Monthly Archives: May 2017

MECHANISMS AND EFFECTIVITY OF SULFATE REDUCING BIOREACTORS USING A CHITINOUS SUBSTRATE IN TREATING MINING INFLUENCED WATER

Mining-influenced water (MIW) is the main environmental challenges associated with the mining industry. Passive MIW remediation can be achieved through microbial activity in sulfate-reducing bioreactors (SRBRs), but their actual removal rates depend on different factors, one of which is the substrate composition. Chitinous materials have demonstrated high metal removal rates, particularly for the two recalcitrant MIW contaminants Zn and Mn, but their removal mechanisms need further study. We studied Cd, Fe, Zn, and Mn removal in bioactive and abiotic SRBRs to elucidate the metal removal mechanisms and the differences in metal and sulfate removal rates using a chitinous material as substrate. We found that sulfate-reducing bacteria are effective in increasing metal and sulfate removal rates and duration of operation in SRBRs, and that the main mechanism involved was metal precipitation as sulfides. The solid residues provided evidence of the presence of sulfides in the bioactive column, more specifically ZnS, according to XPS analysis. The feasibility of passive treatments with a chitinous substrate could be an important option for MIW remediation.

FBI Releases Article on Protecting Business Email Systems

Original release date: May 31, 2017

The Federal Bureau of Investigation (FBI) has released an article on Building a Digital Defense with an Email Fortress. FBI warns that scammers commonly target business email accounts with phishing and social engineering schemes. Strategies for preventing email compromises include avoiding the use of free web-based email accounts; using multi-factor authentication; and updating firewalls, antivirus programs, and spam filters.

US-CERT encourages users and administrators to review the FBI article for more information and refer to US-CERT Tips on Using Caution with Email Attachments and Avoiding Social Engineering and Phishing Attacks.


This product is provided subject to this Notification and this Privacy & Use policy.

NATIONAL STORMWATER CALCULATOR: LOW IMPACT DEVELOPMENT STORMWATER CONTROL COST ESTIMATION. BUILDING BLOCKS GREEN & COMPLETE STREETS – CENTRAL FALLS, RI

Stormwater discharges continue to cause impairment of our Nation’s waterbodies. EPA has developed the National Stormwater Calculator (SWC) to help support local, state, and national stormwater management objectives to reduce runoff through infiltration and retention using green infrastructure practices as low impact development (LID) controls. The primary focus of the SWC is to inform site developers on how well they can meet a desired stormwater retention target with and without the use of green infrastructure. It can also be used by landscapers and homeowners. Platform. The SWC is a Windows-based desktop program that requires an internet connection. A mobile web application version that will be compatible with all operating systems is currently being developed and is expected to be released in the fall of 2017.Cost Module. An LID cost estimation module within the application allows planners and managers to evaluate LID controls based on comparison of regional and national project planning level cost estimates (capital and average annual maintenance) and predicted LID control performance. Cost estimation is accomplished based on user-identified size configuration of the LID control infrastructure and other key project and site-specific variables. This includes whether the project is being applied as part of new development or redevelopment and if there are existing site constraints.Climate Scenarios. The SWC allows users to consider how runoff may vary based both on historical weather and potential future climate. To better inform decisions, it is recommended that the user develop a range of SWC results with various assumptions about model inputs such as percent of impervious surface, soil type, sizing of green infrastructure, as well as historical weather and future climate scenarios. This presentation is intended to inform community stakeholders of Central Falls, RI about how to generally use the cost estimation module of the calculator.

House Dust Endotoxin and Peripheral Leukocyte Counts: Results from Two Large Epidemiologic Studies

Author Affiliations open

1Immunity, Inflammation and Disease Laboratory, National Institute of Environmental Health Sciences (NIEHS), National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina, USA

2Epidemiology Branch, NIEHS, NIH, DHHS, Research Triangle Park, North Carolina, USA

3Social & Scientific Systems, Inc., Durham, North Carolina, USA

4Clinical Pathology Group, NIEHS, NIH, DHHS, Research Triangle Park, North Carolina, USA

5Department of Biological Sciences and Center for Human Health and the Environment, North Carolina State University, Raleigh, North Carolina, USA

6Department of Occupational and Environmental Health, College of Public Health, University of Iowa, Iowa City, Iowa 52242, USA

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  • Background:
    The peripheral leukocyte count is a biomarker of inflammation and is associated with human all-cause mortality. Although causes of acute leukocytosis are well-described, chronic environmental determinants of leukocyte number are less well understood.
    Objectives:
    We investigated the relationship between house dust endotoxin concentration and peripheral leukocyte counts in human subjects.
    Methods:
    The endotoxin–leukocyte relationship was evaluated by linear regression in the National Health and Nutrition Examination Survey (NHANES) 2005–2006 (n=6,254) and the Agricultural Lung Health Study (ALHS; n=1,708). In the ALHS, we tested for a gene [Toll-like Receptor 4 (TLR4), encoding the endotoxin receptor]-by-environment interaction in the endotoxin–leukocyte relationship using regression models with an interaction term.
    Results:
    There is a statistically significant, positive association between endotoxin concentration and total leukocyte number [estimated change, 0.186×103/μL (95% CI: 0.070, 0.301×103/μL) per 10-fold change in endotoxin; p=0.004) in the NHANES. Similar positive associations were found for monocytes, lymphocytes, and neutrophils. Stratified analyses revealed possible effect modification by asthma and chronic obstructive pulmonary disease. We observed similar associations in the ALHS. For total leukocytes, there was suggestive evidence in the ALHS of a gene-by-environment interaction for minor allele carrier status at the TLR4 haplotype defined by rs4986790 and rs4986791 (interaction p=0.15).
    Conclusions:
    This is, to our knowledge, the first report of an association between house dust endotoxin and leukocyte count in a national survey. The finding was replicated in a farming population. Peripheral leukocyte count may be influenced by residential endotoxin exposure in diverse settings. https://doi.org/10.1289/EHP661
  • Received: 15 June 2016
    Revised: 13 September 2016
    Accepted: 27 September 2016
    Published: 31 May 2017

    Address correspondence to: M.B. Fessler, MD, National Institute of Environmental Health Sciences, 111 T.W. Alexander Dr., P.O. Box 12233, Maildrop D2-01, Research Triangle Park, NC 27709. Telephone: (919) 541-3701. E-mail: fesslerm@niehs.nih.gov

    Supplemental Material is available online (https://doi.org/10.1289/EHP661).

    The authors declare they have no actual or potential competing financial interests.

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Introduction

Leukocytes are effectors and biomarkers of inflammation. The total count of white blood cells (WBCs) in the circulation is associated with coronary, cancer, and all-cause mortality in human subjects (de Labry et al. 1990; Grimm et al. 1985). Although peripheral WBC count is well-known in clinical medicine to rise acutely during infection, tissue injury, and certain toxic/occupational exposures (Chabot-Richards and George 2014), there is relatively less information available regarding chronic environmental exposures that determine WBC levels in healthy human populations.

Endotoxin [i.e., lipopolysaccharide (LPS)], a glycolipid shed from the outer cell wall of Gram-negative bacteria and detected by Toll-like receptor 4 (TLR4) on the surface of mammalian cells, is arguably the prototypical environmental stimulus of inflammation (Park and Lee 2013). On the high end of the range of human exposure, endotoxin inhalation is thought to underlie the pathogenesis of respiratory and systemic illness in textile fiber and animal confinement workers, among other industries (Liebers et al. 2006). Controlled human exposure studies have revealed that acute endotoxin inhalation not only induces robust neutrophilic airway inflammation (Alexis et al. 2001; Sandström et al. 1992) but also increases peripheral WBC count and other inflammatory biomarkers, such as serum C-reactive protein (CRP) (Dillon et al. 2011; Michel et al. 1992, 1995, 1997).

Low-level endotoxin is ubiquitous in the environment. Studies focused on pediatric populations have shown that house dust endotoxin is associated with reduced sensitization (Gereda et al. 2000) and atopy (Braun-Fahrländer et al. 2002). House dust endotoxin has also been linked to worsened asthma symptoms (Thorne et al. 2005, 2015). To our knowledge, no reports have investigated whether there is a relationship between house dust endotoxin and peripheral WBC count or other inflammatory biomarkers. Given that house dust endotoxin exposure can be reduced by simple interventions (Chen et al. 2012; Gereda et al. 2001; Ownby et al. 2013; Thorne et al. 2009) and that WBC count has been linked to several human diseases (Grimm et al. 1985), confirmation of an endotoxin–WBC relationship might have public health implications.

The National Health and Nutrition Examination Survey (NHANES) is a biennial, cross-sectional population-based survey of the noninstitutionalized, primarily urban and suburban U.S. population. Our group quantified endotoxin levels in dust samples collected from nearly 7,000 households in NHANES 2005–2006 using extreme quality assurance (QA) measures (Thorne et al. 2015). We hypothesized that endotoxin would be positively related to peripheral WBC count and serum CRP. Given that cigarette smoke contains endotoxin and potentiates endotoxin signaling (Hasday et al. 1999; Kulkarni et al. 2007; Pace et al. 2008) and that asthma modifies the acute response to inhaled endotoxin (Hernandez et al. 2012), we hypothesized that smoking and inflammatory lung diseases would modify the relationship of endotoxin to these inflammatory outcome measures. To test the generalizability of our findings, we also evaluated the relationship of endotoxin to WBC count and differential in the Agricultural Lung Health Study (ALHS), a sub-study of the Agricultural Health Study (AHS), an adult U.S. farming cohort (Alavanja et al. 1996).

Methods

Study Populations

The NHANES 2005–2006 (CDC 2005) used a complex multistage design to assess the health and nutritional status of the civilian, noninstitutionalized U.S. population and was approved by the National Center for Health Statistics Research Ethics Review Board (Fessler et al. 2009, 2013; Jaramillo et al. 2013). All NHANES participants provided informed consent. To ensure adequate sample sizes of certain population subgroups, NHANES oversampled low-income persons, adolescents (12–19 y), elderly subjects (≥60 y), African Americans, and Mexican Americans, among others. A detailed description is posted at http://www.cdc.gov/nchs/nhanes.htm. Of 6,963 participants aged ≥1 year with household endotoxin data, blood samples were collected from 6,254 (89.8%). WBC differentials were not available for 19 (0.3%) participants, resulting in a WBC differential study population of 6,235.

The ALHS is a case–control study of current asthma nested within the AHS, a prospective cohort of licensed pesticide applicators (n=52,395) and their spouses (n=32,347) originally enrolled from 1993–1997 in North Carolina and Iowa, approved by the Institutional Review Board at the National Institute of Environmental Health Sciences (Alavanja et al. 1996). All AHS participants provided informed consent. The ALHS enrolled 3,301 individuals identified during a follow-up telephone interview administered from 2005–2010 as either asthma cases (n=1,223) or noncases (n=2,078). A subset of participants had bedroom dust samples collected for endotoxin analysis (2,485 independent households). Complete blood cell count with differential data were available for 1,941 participants. After exclusion of 216 samples with >20% smudge cells and 17 samples with platelet clumping, the final study population with house dust endotoxin and blood cell count data was n=1,708. The analysis used the following releases of AHS data: P3REL201209.00, P1REL201209.00, and AHSREL201304.00.

Endotoxin Analysis

In both studies, combined bed and bedroom floor dust samples were collected at each participant’s home and were sieved before being frozen according to a common protocol (Thorne et al. 2015). Endotoxin was extracted and analyzed at four dilutions using the kinetic chromogenic Limulus amebocyte lysate assay at the same laboratory, adopting multiple quality assurance (QA) measures (Thorne et al. 2015; Vojta et al. 2002). The lower limit of detection (LOD) was 0.000488 endotoxin units per milligram of dust.

Hematologic Measurements

In NHANES, a Beckman Coulter MAXM was used for blood cell analysis, and standard procedures were followed. Platelet count was derived from the platelet histogram and multiplied by a calibration constant. Erythrocyte mean corpuscular volume (MCV) was derived from the erythrocyte histogram and used to compute hematocrit as follows: erythrocyte count×MCV/10. Hemoglobin concentration was determined by absorbance found through photocurrent transmittance. WBC differential was determined using VCS technology. CRP was measured by latex-enhanced nephelometry. In the ALHS, peripheral blood smears were prepared for each sample and were stained with a modified Romanowsky stain using a Wescor Aerospray® Hematology Slide Stainer Cytocentrifuge; automated total WBC counts were obtained using a Drew Scientific HEMAVET Multispecies Hematology Analyzer, and manual differentials were counted on peripheral blood smears. Smudge cell percentages were calculated by counting the number of ruptured or smudge cells present per 100 intact WBCs. Platelet clumping was also recorded.

Covariates and Effect Modification Variables

In NHANES, covariates were obtained from the questionnaire (age, race/ethnicity, gender, smoking, asthma, COPD, wheeze, antiinflammatory medication usage), from laboratory analyses (cotinine, atopy), and from physical examination (height, weight). Participants with asthma were defined as those who answered in the affirmative to the question “Has a doctor ever told you that you had asthma?” Current asthmatics additionally replied in the affirmative to “Do you still have asthma?” For assessment of wheezing, participants were asked “Have you had wheezing or whistling in your chest at any time in the past 12 mo?” We have previously used “wheeze” from the NHANES questionnaire as an alternate, symptom-based phenotypic categorization of lung disease that captures a broad base of diagnosed and undiagnosed respiratory disorders (Fessler et al. 2009; Thorne et al. 2015). Participants with chronic obstructive pulmonary disease (COPD) were defined as those who answered in the affirmative to a question asking whether “a physician or health care professional has told you that you have emphysema or chronic bronchitis” and/or to coughing up phlegm on most days for ≥3 consecutive months for ≥2 consecutive years in the context of a ≥10 pack-year history of cigarette smoking. Medications defined as antiinflammatory included nonsteroidal antiinflammatory drugs (NSAIDs), systemic and inhaled glucocorticoids, cytotoxic drugs (e.g., methotrexate, mercaptopurine, hydroxyurea, azathioprine, leflunomide, cyclosporine), biologic agents (e.g., etanercept), and leukotriene inhibitors. Atopy was defined as ≥detectable (≥0.35 kU/L) serum allergen-specific immunoglobulin E (IgE), which was measured using a Pharmacia Diagnostics ImmunoCAP 1000 system, now known as Thermo Scientific™ ImmunoCAP Specific IgE, as previously reported (Jaramillo et al. 2013). Serum cotinine was measured by isotope dilution high-performance liquid chromatography/atmospheric pressure chemical ionization tandem mass spectrometry.

In the ALHS, current asthma cases of three categories were pooled for analysis in order to be inclusive: self-reported current doctor-diagnosed asthma (based on positive responses to “have you ever been diagnosed with asthma?” and “do you still have asthma?”) without a self-reported previous diagnosis of COPD or emphysema (n=876), possible undiagnosed current asthma based on symptoms and asthma medication usage in nonsmokers or former smokers (<10 pack-years) who did not report current asthma or COPD (n=309), and doctor-diagnosed current asthma plus COPD among subjects with <10 pack-years of smoking (n=38). Individuals randomly selected from the AHS who reported neither current asthma nor asthma symptoms (i.e., wheeze or being awakened by respiratory symptoms) or use of asthma medications in the past 12 mo were considered noncases.

Gene-by-Environment (G×E) Interaction Analysis

To determine if the relationship of house dust endotoxin to WBC count depends on genotype, we performed a gene by environment interaction analysis within the ALHS, focusing on two SNPs in TLR4. The SNPs were selected from previous reports showing attenuated in vivo proinflammatory responses, including peripheral leukocytosis, after endotoxin inhalation in human volunteers (Arbour et al. 2000; Michel et al. 2003). The selected SNPs, rs4986790 and rs4986791 [located in TLR4 (Toll-like receptor 4, OMIM: 603030)], were genotyped on the UK Biobank Axiom Array (Axiom_UKB_WCSG) by Affymetrix Axiom Genotyping Services (Affymetrix, Inc.) using DNA extracted from blood or saliva collected from ALHS participants during the home visit. Both SNPs passed all quality control metrics including Hardy–Weinberg Equilibrium, missing rate <5%, and a minor allele frequency >5%. All samples had a missing call rate <5%, and gender discrepancies, population outliers, and unexpected duplicates were excluded. For statistical modeling, we used a haplotype approach, combining both SNPs and categorizing individuals as carriers (≥1 copy of a minor allele) and noncarriers (homozygous for both major alleles) as previously described (Arbour et al. 2000; Werner et al. 2003). The final data set included 1,646 individuals with genotype and house dust endotoxin data.

Statistical Analysis

To account for the complex sampling design used in NHANES and to assure unbiased variance estimates, sample weights were used in all analyses, and standard errors (SEs), confidence intervals (CIs), and p-values were developed in accordance with the complex survey design using Taylor series linearization methods. All analyses were conducted using SAS Survey statistical software (v9.4; SAS Institute Inc.) survey procedures (SURVEYFREQ, SURVEYMEAN, SURVEYREG). Descriptive statistics were generated (means or percentages and associated standard errors). All blood parameters were assessed for normality. Linear regression analyses were performed to assess the change in blood cell count and log10CRP (NHANES only) per log10 change in endotoxin (Thorne et al. 2015). Covariates in the NHANES analysis included age, sex, race/ethnicity, poverty-to-income ratio (PIR), body mass index (BMI), and cotinine.

Covariates in the ALHS analysis included age, sex, race/ethnicity, smoking status (current/former/never), pack-years, asthma status, BMI, season, and state (IA vs. NC). Interactions between house dust endotoxin and age [juvenile (<18 y old) vs. adult (≥18 y old)], sex, race/ethnicity, smoking status (current, former, never), and cotinine level [10 ng/ml cut point; a threshold that has been associated with active smoking (Pirkle et al. 1996)] were tested by including a product term in the model. The relationship between house dust endotoxin and WBC count was rendered graphically as a weighted penalized B-spline (SAS v9.4) to facilitate depiction of the WBC trend throughout the full range of house dust endotoxin concentrations. G×E interaction analyses were performed using linear regression models, including an interaction term and main effects (SNP and log10 endotoxin EU/mg) with WBC count as the outcome, and were adjusted for asthma status, age, sex, state, smoking (never/past/current), pack-years, season, and BMI (categorical variable). Statistical significance was set at p<0.05 (main effects) or p<0.10 (interactions).

Results

As shown in Table S1, the NHANES 2005–2006 study population with available endotoxin and total WBC data (n=6,254) was approximately equally divided between the sexes, with a mean±SE age of 37.7±0.6 years (range, 1,−85), and was predominantly (68.9%) non-Hispanic white, with the remainder represented by non-Hispanic black, Mexican American, and Other categories. Approximately half of subjects ≥20 years old were never smokers, one-quarter were former smokers, and the remainder were current smokers. Current asthma was reported by 8.9% of the participants, COPD by 7.5%, and antiinflammatory medication usage by 8.4%. The overall geometric mean endotoxin concentration (weighted) was 15.5 EU/mg [median=16.2 EU/mg, interquartile range (IQR)=8.2−34.3 EU/mg, minimum=0.0003 EU/mg (adjusted LOD), maximum=9089.1 EU/mg].

The mean value for peripheral WBC count in the overall NHANES study population was 7.43×103/μl. Bivariate analysis (see Table S2) revealed that current smokers, subjects with cotinine ≥10 ng/mL [a threshold that has been associated with active smoking (Pirkle et al. 1996)], and subjects with COPD all had increased WBC and that the latter two groups had increased serum CRP, whereas current asthmatics had an increase in CRP but not in WBC count. All WBC subtypes [monocytes, lymphocytes, neutrophils, eosinophils, and basophils; data available for 6,235 (89.5%) of participants with endotoxin data] were increased in current smokers and in subjects with cotinine ≥10 ng/mL, whereas in subjects with COPD, all WBC subtypes were increased with the exception of lymphocytes, which were decreased (see Table S3). Atopic subjects had equivalent WBC and CRP values to those in nonatopic subjects but had increased eosinophils, as expected.

As shown in Table 1, linear regression revealed a statistically significant, positive relationship between endotoxin and total WBC count in the overall population [estimated change (β coefficient) in leukocytes, 0.186×103/μL (95% CI: 0.070, 0.301×103/μL) per 10-fold change in endotoxin; p=0.004]. A spline fit of the WBC count along the full range of endotoxin concentrations revealed a possible plateauing of WBC toward the upper end of the endotoxin distribution but no evident no-effect threshold (Figure 1). Stratified analyses of the endotoxin–WBC relationship were performed to examine COPD, atopy, current asthma, and wheeze for possible effect modification (Table 1). Although the endotoxin–WBC relationship was negligible in the relatively small COPD and nonatopic asthma groups, there were no statistically significant interactions. Additional analyses revealed no significant relationship of endotoxin to hematocrit, hemoglobin concentration, or platelet count (data not shown). As shown in Table 1, a positive endotoxin–CRP relationship was also noted that was of borderline statistical significance (p=0.07 in the overall population).

Table 1. Linear regression of the change in white blood cells and serum C-reactive protein associated with a 10-fold increase in house dust endotoxin concentration, in the overall NHANES 2005–2006 study population and within disease/symptom strata.
White blood cells (1000 cells/μL) C-reactive proteine
Participant Subgroupa n β 95% CI p-Value n β 95% CI 10β p-Value
Overall 5,474 0.186 (0.070, 0.301) 0.004 5,486 0.031 (−0.003, 0.065) 1.073 0.07
COPD
Yes 265 −0.099 (−0.815, 0.617) 0.77 266 0.104 (−0.027, 0.235) 1.271 0.11
No 5,209 0.205 (0.093, 0.317) 0.001 5,220 0.023 (−0.013, 0.059) 1.054 0.20
Atopyb
 Yes 2,612 0.210 (−0.002, 0.422) 0.05 2,619 0.039 (−0.009, 0.086) 1.093 0.10
 No 2,745 0.158 (0.013, 0.304) 0.04 2,750 0.024 (−0.034, 0.083) 1.058 0.39
Current asthma
Yes 513 0.184 (−0.424, 0.792) 0.53 514 0.093 (0.008, 0.178) 1.240 0.03
Noc 4,943 0.186 (0.098, 0.275) <0.001 4,954 0.021 (−0.016, 0.059) 1.051 0.24
Current asthma typed
Atopic 344 0.282 (−0.525, 1.090) 0.47 345 0.071 (−0.037, 0.178) 1.176 0.18
 Non-atopic 158 −0.019 (−0.619, 0.581) 0.95 158 0.094 (−0.015, 0.204) 1.243 0.09
 No asthma 4,838 0.178 (0.090, 0.266) 0.001 4,849 0.024 (−0.018, 0.065) 1.056 0.25
Wheeze
Atopic 431 0.312 (−0.170, 0.793) 0.19 431 0.074 (−0.028, 0.175) 1.185 0.14
Non-atopic 300 0.353 (−0.236, 0.943) 0.22 300 0.192 (0.085, 0.300) 1.557 0.002
No wheeze 4,625 0.140 (0.046, 0.234) 0.006 4,637 0.012 (−0.031, 0.054) 1.028 0.56

Note: 10β expected ratio increase in C-reactive protein for every 10-fold increase in HDE concentration; COPD, chronic obstructive pulmonary disease; NHANES, National Health and Nutrition Examination Survey.

a Adjusted for age, sex, race/ethnicity, cotinine, poverty-to-income ratio (PIR), and body mass index (BMI). Overall data include participants with missing stratification status.

b Sensitized to ≥1 allergen-specific immunoglobulin E (IgE).

c Includes participants who either answered “no” to doctor-diagnosed asthma, or who answered “yes” to doctor-diagnosed asthma but “no” to “Do you still have asthma?”

d Captures the same participants as the “Current asthma” categorization above with the exception that it only includes participants with nonmissing data for atopic status.

e Linear analysis performed on log10-transformed C-reactive protein values (raw values in milligrams/deciliter).

Line graph with a regression line. The white blood cell count in 1000 cells per microliter is plotted in the range of 6.50 to 8.00 (y-axis) across endotoxin in endotoxin units per milligram (x-axis) plotted in the range of 2 to 100. The 10th endotoxin percentile and 90th endotoxin percentile are marked.

Figure 1. Weighted penalized B-spline of white blood cell (WBC) count through the full range of household dust endotoxin concentration in the National Health and Nutrition Examination Survey (NHANES) 2005–2006 study population.

In a sensitivity analysis of the endotoxin–WBC association, we excluded subjects (n=479) who reported usage of antiinflammatory medications during the month before blood collection and found no material changes in the association (see Table S4). Although we found no relationship of endotoxin concentration to the WBC differential [i.e., WBC percentage of neutrophils, monocytes, lymphocytes, eosinophils, and basophils (data not shown)], linear regression revealed a statistically significant, positive relationship of endotoxin to absolute counts of monocytes, lymphocytes, and neutrophils in the overall study population (Table 2).

Table 2. Linear regression of the change in absolute count of WBC subtypes associated with a 10-fold increase in house dust endotoxin concentration in the NHANES 2005–2006 study population.
Absolute cell counta n β 95% CI p-value
Monocyte (1,000/μL) 5,457 0.015 (0.005, 0.026) 0.006
Lymphocyte (1,000/μL) 5,457 0.074 (0.025, 0.123) 0.006
Eosinophil (1,000/μL) 5,457 0.006 (−0.010, 0.022) 0.43
Basophil (1,000/μL) 5,457 −0.00002 (−0.004, 0.004) 0.99
Neutrophil (1,000/μL) 5,457 0.094 (0.011, 0.177) 0.03

Note: CI, confidence interval; NHANES, National Health and Nutrition Examination Survey; WBC, white blood cell.

aAdjusted for age, gender, race/ethnicity, cotinine, poverty-to-income ratio (PIR), and body mass index (BMI).

We also tested for possible effect modification by demographic and cigarette smoking–related factors of the relationship of endotoxin to WBC count and serum CRP. Neither age, gender, nor race/ethnicity affected the endotoxin–WBC and endotoxin–CRP relationships (see Table S5). The relationship of endotoxin to leukocyte subtypes, stratified by demographic characteristics, is shown in Table S6.

Cigarette smoke exposure, evaluated either by questionnaire (i.e., current/former/never smoker) or by serum cotinine concentration (cut point, 10 ng/ml), did not modify the endotoxin–WBC or endotoxin–CRP relationship (data not shown). However, a complex reciprocal interaction was noted between endotoxin and smoking in the relationship to eosinophil and basophil counts (Table 3). Subjects with cotinine ≥10 ng/mL had positive endotoxin–basophil and inverse endotoxin–eosinophil associations, whereas the opposite directionality for both cell types was noted for subjects with low cotinine. Supporting the robustness of this finding, consistent findings were noted using smoking status by questionnaire in place of cotinine. However, given the low values for counts of both eosinophils, these findings must be interpreted with caution.

Table 3. Linear regression of the change in count of leukocyte subtypes in NHANES 2005–2006 study population associated with a 10-fold increase in house dust endotoxin concentration within strata of smoking status and serum cotinine.
Cell counta Participant strata n β 95% CI p-Value Interaction p-valueb
Monocytes (1,000/μL) Current smokersc 717 0.013 (−0.015, 0.041) 0.34 0.71
Former smokersc 752 0.024 (−0.005, 0.054) 0.10
Never smokersc 1,539 0.005 (−0.014, 0.024) 0.59
Current smokers 721 0.014 (−0.014, 0.041) 0.30 0.63
Former smokers 757 0.026 (−0.004, 0.055) 0.08
Never smokers 1,550 0.004 (−0.015, 0.023) 0.67
Cotinine<10 ng/mL 4,432 0.012 (−0.000, 0.023) 0.05 0.33
Cotinine≥
10 ng/mL
1,025 0.030 (0.006, 0.054) 0.02
Lymphocytes (1,000/μL) Current smokersc 717 0.027 (−0.089, 0.143) 0.63 0.85
Former smokersc 752 0.073 (−0.098, 0.244) 0.38
Never smokersc 1,539 0.025 (−0.116, 0.167) 0.71
Current smokers 721 0.036 (−0.083, 0.155) 0.53 0.81
Former smokers 757 0.069 (−0.101, 0.239) 0.40
Never smokers 1,550 0.027 (−0.117, 0.171) 0.70
Cotinine<10
 ng/mL
4,432 0.069 (0.004, 0.134) 0.04 0.93
Cotinine≥10
 ng/mL
1,025 0.066 (−0.008, 0.140) 0.08
Eosinophils (1,000/μL) Current smokersc 717 −0.019 (−0.044, 0.006) 0.12 0.24
Former smokersc 752 0.007 (−0.010, 0.024) 0.38
Never smokersc 1,539 0.005 (−0.020, 0.031) 0.67
Current smokers 721 −0.019 (−0.044, 0.006) 0.13 0.26
Former smokers 757 0.007 (−0.010, 0.023) 0.42
Never smokers 1,550 0.005 (−0.020, 0.030) 0.67
Cotinine<10
 ng/mL
4,432 0.012 (−0.005, 0.029) 0.16 0.03
Cotinine≥
10 ng/mL
1,025 −0.014 (−0.033, 0.004) 0.12
Basophils (1,000/μL) Current smokersc 717 0.003 (−0.005, 0.011) 0.43 0.22
Former smokersc 752 −0.000 (−0.011, 0.011) 0.98
Never smokersc 1,539 −0.000 (−0.006, 0.006) 0.89
Current smokers 721 0.003 (−0.005, 0.011) 0.41 0.28
Former smokers 757 0.000 (−0.012, 0.012) 0.99
Never smokers 1,550 −0.000 (−0.006, 0.006) 0.98
Cotinine<10
 ng/mL
4,432 −0.002 (−0.007, 0.004) 0.50 0.03
Cotinine≥
10 ng/mL
1,025 0.005 (−0.001, 0.011) 0.10
Neutrophils (1,000/μL) Current smokersc 717 0.097 (−0.144, 0.337) 0.40 0.27
Former smokersc 752 0.278 (0.041, 0.515) 0.02
Never smokersc 1,539 0.023 (−0.134, 0.181) 0.76
Current smokers 721 0.135 (−0.139, 0.408) 0.31 0.19
Former smokers 757 0.304 (0.060, 0.549) 0.02
Never smokers 1,550 0.018 (−0.130, 0.166) 0.80
Cotinine<
10 ng/mL
4,432 0.063 (−0.012, 0.138) 0.09 0.19
Cotinine≥10 
ng/mL
1,025 0.216 (−0.019, 0.451) 0.07

Note: CI, confidence interval; NHANES, National Health and Nutrition Examination Survey.

aAdjusted for age, sex, race, poverty-to-income ratio (PIR), and body mass index (BMI).

bp-Value of interaction term in nonstratified model.

cModel also adjusted for cotinine.

To test the generalizability of our primary finding, that of the positive association between endotoxin and peripheral WBC count, we repeated the analysis in the ALHS. The ALHS is a case–control study of current asthma nested within the AHS, an adult U.S. farming cohort of pesticide applicators (mostly farmers) and their spouses. Endotoxin concentrations in dust collected from the same household locations as in NHANES (bedding surface and bedroom floor) were measured by the same laboratory as used for the NHANES analysis and using identical analytic procedures.

Characteristics of the ALHS study population of 1,708 subjects (n=645 asthma cases, n=1,063 controls) that had endotoxin and peripheral blood cell count data are shown in Tables S7–S8. Compared with the NHANES 2005–2006 study population, the ALHS study population by design had a higher percentage of asthma cases and was restricted to adults (99% were ≥40 years old). The study population was also composed much more predominantly of white subjects and had a lower frequency of current smokers. House dust endotoxin concentrations were higher in the ALHS than in the NHANES study population, with an overall geometric mean endotoxin concentration of 30.7 EU/mg [median=45.0 EU/mg, IQR=20.1−76.3 EU/mg, minimum=0.0003 EU/mg (adjusted LOD), maximum 4,452 EU/mg]. As shown in Table 4, a statistically significant, positive association was found between endotoxin concentration and total WBC count. As in NHANES, there was no relationship of endotoxin to the percentages (i.e., differential) of any of the WBC subtypes (data not shown). However, a statistically significant, positive association was observed between endotoxin and absolute neutrophil count (Table 4). The magnitude of the endotoxin–WBC relationship was similar between the two studies (β values in Tables 1 and 4), and the endotoxin–neutrophil relationship was virtually identical between the two studies (β values in Tables 2 and 4).

Table 4. Linear regression of the change in absolute count of total WBC and WBC subtypes associated with a 10-fold increase in house dust endotoxin concentration in the ALHS.
Absolute cell counta n β 95% CI p-Value
WBC (1,000/μL) 1,659 0.127 (0.019, 0.235) 0.02
Monocytes (1,000/μL) 1,659 0.011 (−0.005, 0.027) 0.19
Lymphocytes (1,000/μL) 1,659 0.022 (−0.029, 0.073) 0.39
Eosinophils (1,000/μL) 1,659 −0.0004 (−0.012, 0.011) 0.95
Neutrophils (1,000/μL) 1,659 0.093 (0.001, 0.185) 0.046

Note: ALHS, Agricultural Lung Health Study; CI, confidence interval; WBC, white blood cell.

aAdjusted for age, sex, race, asthma status, state (NC vs. IA), smoking (current/former/never), pack-years, season, and body mass index (BMI). An analysis of the endotoxin–basophil relationship was not possible because all subjects had 0% basophils. n=1,659 for analysis because covariate data were missing for 49 subjects.

Minor allele status at two TLR4 loci that are in high linkage disequilibrium, rs4986790 and rs4986791, confers reduced responsiveness to inhaled LPS, including attenuated leukocytosis (Arbour et al. 2000; Michel et al. 2003). Thus, we hypothesized that these variants may modify the association between endotoxin and total WBC count. Individuals in the ALHS study population (n=1,646) were categorized as noncarriers (89%) if they were homozygous for both major alleles or as a variant carrier (11%) if one of the minor alleles was present, as described previously (Werner et al. 2003). Using this model, we found suggestive evidence that a G×E interaction may exist because a positive endotoxin–WBC relationship was found only in TLR4 variant noncarriers (interaction p-value=0.15; Table 5).

Table 5. Gene by environment interaction analysis of WBC count and log10 house dust endotoxin in the ALHS study population.
Haplotype-stratified βa (95% CI)
Gene/SNP Minor allele frequency AA/CC n=1,463 G-/T- n=183 Interaction p-valueb
TLR4
rs4986790 G: 0.05 0.17 (0.05, 0.29)* −0.20 (−0.57, 0.17) 0.15
rs4986791 T: 0.06

Note: ALHS, Agricultural Lung Health Study; CI, confidence interval; SNP, single nucleotide polymorphism; TLR4, Toll-like receptor 4; WBC, white blood cell.

*p<0.01.

aModel: Haplotype is coded as noncarrier (participant was homozygous for both major alleles; AA/CC frequency: 0.89) versus carrier. Recessive is coded 0,1 based on being homozygous for the minor allele. Linear regression model of estimated change in WBC count per log10 endotoxin (linear variable), adjusted for asthma status, age, sex, state (IA vs. NC), smoking (never/past/current), pack-years, season, and body mass index (BMI) (categorical variable), and stratified by TLR4 genotype (nonvariant carrier vs. variant carrier).

bInteraction p-Value generated from linear regression models of WBC count and log10 endotoxin with a gene-by-environment interaction term adjusted for asthma status, age, gender, state (IA vs. NC), smoking (never/past/current), pack- years, season, and BMI (categorical variable).

Discussion

The peripheral WBC count is an established predictor of human all-cause mortality (de Labry et al. 1990; Grimm et al. 1985). In addition, peripheral leukocyte counts have been associated with multiple clinical outcomes, including obesity (Schwartz and Weiss 1991), acute myocardial infarction (Liebetrau et al. 2015), and chronic kidney disease (Erlinger et al. 2003). Beyond being mere biomarkers, several leukocyte subtypes—neutrophils, monocytes, and monocyte-derived macrophages, in particular—have proven causal roles in atherosclerosis, cancer, and other major chronic diseases (Fredman and Spite 2013; Kim and Bae 2016). Thus, identification of common and potentially mitigable environmental determinants of leukocyte number and activation may reveal new features of human leukocyte biology and may also suggest potential strategies for public health intervention.

When we analyzed the U.S. NHANES survey, we found that residential exposure to endotoxin, as quantified by endotoxin concentration, is positively associated with the total count of circulating WBCs. Consistent with household endotoxin influencing systemic inflammatory state in human subjects, a positive relationship of endotoxin to plasma CRP of borderline statistical significance was also noted. The endotoxin–WBC relationship was not meaningfully affected by a sensitivity analysis restricted to subjects not reporting usage of antiinflammatory medications, suggesting that the association is not an artifact of drugs that can alter leukocyte survival (Saffar et al. 2011). The relationship was also comparable among current/former/never smokers and between subjects with cotinine levels above versus below 10 ng/ml.

Our finding that cigarette smoke but not endotoxin is associated with elevated eosinophils and basophils suggests that these two exposures may operate through distinct biological mechanisms. A cotinine cut point of 10 ng/ml revealed opposite effects of cigarette exposure on the endotoxin–eosinophil and endotoxin–basophil relationships, suggesting complex, reciprocal interactions between cigarette smoke and endotoxin on these two leukocyte subtypes. In subjects with cotinine ≥10 ng/mL, endotoxin was related positively to basophils and inversely to eosinophils, whereas the opposite directionality of the relationships between endotoxin and both cell types was found in subjects with cotinine <10 ng/mL. Although a specific underlying biological mechanism for this reciprocal effect is not revealed by our analyses, these two cell types differentiate from a common progenitor, and growth factors have been identified that induce selective commitment of this progenitor to the basophil versus eosinophil lineage (Ohmori et al. 2009; Tanno et al. 1987). Thus, future studies may be warranted to define whether smoking “switches” endotoxin-induced basophil/eosinophil commitment preference, perhaps through modification of the cytokine milieu.

The magnitude of the endotoxin relationship to WBC count we report is modest. Whereas in bivariate comparison, current smokers in NHANES had a mean WBC count of 8.3×103/μL compared with 7.2×103/μL in former smokers, smoothed analysis of the endotoxin–WBC relationship indicated a point estimate of ∼7.1×103/μL for WBC toward the low end of the endotoxin range compared with ∼7.6×103/μL toward the upper end (Figure 1). Although this range of ∼0.5×103/μL is relatively small, it may not be biologically insignificant given that a difference of 1×103/μL in WBC count, even within the normal range, has been associated with a 14% change in coronary heart disease death (Grimm et al. 1985) and a 20% change in all-cause mortality (de Labry et al. 1990).

Several genetic variants have been identified in humans that putatively modify LPS responsiveness. In particular, the TLR4 polymorphism rs4986790 (Asp299Gly) has been shown in direct functional studies to reduce cellular proinflammatory functions triggered by LPS, perhaps because of reduced recruitment of intracellular adaptor proteins to the receptor (Figueroa et al. 2012; Long et al. 2014). Consistent with a prior report that subjects with a minor allele at rs4986790 (and rs4986790, which is in high linkage disequilibrium) have an attenuated increase in peripheral WBCs after experimental LPS inhalation (Michel et al. 2003), in the ALHS, we found suggestive evidence that minor allele carriers at these loci do not exhibit an increase in WBC count with increasing endotoxin concentration. In recent years, a growing number of additional genetic polymorphisms have been identified in the TLR4 signaling pathway that are associated with an altered biological response to LPS (Netea et al. 2012). Variants have also been identified outside the TLR4 pathway that associate with an exaggerated in vivo inflammatory response to LPS, including the GSTM1-null genotype (Dillon et al. 2011) and the APOε4 allele (Gale et al. 2014). Given this information, it is possible that a more comprehensive evaluation might identify other genes involved in differential susceptibility of WBC count to endotoxin exposure. In addition, it has recently been shown that LPS and other exposures can induce epigenetic modifications (i.e., histone modification, DNA methylation, microRNA induction) that reprogram inflammatory responsiveness to LPS (Chiariotti et al. 2016). Taken together, as more genetic and epigenetic determinants of the LPS response become catalogued by the field, future studies aiming to correlate environmental endotoxin exposure to in vivo biologic measures such as WBC count will likely benefit from patient-level measurement of these determinants and perhaps from patient-level functional measurement of LPS responsiveness (e.g., using ex vivo cell-based assays).

As in NHANES 2005–2006, a positive endotoxin–WBC association was observed in the ALHS, an occupationally and geographically distinct cohort with a higher endotoxin concentration. Unlike the NHANES, in which significant endotoxin relationships to absolute counts of neutrophils, lymphocytes, and monocytes were observed, in the ALHS, endotoxin concentration was related only to neutrophil count, suggesting that the WBC relationship in the ALHS was largely driven by this cell type. Because peripheral neutrophil count is a highly sensitive biomarker of LPS inhalation in humans (Michel et al. 1997), the concordance of both studies on this finding is not unexpected. Future studies are warranted to address potential interactions of environmental endotoxin with other components of the exposome in inducing biological effects. Given that inhalation of different agricultural grain dusts induces distinct effects on peripheral lymphocyte number despite comparable endotoxin contamination (Clapp et al. 1993), it appears likely that environmental coexposures may modify the host response to endotoxin. Pesticides have recently been identified that attenuate the proinflammatory response of macrophages to LPS (Helali et al. 2016). The detailed collection of pesticide exposure data in the AHS will likely facilitate future molecular epidemiology addressing these issues (Hofmann et al. 2015).

Our study has limitations. Despite the strong biological plausibility of the endotoxin effect on WBC number, the cross-sectional design of the NHANES precludes inferences of causality. Endotoxin could be a proxy for other environmental microbial exposures, such as Gram-positive bacterial and fungal components (Sordillo et al. 2011), that drive leukocytosis. The endotoxin–WBC relationship persisted after adjustment for multiple covariates; nonetheless, we cannot exclude the possibility of residual confounding. Genetic data were not collected in NHANES 2005–2006, nor were measurements of inflammatory biomarkers other than WBC count and CRP. Several studies have shown poor correlation between dust endotoxin and airborne endotoxin levels (Mazique et al. 2011; Park et al. 2000, 2001) and have suggested that portable endotoxin monitors may be superior to stationary monitors for judging individual exposure (Rabinovitch et al. 2005). However, compared with other sampling sites for household endotoxin, bed dust, one of the primary sources of dust for our analysis in both NHANES and the ALHS, has been shown to have superior (reduced) within-home variance (Park et al. 2000).

Conclusions

We have used national survey data in a largely urban/suburban study population to report for the first time that house dust endotoxin is positively associated with peripheral WBC count and serum CRP. The endotoxin–WBC association was also observed in a cohort of adult farmers and their spouses, with possible effect modification by a TLR4 polymorphism. Taken together with the existing literature on experimental human endotoxin inhalation, these findings suggest the provocative postulate that leukocyte number and systemic inflammatory state in healthy human subjects may be measurably influenced by “real-world” residential exposure to endotoxin in diverse settings.

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Time-series Analysis of Heat Waves and Emergency Department Visits in Atlanta, 1993 to 2012

Author Affiliations open

1Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, USA

2Department of Environmental Health, Emory University, Atlanta, Georgia, USA

3Department of Geography, University of Georgia, Athens, Georgia, USA

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  • Background:
    Heat waves are extreme weather events that have been associated with adverse health outcomes. However, there is limited knowledge of heat waves’ impact on population morbidity, such as emergency department (ED) visits.
    Objectives:
    We investigated associations between heat waves and ED visits for 17 outcomes in Atlanta over a 20-year period, 1993–2012.
    Methods:
    Associations were estimated using Poisson log-linear models controlling for continuous air temperature, dew-point temperature, day of week, holidays, and time trends. We defined heat waves as periods of ≥2 consecutive days with temperatures beyond the 98th percentile of the temperature distribution over the period from 1945–2012. We considered six heat wave definitions using maximum, minimum, and average air temperatures and apparent temperatures. Associations by heat wave characteristics were examined.
    Results:
    Among all outcome-heat wave combinations, associations were strongest between ED visits for acute renal failure and heat waves defined by maximum apparent temperature at lag 0 [relative risk (RR) = 1.15; 95% confidence interval (CI): 1.03–1.29], ED visits for ischemic stroke and heat waves defined by minimum temperature at lag 0 (RR = 1.09; 95% CI: 1.02–1.17), and ED visits for intestinal infection and heat waves defined by average temperature at lag 1 (RR = 1.10; 95% CI: 1.00–1.21). ED visits for all internal causes were associated with heat waves defined by maximum temperature at lag 1 (RR = 1.02; 95% CI: 1.00, 1.04).
    Conclusions:
    Heat waves can confer additional risks of ED visits beyond those of daily air temperature, even in a region with high air-conditioning prevalence. https://doi.org/10.1289/EHP44
  • Received: 29 February 2016
    Revised: 13 October 2016
    Accepted: 24 October 2016
    Published: 31 May 2017

    Address correspondence to H.H. Chang, Department of Biostatistics and Bioinformatics, Emory University, 1518 Clifton Rd. NE, Atlanta, Georgia 30322. Telephone: (404) 712-4627; E-mail: howard.chang@emory.edu

    Supplemental Material is available online (https://doi.org/10.1289/EHP44).

    The authors declare they have no actual or potential competing financial interests.

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Introduction

Heat waves are extreme weather events that can exert notable impacts on the economy and public health (Field et al. 2014). Although the definition of a heat wave varies by country and region, it is commonly characterized by a period of sustained abnormally hot weather compared to historical observations (Meehl and Tebaldi 2004). In the United States, a heat wave is often identified as a period of two or more exceedingly hot days, but the temperature metric used and the definition of extreme temperature can vary (Anderson et al. 2013; Chen et al. 2015). While the occurrence of heat waves is mostly a natural phenomenon, human activities that contribute to climate change are thought to increase the severity of heat waves (Meehl et al. 2007). Additionally, projections from global climate models indicate that the number of severe heat waves is likely to increase in the future due to increased emissions of greenhouse gases and greater urban heat island effects (Duffy and Tebaldi 2012; Coumou et al. 2013).

Heat waves have been consistently associated with increased risk of mortality based on evidence from historical extreme events (Semenza et al. 1996) and recent epidemiological studies (Anderson and Bell 2011; D’Ippoliti et al. 2010; Hajat et al. 2006; Wang et al. 2015). High ambient temperature can cause heat-related illnesses such as heat exhaustion and heat stroke, or aggravate several common cardiovascular and pulmonary conditions (Borden and Cutter 2008; Bouchama et al. 2007; Wilker et al. 2012). In the United States, extreme heat accounted for about 31% of all the weather-related deaths during 2006 to 2010 (Berko et al. 2014). A large study of 43 cities in the United States estimated that the daily mortality rate during heat wave days was 3.7% higher on average than non–heat wave days during 1987–2005 (Anderson and Bell, 2011). Epidemiologic studies have shown that the association between high temperature and mortality has decreased over the past few decades; however, contemporary health risks are still substantial (Bobb et al. 2014; Davis et al. 2003; Gasparrini et al. 2015). The decrease may be attributed to successful adaptation and mitigation strategies, such as heat warning systems, communication campaigns that lead to behavior changes, and increases in air-conditioning prevalence (Boeckmann and Rohn 2014; Hondula et al. 2015).

The elderly and children have been identified as two susceptible populations for heat-related mortality and morbidity (Schifano et al. 2009; Vanos 2015). The elderly population is at a higher risk because of physiology and behavioral reasons, such as existing cardiovascular diseases, impaired kidney function, and living alone with limited social support (Kovats and Kristie 2006). Individuals who are confined to bed and unable to care for themselves may be at high risk of death during heat waves, possibly due to their limited access to emergency care (Hajat et al. 2006; Knowlton et al. 2009). Children are vulnerable in part because their renal systems are particularly stressed by a series of thermoregulatory adjustments under excessive heat (Xu et al. 2012), as well as their activity patterns (Vanos 2015).

While numerous studies worldwide have examined relationships between heat waves and mortality, fewer studies have examined associations between heat waves and morbidity using indicators such as hospital admissions and emergency department (ED) visits (reviewed by Li et al. 2015). In the United States, national studies of hospital admissions have relied on the Medicare database in which the at-risk population is restricted to those ≥65 y old (Bobb et al. 2014; Gronlund et al. 2014). A study of ED visits in North Carolina from 2007 to 2011 found increased visits during heat wave days compared to non–heat wave days, especially among the elderly, adolescents, and people who had high occupational exposure to heat (Fuhrmann et al. 2016). Similar increases in ED visits were found during the 2006 heat wave in Paris (Josseran et al. 2009) and during the 2011 heat wave in Sydney, Australia (Schaffer et al. 2012). Time-series and case-crossover studies of ED visits and heat waves have also been conducted in Houston, Texas, United States (Zhang et al. 2015), Australia (Toloo et al 2014), and China (Sun et al. 2014).

The objective of this study was to estimate warm season associations between heat waves and daily ED visits in Atlanta, Georgia, during the period from 1993 to 2012. To our knowledge, our 20-year study is the longest among other ED visit time-series studies, and fills an important knowledge gap on the impacts of heat waves on population morbidity as measured by ED visits. In previous U.S. national studies, associations between heat waves and mortality are generally found to be weaker in Atlanta and in the southeastern United States than in other parts of the country (Anderson and Bell 2011; Davis et al. 2016). However, the southeastern United States tends to experience more intense heat waves with higher temperature and humidity than rest of the United States (Bonan 1997). Atlanta has also experienced rates of increase in heat wave frequency and duration that are higher than the national averages from 1961 to 2010 (Habeeb et al. 2015).

Here, we build upon previous work in Atlanta in which, similar to studies in other regions (Basu et al. 2012; Ghirardi et al. 2015; Petitte et al. 2016; Saha et al. 2015), we observed associations between continuous maximum temperature and maximum apparent temperature and ED visits for all internal causes, heat illness, fluid/electrolyte imbalances, renal diseases, asthma/wheeze, diabetes, and intestinal infections (Winquist et al. 2016). For the present study, we were specifically interested in the added effect of extreme heat over a sustained period beyond the continuous temperature–response relationships. In examining the added effect of extreme heat, we considered six heat wave definitions using various temperature metrics that can provide results relevant to public health intervention. While maximum temperature is the most commonly used temperature metric, we also considered minimum temperature and apparent temperature that may strongly influence the body’s warming and cooling mechanisms. Different temperature metrics are also less correlated at the extremes and occur at different hours of the day. Studies have shown that temperature metrics most associated with adverse health outcomes can vary across cities (Barnett et al. 2010; Davis et al. 2016).

Exposure to high heat is often avoidable. Our findings can play an important role in supporting local emergency preparedness, performing detailed risk assessment, and protecting public health (Ebi and Schmier 2005; Frumkin et al. 2008). For example, identification of heat metrics most associated with adverse health outcomes may result in more effective local warning systems. During extreme heat events, Atlanta provides cooling centers and free public pool access. Developing targeted communication strategies for specific subpopulations, such as outdoor workers, the elderly without air-conditioning, or those with preexisting medical conditions (as examined here), may further reduce the health impacts of extreme heat events.

Materials and Methods

Data Sources

For the period from 1993 to 2004, individual records of ED visits were obtained from hospitals within the 20-county Atlanta metropolitan area; for the period from 2005 to 2012, ED visit data were obtained from the Georgia Hospital Association. A comparison of data for the 2002–2004 period indicated minimal differences by hospital in visits captured between the two data sources. ED records from both data sources included admission date, and primary and secondary International Classification of Diseases, Ninth Revision (ICD-9) diagnosis codes (U.S. Dept. Health and Human Services 1991). We calculated daily counts of ED visits for 17 adverse health outcomes of interest; outcomes were the same as those analyzed previously by Winquist et al. (2016), and represented outcomes associated with heat in previous studies. The outcomes were defined either by the primary ICD-9 codes only, or by the presence of the selected codes in any of the diagnosis variables (i.e., primary or secondary). Inclusion of secondary diagnoses for some outcomes was based on the finding that stronger associations between temperature and these outcomes were observed when secondary diagnoses were included (Winquist et al. 2016). The outcomes were defined as follows: fluid and electrolyte imbalance (primary ICD-9 code 276), all renal diseases (primary ICD-9 codes 580–593), nephritis and nephrotic syndrome (primary ICD-9 codes 580–589), acute renal failure (primary ICD-9 code 584), all circulatory system diseases (primary or secondary ICD-9 codes 390–459), hypertension (primary or secondary ICD-9 codes 401–405), ischemic heart disease (primary or secondary ICD-9 codes 410–414), dysrhythmia (primary or secondary ICD-9 code 427), congestive heart failure (primary or secondary ICD-9 code 428), ischemic stroke (primary or secondary ICD-9 codes 433–437), all respiratory system diseases (primary ICD-9 codes 460–519), pneumonia (primary ICD-9 codes 480–486), chronic obstructive pulmonary disease (primary or secondary ICD-9 codes 491, 492, and 496), asthma/wheeze (primary ICD-9 codes 493 or 786.07); diabetes (primary ICD-9 codes 249 and 250), and intestinal infections (primary ICD-9 codes 001–009). We also considered all internal causes (ICD-9 codes 001–799).

Weather data for Atlanta were obtained from the National Climatic Data Center for the first-order weather station located at the Atlanta Hartsfield International Airport. These data were used to calculate daily metrics (i.e., maximum, minimum, average) of dry bulb temperature (i.e., MAXT, MINT, AVGT), apparent temperature (MAXAT, MINAT, AVGAT), and dew-point temperature. Apparent temperature is a measure that combines temperature and humidity in the metric (Steadman 1984), defined as AT(°C)=−1.3+0.92T+2.2e, where T is ambient air temperature (°C) and e is water vapor pressure (kPa). There is no universally recognized definition of heat wave; however, a heat wave event should reflect duration and intensity of extreme heat. We examined six heat wave metrics. We first identified heat wave periods with ≥2 consecutive days with daily maximum, minimum, or average temperature or apparent temperature beyond the 98th percentiles. The 98th percentile threshold values were determined based on the distributions of year-round daily maximum, minimum, and average temperatures and apparent temperature over all available station records in Atlanta during 1945–2012. Heat wave days were then defined as days within each heat wave period except the first day to only capture effects of sustained heat over ≥2 days. Hence, we treated the first day of a heat wave period as a non–heat wave day. We also characterized heat waves according to their duration, timing, and intensity. For duration, heat wave days were categorized as being the first, second, third, or later day within each heat wave. For timing, each heat wave was categorized as being the first, second, or later heat wave within each year. Finally, the intensity of a heat wave was characterized by the average temperature across days of the heat wave, using the temperature metric that defined the heat wave.

Statistical Analysis

We assessed the increase in risk of ED visits during heat wave days compared to non–heat wave days, using a Poisson log-linear model, allowing for over-dispersion. We restricted the analysis to warm seasons (May to September). The primary model was specified as:

where μat is the expected number of ED visits for health outcome a on day t; β1 is the log relative risk for ED visits on heat wave days vs. non–heat wave days; HWtb is 0 when day t is a non–heat wave day (including the first day of every heat wave), and 1 when day t is the second or later days in heat wave under definition b; Temtb is the temperature (in Celsius) with the same metric used in heat wave definition b on day t, modeled as a smooth function using natural cubic splines (ns) with 4 degrees of freedom to account for possible nonlinear relationships with ED visits; DPTt is the maximum dew-point temperature (in Celsius) on day t to capture the strongest level of human discomfort during the day, but it is not included in models with heat waves defined using apparent temperature, as these models already include control for continuous apparent temperature (via the Temtb term), which incorporates a measure of humidity; DOWtk is the categorical variable for day k of the week on day t; HOLIDAYtk includes dichotomous variables that indicate days on which federal holidays are observed; HOSPITALtk denotes hospital indicators to account for hospitals’ contributions to the total ED visits, coded 1 when hospital k contributes ED visits on day t; and ns(DATEt) includes a common smooth function of day of the warm season with monthly knots across years, and a year-specific linear function for day of the warm season to capture between-year differences (Winquist et al 2016). We used a larger degrees of freedom to model calendar date per year compared to previous time-series analyses of daily mortality or hospital admissions among the elderly because ED visits can exhibit finer-scale temporal trends (e.g., over a few weeks compared to months). For example, ED visits for respiratory diseases can peak during the first few weeks in early fall due to a back-to-school effect among the pediatric population. The use of indicators to account for hospitals’ contributions to the total ED visit count and the nonlinear temporal trend may minimize potential bias in the assessment of acute heat wave effects resulting from the change in the ED visit data source between 2004 and 2005. We examined heat waves lagged up to 3 days while controlling for temperature at the same lag. Models for each outcome, heat wave definition, and lag combination were fitted separately.

Sensitivity analyses were conducted by increasing the degrees of freedom for continuous temperature Temtb from 4 to 6, replacing maximum dew-point temperature (DPTt) by minimum or average dew-point temperature or by relative humidity, and considering defining Temtb using different metrics that are not the same as the heat wave definition.

To examine associations by heat wave characteristics, the main heat wave indicator (HWtb) was replaced by categorical variables for heat wave duration or timing with non–heat wave days serving as the reference; all other covariates remained the same. We also assessed the effects of heat wave intensity by replacing HWtb with the average temperature during the heat wave for heat wave days and a value of 0 for non–heat wave days. Hence, in the intensity analysis, heat wave effects were based on continuous exposure metrics.

Results

Table 1 presents descriptive statistics of the ED visits. This study included a total of 9,856,015 ED visits to Atlanta metropolitan area hospitals during 1993–2012, of which 6,994,110 had primary ICD-9 codes indicating internal causes. The overall mean daily count of ED visits for internal causes was 2,286, with the overall mean daily counts of cause-specific ED visits ranging from six for acute renal failure to 622 for all circulatory diseases. For most outcomes, the mean daily counts during heat waves defined by minimum temperature were the highest, while those during heat waves defined by maximum temperature were the lowest. The mean daily counts during heat waves defined by daily temperature were similar to those defined by apparent temperature using daily maximum, minimum, or average.

Table 1. Descriptive statistics for daily emergency department (ED) visits based during May to September in Atlanta, 1993–2012.
Mean daily ED visits, during heat waves defined by:
Outcome ICD-9 Code(s) Total ED visits Mean daily ED visits MAXT MINT AVGT MAXAT MINAT AVGAT
All ED visits All 9,856,015 3,220 2,570 3,249 2,827 2,664 3,193 2,969
All internal causes 001–799 6,994,110 2,286 1,831 2,366 2,043 1,909 2,332 2,165
Fluid and electrolytea imbalance 276 66,369 22 22 27 24 23 26 25
All renal diseasea 580–593 140,678 46 43 56 49 46 56 52
Nephritis and nephrotic syndromea 580–589 22,412 7 9 11 10 9 11 11
Acute renal failurea 584 19,274 6 8 10 9 8 10 10
All circulatory system diseaseb 390–459 1,905,253 622 533 761 634 573 740 694
Hypertensionb 401–405 1,501,108 490 431 624 517 464 605 568
Ischemic heart diseaseb 410–414 367,013 120 102 145 123 110 143 134
Dysrhythmiab 427 285,998 93 79 115 96 86 113 105
Congestive heart failureb 428 227,586 74 62 90 74 67 88 82
Ischemic strokeb 433–437 71,302 23 20 27 23 22 27 25
All respiratory system diseasea 460–519 900,570 294 207 253 224 206 253 232
Pneumoniaa 480–486 90,587 29 20 26 22 20 25 23
Chronic obstructive pulmonary diseaseb 491–492, 496 224,127 73 61 87 73 65 85 79
Asthma/wheezea 493 or 7896.09/.07 177,020 58 43 49 44 41 51 47
Diabetes mellitusa 250 or 249 70,076 22 20 27 23 21 28 25
Intestinal infectiona 001–009 30,610 10 10 12 11 10 11 11
Note: Mean daily visits are calculated across the entire study period and during heat wave days. Heat waves are defined as periods of ≥2 consecutive days with temperature (T) or apparent temperature (at) exceeding the 98th percentile using daily maximum (MAX), minimum (MIN), or average (AVG). ED visits occurring on the first day of each heat wave period are excluded from the summary to only reflect visits occurring during sustained heat over 2 or more days.
aprimary ICD-9 codes only.
bpresence of the selected ICD-9 codes in any of the diagnoses (i.e. primary and secondary).

Table 2 presents a summary of the heat waves occurring in Atlanta from 1993–2012, according to the six different heat wave definitions. Heat waves defined by maximum temperature (≥2 consecutive days exceeding the 98th percentile threshold of 35.0°C) had the fewest heat wave days overall (n=91 days) with average durations of 3.1 days per heat wave. All heat wave definitions had a median duration of 2 days. Heat wave days defined by minimum temperature had the most heat waves overall (n=232 days). Table S1 shows the pairwise concordance and discordance between heat wave days defined using different heat wave metrics. Overall, there was only moderate concordance across the different definitions. The concordance percent ranged from 25% for heat waves defined by maximum temperature and minimum apparent temperature to 65% for heat waves defined by maximum apparent temperature and average temperature.

aThresholds are determined among records from 1945 to 2012.

Table 2. Descriptive statistics of heat wave characteristics during May to September in Atlanta, Georgia, 1993–2012.
Heat wave definitions 98th percentilea threshold temperature (°C) Total number of heat wave days Average number of heat waves per year Average duration of heat waves (after first day, in days) Mean temperature during heat waves (°C)
MAXT 35.0 91 1.5 3.1 36.6
MINT 23.3 232 3.2 3.6 24.2
AVGT 28.9 123 1.8 3.5 30.2
MAXAT 35.6 109 1.9 3.0 37.2
MINAT 26.1 96 1.8 2.7 27.2
AVGAT 30.6 118 1.8 3.3 31.9
Note: Heat wave periods are defined as periods of ≥2 consecutive days with temperature (T) or apparent temperature (at) exceeding the 98th percentile using daily maximum (MAX), minimum (MIN), or average (AVG). The first day of each heat wave period is excluded from the summary to only reflect characteristics occurring during sustained heat over 2 or more days.

ED visits for all internal causes were associated with heat wave days defined by maximum temperature at lag 0 [relative risk (RR) = 1.02; confidence interval (CI): 1.00, 1.03) and lag 1 (RR = 1.02; 95% CI: 1.00, 1.04), controlling for continuous maximum temperature at the same lag. Observed associations between heat wave days and all other outcomes are presented in Figure 1; these associations consider heat waves at lags 0 and 1, controlling for continuous temperature at the same lag. RR estimates and 95% CIs for all ED outcomes by heat wave metrics are also given in Table S2. Among renal diseases, we observed significant heat wave associations for nephritis and nephrotic syndrome (lag 0, minimum temperature RR = 1.07; 95% CI: 1.00, 1.14) and acute renal failure (lag 0, minimum temperature RR = 1.07; 95% CI: 1.00, 1.15; lag 0, maximum apparent temperature RR = 1.15; 95% CI: 1.03, 1.29). Significant associations were also detected for total circulatory diseases (lag 0, minimum temperature RR = 1.01; 95% CI: 1.00, 1.02; lag 1, minimum temperature RR = 1.01; 95% CI: 1.00, 1.02), as well as specific causes, including: hypertension (lag 0, maximum temperature RR = 1.02; 95% CI: 1.00, 1.04; lag 1, minimum temperature RR = 1.02; 95% CI: 1.01, 1.03), ischemic heart disease (lag 0 minimum temperature RR = 1.02; 95% CI: 1.00, 1.04; lag 1, minimum temperature RR = 1.02; 95% CI: 1.00, 1.04), dysrhythmia (lag 0, minimum temperature RR = 1.02; 95% CI: 1.00, 1.04), congestive heart failure (lag 1, minimum temperature RR = 1.02; 95% CI: 1.00, 1.05), and ischemic stroke (lag 0, maximum temperature RR = 1.09; 95% CI: 1.02, 1.17). Associations of heat waves and ED visits for intestinal infections were also significant (lag 0, minimum temperature RR = 1.08; 95% CI: 1.01, 1.16; lag 1 average temperature RR = 1.10; 95% CI: 1.00, 1.21). We found no positive significant associations between heat waves and ED visits for total and most respiratory outcomes. Associations at lag 2 and lag 3 were weaker and mostly null for all outcomes, except for diabetes mellitus (lag 2, maximum temperature RR = 1.08; 95% CI: 1.01, 1.15).

Figure 1 presents associations between individual BDEs as well as total PBDE levels in maternal hair and the odds of cryptorchidism. Every 10-fold increase in maternal hair BDE-99 [OR=2.53 (95% CI: 1.29. 4.95; p<0.007)], BDE-100 [OR=2.45 (95% CI: 1.31, 4.56; p<0.005)] or BDE-154 [OR=1.88 (95% CI: 1.08, 3.28; p<0.026)] was associated with elevated risk of cryptorchidism in male infants.

Eight panels of line graphs with confidence intervals indicating relative risk (x-axis) across LAG (y-axis) for fluid and electrolyte imbalance, all renal disease, nephritis and nephrotic syndrome, acute renal failure, all circulatory diseases, hypertension, ischemic heart disease, and dysrhythmia.
Figure 1. Estimated relative risks and 95% confidence intervals for emergency department (ED) visits associated with heat wave days compared to non–heat wave days. Heat waves were defined as periods of ≥2 consecutive days with temperature (T) or apparent temperature (AT) exceeding the 98th percentile using daily maximum (MAX), minimum (MIN), or average (AVG) temperature. The first day of each heat wave period was considered as a non–heat wave day.

Figure S1 presents results of the sensitivity analysis of varying the continuous temperature metric in the health model for lag 0 heat wave exposures defined using maximum temperature, minimum temperature, or maximum apparent temperature. While most RRs are robust against the choice of the continuous temperature metric, when the continuous temperature metric is different from the heat wave definition, we observed stronger associations for some outcomes. This may be due to residual confounding, such that the heat wave association no longer presents the added effect beyond the continuous temperature. Figure S2 presents results of the sensitivity analyses by replacing the continuous maximum dew-point temperature in the health model with minimum or average dew-point temperature, as well as with maximum, average, or minimum relative humidity. For lag 0 and lag 1 heat wave metrics defined using maximum or minimum temperature, we found the heat wave and ED visit associations to be robust against the choice of confounders. In a few cases for heat waves defined using maximum temperature, additional significant associations were detected when relative humidity was used. This may be due to residual confounding because the magnitude of relative humidity may not fully capture human discomfort as compared to dew-point temperature. Associations by the order of days in each heat wave, defined using maximum or minimum temperature, and the sequence of the heat wave within a year and ED visit outcomes at lag 0 and lag 1 are given in Tables S3 and S4. For most outcomes, the associations were similar in magnitude across different heat wave days. However, stronger associations at later compared to earlier days in a heat wave were found for intestinal infection (day 3, lag 0, minimum temperature RR = 1.16; 95% CI: 1.04, 1.29), acute renal failure (day 4, lag 1, minimum temperature RR = 1.17; 95% CI: 1.04, 1.31), and ischemic stroke (day 4, lag 1, maximum temperature RR = 1.17; 95% CI: 1.02, 1.34). We found that associations were stronger for heat waves occurring later than those occurring earlier within a year for some outcomes, including acute renal failure (third or later heat wave, lag 0, maximum temperature RR = 1.15; 95% CI: 1.01, 1.31) and ischemic stroke (second heat wave, lag 0, maximum temperature RR = 1.13; 95% CI: 1.04, 1.22).

Table 3 presents associations between heat wave intensity (as measured by average temperature during a heat wave) and ED visits for selected outcomes at lag 0 and lag 1, using heat waves defined by minimum or maximum temperature. Estimates for all heat wave metrics are given in Table S5. A 1°C increase in temperature during a heat wave was associated with a RR of 1.0025 (lag 0, maximum temperature, 95% CI: 1.0007, 1.0044) for ischemic stroke, a RR of 1.0029 (lag 0, minimum temperature, 95% CI: 1.0001, 1.0057) for acute renal failure, and a RR of 1.0032 (lag 1, minimum temperature, 95% CI: 1.0004, 1.0060) for intestinal infection. These results indicate a potential exposure–response relationship for heat wave intensity.

Table 3. Relative risk (RR) estimates and 95% confidence interval (CI) for selected emergency department (ED) visit outcomes associated with a 1°C increase in heat wave average temperature in Atlanta, Georgia, 1993–2012.
Outcome Heat wave Lag 0 RR (95% CI) Lag 1 RR (95% CI)
Nephritis and nephrotic syndrome MAXT 1.0009 (0.9980, 1.0038) 0.9993 (0.9964, 1.0022)
MINT 1.0027 (1.0000, 1.0054) 1.0015 (0.9988, 1.0042)
Acute renal failure MAXT 1.0015 (0.9984, 1.0046) 0.9993 (0.9963, 1.0023)
MINT 1.0029 (1.0001, 1.0057) 1.0015 (0.9987, 1.0044)
Hypertension MAXT 1.0006 (1.0001, 1.0011) 1.0002 (0.9997, 1.0007)
MINT 1.0003 (0.9999, 1.0007) 1.0007 (1.0003, 1.0011)
Ischemic heart disease MAXT 1.0003 (0.9994, 1.0011) 0.9998 (0.9990, 1.0007)
MINT 1.0008 (1.0000, 1.0015) 1.0009 (1.0001, 1.0016)
Ischemic stroke MAXT 1.0025 (1.0007, 1.0044) 1.0013 (0.9994, 1.0031)
MINT 1.0011 (0.9994, 1.0027) 1.0002 (0.9985, 1.0019)
Intestinal infection MAXT 0.9992 (0.9962, 1.0023) 1.0014 (0.9983, 1.0044)
MINT 1.0032 (1.0004, 1.0060) 1.0013 (0.9985, 1.0041)
Note: Heat waves are defined as periods of ≥2 consecutive days with minimum temperature (MINT) or maximum temperature (MAXT) exceeding the 98th percentile. The reference period includes any non-heat wave day and the first day of every heat wave period. The exposure metric for days during a specific heat wave is the average temperature of the heat wave, while reference days are assigned a value of zero.

Discussion

In this 20-year time-series analysis of sustained extreme heat exposures and daily ED visits in Atlanta, we found the strongest evidence of significant associations for renal and circulatory outcomes, particularly acute renal failure and ischemic stroke (lags 0 and 1), and intestinal infections among the outcomes examined. When exposed to extreme heat, acute thermoregulatory adjustments accelerate heat loss in the body (Libert et al. 1988). Acute renal failure can happen when the adjustment produces stress on the renal system. The kidney is mainly responsible for maintaining the balance of body fluid and electrolyte (Karmarkar and MacNab 2012). Several studies have found significant association between heat wave and hospitalization for renal diseases (Bobb et al. 2014; Fletcher et al. 2012; Hansen et al. 2008) Among the limited studies that examined cause-specific ED visits in relation to heat waves, Knowlton et al. (2009) found that ED visits for acute renal failure were higher during the 2006 California heat wave compared with reference periods before and after the heat wave (RR = 1.15; 95% CI: 1.11–1.19). During three heat warning events in North Carolina, Fuhrmann et al. (2016) also found significant increases in ED visits for acute renal failure with percent excess visits ranging from 28% to 34%. These previous estimates are similar to those obtained in our current 20-year time-series analysis in Atlanta. Regarding associations between heat waves and intestinal infection, sustained heat may enhance environmental bacterial growth conditions. High temperature has been associated with bacillary dysentery cases in China (Zhang et al. 2008) and incidence of hospital admissions for infectious gastroenteritis and inflammatory bowel disease (Manser et al. 2013). Xu et al. (2012) also found high temperature to be associated with pediatric ED visits for intestinal infections in Brisbane, Australia.

Epidemiologic studies have consistently found that cardiovascular, cerebrovascular, and respiratory illnesses account for a large proportion of increased mortality and hospital admissions during heat waves (Fouillet et al. 2006; Kovats and Kristie 2006; Michelozzi et al. 2009). However, heat wave studies assessing ED visits for these diseases have shown contradictory findings. In studies in New York and Taiwan, the number of ED visits for cardiovascular and respiratory illness were significantly higher during heat wave days compared to non–heat wave days, especially among the elderly (Lin et al. 2009; Wang et al. 2012). Some studies did not observe such associations (Hansen et al. 2008; Zacharias et al. 2014). One study in Europe observed that high temperature had a positive impact on respiratory admissions, but not for cardiovascular admissions (Michelozzi et al. 2009); similarly, Basu et al. (2012) found both positive and negative associations between temperature and ED visits for different circulatory and respiratory diseases. Heterogeneity in associations may be due to differences in population composition, geographical location, outcome and heat wave definitions, and population resilience. Our previous study in Atlanta examining associations between continuous daily maximum temperature and ED visits also identified several associations for cardiovascular and respiratory conditions (Winquist et al. 2016). Here we found that sustained high heat does confer additional risks over the risks associated with high continuous temperature for several circulatory diseases in our study region in the southeastern United States.

We examined six definitions for heat waves using daily maximum, minimum, and average of temperature or apparent temperature. The greater frequency of minimum temperature heat waves is likely associated with positive trends in low-level moisture in our study area that in turn increases the frequency of days with high minimum temperatures (Dai 2006; Brown and DeGaetano 2013). This is because water vapor is a greenhouse gas and can increase nighttime minimum temperature.

We did not evaluate the association between heat waves and heat illness (ICD-9 code 992) due to model convergence issues, although this outcome had a very strong association with continuous maximum temperature in previous analyses (Winquist et al. 2016). The definition of heat illness ED visits includes outcomes such as heat stroke, heat syncope, heat cramps, and heat exhaustion that can arise from various activities (Nelson et al. 2011). In our 20-year study period, there were only a total of 9,155 heat illness ED visits, and 23.2% occurred during heat waves defined using minimum temperature. Heat stroke is a life-threatening condition (Leon and Helwig 2010). The admission rate and case fatality rate have been reported to be substantially higher for heat stroke ED visits than any other type of ED visit (Wu et al. 2014). Hence, quantifying the added effect of heat waves on heat illness should be considered in future studies with a longer time-series or larger study population.

There are several considerations when interpreting the results of this study. First, similar to other heat wave and morbidity studies (Hajat et al. 2014; Sun et al. 2014), we chose to estimate the additional effect of heat wave beyond daily high temperature, but some studies reported heat wave effects that include the effect of high temperature (e.g., by not controlling for continuous temperature in the models) (Bobb et al. 2014; Toloo et al. 2014; Zhang et al. 2015). We also did not consider the first day of a heat wave period as added temperature effect in order to only capture sustained heat effects over 2 or more days. Specifically, we assumed that the first day of a heat wave is no different from another hot day. This approach differs from previous studies and may impact comparability with other studies. We evaluated the relative importance of the added and sustained heat wave effect by calculating the pseudo-R2 for the nonlinear daily temperature effect and the heat wave effect for three outcomes at lag 0: acute renal failure (minimum temperature), ischemic stroke (maximum temperature), and intestinal infection (minimum temperature). R2 measures the variation in ED visits explained by each covariate. The corresponding daily temperature/heat wave R2 for these three outcomes are 0.2%/0.01%, 0.009%/0.02%, and 0.09%/0.04%. Hence, temperature explains more of the variability in daily ED visits for acute renal failure than heat waves, while heat waves explain more of the variability in daily ED visits for ischemic stroke and intestinal infection than temperature.

Second, the study is restricted to the Atlanta metropolitan area, and the results may not apply to other areas. For example, the prevalence of air-conditioning in Atlanta is higher than some other locations in the United States. The Atlanta metropolitan area has an air-conditioning prevalence of 94%, according to the 2011 American Housing Survey (Donovan et al. 2013), that likely modifies Atlanta residents’ personal exposures to ambient heat in a way that dampens the impacts of heat waves on health. However, high prevalence does not necessarily lead to high utilization rate due to economic constraints (Hayden et al. 2011).

Third, in the epidemiologic model, we controlled for the continuous temperature using the same metric as the heat wave. This may not fully control for the effects of temperature if different continuous temperature metrics have independent health impacts. For example, the observed associations with heat waves defined by minimum temperature may be due to the continuous effect of maximum temperature that is only partially controlled for by the inclusion of minimum temperature in the model.

Fourth, we did not control for air pollution as a confounder, as done in some studies (Benmarhnia et al. 2014; Schwartz and Dockery 1992; Tong et al. 2010). Ambient air pollution concentrations, such as fine particulate matter and ozone, may be higher during heat waves as a result of increased emissions due to higher electricity demands, and from increased formation of secondary pollutants due to favorable meteorological conditions. By not including daily air pollution concentration in the health model, our estimated heat wave associations include the effects that are potentially mediated through increases in air pollution (Buckley et al. 2014).

Finally, we examined various heat wave definitions, exposure lags, and different aggregations of health outcomes without formally accounting for multiple testing. Some statistically significant associations, as indicated by the RRs and 95% CIs excluding 1, may be due to type I error. However, we note that across lags, we found a larger number of significant associations at lags 0 and 1 compared to longer lags, which supports our a priori hypothesis that the adverse health impacts of sustained high heat is acute.

Conclusions

Our results support the hypothesis that heat wave events are associated with increased morbidity as measured by ED visits, even in an area with high air-conditioning prevalence. Prolonged heat exposure can confer added adverse health impacts beyond the risk due to higher daily temperature, particularly for renal diseases, cardiovascular diseases, and intestinal infection. We found some evidence that longer heat wave duration, later timing in the year, and higher heat wave intensity were associated with higher risks. Associations of heat waves with ED visits can be sensitive to heat wave definitions, and we found stronger and more frequent associations when heat waves are defined using minimum or maximum temperatures compared to average temperature. Local heat warning systems typically include daily maximum temperature and heat index as criteria. Our findings suggest that minimum or nighttime temperatures may also be useful for some adverse health outcomes.

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OCCURRENCE OF CARBAPENEM-RESISTANT ESCHERICHIA COLI FROM WASTEWATER IN THE UNITED STATES: A RETROSPECTIVE ANALYSIS OF ISOLATES FROM 2005

E. coli isolates from primary and secondary effluents collected from seven WWTPs between 2003 and 2004 were recovered and then screened using one of four antibiotics (trimethoprim-sulfamethoxazole, ampicillin, tetracycline, and trimethoprim). We now report on the testing of a subset of these isolates to determine whether they met the Centers for Disease Control and Prevention (CDC) 2012 CRE definition (intermediate or full resistance to one or more carbapenem antibiotics (imipenem) and resistant to at least two extended-spectrum cephalosporins (cefotaxime, ceftazidime)) or the updated CDC 2015 definition (resistant to a carbapenem antibiotic or producing a carbapenemase). Based on minimum inhibitory concentrations (MICs), isolates classified as nonsusceptible to imipenem or resistant to the two cephalosporin antibiotics or resistant to a fluoroquinolone (ciprofloxacin) were used for PCR assays targeting nine carbapenemase and extended-spectrum -lactamase (ESBL) genes. Of the 500 antibiotic-resistant E. coli isolates tested, the most prevalent resistance was to cefotaxime (3.6%), followed by ciprofloxacin (2.6%), ceftazidime (2.2%) and imipenem (1.8%). Six (1.2%) isolates were nonsusceptible to imipenem, and resistant to cefotaxime and ceftazidime, meeting the CDC 2012 CRE definition. According to the CDC’s updated definition, eight (1.6%) isolates were CRE with full resistance to imipenem; only two of these eight isolates were also determined to be CRE according to the CDC 2012 CRE definition. While none of these isolates were positive using the modified Hodge’s test, all 12 CRE isolates determined by both of the CDC definitions showed either ESBL production or having at least two ESBL genes. These results suggested that the production of ESBLs conferred resistance to carbapenem antibiotics, but we have no evidence of carbapenem specific hydrolyzing enzymes. In contrast, seven of 85 CRE E. coli isolates recovered in 2015 showed the production of carbapenemase as well as ESBL. Additionally, from the present study, 32 isolates, including the 12 CRE isolates, were selected based on the aforementioned MICs for further PCR assays. While 9% of 32 isolates were negative for all target genes, 78% and 16% were positive for more than 2 and 4 genes respectively, indicating multiple mechanisms of antibiotic resistance. This study demonstrates the occurrence of CRE E. coli in wastewater collected before the widespread use of carbapenem antibiotics in healthcare settings in the U.S. and provides additional information about their potential multiple mechanisms of antibiotic resistance.

BUILDING SCIENTIFIC CONFIDENCE IN THE DEVELOPMENT AND EVALUATION OF READ-ACROSS USING TOX21 APPROACHES (SOT ANNUAL MEETING)

Acceptance of read-across is an ongoing challenge and several efforts are underway to identify and address major uncertainties associated with read-across. Several approaches have been proposed but to date few case studies if any have evaluated how Tox21 approaches may be instructive in substantiating category rationales, or indeed their associated read-across justifications. Here we have identified a handful of former OECD HPV categories and evaluated to what extent data from HTS assays notably that generated through ToxCast are helpful in strengthening the rationale underlying those categories and their associated read-across. A handful of categories were identified based on the overlap between the HPV chemicals and the ToxCast inventory. Those selected included the ethylene glycols and primary aliphatic alcohols categories. The findings so far are mixed, highlighting the importance of carefully interpreting the ToxCast data in its appropriate context.

Space Station’s View of Florida at Night

Expedition 51 Flight Engineer Thomas Pesquet captured this nighttime photo of Florida from the International Space Station. Bright lights include the Miami-Fort Lauderdale area, and Orlando, with Cape Canaveral to the east, where launch preparations for SpaceX’s next cargo mission are underway at NASA’s Kennedy Space Center.

The Potential for Health Monitoring in Expandable Space Modules: The Bigelow Expandable Activity Module on the ISS

Abstract: Expandable modules for use in space and on the Moon or Mars offer a great opportunity for volume and mass savings in future space exploration missions. This type of module can be compressed into a relatively small shape on the ground, allowing them to fit into space vehicles with a smaller cargo/fairing size than a traditional solid, metallic structure based module would allow. In April 2016, the Bigelow Expandable Activity Module (BEAM) was berthed to the International Space Station (ISS). B…

Spacecraft Charging Considerations and Design Efforts for the Orion Crew Module

Abstract: The Orion Crew Module (CM) is nearing completion for the next flight, designated as Exploration Mission 1 (EM-1). For the uncrewed mission, the flight path will take the CM through a Perigee Raise Maneuver (PRM) out to an altitude of approximately 1800 km, followed by a Trans-Lunar Injection burn, a pass through the Van Allen belts then out to the moon for a lunar flyby, a Distant Retrograde Insertion (DRI) burn, a Distant Retrograde Orbit (DRO), a Distant Retrograde Departure (DRD) burn, a s…

EVA Systems Technology Gaps and Priorities 2017

Abstract: Performance of Extra-Vehicular Activities (EVA) has been and will continue to be a critical capability for human space flight. Human exploration missions beyond LEO will require EVA capability for either contingency or nominal activities to support mission objectives and reduce mission risk. EVA systems encompass a wide array of products across pressure suits, life support systems, EVA tools and unique spacecraft interface hardware (i.e. EVA Translation Paths and EVA Worksites). In a fiscally…

NASA-STD-6001B Test 7: Impact of Test Methodology and Detection Advancements on the Obsolescence of Historical Offgas Data

Abstract: NASA-STD-6001B states “all nonmetals tested in accordance with NASA-STD-6001 should be retested every 10 years or as required by the responsible program/project.” The retesting of materials helps ensure the most accurate data are used in material selection. Manufacturer formulas and processes can change over time, sometimes without an update to product number and material information. Material performance in certain NASA-STD-6001 tests can be particularly vulnerable to these changes, such as …