Engaging adolescents in research: Home self-collection of biological samples and health questionnaires.

Adolescent health research with biomarker data collection is limited due to difficulties in recruiting and engaging this age group. Thus, successful recruitment, engagement, and retention of adolescents in translational research are necessary to elucidate factors influencing mental and physical health conditions, uncover novel biomarkers, and expand prevention and treatment options. This paper describes strategies for effective recruitment and retention of adolescents in a research study, using a project examining depressive symptoms and the microbiome to illustrate these approaches. This cross-sectional study collected electronic self-reported survey data and self-collected biospecimens (stool and salivary samples) from adolescents 13-19 years old. All but two participants completed the questionnaires, with few missing responses. 94% provided at least one salivary sample and 89% supplied a stool sample. Participants were able to adhere to the study instructions. Using a participant-centered approach, our study successfully recruited and engaged the targeted 90 participants in self-collection of electronic survey data and biospecimens. Successful strategies of recruitment and retention included: 1) on-site clinic recruitment by research team, 2) active involvement of parents as appropriate, 3) use of electronic surveys and self-collection of biospecimens to foster control and ease of participation while addressing privacy concerns, 4) noninvasive collection of data on biospecimen, 5) frequent texting to communicate with participants, 6) flexibility in the pickup and transferring of biospecimens to accommodate adolescent schedules, 7) developmentally appropriate research, 8) participant reimbursement, and 9) sensitivity toward discussing stool sample materials. As a result of these strategies, adolescent participation in the research proved feasible.

Influence of environmental, geographic, socio-demographic, and epidemiological factors on presence of malaria at the community level in two continents.

The interactions of environmental, geographic, socio-demographic, and epidemiological factors in shaping mosquito-borne disease transmission dynamics are complex and changeable, influencing the abundance and distribution of vectors and the pathogens they transmit. In this study, 27 years of cross-sectional malaria survey data (1990-2017) were used to examine the effects of these factors on Plasmodium falciparum and Plasmodium vivax malaria presence at the community level in Africa and Asia. Monthly long-term, open-source data for each factor were compiled and analyzed using generalized linear models and classification and regression trees. Both temperature and precipitation exhibited unimodal relationships with malaria, with a positive effect up to a point after which a negative effect was observed as temperature and precipitation increased. Overall decline in malaria from 2000 to 2012 was well captured by the models, as was the resurgence after that. The models also indicated higher malaria in regions with lower economic and development indicators. Malaria is driven by a combination of environmental, geographic, socioeconomic, and epidemiological factors, and in this study, we demonstrated two approaches to capturing this complexity of drivers within models. Identifying these key drivers, and describing their associations with malaria, provides key information to inform planning and prevention strategies and interventions to reduce malaria burden.

A Community-Guided Approach to Bronchiolitis: A Needs Assessment and Illness Perception Study.

The factors influencing caregivers' understanding of pediatric respiratory diseases, such as bronchiolitis, can guide patient care and the acceptability of treatment methods within the healthcare system. This study aims to identify illness perceptions and perform a needs assessment among caregivers of children diagnosed with respiratory diseases. This is a prospective, cross-sectional, questionnaire-driven study of a representative sample of caregivers whose children had an acute respiratory illness. The telephone-administered questionnaire was comprised of (1) demographic items; (2) illness perception questionnaire-revised (IPQ-R); and (3) items about personal barriers, the latter 2 of which employed a 5-point Likert response. Cronbach's alpha (α) was used to measure the internal consistency reliability for each item within the IPQ-R. The Pearson 2-tailed correlation coefficient was used to correlate questionnaire items. We included 75 caregivers whose children have been diagnosed with bronchiolitis (51%), reactive airway disease (RAD) (35%), asthma (33%), and wheezing (44%). We found no significance between the child's diagnosis and the site of recruitment. The most important components of the illness perception were illness coherence (α=0.849), psychological attributions (α=0.903), and barriers to diagnosis (α=0.633). Understanding caregivers' perceptions of respiratory diseases will lead to better treatment acceptance. We must clarify the terms used to define bronchiolitis from viral-induced wheezing, RAD, and the first asthma episode in older infants. Identifying caregivers' gaps in knowledge will help establish a cohesive approach to personalized treatment of respiratory diseases in children and their diagnosis.

First vaccine confidence and access index in Argentina: Comparison of results from 2019 to 2022.

In this article, we present and empirically illustrate two concepts about vaccines and the way they are perceived by the Argentinean population and the easiness in accessing vaccination in developing countries. First, we focus on the perceptions of people about vaccines in general and develop a confidence index, and second, we analyze barriers to vaccination, measuring the burden citizens have when they intend to receive immunization (or as caretakers, trying to comply with the vaccination calendar of children and adolescents): for this second concept, we develop an access index. The data comes from representative annual surveys from Argentina from 2019 until 2022 (each one with approximately 7000 responders), which allows us to describe trends and check for changes in the confidence in vaccines and barriers towards vaccination. We find high confidence in vaccines in Argentina, although there is a "structural break" in the confidence for all years after 2020. Because we changed the questionnaire and methodology regarding the access to vaccines index in 2022, the discussion focuses on the cross-section of 2022, observing that barriers to vaccination tend to affect less educated caretakers.

Prevalence and correlates of alcohol and drug harms to others: Findings from the 2020 U.S. National Alcohol Survey.

OBJECTIVE: Measure prevalence and overlap of secondhand harms from other people's use of alcohol, cannabis, opioid, or other drugs and examine sociodemographic and other correlates of these secondhand harms. METHODS: This cross-sectional analysis used data from 7,799 respondents (51.6% female; 12.9% Black, 15.6% Hispanic/Latiné; mean age: 47.6) in the 2020 U.S. National Alcohol Survey. Secondhand harms included family/marriage difficulties, traffic accidents, vandalism, physical harm, and financial difficulties. Weighted prevalence estimates provided nationally representative estimates of these harms. Logistic regression assessed associations between individual characteristics and secondhand harms. RESULTS: Lifetime prevalence of secondhand harms from alcohol, cannabis, opioid, or other drugs was 34.2%, 5.5%, 7.6%, and 8.3%, respectively. There was substantial overlap among lifetime harms: almost 30% of those reporting secondhand alcohol harms also reported secondhand drug harms. Significant correlates of secondhand substance harms included female sex (alcohol, other drug); white (alcohol, opioid), American Indian/Alaska Native (opioid), and Black (cannabis) race/ethnicity; and separated/divorced/widowed marital status (opioid). Those reporting family history of alcohol problems had significantly higher odds of reporting secondhand harms across substance types. Individuals who reported frequent cannabis use had higher odds of reporting secondhand alcohol and opioid harms compared to those with no cannabis use, (aOR=1.55; aOR=2.38), but lower odds of reporting secondhand cannabis harms (aOR=0.51). CONCLUSIONS: Although less prevalent than secondhand alcohol harms, 14% of participants reported secondhand harms from someone else's drug use and frequently experienced secondhand harms attributed to multiple substances. Population-focused interventions are needed to reduce the total burden of alcohol and drug use.

Understanding Patients' Negative Experiences with Telehealth: A Content Analysis of Survey Data.

Understanding differences in how demographic groups experience telehealth may be relevant in addressing potential disparities in telehealth usage. We seek to identify and examine themes most pertinent to patients' negative telehealth experiences by age and race in order to inform interventions to improve patients' future telehealth experiences. We performed a content analysis of Press Ganey patient experience surveys from adult patients at 17 primary care sites of a large, public healthcare system with visits from April 30, 2020 to August 27, 2021. We used sentiment analysis to identify negative comments. We coded for content themes and analyzed their frequency, stratifying by age and race. We analyzed 745 negative comments. Most frequent themes differed by demographic categories, but overall, the most commonly applied codes were "Contacting the Clinic" (n = 97), "Connectivity" (n = 84), and "Webside Manner" (n = 79). The top three codes accounted for >40% of the negative codes in each race category and >35% of the negative codes in each age category. While there were common negative experiences among groups, patients of different demographics highlighted different aspects of their telehealth experiences for potential improvement.

The Affordable Care Act and income-based disparities in health care coverage and spending among nonelderly adults with cancer.

The Patient Protection and Affordable Care Act (ACA) significantly reduced uninsured individuals and improved financial protection; however, escalating costs of cancer treatment has led to substantial out-of-pocket expenses, causing severe financial and mental health distress for individuals with cancer. Mixed evidence on the ACA's ongoing impact highlights the necessity of assessing health-spending changes across income groups for informed policy interventions. In our nationally representative survey evaluating the early- and long-term effects of the ACA on nonelderly adult patients with cancer, we categorized individuals-based income subgroups defined by the ACA for eligibility. We found that ACA implementation increased insurance coverage, which was particularly evident after 2 years of implementation. Early post-ACA (within two years of implementation), there were declines in out-of-pocket spending for the lowest and low-income groups by 26.52% and 38.31%, respectively, persisting long-term only for the lowest-income group. High-income groups experienced continuously increased out-of-pocket and premium spending by 25.39% and 34.28%, respectively, with a notable 122% increase in the risk of high-burden spending. This study provides robust evidence of income-based disparities in financial burden for cancer care, emphasizing the need for health care policies promoting equitable care and addressing spending disparities across income brackets.

Wellbeing and Happiness and Their Association With Working Conditions at Sea: A Cross-sectional Study Among the Global Workforce of Seafarers.

The aim of this study was to investigate whether seafarers' self-reported work experiences were associated with wellbeing and happiness while onboard. The study also examined which indicators of the work experiences had an effect in what direction. We analyzed the survey responses from 13 008 seafarers onboard, from 154 different nationalities, serving in 44 different international shipping companies. The outcome measures were wellbeing and happiness, and the exposure variables were work environment factors: satisfaction, expectations, ideal, skills and training, challenges, and workload. General psychosocial work environment onboard and socioeconomic independent variables were also included. We conducted different logistic regression analyses, and presented the results as odds ratios (OR) and 95% confidence intervals (CIs). The study found that most seafarers reported high levels of wellbeing and happiness and that these were significantly associated to the work environment factors, except for workload. A stratified analysis, showed that workload modified the effect of the other work environment factors. The study found that there were independently significant associations between work related factors and wellbeing and happiness among seafarers at sea. The findings suggest that a greater emphasis on these outcomes could have a positive impact both on crew retention and safety at sea.

Using unsupervised clustering approaches to identify common mental health profiles and associated mental health-care service-use patterns in Ontario, Canada.

Mental health is a complex, multidimensional concept that goes beyond clinical diagnoses, including psychological distress, life stress, and well-being. In this study, we aimed to use unsupervised clustering approaches to identify multidimensional mental health profiles that exist in the population, and their associated service-use patterns. The data source was the 2012 Canadian Community Health Survey-Mental Health, linked to administrative health-care data; all Ontario, Canada, adult respondents were included. We used a partitioning around medoids clustering algorithm with Gower's proximity to identify groups with distinct combinations of mental health indicators and described them according to their sociodemographic and service-use characteristics. We identified 4 groups with distinct mental health profiles, including 1 group that met the clinical threshold for a depressive diagnosis, with the remaining 3 groups expressing differences in positive mental health, life stress, and self-rated mental health. The 4 groups had different age, employment, and income profiles and exhibited differential access to mental health-care services. This study represents the first step in identifying complex profiles of mental health at the population level in Ontario. Further research is required to better understand the potential causes and consequences of belonging to each of the mental health profiles identified. This article is part of a Special Collection on Mental Health.

Use of Daily Web-Based, Real-Time Feedback to Improve Patient and Family Experience.

Real-time feedback is a growing trend in patient- and family experience (PFE) work as it allows for immediate service recovery, though it typically requires a significant investment of time and financial resources. We describe a partnership with our "edutainment" system to administer an automated daily experience question (the "Daily Pulse Measure [DPM]") that allowed targeted just-in-time responses to low scores with minimal administrative cost. Through a series of Plan-Do-Study-Act cycles guided by family feedback, the question was created and modified, and the use of the question spread to all hospital units. The response rate was 23%, similar to our Hospital Consumer Assessment of Healthcare Providers and Systems survey response rate of 24% during the study period. Though the DPM did not have a consistent impact on the results of the 2 PFE survey questions we evaluated, units with improved PFE scores after the DPM roll-out tended to have more robust service recovery than those with low scores.

High-risk pooling for mitigating risk selection incentives in health insurance markets with sophisticated risk equalization: an application based on health survey information.

BACKGROUND: Despite sophisticated risk equalization, insurers in regulated health insurance markets still face incentives to attract healthy people and avoid the chronically ill because of predictable differences in profitability between these groups. The traditional approach to mitigate such incentives for risk selection is to improve the risk-equalization model by adding or refining risk adjusters. However, not all potential risk adjusters are appropriate. One example are risk adjusters based on health survey information. Despite its predictiveness of future healthcare spending, such information is generally considered inappropriate for risk equalization, due to feasibility challenges and a potential lack of representativeness. METHODS: We study the effects of high-risk pooling (HRP) as a strategy for mitigating risk selection incentives in the presence of sophisticated- though imperfect- risk equalization. We simulate a HRP modality in which insurers can ex-ante assign predictably unprofitable individuals to a 'high risk pool' using information from a health survey. We evaluate the effect of five alternative pool sizes based on predicted residual spending post risk equalization on insurers' incentives for risk selection and cost control, and compare this to the situation without HRP. RESULTS: The results show that HRP based on health survey information can substantially reduce risk selection incentives. For example, eliminating the undercompensation for the top-1% with the highest predicted residual spending reduces selection incentives against the total group with a chronic disease (60% of the population) by approximately 25%. Overall, the selection incentives gradually decrease with a larger pool size. The largest marginal reduction is found moving from no high-risk pool to HRP for the top 1% individuals with the highest predicted residual spending. CONCLUSION: Our main conclusion is that HRP has the potential to considerably reduce remaining risk selection incentives at the expense of a relatively small reduction of incentives for cost control. The extent to which this can be achieved, however, depends on the design of the high-risk pool.

Who continues to work after retirement age?

BACKGROUND: Demographic changes in all industrialized countries have led to a keen interest in extending working lives for older workers. To achieve this goal, it is essential to understand the patterns of retirement and specifically what characterizes individuals who continue to work beyond retirement age. Thus, the aim of this paper was to contribute to the international body of empirical knowledge about individuals who continue in the workforce after retirement age. We present evidence from Denmark and examine what characterizes individuals who continue in the workforce after retirement age and investigate the likelihood of continued work after retirement age while controlling for a set of socio-economic and lifestyle factors. METHODS: The study population consisted of 5,474 respondents to the Copenhagen Aging and Midlife Biobank (CAMB) 2021 survey, divided into two groups. The first group included subjects (n = 1,293) who stayed longer in the workforce even though they had the possibility to retire. The second group consisted of subjects who had retired full-time at the time of the survey (n = 4,181). Survey data was linked to register data to provide a broader dataset. In order to investigate the heterogeneity between the two groups in terms of important socio-economic, work-related and health-related variables, t-test, Mann-Whitney U (Wilcoxon Rank) test, and chi-square tests were employed. Further, to examine the probability of an individual working after retirement age a logit model with step-wise inclusion was utilized. RESULTS: Overall, individuals who continue to work even though they could retire tend to be wealthier, healthier, and males compared to individuals who are retired full-time. Further, there are more older workers who have partners and are co-habitants than retirees. The likelihood of continuing in the workforce past retirement age is affected by several work-related factors as well as life-style factors. The likelihood of working past retirement age decreases by years spent in the workforce (marginal effect of -0.003), if you have a partner (-0.080) and if your partner is outside of the workforce (marginal effect of -0.106). The likelihood increases by health (marginal effect of -0.044 of moving from excellent/very good health to good health or to fair/poor health, physical working capability (marginal effect of -0.083 of moving from no/some problems to severe problems or cannot work at all) and income (marginal effect of 0.083 from moving from the lowest income-quantile to higher quantiles). CONCLUSION: These results are in line with the previous literature and suggest the importance of designing retirement policies that tailor the transition toward retirement according to specific characteristics of both the individual and the segment of occupation.

Association between domestic violence and unintended, terminated pregnancy and complications during pregnancy among Indian women: Findings from nationally representative survey.

Background: Women experiencing domestic violence might have restrictions in the pregnancy-related decision-making and care-seeking process leading to adverse pregnancy outcomes. We explored the association between domestic violence and undesirable pregnancy events. Methods: 63,796 women aged 18-49 years covered under the domestic violence module of National Family Health Survey-5, 2019-21. Stratification and clustering in the complex sampling design of the survey were accounted in analysis. Using Poisson regression, prevalence ratio (PR) was reported to provide association of domestic violence with undesirable pregnancy events. Results: Prevalence of undesirable pregnancy events was 25.0% (95%CI: 24.4%-25.7%) amongst women aged 18-49 years. Prevalence of unintended, terminated pregnancy and complications during pregnancy was 3.2%, 5.1% and 20.9%, respectively. Women aged ≥35 years, educated, unemployed, primi or multi parity, intimate partner/husband being uneducated, facing problem with access to healthcare, belonging to large number of household members (≥4) and poorest or poorer quintile had significantly higher chance of having undesirable pregnancy events. Sexual violence (aPR: 1.11, p = 0.02) had higher chance of having undesirable pregnancy events. Conclusion: One-fourth of reproductive-age group women had undesirable pregnancy events. Sexual violence was significantly associated with these events. Effective policy should protect women from domestic violence to promote maternal well-being.

Evaluating Machine Learning Stability in Predicting Depression and Anxiety Amidst Subjective Response Errors.

Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) pose significant burdens on individuals and society, necessitating accurate prediction methods. Machine learning (ML) algorithms utilizing electronic health records and survey data offer promising tools for forecasting these conditions. However, potential bias and inaccuracies inherent in subjective survey responses can undermine the precision of such predictions. This research investigates the reliability of five prominent ML algorithms-a Convolutional Neural Network (CNN), Random Forest, XGBoost, Logistic Regression, and Naive Bayes-in predicting MDD and GAD. A dataset rich in biomedical, demographic, and self-reported survey information is used to assess the algorithms' performance under different levels of subjective response inaccuracies. These inaccuracies simulate scenarios with potential memory recall bias and subjective interpretations. While all algorithms demonstrate commendable accuracy with high-quality survey data, their performance diverges significantly when encountering erroneous or biased responses. Notably, the CNN exhibits superior resilience in this context, maintaining performance and even achieving enhanced accuracy, Cohen's kappa score, and positive precision for both MDD and GAD. This highlights the CNN's superior ability to handle data unreliability, making it a potentially advantageous choice for predicting mental health conditions based on self-reported data. These findings underscore the critical importance of algorithmic resilience in mental health prediction, particularly when relying on subjective data. They emphasize the need for careful algorithm selection in such contexts, with the CNN emerging as a promising candidate due to its robustness and improved performance under data uncertainties.

Orthopaedic Hand Patient Support Systems Have Valuable Insight to Patient Function and Pain.

Patient-reported outcome measures (PROs) are increasingly used in clinical assessment. Research on how patient support systems contribute to physician understanding of patient condition is limited. Thus, insights from significant others may provide value, especially when concerns exist regarding patient response validity. Patients recruited from the pre-operative environment undergoing orthopaedic hand procedures responded to PROMIS-Pain Interference (PI), PROMIS-Upper Extremity (UE), PROMIS-Depression (D), and QuickDASH. They then selected a significant other (SO) to do the same. Patients and SOs were also asked to complete the West Haven-Yale Multidimensional Pain Inventory (WHYMPI) as a measure of support-related responses. Patient and SO responses were compared, and support-related responses were added in subsequent analyses to examine their effect on SO PRO assessment.

Dying at home: What is needed? Findings from a nationwide retrospective cross-sectional online survey of bereaved people in Germany.

OBJECTIVES: According to the "Last Year of Life in Cologne" study, 68% of patients with a serious and terminal illness wanted to die at home, but 42% died in hospital. Only 1 in 5 died at home. Most people want to spend their last days and hours at home, but the reality is that this is not always possible. Recommendations are needed on how best to support families to enable people to die at home - if this is their preferred place of death. Our aim was to identify the factors that make it possible for people to die at home and to analyze factors of dying at home. METHODS: Germany-wide quantitative cross-sectional online survey of bereaved adult relatives. RESULTS: The needs of 320 relatives of patients who wished to die at home were explored. Of these, 198 patients died at home and 122 did not. In the last 3 months of life, caregivers needed support in managing out-of-hours care (p < 0.001), financing (p = 0.012), preparing and organizing home care (both p < 0.001), communicating with the patient and medical staff (p = 0.012 and p = 0.009, respectively), and pain management (p < 0.001). Relatives whose next of kin did not die at home had higher needs, suggesting that these factors are key to home care of the dying. SIGNIFICANCE OF RESULTS: The process of dying at home begins long before the actual dying phase. To minimize caregiver burden and improve symptom management, advanced home care plans are needed, with ongoing reassessment of family preferences and abilities.

Perceptions of environmental changes among a climate-vulnerable population from Bangladesh.

Effective climate change adaptation requires a thorough understanding of whether and how affected populations perceive climatic and environmental changes. Existing research has been inconclusive regarding the consistency of these perceptions compared to objective meteorological indicators. Moreover, no systematic comparison has been done for the perception of discrete environmental events such as floods or erosion. This study relies on novel panel survey data of approximately 1700 households residing along the Jamuna River in Bangladesh as well as on unique individual-level, satellite-based erosion data. It compares respondents' perceptions of environmental events, namely riverbank erosion, and three climate change indicators, specifically long-term temperature change and changes in precipitation during wet and dry seasons, to objective measurements using satellite imagery and climatic time-series data (CRU TS). I find that long-term temperature change is perceived more accurately than long-term changes in precipitation. Given that educational attainment and climate change literacy among the study population are low, this indicates that global temperature increases are felt even by remote populations who have never heard the term climate change. Erosion is strongly overestimated, especially by those respondents who had been personally affected by it. Since human behavior is guided by perceptions rather than objective data, this has important policy implications, underlining the importance of considering people's perceptions if the goal is to assist them in adapting to environmental changes. Supplementary Information: The online version contains supplementary material available at 10.1007/s10584-024-03678-6.

Police discrimination and police distrust among ethnic minority adolescents in Germany.

In light of ongoing debates about racially motivated police violence, this paper examines two separate but interrelated phenomena: instances of police discrimination and mistrust in police and the judicial system among ethnic minorities in Germany. Analyses are carried out based on waves 1, 3, and 5 of the CILS4EU-DE data collected among 14 to 20 year-old respondents in Germany. The focus of the paper lies on young men from the Middle East, as well as Northern and Sub-Saharan Africa, who-as our study demonstrates-tend to disproportionally more often report discrimination experiences and particularly low levels of trust in police and courts compared to other ethnic minorities and the majority populations in Germany, and partially also in comparison to their female counterparts. We also show that more frequent experiences of police discrimination are associated with greater distrust of the police and partially also with courts among young men from the Middle East, North and Sub-Saharan Africa. Female adolescents from similar backgrounds are also more distrustful of the police, but this is not explained by their own experiences of police discrimination.

Machine Learning Models for Low Back Pain Detection and Factor Identification: Insights From a 6-Year Nationwide Survey.

This study aimed to enhance performance, identify additional predictors, and improve the interpretability of biopsychosocial machine learning models for low back pain (LBP). Using survey data from a 6-year nationwide study involving 17,609 adults aged ≥50 years (Korea National Health and Nutrition Examination Survey), we explored 119 factors to detect LBP in individuals who reported experiencing LBP for at least 30 days within the previous 3 months. Our primary model, model 1, employed eXtreme Gradient Boosting (XGBoost) and selected primary factors (PFs) based on their feature importance scores. To extend this, we introduced additional factors, such as lumbar X-ray findings, physical activity, sitting time, and nutrient intake levels, which were available only during specific survey periods, into models 2 to 4. Model performance was evaluated using the area under the curve, with predicted probabilities explained by SHapley Additive exPlanations. Eleven PFs were identified, and model 1 exhibited an enhanced area under the curve .8 (.77-.84, 95% confidence interval). The factors had varying impacts across individuals, underscoring the need for personalized assessment. Hip and knee joint pain were the most significant PFs. High levels of physical activity were found to have a negative association with LBP, whereas a high intake of omega-6 was found to have a positive association. Notably, we identified factor clusters, including hip joint pain and female sex, potentially linked to osteoarthritis. In summary, this study successfully developed effective XGBoost models for LBP detection, thereby providing valuable insight into LBP-related factors. Comprehensive LBP management, particularly in women with osteoarthritis, is crucial given the presence of multiple factors. PERSPECTIVE: This article introduces XGBoost models designed to detect LBP and explores the multifactorial aspects of LBP through the application of SHapley Additive exPlanations and network analysis on the 4 developed models. The utilization of this analytical system has the potential to aid in devising personalized management strategies to address LBP.