Dropouts From Sublingual Immunotherapy and the Transition to Subcutaneous Immunotherapy in House Dust Mite-Sensitized Allergic Rhinitis Patients

Purpose: Both subcutaneous immunotherapy (SCIT) and sublingual immunotherapy (SLIT) are effective in reducing symptoms and medication scores and inducing long-term efficacy in patients with allergic rhinitis (AR). However, SLIT has been associated with poor patient adherence. This study investigates the factors impacting dropout rates from SLIT in house dust mite (HDM)-sensitized AR patients.Methods: A retrospective study was performed to analyze dropout rates and reasons in AR patients receiving Dermatophagoides farinae (Der f) SLIT with a follow-up period of 2 years.Results: A total of 719 HDM-sensitized AR patients received Der f-SLIT. Dropout rates increased with time and most occurred after 1 year of SLIT. By month 24, 654 (91%) patients had discontinued SLIT. The dropout rates by month 24 were 100, 90.1, and 91.1% in children <5 years old, children aged 5–18 years old, and adults ≥ 18 years old, respectively. Combination with allergic asthma and mono- or multi-sensitization to other aeroallergens did not affect the dropout rates. The most common self-reported reasons for dropouts were refusal of continuation, dissatisfaction with the efficacy, transition to SCIT, and adverse effects. Refusal of continuation increased with age, whereas transition to SCIT decreased with age. Ninety-seven cases transitioned from SLIT to SCIT, and the transition rates increased with time. Comorbid allergic asthma did not affect the transition rates. However, multi-sensitization was associated with a slightly higher rate of transition to SCIT. The most common reason for the transition was dissatisfaction with the efficacy (54.6%), which was only reported by patients older than 5 years. For children who began SLIT at younger than 5 years old, the most common reason (81.2%) for transition was age reaching 5 years.Conclusions: HDM-SLIT has a very high dropout rate, which is mainly due to refusal of continuation and dissatisfaction with the efficacy. Transitioning from SLIT to SCIT may help keep these patients on AIT and thus increase adherence and long-term efficacy.

Vaccination dropout rates among children aged 12-23 months in Democratic Republic of the Congo: a cross-sectional study

Background Overall, 1.8 million children fail to receive the 3-dose series for diphtheria, tetanus and pertussis each year in the Democratic Republic of the Congo (DRC). Currently, an emergency plan targeting 9 provinces including Kinshasa, the capital of the DRC, is launched to reinforce routine immunization. Mont Ngafula II was the only health district that experienced high vaccination dropout rates for nearly five consecutive years. This study aimed to identify factors predicting high immunization dropout rates among children aged 12-23 months in the Mont Ngafula II health district. Methods A cross-sectional household survey was conducted among 418 children in June-July 2019 using a two-stage sampling design. Socio-demographic and perception data were collected through a structured interviewer-administered questionnaire. The distribution of 2017-2018 immunization coverage and dropout rate was extracted from the local health district authority and mapped. Logistic random effects regression models were used to identify predictors of high vaccination dropout rates. Results Of the 14 health areas in the Mont Ngafula II health district, four reported high vaccine coverage, only one recorded low vaccine coverage, and three reported both low vaccine coverage and high dropout rate. In the final multivariate logistic random effects regression model, the predictors of immunization dropout among children aged 12-23 months were: living in rural areas, unavailability of seats, non-compliance with the order of arrival during vaccination in health facilities, and lack of a reminder system on days before the scheduled vaccination. Conclusions Our results advocate for prioritizing targeted interventions and programs to strengthen interpersonal communication between immunization service providers and users during vaccination in health facilities and to implement an SMS reminder system on days before the scheduled vaccination.

Comparing success of female students to their male counterparts in the STEM fields: an empirical analysis from enrollment until graduation using longitudinal register data

Background In this paper, we investigate the predictors for enrollment and success in Science, Technology, Engineering, and Mathematics (STEM) programs in higher education. We develop a sequential logit model in which students enroll in STEM education, may drop out from STEM higher education, or continue studying until they graduate in an STEM field. We use rich Dutch register data on student characteristics and high school exam grades to explain the differences in enrollment, success, and dropout rates. Results We find that females are less likely to enroll in STEM-related fields, while students with higher high school mathematics grades are more likely to enroll in STEM. Female students have lower first-year dropout rates at university of applied sciences STEM programs. With respect to study success, we find that conditional on enrollment in STEM, women are less likely to graduate than men within the nominal duration or the nominal duration plus one additional year. However, female students do perform equally well as male students in terms of graduation within 10 years. Conclusions We conclude that STEM programs are less popular among female students and that female students are less likely to graduate on time. However, females perform equally well in STEM higher education in the long run. For this reason, policy should be geared at increasing study success in terms of nominal graduation rates among female STEM students.

Gamification Increases Completion Rates in Massive Open Online Courses

Massive Open Online Courses (MOOCs) aim at unlimited participation and open access via the web. There are concerns about the actual value of such courses. This is predominantly due to higher dropout rates. According to studies, only 7-13% go on to complete these courses. The high dropout rate in MOOCs is a challenge for education providers. This paper aims to explore reasons for high dropout rates within MOOCs and how they can be minimized. With this in mind, two research questions have been set for this study: 1) Why do MOOC participants not complete their courses? 2) How can the course completion rate be increased? Implementation of the strategies investigated in this paper can increase completion rates in MOOCs. In conclusion, after analyzing the collected data, the final results have shown that gamification increased the completion rate of MOOCs.

Does Confinement Affect Treatment Dropout Rates in Patients With Gambling Disorder? A Nine-Month Observational Study

Background and Aims: COVID-19 pandemic and confinement have represented a challenge for patients with gambling disorder (GD). Regarding treatment outcome, dropout may have been influenced by these adverse circumstances. The aims of this study were: (a) to analyze treatment dropout rates in patients with GD throughout two periods: during and after the lockdown and (b) to assess clinical features that could represent vulnerability factors for treatment dropout.Methods: The sample consisted of n=86 adults, mostly men (n=79, 91.9%) and with a mean age of 45years old (SD=16.85). Patients were diagnosed with GD according to DSM-5 criteria and were undergoing therapy at a Behavioral Addiction Unit when confinement started. Clinical data were collected through a semi-structured interview and protocolized psychometric assessment. A brief telephone survey related to COVID-19 concerns was also administered at the beginning of the lockdown. Dropout data were evaluated at two moments throughout a nine-month observational period (T1: during the lockdown, and T2: after the lockdown).Results: The risk of dropout during the complete observational period was R=32/86=0.372 (37.2%), the Incidence Density Rate (IDR) ratio T2/T1 being equal to 0.052/0.033=1.60 (p=0.252). Shorter treatment duration (p=0.007), lower anxiety (p=0.025), depressive symptoms (p=0.045) and lower use of adaptive coping strategies (p=0.046) characterized patients who abandoned treatment during the lockdown. Briefer duration of treatment (p=0.001) and higher employment concerns (p=0.044) were highlighted in the individuals who dropped out after the lockdown. Treatment duration was a predictor of dropout in both periods (p=0.005 and p<0.001, respectively).Conclusion: The present results suggest an impact of the COVID-19 pandemic on treatment dropout among patients with GD during and after the lockdown, being treatment duration a predictor of dropout. Assessing vulnerability features in GD may help clinicians identify high-risk individuals and enhance prevention and treatment approaches in future similar situations.

When more schooling is not worth the effort: another look at the dropout decisions of disadvantaged students

This paper shows that when student heterogeneity is introduced in the analysis, differences in the quality of education and in the probability of repetition, typical in developing countries, mark the contrast between an attractive and an inconvenient investment in education. The methodology associates educational quality and repetition rates with educational returns. In particular, it makes apparent that lower secondary education, in the case of Uruguay, is an inconvenient investment for disadvantaged students, even disregarding the possibility of such students not being able to afford the opportunity costs, this fact probably also explains the heavy dropout rates of this student type in many developing countries.

A Machine Learning Approach to Detect Student Dropout at University

In universities, student dropout is a major concern that reflects the university's quality. Some characteristics cause students to drop out of university. A high dropout rate of students affects the university's reputation and the student's careers in the future. Therefore, there's a requirement for student dropout analysis to enhance academic plan and management to scale back student's drop out from the university also on enhancing the standard of the upper education system. The machine learning technique provides powerful methods for the analysis and therefore the prediction of the dropout. This study uses a dataset from a university representative to develop a model for predicting student dropout. In this work, machine- learning models were used to detect dropout rates. Machine learning is being more widely used in the field of knowledge mining diagnostics. Following an examination of certain studies, we observed that dropout detection may be done using several methods. We've even used five dropout detection models. These models are Decision tree, Naïve bayes, Random Forest Classifier, SVM and KNN. We used machine-learning technology to analyze the data, and we discovered that the Random Forest classifier is highly promising for predicting dropout rates, with a training accuracy of 94% and a testing accuracy of 86%.

PP428 Rechargeable Versus Recharge-Free Sacral Neurostimulation: An Australian Health Economics Analysis

IntroductionSacral neuromodulation is a well-established therapy for urinary and fecal incontinence. Currently available sacral neurostimulators require replacement every three to five years due to battery depletion. New rechargeable sacral neurostimulators with a potential 15-year battery life now have regulatory approval in Australia. However, the initial outlay for the device is higher than for the predicate devices. Our objective was to assess the economic value of rechargeable devices, compared with recharge-free implants, and to identify the patients most likely to benefit from the introduction of this novel technology in Australia.MethodsThe Medicare database was reviewed to quantify populations likely to derive benefits from rechargeable technology. Cost minimization and budget impact analyses were conducted from a payer perspective. Cost inputs were derived from Medicare and the Private Hospital Data Bureau. Two scenarios were modeled comparing the three and five-year battery life of the recharge-free devices with 15 years for a rechargeable device. Sensitivity testing was conducted based on potential uptake and dropout rates (due to death, dementia, etc.).ResultsRechargeable neurostimulators were found to be dominant (cost-saving) in all modeled scenarios, facilitated by a reduction in the frequency of battery replacement procedures and their associated risks for patients. Rechargeability also facilitated higher power settings for optimal symptom control, without trading off device longevity. Younger patients are expected to derive the greatest benefit from the extended battery life as data showed that 40 percent of the implantations were for patients younger than 65 years. The key uncertainty in this analysis was the limited real-world data on patient selection and preferences, which may influence uptake and dropout rates.ConclusionsRechargeable sacral neurostimulators deliver cost savings to the healthcare system due to their extended battery life. Fewer replacement surgeries are an important patient-relevant outcome, especially for younger populations. This finding is important because it demonstrates the economic value of rechargeability to payers and provides robust evidence supporting therapy access for privately insured patients in Australia.

Who Volunteers? Results from a Regular Cognitive Monitoring Study

Previous research indicates volunteering promotes well-being of individuals and communities. Volunteering in later-life may buffer some of the negative health effects experienced during retirement, facilitating opportunities for older adults to engage in meaningful activities and stay active. The current study examined characteristics of older adults who volunteered outside of participation in a regular cognitive monitoring study. All 124 members (M= 76.87, SD= 7.47; 80 volunteers, 44 non-volunteers) of a regular cognitive monitoring study, requiring completion of a 15-minute cognitive online test once a month, with complete data on personal characteristics, volunteer activities, as well as study adherence and dropout rates were included. ANCOVA and logistic regression analyses adjusted for sociodemographic characteristics were used to assess differences between volunteers and non-volunteers. Results indicated that volunteers were less educated (p<.05), and slightly more likely to be younger and women compared to non-volunteers. There were no differences in cognitive performance (ps>.05). Volunteers had lower scores for neuroticism (p=.02) and were marginally higher agreeable and extraverted (ps<.09). Volunteers needed more reminders to complete the monthly test (ps<.01) but had lower dropout rates (p=.001). The most frequent type of volunteer activity reported was religious. Volunteers were motivated mainly by altruism, although most reported multiple reasons such as building social relationships and feeling important. Findings provide information about characteristics that can help identify older adults who are likely to volunteer. Results regarding study adherence may have implications for promoting recruitment and retention among older adult volunteers.