Air pollution effects on clinic visits for lower respiratory illness

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Air pollution effects on clinic visits for lower
respiratory illness

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Outline

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

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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
  mortality.

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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
  countries.

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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
  apply.

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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

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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
  data
• Ambient air quality monitoring data
• Less accurate

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What is a health effect?

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

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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,
  bronchitis
• Long-term inflammatory changes in bronchial walls
• Increased incidence of lung cancer
• Increased mortality from cardio-respiratory
  diseases

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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
  volunteers
• Establish a dose-response relationship

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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

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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
  communities.
• 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

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Epidemiological studies

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

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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
  population.
• Aggregate data in a large area to represent
  population exposures.
• Misclassification is often compounded.

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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.

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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.

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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.

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Objective

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

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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
  persons/km2

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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

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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
  1998.
• 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).

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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,
  respectively.

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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.

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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.

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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.

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• 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
  .

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• 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 .

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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
  that
• 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.

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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.

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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
  processes.
• The regression terms/confounding variables were
  chosen through extensive exploratory data
  analyses.

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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

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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
  phase.

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• 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
  error.

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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
  location,
• To obtain an overall pollution coefficient
  estimate across multiple locations.

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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 .

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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.

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• 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 ( )

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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
  .

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

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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.

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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.

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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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Discussion

• 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.

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Discussion

• 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.

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Discussion

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

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Discussion

• 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.

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Discussion

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

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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?

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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)