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Climate change is imposing increasingly severe impacts on public health. Addressing these impacts requires heightened awareness of climate-driven health conditions and appropriate clinical practices to manage these conditions. Within this context, the 2(nd) Annual Clinical Climate Change Conference, held January 24, 2020 at the New York Academy of Medicine, brought together more than 150 allied health practitioners from across the United States for a one-day conference showcasing the state of the science on the climate and health. Eight platform presentations—including a keynote address from Karenna Gore of the Center for Earth Ethics at Union Theological Seminary—covered a range of environmentally induced, climate-related disease areas as well as topics related to environmental justice. Additionally, key workshops engaged participants in the clinical management of climate-related health conditions. Communicating the existing evidence base for climate change-driven impacts on human health is crucial for preparing practitioners to identify and address these impacts. Further partnership between researchers and practitioners to extend and disseminate this evidence base will yield important advancements toward protecting patients and improving health outcomes in an era of climate crisis.

ObjectivesTo examine the factors associated with COVID-19 vaccine receipt among healthcare workers and the role of vaccine confidence in decisions to vaccinate, and to better understand concerns related to COVID-19 vaccination.DesignCross-sectional anonymous survey among front-line, support service and administrative healthcare workers.SettingTwo large integrated healthcare systems (one private and one public) in New York City during the initial roll-out of the COVID-19 vaccine.Participants1933 healthcare workers, including nurses, physicians, allied health professionals, environmental services staff, researchers and administrative staff.Primary outcome measuresThe primary outcome was COVID-19 vaccine receipt during the initial roll-out of the vaccine among healthcare workers.ResultsAmong 1933 healthcare workers who had been offered the vaccine, 81% had received the vaccine at the time of the survey. Receipt was lower among black (58%; OR: 0.14, 95% CI 0.1 to 0.2) compared with white (91%) healthcare workers, and higher among non-Hispanic (84%) compared with Hispanic (69%; OR: 2.37, 95% CI 1.8 to 3.1) healthcare workers. Among healthcare workers with concerns about COVID-19 vaccine safety, 65% received the vaccine. Among healthcare workers who agreed with the statement that the vaccine is important to protect family members, 86% were vaccinated. Of those who disagreed, 25% received the vaccine (pConclusionsOur data demonstrated high overall receipt among healthcare workers. Even among healthcare workers with concerns about COVID-19 vaccine safety, side effects or being experimented on, over 50% received the vaccine. Attitudes around the importance of COVID-19 vaccination to protect others played a large role in healthcare workers’ decisions to vaccinate. We observed striking inequities in COVID-19 vaccine receipt, particularly affecting black and Hispanic workers. Further research is urgently needed to address issues related to vaccine equity and uptake in the context of systemic racism and barriers to care. This is particularly important given the influence healthcare workers have in vaccine decision-making conversations in their communities.

Responses of Seropositive Persons to a Single SARS-CoV-2 Vaccine Some persons who have recovered from Covid-19 have antibodies to the spike protein of SARS-CoV-2. In 43 such persons who had receive...

Climate change is an urgent challenge amplified by socioeconomic factors that demands thoughtful public health responses from OEM professionals. This guidance statement from the American College of Occupational and Environmental Medicine focuses on the different strategies that these health professionals can implement to protect workers from health impacts associated with climate change hazards, foster workplace resilience in the face of rapidly changing environments, and take the necessary steps to mitigate the effects of global climate change.

Risk factors for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are not well-defined in resident physicians and fellows (trainees). We aimed to identify sociodemographic, occupational and community factors associated with SARS-CoV-2 infection among trainees during the first wave of the coronavirus disease 2019 (COVID-19) pandemic in New York City (NYC). In this retrospective cohort study, we administered an electronic survey between June 26 and August 31, 2020 to trainees at the Mount Sinai Health System in NYC to assess risk factors for SARS-CoV-2 infection between February 1 and June 30, 2020. We used Bayesian generalized linear mixed effect regression and structural equation models to examine associations. SAR-CoV-2 infection was determined by self-reported IgG antibody and reverse transcriptase-polymerase chain reaction results and confirmed with laboratory results. Among 2354 trainees invited to participate, 328 (14%) completed the survey and reported test results. The cumulative incidence of SARS-CoV-2 infection was 20.1%. Assignment to medical-surgical units (odds ratio [OR], 2.51; 95% CI, 1.18-5.34), and training in emergency medicine, critical care and anesthesiology (OR, 2.93; 95% CI, 1.24-6.92) were independently associated with infection. Deployment to care for unfamiliar patient populations was protective against infection (OR, 0.16; 95% CI, 0.03-0.73). Community factors were not significantly associated with infection after adjustment for occupational factors. Our findings may inform tailored infection prevention strategies for trainees responding to the COVID-19 pandemic.

ObjectivesTo examine factors associated with COVID-19 vaccine receipt among healthcare workers, including healthcare worker job type, race, and gender, as well as the role of vaccine confidence in decisions to vaccinate, and to better understand specific concerns related to COVID-19 vaccination among healthcare workers.DesignCross-sectional anonymous survey among front-line, support service, and administrative healthcare workers.SettingTwo large integrated healthcare systems (one private and one public) in New York City during the initial rollout of the COVID-19 vaccine among healthcare workers.Participants1,933 healthcare workers, including nurses, physicians, allied health professionals, environmental services staff, researchers, and administrative staff.Main Outcome MeasuresThe primary outcome was COVID-19 vaccine receipt during the initial rollout of the vaccine among healthcare workers.ResultsAmong 1,933 healthcare workers who had been offered the vaccine, 81% had received the vaccine at the time of the survey. Receipt was lower among Black (58%) compared with White (91%) healthcare workers; and lower among Hispanic (69%) compared with non-Hispanic (84%) healthcare workers. Among healthcare workers with concerns about COVID-19 vaccine safety, 65% received the vaccine. Among healthcare workers who agreed with the statement that the vaccine is important to protect family members, 86% were vaccinated. Of those who disagreed, 25% received the vaccine. Across all participants, 27% expressed concern about being experimented on with the COVID-19 vaccine. In a multivariable analysis, concern about being experimented on with the COVID-19 vaccine, concerns about COVID-19 vaccine safety, lack of influenza vaccine receipt, disagreeing that COVID-19 vaccination is important to protect family members, and Black race were independently associated with COVID-19 vaccine non-receipt. Over 70% of all healthcare workers responded that they had been approached for vaccine advice multiple times by family, community members, and patients.ConclusionsOur data demonstrated high overall receipt among healthcare workers. Even among healthcare workers with concerns about COVID-19 vaccine safety, side effects, or being experimented on, over 50% received the vaccine. Attitudes around the importance of COVID-19 vaccination to protect others played a large role in healthcare workers’ decisions to vaccinate. We observed striking inequities in COVID-19 vaccine receipt, particularly affecting Black and Hispanic workers. Further research is urgently needed in developing strategies with healthcare workers to address issues related to vaccine equity and uptake in the context of systemic racism and barriers to care. This is particularly important given the influence healthcare workers have in vaccine decision-making conversations in their communities.SUMMARY BOXESWhat is already known?High uptake of effective COVID-19 vaccines among healthcare workers is critical to pandemic response.In studies of potential COVID-19 vaccine acceptance prior to COVID-19 vaccine availability, people who identified as Black were less likely to indicate they would accept the vaccine.Understanding reasons why some healthcare workers chose not to get the COVID-19 vaccine will help us develop interventions to improve COVID-19 vaccine confidence among healthcare workers and in their communities.What this study addsWe demonstrate high receipt of COVID-19 vaccines in the initial rollout among healthcare workers.Attitudes around the importance of COVID-19 vaccination to protect others played a large role in healthcare workers’ decisions to vaccinate.We observed substantially lower rates of receipt among Black and Hispanic healthcare workers, independent of differences in vaccine-related beliefs. A quarter of healthcare workers expressed concerns about being experimented on. These results suggest systemic racism may be a critical barrier to equitable vaccination.Our results highlight that healthcare workers of all types, including those with non-patient-facing roles, play an important role as sources of COVID-19 vaccine information in their communities.

Background Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. Objective We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. Methods Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily. Results Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19–related symptom compared to all other symptom-free days (P=.01). Conclusions Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19–related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.

BACKGROUND Changes in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with infection and observed prior to its clinical identification. OBJECTIVE We performed an evaluation of HRV collected by a wearable device to identify and predict COVID-19 and its related symptoms. METHODS Health care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study app, which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study, measuring HRV throughout the follow-up period. Surveys assessing infection and symptom-related questions were obtained daily. RESULTS Using a mixed-effect cosinor model, the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), an HRV metric, differed between subjects with and without COVID-19 (P=.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (P=.01). Significant changes in the mean and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19–related symptom compared to all other symptom-free days (P=.01). CONCLUSIONS Longitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can predict the diagnosis of COVID-19 and identify COVID-19–related symptoms. Prior to the diagnosis of COVID-19 by nasal swab polymerase chain reaction testing, significant changes in HRV were observed, demonstrating the predictive ability of this metric to identify COVID-19 infection.

BackgroundChanges in autonomic nervous system function, characterized by heart rate variability (HRV), have been associated with and observed prior to the clinical identification of infection. We performed an evaluation of this metric collected by wearable devices, to identify and predict Coronavirus disease 2019 (COVID-19) and its related symptoms.MethodsHealth care workers in the Mount Sinai Health System were prospectively followed in an ongoing observational study using the custom Warrior Watch Study App which was downloaded to their smartphones. Participants wore an Apple Watch for the duration of the study measuring HRV throughout the follow up period. Survey’s assessing infection and symptom related questions were obtained daily.FindingsUsing a mixed-effect COSINOR model the mean amplitude of the circadian pattern of the standard deviation of the interbeat interval of normal sinus beats (SDNN), a HRV metric, differed between subjects with and without COVID-19 (p=0.006). The mean amplitude of this circadian pattern differed between individuals during the 7 days before and the 7 days after a COVID-19 diagnosis compared to this metric during uninfected time periods (p=0.01). Significant changes in the mean MESOR and amplitude of the circadian pattern of the SDNN was observed between the first day of reporting a COVID-19 related symptom compared to all other symptom free days (p=0.01).InterpretationLongitudinally collected HRV metrics from a commonly worn commercial wearable device (Apple Watch) can identify the diagnosis of COVID-19 and COVID-19 related symptoms. Prior to the diagnosis of COVID-19 by nasal PCR, significant changes in HRV were observed demonstrating its predictive ability to identify COVID-19 infection.FundingSupport was provided by the Ehrenkranz Lab For Human Resilience, the BioMedical Engineering and Imaging Institute, The Hasso Plattner Institute for Digital Health at Mount Sinai, The Mount Sinai Clinical Intelligence Center and The Dr. Henry D. Janowitz Division of Gastroenterology.

Workers are uniquely susceptible to the health hazards imposed by environmental changes. Occupational and environmental medicine (OEM) providers are at the forefront of emerging health issues pertaining to working populations including climate change, and must be prepared to recognize, respond to, and mitigate climate change-related health effects in workers. This guidance document from the American College of Occupational and Environmental Medicine focuses on North American workers health effects that may occur as a result of climate change and describes the responsibilities of the OEM provider in responding to these health challenges.

Welding fume exposure has been associated with structural brain changes and a wide variety of clinical and sub-clinical outcomes including cognitive, behavioral and motor abnormalities. Respirator use has been shown to decrease exposure to welding fumes; however, the associations between respirator use and health outcomes, particularly neurologic health, have been understudied. In this preliminary study, we used diffusion tensor imaging (DTI) to investigate the effectiveness of respirator use in protecting workers’ white matter (WM) from the harmful effects related to welding fume exposure. Fractional anisotropy (FA), a common DTI measurement of water diffusion properties, was used as a marker of WM microstructure integrity. We hypothesized that FA in brain regions involved in motor and neurocognitive functions would differ between welders reporting respirator use compared to those not using a respirator. We enrolled a pilot cohort of 19 welders from labor unions in the New York City area. All welders completed questionnaires to assess welding history and occupational health. All completed a DTI acquisition on a 3 T Siemens scanner. Partial least squares discriminant analysis (PLS-DA), a bioinformatic analytical strategy, was used to model the divergence of WM microstructures in 48 regions defined by the ICBM-DTI-81 atlas between respirator users compared to non-users. This yielded an effective discrimination of respirator users from non-users, with the uncinate fasciculus, the cerebellar peduncle and the superior longitudinal fasciculus contributing most to the discrimination of these groups. These white matter tracts are involved in widespread motor and cognitive functions. To our knowledge, this study is the first to suggest a protective effect of respirator on WM microstructure, indicating that the lack of respirator may present unsafe working conditions for welders. These preliminary findings may inform a larger, longitudinal intervention study that would be more appropriate to investigate the potential protective effect of respirator usage on brain white matter in welders.

The institution of specific Occupational Health and Safety (OHS) training programs open to international trainees from developing countries in some European, American and Asian universities is now a well-established reality. Courses and seminars that focus particularly on this subject, widely varying in approach and duration, have been held for years at these universities; these academic institutions have combined their potential to attract students from developing countries with the scheduling of interesting lectures and training activities, depending on the availability of funds sufficient to cover travel and lodging costs. Interdisciplinarity is the key to the entire program and is its main strength, as the trainees have the opportunity to condense the technical notions and methodological aspects of different disciplines (occupational health, industrial hygiene, safety management, ergonomics) in one course. We firmly believe that these programs are a precious instrument for the training of occupational health professionals from low-income countries, as they are able to address their choices correctly, hopefully achieving the goal of reducing the human costs of development.

BackgroundAcute and chronic low back pain (LBP) are different conditions with different treatments. However, they are coded in electronic health records with the same ICD-10 code (M54.5) and can be differentiated only by retrospective chart reviews. This prevents efficient definition of data-driven guidelines for billing and therapy recommendations, such as return-to-work options.ObjectiveTo solve this issue, we evaluate the feasibility of automatically distinguishing acute LBP episodes by analyzing free text clinical notes.MethodsWe used a dataset of 17,409 clinical notes from different primary care practices; of these, 891 documents were manually annotated as “acute LBP” and 2,973 were generally associated with LBP via the recorded ICD-10 code. We compared different supervised and unsupervised strategies for automated identification: keyword search; topic modeling; logistic regression with bag-of-n-grams and manual features; and deep learning (ConvNet). We trained the supervised models using either manual annotations or ICD-10 codes as positive labels.ResultsConvNet trained using manual annotations obtained the best results with an AUC-ROC of 0.97 and F-score of 0.69. ConvNet’s results were also robust to reduction of the number of manually annotated documents. In the absence of manual annotations, topic models performed better than methods trained using ICD-10 codes, which were unsatisfactory for identifying LBP acuity.ConclusionsThis study uses clinical notes to delineate a potential path toward systematic learning of therapeutic strategies, billing guidelines, and management options for acute LBP at the point of care.

BACKGROUND Acute and chronic low back pain (LBP) are different conditions with different treatments. However, they are coded in electronic health records with the same International Classification of Diseases, 10th revision (ICD-10) code (M54.5) and can be differentiated only by retrospective chart reviews. This prevents an efficient definition of data-driven guidelines for billing and therapy recommendations, such as return-to-work options. OBJECTIVE The objective of this study was to evaluate the feasibility of automatically distinguishing acute LBP episodes by analyzing free-text clinical notes. METHODS We used a dataset of 17,409 clinical notes from different primary care practices; of these, 891 documents were manually annotated as acute LBP and 2973 were generally associated with LBP via the recorded ICD-10 code. We compared different supervised and unsupervised strategies for automated identification: keyword search, topic modeling, logistic regression with bag of n-grams and manual features, and deep learning (a convolutional neural network-based architecture [ConvNet]). We trained the supervised models using either manual annotations or ICD-10 codes as positive labels. RESULTS ConvNet trained using manual annotations obtained the best results with an area under the receiver operating characteristic curve of 0.98 and an F score of 0.70. ConvNet’s results were also robust to reduction of the number of manually annotated documents. In the absence of manual annotations, topic models performed better than methods trained using ICD-10 codes, which were unsatisfactory for identifying LBP acuity. CONCLUSIONS This study uses clinical notes to delineate a potential path toward systematic learning of therapeutic strategies, billing guidelines, and management options for acute LBP at the point of care.

BackgroundAcute and chronic low back pain (LBP) are different conditions with different treatments. However, they are coded in electronic health records with the same International Classification of Diseases, 10th revision (ICD-10) code (M54.5) and can be differentiated only by retrospective chart reviews. This prevents an efficient definition of data-driven guidelines for billing and therapy recommendations, such as return-to-work options.ObjectiveThe objective of this study was to evaluate the feasibility of automatically distinguishing acute LBP episodes by analyzing free-text clinical notes.MethodsWe used a dataset of 17,409 clinical notes from different primary care practices; of these, 891 documents were manually annotated as acute LBP and 2973 were generally associated with LBP via the recorded ICD-10 code. We compared different supervised and unsupervised strategies for automated identification: keyword search, topic modeling, logistic regression with bag of n-grams and manual features, and deep learning (a convolutional neural network-based architecture [ConvNet]). We trained the supervised models using either manual annotations or ICD-10 codes as positive labels.ResultsConvNet trained using manual annotations obtained the best results with an area under the receiver operating characteristic curve of 0.98 and an F score of 0.70. ConvNet’s results were also robust to reduction of the number of manually annotated documents. In the absence of manual annotations, topic models performed better than methods trained using ICD-10 codes, which were unsatisfactory for identifying LBP acuity.ConclusionsThis study uses clinical notes to delineate a potential path toward systematic learning of therapeutic strategies, billing guidelines, and management options for acute LBP at the point of care.

In order to explore the correlation between physical activity, fitness, and injury among police officers, a cross section of active-duty members of the Minneapolis Police Department were surveyed about their level of fitness, physical activity, and prevalence of injury and chronic pain within the past year. In the study, officers with the highest self-reported fitness levels were less likely to experience sprains (OR 0.27, 95% CI 0.08-0.88), back pain (OR 0.48, 95% CI 0.09-0.88), and chronic pain (OR 0.21, 95% CI 0.06-0.73) than those who considered themselves less fit. Officers who were the most physically active were about a third as likely to report back pain (OR 0.37, 95% CI 0.10-0.73) and less than half as likely to report chronic pain (OR 0.42, 95% CI 0.19-0.91) as those who engaged in less activity. And officers with a BMI greater than 35 were 3 times more likely to report back pain (OR 3.36, 95% CI 1.17-9.66) than those whose BMI fell in the normal range (18-25). Thus, officers who engage in higher levels of physical activity and are more physically fit have a lower prevalence of musculoskeletal injuries and chronic pain.

 Tuberculosis (TB) screening of healthcare workers (HCWs) is an important component of hospital infection control programs. Many institutions have replaced tuberculin skin tests (TSTs) with interferon-release gamma assays (IGRAs).  Challenges of IGRA tests include daily fluctuations of IGRA values, serial testing variability, higher conversion rates of IGRAs than TSTs, and indeterminate IGRA results. In addition, reversions of positive QuantiFERON® TB test (QFT) with low cut-off values (0.35 IU/mL to 1 IU/mL) to negative have also raised concerns.  Additional data is needed for IGRA use in HCWs.  An Employee Health algorithm was created prior to implementation of QFT testing at our institution (Figure 1). Background 1The Ohio State University College of Medicine, Columbus, Ohio.