Time Series Behavior of Occupation Exposure Data

Prior studies have observed that exposure variability increased as a function of sampling duration and attributed this phenomenon to autocorrelation. This study confirmed such behavior in occupational exposure data after controlling for factors likely to contribute to variability and assessed the impact of non-stationarity, as well as autocorrelation, on the results. Consecutive shift-long exposure measurements for 54 workers from five different data sets in 149 time series were analyzed to evaluate the variance as the interval between measurements increased. When the data were combined a clear increasing trend in the variance was observed with lag. However, a breakdown by data set revealed that the trend was present in only one of the five data sets. The effect was further isolated to 42% of the workers who contributed data and to less than 1/3 of the total number of time series analyzed. Autocorrelation and non-stationary behavior explained the increase in 60% of the time series where the trend was evident. Analysis of the entire database revealed that a small percentage of time series produced significant first-order autocorrelation coefficients or were non-stationary over the interval in which sampling was conducted. If these results are typical of other workplaces, sampling strategies may not need to address problems associated with autocorrelation or nonstationarity.Master of Science in Public Healt

Kriged and modeled ambient air levels of benzene in an urban environment: an exposure assessment study

<p>Abstract</p> <p>Background</p> <p>There is increasing concern regarding the potential adverse health effects of air pollution, particularly hazardous air pollutants (HAPs). However, quantifying exposure to these pollutants is problematic.</p> <p>Objective</p> <p>Our goal was to explore the utility of kriging, a spatial interpolation method, for exposure assessment in epidemiologic studies of HAPs. We used benzene as an example and compared census tract-level kriged predictions to estimates obtained from the 1999 U.S. EPA National Air Toxics Assessment (NATA), Assessment System for Population Exposure Nationwide (ASPEN) model.</p> <p>Methods</p> <p>Kriged predictions were generated for 649 census tracts in Harris County, Texas using estimates of annual benzene air concentrations from 17 monitoring sites operating in Harris and surrounding counties from 1998 to 2000. Year 1999 ASPEN modeled estimates were also obtained for each census tract. Spearman rank correlation analyses were performed on the modeled and kriged benzene levels. Weighted kappa statistics were computed to assess agreement between discretized kriged and modeled estimates of ambient air levels of benzene.</p> <p>Results</p> <p>There was modest correlation between the predicted and modeled values across census tracts. Overall, 56.2%, 40.7%, 31.5% and 28.2% of census tracts were classified as having 'low', 'medium-low', 'medium-high' and 'high' ambient air levels of benzene, respectively, comparing predicted and modeled benzene levels. The weighted kappa statistic was 0.26 (95% confidence interval (CI) = 0.20, 0.31), indicating poor agreement between the two methods.</p> <p>Conclusions</p> <p>There was a lack of concordance between predicted and modeled ambient air levels of benzene. Applying methods of spatial interpolation for assessing exposure to ambient air pollutants in health effect studies is hindered by the placement and number of existing stationary monitors collecting HAP data. Routine monitoring needs to be expanded if we are to use these data to better assess environmental health risks in the future.</p

Pm

There is limited evidence on the role of exposure to chemical constituents of fine particulate matter (P

Effects of aerosol Particle Size On the Measurement of Airborne PM

Previous validation studies found a good linear correlation between the low-cost particulate matter sensors (LCPMS) and other research grade particulate matter (PM) monitors. This study aimed to determine if different particle size bins of PM would affect the linear relationship and agreement between the Dylos DC1700 (LCPMS) particle count measurements (converted to P

Characterization of Urinary Concentrations of Heavy Metals Among Socioeconomically Disadvantaged Black Pregnant Women

The objective of this study was to characterize exposures to metals using biological samples collected on socioeconomically disadvantaged black pregnant women. We obtained 131 anonymous urine samples provided by black pregnant women visiting a Medicaid-serving prenatal clinic in Houston, TX, from March 27, 2017 to April 11, 2017. We analyzed urine samples for 15 metals including cadmium (Cd), arsenic (As), lead (Pb), and nickel (Ni) and for creatinine and cotinine. We found that median concentrations of zinc (Zn), selenium (Se), and aluminum (Al) among black pregnant women in this study were 1.5 to 3 times higher than levels reported among a cohort of well-educated non-Hispanic white pregnancy planners. We also observed elevated levels of urinary Cd and antimony (Sb) as compared with those reported for a nationally representative sample of adult women in the USA. Based on the results of an exploratory factor analysis, potential sources of metal exposures in this population may arise in home environments or be due to diet, industrial and natural sources, or traffic

Temperature, Placental abruption and Stillbirth

BACKGROUND: Pregnant women may be vulnerable to changes in ambient temperature and warming climates. Recent evidence suggests that temperature increases are associated with placental abruption, a risk factor for stillbirth. OBJECTIVES: We investigated the effect of acute exposures to apparent temperature on stillbirths in Harris County, Texas, 2008-2013. METHODS: We conducted a case-crossover study to investigate the association between temperature and stillbirth among 708 women. We used data from the National Climatic Data Center to estimate maternal exposure to daily average apparent temperature over the days (lag days 1 through 6) preceding the stillbirth event. We employed symmetric bidirectional sampling to select six control periods one to three weeks before and after each event and applied conditional logistic regression to examine associations between increases of apparent temperature and stillbirths during the warm season (May-September). We adjusted for fine particulate matter (PM RESULTS: Independent of air pollutant exposures, a 10 °F increase in apparent temperature in the week preceding delivery (lag days 1 to 6) was positively associated with a 45% (adjusted OR = 1.45, 95% confidence interval (CI): 1.18, 1.77) increase in risk for stillbirth. Risks were elevated for stillbirths occurring in June through August, for Hispanic and non-Hispanic Black women, but not for non-Hispanic Whites. We also observed elevated risks associated with temperature increases in the few days preceding delivery among stillbirths caused by placental abruption, with the risk being highest on lag day 1 (OR = 1.93, 95% CI: 1.15, 3.23). CONCLUSIONS: Independent of maternal ambient air pollutant exposure, we found evidence of an association between apparent temperature increases in the week preceding an event and risk of stillbirth. Risks for stillbirth varied by race/ethnicity. Further, in the first study to evaluate the impact of temperature on a specific complication during pregnancy, the risks were higher among mothers with placental abruption

A scalable two-stage Bayesian approach accounting for exposure measurement error in environmental epidemiology

Accounting for exposure measurement errors has been recognized as a crucial problem in environmental epidemiology for over two decades. Bayesian hierarchical models offer a coherent probabilistic framework for evaluating associations between environmental exposures and health effects, which take into account exposure measurement errors introduced by uncertainty in the estimated exposure as well as spatial misalignment between the exposure and health outcome data. While two-stage Bayesian analyses are often regarded as a good alternative to fully Bayesian analyses when joint estimation is not feasible, there has been minimal research on how to properly propagate uncertainty from the first-stage exposure model to the second-stage health model, especially in the case of a large number of participant locations along with spatially correlated exposures. We propose a scalable two-stage Bayesian approach, called a sparse multivariate normal (sparse MVN) prior approach, based on the Vecchia approximation for assessing associations between exposure and health outcomes in environmental epidemiology. We compare its performance with existing approaches through simulation. Our sparse MVN prior approach shows comparable performance with the fully Bayesian approach, which is a gold standard but is impossible to implement in some cases. We investigate the association between source-specific exposures and pollutant (nitrogen dioxide (NO$_2$))-specific exposures and birth outcomes for 2012 in Harris County, Texas, using several approaches, including the newly developed method.Comment: 34 pages, 8 figure