On the Complexity of Health Data Protection-in-Practice: Insights from a Longitudinal Qualitative Study

Digitalization of healthcare presents opportunities for improving the quality of healthcare services and promises economic benefits. However, the success of digital health and the benefits cannot be actualized without considering health data protection practices in the process of healthcare service delivery. Despite the criticality of protecting health data in the system use lifecycle (from recording to consuming and taking informed actions), there is a paucity of research to investigate this complex phenomenon. Using longitudinal qualitative data on a state-wide digital health transformation project, we contextually theorize the practices for protecting health data. Our study reveals five types of health data protectionin-practice, namely data minimization, informal encoding, accuracy, improving cyber-awareness, and appropriate access management. Our results provide new insights into information system use (especially, effective use), and highlight practices that can improve health data protection

Socio-technical Challenges to the Effective Use of Health Information Systems (IS) and Data Protection: A Contextual Theorization of the Dark Side of IS Use

Information Systems (IS) research on health IS use has suffered from a positivity bias – largely focusing on upside gains rather than the potential dark side of usage practices. Exploring the dark side and failures in health IS use, such as shortcomings in data privacy and cybersecurity, can provide useful insights for research, practice, and policy. Through qualitative analyses of three datasets collected between 2015 and 2021, we theorize challenges to the effective use of IS and data protection in Australian health services. We propose a contextualized theory of ‘health records misuse’ with two overarching dimensions: data misfit and improper data processing. We explain sub-categories of data misfit: availability misfit, meaning misfit, and place misfit, as well as sub-categories of improper data processing: improper interaction and improper data recording and use. Our findings demonstrate how health records misuse arises from socio-technical systems, and impacts health service delivery and patient safety

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Ten‐Year Secular Trends in Youth Violence: Results From the Philadelphia Youth Risk Behavior Survey 2003‐2013

BACKGROUNDYouth violence reduction is a public health priority, yet few studies have examined secular trends in violence among urban youth, who may be particularly vulnerable to numerous forms of violence. This study examines 10‐year secular trends in the prevalence of violence‐related behaviors among Philadelphia high school students.METHODSRepeated cross‐sectional data were analyzed from 5 waves of the Philadelphia Youth Risk Behavior Survey (YRBS) from 2003 to 2013. Sex‐specific multivariate regression models were used to examine secular trends in multiple types of violence, accounting for age, race/ethnicity, and sampling strategy.RESULTSIn 2013, the most prevalent violent behavior was physical fighting among boys (38.4%) and girls (32.7%). Among girls, the prevalence of sexual assault and suicide attempts declined between 2003 and 2013 (β = −0.13, p = .04 and β = −0.14, p = .007, respectively). Among boys, significant declines in carrying a weapon (β = −0.31, p < .001), carrying a gun (β = −0.16, p = .01), and physical fighting (β = −0.35, p = .001) were observed.CONCLUSIONSWhereas the prevalence of some forms of violence stabilized or declined among Philadelphia youth during 2003‐2013 time span, involvement in violence‐related behaviors remains common among this population. Continued surveillance and evidence‐based violence reduction strategies are needed to address violence among urban youth.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136403/1/josh12491_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136403/2/josh12491.pd

Genome-wide fine-scale recombination rate variation in Drosophila melanogaster

Estimating fine-scale recombination maps of Drosophila from population genomic data is a challenging problem, in particular because of the high background recombination rate. In this paper, a new computational method is developed to address this challenge. Through an extensive simulation study, it is demonstrated that the method allows more accurate inference, and exhibits greater robustness to the effects of natural selection and noise, compared to a well-used previous method developed for studying fine-scale recombination rate variation in the human genome. As an application, a genome-wide analysis of genetic variation data is performed for two Drosophila melanogaster populations, one from North America (Raleigh, USA) and the other from Africa (Gikongoro, Rwanda). It is shown that fine-scale recombination rate variation is widespread throughout the D. melanogaster genome, across all chromosomes and in both populations. At the fine-scale, a conservative, systematic search for evidence of recombination hotspots suggests the existence of a handful of putative hotspots each with at least a tenfold increase in intensity over the background rate. A wavelet analysis is carried out to compare the estimated recombination maps in the two populations and to quantify the extent to which recombination rates are conserved. In general, similarity is observed at very broad scales, but substantial differences are seen at fine scales. The average recombination rate of the X chromosome appears to be higher than that of the autosomes in both populations, and this pattern is much more pronounced in the African population than the North American population. The correlation between various genomic features—including recombination rates, diversity, divergence, GC content, gene content, and sequence quality—is examined using the wavelet analysis, and it is shown that the most notable difference between D. melanogaster and humans is in the correlation between recombination and diversity