Introduction: People who experience human trafficking (HT) visit emergency departments (ED). The International Classification of Diseases, Clinical Modification (ICD-10-CM) introduced codes to document HT in June 2018. The aim of this study is to identify characteristics of ED patients who experienced forced labor or sexual exploitation as a documented external cause of morbidity in US visits.
Methods: Nationally representative surveillance based on patient visits to 989 hospital-owned EDs in the Nationwide Emergency Department Sample in 2019 became available in 2021. Eight ICD-10-CM codes to classify HT as an external cause of morbidity were combined into one HT variable for analysis in 2021-2022.
Results: A weighted count of 517 of 33.1 million ED visits (0.0016%) documented HT as an external cause of morbidity. Of them, sexual exploitation (71.6%) was documented more frequently than labor exploitation (28.4%). Most HT-related codes were visits by females (87.3%) from large metropolitan areas, and identified as white. Approximately 40% of visits were from ZIP codes with a median household income less than $48,000 annually. Relative to all other ED visits, patients with HT as an external cause of morbidity had higher odds of being female (OR = 6.54, 95% CI:3.59, 11.92) and being a minor (OR = 1.76, 95% CI:1.02, 3.04).
Conclusion: HT was rarely documented as an external cause of morbidity in 989 hospitals' ED visits from a nationally representative sample in 2019. Documentation of recently added HT ICD-10-CM codes does not appear to have been implemented sufficiently to yield an unbiased representation of those who experienced HT and presented in the ED. Efforts to enhance the utility of ICD-10-CM HT codes for surveillance and documentation must first address ED personnel training on identification and response to HT. In doing so, ED personnel also need to address ethical concerns (e.g. stigma, confidentiality, risk of patient harm) and allow for informed consent among trafficked patients in order to be scaled up responsibly.
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http://dx.doi.org/10.1016/j.ajem.2022.11.017 | DOI Listing |
Matern Child Nutr
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School of Health Science, Western Sydney University, Campbelltown, NSW, Australia.
Early initiation of breastfeeding (EIBF) and exclusive breastfeeding (EBF) are highly effective forms of preventive medicine in many low- and middle-income countries, including Anglophone and Francophone West African countries. Despite the proven benefits of EIBF and EBF in reducing mortality and morbidity, there is limited systematic evidence from West African countries. Hence, the aim of this systematic review and meta-analysis was to estimate the pooled prevalence of EIBF and EBF in Anglophone and Francophone West African countries.
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December 2024
Urology Department, Ankara University Faculty of Medicine, 06480 Ankara, Turkey.
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BMC Psychiatry
January 2025
Department of Epidemiology and Biostatistics, Institute of Health, School of Public Health, Jimma University, Jimma, Ethiopia.
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QUEST Center for Responsible Research, Berlin Institute of Health at Charité Universitätsmedizin Berlin, Berlin, Germany.
Background: Machine learning (ML) is increasingly used to predict clinical deterioration in intensive care unit (ICU) patients through scoring systems. Although promising, such algorithms often overfit their training cohort and perform worse at new hospitals. Thus, external validation is a critical - but frequently overlooked - step to establish the reliability of predicted risk scores to translate them into clinical practice.
View Article and Find Full Text PDFNat Med
January 2025
Marc and Jennifer Lipschultz Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Predicting whether a patient with cancer will benefit from immune checkpoint inhibitors (ICIs) without resorting to advanced genomic or immunologic assays is an important clinical need. To address this, we developed and evaluated SCORPIO, a machine learning system that utilizes routine blood tests (complete blood count and comprehensive metabolic profile) alongside clinical characteristics from 9,745 ICI-treated patients across 21 cancer types. SCORPIO was trained on data from 1,628 patients across 17 cancer types from Memorial Sloan Kettering Cancer Center.
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