The influence of active coping and perceived stress on health disparities in a multi-ethnic low income sample

<p>Abstract</p> <p>Background</p> <p>Extensive research has shown that ethnic health disparities are prevalent and many psychological and social factors influence health disparities. Understanding what factors influence health disparities and how to eliminate health disparities has become a major research objective. The purpose of this study was to examine the impact of coping style, stress, socioeconomic status (SES), and discrimination on health disparities in a large urban multi-ethnic sample.</p> <p>Methods</p> <p>Data from 894 participants were collected via telephone interviews. Independent variables included: coping style, SES, sex, perceived stress, and perceived discrimination. Dependent variables included self-rated general and oral health status. Data analysis included multiple linear regression modeling.</p> <p>Results</p> <p>Coping style was related to oral health for Blacks (B = .23, p < .05) and for Whites there was a significant interaction (B = -.59, p < .05) between coping style and SES for oral health. For Blacks, active coping was associated with better self-reported health. For Whites, low active coping coupled with low SES was significantly associated with worse oral health. Coping style was not significantly related to general health. Higher perceived stress was a significant correlate of poorer general health for all ethnoracial groups and poorer oral health for Hispanics and Blacks. SES was directly related to general health for Hispanics (.B = .27, p < .05) and Whites (B = .23, p < .05) but this relationship was mediated by perceived stress.</p> <p>Conclusion</p> <p>Our results indicate that perceived stress is a critical component in understanding health outcomes for all ethnoracial groups. While SES related significantly to general health for Whites and Hispanics, this relationship was mediated by perceived stress. Active coping was associated only with oral health.</p

Testing a model for identifying nursing and paramedic students’ risk in level 1 human bioscience

BACKGROUND Factors such as the number of hours of paid work, class attendance and university admission scores have been shown to impact on student success. Conflicting results have been demonstrated for factors such as age, healthcare work experience and previous study of biology. Self-efficacy has been linked to academic achievement and performance in science courses has been directly linked to overall academic achievement. At our regional university the student demographic profile of students enrolled in Human Bioscience 1 indicates that up to 50% of students complete the final year of high school and consequently at least half enter through alternative pathways. AIMS 1. Gather detailed information regarding the student demographic profile 2. Develop a model for student risk independent of university admission scores. 3. Prospectively test the model METHOD Students enrolled in Human Bioscience 1 (n=1328) in 2011-12 were invited to undertake an online survey and short science test. The response rate was 70.4%. Data was matched to student final results. A linear model using restricted maximum likelihood was used to analyse the data. Validation of the model was undertaken using 10% of the data randomly selected with the remaining 90% of the data being used to estimate the model. Predicted TotalMark was then compared with the observed TotalMark. Prospective testing of the model was undertaken with data collected during session 1 of 2014. RESULTS Socioeconomic status and the short science test score were not significant to the model. The average correlation between the predicted TotalMark from the model and the actual observed TotalMark for 2011-12 cohort was 0.319 (Range 0.086 – 0.538). A comparison of the demographic characteristics between the two groups, 2011-12 and 2014, indicated similar profiles. Both groups contain students enrolled to study by distance mode and on a campus. The model was used to predict the potential mark to be achieved by the 2014 students. The predicted marks were classified as pass or fail based on a mark >49.5%. McNemars test was then used to determine if this prediction was significantly different from the observed pass/fail result. Only 8 of 300 students were misclassified. The test showed there was no significant difference between observed and predicted values. DISCUSSION Students enrolled in the nursing course are less likely to succeed than their paramedic counterparts with similar backgrounds. The risk is further increased for students aged less than 25 years and students who are the first in their family to attend university. The predictive model highlighted the tension between previous health care work experience and study success. If the student had work experience but was studying on campus the work experience had a negative impact. This may be related to age as those studying on campus are younger overall and work experience is unlikely to have been at a high level of responsibility. This contrasted with those studying by distance, most of who were older and had a higher level of responsibility due to prior technical college qualifications. Limitations to the model include factors such as carer responsibility and measures of the students’ self-efficacy, engagement and anxiety all of which have been shown to impact on student success. Socioeconomic status was determined by geographic location which may not accurately reflect the status for an individual student. IMPLICATIONS With the increasing diversity of undergraduate student populations, the ability to identify students at risk of attrition is more important than ever. The regression model provides a mechanism to identify students who may be assisted by targeted strategies, such as pre-study resources and inclusion of mechanisms for building study resilience

An Investigation of School Socioeconomic Staus on adolescent Athletes\u27 Baseline and Post-Injury Concussion Assessments

Please enjoy Volume 5, Issue 1 of the JSMAHS. In this issue you will find Professional and under graduate research abstracts, case reports, and critically appraised topics. Thank you for viewing this 5th Annual OATA Special Editio

Intercomparison Methods for Satellite Measurements of Atmospheric Composition: Application to Tropospheric Ozone from TES and OMI

We analyze the theoretical basis of three different methods to validate and intercompare satellite measurements of atmospheric composition, and apply them to tropospheric ozone retrievals from the Tropospheric Emission Spectrometer (TES) and the Ozone Monitoring Instrument (OMI). The first method (in situ method) uses in situ vertical profiles for absolute instrument validation; it is limited by the sparseness of in situ data. The second method (CTM method) uses a chemical transport model (CTM) as an intercomparison platform; it provides a globally complete intercomparison with relatively small noise from model error. The third method (averaging kernel smoothing method) involves smoothing the retrieved profile from one instrument with the averaging kernel matrix of the other; it also provides a global intercomparison but dampens the actual difference between instruments and adds noise from the a priori. We apply the three methods to a full year (2006) of TES and OMI data. Comparison with in situ data from ozonesondes shows mean positive biases of 5.3 parts per billion volume (ppbv) (10%) for TES and 2.8 ppbv (5%) for OMI at 500 hPa. We show that the CTM method (using the GEOS-Chem CTM) closely approximates results from the in situ method while providing global coverage. It reveals that differences between TES and OMI are generally less than 10 ppbv (18%), except at northern mid-latitudes in summer and over tropical continents. The CTM method further allows for CTM evaluation using both satellite observations. We thus find that GEOS-Chem underestimates tropospheric ozone in the tropics due to possible underestimates of biomass burning, soil, and lightning emissions. It overestimates ozone in the northern subtropics and southern mid-latitudes, likely because of excessive stratospheric influx of ozone.Earth and Planetary SciencesEngineering and Applied Science

Ecological and Genomic Attributes of Novel Bacterial Taxa That Thrive in Subsurface Soil Horizons.

While most bacterial and archaeal taxa living in surface soils remain undescribed, this problem is exacerbated in deeper soils, owing to the unique oligotrophic conditions found in the subsurface. Additionally, previous studies of soil microbiomes have focused almost exclusively on surface soils, even though the microbes living in deeper soils also play critical roles in a wide range of biogeochemical processes. We examined soils collected from 20 distinct profiles across the United States to characterize the bacterial and archaeal communities that live in subsurface soils and to determine whether there are consistent changes in soil microbial communities with depth across a wide range of soil and environmental conditions. We found that bacterial and archaeal diversity generally decreased with depth, as did the degree of similarity of microbial communities to those found in surface horizons. We observed five phyla that consistently increased in relative abundance with depth across our soil profiles: Chloroflexi, Nitrospirae, Euryarchaeota, and candidate phyla GAL15 and Dormibacteraeota (formerly AD3). Leveraging the unusually high abundance of Dormibacteraeota at depth, we assembled genomes representative of this candidate phylum and identified traits that are likely to be beneficial in low-nutrient environments, including the synthesis and storage of carbohydrates, the potential to use carbon monoxide (CO) as a supplemental energy source, and the ability to form spores. Together these attributes likely allow members of the candidate phylum Dormibacteraeota to flourish in deeper soils and provide insight into the survival and growth strategies employed by the microbes that thrive in oligotrophic soil environments.IMPORTANCE Soil profiles are rarely homogeneous. Resource availability and microbial abundances typically decrease with soil depth, but microbes found in deeper horizons are still important components of terrestrial ecosystems. By studying 20 soil profiles across the United States, we documented consistent changes in soil bacterial and archaeal communities with depth. Deeper soils harbored communities distinct from those of the more commonly studied surface horizons. Most notably, we found that the candidate phylum Dormibacteraeota (formerly AD3) was often dominant in subsurface soils, and we used genomes from uncultivated members of this group to identify why these taxa are able to thrive in such resource-limited environments. Simply digging deeper into soil can reveal a surprising number of novel microbes with unique adaptations to oligotrophic subsurface conditions

Inventory of Boreal Fire Emissions for North America in 2004: The Importance of Peat Burning and Pyro-Convective Injection

The summer of 2004 was one of the largest fire seasons on record for Alaska and western Canada. We construct a daily bottom-up fire emission inventory for that season, including consideration of peat burning and high-altitude (buoyant) injection, and evaluate it in a global chemical transport model (the GEOS-Chem CTM) simulation of CO through comparison with MOPITT satellite and ICARTT aircraft observations. The inventory is constructed by combining daily area burned reports and MODIS fire hot spots with estimates of fuel consumption and emission factors based on ecosystem type. We estimate the contribution from peat burning using drainage and peat distribution maps for Alaska and Canada; 17% of the reported 5.1 × 106 ha burned were located in peatlands in 2004. Our total estimate of North American fire emissions during the summer of 2004 is 30 Tg CO, including 11 Tg from peat. Including peat burning in the GEOS-Chem simulation improves agreement with MOPITT observations. The long-range transport of fire plumes observed by MOPITT suggests that the largest fires injected a significant fraction of their emissions in the upper troposphere.Earth and Planetary SciencesEngineering and Applied Science

The Vaginal Microbiome: Disease, Genetics and the Environment

The vagina is an interactive interface between the host and the environment. Its surface is covered by a protective epithelium colonized by bacteria and other microorganisms. The ectocervix is nonsterile, whereas the endocervix and the upper genital tract are assumed to be sterile in healthy women. Therefore, the cervix serves a pivotal role as a gatekeeper to protect the upper genital tract from microbial invasion and subsequent reproductive pathology. Microorganisms that cross this barrier can cause preterm labor, pelvic inflammatory disease, and other gynecologic and reproductive disorders. Homeostasis of the microbiome in the vagina and ectocervix plays a paramount role in reproductive health. Depending on its composition, the microbiome may protect the vagina from infectious or non-infectious diseases, or it may enhance its susceptibility to them. Because of the nature of this organ, and the fact that it is continuously colonized by bacteria from birth to death, it is virtually certain that this rich environment evolved in concert with its microbial flora. Specific interactions dictated by the genetics of both the host and microbes are likely responsible for maintaining both the environment and the microbiome. However, the genetic basis of these interactions in both the host and the bacterial colonizers is currently unknown. _Lactobacillus_ species are associated with vaginal health, but the role of these species in the maintenance of health is not yet well defined. Similarly, other species, including those representing minor components of the overall flora, undoubtedly influence the ability of potential pathogens to thrive and cause disease. Gross alterations in the vaginal microbiome are frequently observed in women with bacterial vaginosis, but the exact etiology of this disorder is still unknown. There are also implications for vaginal flora in non-infectious conditions such as pregnancy, pre-term labor and birth, and possibly fertility and other aspects of women&#x2019;s health. Conversely, the role of environmental factors in the maintenance of a healthy vaginal microbiome is largely unknown. To explore these issues, we have proposed to address the following questions:&#xd;&#xa;&#xd;&#xa;*1.&#x9;Do the genes of the host contribute to the composition of the vaginal microbiome?* We hypothesize that genes of both host and bacteria have important impacts on the vaginal microbiome. We are addressing this question by examining the vaginal microbiomes of mono- and dizygotic twin pairs selected from the over 170,000 twin pairs in the Mid-Atlantic Twin Registry (MATR). Subsequent studies, beyond the scope of the current project, may investigate which host genes impact the microbial flora and how they do so.&#xd;&#xa;*2.&#x9;What changes in the microbiome are associated with common non-infectious pathological states of the host?* We hypothesize that altered physiological (e.g., pregnancy) and pathologic (e.g., immune suppression) conditions, or environmental exposures (e.g., antibiotics) predictably alter the vaginal microbiome. Conversely, certain vaginal microbiome characteristics are thought to contribute to a woman&#x2019;s risk for outcomes such as preterm delivery. We are addressing this question by recruiting study participants from the ~40,000 annual clinical visits to women&#x2019;s clinics of the VCU Health System.&#xd;&#xa;*3.&#x9;What changes in the vaginal microbiome are associated with relevant infectious diseases and conditions?* We hypothesize that susceptibility to infectious disease (e.g. HPV, _Chlamydia_ infection, vaginitis, vaginosis, etc.) is impacted by the vaginal microbiome. In turn, these infectious conditions clearly can affect the ability of other bacteria to colonize and cause pathology. Again, we are exploring these issues by recruiting participants from visitors to women&#x2019;s clinics in the VCU Health System.&#xd;&#xa;&#xd;&#xa;Three kinds of sequence data are generated in this project: i) rDNA sequences from vaginal microbes; ii) whole metagenome shotgun sequences from vaginal samples; and iii) whole genome shotgun sequences of bacterial clones selected from vaginal samples. The study includes samples from three vaginal sites: mid-vaginal, cervical, and introital. The data sets also include buccal and perianal samples from all twin participants. Samples from these additional sites are used to test the hypothesis of a per continuum spread of bacteria in relation to vaginal health. An extended set of clinical metadata associated with these sequences are deposited with dbGAP. We have currently collected over 4,400 samples from ~100 twins and over 450 clinical participants. We have analyzed and deposited data for 480 rDNA samples, eight whole metagenome shotgun samples, and over 50 complete bacterial genomes. These data are available to accredited investigators according to NIH and Human Microbiome Project (HMP) guidelines. The bacterial clones are deposited in the Biodefense and Emerging Infections Research Resources Repository (&#x22;http://www.beiresources.org/&#x22;:http://www.beiresources.org/). &#xd;&#xa;&#xd;&#xa;In addition to the extensive sequence data obtained in this study, we are collecting metadata associated with each of the study participants. Thus, participants are asked to complete an extensive health history questionnaire at the time samples are collected. Selected clinical data associated with the visit are also obtained, and relevant information is collected from the medical records when available. This data is maintained securely in a HIPAA-compliant data system as required by VCU&#x2019;s Institutional Review Board (IRB). The preponderance of these data (i.e., that judged appropriate by NIH staff and VCU&#x2019;s IRB are deposited at dbGAP (&#x22;http://www.ncbi.nlm.nih.gov/gap&#x22;:http://www.ncbi.nlm.nih.gov/gap). Selected fields of this data have been identified by NIH staff as &#x2018;too sensitive&#x2019; and are not available in dbGAP. Individuals requiring access to these data fields are asked to contact the PI of this project or NIH Program Staff. &#xd;&#xa