EDs must be able to respond to a surge in medical need for both seasonal and pandemic influenza. Because many EDs already operate at or near-capacity, accurate and timely surveillance, coupled with planned response measures, is essential. This study validates the use of weekly city-level GFT as an ED surveillance tool because of its correlation with both positive influenza test results and volume of patients with ILI presenting to the ED. There was a less strong relationship with ED crowding measures, such as prolonged length of stay, which would be expected to occur with higher demand. This relationship was stronger in the pediatric ED than in the adult ED, where there did not appear to be as great an increase in ED visits surrounding increased influenza activity. These relationships highlight the potential usefulness of Internet-based search data on ED-based strategies to better match the supply of ED resources to surges in demand that occur during influenza outbreaks.
GFT provides near-real-time surveillance data 7–10 days before the CDC’s US Influenza Sentinel Provider Surveillance Network [25
]. By harnessing health-related searches on the Internet, GFT combines focused information from a large spectrum of the population with geospatial data to create a broad-reaching yet geographically specific surveillance system. The resulting data are provided free of charge via the Internet, with options to narrow data to the state, regional, or city level.
Overall, GFT correlated well with influenza activity in the ED, but not perfectly. The smaller peak in February 2009 represents 2008–2009 seasonal influenza, the peak in June 2009 represents the initial wave of H1N1 influenza, and the main peak in October 2009 represents the major surge of the H1N1 influenza pandemic. GFT showed good to excellent correlation with both the number of positive influenza tests and the number of those patients presenting to the ED, as shown by the numbers of ED patients with ILI. Of note, during the pandemic peak, GFT preceded the actual increase in ED patients with ILI or positive influenza test results by 1 week. The influenza peak in June 2009 was not detected by GFT, possibly because of the previous month’s flurry of Internet activity surrounding the news coverage of the H1N1 outbreak. However, the overall correlation between GFT data and influenza activity remain strong. Cross-correlation analysis showed the highest correlation resulted at either 0 or 1 week, demonstrating that there was no substantial lag in GFT estimates. The combination of good correlation with influenza activity and the anticipatory increase of GFT highlight the potential of using GFT as an influenza surveillance tool for ED staffing and surge capacity planning measures.
GFT had an additional peak in late April that was not mirrored in the number of patients with ILI or positive influenza test results. This peak probably corresponds to the increasing news coverage of the H1N1 pandemic, because it began the day after the CDC declared H1N1 as a national public health emergency. McDonnell et al termed this period as “fear week” and described a surge in the Google search term “swine flu” in the absence of actual documented cases of influenza [34
]. However, fear week reflected more than simply public fear of influenza, because ED patient volumes increased by 7.0% across the United States during that week, with a 19% increase in pediatric ED visits and a 1% increase in adult ED visits compared with baseline [34
]. This increase is comparable to the increase seen with the actual influenza surge in June 2009, measured by the same mechanism, with a 6.6% overall increase in ED visits. This is similar to our ED volume data, where pediatric ED volumes increased during fear week despite the lack of documented influenza cases. These findings reflect one of the inherent limitations of Internet-based surveillance tools, which by definition, may identify the public perceptions of the threat of influenza as a signal and as actual symptoms or cases. However, because the increase in GFT correlates with an increase in ED patient volumes, there remains practical use of the system for surge capacity planning for EDs.
To determine how linking GFT data to a response plan would affect ED crowding, GFT data were correlated with some basic ED crowding metrics. The correlation with ED crowding measures was stronger in the pediatric than in the adult ED. This finding was not surprising, because of the significantly larger proportion of total ED patient volumes in the pediatric ED attributable to influenza and ILI that we observed—a phenomenon which has been previously described in other EDs [34
]. This effect was further increased by the H1N1 virus, which had a greater impact on the younger population and was the main circulating strain observed during the period of this study.
Remaining crowding measures showed that length of stay increased in both the pediatric and the adult populations, whereas in the adult population, there was little correlation with a higher number of visits. This indicates that, during episodes of influenza activity, patients may require more resources in the ED, such as intravenous fluids or other interventions, requiring longer stays. It also demonstrates insufficient supply-demand matching, which is the major problem that underlies ED crowding in general. When divided by acuity level, GFT had moderate correlation with length of stay for levels 1 and 2 admitted pediatric patients and length of stay for levels 4 and 5 discharged pediatric patients, indicating that this resource mismatch impacted both ends of the acuity spectrum and the inpatient services in addition to the ED. Overall, the only adult ED crowding measures that correlated with GFT were waiting time and length of stay of discharged patients, greatest in level 4 and 5 patients.
The main limitation of the study is the lack of regional and temporal generalizability, because this investigation was limited to 1 medical center, in 1 city, over a period of 2 years. This study used city-wide GFT data to correlate with influenza activity and crowding measures at one institution, thus assuming uniform distribution of both influenza activity and Google Flu searching throughout the city. It is additionally unclear how city-wide GFT data would correlate with suburban hospitals. This investigation was intended for demonstration purposes, however, and the GFT tool offers city- and region-specific data to allow others to evaluate regional generalizability. Furthermore, the data for this study covered 2 years, and this period was largely dominated by the 2009 novel H1N1 pandemic. Extension of this validation to more typical influenza seasons will improve the temporal generalizability. The use of cross-correlation analysis allows for validation and comparison of GFT data, but is not predictive. Further modeling will be necessary to determine the full predictive usefulness of GFT. The temporal precision of our data measurement was limited by the weekly scale with which GFT stores data. Evaluating data on a daily scale may have improved the ability to identify the lead time between GFT and measures of regional influenza activity through the use of daily lags in the cross-correlation analysis.
During the study period, we found that GFT had good correlation with both ED cases of influenza and the number of patients presenting to the ED with ILI, validating GFT as an influenza surveillance tool in Baltimore. GFT correlated well with several pediatric ED crowding measures and those for low-acuity adult patients. This highlights the potential value of linking GFT with an ED response plan. To fully use this new surveillance tool, additional analysis must be performed to incorporate GFT data in a predictive model, which can then be linked to a response plan.