Co-occurrence and clustering of health conditions at age 11: cross-sectional findings from the Millennium Cohort Study

OBJECTIVES: To identify patterns of co-occurrence and clustering of 6 common adverse health conditions in 11-year-old children and explore differences by sociodemographic factors. DESIGN: Nationally representative prospective cohort study. SETTING: Children born in the UK between 2000 and 2002. PARTICIPANTS: 11 399 11-year-old singleton children for whom data on all 6 health conditions and sociodemographic information were available (complete cases). MAIN OUTCOME MEASURES: Prevalence, co-occurrence and clustering of 6 common health conditions: wheeze; eczema; long-standing illness (excluding wheeze and eczema); injury; socioemotional difficulties (measured using Strengths and Difficulties Questionnaire) and unfavourable weight (thin/overweight/obese vs normal). RESULTS: 42.4% of children had 2 or more adverse health conditions (co-occurrence). Co-occurrence was more common in boys and children from lower income households. Latent class analysis identified 6 classes: 'normative' (57.4%): 'atopic burdened' (14.0%); 'socioemotional burdened' (11.0%); 'unfavourable weight/injury' (7.7%); 'eczema/injury' (6.0%) and 'eczema/unfavourable weight' (3.9%). As with co-occurrence, class membership differed by sociodemographic factors: boys, children of mothers with lower educational attainment and children from lower income households were more likely to be in the 'socioemotional burdened' class. Children of mothers with higher educational attainment were more likely to be in the 'normative' and 'eczema/unfavourable weight' classes. CONCLUSIONS: Co-occurrence of adverse health conditions at age 11 is common and is associated with adverse socioeconomic circumstances. Holistic, child focused care, particularly in boys and those in lower income groups, may help to prevent and reduce co-occurrence in later childhood and adolescence

Adverse Birth Outcomes and Maternal Exposure to Trichloroethylene and Tetrachloroethylene through Soil Vapor Intrusion in New York State

Background: Industrial spills of volatile organic compounds (VOCs) in Endicott, New York (USA), have led to contamination of groundwater, soil, and soil gas. Previous studies have reported an increase in adverse birth outcomes among women exposed to VOCs in drinking water

A new combined strategy to implement a community occupational therapy intervention: designing a cluster randomized controlled trial

Contains fulltext : 97575.pdf (publisher's version ) (Open Access)BACKGROUND: Even effective interventions for people with dementia and their caregivers require specific implementation efforts. A pilot study showed that the highly effective community occupational therapy in dementia (COTiD) program was not implemented optimally due to various barriers. To decrease these barriers and make implementation of the program more effective a combined implementation (CI) strategy was developed. In our study we will compare the effectiveness of this CI strategy with the usual educational (ED) strategy. METHODS: In this cluster randomized, single-blinded, controlled trial, each cluster consists of at least two occupational therapists, a manager, and a physician working at Dutch healthcare organizations that deliver community occupational therapy. Forty-five clusters, stratified by healthcare setting (nursing home, hospital, mental health service), have been allocated randomly to either the intervention group (CI strategy) or the control group (ED strategy). The study population consists of the professionals included in each cluster and community-dwelling people with dementia and their caregivers. The primary outcome measures are the use of community OT, the adherence of OTs to the COTiD program, and the cost effectiveness of implementing the COTiD program in outpatient care. Secondary outcome measures are patient and caregiver outcomes and knowledge of managers, physicians and OTs about the COTiD program. DISCUSSION: Implementation research is fairly new in the field of occupational therapy, making this a unique study. This study does not only evaluate the effects of the CI-strategy on professionals, but also the effects of professionals' degree of implementation on client and caregiver outcomes. CLINICAL TRIALS REGISTRATION: NCT01117285

FEL research and development at STFC Daresbury laboratory

In this paper we present an overview of current and proposed FEL developments at STFC Daresbury Laboratory in the UK. We discuss progress on the ALICE IR-FEL since first lasing in October 2010, covering the optimisation of the FEL performance, progress on the demonstration of a single shot cross correlation experiment and the results obtained so far with a Scanning Near-Field Optical Microscopy beamline. We discuss a proposal for a 250 MeV single pass FEL test facility named CLARA to be built at Daresbury and dedicated to research for future light source applications. Finally we present a brief overview of other recent research highlights

Real-time imputation of missing predictor values in clinical practice

Use of prediction models is widely recommended by clinical guidelines, but usually requires complete information on all predictors that is not always available in daily practice. We describe two methods for real-time handling of missing predictor values when using prediction models in practice. We compare the widely used method of mean imputation (M-imp) to a method that personalizes the imputations by taking advantage of the observed patient characteristics. These characteristics may include both prediction model variables and other characteristics (auxiliary variables). The method was implemented using imputation from a joint multivariate normal model of the patient characteristics (joint modeling imputation; JMI). Data from two different cardiovascular cohorts with cardiovascular predictors and outcome were used to evaluate the real-time imputation methods. We quantified the prediction model's overall performance (mean squared error (MSE) of linear predictor), discrimination (c-index), calibration (intercept and slope) and net benefit (decision curve analysis). When compared with mean imputation, JMI substantially improved the MSE (0.10 vs. 0.13), c-index (0.70 vs 0.68) and calibration (calibration-in-the-large: 0.04 vs. 0.06; calibration slope: 1.01 vs. 0.92), especially when incorporating auxiliary variables. When the imputation method was based on an external cohort, calibration deteriorated, but discrimination remained similar. We recommend JMI with auxiliary variables for real-time imputation of missing values, and to update imputation models when implementing them in new settings or (sub)populations.Comment: 17 pages, 6 figures, to be published in European Heart Journal - Digital Health, accepted for MEMTAB 2020 conferenc

Systematic review finds "spin" practices and poor reporting standards in studies on machine learning-based prediction models

Objectives We evaluated the presence and frequency of spin practices and poor reporting standards in studies that developed and/or validated clinical prediction models using supervised machine learning techniques. Study Design and Setting We systematically searched PubMed from 01/2018 to 12/2019 to identify diagnostic and prognostic prediction model studies using supervised machine learning. No restrictions were placed on data source, outcome, or clinical specialty. Results We included 152 studies: 38% reported diagnostic models and 62% prognostic models. When reported, discrimination was described without precision estimates in 53/71 abstracts (74.6% [95% CI 63.4–83.3]) and 53/81 main texts (65.4% [95% CI 54.6–74.9]). Of the 21 abstracts that recommended the model to be used in daily practice, 20 (95.2% [95% CI 77.3–99.8]) lacked any external validation of the developed models. Likewise, 74/133 (55.6% [95% CI 47.2–63.8]) studies made recommendations for clinical use in their main text without any external validation. Reporting guidelines were cited in 13/152 (8.6% [95% CI 5.1–14.1]) studies. Conclusion Spin practices and poor reporting standards are also present in studies on prediction models using machine learning techniques. A tailored framework for the identification of spin will enhance the sound reporting of prediction model studies

Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models

Background and Objectives We sought to summarize the study design, modelling strategies, and performance measures reported in studies on clinical prediction models developed using machine learning techniques. Methods We search PubMed for articles published between 01/01/2018 and 31/12/2019, describing the development or the development with external validation of a multivariable prediction model using any supervised machine learning technique. No restrictions were made based on study design, data source, or predicted patient-related health outcomes. Results We included 152 studies, 58 (38.2% [95% CI 30.8–46.1]) were diagnostic and 94 (61.8% [95% CI 53.9–69.2]) prognostic studies. Most studies reported only the development of prediction models (n = 133, 87.5% [95% CI 81.3–91.8]), focused on binary outcomes (n = 131, 86.2% [95% CI 79.8–90.8), and did not report a sample size calculation (n = 125, 82.2% [95% CI 75.4–87.5]). The most common algorithms used were support vector machine (n = 86/522, 16.5% [95% CI 13.5–19.9]) and random forest (n = 73/522, 14% [95% CI 11.3–17.2]). Values for area under the Receiver Operating Characteristic curve ranged from 0.45 to 1.00. Calibration metrics were often missed (n = 494/522, 94.6% [95% CI 92.4–96.3]). Conclusion Our review revealed that focus is required on handling of missing values, methods for internal validation, and reporting of calibration to improve the methodological conduct of studies on machine learning–based prediction models. Systematic review registration PROSPERO, CRD42019161764

A Multi-site Resting State fMRI Study on the Amplitude of Low Frequency Fluctuations in Schizophrenia

Background: This multi-site study compares resting state fMRI amplitude of low frequency fluctuations (ALFF) and fractional ALFF (fALFF) between patients with schizophrenia (SZ) and healthy controls (HC). Methods: Eyes-closed resting fMRI scans (5:38 min; n = 306, 146 SZ) were collected from 6 Siemens 3T scanners and one GE 3T scanner. Imaging data were pre-processed using an SPM pipeline. Power in the low frequency band (0.01–0.08 Hz) was calculated both for the original pre-processed data as well as for the pre-processed data after regressing out the six rigid-body motion parameters, mean white matter (WM) and cerebral spinal fluid (CSF) signals. Both original and regressed ALFF and fALFF measures were modeled with site, diagnosis, age, and diagnosis × age interactions. Results: Regressing out motion and non-gray matter signals significantly decreased fALFF throughout the brain as well as ALFF in the cortical edge, but significantly increased ALFF in subcortical regions. Regression had little effect on site, age, and diagnosis effects on ALFF, other than to reduce diagnosis effects in subcortical regions. There were significant effects of site across the brain in all the analyses, largely due to vendor differences. HC showed greater ALFF in the occipital, posterior parietal, and superior temporal lobe, while SZ showed smaller clusters of greater ALFF in the frontal and temporal/insular regions as well as in the caudate, putamen, and hippocampus. HC showed greater fALFF compared with SZ in all regions, though subcortical differences were only significant for original fALFF. Conclusions: SZ show greater eyes-closed resting state low frequency power in frontal cortex, and less power in posterior lobes than do HC; fALFF, however, is lower in SZ than HC throughout the cortex. These effects are robust to multi-site variability. Regressing out physiological noise signals significantly affects both total and fALFF measures, but does not affect the pattern of case/control differences

Increased Matrix Metalloproteinase (MMPs) Levels Do Not Predict Disease Severity or Progression in Emphysema

Rationale: Though matrix metalloproteinases (MMPs) are critical in the pathogenesis of COPD, their utility as a disease biomarker remains uncertain. This study aimed to determine whether bronchoalveolar lavage (BALF) or plasma MMP measurements correlated with disease severity or functional decline in emphysema. Methods: Enzyme-linked immunosorbent assay and luminex assays measured MMP-1, -9, -12 and tissue inhibitor of matrix metalloproteinase-1 in the BALF and plasma of non-smokers, smokers with normal lung function and moderate-to-severe emphysema subjects. In the cohort of 101 emphysema subjects correlative analyses were done to determine if MMP or TIMP-1 levels were associated with key disease parameters or change in lung function over an 18-month time period. Main Results: Compared to non-smoking controls, MMP and TIMP-1 BALF levels were significantly elevated in the emphysema cohort. Though MMP-1 was elevated in both the normal smoker and emphysema groups, collagenase activity was only increased in the emphysema subjects. In contrast to BALF, plasma MMP-9 and TIMP-1 levels were actually decreased in the emphysema cohort compared to the control groups. Both in the BALF and plasma, MMP and TIMP-1 measurements in the emphysema subjects did not correlate with important disease parameters and were not predictive of subsequent functional decline. Conclusions: MMPs are altered in the BALF and plasma of emphysema; however, the changes in MMPs correlate poorly with parameters of disease intensity or progression. Though MMPs are pivotal in the pathogenesis of COPD, these findings suggest that measuring MMPs will have limited utility as a prognostic marker in this disease. © 2013 D'Armiento et al