Modeling spatial effects of PM2.5 on term low birth weight in Los Angeles County

Abstract

Air pollution epidemiological studies suggest that elevated exposure to fine particulate matter (PM2.5) is associated with higher prevalence of term low birth weight (TLBW). Previous studies have generally assumed the exposure-response of PM2.5 on TLBW to be the same throughout a large geographical area. Health effects related to PM2.5 exposures, however, may not be uniformly distributed spatially, creating a need for studies that explicitly investigate the spatial distribution of the exposure-response relationship between individual-level exposure to PM2.5 and TLBW. Here, we examine the overall and spatially varying exposure-response relationship between PM2.5 and TLBW throughout urban Los Angeles (LA) County, California. We estimated PM2.5 from a combination of land use regression (LUR), aerosol optical depth from remote sensing, and atmospheric modeling techniques. Exposures were assigned to LA County individual pregnancies identified from electronic birth certificates between the years 1995-2006 (N=1,359,284) provided by the California Department of Public Health. We used a single pollutant multivariate logistic regression model, with multilevel spatially structured and unstructured random effects set in a Bayesian framework to estimate global and spatially varying pollutant effects on TLBW at the census tract level. Overall, increased PM2.5 level was associated with higher prevalence of TLBW county-wide. The spatial random effects model, however, demonstrated that the exposure-response for PM2.5 and TLBW was not uniform across urban LA County. Rather, the magnitude and certainty of the exposure-response estimates for PM2.5 on log odds of TLBW were greatest in the urban core of Central and Southern LA County census tracts. These results suggest that the effects may be spatially patterned, and that simply estimating global pollutant effects obscures disparities suggested by spatial patterns of effects. Studies that incorporate spatial multilevel modeling with random coefficients allow us to identify areas where air pollutant effects on adverse birth outcomes may be most severe and policies to further reduce air pollution might be most effective.

Effect of Environmental Factors on Low Weight in Non-Premature Births: A Time Series Analysis

“…As described in detail previously [18] a growing number of studies have explored the association between air pollutants and LBW, with the most consistent findings showing positive associations with PM 2.5 , PM 10 and NO 2 [14,23,24]. Insofar as the underlying pathophysiological mechanism is concerned, PM 2.5 and ultrafine particles appear to be those which contribute most, linked to a joint action between oxidative stress and pro-inflammatory state.…”

“…LBW, defined as any newborn having a weight of less than 2,500g [7], can give rise to lifelong impairments due to respiratory, circulatory and neurological diseases and disorders [8, 9]. The above types of studies tend to have a cohort design [10–12], whereas those that favour a "short-term" methodology based on statistical analysis and, in addition, are generally geographical in nature, are less numerous [13,14]. While cohort studies have the advantage of pinpointing a cause-effect relationship more clearly, they suffer the disadvantage of being more expensive, in that they require a long spatio-temporal follow-up of exposed persons [15].…”

A Review on Predicting Ground PM2.5 Concentration Using Satellite Aerosol Optical Depth

“…In addition to the models mentioned above, other researchers used linear correlations [16,30,31,37,42,58,71,113,115,117], GAM [23,24,53,65,77], LUR [66,69,70,78,91,122], Kriging [88,90,108] or the nonlinear regression model. Those PM 2.5 estimating models all regard AOD as the primary independent variable.…”

Distribution of PM2.5 Air Pollution in Mexico City: Spatial Analysis with Land-Use Regression Model

“…They also offer more advantages than other approaches because of the statistical relation between the pollutant concentration and the sites where pollutants are monitored, which could also be established with other factors such as population density, traffic, altitude, and weather conditions, amongst other aspects. This analysis has been used in Europe [18,19] and the United States to model air pollution exposure [20,21] and, more recently, in Asian and Middle Eastern countries like China [22,23], Iran [24], and India [25]. However, it has never been used in Mexico City.…”