Towards probabilistic decision support in public health practice: Predicting recent transmission of tuberculosis from patient attributes

Journal of Biomedical Informatics

Volumn 53, Issue , 2015, Pages 237-242

  Mamiya, Hiroshi ^a,e   Schwartzman, Kevin ^a,d,e   Verma, Aman ^a,e   Jauvin, Christian ^a   Behr, Marcel ^a,b,e   Buckeridge, David ^a,c,e  

^a MCGILL UNIVERSITY   (Canada)

^b MCGILL UNIVERSITY HEALTH CENTRE   (Canada)

^c Gouvernement du Quebec   (Canada)

^d MCGILL UNIVERSITY   (Canada)

^e MCGILL UNIVERSITY   (Canada)

Author keywords

Decision support model; Public health; Statistical prediction; Transmission; Tuberculosis

Indexed keywords

DECISION SUPPORT SYSTEMS; FORECASTING; LOGISTIC REGRESSION; PUBLIC HEALTH; TRANSMISSIONS;

DECISION SUPPORT MODELS; DECISION SUPPORT SOFTWARE; LOGISTIC REGRESSION MODELING; POSITIVE PREDICTIVE VALUES; RECEIVER OPERATING CHARACTERISTIC CURVES; SENSITIVITY AND SPECIFICITY; STATISTICAL PREDICTION; TUBERCULOSIS;

PREDICTIVE ANALYTICS;

DNA;

ADULT; AREA UNDER THE CURVE; ARTICLE; COMPUTER PROGRAM; DECISION SUPPORT SYSTEM; DEMOGRAPHY; DIAGNOSTIC TEST ACCURACY STUDY; DISEASE TRANSMISSION; DNA FINGERPRINTING; EXPERIENCE; FEMALE; HEALTH CARE PERSONNEL; HUMAN; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; MALE; PREDICTIVE VALUE; PRIORITY JOURNAL; PROBABILITY; PUBLIC HEALTH SERVICE; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; STANDARD; TUBERCULOSIS; ALGORITHM; BAYES THEOREM; BIOLOGY; CANADA; EPIDEMIC; MEDICAL INFORMATICS; MIDDLE AGED; PROCEDURES; REPRODUCIBILITY; STATISTICAL MODEL; TRANSMISSION;

ADULT; ALGORITHMS; AREA UNDER CURVE; BAYES THEOREM; COMPUTATIONAL BIOLOGY; DECISION SUPPORT TECHNIQUES; DISEASE OUTBREAKS; FEMALE; HUMANS; LOGISTIC MODELS; MALE; MIDDLE AGED; PREDICTIVE VALUE OF TESTS; PROBABILITY; PUBLIC HEALTH INFORMATICS; QUEBEC; REPRODUCIBILITY OF RESULTS; ROC CURVE; SENSITIVITY AND SPECIFICITY; TUBERCULOSIS;

EID: 84924435692     PISSN: 15320464     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbi.2014.11.006     Document Type: Article

References (34)

1. Tuberculosis in Canada 2009 - pre-release [Internet]; 2009. http://publications.gc.ca/collections/collection_2012/aspc-phac/HP37-5-1-2009-eng.pdf.
2. Raviglione MC. Tuberculosis. The essentials: Lung Biology in Health and Disease, 4th ed.; 2009.
3. Nguyen L.N., Gilbert G.L., Marks G.B. Molecular epidemiology of tuberculosis and recent developments in understanding the epidemiology of tuberculosis. Respirology 2004, 9(3):313-319.
4. Centers for Disease Control and Prevention. Guidelines for the investigation of contacts of persons with infectious tuberculosis; recommendations from the National Tuberculosis Controllers Association and CDC, and Guidelines for using the QuantiFERON®-TB Gold test for detecting Mycobacterium tuberculosis infection, United States. MMWR, vol. 54(RR-15); 2005.
5. Nava-Aguilera E., Andersson N., Harris E., Mitchell S., Hamel C., Shea B., et al. Risk factors associated with recent transmission of tuberculosis: systematic review and meta-analysis. Int J Tuberc Lung D 2009, 13(1):17-26.
6. Tamblyn R., Eguale T., Buckeridge D.L., Huang A., Hanley J., Reidel K., et al. The effectiveness of a new generation of computerized drug alerts in reducing the risk of injury from drug side effects: a cluster randomized trial. J Am Med Infor Assoc: JAMIA 2012.
7. Van Soolingen D. Molecular epidemiology of tuberculosis and other mycobacterial infections: main methodologies and achievements. J Intern Med 2001, 249(1):1-26.
8. van Embden J.D., Cave M.D., Crawford J.T., Dale J.W., Eisenach K.D., Gicquel B., et al. Strain identification of Mycobacterium tuberculosis by DNA fingerprinting: recommendations for a standardized methodology. J Clin Microbiol 1993, 31(2):406-409.
9. Goyal M., Saunders N.A., van Embden J.D., Young D.B., Shaw R.J. Differentiation of Mycobacterium tuberculosis isolates by spoligotyping and IS6110 restriction fragment length polymorphism. J Clin Microbiol 1997, 35(3):647-651.
10. Kulaga S., Behr M., Musana K., Brinkman J., Menzies D., Brassard P., et al. Molecular epidemiology of tuberculosis in Montreal. CMAJ 2002, 167(4):353-354.
11. Alland D., Kalkut G.E., Moss A.R., McAdam R.A., Hahn J.A., Bosworth W., et al. Transmission of tuberculosis in New York City. An analysis by DNA fingerprinting and conventional epidemiologic methods. N Engl J Med 1994, 330(24):1710-1716.
12. Farnia P., Mohammadi F., Masjedi M.R., Varnerot A., Zarifi A.Z., Tabatabee J., et al. Evaluation of tuberculosis transmission in Tehran: using RFLP and spoloigotyping methods. J Infect 2004, 49(2):94-101.
13. Gutierrez M.C., Vincent V., Aubert D., Bizet J., Gaillot O., Lebrun L., et al. Molecular fingerprinting of Mycobacterium tuberculosis and risk factors for tuberculosis transmission in Paris, France, and surrounding area. J Clin Microbiol 1998, 36(2):486-492.
14. Long R. Canadian tuberculosis standards, 6th ed.; 2007.
15. Vergouwe Y., Royston P., Moons K.G., Altman D.G. Development and validation of a prediction model with missing predictor data: a practical approach. J Clin Epidemiol 2010, 63(2):205-214.
16. Schafer J.L. Multiple imputation: a primer. Stat Methods Med Res 1999, 8(1):3-15.
17. van Buuren S. Multiple imputation of discrete and continuous data by fully conditional specification. Stat Methods Med Res 2007, 16(3):219-242.
18. van Buuren S., Groothuis-Oudshoorn K. MICE: multivariate imputation by chained equations in R. J Stat Softw 2011, 45(3).
19. R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2012.
20. Tu J.V. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 1996, 49(11):1225-1231.
21. Sargent D.J. Comparison of artificial neural networks with other statistical approaches: results from medical data sets. Cancer 2001, 91(8 Suppl):1636-1642.
22. Dreiseitl S., Ohno-Machado L., Kittler H., Vinterbo S., Billhardt H., Binder M. A comparison of machine learning methods for the diagnosis of pigmented skin lesions. J Biomed Inform 2001, 34(1):28-36.
23. McLaren C.E., Chen W.P., Nie K., Su M.Y. Prediction of malignant breast lesions from MRI features: a comparison of artificial neural network and logistic regression techniques. Acad Radiol 2009, 16(7):842-851.
24. Al Housseini A., Newman T., Cox A., Devoe L.D. Prediction of risk for cesarean delivery in term nulliparas: a comparison of neural network and multiple logistic regression models. Am J Obstet Gynecol 2009, 201(1). 113(e1-6).
25. Raftery AE. Bayesian Model Selection in Social Research. MARSDEN PV, editor: CAMBRIDGE; 1995.
26. Sauerbrei W., Meier-Hirmer C., Benner A., Royston P. Multivariable regression model building by using fractional polynomials: description of SAS, STATA and R programs. Comput Stat Data Anal 2006, 50(12):3464-3485.
27. Hoeting Jennifer A., Madigan D.M., Raftery Adrian E., Volinsky Chris T. Bayesian model averaging: a tutorial. Stat Sci 1999, 14(4):382-417.
28. Genell A., Nemes S., Steineck G., Dickman P.W. Model selection in medical research: a simulation study comparing bayesian model averaging and stepwise regression. BMC Med Res Methodol 2010, 10(1):108.
29. Prost L., Makowski D., Jeuffroy M.-H. Comparison of stepwise selection and Bayesian model averaging for yield gap analysis. Ecol Model 2008, 219(1-2):66-76.
30. Adrian Raftery JH, Chris Volinsky, Ian Painter, Ka Yee Yeung. BMA: Bayesian model averaging. Version 3.15.1; 2012.
31. Marcus J.L., Katz M.H., Katz K.A., Bernstein K.T., Wolf W., Klausner J.D. Prediction model to maximize impact of syphilis partner notification - San Francisco, 2004-2008. Sex Transm Dis 2010, 37(2):109-114.
32. Schaffzin J.K., Koumans E.H., Kahn R.H., Markowitz L.E. Evaluation of syphilis reactor grids: optimizing impact. Sex Transm Dis 2003, 30(9):700-706.
33. Friedman D.J., Parrish R.G., Ross D.A. Electronic health records and US public health: current realities and future promise. Am J Public Health 2013, 103(9):1560-1567.
34. Shapiro J.S. Evaluating public health uses of health information exchange. J Biomed Inform 2007, 40(6 Suppl):S46-S49.