tetano
Editor, Senior Moderator
PLoS One. 2013 Oct 11;8(10):e77244. doi: 10.1371/journal.pone.0077244.
Exploring a Proposed WHO Method to Determine Thresholds for Seasonal Influenza Surveillance.
Tay EL, Grant K, Kirk M, Mounts A, Kelly H.
Source
Victoria Infectious Diseases Reference Laboratory, Melbourne, Australia ; National Centre for Epidemiology and Population Health, The Australian National University, Canberra, Australia.
Abstract
INTRODUCTION:
Health authorities find thresholds useful to gauge the start and severity of influenza seasons. We explored a method for deriving thresholds proposed in an influenza surveillance manual published by the World Health Organization (WHO).
METHODS:
For 2002-2011, we analysed two routine influenza-like-illness (ILI) datasets, general practice sentinel surveillance and a locum medical service sentinel surveillance, plus laboratory data and hospital admissions for influenza. For each sentinel dataset, we created two composite variables from the product of weekly ILI data and the relevant laboratory data, indicating the proportion of tested specimens that were positive. For all datasets, including the composite datasets, we aligned data on the median week of peak influenza or ILI activity and assigned three threshold levels: seasonal threshold, determined by inspection; and two intensity thresholds termed average and alert thresholds, determined by calculations of means, medians, confidence intervals (CI) and percentiles. From the thresholds, we compared the seasonal onset, end and intensity across all datasets from 2002-2011. Correlation between datasets was assessed using the mean correlation coefficient.
RESULTS:
The median week of peak activity was week 34 for all datasets, except hospital data (week 35). Means and medians were comparable and the 90% upper CIs were similar to the 95(th) percentiles. Comparison of thresholds revealed variations in defining the start of a season but good agreement in describing the end and intensity of influenza seasons, except in hospital admissions data after the pandemic year of 2009. The composite variables improved the agreements between the ILI and other datasets. Datasets were well correlated, with mean correlation coefficients of >0.75 for a range of combinations.
CONCLUSIONS:
Thresholds for influenza surveillance are easily derived from historical surveillance and laboratory data using the approach proposed by WHO. Use of composite variables is helpful for describing influenza season characteristics.
PMID:
24146973
[PubMed - in process]
Free full text
http://www.ncbi.nlm.nih.gov/pubmed/24146973
Exploring a Proposed WHO Method to Determine Thresholds for Seasonal Influenza Surveillance.
Tay EL, Grant K, Kirk M, Mounts A, Kelly H.
Source
Victoria Infectious Diseases Reference Laboratory, Melbourne, Australia ; National Centre for Epidemiology and Population Health, The Australian National University, Canberra, Australia.
Abstract
INTRODUCTION:
Health authorities find thresholds useful to gauge the start and severity of influenza seasons. We explored a method for deriving thresholds proposed in an influenza surveillance manual published by the World Health Organization (WHO).
METHODS:
For 2002-2011, we analysed two routine influenza-like-illness (ILI) datasets, general practice sentinel surveillance and a locum medical service sentinel surveillance, plus laboratory data and hospital admissions for influenza. For each sentinel dataset, we created two composite variables from the product of weekly ILI data and the relevant laboratory data, indicating the proportion of tested specimens that were positive. For all datasets, including the composite datasets, we aligned data on the median week of peak influenza or ILI activity and assigned three threshold levels: seasonal threshold, determined by inspection; and two intensity thresholds termed average and alert thresholds, determined by calculations of means, medians, confidence intervals (CI) and percentiles. From the thresholds, we compared the seasonal onset, end and intensity across all datasets from 2002-2011. Correlation between datasets was assessed using the mean correlation coefficient.
RESULTS:
The median week of peak activity was week 34 for all datasets, except hospital data (week 35). Means and medians were comparable and the 90% upper CIs were similar to the 95(th) percentiles. Comparison of thresholds revealed variations in defining the start of a season but good agreement in describing the end and intensity of influenza seasons, except in hospital admissions data after the pandemic year of 2009. The composite variables improved the agreements between the ILI and other datasets. Datasets were well correlated, with mean correlation coefficients of >0.75 for a range of combinations.
CONCLUSIONS:
Thresholds for influenza surveillance are easily derived from historical surveillance and laboratory data using the approach proposed by WHO. Use of composite variables is helpful for describing influenza season characteristics.
PMID:
24146973
[PubMed - in process]
Free full text
http://www.ncbi.nlm.nih.gov/pubmed/24146973