Notes from the Field: High Prevalence of Fentanyl Detected by the Maryland Emergency Department Drug Surveillance System - Baltimore, Maryland, 2019.
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ABSTRACT:
SUBMITTER: Dezman Z
PROVIDER: S-EPMC7315791 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundWe sought to determine the prevalence and sociodemographic and clinical correlates of acute and convalescent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), hepatitis C virus (HCV), and human immunodeficiency virus (HIV) infections among emergency department (ED) patients in Baltimore.MethodsRemnant blood samples from 7450 unique patients were collected over 4 months in 2020 for SARS-CoV-2 antibody (Ab), HCV Ab, and HIV-1/2 antigen and Ab. Among them, 5012 patients were tested by polymerase chain reaction for SARS-CoV-2 based on clinical suspicion. Sociodemographics, ED clinical presentations, and outcomes associated with coinfections were assessed.ResultsOverall, 729 (9.8%) patients had SARS-CoV-2 (acute or convalescent), 934 (12.5%) HCV, 372 (5.0%) HIV infection, and 211 patients (2.8%) had evidence of any coinfection (HCV/HIV, 1.5%; SARS-CoV-2/HCV, 0.7%; SARS-CoV-2/HIV, 0.3%; SARS-CoV-2/HCV/HIV, 0.3%). The prevalence of SARS-CoV-2 (acute or convalescent) was significantly higher in those with HCV or HIV vs those without (13.6% vs 9.1%, P < .001). Key sociodemographic disparities (race, ethnicity, and poverty) and specific ED clinical characteristics were significantly correlated with having any coinfections vs no infection or individual monoinfection. Among those with HCV or HIV, aged 18-34 years, Black race, Hispanic ethnicity, and a cardiovascular-related chief complaint had a significantly higher odds of having SARS-CoV-2 (prevalence ratios: 2.02, 2.37, 5.81, and 2.07, respectively).ConclusionsThe burden of SARS-CoV-2, HCV, and HIV co-pandemics and their associations with specific sociodemographic disparities, clinical presentations, and outcomes suggest that urban EDs should consider implementing integrated screening and linkage-to-care programs for these 3 infections.
| S-EPMC8982772 | biostudies-literature
Project description:BackgroundAbout 30% of home healthcare patients are hospitalized or visit an emergency department (ED) during a home healthcare (HHC) episode. Novel data science methods are increasingly used to improve identification of patients at risk for negative outcomes.ObjectivesThe aim of the study was to identify patients at heightened risk hospitalization or ED visits using HHC narrative data (clinical notes).MethodsThis study used a large database of HHC visit notes (n = 727,676) documented for 112,237 HHC episodes (89,459 unique patients) by clinicians of the largest nonprofit HHC agency in the United States. Text mining and machine learning algorithms (Naïve Bayes, decision tree, random forest) were implemented to predict patient hospitalization or ED visits using the content of clinical notes. Risk factors associated with hospitalization or ED visits were identified using a feature selection technique (gain ratio attribute evaluation).ResultsBest performing text mining method (random forest) achieved good predictive performance. Seven risk factors categories were identified, with clinical factors, coordination/communication, and service use being the most frequent categories.DiscussionThis study was the first to explore the potential contribution of HHC clinical notes to identifying patients at risk for hospitalization or an ED visit. Our results suggest that HHC visit notes are highly informative and can contribute significantly to identification of patients at risk. Further studies are needed to explore ways to improve risk prediction by adding more data elements from additional data sources.
| S-EPMC7606545 | biostudies-literature
Project description:ObjectivesHospital emergency departments (EDs) are a place where many Americans seek treatment of dental conditions. Racial and ethnic minorities consistently have higher rates of ED utilization than whites for dental conditions. The reasons for these disparities and significant public health concerns are investigated less often. In this paper, we measure trends in racial disparities in ED discharges for dental conditions in Maryland from 2010 to 2013. To understand these disparities, we also describe differences between racial groups in age, gender, income, location, payer, comorbidities, and the availability of dental care.Methods2010-2013 State Emergency Department Data for Maryland were used in the analysis. Rates per 100,000 of the population are calculated using information from census population estimates. Cost-to-charge ratios are used to estimate the costs of ED discharges. Dental/oral health-related conditions (DOHRC) are defined as discharge diagnoses of ICD-9-CM codes 520.0 through 529.9. Descriptive statistics and fixed effects logistic regression models with a rare event correction are used to analyze the data.ResultsBlacks, especially females aged 25-34, have larger proportions of total ED discharges due to DOHRC, and higher population rates of DOHRC, than any other racial or ethnic group. In 2013, Blacks represented 30% of Maryland's population and accounted for 52% of ED costs for DOHRC. Hispanics and those of other races have much lower rates of DOHRC discharges. The regression results show that the high proportion of DOHRC discharges among Blacks may be explained by the concentration of Blacks in low-income central cities with less access to dental care.ConclusionThere are significant racial disparities in the ED utilization for DOHRC in Maryland. These disparities reflect the lack of access to dental care due to both cost and geographic limitations. This results in high healthcare costs and ineffective solutions for patients. Addressing oral health disparities will require policy solutions that are targeted to the populations most at need, and action plans that combine community and state level efforts.
| S-EPMC5515044 | biostudies-literature
Project description:BackgroundSyndromic surveillance provides public health intelligence to aid in early warning and monitoring of public health impacts (e.g. seasonal influenza), or reassurance when an impact has not occurred. Using information collected during routine patient care, syndromic surveillance can be based on signs/symptoms/preliminary diagnoses. This approach makes syndromic surveillance much timelier than surveillance requiring laboratory confirmed diagnoses. The provision of healthcare services and patient access to them varies globally. However, emergency departments (EDs) exist worldwide, providing unscheduled urgent care to people in acute need. This provision of care makes ED syndromic surveillance (EDSyS) a potentially valuable tool for public health surveillance internationally. The objective of this study was to identify and describe the key characteristics of EDSyS systems that have been established and used globally.MethodsWe systematically reviewed studies published in peer review journals and presented at International Society of Infectious Disease Surveillance conferences (up to and including 2017) to identify EDSyS systems which have been created and used for public health purposes. Search criteria developed to identify "emergency department" and "syndromic surveillance" were applied to NICE healthcare, Global Health and Scopus databases.ResultsIn total, 559 studies were identified as eligible for inclusion in the review, comprising 136 journal articles and 423 conference abstracts/papers. From these studies we identified 115 EDSyS systems in 15 different countries/territories across North America, Europe, Asia and Australasia. Systems ranged from local surveillance based on a single ED, to comprehensive national systems. National EDSyS systems were identified in 8 countries/territories: 2 reported inclusion of ≥85% of ED visits nationally (France and Taiwan).ConclusionsEDSyS provides a valuable tool for the identification and monitoring of trends in severe illness. Technological advances, particularly in the emergency care patient record, have enabled the evolution of EDSyS over time. EDSyS reporting has become closer to 'real-time', with automated, secure electronic extraction and analysis possible on a daily, or more frequent basis. The dissemination of methods employed and evidence of successful application to public health practice should be encouraged to support learning from best practice, enabling future improvement, harmonisation and collaboration between systems in future.Prospero numberCRD42017069150 .
| S-EPMC7724621 | biostudies-literature
Project description:IntroductionOn January 1, 2014, the State of Maryland implemented the Global Budget Revenue (GBR) program. We investigate the impact of GBR on length of stay (LOS) for inpatients in emergency departments (ED) in Maryland.MethodsWe used the Hospital Compare data reports from the Centers for Medicare and Medicaid Services (CMS) and CMS Cost Reports Hospital Form 2552-10 from January 1, 2012-March 31, 2016, with GBR hospitals from Maryland and hospitals from West Virginia (WV), Delaware (DE), and Rhode Island (RI). We implemented difference-in-differences analysis and investigated the impact of GBR implementation on the LOS or ED1b scores of Maryland hospitals using a mixed-effects model with a state-level fixed effect, a hospital-level random effect, and state-level heterogeneity.ResultsThe GBR impact estimator was 9.47 (95% confidence interval [CI], 7.06 to 11.87, p-value<0.001) for Maryland GBR hospitals, which implies, on average, that GBR implementation added 9.47 minutes per year to the time that hospital inpatients spent in the ED in the first two years after GBR implementation. The effect of the total number of hospital beds was 0.21 (95% CI, 0.089 to 0.330, p-value = 0 .001), which suggests that the bigger the hospital, the longer the ED1b score. The state-level fixed effects for WV were -106.96 (95% CI, -175.06 to -38.86, p-value = 0.002), for DE it was 6.51 (95% CI, -8.80 to 21.82, p-value=0.405), and for RI it was -54.48 (95% CI, -82.85 to -26.10, p-value<0.001).ConclusionOur results indicate that GBR implementation has had a statistically significant negative impact on the efficiency measure ED1b of Maryland hospital EDs from January 2014 to April 2016. We also found that the significant state-level fixed effect implies that the same inpatient might experience different ED processing times in each of the four states that we studied.
| S-EPMC6860385 | biostudies-literature
Project description: Not available
| S-EPMC9984967 | biostudies-literature
Project description:Waterpipe smoking has been growing in popularity in the United States and worldwide. Most tobacco control regulations remain limited to cigarettes. Few studies have investigated waterpipe tobacco smoke exposures in a real world setting. We measured carbon monoxide (CO), particulate matter (PM)2.5, and airborne nicotine concentrations in seven waterpipe cafes in the greater Baltimore area. Area air samples were collected between two and five hours, with an average sampling duration of three hours. Waterpipe smoking behaviors were observed at each venue. Indoor air samplers for CO, PM2.5, and airborne nicotine were placed in the main seating area 1-2 m above the floor. Indoor airborne concentrations of PM2.5 and CO were markedly elevated in waterpipe cafes and exceeded concentrations that were observed in cigarette smoking bars. Air nicotine concentrations, although not as high as in venues that allow cigarette smoking, were markedly higher than in smoke-free bars and restaurants. Concentrations of PM approached occupational exposure limits and CO exceeded occupational exposure guidelines suggesting that worker protection measures need to be considered. This study adds to the literature indicating that both employees and patrons of waterpipe venues are at increased risk from complex exposures to secondhand waterpipe smoke.
| S-EPMC4333110 | biostudies-literature
Project description:Around 42,000 children suffer from severe sepsis each year in the US alone, resulting in significant morbidity, mortality and billion dollar expenditures in the US healthcare system. Sepsis recognition is a clinical challenge in children. Biomarkers are needed to tailor appropriate antimicrobial therapies and improve risk stratification. The goal of this study was to determine if gene expression profiles from peripheral blood were associated with pathogen type and sepsis severity in children treated for suspected sepsis.
2020-01-01 | GSE119217 | GEO
Project description:BACKGROUND:Machine learning has been used extensively in clinical text classification tasks. Deep learning approaches using word embeddings have been recently gaining momentum in biomedical applications. In an effort to automate the identification of altered mental status (AMS) in emergency department provider notes for the purpose of decision support, we compare the performance of classic bag-of-words-based machine learning classifiers and novel deep learning approaches. METHODS:We used a case-control study design to extract an adequate number of clinical notes with AMS and non-AMS based on ICD codes. The notes were parsed to extract the history of present illness, which was used as the clinical text for the classifiers. The notes were manually labeled by clinicians. As a baseline for comparison, we tested several traditional bag-of-words based classifiers. We then tested several deep learning models using a convolutional neural network architecture with three different types of word embeddings, a pre-trained word2vec model and two models without pre-training but with different word embedding dimensions. RESULTS:We evaluated the models on 1130 labeled notes from the emergency department. The deep learning models had the best overall performance with an area under the ROC curve of 98.5% and an accuracy of 94.5%. Pre-training word embeddings on the unlabeled corpus reduced training iterations and had performance that was statistically no different than the other deep learning models. CONCLUSION:This supervised deep learning approach performs exceedingly well for the detection of AMS symptoms in clinical text in our environment. Further work is needed for the generalizability of these findings, including evaluation of these models in other types of clinical notes and other environments. The results seem promising for the ultimate use of these types of classifiers in combination with other information derived from the electronic health records as input for clinical decision support.
| S-EPMC6701023 | biostudies-literature
Project description:Many individuals remain sexually active into their eighth decade. Surveillance data suggest that rates of sexually transmitted infections in older patients are increasing. We compared demographics, risk behaviors, and predictors of acute infections in patients 50 years and older versus younger patients attending sexually transmitted disease clinics in Baltimore, Maryland. This was a retrospective study from a large electronic database of visits to two urban sexually transmitted disease clinics between 2005 and 2010. Proportions were compared using the Chi square test. Logistic regression was used to assess predictors of acute sexually transmitted infections in older versus younger groups. It was found that patients over 50 were more likely than younger patients to report never using condoms (32.6% [CI 0.31-0.34] versus 24.1% [CI 0.23-0.25]). The overall prevalence of acute sexually transmitted infections was 18.1% (CI 0.17-0.19) in older and 25.8% (CI 0.25-0.27) in younger patients. Older women were more likely to be diagnosed with trichomoniasis (21.5% [CI 18.6-24.5] versus 13.1% [CI 11.5-14.8]). Black race was predictive of having an acute sexually transmitted infections in younger men (OR 2.2 [CI 1.47-3.35]) and women (OR 2.7 [CI 1.34-5.30]) but not in older men (OR 1.2 [CI 0.79-1.73]) or women (OR 1.2 [CI 0.43-3.15]). Older age was associated with a decreased risk of acute STI diagnosis in younger men and older women only, while having had sex for money or drugs in the past month was predictive only in younger women. Reporting symptoms and increasing numbers of sexual partners in the last six months was predictive of acute sexually transmitted infection diagnosis in all age groups. Older patients seeking care at sexually transmitted disease clinics engage in important risk behaviors. Race, a factor predictive of acute sexually transmitted infections in younger patients is not a significant predictor of sexually transmitted infections in older persons.
| S-EPMC5554957 | biostudies-other