Early detection- of bioterrorism and infectious disease outbreaks caused by emerging pathogens is very important for public health, to allow prompt implementation of control measures. Routinely collected, automated health services data, including microbiology laboratory tests, ambulatory care and emergency department visits, hospitalizations, diagnostic tests, and prescription drug data could potentially be very useful for disease outbreak detection. However, mathematical, computational and statistical models are needed to implement such systems whenever the outcomes under surveillance already exist at some baseline level that does not require action. For example, if appropriate signal detection methods were available, identification of an anthrax bioterrorism attack might be accelerated through recognition of an unusual number of patients seeking care for cough and fever.; In this project, we will develop models for the early detection of infectious disease outbreaks and for monitoring an outbreak after it has been detected. This includes (i) models describing the natural temporal and geographical variation in the number of people utilizing the health services of interest, in order to adjust for e.g. seasonal and day-of-week effects and (ii) different space-time aberration detection models that will generate a signal when an outbreak have occurred. These models will be applied at different geographical scales, from individual wards of a single hospital to a whole country, as well as for different data specificity from very general symptoms such as fever to specific microbial disease strains and antimicrobial resistance profiles that migrate from one bacterial species to another. We will develop and test our new methods and models in two health plans (Harvard Pilgrim Health Care in Massachusetts, and Kaiser Permanente Northern California) that cover over 4 million people, a single large US referral hospital (Brigham and Women's), a statewide (Massachusetts) registry of MRSA, and a national (Argentine) consortium of 55 hospitals that monitors antibiotic resistance. The models and methods will be evaluated using both historical data from these health systems and simulated data based on different infectious disease transmission dynamics models.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project--Cooperative Agreements (U01)
Project #
7U01GM076672-05
Application #
7766229
Study Section
Special Emphasis Panel (ZGM1-CBCB-2 (MI))
Program Officer
Anderson, James J
Project Start
2006-02-01
Project End
2012-01-31
Budget Start
2010-02-01
Budget End
2012-01-31
Support Year
5
Fiscal Year
2010
Total Cost
$499,291
Indirect Cost
Name
University of California Irvine
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
046705849
City
Irvine
State
CA
Country
United States
Zip Code
92697
Prates, Marcos O; Kulldorff, Martin; Assunção, Renato M (2014) Relative risk estimates from spatial and space-time scan statistics: are they biased? Stat Med 33:2634-44
Yih, W Katherine; Cocoros, Noelle M; Crockett, Molly et al. (2014) Automated influenza-like illness reporting--an efficient adjunct to traditional sentinel surveillance. Public Health Rep 129:55-63
Costa, Marcelo A; Kulldorff, Martin (2014) Maximum linkage space-time permutation scan statistics for disease outbreak detection. Int J Health Geogr 13:20
Lee, Bruce Y; Bartsch, Sarah M; Huang, Susan S (2013) Reply to Crnich and Drinka. Infect Control Hosp Epidemiol 34:647-8
Lee, Bruce Y; Wong, Kim F; Bartsch, Sarah M et al. (2013) The Regional Healthcare Ecosystem Analyst (RHEA): a simulation modeling tool to assist infectious disease control in a health system. J Am Med Inform Assoc 20:e139-46
Lee, Bruce Y; Singh, Ashima; Bartsch, Sarah M et al. (2013) The potential regional impact of contact precaution use in nursing homes to control methicillin-resistant Staphylococcus aureus. Infect Control Hosp Epidemiol 34:151-60
Lee, Bruce Y; Yilmaz, S Levent; Wong, Kim F et al. (2013) Modeling the regional spread and control of vancomycin-resistant enterococci. Am J Infect Control 41:668-73
Lee, Bruce Y; Bartsch, Sarah M; Wong, Kim F et al. (2013) The importance of nursing homes in the spread of methicillin-resistant Staphylococcus aureus (MRSA) among hospitals. Med Care 51:205-15
Lewis, Bryan; Eubank, Stephen; Abrams, Allyson M et al. (2013) in silico surveillance: evaluating outbreak detection with simulation models. BMC Med Inform Decis Mak 13:12
Viñas, María R; Tuduri, Ezequiel; Galar, Alicia et al. (2013) Laboratory-based prospective surveillance for community outbreaks of Shigella spp. in Argentina. PLoS Negl Trop Dis 7:e2521

Showing the most recent 10 out of 28 publications