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

Adjusted Linear Regression Estimates of the association between demographic and clinical characteristics and Neutrophil and Lymphocyte Values (n = 7736).

<p>Adjusted Linear Regression Estimates of the association between demographic and clinical characteristics and Neutrophil and Lymphocyte Values (n = 7736).</p

Average Values and Racial Differences of Neutrophil Lymphocyte Ratio among a Nationally Representative Sample of United States Subjects

<div><p>Introduction</p><p>Several studies reported the negative impact of elevated neutrophil/lymphocyte ratio (NLR) on outcomes in many surgical and medical conditions. Previous studies used arbitrary NLR cut-off points according to the average of the populations under study. There is no data on the average NLR in the general population. The aim of this study is to explore the average values of NLR and according to race in adult non-institutional United States individuals by using national data.</p><p>Methods</p><p>The National Health and Nutrition Examination Survey (NHANES) of aggregated cross-sectional data collected from 2007 to 2010 was analyzed; data extracted included markers of systemic inflammation (neutrophil count, lymphocyte count, and NLR), demographic variables and other comorbidities. Subjects who were prescribed steroids, chemotherapy, immunomodulators and antibiotics were excluded. Adjusted linear regression models were used to examine the association between demographic and clinical characteristics and neutrophil counts, lymphocyte counts, and NLR.</p><p>Results</p><p>Overall 9427 subjects are included in this study. The average value of neutrophils is 4.3k cells/mL, of lymphocytes 2.1k cells/mL; the average NLR is 2.15. Non-Hispanic Black and Hispanic participants have significantly lower mean NLR values (1.76, 95% CI 1.71–1.81 and 2.08, 95% CI 2.04–2.12 respectively) when compared to non-Hispanic Whites (2.24, 95% CI 2.19–2.28–p<0.0001). Subjects who reported diabetes, cardiovascular disease, and smoking had significantly higher NLR than subjects who did not. Racial differences regarding the association of smoking and BMI with NLR were observed.</p><p>Conclusions</p><p>This study is providing preliminary data on racial disparities in a marker of inflammation, NLR, that has been associated with several chronic diseases outcome, suggesting that different cut-off points should be set according to race. It also suggests that racial differences exist in the inflammatory response to environmental and behavioral risk factors.</p></div

Linear Regression Estimates (β coefficients and 95% CI) of the association between clinical and demographic characteristics and NLR according to racial subgroups.

<p>*p = 0.04;</p>#<p>p<0.0001;</p><p>∧p = 0.01.</p><p>Linear Regression Estimates (β coefficients and 95% CI) of the association between clinical and demographic characteristics and NLR according to racial subgroups.</p

Sample characteristics among NHANES 2007–2010 participants (n = 9427).

<p>Sample characteristics among NHANES 2007–2010 participants (n = 9427).</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

Correlation between FEMA (y) and self-reported (x) flood heights according to living in an apartment (triangles) or not (circles).

<p>Correlation between FEMA (y) and self-reported (x) flood heights according to living in an apartment (triangles) or not (circles).</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

Description of the study population.

<p>Description of the study population.</p