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 index of rural access: an innovative integrated approach for measuring primary care access

<p>Abstract</p> <p>Background</p> <p>The problem of access to health care is of growing concern for rural and remote populations. Many Australian rural health funding programs currently use simplistic rurality or remoteness classifications as proxy measures of access. This paper outlines the development of an alternative method for the measurement of access to primary care, based on combining the three key access elements of spatial accessibility (availability and proximity), population health needs and mobility.</p> <p>Methods</p> <p>The recently developed two-step floating catchment area (2SFCA) method provides a basis for measuring primary care access in rural populations. In this paper, a number of improvements are added to the 2SFCA method in order to overcome limitations associated with its current restriction to a single catchment size and the omission of any distance decay function. Additionally, small-area measures for the two additional elements, health needs and mobility are developed. By utilising this improved 2SFCA method, the three access elements are integrated into a single measure of access. This index has been developed within the state of Victoria, Australia.</p> <p>Results</p> <p>The resultant index, the Index of Rural Access, provides a more sensitive and appropriate measure of access compared to existing classifications which currently underpin policy measures designed to overcome problems of limited access to health services. The most powerful aspect of this new index is its ability to identify access differences within rural populations at a much finer geographical scale. This index highlights that many rural areas of Victoria have been incorrectly classified by existing measures as homogenous in regards to their access.</p> <p>Conclusion</p> <p>The Index of Rural Access provides the first truly integrated index of access to primary care. This new index can be used to better target the distribution of limited government health care funding allocated to address problems of poor access to primary health care services in rural areas.</p

The first myriapod genome sequence reveals conservative arthropod gene content and genome organisation in the centipede Strigamia maritima.

Myriapods (e.g., centipedes and millipedes) display a simple homonomous body plan relative to other arthropods. All members of the class are terrestrial, but they attained terrestriality independently of insects. Myriapoda is the only arthropod class not represented by a sequenced genome. We present an analysis of the genome of the centipede Strigamia maritima. It retains a compact genome that has undergone less gene loss and shuffling than previously sequenced arthropods, and many orthologues of genes conserved from the bilaterian ancestor that have been lost in insects. Our analysis locates many genes in conserved macro-synteny contexts, and many small-scale examples of gene clustering. We describe several examples where S. maritima shows different solutions from insects to similar problems. The insect olfactory receptor gene family is absent from S. maritima, and olfaction in air is likely effected by expansion of other receptor gene families. For some genes S. maritima has evolved paralogues to generate coding sequence diversity, where insects use alternate splicing. This is most striking for the Dscam gene, which in Drosophila generates more than 100,000 alternate splice forms, but in S. maritima is encoded by over 100 paralogues. We see an intriguing linkage between the absence of any known photosensory proteins in a blind organism and the additional absence of canonical circadian clock genes. The phylogenetic position of myriapods allows us to identify where in arthropod phylogeny several particular molecular mechanisms and traits emerged. For example, we conclude that juvenile hormone signalling evolved with the emergence of the exoskeleton in the arthropods and that RR-1 containing cuticle proteins evolved in the lineage leading to Mandibulata. We also identify when various gene expansions and losses occurred. The genome of S. maritima offers us a unique glimpse into the ancestral arthropod genome, while also displaying many adaptations to its specific life history.This work was supported by the following grants: NHGRIU54HG003273 to R.A.G; EU Marie Curie ITN #215781 “Evonet” to M.A.; a Wellcome Trust Value in People (VIP) award to C.B. and Wellcome Trust graduate studentship WT089615MA to J.E.G; Marine rhythms of Life” of the University of Vienna, an FWF (http://www.fwf.ac.at/) START award (#AY0041321) and HFSP (http://www.hfsp.org/) research grant (#RGY0082/2010) to KT-­‐R; MFPL Vienna International PostDoctoral Program for Molecular Life Sciences (funded by Austrian Ministry of Science and Research and City of Vienna, Cultural Department -­‐Science and Research to T.K; Direct Grant (4053034) of the Chinese University of Hong Kong to J.H.L.H.; NHGRI HG004164 to G.M.; Danish Research Agency (FNU), Carlsberg Foundation, and Lundbeck Foundation to C.J.P.G.; U.S. National Institutes of Health R01AI55624 to J.H.W.; Royal Society University Research fellowship to F.M.J.; P.D.E. was supported by the BBSRC via the Babraham Institute;This is the final version of the article. It first appeared from PLOS via http://dx.doi.org/10.1371/journal.pbio.100200

Diet-related chronic disease in the northeastern United States: a model-based clustering approach

Background: Obesity and diabetes are global public health concerns. Studies indicate a relationship between socioeconomic, demographic and environmental variables and the spatial patterns of diet-related chronic disease. In this paper, we propose a methodology using model-based clustering and variable selection to predict rates of obesity and diabetes. We test this method through an application in the northeastern United States. Methods: We use model-based clustering, an unsupervised learning approach, to find latent clusters of similar US counties based on a set of socioeconomic, demographic, and environmental variables chosen through the process of variable selection. We then use Analysis of Variance and Post-hoc Tukey comparisons to examine differences in rates of obesity and diabetes for the clusters from the resulting clustering solution. Results: We find access to supermarkets, median household income, population density and socioeconomic status to be important in clustering the counties of two northeastern states. The results of the cluster analysis can be used to identify two sets of counties with significantly lower rates of diet-related chronic disease than those observed in the other identified clusters. These relatively healthy clusters are distinguished by the large central and large fringe metropolitan areas contained in their component counties. However, the relationship of socio-demographic factors and diet-related chronic disease is more complicated than previous research would suggest. Additionally, we find evidence of low food access in two clusters of counties adjacent to large central and fringe metropolitan areas. While food access has previously been seen as a problem of inner-city or remote rural areas, this study offers preliminary evidence of declining food access in suburban areas. Conclusions: Model-based clustering with variable selection offers a new approach to the analysis of socioeconomic, demographic, and environmental data for diet-related chronic disease prediction. In a test application to two northeastern states, this method allows us to identify two sets of metropolitan counties with significantly lower diet-related chronic disease rates than those observed in most rural and suburban areas. Our method could be applied to larger geographic areas or other countries with comparable data sets, offering a promising method for researchers interested in the global increase in diet-related chronic disease