Working Group I: Effects of PM on Mortality; Air Quality and - - PowerPoint PPT Presentation

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Working Group I: Effects of PM on Mortality; Air Quality and - - PowerPoint PPT Presentation

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Sujit K. Ghosh Working Group I: Effects of PM on Mortality; Air Quality and Morbidity Sujit K. Ghosh and http://www.stat.ncsu.edu/people/ghosh/ sujit.ghosh@ncsu.edu Presented at: Statistical Methods and Analysis of Environmental Health Data

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Sujit K. Ghosh

Working Group I: Effects of PM on Mortality; Air Quality and Morbidity Sujit K. Ghosh

and

http://www.stat.ncsu.edu/people/ghosh/ sujit.ghosh@ncsu.edu Presented at: Statistical Methods and Analysis of Environmental Health Data SAMSI-SAVI Workshop, Piramal Tower Annex, Mumbai, India

http://www.tinyurl.com/sami-savi-2016

SAMSI-SAVI Workshop 1 June 3, 2016

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Sujit K. Ghosh

Current Team

  • Sujit Ghosh (Co-Leader), NCSU/SAMSI, USA
  • Brian Reich (Co-Leader), NCSU, USA
  • Sirajuddin Ahmed, Jamia Milia Islamia University, India
  • Safraj Shahul Hameed, PHFI, India
  • Sanjoy Maji, Jamia Milia Islamia University, India
  • Parul Goel, National Center for Disease Control, India

Initial data sets are provided by Sanjoy Maji.

SAMSI-SAVI Workshop 2 June 3, 2016

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Sujit K. Ghosh

Outline

  • The Delhi Data set
  • Scientific Questions and Challenges
  • Methodologies explored
  • Preliminary Results
  • Next Steps...

SAMSI-SAVI Workshop 3 June 3, 2016

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Sujit K. Ghosh

The Delhi Data Sanjoy Maji has obtained daily data (FY 2010-2012) on following variables:

  • Cause specific deaths: accidental, respiratory, circulatory and total number of deaths

Available by gender and age group

  • Air pollutant: SOx, NOx, RSPM

Stations: Industrial area: Mayapuri Indl. Area, Shahdara,Shahzada Bagh, Janakpuri Residential area: N.Y. School, Nizamuddin, Pritampura,Siri Fort and Town Hall (under National Ambient Air Quality Program, monitoring stations are operated twice a week only!)

  • Met data: Max temp, Min temp, RH

(obtained from Indian Meteorological station at Safdarjung)

SAMSI-SAVI Workshop 4 June 3, 2016

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Sujit K. Ghosh SAMSI-SAVI Workshop 5 June 3, 2016

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Sujit K. Ghosh

Scientific Questions and Challenges

  • It is typical to use quasi-Poisson models to relate number of deaths to pollutant levels

after adjusting for other concomitant variables. What appropriate statistical methodologies be used to address the missing values or change of support?

  • How can we create an Air Quality Index (AQI) by combining several pollutants?
  • How do we relate the AQI to mortality data and create a warning system (based on

futurecasts)?

  • Is it possible to identify association between cause-specific deaths and air pollutants

(after adjusting for other variables)

  • Do the associations (if any) are of different magnitude for different subgroups of

populations and zonal regions? E.g., by gender, age group, industrial vs. residential, etc.

SAMSI-SAVI Workshop 6 June 3, 2016

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Sujit K. Ghosh

Methodologies explored

  • Create an Air Quality Index (AQI) by combining several (appropriately scaled)

pollutants

  • Xj(s, t) = Pollutant j measured at site (station) s and time (day) t
  • ¯

Xj(t) = S

s=1 Xj(s, t)/S: Average level of pollutant j measured on time (day) t

  • However, in most cases Xj(s, t) are not available for all triplet (s, t, j)!!
  • We thus need an imputation model to compute ¯

Xj(t). Explored several models and

more needs to be explored.

  • With imputed versions of each ¯

Xj(t) we would determine ‘optimal’ weights to create

AQI: λ(t) = J

j=1 wj ¯

Zj(t)

where ¯

Zj(t) is an appropriately scaled version of ¯ Xj(t)

SAMSI-SAVI Workshop 7 June 3, 2016

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Sujit K. Ghosh

  • Initial explorations included looking at Principal Component Analysis (PCA).

Alternatively, we can use Independent Component Analysis (ICA).

  • However, how do we associate the AQI to health (mortality)?
  • We use the popular quasi-Poisson models adjusting for other concommitant variables

(smooth functions of time and met variavles) and the AQI

  • A simple version of the model would like like:

Y (t) ∼ Poisson(µt) µt = splines(t, Wt) + g(λt(w))

where Wt are available met data and g is an unknown monotone function of the AQI (which depends on unknown weights)

  • Simultaneous estimation of g and w = (w1, . . . , wJ) is non-trivial. E.g., we plan to

explore projection pursuit (or single index) regression methods

SAMSI-SAVI Workshop 8 June 3, 2016

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Sujit K. Ghosh

Preliminary Results

  • We explored linear regression based methodologies to build initial imputation models
  • For each pair (j, t) we use available values of Xj(s, t) from several existing

monitoring stations and met variable to build the regression models that have ’respectable’ predictive power (e.g., adjusted R2 > 0.5 etc.)

SAMSI-SAVI Workshop 9 June 3, 2016

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Sujit K. Ghosh

Mayapuri Indl. Area Pritampura Civili.Lines IHBAS R.K.Puram 500 1000 1500

PM10

Station Concentration Manual Real time

SAMSI-SAVI Workshop 10 June 3, 2016

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Sujit K. Ghosh

Mayapuri Indl. Area Pritampura Civili.Lines IHBAS R.K.Puram 200 400 600

NO2

Station Concentration Manual Real time

SAMSI-SAVI Workshop 11 June 3, 2016

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Sujit K. Ghosh

Mayapuri Indl. Area Pritampura Civili.Lines IHBAS R.K.Puram 50 100 150 200 250 300

SO2

Station Concentration Manual Real time

SAMSI-SAVI Workshop 12 June 3, 2016

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Sujit K. Ghosh

200 600 1000 200 600

Mayapuri Indl. Area

Day Concentration 200 600 1000 100 300 500

Shahdara

Day Concentration 200 600 1000 200 600

Shahzada Bagh

Day Concentration 200 600 1000 100 300 500 700

Janakpuri

Day Concentration 200 600 1000 400 800 1200

N.Y. School

Day Concentration 200 600 1000 200 400

Nizamuddin

Day Concentration 200 600 1000 200 400 600

Pritampura

Day Concentration 200 600 1000 200 400

Siri Fort

Day Concentration 200 600 1000 500 1000

Town Hall

Day Concentration Obs Impute Obs Impute Obs Impute

SAMSI-SAVI Workshop 13 June 3, 2016

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Sujit K. Ghosh

Next Steps...

  • Explore more sophisticated imputation models to compute AQI. E.g.,

Robbins, M., Ghosh, S. K. and Habiger, J. (2013). Imputation in High Dimensional Economic Data as Applied to the Agricultural Resource Management Survey, Journal

  • f the American Statistical Association, 108, 81-95.

http://www.tandfonline.com/doi/abs/10.1080/01621459.2012.734158

  • Explore more sophisticated quasi-Poisson model based on projection pursuit type

regression models. E.g. Lingjarde, O. C. and Liestol, K. (1998) Generalized Projection Pursuit Regression, SIAM Journal of Scientific Computing, 20, 844-857.

http://dx.doi.org/10.1137/S1064827595296574

  • Incorporate measure of uncertainty due to imputation

SAMSI-SAVI Workshop 14 June 3, 2016

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Sujit K. Ghosh

  • Develop plans for collaborations and training (especially on the use of R and

advanced statistical modeling)

  • E.g., SAMSI uses web-based interface like webex and SAKAI for regular research

group meeting

  • Arrange for short visits for training on an annual basis
  • E.g., International Indian Statistical Association (IISA) organizes annual conferences

in U.S. and India on alternative years

  • SAMSI will launch a yearlong program on Climate:

https://www.samsi.info/programs-and-activities/year-long-research-progra

  • Create a (secure) Dropbox (or similar web-based sharing tools) to share data, codes

and research materials

  • Other ideas?....

SAMSI-SAVI Workshop 15 June 3, 2016

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Sujit K. Ghosh

Additional Questions?, Feedback?

Interested to join or organize a forum on an emerging area of methodological research? Contact me... Sujit Ghosh, Deputy Director (ghosh@samsi.info) Statistical and Applied Mathematical Sciences Institute RTP , NC, USA (www.samsi.info)

SAMSI-SAVI Workshop 16 June 3, 2016