AMOS 2015 Presentation

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Transcript of AMOS 2015 Presentation

  • Temperature and humidity effects on hospital admissions in Darwin, AustraliaJames Goldie

    With Steven Sherwood, Lisa Alexander & Donna Green

    17 July 2015

  • This talk

    !Background

    Heat stress andepidemiology

    ?Our studyDarwin hospital

    admissions

    #Results &

    DiscussionOvernight humidity

  • How we overheat

    https://www.flickr.com/photos/hddod/203141621

    When its hot,

    we sweatWhen its

    humid, our sweat drips

    (and doesnt cool us)

    When its hot and humid,

    were in trouble!

  • Epidemiology: statistical analysis of public health

    Epi models can make inferences about health relationships and predict future health responses

    &Health

    response

    Climate

    predictor(s)

    TemperatureHumidityPollution

    Hospital admissionsMortalities

    Ambulance callouts

    ~

  • Wheres the humidity?

    Google Earth: Data SIO, NOAA, US Navy, NGA, GEBCO, Image Landsat

    Melbourne: ~ 1520 hPa

    Sydney: ~ 25 hPaAdelaide: ~ 1520 hPa

    Perth: ~ 20 hPa

    Brisbane: ~ 2530 hPa

    Cairns: ~ 3035 hPa

    Darwin: ~ 3035 hPa

  • There arent enough epi studies looking at humidity in the tropics!

  • #*

    #* Darwin Airport HadISD Station

    Included Darwin Airport SLAs

    Other 2006 SLAs

    0 30 60 90 12015Kilometers

    Darwin Airport CohortDarwin residents (~ 18k admissions)

    ResponseDaily hospital admission count

    PredictorsDaily temperature and relative humidity

    Tmax, Tmin, Tmean, RHmax, RHmin, Rhmean

    Our study in Darwin

    Darwin Airport weather station

    Cohort areas

    Non-cohort areas

    &

    '

  • Linear effects studies with GLMs

    admission count ~logged population offset +predictor

    Non-linear effects studied with subset

    Daily series split into five equal binsAdmission rates of bins compared w/

    Two analyses

    Each analysis performed for one predictor, then two

    https://upload.wikimedia.org/wikipedia/commons/c/cc/Darwin_Australia_aerial_photo_1984.JPEG

  • Linear analysis of one predictor

    Predictor Tmax Tmin Tmean RHmax RHmin RHmeanEffect Size 1.74% -0.19% 0.13% 3.73% 0.02% 1.21%P-value 0.049 0.800 0.863 < 0.001 0.968 0.049

    Max. relative humidity is extremely significantMax. temp, mean relative humidity are also significant

    % change per 2 C change % change per 10 p.p. change

  • Non-linear analysis of one predictor

    2.2

    2.3

    2.4

    2.5

    2.6

    Tmax Tmin Tmean RHmax RHmin RHmeanDaily predictor

    Mea

    n da

    ily a

    dmis

    sion

    rate

    (per

    100

    k re

    side

    nts)

    with

    95%

    con

    fiden

    ce in

    terv

    al

    Predictor bin

    P020P2040P4060P6080P80100

    Estimates (points) and 95% confidence intervals (lines) for each bin

  • 2.0

    2.5

    3.0

    3.5

    4.0

    P020 P2040 P4060 P6080 P80100Tmax bin

    Mea

    n da

    ily a

    dmis

    sion

    rate

    (per

    100

    k re

    side

    nts)

    with

    95%

    con

    fiden

    ce in

    terv

    al

    RHmean bin

    P020P2040P4060P6080P80100

    Non-linear analysis of two predictors: RHmean within TmaxEstimates (points) and 95% confidence intervals (lines) for each bin

  • What do the results mean?

    Overnight humidity is important!Other studies found no humidity effectsbut they werent in the tropics!

    Humidity and temperature act at different times of dayHeat policies assume equal contributions throughout the day

    Humid heat affects sleepMore wakefulness, less deep sleep on humid/hot days

    (

    https://www.flickr.com/photos/mindfulness/21264368/

    )

  • Summary

    Epidemiology: stats meets healthNot enough epi studies looking at humidity in the tropics

    Hospital admissions of Darwin residentsDaily count of selected hospital admissions

    Daytime temp, overnight humidity affect admissionsHeat policies assume equal contributions throughout the day#

    &

  • Thanks!Questions?

    J. Goldie, S. C. Sherwood, D. Green & L. Alexander (Accepted). Temperature and humidity effects on hospital morbidity in Darwin, Australia. Annals of Global Health.+

  • References

    Tong, S., Wang, X. Y., Yu, W., Chen, D., & Wang, X. (2014). The impact of heatwaves on mortality in Australia: a multicity study. BMJ Open, 4(2), e003579. doi:10.1136/bmjopen-2013-003579

    Vaneckova, P., Neville, G., Tippett, V., Aitken, P., FitzGerald, G., & Tong, S. (2011). Do Biometeorological Indices Improve Modeling Outcomes of Heat-Related Mortality? Journal of Applied Meteorology and Climatology, 50(6), 11651176. doi:10.1175/2011JAMC2632.