Perfluorooctanoic acid and chronic kidney disease: Longitudinal analysis of a Mid-Ohio Valley community.

INTRODUCTION: Perfluorooctanoic acid (PFOA) is an environmentally persistent chemical found at low-levels in the serum of almost all U.S. residents. Chronic kidney disease (CKD) has been positively associated with serum PFOA in prior cross-sectional studies and in one occupational mortality study, while other investigations have found no association between kidney function and PFOA. METHODS: We conducted a longitudinal analysis of chronic kidney disease among adults, aged ≥20 years, (N=32,254) in a Mid-Ohio Valley community cohort, exposed to high PFOA levels from contaminated drinking water. Estimated retrospective yearly serum PFOA concentrations (1951-2011) were previously modeled in this population. Information about lifetime history of CKD diagnosis was collected during surveys in 2008-2011; self-reported CKD diagnoses were validated through medical record review. Using a Cox proportional hazards model, we retrospectively examined the association between validated adult onset CKD, and modeled PFOA exposure, from time of first exposure. We also analyzed data for the cohort prospectively, among people with no CKD diagnosis prior to enrollment in a baseline survey in 2005-2006. Both the full cohort and a non-diabetic subset were analyzed, retrospectively and prospectively. RESULTS: Neither in retrospective nor in prospective analyses did we find a significant (α=0.05) trend between PFOA exposure and CKD. In the full cohort, estimated hazard ratios by quintile of cumulative serum PFOA in the retrospective analysis were 1.00 (referent), 1.26, 1.12, 1.12 and 1.24 (trend test for log cumulative exposure: p=0.80). CONCLUSION: Our analyses suggest that CKD is not associated with exposure to PFOA.

Perfluorooctanoic acid exposure and natural menopause: A longitudinal study in a community cohort.

INTRODUCTION: Perfluorooctanoic acid (PFOA), a suspected endocrine disruptor, is a bio-persistent chemical found at low levels in the serum of nearly all U.S. residents. Early menopause has been positively associated with serum PFOA in prior cross-sectional studies. METHODS: We conducted a longitudinal analysis of age at menopause among women, aged ≥40 years, (N=8759) in a Mid-Ohio Valley community cohort, exposed to high PFOA levels via contaminated drinking water. Using estimated retrospective year-specific serum PFOA concentrations (1951-2011), we examined the associations between PFOA, as cumulative exposure or year-specific serum estimates, and natural menopause using a Cox proportional hazards models. As participants were initially recruited in 2005-2006, we also analyzed the cohort prospectively (i.e., from the time of enrollment), using both modeled cumulative PFOA, and PFOA serum levels measured in 2005-2006. Women with hysterectomy (a competing risk) were either censored or excluded from the analysis. RESULTS: Neither in the retrospective nor the prospective cohort did we find a significant (at α=0.05) trend between PFOA exposure and natural menopause. The non-significant, hazard ratios by quintile of increasing cumulative serum PFOA were 1.00 (referent), 1.00, 1.09, 1.05 and 1.06 (trend test for log cumulative exposure: p=0.37) with hysterectomies censored, and 1.00 (referent), 1.06, 1.13, 1.09 and 1.11 (trend test for log cumulative exposure: p=0.85) with hysterectomies excluded. Year-specific serum estimates were also not associated with early menopause. CONCLUSION: Our data suggest that earlier age at menopause is not associated with PFOA exposure.

Associations between ambient air pollutant mixtures and pediatric asthma emergency department visits in three cities: a classification and regression tree approach.

BACKGROUND: Characterizing multipollutant health effects is challenging. We use classification and regression trees to identify multipollutant joint effects associated with pediatric asthma exacerbations and compare these results with those from a multipollutant regression model with continuous joint effects. METHODS: We investigate the joint effects of ozone, NO2 and PM2.5 on emergency department visits for pediatric asthma in Atlanta (1999-2009), Dallas (2006-2009) and St. Louis (2001-2007). Daily concentrations of each pollutant were categorized into four levels, resulting in 64 different combinations or "Day-Types" that can occur. Days when all pollutants were in the lowest level were withheld as the reference group. Separate regression trees were grown for each city, with partitioning based on Day-Type in a model with control for confounding. Day-Types that appeared together in the same terminal node in all three trees were considered to be mixtures of potential interest and were included as indicator variables in a three-city Poisson generalized linear model with confounding control and rate ratios calculated relative to the reference group. For comparison, we estimated analogous joint effects from a multipollutant Poisson model that included terms for each pollutant, with concentrations modeled continuously. RESULTS AND DISCUSSION: No single mixture emerged as the most harmful. Instead, the rate ratios for the mixtures suggest that all three pollutants drive the health association, and that the rate plateaus in the mixtures with the highest concentrations. In contrast, the results from the comparison model are dominated by an association with ozone and suggest that the rate increases with concentration. CONCLUSION: The use of classification and regression trees to identify joint effects may lead to different conclusions than multipollutant models with continuous joint effects and may serve as a complementary approach for understanding health effects of multipollutant mixtures.

Joint effects of ambient air pollutants on pediatric asthma emergency department visits in Atlanta, 1998-2004.

BACKGROUND: Because ambient air pollution exposure occurs as mixtures, consideration of joint effects of multiple pollutants may advance our understanding of the health effects of air pollution. METHODS: We assessed the joint effect of air pollutants on pediatric asthma emergency department visits in Atlanta during 1998-2004. We selected combinations of pollutants that were representative of oxidant gases and secondary, traffic, power plant, and criteria pollutants, constructed using combinations of criteria pollutants and fine particulate matter (PM2.5) components. Joint effects were assessed using multipollutant Poisson generalized linear models controlling for time trends, meteorology, and daily nonasthma upper respiratory emergency department visit counts. Rate ratios (RRs) were calculated for the combined effect of an interquartile range increment in each pollutant's concentration. RESULTS: Increases in all of the selected pollutant combinations were associated with increases in warm-season pediatric asthma emergency department visits (eg, joint-effect RR = 1.13 [95% confidence interval = 1.06-1.21] for criteria pollutants, including ozone, carbon monoxide, nitrogen dioxide, sulfur dioxide, and PM2.5). Cold-season joint effects from models without nonlinear effects were generally weaker than warm-season effects. Joint-effect estimates from multipollutant models were often smaller than estimates based on single-pollutant models, due to control for confounding. Compared with models without interactions, joint-effect estimates from models including first-order pollutant interactions were largely similar. There was evidence of nonlinear cold-season effects. CONCLUSIONS: Our analyses illustrate how consideration of joint effects can add to our understanding of health effects of multipollutant exposures and also illustrate some of the complexities involved in calculating and interpreting joint effects of multiple pollutants.

Classification and regression trees for epidemiologic research: an air pollution example.

BACKGROUND: Identifying and characterizing how mixtures of exposures are associated with health endpoints is challenging. We demonstrate how classification and regression trees can be used to generate hypotheses regarding joint effects from exposure mixtures. METHODS: We illustrate the approach by investigating the joint effects of CO, NO2, O3, and PM2.5 on emergency department visits for pediatric asthma in Atlanta, Georgia. Pollutant concentrations were categorized as quartiles. Days when all pollutants were in the lowest quartile were held out as the referent group (n = 131) and the remaining 3,879 days were used to estimate the regression tree. Pollutants were parameterized as dichotomous variables representing each ordinal split of the quartiles (e.g. comparing CO quartile 1 vs. CO quartiles 2-4) and considered one at a time in a Poisson case-crossover model with control for confounding. The pollutant-split resulting in the smallest P-value was selected as the first split and the dataset was partitioned accordingly. This process repeated for each subset of the data until the P-values for the remaining splits were not below a given alpha, resulting in the formation of a "terminal node". We used the case-crossover model to estimate the adjusted risk ratio for each terminal node compared to the referent group, as well as the likelihood ratio test for the inclusion of the terminal nodes in the final model. RESULTS: The largest risk ratio corresponded to days when PM2.5 was in the highest quartile and NO2 was in the lowest two quartiles (RR: 1.10, 95% CI: 1.05, 1.16). A simultaneous Wald test for the inclusion of all terminal nodes in the model was significant, with a chi-square statistic of 34.3 (p = 0.001, with 13 degrees of freedom). CONCLUSIONS: Regression trees can be used to hypothesize about joint effects of exposure mixtures and may be particularly useful in the field of air pollution epidemiology for gaining a better understanding of complex multipollutant exposures.

Using self-organizing maps to develop ambient air quality classifications: a time series example.

BACKGROUND: Development of exposure metrics that capture features of the multipollutant environment are needed to investigate health effects of pollutant mixtures. This is a complex problem that requires development of new methodologies. OBJECTIVE: Present a self-organizing map (SOM) framework for creating ambient air quality classifications that group days with similar multipollutant profiles. METHODS: Eight years of day-level data from Atlanta, GA, for ten ambient air pollutants collected at a central monitor location were classified using SOM into a set of day types based on their day-level multipollutant profiles. We present strategies for using SOM to develop a multipollutant metric of air quality and compare results with more traditional techniques. RESULTS: Our analysis found that 16 types of days reasonably describe the day-level multipollutant combinations that appear most frequently in our data. Multipollutant day types ranged from conditions when all pollutants measured low to days exhibiting relatively high concentrations for either primary or secondary pollutants or both. The temporal nature of class assignments indicated substantial heterogeneity in day type frequency distributions (~1%-14%), relatively short-term durations (<2 day persistence), and long-term and seasonal trends. Meteorological summaries revealed strong day type weather dependencies and pollutant concentration summaries provided interesting scenarios for further investigation. Comparison with traditional methods found SOM produced similar classifications with added insight regarding between-class relationships. CONCLUSION: We find SOM to be an attractive framework for developing ambient air quality classification because the approach eases interpretation of results by allowing users to visualize classifications on an organized map. The presented approach provides an appealing tool for developing multipollutant metrics of air quality that can be used to support multipollutant health studies.

Feasibility of developing a passive sampler for sampling heavy metals in BMPs for stormwater runoff management.

Structural Best Management Practices (BMPs) have been used for stormwater treatment and management for several decades. How to monitor these BMPs performance reliably and economically is a challenge. This paper reports the feasibility of developing a flow through passive sampler (PS) based on Amberlite IRC748 ion exchange resin operated in kinetic regime for sampling heavy metals in BMPs (infiltration systems) for stormwater treatment and management. Tests were conducted using batch reactors and laboratory-scale BMPs. Batch reactor results indicate that PSs performed desirably with consistent and rapid metal uptake, and thus, the resin-based PS is feasible to be used for stormwater sampling. In lab-scale BMPs tests, the resin PSs were employed for sampling influent and effluent of BMPs loaded with synthetic stormwater for storm durations of 0.5, 3, and 12 hours. The removal efficiency of heavy metals in the BMPs as predicted by PSs was very similar to the actual treatment efficiencies obtained from control BMPs, with errors ranging from -5% to 2%, indicating that the PSs can be used for sampling stormwater and monitoring BMPs. The next step for this sampler will be to develop a method for evaluating the volume of water passing the PS during the sampling period.

Effects of instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, Georgia.

Data from the U.S. Environmental Protection Agency Air Quality System, the Southeastern Aerosol Research and Characterization database, and the Assessment of Spatial Aerosol Composition in Atlanta database for 1999 through 2002 have been used to characterize error associated with instrument precision and spatial variability on the assessment of the temporal variation of ambient air pollution in Atlanta, GA. These data are being used in time series epidemiologic studies in which associations of acute respiratory and cardiovascular health outcomes and daily ambient air pollutant levels are assessed. Modified semivariograms are used to quantify the effects of instrument precision and spatial variability on the assessment of daily metrics of ambient gaseous pollutants (SO2, CO, NOx, and O3) and fine particulate matter ([PM2.5] PM2.5 mass, sulfate, nitrate, ammonium, elemental carbon [EC], and organic carbon [OC]). Variation because of instrument imprecision represented 7-40% of the temporal variation in the daily pollutant measures and was largest for the PM2.5 EC and OC. Spatial variability was greatest for primary pollutants (SO2, CO, NOx, and EC). Population-weighted variation in daily ambient air pollutant levels because of both instrument imprecision and spatial variability ranged from 20% of the temporal variation for O3 to 70% of the temporal variation for SO2 and EC. Wind

Air Pollution and Preterm Birth in the U.S. State of Georgia (2002-2006): Associations with Concentrations of 11 Ambient Air Pollutants Estimated by Combining Community Multiscale Air Quality Model (CMAQ) Simulations with Stationary Monitor Measurements.

BACKGROUND: Previous epidemiologic studies suggest associations between preterm birth and ambient air pollution. OBJECTIVE: We investigated associations between 11 ambient air pollutants, estimated by combining Community Multiscale Air Quality model (CMAQ) simulations with measurements from stationary monitors, and risk of preterm birth (< 37 weeks of gestation) in the U.S. state of Georgia. METHODS: Birth records for singleton births ≥ 27 weeks of gestation with complete covariate information and estimated dates of conception between 1 January 2002 and 28 February 2006 were obtained from the Office of Health Indicators for Planning, Georgia Department of Public Health (n = 511,658 births). Daily pollutant concentrations at 12-km resolution were estimated for 11 ambient air pollutants. We used logistic regression with county-level fixed effects to estimate associations between preterm birth and average pollutant concentrations during the first and second trimester. Discrete-time survival models were used to estimate third-trimester and total pregnancy associations. Effect modification was investigated by maternal education, race, census tract poverty level, and county-level urbanicity. RESULTS: Trimester-specific and total pregnancy associations (p < 0.05) were observed for several pollutants. All the traffic-related pollutants (carbon monoxide, nitrogen dioxide, PM2.5 elemental carbon) were associated with preterm birth [e.g., odds ratios for interquartile range increases in carbon monoxide during the first, second, and third trimesters and total pregnancy were 1.005 (95% CI: 1.001, 1.009), 1.007 (95% CI: 1.002, 1.011), 1.010 (95% CI: 1.006, 1.014), and 1.011 (95% CI: 1.006, 1.017)]. Associations tended to be higher for mothers with low educational attainment and African American mothers. CONCLUSION: Several ambient air pollutants were associated with preterm birth; associations were observed in all exposure windows. CITATION: Hao H, Chang HH, Holmes HA, Mulholland JA, Klein M, Darrow LA, Strickland MJ. 2016. Air pollution and preterm birth in the U.S. state of Georgia (2002-2006): associations with concentrations of 11 ambient air pollutants estimated by combining Community Multiscale Air Quality Model (CMAQ) simulations with stationary monitor measurements. Environ Health Perspect 124:875-880; http://dx.doi.org/10.1289/ehp.1409651.

Characterizing the spatial distribution of multiple pollutants and populations at risk in Atlanta, Georgia.

BACKGROUND: Exposure metrics that identify spatial contrasts in multipollutant air quality are needed to better understand multipollutant geographies and health effects from air pollution. Our aim is to improve understanding of: (1) long-term spatial distributions of multiple pollutants; and (2) demographic characteristics of populations residing within areas of differing air quality. METHODS: We obtained average concentrations for ten air pollutants (p=10) across a 12 km grid (n=253) covering Atlanta, Georgia for 2002-2008. We apply a self-organizing map (SOM) to our data to derive multipollutant patterns observed across our grid and classify locations under their most similar pattern (i.e, multipollutant spatial type (MST)). Finally, we geographically map classifications to delineate regions of similar multipollutant characteristics and characterize associated demographics. RESULTS: We found six MSTs well describe our data, with profiles highlighting a range of combinations, from locations experiencing generally clean air to locations experiencing conditions that were relatively dirty. Mapping MSTs highlighted that downtown areas were dominated by primary pollution and that suburban areas experienced relatively higher levels of secondary pollution. Demographics show the largest proportion of the overall population resided in downtown locations experiencing higher levels of primary pollution. Moreover, higher proportions of nonwhites and children in poverty reside in these areas when compared to suburban populations that resided in areas exhibiting relatively lower pollution. CONCLUSION: Our approach reveals the nature and spatial distribution of differential pollutant combinations across urban environments and provides helpful insights for identifying spatial exposure and demographic contrasts for future health studies.

Head injury does not alter disease progression or neuropathologic outcomes in ALS.

OBJECTIVES: To study the effects of head injury on disease progression and on neuropathologic outcomes in amyotrophic lateral sclerosis (ALS). METHODS: Patients with ALS were surveyed to obtain head injury history, and medical records were reviewed. Linear regression was performed to determine if head injury was a predictor for mean monthly decline of Amyotrophic Lateral Sclerosis Functional Rating Scale-revised (ALSFRS-R), while controlling for confounders. Head injury history was obtained from family members of ALS autopsy cases. The frequency of tau proteinopathy, brain TDP-43 inclusions, and pathologic findings of Alzheimer disease (AD) were examined in ALS cases with head injury compared to cases without. Logistic regression was performed with each neuropathologic diagnosis as an outcome measure and head injury as a predictor variable. RESULTS: No difference was seen in rate of decline of the ALSFRS-R between patients with head injury (n = 24) and without (n = 76), with mean monthly decline of -0.9 for both groups (p = 0.18). Of 47 ALS autopsy cases (n = 9 with head injury, n = 38 without), no significant differences were seen in the frequency of tau proteinopathy (11% with head injury; 24% without), TDP-43 in the brain (44% with head injury; 45% without), or AD pathology (33% with head injury; 26% without). Independent logistic regression models showed head injury was not a predictor of tau pathology (p = 0.42) or TDP-43 in the brain (p = 0.99). CONCLUSIONS: Head injury was not associated with faster disease progression in ALS and did not result in a specific neuropathologic phenotype. The tau pathology described with chronic traumatic encephalopathy was found in ALS autopsy cases both with and without head injury.

Impact of ambient fine particulate matter carbon measurement methods on observed associations with acute cardiorespiratory morbidity.

Elemental carbon (EC) and organic carbon (OC) represent a substantial portion of particulate matter <2.5 µm in diameter (PM2.5), and have been associated with adverse health effects. EC and OC are commonly measured using the National Institute of Occupational Safety and Health (NIOSH) method or the Interagency Monitoring of Protected Visual Environments (IMPROVE) method. Measurement method differences could have an impact on observed epidemiologic associations. Daily speciated PM2.5 data were obtained from the St Louis-Midwest Supersite, and St Louis emergency department (ED) visit data were obtained from the Missouri Hospital Association for the period June 2001 to April 2003. We assessed acute associations between cardiorespiratory ED visits and EC and OC from NIOSH and IMPROVE methods using Poisson generalized linear models controlling for temporal trends and meteorology. Associations were generally similar for EC and OC from the different measurement methods. The most notable difference between methods was observed for congestive heart failure and EC (for example, warm season rate ratios (95% confidence intervals) per interquartile range change in EC concentration were: NIOSH=1.06 (0.99-1.13), IMPROVE=1.01 (0.96-1.07)). Overall, carbon measurement method had little impact on acute associations between EC, OC, and ED visits. Some specific differences were observed, however, which may be related to particle composition.

Impact of a community-based lymphedema management program on episodes of Adenolymphangitis (ADLA) and lymphedema progression--Odisha State, India.

BACKGROUND: Lymphedema management programs have been shown to decrease episodes of adenolymphangitis (ADLA), but the impact on lymphedema progression and of program compliance have not been thoroughly explored. Our objectives were to determine the rate of ADLA episodes and lymphedema progression over time for patients enrolled in a community-based lymphedema management program. We explored the association between program compliance and ADLA episodes as well as lymphedema progression. METHODOLOGY/PRINCIPAL FINDINGS: A lymphedema management program was implemented in Odisha State, India from 2007-2010 by the non-governmental organization, Church's Auxiliary for Social Action, in consultation with the Centers for Disease Control and Prevention. A cohort of patients was followed over 24 months. The crude 30-day rate of ADLA episodes decreased from 0.35 episodes per person-month at baseline to 0.23 at 24 months. Over the study period, the percentage of patients who progressed to more severe lymphedema decreased (P-value  = 0.0004), while those whose lymphedema regressed increased over time (P-value<0.0001). Overall compliance to lymphedema management, lagged one time point, appeared to have little to no association with the frequency of ADLA episodes among those without entry lesions (RR = 0.87 (0.69, 1.10)) and was associated with an increased rate (RR = 1.44 (1.11, 1.86)) among those with entry lesions. Lagging compliance two time points, it was associated with a decrease in the rate of ADLA episodes among those with entry lesions (RR = 0.77 (95% CI: 0.59, 0.99)) and was somewhat associated among those without entry lesions (RR = 0.83 (95% CI: 0.64, 1.06)). Compliance to soap was associated with a decreased rate of ADLA episodes among those without inter-digital entry lesions. CONCLUSIONS/SIGNIFICANCE: These results indicate that a community-based lymphedema management program is beneficial for lymphedema patients for both ADLA episodes and lymphedema. It is one of the first studies to demonstrate an association between program compliance and rate of ADLA episodes.

Impact of a patient education program on disparities in kidney transplant evaluation.

BACKGROUND AND OBJECTIVES: In 2007, the Emory Transplant Center (ETC) kidney transplant program implemented a required educational session for ESRD patients referred for renal transplant evaluation to increase patient awareness and decrease loss to follow-up. The purpose of this study was to evaluate the association of the ETC education program on completion of the transplant evaluation process. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Incident, adult ESRD patients referred from 2005 to 2008 were included. Patient data were abstracted from medical records and linked with data from the United States Renal Data System. Evaluation completion was compared by pre- and posteducational intervention groups in binomial regression models accounting for temporal confounding. RESULTS: A total of 1126 adult ESRD patients were examined in two transplant evaluation eras (75% pre- and 25% postintervention). One-year evaluation completion was higher in the post- versus preintervention group (80.4% versus 44.7%, P<0.0001). In adjusted analyses controlling for time trends, the adjusted probability of evaluation completion at 1 year was higher among the intervention versus nonintervention group (risk ratio=1.38, 95% confidence interval=1.12-1.71). The effect of the intervention was stronger among black patients and those patients living in poor neighborhoods (likelihood ratio test for interaction, P<0.05). CONCLUSIONS: Standardizing transplant education may help reduce some of the racial and socioeconomic disparities observed in kidney transplantation.

Risk estimation with epidemiologic data when response attenuates at high-exposure levels.

BACKGROUND: In occupational studies, which are commonly used for risk assessment for environmental settings, estimated exposure-response relationships often attenuate at high exposures. Relative risk (RR) models with transformed (e.g., log- or square root-transformed) exposures can provide a good fit to such data, but resulting exposure-response curves that are supralinear in the low-dose region may overestimate low-dose risks. Conversely, a model of untransformed (linear) exposure may underestimate risks attributable to exposures in the low-dose region. METHODS: We examined several models, seeking simple parametric models that fit attenuating exposure-response data well. We have illustrated the use of both log-linear and linear RR models using cohort study data on breast cancer and exposure to ethylene oxide. RESULTS: Linear RR models fit the data better than do corresponding log-linear models. Among linear RR models, linear (untransformed), log-transformed, square root-transformed, linear-exponential, and two-piece linear exposure models all fit the data reasonably well. However, the slopes of the predicted exposure-response relations were very different in the low-exposure range, which resulted in different estimates of the exposure concentration associated with a 1% lifetime excess risk (0.0400, 0.00005, 0.0016, 0.0113, and 0.0100 ppm, respectively). The linear (in exposure) model underestimated the categorical exposure-response in the low-dose region, whereas log-transformed and square root-transformed exposure models overestimated it. CONCLUSION: Although a number of models may fit attenuating data well, models that assume linear or nearly linear exposure-response relations in the low-dose region of interest may be preferred by risk assessors, because they do not depend on the choice of a point of departure for linear low-dose extrapolation and are relatively easy to interpret.