Does climate predict the timing of peak influenza activity in the united states
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Does Climate Predict the Timing of Peak Influenza Activity in the United States? PowerPoint PPT Presentation

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

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

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Does climate predict the timing of peak influenza activity in the united states

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


Objective

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.


Background

Background

Environmental factors and influenza


Temperature and relative humidity

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)


Solar radiation

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)


Latitude and longitude

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)


Does climate predict the timing of peak influenza activity in the

Data


Pediatric hospitals and us climate zones

Pediatric Hospitals and US Climate Zones


Definition of influenza case and influenza season

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


Identifying the peak week for an influenza season

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


Choosing peak week from multiple candidate weeks

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’)


Choosing peak week from multiple candidate weeks1

Choosing “Peak Week” from Multiple Candidate Weeks

City peak week

RSV

Viral isolate

16

22

weeks

7 weeks

7 weeks


Meteorological variables

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


Statistical analyses

Statistical Analyses


Multilevel models

Multilevel Models

  • correlations in city-level measurements

  • correlations in measurements from same influenza season

    Multilevel regression models

    random effects for city and influenza season


Multi collinearity

Multi-collinearity

  • Dew point, temperature, solar radiation and latitude correlated variables

     Cannot put all in model and assess significance of each variable


Bayesian hierarchical model

Bayesian Hierarchical Model

  • WinBUGS 1.4 and R software


Results and interpretation

Resultsand Interpretation


Multilevel regression results

Multilevel Regression Results

*sensitivity analyses showed that the results are robust to the period of averaging


Average peak week versus average solar radiation

Average peak week versus average solar radiation


Pediatric hospitals and us climate zones1

Pediatric Hospitals and US Climate Zones


Limitations of study

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


Conclusion

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.


Acknowledgements

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


Extra

EXTRA


Outline

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


Does climate predict the timing of peak influenza activity in the

Goal

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


Multilevel regression results1

Multilevel Regression Results

*sensitivity analyses showed that the results are robust to the period of averaging


Choosing peak week from multiple candidate weeks2

Choosing “Peak Week” from Multiple Candidate Weeks

City peak week

RSV

Viral isolate

16

22

weeks

7 weeks

7 weeks


Choosing peak week from multiple candidate weeks3

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


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