Breast cancer is the most common cancer among women. Common variants at 27 loci have been identified as associated with susceptibility to breast cancer, and these account for ~9% of the familial risk of the disease. We report here a meta-analysis of 9 genome-wide association studies, including 10,052 breast cancer cases and 12,575 controls of European ancestry, from which we selected 29,807 SNPs for further genotyping. These SNPs were genotyped in 45,290 cases and 41,880 controls of European ancestry from 41 studies in the Breast Cancer Association Consortium (BCAC). The SNPs were genotyped as part of a collaborative genotyping experiment involving four consortia (Collaborative Oncological Gene-environment Study, COGS) and used a custom Illumina iSelect genotyping array, iCOGS, comprising more than 200,000 SNPs. We identified SNPs at 41 new breast cancer susceptibility loci at genome-wide significance (P < 5 × 10−8). Further analyses suggest that more than 1,000 additional loci are involved in breast cancer susceptibility.

Analysis of 4,405 variants in 89,050 European subjects from 41 case-control studies identified three independent association signals for estrogen-receptor-positive tumors at 11q13. The strongest signal maps to a transcriptional enhancer element in which the G allele of the best candidate causative variant rs554219 increases risk of breast cancer, reduces both binding of ELK4 transcription factor and luciferase activity in reporter assays, and may be associated with low cyclin D1 protein levels in tumors. Another candidate variant, rs78540526, lies in the same enhancer element. Risk association signal 2, rs75915166, creates a GATA3 binding site within a silencer element. Chromatin conformation studies demonstrate that these enhancer and silencer elements interact with each other and with their likely target gene, CCND1.

The poor mental and physical health of people with disabilities has been well documented and there is evidence to suggest that inequalities in health between people with and without disabilities may be at least partly explained by the socioeconomic disadvantage (e.g. low education, unemployment) experienced by people with disabilities. Although there are fewer studies documenting inequalities in social capital, the evidence suggests that people with disabilities are also disadvantaged in this regard. We drew on Bourdieu's conceptualisation of social capital as the resources that flow to individuals from their membership of social networks. Using data from the General Social Survey 2010 of 15,028 adults living in private dwellings across non-remote areas of Australia, we measured social capital across three domains: informal networks (contact with family and friends); formal networks (group membership and contacts in influential organisations) and social support (financial, practical and emotional). We compared levels of social capital and self-rated health for people with and without disabilities and for people with different types of impairments (sensory and speech, physical, psychological and intellectual). Further, we assessed whether differences in levels of social capital contributed to inequalities in health between people with and without disabilities. We found that people with disabilities were worse off than people without disabilities in regard to informal and formal networks, social support and self-rated health status, and that inequalities were greatest for people with intellectual and psychological impairments. Differences in social capital did not explain the association between disability and health. These findings underscore the importance of developing social policies which promote the inclusion of people with disabilities, according to the varying needs of people with different impairments types. Given the changing policy environment, ongoing monitoring of the living circumstances of people with disabilities, including disaggregation of data by impairment type, is critical.

In respiratory health research, interest often lies in estimating the effect of an exposure on a health outcome. If randomization of the exposure of interest is not possible, estimating its effect is typically complicated by confounding bias. This can often be dealt with by controlling for the variables causing the confounding, if measured, in the statistical analysis. Common statistical methods used to achieve this include multivariable regression models adjusting for selected confounding variables or stratification on those variables. Therefore, a key question is which measured variables need to be controlled for in order to remove confounding. An approach to confounder-selection based on the use of causal diagrams (often called directed acyclic graphs) is discussed. A causal diagram is a visual representation of the causal relationships believed to exist between the variables of interest, including the exposure, outcome and potential confounding variables. After creating a causal diagram for the research question, an intuitive and easy-to-use set of rules can be applied, based on a foundation of rigorous mathematics, to decide which measured variables must be controlled for in the statistical analysis in order to remove confounding, to the extent that is possible using the available data. This approach is illustrated by constructing a causal diagram for the research question: 'Does personal smoking affect the risk of subsequent asthma?'. Using data taken from the Tasmanian Longitudinal Health Study, the statistical analysis suggested by the causal diagram approach was performed.

Paid maternity leave has become a standard benefit in many countries throughout the world. Although maternal health has been central to the rationale for paid maternity leave, no review has specifically examined the effect of paid maternity leave on maternal health. The aim of this paper is to provide a systematic review of studies that examine the association between paid maternity leave and maternal health. We conducted a comprehensive search of electronic databases (Medline, Embase, CINAHL, PsycINFO, Web of Science, Sociological Abstracts) and Google Scholar. We searched websites of relevant organisations, reference lists of key papers and journals, and citation indices for additional studies including those not in refereed journals. There were no language restrictions. Studies were included if they compared paid maternity leave versus no paid maternity leave, or different lengths of paid leave. Data were extracted and an assessment of bias was performed independently by authors. Seven studies were identified, with participants from Australia, Sweden, Norway, USA, Canada, and Lebanon. All studies used quantitative methodologies, including cohort, cross-sectional, and repeated cross-sectional designs. Outcomes included mental health and wellbeing, general health, physical wellbeing, and intimate partner violence. The four studies that examined leave at an individual level showed evidence of maternal health benefits, whereas the three studies conducting policy-level comparisons reported either no association or evidence of a negative association. The synthesis of the results suggested that paid maternity leave provided maternal health benefits, although this varied depending on the length of leave. This has important implications for public health and social policy. However, all studies were subject to confounding bias and many to reverse causation. Given the small number of studies and the methodological limitations of the evidence, longitudinal studies are needed to further clarify the effects of paid maternity leave on the health of mothers in paid employment.