Abstract
For several decades, the World Health Organization has collected, maintained, and distributed invaluable country-specific disease surveillance data that allow experts to develop new analytical tools for disease tracking and forecasting. To capture the extent of available data within these sources, we proposed a completeness metric based on the effective time series length. Using FluNet records for 29 Pan-American countries from 2005 to 2019, we explored whether completeness was associated with health expenditure indicators adjusting for surveillance system heterogeneity. We observed steady improvements in completeness by 4.2–6.3% annually, especially after the A(H1N1)-2009 pandemic, when 24 countries reached > 95% completeness. Doubling in decadal health expenditure per capita was associated with ~ 7% increase in overall completeness. The proposed metric could navigate experts in assessing open access data quality and quantity for conducting credible statistical analyses, estimating disease trends, and developing outbreak forecasting systems.
Highlights
Global and national surveillance systems serve two critical functions: monitoring disease trend trajectories to inform health policies and providing early outbreak warnings that require local, regional, or global r esponses[1]
We proposed a metric of completeness based on the effective time series length (ETSL) to capture the extent of the available time series data within FluNet records
We found no resources that compile these surveillance system characteristics to allow for clear side-by-side comparison across continental countries
Summary
Global and national surveillance systems serve two critical functions: monitoring disease trend trajectories to inform health policies and providing early outbreak warnings that require local, regional, or global r esponses[1]. The WHO has established high standards by using a comprehensive set of monitoring and evaluation (M&E) metrics to routinely collect worldwide records[5,6] These metrics track the production of surveillance data and provide critical information for effective analysis and interpretation of the data. As the velocity and volume of collected data increased from 1998–2010, so did opportunities to utilize multiple data streams and disseminate surveillance records more broadly This gave rise to web-based platforms like FluID that actively collect, deposit, and report influenza health records using various influenza case definitions. The merging of multiple data streams has been shown to improve rates of influenza testing and diagnosis, which greatly influence the reporting completeness of surveillance d ata[3,4,14]
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