Results of Anorectal Physiological Testing in Patients with Limited English Proficiency.

BACKGROUND: Patients with limited English proficiency (LEP) experience barriers to healthcare. These include language barriers and difficulty accessing medical subspecialties. Consequently, patients with LEP may be underrepresented, and may be more likely to have abnormal results, among individuals referred for anorectal testing. AIMS: To explore whether differences exist in the results of high-resolution anorectal manometry (HRM), rectal sensory testing (RST), and balloon expulsion testing (BET) between patients with LEP and English proficiency (EP). METHODS: The electronic health records at Mayo Clinic, Rochester were used to identify constipated patients without organic anorectal disease who had undergone anorectal testing in 2018, 2019, and 2020. The language spoken by the patients was determined. HRM, RST, and BET results were compared. Nominal logistic regression explored the influence of age, gender, test operator, and LEP on the likelihood of abnormal findings. KEY RESULTS: Among 3298 patients (80% female, mean age ± standard deviation 46 ± 16 years), 67 (2%) had LEP. HRM measurements were similar in LEP and EP patients. However, LEP patients were more likely to have abnormal BET and RST. Logistic regression revealed that age (older than 50 years), gender, test operator, and LEP influenced the results of BET and RST, with LEP having the strongest influence. CONCLUSIONS: Results of anorectal testing in constipated patients differ between LEP and EP patients. This is likely to represent a difference in disease prevalence between these groups, for example, due to referral bias, rather than a difference in physiology or a language barrier.

Gastric Motor Dysfunction in Patients With Functional Gastroduodenal Symptoms.

OBJECTIVES: The pathophysiology of dyspeptic symptoms is complex. The aim of this study was to evaluate the association of gastric emptying (GE), gastric accommodation (GA), and respiratory sinus arrhythmia (RSA, to assess vagal dysfunction) in a large cohort with functional gastroduodenal symptoms. METHODS: We reviewed demographic, clinical features, and results of gastric motor and vagal function studies of 1,287 patients (74.0% females, mean age 43.1±15.4 years) who had undergone both single photon emission computed tomography GA and scintigraphic GE. Accommodation was based on postprandial to fasting gastric volume ratio (VR). Electrocardiograms were available and analyzed for RSA in 300 patients. RESULTS: There were 29.8% patients with normal GE and GA, 21.9% with abnormal GA only, 27.1% with abnormal GE only, and 21.1% with abnormal GA and GE. There were numerical differences in GA among patients with normal, accelerated, and delayed GE (P=0.062, by χ2). Increased GA (VR >3.85) was more prevalent in patients with delayed GE compared to accelerated GE (14.0% vs. 6.8%, P=0.004). Decreased VRs (median 2.9) were observed with accelerated GE compared to normal GE (median 3.1, P<0.05). Nausea and vomiting were more prevalent (in contrast to the less prevalent bloating) in patients with delayed compared to accelerated or normal GE (all P<0.05). In patients with diminished RSA, there was higher prevalence of reduced GA (41.5%) compared to those with preserved RSA (29.2%, P=0.031). Multivariable analysis showed associations of the main abdominal symptoms with gender, body mass index, gastric emptying, diabetes, and prior abdominal surgery. CONCLUSIONS: Patients with symptoms of functional gastroduodenal disorders may have one or more gastric motor dysfunctions and reduced RSA; among the patients with abnormal gastric motor functions, vomiting suggests delayed GE, whereas reduced RSA is associated with reduced GA.

Joint Modeling of Ability and Differential Speed Using Responses and Response Times.

With computerized testing, it is possible to record both the responses of test takers to test questions (i.e., items) and the amount of time spent by a test taker in responding to each question. Various models have been proposed that take into account both test-taker ability and working speed, with the many models assuming a constant working speed throughout the test. The constant working speed assumption may be inappropriate for various reasons. For example, a test taker may need to adjust the pace due to time mismanagement, or a test taker who started out working too fast may reduce the working speed to improve accuracy. A model is proposed here that allows for variable working speed. An illustration of the model using the Amsterdam Chess Test data is provided.

Why item response theory should be used for longitudinal questionnaire data analysis in medical research.

BACKGROUND: Multi-item questionnaires are important instruments for monitoring health in epidemiological longitudinal studies. Mostly sum-scores are used as a summary measure for these multi-item questionnaires. The objective of this study was to show the negative impact of using sum-score based longitudinal data analysis instead of Item Response Theory (IRT)-based plausible values. METHODS: In a simulation study (varying the number of items, sample size, and distribution of the outcomes) the parameter estimates resulting from both modeling techniques were compared to the true values. Next, the models were applied to an example dataset from the Amsterdam Growth and Health Longitudinal Study (AGHLS). RESULTS: The results show that using sum-scores leads to overestimation of the within person (repeated measurement) variance and underestimation of the between person variance. CONCLUSIONS: We recommend using IRT-based plausible value techniques for analyzing repeatedly measured multi-item questionnaire data.

Harmonization of Neuroticism and Extraversion phenotypes across inventories and cohorts in the Genetics of Personality Consortium: an application of Item Response Theory.

Mega- or meta-analytic studies (e.g. genome-wide association studies) are increasingly used in behavior genetics. An issue in such studies is that phenotypes are often measured by different instruments across study cohorts, requiring harmonization of measures so that more powerful fixed effect meta-analyses can be employed. Within the Genetics of Personality Consortium, we demonstrate for two clinically relevant personality traits, Neuroticism and Extraversion, how Item-Response Theory (IRT) can be applied to map item data from different inventories to the same underlying constructs. Personality item data were analyzed in >160,000 individuals from 23 cohorts across Europe, USA and Australia in which Neuroticism and Extraversion were assessed by nine different personality inventories. Results showed that harmonization was very successful for most personality inventories and moderately successful for some. Neuroticism and Extraversion inventories were largely measurement invariant across cohorts, in particular when comparing cohorts from countries where the same language is spoken. The IRT-based scores for Neuroticism and Extraversion were heritable (48 and 49 %, respectively, based on a meta-analysis of six twin cohorts, total N = 29,496 and 29,501 twin pairs, respectively) with a significant part of the heritability due to non-additive genetic factors. For Extraversion, these genetic factors qualitatively differ across sexes. We showed that our IRT method can lead to a large increase in sample size and therefore statistical power. The IRT approach may be applied to any mega- or meta-analytic study in which item-based behavioral measures need to be harmonized.

Longitudinal measurement in health-related surveys. A Bayesian joint growth model for multivariate ordinal responses.

Longitudinal surveys measuring physical or mental health status are a common method to evaluate treatments. Multiple items are administered repeatedly to assess changes in the underlying health status of the patient. Traditional models to analyze the resulting data assume that the characteristics of at least some items are identical over measurement occasions. When this assumption is not met, this can result in ambiguous latent health status estimates. Changes in item characteristics over occasions are allowed in the proposed measurement model, which includes truncated and correlated random effects and a growth model for item parameters. In a joint estimation procedure adopting MCMC methods, both item and latent health status parameters are modeled as longitudinal random effects. Simulation study results show accurate parameter recovery. Data from a randomized clinical trial concerning the treatment of depression by increasing psychological acceptance showed significant item parameter shifts. For some items, the probability of responding in the middle category versus the highest or lowest category increased significantly over time. The resulting latent depression scores decreased more over time for the experimental group than for the control group and the amount of decrease was related to the increase in acceptance level.

Assessing an alternative for "negative variance components": A gentle introduction to Bayesian covariance structure modeling for negative associations among patients with personalized treatments.

The multilevel model (MLM) is the popular approach to describe dependences of hierarchically clustered observations. A main feature is the capability to estimate (cluster-specific) random effect parameters, while their distribution describes the variation across clusters. However, the MLM can only model positive associations among clustered observations, and it is not suitable for small sample sizes. The limitation of the MLM becomes apparent when estimation methods produce negative estimates for random effect variances, which can be seen as an indication that observations are negatively correlated. A gentle introduction to Bayesian covariance structure modeling (BCSM) is given, which makes it possible to model also negatively correlated observations. The BCSM does not model dependences through random (cluster-specific) effects, but through a covariance matrix. We show that this makes the BCSM particularly useful for small data samples. We draw specific attention to detect effects of a personalized intervention. The effect of a personalized treatment can differ across individuals, and this can lead to negative associations among measurements of individuals who are treated by the same therapist. It is shown that the BCSM enables the modeling of negative associations among clustered measurements and aids in the interpretation of negative clustering effects. Through a simulation study and by analysis of a real data example, we discuss the suitability of the BCSM for small data sets and for exploring effects of individualized treatments, specifically when (standard) MLM software produces negative or zero variance estimates. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

R-package LNIRT for joint modeling of response accuracy and times.

In computer-based testing it has become standard to collect response accuracy (RA) and response times (RTs) for each test item. IRT models are used to measure a latent variable (e.g., ability, intelligence) using the RA observations. The information in the RTs can help to improve routine operations in (educational) testing, and provide information about speed of working. In modern applications, the joint models are needed to integrate RT information in a test analysis. The R-package LNIRT supports fitting joint models through a user-friendly setup which only requires specifying RA, RT data, and the total number of Gibbs sampling iterations. More detailed specifications of the analysis are optional. The main results can be reported through the summary functions, but output can also be analysed with Markov chain Monte Carlo (MCMC) output tools (i.e., coda, mcmcse). The main functionality of the LNIRT package is illustrated with two real data applications.

Rural and urban residents' attitudes and preferences toward COVID-19 prevention behaviors in a midwestern community.

Rural populations are more vulnerable to the impacts of COVID-19 compared to their urban counterparts as they are more likely to be older, uninsured, to have more underlying medical conditions, and live further from medical care facilities. We engaged the Southeastern MN (SEMN) community (N = 7,781, 51% rural) to conduct a survey of motivators and barriers to masking to prevent COVID-19. We also assessed preferences for types of and modalities to receive education/intervention, exploring both individual and environmental factors primarily consistent with Social Cognitive Theory. Our results indicated rural compared to urban residents performed fewer COVID-19 prevention behaviors (e.g. 62% rural vs. 77% urban residents reported wearing a mask all of the time in public, p<0.001), had more negative outcome expectations for wearing a mask (e.g. 50% rural vs. 66% urban residents thought wearing a mask would help businesses stay open, p<0.001), more concerns about wearing a mask (e.g. 23% rural vs. 14% urban were very concerned about being 'too hot', p<0.001) and lower levels of self-efficacy for masking (e.g. 13.9±3.4 vs. 14.9±2.8, p<0.001). It appears that masking has not become a social norm in rural SEMN, with almost 50% (vs. 24% in urban residents) disagreeing with the expectation 'others in my community will wear a mask to stop the spread of Coronavirus'. Except for people (both rural and urban) who reported not being at all willing to wear a mask (7%), all others expressed interest in future education/interventions to help reduce masking barriers that utilized email and social media for delivery. Creative public health messaging consistent with SCT tailored to rural culture and norms is needed, using emails and social media with pictures and videos from role models they trust, and emphasizing education about when masks are necessary.

Procedural and anthropometric factors associated with musculoskeletal injuries among gastroenterology endoscopists.

BACKGROUND AND AIMS: Musculoskeletal (MSK) injuries among gastroenterologists are common. Our study describes risk factors and consequences of injury by comparing provider-specific anthropometric and objective procedural data to self-reported injury patterns. METHODS: A validated MSK symptom survey was sent to gastroenterologists to gauge prevalence, distribution, and severity of active injury. Respondents' procedural activities over 7 years were collected via an endoscopic database. RESULTS: 64 surveys were completed. 54 respondents had active pain; 53.1% reported activity-limiting injury. Activity-limiting injuries lead to longer colonoscopy times (25.3 vs. 22.1 min, P = 0.03) and lower procedural volumes (532 vs. 807, P = 0.01). Hand/wrist injuries yielded longer colonoscopy insertion times (9.35 vs. 8.21 min, P = 0.03) and less hands-on scope hours (81.2 vs. 111.7 h, P = 0.04). Higher esophagogastroduodenoscopy volume corelated with shoulder injury (336.5 vs. 243.1 EGDs/year, P = 0.04). Females had more foot injuries (P = 0.04). CONCLUSION: Activity-limiting MSK symptoms/injuries affect over 50% of endoscopists with negative impact on procedural volume and efficiency.

A mixture model for the joint analysis of latent developmental trajectories and survival.

A general joint modeling framework is proposed that includes a parametric stratified survival component for continuous time survival data, and a mixture multilevel item response component to model latent developmental trajectories given mixed discrete response data. The joint model is illustrated in a real data setting, where the utility of longitudinally measured cognitive function as a predictor for survival is investigated in a group of elderly persons. The object is partly to determine whether cognitive impairment is accompanied by a higher mortality rate. Time-dependent cognitive function is measured using the generalized partial credit model given occasion-specific mini-mental state examination response data. A parametric survival model is applied for the survival information, and cognitive function as a continuous latent variable is included as a time-dependent explanatory variable along with other explanatory information. A mixture model is defined, which incorporates the latent developmental trajectory and the survival component. The mixture model captures the heterogeneity in the developmental trajectories that could not be fully explained by the multilevel item response model and other explanatory variables. A Bayesian modeling approach is pursued, where a Markov chain Monte Carlo algorithm is developed for simultaneous estimation of the joint model parameters. Practical issues as model building and assessment are addressed using the DIC and various posterior predictive tests.

Hepatic iron overload or cirrhosis may occur in acquired copper deficiency and is likely mediated by hypoceruloplasminemia.

BACKGROUND: The metabolic fates of copper and iron are closely linked through ceruloplasmin and hephaestin. Ceruloplasmin is the principal copper carrying protein and decreases in acquired copper deficiency. Congenital absence of ceruloplasmin (aceruloplasminemia) results in tissue iron overload. Animal studies suggest hypoceruloplasminemia and impaired hephaestin function result in tissue iron accumulation. OBJECTIVES: There are no data on hepatic function, pathology, and iron status in patients with acquired copper deficiency. This report studies these issues in 4 patients with acquired copper deficiency. STUDY: This is a retrospective review of hepatic status (imaging, liver function tests, liver biopsy) in 4 patients with neurologic and hematologic manifestations of acquired copper deficiency who also had imaging and/or pathologic evidence of hepatic dysfunction. RESULTS: Two patients (cases 1 and 2) showed imaging evidence of cirrhosis and pathologic evidence of cirrhosis or advanced fibrosis. Two patients (cases 3 and 4) had pathologic evidence of hepatic iron overload. All patients had some evidence of abnormality on liver function tests. CONCLUSIONS: Acquired copper deficiency causes a secondary ceruloplasmin deficiency which can result in hepatic iron overload and/or cirrhosis.

Factors associated with willingness to wear a mask to prevent the spread of COVID-19 in a Midwestern Community.

OBJECTIVE: To identify motivators and barriers to wearing a mask to prevent COVID-19. PARTICIPANTS AND METHODS: An anonymous, online survey of adults from Southeastern Minnesota conducted August 2020. We assessed willingness to wear a mask and its associations with socio-demographics, COVID-19-related factors and prevention behaviors using multivariable ordinal logistic regression. RESULTS: Of 7,786 respondents (78% women, 51% rural), 9% reported 'not at all willing', 27% 'willing', and 64% 'very willing' to wear a mask. Factors independently associated with willingness to wear a mask were: urban residence (OR = 1.23, 95% CI 1.05-1.44, p = 0.009); college degree or greater (OR 1.42, CI 1.05-1.93, p = 0.025); age (18-29 years OR 1.29, CI 01.02-1.64, p = 0.038; 30-39 OR = 1.37, CI 1.12-1.69, p = 0.003; 60-69 OR = 1.44, CI 1.09-1.91, p = 0.011; 70-89 OR 2.09, CI 1.32-3.37, p = 0.002; 40-49 reference group); and (all p < 0.001) democratic party affiliation (OR 1.79, CI 1.40-2.29), correct COVID-19 knowledge (OR 1.50, CI 1.28-1.75), 5 + COVID-19 prevention behaviors (OR 2.74, CI 1.98-3.81), positive perceived impacts for wearing a mask (OR 1.55, 1.52-1.59), perceived COVID-19 severity (OR 2.1, CI 1.44-3.1), and greater stress (OR 1.03, CI 1.02-1.04), and trust in the Centers for Disease Control (CDC) (OR 1.78, CI 1.45 -2.19). CONCLUSION: Results from this sample of SEMN residents suggest interventions to enhance COVID-19 knowledge, positive expectations for mask wearing, and trust in the CDC are warranted. Research is needed to understand cultural and other barriers and facilitators among sub-populations, e.g., rural residents less willing to wear a mask.

Shared Decision Making in Gastroenterology: Challenges and Opportunities.

This article reviews the current uses of shared decision making in gastroenterology and discusses additional areas of opportunity for shared decision making, especially in the area of functional gastrointestinal disorders. PubMed, MEDLINE, and Cochrane library databases were searched for articles published during a 10-year period from January 1, 2007, through December 31, 2017. Search terms included shared decision making and gastroenterology, shared decision making in gastrointestinal disease, shared decision making in functional GI disorders, and shared decision making and irritable bowel syndrome. Studies were not included in this review when a health care professional other than a gastroenterologist was involved, eg, an article that reported shared decision making regarding the use of radiation therapy in a patient with advanced rectal cancer in which the health care professional helping to make the decision was an oncologist.

Evaluating cognitive theory: a joint modeling approach using responses and response times.

In current psychological research, the analysis of data from computer-based assessments or experiments is often confined to accuracy scores. Response times, although being an important source of additional information, are either neglected or analyzed separately. In this article, a new model is developed that allows the simultaneous analysis of accuracy scores and response times of cognitive tests with a rule-based design. The model is capable of simultaneously estimating ability and speed on the person side as well as difficulty and time intensity on the task side, thus dissociating information that is often confounded in current analysis procedures. Further, by integrating design matrices on the task side, it becomes possible to assess the effects of design parameters (e.g., cognitive processes) on both task difficulty and time intensity, offering deeper insights into the task structure. A Bayesian approach, using Markov Chain Monte Carlo methods, has been developed to estimate the model. An application of the model in the context of educational assessment is illustrated using a large-scale investigation of figural reasoning ability.

Modeling Dependence Structures for Response Times in a Bayesian Framework.

A multivariate generalization of the log-normal model for response times is proposed within an innovative Bayesian modeling framework. A novel Bayesian Covariance Structure Model (BCSM) is proposed, where the inclusion of random-effect variables is avoided, while their implied dependencies are modeled directly through an additive covariance structure. This makes it possible to jointly model complex dependencies due to for instance the test format (e.g., testlets, complex constructs), time limits, or features of digitally based assessments. A class of conjugate priors is proposed for the random-effect variance parameters in the BCSM framework. They give support to testing the presence of random effects, reduce boundary effects by allowing non-positive (co)variance parameters, and support accurate estimation even for very small true variance parameters. The conjugate priors under the BCSM lead to efficient posterior computation. Bayes factors and the Bayesian Information Criterion are discussed for the purpose of model selection in the new framework. In two simulation studies, a satisfying performance of the MCMC algorithm and of the Bayes factor is shown. In comparison with parameter expansion through a half-Cauchy prior, estimates of variance parameters close to zero show no bias and undercoverage of credible intervals is avoided. An empirical example showcases the utility of the BCSM for response times to test the influence of item presentation formats on the test performance of students in a Latin square experimental design.

Bayesian Covariance Structure Modeling of Responses and Process Data.

A novel Bayesian modeling framework for response accuracy (RA), response times (RTs) and other process data is proposed. In a Bayesian covariance structure modeling approach, nested and crossed dependences within test-taker data (e.g., within a testlet, between RAs and RTs for an item) are explicitly modeled. The local dependences are modeled directly through covariance parameters in an additive covariance matrix. The inclusion of random effects (on person or group level) is not necessary, which allows constructing parsimonious models for responses and multiple types of process data. Bayesian Covariance Structure Models (BCSMs) are presented for various well-known dependence structures. Through truncated shifted inverse-gamma priors, closed-form expressions for the conditional posteriors of the covariance parameters are derived. The priors avoid boundary effects at zero, and ensure the positive definiteness of the additive covariance structure at any layer. Dependences of categorical outcome data are modeled through latent continuous variables. In a simulation study, a BCSM for RAs and RTs is compared to van der Linden's hierarchical model (LHM; van der Linden, 2007). Under the BCSM, the dependence structure is extended to allow variations in test-takers' working speed and ability and is estimated with a satisfying performance. Under the LHM, the assumption of local independence is violated, which results in a biased estimate of the variance of the ability distribution. Moreover, the BCSM provides insight in changes in the speed-accuracy trade-off. With an empirical example, the flexibility and relevance of the BCSM for complex dependence structures in a real-world setting are discussed.

Understanding Therapeutic Change Process Research Through Multilevel Modeling and Text Mining.

Online interventions hold great potential for Therapeutic Change Process Research (TCPR), a field that aims to relate in-therapeutic change processes to the outcomes of interventions. Online a client is treated essentially through the language their counsellor uses, therefore the verbal interaction contains many important ingredients that bring about change. TCPR faces two challenges: how to derive meaningful change processes from texts, and secondly, how to assess these complex, varied, and multi-layered processes? We advocate the use text mining and multi-level models (MLMs): the former offers tools and methods to discovers patterns in texts; the latter can analyse these change processes as outcomes that vary at multiple levels. We (re-)used the data from Lamers et al. (2015) because it includes outcomes and the complete online intervention for clients with mild depressive symptoms. We used text mining to obtain basic text-variables from e-mails, that we analyzed through MLMs. We found that we could relate outcomes of interventions to variables containing text-information. We conclude that we can indeed bridge text mining and MLMs for TCPR as it was possible to relate text-information (obtained through text mining) to multi-leveled TCPR outcomes (using a MLM). Text mining can be helpful to obtain change processes, which is also the main challenge for TCPR. We showed how MLMs and text mining can be combined, but our proposition leaves open how to obtain the most relevant textual operationalization of TCPR concepts. That requires interdisciplinary collaboration and discussion. The future does look bright: based on our proof-of-concept study we conclude that MLMs and text mining can indeed advance TCPR.

Beta-binomial ANOVA for multivariate randomized response data.

There is much empirical evidence that randomized response methods improve the cooperation of the respondents when asking sensitive questions. The traditional methods for analysing randomized response data are restricted to univariate data and only allow inferences at the group level due to the randomized response sampling design. Here, a novel beta-binomial model is proposed for analysing multivariate individual count data observed via a randomized response sampling design. This new model allows for the estimation of individual response probabilities (response rates) for multivariate randomized response data utilizing an empirical Bayes approach. A common beta prior specifies that individuals in a group are tied together and the beta prior parameters are allowed to be cluster-dependent. A Bayes factor is proposed to test for group differences in response rates. An analysis of a cheating study, where 10 items measure cheating or academic dishonesty, is used to illustrate application of the proposed model.

Efficacy of on-demand asimadoline, a peripheral kappa-opioid agonist, in females with irritable bowel syndrome.

BACKGROUND & AIMS: In experimental animal models of irritable bowel syndrome (IBS) and human studies, peripheral kappa opioid agonists have been shown to decrease sensation to colonic distention. The aim of this study was to compare the effects of the kappa opioid agonist, asimadoline, and placebo on episodes of abdominal pain in patients with IBS. METHODS: After a 2-week run-in period, 100 patients with IBS were randomized (3:2 ratio) to receive asimadoline, up to 1 mg 4 times daily, or placebo for 4 weeks in a double-blind study. Pain was scored by daily diary using a 100-mm visual analogue scale. During pain episodes, patients recorded the pain severity, took study medication, and recorded their pain score 2 hours later. The primary end point was the average reduction in pain severity 2 hours after treatment. RESULTS: The average pain reduction 2 hours posttreatment was not significantly different between the groups. Post hoc analyses suggest asimadoline was effective in mixed IBS (P = .003, unadjusted), but may be worse in diarrhea-predominant IBS (P = .065 unadjusted). The anxiety score was reduced modestly by asimadoline (P = .053). No significant adverse effects were noted. CONCLUSIONS: An on-demand dosing schedule of asimadoline was not effective in reducing severity of abdominal pain in IBS. Further studies in visceral pain and IBS appear warranted.