Trust-Building through Social Media Communications in Disaster Management

open4Social media provides a digital space – a meeting place, for different people, often representing one or more groups in a society. The use of this space during a disaster, especially where information needs are high and the availability of factually accurate and ethically sourced data is scarce, has increased substantially over the last 5-10 years. This paper attempts to address communication in social media and trust between the public and figures of authority during a natural disaster in order to suggest communication strategies that can enhance or reinforce trust between these bodies before, during and after a natural disaster.openM. G. Busà; M. T. Musacchio; S. Finan; C. FennelBusa', MARIA GRAZIA; Musacchio, MARIA TERESA; S., Finan; C., Fenne

Human Genomics and Drug Development

Insights into the genetic basis of human disease are helping to address some of the key challenges in new drug development including the very high rates of failure. Here we review the recent history of an emerging, genomics-assisted approach to pharmaceutical research and development, and its relationship to Mendelian randomization (MR), a well-established analytical approach to causal inference. We demonstrate how human genomic data linked to pharmaceutically relevant phenotypes can be used for (1) drug target identification (mapping relevant drug targets to diseases), (2) drug target validation (inferring the likely effects of drug target perturbation), (3) evaluation of the effectiveness and specificity of compound-target engagement (inferring the extent to which the effects of a compound are exclusive to the target and distinguishing between on-target and off-target compound effects), and (4) the selection of end points in clinical trials (the diseases or conditions to be evaluated as trial outcomes). We show how genomics can help identify indication expansion opportunities for licensed drugs and repurposing of compounds developed to clinical phase that proved safe but ineffective for the original intended indication. We outline statistical and biological considerations in using MR for drug target validation (drug target MR) and discuss the obstacles and challenges for scaled applications of these genomics-based approaches

Lipid lowering and Alzheimer's disease risk: a Mendelian randomization study

Objective: To examine whether genetic variation affecting the expression or function of lipid-lowering drug targets isassociated with Alzheimer disease (AD) risk, to evaluate the potential impact of long-term exposure to correspondingtherapeutics.Methods: We conducted Mendelian randomization analyses using variants in genes that encode the protein targets ofseveral approved lipid-lowering drug classes: HMGCR (encoding the target for statins), PCSK9 (encoding the target forPCSK9 inhibitors, eg, evolocumab and alirocumab), NPC1L1 (encoding the target for ezetimibe), and APOB (encodingthe target of mipomersen). Variants were weighted by associations with low-density lipoprotein cholesterol (LDL-C)using data from lipid genetics consortia (n up to 295,826). We meta-analyzed Mendelian randomization estimates forregional variants weighted by LDL-C on AD risk from 2 large samples (total n = 24,718 cases, 56,685 controls).Results: Models for HMGCR, APOB, and NPC1L1 did not suggest that the use of related lipid-lowering drug classeswould affect AD risk. In contrast, genetically instrumented exposure to PCSK9 inhibitors was predicted to increase ADrisk in both of the AD samples (combined odds ratio per standard deviation lower LDL-C inducible by the drug tar-get = 1.45, 95% confidence interval = 1.23–1.69). This risk increase was opposite to, although more modest than, thedegree of protection from coronary artery disease predicted by these same methods for PCSK9 inhibition.Interpretation: We did not identify genetic support for the repurposing of statins, ezetimibe, or mipomersen for ADprevention. Notwithstanding caveats to this genetic evidence, pharmacovigilance for AD risk among users of PCSK9inhibitors may be warranted

Linear regression and the normality assumption

Objectives: Researchers often perform arbitrary outcome transformations to fulfill the normality assumption of a linear regression model. This commentary explains and illustrates that in large data settings, such transformations are often unnecessary, and worse may bias model estimates. / Study Design and Setting: Linear regression assumptions are illustrated using simulated data and an empirical example on the relation between time since type 2 diabetes diagnosis and glycated hemoglobin levels. Simulation results were evaluated on coverage; i.e., the number of times the 95% confidence interval included the true slope coefficient. / Results: Although outcome transformations bias point estimates, violations of the normality assumption in linear regression analyses do not. The normality assumption is necessary to unbiasedly estimate standard errors, and hence confidence intervals and P-values. However, in large sample sizes (e.g., where the number of observations per variable is >10) violations of this normality assumption often do not noticeably impact results. Contrary to this, assumptions on, the parametric model, absence of extreme observations, homoscedasticity, and independency of the errors, remain influential even in large sample size settings. / Conclusion: Given that modern healthcare research typically includes thousands of subjects focusing on the normality assumption is often unnecessary, does not guarantee valid results, and worse may bias estimates due to the practice of outcome transformations

Polygenic risk scores for coronary artery disease and subsequent event risk amongst established cases

BACKGROUND: There is growing evidence that polygenic risk scores (PRS) can identify individuals with elevated lifetime risk of coronary artery disease (CAD). Whether they can also be used to stratify risk of subsequent events among those surviving a first CAD event remains uncertain, with possible biological differences between CAD onset and progression, and the potential for index event bias. METHODS: Using two baseline subsamples of UK Biobank; prevalent CAD cases (N = 10 287) and individuals without CAD (N = 393 108), we evaluated associations between a CAD PRS and incident cardiovascular and fatal outcomes. RESULTS: A 1 S.D. higher PRS was associated with increased risk of incident MI in participants without CAD (OR 1.33; 95% C.I. 1.29, 1.38), but the effect estimate was markedly attenuated in those with prevalent CAD (OR 1.15; 95% C.I. 1.06, 1.25); heterogeneity P = 0.0012. Additionally, among prevalent CAD cases, we found evidence of an inverse association between the CAD PRS and risk of all-cause death (OR 0.91; 95% C.I. 0.85, 0.98) compared to those without CAD (OR 1.01; 95% C.I. 0.99, 1.03); heterogeneity P = 0.0041. A similar inverse association was found for ischaemic stroke (Prevalent CAD (OR 0.78; 95% C.I. 0.67, 0.90); without CAD (OR 1.09; 95% C.I. 1.04, 1.15), heterogeneity P < 0.001). CONCLUSIONS: Bias induced by case stratification and survival into UK Biobank may distort associations of polygenic risk scores derived from case-control studies or populations initially free of disease. Differentiating between effects of possible biases and genuine biological heterogeneity is a major challenge in disease progression research