Developing predictive models of health literacy.

IntroductionLow health literacy (LHL) remains a formidable barrier to improving health care quality and outcomes. Given the lack of precision of single demographic characteristics to predict health literacy, and the administrative burden and inability of existing health literacy measures to estimate health literacy at a population level, LHL is largely unaddressed in public health and clinical practice. To help overcome these limitations, we developed two models to estimate health literacy.MethodsWe analyzed data from the 2003 National Assessment of Adult Literacy (NAAL), using linear regression to predict mean health literacy scores and probit regression to predict the probability of an individual having 'above basic' proficiency. Predictors included gender, age, race/ethnicity, educational attainment, poverty status, marital status, language spoken in the home, metropolitan statistical area (MSA) and length of time in U.S.ResultsAll variables except MSA were statistically significant, with lower educational attainment being the strongest predictor. Our linear regression model and the probit model accounted for about 30% and 21% of the variance in health literacy scores, respectively, nearly twice as much as the variance accounted for by either education or poverty alone.ConclusionsMultivariable models permit a more accurate estimation of health literacy than single predictors. Further, such models can be applied to readily available administrative or census data to produce estimates of average health literacy and identify communities that would benefit most from appropriate, targeted interventions in the clinical setting to address poor quality care and outcomes related to LHL

Genetic Influences on Parental Care in Nicrophorus vespilloides

The burying beetle (Nicrophorus vespilloides) has unusually highly developed parental care; parents prepare and maintain a food resource (thereby providing indirect parental care), feed through direct provisioning by regurgitation, and protect their larvae. Parental care is highly variable and can be uniparental female care, uniparental male care, or biparental. There are genetic components to the parenting behaviour of the burying beetle, the amount of direct and indirect care given, and the size of the brood are heritable and therefore genetic traits. In this thesis I have focused on two candidate genes that I predicted would influence parental care behaviour. The first is foraging, which has been shown to influence a range of social and reproductive behaviours in other insect species. Using QRTPCR and pharmacological manipulations I have investigated the role of Nvfor in adult and juvenile burying beetles. The second gene is inotocin, the insect orthologue of oxytocin. Oxytocin has been shown to influence social behaviour as well as many behaviours associated with reproduction in vertebrates and invertebrates, however the effects of inotocin have not yet been investigated in insects. I have used pharmacological manipulations to investigate the role of inotocin in parental behaviour in female burying beetles. Collectively my results demonstrate the central role of Nvfor in the control of direct parental care and the association with major behavioural changes in both adult and larval burying beetles. I have also demonstrated the possible involvement of oxytocin in the control of aggression towards conspecific larvae. These insights suggest the controlling mechanism for the behavioural changes seen in burying beetles is complex and involves interactions between many genes. Combined with previous research on these genes, it is clear they are key components in the evolution of sociality. Finally, my research indicates the power of the candidate gene approach, and suggests additional components of the related pathways that could be investigated.The University of Exete

Determinants of Racial/Ethnic Disparities in Incidence of Diabetes in Postmenopausal Women in the U.S.: The Women’s Health Initiative 1993–2009

OBJECTIVE To examine determinants of racial/ethnic differences in diabetes incidence among postmenopausal women participating in the Women’s Health Initiative. RESEARCH DESIGN AND METHODS Data on race/ethnicity, baseline diabetes prevalence, and incident diabetes were obtained from 158,833 women recruited from 1993–1998 and followed through August 2009. The relationship between race/ethnicity, other potential risk factors, and the risk of incident diabetes was estimated using Cox proportional hazards models from which hazard ratios (HRs) and 95% CIs were computed. RESULTS Participants were aged 63 years on average at baseline. The racial/ethnic distribution was 84.1% non-Hispanic white, 9.2% non-Hispanic black, 4.1% Hispanic, and 2.6% Asian. After an average of 10.4 years of follow-up, compared with whites and adjusting for potential confounders, the HRs for incident diabetes were 1.55 for blacks (95% CI 1.47–1.63), 1.67 for Hispanics (1.54–1.81), and 1.86 for Asians (1.68–2.06). Whites, blacks, and Hispanics with all factors (i.e., weight, physical activity, dietary quality, and smoking) in the low-risk category had 60, 69, and 63% lower risk for incident diabetes. Although contributions of different risk factors varied slightly by race/ethnicity, most findings were similar across groups, and women who had both a healthy weight and were in the highest tertile of physical activity had less than one-third the risk of diabetes compared with obese and inactive women. CONCLUSIONS Despite large racial/ethnic differences in diabetes incidence, most variability could be attributed to lifestyle factors. Our findings show that the majority of diabetes cases are preventable, and risk reduction strategies can be effectively applied to all racial/ethnic groups

Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach

In this work we present a three-stage Machine Learning strategy to country-level risk classification based on countries that are reporting COVID-19 information. A K% binning discretisation (K = 25) is used to create four risk groups of countries based on the risk of transmission (coronavirus cases per million population), risk of mortality (coronavirus deaths per million population), and risk of inability to test (coronavirus tests per million population). The four risk groups produced by K% binning are labelled as ‘low’, ‘medium-low’, ‘medium-high’, and ‘high’. Coronavirus-related data are then removed and the attributes for prediction of the three types of risk are given as the geopolitical and demographic data describing each country. Thus, the calculation of class label is based on coronavirus data but the input attributes are country-level information regardless of coronavirus data. The three four-class classification problems are then explored and benchmarked through leave-one-country-out cross validation to find the strongest model, producing a Stack of Gradient Boosting and Decision Tree algorithms for risk of transmission, a Stack of Support Vector Machine and Extra Trees for risk of mortality, and a Gradient Boosting algorithm for the risk of inability to test. It is noted that high risk for inability to test is often coupled with low risks for transmission and mortality, therefore the risk of inability to test should be interpreted first, before consideration is given to the predicted transmission and mortality risks. Finally, the approach is applied to more recent risk levels to data from September 2020 and weaker results are noted due to the growth of international collaboration detracting useful knowledge from country-level attributes which suggests that similar machine learning approaches are more useful prior to situations later unfolding

Seed dispersal by Martu peoples promotes the distribution of native plants in arid Australia

Commensal relationships between wild plants and their dispersers play a key ecological and evolutionary role in community structure and function. While non-human dispersers are often considered critical to plant recruitment, human dispersers have received much less attention, especially when it comes to non-domesticated plants. Australia, as a continent historically characterized by economies reliant on non-domesticated plants, is thus a key system for exploring the ecological role of people as seed dispersers in the absence of agriculture. Here, we utilize a controlled observation research design, employing ecological surveys and ethnographic observations to examine how seed dispersal and landscape burning by Martu Aboriginal people affects the distribution of three preferred plants and one (edible, but non-preferred) control species. Using an information theoretic approach, we find that the three preferred plants show evidence of human dispersal, with the strongest evidence supporting anthropogenic dispersal for the wild bush tomato, Solanum diversiflorum

Neighborhood Effects on Health: Concentrated Advantage and Disadvantage

We investigate an alternative conceptualization of neighborhood context and its association with health. Using an index that measures a continuum of concentrated advantage and disadvantage, we examine whether the relationship between neighborhood conditions and health varies by socio-economic status. Using NHANES III data geo-coded to census tracts, we find that while largely uneducated neighborhoods are universally deleterious, individuals with more education benefit from living in highly educated neighborhoods to a greater degree than individuals with lower levels of education

Telehealth-supervised exercise in systemic lupus erythematosus : A pilot study

Objectives: To explore the feasibility and effectiveness of telehealth-supervised exercise for adults with Systemic lupus erythematosus (SLE). Methods: This was a non-randomised controlled pilot trial comparing telehealth-supervised exercise (8 weeks, 2 days/week, 45 min, moderate intensity) plus usual care with usual care alone. Mixed methods were used to assess change in fatigue (FACIT-fatigue), quality of life (SF36), resting fatigue and pain (11-point scale), lower body strength (five-time sit-to-stand) and endurance (30 s sit-to-stand), upper body endurance (30 s arm curl), aerobic capacity (2 min step test), and experience (survey and interviews). Group comparison was performed statistically using a two-sample T-test or Mann–Whitney U-test. Where known, we used MCID or MCII, or assumed a change of 10%, to determine clinically meaningful change within groups over time. Interviews were analysed using reflexive thematic analysis. Results: Fifteen female adults with SLE were included (control group n = 7, exercise group n = 8). Statistically significant differences between groups, in favour of the exercise intervention, were noted for SF36 domain emotional well-being (p = 0.048) and resting fatigue (p = 0.012). There were clinically meaningful improvements over time for FACIT-fatigue (+6.3 ± 8.3, MCID >5.9), SF36 domains physical role functioning (+30%), emotional role functioning (+55%), energy/fatigue (+26%), emotional well-being (+19%), social functioning (+30%), resting pain (−32%), and upper body endurance (+23%) within the exercise group. Exercise attendance was high (98%, 110/112 sessions); participants strongly agreed (n = 5/7, 71%) or agreed (n = 2/7, 29%) they would do telehealth-supervised exercise again and were satisfied with the experience. Four themes emerged: (1) ease and efficiency of exercising from home, (2) value of live exercise instruction, (3) challenges of exercising at home, and (4) continuation of telehealth-supervised exercise sessions. Conclusion: Key findings from this mixed-method investigation suggest that telehealth-supervised exercise was feasible for, and well-accepted by, adults with SLE and resulted in some modest health improvements. We recommend a follow-up RCT with more SLE participants

Enhancing Pollinator Conservation towards Agriculture 4.0: Monitoring of Bees through Object Recognition

In an era of rapid climate change and its adverse effects on food production, technological intervention to monitor pollinator conservation is of paramount importance for environmental monitoring and conservation for global food security. The survival of the human species depends on the conservation of pollinators. This article explores the use of Computer Vision and Object Recognition to autonomously track and report bee behaviour from images. A novel dataset of 9664 images containing bees is extracted from video streams and annotated with bounding boxes. With training, validation and testing sets (6722, 1915, and 997 images, respectively), the results of the COCO-based YOLO model fine-tuning approaches show that YOLOv5m is the most effective approach in terms of recognition accuracy. However, YOLOv5s was shown to be the most optimal for real-time bee detection with an average processing and inference time of 5.1ms per video frame at the cost of slightly lower ability. The trained model is then packaged within an explainable AI interface, which converts detection events into timestamped reports and charts, with the aim of facilitating use by non-technical users such as expert stakeholders from the apiculture industry towards informing responsible consumption and production