2002
DOI: 10.1197/jamia.m1224
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Value of ICD-9-Coded Chief Complaints for Detection of Epidemics

Abstract: To assess the value of ICD-9-coded chief complaints for early detection of epidemics, we measured sensitivity, positive predictive value, and timeliness of Influenza detection using a respiratory set (RS) of ICD-9 codes and an Influenza set (IS). We also measured inherent timeliness of these data using the cross-correlation function

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Cited by 63 publications
(74 citation statements)
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“…We therefore adopted an administrative case definition of ILI, which consisted of a broad bundle of diagnoses, including bronchitis, pneumonia, cold, cough and exacerbations of chronic obstructive pulmonary disease (see electronic supplementary material [ESM] Tables 1 and 2). This case definition, determined in a pilot study of six emergency departments, is similar to those of other studies identifying diagnoses correlated with influenza activity [21,22]. ILI was chosen to represent the common manifestations of influenza, which include PI hospitalisations.…”
Section: Methodsmentioning
confidence: 95%
“…We therefore adopted an administrative case definition of ILI, which consisted of a broad bundle of diagnoses, including bronchitis, pneumonia, cold, cough and exacerbations of chronic obstructive pulmonary disease (see electronic supplementary material [ESM] Tables 1 and 2). This case definition, determined in a pilot study of six emergency departments, is similar to those of other studies identifying diagnoses correlated with influenza activity [21,22]. ILI was chosen to represent the common manifestations of influenza, which include PI hospitalisations.…”
Section: Methodsmentioning
confidence: 95%
“…16,17 Additional studies that measured sensitivity and specificity of outbreak detection systems but not timeliness could be reanalyzed to measure timeliness. [18][19][20][21][22][23][24][25][26][27] Such analyses would provide insights about the timeliness of detection of those outbreaks studied (influenza, gastroenteritides, hepatitis, measles, mumps, meningitis, pertussis, rubella, and nosocomial outbreaks), a variety of detection methods (quality control charts, 15,[18][19][20] time-series analyses, 17,[21][22][23] naïve Bayes, 24 hidden Markov models, 25 and ad hoc algorithms 26,27 ), and a variety of signals Figure 2. Cumulative economic impact with and without post-attack treatment following a hypothetical bioterrorist attack with B. anthracis on a population of 100,000.…”
Section: Previous Work On Measuring Timeliness Of a Detection Systemmentioning
confidence: 99%
“…An exception is Influenza, for which researchers have studied changes that appear in weekly or daily counts of numerous types of routinely collected data. 15,17,43 For many other dis- Legend: Lab, influenza cultures from the UPMC Health System; WebMD, counts of queries to a national Web health site using words such as cold and flu; School, school nurse influenza reporting; Resp. and Viral, categories of emergency department ICD-9-coded chief complaints.…”
Section: Journal Of Public Health Management and Practice/november 2001mentioning
confidence: 99%
“…Using visual examination, abnormal peaks were observed only in DeadBird due to health troubles occurring in the wild bird population (i.e., intoxication). These extreme values were removed based on a method adapted from Tsui et al (Tsui et al 2001): the entire data set was first fitted to a negative binomial (NB) distribution (Supplementary Table S1; Supplementary Data are available online at www.liebertpub.com/vbz), and then, values above the 95% confidence interval were deleted and replaced with the average value of the four previous weeks.…”
Section: Data Modeling and Simulationmentioning
confidence: 99%