1. Studying the effects of air pollution on children’s health Presented by Elizabeth Stanwyck with Dr. Bimal Sinha University of Maryland, Baltimore County June 30, 2008

2. Objective To study the effects of air pollution on the health of children (and the elderly) ▫Focus on respiratory health Elizabeth Stanwyck 30 June 2008 UMBC 2

3. Objective Elizabeth Stanwyck 30 June 2008 UMBC 3

4. Adverse Health Effects Types of responses ▫Binary ▫Ordinal ▫Continuous Possibility of measurement error Measurements can be taken ▫Hourly ▫Daily ▫Monthly ▫Annually Elizabeth Stanwyck 30 June 2008 UMBC 4

5. Adverse Health Effects Common health effects used in these studies: ▫Mortality [binary]  Mortality due to respiratory causes  Mortality due to cardiovascular causes ▫Disease rates [continuous]  Cardiac arrest/cardiovascular events  Respiratory disease ▫Specific outcomes  Cough, wheeze, bronchitis, asthma [mostly binary]  Lung function [continuous] Health effects on ▫Children ▫The elderly Elizabeth Stanwyck 30 June 2008 UMBC 5

6. Pollutants Commonly measured pollutants are ▫Sulfur Dioxide (SO 2 ) ▫Oxides of Nitrogen (NO x, or NO and NO 2 ) ▫Ozone (O 3 ) ▫Particulate Matter (PM 2.5, PM 10 ) ▫Carbon Monoxide (CO) Elizabeth Stanwyck 30 June 2008 UMBC 6

7. Personal Level Covariates Smoking status Mode of cooking and heating Health history Income level / Living conditions Age Ethnicity Body Mass Index Exercise Gender Elizabeth Stanwyck 30 June 2008 UMBC 7

8. Community Level Covariates Distance to nearest busy road/intersection Presence of factories/mills in the community Topography of study region Weather conditions Elizabeth Stanwyck 30 June 2008 UMBC 8

9. NHANES-III Data National Health And Nutrition Examination Study Conducted from 1988-91 and 1992-94 Complex, multi-stage probability sampling design Designed to give a snapshot of the nation’s health Includes: ▫Questionnaire data  Personal covariates: age, race, gender, housing characteristics, family characteristics, smoking  Respiratory and allergy questions ▫Examination data  Height, weight, spirometry measurements ▫Laboratory data  Tests on blood and urine Elizabeth Stanwyck 30 June 2008 UMBC 9

10. A Proposed Model: General Description ▫Molitor et al. (2006) “Bayesian Modeling of Air Pollution Health Effects with Missing Exposure Data” American Journal of Epidemiology ▫Molitor et al. (2007) “Assessing Uncertainty in Spatial Exposure Models for Air Pollution Health Effects Assessment” Environmental Health Perspectives Object: Model the relationship between health effect and exposure to pollution ▫Disease Model  Model the effect of household-level long term pollutant exposure and the effects of various personal-level covariates on lung function [response] ▫Measurement Model  Model the long-term level of pollutant(s) exposure for an individual ▫Exposure Model  Model long-term pollutant exposure using various household-level covariates Elizabeth Stanwyck 30 June 2008 UMBC 10

11. The Data – Pollution (Molitor et al. 2006) Southern California Children’s Health Study ▫Continuous, long-term central site measurements of air pollution in multiple (11) communities ▫Two seasonal short-term household-level measurements at a subset of residences within- communities ▫Pollutants measured:  Ozone – O 3  Nitrogen Dioxide – NO 2  Particulate matter with diameter of 10 μm or less – PM 10 Elizabeth Stanwyck 30 June 2008 UMBC 11

12. The Data – Health Outcomes Questionnaire data on demographic characteristics, health outcomes, activities, housing characteristics Height, weight Lung function (using spirometry): response of interest ▫FVC: forced vital capacity  Measure of lung volume ▫FEV 1 : forced expiry volume in 1 second  Measure of airway flow ▫These measures have been shown to be sensitive indicators of lung response ▫All measurements were taken annually from study entry until high school graduation Elizabeth Stanwyck 30 June 2008 UMBC 12

13. The Data Geocoded locations of all residences and schools Information about the distance from residence to nearest freeway Predicted pollution exposures using CALINE4 ▫Package developed by the California Department of Transportation (CalTrans) to predict air concentrations of PM, CO, and NO 2 near roadways. Elizabeth Stanwyck 30 June 2008 UMBC 13

14. The Model C=11 communities, c = 1, 2,..., 11 i=1, 2,... N c individuals per community Measurements over j=1, 2 seasons Elizabeth Stanwyck 30 June 2008 UMBC 14

15. Disease Model is the health effect of the i th individual in the c th community is a community-level [response] random effect ▫Modeled as ; is the (unobserved) true household-level concentration of a pollutant in community c is the (unobserved) true average concentration of a pollutant in community c is the vector of personal-level covariates that directly affect the health outcome for subject i in community c Elizabeth Stanwyck 30 June 2008 UMBC 15

16. Measurement Model are observed, household-level exposure measurements in community c, in season j, for subject i. are central-site ambient pollution measurements in community c, in season j are central-site ambient pollution measurements in community c, averaged over all seasons Elizabeth Stanwyck 30 June 2008 UMBC 16

17. Exposure Model is a community level [pollutant] random effect ▫Modeled as, are household-level exposure variables Elizabeth Stanwyck 30 June 2008 UMBC 17

18. Elizabeth Stanwyck 30 June 2008 UMBC 18 The Complete Model

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21. Molitor et al. (2006) vs. Molitor et al. (2007) ▫Molitor et al. (2006) “Bayesian Modeling of Air Pollution Health Effects with Missing Exposure Data” American Journal of Epidemiology Molitor et al. (2007) “Assessing Uncertainty in Spatial Exposure Models for Air Pollution Health Effects Assessment” Environmental Health Perspectives Elizabeth Stanwyck 30 June 2008 UMBC 21 DOES NOT INCLUDE SPATIAL AUTOCORRELATION IN THE ANALYSIS DOES INCLUDE SPATIAL AUTOCORRELATION IN THE ANALYSIS

22. Spatial Autocorrelation Standard regression models for exposure prediction assume independence Air pollution has been shown to be spatially correlated within communities Health effects are also often spatially correlated If these correlations are not accounted for within the model, it will lead to biased parameter estimates and inefficient significance tests Elizabeth Stanwyck 30 June 2008 UMBC 22

23. Spatial Autocorrelation Disease Model   within-town spatial error influencing lung function measurements Exposure Model   within-town spatial error influencing “true” long-term pollutant exposure Measurement Model  Elizabeth Stanwyck 30 June 2008 UMBC 23

24. Spatial Autocorrelation Community-specific random effects: ▫  is a between-community spatial error influencing lung function ▫  is a between-community spatial error term influencing exposure Elizabeth Stanwyck 30 June 2008 UMBC 24

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26. Frequentist Analysis: Molitor et al. (2006) (without Spatial Autocorrelation) Use and to estimate Exposure model: Disease model: Fit exposure model, then use fitted values in the disease model via Three approaches to frequentist regression Elizabeth Stanwyck 30 June 2008 UMBC 26

27. Frequentist Analysis (without Spatial Correlation) Naïve model: Weighted single-imputation model: Multiple imputation model: ▫5 sets of multiple first stage NO 2 measurements were generated for each person from, then imputed into the disease model ▫Multiple regression results based on imputed values were combined to get final parameter estimates. Elizabeth Stanwyck 30 June 2008 UMBC 27

28. Bayesian Analysis: Molitor et al. (2006) (without Spatial Autocorrelation) Markov-Chain Monte Carlo method (Gibbs Sampling) using WinBUGS Parameters, latent variables, and missing values can be estimated simultaneously (treated as random variables) 20,000 iterations for burn-in 100,000 iterations to compute posterior distributions Diffuse priors on parameters ▫Regression parameters with normal priors ▫Variance components with flat (uniform) priors Elizabeth Stanwyck 30 June 2008 UMBC 28

29. Elizabeth Stanwyck 30 June 2008 UMBC 29 Molitor et al. (2006) “Bayesian Modeling of Air Pollution Health Effects with Missing Exposure Data” American Journal of Epidemiology

30. Bayesian Analysis (with Spatial Autocorrelation) and are assumed to follow a spatial distribution defined by the CAR (Conditional Autoregressive) model denotes the vector of spatial residual errors excluding the subject i ▫  and  is a weight matrix, specified to reflect the amount of spatial similarity between all pairs of individuals and are assumed to follow a similar CAR model ▫Elements of the weight matrix are specified as the inverse of driving distance between two communities Elizabeth Stanwyck 30 June 2008 UMBC 30

31. Weight matrices based on pairwise spatial similarities: Example 1 Elizabeth Stanwyck 30 June 2008 UMBC 31

32. Weight matrices based on pairwise spatial similarities: Example 2 Elizabeth Stanwyck 30 June 2008 UMBC 32 Images courtesy of Boots, B.N. “Weighting Thiessen Polygons” (1980) Economic Geography

33. Results Elizabeth Stanwyck 30 June 2008 UMBC 33 Molitor et al. (2007) “Assessing Uncertainty in Spatial Exposure Models for Air Pollution Health Effects Assessment” Environmental Health Perspectives

34. Unique to this study.... : outdoor measurements of pollutant concentrations at subjects’ homes for 4 weeks (2 weeks in the summer and 2 weeks in the winter) This allowed a model that could incorporate the relationship between measurements from fixed- site monitors and measurements made at the subjects’ homes What if this information is not available? Elizabeth Stanwyck 30 June 2008 UMBC 34

35. Spatial Interpolation Methods Wong et al. (2004) “Comparison of Spatial Interpolation Methods for the Estimation of Air Quality Data” Journal of Exposure Analysis and Environmental Epidemiology Estimation methods to assess personal exposure to pollutants given only central-site monitoring measurements Four interpolation methods are presented and compared: ▫Spatial Averaging ▫Nearest Neighbor ▫Inverse Distance Weighting ▫Kriging Elizabeth Stanwyck 30 June 2008 UMBC 35

36. Spatial Interpolation Methods All methods use a weighted average; only difference is in the choice of weights where are weights is the air pollution concentration at an unsampled point are the concentrations at neighboring sampled points Elizabeth Stanwyck 30 June 2008 UMBC 36

37. Spatial Interpolation Methods Spatial Averaging ▫All sampled values within a fixed distance from the point of interest are assigned the same weight (based on the number of monitors) Nearest Neighbor ▫The single sample value closest to the point of interest is assigned a weight of 1 Inverse Distance Weighting ▫Samples closer to the point of interest have correspondingly larger weights Kriging ▫Weights are assigned based on spatial autocorrelation statistics Choice of method depends on density and nature of monitoring sites Elizabeth Stanwyck 30 June 2008 UMBC 37

38. Incorporating Topography and Atmospheric Conditions Sheppard et al. (2001) “Correcting for the Effects of Location and Atmospheric Conditions on Air Pollution Exposures in a Case-Crossover Study” Journal of Exposure Analysis and Environmental Epidemiology Under stagnant conditions, distribution of a pollutant may not be uniform – especially if the topography of the study area is very hilly or mountainous Systematic variation in the distribution of a pollutant can ▫alter personal exposure levels and ▫bias health effect analysis Elizabeth Stanwyck 30 June 2008 UMBC 38

39. Incorporating Topography and Atmospheric Conditions Additional covariates that may be useful: ▫Whether or not the study area is subject to a lot of wood-burning ▫Elevation ▫Topographical Index (TI) can be used to classify airshed ▫Outdoor temperature ▫Measure of stagnant weather conditions (e.g. data on daily wind-speeds) ▫Season (winter or summer) ▫Geocoding for subject residences ▫Interactions (e.g., between temperature and stagnation) Elizabeth Stanwyck 30 June 2008 UMBC 39

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41. Another approach to studying the adverse health effects of air pollution in children, based on the paper (title above) by Zhengmin Qian et al. (2004), Environment International. Elizabeth Stanwyck 30 June 2008 UMBC 41

42. Study Area Two districts in each of four Chinese Cities ▫The cities are Chongqing, Guangzhou, Lanzhou, and Wuhan ▫One urban (relatively high pollution levels) and one suburban (relatively low pollution levels) district chosen in each city ▫Cities were chosen because they were expected to exhibit a substantial gradient in pollution levels Elizabeth Stanwyck 30 June 2008 UMBC 42

43. Children’s age groups 5-16 years of age All students from one (or two) elementary schools in each district were recruited 99% (7754 of 7817) of the recruited students were represented in questionnaire responses 91% (7058) of the recruited students were used in the analysis Elizabeth Stanwyck 30 June 2008 UMBC 43

44. Pollutants of Interest TSP (total suspended particles) SO2 (sulfur dixoide) NOx (oxides of nitrogen) Size fractionated particulate matter: ▫PM2.5 ▫PM10-2.5 (= PM10 – PM2.5) ▫PM10 NOTE: this study deals with multiple pollutants at the same time (contrast with Molitor et al.) Elizabeth Stanwyck 30 June 2008 UMBC 44

45. Pollutant measurement concerns 8 districts may not be independent in terms of ambient pollution levels ▫High levels of correlation between pollutants ▫This multicollinearity interferes with estimates of the exposure-response relationship Exposure assessment in this study is indirect (as opposed to direct biological/personal monitoring) Elizabeth Stanwyck 30 June 2008 UMBC 45

46. Children’s health outcomes 6 respiratory health outcomes were explored, based on questionnaire responses [binary] ▫Cough ▫Phlegm ▫Cough with phlegm ▫Wheeze ▫Asthma ▫Bronchitis Elizabeth Stanwyck 30 June 2008 UMBC 46

47. Data sources Pollutant level data were obtained from municipal sources and school-yard monitors Health outcome data were obtained from questionnaires Covariate data were obtained from questionnaires Elizabeth Stanwyck 30 June 2008 UMBC 47

48. Time scale of the study 1993-1996 Health and covariate data (questionnaires) were collected during the years 1993-1996 TSP, NOx, and SO2 measurements were collected during the years 1993-1996 PM10, PM10-2.5, and PM2.5 measurements were collected during the years 1995-1996 This is not a time-series analysis Elizabeth Stanwyck 30 June 2008 UMBC 48

49. Analysis – Stage 1 Data were analyzed using a two-stage procedure ▫Stage 1: group the 8 districts into 4 district clusters using hierarchical clustering.  This will create homogeneous study areas  The “cluster number” will serve as a single aggregate measure of the pollutants under study (by ordering the clusters according to pollutant levels) Elizabeth Stanwyck 30 June 2008 UMBC 49

50. Analysis – Stage 1 ▫District classification was driven by particulate matter pollution (TSP, PM10, PM10-2.5, PM2.5) ▫Ordering of clusters:  Total pollution, TSP-PM10 and PM10-2.5: C4>C3>C2>C1  PM2.5: C4>C2>C3>C1  SO2: C3>C4>C2>C1  NOx: C2>C4>C3>C1 Elizabeth Stanwyck 30 June 2008 UMBC 50

51. Analysis – Stage 1 Elizabeth Stanwyck 30 June 2008 UMBC 51

52. Analysis – Stage 2 ▫Stage 2: Logistic Regression  Unconditional logistic regression models were used to calculate covariate-adjusted odds ratios of each health outcome (separately) with respect to district clusters  Gradients of odds ratios were then compared to pollutant gradients Elizabeth Stanwyck 30 June 2008 UMBC 52

53. Analysis – Stage 2 Logistic regression: Elizabeth Stanwyck 30 June 2008 UMBC or 53

54. Analysis – Stage 2 In our application: Elizabeth Stanwyck 30 June 2008 UMBC With X1, X2, X3 = district cluster dummy variables (for clusters 2, 3, and 4) X4 = age X5 = gender indicator variable (1 if male) X6 = indicator for child sleeps in own room X7 = indicator for mother’s education level (1 if more than middle school) X8 = indicator for father’s smoking status (1 if smokes) X9, X10 = indicators for house type (apartment or one-story house) X11 = indicator for cooking oil type (1 if rapeseed oil) X12, X13, X14 = indicators for coal heating exposure X15, X16, X17 = indicators for coal cooking exposure **Notice that α is not really meaningful for interpretation, since none in the study had age zero 54

55. Analysis – Stage 2 Estimates for the regression parameters: Say is the estimate for the effect of cluster 2, then the odds ratio of the effect of cluster 2 with respect to cluster 1 is Likewise, if is the estimated effect for cluster 3, then the odds ratio of the effect of cluster 3 with respect to cluster 1 is Elizabeth Stanwyck 30 June 2008 UMBC 55

56. Analysis – Stage 2 / Results Elizabeth Stanwyck 30 June 2008 UMBC 56

57. Results Crude prevalence rates of phlegm, cough with phlegm, bronchitis and wheeze had the same ranking order as the combined pollution levels (C4>C3>C2>C1). Cluster 1 was treated as a reference group, since that cluster had the lowest of all crude prevalence rates Odds ratios of cough with phlegm and wheeze had the same ranking order as combined pollution levels (C4>C3>C2>C1) Odds ratios were significantly higher in other clusters than in the reference for all outcomes Elizabeth Stanwyck 30 June 2008 UMBC 57

58. Results Integrated pollution levels were associated with prevalence of cough with phlegm and wheeze PM10-2.5 and TSP were associated with prevalence of cough with phlegm and wheeze PM2.5 was associated with prevalence of cough, phlegm, bronchitis, and asthma SO2 and NOx were not associated with any of the health outcomes Elizabeth Stanwyck 30 June 2008 UMBC 58

59. Results/Conclusions Relationships between ambient air pollutant mixture exposure and prevalence of cough with phlegm and wheeze are: ▫Monotonic ▫Positive ▫Statistically significant These relationships are driven by particulate matter pollution levels across the district clusters. Elizabeth Stanwyck 30 June 2008 UMBC 59

60. Concerns Toxicological importance of different pollutants cannot be reasonably weighted Health effects are self-reported, and thus there is potential for misclassification and/or recall bias Assumption: community mean concentrations of pollutant levels are a good surrogate for personal exposure/dosage Cluster analysis is highly empirical, and it may be difficult to extend the conclusions of this study to other cities/districts Elizabeth Stanwyck 30 June 2008 UMBC 60

61. Future Direction Develop a model that will simultaneously incorporate two (or more) health outcomes ▫Two binary outcomes ▫Two continuous outcomes ▫One binary and one continuous outcome Develop a model that will simultaneously incorporate two (or more) pollutants ▫Handle multicollinearity among pollutants Combine the models to create a model involving multiple pollutants and multiple health outcomes Elizabeth Stanwyck 30 June 2008 UMBC 61

62. Elizabeth Stanwyck: estanwy1@math.umbc.eduestanwy1@math.umbc.edu Dr. Bimal Sinha: sinha@umbc.edusinha@umbc.edu Elizabeth Stanwyck 30 June 2008 UMBC 62