Development of an Algorithm to Identify Patients with Physician-Documented Insomnia.

We developed an insomnia classification algorithm by interrogating an electronic medical records (EMR) database of 314,292 patients. The patients received care at Massachusetts General Hospital (MGH), Brigham and Women's Hospital (BWH), or both, between 1992 and 2010. Our algorithm combined structured variables (such as International Classification of Diseases 9th Revision [ICD-9] codes, prescriptions, laboratory observations) and unstructured variables (such as text mentions of sleep and psychiatric disorders in clinical narrative notes). The highest classification performance of our algorithm was achieved when it included a combination of structured variables (billing codes for insomnia, common psychiatric conditions, and joint disorders) and unstructured variables (sleep disorders and psychiatric disorders). Our algorithm had superior performance in identifying insomnia patients compared to billing codes alone (area under the receiver operating characteristic curve [AUROC] = 0.83 vs. 0.55 with 95% confidence intervals [CI] of 0.76-0.90 and 0.51-0.58, respectively). When applied to the 314,292-patient population, our algorithm classified 36,810 of the patients with insomnia, of which less than 17% had a billing code for insomnia. In conclusion, an insomnia classification algorithm that incorporates clinical notes is superior to one based solely on billing codes. Compared to traditional methods, our study demonstrates that a classification algorithm that incorporates physician notes can more accurately, comprehensively, and quickly identify large cohorts of insomnia patients.

Predictive Modeling of Physician-Patient Dynamics That Influence Sleep Medication Prescriptions and Clinical Decision-Making.

Insomnia remains under-diagnosed and poorly treated despite its high economic and social costs. Though previous work has examined how patient characteristics affect sleep medication prescriptions, the role of physician characteristics that influence this clinical decision remains unclear. We sought to understand patient and physician factors that influence sleep medication prescribing patterns by analyzing Electronic Medical Records (EMRs) including the narrative clinical notes as well as codified data. Zolpidem and trazodone were the most widely prescribed initial sleep medication in a cohort of 1,105 patients. Some providers showed a historical preference for one medication, which was highly predictive of their future prescribing behavior. Using a predictive model (AUC = 0.77), physician preference largely determined which medication a patient received (OR = 3.13; p = 3 × 10-37). In addition to the dominant effect of empirically determined physician preference, discussion of depression in a patient's note was found to have a statistically significant association with receiving a prescription for trazodone (OR = 1.38, p = 0.04). EMR data can yield insights into physician prescribing behavior based on real-world physician-patient interactions.

Characterizing Sleep Issues Using Twitter.

BACKGROUND: Sleep issues such as insomnia affect over 50 million Americans and can lead to serious health problems, including depression and obesity, and can increase risk of injury. Social media platforms such as Twitter offer exciting potential for their use in studying and identifying both diseases and social phenomenon. OBJECTIVE: Our aim was to determine whether social media can be used as a method to conduct research focusing on sleep issues. METHODS: Twitter posts were collected and curated to determine whether a user exhibited signs of sleep issues based on the presence of several keywords in tweets such as insomnia, "can't sleep", Ambien, and others. Users whose tweets contain any of the keywords were designated as having self-identified sleep issues (sleep group). Users who did not have self-identified sleep issues (non-sleep group) were selected from tweets that did not contain pre-defined words or phrases used as a proxy for sleep issues. RESULTS: User data such as number of tweets, friends, followers, and location were collected, as well as the time and date of tweets. Additionally, the sentiment of each tweet and average sentiment of each user were determined to investigate differences between non-sleep and sleep groups. It was found that sleep group users were significantly less active on Twitter (P=.04), had fewer friends (P<.001), and fewer followers (P<.001) compared to others, after adjusting for the length of time each user's account has been active. Sleep group users were more active during typical sleeping hours than others, which may suggest they were having difficulty sleeping. Sleep group users also had significantly lower sentiment in their tweets (P<.001), indicating a possible relationship between sleep and pyschosocial issues. CONCLUSIONS: We have demonstrated a novel method for studying sleep issues that allows for fast, cost-effective, and customizable data to be gathered.

Contaminant levels in Gulf of Mexico reef fish after the Deepwater Horizon oil spill as measured by a fishermen-led testing program.

The BP oil disaster posed a significant threat to the U.S. seafood industry. Invertebrates (shrimp, oyster, crab) and other nearshore species comprised the majority of postspill testing by federal and state agencies. Deeper water finfish were sampled less frequently, despite population ranges that overlapped with affected waters. We report on a voluntary testing program with Gulf of Mexico commercial fishermen to ensure the safety of their catch. Seven species of reef fish were tested for polycyclic aromatic hydrocarbons, several metals, and a constituent of Corexit 9500A and 9527A dispersants. Only two of 92 samples had detectable levels of benzo(a)pyrene-equivalents (a combined measure of carcinogenic potency across 7 different PAHs), which were still below federal safety thresholds. PAH ratios for these samples suggest pyrogenic (not petrogenic) contamination - indicating potential sources other than Deepwater Horizon. Metals were largely absent (cadmium, lead) or consistent with levels previously reported (mercury, arsenic). One notable exception was tilefish, which showed mercury concentrations lower than expected. We did not detect dispersant in any of our samples, indicating that it was not present in these species during the study period. Our findings suggest minimal risk to public health from these seafoods as a result of the disaster; however, the most contaminated areas were not sampled through this program.

A quantitative synthesis of mercury in commercial seafood and implications for exposure in the United States.

BACKGROUND: Mercury (Hg) is a toxic metal that presents public health risks through fish consumption. A major source of uncertainty in evaluating harmful exposure is inadequate knowledge of Hg concentrations in commercially important seafood. OBJECTIVES: We examined patterns, variability, and knowledge gaps of Hg in common commercial seafood items in the United States and compared seafood Hg concentrations from our database to those used for exposure estimates and consumption advice. METHODS: We developed a database of Hg concentrations in fish and shellfish common to the U.S. market by aggregating available data from government monitoring programs and the scientific literature. We calculated a grand mean for individual seafood items, based on reported means from individual studies, weighted by sample size. We also compared database results to those of federal programs and human health criteria [U.S. Food and Drug Administration Hg Monitoring Program (FDA-MP), U.S. Environmental Protection Agency (EPA)]. RESULTS: Mean Hg concentrations for each seafood item were highly variable among studies, spanning 0.3-2.4 orders of magnitude. Farmed fish generally had lower grand mean Hg concentrations than their wild counterparts, with wild seafood having 2- to 12-fold higher concentrations, depending on the seafood item. However, farmed fish are relatively understudied, as are specific seafood items and seafood imports from Asia and South America. Finally, we found large discrepancies between mean Hg concentrations estimated from our database and FDA-MP estimates for most seafood items examined. CONCLUSIONS: The high variability in Hg in common seafood items has considerable ramifications for public health and the formulation of consumption guidelines. Exposure and risk analyses derived from smaller data sets do not reflect our collective, available information on seafood Hg concentrations.

Response of juvenile scalloped hammerhead sharks to electric stimuli.

Sharks can use their electrosensory system to detect electric fields in their environment. Measurements of their electrosensitivity are often derived by calculating the voltage gradient from a model of the charge distribution for an ideal dipole. This study measures the charge distribution around a dipole in seawater and confirms the close correspondence with the model. From this, it is possible to predict how the sharks will respond to dipolar electric fields comprised of differing parameters. We tested these predictions by exposing sharks to different sized dipoles and levels of applied current that simulated the bioelectric fields of their natural prey items. The sharks initiated responses from a significantly greater distance with larger dipole sizes and also from a significantly greater distance with increasing levels of electric current. This study is the first to provide empirical evidence supporting a popular theoretical model and test predictions about how sharks will respond to a variety of different electric stimuli.