Data Integration Model for Air Quality: A Hierarchical Approach to the Global Estimation of Exposures to Ambient Air Pollution

This is the author accepted manuscript. Available from arXiv via the URL in this record.Air pollution is a major risk factor for global health, with both ambient and household air pollution contributing substantial components of the overall global disease burden. One of the key drivers of adverse health effects is fine particulate matter ambient pollution (PM2:5) to which an estimated 3 million deaths can be attributed annually. The primary source of information for estimating exposures has been measurements from ground monitoring networks but, although coverage is increasing, there remain regions in which monitoring is limited. Ground monitoring data therefore needs to be supplemented with information from other sources, such as satellite retrievals of aerosol optical depth and chemical transport models. A hierarchical modelling approach for integrating data from multiple sources is proposed allowing spatially-varying relationships between ground measurements and other factors that estimate air quality. Set within a Bayesian framework, the resulting Data Integration Model for Air Quality (DIMAQ) is used to estimate exposures, together with associated measures of uncertainty, on a high resolution grid covering the entire world. Bayesian analysis on this scale can be computationally challenging and here approximate Bayesian inference is performed using Integrated Nested Laplace Approximations. Model selection and assessment is performed by cross-validation with the final model offering substantial increases in predictive accuracy, particularly in regions where there is sparse ground monitoring, when compared to previous approaches: root mean square error (RMSE) reduced from 17.1 to 10.7, and population weighted RMSE from 23.1 to 12.1 gm3. Based on summaries of the posterior distributions for each grid cell, it is estimated that 92% of the world’s population reside in areas exceeding the World Health Organization’s Air Quality Guidelines.Matthew Lloyd Thomas is supported by a scholarship from the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics at Bath (SAMBa), under the project EP/L015684/1. Amelia Jobling was supported for this work by WHO contracts APW 201255146 and 201255393

A pilot randomized controlled trial of a stepped care intervention package for depression in primary care in Nigeria

Background Depression is common in primary care and is often unrecognized and untreated. Studies are needed to demonstrate the feasibility of implementing evidence-based depression care provided by primary health care workers (PHCWs) in sub-Saharan Africa. We carried out a pilot two-parallel arm cluster randomized controlled trial of a package of care for depression in primary care. Methods Six primary health care centers (PHCC) in two Local Government Areas of Oyo State, South West Nigeria were randomized into 3 intervention and 3 control clinics. Three PHCWs were selected for training from each of the participating clinics. The PHCWs from the intervention clinics were trained to deliver a manualized multicomponent stepped care intervention package for depression consisting of psychoeducation, activity scheduling, problem solving treatment and medication for severe depression. Providers from the control clinics delivered care as usual, enhanced by a refresher training on depression diagnosis and management. Outcome measures Patient’s Health Questionnaire (PHQ-9), WHO quality of Life instrument (WHOQOL-Bref) and the WHO disability assessment schedule (WHODAS) were administered in the participants’ home at baseline, 3 and 6 months. Results About 98% of the consecutive attendees to the clinics agreed to have the screening interview. Of those screened, 284 (22.7%) were positive (PHQ-9 score ≥ 8) and 234 gave consent for inclusion in the study: 165 from intervention and 69 from control clinics. The rates of eligible and consenting participants were similar in the control and intervention arms. In all 85.9% (92.8% in intervention and 83% in control) of the participants were successfully administered outcome assessments at 6 months. The PHCWs had little difficulty in delivering the intervention package. At 6 months follow up, depression symptoms had improved in 73.0% from the intervention arm compared to 51.6% control. Compared to the mean scores at baseline, there was improvement in the mean scores on all outcome measures in both arms at six months. Conclusion The results provide support for the feasibility of conducting a fully-powered randomized study in this setting and suggest that the instruments used may have the potential to detect differences between the arms

Kinome-wide interaction modelling using alignment-based and alignment-independent approaches for kinase description and linear and non-linear data analysis techniques

<p>Abstract</p> <p>Background</p> <p>Protein kinases play crucial roles in cell growth, differentiation, and apoptosis. Abnormal function of protein kinases can lead to many serious diseases, such as cancer. Kinase inhibitors have potential for treatment of these diseases. However, current inhibitors interact with a broad variety of kinases and interfere with multiple vital cellular processes, which causes toxic effects. Bioinformatics approaches that can predict inhibitor-kinase interactions from the chemical properties of the inhibitors and the kinase macromolecules might aid in design of more selective therapeutic agents, that show better efficacy and lower toxicity.</p> <p>Results</p> <p>We applied proteochemometric modelling to correlate the properties of 317 wild-type and mutated kinases and 38 inhibitors (12,046 inhibitor-kinase combinations) to the respective combination's interaction dissociation constant (K_d). We compared six approaches for description of protein kinases and several linear and non-linear correlation methods. The best performing models encoded kinase sequences with amino acid physico-chemical z-scale descriptors and used support vector machines or partial least- squares projections to latent structures for the correlations. Modelling performance was estimated by double cross-validation. The best models showed high predictive ability; the squared correlation coefficient for new kinase-inhibitor pairs ranging P²= 0.67-0.73; for new kinases it ranged P²_kin= 0.65-0.70. Models could also separate interacting from non-interacting inhibitor-kinase pairs with high sensitivity and specificity; the areas under the ROC curves ranging AUC = 0.92-0.93. We also investigated the relationship between the number of protein kinases in the dataset and the modelling results. Using only 10% of all data still a valid model was obtained with P²= 0.47, P²_kin= 0.42 and AUC = 0.83.</p> <p>Conclusions</p> <p>Our results strongly support the applicability of proteochemometrics for kinome-wide interaction modelling. Proteochemometrics might be used to speed-up identification and optimization of protein kinase targeted and multi-targeted inhibitors.</p

Challenges in developing methods for quantifying the effects of weather and climate on water-associated diseases: A systematic review

Infectious diseases attributable to unsafe water supply, sanitation and hygiene (e.g. Cholera, Leptospirosis, Giardiasis) remain an important cause of morbidity and mortality, especially in low-income countries. Climate and weather factors are known to affect the transmission and distribution of infectious diseases and statistical and mathematical modelling are continuously developing to investigate the impact of weather and climate on water-associated diseases. There have been little critical analyses of the methodological approaches. Our objective is to review and summarize statistical and modelling methods used to investigate the effects of weather and climate on infectious diseases associated with water, in order to identify limitations and knowledge gaps in developing of new methods. We conducted a systematic review of English-language papers published from 2000 to 2015. Search terms included concepts related to water-associated diseases, weather and climate, statistical, epidemiological and modelling methods. We found 102 full text papers that met our criteria and were included in the analysis. The most commonly used methods were grouped in two clusters: process-based models (PBM) and time series and spatial epidemiology (TS-SE). In general, PBM methods were employed when the bio-physical mechanism of the pathogen under study was relatively well known (e.g. Vibrio cholerae); TS-SE tended to be used when the specific environmental mechanisms were unclear (e.g. Campylobacter). Important data and methodological challenges emerged, with implications for surveillance and control of water-associated infections. The most common limitations comprised: non-inclusion of key factors (e.g. biological mechanism, demographic heterogeneity, human behavior), reporting bias, poor data quality, and collinearity in exposures. Furthermore, the methods often did not distinguish among the multiple sources of time-lags (e.g. patient physiology, reporting bias, healthcare access) between environmental drivers/exposures and disease detection. Key areas of future research include: disentangling the complex effects of weather/climate on each exposure-health outcome pathway (e.g. person-to-person vs environment-to-person), and linking weather data to individual cases longitudinally

Do regional brain volumes and major depressive disorder share genetic architecture?:A study of Generation Scotland (<i>n</i>=19,762), UK Biobank (<i>n</i>=24,048) and the English Longitudinal Study of Ageing (<i>n</i>=5,766)

Major depressive disorder (MDD) is a heritable and highly debilitating condition. It is commonly associated with subcortical volumetric abnormalities, the most replicated of these being reduced hippocampal volume. Using the most recent published data from Enhancing Neuroimaging Genetics through Meta-analysis (ENIGMA) consortium's genome-wide association study of regional brain volume, we sought to test whether there is shared genetic architecture between seven subcortical brain volumes and intracranial volume (ICV) and MDD. We explored this using linkage disequilibrium score regression, polygenic risk scoring (PRS) techniques, Mendelian randomisation (MR) analysis and BUHMBOX. Utilising summary statistics from ENIGMA and Psychiatric Genomics Consortium, we demonstrated that hippocampal volume was positively genetically correlated with MDD (rG=0.46, P=0.02), although this did not survive multiple comparison testing. None of the other six brain regions studied were genetically correlated and amygdala volume heritability was too low for analysis. Using PRS analysis, no regional volumetric PRS demonstrated a significant association with MDD or recurrent MDD. MR analysis in hippocampal volume and MDD identified no causal association, however, BUHMBOX analysis identified genetic subgrouping in GS:SFHS MDD cases only (P=0.00281). In this study, we provide some evidence that hippocampal volume and MDD may share genetic architecture in a subgroup of individuals, albeit the genetic correlation did not survive multiple testing correction and genetic subgroup heterogeneity was not replicated. In contrast, we found no evidence to support a shared genetic architecture between MDD and other regional subcortical volumes or ICV

A protective personal factor against disability and dependence in the elderly: an ordinal regression analysis with nine geographically-defined samples from Spain

Background Sense of Coherence (SOC) is defined as a tendency to perceive life experiences as comprehensible, manageable and meaningful. The construct is split in three major domains: Comprehensibility, Manageability, and Meaningfulness. SOC has been associated with successful coping strategies in the face of illness and traumatic events and is a predictor of self-reported and objective health in a variety of contexts. In the present study we aim to evaluate the association of SOC with disability and dependence in Spanish elders. Methods A total of 377 participants aged 75 years or over from nine locations across Spain participated in the study (Mean age: 80.9 years; 65.3% women). SOC levels were considered independent variables in two ordinal logistic models on disability and dependence, respectively. Disability was established with the World health Organization-Disability Assessment Schedule 2.0 (36-item version), while dependence was measured with the Extended Katz Index on personal and instrumental activities of daily living. The models included personal (sex, age, social contacts, availability of an intimate confidant), environmental (municipality size, access to social resources) and health-related covariates (morbidity). Results High Meaningfulness was a strong protective factor against both disability (Odds Ratio [OR] = 0.50; 95% Confidence Interval [CI] = 0.29–0.87) and dependence (OR = 0.33; 95% CI = 0.19–0.58) while moderate and high Comprehensibility was protective for disability (OR = 0.40; 95% CI = 0.22–0.70 and OR = 0.39; 95%CI = 0.21–0.74), but not for dependence. Easy access to social and health resources was also highly protective against both disability and dependence. Conclusions Our results are consistent with the view that high levels of SOC are protective against disability and dependence in the elderly. Elderly individuals with limited access to social and health resources and with low SOC may be a group at risk for dependence and disability in Spain.This project was partially funded by a research contract in support of the project “Epidemiological Study of Dementia in Spain” signed by the Pfizer Foundation and Carlos III Institute of HealthS

Publication Bias in Antipsychotic Trials: An Analysis of Efficacy Comparing the Published Literature to the US Food and Drug Administration Database

A comparison of data held by the U.S. Food and Drug Administration (FDA) against data from journal reports of clinical trials enables estimation of the extent of publication bias for antipsychotics

Nasal Delivery of an Adenovirus-Based Vaccine Bypasses Pre-Existing Immunity to the Vaccine Carrier and Improves the Immune Response in Mice

Pre-existing immunity to human adenovirus serotype 5 (Ad5) is common in the general population. Bypassing pre-existing immunity could maximize Ad5 vaccine efficacy. Vaccination by the intramuscular (I.M.), nasal (I.N.) or oral (P.O.) route with Ad5 expressing Ebola Zaire glycoprotein (Ad5-ZGP) fully protected naïve mice against lethal challenge with Ebola. In the presence of pre-existing immunity, only mice vaccinated I.N. survived. The frequency of IFN-γ+ CD8+ T cells was reduced by 80% and by 15% in animals vaccinated by the I.M. and P.O. routes respectively. Neutralizing antibodies could not be detected in serum from either treatment group. Pre-existing immunity did not compromise the frequency of IFN-γ+ CD8+ T cells (3.9±1% naïve vs. 3.6±1% pre-existing immunity, PEI) nor anti-Ebola neutralizing antibody (NAB, 40±10 reciprocal dilution, both groups). The number of INF-γ+ CD8+ cells detected in bronchioalveolar lavage fluid (BAL) after I.N. immunization was not compromised by pre-existing immunity to Ad5 (146±14, naïve vs. 120±16 SFC/million MNCs, PEI). However, pre-existing immunity reduced NAB levels in BAL by ∼25% in this group. To improve the immune response after oral vaccination, the Ad5-based vaccine was PEGylated. Mice given the modified vaccine did not survive challenge and had reduced levels of IFN-γ+ CD8+ T cells 10 days after administration (0.3±0.3% PEG vs. 1.7±0.5% unmodified). PEGylation did increase NAB levels 2-fold. These results provide some insight about the degree of T and B cell mediated immunity necessary for protection against Ebola virus and suggest that modification of the virus capsid can influence the type of immune response elicited by an Ad5-based vaccine