Faculty Bibliography

IMPORTANCE:In the US, unaccompanied migrant children and adolescents (hereinafter referred to as children) are predominantly from Central America's Northern Triangle. While unaccompanied migrant children are at high risk for psychiatric sequelae due to complex traumatic exposures, longitudinal investigations of psychiatric distress after resettlement are lacking. OBJECTIVE:To identify factors associated with emotional distress and longitudinal changes in emotional distress among unaccompanied migrant children in the US. DESIGN, SETTING, AND PARTICIPANTS:For this retrospective cohort study, the 15-item Refugee Health Screener (RHS-15) was administered between January 1, 2015, and December 31, 2019, to unaccompanied migrant children as part of their medical care to detect emotional distress. Follow-up RHS-15 results were included if they were completed before February 29, 2020. Median follow-up interval was 203 days (IQR, 113-375 days). The study was conducted in a federally qualified health center that provides medical, mental health, and legal services. Unaccompanied migrant children who completed the initial RHS-15 were eligible for analysis. Data were analyzed from April 18, 2022, to April 23, 2023. EXPOSURES:Traumatic events before migration, during migration, during detention, and after resettlement in the US. MAIN OUTCOMES AND MEASURES:Emotional distress, including symptoms of posttraumatic stress disorder, anxiety, and depressive symptoms, as indicated by the RHS-15 (ie, score ≥12 on items 1-14 or ≥5 on item 15). RESULTS:In total, 176 unaccompanied migrant children completed an initial RHS-15. They were primarily from Central America's Northern Triangle (153 [86.9%]), were mostly male (126 [71.6%]), and had a mean (SD) age of 16.9 (2.1) years. Of the 176 unaccompanied migrant children, 101 (57.4%) had screen results above the positive cutoff. Girls were more likely to have positive screen results than boys (odds ratio, 2.48 [95% CI, 1.15-5.34]; P = .02). Follow-up scores were available for 68 unaccompanied migrant children (38.6%). On the follow-up RHS-15, most scored above the positive cutoff (44 [64.7%]). Three-quarters of unaccompanied migrant children who scored above the positive cutoff initially continued to have positive scores at follow-up (30 of 40), and half of those with negative screen scores initially had positive scores at follow-up (14 of 28). Female vs male unaccompanied migrant children (unstandardized β = 5.14 [95% CI, 0.23-10.06]; P = .04) and initial total score (unstandardized β = 0.41 [95% CI, 0.18-0.64]; P = .001) were independently associated with increased follow-up RHS-15 total score. CONCLUSIONS AND RELEVANCE:The findings suggest that unaccompanied migrant children are at high risk for emotional distress, including symptoms of depression, anxiety, and posttraumatic stress. The persistence of emotional distress suggests that unaccompanied migrant children would benefit from ongoing psychosocial and material support after resettlement.

With the increasing accuracy of continuous glucose monitors (CGM) have come calls for the Food and Drug Administration (FDA) to label these devices as safe for nonadjunctive dosing of insulin. However, there is evidence that these devices are subject to sporadic, unpredictable, large errors. A text analysis of reports to the FDA MAUDE database since 2015 reveals over 25 000 complaints of CGM sensor inaccuracy, with instances directly leading to serious outcomes. These new data were not considered at a recent FDA Advisory Panel meeting that voted to approve Dexcom G5 relabeling for nonadjunctive use. Social media is another source of surveillance data providing evidence of large CGM inaccuracies in real-world use. We need to improve safety procedures, not remove them. CGMs offer unique information and alerts for managing diabetes, but the issue is not whether they are better than other approaches to monitoring glucose, but how they can be best used in conjunction with devices that offer the confirmatory readings needed for patient safety.

INTRODUCTION: Use of free text in syndromic surveillance requires managing the substantial word variation that results from use of synonyms, abbreviations, acronyms, truncations, concatenations, misspellings, and typographic errors. Failure to detect these variations results in missed cases, and traditional methods for capturing these variations require ongoing, labor-intensive maintenance. OBJECTIVES: This paper examines the problem of word variation in chief-complaint data and explores three semi-automated approaches for addressing it. METHODS: Approximately 6 million chief complaints from patients reporting to emergency departments at 54 hospitals were analyzed. A method of text normalization that models the similarities between words was developed to manage the linguistic variability in chief complaints. Three approaches based on this method were investigated: 1) automated correction of spelling and typographical errors; 2) use of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes to select chief complaints to mine for overlooked vocabulary; and 3) identification of overlooked vocabulary by matching words that appeared in similar contexts. RESULTS: The prevalence of word errors was high. For example, such words as diarrhea, nausea, and vomiting were misspelled 11.0%-18.8% of the time. Approximately 20% of all words were abbreviations or acronyms whose use varied substantially by site. Two methods, use of ICD-9-CM codes to focus searches and the automated pairing of words by context, both retrieved relevant but previously unexpected words. Text normalization simultaneously reduced the number of false positives and false negatives in syndrome classification, compared with commonly used methods based on word stems. In approximately 25% of instances, using text normalization to detect lower respiratory syndrome would have improved the sensitivity of current word-stem approaches by approximately 10%-20%. CONCLUSIONS: Incomplete vocabulary and word errors can have a substantial impact on the retrieval performance of free-text syndromic surveillance systems. The text normalization methods described in this paper can reduce the effects of these problems