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Demonstrating the use of high-volume electronic medical claims data to monitor local and regional influenza activity in the US.

Viboud C, Charu V, Olson D, Ballesteros S, Gog J, Khan F, Grenfell B, Simonsen L - PLoS ONE (2014)

Bottom Line: To test IMS-Health performances at the city level, we compared IMS-ILI indicators to syndromic surveillance data for New York City.Seasonal intensity estimates were weakly correlated across datasets in all age data (rho≤0.52), moderately correlated among adults (rho≥0.64) and uncorrelated among school-age children.City-level IMS-ILI indicators were highly consistent with reference syndromic data (rho≥0.86).

View Article: PubMed Central - PubMed

Affiliation: Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America.

ABSTRACT

Introduction: Fine-grained influenza surveillance data are lacking in the US, hampering our ability to monitor disease spread at a local scale. Here we evaluate the performances of high-volume electronic medical claims data to assess local and regional influenza activity.

Material and methods: We used electronic medical claims data compiled by IMS Health in 480 US locations to create weekly regional influenza-like-illness (ILI) time series during 2003-2010. IMS Health captured 62% of US outpatient visits in 2009. We studied the performances of IMS-ILI indicators against reference influenza surveillance datasets, including CDC-ILI outpatient and laboratory-confirmed influenza data. We estimated correlation in weekly incidences, peak timing and seasonal intensity across datasets, stratified by 10 regions and four age groups (<5, 5-29, 30-59, and 60+ years). To test IMS-Health performances at the city level, we compared IMS-ILI indicators to syndromic surveillance data for New York City. We also used control data on laboratory-confirmed Respiratory Syncytial Virus (RSV) activity to test the specificity of IMS-ILI for influenza surveillance.

Results: Regional IMS-ILI indicators were highly synchronous with CDC's reference influenza surveillance data (Pearson correlation coefficients rho≥0.89; range across regions, 0.80-0.97, P<0.001). Seasonal intensity estimates were weakly correlated across datasets in all age data (rho≤0.52), moderately correlated among adults (rho≥0.64) and uncorrelated among school-age children. IMS-ILI indicators were more correlated with reference influenza data than control RSV indicators (rho = 0.93 with influenza v. rho = 0.33 with RSV, P<0.05). City-level IMS-ILI indicators were highly consistent with reference syndromic data (rho≥0.86).

Conclusion: Medical claims-based ILI indicators accurately capture weekly fluctuations in influenza activity in all US regions during inter-pandemic and pandemic seasons, and can be broken down by age groups and fine geographical areas. Medical claims data provide more reliable and fine-grained indicators of influenza activity than other high-volume electronic algorithms and should be used to augment existing influenza surveillance systems.

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Related in: MedlinePlus

Spatial variation in local influenza activity: 2009 influenza pandemic patterns in 21 cities and county regions of New York State.Weekly IMS-ILI indicators are represented for the period May 2009 to April 2010.
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pone-0102429-g004: Spatial variation in local influenza activity: 2009 influenza pandemic patterns in 21 cities and county regions of New York State.Weekly IMS-ILI indicators are represented for the period May 2009 to April 2010.

Mentions: Next, we explored city-level disease curves (Figure 4). We focus on the 2009 pandemic period in 21 cities and counties in New York State, for which spatial heterogeneity has been well documented [3], [11]. Influenza pandemic patterns appear highly heterogeneous at such a local scale. In particular, the New York City boroughs display intense influenza activity in spring 2009 followed by a moderate outbreak in fall 2009, while locations in upstate New York experienced a dominant fall wave. Comparison of IMS-ILI time series for 4 boroughs of New York City with available data (Manhattan, Queens, Bronx and Brooklyn), revealed high consistency in IMS surveillance within this local area (Figure 4: pairwise weekly correlation ≥0.87; P<0.0001). This analysis confirms the robustness of the IMS system for local disease monitoring, as we would expect high population connectivity within New York City, resulting in highly synchronous disease patterns between boroughs.


Demonstrating the use of high-volume electronic medical claims data to monitor local and regional influenza activity in the US.

Viboud C, Charu V, Olson D, Ballesteros S, Gog J, Khan F, Grenfell B, Simonsen L - PLoS ONE (2014)

Spatial variation in local influenza activity: 2009 influenza pandemic patterns in 21 cities and county regions of New York State.Weekly IMS-ILI indicators are represented for the period May 2009 to April 2010.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4114744&req=5

pone-0102429-g004: Spatial variation in local influenza activity: 2009 influenza pandemic patterns in 21 cities and county regions of New York State.Weekly IMS-ILI indicators are represented for the period May 2009 to April 2010.
Mentions: Next, we explored city-level disease curves (Figure 4). We focus on the 2009 pandemic period in 21 cities and counties in New York State, for which spatial heterogeneity has been well documented [3], [11]. Influenza pandemic patterns appear highly heterogeneous at such a local scale. In particular, the New York City boroughs display intense influenza activity in spring 2009 followed by a moderate outbreak in fall 2009, while locations in upstate New York experienced a dominant fall wave. Comparison of IMS-ILI time series for 4 boroughs of New York City with available data (Manhattan, Queens, Bronx and Brooklyn), revealed high consistency in IMS surveillance within this local area (Figure 4: pairwise weekly correlation ≥0.87; P<0.0001). This analysis confirms the robustness of the IMS system for local disease monitoring, as we would expect high population connectivity within New York City, resulting in highly synchronous disease patterns between boroughs.

Bottom Line: To test IMS-Health performances at the city level, we compared IMS-ILI indicators to syndromic surveillance data for New York City.Seasonal intensity estimates were weakly correlated across datasets in all age data (rho≤0.52), moderately correlated among adults (rho≥0.64) and uncorrelated among school-age children.City-level IMS-ILI indicators were highly consistent with reference syndromic data (rho≥0.86).

View Article: PubMed Central - PubMed

Affiliation: Fogarty International Center, National Institutes of Health, Bethesda, Maryland, United States of America.

ABSTRACT

Introduction: Fine-grained influenza surveillance data are lacking in the US, hampering our ability to monitor disease spread at a local scale. Here we evaluate the performances of high-volume electronic medical claims data to assess local and regional influenza activity.

Material and methods: We used electronic medical claims data compiled by IMS Health in 480 US locations to create weekly regional influenza-like-illness (ILI) time series during 2003-2010. IMS Health captured 62% of US outpatient visits in 2009. We studied the performances of IMS-ILI indicators against reference influenza surveillance datasets, including CDC-ILI outpatient and laboratory-confirmed influenza data. We estimated correlation in weekly incidences, peak timing and seasonal intensity across datasets, stratified by 10 regions and four age groups (<5, 5-29, 30-59, and 60+ years). To test IMS-Health performances at the city level, we compared IMS-ILI indicators to syndromic surveillance data for New York City. We also used control data on laboratory-confirmed Respiratory Syncytial Virus (RSV) activity to test the specificity of IMS-ILI for influenza surveillance.

Results: Regional IMS-ILI indicators were highly synchronous with CDC's reference influenza surveillance data (Pearson correlation coefficients rho≥0.89; range across regions, 0.80-0.97, P<0.001). Seasonal intensity estimates were weakly correlated across datasets in all age data (rho≤0.52), moderately correlated among adults (rho≥0.64) and uncorrelated among school-age children. IMS-ILI indicators were more correlated with reference influenza data than control RSV indicators (rho = 0.93 with influenza v. rho = 0.33 with RSV, P<0.05). City-level IMS-ILI indicators were highly consistent with reference syndromic data (rho≥0.86).

Conclusion: Medical claims-based ILI indicators accurately capture weekly fluctuations in influenza activity in all US regions during inter-pandemic and pandemic seasons, and can be broken down by age groups and fine geographical areas. Medical claims data provide more reliable and fine-grained indicators of influenza activity than other high-volume electronic algorithms and should be used to augment existing influenza surveillance systems.

Show MeSH
Related in: MedlinePlus