Interrelationships between the structural and functional aspects of social relationships, comorbidity and health related quality of life in breast cancer survivors

It is widely acknowledged in theoretical and empirical literature that social relationships, comprising of structural measures (social networks) and functional measures (perceived social support) have an undeniable effect on health outcomes. However, the actual mechanism of this effect has yet to be clearly understood or explicated. In addition, comorbidity is found to adversely affect social relationships and health related quality of life (a valued outcome measure in cancer patients and survivors). ^ This cross sectional study uses selected baseline data (N=3088) from the Women's Healthy Eating and Living (WHEL) study. Lisrel 8.72 was used for the latent variable structural equation modeling. Due to the ordinal nature of the data, Weighted Least Squares (WLS) method of estimation using Asymptotic Distribution Free covariance matrices was chosen for this analysis. The primary exogenous predictor variables are Social Networks and Comorbidity; Perceived Social Support is the endogenous predictor variable. Three dimensions of HRQoL, physical, mental and satisfaction with current quality of life were the outcome variables. ^ This study hypothesizes and tests the mechanism and pathways between comorbidity, social relationships and HRQoL using latent variable structural equation modeling. After testing the measurement models of social networks and perceived social support, a structural model hypothesizing associations between the latent exogenous and endogenous variables was tested. The results of the study after listwise deletion (N=2131) mostly confirmed the hypothesized relationships (TLI, CFI >0.95, RMSEA = 0.05, p=0.15). Comorbidity was adversely associated with all three HRQoL outcomes. Strong ties were negatively associated with perceived social support; social network had a strong positive association with perceived social support, which served as a mediator between social networks and HRQoL. Mental health quality of life was the most adversely affected by the predictor variables. ^ This study is a preliminary look at the integration of structural and functional measures of social relationships, comorbidity and three HRQoL indicators using LVSEM. Developing stronger social networks and forming supportive relationships is beneficial for health outcomes such as HRQoL of cancer survivors. Thus, the medical community treating cancer survivors as well as the survivor's social networks need to be informed and cognizant of these possible relationships. ^

Revising the National Ambient Air Quality Standards: The integration of science and policy

Under the Clean Air Act, Congress granted discretionary decision making authority to the Administrator of the Environmental Protection Agency (EPA). This discretionary authority involves setting standards to protect the public's health with an "adequate margin of safety" based on current scientific knowledge. The Administrator of the EPA is usually not a scientist, and for the National Ambient Air Quality Standard (NAAQS) for particulate matter (PM), the Administrator faced the task of revising a standard when several scientific factors were ambiguous. These factors included: (1) no identifiable threshold below which health effects are not manifested, (2) no biological basis to explain the reported associations between particulate matter and adverse health effects, and (3) no consensus among the members of the Clean Air Scientific Advisory Committee (CASAC) as to what an appropriate PM indicator, averaging period, or value would be for the revised standard. ^ This project recommends and demonstrates a tool, integrated assessment (IA), to aid the Administrator in making a public health policy decision in the face of ambiguous scientific factors. IA is an interdisciplinary approach to decision making that has been used to deal with complex issues involving many uncertainties, particularly climate change analyses. Two IA approaches are presented; a rough set analysis by which the expertise of CASAC members can be better utilized, and a flag model for incorporating the views of stakeholders into the standard setting process. ^ The rough set analysis can describe minimal and maximal conditions about the current science pertaining to PM and health effects. Similarly, a flag model can evaluate agreement or lack of agreement by various stakeholder groups to the proposed standard in the PM review process. ^ The use of these IA tools will enable the Administrator to (1) complete the NAAQS review in a manner that is in closer compliance with the Clean Air Act, (2) expand the input from CASAC, (3) take into consideration the views of the stakeholders, and (4) retain discretionary decision making authority. ^

Prevalence of depressive symptoms in urban middle and high school Hispanic and African American students

This cross-sectional study examined the prevalence of depressive symptoms in urban Hispanic and African American middle and high school students (N=1,292) using data collected from a multi-component, multi-wave violence and substance use intervention program targeted at a large urban school district in Texas. Chi-square analysis was used to examine differences in race/ethnicity, gender, grade level and whether or not a student had been held back/repeated a grade in school. Univariate and multivariate logistic regression were used to analyze the association between depressive symptoms and demographic variables. Being female and being held back/repeating a grade was significantly associated with depressive symptoms in both univariate and multivariate analyses. Overall 16% of the students reported depressive symptoms; Hispanic youth had a higher prevalence of depressive symptoms (16.8%) than the African American youth (14.8%). Minority females and those who had been held back/repeated a grade reported a prevalence of 19.4% and 21.2%, respectively. Further research is needed to understand why Hispanic youth continue to report a higher prevalence of depressive symptoms than other minorities. Additionally research is required to further explore the association between academic performance and depressive symptoms in urban minorities, specifically the effect of being held back/repeating a grade.^

Maternal health care utilization in rural Jordanian villages: Patterns and predictors

Background. Different individual (demographic) characteristic and health system related characteristics have been identified in the literature to contribute to different rates of maternal health care utilization in developing countries. This study is going to evaluate the individual and quality of health predictors of maternal health care utilization in rural Jordanian villages. ^ Methods. Data from a 2004 survey was used. Individual (predisposing and enabling) variables, quality of health care variables, and maternal care utilization variables were selected for 477 women who had a live birth during the last 5 years. The conceptual framework used in this study will be the Aday-Andersen model for health services utilization. ^ Results. 82.4% of women received at least one antenatal care visit. Individually, village of residence (p=0.036), parity (p=0.048), education (p=0.006), and health insurance (p=0.029) were found to be significant; in addition to respectful treatment (p=0.045) and clean facilities (p=0.001) were the only quality of health care factors found to be significant in predicting antenatal care use. Using logistic regression, living in southern villages (OR=4.7, p=0.01) and availability of transportation (sometimes OR=3.2, p=0.01 and never OR=2.4, p<0.05) were the only two factors to influence maternal care use. ^ Conclusions. Living in the South and transportation are major barriers to maternal care utilization in rural Jordan. Other important cultural factors of interest in some villages should be addressed in future research. Perceptions of women regarding quality of health services should be seriously taken into account. ^

The characterization of residential fungal spores and the relationship with housing characteristics in the Houston area

A number of indoor environmental factors, including bioaerosol or aeroallergen concentrations have been identified as exacerbators for asthma and allergenic conditions of the respiratory system. People generally spend 90% to 95% of their time indoors. Therefore, understanding the environmental factors that affect the presence of aeroallergens indoors as well as outdoors is important in determining their health impact, and in identifying potential intervention methods. This study aimed to assess the relationship between indoor airborne fungal spore concentrations and indoor surface mold levels, indoor versus outdoor airborne fungal spore concentrations and the effect of previous as well as current water intrusion. Also, the association between airborne concentration of indoor fungal spores and surface mold levels and the age of the housing structure were examined. Further, the correlation between indoor concentrations of certain species was determined as well. ^ Air and surface fungal measurements and related information were obtained from a Houston-area data set compiled from visits to homes filing insurance claims. During the sampling visit these complaint homes exhibited either visible mold or a combination of visible mold and water intrusion problems. These data were examined to assess the relationships between the independent and dependent variables using simple linear regression analysis, and independent t-tests. To examine the correlation between indoor concentrations of certain species, Spearman correlation coefficients were used. ^ There were 126 houses sampled, with spring, n=43 (34.1%), and winter, n=42 (33.3%), representing the seasons with the most samples. The summer sample illustrated the highest geometric mean concentration of fungal spores, GM=5,816.5 relative to winter, fall and spring (GM=1,743.4, GM=3,683.5 and GM=2,507.4, respectively). In all seasons, greater concentrations of fungal spores were observed during the cloudy weather conditions. ^ The results indicated no statistically significant association between outdoor total airborne fungal spore concentration and total living room airborne fungal spore concentration (β = 0.095, p = 0.491). Second, living room surface mold levels were not associated with living room airborne fungal spore concentration, (β= 0.011, p = 0.669). Third, houses with and without previous water intrusion did not differ significantly with respect to either living room (t(111) = 0.710, p = 0.528) or bedroom (t(111) =1.673, p = 0.162) airborne fungal spore concentrations. Likewise houses with and without current water intrusion did not differ significantly with respect to living room (t(109)=0.716, p = 0.476) or bedroom (t(109) = 1.035, p = 0.304) airborne fungal spore concentration. Fourth, houses with and without current water intrusion did not differ significantly with respect to living room (χ 2 (5) = 5.61, p = 0.346), or bedroom (χ 2 (5) = 1.80, p = 0.875) surface mold levels. Fifth, the age of the house structure did not predict living room (β = 0.023, p = 0.102) and bedroom (β = 0.023, p = 0.065) surface mold levels nor living room (β = 0.002, p = 0.131) and bedroom (β = 0.001, p = 0.650) fungal spore airborne concentration. Sixth, in houses with visually observed mold growth there was statistically significant differences between the mean living room concentrations and mean outdoor concentrations for Cladosporium (t (107) = 11.73, p < 0.0001), Stachybotrys (t (106)=2.288, p = 0.024, and Nigrosporia (t (102) = 2.267, p = 0.025). Finally, there was a significant correlation between several living room fungal species pairs, namely, Cladosporium and Stachybotrys (r = 0.373, p <0.01, n=65), Curvularia and Aspergillus/Penicillium (r = 0.205, p < 0.05, n= 111)), Curvularia and Stachybotrys (r = 0.205, p < 0.05, n=111), Nigrospora and Chaetomium (r = 0.254, p < 0.01, n=105) and Stachybotrys and Nigrospora (r = 0.269, p < 0.01, n=105). ^ This study has demonstrated several positive findings, i.e., significant pairwise correlations of concentrations of several fungal species in living room air, and significant differences between indoor and outdoor concentrations of three fungal species in homes with visible mold. No association was observed between indoor and outdoor fungal spore concentrations. Neither living room nor bedroom airborne spore concentrations and surface mold levels were related to the age of the house or to water intrusion, either previous or current. Therefore, these findings suggest the need for evaluating additional parameters, as well as combinations of factors such as humidity, temperature, age of structure, ventilation, and room size to better understand the determinants of airborne fungal spore concentrations and surface mold levels in homes. ^