ICD-9

Previous reports from participating facilities in North Dakota illustrated that ILI syndrome data from syndromic surveillance data, which is based on chief complaints logs, had a close correlation to the traditional ILI surveillance and that frequency slope of the ILI syndrome was also closely correlated to that of the cases that tested positive for influenza. The facility used in this report submits ICD-9 codes to the North Dakota Department of Health (NDDoH). By comparing the NDDoH ILI syndrome to influenza laboratory testing data and ICD-9 code specific to influenza (487) we found that syndromic surveillance data for ILI closely followed the influenza testing trend as well as the ICD-9 code trend.

Objective

The objective of this report is to evaluate the correlation between influenza-like illness (ILI) syndrome classification using chief complaint data and discharge diagnosis International Classification of Disease, Ninth Revision (ICD-9) code for influenza with the laboratory data from one hospital in North Dakota over a period of three influenza seasons.

The Veterans Health Administration (VHA) operates over 880 outpatient clinics across the nation. The Johns Hopkins Applied Physics Laboratory’s Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) utilizes VHA ICD9 coded outpatient visit data for the detection of abnormal patterns of disease occurrence. The hemorrhagic illness (HI) syndrome category in ESSENCE is comprised of 25 different ICD9 codes, including 12 codes specific for viral hemorrhagic fever (VHF) (e.g., ebola, yellow fever, CrimeanCongo hemorrhagic fever, lassa, etc.) and 13 nonspecific conditions (e.g., purpura not otherwise specified (NOS), thrombocytopathy, and coagulation defect NOS).

Objective

We sought to evaluate the functionality of the diagnosis codes which fall into the syndrome category of hemorrhagic illness.

The CDC recently developed sub-syndromes for classifying disease to enhance syndromic surveillance of natural outbreaks and bioterrorism. They have developed ICD9 classifiers for six GI Illness subsyndromes: Abdominal Pain, Nausea and Vomiting, Diarrhea, Anorexia, Intestinal infections, and Food poisoning. If the number of visits for sub-syndromes varies significantly by age it may impact the design of outbreak detection methods.

Objective

We hypothesized that the percentage of visits for the GI sub-syndromes varied significantly with age.

ICD-9-CM codes have been proposed to be used as adjuncts to existing public health reporting systems and are commonly used for public health surveillance and research purposes. However these codes have been found to have variable accuracy for both healthcare billing as well as for disease classification due to both coding and physician errors, and these codes have never been comprehensively validated for their use for surveillance. Quantification of the positive predictive value for ICD-9 CM diagnosis codes is crucial for assessing their utility for public health disease surveillance and research.

Objective

To quantify the positive predictive values of ICD-9 CM diagnosis codes for public health surveillance of communicable diseases.

Measures aimed at controlling epidemics of infectious diseases critically benefit from early outbreak recognition. Through a manual electronic medical record (EMR) review of 5,127 outpatient encounters at the Veterans Administration health system (VA), we previously developed single-case detection algorithms (CDAs) aimed at uncovering individuals with influenza-like illness (ILI). In this work, we evaluate the impact of using CDAs of varying statistical performance on the time and workload required to find a community-wide influenza outbreak through a VA-based syndromic surveillance system (SSS). The CDAs utilize various logical arrangements of EMR data, including ICD-9 codes, structured clinical parameters, and/or an automated analysis of the free-text of the full clinical note. The 18 ILI CDAs used here are limited to the most successful representatives of ICD-9-only and EMR-based case detectors.

Objective

This work uses a mathematical model of a plausible influenza epidemic to begin to test the influence of CDAs on the performance of a SSS.

Objective

Understanding the baseline dynamics of syndrome counts is essential for use in prospective syndromic surveillance. Therefore we studied to what extent the known seasonal dynamics of gastro-intestinal (GI) pathogens explain the dynamics in GI syndrome in general practitioner and hospital data.

 

While there has been some work to evaluate different data sources for syndromic surveillance of influenza, no one has yet assessed the utility of simultaneously restricting data to specific visit settings and patient age-groups using data drawn from a single source population. Furthermore, most studies have been limited to the emergency departments (ED), with few evaluating the timeliness of data from community-based primary care.

Objective

Using physician billing data from a single source population, we aimed to compare age-group and visit setting specific patterns in the timing of patients presenting to community-based healthcare settings and hospital ED for influenza-like-illnesses (ILI). We thus evaluate the utility of focusing on particular age-groups and care settings for syndromic surveillance of ILI in ambulatory care.

In order to detect influenza outbreaks, the New York State Department of Health emergency department (ED) syndromic surveillance system uses patients’ chief complaint (CC) to assign visits to respiratory and fever syndromes. Recently, the CDC developed a more specific set of “sub-syndromes” including one that included only patients with a CC of flu or having a final ICD9 diagnosis of flu. Our own experience was that although flu may be a common presentation in the ED during the flu season, it is not commonly diagnosed as such. Emergency physicians usually use a symptomatic diagnosis in preference, probably because rapid testing is generally unavailable or may not change treatment. The flu subsyndrome is based on a specific ICD9 code for influenza. It is unknown whether patient visits that meet these restrictive criteria are sufficiently common to be of use, or whether patients who identify themselves as having the flu are correct.

Objective

Our objective was to examine the CC and ICD9 classifiers for the influenza sub-syndrome to assess the frequency of visits and the agreement between the CC, ICD9 code and chart review for these patient visits.

The Centers for Disease Control and Prevention BioSense has developed chief complaint (CC) and ICD9 sub syndrome classifiers for the major syndromes for early event detection and situational awareness. The prevalence of these sub-syndromes in the emergency department population and the performance of these CC classifiers have been little studied. Chart reviews have been used in the past to study this type of question but because of the large number of cases to review, the labor involved would be prohibitive. Therefore, we used an ICD9 code classifier for a syndrome as a surrogate by chart reviews to estimate the performance of a CC classifier.

Objective

To determine the prevalence of the sub-syndromes based on the ICD9 classifiers, and to determine the sensitivity, specificity, positive predictive value and negative predictive value of CC classifiers for the sub-syndromes associated with the respiratory and gastrointestinal syndromes using the ICD9 classifier as the criterion standard.