The temporal lag structure of short-term associations of fine particulate matter chemical constituents and cardiovascular and respiratory hospitalizations

Abstract

Background: In air pollution time-series studies, the temporal pattern of the association of fine particulate matter (PM2.5; particulate matter ≤ 2.5 µm in aerodynamic diameter) and health end points has been observed to vary by disease category. The lag pattern of PM2.5 chemical constituents has not been well investigated, largely because daily data have not been available.

Objectives: We explored the lag structure for hospital admissions using daily PM2.5 chemical constituent data for 5 years in the Denver Aerosol Sources and Health (DASH) study.

Methods: We measured PM2.5 constituents, including elemental carbon, organic carbon, sulfate, and nitrate, at a central residential site from 2003 through 2007 and linked these daily pollution data to daily hospital admission counts in the five-county Denver metropolitan area. Total hospital admissions and subcategories of respiratory and cardiovascular admissions were examined. We assessed the lag structure of relative risks (RRs) of hospital admissions for PM2.5 and four constituents on the same day and from 1 to 14 previous days from a constrained distributed lag model; we adjusted for temperature, humidity, longer-term temporal trends, and day of week using a generalized additive model.

Results: RRs were generally larger at shorter lags for total cardiovascular admissions but at longer lags for total respiratory admissions. The delayed lag pattern was particularly prominent for asthma. Elemental and organic carbon generally showed more immediate patterns, whereas sulfate and nitrate showed delayed patterns.

Conclusion: In general, PM2.5 chemical constituents were found to have more immediate estimated effects on cardiovascular diseases and more delayed estimated effects on respiratory diseases, depending somewhat on the constituent.

Figure 1

Map showing the DASH monitoring site within the five-county Denver metropolitan area and total hospital admissions by ZIP code for 2003 through 2007. STN, Speciation Trends Network; ID, U.S. EPA database monitoring identification code.

Figure 2

The pattern of RRs from lag 0 to lag 14 estimated from a constrained distributed lag model of total cardiovascular disease and respiratory disease hospitalizations for an IQR increase in PM_2.5 mass and the four chemical components using data from 2003 through 2007 within the five-county Denver metropolitan area.

Figure 3

The pattern of RRs from lag 0 to lag 14 estimated from a constrained distributed lag model of cause-specific hospitalizations for cardiovascular diseases [ischemic heart disease (IHD), congestive heart failure (CHF), cerebrovascular disease (CBVD), and ischemic stroke (IS)] and for respiratory diseases (COPD, asthma, and pneumonia) for an IQR increase in PM_2.5 and the four chemical components. Data from 2003 through 2007 and the five-county Denver metropolitan area.