tetano
Editor, Senior Moderator
BMC Infect Dis. 2014 Sep 17;14(1):505. [Epub ahead of print]
Simulation-guided design of serological surveys of the cumulative incidence of influenza infection.
Wu KM, Riley S.
Abstract
BACKGROUND:
Influenza infection does not always cause clinical illnesses, so serological surveillance has been usedto determine the true burden of influenza outbreaks. This study investigates the accuracy of measuringcumulative incidence of influenza infection using different serological survey designs.
METHODS:
We used a simple transmission model to simulate a typical influenza epidemic and obtained the seroprevalenceover time. We also constructed four illustrative scenarios for baseline levels of antibodiesprior and levels of boosting following infection in the simulated studies. Although illustrative, threeof the four scenarios were based on the most detailed empirical data available. We used standardanalytical methods to calculate estimated seroprevalence and associated confidence intervals for eachof the four scenarios for both cross-sectional and longitudinal study designs. We tested the sensitivityof our results to changes in the sampled size and in our ability to detect small changes in antibodylevels.
RESULTS:
There were substantial differences between the background antibody titres and levels of boostingwithin three of our illustrative scenarios which were based on empirical data. These differences propagatedthrough to different and substantial patterns of bias for all scenarios other than those with verylow background titre and high levels of boosting. The two survey designs result in similar seroprevalenceestimates in general under these scenarios, but when background immunity was high, simulatedcross-sectional studies had higher biases. Sensitivity analyses indicated that an ability to accuratelydetect low levels of antibody boosting within paired sera would substantially improve the performanceof serological surveys, even under difficult conditions.
CONCLUSIONS:
Levels of boosting and background immunity significantly affect the accuracy of seroprevalence estimations,and depending on these levels of immunity responses, different survey designs should beused to estimate seroprevalences. These results suggest that under current measurement criteria, cumulativeincidence measured by serological surveys might have been substantially underestimated byfailing to include all infections, including mild and asymptomatic infections, in certain scenarios. Dilutionprotocols more highly resolved than serial 2-fold dilution should be considered for serologicalsurveys.
PMID:
25231414
[PubMed - as supplied by publisher]
Free full text
http://www.ncbi.nlm.nih.gov/pubmed/25231414
Simulation-guided design of serological surveys of the cumulative incidence of influenza infection.
Wu KM, Riley S.
Abstract
BACKGROUND:
Influenza infection does not always cause clinical illnesses, so serological surveillance has been usedto determine the true burden of influenza outbreaks. This study investigates the accuracy of measuringcumulative incidence of influenza infection using different serological survey designs.
METHODS:
We used a simple transmission model to simulate a typical influenza epidemic and obtained the seroprevalenceover time. We also constructed four illustrative scenarios for baseline levels of antibodiesprior and levels of boosting following infection in the simulated studies. Although illustrative, threeof the four scenarios were based on the most detailed empirical data available. We used standardanalytical methods to calculate estimated seroprevalence and associated confidence intervals for eachof the four scenarios for both cross-sectional and longitudinal study designs. We tested the sensitivityof our results to changes in the sampled size and in our ability to detect small changes in antibodylevels.
RESULTS:
There were substantial differences between the background antibody titres and levels of boostingwithin three of our illustrative scenarios which were based on empirical data. These differences propagatedthrough to different and substantial patterns of bias for all scenarios other than those with verylow background titre and high levels of boosting. The two survey designs result in similar seroprevalenceestimates in general under these scenarios, but when background immunity was high, simulatedcross-sectional studies had higher biases. Sensitivity analyses indicated that an ability to accuratelydetect low levels of antibody boosting within paired sera would substantially improve the performanceof serological surveys, even under difficult conditions.
CONCLUSIONS:
Levels of boosting and background immunity significantly affect the accuracy of seroprevalence estimations,and depending on these levels of immunity responses, different survey designs should beused to estimate seroprevalences. These results suggest that under current measurement criteria, cumulativeincidence measured by serological surveys might have been substantially underestimated byfailing to include all infections, including mild and asymptomatic infections, in certain scenarios. Dilutionprotocols more highly resolved than serial 2-fold dilution should be considered for serologicalsurveys.
PMID:
25231414
[PubMed - as supplied by publisher]
Free full text
http://www.ncbi.nlm.nih.gov/pubmed/25231414