Integrating a framework for conducting public health systems research into statewide operations-based exercises to improve emergency preparedness

<p>Abstract</p> <p>Background</p> <p>Due to the uncommon nature of large-scale disasters and emergencies, public health practitioners often turn to simulated emergencies, known as “exercises”, for preparedness assessment and improvement. Under the right conditions, exercises can also be used to conduct original public health systems research. This paper describes the integration of a research framework into a statewide operations-based exercise program in California as a systems-based approach for studying public health emergency preparedness and response.</p> <p>Methods</p> <p>We developed a research framework based on the premise that operations-based exercises conducted by medical and public health agencies can be described using epidemiologic concepts. Using this framework, we conducted a survey of key local and regional medical and health agencies throughout California following the 2010 Statewide Medical and Health Exercise. The survey evaluated: (1) the emergency preparedness capabilities activated and functions performed in response to the emergency scenario, and (2) the major challenges to inter-organizational communications and information management.</p> <p>Results</p> <p>Thirty-five local health departments (LHDs), 24 local emergency medical services (EMS) agencies, 121 hospitals, and 5 Regional Disaster Medical and Health Coordinators/Specialists (RDMHC) responded to our survey, representing 57%, 77%, 26% and 83%, respectively, of target agencies in California. We found two sets of response capabilities were activated during the 2010 Statewide Exercise: a set of core capabilities that were common across all agencies, and a set of agency-specific capabilities that were more common among certain agency types. With respect to one response capability in particular, inter-organizational information sharing, we found that the majority of respondents’ comments were related to the complete or partial failure of communications equipment or systems.</p> <p>Conclusions</p> <p>Using the 2010 Statewide Exercise in California as an opportunity to develop our research framework, we characterized several aspects of the public health and medical system’s response to a standardized emergency scenario. From a research perspective, this study provides a potential new framework for conducting exercise-based research. From a practitioner’s perspective, our results provide a starting point for preparedness professionals’ dialogue about expected and actual organizational roles, responsibilities, and resource capacities within the public health system. Additionally, the identification of specific challenges to inter-organizational communications and information management offer specific areas for intervention.</p

Exploring the utility of the Multidimensional State Boredom Scale.

Background: State boredom–the experience of boredom in the moment – is related to a number of psychosocial issues. Until the recent creation of the Multidimensional State Boredom Scale (MSBS), research was constrained by the lack of a comprehensive, validated measure. However, the MSBS could benefit from further evaluation. Aim: To more thoroughly validate the MSBS. Methods: In two studies, participants were induced into a state of either boredom or non-boredom, and then completed the MSBS. Results: Discriminant analysis showed that the full MSBS was able to correctly classify 68.1% (Study 2) – 84.1% (Study 1) of participants into their experimental condition. Based on 14 further DA analysis, a subset of eight items (a potential short form) is proposed. Differential item functioning (Study 1) found only one item to which responding differed by gender. Discussion: Use of the MSBS, including the full scale versus the short form, is discussed. Which experiential components of boredom may be particularly important for classifying bored individuals, and the issue of variability across boredom manipulations, are also considered

Polygenic risk of prediabetes, undiagnosed diabetes, and incident type 2 diabetes stratified by diabetes risk factors

Context: Early diagnosis of type 2 diabetes is crucial to reduce severe comorbidities and complications. Current screening recommendations for type 2 diabetes include traditional risk factors, primarily body mass index (BMI) and family history, however genetics also plays a key role in type 2 diabetes risk. It is important to understand whether genetic predisposition to type 2 diabetes modifies the effect of these traditional factors on type 2 diabetes risk. Objective: This work aimed to investigate whether genetic risk of type 2 diabetes modifies associations between BMI and first-degree family history of diabetes with 1) prevalent prediabetes or undiagnosed diabetes; and 2) incident confirmed type 2 diabetes. Methods: We included 431 658 individuals aged 40 to 69 years at baseline of multiethnic ancestry from the UK Biobank. We used a multiethnic polygenic risk score for type 2 diabetes (PRST2D) developed by Genomics PLC. Prediabetes or undiagnosed diabetes was defined as baseline glycated hemoglobin greater than or equal to 42 mmol/mol (6.0%), and incident type 2 diabetes was derived from medical records. Results: At baseline, 43 472 participants had prediabetes or undiagnosed diabetes, and 17 259 developed type 2 diabetes over 15 years follow-up. Dose-response associations were observed for PRST2D with each outcome in each category of BMI or first-degree family history of diabetes. Those in the highest quintile of PRST2D with a normal BMI were at a similar risk as those in the middle quintile who were overweight. Participants who were in the highest quintile of PRST2D and did not have a first-degree family history of diabetes were at a similar risk as those with a family history who were in the middle category of PRST2D. Conclusion: Genetic risk of type 2 diabetes remains strongly associated with risk of prediabetes, undiagnosed diabetes, and future type 2 diabetes within categories of nongenetic risk factors. This could have important implications for identifying individuals at risk of type 2 diabetes for prevention and early diagnosis programs

Carbon Dioxide Gas Sensors and Method of Manufacturing and Using Same

A gas sensor includes a substrate and a pair of interdigitated metal electrodes selected from the group consisting of Pt, Pd, Au, Ir, Ag, Ru, Rh, In, and Os. The electrodes each include an upper surface. A first solid electrolyte resides between the interdigitated electrodes and partially engages the upper surfaces of the electrodes. The first solid electrolyte is selected from the group consisting of NASICON, LISICON, KSICON, and .beta.''-Alumina (beta prime-prime alumina in which when prepared as an electrolyte is complexed with a mobile ion selected from the group consisting of Na.sup.+, K.sup.+, Li.sup.+, Ag.sup.+, H.sup.+, Pb.sup.2+, Sr.sup.2+ or Ba.sup.2+). A second electrolyte partially engages the upper surfaces of the electrodes and engages the first solid electrolyte in at least one point. The second electrolyte is selected from the group of compounds consisting of Na.sup.+, K.sup.+, Li.sup.+, Ag.sup.+, H.sup.+, Pb.sup.2+, Sr.sup.2+ or Ba.sup.2+ ions or combinations thereof

Concomitant medication use and clinical outcome of repetitive Transcranial Magnetic Stimulation (rTMS) treatment of Major Depressive Disorder.

BackgroundRepetitive Transcranial Magnetic Stimulation (rTMS) is commonly administered to Major Depressive Disorder (MDD) patients taking psychotropic medications, yet the effects on treatment outcomes remain unknown. We explored how concomitant medication use relates to clinical response to a standard course of rTMS.MethodsMedications were tabulated for 181 MDD patients who underwent a six-week rTMS treatment course. All patients received 10&nbsp;Hz rTMS administered to left dorsolateral prefrontal cortex (DLPFC), with 1&nbsp;Hz administered to right DLPFC in patients with inadequate response to and/or intolerance of left-sided stimulation. Primary outcomes were change in Inventory of Depressive Symptomatology Self Report (IDS-SR30) total score after 2, 4, and 6&nbsp;weeks.ResultsUse of benzodiazepines was associated with less improvement at week 2, whereas use of psychostimulants was associated with greater improvement at week 2 and across 6&nbsp;weeks. These effects were significant controlling for baseline variables including age, overall symptom severity, and severity of anxiety symptoms. Response rates at week 6 were lower in benzodiazepine users versus non-users (16.4% vs. 35.5%, p&nbsp;=&nbsp;0.008), and higher in psychostimulant users versus non-users (39.2% vs. 22.0%, p&nbsp;=&nbsp;0.02).ConclusionsConcomitant medication use may impact rTMS treatment outcome. While the differences reported here could be considered clinically significant, results were not corrected for multiple comparisons and findings should be replicated before clinicians incorporate the evidence into clinical practice. Prospective, hypothesis-based treatment studies will aid in determining causal relationships between medication treatments and outcome

Assessing the importance of primary care diagnoses in the UK Biobank.

The UK Biobank has made general practitioner (GP) data (censoring date 2016-2017) available for approximately 45% of the cohort, whilst hospital inpatient and death registry (referred to as "HES/Death") data are available cohort-wide through 2018-2022 depending on whether the data comes from England, Wales or Scotland. We assessed the importance of case ascertainment via different data sources in UKB for three diseases that are usually first diagnosed in primary care: Parkinson's disease (PD), type 2 diabetes (T2D), and all-cause dementia. Including GP data at least doubled the number of incident cases in the subset of the cohort with primary care data (e.g. from 619 to 1390 for dementia). Among the 786 dementia cases that were only captured in the GP data before the GP censoring date, only 421 (54%) were subsequently recorded in HES. Therefore, estimates of the absolute incidence or risk-stratified incidence are misleadingly low when based only on the HES/Death data. For incident cases present in both HES/Death and GP data during the full follow-up period (i.e. until the HES censoring date), the median time difference between an incident diagnosis of dementia being recorded in GP and HES/Death was 2.25 years (i.e. recorded 2.25 years earlier in the GP records). Similar lag periods were also observed for PD (median 2.31 years earlier) and T2D (median 2.82 years earlier). For participants with an incident GP diagnosis, only 65.6% of dementia cases, 69.0% of PD cases, and 58.5% of T2D cases had their diagnosis recorded in HES/Death within 7 years since GP diagnosis. The effect estimates (hazard ratios, HR) of established risk factors for the three health outcomes mostly remain in the same direction and with a similar strength of association when cases are ascertained either using HES only or further adding GP data. The confidence intervals of the HR became narrower when adding GP data, due to the increased statistical power from the additional cases. In conclusion, it is desirable to extend both the coverage and follow-up period of GP data to allow researchers to maximise case ascertainment of chronic health conditions in the UK

CO2 Sensors Based on Nanocrystalline SnO2 Doped with CuO

Nanocrystalline tin oxide (SnO2) doped with copper oxide (CuO) has been found to be useful as an electrical-resistance sensory material for measuring the concentration of carbon dioxide in air. SnO2 is an n-type semiconductor that has been widely used as a sensing material for detecting such reducing gases as carbon monoxide, some of the nitrogen oxides, and hydrocarbons. Without doping, SnO2 usually does not respond to carbon dioxide and other stable gases. The discovery that the electrical resistance of CuO-doped SnO2 varies significantly with the concentration of CO2 creates opportunities for the development of relatively inexpensive CO2 sensors for detecting fires and monitoring atmospheric conditions. This discovery could also lead to research that could alter fundamental knowledge of SnO2 as a sensing material, perhaps leading to the development of SnO2-based sensing materials for measuring concentrations of oxidizing gases. Prototype CO2 sensors based on CuO-doped SnO2 have been fabricated by means of semiconductor-microfabrication and sol-gel nanomaterial-synthesis batch processes that are amendable to inexpensive implementation in mass production

Special issue introduction: Approaching spatial uncertainty visualization to support reasoning and decision making

While research on uncertainty and decision-making has a long history across several disciplines, recent technological developments compel researchers to rethink how to best address and advance the understanding of how humans reason and make decisions under spatial uncertainty. This introduction presents a visual summary graphic to provide an overview of each article in this special issue. Upon viewing these visual summaries, the reader will find that each of these articles covers different topics in the uncertainty visualization domain, offering complementary research in this field. Extending this body of research and finding new ways to explore how these visualizations may help or hinder the analytical and reasoning process of humans continues to be a necessary step towards designing more effective uncertainty visualizations to support reasoning and decision-making

Kidney function, albuminuria, and their modification by genetic factors and risk of incident dementia in UK Biobank.

BACKGROUND: Associations between kidney function and dementia risk are inconclusive. Chronic kidney disease (CKD) severity is determined by levels of both estimated glomerular filtration rate (eGFR) and the urine albumin to creatinine ratio (ACR). However, whether there is a graded increase in dementia risk for worse eGFR in each ACR category is unclear. Also, whether genetic risk for dementia impacts the associations is unknown. The current study aims to investigate the associations between eGFR and albuminuria with dementia risk both individually and jointly, whether the associations vary by different follow-up periods, and whether genetic factors modified the associations. METHODS: In 202,702 participants aged ≥ 60 years from the UK Biobank, Cox proportional-hazards models were used to examine the associations between eGFR and urine albumin creatinine ratio (ACR) with risk of incident dementia. GFR was estimated based on serum creatinine, cystatin C, or both. The models were restricted to different follow-up periods (< 5 years, 5-10 years, and ≥ 10 years) to investigate potential reverse causation. RESULTS: Over 15 years of follow-up, 6,042 participants developed dementia. Decreased kidney function (eGFR < 60 ml/min/1.73m2) was associated with an increased risk of dementia (Hazard Ratio [HR] = 1.42, 95% Confidence Interval [CI] 1.28-1.58), compared to normal kidney function (≥ 90 ml/min/1.73m2). The strength of the association remained consistent when the models were restricted to different periods of follow-up. The HRs for incident dementia were 1.16 (95% CI 1.07-1.26) and 2.24 (95% CI 1.79-2.80) for moderate (3-30 mg/mmol) and severely increased ACR (≥ 30 mg/mmol) compared to normal ACR (< 3 mg/mmol). Dose-response associations were observed when combining eGFR and ACR, with those in the severest eGFR and ACR group having the greatest risk of dementia (HR = 4.70, 95% CI 2.34-9.43). APOE status significantly modified the association (p = 0.04), with stronger associations observed among participants with a lower genetic risk of dementia. There was no evidence of an interaction between kidney function and non-APOE polygenic risk of dementia with dementia risk (p = 0.42). CONCLUSIONS: Kidney dysfunction and albuminuria were individually and jointly associated with higher dementia risk. The associations were greater amongst participants with a lower genetic risk of dementia based on APOE, but not non-APOE polygenic risk

Novel Carbon Dioxide Microsensor Based on Tin Oxide Nanomaterial Doped With Copper Oxide

Carbon dioxide (CO2) is one of the major indicators of fire and therefore its measurement is very important for low-false-alarm fire detection and emissions monitoring. However, only a limited number of CO2 sensing materials exist due to the high chemical stability of CO2. In this work, a novel CO2 microsensor based on nanocrystalline tin oxide (SnO2) doped with copper oxide (CuO) has been successfully demonstrated. The CuO-SnO2 based CO2 microsensors are fabricated by means of microelectromechanical systems (MEMS) technology and sol-gel nanomaterial-synthesis processes. At a doping level of CuO: SnO2 = 1:8 (molar ratio), the resistance of the sensor has a linear response to CO2 concentrations for the range of 1 to 4 percent CO2 in air at 450 C. This approach has demonstrated the use of SnO2, typically used for the detection of reducing gases, in the detection of an oxidizing gas