Early childhood lower respiratory illness and air pollution.

BACKGROUND: Few studies of air pollutants address morbidity in preschool children. In this study we evaluated bronchitis in children from two Czech districts: Teplice, with high ambient air pollution, and Prachatice, characterized by lower exposures. OBJECTIVES: Our goal was to examine rates of lower respiratory illnesses in preschool children in relation to ambient particles and hydrocarbons. METHODS: Air monitoring for particulate matter < 2.5 microm in diameter (PM(2.5)) and polycyclic aromatic hydrocarbons (PAHs) was conducted daily, every third day, or every sixth day. Children born May 1994 through December 1998 were followed to 3 or 4.5 years of age to ascertain illness diagnoses. Mothers completed questionnaires at birth and at follow-up regarding demographic, lifestyle, reproductive, and home environmental factors. Longitudinal multivariate repeated-measures analysis was used to quantify rate ratios for bronchitis and for total lower respiratory illnesses in 1,133 children. RESULTS: After adjustment for season, temperature, and other covariates, bronchitis rates increased with rising pollutant concentrations. Below 2 years of age, increments in 30-day averages of 100 ng/m(3) PAHs and of 25 microg/m(3) PM(2.5) resulted in rate ratios (RRs) for bronchitis of 1.29 [95 % confidence interval (CI), 1.07-1.54] and 1.30 (95% CI, 1.08-1.58), respectively; from 2 to 4.5 years of age, these RRs were 1.56 (95% CI, 1.22-2.00) and 1.23 (95% CI, 0.94-1.62), respectively. CONCLUSION: Ambient PAHs and fine particles were associated with early-life susceptibility to bronchitis. Associations were stronger for longer pollutant-averaging periods and, among children > 2 years of age, for PAHs compared with fine particles. Preschool-age children may be particularly vulnerable to air pollution-induced illnesses.

Air pollution and lymphocyte phenotype proportions in cord blood.

Effects of air pollution on morbidity and mortality may be mediated by alterations in immune competence. In this study we examined short-term associations of air pollution exposures with lymphocyte immunophenotypes in cord blood among 1,397 deliveries in two districts of the Czech Republic. We measured fine particulate matter < 2.5 microm in diameter (PM2.5) and 12 polycyclic aromatic hydrocarbons (PAHs) in 24-hr samples collected by versatile air pollution samplers. Cord blood samples were analyzed using a FACSort flow cytometer to determine phenotypes of CD3+ T-lymphocytes and their subsets CD4+ and CD8+, CD19+ B-lymphocytes, and natural killer cells. The mothers were interviewed regarding sociodemographic and lifestyle factors, and medical records were abstracted for obstetric, labor and delivery characteristics. During the period 1994 to 1998, the mean daily ambient concentration of PM2.5 was 24.8 microg/m3 and that of PAHs was 63.5 ng/m3. In multiple linear regression models adjusted for temperature, season, and other covariates, average PAH or PM2.5 levels during the 14 days before birth were associated with decreases in T-lymphocyte phenotype fractions (i.e., CD3+ CD4+, and CD8+), and a clear increase in the B-lymphocyte (CD19+) fraction. For a 100-ng/m3 increase in PAHs, which represented approximately two standard deviations, the percentage decrease was -3.3% [95% confidence interval (CI), -5.6 to -1.0%] for CD3+, -3.1% (95% CI, -4.9 to -1.3%) for CD4+, and -1.0% (95% CI, -1.8 to -0.2%) for CD8+ cells. The corresponding increase in the CD19+ cell proportion was 1.7% (95% CI, 0.4 to 3.0%). Associations were similar but slightly weaker for PM2.5. Ambient air pollution may influence the relative distribution of lymphocyte immunophenotypes of the fetus.

Statistical approaches to estimating mean water quality concentrations with detection limits.

We review statistical methodology for estimating mean concentrations of potentially toxic pollutants in water, for small samples that are not normally distributed and often contain substantial numbers of nondetects, i.e. samples that are only known to be below some set of fixed thresholds. Maximum likelihood estimation (MLE) and regression on order statistics (ROS) are two main approaches that dominate the literature, with transformation bias under non-normality that increases with the severity of censoring being the main problem. We consider exact maximum likelihood estimators in conjunction with the Box-Cox transformation and propose the Quenouille-Tukey Jackknife as a method for bias reduction and variance estimation. Exact maximum likelihood estimators resulting from the expectation-maximization (EM) algorithm are exhibited in a simple heuristic form that also provides estimated values for the nondetects as subsidiary outputs. We show in simulationsthatthetwo main approaches perform well for the log-normal and gamma distributions as long as the jackknife is employed to reduce bias. Bias corrections to MLE used in the literature are shown to correct in the wrong direction under severe censoring. The jackknife is also used for estimating the variance of the both the MLE and ROS estimators. Robustness is improved by searching a class of power transformations (Box-Cox) for the best approximating normal distribution. We conclude that both the exact MLE and ROS procedures can be useful under varying experimental conditions. Limited simulations indicate that the ROS procedure is unbiased and has a smaller variance than the MLE under the log-normal distribution and is robust. The MLE performed better in simulations involving the gamma as the underlying distribution. We also compare the estimators for the mean and variance that one obtains from typical sets of water quality data, analyzing for copper, alumnium, arsenic, chromium, nickel, and lead.