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"Friedman, Sam"
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The class ceiling : why it pays to be privileged
Politicians continually tell us that anyone can get ahead. But is that really true? This important book takes readers behind the closed doors of elite employers to reveal how class affects who gets to the top. Friedman and Laurison show that a powerful 'class pay gap' exists in Britain's elite occupations. Even when those from working-class backgrounds make it into prestigious jobs they earn, on average, 16% less than colleagues from privileged backgrounds. But why is this the case? Drawing on 175 interviews across four case studies--television, accountancy, architecture, and acting--they explore the complex barriers facing the upwardly mobile. -- Dust jacket.
Book
The Class Pay Gap in Higher Professional and Managerial Occupations
This article demonstrates how class origin shapes earnings in higher professional and managerial employment. Taking advantage of newly released data in Britain's Labour Force Survey, we examine the relative openness of different high-status occupations and the earnings of the upwardly mobile within them. In terms of access, we find a distinction between traditional professions, such as law, medicine, and finance, which are dominated by the children of higher managers and professionals, and more technical occupations, such as engineering and IT, that recruit more widely. Moreover, even when people who are from working-class backgrounds are successful in entering high-status occupations, they earn 17 percent less, on average, than individuals from privileged backgrounds. This class-origin pay gap translates to up to £7,350 ($11,000) lower annual earnings. This difference is partly explained by the upwardly mobile being employed in smaller firms and working outside London, but it remains substantial even net of a variety of important predictors of earnings. These findings underline the value of investigating differences in mobility rates between individual occupations as well as illustrating how, beyond entry, the mobile population often faces an earnings \"class ceiling\" within high-status occupations.
Journal Article
Cross-modal autoencoder framework learns holistic representations of cardiovascular state
A fundamental challenge in diagnostics is integrating multiple modalities to develop a joint characterization of physiological state. Using the heart as a model system, we develop a cross-modal autoencoder framework for integrating distinct data modalities and constructing a holistic representation of cardiovascular state. In particular, we use our framework to construct such cross-modal representations from cardiac magnetic resonance images (MRIs), containing structural information, and electrocardiograms (ECGs), containing myoelectric information. We leverage the learned cross-modal representation to (1) improve phenotype prediction from a single, accessible phenotype such as ECGs; (2) enable imputation of hard-to-acquire cardiac MRIs from easy-to-acquire ECGs; and (3) develop a framework for performing genome-wide association studies in an unsupervised manner. Our results systematically integrate distinct diagnostic modalities into a common representation that better characterizes physiologic state.
A challenge in diagnostics is integrating different data modalities to characterize physiological state. Here, the authors show, using the heart as a model system, that cross-modal autoencoders can integrate and translate modalities to improve diagnostics and identify associated genetic variants.
Journal Article
A New Model of Social Class? Findings from the BBC's Great British Class Survey Experiment
The social scientific analysis of social class is attracting renewed interest given the accentuation of economic and social inequalities throughout the world. The most widely validated measure of social class, the Nuffield class schema, developed in the 1970s, was codified in the UK's National Statistics Socio-Economic Classification (NS-SEC) and places people in one of seven main classes according to their occupation and employment status. This principally distinguishes between people working in routine or semi-routine occupations employed on a 'labour contract' on the one hand, and those working in professional or managerial occupations employed on a 'service contract' on the other. However, this occupationally based class schema does not effectively capture the role of social and cultural processes in generating class divisions. We analyse the largest survey of social class ever conducted in the UK, the BBC's 2011 Great British Class Survey, with 161,400 web respondents, as well as a nationally representative sample survey, which includes unusually detailed questions asked on social, cultural and economic capital. Using latent class analysis on these variables, we derive seven classes. We demonstrate the existence of an 'elite', whose wealth separates them from an established middle class, as well as a class of technical experts and a class of 'new affluent' workers. We also show that at the lower levels of the class structure, alongside an ageing traditional working class, there is a 'precariat' characterised by very low levels of capital, and a group of emergent service workers. We think that this new seven class model recognises both social polarisation in British society and class fragmentation in its middle layers, and will attract enormous interest from a wide social scientific community in offering an up-to-date multi-dimensional model of social class.
Journal Article
The Price of the Ticket: Rethinking the Experience of Social Mobility
Increasing social mobility is the 'principal goal' of the current Coalition Government's social policy. However, while mainstream political discourse frames mobility as an unequivocally progressive force, there is a striking absence of studies examining the long-term impact of mobility on individuals themselves. In British sociology the most influential research was carried out by Goldthorpe 40 years ago and argued that the mobile were overwhelmingly content with their trajectories. However, using a critique of Goldthorpe as its springboard, this article calls for a new research agenda in mobility studies. In particular, it proposes a large-scale re-examination of the mobility experience — one which addresses the possibility that people make sense of social trajectories not just through 'objective' markers of economic or occupational success, but also through symbols and artifacts of class-inflected cultural identity. Such enquiry may yield a richer account that explains both the potential social benefits and the costs of mobility.
Journal Article
From Aristocratic to Ordinary
How do elites signal their superior social position via the consumption of culture? We address this question by drawing on 120 years of “recreations” data (N = 71,393) contained within Who’s Who, a unique catalogue of the British elite. Our results reveal three historical phases of elite cultural distinction: first, a mode of aristocratic practice forged around the leisure possibilities afforded by landed estates, which waned significantly in the late-nineteenth century; second, a highbrow mode dominated by the fine arts, which increased sharply in the early-twentieth century before gently receding in the most recent birth cohorts; and, third, a contemporary mode characterized by the blending of highbrow pursuits with everyday forms of cultural participation, such as spending time with family, friends, and pets. These shifts reveal changes not only in the contents of elite culture but also in the nature of elite distinction, in particular, (1) how the applicability of emulation and (mis) recognition theories has changed over time, and (2) the emergence of a contemporary mode that publicly emphasizes everyday cultural practice (to accentuate ordinariness, authenticity, and cultural connection) while retaining many tastes that continue to be (mis) recognized as legitimate.
Journal Article
Association between transcriptomic metrics of exogenous antigen presentation and adaptive immunity with locoregional recurrence in localized estrogen receptor negative breast cancer: retrospective review of multi-institutional datasets
Background
Transcriptomic features of breast cancer locoregional recurrence (LRR) remain poorly understood. We therefore sought to investigate transcriptomic features associated with LRR in newly diagnosed invasive breast tumors from our institutional dataset.
Methods
Transcriptomic profiling was performed on 632 tumors from consecutive patients treated within our health system for newly diagnosed non-metastatic breast cancer. Univariable Cox models identified genes whose expression was associated with LRR (
q
-value < 0.05). Up-regulated (UR) genes were defined as hazard ratio (HR) > 1 and down-regulated (DR) genes were defined as HR < 1. Gene set enrichment analyses were performed for UR and DR gene sets and validated within two external cohorts of ER- tumors.
Results
With a median follow-up of 7.6 years, we observed 38 LRRs: 28/481 (5.8%) in ER + and 10/151 (6.6%) in ER-. There were 43 UR and 7 DR genes associated with LRR in ER + tumors, while 417 UR and 1150 DR genes were associated with LRR in ER- tumors. UR genes in ER + tumors were enriched for roles in cell proliferation (
q
< 0.05). In contrast, LRR in ER- tumors was most strongly associated with DR genes enriched for MHC-II-mediated antigen presentation and T cell activation (
q
< 0.05). In external cohorts of ER- tumors, 97 significant DR genes (
p
< 0.05) were enriched for 18 pathways, including 5 pathways involved in MHC-II signaling, antigen presentation and T-cell activation.
Conclusions
Transcriptomic patterns associated with LRR appear distinct between ER + and ER- tumors. In ER + tumors, LRR appears predominantly associated with proliferation, whereas ER- LRR suggests a robust pattern of suppressed antigen presentation via MHC-II.
Journal Article
Artificial intelligence-enabled analysis of handheld single-lead electrocardiograms to predict incident atrial fibrillation: an analysis of the VITAL-AF randomized trial
Whether artificial intelligence (AI) analysis of single-lead ECG (1 L ECG) can predict incident AF is unknown. In the VITAL-AF trial (ClinicalTrials.gov NCT03515057, registered 2/24/2021) of primary care patients aged ≥65 years undergoing handheld 1 L ECG screening, we tested three AI approaches to incident AF prediction, and compared the best model to the CHARGE-AF risk score. In a test set of 4,221 individuals, a published AI model trained using single standard ECG leads (“1 L ECG-AI”) provided similar 2-year AF discrimination to models trained with VITAL-AF data. In the full VITAL-AF sample of 15,694 individuals without prevalent AF (2-year incident AF 3.1%), 1 L ECG-AI with age/sex (1 L ECG-AI AS) had comparable discrimination (area under the receiver operating characteristic curve [AUROC] 0.695[0.637–0.742]; average precision [AP] 0.060[0.050–0.078]) to CHARGE-AF (AUROC 0.679[0.623-0.730]; AP 0.062[0.052–0.080], AUROC
p
= 0.46, AP
p
= 0.92). Net reclassification improvement was favorable versus age ≥65 years (0.27[0.22–0.32]). 1 L ECG-AI may increase efficiency and reach of AF screening.
Journal Article
Unsupervised deep learning of electrocardiograms enables scalable human disease profiling
The 12-lead electrocardiogram (ECG) is inexpensive and widely available. Whether conditions across the human disease landscape can be detected using the ECG is unclear. We developed a deep learning denoising autoencoder and systematically evaluated associations between ECG encodings and ~1,600 Phecode-based diseases in three datasets separate from model development, and meta-analyzed the results. The latent space ECG model identified associations with 645 prevalent and 606 incident Phecodes. Associations were most enriched in the circulatory (
n
= 140, 82% of category-specific Phecodes), respiratory (
n
= 53, 62%) and endocrine/metabolic (
n
= 73, 45%) categories, with additional associations across the phenome. The strongest ECG association was with hypertension (p < 2.2×10
-308
). The ECG latent space model demonstrated more associations than models using standard ECG intervals, and offered favorable discrimination of prevalent disease compared to models comprising age, sex, and race. We further demonstrate how latent space models can be used to generate disease-specific ECG waveforms and facilitate individual disease profiling.
Journal Article
Genetic Architectures of Medical Images Revealed by Registration of Multiple Modalities
The advent of biobanks with vast quantities of medical imaging and paired genetic measurements creates huge opportunities for a new generation of genotype–phenotype association studies. However, disentangling biological signals from the many sources of bias and artifacts remains difficult. Using diverse medical images and time-series (ie, magnetic resonance imagings [MRIs], electrocardiograms [ECGs], and dual-energy X-ray absorptiometries [DXAs]), we show how registration, both spatial and temporal, guided by domain knowledge or learned de novo, helps uncover biological information. A multimodal autoencoder comparison framework quantifies and characterizes how registration affects the representations that unsupervised and self-supervised encoders learn. In this study we (1) train autoencoders before and after registration with nine diverse types of medical image, (2) demonstrate how neural network-based methods (VoxelMorph, DeepCycle, and DropFuse) can effectively learn registrations allowing for more flexible and efficient processing than is possible with hand-crafted registration techniques, and (3) conduct exhaustive phenotypic screening, comprised of millions of statistical tests, to quantify how registration affects the generalizability of learned representations. Genome- and phenome-wide association studies (GWAS and PheWAS) uncover significantly more associations with registered modality representations than with equivalently trained and sized representations learned from native coordinate spaces. Specifically, registered PheWAS yielded 61 more disease associations for ECGs, 53 more disease associations for cardiac MRIs, and 10 more disease associations for brain MRIs. Registration also yields significant increases in the coefficient of determination when regressing continuous phenotypes (eg, 0.36 ± 0.01 with ECGs and 0.11 ± 0.02 for DXA scans). Our findings reveal the crucial role registration plays in enhancing the characterization of physiological states across a broad range of medical imaging data types. Importantly, this finding extends to more flexible types of registration, such as the cross-modal and the circular mapping methods presented here.
Journal Article