Does Climate Predict the Timing of Peak Influenza Activity in the United States? - PowerPoint PPT Presentation

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Does Climate Predict the Timing of Peak Influenza Activity in the United States?

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  1. Does Climate Predict the Timing of Peak Influenza Activity in the United States? Katia Charland, David Buckeridge, Jessica Sturtevant, Forrest Melton, John Brownstein Children’s Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Boston, United States McGill Clinical and Health Informatics, McGill University, Montreal, Canada NASA Ames Research Center, Mountain View, CA

  2. Objective To assess the strength of the association between solar radiation temperature dew point latitude/longitude and the timing of peak influenza activity in the United States.

  3. Background Environmental factors and influenza

  4. Temperature and Relative Humidity -low temp  preserves protective lipid coating (Polozov IV et al, 2007) -animal studies: low temp, low relative humidity increased rates of aerosol but not contact transmission (Lowen AC et al 2007; Lowen AC et al 2008) -El Nino/ENSO cold phases  increased morbidity and excess mortality (Viboud C et al, 2004)

  5. Solar Radiation Hypothesized effects: -affects innate immunity -inactivates influenza virus (Hope-Simpson RE 1981; Cannell JJ et al 2006; Jensen MM, 1964; Carrillo-Vico A et al 2005)

  6. Latitude and Longitude Viral data, 19 temperate countries, 1997-2005 Countries closer to the equator experienced earlier influenza A epidemics compared to countries situated further from the equator. (Finkelman BS et al 2007)

  7. Data

  8. Pediatric Hospitals and US Climate Zones

  9. Definition of influenza case and influenza season • Influenza case: inpatient visit with ICD-9 code 486.00, 487.00, 487.10, 487.8 (Marsden-Haug et al, 2007) • Influenza season: 40th week of calendar year to 39th week of the following calendar year • Computed # influenza cases/week, Oct 1, 2000 – Sept 30, 2005

  10. Identifying the Peak Week for an Influenza Season Candidates for ‘peak week’: -week with max # cases -week with # cases within 5% of maximum • Approx 70% cases  a single candidate

  11. Choosing “Peak Week” from Multiple Candidate Weeks Choice guided by: WHO-NREVSS regional viral isolate peak week (‘Regional viral isolate peak week’) and Pediatric hospitalizations with RSV specific ICD-9 code 466.11 (‘RSV peak week’)

  12. Choosing “Peak Week” from Multiple Candidate Weeks City peak week RSV Viral isolate 16 22 weeks 7 weeks 7 weeks

  13. Meteorological Variables • Daily temperature, dew point, solar radiation measurements • for each city, averaged over Oct 1 – Dec 31 for each influenza season • Assessed sensitivity to time period of averaging

  14. Statistical Analyses

  15. Multilevel Models • correlations in city-level measurements • correlations in measurements from same influenza season Multilevel regression models random effects for city and influenza season

  16. Multi-collinearity • Dew point, temperature, solar radiation and latitude correlated variables  Cannot put all in model and assess significance of each variable

  17. Bayesian Hierarchical Model • WinBUGS 1.4 and R software

  18. Resultsand Interpretation

  19. Multilevel Regression Results *sensitivity analyses showed that the results are robust to the period of averaging

  20. Average peak week versus average solar radiation

  21. Pediatric Hospitals and US Climate Zones

  22. Limitations of Study • Administrative data- not collected with study objective in mind • Multiple peaks • Ecological study • Relatively small sample size 35*5 measurements but correlations reduce total information in the data • Only 5 influenza seasons

  23. Conclusion • Solar radiation and latitude significantly related to peak timing • Is solar radiation the biological basis for the significance of latitude? Though our period of study is relatively short, our results concerning the importance of solar radiation as a predictor of epidemic timing in the context of surveillance are promising.

  24. Acknowledgements Thanks to: Dionne Graham for help in retrieving PHIS data. and Drs. Marc Lipsitch, Ben Reis and Ken Mandl for edits and helpful comments and suggestions. Thank You

  25. EXTRA

  26. Outline • Objective • Background Temperature, Relative Humidity, Solar Radiation, Latitude, Longitude • Data Pediatric Hospitalization and Meteorological • Statistical Analyses - Multilevel Models • Results and Interpretation • Conclusion • Future Research

  27. Goal To determine whether the spatio-temporal patterns of peak influenza activity in temperate regions are related to environmental factors.

  28. Multilevel Regression Results *sensitivity analyses showed that the results are robust to the period of averaging

  29. Choosing “Peak Week” from Multiple Candidate Weeks City peak week RSV Viral isolate 16 22 weeks 7 weeks 7 weeks

  30. Choosing “Peak Week” from Multiple Candidate Weeks If > 3 candidates  consider 2 closest to WHO-NREVSS regional viral isolate peak week 3 criteria to choose between remaining 2 weeks: 1. Closest to regional viral isolate peak week 2. Furthest from inpatient RSV peak week (ICD-9 code 466.11) 3. Greatest total # visits in 7 week period centered at candidate week city influenza peak week= satisfies at least 2 criteria