Determinants of self-rated health in women: a population-based study in Armavir Marz, Armenia, 2001 & 2004

<p>Abstract</p> <p>Background</p> <p>The former soviet Republic of Armenia entered a turbulent and long-lasting economic transition when it declared its independence in 1991. This analysis sought to identify the determinants of poor self-rated health as an indirect measure of health status and mortality prognosis in an adult female population during a period of socio-economic transition in Armenia.</p> <p>Methods</p> <p>Differences in self-rated health in women respondents were analyzed along three main dimensions: social, behavioral/attitudinal, and psychological. The data used were generated from cross-sectional household health surveys conducted in Armavir <it>marz </it>in 2001 and 2004. The surveys utilized the same instruments and study design (probability proportional to size, multistage cluster sampling with a combination of interviewer-administered and self-administered surveys) and generated two independent samples of households representative of Armavir <it>marz</it>. Binary logistic regression models with self-rated health as the outcome were fitted to the 2001 and 2004 datasets and a combined 2001/2004 dataset.</p> <p>Results</p> <p>Overall, 2 038 women aged 18 and over participated in the two surveys (1 019 in each). The rate of perceived "poor" health was relatively high in both surveys: 38.1% in 2001 and 27.0% in 2004. The sets of independent predictors of poor self-rated health were similar in all three models and included severe and moderate material deprivation, probable and possible depression, low level of education, and having ever smoked. These predictors mediated the effect of women's economic activity (including unemployment), ethnicity, low access to/utilization of healthcare services, and living alone on self-rated health.</p> <p>Conclusion</p> <p>Material deprivation was the most influential predictor of self-rated health. Thus, social reforms to decrease the gap between the rich and poor are recommended as a powerful tool for reducing health inequalities and improving the health status of the population.</p

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe