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Environmental parameters, including built and sociodemographic environments, can impact diabetes control (DC). Epidemiological studies have associated specific environmental factors with DC; however, the impact of multidimensional environmental status has not been assessed. The Environmental Quality Index (EQI), a comprehensive quantitative metric capturing five environmental domains, was considered as an exposure. Age-adjusted rates of DC prevalence for each county in the United States were used as an outcome. DC was defined as the proportion of adults aged 20+ years with a previous diabetes diagnosis who currently do not have high fasting blood glucose (≥126 mg/dL) or elevated HbA1c (≥6.5). We conducted county-level analyses of DC prevalence rates for the years 2004–2012 in association with EQI for 2006–2010 and domain-specific indices using random intercept multilevel linear regression models clustered by state and controlled for county-level rates of obesity and physical inactivity. Analyses were stratified by rural–urban strata, and results are reported as prevalence rate differences (PRD) with 95% CIs comparing highest quintile/worst environmental quality to lowest quintile/best environmental quality. The association of DC with cumulative environmental quality was negative after control for all counties (PRD −0.32, 95% CI: −0.38, −0.27); suggesting that rates of DC worsen as environmental quality declines. While overall environmental quality exerts effects on DC that vary across the rural–urban spectrum, poor sociodemographic, and built environmental factors are associated with decreased DC nationally. These data suggest improvements in environmental quality mediated by larger-scale policy and practice interventions may improve glycemic control and reduce the morbidity and mortality arising from hyperglycemia.

Air pollution exposure is associated with negative health consequences among children and adolescents. Physical activity is recommended for all children/adolescents due to benefits to health and development. However, it is unclear if physically active children have additional protective benefits when exposed to higher levels of air pollution, compared to less active children. This systematic review evaluates all available literature since 2000 and examines if effect measure modification (EMM) exists between air pollution exposure and health outcomes among children/adolescents partaking in regular physical activity. PubMed, Science Direct, Scopus, Web of Science, and ProQuest AgriculturalEnvironmental Science databases were queried, identifying 2686 articles. Title/abstract screening and full-text review eliminated 2620 articles, and 56 articles were removed for evaluating individuals21, leaving 10 articles for review. Of the included articles, half were conducted in China, three in the United States, and one each in Indonesia and Germany. Seven articles identified EMM between active children and air-pollution related health outcomes. Five of these indicated that children/adolescents do not experience any additional benefits from being physically active in higher levels of air pollution, with some studies implying active children may experience additional detriments, compared to less active children. However, the remaining two EMM studies highlighted modest benefits of having a higher activity level, even in polluted air. Overall, active children/adolescents may be at greater risk from air pollution exposure, but results were not consistent across all studies. Future studies assessing the intersection between air pollution and regular physical activity among children would be useful.

BACKGROUND AND AIM: Living in a disadvantaged neighborhood has been linked to many adverse health outcomes. Chronic social and economic stressors associated with disadvantaged neighborhoods may als...

Summary Geographic differences in eosinophilic esophagitis (EoE) prevalence suggest the possibility that environmental exposures contribute to EoE pathogenesis. We aimed to examine the association between environmental quality and risk of EoE, using the Environmental Quality Index (EQI), which provides quantification of environmental quality in five domains: air, land, water, built, and sociodemographic for all counties in the United States. To do this, we performed a case–control study in a large pathology database. EoE cases were defined by ≥15 eosinophils per high-power field with other pathologic diagnoses excluded; controls did not have EoE. The pathology data were geocoded and linked with the EQI by county of residence. Logistic regression was used to estimate odds ratio (OR and 95% confidence interval [CI]) of EoE with overall EQI and for each domain, after adjusting for sex, age, and proportion minority race or ethnicity at the county level (higher EQI score indicates worse environmental quality). Of 29,802 EoE cases and 593,329 controls analyzed, odds of EoE were highest in the worst quintile of EQI (OR 1.25; 95% CI: 1.04–1.50), which was largely explained by poor scores in the water domain (OR: 1.33; 1.17–1.50). Conversely, odds of EoE were reduced with higher scores in the air domain (OR: 0.87, 0.74–1.03) and land domain (OR 0.87; 0.76–0.99). Poor EQI, mostly reflected by poor water quality, was associated with increased odds of EoE, while poor air and land quality were inversely associated with EoE. Additional work to identify specific water pollutants that may have an etiologic role in EoE may be warranted.

Aims/Introduction Caloric excess and physical inactivity fail to fully account for the rise of diabetes prevalence. Individual environmental pollutants can disrupt glucose homeostasis and promote metabolic dysfunction. However, the impact of cumulative exposures on diabetes risk is unknown. Materials and Methods The Environmental Quality Index, a county‐level index composed of five domains, was developed to capture the multifactorial ambient environmental exposures. The Environmental Quality Index was linked to county‐level annual age‐adjusted population‐based estimates of diabetes prevalence rates. Prevalence differences (PD, annual difference per 100,000 persons) and 95% confidence intervals (CI) were estimated using random intercept mixed effects linear regression models. Associations were assessed for overall environmental quality and domain‐specific indices, and all analyses were stratified by four rural‐urban strata. Results Comparing counties in the highest quintile/poorest environmental quality to those in the lowest quintile/best environmental quality, counties with poor environmental quality demonstrated lower total diabetes prevalence rates. Associations varied by rural–urban strata; overall better environmental quality was associated with lower total diabetes prevalence rates in the less urbanized and thinly populated strata. When considering all counties, good sociodemographic environments were associated with lower total diabetes prevalence rates (prevalence difference 2.77, 95% confidence interval 2.71–2.83), suggesting that counties with poor sociodemographic environments have an annual prevalence rate 2.77 per 100,000 persons higher than counties with good sociodemographic environments. Conclusions Increasing attention has focused on environmental exposures as contributors to diabetes pathogenesis, and the present findings suggest that comprehensive approaches to diabetes prevention must include interventions to improve environmental quality., Caloric excess and physical inactivity fail to fully account for the rise of diabetes prevalence. The impact of cumulative exposures on diabetes risk is unknown. Using a measure of cumulative environmental exposures, we showed that overall environmental quality was strongly associated with total diabetes prevalence in the less urbanized and thinly populated strata. Increasing attention has focused on environmental exposures as contributors to diabetes pathogenesis, and the present findings suggest comprehensive approaches to diabetes prevention must include interventions to improve environmental quality.

BACKGROUND: Many studies neglect to account for variation in population served by community water systems (CWSs) when aggregating CWS-level contaminant concentrations to county-level. OBJECTIVE: In an ecological epidemiologic analysis, we explored two methods – unweighted and weighted (proportion of CWS population served by county population) – to account for population served by CWS in association between arsenic and three cancers to determine the impact of population served on aggregated measures of exposure. METHODS: CWS arsenic concentration data for 19 states were obtained from Centers for Disease Control and Prevention (CDC) National Environmental Public Health Tracking Network for 2000-2010, aggregated to county-level, and linked to county-level cancer data for 2011-2015 from National Cancer Institute and CDC State Cancer Profiles. Negative binomial regression models estimated adjusted risk ratios (aRR) and 95% confidence intervals (CI) between county-level bladder, colorectal, and kidney cancers and quartiles of aggregated cumulative county-level arsenic concentration (ppb-years). RESULTS: We observed positive associations between highest quartile of exposure, compared to lowest, of aggregated cumulative county-level arsenic concentration (ppb-year) for bladder [weighted aRR: 1.89(1.53, 2.35)], colorectal [1.64(1.33, 2.01)], and kidney [1.69(1.37, 2.09)] cancers. We observed stronger associations utilizing the weighted exposure assessment method. However, inferences from this study are limited due to the ecologic nature of the analyses and different analytic study designs are needed to assess the utility that the weighted by CWS population served metric has for exposure assessment. SIGNIFICANCE: Weighting by CWS population served accounts for some potential exposure assignment error in epidemiologic analysis.

Background Causes of most birth defects are largely unknown. Genetics, maternal factors (e.g., age, smoking) and environmental exposures have all been linked to some birth defects, including neural tube, oral cleft, limb reduction, and gastroschisis; however, the contribution of cumulative exposures across several environmental domains in association with these defects is not well understood. Methods The Environmental Quality Index (EQI) and its domains (air, water, land, sociodemographic, built) were used to estimate county-level cumulative environmental exposures from 2006-2010 and matched to birth defects identified from Texas Birth Defects Registry and live birth records from births in years 2007-2010 (N = 1,610,709). Poisson regression models estimated prevalence ratios (PR) and 95% confidence intervals (CI) for associations between 10 birth defects and the EQI. Results We observed some positive associations between worst environmental quality and neural tube, anencephaly, spina bifida, oral cleft, cleft palate, cleft lip with and without cleft palate, and gastroschisis [PR range: 1.12-1.55], but near null associations with limb reduction defects. Among domain specific results, we observed the strongest positive associations with the sociodemographic domain across birth defects but varied positive associations among the air and water domains, and negative or null associations with the land and built domains. Overall, few exposure-response patterns were evident. Conclusions Our results highlight the complexities of cumulative, simultaneous environmental exposures in the prevalence rates of 10 selected birth defects. We were able to explore the impact of overall and domain specific environmental quality on birth defects and identify potential domain specific drivers of these associations.

Background: Environmental parameters, including built and sociodemographic environments, can impact diabetes control (DC). Indeed, epidemiological studies have associated specific environmental factors with DC; however, the impact of multidimensional environmental status has not been assessed. Methods: The Environmental Quality Index (EQI) is a comprehensive quantitative metric that captures five environmental domains. DC was defined as the proportion of adults aged 20+ years with a previous diabetes diagnosis, high fasting blood glucose, and/or elevated hemoglobin A1c (HbA1c) who currently do not have high fasting blood glucose or elevated HbA1c. We conducted county-level analyses of age-adjusted DC prevalence rates for 2004-2012 in association with EQI for 2006-2010 and domain-specific indices using random intercept multilevel linear regression models clustered by state and controlled for county-level rates of obesity and physical inactivity. Analyses were stratified by rural-urban strata and results are reported as prevalence rate differences (PRD) with 95% confidence intervals (CIs) comparing highest quintile/worst environmental quality to lowest quintile/best environmental quality. Findings: The association of DC with cumulative environmental quality is negative after controlling for obesity and leisure time physical inactivity for all counties (PRD -0.32,95% CI:-0.38,-0.27). This suggests that rates of DC worsen as environmental quality declines. While overall environmental quality exerts effects on DC that vary across the rural-urban spectrum, poor sociodemographic and built environmental factors are associated with decreased DC nationally. Interpretation: These data suggest that improvements in environmental quality mediated by larger scale policy and practice interventions may improve glycemic control and reduce the morbidity and mortality arising from hyperglycemia. Funding Statement: This work was supported by the National Institutes of Health [P30 DK092949 to JSJ (via the Chicago Center for Diabetes Translational Research), P30 ES027792 (ChicAgo Center on Health and EnvironmenT), and R01 ES028879 (to RMS)], the American Diabetes Association [1-17-JDF-033 to RMS], and the U.S. Environmental Protection Agency [EP-17-D-000063 to JSJ]. A.K.K. supported in part by an appointment to the Internship/Research Participation Program at Office of Research and Development (Center for Public Health & Environmental Assessment), U.S. Environmental Protection Agency, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and the U.S. Environmental Protection Agency. Declaration of Interests: The authors do not have any conflicts of interest pertinent to this manuscript.

Gastrointestinal (GI) illnesses are associated with various environmental factors, such as water quality, stormwater runoff, agricultural runoff, sewer overflows, and wastewater treatment plant effluents. However, rather than assessing an individual factor alone, two indices incorporating a combination of ecological and environmental stressors were created to represent (1) overall watershed integrity, Index of Watershed Integrity (IWI) and (2) catchment integrity, Index of Catchment Integrity (ICI). These indices could provide a more comprehensive understanding of how watershed/catchment integrity potentially impact the rates of GI illness, compared to assessing an individual stressor alone. We utilized the IWI and ICI, as well as agricultural and urban land uses, to assess associations at the county level with the rates of GI illness in a population of adults over 65 years of age. Our findings demonstrated that both watershed and catchment integrity are associated with reduced hospitalizations for any GI outcomes, though association varied by urbanicity. We believe that improved versions of the IWI and ICI may potentially be useful indicators for public health analyses in other circumstances, particularly when considering rural areas or to capture the complex stressors impacting the ecological health of a watershed.

BACKGROUND Individual environmental exposures are associated with cancer development; however, environmental exposures occur simultaneously. The Environmental Quality Index (EQI) is a county-level measure of cumulative environmental exposures that occur in 5 domains. METHODS The EQI was linked to county-level annual age-adjusted cancer incidence rates from the Surveillance, Epidemiology, and End Results (SEER) Program state cancer profiles. All-site cancer and the top 3 site-specific cancers for male and female subjects were considered. Incident rate differences (IRDs; annual rate difference per 100,000 persons) and 95% confidence intervals (CIs) were estimated using fixed-slope, random intercept multilevel linear regression models. Associations were assessed with domain-specific indices and analyses were stratified by rural/urban status. RESULTS Comparing the highest quintile/poorest environmental quality with the lowest quintile/best environmental quality for overall EQI, all-site county-level cancer incidence rate was positively associated with poor environmental quality overall (IRD, 38.55; 95% CI, 29.57-47.53) and for male (IRD, 32.60; 95% CI, 16.28-48.91) and female (IRD, 30.34; 95% CI, 20.47-40.21) subjects, indicating a potential increase in cancer incidence with decreasing environmental quality. Rural/urban stratified models demonstrated positive associations comparing the highest with the lowest quintiles for all strata, except the thinly populated/rural stratum and in the metropolitan/urbanized stratum. Prostate and breast cancer demonstrated the strongest positive associations with poor environmental quality. CONCLUSION We observed strong positive associations between the EQI and all-site cancer incidence rates, and associations differed by rural/urban status and environmental domain. Research focusing on single environmental exposures in cancer development may not address the broader environmental context in which cancers develop, and future research should address cumulative environmental exposures. Cancer 2017;123:2901–8. © 2017 American Cancer Society.

The science behind the combined effect of (and possible interaction between) physical activity and air pollution exposure on health endpoints is not well established, despite the fact that independent effects of physical activity and air pollution on health are well known. The objective of this review is to systematically assess the available literature pertaining to exposure to air pollution while being physically active, in order to assess statistical interaction. Articles published during 2000–2020 were identified by searching PubMed, Science Direct, and ProQuest Agricultural & Environmental Science Database for terms encompassing air pollution and exercise/physical activity. Articles were included if they examined the following four scenarios: at rest in clean air, physical activity in clean air, at rest in polluted air, and physical activity in polluted air. Risk of bias assessment was performed on all included articles. We identified 25 articles for inclusion and determined risk of bias was low to moderate. Nine articles identified evidence of statistical interaction between air pollution exposure and physical activity, while 16 identified no such interaction. However, pollutant levels, exercise intensity, and the population studied appeared to influence statistical interaction. Even in low levels of air pollution, low-intensity activities (i.e., walking), may intensify the negative impacts of air pollution, particularly among those with pre-existing conditions. However, among healthy adults, the review suggests that exercise is generally beneficial even in high air pollution environments. Particularly, the review indicates that moderate to high-intensity exercise may neutralize any short-term negative effects of air pollution.

Background The United States (U.S.) suffers from high infant mortality (IM) rates and there are significant racial/ethnic differences in these rates. Prior studies on the environment and infant mortality are generally limited to singular exposures. We utilize the Environmental Quality Index (EQI), a measure of cumulative environmental exposure (across air, water, land, sociodemographic, and land domains) for U.S. counties from 2000 to 2005, to investigate associations between ambient environment and IM across maternal race/ethnicity. Methods We linked 2000–2005 infant data from the U.S. Centers for Disease Control and Prevention to the EQI (n = 22,702,529; 144,741 deaths). We utilized multi-level regression to estimate associations between quartiles of county-level EQI and IM. We also considered associations between quartiles of county level domain specific indices with IM. We controlled for rural-urban status (RUCC1: urban, metropolitan; RUCC2: urban, non-metropolitan; RUCC3: less urbanized; RUCC4: thinly populated), maternal age, maternal education, marital status, infant sex, and stratified on race/ethnicity. Additionally, we estimated associations for linear combinations of environmental quality and rural-urban status. Results We found a mix of positive, negative, and null associations and our findings varied across domain and race/ethnicity. Poorer overall environmental quality was associated with decreased odds among Non-Hispanic whites (OR and 95% CI: EQIQ4 (ref. EQIQ1): 0.84[0.80,0.89]). For Non-Hispanic blacks and Hispanics, some increased odds were observed. Poorer air quality was monotonically associated with increased odds among Non-Hispanic whites (airQ4 (ref. airQ1): 1.05[0.99,1.11]) and blacks (airQ4 (ref. airQ1): 1.09 [0.9,1.31]). Rural status was associated with increased IM odds among Hispanics (RUCC4-Q4:1.36[1.04,1.78]; RUCC1-Q4: 1.04[0.92,1.16], ref. for both RUCC1-Q1). Conclusions This study is the first to report on associations between ambient environmental quality and IM across the United States. It corroborates prior research suggesting an association between air pollution and IM and identifies residence in thinly populated (rural) areas as a potential risk factor towards IM amongst Hispanics. Some of the counterintuitive findings highlight the need for additional research into potentially differential drivers of environmental quality across the rural-urban continuum, especially with regards to the sociodemographic environment. Electronic supplementary material The online version of this article (10.1186/s13690-018-0306-0) contains supplementary material, which is available to authorized users.

Air pollution is known to be associated with morbidity and mortality, while physical activity is consistently encouraged due to its health benefits. It is well established that exercise increases t...

Disinfectant byproducts (DBPs) haloacetic acids (HAA5) and total trihalomethanes (TTHM) used in community water systems (CWS) are potential bladder and colorectal carcinogens. Studies neglect to ac...

Prevalence of end-stage renal disease (ESRD) in the U.S. increased by 74% from 2000 to 2013, with a 5-year survival of only 42%. To investigate associations between environmental quality and ESRD s...

Prostate cancer (PC) is one of the most common cancer types in the United States (US). Cumulative environmental exposures have been associated with PC incidence. However, the impact of cumulative e...

Physical inactivity is a primary contributor to the obesity epidemic, but may be promoted or hindered by environmental factors. To examine how cumulative environmental quality may modify the inactivity-obesity relationship, we conducted a cross-sectional study by linking county-level Behavioral Risk Factor Surveillance System data with the Environmental Quality Index (EQI), a composite measure of five environmental domains (air, water, land, built, sociodemographic) across all U.S. counties. We estimated the county-level association (N = 3,137 counties) between 2009 age-adjusted leisure-time physical inactivity (LTPIA) and 2010 age-adjusted obesity from BRFSS across EQI tertiles using multi-level linear regression, with a random intercept for state, adjusted for percent minority and rural-urban status. We modelled overall and sex-specific estimates, reporting prevalence differences (PD) and 95% confidence intervals (CI). In the overall population, the PD increased from best (PD = 0.341 (95% CI: 0.287, 0.396)) to worst (PD = 0.645 (95% CI: 0.599, 0.690)) EQI tertile. We observed similar trends in males from best (PD = 0.244 (95% CI: 0.194, 0.294)) to worst (PD = 0.601 (95% CI: 0.556, 0.647)) quality environments, and in females from best (PD = 0.446 (95% CI: 0.385, 0.507)) to worst (PD = 0.655 (95% CI: 0.607, 0.703)). We found that poor environmental quality exacerbates the LTPIA-obesity relationship. Efforts to improve obesity through LTPIA may benefit from considering this relationship.

As of 2014, approximately 7.4% of U.S. adults had current asthma. The etiology of asthma is complex, involving genetics, behavior, and environmental factors. To explore the association between cumulative environmental quality and asthma prevalence in U.S. adults, we linked the U.S. Environmental Protection Agency’s Environmental Quality Index (EQI) to the MarketScan(®) Commercial Claims and Encounters Database. The EQI is a summary measure of five environmental domains (air, water, land, built, sociodemographic). We defined asthma as having at least 2 claims during the study period, 2003-2013. We used a Bayesian approach with non-informative priors, implementing mixed-effects regression modeling with a Poisson link function. Fixed effects variables were EQI, sex, race, and age. Random effects were counties. We modeled quintiles of the EQI comparing higher quintiles (worse quality) to lowest quintile best quality) to estimate prevalence ratios (PR) and credible intervals (CIs). We estimated associations using the cumulative EQI and domain-specific EQIs; we assessed U.S. overall (non-stratified) as well as stratified by rural-urban continuum codes (RUCC) to assess rural/urban heterogeneity. Among the 71,577,118 U.S. adults with medical claims who could be geocoded to county of residence, 1,147,564 (1.6%) met the asthma definition. Worse environmental quality was associated with increased asthma prevalence using the non-RUCC-stratified cumulative EQI, comparing the worst to best EQI quintile (PR:1.27; 95% CI: 1.21, 1.34). Patterns varied among different EQI domains, as well as by rural/urban status. Poor environmental quality may increase asthma prevalence, but domain-specific drivers may operate differently depending on rural/urban status.

OBJECTIVES: To estimate county-level adult life expectancy for Whites, Black/African Americans (Black), American Indian/Alaska Native (AIAN) and Asian/Pacific Islander (Asian) populations and assess the difference across racial groups in the relationship among life expectancy, rurality and specific race proportion. METHODS: We used individual-level death data to estimate county-level life expectancy at 25 (e25) for Whites, Black, AIAN and Asian in the contiguous US for 2000–2005. Race-sex-stratified models were used to examine the associations among e25, rurality and specific race proportion, adjusted for socioeconomic variables. RESULTS: Lower e25 was found in the central US for AIANs and in the west coast for Asians. We found higher e25 in the most rural areas for Whites but in the most urban areas for AIAN and Asians. The associations between specific race proportion and e25 were positive or null for Whites, but were negative for Blacks, AIAN, and Asians. The relationship between specific race proportion and e25 varied across rurality. CONCLUSIONS: Identifying differences in adult life expectancy, both across and within racial groups, provides new insights into the geographic determinants of life expectancy disparities.

Manganese (Mn), an essential element, can be neurotoxic in high doses. This cross-sectional study explored the cognitive function of adults residing in two towns (Marietta and East Liverpool, Ohio, USA) identified as having high levels of environmental airborne Mn from industrial sources. Air-Mn site surface emissions method modeling for total suspended particulate (TSP) ranged from 0.03 to 1.61 μg/m3 in Marietta and 0.01–6.32 μg/m3 in East Liverpool. A comprehensive screening test battery of cognitive function, including the domains of abstract thinking, attention/concentration, executive function and memory was administered. The mean age of the participants was 56 years (±10.8 years). Participants were mostly female (59.1) and primarily white (94.6%). Significant relationships (p < 0.05) were found between Mn exposure and performance on working and visuospatial memory (e.g., Rey-O Immediate β = −0.19, Rey-O Delayed β = −0.16) and verbal skills (e.g., Similarities β = −0.19). Using extensive cognitive testing and computer modeling of 10-plus years of measured air monitoring data, this study suggests that long-term environmental exposure to high levels of air-Mn, the exposure metric of this paper, may result in mild deficits of cognitive function in adult populations.

This study was conducted to derive receptor-specific outdoor exposure concentrations of total suspended particulate (TSP) and respirable (dae ≤ 10 μm) air manganese (air-Mn) for East Liverpool and Marietta (Ohio) in the absence of facility emissions data, but where long-term air measurements were available. Our “site-surface area emissions method” used U.S. Environmental Protection Agency’s (EPA) AERMOD (AMS/EPA Regulatory Model) dispersion model and air measurement data to estimate concentrations for residential receptor sites in the two communities. Modeled concentrations were used to create ratios between receptor points and calibrated using measured data from local air monitoring stations. Estimated outdoor air-Mn concentrations were derived for individual study subjects in both towns. The mean estimated long-term air-Mn exposure levels for total suspended particulate were 0.35 μg/m3 (geometric mean [GM]) and 0.88 μg/m3 (arithmetic mean [AM]) in East Liverpool (range: 0.014–6.32 μg/m3) and 0.17 μg/m3 (GM) and 0.21 μg/m3 (AM) in Marietta (range: 0.03–1.61 μg/m3). Modeled results compared well with averaged ambient air measurements from local air monitoring stations. Exposure to respirable Mn particulate matter (PM10; PM

Background Particulate matter ≤ 2.5 μm in aerodynamic diameter (PM2.5) has been consistently associated with preterm birth (PTB) to varying degrees, but roles of PM2.5 species have been less studied. Objective We estimated risk differences (RD) of PTB (reported per 106 pregnancies) associated with change in ambient concentrations of elemental carbon (EC), organic carbon (OC), nitrates (NO3), and sulfates (SO4). Methods From live birth certificates from three states, we constructed a cohort of singleton pregnancies at or beyond 20 weeks of gestation from 2000 through 2005 (n = 1,771,225; 8% PTB). We estimated mean species exposures for each week of gestation from monitor-corrected Community Multi-Scale Air Quality modeling data. RDs and 95% confidence intervals (CIs) for four PTB categories were estimated for each exposure using linear regression, adjusted for maternal race/ethnicity, marital status, education, age, smoking, maximum temperature, ozone, and season of conception. We also adjusted for other species in multi-species models. Results RDs varied by exposure window and outcome period. EC was positively associated with PTB after 27 and before 35 weeks of gestation. For example, for a 0.25-μg/m3 increase in EC exposure during gestational week 9, RD = 96 (95% CI: –20, 213) and RD = 145 (95% CI: –50, 341) for PTB during weeks 28–31 and 32–34, respectively. Associations with OCs were null or negative. RDs for NO3 were elevated with exposure in early weeks of gestation, and null in later weeks. RDs for SO4 exposure were positively associated with PTB, though magnitude varied across gestational weeks. We observed effect measure modification for associations between EC and PTB by race/ethnicity and smoking status. Conclusion EC and SO4 may contribute to associations between PM2.5 and PTB. Associations varied according to the timing of exposure and the timing of PTB. Citation Rappazzo KM, Daniels JL, Messer LC, Poole C, Lobdell DT. 2015. Exposure to elemental carbon, organic carbon, nitrate, and sulfate fractions of fine particulate matter and risk of preterm birth in New Jersey, Ohio, and Pennsylvania (2000–2005). Environ Health Perspect 123:1059–1065; http://dx.doi.org/10.1289/ehp.1408953

Background Excessive exposure to manganese (Mn) may cause parkinsonian-like motor and tremor symptoms and adverse cognitive effects, including problems with executive functioning (EF), resembling those found in later-stage Parkinson’s disease (PD). Studies seeking to differentiate PD patients into subgroups with associated cognitive and functional outcomes using motor and tremor symptoms identified tremor-dominant (TD) and non-tremor dominant (NTD) subtypes. It is unclear whether differing patterns of pathophysiology and symptoms exist in Mn neurotoxicity, as they do in PD. Methods Residents of East Liverpool (n = 83) and Marietta, OH (n = 99) exposed to chronic (>10 years) environmental Mn through industrial pollution were administered neuropsychological measures and a physician-rated scale of movement-disorder symptoms. Two-step cluster analysis was used to group residents based on tremor symptoms, bradykinesia/rigidity symptoms, gait disturbance, and executive function. Cluster membership was validated using modeled air-Mn exposure and a computerized tremor measure. Results Elevated tremor and motor symptoms and executive dysfunction were observed, and TD and NTD symptom clusters were identified. Two additional clusters were also identified: Executive Dysfunction and Normal Functioning. The NTD residents, with elevated levels of gait disturbance and other movement disorder symptoms, did not evidence EF impairment, as predicted. Instead, residents with EF impairment formed their own cluster, and were relatively free of movement disorder symptoms. Conclusions Results resemble reports in the PD literature with TD and NTD clusters identified, but executive dysfunction did not cluster with NTD symptoms. PD and Mn exposure likely have differing pathophysiology and developmental courses, and therefore different symptom patterns, even when similar symptoms are present.

Fecundity, the biologic capacity to reproduce, is essential for the health of individuals and is, therefore, fundamental for understanding human health at the population level. Given the absence of a population (bio)marker, fecundity is assessed indirectly by various individual-based (e.g. semen quality, ovulation) or couple-based (e.g. time-to-pregnancy) endpoints. Population monitoring of fecundity is challenging, and often defaults to relying on rates of births (fertility) or adverse outcomes such as genitourinary malformations and reproductive site cancers. In light of reported declines in semen quality and fertility rates in some global regions among other changes, the question as to whether human fecundity is changing needs investigation. We review existing data and novel methodological approaches aimed at answering this question from a transdisciplinary perspective. The existing literature is insufficient for answering this question; we provide an overview of currently available resources and novel methods suitable for delineating temporal patterns in human fecundity in future research.

BACKGROUND: Individual environmental exposures are associated with cancer development; however, environmental exposures occur simultaneously. The Environmental Quality Index (EQI) is a county-level measure of cumulative environmental exposures that occur in 5 domains. METHODS: The EQI was linked to county-level annual age-adjusted cancer incidence rates from the Surveillance, Epidemiology, and End Results (SEER) Program state cancer profiles. All-site cancer and the top 3 site-specific cancers for male and female subjects were considered. Incident rate differences (IRDs; annual rate difference per 100,000 persons) and 95% confidence intervals (CIs) were estimated using fixed-slope, random intercept multilevel linear regression models. Associations were assessed with domain-specific indices and analyses were stratified by rural/urban status. RESULTS: Comparing the highest quintile/poorest environmental quality with the lowest quintile/best environmental quality for overall EQI, all-site county-level cancer incidence rate was positively associated with poor environmental quality overall (IRD, 38.55; 95% CI, 29.57-47.53) and for male (IRD, 32.60; 95% CI, 16.28-48.91) and female (IRD, 30.34; 95% CI, 20.47-40.21) subjects, indicating a potential increase in cancer incidence with decreasing environmental quality. Rural/urban stratified models demonstrated positive associations comparing the highest with the lowest quintiles for all strata, except the thinly populated/rural stratum and in the metropolitan/urbanized stratum. Prostate and breast cancer demonstrated the strongest positive associations with poor environmental quality. CONCLUSION: We observed strong positive associations between the EQI and all-site cancer incidence rates, and associations differed by rural/urban status and environmental domain. Research focusing on single environmental exposures in cancer development may not address the broader environmental context in which cancers develop, and future research should address cumulative environmental exposures. Cancer 2017;123:2901-8. © 2017 American Cancer Society.

Background: This study investigated the validity of self-reported concentration and memory problems (CMP) in residents environmentally exposed to manganese (Mn). Method: Self-report of CMP from a health questionnaire (HQ) and the Symptom Checklist-90-Revised (SCL-90-R) was compared to neuropsychological assessment (Trails A&B; Digit Span; Digit Symbol; Similarities; Auditory Consonant Trigrams, ACT; NAB Memory; Rey–Osterrieth, Rey–O, Delayed). Participants included 146 residents from Ohio exposed to air-Mn, with a modeled average concentration of 0.55 µg m–3 (range = 0.01–4.58). Results: Residents were primarily White (94.5%), aged 30–64 years (M = 51.24), with a minimum of 10 years of residence (range = 10–64). Ninety-four (65.3%) participants reported concentration problems, and 107 residents (73.3%) reported memory problems. More participants endorsed CMP on the SCL-90-R than on the HQ. The prevalence of self-reported CMP was higher for women than for men (88.4% vs. 68.3%). Point-biserial and Pearson’s correlations between self-reported CMP and neuropsychological test scores were nonsignificant and weak for both the HQ (rpb = −.20 to rpb = .04) and the SCL-90-R (r = −.12 to r = .007). Greater levels of depression, anxiety, and female sex predicted having more self-reported CMP on both the HQ and the SCL-90-R. Air-Mn and blood-Mn were not associated with self-reported CMP. Residential distance from the Mn source accounted for a small proportion of variance (sr2 = .04), although depression remained the largest predictor (sr2 = .21). Conclusion: These results indicate that self-report of CMP in Mn-exposed residents appear to be invalid when compared to neuropsychological test scores. The participants’ misperception of having CMP is associated with less education and higher levels of depression. Neuropsychological assessment is recommended to attain valid results.

Prevalence of end-stage renal disease (ESRD) in the US increased by 74% from 2000 to 2013. To investigate the role of the broader environment on ESRD survival time, we evaluated average distance to the nearest hospital by county (as a surrogate for access to healthcare) and the Environmental Quality Index (EQI), an aggregate measure of ambient environmental quality composed of five domains (air, water, land, built, and sociodemographic), at the county level across the US. Associations between average hospital distance, EQI, and survival time for 1,092,281 people diagnosed with ESRD between 2000 and 2013 (age 18+, without changes in county residence) from the US Renal Data System were evaluated using proportional-hazards models adjusting for gender, race, age at first ESRD service date, BMI, alcohol and tobacco use, and rurality. The models compared the average distance to the nearest hospital (20 miles) and overall EQI percentiles [0-5), [5-20), [20-40), [40-60), [60-80), [80-95), and [95-100], where lower percentiles are interpreted as better EQI. In the full, non-stratified model with both distance and EQI, there was increased survival for patients over 20 miles from a hospital compared to those under 10 miles from a hospital (hazard ratio = 1.14, 95% confidence interval = 1.12-1.15) and no consistent direction of association across EQI strata. In the full model stratified by average hospital distance, under 10 miles from a hospital had increased survival in the worst EQI strata (median survival 3.0 vs. 3.5 years for best vs. worst EQI, respectively), however for people over 20 miles from a hospital, median survival was higher in the best (4.2 years) vs worst (3.4 years) EQI. This association held across different rural/urban categories and age groups. These results demonstrate the importance of considering multiple factors when studying ESRD survival and future efforts should consider additional components of the broader environment.

Under the Clean Water Act, the US Environmental Protection Agency (EPA) collects information from states on intended use and impairment of each water body. We explore the feasibility of using these data, collected for regulatory purposes, for public health analyses. Combining EPA impairment data and stream hydrology information we estimated the percent of stream length impaired for any use, recreational use, or drinking water use per county in the US as exposure variables. For health outcomes we abstracted county-level hospitalization rates of gastrointestinal infections, GI (ICD-9CM 001-009 excluding 008.45) and gastrointestinal symptoms, GS (ICD-9CM 558.9, 787) among US adults aged 65 years and older from the Center for Medicare and Medicaid Services (1991–2004). Linear mixed-effects models were used to assess county-level associations between percent impaired waters and hospitalization rates adjusted for population density, a proxy for person-to-person transmission. Contrary to expectation, both GI and GS were negatively associated with any water impairment in adjusted models (GI: −0.052, 95 % CI: −0.077, −0.028; GS: −0.438, 95 % CI: −0.702, −0.174). GI was also negatively associated with recreational water impairment (−0.079, 95 % CI: −0.123, −0.036 after adjustment). Neither outcome was associated with drinking water impairment. Limited state data were reported to the EPA for specific recreational (27 states) and drinking (13 states) water impairment, thus limiting the power of the study. Though limited, this analysis demonstrates the feasibility of utilizing regulatory data for public health analyses.

Waste and materials management, land use planning, transportation and infrastructure including water and energy can have indirect or direct beneficial impacts on the environment and public health. The potential for impact, however, is rarely viewed in an integrated fashion. To facilitate such an integrated view in support of community-based policy decision making, we catalogued and evaluated associations between common, publically available, Environmental (e), Health (h), and Sustainability (s) metrics and sociodemographic measurements (n = 10) for 50 populous U.S. cities. E, H, S indices combined from two sources were derived from component (e) (h) (s) metrics for each city. A composite EHS Index was derived to reflect the integration across the E, H, and S indices. Rank order of high performing cities was highly dependent on the E, H and S indices considered. When viewed together with sociodemographic measurements, our analyses further the understanding of the interplay between these broad categories and reveal significant sociodemographic disparities (e.g., race, education, income) associated with low performing cities. Our analyses demonstrate how publically available environmental, health, sustainability and socioeconomic data sets can be used to better understand interconnections between these diverse domains for more holistic community assessments.

Objectives Estimating gestational age is usually based on date of last menstrual period (LMP) or clinical estimation (CE); both approaches introduce potential bias. Differences in methods of estimation may lead to misclassification and inconsistencies in risk estimates, particularly if exposure assignment is also gestation-dependent. This paper examines a ‘what-if’ scenario in which alternative methods are used and attempts to elucidate how method choice affects observed results. Methods We constructed two 20-week gestational age cohorts of pregnancies between 2000 and 2005 (New Jersey, Pennsylvania, Ohio, USA) using live birth certificates: one defined preterm birth (PTB) status using CE and one using LMP. Within these, we estimated risk for 4 categories of preterm birth (PTBs per 10 6 pregnancies) and risk differences (RD (95% CIs)) associated with exposure to particulate matter (PM 2.5 ). Results More births were classified preterm using LMP (16%) compared with CE (8%). RD divergences increased between cohorts as exposure period approached delivery. Among births between 28 and 31 weeks, week 7 PM 2.5 exposure conveyed RDs of 44 (21 to 67) for CE and 50 (18 to 82) for LMP populations, while week 24 exposure conveyed RDs of 33 (11 to 56) and −20 (−50 to 10), respectively. Conclusions Different results from analyses restricted to births with both CE and LMP are most likely due to differences in dating methods rather than selection issues. Results are sensitive to choice of gestational age estimation, though degree of sensitivity can vary by exposure timing. When both outcome and exposure depend on estimate of gestational age, awareness of nuances in the method used for estimation is critical.

Introduction: Understanding racial/ethnic disparities in mortality is an important goal for public health in the U.S. We examined the role environmental quality may have on mortality across race/et...

Background: Assessing cumulative effects of the multiple environmental factors influencing mortality remains a challenging task. Objectives: This study aimed to examine the associations between cumulative environmental quality and all-cause and leading cause-specific (heart disease, cancer, and stroke) mortality rates. Methods: We used the overall Environmental Quality Index (EQI) and its five domain indices (air, water, land, built, and sociodemographic) to represent environmental exposure. Associations between the EQI and mortality rates (CDC WONDER) for counties in the contiguous United States (n = 3,109) were investigated using multiple linear regression models and random intercept and random slope hierarchical models. Urbanicity, climate, and a combination of the two were used to explore the spatial patterns in the associations. Results: We found 1 standard deviation increase in the overall EQI (worse environment) was associated with a mean 3.22% (95% CI: 2.80%, 3.64%) increase in all-cause mortality, a 0.54% (95% CI: –0.17%, 1.25%) increase in heart disease mortality, a 2.71% (95% CI: 2.21%, 3.22%) increase in cancer mortality, and a 2.25% (95% CI: 1.11%, 3.39%) increase in stroke mortality. Among the environmental domains, the associations ranged from –1.27% (95% CI: –1.70%, –0.84%) to 3.37% (95% CI: 2.90%, 3.84%) for all-cause mortality, –2.62% (95% CI: –3.52%, –1.73%) to 4.50% (95% CI: 3.73%, 5.27%) for heart disease mortality, –0.88% (95% CI: –2.12%, 0.36%) to 3.72% (95% CI: 2.38%, 5.06%) for stroke mortality, and –0.68% (95% CI: –1.19%, –0.18%) to 3.01% (95% CI: 2.46%, 3.56%) for cancer mortality. Air had the largest associations with all-cause, heart disease, and cancer mortality, whereas the sociodemographic index had the largest association with stroke mortality. Across the urbanicity gradient, no consistent trend was found. Across climate regions, the associations ranged from 2.29% (95% CI: 1.87%, 2.72%) to 5.30% (95% CI: 4.30%, 6.30%) for overall EQI, and larger associations were generally found in dry areas for both overall EQI and domain indices. Conclusions: These results suggest that poor environmental quality, particularly poor air quality, was associated with increased mortality and that associations vary by urbanicity and climate region. Citation: Jian Y, Messer LC, Jagai JS, Rappazzo KM, Gray CL, Grabich SC, Lobdell DT. 2017. Associations between environmental quality and mortality in the contiguous United States, 2000–2005. Environ Health Perspect 125:355–362; http://dx.doi.org/10.1289/EHP119

Increasing coastal populations and storm intensity may lead to more adverse health effects from tropical storms and hurricanes. Exposure during pregnancy can influence birth outcomes through mechanisms related to healthcare, infrastructure disruption, stress, nutrition, and injury. However, accurate estimation of health effects may be limited by nonspecific exposure definitions that create potential misclassification. The two predominant hurricane exposure assignments are (1) the county of a FEMA presidential disaster declaration; and (2) the specified area within a storm track. The authors propose a third method: meteorological severity of wind speed. Based on the Saffir-Simpson categories, wind speed was examined through binary and quartile comparisons. All three methods of exposure classification were compared by examining the associations with county-level preterm birth and low-birth-weight rates among Florida women who were pregnant during the 2004 hurricane season. The county-level environme...

This report describes the use of medications as a proxy when medical record reviews are unavailable, to study the health effects of residents environmentally exposed to air-manganese (n = 185) compared to unexposed residents (n = 90). Participants' current medication lists and medication questionnaire responses were collected in clinical interviews and categorized into 13 domains. Exposed participants reported fewer hours of sleep than controls (6.6 vs. 7.0). The exposed used significantly more medications than unexposed participants (82.2 % vs. 67.8 %) and, when adjusting for age, education, and personal income, also for pain (aOR = 2.40) and hypothyroidism (aOR = 7.03). Exposed participants with higher air-Mn concentrations, monitored for 10 years by the U.S. Environmental Protection Agency, were 1.5 times more likely to take pain medications. The exposed participants take significantly more medications than unexposed participants in the categories of hypothyroidism, pain, supplements, and total medications.

Objectives. An environmental quality index (EQI) for all counties in the United States is under development to explore the relationship between environmental insults and human health. The EQI is potentially useful for investigators researching health disparities to account for other concurrent environmental conditions. This article focused on the identification and assessment of data sources used in developing the EQI. Data source strengths, limitations, and utility were addressed. Methods. Five domains were identified that contribute to environmental quality: air, water, land, built, and sociodemographic environments. An inventory of possible data sources was created. Data sources were evaluated for appropriate spatial and temporal coverage and data quality. Results. The overall data inventory identified multiple data sources for each domain. From the inventory (187 sources, 617 records), the air, water, land, built environment, and sociodemographic domains retained 2, 9, 7, 4, and 2 data sources for inclusion in the EQI, respectively. However, differences in data quality, geographic coverage, and data availability existed between the domains. Conclusions. The data sources identified for use in the EQI may be useful to researchers, advocates, and communities to explore specific environmental quality questions.

Objective The purpose of this review is to evaluate the impact of recent epidemiologic literature on the National Research Council (NRC) assessment of the lung and bladder cancer risks from ingesting low concentrations (< 100 μg/L) of arsenic-contaminated water. Data sources, extraction, and synthesis PubMed was searched for epidemiologic studies pertinent to the lung and bladder cancer risk estimates from low-dose arsenic exposure. Articles published from 2001, the date of the NRC assessment, through September 2010 were included. Fourteen epidemiologic studies on lung and bladder cancer risk were identified as potentially useful for the analysis. Conclusions Recent epidemiologic studies that have investigated the risk of lung and bladder cancer from low arsenic exposure are limited in their ability to detect the NRC estimates of excess risk because of sample size and less than lifetime exposure. Although the ecologic nature of the Taiwanese studies on which the NRC estimates are based present certain limitations, the data from these studies have particular strengths in that they describe lung and bladder cancer risks resulting from lifetime exposure in a large population and remain the best data on which to conduct quantitative risk assessment. Continued follow-up of a population in northeastern Taiwan, however, offers the best opportunity to improve the cancer risk assessment for arsenic in drinking water. Future studies of arsenic < 100 μg/L in drinking water and lung and bladder cancer should consider adequacy of the sample size, the synergistic relationship of arsenic and smoking, duration of arsenic exposure, age when exposure began and ended, and histologic subtype.

Assessing the overall impact of cumulative air pollution programs on environmental public health is a daunting task. Air quality has improved substantially in the United States in recent decades, in large part because of increasingly stringent federal and state air quality regulations. Although many studies have documented links between better air quality and improvements in a variety of human health metrics (Clancy et al. 2002; Friedman et al. 2001; Hedley et al. 2002; Heinrich et al. 2002; Pope 1989; Pope et al. 2009), direct evidence concerning the extent to which specific control measures have improved health is lacking. This lack of evidence is due in part to inherent difficulties in environmental health research concerning effects of relatively low-level exposures on multifactorial health outcomes with long latency periods that often are associated with small changes in relative risk (RR). In addition, complex interactions between interventions over time make it difficult to isolate the environmental health effects of any one regulation. No single study or study design is likely to characterize the entire range of public health improvements attributable to an air quality regulation (Health Effects Institute 2003). Although the study of the impact of cumulative air pollution control programs on environmental public health is fairly unique compared with those involving individual programs, and formal research approaches are still in early phases of development, several recently published research studies have contributed relevant insights by taking advantage of episodic, one-time events or natural experiments such as a coal ban (Clancy et al. 2002), traffic reductions (Friedman et al. 2001; Tonne et al. 2008), and closure of a steel mill (Parker et al. 2008; Pope 1989). Burnett et al. (2005) proposed a “measure of progress” equivalent to the percent reduction in the RR of adverse health outcomes attributable to reductions in ambient air pollutant concentrations. They note that their proposed approach is applicable to single and multiple cities, as well as single and multiple pollutants. However, they demonstrate their approach only with time-series data on mortality associated with ambient nitrogen dioxide (NO2) concentrations from 1981 to 1999 in multiple Canadian cities. Jerrett et al. (2007) pointed out that evidence of short-term health improvements based on studies of “natural experiments” and well-delineated interventions may not apply directly to effects of gradual air quality improvements over many years, and when the follow-up period is long, many other factors, such as random error and systematic biases, especially for low RRs (< 1.05), can obfuscate the linkage of air quality improvements to health benefits. Thus, it is important to determine a priori which pollutant–outcome relationships are most likely to result in observable impacts on health in a particular population given projected changes in air pollutant concentrations and estimated risks associated with exposures of concern. Of course, even when benefits are not statistically detectable based on observational data, it is helpful to decision makers to know the circumstances in which health benefits of pollution reductions are expected to occur. This challenge was recently addressed by Pope et al. (2009) for ambient particulate matter (PM). They used an ecological analytic approach whereby they examined associations between noticeable changes in ambient PM levels and differences in life expectancy estimates [~ 0.61 years per 10-μg/m3 decrease in PM ≤ 2.5 μm in aerodynamic diameter (PM2.5)] across multiple metropolitan areas in the United States between 1980 and 2000 using multiple regression models adjusted for socioeconomic status, demographic characteristics, and smoking. The models were similar in structure to those previously used in a cross-sectional analysis of air pollution effects on mortality by Ozkaynak and Thurston (1987). This approach can be applied at a much smaller geographical or urban scale if the necessary health, air pollution, demographic, and other explanatory data can be accessed or estimated at fine-scale spatial resolution. In the present study, we used both conventional sample-size–based approaches and the strategy presented by Pope et al. (2009) to assess the feasibility of conducting a study on the impact that cumulative air pollution reduction programs may have on environmental public health within a small geographic area, New Haven, Connecticut (USA). We then evaluated the strengths and limitations of these approaches in the context of urban scale assessments. We chose New Haven for this assessment because it was designated as one of two Connecticut counties in nonattainment of the PM2.5 standard in 1997 [U.S. Environmental Protection Agency (EPA) 2010], and because a variety of air pollution reduction activities have subsequently been implemented at multiple jurisdiction levels by various federal, state, local, and voluntary actions, including the CARE (Community Action for a Renewed Environment) program (U.S. EPA 2011). National and regional initiatives have resulted in large reductions in ambient nitrogen oxides (NOx) from mobile sources in New Haven, and the Northeast region also adopted more stringent vehicle emission standards earlier than did other parts of the United States and had faster fleet turnover. In addition, New Haven has implemented a number of voluntary air pollution reduction activities such as promoting smoke-free homes, use of ultra-low-sulfur diesel fuel, school bus retrofits, solvent reduction workshops, and Tools for Schools (City of New Haven 2004).

Background Bayingnormen is a region located in western Inner Mongolia China, with a population that is exposed to a wide range of drinking water arsenic concentrations. The relationship between maternal drinking water arsenic exposure and perinatal endpoints (term birth weight, preterm birth, stillbirth and neonatal death) in this region was evaluated in this study. Methods An analysis was conducted of all singleton deliveries in a defined geographical area of Inner Mongolia from December 1996 to December 1999 (n=9890). Outcome and covariate data were abstracted from prenatal care records. Exposure was based on well-water measures for the maternal subvillage. Mean birth weight at term was compared across four arsenic categories using analysis of covariance. ORs for stillbirth, preterm birth and neonatal death were estimated by logistic regression with arsenic exposure dichotomised at 50 μg/l. Results Term birth weight was 0.05 kg higher (95% CI 0.02 to 0.08) in the highest exposure category (>100 μg/l) compared to the reference (below limit of detection to 20 μg/l). Arsenic >50 μg/l was associated with an increased risk of neonatal death (OR 2.01, 95% CI 1.12 to 3.59). No relationship was found between maternal arsenic exposure and preterm or stillbirth delivery. Conclusions At the levels observed in our study, arsenic does not appear to contribute to adverse birth outcomes. Exposure may play a role in neonatal death; however, the neonatal death rate in this population was low and this potential association merits further research.