Time-tradeoff utilities for identifying and evaluating a minimum data set for time-critical biosurveillance.
BACKGROUND
Researchers and policy makers are interested in identifying, implementing, and evaluating a national minimum data set for biosurveillance. However, work remains to be done to establish methods for measuring the value of such data.


OBJECTIVE
The purpose of this article is to establish and evaluate a method for measuring the utility of biosurveillance data.


METHODS
The authors derive an expected utility model in which the value of data may be determined by trading data relevance for time delay in receiving data. In a sample of 23 disease surveillance practitioners, the authors test if such tradeoffs are sensitive to the types of data elements involved (chief complaint v. emergency department [ED] log of visit) and proportional changes to the time horizon needed for receiving data (24 v. 48 h). In addition, they evaluate the logical error rate: the proportion of responses that scored less relevant data as having higher utility.


RESULTS
Utilities of chief complaints were significantly higher than ED log of visit, F(1, 21)= 5.60, P < 0.05, suggesting the method is sensitive. Further utilities did not depend on time horizon used in the exercise, F(1, 21) = 0.00, P = ns. Of 92 time tradeoffs elicited, there were 5 logical errors (i.e., 5% logical error rate).


CONCLUSIONS
In this article, the authors establish a time-tradeoff exercise for valuing biosurveillance data. Empirically, the method shows initial promise for evaluating a minimum data set for biosurveillance. Future applications of this approach may prove useful in disease surveillance planning and evaluation.
Doctor
Jason N
JN
Department of Clinical Pharmacy and Pharmaceutical Economics and Policy, University of Southern California, Los Angeles, CA 90089-9004, USA. jdoctor@usc.edu
Baseman
Janet G
JG
Lober
William B
WB
Davies
Jac
J
Kobayashi
John
J
Karras
Bryant T
BT
Fuller
Sherrilynne
S
D000328:Adult; D001699:Biometry; D003141:Communicable Diseases / Q000453:epidemiology; D004196:Disease Outbreaks; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011159:Population Surveillance / Q000379:methods*; D011634:Public Health / Q000706:statistics & numerical data; D013997:Time Factors; D014481:United States; D014861:Washington / Q000453:epidemiology