Air pollution during pregnancy and neonatal outcome: a review

There is increasing evidence of the adverse impact of prenatal exposure to air pollution. This is of particular interest, as exposure during pregnancy--a crucial time span of important biological development--may have long-term implications. The aims of this review are to show current epidemiological evidence of known effects of prenatal exposure to air pollution and present possible mechanisms behind this process. Harmful effects of exposure to air pollution during pregnancy have been shown for different birth outcomes: higher infant mortality, lower birth weight, impaired lung development, increased later respiratory morbidity, and early alterations in immune development. Although results on lower birth weight are somewhat controversial, evidence for higher infant mortality is consistent in studies published worldwide. Possible mechanisms include direct toxicity of particles due to particle translocation across tissue barriers or particle penetration across cellular membranes. The induction of specific processes or interaction with immune cells in either the pregnant mother or the fetus may be possible consequences. Indirect effects could be oxidative stress and inflammation with consequent hemodynamic alterations resulting in decreased placental blood flow and reduced transfer of nutrients to the fetus. The early developmental phase of pregnancy is thought to be very important in determining long-term growth and overall health. So-called "tracking" of somatic growth and lung function is believed to have a huge impact on long-term morbidity, especially from a public health perspective. This is particularly important in areas with high levels of outdoor pollution, where it is practically impossible for an individual to avoid exposure. Especially in these areas, good evidence for the association between prenatal exposure to air pollution and infant mortality exists, clearly indicating the need for more stringent measures to reduce exposure to air pollution.

Late Holocene air temperature variability reconstructed from the sediments of Laguna Escondida, Patagonia, Chile (45 degrees 30 ' S)

Climate and environmental reconstructions from natural archives are important for the interpretation of current climatic change. Few quantitative high-resolution reconstructions exist for South America which is the only land mass extending from the tropics to the southern high latitudes at 56°S. We analyzed sediment cores from two adjacent lakes in Northern Chilean Patagonia, Lago Castor (45°36′S, 71°47′W) and Laguna Escondida (45°31′S, 71°49′W). Radiometric dating (210Pb, 137Cs, 14C-AMS) suggests that the cores reach back to c. 900 BC (Laguna Escondida) and c. 1900 BC (Lago Castor). Both lakes show similarities and reproducibility in sedimentation rate changes and tephra layer deposition. We found eight macroscopic tephras (0.2–5.5 cm thick) dated at 1950 BC, 1700 BC, at 300 BC, 50 BC, 90 AD, 160 AD, 400 AD and at 900 AD. These can be used as regional time-synchronous stratigraphic markers. The two thickest tephras represent known well-dated explosive eruptions of Hudson volcano around 1950 and 300 BC. Biogenic silica flux revealed in both lakes a climate signal and correlation with annual temperature reanalysis data (calibration 1900–2006 AD; Lago Castor r = 0.37; Laguna Escondida r = 0.42, seven years filtered data). We used a linear inverse regression plus scaling model for calibration and leave-one-out cross-validation (RMSEv = 0.56 °C) to reconstruct sub decadal-scale temperature variability for Laguna Escondida back to AD 400. The lower part of the core from Laguna Escondida prior to AD 400 and the core of Lago Castor are strongly influenced by primary and secondary tephras and, therefore, not used for the temperature reconstruction. The temperature reconstruction from Laguna Escondida shows cold conditions in the 5th century (relative to the 20th century mean), warmer temperatures from AD 600 to AD 1150 and colder temperatures from AD 1200 to AD 1450. From AD 1450 to AD 1700 our reconstruction shows a period with stronger variability and on average higher values than the 20th century mean. Until AD 1900 the temperature values decrease but stay slightly above the 20th century mean. Most of the centennial-scale features are reproduced in the few other natural climate archives in the region. The early onset of cool conditions from c. AD 1200 onward seems to be confirmed for this region.

The National Morbidity, Mortality, and Air Pollution Study Database in R

The NMMAPS data package contains daily mortality, air pollution, and weather data originally assembled as part of the National Morbidity,Mortality, and Air Pollution Study (NMMAPS). The data have recently been updated and are available for 108 United States cities for the years 1987--2000. The package provides tools for building versions of the full database in a structured and reproducible manner. These database derivatives may be more suitable for particular analyses. We describe how to use the package to implement a multi-city time series analysis of mortality and PM(10). In addition we demonstrate how to reproduce recent findings based on the NMMAPS data.

Ozone and Mortality: A Meta-Analysis of Time-Series Studies and Comparison to a Multi-City Study (The National Morbidity, Mortality, and Air Pollution Study)

While many time-series studies of ozone and daily mortality identified positive associations,others yielded null or inconclusive results. We performed a meta-analysis of 144 effect estimates from 39 time-series studies, and estimated pooled effects by lags, age groups,cause-specific mortality, and concentration metrics. We compared results to estimates from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS), a time-series study of 95 large U.S. cities from 1987 to 2000. Both meta-analysis and NMMAPS results provided strong evidence of a short-term association between ozone and mortality, with larger effects for cardiovascular and respiratory mortality, the elderly, and current day ozone exposure as compared to other single day lags. In both analyses, results were not sensitive to adjustment for particulate matter and model specifications. In the meta-analysis we found that a 10 ppb increase in daily ozone is associated with a 0.83 (95% confidence interval: 0.53, 1.12%) increase in total mortality, whereas the corresponding NMMAPS estimate is 0.25%(0.12, 0.39%). Meta-analysis results were consistently larger than those from NMMAPS,indicating publication bias. Additional publication bias is evident regarding the choice of lags in time-series studies, and the larger heterogeneity in posterior city-specific estimates in the meta-analysis, as compared with NMAMPS.

A BAYESIAN HIERARCHICAL MODEL FOR CONSTRAINED DISTRIBUTED LAG FUNCTIONS: ESTIMATING THE TIME COURSE OF HOSPITALIZATION ASSOCIATED WITH AIR POLLUTION EXPOSURE

Numerous time series studies have provided strong evidence of an association between increased levels of ambient air pollution and increased levels of hospital admissions, typically at 0, 1, or 2 days after an air pollution episode. An important research aim is to extend existing statistical models so that a more detailed understanding of the time course of hospitalization after exposure to air pollution can be obtained. Information about this time course, combined with prior knowledge about biological mechanisms, could provide the basis for hypotheses concerning the mechanism by which air pollution causes disease. Previous studies have identified two important methodological questions: (1) How can we estimate the shape of the distributed lag between increased air pollution exposure and increased mortality or morbidity? and (2) How should we estimate the cumulative population health risk from short-term exposure to air pollution? Distributed lag models are appropriate tools for estimating air pollution health effects that may be spread over several days. However, estimation for distributed lag models in air pollution and health applications is hampered by the substantial noise in the data and the inherently weak signal that is the target of investigation. We introduce an hierarchical Bayesian distributed lag model that incorporates prior information about the time course of pollution effects and combines information across multiple locations. The model has a connection to penalized spline smoothing using a special type of penalty matrix. We apply the model to estimating the distributed lag between exposure to particulate matter air pollution and hospitalization for cardiovascular and respiratory disease using data from a large United States air pollution and hospitalization database of Medicare enrollees in 94 counties covering the years 1999-2002.