2003 Progress Report: Relating Cardiovascular Disease Risk to Ambient Air Pollutants Using Geographic Information Systems Technology and Bayesian Neural Networks: The AHSMOG Study

EPA Grant Number: R830547
Title: Relating Cardiovascular Disease Risk to Ambient Air Pollutants Using Geographic Information Systems Technology and Bayesian Neural Networks: The AHSMOG Study
Investigators: Knutsen, Synnove F. , Beeson, Larry , Ghamsary, Mark , Soret, Samuel
Institution: Loma Linda University
EPA Project Officer: Chung, Serena
Project Period: February 1, 2003 through December 31, 2006 (Extended to January 31, 2009)
Project Period Covered by this Report: February 1, 2003 through December 31, 2004
Project Amount: $964,436
RFA: Epidemiologic Research on Health Effects of Long-Term Exposure to Ambient Particulate Matter and Other Air Pollutants (2002) RFA Text |  Recipients Lists
Research Category: Health Effects , Particulate Matter , Air

Objective:

The primary objective of this research project is to determine the association between cardiovascular disease and long-term particulate ambient pollutants in 6,338 non-smoking California Seventh-day Adventists. The outcomes include both fatal and non-fatal coronary heart disease (CHD) as well as other cardiovascular disease during 22 years of followup. Further, the objectives are to assess the same effects in sensitive subgroups (e.g., prevalent cardiovascular disease [CVD], hypertensives, diabetics, and elderly). This research project also aims to assess whether other pollutants (specifically gaseous pollutants) modify the association between particulate pollution and CVD.

Approach

  • Utilize data from the existing Adventist Health and Smog (AHSMOG) Study, which has been updated through March 2000 through the current U.S. Environmental Protection Agency (EPA) Science To Achieve Results (STAR) Grant R827998. This data includes monthly indices of air pollutants to zip code centroids, monthly residence and work location histories, outcome assessment (CHD, fatal and non-fatal), and assessment of relevant confounders (smoking, environmental tobacco smoke, diet, exercise, etc.).
  • Develop new indices of ambient air pollutants for the individual subjects in the AHSMOG Study using Geographic Information Systems (GIS) technology and stochastic models that include error estimates of the indices.
  • Develop non-linear statistical models using Bayesian neural networks to develop alternative analytical strategies for modeling the relationship between different ambient air pollutants and risk of CHD where several pollutants and latent (unobserved) and missing values can be incorporated.
  • Compare the new analytical methods to the classic or conventional methods previously used in the AHSMOG Study.

Progress Summary:

Air Pollution Estimates

The AHSMOG Study already had monthly air pollution estimates for each subject since the start of the Study in 1973 and for some pollutants (PM2.5) back to 1966. These were developed using a deterministic method with interpolation to the centroid of each zip code. The AHSMOG Study had used information from all relevant monitoring stations in California to develop their ambient air pollution estimates; however, in meetings between the STAR grantees and EPA, it was decided to use a common air pollution database from EPA. This will allow a comparison of estimated ambient air pollution values by the four studies. For AHSMOG, this also will allow a comparison with our previous, comprehensive methods for estimation of ambient air pollution. This decision, however, has delayed the progress of the study in obtaining air pollution estimates using geostatistical methods.

To date, we have geocoded all street addresses for subjects on a monthly basis from baseline in 1977 to 1999 or date of death or loss to followup. In addition, we have geocoded monthly workplace zipcode for each subject in the same time period. Currently, we are collaborating with the research team at Environmental Systems Research Institute (ESRI) to develop subject-specific ambient air pollution estimates using geostatistical data analysis. The deadline for completing this task is the end of September 2004.

Assessment of Outcome

Incident CHD. From 1977-1982, we have information on incident myocardial infarctions (MI). For the period 1983-1999, we have self-reported incidence of acute MI with additional information on the name and address of the hospital in which these cases were diagnosed. Validity of this information is verified by obtaining medical records from the individual hospitals.

A total of 568 subjects have reported that they had an acute MI since 1982. Letters have been sent to the hospitals in which the subject was diagnosed with their self-reported MI. Of the requests sent to hospitals, valid medical records have been received for 318 subjects. These are in the process of being examined by a physician for verification of the self-reported MI. Among the rest, we have the following results:

  • 16 report that records for that year have been destroyed.
  • 129 provided no response. New mailings have been made.
  • 105 would not release patient medical records. Of these, 63 hospitals want a new consent from the subjects even though the Health Insurance Portability and Accountability Act (HIPAA) rules have exemptions for consents given before April 2003. The remaining hospitals have another explanation for why medical records have not been released. We have written new letters to these reiterating the HIPAA exemptions or addressing other concerns and are following up as we receive their replies.

Cardiovascular Disease Mortality. All death certificates have been coded by a certified nosologist and all mortality outcomes have thus been updated. A total of 2,462 deaths have occurred in the cohort since 1977. Of these, 2,393 were natural cause deaths (ICD-9: < 800) and 644 were the result of ischemic heart disease (ICD-9: 410-414).

Outcomes in Sensitive Subgroups. The following sensitive subgroups have been identified: (1) older age (> 64- and > 74-years old); (2) prevalent CHD; (3) prevalent CHD, stroke, diabetes or hypertension; (4) past smokers; and (5) prevalent chronic obstructive pulmonary disease (COPD). Preliminary analyses of sensitive subgroups using previously estimated ambient air pollution do not indicate any increased risk associated with particulate matter air pollution in sensitive subgroups, except for persons with COPD where the risk of fatal CHD seems to be elevated, especially in males. Further analyses using ambient air pollution estimates using GIS methodology, however, will be done in accordance with the objectives of this grant.

Progress on Analyses and Results

As mentioned, we still do not have individual estimates of ambient air pollution for the subjects using the EPA air quality database. We have analyzed, however, fatal CHD associated with particulate air pollution using our previous air pollution estimates. The results show a detrimental effect of particulate pollution, especially PM2.5, in females but not in males. The association in females was strengthened in two-pollutant models with ozone.

Obtaining medical records of self-reported MI cases has been much more challenging than first expected because of several factors. First, some hospitals do not keep records for more than 10 years. Secondly, because of the HIPAA regulations coming into effect in April 2003, hospitals are very reluctant to give out any records despite the fact that the HIPAA regulations specifically exempt records of persons having given their consent prior to the implementation of the HIPAA rules. In spite of this, many hospitals are requesting that new consent forms using their specific forms be obtained from the subjects. At this point, many of the subjects are dead, and it may be necessary to obtain consent from close family members.

The progress on developing GIS-based individual ambient air pollution estimates is moving forward. Currently, we are using the services of ESRI in this effort.

Likewise, the development of statistical models using neural networks and Bayesian neural networks is progressing. The first paper on this work was presented at the Hawaii International Conference on Statistics in Honolulu in June 2004. The plan is to have the statistical models tested out using our previous air pollution estimates by the time the GIS-based air pollution estimates are ready at the end of September 2004.

Two abstracts have been accepted for presentation at the International Society for Environmental Epidemiology meeting in New York in August 2004 using previous air pollution estimates. Two manuscripts are in their final stages for submission—the first on particulate matter and fatal CHD, the other on particulate matter and risk of non-Hodgkin’s lymphoma. These also use previously developed ambient air pollution estimates.

Future Activities:

In Year 2 of the project, we will obtain outstanding medical records and verify the self-reported non-fatal MIs from these. We also will complete the development of the air pollution estimates using GIS as well as traditional methods from the databases obtained from EPA. The development of the statistical models using Bayesian neural networks also will be further developed in Year 2 of the project.

We expect to start analyses of the air pollution-disease relationship in the latter half of Year 2 of the project.

Publications that we expect to submit from this grant in Year 2 of the project will be a methods paper on development of the Bayesian neural network and development of the GIS based ambient air pollution estimates. We further hope to submit the first paper on the association between cardiovascular disease and ambient air pollution using GIS-developed air pollution estimates

In Year 3 of the project, we expect to do most of the analyses and writing of papers to address the specific objectives.

Journal Articles:

No journal articles submitted with this report: View all 25 publications for this project

Supplemental Keywords:

ambient air, ozone, particulate matter, exposure, risk, risk assessment, health effects, human health, sensitive populations, population, elderly, cumulative effects, susceptibility, epidemiology, modeling, monitoring, analytical, Bayesian neural networks, GIS, southwest, California, CA,, RFA, Economic, Social, & Behavioral Science Research Program, Health, Scientific Discipline, Air, ENVIRONMENTAL MANAGEMENT, particulate matter, Health Risk Assessment, Risk Assessments, Susceptibility/Sensitive Population/Genetic Susceptibility, Disease & Cumulative Effects, Biochemistry, Environmental Statistics, genetic susceptability, Biology, Risk Assessment, ambient air quality, elderly adults, health effects, sensitive populations, health risk analysis, air pollutants, long term exposure, acute lung injury, Bayesian approach, cardiovascular vulnerability, exposure, Bayesian method, Bayesian neural networks, air pollution, chronic health effects, human exposure, statistical models, susceptibility, particulate exposure, sensitive subjects, Acute health effects, elderly, GIS, sensitive subgroups, cardiotoxicity, mortality, tobacco smoke, age dependent response, cardiovascular disease, cumulative effects, exposure assessment, human health risk, respiratory, genetic susceptibility, cardiopulmonery responses, toxics

Relevant Websites:

http://www.llu.edu/llu/health/ahsmog.htm Exit

Progress and Final Reports:

Original Abstract
  • 2004 Progress Report
  • 2005 Progress Report
  • 2006 Progress Report
  • 2007 Progress Report
  • Final Report