Relationships Between Coping Behaviors and Social Loneliness in Adults With Self-reported Hearing Problems.

OBJECTIVES: To determine the association between various coping behaviors and social loneliness (self-reported deficits in social integration and embeddedness) in adults with self-reported hearing problems. It is hypothesized that adults who frequently use adequate coping behaviors experience less feelings of social loneliness than persons who use these behaviors less often. DESIGN: Cross-sectional data of 686 participants with hearing-impairment (24-75 years of age) of the online Netherlands Longitudinal Study on Hearing were analyzed. Six coping behaviors were measured using six subscales of the Communication Profile for the Hearing Impaired (maladaptive behavior, verbal strategies, nonverbal strategies, self-acceptance, acceptance of loss, and stress and withdrawal). The De Jong-Gierveld loneliness scale was used to measure social loneliness. Multiple logistic multinomial regression analyses were applied to determine associations between each of the coping behaviors and (1) moderate social loneliness (reference category: no loneliness) and (2) severe social loneliness (reference category: no loneliness). Potential subgroup effects and confounders were examined. RESULTS: Almost two-thirds of the sample reported feeling moderately or severely socially lonely. Significantly less feelings of social loneliness were experienced by participants who reported relatively high levels of self-acceptance or acceptance of loss, relatively infrequent use of maladaptive behavior, or relatively low levels of stress and withdrawal. Particularly those participants whose hearing loss dated back to ≤5 years, better coping with verbal strategies was associated with a lower likelihood of either moderate or severe social loneliness. More frequent use of nonverbal strategies was only associated with a lower likelihood of severe social loneliness for participants with paid work. CONCLUSIONS: To the best of our knowledge, this study is the first in which the relationship between a wide range of hearing coping behaviors and social loneliness was studied. The results show that more frequent use of adequate coping behaviors is significantly associated with less feelings of social loneliness. The findings underline the importance of recognizing and tackling inadequate coping behaviors so that social loneliness can be prevented or combated.

Neighbourhood characteristics and prevalence and severity of depression: pooled analysis of eight Dutch cohort studies.

BACKGROUND: Studies on neighbourhood characteristics and depression show equivocal results.AimsThis large-scale pooled analysis examines whether urbanisation, socioeconomic, physical and social neighbourhood characteristics are associated with the prevalence and severity of depression. METHOD: Cross-sectional design including data are from eight Dutch cohort studies (n = 32 487). Prevalence of depression, either DSM-IV diagnosis of depressive disorder or scoring for moderately severe depression on symptom scales, and continuous depression severity scores were analysed. Neighbourhood characteristics were linked using postal codes and included (a) urbanisation grade, (b) socioeconomic characteristics: socioeconomic status, home value, social security beneficiaries and non-Dutch ancestry, (c) physical characteristics: air pollution, traffic noise and availability of green space and water, and (d) social characteristics: social cohesion and safety. Multilevel regression analyses were adjusted for the individual's age, gender, educational level and income. Cohort-specific estimates were pooled using random-effects analysis. RESULTS: The pooled analysis showed that higher urbanisation grade (odds ratio (OR) = 1.05, 95% CI 1.01-1.10), lower socioeconomic status (OR = 0.90, 95% CI 0.87-0.95), higher number of social security beneficiaries (OR = 1.12, 95% CI 1.06-1.19), higher percentage of non-Dutch residents (OR = 1.08, 95% CI 1.02-1.14), higher levels of air pollution (OR = 1.07, 95% CI 1.01-1.12), less green space (OR = 0.94, 95% CI 0.88-0.99) and less social safety (OR = 0.92, 95% CI 0.88-0.97) were associated with higher prevalence of depression. All four socioeconomic neighbourhood characteristics and social safety were also consistently associated with continuous depression severity scores. CONCLUSIONS: This large-scale pooled analysis across eight Dutch cohort studies shows that urbanisation and various socioeconomic, physical and social neighbourhood characteristics are associated with depression, indicating that a wide range of environmental aspects may relate to poor mental health.Declaration of interestNone.

Machine Learning Prediction Models for Neurodevelopmental Outcome After Preterm Birth: A Scoping Review and New Machine Learning Evaluation Framework.

BACKGROUND AND OBJECTIVES: Outcome prediction of preterm birth is important for neonatal care, yet prediction performance using conventional statistical models remains insufficient. Machine learning has a high potential for complex outcome prediction. In this scoping review, we provide an overview of the current applications of machine learning models in the prediction of neurodevelopmental outcomes in preterm infants, assess the quality of the developed models, and provide guidance for future application of machine learning models to predict neurodevelopmental outcomes of preterm infants. METHODS: A systematic search was performed using PubMed. Studies were included if they reported on neurodevelopmental outcome prediction in preterm infants using predictors from the neonatal period and applying machine learning techniques. Data extraction and quality assessment were independently performed by 2 reviewers. RESULTS: Fourteen studies were included, focusing mainly on very or extreme preterm infants, predicting neurodevelopmental outcome before age 3 years, and mostly assessing outcomes using the Bayley Scales of Infant Development. Predictors were most often based on MRI. The most prevalent machine learning techniques included linear regression and neural networks. None of the studies met all newly developed quality assessment criteria. Studies least prone to inflated performance showed promising results, with areas under the curve up to 0.86 for classification and R2 values up to 91% in continuous prediction. A limitation was that only 1 data source was used for the literature search. CONCLUSIONS: Studies least prone to inflated prediction results are the most promising. The provided evaluation framework may contribute to improved quality of future machine learning models.