OBJECTIVE: to evaluate the relative risk of smoking cessation treatment dropout during its intensive phase.
METHODS: a retrospective and quantitative cohort study was developed from the electronic medical records of individuals who started smoking cessation treatment between 2015 and 2019 at a specialty clinic in a city in the interior of São Paulo, Brazil. The relative risk of dropping out of treatment was calculated using the Poisson regression model.
RESULTS: it was observed that out of the 396 (100.0%) individuals who started the treatment, 109 (27.5%) abandoned it before the end of the intensive phase. For each one-year increase in age, the risk of dropping out of smoking cessation treatment decreased by an average of 2%.
CONCLUSIONS: the risk of dropping out of smoking cessation treatment is higher among younger individuals. It is necessary to rethink the care offered to younger adults to promote the continuity of treatment.

According to the main international guidelines, patients with obesity and psychiatric/psychological disorders who cannot be addressed to surgery are recommended to follow a nutritional approach and a psychological treatment. A total of 94 patients (T0) completed a battery of self-report measures: Symptom Checklist-90-Revised (SCL-90-R), Barratt Impulsiveness Scale-11 (BIS-11), Binge-Eating Scale (BES), Obesity-Related Well-Being Questionnaire-97 (ORWELL-97), and Minnesota Multiphasic Personality Inventory-2 (MMPI-2). Then, twelve sessions of a brief psychodynamic psychotherapy were delivered, which was followed by the participants completing the follow-up evaluation (T1). Two groups of patients were identified: Group 1 (n = 65), who fully completed the assessment in both T0 and T1; and Group 2-dropout (n = 29), who fulfilled the assessment only at T0 and not at T1. Machine learning models were implemented to investigate which variables were most associated with treatment failure. The classification tree model identified patients who were dropping out of treatment with an accuracy of about 80% by considering two variables: the MMPI-2 Correction (K) scale and the SCL-90-R Phobic Anxiety (PHOB) scale. Given the limited number of studies on this topic, the present results highlight the importance of considering the patient\'s level of adaptation and the social context in which they are integrated in treatment planning. Cautionary notes, implications, and future directions are discussed.

BACKGROUND: Dietary behaviors significantly influence health outcomes across populations. Unhealthy diets are linked to serious diseases and substantial economic burdens, contributing to approximately 11 million deaths and significant disability-adjusted life years annually. Digital dietary interventions offer accessible solutions to improve dietary behaviors. However, attrition, defined as participant dropout before intervention completion, is a major challenge, with rates as high as 75%-99%. High attrition compromises intervention validity and reliability and exacerbates health disparities, highlighting the need to understand and address its causes.
OBJECTIVE: This study systematically reviews the literature on attrition in digital dietary interventions to identify the underlying causes, propose potential solutions, and integrate these findings with behavior theory concepts to develop a comprehensive theoretical framework. This framework aims to elucidate the behavioral mechanisms behind attrition and guide the design and implementation of more effective digital dietary interventions, ultimately reducing attrition rates and mitigating health inequalities.
METHODS: We conducted a systematic review, meta-analysis, and thematic synthesis. A comprehensive search across 7 electronic databases (PubMed, MEDLINE, Embase, CENTRAL, Web of Science, CINAHL Plus, and Academic Search Complete) was performed for studies published between 2013 and 2023. Eligibility criteria included original research exploring attrition in digital dietary interventions. Data extraction focused on study characteristics, sample demographics, attrition rates, reasons for attrition, and potential solutions. We followed ENTREQ (Enhancing the Transparency in Reporting the Synthesis of Qualitative Research) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and used RStudio (Posit) for meta-analysis and NVivo for thematic synthesis.
RESULTS: Out of the 442 identified studies, 21 met the inclusion criteria. The meta-analysis showed mean attrition rates of 35% for control groups, 38% for intervention groups, and 40% for observational studies, with high heterogeneity (I²=94%-99%) indicating diverse influencing factors. Thematic synthesis identified 15 interconnected themes that align with behavior theory concepts. Based on these themes, the force-resource model was developed to explore the underlying causes of attrition and guide the design and implementation of future interventions from a behavior theory perspective.
CONCLUSIONS: High attrition rates are a significant issue in digital dietary interventions. The developed framework conceptualizes attrition through the interaction between the driving force system and the supporting resource system, providing a nuanced understanding of participant attrition, summarized as insufficient motivation and inadequate or poorly matched resources. It underscores the critical necessity for digital dietary interventions to balance motivational components with available resources dynamically. Key recommendations include user-friendly design, behavior-factor activation, literacy training, force-resource matching, social support, personalized adaptation, and dynamic follow-up. Expanding these strategies to a population level can enhance digital health equity. Further empirical validation of the framework is necessary, alongside the development of behavior theory-guided guidelines for digital dietary interventions.
BACKGROUND: PROSPERO CRD42024512902; https://tinyurl.com/3rjt2df9.

Attrition is a particular concern in studies examining the efficacy of a treatment for Alzheimer disease. Analyzing reasons for withdrawal in Alzheimer studies is crucial to ruling out attrition bias, which can undermine a study\'s validity. In contrast, attrition in studies using repetitive transcranial magnetic stimulation (rTMS) has received much less attention. Our goal was to identify any commonalities between participants who withdrew for the same reasons. Three independent coders rated each response concerning the reasons for withdrawal, and frequency tables were generated to characterize the participants within each category. This study was conducted on the 28 withdrawn cases from a 7-month study investigating the short-term and long-term therapeutic effects of rTMS for Alzheimer disease among 156 participants across 3 sites of the study. Seven reasons for withdrawal were identified, with health and medical changes being the most commonly reported reason (7 participants). Personal issues involving family or caregivers were the next most common (5 participants), and the remaining 5 categories consisted of 3 participants each. Although the limited sample size prevented the use of inferential statistics, our findings highlight the need for more transparent reporting of attrition rates and withdrawal reasons by rTMS researchers.

UNASSIGNED: The objective of the study was to investigate dropout rates and discern potential factors contributing to the discontinuation of treatment provided to transgender individuals by the Transdisciplinary Gender Identity Program at the Hospital de Clínicas de Porto Alegre (PROTIG).
UNASSIGNED: This study employs a descriptive, cross-sectional, retrospective design to analyze socio-demographic and clinical data obtained from medical records of patients treated at PROTIG between 2000 and 2018. A structured form, devised by PROTIG\'s professional team, was utilized to extract and evaluate several variables including: age, gender, education level, diagnosis of F64 according to the International Classification of Diseases (ICD-10: Version: 2010), clinical comorbidities (coded by ICD-10), laboratory diagnosis of sexually transmitted infections, distance between patients\' residence and the hospital, and year of entry into PROTIG. The patient cohort was stratified into two categories based on their duration of attendance: dropout (defined as attendance for up to 365 days) and non-dropout (attendance exceeding 365 days). Categorical variables between dropout and non-dropout groups were compared using Pearson\'s chi-square test. Additionally, Poisson regression analysis was utilized, employing a 95% confidence interval (CI) and setting the significance level at 0.05.
UNASSIGNED: The study included a total of 888 patients accessing PROTIG, with 275 (31%) classified in the dropout group. Of the patient population, 65.5% (n = 582) self-identified as transgender women, while 34.5% (n = 306) identified as transgender men. Significant differences were noted between the dropout and non-dropout groups. Specifically, differences were noted among transgender women (p < 0.001), individuals with lower levels of education (p < 0.001), those with fewer diagnoses classified under ICD-10 as F64 (p < 0.001), individuals exhibiting fewer clinical comorbidities recorded in ICD-10 (p < 0.001), and those who commenced inclusion in PROTIG after 2010 (p < 0.001).
UNASSIGNED: There exists a notable rate of treatment discontinuation among individuals receiving care at PROTIG, with statistically significant variances observed between groups. We posit potential rationales for this discontinuation, informed by care experiences and feedback from group attendees: Increased accessibility to outpatient services in our jurisdiction for Transgender Care, along with heightened societal awareness of gender identity fostering diverse gender expression avenues devoid of reliance on gender-affirming surgical interventions.

BACKGROUND: Cancer presents a growing global burden, not least in African countries such as Ghana where high cancer treatment dropouts has been identified due to numerous social, cultural and financial reasons. There is little understanding regarding patterns of treatment access behaviour, especially in Northern Ghana, which this study was designed to explore.
METHODS: Through cross-sector collaboration, we extracted and clinically validated cancer patient records available in the Tamale Teaching Hospital. These were analysed descriptively and through multi-variate logistic regression. A treatment mapping process was also applied to highlight challenges in data collection. Multiple imputation with chained equations was conducted for high levels of missing data. Sensitivity analysis was applied to assess the impact of missing data.
RESULTS: Treatment drop-out was high even when uncertainty due to missing data was accounted for, and only 27 % of patients completely engaged with treatment. High drop-out was found for all cancers including those covered by the Ghana National Health Insurance scheme. Multi-variate logistic regression revealed that social, health condition and systemic factors influence treatment engagement until completion. High missing data was observed for liver, ovarian, colorectal, gastric, bladder, oesophageal and head and neck and skin cancers, and soft tissue sarcomas, which limited model fitting.
CONCLUSIONS: Treatment drop-out is a critical issue in Northern Ghana. There was high missing data due to the dynamic, complex and decentralised treatment pathway. Future studies are needed to understand the complex challenges in data recording.
UNASSIGNED: Treatment drop out is a pertinent issue that policy makers should look to address. Further discussion with stakeholders involved in cancer treatment and data collection is required to better understand challenges to routine data collection in the local setting. This will allow policy to be designed to cater for the impact of multiple intersecting health and social factors on treatment completion.

BACKGROUND: Participant discontinuation from study treatment in a clinical trial can leave a trial underpowered, produce bias in statistical analysis, and limit interpretability of study results. Retaining participants in clinical trials for the full study duration is therefore as important as participant recruitment.
OBJECTIVE: This analysis aims to identify associations of pre-randomization characteristics of participants with premature discontinuation during the blinded phase of the Anti-Amyloid treatment in Asymptomatic AD (A4) Study.
METHODS: All A4 trial randomized participants were classified as having prematurely discontinued study during the blinded period of the study for any reason (dropouts) or completed the blinded phase of the study on treatment (completers).
METHODS: The trial was conducted across 67 study sites in the United States, Canada, Japan and Australia through the global COVID-19 pandemic.
METHODS: The sample consisted of all 1169 A4 trial randomized participants.
METHODS: Pre-randomization demographic, clinical, amyloid PET and genetic predictors of study discontinuation were evaluated using a univariate generalized linear mixed model (GLMM), with discontinuation status as the binary outcome, each predictor as a fixed effect, and site as a random effect to account for differences among study sites in the trial. Characteristics significant at p<0.10 were then included in a multivariable GLMM.
RESULTS: Among randomized participants, 339 (29%) discontinued the study during the blinded period (median follow-up time in trial: 759 days). From the multivariable analysis, the two main predictors of study discontinuation were screening State-Trait Anxiety Inventory (STAI) scores (OR = 1.07 [95%CI = 1.02; 1.12]; p=0.002) and age (OR = 1.06 [95%CI = 1.03; 1.09]; p<0.001). Participants with a family history of dementia (OR = 0.75 [95%CI = 0.55; 1.01]; p=0.063) and APOE ε4 carriers (OR = 0.79 [95%CI = 0.6; 1.04]; p=0.094) were less likely to discontinue from the study, with the association being marginally significant. In these analyses, sex, race and ethnicity, cognitive scores and amyloid/tau PET scores were not associated with study dropout.
CONCLUSIONS: In the A4 trial, older participants and those with higher levels of anxiety at baseline as measured by the STAI were more likely to discontinue while those who had a family history of dementia or were APOE ε4 carriers were less likely to drop out. These findings have direct implications for future preclinical trial design and selection processes to identify those individuals at greatest risk of dropout and provide information to the study team to develop effective selection and retention strategies in AD prevention studies.

OBJECTIVE: Web-based cognitive bias modification for interpretation (CBM-I) can improve interpretation biases and anxiety symptoms but faces high rates of dropout. This study tested the effectiveness of web-based CBM-I relative to an active psychoeducation condition and the addition of low-intensity telecoaching for a subset of CBM-I participants.
METHODS: 1,234 anxious community adults (Mage = 35.09 years, 81.2% female, 72.1% white, 82.6% not Hispanic) were randomly assigned at Stage 1 of a sequential, multiple-assignment randomized trial to complete five weekly sessions of CBM-I or psychoeducation on our team\'s public research website. After the first session, for Stage 2, an algorithm attempted to classify CBM-I participants as higher (vs. lower) risk for dropping out; those classified as higher risk were then randomly assigned to complete four brief weekly telecoaching check-ins (vs. no coaching).
RESULTS: As hypothesized (https://doi.org/j2xr; Daniel, Eberle, & Teachman, 2020), CBM-I significantly outperformed psychoeducation at improving positive and negative interpretation biases (Recognition Ratings, Brief Body Sensations Interpretation Questionnaire) and anxiety symptoms (Overall Anxiety Severity and Impairment Scale, Anxiety Scale from Depression Anxiety Stress Scales-Short Form), with smaller treatment gains remaining significant at 2-month follow-up. Unexpectedly, CBM-I had significantly worse treatment dropout outcomes than psychoeducation, and adding coaching (vs. no coaching) did not significantly improve efficacy or dropout outcomes (notably, many participants chose not to interact with their coach).
CONCLUSIONS: Web-based CBM-I appears effective, but supplemental coaching may not mitigate the challenge of dropout. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

UNASSIGNED: Vaccination is a cost-effective public health program that helps reduce significant morbidity and mortality in children under the age of five. Worldwide, the number of vaccine-preventable causes of child death has significantly decreased since the Expanded Program of Immunization (EPI) was introduced. However, for a variety of reasons, 23 million children did not have adequate access to vaccines in 2020. Therefore, this study aimed to evaluate the determinants of pneumonia conjugate vaccine (PCV) dropout among children aged 12-23 months in Ethiopia.
UNASSIGNED: The study analyzed cross-sectional data obtained from the 2019 mini Ethiopian demographic and health survey. Multilevel binary logistic regression analysis was utilized, and the best fit model was chosen using the Akaike Information Criteria. The study comprised a weighted sample of 989 children aged 12 to 23 months. The study presented the Adjusted Odds Ratio (AOR) along with a 95% Confidence Interval (CI) to identify the significant factors influencing PCV dropout.
UNASSIGNED: The PCV dropout rate was reported at 20.2% in this study. In the multilevel analysis, possession of a health card (AOR = 0.076, 95% CI: 0.019, 0.04), vaccination for PCV 2 (AOR =0.002, 95% CI: 0.023, 0.263), and region 7 (AOR = 6.98, 95% CI: 10.1, 48.31) were significantly associated with children\'s PCV dropout.
UNASSIGNED: Having a health card, having received the PCV 2 vaccinations, and region were significant predictors of PCV dropout. Consequently, health education on immunization for all mothers and region-specific, customized public health interventions are needed to reduce the vaccination dropout rate.

BACKGROUND: Each year, vaccine-preventable diseases cost the lives of 8.8 million under-five children. Although vaccination prevents 1-2 million childhood deaths worldwide, measles vaccination dropouts are not well studied in developing countries, particularly in Ethiopia. Therefore, this study aims to assess the spatial distribution of the measles vaccination dropout and its determinants among under-five children in Ethiopia.
METHODS: Data from Ethiopian Demographic and Health Survey 2019 was used for data analysis. The study used a total of 5,753 children. Spatial autocorrelations was used to determine the spatial dependency of measles vaccination dropout. Ordinary interpolation was employed to forecast measles vaccination dropout. Factors associated with measles vaccination dropout were declared significant at p-values <0.05. The data were interpreted using the confidence interval and adjusted odds ratio. A model with the lowest deviance and highest logliklihood ratio was selected as the best-fit model.
RESULTS: In Ethiopia, one in three under-five children had measles vaccination dropouts. Factors such as birth interval (AOR = 1.87, 95% CI: 1.30, 2.70), unmarried marital status women (AOR = 3.98, 95% CI: 1.08, 8.45), ≤1 number of under-five children (AOR = 3.86, 95% CI: 2.56, 5.81), rural place of residence (AOR = 2.43, 95% CI: 2.29, 3.11), low community-level ANC utilization (AOR = 3.20, 95% CI: 2.53, 3.56), and residing in Benishangul Gumuz (AOR = 1.80, 95% CI: 1.061, 3.06) had higher odds of measles vaccination dropout.
CONCLUSIONS: Measles vaccination dropout rates in Ethiopia among under-five children were high compared to the maximum tolerable vaccination dropout level of 10% by the WHO. Both individual and community-level variables were determinants of measles vaccination dropout. The ministry of health in Ethiopia should give attention to those mothers of under-five children who reported underutilization of ANC services and rural residences while designing policies and strategies in areas of high spatial clustering of vaccine dropout in Ethiopia.