scholarly journals How Many Urine Samples Are Needed to Accurately Assess Exposure to Non-Persistent Chemicals? The Biomarker Reliability Assessment Tool (BRAT) for Scientists, Research Sponsors, and Risk Managers

2020 ◽  
Vol 17 (23) ◽  
pp. 9102
Author(s):  
Marc-André Verner ◽  
Hassan Salame ◽  
Conrad Housand ◽  
Linda S. Birnbaum ◽  
Maryse F. Bouchard ◽  
...  
Keyword(s):  
Decision Making ◽  
Monte Carlo ◽  
Human Exposure ◽  
Assessment Tool ◽  
Reliability Assessment ◽  
Population Based ◽  
Environmental Chemicals ◽  
Urine Samples ◽  
Risk Managers

In epidemiologic and exposure research, biomonitoring is often used as the basis for assessing human exposure to environmental chemicals. Studies frequently rely on a single urinary measurement per participant to assess exposure to non-persistent chemicals. However, there is a growing consensus that single urine samples may be insufficient for adequately estimating exposure. The question then arises: how many samples would be needed for optimal characterization of exposure? To help researchers answer this question, we developed a tool called the Biomarker Reliability Assessment Tool (BRAT). The BRAT is based on pharmacokinetic modeling simulations, is freely available, and is designed to help researchers determine the approximate number of urine samples needed to optimize exposure assessment. The BRAT performs Monte Carlo simulations of exposure to estimate internal levels and resulting urinary concentrations in individuals from a population based on user-specified inputs (e.g., biological half-life, within- and between-person variability in exposure). The BRAT evaluates—through linear regression and quantile classification—the precision/accuracy of the estimation of internal levels depending on the number of urine samples. This tool should guide researchers towards more robust biomonitoring and improved exposure classification in epidemiologic and exposure research, which should in turn improve the translation of that research into decision-making.

scholarly journals A Bayesian approach to estimating the population prevalence of mood and anxiety disorders using multiple measures

2021 ◽  
Vol 30 ◽  
Author(s):  
Jordan Edwards ◽  
A. Demetri Pananos ◽  
Amardeep Thind ◽  
Saverio Stranges ◽  
Maria Chiu ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Anxiety Disorders ◽  
Administrative Data ◽  
Population Based ◽  
Accurate Estimate ◽  
Service Planning ◽  
Prevalence Estimate ◽  
Population Prevalence ◽  
Mood And Anxiety Disorders ◽  
Prevalence Estimates

Abstract Aims There is currently no universally accepted measure for population-based surveillance of mood and anxiety disorders. As such, the use of multiple linked measures could provide a more accurate estimate of population prevalence. Our primary objective was to apply Bayesian methods to two commonly employed population measures of mood and anxiety disorders to make inferences regarding the population prevalence and measurement properties of a combined measure. Methods We used data from the 2012 Canadian Community Health Survey – Mental Health linked to health administrative databases in Ontario, Canada. Structured interview diagnoses were obtained from the survey, and health administrative diagnoses were identified using a standardised algorithm. These two prevalence estimates, in addition to data on the concordance between these measures and prior estimates of their psychometric properties, were used to inform our combined estimate. The marginal posterior densities of all parameters were estimated using Hamiltonian Monte Carlo (HMC), a Markov Chain Monte Carlo technique. Summaries of posterior distributions, including the means and 95% equally tailed posterior credible intervals, were used for interpretation of the results. Results The combined prevalence mean was 8.6%, with a credible interval of 6.8–10.6%. This combined estimate sits between Bayesian-derived prevalence estimates from administrative data-derived diagnoses (mean = 7.4%) and the survey-derived diagnoses (mean = 13.9%). The results of our sensitivity analysis suggest that varying the specificity of the survey-derived measure has an appreciable impact on the combined posterior prevalence estimate. Our combined posterior prevalence estimate remained stable when varying other prior information. We detected no problematic HMC behaviour, and our posterior predictive checks suggest that our model can reliably recreate our data. Conclusions Accurate population-based estimates of disease are the cornerstone of health service planning and resource allocation. As a greater number of linked population data sources become available, so too does the opportunity for researchers to fully capitalise on the data. The true population prevalence of mood and anxiety disorders may reside between estimates obtained from survey data and health administrative data. We have demonstrated how the use of Bayesian approaches may provide a more informed and accurate estimate of mood and anxiety disorders in the population. This work provides a blueprint for future population-based estimates of disease using linked health data.

Exploring the Differences in Macrocognition Between Experts and Non-CHD Experts Managing Congenital Heart Disease (CHD)

2021 ◽  
Vol 10 (1) ◽  
pp. 335-339
Author(s):  
Azadeh Assadi ◽  
Peter C. Laussen ◽  
Patricia Trbovich
Keyword(s):  
Decision Making ◽  
Emergency Medicine ◽  
Congenital Heart Disease ◽  
Heart Disease ◽  
Congenital Heart ◽  
Pediatric Emergency ◽  
Acute Management ◽  
Naturalistic Decision Making ◽  
The Face

Background and aims: Children with congenital heart disease (CHD) are at risk of deterioration in the face of common childhood illnesses, and their resuscitation and acute management is often best achieved with the guidance of CHD experts. Access to such expertise may be limited outside specialty heart centers and the fragility of these patients is cause for discomfort among many emergency medicine physicians. An understanding of the differences in macrocognition of these clinicians could shed light on some of the causes of discomfort and facilitate the development of a sociotechnological solution to this problem. Methods: Cardiac intensivists (CHD experts) and pediatric emergency medicine physicians (non-CHD experts) in a major academic cardiac center were interviewed using the critical decision method. Interview transcripts were coded deductively based on Klein’s macrocognitive framework and inductively to allow for new or modified characterization of dimensions. Results: While both CHD-experts and non-CHD experts relied on the macrocognitive functions of sensemaking, naturalistic decision making and detecting problems, the specific data and mental models used to understand the patients and course of therapy differed between CHD-experts and non-CHD experts. Conclusion: Characterization of differences between the macrocognitive processes of CHD experts and non-CHD experts can inform development of sociotechnological solutions to augment decision making pertaining to the acute management of pediatric CHD patients.

Post-Operative Dysphagia in Anterior Cervical Discectomy and Fusion

2021 ◽  
pp. 000348942110155
Author(s):  
Leonard Haller ◽  
Khush Mehul Kharidia ◽  
Caitlin Bertelsen ◽  
Jeffrey Wang ◽  
Karla O’Dell
Keyword(s):  
Risk Factors ◽  
Medical Records ◽  
Assessment Tool ◽  
Demographic Data ◽  
Anterior Cervical Discectomy ◽  
Cervical Discectomy ◽  
Long Term Follow Up

Objective: We sought to identify risk factors associated with long-term dysphagia, characterize changes in dysphagia over time, and evaluate the incidence of otolaryngology referrals for patients with long-term dysphagia following anterior cervical discectomy with fusion (ACDF). Methods: About 56 patients who underwent ACDF between May 2017 to February 2019 were included in the study. All patients were assessed for dysphagia using the Eating Assessment Tool (EAT-10) survey preoperatively and late postoperatively (≥1 year). Additionally, 28 patients were assessed for dysphagia early postoperatively (2 weeks—3 months). Demographic data, medical comorbidities, intraoperative details, and post-operative otolaryngology referral rates were collected from electronic medical records. Results: Of the 56 patients enrolled, 21 patients (38%) had EAT-10 scores of 3 or more at long-term follow-up. None of the demographics, comorbidities, or surgical factors assessed were associated with long-term dysphagia. Patients who reported no long-term dysphagia had a mean EAT-10 score of 6.9 early postoperatively, while patients with long-term symptoms had a mean score of 18.1 ( P = .006). Of the 21 patients who reported persistent dysphagia symptoms, 3 (14%) received dysphagia testing or otolaryngology referrals post-operatively. Conclusion: Dysphagia is a notable side effect of ACDF surgery, but there are no significant demographics, comorbidities, or surgical risk factors that predict long-term dysphagia. Early postoperative characterization of dysphagia using the EAT-10 questionnaire can help predict long-term symptoms. There is inadequate screening and otolaryngology follow-up for patients with post-ACDF dysphagia.

Sign in / Sign up

Export Citation Format

Share Document