic finding were significant predictors in distinguishing invasive from DCIS in all age groups. Family history of breast cancer, mass shape and mass margins were significant positive predictors of invasive cancer in the older group whereas calcification distribution was a negative predictor of invasive cancer (i.e. predicted DCIS). In the middle age group–mass margins, and in the younger group–mass size were positive predictors of invasive cancer. CONCLUSIONS: Clinical and mammographic finding features predict invasive breast cancer versus DCIS better in older women than younger women. Specific predictive variables differ based on age.