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Air pollution effects on clinic visits for lower respiratory illness


Introduction to air pollution and health. The study objective and design ... The potential for air pollution at high concentrations to cause excess deaths ... – PowerPoint PPT presentation

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Title: Air pollution effects on clinic visits for lower respiratory illness

Air pollution effects on clinic visits for lower
respiratory illness

  • Introduction to air pollution and health
  • The study objective and design
  • Environment and health data
  • Statistical models
  • Main findings
  • Discussion

Development of modern research
  • The potential for air pollution at high
    concentrations to cause excess deaths was
    established in the mid-twentieth century by a
    series of air pollution disasters in the US and
    Europe which caused striking increases in

Development of modern research
  • By the early 1990's, time series studies, each
    conducted at a single location, showed that air
    pollution levels, even at much lower
    concentrations, were associated with increased
    rates of mortality and morbidity in cities in the
    United States, Europe and other developed

Development of modern research
  • At present, although these relative rates are
    small, the burden of disease attributable to air
    pollution may be substantial considering the very
    large population exposed to air pollution and to
    whom the relative rates of mortality or morbidity

Development of modern research
  • Investment in research programs designed to
    answer some of important questions
  • Powerful tools are available for data collection
    and analysis
  • Far more data available
  • Understanding of the power and limitations of
    statistical methods
  • Such an interesting challenge that many
    disciplines are involved in its full understanding

Exposure assessment
  • Target organ dose
  • Less easy to estimate organ dose
  • Personal exposure models
  • Pollutant concentration and time activities
  • Enhanced by other factors exercise, smoking,
    viral infections
  • Microenvironmental models
  • Indoor/outdoor concentrations with time-activity
  • Ambient air quality monitoring data
  • Less accurate

What is a health effect?
  • Minor changes in respiratory function and
    bronchial activity
  • Increases in respiratory symptom prevalence and
  • Acute asthma attack, exacerbations of bronchitis,
    wheezing, serious illness e.g. cancer, hospital
    admissions for acute asthma or bronchitis
  • Deaths

Time scales of exposure-effect
  • Acute health effects (minutes to months)
  • Inflammatory cells in the lung
  • Deaths from respiratory and cardiac diseases
  • Chronic health effects (months to years)
  • Increased prevalence of cough, wheeze, asthma,
  • Long-term inflammatory changes in bronchial walls
  • Increased incidence of lung cancer
  • Increased mortality from cardio-respiratory

Health effects studies
  • Experimental studies
  • In vivo exposure in animals
  • In vitro exposure of human or animal tissue or
    bacterial cultures
  • Controlled-chamber experiments
  • Under controlled conditions on dogs or human
  • Establish a dose-response relationship

Epidemiological studies
  • Short-term studies
  • Ecological studies examines the effects of
    day-to-day changes in air pollution levels on
    routinely measured health outcomes such as
    hospital admissions
  • Panel studies on panels of individual volunteers
  • Reflect real-life exposure conditions
  • Usually not possible to infer causality

Epidemiological studies
  • Long-term studies
  • Cross-sectional studies the prevalence of
    disease in different communities is compared with
    the ambient level of pollution in those
  • Cohort studies follow up a group over a period
    of time
  • Large sample size is required
  • The effect of confounding factors and problem of
    estimating exposure over the whole latent period

Epidemiological studies
  • Extensive application to air pollution
  • Because of large degree of variation of air
    pollution levels over time and across geographic
  • Inexpensive database
  • Monitoring networks for regulatory objectives
  • Routinely collected mortality and morbidity
    statistics by government and insurance agency

Epidemiological studies
  • Time-domain methods to demonstrate associations
    between air pollution and various health effects
    in single cities.
  • Two common features
  • Mainly carried out in places with a large
  • Aggregate data in a large area to represent
    population exposures.
  • Misclassification is often compounded.

Possible solutions
  • Create less heterogeneous exposures by clustering
    hospitals around a monitoring station as
    suggested by Burnett et al.
  • Exposure attribution based on clustered hospitals
    remains a serious challenge because some
    hospitals are located as far as 200 km away from
    any monitoring stations.

Possible solutions
  • Known census clusters will provide exposure
    populations with smaller and more homogeneous
    regions (Zidek et al.).
  • Many important explanatory factors are either
    unmeasured or unavailable in all clusters.
  • Census areas are not equivalent to clinic
    catchment areas.
  • Daily outcomes in small census subdivision are
    sparse when the health outcome is the case for
    serious illness.

Small area design
  • Cluster clinics around a monitoring station to
    create relatively homogeneous area of size about
    20 km2.
  • Population at risk of each area is the estimated
    service coverage of all clinics in that area.
  • Population exposure is represented by
    measurements from the monitoring station.
  • Health outcome is daily clinic visit for lower
    respiratory illness.

  • Use daily pollutant levels and clinic visits for
    lower respiratory illness data recorded in 50
    small areas to estimate air pollution health

Statistical Analysis
  • Estimate population at risk for each area and
    convert daily clinic visit counts to daily rates.
  • Phase I Use linear models to model temporal
    patterns in order to obtain estimated
    pollution-health effect for each area.
  • Phase II Use Bayesian hierarchical models to
    combine the estimated pollution-health effects
    across the 50 communities.

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The Data
  • Study communities include 50 townships and city
    districts across the island
  • Include rural, urban and industrial areas
  • Population densities range from 250 to 28,000

The Data
  • Environmental variables
  • Daily average for NO2, SO2 and PM10
  • Daily maximum O3 and maximum 8-hour running
    average for CO
  • Daily maximum temperature and average dew point

The Data
  • Clinic Visits
  • Huge computerized clinic visit records contain
    clinic's ID, township names, date-of-visit,
    patient's ID, gender, birthday, cause-of-visit
    and others.
  • One-year records from the 50 study communities in
  • Clinic visits due to lower respiratory illness
    like acute bronchitis, acute bronchiolitis, and
    pneumonia are used as health effects.
  • Classify the population at risk into 3 age
    groups children (0-14), adults (15-64) and
    elderly (65).

Data Summary
  • Estimated population at risk ranged from 19,000
    to 278,000.
  • The averages of daily average NO2, SO2, PM10, and
    CO levels were 23.6 ppb, 5.4 ppb, 58.9
    , 1.0 ppm, and daily maximum O3 levels 54.2 ppb.
  • The average of daily rates of clinic visits due
    to lower respiratory illness was 1.34 per 1000.
  • The average rates are 2.39, 0.88 and 1.02 the
    children, adults and elderly groups,

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Population at Risk
  • Define population at risk for a selected
    community as those who would go to the clinics in
    the community whenever they need to make medical
    visits, which is the service coverage of all the
    clinics in the community.
  • Include some non-resident daytime workers who may
    visit clinic in the community, but exclude
    residents who prefer to use medical resources
    outside the community.

Population Estimation
  • Similar to estimating the number of unseen
    species in ecological studies, using only the
    numbers of individuals captured during a fixed
    interval of time.
  • Use clinic visits due to all diseases recorded in
    the study communities during 1998 to estimate
    population at risk.
  • An individual's times of clinic visits in a
    community during one year is analogous to a
    species having members captured during one
    unit of time.

Population Estimation
  • For the species problem, the members are
    assumed unrelated, while one person's clinic
    visits are generally correlated.
  • Assumption may still be satisfied when we only
    count the first visit for consecutive visits with
    same diagnosis in a short time period.

  • Let be the number of people having exactly
    clinic visits in a community during 1998.
  • is the total number of different
    people having made at least one clinic visit in
    that community in 1998.
  • The number of people who made no clinic visits in
    1998 but would do so if they were later sick is

  • Assume that all people will eventually get
    sick and visit one of the clinics in this
    community in the coming years.
  • The expected number of is denoted by in
    unseen species problem.
  • Efron and Thisted (Biometrika,76) proposed
  • with ,
    where B is .

Population Estimation
  • Ideally, one should choose an appropriate value
    to obtain less biased population estimation
    without excess uncertainty.
  • Our choice of is based on the observation
  • Patient's medical seeking behavior was stable
    under the NHI program
  • Limited changes in the demographics of study
    communities in the past six years in Taiwan.

Population Estimation
  • Validity of the population estimator
  • We estimated the number of people not recorded in
    the database of 1997 but who appeared in 1998.
  • Mean absolute value of the relative difference
    between estimated additional subjects, ,
    and actually observed new patients in 1998 was
    less than 2 across study communities.

Phase I modeling
  • Use daily visit rate in log scale instead of
    count as response variable.
  • Daily series of rates for each sub-population by
    area and age group are modeled separately.
  • Our models are general linear regressions with
    seasonal autoregressive moving average residual
  • The regression terms/confounding variables were
    chosen through extensive exploratory data

The Model
  • where yiat is the observed clinic visit rate of
    the ath age group in the ith community at the
    tth day.
  • POLLi, t-h is the level of pollutant at day t-h,
    where t is the current day and h ranges from 0
    to 2.
  • is the pollution coefficient.
  • The error term

Model Selection
  • The model was examined at several communities
    with a mean R-squared 0.53 in fitting the data
    of all the sub populations.
  • Ideally, we can explore the data to find the best
    models for each setting of the combination of 5
    air pollutants, 3 time lags, and 4 age categories
    in all 50 locations, respectively.
  • Because of efficiency considerations we apply
    this single regression model to all
    sub-populations in all 50 locations at this

  • Health impact is measured as the percentage
    increase in clinic visit rates that corresponds
    to a 10 increase in local air pollution levels.
  • The percentage change is expressed by
    , where is the estimated
    pollution coefficients for community i age group
    a, and lag h, and is the corresponding
    average pollution level.
  • The 95 confidence interval for the percentage
    change is constructed by replacing with
  • , where is the standard

Phase II modeling
  • The second phase of hierarchical modeling is to
    use variables of community's characteristics and
    spatial dependency
  • To modify pollution coefficient estimate in each
  • To obtain an overall pollution coefficient
    estimate across multiple locations.

Three stages
  • First, the estimated 50 pollution coefficients
    for a single pollutant, a fixed age group and
    time lag, denoted as are
    assumed to be multivariate normal, that is
  • where and
    ,and is the estimate of standard
    error of .

Second, spatial variation among the 50 mean
pollution coefficients is modeled as
  • where dij is Euclidean distance between the air
    monitoring stations for communities i and j,
    and R is a range parameter.
  • Based on empirical correlograms for the 50
    estimated pollution coefficients, the range
    parameter R is fixed at 5 km.

  • For the current study, we construct the
    regression terms
  • The intercept can be interpreted as an
    overall pollution coefficient for any location
    with mean predictors.
  • The other coefficients, , reflect the
    modification or adjustment on its local pollution
    coefficient ( )

Third, complete the hierarchical structure with a
proper prior model for and
  • We use conjugate priors, and
  • The hyper parameters, , in our model
    are chosen to reflect no information on and

  • The Bayesian inference is based on the posterior
    distribution of and given the Phase I
    estimates and the specified hyper
  • Samples from these posteriors can be obtained
    from the MCMC algorithm, or simply use BUGS

Results Phase I
  • Variation in clinic visits was likely related to
    variation in NO2, CO, SO2 and PM10 exposures.
  • No significant effect for ozone exposures.
  • Significant association was seen at current day
    but less significant at 1-day lag.
  • Significant intra-community and inter-community
    variability in the estimated percentage changes
    of clinic visit rates.

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Results Phase II
  • The 95 posterior support intervals of the
    estimated overall pollution coefficient ( )
    showed that clinic visits were related to NO2,
    CO, SO2 and PM10 exposures but not O3.
  • An individual community's pollution coefficient
    for NO2 was negatively adjusted by long-term PM10
    and O3 exposure.

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Modification of acute effect
  • The acute effect of SO2 was adjusted by
  • (-) area's population density, PM10 and SO2
  • () area's annual CO and O3 concentrations.
  • The acute effect of CO was adjusted by
  • (-) area's population density, PM10 and O3.
  • The acute effect of PM10 was adjusted by
  • (-) long-term exposure of PM10
  • () long-term exposure of CO positively.

Modification of acute effect
  • In summary, area's annual PM10 level is a major
    effect modifier. The short-term effects of air
    pollution on lower respiratory illness would be
    lower in areas with a large PM10 average.
  • Yearly averages of community's NO2 and SO2
    levels, however, had no significant influence on
    the acute effects of the 5 pollutants in the
    Phase II models.

Main findings
  • NO2 had the greatest estimated percentage
    increases in daily clinic visit rates
  • The pollution effects were always the greatest
    for current-day exposures and decreased
    significantly as exposure time lags increased
  • The elderly being the most susceptible.
  • The short-term effects of air pollution on lower
    respiratory illness would be lower in areas with
    a large PM10 average.

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  • Few epidemiologic studies have related clinic
    visits of minor illness to ambient air pollution.
  • Studies on minor health effects of air pollution
    should be encouraged even though currently major
    on-going epidemiologic studies on air pollution
    are about mortality.

  • From scientific viewpoints, the studies on minor
    health effects can strengthen consistency in the
    biological plausibility of mortality effects by
    air pollution.
  • From public health viewpoints, a minor health
    effect usually impacts on large-scale population
    and can lead to the death of susceptible

  • Population at risk estimation is an important
    issue in environmental health studies.
  • High collinearity among air pollutants prevents
    us from using multi-pollutant models.

  • Gaussian linear process for rates versus Poisson
    process for counts
  • Linear predictors of these two models are the
    same except one constant term of population at
    risk in log scale
  • A minor difference between these two models is
    the assumed variance structure
  • Gaussian process provides us with flexible model
    selection, diagnostics and simplified computation.

  • Joint tempo-spatial models can fit the multiple
    time series of rates data simultaneously.
  • However, model selection and calculations are

Some other challenging issues of epidemiologic
studies on air pollution
  • Why the exposure-response slopes for individual
    air pollutants varied significantly among
    different study sites?
  • Whether the pollution effects were from single
    pollutant or mixtures of air pollutants?
  • What was the relationship between chronic and
    acute exposure effects?

Related works on air pollution health effects
  • Proposed a subject-domain model for estimating
    the schoolchildrens risks of illness absence
    (Hwang et al., 2000)
  • On emergency room visit for respiratory disease
    in Taipei (Hwang and Lin, 2001)
  • Mortality in association with ozone and particles
    (Chiang and Hwang, 2001)