The Impact of Bisphenol A and Triclosan on Immune Parameters in the U.S. Population, NHANES 2003–2006

Background: Exposure to environmental toxicants is associated with numerous disease outcomes, many of which involve underlying immune and inflammatory dysfunction. Objectives: To address the gap between environmental exposures and immune dysfunction, we investigated the association of two endocrine-disrupting compounds (EDCs) with markers of immune function. Methods: Using data from the 2003–2006 National Health and Nutrition Examination Survey, we compared urinary bisphenol A (BPA) and triclosan levels with serum cytomegalovirus (CMV) antibody levels and diagnosis of allergies or hay fever in U.S. adults and children ≥ 6 years of age. We used multivariate ordinary least squares linear regression models to examine the association of BPA and triclosan with CMV antibody titers, and multivariate logistic regression models to investigate the association of these chemicals with allergy or hay fever diagnosis. Statistical models were stratified by age (\u3c 18 years and ≥ 18 years). Results: In analyses adjusted for age, sex, race, body mass index, creatinine levels, family income, and educational attainment, in the ≥ 18-year age group, higher urinary BPA levels were associated with higher CMV antibody titers (p \u3c 0.001). In the \u3c 18-year age group, lower levels of BPA were associated with higher CMV antibody titers (p \u3c 0.05). However, triclosan, but not BPA, showed a positive association with allergy or hay fever diagnosis. In the \u3c 18-year age group, higher levels of triclosan were associated with greater odds of having been diagnosed with allergies or hay fever (p \u3c 0.01). Conclusions: EDCs such as BPA and triclosan may negatively affect human immune function as measured by CMV antibody levels and allergy or hay fever diagnosis, respectively, with differential consequences based on age. Additional studies should be done to investigate these findings

What Were the Information Voids? A Qualitative Analysis of Questions Asked by Dear Pandemic Readers between August 2020-August 2021

In the current infodemic, how individuals receive information (channel), who it is coming from (source), and how it is framed can have an important effect on COVID-19 related mitigation behaviors. In light of these challenges presented by the infodemic, Dear Pandemic (DP) was created to directly address persistent questions related to COVID-19 and other health topics in the online environment. This is a qualitative analysis of 3806 questions that were submitted by DP readers to a question box on the Dear Pandemic website between August 30, 2020 and August 29, 2021. Analyses resulted in four themes: the need for clarification of other sources; lack of trust in information; recognition of possible misinformation; and questions on personal decision-making. Each theme reflects an unmet informational need of Dear Pandemic readers, which may be reflective of the broader informational gaps in our science communication efforts. This study highlights the role of an ad hoc risk communication platform in the current environment and uses questions submitted to the Dear Pandemic question box to identify informational needs of DP readers over the course of the COVID-19 pandemic. These findings may help clarify how organizations addressing health misinformation in the digital space can contribute to timely, responsive science communication and improve future communication efforts

Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset

© 2015 Luo et al. For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease

Consistency and precision of cancer reporting in a multiwave national panel survey

Abstract Background Many epidemiological studies rely on self-reported information, the accuracy of which is critical for unbiased estimates of population health. Previously, accuracy has been analyzed by comparing self-reports to other sources, such as cancer registries. Cancer is believed to be a well-reported condition. This paper uses novel panel data to test the consistency of cancer reports for respondents with repeated self-reports. Methods Data come from 978 adults who reported having been diagnosed with cancer in at least one of four waves of the Panel Study of Income Dynamics, 1999-2005. Consistency of cancer occurrence reports and precision of timing of onset were studied as a function of individual and cancer-related characteristics using logistic and ordered logistic models. Results Almost 30% of respondents gave inconsistent cancer reports, meaning they said they never had cancer after having said they did have cancer in a previous interview; 50% reported the year of diagnosis with a discrepancy of two or more years. More recent cancers were reported with a higher consistency and timing precision; cervical cancer was reported more inaccurately than other cancer types. Demographic and socio-economic factors were only weak predictors of reporting quality. Conclusions Results suggest that retrospective reports of cancer contain significant measurement error. The errors, however, are fairly random across different social groups, meaning that the results based on the data are not systematically biased by socio-economic factors. Even for health events as salient as cancer, researchers should exercise caution about the presumed accuracy of self-reports, especially if the timing of diagnosis is an important covariate.http://deepblue.lib.umich.edu/bitstream/2027.42/112656/1/12963_2010_Article_108.pd

Seropositivity to Cytomegalovirus, Inflammation, All-Cause and Cardiovascular Disease-Related Mortality in the United States

Studies have suggested that CMV infection may influence cardiovascular disease (CVD) risk and mortality. However, there have been no large-scale examinations of these relationships among demographically diverse populations. The inflammatory marker C-reactive protein (CRP) is also linked with CVD outcomes and mortality and may play an important role in the pathway between CMV and mortality. We utilized a U.S. nationally representative study to examine whether CMV infection is associated with all-cause and CVD-related mortality. We also assessed whether CRP level mediated or modified these relationships., 2006 (N = 14153) in the National Health and Nutrition Examination Survey (NHANES) III (1988–1994). Cox proportional hazard models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for all-cause and CVD-related mortality by CMV serostatus. After adjusting for multiple confounders, CMV seropositivity remained statistically significantly associated with all-cause mortality (HR 1.19, 95% CI: 1.01, 1.41). The association between CMV and CVD-related mortality did not achieve statistical significance after confounder adjustment. CRP did not mediate these associations. However, CMV seropositive individuals with high CRP levels showed a 30.1% higher risk for all-cause mortality and 29.5% higher risk for CVD-related mortality compared to CMV seropositive individuals with low CRP levels.CMV was associated with a significant increased risk for all-cause mortality and CMV seropositive subjects who also had high CRP levels were at substantially higher risk for both for all-cause and CVD-related mortality than subjects with low CRP levels. Future work should target the mechanisms by which CMV infection and low-level inflammation interact to yield significant impact on mortality

Cross-sectional associations between multiple lifestyle behaviors and health-related quality of life in the 10,000 steps cohort

Background: The independent and combined influence of smoking, alcohol consumption, physical activity, diet, sitting time, and sleep duration and quality on health status is not routinely examined. This study investigates the relationships between these lifestyle behaviors, independently and in combination, and health-related quality of life (HRQOL). Methods: Adult members of the 10,000 Steps project (n = 159,699) were invited to participate in an online survey in November-December 2011. Participant socio-demographics, lifestyle behaviors, and HRQOL (poor self-rated health; frequent unhealthy days) were assessed by self-report. The combined influence of poor lifestyle behaviors were examined, independently and also as part of two lifestyle behavior indices, one excluding sleep quality (Index 1) and one including sleep quality (Index 2). Adjusted Cox proportional hazard models were used to examine relationships between lifestyle behaviors and HRQOL. Results: A total of 10,478 participants provided complete data for the current study. For Index 1, the Prevalence Ratio (p value) of poor self-rated health was 1.54 (p = 0.001), 2.07 (p≤0.001), 3.00 (p≤0.001), 3.61 (p≤0.001) and 3.89 (p≤0.001) for people reporting two, three, four, five and six poor lifestyle behaviors, compared to people with 0-1 poor lifestyle behaviors. For Index 2, the Prevalence Ratio (p value) of poor self-rated health was 2.26 (p = 0.007), 3.29 (p≤0.001), 4.68 (p≤0.001), 6.48 (p≤0.001), 7.91 (p≤0.001) and 8.55 (p≤0.001) for people reporting two, three, four, five, six and seven poor lifestyle behaviors, compared to people with 0-1 poor lifestyle behaviors. Associations between the combined lifestyle behavior index and frequent unhealthy days were statistically significant and similar to those observed for poor self-rated health. Conclusions: Engaging in a greater number of poor lifestyle behaviors was associated with a higher prevalence of poor HRQOL. This association was exacerbated when sleep quality was included in the index. © 2014 Duncan et al

Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world

ISSN:2397-337

Fight Like a Nerdy Girl: The Dear Pandemic Playbook for Combating Health Misinformation

Raging alongside the COVID-19 pandemic, a parallel “infodemic” – an overwhelming swirl of information, both good and bad – has seriously compromised pandemic response. Medical falsehood is not a new problem; in the words of medical sociologist Nikolas Christakis, “everywhere you see the spread of germs, for the last few thousand years, you see right behind it the spread of lies.” But its ability to scale thanks to modern digital platforms represents a new and greatly intensified threat. Indeed, the impact of harmful information during the pandemic has been so profound that premier scientific leaders including the Director-General of the World Health Organization and the U.S. Surgeon General have issued urgent calls for the health sector workforce to proactively fight back. Like many other scientists, our all-woman team of “Nerdy Girls” took seriously this call. In March 2020 we launched a public education campaign on social media to do our part to fight the infodemic. Over 18 months and more than two thousand Facebook posts later, we have refined a set of core communication principles and named them with the mnemonic LET’S LEARN. We anticipate that these principles will feel intuitively familiar to health promotion professionals. Formalizing them into a framework provides shared language with which we can support each other as we navigate the new professional frontier of infodemic management

Dear Pandemic: A topic modeling analysis of COVID-19 information needs among readers of an online science communication campaign.

BACKGROUND: The COVID-19 pandemic was accompanied by an infodemic -an overwhelming excess of accurate, inaccurate, and uncertain information. The social media-based science communication campaign Dear Pandemic was established to address the COVID-19 infodemic, in part by soliciting submissions from readers to an online question box. Our study characterized the information needs of Dear Pandemic\u27s readers by identifying themes and longitudinal trends among question box submissions. METHODS: We conducted a retrospective analysis of questions submitted from August 24, 2020, to August 24, 2021. We used Latent Dirichlet Allocation topic modeling to identify 25 topics among the submissions, then used thematic analysis to interpret the topics based on their top words and submissions. We used t-Distributed Stochastic Neighbor Embedding to visualize the relationship between topics, and we used generalized additive models to describe trends in topic prevalence over time. RESULTS: We analyzed 3839 submissions, 90% from United States-based readers. We classified the 25 topics into 6 overarching themes: \u27Scientific and Medical Basis of COVID-19,\u27 \u27COVID-19 Vaccine,\u27 \u27COVID-19 Mitigation Strategies,\u27 \u27Society and Institutions,\u27 \u27Family and Personal Relationships,\u27 and \u27Navigating the COVID-19 Infodemic.\u27 Trends in topics about viral variants, vaccination, COVID-19 mitigation strategies, and children aligned with the news cycle and reflected the anticipation of future events. Over time, vaccine-related submissions became increasingly related to those surrounding social interaction. CONCLUSIONS: Question box submissions represented distinct themes that varied in prominence over time. Dear Pandemic\u27s readers sought information that would not only clarify novel scientific concepts, but would also be timely and practical to their personal lives. Our question box format and topic modeling approach offers science communicators a robust methodology for tracking, understanding, and responding to the information needs of online audiences