High-Fidelity, Indigenously Prepared, Low-Cost Moulage as a Valid Simulation Tool to Improve Trauma Education.

Background Simulation-based trauma education facilitates repeated practice in a controlled and safer environment for the learner without any risk to the patient's well-being. Moulage contributes to the perception of reality during training using standardized patients. However, the high cost of commercial moulage items is often prohibitive for regular use. This study aimed to assess the effectiveness of indigenously prepared, low-cost moulage as a valid simulation tool to improve trauma education, explore possible replacements of commercial moulage products, and determine their merits and demerits. Methodology Readily available economic items were used to make low-cost moulage on the simulated patients to replicate trauma victims. A cross-sectional design used a pre-validated Modified Moulage Authenticity Rating Scale to collect data from 61 participants of Advanced Trauma Life Support and Advanced Trauma Care for Nurses courses to analyze the effectiveness and fidelity of moulage. Results In total, 54 (89%) participants scored the low-cost moulage to provide high fidelity effectively. The majority of respondents graded the authenticity of moulage as good. Overall, 46 (75%) participants felt moulage injuries were quite realistic. All agreed that the moulage-based simulation offered a good teaching-learning alternative to assess and manage trauma victims. Further, 45 (73%) participants felt they were in an actual clinical situation, and 58 (95%) stated it could help them in their clinical practice. Conclusions Indigenously prepared, low-cost moulage is a feasible and cost-effective means to enhance fidelity in simulation-based trauma education. It can also be a possible replacement for commercial moulage. Further research is needed to rigorously evaluate the effectiveness of indigenously prepared, cost-effective moulage in trauma education to enhance patient care outcomes. This technique can also be easily translated into other simulation-based medical education domains.

Effectiveness of Large-Scale Preparedness Training on Electrocardiogram for Medical, Surgical, and Pre-clinical Doctors: A Need-Based Initiative for COVID-19 Patient Care.

INTRODUCTION: One of the competencies expected of all doctors posted in coronavirus disease 2019 (COVID-19) wards, is ECG rhythm identification, interpretation, and intervention for immediate management of patients. This study was undertaken to evaluate the effectiveness of the ECG training module as a component of preparedness training to combat COVID-19. METHODOLOGY: This was a cross-sectional study conducted during training on ECG rhythm identification, interpretation, its management in COVID-19 patients. Study participants included faculty, senior residents, junior residents, and interns of medical, surgical, and paraclinical disciplines. The training session included one hour of didactic lecture and one and half hours of interactive session during which case scenarios were discussed. An objective assessment was conducted through pre-test and post-test. Mean of pre and post-test scores were compared using paired t-test for evaluating statistical significance. Feedback was also taken from participants. RESULTS: Out of the 800 participants who gave consent, only 682 who completed both pre and post-test were included in the final analysis. Mean pre-test and post-test scores were 9.29/15 (61.9%) and 11.63/15 (77.5%), respectively, with a mean improvement of +2.34/15 (+15.6%). Of the participants, 38.6% obtained low scores in pre-test and 82% of respondents agreed that knowledge and skills gained from training would be useful in providing patient care. CONCLUSION:  Low baseline knowledge on ECG highlights the need for re-training doctors posted in COVID-19 care on cardiac rhythm identification and interpretation. Interactive training is effective in improving ECG interpretative skills among doctors across disciplines and is the appropriate method to retrain/reskill, especially for large-scale capacity building.

Rapid Workforce Development to Combat the COVID-19 Pandemic: Experience From a Tertiary Health Care Centre in North India.

Introduction During a large-scale disease outbreak, one needs to respond to the situation quickly towards capacity building, by identifying areas that require training and planning a workable strategy and implementing it. There are limited studies focused on fast-track workforce creation under challenging circumstances that demand mandatory social distancing and discouragement of gatherings. This study was conducted to analyze the planning process and implementation of fast-track training during the Coronavirus disease (COVID-19) pandemic, and evaluate its effectiveness in building a rapid, skilled, and massive workforce. Methods A cross-sectional study was conducted to evaluate rapid preparedness training delivered from March to June 2020, based on documents and data regarding the process, planning, and implementation for large-scale capacity building. Pre-test and post-test scores were compared to assess the effectiveness of training. The number of personnel trained was evaluated to determine the efficiency of the training program. Data on COVID-19 among health care workers (HCWs) were analyzed. Results The Advanced Center of Continuous Professional Development acted as the central facility, quickly responding to the situation. A total of 327 training sessions were conducted, including 76 online sessions with 153 instructors. The capacity-building of 2,706 individuals (913 clinicians and 1,793 nurses, paramedics, and non-medical staff) was achieved through multiple parallel sessions on general precautionary measures and specialized skills within four months. The rate of hospital staff infected with COVID-19 was found to be 0.01% over five months. Conclusions A fast-track, efficient, large-scale workforce can be created through a central facility even under challenging circumstances which restrict gatherings and require physical distancing. A training action plan for disease outbreaks would be a useful resource to tackle such medical emergencies affecting substantial populations in future.

Novel text analytics approach to identify relevant literature for human health risk assessments: A pilot study with health effects of in utero exposures.

BACKGROUND: Systematic reviews involve mining literature databases to identify relevant studies. Identifying potentially relevant studies can be informed by computational tools comparing text similarity between candidate studies and selected key (i.e., seed) references. Challenge Using computational approaches to identify relevant studies for risk assessments is challenging, as these assessments examine multiple chemical effects across lifestages (e.g., human health risk assessments) or specific effects of multiple chemicals (e.g., cumulative risk). The broad scope of potentially relevant literature can make selection of seed references difficult. Approach We developed a generalized computational scoping strategy to identify human health relevant studies for multiple chemicals and multiple effects. We used semi-supervised machine learning to prioritize studies to review manually with training data derived from references cited in the hazard identification sections of several US EPA Integrated Risk Information System (IRIS) assessments. These generic training data or seed studies were clustered with the unclassified corpus to group studies based on text similarity. Clusters containing a high proportion of seed studies were prioritized for manual review. Chemical names were removed from seed studies prior to clustering resulting in a generic, chemical-independent method for identifying potentially human health relevant studies. We developed a case study that focused on identifying the array of chemicals that have been studied with respect to in utero exposure to test the recall of this novel literature searching strategy. We then evaluated the general strategy of using generic, chemical-independent training data with two previous IRIS assessments by comparing studies predicted relevant to those used in the assessments (i.e., total relevant). Outcome A keyword search designed to retrieve studies that examined the in utero effects of environmental chemicals identified over 54,000 candidate references. Clustering algorithms were applied using 1456 studies from multiple IRIS assessments with chemical names removed as training data or seeds (i.e., semi-supervised learning). Using a six-algorithm ensemble approach 2602 articles, or approximately 5% of candidate references, were "voted" relevant by four or more clustering algorithms and manual review confirmed nearly 50% of these studies were relevant. Further evaluations on two IRIS assessments, using a nine-algorithm ensemble approach and a set of generic, chemical-independent, externally-derived seed studies correctly identified 77-83% of hazard identification studies published in the assessments and eliminated the need to manually screen more than 75% of search results on average. Limitations The chemical-independent approach used to build the training literature set provides a broad and unbiased picture across a variety of endpoints and environmental exposures but does not systematically identify all available data. Variance between actual and predicted relevant studies will be greater because of the external and non-random origin of seed study selection. This approach depends on access to readily available generic training data that can be used to locate relevant references in an unclassified corpus. Impact A generic approach to identifying human health relevant studies could be an important first step in literature evaluation for risk assessments. This initial scoping approach could facilitate faster literature evaluation by focusing reviewer efforts, as well as potentially minimize reviewer bias in selection of key studies. Using externally-derived training data has applicability particularly for databases with very low search precision where identifying training data may be cost-prohibitive.

Salivary Interleukin-6 Levels among Polycystic Ovary Syndrome Patients with and Without Chronic Periodontitis - A Comparative Study.

BACKGROUND: Periodontitis is associated with various systemic diseases one of which is poly cystic ovarian syndrome (PCOS). PCOS is a genetically complex endocrinopathy of uncertain etiology affecting women of the reproductive age group which results in the most common cause of anovulatory infertility, menstrual dysfunction, and hirsutism. PCOS has a close association with cardiometabolic risk profile, insulin resistance (IR), hyperinsulinemia, central obesity, dyslipidemia, and increasing the prevalence of cardiovascular risk factors. The common pathway is the chronic low-grade inflammation which is constituted by pro-inflammatory cytokine interleukin (IL)-6. AIM: The aim of the study was to compare salivary IL-6 levels among polycystic ovary syndrome (PCOS) patients with and without chronic periodontitis. MATERIALS AND METHODS: Newly diagnosed PCOS patients were selected for the study, and the periodontal parameters were recorded. Group A consists of 42 patients of PCOS with periodontitis and Group B consists of 42 patients of PCOS without periodontitis. Salivary levels of IL-6 were compared between the two groups and were assessed by enzyme-linked immunosorbent assay kit (bioassay). RESULTS: The mean pocket depth in Group A was 4.23 ± 0.134 and that of Group B was 1.30 ± 0.06. The mean bleeding on probing in Group A was 1.40 ± 0.40 and in Group B it was 0.91 ± 0.18. The mean clinical attachment level in Group A was 4.87 ± 0.124 and that of Group B was 1.30 ± 0.06. The mean difference in clinical parameters was statistically significant between the groups (P ≤ 0.001). IL-6 level in group A is 102.59 ± 18.2 and in Group B it was 51.3 ± 25.3. CONCLUSION: Salivary IL-6 levels show a double-fold increase in PCOS with periodontitis than in PCOS without periodontitis. This study reflects the importance of periodontal health and the prevention of periodontal disease so as to minimize IR in PCOS patients with periodontitis.

Clinical profile and outcome of pigment-induced nephropathy.

BACKGROUND: Pigment nephropathy represents one of the most severe complications of rhabdomyolysis or hemolysis. METHODS: We performed a retrospective observational study to analyze the etiology, clinical manifestation, laboratory profile and outcome in patients with biopsy-proven pigment-induced nephropathy between January 2011 and December 2016. History, clinical examination findings, laboratory investigations and outcome were recorded. RESULTS: A total of 46 patients were included with mean follow-up of 14 ± 5.5 months. Mean age was 40.15 ± 12.3 years, 65% were males (male:female, 1.8:1) and ∼37 (80.4%) had oliguria. Mean serum creatinine at presentation and peak creatinine were 7.5 ± 2.2 and 12.1 ± 4.3 mg/dL, respectively. Evidence of rhabdomyolysis was noted in 26 patients (64%) and hemolysis in 20 patients (36%). Etiology of rhabdomyolysis include snake envenomation (10 patients), seizures (7), strenuous exercise (5), wasp sting (2) and rifampicin induced (2). The causes of hemolysis include rifampicin induced (7 patients), sepsis (5), malaria (3), mismatched blood transfusion/transfusion reaction (3) and paroxysmal nocturnal hemoglobinuria (2). On renal biopsy, two patients had acute interstitial nephritis and two had immunoglobulin A deposits in addition to pigment nephropathy. All except one (97.8%) required hemodialysis (HD) during hospital stay and mean number of HD sessions was 9 ± 2. A total of three patients with sepsis/disseminated intravascular coagulation died, all had associated hemolysis. On statistical analysis, there was no difference between AKI due to rhabdomyolysis and hemolysis except for high creatine phosphokinase in patients with rhabdomyolysis and Lactate dehydrogenase level in patients with hemolysis. At mean follow-up, five patients (12%) progressed to chronic kidney disease (CKD). CONCLUSIONS: Pigment nephropathy due to rhabdomyolysis and hemolysis is an important cause of renal failure requiring HD. The prognosis was relatively good and depends on the etiology; however, long-term studies and follow-up are needed to assess the true incidence of CKD due to pigment nephropathy.

Social Listening: A Content Analysis of E-Cigarette Discussions on Twitter.

BACKGROUND: Electronic cigarette (e-cigarette) use has increased in the United States, leading to active debate in the public health sphere regarding e-cigarette use and regulation. To better understand trends in e-cigarette attitudes and behaviors, public health and communication professionals can turn to the dialogue taking place on popular social media platforms such as Twitter. OBJECTIVE: The objective of this study was to conduct a content analysis to identify key conversation trends and patterns over time using historical Twitter data. METHODS: A 5-category content analysis was conducted on a random sample of tweets chosen from all publicly available tweets sent between May 1, 2013, and April 30, 2014, that matched strategic keywords related to e-cigarettes. Relevant tweets were isolated from the random sample of approximately 10,000 tweets and classified according to sentiment, user description, genre, and theme. Descriptive analyses including univariate and bivariate associations, as well as correlation analyses were performed on all categories in order to identify patterns and trends. RESULTS: The analysis revealed an increase in e-cigarette-related tweets from May 2013 through April 2014, with tweets generally being positive; 71% of the sample tweets were classified as having a positive sentiment. The top two user categories were everyday people (65%) and individuals who are part of the e-cigarette community movement (16%). These two user groups were responsible for a majority of informational (79%) and news tweets (75%), compared to reputable news sources and foundations or organizations, which combined provided 5% of informational tweets and 12% of news tweets. Personal opinion (28%), marketing (21%), and first person e-cigarette use or intent (20%) were the three most common genres of tweets, which tended to have a positive sentiment. Marketing was the most common theme (26%), and policy and government was the second most common theme (20%), with 86% of these tweets coming from everyday people and the e-cigarette community movement combined, compared to 5% of policy and government tweets coming from government, reputable news sources, and foundations or organizations combined. CONCLUSIONS: Everyday people and the e-cigarette community are dominant forces across several genres and themes, warranting continued monitoring to understand trends and their implications regarding public opinion, e-cigarette use, and smoking cessation. Analyzing social media trends is a meaningful way to inform public health practitioners of current sentiments regarding e-cigarettes, and this study contributes a replicable methodology.

Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning.

BACKGROUND: Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public's knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. OBJECTIVE: Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. METHODS: Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. RESULTS: Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. CONCLUSIONS: Social media outlets like Twitter can uncover real-time snapshots of personal sentiment, knowledge, attitudes, and behavior that are not as accessible, at this scale, through any other offline platform. Using the vast data available through social media presents an opportunity for social science and public health methodologies to utilize computational methodologies to enhance and extend research and practice. This study was successful in automating a complex five-category manual content analysis of e-cigarette-related content on Twitter using machine learning techniques. The study details machine learning model specifications that provided the best accuracy for data related to e-cigarettes, as well as a replicable methodology to allow extension of these methods to additional topics.

Human health risk implications of multiple sources of faecal indicator bacteria in a recreational waterbody.

We simulate the influence of multiple sources of enterococci (ENT) as faecal indicator bacteria (FIB) in recreational water bodies on potential human health risk by considering waters impacted by human and animal sources, human and non-pathogenic sources, and animal and non-pathogenic sources. We illustrate that risks vary with the proportion of culturable ENT in water bodies derived from these sources and estimate corresponding ENT densities that yield the same level of health protection that the recreational water quality criteria in the United States seeks (benchmark risk). The benchmark risk is based on epidemiological studies conducted in water bodies predominantly impacted by human faecal sources. The key result is that the risks from mixed sources are driven predominantly by the proportion of the contamination source with the greatest ability to cause human infection (potency), not necessarily the greatest source(s) of FIB. Predicted risks from exposures to mixtures comprised of approximately 30% ENT from human sources were up to 50% lower than the risks expected from purely human sources when contamination is recent and ENT levels are at the current water quality criteria levels (35 CFU 100 mL(-1)). For human/non-pathogenic, human/gull, human/pig, and human/chicken faecal mixtures with relatively low human contribution, the predicted culturable enterococci densities that correspond to the benchmark risk are substantially greater than the current water quality criteria values. These findings are important because they highlight the potential applicability of site specific water quality criteria for waters that are predominantly un-impacted by human sources.

Application of multicriteria decision analysis in environmental decision making.

Decision making in environmental projects can be complex and seemingly intractable, principally because of the inherent trade-offs between sociopolitical, environmental, ecological, and economic factors. The selection of appropriate remedial and abatement strategies for contaminated sites, land use planning, and regulatory processes often involves multiple additional criteria such as the distribution of costs and benefits, environmental impacts for different populations, safety, ecological risk, or human values. Some of these criteria cannot be easily condensed into a monetary value, partly because environmental concerns often involve ethical and moral principles that may not be related to any economic use or value. Furthermore, even if it were possible to aggregate multiple criteria rankings into a common unit, this approach would not always be desirable because the ability to track conflicting stakeholder preferences may be lost in the process. Consequently, selecting from among many different alternatives often involves making trade-offs that fail to satisfy 1 or more stakeholder groups. Nevertheless, considerable research in the area of multicriteria decision analysis (MCDA) has made available practical methods for applying scientific decision theoretical approaches to complex multicriteria problems. This paper presents a review of the available literature and provides recommendations for applying MCDA techniques in environmental projects. A generalized framework for decision analysis is proposed to highlight the fundamental ingredients for more structured and tractable environmental decision making.