We applied time-series methods to multivariate sentinel surveillance data recorded in Hong Kong during 1998–2007. Our study demonstrates that simultaneous monitoring of multiple streams of influenza surveillance data can improve the accuracy and timeliness of alerts compared with monitoring of aggregate data or of any single stream alone

Alonso

Benjamin J. Cowling

Besculides

Cowling

Eric H. Y. Lau

Gabriel M. Leung

Glezen

Goldenberg

Hutwagner

Kleinman

Lai-Ming Ho

Leung

Mohtashemi

Reis

Emerging Infectious Diseases

English

PubMed

We applied time-series methods to multivariate sentinel surveillance data recorded in Hong Kong during 1998-2007. Our study demonstrates that simultaneous monitoring of multiple streams of influenza surveillance data can improve the accuracy and timeliness of alerts compared with monitoring of aggregate data or of any single stream alone.published_or_final_versio

Cowling, BJ

Lau, EHY

Ho, LM

Leung, GM

HKU Scholars Hub

Title Optimizing use of multistream influenza sentinel surveillancedataAuthor(s) Lau, EHY; Cowling, BJ; Ho, LM; Leung, GMCitation Emerging Infectious Diseases, 2008, v. 14 n. 7, p. 1154-1157Issued Date 2008URL http://hdl.handle.net/10722/60315RightsOptimizing Use of Multistream Infl uenza Sentinel Surveillance DataEric H. Y. Lau,* Benjamin J. Cowling,* Lai-Ming Ho,* and Gabriel M. Leung*We applied time-series methods to multivariate sentinel surveillance data recorded in Hong Kong during 1998–2007. Our study demonstrates that simultaneous monitoring of multiple streams of infl uenza surveillance data can improve the accuracy and timeliness of alerts compared with moni-toring of aggregate data or of any single stream alone. The use of separate data streams based on sentinel sur-veillance has long been an accepted approach to moni-tor community incidence and to enable timely detection of infectious disease outbreaks (1,2). Recently, more attention has been given to the combined analysis of multivariate sentinel data (3–5).  In this study we explored the possibility of improving the ability to more quickly detect peak periods of infl uenza activity in Hong Kong through simultaneous monitoring of multiple streams of sentinel surveillance data. Our fi ndings have general implications in the choice of surveillance al-gorithms where multistream data are available.The StudyThe local Department of Health publishes weekly re-ports (6) from a network of 50 private-sector sentinel gen-eral practitioners (GP) and 62 public-sector sentinel gen-eral outpatient clinics (GOPC) on the proportion of patients seeking treatment for infl uenza-like illness (ILI), defi ned as fever plus cough or sore throat (7). In this study, we used the GP and GOPC sentinel surveillance data in 9 annual infl uenza seasons from 1998–1999 to 2006–2007, stratifi ed by 4 geographic regions in Hong Kong—Hong Kong Is-land, Kowloon, New Territories East, and New Territories West—resulting in 8 separate data streams (Figure). Each month a median of 1,555 specimens (interquartile range 1,140–2,740), primarily from hospitals, were sent to the Government Virus Unit of the Department of Health (7). We calculated the highest proportion of positive infl u-enza isolations each season, and used these laboratory data to defi ne the onset of each peak activity period when the proportion of positive infl uenza A or B isolates exceeded 30% of the maximum seasonal level (7).Dynamic linear models (8) were used to generate alerts (online Technical Appendix, available from www.cdc.gov/EID/content/13/7/1154-Techapp.pdf). We determined that an aberration had occurred when the current observation fell outside a forecast interval generated by the model. For meth-ods based on monitoring of single data streams only, an ab-erration triggers an alert. For simultaneous monitoring of all 8 data streams, we monitored separate aberrations as above and generated alerts based on the fi rst occurrence of any aberration (M1), 2 simultaneous aberrations (M2), the fi rst occurrence of 3 simultaneous aberrations (M3), any 2 aber-rations within a 2-week period (M4), and any 3 aberrations within a 2-week period (M5). In the multistream analyses, we compared alerts produced by univariate models, which effectively assumed independence between the data streams, and multivariate models, which allowed for correlation be-tween the data streams (online Technical Appendix).Alerts were compared in terms of their sensitivity, specifi city, and timeliness in detecting the onset of peak levels of infl uenza activity (9). We combined these metrics and estimated the area under the weighted receiver operat-ing characteristic curve (AUWROC) as an overall measure of performance (10). The Table shows the highest AU-WROC, for each method, from a predefi ned selection of parameter combinations and the sensitivity and timeliness at a fi xed specifi city of 95%. On the basis of aggregated data, we determined that alerts generated from the GOPC network achieved a higher AUWROC and better timeli-ness than those from the GP network. However, the best AUWROC from each of the data streams was produced by the GP New Territories East data, which outperformed the aggregate GP data. Conversely, for GOPC data, the perfor-mance of aggregate data was superior to that of any single data stream.The Table also shows simultaneous monitoring re-sults for all 8 geographic data streams from both GPs and GOPCs. For the univariate (independent) models for each data stream, methods based on simultaneous alerts perform well. The optimal methods were M2 and M3 with AU-WROC of 0.89 and 0.90 and timeliness of 1.22 and 1.47 weeks, respectively, for a fi xed specifi city of 0.95. In gen-eral, univariate models performed better than multivariate models. Empirical correlation derived from one of the fi t-ted multivariate models is shown in the online Technical Appendix; correlation structures under other models were similar (data not shown).Results were insensitive to the choice of parameters (online Technical Appendix). The results also held when we varied the defi nition of the start of peak infl uenza activ-ity between 10% and 50% of peak seasonal levels (online Technical Appendix).DISPATCHES1154 Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 14, No. 7, July 2008*University of Hong Kong, Hong Kong Special Administrative Re-gion, People’s Republic of China DOI: 10.3201/eid1407.080060Multistream Infl uenza SurveillanceConclusionsA primary objective of sentinel surveillance is to pro-vide sensitive, specifi c, and timely alerts at the beginning of increased disease activity (11). We evaluated the perfor-mance of multistream sentinel surveillance of ILI in detect-ing the onset of peak infl uenza activity. Splitting sentinel data into separate geographic-based streams and monitoring all 8 streams for 2 or 3 simultane-ous aberrations provided substantial improvements in AU-WROC and also in timeliness for a fi xed specifi city when compared with monitoring aggregated data or any single data stream. We also used multivariate models with vari-ous alternative correlation structures between data streams, but use of these more complex models did not appear to improve performance (Table), possibly because correlation between streams vary year to year; the multivariate model is based on constant correlations (online Technical Appen-dix). It is possible that other complex multivariate mod-els may allow even greater improvement in performance; however, simultaneous monitoring of data streams may be more practical because univariate models may be applied in a spreadsheet (7).Although the relative performance of GP and GOPC sentinels may not be directly generalizable to other settings with differences in infectious disease dynamics and health-care systems, the implications for data collection are never-theless relevant. Inclusion of data streams should be based on their value to the overall surveillance system, rather than independent performance. For example, simultaneous monitoring of data streams where some have lower speci-fi city and others have higher specifi city could still improve overall timeliness. Specifi cally regarding Hong Kong, it is unclear why alerts from the private GP network have better timeliness than those from the public GOPC network. Although we note that both networks have different catchment popula-tions, the GOPC network typically serves elderly and lower income groups (12), whereas infl uenza would be more like-ly to affect children at the start of the infl uenza season (13). Differences between geographic regions could be real, when disease progresses from 1 region to another (14); however, this circumstance is unlikely in Hong Kong, an area of only 1,000 km2, where a high degree of mixing occurs among a population of 7 million persons. Geographic heterogeneity could also be explained by differential socioeconomics and demographics between different regions, associated differ-ences in access to healthcare and health-seeking behavior issues, or small area variations in reporting behavior among the sentinel practices.A potential caveat of our analysis is the small number of annual cycles of sentinel data available for study. How-ever, until recently, few subtropical or tropical regions had begun infl uenza sentinel surveillance. Another limitation is the absence of a generally agreed-upon standard in defi ning a peak infl uenza season. In our analysis, the start of peak activity was defi ned as laboratory isolation rates exceed-ing 30% of the annual level; however, we found that our results were not sensitive to other reasonable thresholds. In addition, we compared methods with only a few chosen parameter combinations; sensitivity analyses showed that  Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 14, No. 7, July 2008 1155 Figure. Nine annual cycles (unbroken lines) of general practitioner (A) and general outpatient clinic (B) geogra- phic sentinel surveillance data from Hong Kong Island, Kowloon, New Territories East, and New Territories West, 1998–2007. The monthly proportions of laboratory samples testing positive for infl uenza isolates are overlaid as gray bars, and the beginning of each annual period of peak activity (inferred from the laboratory data) is marked with a vertical dotted line. ILI, infl uenza-like illness.the results were not sensitive to the smoothing parameter or the specifi cation of correlations between streams. Finally, alerts generated by other more complicated combinations of aberrations might provide further enhancements. How-ever, the value of simultaneously monitoring separate data streams (15) has already been demonstrated by the simple combinations chosen here.AcknowledgmentsWe gratefully acknowledge the sentinel practitioners, who through their own goodwill, have been providing weekly data to the Hong Kong Department of Health for infectious disease sur-veillance. We also thank Irene Wong for technical assistance. This research was in part funded by the Research Fund for the Control of Infectious Diseases of the Food and Health Bureau of the Hong Kong SAR Government (grant no. 04050102) and by the Area of Excellence Scheme of the University Grants Commit-tee (grant no. AoE/M–12/06).Dr Lau is a postdoctoral fellow in the School of Public Health at the University of Hong Kong. He is interested in the method-ology and application of statistical and mathematical models to infectious diseases.References  1.  Besculides M, Heffernan R, Mostashari F, Weiss D. Evaluation of school absenteeism data for early outbreak detection, New York City. BMC Public Health. 2005;5:105. DOI: 10.1186/1471-2458-5-105  2.  Goldenberg A, Shmueli G, Caruana RA, Fienberg SE. Early statis-tical detection of anthrax outbreaks by tracking over-the-counter medication sales. Proc Natl Acad Sci U S A. 2002;99:5237–40. DOI: 10.1073/pnas.042117499  3.  Mohtashemi M, Kleinman K, Yih WK. Multi-syndrome analysis of time series using PCA: a new concept for outbreak investigation. Stat Med. 2007;26:5203–24. DOI: 10.1002/sim.2872  4.  Reis BY, Kohane IS, Mandl KD. An epidemiological network mod-el for disease outbreak detection. PLoS Med. 2007;4:e210. DOI: 10.1371/journal.pmed.0040210  5.  Fricker RD. Directionally sensitive multivariate statistical process control procedures with application to syndromic surveillance. Ad-vances in Disease Surveillance. 2007;3:1–17. Available from http://www.isdsjournal.org/article/view/3%3B1/831  6.  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DOI: 10.1016/j.socscimed.2004.12.02913.  Glezen WP, Couch RB. Interpandemic infl uenza in the Houston area, 1974–76. N Engl J Med. 1978;298:587–92.DISPATCHES1156 Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 14, No. 7, July 2008Table. Performance of alerts generated by individual monitoring of aggregate data and separate data streams, and simultaneous monitoring of multiple data streams by using univariate and multivariate time series models, Hong Kong, 1998–2007* Univariate models Multivariate models†Data AUWROC Sensitivity‡ Timeliness, wk‡ AUWROC Sensitivity‡ Timeliness, wk‡Aggregated data GP 0.78 1.00 2.41 – – – GOPC 0.86 1.00 1.50 – – –Single stream GP     HK 0.75 1.00 2.36 0.73 0.87 2.64   KL 0.66 1.00 2.71 0.62 0.88 3.06  NTE 0.89 1.00 2.00 0.76 0.90 2.04   NTW 0.80 1.00 2.07 0.80 0.91 2.24 GOPC   HK 0.79 1.00 2.21 0.71 0.89 2.42  KL 0.78 1.00 2.46 0.62 0.96 3.15  NTE 0.79 0.95 2.22 0.79 0.96 2.26  NTW 0.73 1.00 2.55 0.72 1.00 2.52Multiple streams M1: First aberration 0.84 1.00 1.57 0.86 1.00 1.66 M2: 2 simultaneous aberrations 0.89 1.00 1.22 0.82 1.00 1.77 M3: 3 simultaneous aberrations 0.90 1.00 1.47 0.80 1.00 1.70 M4: Any 2 aberrations in 2 wk 0.81 1.00 2.63 0.72 1.00 2.43 M5: Any 2 aberrations in 2 wk 0.83 1.00 2.44 0.77 1.00 2.11*AUWROC, area under the weighted receiver operating characteristic curve; GP, general practitioner; GOPC, general outpatient clinic; HK, Hong Kong Island; KL, Kowloon; NTE, New Territories East; NTW, New Territories West. †See online Technical Appendix (available from www.cdc.gov/EID/content/14/7/1154-Techapp.pdf) for more detailed description of the multivariate model.‡At a fixed specificity of 0.95. Multistream Infl uenza Surveillance14.  Alonso WJ, Viboud C, Simonsen L, Hirano EW, Daufenbach LZ, Miller MA. Seasonality of infl uenza in Brazil: a traveling wave from the Amazon to the subtropics. Am J Epidemiol. 2007;165:1434–42. DOI: 10.1093/aje/kwm01215.  Kleinman KP, Abrams A, Mandl K, Platt R. Simulation for assessing statistical methods of biologic terrorism surveillance. MMWR Morb Mortal Wkly Rep. 2005;54(Suppl):101–8.Address for correspondence: Benjamin J. Cowling, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 21 Sassoon Rd, Pokfulam, Hong Kong, People’s Republic of China; email: bcowling@hku.hk Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 14, No. 7, July 2008 1157 

Optimizing use of multistream influenza sentinel surveillance data

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Optimizing Use of Multistream Influenza Sentinel Surveillance Data

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Abrams AM. Assessing surveillance using sensitivity, speciﬁ city and timeliness. Stat Methods Med Res.

An epidemiological network model for disease outbreak detection. PLoS Med.

Bayesian forecasting and dynamic models. 2nd ed.

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Directionally sensitive multivariate statistical process control procedures with application to syndromic surveillance.

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Multi-syndrome analysis of time series using PCA: a new concept for outbreak investigation. Stat Med.

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Using laboratory-based surveillance data for prevention: an algorithm for detecting Salmonella outbreaks. Emerg Infect Dis.

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Optimizing Use of Multistream Influenza Sentinel Surveillance Data

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