Bayesian Multilevel Models Research Articles

Mapping mental health inequalities: The intersecting effects of gender, race, class, and ethnicity on ADHD diagnosis.

While the effects of social stratification by gender, race, class, and ethnicity on health inequalities are well-documented, our understanding of the intersecting consequences of these social dimensions on diagnosis remains limited. This is particularly the case in studies of mental health, where "paradoxical" patterns of stratification have been identified. Using a Bayesian multi-level random-effects Poisson model and a nationally representative random sample of 138,009 households from the National Survey of Children's Health, this study updates and extends the literature on mental health inequalities through an intersectional investigation of one of the most commonly diagnosed psychiatric conditions of childhood/adolescence: attention-deficit hyperactivity disorder (ADHD). Findings indicate that gender, race, class, and ethnicity combine in mutually constitutive ways to explain between-group variation in ADHD diagnosis. Observed effects underscore the importance and feasibility of an intersectional, multi-level modelling approach and data mapping technique to advance our understanding of social subgroups more/less likely to be diagnosed with mental health conditions.

Personality-specific pathways from bullying victimization to adolescent alcohol use: a multilevel longitudinal moderated mediation analysis.

Bullying victimization is common in adolescence and has been associated with a broad variety of psychopathology and alcohol use. The present study assessed time-varying associations between bullying victimization and alcohol use through internalizing and externalizing symptoms and whether this indirect association throughout time is moderated by personality. This 5-year longitudinal study (3,800 grade 7 adolescents) used Bayesian multilevel moderated mediation models: independent variable was bullying victimization; moderators were four personality dimensions (anxiety sensitivity, hopelessness, impulsivity, and sensation seeking); internalizing symptoms (anxiety, depressive symptoms) and externalizing symptoms (conduct, hyperactivity problems) were the mediators; and alcohol use, the outcome. Results indicated significant between, within, and lagged effects on alcohol use through internalizing and externalizing symptoms. There were significant between and within effects on alcohol use through internalizing symptoms for adolescents with high anxiety sensitivity and hopelessness, and significant between, within, and lagged effects on alcohol use through externalizing symptoms for adolescents with high impulsivity and sensation seeking. These findings implicate two risk pathways that account for how bullying victimization enhances alcohol use risk and emphasize the importance of personality profiles that can shape the immediate and long-term consequences of victimization.

Global, regional, and national prevalence estimates of physical or sexual, or both, intimate partner violence against women in 2018

SummaryBackgroundIntimate partner violence against women is a global public health problem with many short-term and long-term effects on the physical and mental health of women and their children. The Sustainable Development Goals (SDGs) call for its elimination in target 5.2. To monitor governments' progress towards SDG target 5.2, this study aimed to provide global, regional, and country baseline estimates of physical or sexual, or both, violence against women by male intimate partners.MethodsThis study developed global, regional, and country estimates, based on data from the WHO Global Database on Prevalence of Violence Against Women. These data were identified through a systematic literature review searching MEDLINE, Global Health, Embase, Social Policy, and Web of Science, and comprehensive searches of national statistics and other websites. A country consultation process identified additional studies. Included studies were conducted between 2000 and 2018, representative at the national or sub-national level, included women aged 15 years or older, and used act-based measures of physical or sexual, or both, intimate partner violence. Non-population-based data, including administrative data, studies not generalisable to the whole population, studies with outcomes that only provided the combined prevalence of physical or sexual, or both, intimate partner violence with other forms of violence, and studies with insufficient data to allow extrapolation or imputation were excluded. We developed a Bayesian multilevel model to jointly estimate lifetime and past year intimate partner violence by age, year, and country. This framework adjusted for heterogeneous age groups and differences in outcome definition, and weighted surveys depending on whether they were nationally or sub-nationally representative. This study is registered with PROSPERO (number CRD42017054100).FindingsThe database comprises 366 eligible studies, capturing the responses of 2 million women. Data were obtained from 161 countries and areas, covering 90% of the global population of women and girls (15 years or older). Globally, 27% (uncertainty interval [UI] 23–31%) of ever-partnered women aged 15–49 years are estimated to have experienced physical or sexual, or both, intimate partner violence in their lifetime, with 13% (10–16%) experiencing it in the past year before they were surveyed. This violence starts early, affecting adolescent girls and young women, with 24% (UI 21–28%) of women aged 15–19 years and 26% (23–30%) of women aged 19–24 years having already experienced this violence at least once since the age of 15 years. Regional variations exist, with low-income countries reporting higher lifetime and, even more pronouncedly, higher past year prevalence compared with high-income countries.InterpretationThese findings show that intimate partner violence against women was already highly prevalent across the globe before the COVID-19 pandemic. Governments are not on track to meet the SDG targets on the elimination of violence against women and girls, despite robust evidence that intimate partner violence can be prevented. There is an urgent need to invest in effective multisectoral interventions, strengthen the public health response to intimate partner violence, and ensure it is addressed in post-COVID-19 reconstruction efforts.FundingUK Department for International Development through the UN Women–WHO Joint Programme on Strengthening Violence against Women Data, and UNDP-UN Population Fund-UNICEF-WHO-World Bank Special Programme of Research, Development, and Research Training in Human Reproduction, a cosponsored programme executed by WHO.

Foraging as sampling without replacement: A Bayesian statistical model for estimating biases in target selection.

Foraging entails finding multiple targets sequentially. In humans and other animals, a key observation has been a tendency to forage in 'runs' of the same target type. This tendency is context-sensitive, and in humans, it is strongest when the targets are difficult to distinguish from the distractors. Many important questions have yet to be addressed about this and other tendencies in human foraging, and a key limitation is a lack of precise measures of foraging behaviour. The standard measures tend to be run statistics, such as the maximum run length and the number of runs. But these measures are not only interdependent, they are also constrained by the number and distribution of targets, making it difficult to make inferences about the effects of these aspects of the environment on foraging. Moreover, run statistics are underspecified about the underlying cognitive processes determining foraging behaviour. We present an alternative approach: modelling foraging as a procedure of generative sampling without replacement, implemented in a Bayesian multilevel model. This allows us to break behaviour down into a number of biases that influence target selection, such as the proximity of targets and a bias for selecting targets in runs, in a way that is not dependent on the number of targets present. Our method thereby facilitates direct comparison of specific foraging tendencies between search environments that differ in theoretically important dimensions. We demonstrate the use of our model with simulation examples and re-analysis of existing data. We believe our model will provide deeper insights into visual foraging and provide a foundation for further modelling work in this area.

Effect of a “feedback prompt” from a disinfection tracking system on portable medical equipment disinfection

Portable medical equipment (PME) may contribute to transmission of multidrug-resistant organisms without proper disinfection. We studied whether a Disinfection Tracking System (DTS) with feedback prompt, attached to PME, can increase the frequency of PME disinfection. DTS devices were placed on 10 workstations-on-wheels (WOWs) and 5 vitals machine (VM). After a 25 day "screen-off" period, the DTS device screens were turned on to display the number of hours since the last recorded disinfection event for a 42 day period. We used a Bayesian multilevel zero-inflated negative binomial model to compare the number of events in the display "screen-off" to the "screen-on" period. During the "screen-off" period, there were 1.26 and 0.49 mean disinfection events and during the "screen-on" period, there were 1.76 and 0.50 mean disinfection events for WOWs and VM, respectively, per day. The model estimated mean events per device per day in the the "screen-on" period for WOW's were 1.32 (1.10 - 1.57) times greater than those in the"screen-off" period and the "screen-on" period for VM devices was 1.37 (0.89 - 2.01) times greater than those in the "screen-off" period. The rate of disinfection events for WOWs increased following the implementation of the DTS feedback prompt.

Impact of social distancing on early SARS-CoV-2 transmission in the United States.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a viral pathogen that quickly became a global pandemic in the winter of 2020-2021. In response, governments issued social distancing orders to minimize transmission by reducing community contacts. We tested the efficacy of this social distancing at the state level during the first 2months of the pandemic in the United States. We utilized data on daily SARS-CoV-2 case numbers and human community mobility (anonymized, aggregated cell phone location data stratified into six categories used as an index of social distancing), the date of government-issued social distancing orders, demographics, urbanization and public transportation. We implemented cross-correlation to identify lag times between declines in mobility and SARS-CoV-2 cases. Incorporating state-specific lag times, we tested for associations between case counts and mobility metrics using Bayesian multilevel models. Decreased mobility around grocery stores/pharmacies, retail/recreation locations, transit stations and workplaces was correlated with decreases in SARS-CoV-2 cases with significant lag times of ≥21days. Social distancing orders were associated with fewer cumulative SARS-CoV-2 cases when they were put in place earlier. Community mobility had already started declining prior to most social distancing orders, especially the more restrictive orders implemented later in the pandemic. Social distancing is an important tool that has been implemented throughout the pandemic to decrease SARS-CoV-2 transmission, although with significant social and economic impacts. Our results suggest that declines in cases were observed several weeks subsequent to implementation of social distancing measures, and that implementing social distancing earlier could potentially minimize the duration of time these policies need to be in effect. Our findings can inform ongoing management of this pandemic and other emerging infectious disease outbreaks by identifying areas where reductions in mobility are associated with reduced disease transmission, and the expected time frame between behavioural changes and measurable population outcomes.

Age-At-Injury Influences the Glial Response to Traumatic Brain Injury in the Cortex of Male Juvenile Rats.

Few translational studies have examined how age-at-injury affects the glial response to traumatic brain injury (TBI). We hypothesized that rats injured at post-natal day (PND) 17 would exhibit a greater glial response, that would persist into early adulthood, compared to rats injured at PND35. PND17 and PND35 rats (n = 75) received a mild to moderate midline fluid percussion injury or sham surgery. In three cortical regions [peri-injury, primary somatosensory barrel field (S1BF), perirhinal], we investigated the glial response relative to age-at-injury (PND17 or PND35), time post-injury (2 hours, 1 day, 7 days, 25 days, or 43 days), and post-natal age, such that rats injured at PND17 or PND35 were compared at the same post-natal-age (e.g., PND17 + 25D post-injury = PND42; PND35 + 7D post-injury = PND42). We measured Iba1 positive microglia cells (area, perimeter) and quantified their activation status using skeletal analysis (branch length/cell, mean processes/cell, cell abundance). GFAP expression was examined using immunohistochemistry and pixel analysis. Data were analyzed using Bayesian multivariate multi-level models. Independent of age-at-injury, TBI activated microglia (shorter branches, fewer processes) in the S1BF and perirhinal cortex with more microglia in all regions compared to uninjured shams. TBI-induced microglial activation (shorter branches) was sustained in the S1BF into early adulthood (PND60). Overall, PND17 injured rats had more microglial activation in the perirhinal cortex than PND35 injured rats. Activation was not confounded by age-dependent cell size changes, and microglial cell body sizes were similar between PND17 and PND35 rats. There were no differences in astrocyte GFAP expression. Increased microglial activation in PND17 brain-injured rats suggests that TBI upregulates the glial response at discrete stages of development. Age-at-injury and aging with an injury are translationally important because experiencing a TBI at an early age may trigger an exaggerated glial response.

Bayesian Analysis of State Voter Registration Database Integrity

Abstract How do we ensure a statewide voter registration database’s accuracy and integrity, especially when the database depends on aggregating decentralized, sub-state data with different list maintenance practices? We develop a Bayesian multivariate multilevel model to account for correlated patterns of change over time in multiple response variables, and label statewide anomalies using deviations from model predictions. We apply our model to California’s 22 million registered voters, using 25 snapshots from the 2020 presidential election. We estimate countywide change rates for multiple response variables such as changes in voter’s partisan affiliation and jointly model these changes. The model outperforms a simple interquartile range (IQR) detection when tested with synthetic data. This is a proof-of-concept that demonstrates the utility of the Bayesian methodology, as despite the heterogeneity in list maintenance practices, a principled, statistical approach is useful. At the county level, the total numbers of anomalies are positively correlated with the average election cost per registered voter between 2017 and 2019. Given the recent efforts to modernize and secure voter list maintenance procedures in the For the People Act of 2021, we argue that checking whether counties or municipalities are behaving similarly at the state level is also an essential step in ensuring electoral integrity.

Artificial intelligence prediction of cholecystectomy operative course from automated identification of gallbladder inflammation.

Operative courses of laparoscopic cholecystectomies vary widely due to differing pathologies. Efforts to assess intra-operative difficulty include the Parkland grading scale (PGS), which scores inflammation from the initial view of the gallbladder on a 1-5 scale. We investigated the impact of PGS on intra-operative outcomes, including laparoscopic duration, attainment of the critical view of safety (CVS), and gallbladder injury. We additionally trained an artificial intelligence (AI) model to identify PGS. One surgeon labeled surgical phases, PGS, CVS attainment, and gallbladder injury in 200 cholecystectomy videos. We used multilevel Bayesian regression models to analyze the PGS's effect on intra-operative outcomes. We trained AI models to identify PGS from an initial view of the gallbladder and compared model performance to annotations by a second surgeon. Slightly inflamed gallbladders (PGS-2) minimally increased duration, adding 2.7 [95% compatibility interval (CI) 0.3-7.0] minutes to an operation. This contrasted with maximally inflamed gallbladders (PGS-5), where on average 16.9 (95% CI 4.4-33.9) minutes were added, with 31.3 (95% CI 8.0-67.5) minutes added for the most affected surgeon. Inadvertent gallbladder injury occurred in 25% of cases, with a minimal increase in gallbladder injury observed with added inflammation. However, up to a 28% (95% CI - 2, 63) increase in probability of a gallbladder hole during PGS-5 cases was observed for some surgeons. Inflammation had no substantial effect on whether or not a surgeon attained the CVS. An AI model could reliably (Krippendorff's α = 0.71, 95% CI 0.65-0.77) quantify inflammation when compared to a second surgeon (α = 0.82, 95% CI 0.75-0.87). An AI model can identify the degree of gallbladder inflammation, which is predictive of cholecystectomy intra-operative course. This automated assessment could be useful for operating room workflow optimization and for targeted per-surgeon and per-resident feedback to accelerate acquisition of operative skills.

Adaptive MR-Guided Stereotactic Radiotherapy is Beneficial for Ablative Treatment of Lung Tumors in High-Risk Locations.

PurposeTo explore the benefit of adaptive magnetic resonance-guided stereotactic body radiotherapy (MRgSBRT) for treatment of lung tumors in different locations with a focus on ultracentral lung tumors (ULT).Patients & MethodsA prospective cohort of 21 patients with 23 primary and secondary lung tumors was analyzed. Tumors were located peripherally (N = 10), centrally (N = 2) and ultracentrally (N = 11, planning target volume (PTV) overlap with proximal bronchi, esophagus and/or pulmonary artery). All patients received MRgSBRT with gated dose delivery and risk-adapted fractionation. Before each fraction, the baseline plan was recalculated on the anatomy of the day (predicted plan). Plan adaptation was performed in 154/165 fractions (93.3%). Comparison of dose characteristics between predicted and adapted plans employed descriptive statistics and Bayesian linear multilevel models. The posterior distributions resulting from the Bayesian models are presented by the mean together with the corresponding 95% compatibility interval (CI).ResultsPlan adaptation decreased the proportion of fractions with violated planning objectives from 94% (predicted plans) to 17% (adapted plans). In most cases, inadequate PTV coverage was remedied (predicted: 86%, adapted: 13%), corresponding to a moderate increase of PTV coverage (mean +6.3%, 95% CI: [5.3–7.4%]) and biologically effective PTV doses (BED10) (BEDmin: +9.0 Gy [6.7–11.3 Gy], BEDmean: +1.4 Gy [0.8–2.1 Gy]). This benefit was smaller in larger tumors (−0.1%/10 cm³ PTV [−0.2 to −0.02%/10 cm³ PTV]) and ULT (−2.0% [−3.1 to −0.9%]). Occurrence of exceeded maximum doses inside the PTV (predicted: 21%, adapted: 4%) and violations of OAR constraints (predicted: 12%, adapted: 1%, OR: 0.14 [0.04–0.44]) was effectively reduced. OAR constraint violations almost exclusively occurred if the PTV had touched the corresponding OAR in the baseline plan (18/19, 95%).ConclusionAdaptive MRgSBRT is highly recommendable for ablative treatment of lung tumors whose PTV initially contacts a sensitive OAR, such as ULT. Here, plan adaptation protects the OAR while maintaining best-possible PTV coverage.

Sources of Information Waste in Neuroimaging: Mishandling Structures, Thinking Dichotomously, and Over-Reducing Data

Neuroimaging relies on separate statistical inferences at tens of thousands of spatial locations. Such massively univariate analysis typically requires an adjustment for multiple testing in an attempt to maintain the family-wise error rate at a nominal level of 5%. First, we examine three sources of substantial information loss that are associated with the common practice under the massively univariate framework: (a) the hierarchical data structures (spatial units and trials) are not well maintained in the modeling process; (b) the adjustment for multiple testing leads to an artificial step of strict thresholding; (c) information is excessively reduced during both modeling and result reporting. These sources of information loss have far-reaching impacts on result interpretability as well as reproducibility in neuroimaging. Second, to improve inference efficiency, predictive accuracy, and generalizability, we propose a Bayesian multilevel modeling framework that closely characterizes the data hierarchies across spatial units and experimental trials. Rather than analyzing the data in a way that first creates multiplicity and then resorts to a post hoc solution to address them, we suggest directly incorporating the cross-space information into one single model under the Bayesian framework (so there is no multiplicity issue). Third, regardless of the modeling framework one adopts, we make four actionable suggestions to alleviate information waste and to improve reproducibility: (1) model data hierarchies, (2) quantify effects, (3) abandon strict dichotomization, and (4) report full results. We provide examples for all of these points using both demo and real studies, including the recent Neuroimaging Analysis Replication and Prediction Study (NARPS).

A Comprehensive Reliability Assessment Method for Existing Railway Bridges Based on Bayesian Theory

The increase in traffic volume and train load poses new challenges to the reliability assessment of existing railway bridges. To construct a comprehensive assessment method for the safety and reliability of existing railway bridges, firstly, the risk factors of railway bridge structures are analyzed and the evaluation criteria are determined; secondly, based on the accident tree theory, a multilevel Bayesian network model with key points is established, and the ability of the Bayesian network bidirectional reasoning and sensitivity analysis is used to evaluate the structural safety; finally, the result was applied to the marina northern Songhua River extra-large bridge to verify the applicability of the comprehensive evaluation of the reliability of an existing railway bridge. This approach provides a theoretical basis for the maintenance and reinforcement of the Songhua River Bridge along the Bin‐North Line.

Bayesian Multilevel Modeling for the Intersections of Race and Gender

Bayesian Multilevel Modeling for the Intersections of Race and Gender

Adipose tissue distribution from body MRI is associated with cross-sectional and longitudinal brain age in adults

There is an intimate body-brain connection in ageing, and obesity is a key risk factor for poor cardiometabolic health and neurodegenerative conditions. Although research has demonstrated deleterious effects of obesity on brain structure and function, the majority of studies have used conventional measures such as waist-to-hip ratio, waist circumference, and body mass index. While sensitive to gross features of body composition, such global anthropometric features fail to describe regional differences in body fat distribution and composition. The sample consisted of baseline brain magnetic resonance imaging (MRI) acquired from 790 healthy participants aged 18–94 years (mean ± standard deviation (SD) at baseline: 46.8 ± 16.3), and follow-up brain MRI collected from 272 of those individuals (two time-points with 19.7 months interval, on average (min = 9.8, max = 35.6). Of the 790 included participants, cross-sectional body MRI data was available from a subgroup of 286 participants, with age range 19–86 (mean = 57.6, SD = 15.6). Adopting a mixed cross-sectional and longitudinal design, we investigated cross-sectional body magnetic resonance imaging measures of adipose tissue distribution in relation to longitudinal brain structure using MRI-based morphometry (T1) and diffusion tensor imaging (DTI). We estimated tissue-specific brain age at two time points and performed Bayesian multilevel modelling to investigate the associations between adipose measures at follow-up and brain age gap (BAG) – the difference between actual age and the prediction of the brain’s biological age – at baseline and follow-up. We also tested for interactions between BAG and both time and age on each adipose measure. The results showed credible associations between T1-based BAG and liver fat, muscle fat infiltration (MFI), and weight-to-muscle ratio (WMR), indicating older-appearing brains in people with higher measures of adipose tissue. Longitudinal evidence supported interaction effects between time and MFI and WMR on T1-based BAG, indicating accelerated ageing over the course of the study period in people with higher measures of adipose tissue. The results show that specific measures of fat distribution are associated with brain ageing and that different compartments of adipose tissue may be differentially linked with increased brain ageing, with potential to identify key processes involved in age-related transdiagnostic disease processes.

Rethinking the Ranks of Visual Channels.

Data can be visually represented using visual channels like position, length or luminance. An existing ranking of these visual channels is based on how accurately participants could report the ratio between two depicted values. There is an assumption that this ranking should hold for different tasks and for different numbers of marks. However, there is surprisingly little existing work that tests this assumption, especially given that visually computing ratios is relatively unimportant in real-world visualizations, compared to seeing, remembering, and comparing trends and motifs, across displays that almost universally depict more than two values. To simulate the information extracted from a glance at a visualization, we instead asked participants to immediately reproduce a set of values from memory after they were shown the visualization. These values could be shown in a bar graph (position (bar)), line graph (position (line)), heat map (luminance), bubble chart (area), misaligned bar graph (length), or 'wind map' (angle). With a Bayesian multilevel modeling approach, we show how the rank positions of visual channels shift across different numbers of marks (2, 4 or 8) and for bias, precision, and error measures. The ranking did not hold, even for reproductions of only 2 marks, and the new probabilistic ranking was highly inconsistent for reproductions of different numbers of marks. Other factors besides channel choice had an order of magnitude more influence on performance, such as the number of values in the series (e.g., more marks led to larger errors), or the value of each mark (e.g., small values were systematically overestimated). Every visual channel was worse for displays with 8 marks than 4, consistent with established limits on visual memory. These results point to the need for a body of empirical studies that move beyond two-value ratio judgments as a baseline for reliably ranking the quality of a visual channel, including testing new tasks (detection of trends or motifs), timescales (immediate computation, or later comparison), and the number of values (from a handful, to thousands).

Heterogeneity in the association between weather and pain severity among patients with chronic pain: a Bayesian multilevel regression analysis.

Previous studies on the association between weather and pain severity among patients with chronic pain have produced mixed results. In part, this inconsistency may be due to differences in individual pain responses to the weather. To test the hypothesis that there might be subgroups of participants with different pain responses to different weather conditions, we examined data from a longitudinal smartphone-based study, Cloudy with a Chance of Pain, conducted between January 2016 and April 2017. The study recruited more than 13,000 participants and recorded daily pain severity on a 5-point scale (range: no pain to very severe pain) along with hourly local weather data for up to 15 months. We used a Bayesian multilevel model to examine the weather-pain association. We found 1 in 10 patients with chronic pain were sensitive to the temperature, 1 in 25 to relative humidity, 1 in 50 to pressure, and 3 in 100 to wind speed, after adjusting for age, sex, belief in the weather-pain association, mood, and activity level. The direction of the weather-pain association differed between people. Although participants seem to be differentially sensitive to weather conditions, there is no definite indication that participants' underlying pain conditions play a role in weather sensitivity. This study demonstrated that weather sensitivity among patients with chronic pain is more apparent in some subgroups of participants. In addition, among those sensitive to the weather, the direction of the weather-pain association can differ.

Current Inequities in Smoking Prevalence on District Level in Iran: A Systematic Analysis on the STEPS Survey.

Background: The prevalence of tobacco smoking and its burden on societies is not homogenous at the national and district levels. This nationwide study aimed to investigate current inequalities in the prevalence of smoking at the district level and the association of smoking behaviors with gender, wealth, education, and urbanization in Iran. Study design: A cross-sectional study. Methods: This study was conducted by analyzing the data of the STEPS survey 2016 with 30541 participants. The small-area estimation method using the Bayesian spatial hierarchical multilevel regression model was employed to generate district-level prevalence of all types of smoking by gender. The inequalities between the groups by wealth, education, and urbanization were investigated via concentration index. Results: The prevalence rates of current daily cigarette smoking were found to be at the range of 4.6-40.9 and 0-4.5 among men and women, respectively. Current daily cigarette smoking was higher in men than in women: 19.0 (95% CI: 9.5-28.7) vs 0.7 (95% CI: 0-6.9). Women with lower wealth, education, or urbanization were more likely to smoke tobacco or be exposed to secondhand smoking. On the other hand, men with higher wealth or education indices were more likely to smoke tobacco. Men with lower wealth, education, or urbanization were more likely to be exposed to secondhand smoking. Conclusion: The smoking behavior varied significantly at the district level in Iran. Gender, wealth, education, and urbanization were determinants of smoking prevalence.

Spatial characteristics of vegetation habitat suitability and mountainous settlements and their quantitative relationships in upstream of Min River, southwestern of China

Vegetation habitat suitability (HS) and mountainous settlements are continuously varying, and display spatial heterogeneity in their spatial arrangement at multiple scales substantially. This paper focuses on spatial characteristics of vegetation HS and mountainous settlement and their quantitative relationships in the upstream of Min River, southwest of China. Based on the spatially distributed maps of vegetation HS types and different nationality mountainous settlements, we first described spatial differentiation and cluster of vegetation HS from global and local standpoints through lacunarity analysis and Multilevel Bayesian model. Then, we depicted the effects of nationality attribute on the spatially distributed characteristics of mountainous settlements. Finally, we revealed the relationships between the geometrical features of mountainous settlements and vegetation HS. The detailed results were as following: 1) The vegetation habitats are classified into four types based on the vegetation HS indexes, the largest spatially heterogeneities of comprehensive, unsuitable and worst unsuitable vegetation HS types occurred in Wenchuan county, but the largest spatially heterogeneities of suitable and sub-suitable vegetation HS categories appeared in Songpan county. The largest spatially clusters of comprehensive, suitable and sub-suitable vegetation HS categories were in Wenchuan, Songpan and Lixian counties, respectively. This suggested that their spatial clusters were low and spatial heterogeneity were high. However, the largest spatially clusters of unsuitable and worst unsuitable vegetation HS types were in Maoxian county. 2) The largest spatially cluster occurred in the Han nationality mountainous settlements revealed their closure characteristics in mountainous regions. Meanwhile, the spatial cluster of all mountainous settlements across watershed approximated to Tibetan nationality mountainous settlement, indicating that the spatial heterogeneity and layout characteristics of Tibetan nationality mountainous settlement could reflect the spatial differentiation of all settlements across watershed. 3) The average values of vegetation HS indexes for Tibetan-Qiang-Hui-Han nationality mountainous settlements were 2.8157, 2.6217, 2.5291 and 2.6441. It meant that the spatial distribution of mountainous settlements perfectly match with spatial pattern of vegetation habitat. Moreover, the sensitivities of geometrical feature characteristics for all mountainous settlements to vegetation HS were similar to Qiang nationality mountainous settlement. The quantitative results obtained from analysis on spatial characteristics of vegetation HS and mountainous settlements, and their interactive relationships could provide effective data supports for ecological construction of vegetation habitat and spatial reconstruction of mountainous settlements.

Prevalence of adult sexual abuse in men with mental illness: Bayesian meta-analysis.

Sexual abuse is a broad category of traumatic experiences that includes rape and any unwanted sexual contact with a body part or foreign object, whether penetrative, oral or otherwise. Although patients with mental illness have a higher risk of becoming victims of sexual abuse in adulthood, few studies investigate the proportion of male victims in this population. Their underrepresentation in research is a barrier to understanding the negative outcomes associated with sexual abuse in men. We estimated the prevalence of recent (past year) and adulthood sexual abuse perpetrated by any perpetrator and separately by intimate partners in males diagnosed with a mental illness. To model the prevalences and heterogeneity arising from reports, we used Bayesian multilevel models. Prevalences were estimated for mixed-diagnosis, substance misuse, intellectual disability and post-traumatic stress disorder samples, and studies reporting specifically on intimate partner violence. This review was registered through PROSPERO (CRD42020169299). Estimated adult sexual abuse was 5.3% (95% Credibility Interval 1.6-12.8%) for past-year abuse and 14.1% (95% Credibility Interval 7.3-22.4%) for abuse in adulthood. There was considerable heterogeneity of prevalence between studies and diagnosis groups. Our analyses show that the prevalence of sexual abuse of males diagnosed with a mental illness was much higher than for men in the general population. This has important implications regarding the proportion of undetected or untreated sexually abused men in clinical practice.

Namilumab or infliximab compared with standard of care in hospitalised patients with COVID-19 (CATALYST): a randomised, multicentre, multi-arm, multistage, open-label, adaptive, phase 2, proof-of-concept trial

BackgroundDysregulated inflammation is associated with poor outcomes in COVID-19. We aimed to assess the efficacy of namilumab (a granulocyte-macrophage colony stimulating factor inhibitor) and infliximab (a tumour necrosis factor inhibitor) in hospitalised patients with COVID-19, to prioritise agents for phase 3 trials.MethodsIn this randomised, multicentre, multi-arm, multistage, parallel-group, open-label, adaptive, phase 2, proof-of-concept trial (CATALYST), we recruited patients (aged ≥16 years) admitted to hospital with COVID-19 pneumonia and C-reactive protein (CRP) concentrations of 40 mg/L or greater, at nine hospitals in the UK. Participants were randomly assigned with equal probability to usual care or usual care plus a single intravenous dose of namilumab (150 mg) or infliximab (5 mg/kg). Randomisation was stratified by care location within the hospital (ward vs intensive care unit [ICU]). Patients and investigators were not masked to treatment allocation. The primary endpoint was improvement in inflammation, measured by CRP concentration over time, analysed using Bayesian multilevel models. This trial is now complete and is registered with ISRCTN, 40580903.FindingsBetween June 15, 2020, and Feb 18, 2021, we screened 299 patients and 146 were enrolled and randomly assigned to usual care (n=54), namilumab (n=57), or infliximab (n=35). For the primary outcome, 45 patients in the usual care group were compared with 52 in the namilumab group, and 29 in the usual care group were compared with 28 in the infliximab group. The probabilities that the interventions were superior to usual care alone in reducing CRP concentration over time were 97% for namilumab and 15% for infliximab; the point estimates for treatment–time interactions were –0·09 (95% CI –0·19 to 0·00) for namilumab and 0·06 (–0·05 to 0·17) for infliximab. 134 adverse events occurred in 30 (55%) of 55 patients in the namilumab group compared with 145 in 29 (54%) of 54 in the usual care group. 102 adverse events occurred in 20 (69%) of 29 patients in the infliximab group compared with 112 in 17 (50%) of 34 in the usual care group. Death occurred in six (11%) patients in the namilumab group compared with ten (19%) in the usual care group, and in four (14%) in the infliximab group compared with five (15%) in the usual care group.InterpretationNamilumab, but not infliximab, showed proof-of-concept evidence for reduction in inflammation—as measured by CRP concentration—in hospitalised patients with COVID-19 pneumonia. Namilumab should be prioritised for further investigation in COVID-19.FundingMedical Research Council.