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Time series study on air pollution and mortality in Indian cities

Time series study on air pollution and mortality in Indian cities. R Uma, Kaplana Balakrishnan, Rajesh kumar. Background. Air quality issues are of major concern for many cities in Asia and other developing countries

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Time series study on air pollution and mortality in Indian cities

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  1. Time series study on air pollution and mortality in Indian cities R Uma, Kaplana Balakrishnan, Rajesh kumar

  2. Background • Air quality issues are of major concern for many cities in Asia and other developing countries • Increasing attention from policy makers, legal body, NGOs, research, academic institutions and funding agencies • Many initiatives, but gaps in research still exist

  3. PAPA study: Indian cities • Selection based on geographical representation, population, data availability, Research team capability • Delhi: National capital with 13.8 million population • Ludhiana: Industrial town in Punjab, North India with population of about 1.5 million • Chennai: Located in coastal area of Southern India with population of 4.5 million

  4. Aim and Objectives of the study • Aim: To generate site specific database on effect of air pollution on mortality for Indian cities Delhi, India • Specific objectives: • To assess the time series data on air quality parameters and mortality to study the relationship between air pollution and mortality due to respiratory diseases in Indian cities • To assess the daily change in mortality in relation with change in air quality after controlling for the exogenous parameters

  5. Salient features of the study • Multidisciplinary team • Meeting ICMR guidelines on ethical aspects • Review and guidance from ISOC • QA/QC audit • Capacity building • Training on developing exposure series • R Package • Core model for time series analysis

  6. Study Locations

  7. Methodology • Collection of retrospective time series data (2002, 2003 & 2004) on • Ambient air quality • Mortality data • Meteorological data (Temperature, humidity, visiblity) • Statistical analysis of data to study the association of age specific death (all cause mortality) with exposure to air pollution

  8. Descriptive Statistics of RSPM in Delhi

  9. Distribution of RSPM (PM 10) levels in Chennai

  10. Descriptive Statistics of Air Pollutants - Ludhiana

  11. Descriptive Statistics of Mortality-Ludhiana (2004) • Total deaths : 9322 • Number of male deaths : 6103 • Number of female deaths : 3219 Mean SD Min Max 25.5 6.17 9.0 48

  12. Mortality data descriptives in Chennai

  13. Core model • Generalized Additive Model (GAM) with penalized and natural spline smoothers in R. • Quasi-Poisson function with all natural causes as the dependent variables. • Smoothers for time, temp,RH • Day of week terms (i.e, dichotomous variables for each day of the week from Monday through Saturday). • Exposure at single-day lags of 0 to 3 days & a cumulative two-day average of lags considered.

  14. Summary results of model -Delhi

  15. Model result-Chennai GAM output from all single monitors GAM output from the best single monitors

  16. Gam Model Results – Ludhiana (After Selecting Base Model, Natural Spline (6,4,4)) * RSPM Effects are significant with lag2 and lag3

  17. Work in progress • Sensitivity analyses for different exposure series • Multi-pollutant models • Population (Exposure) weighted assignment for individual monitors using spatial models • Gender specific analysis • Cause specific impact estimations

  18. Conclusions • The data processing steps for the pollutant and mortality data could be completed as envisaged as a result of co-operation and access to raw data provided by the local bodies in-charge of routine data collection. • The results of models developed thus far show that the estimates for impacts of PM10 (an approximately 0.2% to 0.6% increase in all-cause mortality for every 10 g/m3 of exposure) are in the range reported by other on-going PAPA studies and earlier studies reported in North America and Europe (using similar statistical methods). • More sophisticated analyses including use of spatial models to estimate population-weighted exposures from individual monitors and use of EM algorithms to address missing data and Poisson auto-correlation are being undertaken to refine these initial estimates. • Data quality issues may limit development of multi-pollutant models and differential cause specific estimates. • Consistency of results obtained using similar methodological approaches between Indian cities and other Asian cities indicate that routinely collected pollutant and mortality data may be reliably used in time-series analyses of air pollution related health impacts. However, significant challenges remain in making clean data readily accessible to environmental health researchers.

  19. Thank you

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