Psychiatric comorbidity in older long-term abstinent alcoholics.

BACKGROUND: We compared 89 older abstinent alcoholics (OAA, mean abstinence of 14.8 years), to 53 age and gender-comparable older non-alcoholic controls (ONC) with regard to lifetime and current psychiatric diagnoses, lifetime psychiatric symptom counts, and psychological measures in the mood, anxiety, and externalizing disorder domains. We compared these findings with our previously reported results in analogous middle-aged samples (MAA versus MNC). METHODS: The methods used were the same as in our previous study of MAA versus MNC. RESULTS: OAA had more lifetime psychiatric and mood disorder diagnoses than ONC. They also had more lifetime symptoms and psychological test evidence of psychiatric disorder in all domains. However, OAA were less different from ONC than were MAA from MNC on most psychiatric and psychological measures. In both studies, differences between alcoholics and controls were dramatically larger in the externalizing compared with the mood and anxiety domains, and there was little evidence that psychiatric comorbidity measures impacted abstinence duration. CONCLUSIONS: The finding that OAA had less psychiatric illness than MAA may involve a combination of selective survivorship, selection bias, and cohort differences. Although selection bias may be present in clinical studies of samples of any age, it is a more potent problem in older samples. However, given these potential biases, our results underestimate psychiatric comorbidity in OAA, strengthening our finding of increased psychiatric disorder in OAA versus ONC.

Detecting Abrupt Changes in a Piecewise Locally Stationary Time Series.

Non-stationary time series arise in many settings, such as seismology, speech-processing, and finance. In many of these settings we are interested in points where a model of local stationarity is violated. We consider the problem of how to detect these change-points, which we identify by finding sharp changes in the time-varying power spectrum. Several different methods are considered, and we find that the symmetrized Kullback-Leibler information discrimination performs best in simulation studies. We derive asymptotic normality of our test statistic, and consistency of estimated change-point locations. We then demonstrate the technique on the problem of detecting arrival phases in earthquakes.

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.

Statistical parametric mapping of brain morphology: sensitivity is dramatically increased by using brain-extracted images as inputs.

A major attraction of voxel-based morphometry (VBM) is that it allows researchers to explore large datasets with minimal human intervention. However, the validity and sensitivity of the Statistical Parametric Mapping (SPM2) approach to VBM are the subject of considerable debate. We visually inspected the SPM2 gray matter segmentations for 101 research participants and found a gross inclusion of non-brain tissue surrounding the entire brain as gray matter in five subjects and focal areas bordering the brain in which non-brain tissue was classified as gray matter in many other subjects. We also found many areas in which the cortical gray matter was incorrectly excluded from the segmentation of the brain. The major source of these errors was the misregistration of individual brain images with the reference T1-weighted brain template. These errors could be eliminated if SPM2 operated on images from which non-brain tissues (scalp, skull, and meninges) are removed (brain-extracted images). We developed a modified SPM2 processing pipeline that used brain-extracted images as inputs to test this hypothesis. We describe the modifications to the SPM2 pipeline that allow analysis of brain-extracted inputs. Using brain-extracted inputs eliminated the non-brain matter inclusions and the cortical gray matter exclusions noted above, reducing the residual mean square errors (RMSEs, the error term of the SPM2 statistical analyses) by over 30%. We show how this reduction in the RMSEs profoundly affects power analyses. SPM2 analyses of brain-extracted images may require sample sizes only half as great as analyses of non-brain-extracted images.

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.

Controlling for premorbid brain size in imaging studies: T1-derived cranium scaling factor vs. T2-derived intracranial vault volume.

Intracranial vault (ICV) volume, obtained from T2-weighted magnetic resonance imaging (MRI), is generally used to estimate premorbid brain size in imaging studies. T1-weighted sequences lack the signal characteristics for ICV measurements [they have poor contrast at the outer boundary of sulcal cranium scaling factor (CSF)] but are valuable in imaging studies due to their excellent gray vs. white matter contrast. Smith et al. [NeuroImage 17 (2002) 479] suggested a T1-derived cranium scaling factor as an alternative control variable for premorbid brain size in cross-sectional studies. This index, which is computed using the SIENAX software, is a scaling factor comparing an individual's skull to a template skull derived from the Montreal Neurological Institute (MNI) average of 152 T1 studies (the MNI152). SIENAX computes coarsely defined estimates for the individual and MNI skulls rather than well-defined volumes. To test how well this approach would work as a control variable for premorbid brain size in cross-sectional studies, we compared the T1-derived cranium scaling factor to T2-derived ICV measurements in a sample of 92 individuals: 39 white males, 22 white females, and 31 African-American males, with an age range of 26-78 years. The correlation between T1- and T2-derived variables was 0.94 and did not differ across subject groups. The T1-derived cranium scaling factor accounted for a statistically significant portion (87%) of the variance of the T2-derived ICV measure and thus is a good surrogate for ICV measurement of premorbid brain size as a reference measure in MRI atrophy studies. Furthermore, neither race, sex, nor age accounted for any additional variance in ICV, indicating that neither race-, gender-, nor age-associated cranial bone thickness effects were present in this data set.

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.