Hotter summers caused by global warming and increased workload and duration are endangering the health of farmworkers, a high-risk population for heat-related illness (HRI), and deaths. Although prior studies using wearable sensors show the feasibility of employing field-collected data for HRI monitoring, existing devices still have limitations, such as data loss from motion artifacts, device discomfort from rigid electronics, difficulties with administering ingestible sensors, and low temporal resolution. Here, this paper introduces a wireless, wearable bioelectronic system with functionalities for continuous monitoring of skin temperature, electrocardiograms (ECG), heart rates (HR), and activities, configured in a single integrated package. Advanced nanomanufacturing based on laser machining allows rapid device fabrication and direct incorporation of sensors with a highly breathable substrate, allowing for managing excessive sweating and multimodal stresses. To validate the device's performance in agricultural settings, the device is applied to multiple farmworkers at various operations, including fernery, nursery, and crop. The accurate data recording, including high-fidelity ECG (signal-to-noise ratio: >20 dB), accurate HR (r = 0.89, r2 = 0.65 in linear correlation), and reliable temperature/activity, confirms the device's capability for multiparameter health monitoring of farmworkers.
AIMS: Various cardiovascular risk prediction models have been developed for patients with type 2 diabetes mellitus. Yet few models have been validated externally. We perform a comprehensive validation of existing risk models on a heterogeneous population of patients with type 2 diabetes using secondary analysis of electronic health record data. METHODS: Electronic health records of 47,988 patients with type 2 diabetes between 2013 and 2017 were used to validate 16 cardiovascular risk models, including 5 that had not been compared previously, to estimate the 1-year risk of various cardiovascular outcomes. Discrimination and calibration were assessed by the c-statistic and the Hosmer-Lemeshow goodness-of-fit statistic, respectively. Each model was also evaluated based on the missing measurement rate. Sub-analysis was performed to determine the impact of race on discrimination performance. RESULTS: There was limited discrimination (c-statistics ranged from 0.51 to 0.67) across the cardiovascular risk models. Discrimination generally improved when the model was tailored towards the individual outcome. After recalibration of the models, the Hosmer-Lemeshow statistic yielded p-values above 0.05. However, several of the models with the best discrimination relied on measurements that were often imputed (up to 39% missing). CONCLUSION: No single prediction model achieved the best performance on a full range of cardiovascular endpoints. Moreover, several of the highest-scoring models relied on variables with high missingness frequencies such as HbA1c and cholesterol that necessitated data imputation and may not be as useful in practice. An open-source version of our developed Python package, cvdm, is available for comparisons using other data sources.
Background: Patients develop pressure injuries (PIs) in the hospital owing to low mobility, exposure to localized pressure, circulatory conditions, and other predisposing factors. Over 2.5 million Americans develop PIs annually. The Center for Medicare and Medicaid considers hospital-acquired PIs (HAPIs) as the most frequent preventable event, and they are the second most common claim in lawsuits. With the growing use of electronic health records (EHRs) in hospitals, an opportunity exists to build machine learning models to identify and predict HAPI rather than relying on occasional manual assessments by human experts. However, accurate computational models rely on high-quality HAPI data labels. Unfortunately, the different data sources within EHRs can provide conflicting information on HAPI occurrence in the same patient. Furthermore, the existing definitions of HAPI disagree with each other, even within the same patient population. The inconsistent criteria make it impossible to benchmark machine learning methods to predict HAPI. Objective: The objective of this project was threefold. We aimed to identify discrepancies in HAPI sources within EHRs, to develop a comprehensive definition for HAPI classification using data from all EHR sources, and to illustrate the importance of an improved HAPI definition. Methods: We assessed the congruence among HAPI occurrences documented in clinical notes, diagnosis codes, procedure codes, and chart events from the Medical Information Mart for Intensive Care III database. We analyzed the criteria used for the 3 existing HAPI definitions and their adherence to the regulatory guidelines. We proposed the Emory HAPI (EHAPI), which is an improved and more comprehensive HAPI definition. We then evaluated the importance of the labels in training a HAPI classification model using tree-based and sequential neural network classifiers. Results: We illustrate the complexity of defining HAPI, with <13% of hospital stays having at least 3 PI indications documented across 4 data sources. Although chart events were the most common indicator, it was the only PI documentation for >49% of the stays. We demonstrate a lack of congruence across existing HAPI definitions and EHAPI, with only 219 stays having a consensus positive label. Our analysis highlights the importance of our improved HAPI definition, with classifiers trained using our labels outperforming others on a small manually labeled set from nurse annotators and a consensus set in which all definitions agreed on the label. Conclusions: Standardized HAPI definitions are important for accurately assessing HAPI nursing quality metric and determining HAPI incidence for preventive measures. We demonstrate the complexity of defining an occurrence of HAPI, given the conflicting and incomplete EHR data. Our EHAPI definition has favorable properties, making it a suitable candidate for HAPI classification tasks.
To determine the hepatitis C virus (HCV) care cascade among persons who were born during 1945 to 1965 and received outpatient care on or after January 2014 at a large academic healthcare system. Deidentified electronic health record data in an existing research database were analyzed for this study. Laboratory test results for HCV antibody and HCV ribonucleic acid (RNA) indicated seropositivity and confirmatory testing. HCV genotyping was used as a proxy for linkage to care. A direct-acting antiviral (DAA) prescription indicated treatment initiation, an undetectable HCV RNA at least 20 weeks after initiation of antiviral treatment indicated a sustained virologic response. Of the 121,807 patients in the 1945 to 1965 birth cohort who received outpatient care between January 1, 2014 and June 30, 2017, 3399 (3%) patients were screened for HCV; 540 (16%) were seropositive. Among the seropositive, 442 (82%) had detectable HCV RNA, 68 (13%) had undetectable HCV RNA, and 30 (6%) lacked HCV RNA testing. Of the 442 viremic patients, 237 (54%) were linked to care, 65 (15%) initiated DAA treatment, and 32 (7%) achieved sustained virologic response. While only 3% were screened for HCV, the seroprevalence was high in the screened sample. Despite the established safety and efficacy of DAAs, only 15% initiated treatment during the study period. To achieve HCV elimination, improved HCV screening and linkage to HCV care and DAA treatment are needed.
Background: The purpose of this systematic review is to examine cooling intervention research in outdoor occupations, evaluate the effectiveness of such interventions, and offer recommendations for future studies. This review focuses on outdoor occupational studies conducted at worksites or simulated occupational tasks in climatic chambers. Methods: This systematic review was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. PubMed, Embase, and Web of Science were searched to identify original research on intervention studies published in peer-reviewed journals that aimed at reducing heat stress or heat-related illness from January 2000 to August 2020. Results: A systematic search yielded a total of 1042 articles, of which 21 met the inclusion criteria. Occupations with cooling intervention studies included agriculture (n = 5), construction (n = 5), industrial workers (n = 4), and firefighters (n = 7). The studies focused on multiple types of cooling interventions cooling gear (vest, bandanas, cooling shirts, or head-cooling gel pack), enhanced heat dissipation clothing, forearm or lower body immersion in cold water, water dousing, ingestion of a crushed ice slush drink, electrolyte liquid hydration, and modified Occupational Safety and Health Administration recommendations of drinking water and resting in the shade. Conclusion: Current evidence indicates that using multiple cooling gears along with rest cycles may be the most effective method to reduce heat-related illness. Occupational heat-related illnesses and death may be mitigated by targeted cooling intervention and workplace controls among workers of vulnerable occupational groups and industries.
Sequential pattern mining can be used to extract meaningful sequences from electronic health records. However, conventional sequential pattern mining algorithms that discover all frequent sequential patterns can incur a high computational and be susceptible to noise in the observations. Approximate sequential pattern mining techniques have been introduced to address these shortcomings yet, existing approximate methods fail to reflect the true frequent sequential patterns or only target single-item event sequences. Multi-item event sequences are prominent in healthcare as a patient can have multiple interventions for a single visit. To alleviate these issues, we propose GASP, a graph-based approximate sequential pattern mining, that discovers frequent patterns for multi-item event sequences. Our approach compresses the sequential information into a concise graph structure which has computational benefits. The empirical results on two healthcare datasets suggest that GASP outperforms existing approximate models by improving recoverability and extracts better predictive patterns.
Introduction: Agricultural workers perform intense labor outside in direct sunlight and in humid environmental conditions exposing them to a high risk of heat-related illness (HRI). To implement effective cooling interventions in occupational settings, it is important to consider workers’ perceptions. To date, an analysis of agricultural workers’ experience and perception of cooling devices used in the field while working has not been published. Methods: Qualitatively data from 61 agricultural workers provided details of their perceptions and experiences with cooling interventions. Results: The participants in the bandana group reported the bandana was practical to use at work and did not interfere with their work routine. Cooling vest group participants agreed that the vest was effective at cooling them, but the practicality of using the vest at work was met with mixed reviews. Conclusion: The findings of this qualitative study support and extend existing research regarding personal cooling and heat prevention research interventions with vulnerable occupational groups. Personal cooling gear was well received and utilized by the agricultural workers. Sustainable heat prevention studies and governmental protection strategies for occupational heat stress are urgently needed to reduce the risk of heat-related morbidity, mortality, and projected climate change health impacts on outdoor workers.
From electronic health records (EHRs), the relationship between patients' conditions, treatments, and outcomes can be discovered and used in various healthcare research tasks such as risk prediction. In practice, EHRs can be stored in one or more data warehouses, and mining from distributed data sources becomes challenging. Another challenge arises from privacy laws because patient data cannot be used without some patient privacy guarantees. Thus, in this paper, we propose a privacy-preserving framework using sequential pattern mining in distributed data sources. Our framework extracts patterns from each source and shares patterns with other sources to discover discriminative and representative patterns that can be used for risk prediction while preserving privacy. We demonstrate our framework using a case study of predicting Cardiovascular Disease in patients with type 2 diabetes and show the effectiveness of our framework with several sources and by applying differential privacy mechanisms.
Introduction Although a considerable proportion of Asians in the USA experience depression, anxiety and poor sleep, these health issues have been underestimated due to the model minority myth about Asians, the stigma associated with mental illness, lower rates of treatment seeking and a shortage of culturally tailored mental health services. Indeed, despite emerging evidence of links between psychosocial risk factors, the gut microbiome and depression, anxiety and sleep quality, very few studies have examined how these factors are related in Chinese and Korean immigrants in the USA. The purpose of this pilot study was to address this issue by (a) testing the usability and feasibility of the study's multilingual survey measures and biospecimen collection procedure among Chinese and Korean immigrants in the USA and (b) examining how stress, discrimination, acculturation and the gut microbiome are associated with depression, anxiety and sleep quality in this population. Method and analysis This is a cross-sectional pilot study among first and second generations of adult Chinese and Korean immigrants in the greater Atlanta area (Georgia, USA). We collected (a) gut microbiome samples and (b) data on psychosocial risk factors, depression, anxiety and sleep disturbance using validated, online surveys in English, Chinese and Korean. We aim to recruit 60 participants (30 Chinese, 30 Korean). We will profile participants' gut microbiome using 16S rRNA V3-V4 sequencing data, which will be analysed by QIIME 2. Associations of the gut microbiome and psychosocial factors with depression, anxiety and sleep disturbance will be analysed using descriptive and inferential statistics, including linear regression. Ethics and dissemination This study has been approved by the Institutional Review Board at Emory University (IRB ID: STUDY00000935). Results will be made available to Chinese and Korean community members, the funder and other researchers and the broader scientific community.
Background: Periodontal disease in pregnancy is considered a risk factor for adverse birth outcomes. Periodontal disease has a microbial etiology, however, the current state of knowledge about the subgingival microbiome in pregnancy is not well understood. Objective: To characterize the structure and diversity of the subgingival microbiome in early and late pregnancy and explore relationships between the subgingival microbiome and preterm birth among pregnant Black women. Methods: This longitudinal descriptive study used 16S rRNA sequencing to profile the subgingival microbiome of 59 Black women and describe microbial ecology using alpha and beta diversity metrics. We also compared microbiome features across early (8-14 weeks) and late (24-30 weeks) gestation overall and according to gestational age at birth outcomes (spontaneous preterm, spontaneous early term, full term). Results: In this sample of Black pregnant women, the top twenty bacterial taxa represented in the subgingival microbiome included a spectrum representative of various stages of biofilm progression leading to periodontal disease, including known periopathogens Porphyromonas gingivalis and Tannerella forsythia. Other organisms associated with periodontal disease reflected in the subgingival microbiome included several Prevotella spp., and Campylobacter spp. Measures of alpha or beta diversity did not distinguish the subgingival microbiome of women according to early/late gestation or full term/spontaneous preterm birth; however, alpha diversity differences in late pregnancy between women who spontaneously delivered early term and women who delivered full term were identified. Several taxa were also identified as being differentially abundant according to early/late gestation, and full term/spontaneous early term births. Conclusions: Although the composition of the subgingival microbiome is shifted toward complexes associated with periodontal disease, the diversity of the microbiome remains stable throughout pregnancy. Several taxa were identified as being associated with spontaneous early term birth. Two, in particular, are promising targets of further investigation. Depletion of the oral commensal Lautropia mirabilis in early pregnancy and elevated levels of Prevotella melaninogenica in late pregnancy were both associated with spontaneous early term birth.