Adam R. Pah

Design and implementation of a privacy preserving electronic health record linkage tool in Chicago

OBJECTIVE To design and implement a tool that creates a secure, privacy preserving linkage of electronic health record (EHR) data across multiple sites in a large metropolitan area in the United States (Chicago, IL), for use in clinical research. METHODS The authors developed and distributed a software application that performs standardized data cleaning, preprocessing, and hashing of patient identifiers to remove all protected health information. The application creates seeded hash code combinations of patient identifiers using a Health Insurance Portability and Accountability Act compliant SHA-512 algorithm that minimizes re-identification risk. The authors subsequently linked individual records using a central honest broker with an algorithm that assigns weights to hash combinations in order to generate high specificity matches. RESULTS The software application successfully linked and de-duplicated 7 million records across 6 institutions, resulting in a cohort of 5 million unique records. Using a manually reconciled set of 11 292 patients as a gold standard, the software achieved a sensitivity of 96% and a specificity of 100%, with a majority of the missed matches accounted for by patients with both a missing social security number and last name change. Using 3 disease examples, it is demonstrated that the software can reduce duplication of patient records across sites by as much as 28%. CONCLUSIONS Software that standardizes the assignment of a unique seeded hash identifier merged through an agreed upon third-party honest broker can enable large-scale secure linkage of EHR data for epidemiologic and public health research. The software algorithm can improve future epidemiologic research by providing more comprehensive data given that patients may make use of multiple healthcare systems.

The Arabidopsis thaliana putative sialyltransferase resides in the Golgi apparatus but lacks the ability to transfer sialic acid

A common feature of the animal sialyltransferases (STs) is the presence of four conserved motifs, namely large (L), small (S), very small (VS) and motif III. Although sialic acid (SA) has not been detected in plants, three orthologues containing sequences similar to the ST motifs have been identified in the Arabidopsis thaliana L. database. In this study, we report that the At3g48820 gene (Gene ID: 824043) codes for a Golgi resident protein lacking the ability to transfer SA to asialofetuin or Galbeta1,3GalNAc and Galbeta1,4GlcNAc oligosaccharide acceptors. Restoration of deteriorated motifs S, VS and motif III by constructing chimeric proteins consisting of the 28-308 amino acid region of the A. thalianaAt3g48820 ST-like protein and the 264-393 amino acid region of the Oryza sativa L. AK107493 ST-like protein, or of the 28-240 amino acid region of the At3g48820 protein and the 204-350 amino acid region of the Homo sapiens L. alpha2,3-ST (NP_008858) was not able to recover sialyltransferase activity. Altering the appropriate amino acid regions of the A. thalianaAt3g48820 ST-like protein to those typical for the mammalian motif III (HHYWE) and VS motif (HDADFE) also did not have any effect. Our data, together with previous results, indicate that A. thaliana in particular, and plants in general, do not have transferases for SA. Substrates for the plant ST-like proteins might be compounds involved in secondary metabolism.

Use of a global metabolic network to curate organismal metabolic networks.

The difficulty in annotating the vast amounts of biological information poses one of the greatest current challenges in biological research. The number of genomic, proteomic, and metabolomic datasets has increased dramatically over the last two decades, far outstripping the pace of curation efforts. Here, we tackle the challenge of curating metabolic network reconstructions. We predict organismal metabolic networks using sequence homology and a global metabolic network constructed from all available organismal networks. While sequence homology has been a standard to annotate metabolic networks it has been faulted for its lack of predictive power. We show, however, that when homology is used with a global metabolic network one is able to predict organismal metabolic networks that have enhanced network connectivity. Additionally, we compare the annotation behavior of current database curation efforts with our predictions and find that curation efforts are biased towards adding (rather than removing) reactions to organismal networks.

Leveraging genome-wide datasets to quantify the functional role of the anti-Shine-Dalgarno sequence in regulating translation efficiency

Studies dating back to the 1970s established that sequence complementarity between the anti-Shine–Dalgarno (aSD) sequence on prokaryotic ribosomes and the 5′ untranslated region of mRNAs helps to facilitate translation initiation. The optimal location of aSD sequence binding relative to the start codon, the full extents of the aSD sequence and the functional form of the relationship between aSD sequence complementarity and translation efficiency have not been fully resolved. Here, we investigate these relationships by leveraging the sequence diversity of endogenous genes and recently available genome-wide estimates of translation efficiency. We show that—after accounting for predicted mRNA structure—aSD sequence complementarity increases the translation of endogenous mRNAs by roughly 50%. Further, we observe that this relationship is nonlinear, with translation efficiency maximized for mRNAs with intermediate levels of aSD sequence complementarity. The mechanistic insights that we observe are highly robust: we find nearly identical results in multiple datasets spanning three distantly related bacteria. Further, we verify our main conclusions by re-analysing a controlled experimental dataset.

Economic insecurity and the rise in gun violence at US schools

Frequent school shootings are a unique US phenomenon that has defied understanding1,2. Uncovering the aetiology of this problem is hampered by the lack of an established dataset3,4. Here we assemble a carefully curated dataset for the period 1990–2013 that is built upon an exhaustive review of existing data and original sources. Using this dataset, we find that the rate of gun violence is time-dependent and that this rate is heightened from 2007 to 2013. We further find that periods of increased shooting rates are significantly correlated with increases in the unemployment rate across different geographic aggregation levels (national, regional and city). Consistent with the hypothesis that increasing uncertainty in the school-to-work transition contributes to school shootings, we find that multiple indicators of economic distress significantly correlate with increases in the rate of gun violence when events at both K12 and post-secondary schools are considered.

Big Data: What Is It and What Does It Mean for Cardiovascular Research and Prevention Policy

Over the past decade, there has been explosive growth in the amount of healthcare-related data generated and interest in harnessing this data for research purposes and informing public policy. Outside of healthcare, specialized software has been developed to tackle the problems that voluminous data creates, and these techniques could be applicable in several areas of cardiovascular research. Cardiovascular risk analysis may benefit from the inclusion of patient genetic and health record data, while cardiovascular epidemiology could benefit from crowd-sourced environmental data. Some of the most significant advances may come from the ability to predict and respond to events in real-time—such as assessing the impact of new public policy at the community level on a weekly basis through electronic health records or monitoring a patient’s cardiovascular health remotely with a smartphone.

Analysis of Musculoskeletal Injuries Sustained in Falls From the United States–Mexico Border Fence

Injuries sustained by unauthorized individuals who jump or fall from the United States-Mexico border fence are frequently treated by trauma centers in border states. The authors investigated patterns of musculoskeletal injury occurring in these individuals to improve emergency department assessment and to identify strategies to prevent future injuries. A retrospective chart review was performed for patients presenting to an urban, level I trauma center with musculoskeletal injuries sustained in a jump or fall from the United States-Mexico border fence between February 2004 and February 2010. Frequency of fracture by site, frequency of open fracture, and associated patterns of injury were recorded. The population was stratified by age and sex to identify disparity in injury pattern. Average length of stay and number of surgical interventions were also recorded. During the study period, 174 individuals who had jumped or fallen from the United States-Mexico border fence were identified. The population contained 93 (53%) women and 81 (47%) men with an average age of 31.5 years (range, 11-56 years). On average (±standard error), men sustained slightly more fractures than women (1.77±0.12 vs 1.43±0.07; P=.015). There were no significant differences in the number of fractures sustained between age groups. Average length of stay for patients admitted to the hospital was 3.5 days. Patients underwent an average of 0.75 surgical interventions during admission. Falls from the United States-Mexico border fence are a significant cause of morbidity among unauthorized immigrants. [Orthopedics. 2017; 40(3):e432-e435.].

Defining the anti-Shine-Dalgarno sequence interaction and quantifying its functional role in regulating translation efficiency

Summary Studies dating back to the 1970s established that binding between the anti-Shine-Dalgarno (aSD) sequence on prokaryotic ribosomes and mRNA helps to facilitate translation initiation. The location of aSD binding relative to the start codon, the full extents of the aSD sequence, and the functional form of the relationship between aSD binding and translation efficiency are important parameters that remain ill defined in the literature. Here, we leverage genome-wide estimates of translation efficiency to determine these parameters and show that anti-Shine-Dalgarno sequence binding increases the translation of endogenous mRNAs on the order of 50%. Our findings highlight the non-linearity of this relationship, showing that translation efficiency is maximized for sequences with intermediate aSD binding strengths. These mechanistic insights are highly robust; we find nearly identical results in ribosome profiling datasets from 3 highly diverged bacteria, as well as independent genome-scale estimates and controlled experimental data using recombinant GFP expression.