Differences in complete blood count and C-reactive protein levels between winter-spring and summer-fall (reference) seasons based on regression coefficients (±2*standard error) from crude and adjusted models, NHANES (1999–2012).

<p><b>Note:</b> %change above zero indicates higher winter-spring than summer-fall levels, while below zero indicates lower winter-spring than summer-fall levels. Regressions were run separately for children and adults (18+ years) populations. Analyses were conducted separately for the overall population and the healthy group, defined as those without any of the 16 self-reported chronic diseases. Regression models, log(biomarkers) = f(season, covariates), were adjusted for age, sex, race, poverty income ratio, and body mass index, and chronic disease status. Additional covariates: education, smoking, and alcohol consumption were adjusted for the adult population. BAS = Basophils number, EOS = Eosinophils number, HCT = Hematocrit, HGB = Hemoglobin, Lym = Lymphocyte number, MCH = Mean cell hemoglobin, MCV = Mean cell volume, Mon = Monocyte number, Neu = Segmented neutrophils number, PLT = Platelet count, RBC = Red cell count, WBC = White blood cell count, NLR = Neu/Lym ratio, CRP = C-reactive protein.</p

Regression coefficients for CRP, Neutrophils (Neu), and White Blood Cell (WBC) between winter-spring and summer-fall in children and adults (18+ years), NHANES (1999–2012).

<p><b>Note:</b> Results from regression models on the overall population, adjusting for age, sex, race (white or non-white), poverty income ratio (PIR>1 or ≤1), and BMI, and chronic disease status (self-reported versus no reporting of 16 major chronic diseases: asthma, arthritis, congestive heart failure, coronary heart disease, angina pectoris, heart attack, stroke, emphysema, overweight, chronic bronchitis, liver diseases, thyroid problem, cancer, diabetes, high cholesterols, and hypertension). Additional covariates: education (<, =, and > high school), smoking, and alcohol consumption were adjusted in the adult data set. Levels of Neu, WBC, CRP, and BMI were natural log transformed. Neu = Segmented neutrophils number, WBC = White blood cell count, CRP = C-reactive protein, Ref = Reference level. Significance levels:</p><p>*, <i>p</i><0.05;</p><p>**, <i>p</i>≤0.01;</p><p>***, <i>p</i>≤0.001.</p><p>Regression coefficients for CRP, Neutrophils (Neu), and White Blood Cell (WBC) between winter-spring and summer-fall in children and adults (18+ years), NHANES (1999–2012).</p

Descriptions of the ten WTC studies included in the meta-analysis.

<p><b>Note</b>: PTSD (%)  =  probable PTSD prevalence. n =  total numbers of participants. FDNY  = Fire Department of the City of New York. PCL = PTSD Checklist-Civilian Version, DSM-IV = Diagnostic and Statistical Manual of Mental Disorders, 4^th edition. ^a, PTSD assessed by a modified PCL. ^b1, those with PTSD alone; ^b2, those with both PTSD and lower respiratory symptoms. ^c, full PTSD. ^d, the percentages (%) of male sex and white ethnic/race, and age in years (age ± standard deviation, median, or age bracket with the largest percentage) were shown in (). ^d, results from 2006–2007 was used in this meta-analysis.</p

Forest plot of odds ratios (ORs and 95% confidence intervals) of probable PTSD risks associated with four specific WTC exposure types common among the civilians.

<p>Note: Individual ORs from the original studies, summary ORs for the exposure subgroups, and the overall OR were presented. Details of the studies (a–j) and cohort types were shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101491#pone-0101491-t001" target="_blank">Table 1</a>. IDs (1–37) corresponded to individual ORs in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101491#pone.0101491.s004" target="_blank">Table S1</a>.</p

Funnel plot of the log odds ratios (ORs) of probable PTSD risks associated with WTC-related exposure for the meta-analysis of the ten studies included in the meta-analysis.

<p>Note: The points correspond to the 37 individual ORs. The funnel shape indicates the expected 95% confidence intervals around the summary estimate (vertical line). Little evidence of publication bias was found based on the symmetry of the funnel plot, which was also confirmed by both the Begg's test (p-value = 0.89) and Egger's test (p-value  =  0.93).</p

Summary of the six WTC exposure types from the ten studies included in the meta-analysis.

<p><b>Note</b>: Dichotomized exposure indicators were derived from exposure classifications used in the original studies. * indicates the reference group. Details of studies (a–j) were shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101491#pone-0101491-t001" target="_blank">Table 1</a>.</p

Geographic distribution of concordance (green) and discordance (purple) in flooding (yes/no) between self-reported and FEMA flooding measures in NYC and LI.

<p>Data were classified by living in apartments (triangles) and not (circles) and were overlaid on the FEMA MOTF flood height layer.</p

Phylogram with the phylogenetic relationships of 16S rRNA gene sequences.

<p>The phylogenetic tree shows the bacterial strains and environmental clones most closely to the T-RFs in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061215#pone-0061215-g003" target="_blank">Figure 3</a>. Whenever possible, closest strains were used for the calculation of the tree but when no strain was available (e.g. many species of <i>Mollicutes</i> could not be isolated so far) the closest clone was used. The tree was calculated by Baysian inference using sequences of 898 bp lengths and shows the affiliation between the clones and closest related sequences of NCBI. The clones of our study are bold marked. Only bootstrap values above 0.9 are given. The scale bar represents 0.1 (10%) of sequence divergence.</p

Associations between flood exposure measurements [self-reported / FEMA-reported, dichotomous (flooding yes/no) / continuous (feet)] and mental health (anxiety, depression, PTSD).

<p>The model was adjusted for age, gender, race, education, existing mental health status, elapsed time since Hurricane Sandy, and living in an apartment.</p

Self-Reported and FEMA Flood Exposure Assessment after Hurricane Sandy: Association with Mental Health Outcomes

<div><p>Hurricane Sandy caused extensive physical and economic damage; the long-term mental health consequences are unknown. Flooding is a central component of hurricane exposure, influencing mental health through multiple pathways that unfold over months after flooding recedes. Here we assess the concordance in self-reported and Federal Emergency Management (FEMA) flood exposure after Hurricane Sandy and determine the associations between flooding and anxiety, depression, and post-traumatic stress disorder (PTSD). Self-reported flood data and mental health symptoms were obtained through validated questionnaires from New York City and Long Island residents (N = 1231) following Sandy. Self-reported flood data was compared to FEMA data obtained from the FEMA Modeling Task Force Hurricane Sandy Impact Analysis. Multivariable logistic regressions were performed to determine the relationship between flooding exposure and mental health outcomes. There were significant discrepancies between self-reported and FEMA flood exposure data. Self-reported dichotomous flooding was positively associated with anxiety (OR_adj: 1.5 [95% CI: 1.1–1.9]), depression (OR_adj: 1.7 [1.3–2.2]), and PTSD (OR_adj: 2.5 [1.8–3.4]), while self-reported continuous flooding was associated with depression (OR_adj: 1.1 [1.01–1.12]) and PTSD (OR_adj: 1.2 [1.1–1.2]). Models with FEMA dichotomous flooding (OR_adj: 2.1 [1.5–2.8]) or FEMA continuous flooding (OR_adj: 1.1 [1.1–1.2]) were only significantly associated with PTSD. Associations between mental health and flooding vary according to type of flood exposure measure utilized. Future hurricane preparedness and recovery efforts must integrate micro and macro-level flood exposures in order to accurately determine flood exposure risk during storms and realize the long-term importance of flooding on these three mental health symptoms.</p></div