A multilevel study of the determinants of area-level inequalities in colorectal cancer survival

Background: In Australia, associations between geographic remoteness, socioeconomic disadvantage, and colorectal cancer (CRC) survival show that survival rates are lowest among residents of geographically remote regions and those living in disadvantaged areas. At present we know very little about the reasons for these inequalities, hence our capacity to intervene to reduce the inequalities is limited. Methods/Design: This study, the first of its type in Australia, examines the association between CRC survival and key area- and individual-level factors. Specifically, we will use a multilevel framework to investigate the possible determinants of area- and individual-level inequalities in CRC survival and quantify the relative contribution of geographic remoteness, socioeconomic and demographic factors, disease stage, and access to diagnostic and treatment services, to these inequalities. The multilevel analysis will be based on survival data relating to people diagnosed with CRC in Queensland between 1996 and 2005 (n = 22,723) from the Queensland Cancer Registry (QCR), area-level data from other data custodians such as the Australian Bureau of Statistics, and individual-level data from the QCR (including extracting stage from pathology records) and Queensland Hospitals. For a subset of this period (2003 and 2004) we will utilise more detailed, individual-level data (n = 1,966) covering a greater range of risk factors from a concurrent research study. Geo-coding and spatial technology will be used to calculate road travel distances from patients’ residence to treatment centres. The analyses will be conducted using a multilevel Cox proportional hazards model with Level 1 comprising individual-level factors (e.g. occupation) and level 2 area level indicators of remoteness and area socioeconomic disadvantage. Discussion: This study focuses on the health inequalities for rural and disadvantaged populations that have often been documented but poorly understood, hence limiting our capacity to intervene. This study utilises and develops emerging statistical and spatial technologies that can then be applied to other cancers and health outcomes. The findings of this study will have direct implications for the targeting and resourcing of cancer control programs designed to reduce the burden of colorectal cancer, and for the provision of diagnostic and treatment services

Translating research in elder care: an introduction to a study protocol series

<p>Abstract</p> <p>Background</p> <p>The knowledge translation field is undermined by two interrelated gaps – underdevelopment of the science and limited use of research in health services and health systems decision making. The importance of context in theory development and successful translation of knowledge has been identified in past research. Additionally, examination of knowledge translation in the long-term care (LTC) sector has been seriously neglected, despite the fact that aging is increasingly identified as a priority area in health and health services research.</p> <p>Aims</p> <p>The aims of this study are: to build knowledge translation theory about the role of organizational context in influencing knowledge use in LTC settings and among regulated and unregulated caregivers, to pilot knowledge translation interventions, and to contribute to enhanced use of new knowledge in LTC.</p> <p>Design</p> <p>This is a multi-level and longitudinal program of research comprising two main interrelated projects and a series of pilot studies. An integrated mixed method design will be used, including sequential and simultaneous phases to enable the projects to complement and inform one another. Inferences drawn from the quantitative and qualitative analyses will be merged to create meta-inferences.</p> <p>Outcomes</p> <p>Outcomes will include contributions to (knowledge translation) theory development, progress toward resolution of major conceptual issues in the field, progress toward resolution of methodological problems in the field, and advances in the design of effective knowledge translation strategies. Importantly, a better understanding of the contextual influences on knowledge use in LTC will contribute to improving outcomes for residents and providers in LTC settings.</p

Quantitative evaluation of the beneficial effects in the mdx mouse of epigallocatechin gallate, an antioxidant polyphenol from green tea

In two separate previous studies, we reported that subcutaneous (sc) or oral administration of (−)-epigallocatechin-3-gallate (EGCG) limited the development of muscle degeneration of mdx mice, a mild phenotype model for Duchenne muscular dystrophy (DMD). However, it was not possible to conclude which was the more efficient route of EGCG administration because different strains of mdx mice, periods of treatment and methods of assessment were used. In this study, we investigated which administration routes and dosages of EGCG are the most effective for limiting the onset of dystrophic lesions in the same strain of mdx mice and applying the same methods of assessment. Three-week-old mdx mice were injected sc for 5 weeks with either saline or a daily average of 3 or 6 mg/kg EGCG. For comparison, age-matched mdx mice were fed for 5 weeks with either a diet containing 0.1% EGCG or a control diet. The effects of EGCG were assessed quantitatively by determining the activities of serum muscle-derived creatine kinase, isometric contractions of triceps surae muscles, integrated spontaneous locomotor activities, and oxidative stress and fibrosis in selected muscles. Oral administration of 180 mg/kg/day EGCG in the diet was found the most effective for significantly improving several parameters associated with muscular dystrophy. However, the improvements were slightly less than those observed previously for sc injection started immediately after birth. The efficacy of EGCG for limiting the development of dystrophic muscle lesions in mice suggests that EGCG may be of benefit for DMD patients

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M&gt;70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0&lt;e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

Design and Performance of a COVID-19 Hospital Recovery Model

Objective:. To determine the accuracy of a predictive model for inpatient occupancy that was implemented at a large New England hospital to aid hospital recovery planning from the COVID-19 surge. Background:. During recovery from COVID surges, hospitals must plan for multiple patient populations vying for inpatient capacity, so that they maintain access for emergency department (ED) patients while enabling time-sensitive scheduled procedures to go forward. To guide pandemic recovery planning, we implemented a model to predict hospital occupancy for COVID and non-COVID patients. Methods:. At a quaternary care hospital in New England, we included hospitalizations from March 10 to July 12, 2020 and subdivided them into COVID, non-COVID nonscheduled (NCNS), and non-COVID scheduled operating room (OR) hospitalizations. For the recovery period from May 25 to July 12, the model made daily hospital occupancy predictions for each population. The primary outcome was the daily mean absolute percentage error (MAPE) and mean absolute error (MAE) when comparing the predicted versus actual occupancy. Results:. There were 444 COVID, 5637 NCNS, and 1218 non-COVID scheduled OR hospitalizations during the recovery period. For all populations, the MAPE and MAE for total occupancy were 2.8% or 22.3 hospitalizations per day; for general care, 2.6% or 17.8 hospitalizations per day; and for intensive care unit, 9.7% or 11.0 hospitalizations per day. Conclusions:. The model was accurate in predicting hospital occupancy during the recovery period. Such models may aid hospital recovery planning so that enough capacity is maintained to care for ED hospitalizations while ensuring scheduled procedures can efficiently return