Decreased Births Among Black Female Adolescents Following School Desegregation

Although the socioeconomic impact of school desegregation in the U.S. has been well documented, little is known about the health consequences of this policy. The purpose of this study was to quantify the associations between school desegregation and adolescent births among black and white females. We compared the change in prevalence of adolescent births in areas that implemented school desegregation plans in the 1970s with areas that implemented school desegregation plans in other decades, using difference-in-difference methods with 1970 and 1980 Census microdata. School desegregation policy in the U.S. in the 1970s was associated with a significant reduction of 3.2 percentage points in the prevalence of births among black female adolescents between 1970 and 1980. This association was specific to black female adolescents and was not observed among white adolescents

The association between blood pressure and years of schooling versus educational credentials: Test of the sheepskin effect

Purpose Attaining a degree may offer greater opportunities for health than years of schooling alone. This study examines whether there is a degree, or “sheepskin”, effect on the association between education and blood pressure. Methods Multivariable-adjusted ordinal and linear regression models assessed associations of years of schooling and degree attainment with systolic and diastolic blood pressure in a sample of 552 adults aged 38–47 years. Results Years of schooling was inversely associated with systolic blood pressure adjusting for age, gender and race (β=−0.4, 95% CL:−0.7,−0.1 mmHg systolic blood pressure/year of schooling). Additional adjustment for mother’s education, childhood verbal intelligence quotient, childhood health and childhood socioeconomic status had minimal impact on effect size (β= −0.3, 95% CI=−0.7,0.0). However, years of schooling was no longer associated with blood pressure in the fully adjusted model which included additional adjustment for degree attained (β=0.0, 95% CL:−0.5, 0.4). In the fully adjusted model (including adjustment for years of schooling), individuals with a graduate degree still had significantly lower systolic blood pressure than HS degree-holders (e.g. β=−9.2, 95% CL:−15.2,−3.2 for graduate vs. high school degree). Findings were similar for diastolic blood pressure. Conclusion The association of years of schooling with blood pressure may be largely due to degree attainment rather than simply the knowledge and skills accumulated due to years of schooling alone

Fatty Acid Desaturase Gene Variants, Cardiovascular Risk Factors, and Myocardial Infarction in the Costa Rica Study

Genetic variation in fatty acid desaturases (FADS) has previously been linked to long-chain polyunsaturated fatty acids (PUFAs) in adipose tissue and cardiovascular risk. The goal of our study was to test associations between six common FADS polymorphisms (rs174556, rs3834458, rs174570, rs2524299, rs174589, rs174627), intermediate cardiovascular risk factors, and non-fatal myocardial infarction (MI) in a matched population based case–control study of Costa Rican adults (n = 1756). Generalized linear models and multiple conditional logistic regression models were used to assess the associations of interest. Analyses involving intermediate cardiovascular risk factors and MI were also conducted in two replication cohorts, The Nurses’ Health Study (n = 1200) and The Health Professionals Follow-Up Study (n = 1295). In the Costa Rica Study, genetic variation in the FADS cluster was associated with a robust linear decrease in adipose gamma-linolenic, arachidonic, and eicosapentaenoic fatty acids, and significant or borderline significant increases in the eicosadienoic, eicosatrienoic, and dihomo-gamma-linolenic fatty acids. However, the associations with adipose tissue fatty acids did not translate into changes in inflammatory biomarkers, blood lipids, or the risk of MI in the discovery or the replication cohorts. In conclusion, fatty acid desaturase polymorphisms impact long-chain PUFA biosynthesis, but their overall effect on cardiovascular health likely involves multiple pathways and merits further investigation

Spatial process models for social network analysis

There has been a recent increase in the use of network models for representing interactions and structure in many complex systems. In this thesis we introduce the use of spatial process models for the statistical analysis of networks, emphasizing applications to social networks. The first methodology we propose is the latent socio-spatial process model. In the spirit of a random effects model, pairwise connections are assumed to be conditionally independent given a latent spatial process evaluated at observed points in a covariate space. This smooths the relationship between connections and covariates in a sample network using relatively few parameters, so the probabilities of connection for a population can be inferred. The second model that is proposed is the meta-distance model. Here, a random function is used to represent the logistic relationship between covariates and binary relations. A spatial covariance structure is assumed for the random function, where the points in space are distances between attribute pairs. A Bayesian framework is used for estimation and prediction. While spatial process models can be very flexible and provide reasonable fit and predictions in many contexts, interpretation of these models can be complicated. To aid in the identification of important covariates, we propose a reference distribution variable selection procedure. An inert variable is appended to the data for analysis, and the posterior distribution of an ``activity\u27\u27 parameter associated with the covariate is used as a reference distribution against which the true variables can be assessed. The approach is Bayesian, but the variable selection has a frequentist flavor. Finally, we illustrate one important application of the proposed methodology. Local network topology can have a significant impact on contact-based processes, such as epidemics. This is demonstrated by looking at susceptible-infected-susceptible and susceptible-infected-removed epidemic models. We explore how using a predictive network model, such as the latent socio-spatial process model, can help in predicting how a disease might spread in a population

Social and Behavioral Risk Marker Clustering Associated with Biological Risk Factors for Coronary Heart Disease: NHANES 2001–2004

Background. Social and behavioral risk markers (e.g., physical activity, diet, smoking, and socioeconomic position) cluster; however, little is known whether clustering is associated with coronary heart disease (CHD) risk. Objectives were to determine if sociobehavioral clustering is associated with biological CHD risk factors (total cholesterol, HDL cholesterol, systolic blood pressure, body mass index, waist circumference, and diabetes) and whether associations are independent of individual clustering components. Methods. Participants included 4,305 males and 4,673 females aged ≥20 years from NHANES 2001–2004. Sociobehavioral Risk Marker Index (SRI) included a summary score of physical activity, fruit/vegetable consumption, smoking, and educational attainment. Regression analyses evaluated associations of SRI with aforementioned biological CHD risk factors. Receiver operator curve analyses assessed independent predictive ability of SRI. Results. Healthful clustering (SRI = 0) was associated with improved biological CHD risk factor levels in 5 of 6 risk factors in females and 2 of 6 risk factors in males. Adding SRI to models containing age, race, and individual SRI components did not improve C-statistics. Conclusions. Findings suggest that healthful sociobehavioral risk marker clustering is associated with favorable CHD risk factor levels, particularly in females. These findings should inform social ecological interventions that consider health impacts of addressing social and behavioral risk factors

Development of a Cardiovascular Risk Score for Use in Low- and Middle-Income Countries123

Summary measures of cardiovascular risk have long been used in public health, but few include nutritional predictors despite extensive evidence linking diet and heart disease. Study objectives were to develop and validate a novel risk score in a case-control study of myocardial infarction (MI) conducted in Costa Rica during 1994–2004. After restricting the data set to healthy participants (n = 1678), conditional logistic regression analyses modeled associations of lifestyle factors (unhealthy diet, decreased physical activity, smoking, waist:hip ratio, low or high alcohol intake, and low socioeconomic status) with risk for MI. Using the estimated coefficients as weights for each component, a regression model was fit to assess score performance. The score was subsequently validated in participants with a history of chronic disease. Higher risk score values were associated with a significantly increased risk of MI [OR = 2.72 (95% CI = 2.28–3.24)]. The findings were replicated in a model (n = 1392) that included the best covariate measures available in the study [OR = 2.71 (95% CI = 2.26–3.26)]. Performance of the score in different subsets of the study population showed c-statistics ranging from 0.63 to 0.67. The new score presents a quantitative summary of modifiable cardiovascular risk factors in the study population