RT Journal Article
SR Electronic
T1 Sentinel versus passive surveillance for measuring changes in dengue incidence: Evidence from three concurrent surveillance systems in Iquitos, Peru
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 040220
DO 10.1101/040220
A1 Sandra Olkowski
A1 Steven T. Stoddard
A1 Eric S. Halsey
A1 Amy C. Morrison
A1 Christopher M. Barker
A1 Thomas W. Scott
YR 2016
UL http://biorxiv.org/content/early/2016/02/18/040220.abstract
AB Monitoring changes in infectious disease incidence is fundamental to outbreak detection and response, intervention outcome monitoring, and identifying environmental correlates of transmission. In the case of dengue, little is known about how consistently surveillance data track disease burden in a population over time. Here we use four years of monthly dengue incidence data from three sources – population-based (‘passive’) surveillance including suspected cases, ‘sentinel’ surveillance with 100% laboratory confirmation and complete reporting, and door-to-door (‘cohort’) surveillance conducted three times per week - in Iquitos, Peru, to quantify their relative consistency and timeliness. Data consistency was evaluated using annual and monthly expansion factors (EFs) as cohort incidence divided by incidence in each surveillance system, to assess their reliability for estimating disease burden (annual) and monitoring disease trends (monthly). Annually, passive surveillance data more closely estimated cohort incidence (average annual EF=5) than did data from sentinel surveillance (average annual EF=19). Monthly passive surveillance data generally were more consistent (ratio of sentinel/passive EF standard deviations=2.2) but overestimated incidence in 26% (11/43) of months, most often during the second half of the annual high season as dengue incidence typically wanes from its annual peak. Increases in sentinel surveillance incidence were correlated temporally (correlation coefficient = 0.86) with increases in the cohort, while passive surveillance data were significantly correlated at both zero-lag and a one-month lag (0.63 and 0.44, respectively). Together these results suggest that, rather than relying on a single data stream, a clearer picture of changes in infectious disease incidence might be achieved by combining the timeliness of sentinel surveillance with the representativeness of passive surveillance.