Final Report: Are Diabetics and the Neurologically Impaired at Increased Risk from Air Pollutant Exposures? A National Analysis

EPA Grant Number: R834900
Title: Are Diabetics and the Neurologically Impaired at Increased Risk from Air Pollutant Exposures? A National Analysis
Investigators: Zanobetti, Antonella , Dominici, Francesca , Koutrakis, Petros , Schwartz, Joel , Wang, Yun
Institution: Harvard T.H. Chan School of Public Health
EPA Project Officer: Ilacqua, Vito
Project Period: April 1, 2011 through March 31, 2014 (Extended to March 31, 2015)
Project Amount: $299,903
RFA: Exploring New Air Pollution Health Effects Links in Existing Datasets (2010) RFA Text |  Recipients Lists
Research Category: Air Quality and Air Toxics , Health Effects , Air

Objective:

We focused this project on two susceptible populations: individuals with neurological disorder, and individuals with diabetes. Using Medicare data to select the susceptible populations, we will estimate county specific mortality risks associated with both short- and long-term exposure to individual pollutants on a national scale. Then we will identify factors that could explain the heterogeneity of these air pollution mortality risks. The specific aims of this project are:

Aim 1: To estimate the chronic effects on mortality of long-term exposure to individual pollutants in several US counties in two susceptible populations defined as individuals with neurological disorders or diabetes.

Aim 2: To estimate the acute effects on mortality of short term effects of individual pollutants in a potentially susceptible population.

Aim 3: To investigate whether markers of susceptibility and vulnerability differentially influence the previously established relationships between individual pollutants and mortality, allowing us to identify subpopulations at increased risk for harmful effects of air pollution. Moreover we will examine effect modification due to the composition of multi-pollutant mixtures and to PM composition.

Summary/Accomplishments (Outputs/Outcomes):

In the first year we worked on preparing the datasets:  

  1. Medicare data, 2000-2008

We used the Medicare beneficiary denominator file from CMS to identify beneficiaries who were enrolled in the Medicare FFS plan. The initial denominator file included near 400 million beneficiaries across the study period, of which we excluded beneficiaries with age<65 years, those enrolled in managed-care programs over an entire year, and those who resided outside of the targeted counties. We calculated person-years for each beneficiary to account for new enrollment, disenrollment, or death during an index year. We then linked the person-years beneficiary data with the Medicare Provider Analysis and Review (MEDPAR) inpatient data to identify all Medicare FFS patients who were hospitalized for the following targeted medical conditions between January 1, 2000 and December 31, 2008:

  1. diabetes ICD-9: 250;
  2. Acute myocardial infarction (AMI, ICD-9: 410);
  3. Dementia ICD-9: 290
  4. Neurological disorders, ICD-9: 320-359.
  5. Inflammatory diseases of the central nervous system (CNS) ICD-9: 320-326;
  6. Hereditary and degenerative diseases of the central nervous system ICD-9: 330-337;
  7. Alzheimer's disease ICD-9: 331.0;
  8. Parkinson's disease ICD-9: 332;
  9. Other disorders of the central nervous system ICD-9: 340-349; d
  10. Disorders of the peripheral nervous system (PNS) ICD-9: 350-359.

The MEDPAR inpatient data includes information on patient demographics (age, sex, race), dates of admission and discharge, date of death and verification code for death, admission sources and types, discharge dispositions, principal and secondary diagnosis codes, and procedure codes, defined by the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). We excluded patients who could not be merged with the Medicare denominator file. One reason for the unsuccessful merges is the incorrect MEDPAR beneficiary identification code or sex code. Patients’ states of residence information were obtained from the Medicare beneficiary denominator file.

  1. Air pollution, weather, and Census data

            We obtained daily air pollution (PM2.5, NO2, CO, and ozone) from the U.S. EPA Air Quality System website, and weather from the NOAA website. Key variables in the pollution data include monitor ID, date, FIPS, and sample value. Key variables in the weather data include daily temperature (mean, min, max), daily dew point, date, and weather station and WBAN IDs.

We also started preliminary analysis.

In year 2 we updated both Medicare and air pollution datasets to include the years up to 2010, and we continued to analyze the data.

We published (Zanobetti, et al., 2014) on the acute effects on mortality and hospital admissions of the short term effects of PM2.5 in individuals with neurological disorder and diabetes. (Aim 2) We found that short-term exposure to fine particles increased the risk of hospitalizations for Parkinson’s disease and diabetes, and of all-cause mortality. While the association between short term exposure to PM2.5 and mortality was higher among Medicare enrollees that had a previous admission for diabetes and neurological disorders than among Medicare enrollees that did not had a prior admission for these diseases, the effect modification was not statistically significant. The same biological responses thought to effect cardiovascular disease through air pollution-mediated systemic oxidative stress, inflammation, and cerebrovascular dysfunction could also be relevant for diabetes and neurodegenerative diseases. We believe that these results provide useful insights regarding the mechanisms by which particles may affect the brain.

During years 2 and 3 we then worked on the chronic effects of PM2.5 in all Medicare enrollees.

We estimated the effects of PM2.5 on first hospital admission for dementia, AD and PD, among approximately 10 million Medicare enrollees >64 years in 50 northeastern US cities (1999–2010). We observed significant associations of long-term PM2.5 city-wide exposure on all three outcomes. Specifically, we estimated a HR of 1.08; 95%CI: 1.05, 1.11 for dementia, 1.15; 95%CI: 1.11, 1.19 for AD and 1.08; 95%CI: 1.04, 1.12 for PD admissions per 1 μg/m3 of increase in annual PM2.5 concentrations. This was the first study to examine and to find a relationship between long-term exposure to PM2.5 and time to the first hospitalization for the most common neurodegenerative diseases. Our findings provide the basis for more studies, as the implications to public health can be crucial (Kioumourtzoglou, et al., 2015 Environmental Health Perspectives [epub ahead of print]).

We started to examine effect modification (Aim 3) by individual and are level characteristics and, when looking at mortality, we also have been looking at modification by particulate composition.

We examined the association between PM2.5 composition and survival. We used k-means cluster analyses to identify spatial patterns in PM2.5 composition across the US. We then examined the impact of PM2.5 and cluster-specific PM2.5 on survival among Medicare enrollees in 81 US cities (2000–2010). We observed a strong impact of annual PM2.5 concentrations on survival (HR = 1.11, 95%CI: 1.01, 1.23 per 10 μg/m3). This effect was modified by particulate composition, with higher effects observed in clusters containing high concentrations of nickel, vanadium and sulfate. We observed null or the opposite associations in clusters with high oceanic and crustal particles. Our findings indicate that long-term exposures to fuel oil combustion and power plant emissions have the highest impact on survival (Kioumourtzoglou, et al., 2015 Epidemiology 26(3):321-327).

In a paper under review (Kioumourtzoglou, et al., 2015 [submitted to Epidemiology]) we conducted a study to assess whether community-level variables, including socioeconomic status (SES) indicators, increased urbanicity and temperature modify the association between long term exposure to PM2.5 and mortality. We used data from approximately 35 million Medicare enrollees living in 207 U.S. cities during 2000-2010. For each city, we calculated annual PM2.5 averages, measured at ambient central monitoring sites, as the exposure of interest. We used a variation of a causal modeling approach and fitted Cox models, separately within each city, and we pooled the city-specific effects, using a random effects meta-regression. In the second stage, we assessed whether temperature and city-level variables, including smoking and obesity rates, poverty, education, %black residents and % developed land modify the association between long term exposure to PM2.5 and mortality.

We found a significant association between long-term PM2.5 effects and survival (HR = 1.19; 95%CI: 1.11-1.28 and 1.17; 95%CI: 1.05-1.31 per 10 μg/m3 increase in the annual and 2-year PM2.5 average concentrations). We observed significantly higher effect estimates in the Southeastern, South and Northwestern U.S. (HR = 1.90; 95%CI: 1.65-2.19, 1.42; 95%CI: 1.17- 1.73 and 1.44; 95%CI: 1.11-1.88 respectively). We observed significant effect modification by temperature, with higher effect estimates in warmer cities. Furthermore, we observed increasing effects with increasing obesity rates, percent of residents and families in poverty, percent of black residents and percent of the population without a high school degree, and lower effects with increasing median household income and percent white residents.

This is the first study to assess modification by temperature and community-level characteristics on the association between long-term PM2.5 exposures and survival. Our findings suggest that living in cities with higher temperatures and communities with low SES is associated with higher effect estimates.

I have also been working in collaboration with Drs. Bell and Dominici on two review papers summarizing the scientific evidence regarding effect modification of associations between short-term exposure to particulate matter (Bell, et al., 2013) and ozone (Bell, et al., 2014) and the risk of death or hospitalization.

Dr Dominici also has published several papers with her group.

In Cefalu and Dominici (Cefalu and Dominici, 2014), the authors consider exposure prediction and confounding adjustment in a health-effects regression model simultaneously. Using theoretical arguments and simulation studies, they show that the bias of a health-effect estimate is influenced by the exposure prediction model, the type of confounding adjustment used in the health-effects regression model, and the relationship between these 2. The authors show that even with a health-effects regression model that properly adjusts for confounding, the use of a predicted exposure can bias the health-effect estimate unless all confounders included in the health-effects regression model are also included in the exposure prediction model. While these results of this article were motivated by studies of environmental contaminants, they apply more broadly to any context where an exposure needs to be predicted.

In another paper (Chung, et al., 2015), Chung and co-authors assembled a data set of 12.5 million Medicare enrollees to determine which PM2.5 constituents are a) associated with mortality controlling for previous-year PM2.5 total mass (main effect); and b) elevated in locations exhibiting stronger associations between previous-year PM2.5 and mortality (effect modification). The authors found that one-standard deviation (SD) increases in 7-year average EC, Si, and NO3- concentrations were associated with 1.3% [95% posterior interval (PI): 0.3, 2.2], 1.4% (95% PI: 0.6, 2.4), and 1.2% (95% PI: 0.4, 2.1) increases in monthly mortality, controlling for previous-year PM2.5. They conclude that long-term exposures to PM2.5 and several constituents were associated with mortality in the elderly population of the eastern United States. Moreover, some constituents increased the association between long-term exposure to PM2.5 and mortality. These results provide new evidence that chemical composition can partly explain the differential toxicity of PM2.5.

Conclusions:

The same biological responses thought to effect cardiovascular disease through air pollution-mediated systemic oxidative stress, inflammation, and cerebrovascular dysfunction could also be relevant for diabetes and neurodegenerative diseases. We believe that these results provide useful insights regarding the mechanisms by which particles may affect the brain

This was the first study to examine and to find a relationship between long-term exposure to PM2.5 and time to the first hospitalization for the most common neurodegenerative diseases. Our findings provide the basis for more studies, as the implications to public health can be crucial

Our findings indicate that long-term exposures to fuel oil combustion and power plant emissions have the highest impact on survival.

This is the first study to assess modification by temperature and community-level characteristics on the association between long-term PM2.5 exposures and survival. Our findings suggest that living in cities with higher temperatures and communities with low SES is associated with higher effect estimates.

 


Journal Articles on this Report : 11 Displayed | Download in RIS Format

Other project views: All 19 publications 11 publications in selected types All 11 journal articles
Type Citation Project Document Sources
Journal Article Bell ML, Zanobetti A, Dominici F. Evidence on vulnerability and susceptibility to health risks associated with short-term exposure to particulate matter: a systematic review and meta-analysis. American Journal of Epidemiology 2013;178(6):865-876. R834900 (2013)
R834900 (Final)
R834798 (2013)
R834798 (2014)
R834798 (Final)
R834798C005 (2013)
R834798C005 (2014)
R834798C005 (Final)
R834894 (2013)
R834894 (Final)
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  • Journal Article Bell ML, Zanobetti A, Dominici F. Who is more affected by ozone pollution? A systematic review and meta-analysis. American Journal of Epidemiology 2014;180(1):15-28. R834900 (2013)
    R834900 (Final)
    R834798 (Final)
    R834798C005 (2014)
    R834798C005 (Final)
    R834894 (Final)
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  • Journal Article Cefalu M, Dominici F. Does exposure prediction bias health-effect estimation?: The relationship between confounding adjustment and exposure prediction. Epidemiology 2014;25(4):583-590. R834900 (Final)
    R834798 (Final)
    R834798C005 (2014)
    R834798C005 (Final)
    R834894 (Final)
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  • Journal Article Chung Y, Dominici F, Wang Y, Coull BA, Bell ML. Associations between long-term exposure to chemical constituents of fine particulate matter (PM2.5) and mortality in Medicare enrollees in the eastern United States. Environmental Health Perspectives 2015;123(5):467-474. R834900 (Final)
    R834798 (2014)
    R834798 (2015)
    R834798 (Final)
    R834798C005 (2014)
    R834798C005 (Final)
    R834894 (Final)
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  • Journal Article Dominici F, Greenstone M, Sunstein CR. Particulate matter matters. Science 2014;344(6181):257-259. R834900 (Final)
    R834798 (Final)
    R834798C005 (2014)
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  • Journal Article Dominici F, Wang Y, Correia AW, Ezzati M, Pope IIII CA, Dockery DW. Chemical composition of fine particulate matter and life expectancy: in 95 US counties between 2002 and 2007. Epidemiology 2015;26(4):556-564. R834900 (Final)
    R834894 (2013)
    R834894 (Final)
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  • Journal Article Kioumourtzoglou M-A, Schwartz J, James P, Dominici F, Zanobetti A. PM2.5 and mortality in 207 US cities: modification by temperature and city characteristics. Epidemiology 2016;27(2):221-227. R834900 (Final)
    R834894 (Final)
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  • Journal Article Kioumourtzoglou M-A, Austin E, Koutrakis P, Dominici F, Schwartz J, Zanobetti A. PM2.5 and survival among older adults: effect modification by particulate composition. Epidemiology 2015;26(3):321-327. R834900 (Final)
    R834798 (2015)
    R834798 (Final)
    R834894 (Final)
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  • Abstract: Epidemiology-Abstract HTML
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  • Journal Article Kioumourtzoglou M-A, Schwartz JD, Weisskopf MG, Melly SJ, Wang Y, Dominici F, Zanobetti A. Long-term PM2.5 exposure and neurological hospital admissions in the northeastern United States. Environmental Health Perspectives 2016:124(1):23-29. R834900 (Final)
    R834798 (2015)
    R834894 (Final)
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  • Journal Article Wang Y, Eldridge N, Metersky ML, Sonnenfeld N, Fine JM, Pandolfi MM, Eckenrode S, Bakullari A, Galusha DH, Jaser L, Verzier NR, Nuti SV, Hunt D, Normand SL, Krumholz HM. Association between hospital performance on patient safety and 30-day mortality and unplanned readmission for medicare fee-for-service patients with acute myocardial infarction. Journal of the American Heart Association 2016;5(7):e003731 (14 pp.). R834900 (Final)
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  • Journal Article Zanobetti A, Dominici F, Wang Y, Schwartz JD. A national case-crossover analysis of the short-term effect of PM2.5 on hospitalizations and mortality in subjects with diabetes and neurological disorders. Environmental Health 2014;13(1):38 (11 pp.). R834900 (2012)
    R834900 (2013)
    R834900 (Final)
    R834798 (Final)
    R834798C005 (2014)
    R834798C005 (Final)
    R834894 (Final)
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  • Supplemental Keywords:

    health effects, vulnerability, susceptibility, particulates, environmental epidemiology

    Progress and Final Reports:

    Original Abstract
    2011 Progress Report
    2012 Progress Report
    2013 Progress Report