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Health Impacts of Aerosols

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    1. Health Impacts of Aerosols Dr. Patrick L. Kinney Associate Professor Columbia University

    3. The Human Respiratory System Three key regions Extrathoracic Tracheobronchial alveolar Particle penetration and deposition vary by region Lung defenses vary by region Vulnerability varies by region

    6. How Can Air Pollution Cause Problems? Irritation of the airways in the extrathoracic region, resulting in symptoms such as runny nose and sneeze Irritation and inflammation of the conducting airways in the tracheobroncial region, resulting in symptoms of cough, wheeze, and shortness of breath (asthma-like symptoms), and possibly long-term effects such as bronchitis or lung cancer Damage to the alveolar cells, resulting in scarring, remodeling, and decreased lung capacity, which may lead eventually to clinically-significant fibrosis or emphysema Penetration through the epithelial lining to the circulatory system and thence to other organs, such as the heart

    7. Health Effects of Airborne Particulate Matter Historical experience during severe episodes provides strong evidence for a cause-effect relationship between air pollution and premature death For example, London 1952 It has been argued, though it cannot be proven, that PM was the responsible pollutant in the London episode

    11. Health Effects, continued Modern epidemiology studies have repeatedly found statistically significant associations between PM and the risk of death and disease Two primary epidemiologic study designs: Studies examining changes in exposure over time, which assess mainly acute effects Studies examining changes in exposure over space, which assess mainly chronic effects Experimental studies also provide supporting information

    12. What are acute studies telling us? Time-series studies show that: Risk of death is acutely elevated on days when PM levels are high Risk of hospital visits or admissions are also acutely related to PM levels Respiratory and cardiovascular causes of death and disease are most associated with PM

    13. Important Disease Categories in the Study of PM Health Effects Respiratory Asthma Bronchitis Pneumonia Emphysema COPD Cardiovascular Heart attack Arrhythmia Congestive heart failure

    14. What are chronic studies saying? Spatial studies show that: Risk of death is higher in cities with higher long-term PM concentrations This relationship remains after controlling for differences in smoking rates, economic status, diet, occupation, and other factors The chronic PM effect is substantially larger than the acute effect and is probably more significant in its overall population impacts

    15. Schematic view of the relationship between long-term and short-term effects Adapted from Kunzli et al. 2001

    16. Description of Categories

    17. What else does the health literature tell us? More recent epidemiologic studies have investigated possible mechanisms of acute effects, by observing physiologic or biochemical changes in groups of subjects followed over time. For example, recent studies have demonstrated associations of PM with: decreased heart rate variability increased arrhythmias levels of substances in the blood that promote inflammation and blood clotting.

    19. What havent we learned? Epidemiologic studies tell us very little about: Which particle components are responsible for the observed health effects? Coarse vs. fine vs. ultrafine modes Sulfate vs. nitrate vs. elemental carbon vs. organic carbon vs. trace elements and metals What sources are most responsible? Motor vehicles vs. coal vs. fuel oil vs. windblown dust Why Not?

    20. Epidemiology is opportunistic In most cases, exposure assessment relies upon air monitoring data collected for regulatory purposes Regulatory air monitoring for PM has seldom provided detailed size or chemical speciation If theres no speciation data, then epidemiologic studies cannot look at differential effects of different PM components Even in cases where speciated PM data exist, it is hard to statistically separate the various components when they correlate with each other

    21. Experimental Studies Can Help Answer the PM Component Questions Controlled exposures of animals to component particles Controlled exposures of human volunteers to component particles In-vitro exposures to cells

    22. What are some advantages of Experimental Studies? Well-characterized air pollution exposures Control of other environmental conditions that might affect the outcome With appropriate study design, can prove a cause-effect relationship Can reliably evaluate shape of exposure-response relationship

    23. What are some limitations of Experimental Studies? Complex pollution mixtures are technically challenging To address this challenge, ambient pollution is sometimes used to generate exposures In the case of PM, concentrators have become popular in experimental studies Animal and in-vitro results may not be directly applicable to human risk assessment Human studies involve healthy volunteers, so may not tell us whats going on in susceptible populations Exposures much higher than ambient are often used, necessitating extrapolation to lower levels Human studies limited to acute effects

    24. Air Pollution Epidemiology Pros: Provides results which are directly relevant for policy makers Assesses effects of the real-world mix of pollutants on relevant human populations No need to extrapolate across species Little need for extrapolation across exposure levels Study population may include susceptible subgroups

    25. Air Pollution Epidemiology Cons Pollutants tend to co-vary, making it hard to identify effects of a specific pollutant Can demonstrate associations between outcome and exposure, but cannot prove cause and effect. Were stuck with causal inference Various schemes exist for building an argument for causality Shape of exposure-response relationship difficult to discern. Difficult to identify thresholds Must control for confounding factors Exposures are always ecologic. Outcomes and covariates often are too

    26. Confounding a serious issue In an analysis of the effects of exposure on an outcome, confounding occurs when there is a third variable, omitted from the analysis, that independently affects the outcome and which is also correlated with the exposure variable of interest In a multi-city study of smoking and heart disease, high fat diet might be a confounder In a time series study of ozone and daily deaths, temperature might be a confounder

    27. Confounding, continued When confounding is present, the estimate of the exposure-response relationship of interest may be biased up or down This is because some of the effect of the omitted confounding variable is attributed to, or is picked up by, the exposure variable of interest If not addressed, confounding may invalidate the findings of a study Solution: control for the confounding variable, either by exclusion, stratification, or by including it as a covariate in the analysis

    28. Effect Modification Occurs when the level of a third variable influences the exposure-response relationship for the variable of interest For example, people who smoke cigarettes are more likely to get lung cancer following asbestos exposure than are non-smokers Same idea as an interaction Does not invalidate a study, but may affect the generalizability of results

    29. What is meant by Ecologic Variable? An ecologic variable is one for which information is not available uniquely for each individual in the study, but rather is available only for groups of individuals Air pollution variables are always ecologic, in that data from one or a few monitoring sites is used to represent exposure for a large group of people Outcome and covariate data sometimes are ecologic For example, daily death counts in a city

    30. Why does this matter? Especially when outcomes and potential confounding variables are ecologic, there is greater concern about potential confounding by group-level factors that are hard to measure and control Old cross sectional epidemiology studies were criticized for this reason (see Lave and Seskin studies from 1970s)

    31. Two Main Study Designs Time Series or temporal, acute, short-term Cross Sectional or cohort, chronic, long-term

    32. Time Series Study Design See Pope and Schwartz 1996 handout Examines acute exposure-response relationships Data must be available at equally spaced intervals (usually days) over extended time period Outcome data may be continuous (e.g., lung function), dichotomous (e.g., symptoms), or count (e.g., deaths) Multiple regression is commonly used to examine exposure-response relationships

    33. The Multiple Linear Regression Model Yt = a + b1 X1t + b2 X2t + + et

    34. Time Series Analysis Statistical Challenges: Residuals (deviations between observed and modeled outcome data) may not be normally distributed For counts, use Poisson regression Residuals may not be independent of each other especially over time Autoregressive terms address this issue Seasonal cycles and weather variables are often important confounders Co-pollutants may also be confounders

    35. Control for Seasonal Confounding General approach is to include a new variable or function which fits the seasonal pattern of the outcome variable, thereby eliminating the opportunity for pollution to explain the seasonal variations The goal is to eliminate any cyclic patterns from the data with periodicities of greater than several weeks, leaving only high frequency data variations Options include filtering using a weighted moving average, fitting of sinusoidal functions of time, fitting generalized additive models (GAM) In general, all seem to perform similarly

    36. Control for Weather Confounding For many health outcomes, such as death, we know that weather is a risk factor. Both heat spells and cold snaps are associated with higher death rates Since weather is closely tied to air pollution concentrations, confounding is possible This problem is easily addressed by including weather variables, such as temperature, in the regression analysis

    37. A Note About GAM In the 1990s, the Splus GAM function became a popular method for fitting the non-linear exposure-response function for seasonal and temperature influences on daily deaths GAM with LOESS involves fitting a local, weighted moving regression between two variables In 2002, it was realized that the Splus GAM/loess algorithm has some problems

    38. GAM continued When GAM/loess is used to fit season and/or temperature effects in a multiple regression model with pollution, The slope estimate for pollution may not converge to its optimal solution, resulting in positive bias Also, the standard error estimate for the slope on pollution is negatively biased But, the effects are small and not qualitatively important

    39. Time Series Results A large number of studies have reported significant associations between daily deaths and/or hospital visit counts and daily average air pollution Particles often appear most important, but CO, SO2, NO2, and/or ozone may also play roles For example, NMMAPS Study

    40. National Mortality and Morbidity Air Pollution Study (NMMAPS) To investigate acute effects of PM10 on daily deaths and hospital admissions, controlling for other pollutants To carry out a comprehensive analysis for multiple US cities using a consistent statistical approach

    42. Cohort Epidemiology Address long-term exposure-response window Large populations in multiple cities enrolled and then followed for many years to determine mortality experience Cox proportional hazards modeling to determine associations with pollution exposure Must control for spatial confounders, e.g., smoking, income, race, diet, occupation Assessment of confounders at individual level is a major advantage over former cross-sectional, ecologic studies

    44. Pope et al., 2002 Context: although acute impacts of PM on mortality have been well-documented, studies of health effects from long-term PM have been less conclusive Objective: to assess the relationship between long-term exposure to fine PM and all-cause, lung cancer, and cardiopulmonary mortality

    45. Methods Added air pollution exposure assessment to an existing long-term study of cancer among 500,000 adults enrolled in 1982 from 50 states in the US Vital status and cause of death recorded for each subject through the end of 1998 Analyzed relationship of mortality risk to fine PM and other pollutant exposures Controlled for important confounders Tested for effect modification

    46. Study Participants Were enrolled by American Cancer Society volunteers, and consisted of friends, neighbors, or acquaintances Aged 30 and over At enrollment, each completed a questionnaire addressing age, sex, weight, height, smoking history, alcohol use, occupational exposures, diet, education, and marital status

    47. Exposure Assessment Each participant was assigned a metropolitan area of residence based on their location at enrollment The annual mean concentrations of all monitors throughout the metropolitan area were averaged Fine PM and other air pollutants were available for various time periods between 1980 and 1998

    55. Conclusion Long-term exposure to combustion-related fine particle air pollution is an important environmental risk factor for cardiopulmonary and lung cancer mortality.

    56. Summary of PM Epidemiology Daily time-series studies have demonstrated small but consistent associations of PM with mortality and hospital admissions, reflecting acute effects Multi-city prospective cohort studies have shown increased mortality risk for cities with higher long-term PM concentrations, reflecting chronic effects

    57. Implications Acute effects are well documented but of uncertain significance due to questions about how much life is lost Chronic effects imply very large impacts on public health. A new US national ambient air quality standard for PM2.5 was established in 1997, largely based on the cohort epidemiology evidence Mechanistic explanation for effects remains unclear but is the subject of current research Weaknesses in exposure assessment limits interpretation

    58. It is also unclear Whether a threshold exists Who is at risk due to Higher exposures Greater susceptibility What particle components are most toxic Which sources should be controlled

    59. Effects of Long-term Air Pollution Exposures on Lung Function in Young Adults the Yale Study There is increasing concern about the human health impacts of long-term particulate matter (PM) exposures. Small airways function represents a potentially sensitive measure of early PM-related effects on the lung. We know e.g., that first signs of adverse effects due to smoking occur in the small airways. Few environmental epidemiology studies have examined lung function outcomes in the age range (late teens to mid 20s) when maximal function is achieved, or have included long-term PM exposure estimates. The Yale cohort study combined physiological measurements of small airways function, with life-time PM (and ozone) exposure estimates among young adults, to test the hypothesis that: Life-time exposures to PM10 and/or Ozone are associated with diminished lung function in a nationwide cohort of young adults.

    60. Yale Study Methods 1723 subjects were recruited over a three year period from freshman (first year) classes at Yale University in New Haven, CT. The present report focuses on the subset of 1578 subjects who lived in the United States prior to attending Yale University. Each subject completed a questionnaire addressing respiratory disease and symptom history, residential history, home characteristics, childhood activity patterns, personal and family smoking history, parental education (SES), and other factors.

    61. Lung Function Assessment Standing height measured in stocking feet. At least 3 maximal forced expirations performed by each subject. Blow selection based on standard ATS recommendations. Adjustment to BTPS. Lung function variables: FVC: Forced vital capacity FEV1: Forced expiratory volume in 1 second, FEF25-75: Mid-maximal flow rate FEF75: Flow rate at 75% of FVC

    62. Exposure Assessment Annual mean PM10 concentrations were computed for all US sites in operation from 1983-1997. Annual means were extrapolated to the years 1972-1982 using site-specific linear regressions (annual mean regressed on year) from 1983-1997. Annual mean PM10 concentrations were interpolated from 3 nearest monitoring sites to subject residential locations for all years from 1972-1997. Life-time average PM10 concentrations were computed for each subject. A similar procedure was used to estimate long-term ozone exposures, based on June-August mean daily maximal.

    63. Data Analysis Multiple linear regression was used to relate lung function to long-term average PM10 and ozone exposures, controlling for covariates. Covariates in the lung function models included height, height squared, sex, race, personal and maternal smoking, and parental education level.

    67. Yale Study Conclusions We found associations between long-term average exposures to ambient PM10 and diminished small-airways function in a college student cohort with varying life-time residential and exposure histories. No associations were observed for ozone in two-pollutant models. To the extent that the study population was of high socioeconomic status, these results may underestimate effects (recent evidence from the ACS cohort showed higher chronic mortality risk at lower SES Pope et al., JAMA, 2002). Results of this study provide new insights into potential pathophysiologic linkages between long-term PM exposures and ill-health.

    68. Remaining Questions on Yale Study Results Do lung function decrements persist into adulthood? Do they increase the risk of chronic lung disease? What component of the particle mix is responsible?

    69. Monitoring Data Needs Daily speciated PM data Daily size-selected PM data for ultrafine and accumulation mode particles New portable, lightweight monitors for personal sampling of PM with speciation Correlation between personal and central-site concentrations for speciated PM Better data on fine-scale spatial patterns of PM species and size classes resulting from source influences in urban areas

    70. Modeling Data Needs Models for near-source impact assessment over fine spatial, but not necessarily temporal, scales Integration of modeling into exposure assessments, to fill the gaps in available monitoring data