Abstract
BackgroundSolomon Islands is one of the least developed countries in the world. Recognising that timely detection of outbreaks is needed to enable early and effective response to disease outbreaks, the Solomon Islands government introduced a simple syndromic surveillance system in 2011. We conducted the first evaluation of the system and the first exploration of a national experience within the broader multi-country Pacific Syndromic Surveillance System to determine if it is meeting its objectives and to identify opportunities for improvement.MethodsWe used a multi-method approach involving retrospective data collection and statistical analysis, modelling, qualitative research and observational methods.ResultsWe found that the system was well accepted, highly relied upon and designed to account for contextual limitations. We found the syndromic algorithm used to identify outbreaks was moderately sensitive, detecting 11.8% (IQR: 6.3–25.0%), 21.3% (IQR: 10.3–36.8%), 27.5% (IQR: 12.8–52.3%) and 40.5% (IQR: 13.5–65.7%) of outbreaks that caused small, moderate, large and very large increases in case presentations to health facilities, respectively. The false alert rate was 10.8% (IQR: 4.8–24.5%). Rural coverage of the system was poor. Limited workforce, surveillance resourcing and other ‘upstream’ health system factors constrained performance.ConclusionsThe system has made a significant contribution to public health security in Solomon Islands, but remains insufficiently sensitive to detect small-moderate sized outbreaks and hence should not be relied upon as a stand-alone surveillance strategy. Rather, the system should sit within a complementary suite of early warning surveillance activities including event-based, in-patient- and laboratory-based surveillance methods. Future investments need to find a balance between actions to address the technical and systems issues that constrain performance while maintaining simplicity and hence sustainability.
Highlights
IntroductionRecognising that timely detection of outbreaks is needed to enable early and effective response to disease outbreaks, the Solomon Islands government introduced a simple syndromic surveillance system in 2011
Solomon Islands is one of the least developed countries in the world
We modelled four different magnitudes (a 50, 100, 150 and 200% increase in expected case presentations), and four different distributions: (i) a single peak event with all additional cases presenting in one reporting period; (ii) a single peak with additional presenting over two reporting periods (i.e., 60% of additional cases presenting in the first reporting period and 40% in the second); (iii) a multi-peak event (i.e., 50% of additional cases presenting in the first reporting period, 20% in the second and 30% in the third); (iv) and a prolonged event (i.e., 10% of cases presenting in the first and fourth reporting period and 40% in the second and third)
Summary
Recognising that timely detection of outbreaks is needed to enable early and effective response to disease outbreaks, the Solomon Islands government introduced a simple syndromic surveillance system in 2011. Many developing countries lack the resources, systems and infrastructure required to implement comprehensive multi-faceted early warning surveillance strategies for infectious diseases of outbreak potential; and rely on relatively rudimentary syndromic surveillance methods for their detection [4,5,6]. In 2011, while recovering from 6 years of civil unrest that resulted in health sector fragility [6] and recognising vulnerability due to the lack of a formalised routine outbreak early warning detection mechanism [12], the Solomon Islands Government (SIG) implemented a simple syndrome-based outbreak detection strategy known as the SI Syndromic Surveillance System (SI-SSS). The objectives of the SI-SSS are “to accurately detect outbreaks in the community quickly so that responses can be initiated promptly, and health impacts minimised” and “to support SI’s compliance with IHR (2005) obligations” The SI-SSS is described in Additional file 1
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