State of the Science in Dried Blood Spots.

BACKGROUND: Advancements in the quality and availability of highly sensitive analytical instrumentation and methodologies have led to increased interest in the use of microsamples. Among microsamples, dried blood spots (DBS) are the most well-known. Although there have been a variety of review papers published on DBS, there has been no attempt at describing the full range of analytes measurable in DBS, or any systematic approach published for characterizing the strengths and weaknesses associated with adoption of DBS analyses. CONTENT: A scoping review of reviews methodology was used for characterizing the state of the science in DBS. We identified 2018 analytes measured in DBS and found every common analytic method applied to traditional liquid samples had been applied to DBS samples. Analytes covered a broad range of biomarkers that included genes, transcripts, proteins, and metabolites. Strengths of DBS enable its application in most clinical and laboratory settings, and the removal of phlebotomy and the need for refrigeration have expanded biosampling to hard-to-reach and vulnerable populations. Weaknesses may limit adoption in the near term because DBS is a nontraditional sample often requiring conversion of measurements to plasma or serum values. Opportunities presented by novel methodologies may obviate many of the current limitations, but threats around the ethical use of residual samples must be considered by potential adopters. SUMMARY: DBS provide a wide range of potential applications that extend beyond the reach of traditional samples. Current limitations are serious but not intractable. Technological advancements will likely continue to minimize constraints around DBS adoption.

You may delay, but time will not. Beta cells lost are never found again: a case for timely initiation of basal insulin in type 2 diabetes.

Since its first use just over a century ago, insulin treatment has evolved dramatically, such that the molecules are physiologic in nature, and treatment can now closely resemble the natural hormone response over 24 hours. Newer, longer-acting basal insulin analogs have provided insulin therapies with improved characteristics and, therefore, ease of use, and can readily be incorporated as part of routine treatment for type 2 diabetes (T2D), but evidence suggests that insulin remains underused in people with T2D. We review the barriers to initiation of basal insulin and the education needed to address these barriers, and we provide practical pointers, supported by evidence, for primary care physicians and advanced practice providers to facilitate timely initiation of basal insulin in the people with T2D who will benefit from such treatment.

Type 2 diabetes is a complex disease. It causes increased amounts of sugar in the blood, which can cause damage to the body. Medications are given to people with type 2 diabetes to keep their blood sugar at normal levels. Unfortunately, type 2 diabetes worsens over time, so regular adjustments to medications are needed to keep blood sugar levels controlled.Basal insulin, which is a type of insulin that works over the entire day, is a key treatment for type 2 diabetes. It works best if it is started as soon as other medications (tablets or non-insulin injections) are not working to control blood sugar levels. Unfortunately, delays in starting basal insulin are common. Some healthcare professionals and people with type 2 diabetes believe insulin is difficult to use. False information on insulin is common; for example, some people with diabetes believe that their symptoms are caused by insulin treatment rather than high blood sugar.This review summarizes key information to encourage effective conversations between healthcare professionals and people with type 2 diabetes about starting basal insulin. Proactive, positive, early discussion of the benefits of basal insulin can help to: 1) address concerns, 2) set appropriate, individual treatment targets, and 3) provide practical information and training to help with injecting insulin. This will give people living with type 2 diabetes the knowledge and confidence to take an active part in managing their diabetes and overcome any barriers to using basal insulin.

Exposure to E-Cigarette Marketing and Susceptibility to Future Vaping among Black and Latino Adolescents in the United States.

Evidence suggests an association between exposure to electronic cigarette (e-cigarette) marketing and e-cigarette use (vaping) among adolescents. However, there is limited evidence on exposure to e-cigarette marketing and susceptibility to future vaping, especially among Black and Latino adolescents. This study aimed to examine associations between exposure to e-cigarette marketing and susceptibility to future vaping among Black and Latino adolescents in the United States (US). Participants (N = 362; equal representation between Black and Latino adolescents) completed a baseline assessment (available in English and Spanish) including sociodemographic characteristics (e.g., racial/ethnic group, age, gender, sexual orientation, etc.), exposure to e-cigarette marketing, and susceptibility to future vaping. Exposure to e-cigarette marketing was recoded and organized into two categories (high exposure = 2 to 3; low exposure = 0 to 1). Cochran-Mantel-Haenszel tests were used to evaluate the association between exposure to e-cigarette marketing and susceptibility to future vaping, stratified by racial/ethnic group. Multiple logistic regressions assessed the association between exposure to e-cigarette marketing and susceptibility to future vaping, controlling for gender, sexual orientation, grade, and academic performance within each racial/ethnic group. Black adolescents reported significantly higher frequencies of exposure to e-cigarette marketing (p = 0.005). A significant interaction was found between exposure to e-cigarette marketing and racial/ethnic group (X2 (1) = 6.294, p = 0.012). Among Black adolescents, high exposure to e-cigarette marketing (vs. low exposure) was associated with a higher probability of susceptibility to future vaping (OR: 2.399, 95% CI 1.147-5.021, p = 0.020). For Latino adolescents, exposure to e-cigarette marketing was not associated with susceptibility to future vaping (OR: 0.503, 95% CI 0.245-1.03, p = 0.062). Future studies should evaluate how and where adolescents are exposed to e-cigarette marketing. Prevention efforts must include the implementation of effective counter-marketing campaigns and the reduction of exposure to e-cigarette marketing among Black and Latino adolescents.

Tracking COVID-19 in the United States With Surveillance of Aggregate Cases and Deaths.

Early during the COVID-19 pandemic, the Centers for Disease Control and Prevention (CDC) leveraged an existing surveillance system infrastructure to monitor COVID-19 cases and deaths in the United States. Given the time needed to report individual-level (also called line-level) COVID-19 case and death data containing detailed information from individual case reports, CDC designed and implemented a new aggregate case surveillance system to inform emergency response decisions more efficiently, with timelier indicators of emerging areas of concern. We describe the processes implemented by CDC to operationalize this novel, multifaceted aggregate surveillance system for collecting COVID-19 case and death data to track the spread and impact of the SARS-CoV-2 virus at national, state, and county levels. We also review the processes established to acquire, process, and validate the aggregate number of cases and deaths due to COVID-19 in the United States at the county and jurisdiction levels during the pandemic. These processes include time-saving tools and strategies implemented to collect and validate authoritative COVID-19 case and death data from jurisdictions, such as web scraping to automate data collection and algorithms to identify and correct data anomalies. This topical review highlights the need to prepare for future emergencies, such as novel disease outbreaks, by having an event-agnostic aggregate surveillance system infrastructure in place to supplement line-level case reporting for near-real-time situational awareness and timely data.

What have we learned about the treatment of type 2 diabetes? The evolving paradigms.

Insulin, the first treatment for diabetes, was discovered >90 years ago. Since then, many new types of insulin have become available, including analogs that more closely mimic the characteristics of endogenous insulin. In addition, oral antidiabetes drugs and other types of injectable therapies have been approved for the treatment of patients with type 2 diabetes. As newer treatments are approved for type 2 diabetes, the choice and-paradoxically-the complexity of treatment increases. The potential benefits of all treatment options must be carefully balanced against potential adverse events to truly analyze the overall efficacy, safety, tolerability, and potential long-term effects. The manner in which outcomes are assessed and the methods employed to make such assessments have changed over time. This review will address these issues as they are related to therapies for type 2 diabetes, including insulin, oral antidiabetes drugs, and incretin-based agents.

Applications of Artificial Intelligence and Machine Learning in Disasters and Public Health Emergencies.

Indexed literature (from 2015 to 2020) on artificial intelligence (AI) technologies and machine learning algorithms (ML) pertaining to disasters and public health emergencies were reviewed. Search strategies were developed and conducted for PubMed and Compendex. Articles that met inclusion criteria were filtered iteratively by title followed by abstract review and full text review. Articles were organized to identify novel approaches and breadth of potential AI applications. A total of 1217 articles were initially retrieved by the search. Upon relevant title review, 1003 articles remained. Following abstract screening, 667 articles remained. Full text review for relevance yielded 202 articles. Articles that met inclusion criteria totaled 56 articles. Those identifying specific roles of AI and ML (17 articles) were grouped by topics highlighting utility of AI and ML in disaster and public health emergency contexts. Development and use of AI and ML have increased dramatically over the past few years. This review discusses and highlights potential contextual applications and limitations of AI and ML in disaster and public health emergency scenarios.

Gecko: A time-series model for COVID-19 hospital admission forecasting.

During the COVID-19 pandemic, concerns about hospital capacity in the United States led to a demand for models that forecast COVID-19 hospital admissions. These short-term forecasts were needed to support planning efforts by providing decision-makers with insight about future demands for health care capacity and resources. We present a SARIMA time-series model called Gecko developed for this purpose. We evaluate its historical performance using metrics such as mean absolute error, predictive interval coverage, and weighted interval scores, and compare to alternative hospital admission forecasting models. We find that Gecko outperformed baseline approaches and was among the most accurate models for forecasting hospital admissions at the state and national levels from January-May 2021. This work suggests that simple statistical methods can provide a viable alternative to traditional epidemic models for short-term forecasting.

Implementation of SARS-CoV-2 Monoclonal Antibody Infusion Sites at Three Medical Centers in the United States: Strengths and Challenges Assessment to Inform COVID-19 Pandemic and Future Public Health Emergency Use.

Monoclonal antibody therapeutics to treat coronavirus disease (COVID-19) have been authorized by the US Food and Drug Administration under Emergency Use Authorization (EUA). Many barriers exist when deploying a novel therapeutic during an ongoing pandemic, and it is critical to assess the needs of incorporating monoclonal antibody infusions into pandemic response activities. We examined the monoclonal antibody infusion site process during the COVID-19 pandemic and conducted a descriptive analysis using data from 3 sites at medical centers in the United States supported by the National Disaster Medical System. Monoclonal antibody implementation success factors included engagement with local medical providers, therapy batch preparation, placing the infusion center in proximity to emergency services, and creating procedures resilient to EUA changes. Infusion process challenges included confirming patient severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positivity, strained staff, scheduling, and pharmacy coordination. Infusion sites are effective when integrated into pre-existing pandemic response ecosystems and can be implemented with limited staff and physical resources.

Chainchecker: An application to visualise and explore transmission chains for Ebola virus disease.

2020 saw the continuation of the second largest outbreak of Ebola virus disease (EVD) in history. Determining epidemiological links between cases is a key part of outbreak control. However, due to the large quantity of data and subsequent data entry errors, inconsistencies in potential epidemiological links are difficult to identify. We present chainchecker, an online and offline shiny application which visualises, curates and verifies transmission chain data. The application includes the calculation of exposure windows for individual cases of EVD based on user defined incubation periods and user specified symptom profiles. It has an upload function for viral hemorrhagic fever data and utility for additional entries. This data may then be visualised as a transmission tree with inconsistent links highlighted. Finally, there is utility for cluster analysis and the ability to highlight nosocomial transmission. chainchecker is a R shiny application which has an offline version for use with VHF (viral hemorrhagic fever) databases or linelists. The software is available at https://shiny.dide.imperial.ac.uk/chainchecker which is a web-based application that links to the desktop application available for download and the github repository, https://github.com/imperialebola2018/chainchecker.

Use of Big Data in Disaster Recovery: An Integrative Literature Review.

OBJECTIVE: Disasters of all varieties have been steadily increasing in frequency. Simultaneously, "big data" has seen explosive growth as a tool in business and private industries while opportunities for robust implementation in disaster management remain nascent. To more explicitly ascertain the current status of big data as applied to disaster recovery, we conducted an integrative literature review. METHODS: Eleven databases were searched using iteratively developed keywords to target big data in a disaster recovery context. All studies were dual-screened by title and abstract followed by dual full-text review to determine if they met inclusion criteria. Articles were included if they focused on big data in a disaster recovery setting and were published in the English-language peer-reviewed literature. RESULTS: After removing duplicates, 25,417 articles were originally identified. Following dual title/abstract review and full-text review, 18 studies were included in the final analysis. Among those, 44% were United States-based and 39% focused on hurricane recovery. Qualitative themes emerged surrounding geographic information systems (GIS), social media, and mental health. CONCLUSIONS: Big data is an evolving tool for recovery from disasters. More research, particularly in real-time applied disaster recovery settings, is needed to further expand the knowledge base for future applications.

Staffing and Capacity Planning for SARS-CoV-2 Monoclonal Antibody Infusion Facilities: A Performance Estimation Calculator Based on Discrete-Event Simulations.

Background: The COVID-19 pandemic has significantly stressed healthcare systems. The addition of monoclonal antibody (mAb) infusions, which prevent severe disease and reduce hospitalizations, to the repertoire of COVID-19 countermeasures offers the opportunity to reduce system stress but requires strategic planning and use of novel approaches. Our objective was to develop a web-based decision-support tool to help existing and future mAb infusion facilities make better and more informed staffing and capacity decisions. Materials and Methods: Using real-world observations from three medical centers operating with federal field team support, we developed a discrete-event simulation model and performed simulation experiments to assess performance of mAb infusion sites under different conditions. Results: 162,000 scenarios were evaluated by simulations. Our analyses revealed that it was more effective to add check-in staff than to add additional nurses for middle-to-large size sites with ≥2 infusion nurses; that scheduled appointments performed better than walk-ins when patient load was not high; and that reducing infusion time was particularly impactful when load on resources was only slightly above manageable levels. Discussion: Physical capacity, check-in staff, and infusion time were as important as nurses for mAb sites. Health systems can effectively operate an infusion center under different conditions to provide mAb therapeutics even with relatively low investments in physical resources and staff. Conclusion: Simulations of mAb infusion sites were used to create a capacity planning tool to optimize resource utility and allocation in constrained pandemic conditions, and more efficiently treat COVID-19 patients at existing and future mAb infusion sites.

Real-world Effect of Monoclonal Antibody Treatment in COVID-19 Patients in a Diverse Population in the United States.

BACKGROUND: Monoclonal antibodies (mAbs) against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are a promising treatment for limiting the progression of coronavirus disease 2019 (COVID-19) and decreasing strain on hospitals. Their use, however, remains limited, particularly in disadvantaged populations. METHODS: Electronic health records were reviewed from SARS-CoV-2 patients at a single medical center in the United States that initiated mAb infusions in January 2021 with the support of the US Department of Health and Human Services' National Disaster Medical System. Patients who received mAbs were compared with untreated patients from the time period before mAb availability who met eligibility criteria for mAb treatment. We used logistic regression to measure the effect of mAb treatment on the risk of hospitalization or emergency department (ED) visit within 30 days of laboratory-confirmed COVID-19. RESULTS: Of 598 COVID-19 patients, 270 (45%) received bamlanivimab and 328 (55%) were untreated. Two hundred thirty-one patients (39%) were Hispanic. Among treated patients, 5/270 (1.9%) presented to the ED or required hospitalization within 30 days of a positive SARS-CoV-2 test, compared with 39/328 (12%) untreated patients (P < .001). After adjusting for age, gender, and comorbidities, the risk of ED visit or hospitalization was 82% lower in mAb-treated patients compared with untreated patients (95% CI, 56%-94%). CONCLUSIONS: In this diverse, real-world COVID-19 patient population, mAb treatment significantly decreased the risk of subsequent ED visit or hospitalization. Broader treatment with mAbs, including in disadvantaged patient populations, can decrease the burden on hospitals and should be facilitated in all populations in the United States to ensure health equity.

Are analogue insulins superior to human insulin in clinical practice?

Insulin analogues were designed to provide more physiologic pharmacokinetic and pharmacodynamic properties compared with human insulin. This article examines the literature over a 2-year period, focusing on studies directly comparing analogue and human insulin in controlled clinical trials and large observational studies documenting the introduction of, or change to, analogue insulin in clinical practice. Findings indicate that analogues provide objective benefits that include improved glycemic control, lower risk of hypoglycemia, lower glucose variability, and (for insulin detemir) reduced weight gain. Recent data with analogues also explore their safety and efficacy in special patient groups such as children and adolescents. These data complement increasing evidence that analogues offer improved acceptability and accessibility to people with diabetes.

Management of hypoglycemia in older adults with type 2 diabetes.

Treatment of older adults with type 2 diabetes (T2D) is complex because they represent a heterogeneous group with a broad range of comorbidities, functional abilities, socioeconomic status, and life expectancy. Older adults with T2D are at high risk of recurring hypoglycemia, a condition associated with marked morbidity and mortality, because their counter-regulatory mechanism to hypoglycemia is attenuated, and recurring hypoglycemic episodes can lead to hypoglycemia unawareness. In addition, polypharmacy, a result of multiple chronic comorbidities (including heart disease, stroke, and chronic kidney disease), can increase the risk of severe hypoglycemia, especially when patients are taking sulfonylureas or insulin. Often the signs of hypoglycemia are nonspecific (sweating, dizziness, confusion, visual disturbances) and are mistaken for neurological symptoms or dementia. Consequences of hypoglycemia include acute and long-term cognitive changes, cardiac arrhythmia and myocardial infarction, serious falls, frailty, and death, often resulting in hospitalization, which come at a high economic cost. The American Diabetes Association has recently added three new recommendations regarding hypoglycemia in the elderly, highlighting individualized pharmacotherapy with glucose-lowering agents with a low risk of hypoglycemia and proven cardiovascular safety, avoidance of overtreatment, and simplifying treatment regimens while maintaining HbA1c targets. Thus, glycemic goals can be relaxed in the older population as part of individualized care, and physicians must make treatment decisions that best serve their patients' circumstances. This article highlights the issues faced by older people with T2D, the risk factors for hypoglycemia in this population, and the challenges faced by health care providers regarding glycemic management in this patient group.

Use of Big Data and Information and Communications Technology in Disasters: An Integrative Review.

ABSTRACTNovel approaches to improving disaster response have begun to include the use of big data and information and communication technology (ICT). However, there remains a dearth of literature on the use of these technologies in disasters. We have conducted an integrative literature review on the role of ICT and big data in disasters. Included in the review were 113 studies that met our predetermined inclusion criteria. Most studies used qualitative methods (39.8%, n=45) over mixed methods (31%, n=35) or quantitative methods (29.2%, n=33). Nearly 80% (n=88) covered only the response phase of disasters and only 15% (n=17) of the studies addressed disasters in low- and middle-income countries. The 4 most frequently mentioned tools were geographic information systems, social media, patient information, and disaster modeling. We suggest testing ICT and big data tools more widely, especially outside of high-income countries, as well as in nonresponse phases of disasters (eg, disaster recovery), to increase an understanding of the utility of ICT and big data in disasters. Future studies should also include descriptions of the intended users of the tools, as well as implementation challenges, to assist other disaster response professionals in adapting or creating similar tools. (Disaster Med Public Health Preparedness. 2019;13:353-367).

A community-wide quality improvement initiative to improve hypertension control and reduce disparities.

Initiatives to improve hypertension control within academic medical centers and closed health systems have been extensively studied, but large community-wide quality improvement (QI) initiatives have been both less common and less successful in the United States. The authors examined a community-wide QI initiative across 226 843 patients from 198 practices in nine counties across upstate New York to improve hypertension control and reduce disparities. The QI initiative focused on (a) providing population and practice-level comparative data, (b) community engagement, especially in underserved communities, and (c) practice-level quality improvement assistance, but was not designed to examine causality of specific components. Across the nine counties, hypertension control rates improved from 61.9% in 2011 to 69.5% in 2016. Improvements were greatest among whites (73.7%-81.5%) and more modest among black patients (58.8%-64.7%). The authors noted a considerable improvement in BP within the group of patients with the highest risk (defined as a BP ≥ 160/100) and a decrease in disparities within this group. The quality collaborative identified five key lessons to help guide future community initiatives: (a) anticipate a plateauing of response; (b) distinguish the needs of disparate populations and create subpopulation-specific strategies to address and reduce disparities; (c) recognize the variation across low SES practices; (d) remain open to the refinement of outcome measures; and (e) continually seek best practices and barriers to success. Overall, a large community-wide QI initiative, involving multiple different stakeholders, was associated with improvements in BP control and modest reductions in some targeted disparities.

Chronic Pain in the Emergency Department: A Pilot Interdisciplinary Program Demonstrates Improvements in Disability, Psychosocial Function, and Healthcare Utilization.

Objective: To evaluate the feasibility of an individualized interdisciplinary chronic pain care plan as an intervention to reduce emergency department (ED) visits and improve clinical outcomes among patients who frequented the ED with concerns related to chronic pain. Methods: A prospective cohort design was used in an urban tertiary care hospital. As a pilot program, fourteen patients with chronic pain who frequented the ED (i.e., >12 ED visits within the last year, of which ≥50% were for chronic pain) received a rapid interdisciplinary assessment and individualized care plan that was uploaded to an electronic medical record system (EMR) accessible to the ED and patient's primary care provider. Patients were assessed at baseline and every three months over a 12-month period. Primary outcomes were self-reported pain and function assessed using psychometrically valid scales. Results: Nine patients completed 12-month follow-up. Missing data and attrition were handled using multiple imputation. Patients who received the intervention reported clinically significant improvements in pain, function, ED visits, symptoms of depression, pain catastrophizing, sleep, health-related quality of life, and risk of future aberrant opioid use. Discussion: Individualized care plans uploaded to an EMR may be worth implementing in hospital EDs for high frequency visitors with chronic pain.

Hovering and Swarming Behavior of Male Tabanus calens (Diptera: Tabanidae) in Tennessee and New Jersey, USA.

Hovering and swarming activity of male Tabanus calens L. were documented at distinctive sites above a woodland road clearing in Knox Co., TN (7 d), and above a slow-flowing river in Morris Co., NJ (6 d). Activity was restricted to the period near sunset. Mean activity typically began about 10 min before sunset and continued until about 10 min after sunset, with an average duration of 18.7 min (Tennessee) and 20.8 min (New Jersey). Swarming began earlier and thus lasted longer (55 min) on one cool New Jersey date. Onset and particularly cessation of activity tended to be statistically correlated with sunset time. Most males hovered at heights of 5-8 m in Tennessee, and they hovered in stable locations for a median of only 10 s (maximum 2 min 57 s) before darting away. Hovering in New Jersey was observed from 0.5-8.0 m above the water. The behavior of T. calens might be especially interesting to study in more detail, given its unusual eye morphology (lack of obvious dorsoventral facet size differences found in males of most other Tabanidae).

Assessing the spatial nonstationarity in relationship between local patterns of HIV infections and the covariates in South Africa: A geographically weighted regression analysis.

Beyond the structural drivers such as distance from the road, rural/urban divide or demographic profiles, not much is known about the spatial relationship between HIV and social covariates. Spatial relations between social covariates and HIV infection of persons above 15 years were explored and mapped using geographically weighted regression model using data from a national HIV household survey conducted in 2008 and comprising 23 369 individuals from approximately 1000 enumeration areas that were randomly selected from the national census. The maps show spatial non-stationarity in relationship between local patterns of HIV prevalence and the social covariates across South Africa. The high prevalence districts have very homogeneous population defined by the following characteristics: Black origin, unfavorable sex ratio (high proportion of females), low socioeconomic status, being single or low marriage rates, multiple sexual partners and intergenerational sex. Markedly, intergenerational sex compounds the risk of acquiring HIV infection for females in poor districts. Identification of key social drivers of HIV and how they vary from location to location can help to effectively guide and focus intervention programs to areas of particular need.