Limits...
Telephone triage service data for detection of influenza-like illness.

Yih WK, Teates KS, Abrams A, Kleinman K, Kulldorff M, Pinner R, Harmon R, Wang S, Platt R - PLoS ONE (2009)

Bottom Line: The degree to which the call data were correlated with either CDC viral isolates or sentinel provider percentage ILI data was highly variable among states.However, in states displaying a higher correlation between the call data and the CDC data, call data may be useful as an adjunct to state-level surveillance data, for example at times when sentinel surveillance is not in operation or in areas where sentinel provider coverage is considered insufficient.Sufficient population coverage, a specific ILI syndrome definition, and the use of a threshold of percentage of calls that are for ILI would likely improve the utility of such data for ILI surveillance purposes.

View Article: PubMed Central - PubMed

Affiliation: Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA, USA. katherine_yih@hphc.org

ABSTRACT

Background: Surveillance for influenza and influenza-like illness (ILI) is important for guiding public health prevention programs to mitigate the morbidity and mortality caused by influenza, including pandemic influenza. Nontraditional sources of data for influenza and ILI surveillance are of interest to public health authorities if their validity can be established.

Methods/principal findings: National telephone triage call data were collected through automated means for purposes of syndromic surveillance. For the 17 states with at least 500,000 inhabitants eligible to use the telephone triage services, call volume for respiratory syndrome was compared to CDC weekly number of influenza isolates and percentage of visits to sentinel providers for ILI. The degree to which the call data were correlated with either CDC viral isolates or sentinel provider percentage ILI data was highly variable among states.

Conclusions: Telephone triage data in the U.S. are patchy in coverage and therefore not a reliable source of ILI surveillance data on a national scale. However, in states displaying a higher correlation between the call data and the CDC data, call data may be useful as an adjunct to state-level surveillance data, for example at times when sentinel surveillance is not in operation or in areas where sentinel provider coverage is considered insufficient. Sufficient population coverage, a specific ILI syndrome definition, and the use of a threshold of percentage of calls that are for ILI would likely improve the utility of such data for ILI surveillance purposes.

Show MeSH

Related in: MedlinePlus

Flow of telephone triage service information, from patient call to analysis.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2668187&req=5

pone-0005260-g001: Flow of telephone triage service information, from patient call to analysis.

Mentions: The flow of information, from the point the patient dialed the phone to receipt of aggregate counts data by the NDP's data center, happened as follows and as shown schematically in Figure 1: Each patient call to Optum was distributed to one of its national call centers according to the availability of personnel to answer, not according to geography. Upon answering a call, the nurse would go online; call up or collect the patient's demographic information, including zip code of residence, in a new call record; and over the course of the conversation consult one or more online “guidelines.” Guidelines were electronic documents about conditions or symptoms, such as “Sore Throat/Adult” or “Influenza/Pediatric.” Whenever a nurse accessed a guideline, the guideline title would automatically be entered into the call record, serving as an indicator of the patient's symptoms. Multiple guidelines could be consulted during a single call, depending on the variety of the patient's symptoms. Each night, patient call records with guideline titles previously determined to be of interest to the NDP for syndromic surveillance purposes were extracted automatically from the Optum data system, in a uniform format specified by the NDP, to a directory accessible to software provided to Optum data-managers by the NDP. The distributed software then mapped patient calls to syndromes (e.g., respiratory), which had been previously defined with CDC collaboration, and then identified which of these represented new episodes of illness, ignoring records of patient calls in any syndrome that occurred within 42 days of an earlier call by the patient for the same syndrome. A call could be counted in multiple syndromes; for example, if the two guidelines “Blood in Stools/Pediatric” and “Cough/Pediatric” were accessed, the call would be counted in three syndromes: Gastrointestinal, Hemorrhagic, and Respiratory. A daily file was created containing counts of new episodes of each syndrome by zip code for the preceding day, and this was sent electronically in encrypted form to the NDP's data center. All extraction, processing, and transfer procedures were automated, so no extra manual data entry or active reporting was required of Optum staff.


Telephone triage service data for detection of influenza-like illness.

Yih WK, Teates KS, Abrams A, Kleinman K, Kulldorff M, Pinner R, Harmon R, Wang S, Platt R - PLoS ONE (2009)

Flow of telephone triage service information, from patient call to analysis.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2668187&req=5

pone-0005260-g001: Flow of telephone triage service information, from patient call to analysis.
Mentions: The flow of information, from the point the patient dialed the phone to receipt of aggregate counts data by the NDP's data center, happened as follows and as shown schematically in Figure 1: Each patient call to Optum was distributed to one of its national call centers according to the availability of personnel to answer, not according to geography. Upon answering a call, the nurse would go online; call up or collect the patient's demographic information, including zip code of residence, in a new call record; and over the course of the conversation consult one or more online “guidelines.” Guidelines were electronic documents about conditions or symptoms, such as “Sore Throat/Adult” or “Influenza/Pediatric.” Whenever a nurse accessed a guideline, the guideline title would automatically be entered into the call record, serving as an indicator of the patient's symptoms. Multiple guidelines could be consulted during a single call, depending on the variety of the patient's symptoms. Each night, patient call records with guideline titles previously determined to be of interest to the NDP for syndromic surveillance purposes were extracted automatically from the Optum data system, in a uniform format specified by the NDP, to a directory accessible to software provided to Optum data-managers by the NDP. The distributed software then mapped patient calls to syndromes (e.g., respiratory), which had been previously defined with CDC collaboration, and then identified which of these represented new episodes of illness, ignoring records of patient calls in any syndrome that occurred within 42 days of an earlier call by the patient for the same syndrome. A call could be counted in multiple syndromes; for example, if the two guidelines “Blood in Stools/Pediatric” and “Cough/Pediatric” were accessed, the call would be counted in three syndromes: Gastrointestinal, Hemorrhagic, and Respiratory. A daily file was created containing counts of new episodes of each syndrome by zip code for the preceding day, and this was sent electronically in encrypted form to the NDP's data center. All extraction, processing, and transfer procedures were automated, so no extra manual data entry or active reporting was required of Optum staff.

Bottom Line: The degree to which the call data were correlated with either CDC viral isolates or sentinel provider percentage ILI data was highly variable among states.However, in states displaying a higher correlation between the call data and the CDC data, call data may be useful as an adjunct to state-level surveillance data, for example at times when sentinel surveillance is not in operation or in areas where sentinel provider coverage is considered insufficient.Sufficient population coverage, a specific ILI syndrome definition, and the use of a threshold of percentage of calls that are for ILI would likely improve the utility of such data for ILI surveillance purposes.

View Article: PubMed Central - PubMed

Affiliation: Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA, USA. katherine_yih@hphc.org

ABSTRACT

Background: Surveillance for influenza and influenza-like illness (ILI) is important for guiding public health prevention programs to mitigate the morbidity and mortality caused by influenza, including pandemic influenza. Nontraditional sources of data for influenza and ILI surveillance are of interest to public health authorities if their validity can be established.

Methods/principal findings: National telephone triage call data were collected through automated means for purposes of syndromic surveillance. For the 17 states with at least 500,000 inhabitants eligible to use the telephone triage services, call volume for respiratory syndrome was compared to CDC weekly number of influenza isolates and percentage of visits to sentinel providers for ILI. The degree to which the call data were correlated with either CDC viral isolates or sentinel provider percentage ILI data was highly variable among states.

Conclusions: Telephone triage data in the U.S. are patchy in coverage and therefore not a reliable source of ILI surveillance data on a national scale. However, in states displaying a higher correlation between the call data and the CDC data, call data may be useful as an adjunct to state-level surveillance data, for example at times when sentinel surveillance is not in operation or in areas where sentinel provider coverage is considered insufficient. Sufficient population coverage, a specific ILI syndrome definition, and the use of a threshold of percentage of calls that are for ILI would likely improve the utility of such data for ILI surveillance purposes.

Show MeSH
Related in: MedlinePlus