Teamwork Makes the Dream Work: Using Team-Based Learning in the Science Classroom

With an overwhelming amount of research and a demand for collaborative learning in the classroom, teachers are tackling challenges at all educational levels that often accompany the social aspects of group work. Team-Based Learning (TBL) is an instructional sequence that shifts instruction from teacher lecture to small-group learning. Through the use of teams and social learning, students are actively engaged and learning through critical-thinking tasks. College students can take responsibility both for their own learning and for each other as learners and fellow human beings. TBL allows the instructors to design opportunities for students to demonstrate what they know and can do in the classroom with the content. This study qualitatively examines students’ perceptions of the pedagogical strategy TBL in an undergraduate science course. TBL practices enabled instructors to prepare students for classes in advance and assist students in deeply learning the material through application of course concepts, allowing them to solve interesting, complex, and real-world problems that are relevant to the teaching profession

Appropriate inclusion of interactions was needed to avoid bias in multiple imputation

OBJECTIVE: Missing data are a pervasive problem, often leading to bias in complete records analysis (CRA). Multiple imputation (MI) via chained equations is one solution, but its use in the presence of interactions is not straightforward. STUDY DESIGN AND SETTING: We simulated data with outcome Y dependent on binary explanatory variables X and Z and their interaction XZ. Six scenarios were simulated (Y continuous and binary, each with no interaction, a weak and a strong interaction), under five missing data mechanisms. We use directed acyclic graphs to identify when CRA and MI would each be unbiased. We evaluate the performance of CRA, MI without interactions, MI including all interactions, and stratified imputation. We also illustrated these methods using a simple example from the National Child Development Study (NCDS). RESULTS: MI excluding interactions is invalid and resulted in biased estimates and low coverage. When XZ was zero, MI excluding interactions gave unbiased estimates but overcoverage. MI including interactions and stratified MI gave equivalent, valid inference in all cases. In the NCDS example, MI excluding interactions incorrectly concluded there was no evidence for an important interaction. CONCLUSIONS: Epidemiologists carrying out MI should ensure that their imputation model(s) are compatible with their analysis model

HIghMass - High HI Mass, HI-Rich Galaxies at z∼0z\sim0: Combined HI and H2_2 Observations

We present resolved HI and CO observations of three galaxies from the HIghMass sample, a sample of HI-massive ($M_{HI} > 10^{10} M_\odot$), gas-rich ($M_{HI}$ in top $5\%$ for their $M_*$) galaxies identified in the ALFALFA survey. Despite their high gas fractions, these are not low surface brightness galaxies, and have typical specific star formation rates (SFR$/M_*$) for their stellar masses. The three galaxies have normal star formation rates for their HI masses, but unusually short star formation efficiency scale lengths, indicating that the star formation bottleneck in these galaxies is in the conversion of HI to H$_2$, not in converting H$_2$ to stars. In addition, their dark matter spin parameters ($\lambda$) are above average, but not exceptionally high, suggesting that their star formation has been suppressed over cosmic time but are now becoming active, in agreement with prior H$\alpha$ observations.Comment: 20 pages, 13 figure

Propensity scores using missingness pattern information: a practical guide.

Electronic health records are a valuable data source for investigating health-related questions, and propensity score analysis has become an increasingly popular approach to address confounding bias in such investigations. However, because electronic health records are typically routinely recorded as part of standard clinical care, there are often missing values, particularly for potential confounders. In our motivating study-using electronic health records to investigate the effect of renin-angiotensin system blockers on the risk of acute kidney injury-two key confounders, ethnicity and chronic kidney disease stage, have 59% and 53% missing data, respectively. The missingness pattern approach (MPA), a variant of the missing indicator approach, has been proposed as a method for handling partially observed confounders in propensity score analysis. In the MPA, propensity scores are estimated separately for each missingness pattern present in the data. Although the assumptions underlying the validity of the MPA are stated in the literature, it can be difficult in practice to assess their plausibility. In this article, we explore the MPA's underlying assumptions by using causal diagrams to assess their plausibility in a range of simple scenarios, drawing general conclusions about situations in which they are likely to be violated. We present a framework providing practical guidance for assessing whether the MPA's assumptions are plausible in a particular setting and thus deciding when the MPA is appropriate. We apply our framework to our motivating study, showing that the MPA's underlying assumptions appear reasonable, and we demonstrate the application of MPA to this study.Economic and Social Research Council [Grant Number ES/J5000/21/1]; Medical Research Council [Project Grant MR/M013278/1]; Health Data Research UK [Grant Number EPNCZO90], which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcom

Propensity scores using missingness pattern information: a practical guide.

Electronic health records are a valuable data source for investigating health-related questions, and propensity score analysis has become an increasingly popular approach to address confounding bias in such investigations. However, because electronic health records are typically routinely recorded as part of standard clinical care, there are often missing values, particularly for potential confounders. In our motivating study-using electronic health records to investigate the effect of renin-angiotensin system blockers on the risk of acute kidney injury-two key confounders, ethnicity and chronic kidney disease stage, have 59% and 53% missing data, respectively. The missingness pattern approach (MPA), a variant of the missing indicator approach, has been proposed as a method for handling partially observed confounders in propensity score analysis. In the MPA, propensity scores are estimated separately for each missingness pattern present in the data. Although the assumptions underlying the validity of the MPA are stated in the literature, it can be difficult in practice to assess their plausibility. In this article, we explore the MPA's underlying assumptions by using causal diagrams to assess their plausibility in a range of simple scenarios, drawing general conclusions about situations in which they are likely to be violated. We present a framework providing practical guidance for assessing whether the MPA's assumptions are plausible in a particular setting and thus deciding when the MPA is appropriate. We apply our framework to our motivating study, showing that the MPA's underlying assumptions appear reasonable, and we demonstrate the application of MPA to this study.Economic and Social Research Council [Grant Number ES/J5000/21/1]; Medical Research Council [Project Grant MR/M013278/1]; Health Data Research UK [Grant Number EPNCZO90], which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcom

Technology for Behavioral Change in Rural Older Adults with Obesity

Background: Mobile health (mHealth) technologies comprise a multidisciplinary treatment strategy providing potential solutions for overcoming challenges of successfully delivering health promotion interventions in rural areas. We evaluated the potential of using technology in a high-risk population. Methods: We conducted a convergent, parallel mixed-methods study using semi-structured interviews, focus groups, and self-reported questionnaires, using purposive sampling of 29 older adults, 4 community leaders and 7 clinicians in a rural setting. We developed codes informed by thematic analysis and assessed the quantitative data using descriptive statistics. Results: All groups expressed that mHealth could improve health behaviors. Older adults were optimistic that mHealth could track health. Participants believed they could improve patient insight into health, motivating change and assuring accountability. Barriers to using technology were described, including infrastructure. Conclusions: Older rural adults with obesity expressed excitement about the use of mHealth technologies to improve their health, yet barriers to implementation exist

The DetectDeviatingCells algorithm was a useful addition to the toolkit for cellwise error detection in observational data

OBJECTIVE: We evaluated the error detection performance of the DetectDeviatingCells (DDC) algorithm, which flags data anomalies at observation (casewise) and variable (cellwise) level in continuous variables. We compared its performance to other approaches in a simulated dataset. STUDY DESIGN AND SETTING: We simulated height and weight data for hypothetical individuals aged 2-20 years. We changed a proportion of height values according to pre-determined error patterns. We applied the DDC algorithm and other error-detection approaches (descriptive statistics, plots, fixed-threshold rules, classic and robust Mahalanobis distance) and we compared error detection performance with sensitivity, specificity, likelihood ratios, predictive values and ROC curves. RESULTS: At our chosen thresholds, error detection specificity was excellent across all scenarios for all methods and sensitivity was higher for multivariable and robust methods. The DDC algorithm performance was similar to other robust multivariable methods. Analysis of ROC curves suggested that all methods had comparable performance for gross errors (e.g. wrong measurement unit), but the DDC algorithm outperformed the others for more complex error patterns (e.g. transcription errors that are still plausible, although extreme). CONCLUSIONS: The DDC algorithm has the potential to improve error detection processes for observational data

Clinical, health systems and neighbourhood determinants of tuberculosis case fatality in urban Blantyre, Malawi : a multilevel epidemiological analysis of enhanced surveillance data

We investigated whether household to clinic distance was a risk factor for death on tuberculosis (TB) treatment in Malawi. Using enhanced TB surveillance data, we recorded all TB treatment initiations and outcomes between 2015 and 2018. Household locations were geolocated, and distances were measured by a straight line or shortest road network. We constructed Bayesian multi-level logistic regression models to investigate associations between distance and case fatality. A total of 479/4397 (10.9%) TB patients died. Greater distance was associated with higher (odds ratio (OR) 1.07 per kilometre (km) increase, 95% credible interval (CI) 0.99–1.16) odds of death in TB patients registered at the referral hospital, but not among TB patients registered at primary clinics (OR 0.98 per km increase, 95% CI 0.92–1.03). Age (OR 1.02 per year increase, 95% CI 1.01–1.02) and HIV-positive status (OR 2.21, 95% CI 1.73–2.85) were also associated with higher odds of death. Model estimates were similar for both distance measures. Distance was a risk factor for death among patients at the main referral hospital, likely due to delayed diagnosis and suboptimal healthcare access. To reduce mortality, targeted community TB screening interventions for TB disease and HIV, and expansion of novel sensitive diagnostic tests are required.Peer reviewe