Distributed lag analyses of daily hospital admissions and source-apportioned fine particle air pollution

Abstract

Background: Past time-series studies of the health effects of fine particulate matter [aerodynamic diameter ≤ 2.5 µm (PM2.5)] have used chemically nonspecific PM2.5 mass. However, PM2.5 is known to vary in chemical composition with source, and health impacts may vary accordingly.

Objective: We tested the association between source-specific daily PM2.5 mass and hospital admissions in a time-series investigation that considered both single-lag and distributed-lag models.

Methods: Daily PM2.5 speciation measurements collected in midtown Manhattan were analyzed via positive matrix factorization source apportionment. Daily and distributed-lag generalized linear models of Medicare respiratory and cardiovascular hospital admissions during 2001-2002 considered PM2.5 mass and PM2.5 from five sources: transported sulfate, residual oil, traffic, steel metal works, and soil.

Results: Source-related PM2.5 (specifically steel and traffic) was significantly associated with hospital admissions but not with total PM2.5 mass. Steel metal works-related PM2.5 was associated with respiratory admissions for multiple-lag days, especially during the cleanup efforts at the World Trade Center. Traffic-related PM2.5 was consistently associated with same-day cardiovascular admissions across disease-specific subcategories. PM2.5 constituents associated with each source (e.g., elemental carbon with traffic) were likewise associated with admissions in a consistent manner. Mean effects of distributed-lag models were significantly greater than were maximum single-day effect models for both steel- and traffic-related PM2.5.

Conclusions: Past analyses that have considered only PM2.5 mass or only maximum single-day lag effects have likely underestimated PM2.5 health effects by not considering source-specific and distributed-lag effects. Differing lag structures and disease specificity observed for steel-related versus traffic-related PM2.5 raise the possibility of distinct mechanistic pathways of health effects for particles of differing chemical composition.

Figure 1

RRs for total respiratory (A) and total cardiovascular (B) admissions per 5th–95th percentile increment in PM_2.5 mass and source-related PM.

Figure 2

RRs for disease-specific respiratory hospital admissions per 5th–95th percentile increment of steel PM (A) and disease-specific cardiovascular hospital admissions per 5th–95th percentile increment of traffic-related PM (B).

Figure 3

Distributed lag sum and simultaneous inclusion of 0- to 3-day lags for steel-related PM and total respiratory hospital admissions (A) and for traffic-related PM and total cardiovascular hospital admissions (B).

Figure 4

RRs for total respiratory-related (A) and total cardiovascular-related (B) hospital admissions per 5th–95th percentile increment for trace element PM_2.5 constituents.