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

Studying the effects of air pollution on children’s healthPresented by Elizabeth Stanwyckwith Dr. Bimal SinhaUniversity of Maryland, Baltimore CountyJune 30, 2008

Objective

•To study the effects of air pollution on the health of children (and the elderly)

▫Focus on respiratory health

Elizabeth Stanwyck 30 June

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Objective


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Adverse Health Effects•Types of responses

▫Binary▫Ordinal▫Continuous

•Possibility of measurement error•Measurements can be taken

▫Hourly▫Daily▫Monthly▫Annually


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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


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Pollutants

•Commonly measured pollutants are▫Sulfur Dioxide (SO2)

▫Oxides of Nitrogen (NOx, or NO and NO2)

▫Ozone (O3)

▫Particulate Matter (PM2.5, PM10)▫Carbon Monoxide (CO)


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Personal Level Covariates

•Smoking status•Mode of cooking and heating•Health history•Income level / Living conditions•Age•Ethnicity•Body Mass Index•Exercise•Gender


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Community Level Covariates

•Distance to nearest busy road/intersection

•Presence of factories/mills in the community

•Topography of study region

•Weather conditions


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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


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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


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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 – O3

Nitrogen Dioxide – NO2 Particulate matter with diameter of 10 μm or less

– PM10


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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▫FEV1: 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


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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 NO2 near roadways.


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The Model

•C=11 communities, c = 1, 2, . . . , 11

• i=1, 2, . . . Nc individuals per community

•Measurements over j=1, 2 seasons


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Disease Model

• is the health effect of the ith individual in the cth 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

•


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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

•


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Exposure Model

• is a community level [pollutant] random effect▫Modeled as ,

• are household-level exposure variables

•


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The Complete Model


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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


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DOES NOT INCLUDE SPATIAL

AUTOCORRELATION IN THE ANALYSIS

DOES INCLUDE SPATIAL

AUTOCORRELATION IN THE ANALYSIS

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


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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


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Spatial Autocorrelation

•Community-specific random effects:▫

is a between-community spatial error influencing lung function

▫

is a between-community spatial error term influencing exposure


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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


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Frequentist Analysis(without Spatial Correlation)• Naïve model:

• Weighted single-imputation model:

• Multiple imputation model:▫ 5 sets of multiple first stage NO2 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.


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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


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Molitor et al. (2006) “Bayesian Modeling of Air Pollution Health Effects with Missing Exposure Data” American Journal of Epidemiology

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


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Weight matrices based on pairwise spatial similarities: Example 1


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Weight matrices based on pairwise spatial similarities: Example 2


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Images courtesy of Boots, B.N. “Weighting Thiessen Polygons” (1980) Economic Geography

Results


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Molitor et al. (2007) “Assessing Uncertainty in Spatial Exposure Models for Air Pollution Health Effects Assessment” Environmental Health Perspectives

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?


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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


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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


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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


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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


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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)


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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.


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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


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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


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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.)


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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)


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Children’s health outcomes

•6 respiratory health outcomes were explored, based on questionnaire responses [binary]▫Cough▫Phlegm▫Cough with phlegm▫Wheeze▫Asthma▫Bronchitis


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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


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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


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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)


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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


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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


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•Logistic regression:


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•In our application:


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With X1, X2, X3 = district cluster dummy variables (for clusters 2, 3, and 4)X4 = ageX5 = gender indicator variable (1 if male)X6 = indicator for child sleeps in own roomX7 = 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 exposureX15, X16, X17 = indicators for coal cooking exposure**Notice that α is not really meaningful for interpretation, since none in the study had age zero

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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


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Analysis – Stage 2 / Results


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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


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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


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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.


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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


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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


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Elizabeth Stanwyck: [email protected]

Dr. Bimal Sinha: [email protected]


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

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