Screening for Domestic Violence Among Adult Women in the United States

BACKGROUND: Domestic violence is a problem frequently encountered in health care settings and a risk factor for physical and mental health problems. OBJECTIVE: To provide nationally representative estimates of rates of domestic violence screening among women, to identify predictors of screening, and to describe settings where women are screened. DESIGN AND PARTICIPANTS: We examined 4,821 women over the age of 18 from the second wave of Healthcare for Communities, a nationally representative household telephone survey conducted in 2000–2001. MEASUREMENTS: Self-reports concerning whether the respondent was ever asked about domestic or family violence by any health care provider. RESULTS: Only 7% (95% CI, 6%–8%) of women reported they were ever asked about domestic violence or family violence by a health care professional. Of women who were asked about abuse, nearly half (46%) were asked in a primary care setting, and 24% were asked in a specialty mental health setting. Women with risk factors for domestic violence were more likely to report being asked about it by a health care professional, but rates were still low. CONCLUSIONS: Self-reported rates of screening for domestic violence are low even among women at higher risk for abuse. These findings reinforce the importance of developing training and raising awareness of domestic violence and its health implications. This is especially true in primary care and mental health specialty settings

When a photograph can be heard: Vision activates the auditory cortex within 110 ms

As the makers of silent movies knew well, it is not necessary to provide an actual auditory stimulus to activate the sensation of sounds typically associated with what we are viewing. Thus, you could almost hear the neigh of Rodolfo Valentino's horse, even though the film was mute. Evidence is provided that the mere sight of a photograph associated with a sound can activate the associative auditory cortex. High-density ERPs were recorded in 15 participants while they viewed hundreds of perceptually matched images that were associated (or not) with a given sound. Sound stimuli were discriminated from non-sound stimuli as early as 110 ms. SwLORETA reconstructions showed common activation of ventral stream areas for both types of stimuli and of the associative temporal cortex, at the earliest stage, only for sound stimuli. The primary auditory cortex (BA41) was also activated by sound images after ∼ 200 ms

Subcortical volumes across the lifespan: data from 18,605 healthy individuals aged 3-90 years

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3?90?years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.This study presents independent research funded by multiple agen-cies. The funding sources had no role in the study design, data collection, analysis, and interpretation of the data. The views expressed inthe manuscript are those of the authors and do not necessarily repre-sent those of any of the funding agencies. Dr. Dima received fundingfrom the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS FoundationTrust and King's College London, the Psychiatry Research Trust and2014 NARSAD Young Investigator Award. Dr. Frangou received sup-port from the National Institutes of Health (R01 MH104284,R01MH113619, R01 MH116147), the European Community's Sev-enth Framework Programme (FP7/2007–2013) (grant agreementn 602450). This work was supported in part through the computa-tional resources and staff expertise provided by Scientific Computingat the Icahn School of Medicine at Mount Sinai, USA. Dr. Agartz wassupported by the Swedish Research Council (grant numbers:521-2014-3487 and 2017-00949). Dr. Alnæs was supported by theSouth Eastern Norway Regional Health Authority (grant number:2019107). Dr. O Andreasen was supported by the Research Councilof Norway (grant number: 223273) and South-Eastern Norway HealthAuthority (grant number: 2017-112). Dr. Cervenka was supported bythe Swedish Research Council (grant number 523-2014-3467).Dr. Crespo-Facorro was supported by the IDIVAL Neuroimaging Unitfor imaging acquisition; Instituto de Salud Carlos III (grant numbers:PI020499, PI050427, PI060507, PI14/00639 and PI14/00918) andthe Fundación Instituto de Investigación Marqués de Valdecilla (grantnumbers: NCT0235832, NCT02534363, and API07/011). Dr. Gurwas supported by the National Institute of Mental Health (grant num-bers: R01MH042191 and R01MH117014). Dr. James was supportedby the Medical Research Council (grant no G0500092). Dr. Saykinreceived support from U.S. National Institutes of Health grants R01AG19771, P30 AG10133 and R01 CA101318. Dr. Thompson,Dr. Jahanshad, Dr. Wright, Dr. Medland, Dr. O Andreasen, Dr. Rinker,Dr. Schmaal, Dr. Veltam, Dr. van Erp, and D.P.H. were supported inpart by a Consortium grant (U54 EB020403 to P.M.T.) from the NIHInstitutes contributing to the Big Data to Knowledge (BD2K) Initiative.FBIRN sample: Data collection and analysis was supported by the National Center for Research Resources at the National Institutes ofHealth (grant numbers: NIH 1 U24 RR021992 (Function BiomedicalInformatics Research Network) and NIH 1 U24 RR025736-01(Biomedical Informatics Research Network Coordinating Center;http://www.birncommunity.org). FBIRN data was processed by theUCI High Performance Computing cluster supported by the NationalCenter for Research Resources and the National Center for AdvancingTranslational Sciences, National Institutes of Health, through GrantUL1 TR000153. Brainscale: This work was supported by NederlandseOrganisatie voor Wetenschappelijk Onderzoek (NWO 51.02.061 toH.H., NWO 51.02.062 to D.B., NWO- NIHC Programs of excellence433-09-220 to H.H., NWO-MagW 480-04-004 to D.B., andNWO/SPI 56-464-14192 to D.B.); FP7 Ideas: European ResearchCouncil (ERC-230374 to D.B.); and Universiteit Utrecht (High Poten-tial Grant to H.H.). UMCU-1.5T: This study is partially funded throughthe Geestkracht Programme of the Dutch Health Research Council(Zon-Mw, grant No 10-000-1001), and matching funds from partici-pating pharmaceutical companies (Lundbeck, AstraZeneca, Eli Lilly,Janssen Cilag) and universities and mental health care organizations(Amsterdam: Academic Psychiatric Centre of the Academic MedicalCenter and the mental health institutions: GGZ Ingeest, Arkin, Dijk enDuin, GGZ Rivierduinen, Erasmus Medical Centre, GGZ Noord Hol-land Noord. Groningen: University Medical Center Groningen and themental health institutions: Lentis, GGZ Friesland, GGZ Drenthe, Dim-ence, Mediant, GGNet Warnsveld, Yulius Dordrecht and Parnassiapsycho-medical center The Hague. Maastricht: Maastricht UniversityMedical Centre and the mental health institutions: GGzE, GGZBreburg, GGZ Oost-Brabant, Vincent van Gogh voor GeestelijkeGezondheid, Mondriaan, Virenze riagg, Zuyderland GGZ, MET ggz,Universitair Centrum Sint-Jozef Kortenberg, CAPRI University of Ant-werp, PC Ziekeren Sint-Truiden, PZ Sancta Maria Sint-Truiden, GGZOverpelt, OPZ Rekem. Utrecht: University Medical Center Utrechtand the mental health institutions Altrecht, GGZ Centraal and Delta.).UMCU-3T: This study was supported by NIMH grant number: R01MH090553 (to RAO). The NIMH had no further role in study design,in the collection, analysis and interpretation of the data, in the writingof the report, and in the decision to submit the paper for publication.Netherlands Twin Register: Funding was obtained from the Nether-lands Organization for Scientific Research (NWO) and The NetherlandsOrganization for Health Research and Development (ZonMW) grants904-61-090, 985-10-002, 912-10-020, 904-61-193,480-04-004,463-06-001, 451-04-034, 400-05-717, 400-07-080, 31160008,016-115-035, 481-08-011, 056-32-010, 911-09-032, 024-001-003,480-15-001/674, Center for Medical Systems Biology (CSMB, NWOGenomics), Biobanking and Biomolecular Resources Research Infra-structure (BBMRI-NL, 184.021.007 and 184.033.111); Spinozapremie(NWO- 56-464-14192), and the Neuroscience Amsterdam researchinstitute (former NCA). The BIG database, established in Nijmegen in2007, is now part of Cognomics, a joint initiative by researchers of theDonders Centre for Cognitive Neuroimaging, the Human Genetics andCognitive Neuroscience departments of the Radboud University Medi-cal Centre, and the Max Planck Institute for Psycholinguistics. TheCognomics Initiative is supported by the participating departments and centers and by external grants, including grants from the Biobankingand Biomolecular Resources Research Infrastructure (Netherlands)(BBMRI-NL) and the Hersenstichting Nederland. The authors alsoacknowledge grants supporting their work from the Netherlands Orga-nization for Scientific Research (NWO), that is, the NWO Brain & Cog-nition Excellence Program (grant 433-09-229), the Vici InnovationProgram (grant 016-130-669 to BF) and #91619115. Additional sup-port is received from the European Community's Seventh FrameworkProgramme (FP7/2007–2013) under grant agreements n 602805(Aggressotype), n 603016 (MATRICS), n 602450 (IMAGEMEND), andn 278948 (TACTICS), and from the European Community's Horizon2020 Programme (H2020/2014–2020) under grant agreements n 643051 (MiND) and n 667302 (CoCA). Betula sample: Data collectionfor the BETULA sample was supported by a grant from Knut and AliceWallenberg Foundation (KAW); the Freesurfer segmentations wereperformed on resources provided by the Swedish National Infrastruc-ture for Computing (SNIC) at HPC2N in Umeå, Sweden. Indiana sample:This sample was supported in part by grants to BCM from SiemensMedical Solutions, from the members of the Partnership for PediatricEpilepsy Research, which includes the American Epilepsy Society, theEpilepsy Foundation, the Epilepsy Therapy Project, Fight Against Child-hood Epilepsy and Seizures (F.A.C.E.S.), and Parents Against ChildhoodEpilepsy (P.A.C.E.), from the Indiana State Department of Health SpinalCord and Brain Injury Fund Research Grant Program, and by a ProjectDevelopment Team within the ICTSI NIH/NCRR Grant NumberRR025761. MHRC study: It was supported in part by RFBR grant20-013-00748. PING study: Data collection and sharing for the Pediat-ric Imaging, Neurocognition and Genetics (PING) Study (National Insti-tutes of Health Grant RC2DA029475) were funded by the NationalInstitute on Drug Abuse and the Eunice Kennedy Shriver National Insti-tute of Child Health & Human Development. A full list of PING investi-gators is at http://pingstudy.ucsd.edu/investigators.html. QTIM sample:The authors are grateful to the twins for their generosity of time andwillingness to participate in our study and thank the many researchassistants, radiographers, and other staff at QIMR Berghofer MedicalResearch Institute and the Centre for Advanced Imaging, University ofQueensland. QTIM was funded by the Australian National Health andMedical Research Council (Project Grants No. 496682 and 1009064)and US National Institute of Child Health and Human Development(RO1HD050735). Lachlan Strike was supported by a University ofQueensland PhD scholarship. Study of Health in Pomerania (SHIP): thisis part of the Community Medicine Research net (CMR) (http://www.medizin.uni-greifswald.de/icm) of the University Medicine Greifswald,which is supported by the German Federal State of Mecklenburg- WestPomerania. MRI scans in SHIP and SHIP-TREND have been supportedby a joint grant from Siemens Healthineers, Erlangen, Germany and theFederal State of Mecklenburg-West Pomerania. This study was furthersupported by the DZHK (German Centre for Cardiovascular Research),the German Centre of Neurodegenerative Diseases (DZNE) and theEU-JPND Funding for BRIDGET (FKZ:01ED1615). TOP study: this wassupported by the European Community's Seventh Framework Pro-gramme (FP7/2007–2013), grant agreement n 602450. The Southernand Eastern Norway Regional Health Authority supported Lars T. Westlye (grant no. 2014-097) and STROKEMRI (grantno. 2013-054). HUBIN sample: HUBIN was supported by the SwedishResearch Council (K2007-62X-15077-04-1, K2008-62P-20597-01-3,K2010-62X-15078-07-2, K2012-61X-15078-09-3), the regional agree-ment on medical training and clinical research between StockholmCounty Council, and the Karolinska Institutet, and the Knut and AliceWallenberg Foundation. The BIG database: this was established in Nij-megen in 2007, is now part of Cognomics, a joint initiative byresearchers of the Donders Centre for Cognitive Neuroimaging, theHuman Genetics and Cognitive Neuroscience departments of theRadboud university medical centre, and the Max Planck Institute forPsycholinguistics. The Cognomics Initiative is supported by the partici-pating departments and centres and by external grants, including grantsfrom the Biobanking and Biomolecular Resources Research Infrastruc-ture (Netherlands) (BBMRI-NL) and the Hersenstichting Nederland. Theauthors also acknowledge grants supporting their work from the Neth-erlands Organization for Scientific Research (NWO), that is, the NWOBrain & Cognition Excellence Program (grant 433-09-229), the ViciInnovation Program (grant 016-130-669 to BF) and #91619115. Addi-tional support is received from the European Community's SeventhFramework Programme (FP7/2007–2013) under grant agreements n 602805 (Aggressotype), n 603016 (MATRICS), n 602450(IMAGEMEND), and n 278948 (TACTICS), and from the EuropeanCommunity's Horizon 2020 Programme (H2020/2014–2020) undergrant agreements n 643051 (MiND) and n 667302 (CoCA)

Should Global Burden of Disease Estimates Include Depression as a Risk Factor for Coronary Heart Disease?

The 2010 Global Burden of Disease Study estimates the premature mortality and disability of all major diseases and injuries. In addition it aims to quantify the risk that diseases and other factors play in the aetiology of disease and injuries. Mental disorders and coronary heart disease are both significant public health issues due to their high prevalence and considerable contribution to global disease burden. For the first time the Global Burden of Disease Study will aim to assess mental disorders as risk factors for coronary heart disease. We show here that current evidence satisfies established criteria for considering depression as an independent risk factor in development of coronary heart disease. A dose response relationship appears to exist and plausible biological pathways have been proposed. However, a number of challenges exist when conducting a rigorous assessment of the literature including heterogeneity issues, definition and measurement of depression and coronary heart disease, publication bias and residual confounding. Therefore, despite some limitations in the available data, it is now appropriate to consider major depression as a risk factor for coronary heart disease in the new Global Burden of Disease Study

T wave abnormalities, high body mass index, current smoking and high lipoprotein (a) levels predict the development of major abnormal Q/QS patterns 20 years later. A population-based study

BACKGROUND: Most studies on risk factors for development of coronary heart disease (CHD) have been based on the clinical outcome of CHD. Our aim was to identify factors that could predict the development of ECG markers of CHD, such as abnormal Q/QS patterns, ST segment depression and T wave abnormalities, in 70-year-old men, irrespective of clinical outcome. METHODS: Predictors for development of different ECG abnormalities were identified in a population-based study using stepwise logistic regression. Anthropometrical and metabolic factors, ECG abnormalities and vital signs from a health survey of men at age 50 were related to ECG abnormalities identified in the same cohort 20 years later. RESULTS: At the age of 70, 9% had developed a major abnormal Q/QS pattern, but 63% of these subjects had not been previously hospitalized due to MI, while 57% with symptomatic MI between age 50 and 70 had no major Q/QS pattern at age 70. T wave abnormalities (Odds ratio 3.11, 95% CI 1.18–8.17), high lipoprotein (a) levels, high body mass index (BMI) and smoking were identified as significant independent predictors for the development of abnormal major Q/QS patterns. T wave abnormalities and high fasting glucose levels were significant independent predictors for the development of ST segment depression without abnormal Q/QS pattern. CONCLUSION: T wave abnormalities on resting ECG should be given special attention and correlated with clinical information. Risk factors for major Q/QS patterns need not be the same as traditional risk factors for clinically recognized CHD. High lipoprotein (a) levels may be a stronger risk factor for silent myocardial infarction (MI) compared to clinically recognized MI

Heterogeneous Light Supply Affects Growth and Biomass Allocation of the Understory Fern Diplopterygium glaucum at High Patch Contrast

Spatial heterogeneity in resource supply is common and responses to heterogeneous resource supply have been extensively documented in clonal angiosperms but not in pteridophytes. To test the hypotheses that clonal integration can modify responses of pteridophytes to heterogeneous resource supply and the integration effect is larger at higher patch contrast, we conducted a field experiment with three homogeneous and two heterogeneous light treatments on the rhizomatous, understory fern Diplopterygium glaucum in an evergreen broad-leaved forest in East China. In homogeneous treatments, all D. glaucum ramets in 1.5 m×1.5 m units were subjected to 10, 40 and 100% natural light, respectively. In the heterogeneous treatment of low patch contrast, ramets in the central 0.5 m×0.5 m plots of the units were subjected to 40% natural light and their interconnected ramets in the surrounding area of the units to 100%; in the heterogeneous treatment of high patch contrast, ramets in the central plots were subjected to 10% natural light and those in the surrounding area to 100%. In the homogeneous treatments, biomass and number of living ramets in the central plots decreased and number of dead ramets increased with decreasing light supply. At low contrast heterogeneous light supply did not affect performance or biomass allocation of D. glaucum in the central plots, but at high contrast it increased lamina biomass and number of living ramets older than annual and modified biomass allocation to lamina and rhizome. Thus, clonal integration can affect responses of understory ferns to heterogeneous light supply and ramets in low light patches can be supported by those in high light. The results also suggest that effects of clonal integration depend on the degree of patch contrast and a significant integration effect may be found only under a relatively high patch contrast

Nine-year comparison of presentation and management of acute coronary syndromes in Ireland: a national cross-sectional survey

BACKGROUND: Shorter time to treatment is associated with lower mortality in acute coronary syndromes (ACS). A previous (1994) survey showed substantial delays for acute myocardial infarction (AMI) in Ireland. The present study compared current practice with 1994 and surveyed acute coronary syndromes as a more complete contemporary evaluation of critical cardiac care than assessing AMI alone. METHODS: Following ethics committee approval, all centres (N = 39) admitting acute cardiac patients to intensive/coronary care unit provided information on 1365 episodes. A cross-sectional survey design was employed. RESULTS: Since 1994, median hospital arrival to thrombolysis time was reduced by 41% (76 to 45 minutes). Thrombolysis was delivered more often in the emergency department in 2003 (48% vs 2%). Thrombolysis when delivered in the emergency department was achieved faster than thrombolysis delivered in intensive/coronary care (35 mins v 60 mins; z = 5.62, p < .0001). Suspected AMI patients who did not subsequently receive thrombolysis took longer to present to hospital (5 h vs 2 h 34 mins; z = 7.33, p < .0001) and had longer transfer times to the intensive/coronary care unit following arrival (2 h 17 mins vs 1 h 10 mins; z = 8.92, p < .0001). Fewer confirmed AMI cases received thrombolysis in 2003 (43% vs 58%). There was an increase in confirmed cases of AMI from 1994 (70% to 87%). CONCLUSIONS: Substantial improvements in time to thrombolysis have occurred since 1994, probably relating to treatment provision in emergency departments. Patient delay pre-hospital is still the principal impediment to effective treatment of ACS. A recent change of definition of AMI may have precluded an exact comparison between 1994 and 2003 data

Physical Stress, Not Biotic Interactions, Preclude an Invasive Grass from Establishing in Forb-Dominated Salt Marshes

Biological invasions have become the focus of considerable concern and ecological research, yet the relative importance of abiotic and biotic factors in controlling the invasibility of habitats to exotic species is not well understood. Spartina species are highly invasive plants in coastal wetlands; however, studies on the factors that control the success or failure of Spartina invasions across multiple habitat types are rare and inconclusive.We examined the roles of physical stress and plant interactions in mediating the establishment of the smooth cordgrass, Spartina alterniflora, in a variety of coastal habitats in northern China. Field transplant experiments showed that cordgrass can invade mudflats and low estuarine marshes with low salinity and frequent flooding, but cannot survive in salt marshes and high estuarine marshes with hypersaline soils and infrequent flooding. The dominant native plant Suaeda salsa had neither competitive nor facilitative effects on cordgrass. A common garden experiment revealed that cordgrass performed significantly better when flooded every other day than when flooded weekly. These results suggest that physical stress rather than plant interactions limits cordgrass invasions in northern China.We conclude that Spartina invasions are likely to be constrained to tidal flats and low estuarine marshes in the Yellow River Delta. Due to harsh physical conditions, salt marshes and high estuarine marshes are unlikely to be invaded. These findings have implications for understanding Spartina invasions in northern China and on other coasts with similar biotic and abiotic environments