A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models

BACKGROUND

Exposure measurement error in copollutant epidemiologic models has the potential to introduce bias in relative risk (RR) estimates. A simulation study was conducted using empirical data to quantify the impact of correlated measurement errors in time-series analyses of air pollution and health.

METHODS

ZIP-code level estimates of exposure for six pollutants (CO, NO_x, EC, PM_2.5, SO₄, O₃) from 1999 to 2002 in the Atlanta metropolitan area were used to calculate spatial, population (i.e. ambient versus personal), and total exposure measurement error. Empirically determined covariance of pollutant concentration pairs and the associated measurement errors were used to simulate true exposure (exposure without error) from observed exposure. Daily emergency department visits for respiratory diseases were simulated using a Poisson time-series model with a main pollutant RR = 1.05 per interquartile range, and a null association for the copollutant (RR = 1). Monte Carlo experiments were used to evaluate the impacts of correlated exposure errors of different copollutant pairs.

RESULTS

Substantial attenuation of RRs due to exposure error was evident in nearly all copollutant pairs studied, ranging from 10 to 40% attenuation for spatial error, 3-85% for population error, and 31-85% for total error. When CO, NO_x or EC is the main pollutant, we demonstrated the possibility of false positives, specifically identifying significant, positive associations for copollutants based on the estimated type I error rate.

CONCLUSIONS

The impact of exposure error must be considered when interpreting results of copollutant epidemiologic models, due to the possibility of attenuation of main pollutant RRs and the increased probability of false positives when measurement error is present.

Assessing the Association between Thermotolerant Coliforms in Drinking Water and Diarrhea: An Analysis of Individual-Level Data from Multiple Studies

BACKGROUND

Fecally contaminated drinking water is believed to be a major contributor to the global burden of diarrheal disease and a leading cause of mortality among young children. However, recent systematic reviews and results from blinded studies of water quality interventions have raised questions about the risk associated with fecally contaminated water, particularly as measured by thermotolerant coliform (TTC) bacteria, a WHO-approved indicator of drinking water quality.

OBJECTIVES

We investigated the association between TTC in drinking water and diarrhea using data from seven previous studies.

METHODS

We obtained individual-level data from available field studies that measured TTC levels in household-drinking water and reported prevalence of diarrhea among household members during the days prior to the visit.

RESULTS

The combined data set included diarrhea prevalence for 26,518 individuals and 8,000 water samples from 4,017 households, yielding 45,052 observations. The odds of diarrhea increased for each log10 increase in TTC/100 mL by 18% (95% CI: 11, 26%) for children < 5 years old and 12% (95% CI: 8, 18%) for all ages. For all ages, the odds of diarrhea increased by 21%, 35% and 49% for those whose household water samples were from 11-100, 101-1,000, and > 1,000 TTC/100 mL, respectively compared to < 1 TTC/100 mL. We found no evidence of increased odds of diarrhea with contamination levels below 11 TTC/100 mL, either in adults or children.

CONCLUSIONS

Our analysis of individual-level data shows increased risk of diarrhea with increasing levels of TTC in drinking water. These results suggest an association between fecally contaminated water and diarrheal disease and provides support for health-based targets for levels of TTC in drinking water and for interventions to improve drinking water quality to prevent diarrhea.

CITATION

Hodge J, Chang HH, Boisson S, Collin SM, Peletz R, Clasen T. 2016. Assessing the association between thermotolerant coliforms in drinking water and diarrhea: an analysis of individual level data from multiple studies. Environ Health Perspect 124:1560-1567; http://dx.doi.org/10.1289/EHP156.

Spatiotemporal hurdle models for zero-inflated count data: Exploring trends in emergency department visits

Motivated by a study exploring spatiotemporal trends in emergency department use, we develop a class of two-part hurdle models for the analysis of zero-inflated areal count data. The models consist of two components—one for the probability of any emergency department use and one for the number of emergency department visits given use. Through a hierarchical structure, the models incorporate both patient- and region-level predictors, as well as spatially and temporally correlated random effects for each model component. The random effects are assigned multivariate conditionally autoregressive priors, which induce dependence between the components and provide spatial and temporal smoothing across adjacent spatial units and time periods, resulting in improved inferences. To accommodate potential overdispersion, we consider a range of parametric specifications for the positive counts, including truncated negative binomial and generalized Poisson distributions. We adopt a Bayesian inferential approach, and posterior computation is handled conveniently within standard Bayesian software. Our results indicate that the negative binomial and generalized Poisson hurdle models vastly outperform the Poisson hurdle model, demonstrating that overdispersed hurdle models provide a useful approach to analyzing zero-inflated spatiotemporal data.

Weighted-SAMGSR: combining significance analysis of microarray-gene set reduction algorithm with pathway topology-based weights to select relevant genes

BACKGROUND

It has been demonstrated that a pathway-based feature selection method that incorporates biological information within pathways during the process of feature selection usually outperforms a gene-based feature selection algorithm in terms of predictive accuracy and stability. Significance analysis of microarray-gene set reduction algorithm (SAMGSR), an extension to a gene set analysis method with further reduction of the selected pathways to their respective core subsets, can be regarded as a pathway-based feature selection method.

METHODS

In SAMGSR, whether a gene is selected is mainly determined by its expression difference between the phenotypes, and partially by the number of pathways to which this gene belongs. It ignores the topology information among pathways. In this study, we propose a weighted version of the SAMGSR algorithm by constructing weights based on the connectivity among genes and then combing these weights with the test statistics.

RESULTS

Using both simulated and real-world data, we evaluate the performance of the proposed SAMGSR extension and demonstrate that the weighted version outperforms its original version. CONCLUSIONS: To conclude, the additional gene connectivity information does faciliatate feature selection.

REVIEWERS

This article was reviewed by Drs. Limsoon Wong, Lev Klebanov, and, I. King Jordan.

Assessment of neighbourhood-level socioeconomic status as a modifier of air pollution-asthma associations among children in Atlanta

BACKGROUND

A broad literature base provides evidence of association between air pollution and paediatric asthma. Socioeconomic status (SES) may modify these associations; however, previous studies have found inconsistent evidence regarding the role of SES.

METHODS

Effect modification of air pollution-paediatric asthma morbidity by multiple indicators of neighbourhood SES was examined in Atlanta, Georgia. Emergency department (ED) visit data were obtained for 5-18 years old with a diagnosis of asthma in 20-county Atlanta during 2002-2008. Daily ZIP Code Tabulation Area (ZCTA)-level concentrations of ozone, nitrogen dioxide, fine particulate matter and elemental carbon were estimated using ambient monitoring data and emissions-based chemical transport model simulations. Pollutant-asthma associations were estimated using a case-crossover approach, controlling for temporal trends and meteorology. Effect modification by ZCTA-level (neighbourhood) SES was examined via stratification.

RESULTS

We observed stronger air pollution-paediatric asthma associations in 'deprivation areas' (eg, ≥20% of the ZCTA population living in poverty) compared with 'non-deprivation areas'. When stratifying analyses by quartiles of neighbourhood SES, ORs indicated stronger associations in the highest and lowest SES quartiles and weaker associations among the middle quartiles.

CONCLUSIONS

Our results suggest that neighbourhood-level SES is a factor contributing vulnerability to air pollution-related paediatric asthma morbidity in Atlanta. Children living in low SES environments appear to be especially vulnerable given positive ORs and high underlying asthma ED rates. Inconsistent findings of effect modification among previous studies may be partially explained by choice of SES stratification criteria, and the use of multiplicative models combined with differing baseline risk across SES populations.

Ambient air pollution epidemiology systematic review and meta-analysis: A review of reporting and methods practice

BACKGROUND

Systematic review and meta-analysis (SRMA) are increasingly employed in environmental health (EH) epidemiology and, provided methods and reporting are sound, contribute to translating science evidence to policy. Ambient air pollution (AAP) is both among the leading environmental causes of mortality and morbidity worldwide, and of growing policy relevance due to health co-benefits associated with greenhouse gas emissions reductions.

OBJECTIVES

We reviewed the published AAP SRMA literature (2009 to mid-2015), and evaluated the consistency of methods, reporting and evidence evaluation using a 22-point questionnaire developed from available best-practice consensus guidelines and emerging recommendations for EH. Our goal was to contribute to enhancing the utility of AAP SRMAs to EH policy.

RESULTS AND DISCUSSION

We identified 43 studies that used both SR and MA techniques to examine associations between the AAPs PM2.5, PM10, NO2, SO2, CO and O3, and various health outcomes. On average AAP SRMAs partially or thoroughly addressed 16 of 22 questions (range 10-21), and thoroughly addressed 13 of 22 (range 5-19). We found evidence of an improving trend over the period. However, we observed some weaknesses, particularly infrequent formal reviews of underlying study quality and risk-of-bias that correlated with lower frequency of thorough evaluation for key study quality parameters. Several other areas for enhanced reporting are highlighted.

CONCLUSIONS

The AAP SRMA literature, in particular more recent studies, indicate broad concordance with current and emerging best practice guidance. Development of an EH-specific SRMA consensus statement including a risk-of-bias evaluation tool, would be a contribution to enhanced reliability and robustness as well as policy utility.

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.

Current Methods and Challenges for Epidemiological Studies of the Associations Between Chemical Constituents of Particulate Matter and Health

Epidemiological studies have been critical for estimating associations between exposure to ambient particulate matter (PM) air pollution and adverse health outcomes. Because total PM mass is a temporally and spatially varying mixture of constituents with different physical and chemical properties, recent epidemiological studies have focused on PM constituents. Most studies have estimated associations between PM constituents and health using the same statistical methods as in studies of PM mass. However, these approaches may not be sufficient to address challenges specific to studies of PM constituents, namely assigning exposure, disentangling health effects, and handling measurement error. We reviewed large, population-based epidemiological studies of PM constituents and health and describe the statistical methods typically applied to address these challenges. Development of statistical methods that simultaneously address multiple challenges, for example, both disentangling health effects and handling measurement error, could improve estimation of associations between PM constituents and adverse health outcomes.

Improving the Accuracy of Daily PM2.5 Distributions Derived from the Fusion of Ground-Level Measurements with Aerosol Optical Depth Observations, a Case Study in North China

The accuracy in estimated fine particulate matter concentrations (PM2.5), obtained by fusing of station-based measurements and satellite-based aerosol optical depth (AOD), is often reduced without accounting for the spatial and temporal variations in PM2.5 and missing AOD observations. In this study, a city-specific linear regression model was first developed to fill in missing AOD data. A novel interpolation-based variable, PM2.5 spatial interpolator (PMSI2.5), was also introduced to account for the spatial dependence in PM2.5 across grid cells. A Bayesian hierarchical model was then developed to estimate spatiotemporal relationships between AOD and PM2.5. These methods were evaluated through a city-specific 10-fold cross-validation procedure in a case study in North China in 2014. The cross validation R(2) was 0.61 when PMSI2.5 was included and 0.48 when PMSI2.5 was excluded. The gap-filled AOD values also effectively improved predicted PM2.5 concentrations with an R(2) = 0.78. Daily ground-level PM2.5 concentration fields at a 12 km resolution were predicted with complete spatial and temporal coverage. This study also indicates that model prediction performance should be assessed by accounting for monitor clustering due to the potential misinterpretation of model accuracy in spatial prediction when validation monitors are randomly selected.

Pediatric Emergency Visits and Short-Term Changes in PM2.5 Concentrations in the U.S. State of Georgia

BACKGROUND

Associations between pediatric emergency department (ED) visits and ambient concentrations of particulate matter ≤ 2.5 μm in diameter (PM2.5) have been reported in previous studies, although few were performed in nonmetropolitan areas.

OBJECTIVE

We estimated associations between daily PM2.5 concentrations, using a two-stage model that included land use parameters and satellite aerosol optical depth measurements at 1-km resolution, and ED visits for six pediatric conditions in the U.S. state of Georgia by urbanicity classification.

METHODS

We obtained pediatric ED visits geocoded to residential ZIP codes for visits with nonmissing PM2.5 estimates and admission dates during 1 January 2002-30 June 2010 for 2- to 18-year-olds for asthma or wheeze (n = 189,816), and for 0- to 18-year-olds for bronchitis (n = 76,243), chronic sinusitis (n = 15,745), otitis media (n = 237,833), pneumonia (n = 52,946), and upper respiratory infections (n = 414,556). Daily ZIP code-level estimates of 24-hr average PM2.5 were calculated by averaging concentrations within ZIP code boundaries. We used time-stratified case-crossover models stratified on ZIP code, year, and month to estimate odds ratios (ORs) between ED visits and same-day and previous-day PM2.5 concentrations at the ZIP code level, and we investigated effect modification by county-level urbanicity.

RESULTS

A 10-μg/m3 increase in same-day PM2.5 concentrations was associated with ED visits for asthma or wheeze (OR = 1.013; 95% CI: 1.003, 1.023) and upper respiratory infections (OR = 1.015; 95% CI: 1.008, 1.022); associations with previous-day PM2.5 concentrations were lower. Differences in the association estimates across levels of urbanicity were not statistically significant.

CONCLUSION

Pediatric ED visits for asthma or wheeze and for upper respiratory infections were associated with PM2.5 concentrations in Georgia.

CITATION

Strickland MJ, Hao H, Hu X, Chang HH, Darrow LA, Liu Y. 2016. Pediatric emergency visits and short-term changes in PM2.5 concentrations in the U.S. state of Georgia. Environ Health Perspect 124:690-696; http://dx.doi.org/10.1289/ehp.1509856.

Warm season temperatures and emergency department visits in Atlanta, Georgia

PURPOSE

Extreme heat events will likely increase in frequency with climate change. Heat-related health effects are better documented among the elderly than among younger age groups. We assessed associations between warm-season ambient temperature and emergency department (ED) visits across ages in Atlanta during 1993-2012.

METHODS

We examined daily counts of ED visits with primary diagnoses of heat illness, fluid/electrolyte imbalances, renal disease, cardiorespiratory diseases, and intestinal infections by age group (0-4, 5-18, 19-64, 65+years) in relation to daily maximum temperature (TMX) using Poisson time series models that included cubic terms for TMX at single-day lags of 0-6 days, controlling for maximum dew-point temperature, time trends, week day, holidays, and hospital participation periods. We estimated rate ratios (RRs) and 95% confidence intervals (CI) for TMX changes from 27°C to 32°C (25th to 75th percentile) and conducted extensive sensitivity analyses.

RESULTS

We observed associations between TMX and ED visits for all internal causes, heat illness, fluid/electrolyte imbalances, renal diseases, asthma/wheeze, diabetes, and intestinal infections. Age groups with the strongest observed associations were 65+years for all internal causes [lag 0 RR (CI)=1.022 (1.016-1.028)] and diabetes [lag 0 RR=1.050 (1.008-1.095)]; 19-64 years for fluid/electrolyte imbalances [lag 0 RR=1.170 (1.136-1.205)] and renal disease [lag 1 RR=1.082 (1.065-1.099)]; and 5-18 years for asthma/wheeze [lag 2 RR=1.059 (1.030-1.088)] and intestinal infections [lag 1 RR=1.120 (1.041-1.205)].

CONCLUSIONS

Varying strengths of associations between TMX and ED visits by age suggest that optimal interventions and health-impact projections would account for varying heat health impacts across ages.

The Impact of a School-Based Water, Sanitation, and Hygiene Program on Absenteeism, Diarrhea, and Respiratory Infection: A Matched-Control Trial in Mali

We conducted a matched-control trial in Mali to assess the effectiveness of a comprehensive school-based water, sanitation, and hygiene (WASH) intervention on pupil absence, diarrhea, and respiratory infections. After completion of the intervention, data were collected from 100 beneficiary schools and 100 matched comparison schools in 5-6 sessions over a 14-month period. Data collection included roll calls to assess absenteeism and interviews with a subset of pupils to assess recent absence and disease symptoms. The odds of pupils being absent at roll call were 23% higher in beneficiary schools than in comparison schools (odds ratio [OR]: 1.23, 95% confidence interval [CI]: 1.06, 1.42). The odds of pupils reporting being absent due to diarrhea (OR: 0.73, 95% CI: 0.56, 0.94) or having had diarrhea (OR: 0.71, 95% CI: 0.60, 0.85) or respiratory infection symptoms (OR: 0.75, 95% CI: 0.65, 0.86) in the past week were lower in beneficiary schools compared with comparison schools. We found that a school-based WASH intervention can have a positive effect on reducing rates of illness, as well as absence due to diarrhea. However, we did not find evidence that these health impacts led to a reduction in overall absence. Higher absence rates are less likely attributable to the intervention than the result of an imbalance in unobserved confounders between study groups.

Method for Fusing Observational Data and Chemical Transport Model Simulations To Estimate Spatiotemporally Resolved Ambient Air Pollution

Investigations of ambient air pollution health effects rely on complete and accurate spatiotemporal air pollutant estimates. Three methods are developed for fusing ambient monitor measurements and 12 km resolution chemical transport model (CMAQ) simulations to estimate daily air pollutant concentrations across Georgia. Temporal variance is determined by observations in one method, with the annual mean CMAQ field providing spatial structure. A second method involves scaling daily CMAQ simulated fields using mean observations to reduce bias. Finally, a weighted average of these results based on prediction of temporal variance provides optimized daily estimates for each 12 × 12 km grid. These methods were applied to daily metrics of 12 pollutants (CO, NO2, NOx, O3, SO2, PM10, PM2.5, and five PM2.5 components) over the state of Georgia for a seven-year period (2002-2008). Cross-validation demonstrates a wide range in optimized model performance across pollutants, with SO2 predicted most poorly due to limitations in coal combustion plume monitoring and modeling. For the other pollutants studied, 54-88% of the spatiotemporal variance (Pearson R(2) from cross-validation) was captured, with ozone and PM2.5 predicted best. The optimized fusion approach developed provides daily spatial field estimates of air pollutant concentrations and uncertainties that are consistent with observations, emissions, and meteorology.

Ambient air pollution and emergency department visits for asthma: a multi-city assessment of effect modification by age

Previous studies have found strong associations between asthma morbidity and major ambient air pollutants. Relatively little research has been conducted to assess whether age is a factor conferring susceptibility to air pollution-related asthma morbidity. We investigated the short-term relationships between asthma emergency department (ED) visits and ambient ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and fine particulate matter (PM2.5) in Atlanta (1993-2009), Dallas (2006-2009), and St. Louis (2001-2007). City-specific daily time-series analyses were conducted to estimate associations by age group (0-4, 5-18, 19-39, 40-64, and 65+ years). Sub-analyses were performed stratified by race and sex. City-specific rate ratios (RRs) were combined by inverse-variance weighting to provide an overall association for each strata. The overall RRs differed across age groups, with associations for all pollutants consistently strongest for children aged 5-18 years. The patterns of association across age groups remained generally consistent when models were stratified by sex and race, although the strong observed associations among 5-18 year olds appeared to be partially driven by non-white and male patients. Our findings suggest that age is a susceptibility factor for asthma exacerbations in response to air pollution, with school-age children having the highest susceptibility.

A longitudinal study of the association between the GNB3 C825T polymorphism and metabolic disturbance in bipolar II patients treated with valproate

This longitudinal study aimed to investigate the associations between the polymorphisms of guanine nucleotide-binding protein subunit β-3 (GNB3) C825T and metabolic disturbance in bipolar II disorder (BP-II) patients being treated with valproate (VPA). A 100 BP-II patients received a 12-week course of VPA treatment, and their body weight and metabolic indices were measured. At baseline, the GNB3 C825T polymorphisms were associated with the triglyceride level (P=0.032) in BP-II patients. During the VPA treatment course, the polymorphisms were not only associated with body mass index (BMI) and waist circumference (P-values=0.009 and 0.001, respectively), but also with total cholesterol, triglyceride, low-density lipoprotein and leptin levels (P-values=0.004, 0.002, 0.031 and 0.015, respectively). Patients with the TT genotype had a lower BMI, smaller waist circumference, and lower levels of lipids and leptin than those with the CT or CC genotypes undergoing the VPA treatment course.

Interactions of acylated methylglucoside derivatives with CO2: simulation and calculations

Carbohydrates have drawn considerable interest from researchers recently due to their affinity for CO2. However, most of the research in this field has focused on peracetylated derivatives. Compared with acetylated carbohydrates, which have already been studied in depth, methyl D-glucopyranoside derivatives are more stable and could have additional applications. Thus, in the present work, ab initio calculations were performed to elucidate the characteristics of the interactions of methylglucoside derivatives with CO2, and to investigate how the binding energy (ΔE) is affected by isomerization or the introduction of various acyl groups. Four methyl D-glucopyranosides (each with two anomers) bearing acetyl, propionyl, butyryl, and isobutyryl moieties, respectively, were designed as substrates, and the 1:1 complexes of a CO2 molecule with each of these sugar substrates were modeled. The results indicate that ΔE is mainly influenced by interaction distance and the number of negatively charged donors or interacting pairs in the complex; the structure of the acyl group present in the substrate is a secondary influence. Except in the case of methyl 2-O-acetyl-D-glucopyranose, the ΔE values of the α- and β-anomers of each methylglucoside were found to be almost the same. Therefore, we would expect the CO2 affinities of the four derivatives studied here to be as strong as or even stronger than that of peracetylated D-glucopyranose. Graphical Abstract The binding energy between methyl D-glucopyranoside derivatives with various substituted acyl groups and CO2 are evaluated by ab initio calculations. The strong interaction between these methyl dglucopyranoside derivatives and CO2 showed the potential of their application for CO2 capture.

Reactive Oxygen Species Generation Linked to Sources of Atmospheric Particulate Matter and Cardiorespiratory Effects

Exposure to atmospheric fine particulate matter (PM2.5) is associated with cardiorespiratory morbidity and mortality, but the mechanisms are not well understood. We assess the hypothesis that PM2.5 induces oxidative stress in the body via catalytic generation of reactive oxygen species (ROS). A dithiothreitol (DTT) assay was used to measure the ROS-generation potential of water-soluble PM2.5. Source apportionment on ambient (Atlanta, GA) PM2.5 was performed using the chemical mass balance method with ensemble-averaged source impact profiles. Linear regression analysis was used to relate PM2.5 emission sources to ROS-generation potential and to estimate historical levels of DTT activity for use in an epidemiologic analysis for the period of 1998-2009. Light-duty gasoline vehicles (LDGV) exhibited the highest intrinsic DTT activity, followed by biomass burning (BURN) and heavy-duty diesel vehicles (HDDV) (0.11 ± 0.02, 0.069 ± 0.02, and 0.052 ± 0.01 nmol min(-1) μg(-1)source, respectively). BURN contributed the largest fraction to total DTT activity over the study period, followed by LDGV and HDDV (45, 20, and 14%, respectively). DTT activity was more strongly associated with emergency department visits for asthma/wheezing and congestive heart failure than PM2.5. This work provides further epidemiologic evidence of a biologically plausible mechanism, that of oxidative stress, for associations of adverse health outcomes with PM2.5 mass and supports continued assessment of the utility of the DTT activity assay as a measure of ROS-generating potential of particles.

Interventions to improve water quality for preventing diarrhoea

BACKGROUND

Diarrhoea is a major cause of death and disease, especially among young children in low-income countries. In these settings, many infectious agents associated with diarrhoea are spread through water contaminated with faeces.In remote and low-income settings, source-based water quality improvement includes providing protected groundwater (springs, wells, and bore holes), or harvested rainwater as an alternative to surface sources (rivers and lakes). Point-of-use water quality improvement interventions include boiling, chlorination, flocculation, filtration, or solar disinfection, mainly conducted at home.

OBJECTIVES

To assess the effectiveness of interventions to improve water quality for preventing diarrhoea.

SEARCH METHODS

We searched the Cochrane Infectious Diseases Group Specialized Register (11 November 2014), CENTRAL (the Cochrane Library, 7 November 2014), MEDLINE (1966 to 10 November 2014), EMBASE (1974 to 10 November 2014), and LILACS (1982 to 7 November 2014). We also handsearched relevant conference proceedings, contacted researchers and organizations working in the field, and checked references from identified studies through 11 November 2014.

SELECTION CRITERIA

Randomized controlled trials (RCTs), quasi-RCTs, and controlled before-and-after studies (CBA) comparing interventions aimed at improving the microbiological quality of drinking water with no intervention in children and adults.

DATA COLLECTION AND ANALYSIS

Two review authors independently assessed trial quality and extracted data. We used meta-analyses to estimate pooled measures of effect, where appropriate, and investigated potential sources of heterogeneity using subgroup analyses. We assessed the quality of evidence using the GRADE approach.

MAIN RESULTS

Forty-five cluster-RCTs, two quasi-RCTs, and eight CBA studies, including over 84,000 participants, met the inclusion criteria. Most included studies were conducted in low- or middle-income countries (LMICs) (50 studies) with unimproved water sources (30 studies) and unimproved or unclear sanitation (34 studies). The primary outcome in most studies was self-reported diarrhoea, which is at high risk of bias due to the lack of blinding in over 80% of the included studies. Source-based water quality improvementsThere is currently insufficient evidence to know if source-based improvements such as protected wells, communal tap stands, or chlorination/filtration of community sources consistently reduce diarrhoea (one cluster-RCT, five CBA studies, very low quality evidence). We found no studies evaluating reliable piped-in water supplies delivered to households. Point-of-use water quality interventionsOn average, distributing water disinfection products for use at the household level may reduce diarrhoea by around one quarter (Home chlorination products: RR 0.77, 95% CI 0.65 to 0.91; 14 trials, 30,746 participants, low quality evidence; flocculation and disinfection sachets: RR 0.69, 95% CI 0.58 to 0.82, four trials, 11,788 participants, moderate quality evidence). However, there was substantial heterogeneity in the size of the effect estimates between individual studies.Point-of-use filtration systems probably reduce diarrhoea by around a half (RR 0.48, 95% CI 0.38 to 0.59, 18 trials, 15,582 participants, moderate quality evidence). Important reductions in diarrhoea episodes were shown with ceramic filters, biosand systems and LifeStraw® filters; (Ceramic: RR 0.39, 95% CI 0.28 to 0.53; eight trials, 5763 participants, moderate quality evidence; Biosand: RR 0.47, 95% CI 0.39 to 0.57; four trials, 5504 participants, moderate quality evidence; LifeStraw®: RR 0.69, 95% CI 0.51 to 0.93; three trials, 3259 participants, low quality evidence). Plumbed in filters have only been evaluated in high-income settings (RR 0.81, 95% CI 0.71 to 0.94, three trials, 1056 participants, fixed effects model).In low-income settings, solar water disinfection (SODIS) by distribution of plastic bottles with instructions to leave filled bottles in direct sunlight for at least six hours before drinking probably reduces diarrhoea by around a third (RR 0.62, 95% CI 0.42 to 0.94; four trials, 3460 participants, moderate quality evidence).In subgroup analyses, larger effects were seen in trials with higher adherence, and trials that provided a safe storage container. In most cases, the reduction in diarrhoea shown in the studies was evident in settings with improved and unimproved water sources and sanitation.

AUTHORS' CONCLUSIONS

Interventions that address the microbial contamination of water at the point-of-use may be important interim measures to improve drinking water quality until homes can be reached with safe, reliable, piped-in water connections. The average estimates of effect for each individual point-of-use intervention generally show important effects. Comparisons between these estimates do not provide evidence of superiority of one intervention over another, as such comparisons are confounded by the study setting, design, and population.Further studies assessing the effects of household connections and chlorination at the point of delivery will help improve our knowledge base. As evidence suggests effectiveness improves with adherence, studies assessing programmatic approaches to optimising coverage and long-term utilization of these interventions among vulnerable populations could also help strategies to improve health outcomes.

A county-level analysis of persons living with HIV in the southern United States

This study uses county-level surveillance data to systematically analyze geographic variation and clustering of persons living with diagnosed HIV (PLWH) in the southern United States in 2011. Clusters corresponding to large metropolitan areas - including Miami, Atlanta, and Baltimore - had HIV prevalence rates higher (p < .001) than the regional rate. Regression analysis within the counties included in these clusters determined that race was a significant indicator for PLWH. These results provide a general picture of the distribution of PLWH in the southern United States at the county level and provide insights for identifying local geographic areas with a high number of PLWH, as well as subpopulations that may have an increased risk of infection.

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.

Exploring associations between multipollutant day types and asthma morbidity: epidemiologic applications of self-organizing map ambient air quality classifications

BACKGROUND

Recent interest in the health effects of air pollution focuses on identifying combinations of multiple pollutants that may be associated with adverse health risks.

OBJECTIVE

Present a methodology allowing health investigators to explore associations between categories of ambient air quality days (i.e., multipollutant day types) and adverse health.

METHODS

First, we applied a self-organizing map (SOM) to daily air quality data for 10 pollutants collected between January 1999 and December 2008 at a central monitoring location in Atlanta, Georgia to define a collection of multipollutant day types. Next, we conducted an epidemiologic analysis using our categories as a multipollutant metric of ambient air quality and daily counts of emergency department (ED) visits for asthma or wheeze among children aged 5 to 17 as the health endpoint. We estimated rate ratios (RR) for the association of multipollutant day types and pediatric asthma ED visits using a Poisson generalized linear model controlling for long-term, seasonal, and weekday trends and weather.

RESULTS

Using a low pollution day type as the reference level, we found significant associations of increased asthma morbidity in three of nine categories suggesting adverse effects when combinations of primary (CO, NO2, NOX, EC, and OC) and/or secondary (O3, NH4, SO4) pollutants exhibited elevated concentrations (typically, occurring on dry days with low wind speed). On days with only NO3 elevated (which tended to be relatively cool) and on days when only SO2 was elevated (which likely reflected plume touchdowns from coal combustion point sources), estimated associations were modestly positive but confidence intervals included the null.

CONCLUSIONS

We found that ED visits for pediatric asthma in Atlanta were more strongly associated with certain day types defined by multipollutant characteristics than days with low pollution levels; however, findings did not suggest that any specific combinations were more harmful than others. Relative to other health endpoints, asthma exacerbation may be driven more by total ambient pollutant exposure than by composition.

Geographic variation and neighborhood factors are associated with low rates of pre-end-stage renal disease nephrology care

Geographic variation of pre-end-stage renal disease (pre-ESRD) nephrology care has not been studied across the United States. Here we sought to identify geographic differences in pre-ESRD care, assess for county-level geographic and sociodemographic risk factors, and correlate with patient outcomes using facility-level mortality. Patients from 5387 dialysis facilities across the United States from 2007 to 2010 were included from the Dialysis Facility Report. Marginal generalized estimating equations were used for modeling with geographic cluster analysis to detect clusters of facilities with low rates of pre-ESRD care. On average, 67% of patients received pre-ESRD care in the United States but with significant variability across regions ranging from 3 to 99%. Five geographic clusters of facilities with low rates of pre-ESRD care were the metropolitan areas of San Francisco, Los Angeles, Chicago, Miami, and Baltimore, along with Southern states along the Mississippi River. Dialysis facilities with the lowest rates of pre-ESRD care were more likely to be located in urban counties with high African-American populations and low educational attainment. A 10% higher proportion of patients receiving pre-ESRD care was associated with 1.3% lower patient mortality as reflected by facility-level mortality. Thus, geographic and sociodemographic factors can be used to design quality improvement initiatives to increase access to nephrology care nationwide and improve patient outcomes.

Ensemble-based source apportionment of fine particulate matter and emergency department visits for pediatric asthma

Epidemiologic studies utilizing source apportionment (SA) of fine particulate matter have shown that particles from certain sources might be more detrimental to health than others; however, it is difficult to quantify the uncertainty associated with a given SA approach. In the present study, we examined associations between source contributions of fine particulate matter and emergency department visits for pediatric asthma in Atlanta, Georgia (2002-2010) using a novel ensemble-based SA technique. Six daily source contributions from 4 SA approaches were combined into an ensemble source contribution. To better account for exposure uncertainty, 10 source profiles were sampled from their posterior distributions, resulting in 10 time series with daily SA concentrations. For each of these time series, Poisson generalized linear models with varying lag structures were used to estimate the health associations for the 6 sources. The rate ratios for the source-specific health associations from the 10 imputed source contribution time series were combined, resulting in health associations with inflated confidence intervals to better account for exposure uncertainty. Adverse associations with pediatric asthma were observed for 8-day exposure to particles generated from diesel-fueled vehicles (rate ratio = 1.06, 95% confidence interval: 1.01, 1.10) and gasoline-fueled vehicles (rate ratio = 1.10, 95% confidence interval: 1.04, 1.17).

Associations between weather and microbial load on fresh produce prior to harvest

Contaminated produce causes approximately 1 million cases of foodborne illness and 1 billion dollars in damages to the U.S. economy annually. The environmental conditions, especially weather, that influence the inoculation, proliferation, and dispersal of microbial load on produce are not well understood. Using a mixed models approach, we examined the relationship of temperature and precipitation to microbial indicators of contamination on fresh produce on the farm over a week-long period prior to harvest. Between 2000 and 2002, we assayed for four microbial indicators of contamination (aerobic plate count, Enterococcus, total coliforms, and Escherichia coli) on 10 produce types in 15 fields in the southern United States. The sample collection times varied, with most occurring between January and May. We collected hourly weather data for the corresponding time period and location. Our results indicated that there was a significant association between the average daily temperature (20°C) and both log aerobic plate count (e.g., an increase of 0.074 log CFU/g [standard error {SE}, 0.023] per °C increase in weekly average temperature) and log Enterococcus (e.g., an increase of 0.15 log CFU/g [SE, 0.031] per °C increase in weekly average temperature) for approximately 5 days prior to sample collection. Daily total precipitation was significantly associated with log coliforms on 2 days (∼0.11 log CFU/g [SE, 0.06] per mm of precipitation) during the week-long lag period prior to harvest. Our results suggest that microbial indicator concentrations may increase as the temperature increases. Precipitation may have a positive but complex relationship with microbial indicators, as precipitation may create moist conditions conducive to bacterial growth, spread contamination onto the field, or wash contamination off of the plant.

Quantifying midbrain serotonin transporter in depression: a preliminary study of diagnosis and naturalistic treatment outcome

INTRODUCTION

Serotonin may play an important role in the pathology of major depressive disorder (MDD). However, the relationship between serotonin transporter (SERT) availability and the medical outcome of antidepressant treatment is uncertain.

METHODS

In this naturalistic study, SERT availability (expressed as the specific uptake ratio, SUR) in the midbrain of 17 drug-free patients with MDD and 17 controls matched for age and gender was measured using SPECT with [(123)I]ADAM. The severity of MDD was measured by the Hamilton Depression Rating Scale before, and after 6 weeks of non-standardized antidepressant treatment.

RESULTS

A total of 12 patients completed the study. The SUR of the patients with MDD was significantly lower than that of the healthy controls. The SUR of SERT was not found to have a linear relationship with the treatment outcome; however, supplemental analysis found a curvilinear relationship between treatment outcome and the SUR of SERT.

DISCUSSION

The findings indicate that the SUR of SERT is lower in patients with MDD; however it did not predict treatment outcome in a linear fashion. Studies with larger sample sizes are required.