Understanding Rates of Marijuana Use and Consequences Among Adolescents in a Changing Legal Landscape.

Purpose of Review:There is not one answer to address whether marijuana use has increased, decreased, or stayed the same given changes in state legalization of medical and non-medical marijuana in the USA. Recent Findings:Evidence suggests some health benefits for medical marijuana; however, initiation of marijuana use is a risk factor for developing problem cannabis use. Though use rates have remained stable over recent years, about one in three 10th graders report marijuana use, most adolescents do not view the drug as harmful, and over 650,000 youth aged 12 to 17 struggle with cannabis use disorder. Summary:Although the health benefits of medical marijuana are becoming better understood, more research is needed. Intervention and prevention programs must better address effects of marijuana, acknowledging that while there may be some benefits medically, marijuana use can affect functioning during adolescence when the brain is still developing

Use of alternative time scales in Cox proportional hazard models: implications for time-varying environmental exposures

Issues surrounding choice of time scales in Cox proportional hazard regression models have received limited attention in the literature. Although the choice between time on study and ‘attained’ age time scales has been examined, the calendar time scale may be of interest when modeling health effects of environmental exposures with noteworthy secular trends such as ambient particulate matter air pollution in large epidemiological cohort studies. The authors use simulation studies to examine performance (bias, mean squared error, coverage probabilities, and power) of models using all three time scales when the primary exposure of interest depends on calendar time. Results show that performance of models fit to the calendar time scale varies inversely with the strength of the linear association between the time-varying primary exposure and calendar time. Although models fit to attained age and time on study that do not adjust for calendar time were relatively robust, the authors conclude that care should be exercised when using time scales that are highly correlated with exposures of interest

The Hispanic Paradox: Race/Ethnicity and Nativity, Immigrant Enclave Residence and Cognitive Impairment Among Older US Adults

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137472/1/jgs14806.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137472/2/jgs14806_am.pd

Cumulative Lead Dose and Cognitive Function in Adults: A Review of Studies That Measured Both Blood Lead and Bone Lead

OBJECTIVE: We review empirical evidence for the relations of recent and cumulative lead dose with cognitive function in adults. DATA SOURCES: A systematic search of electronic databases resulted in 21 environmental and occupational studies from 1996 to 2006 that examined and compared associations of recent (in blood) and cumulative (in bone) lead doses with neurobehavioral outcomes. DATA EXTRACTION: Data were abstracted after consideration of exclusion criteria and quality assessment, and then compiled into summary tables. CONCLUSIONS: At exposure levels encountered after environmental exposure, associations with bio-markers of cumulative dose (mainly lead in tibia) were stronger and more consistent than associations with blood lead levels. Similarly, in studies of former workers with past occupational lead exposure, associations were also stronger and more consistent with cumulative dose than with recent dose (in blood). In contrast, studies of currently exposed workers generally found associations that were more apparent with blood lead levels; we speculate that the acute effects of high, recent dose may mask the chronic effects of cumulative dose. There is moderate evidence for an association between psychiatric symptoms and lead dose but only at high levels of current occupational lead exposure or with cumulative dose in environmentally exposed adults

Ambient Particulate Matter Air Pollution and Venous Thromboembolism in the Women’s Health Initiative Hormone Therapy Trials

BackgroundThe putative effects of postmenopausal hormone therapy on the association between particulate matter (PM) air pollution and venous thromboembolism (VTE) have not been assessed in a randomized trial of hormone therapy, despite its widespread use among postmenopausal women.ObjectiveIn this study, we examined whether hormone therapy modifies the association of PM with VTE risk.MethodsPostmenopausal women 50–79 years of age (n = 26,450) who did not have a history of VTE and who were not taking anticoagulants were enrolled in the Women’s Health Initiative Hormone Therapy trials at 40 geographically diverse U.S. clinical centers. The women were randomized to treatment with estrogen versus placebo (E trial) or to estrogen plus progestin versus placebo (E + P trial). We used age-stratified Cox proportional hazard models to examine the association between time to incident, centrally adjudicated VTE, and daily mean PM concentrations spatially interpolated at geocoded addresses of the participants and averaged over 1, 7, 30, and 365 days.ResultsDuring the follow-up period (mean, 7.7 years), 508 participants (2.0%) had VTEs at a rate of 2.6 events per 1,000 person-years. Unadjusted and covariate-adjusted VTE risk was not associated with concentrations of PM 0.05) regardless of PM averaging period, either before or after combining data from both trials [e.g., combined trial-adjusted hazard ratios (95% confidence intervals) per 10 μg/m3 increase in annual mean PM2.5 and PM10, were 0.93 (0.54–1.60) and 1.05 (0.72–1.53), respectively]. Findings were insensitive to alternative exposure metrics, outcome definitions, time scales, analytic methods, and censoring dates.ConclusionsIn contrast to prior research, our findings provide little evidence of an association between short-term or long-term PM exposure and VTE, or clinically important modification by randomized exposure to exogenous estrogens among postmenopausal women

The relationship between urban sprawl and coronary heart disease in women

Studies have reported relationships between urban sprawl, physical activity, and obesity, but—to date—no studies have considered the relationship between sprawl and coronary heart disease (CHD) endpoints. In this analysis, we use longitudinal data on post-menopausal women from the Women’s Health Initiative (WHI) Clinical Trial to analyze the relationship between metropolitan statistical area (MSA)-level urban compactness (the opposite of sprawl) and CHD endpoints including death, any CHD event, and myocardial infarction. Models control for individual and neighborhood sociodemographic characteristics. Women who lived in more compact communities at baseline had a lower probability of experiencing a CHD event and CHD death or MI during the study follow-up period. One component of compactness, high residential density, had a particularly noteworthy effect on outcomes. Finally, exploratory analyses showed evidence that the effects of compactness were moderated by race and region

Individual and Neighborhood Socioeconomic Status and the Association between Air Pollution and Cardiovascular Disease

BACKGROUND: Long-term fine particulate matter (PM2.5) exposure is linked with cardiovascular disease, and disadvantaged status may increase susceptibility to air pollution-related health effects. In addition, there are concerns that this association may be partially explained by confounding by socioeconomic status (SES). OBJECTIVES: We examined the roles that individual- and neighborhood-level SES (NSES) play in the association between PM2.5 exposure and cardiovascular disease. METHODS: The study population comprised 51,754 postmenopausal women from the Women's Health Initiative Observational Study. PM2.5 concentrations were predicted at participant residences using fine-scale regionalized universal kriging models. We assessed individual-level SES and NSES (Census-tract level) across several SES domains including education, occupation, and income/wealth, as well as through an NSES score, which captures several important dimensions of SES. Cox proportional-hazards regression adjusted for SES factors and other covariates to determine the risk of a first cardiovascular event. RESULTS: A 5 μg/m3 higher exposure to PM2.5 was associated with a 13% increased risk of cardiovascular event [hazard ratio (HR) 1.13; 95% confidence interval (CI): 1.02, 1.26]. Adjustment for SES factors did not meaningfully affect the risk estimate. Higher risk estimates were observed among participants living in low-SES neighborhoods. The most and least disadvantaged quartiles of the NSES score had HRs of 1.39 (95% CI: 1.21, 1.61) and 0.90 (95% CI: 0.72, 1.07), respectively. CONCLUSIONS: Women with lower NSES may be more susceptible to air pollution-related health effects. The association between air pollution and cardiovascular disease was not explained by confounding from individual-level SES or NSES. Citation: Chi GC, Hajat A, Bird CE, Cullen MR, Griffin BA, Miller KA, Shih RA, Stefanick ML, Vedal S, Whitsel EA, Kaufman JD. 2016. Individual and neighborhood socioeconomic status and the association between air pollution and cardiovascular disease. Environ Health Perspect 124:1840-1847; http://dx.doi.org/10.1289/EHP199

The Women's Health Initiative: The Food Environment, Neighborhood Socioeconomic Status, BMI, and Blood Pressure

Using data (n=60,775 women) from the Women’s Health Initiative Clinical Trial (WHI CT)— a national study of postmenopausal women aged 50 to 79 years — we analyzed cross-sectional associations between the availability of different types of food outlets in the 1.5 miles surrounding a woman’s residence, census tract neighborhood socioeconomic status (NSES), body mass index (BMI) and blood pressure (BP)

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing