Internet search data may reveal people’s intentions to migrate, as aspiring migrants tend to use online search engines to explore migration opportunities. However, unlike official migration statistics, search data may only reflect the behavior of a self-selected subset of the population, raising concerns about its generalizability. This article integrates traditional survey data - Gallup World Poll (GWP) - with Google Trends, search engine market share, and internet adoption rate to examine the extent to which search trends of migration-related topics can serve as proxies for migration intentions. The results reveal that, on a global scale, passport-related search queries strongly correlate with individuals’ intentions to migrate. However, at the country level, particularly in the global south, migration intentions are more accurately predicted by the adoption rates of Google search rather than search topics per se. These findings underscore the importance of detecting and correcting user selection biases when leveraging digital trace data for migration research, ensuring robust and representative insights into the spatial-temporal patterns of human mobility.

Background/aims: Shift workers, such as medical personnel, and pilots, are facing an increased risk of depressive symptoms. Depressive symptoms significantly impact an individual’s quality of life and affect work performance, decision-making abilities, and overall public safety. This study aims to establish a multidimensional depressive symptom prediction model based on a large sample of commercial airline pilots to facilitate early identification, prevention, and personalized intervention strategies. Methods: This population-based study included 11,111 participants, with 7918 pilots in the training set and 3193 pilots in the external validation set. Depressive symptom severity was assessed using the Patient Health Questionnaire-9 (PHQ-9). Physiological, psychological, and lifestyle factors potentially associated with depressive symptom risk were collected. The optimal predictors for model development were selected using the Boruta algorithm combined with the LASSO method, and a nomogram was developed using multivariate logistic regression to predict depressive symptoms in pilots. The model performance was evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, and accuracy measures, such as the Brier score and Spiegelhalter z-test. Additionally, decision curve analysis (DCA) was performed to assess the model's clinical utility. Results: A total of 7918 pilots were included in the training set and 3193 were included in the external validation set. Five characteristic indicators were selected based on their significance in the prediction of depressive symptom risk: living status, alcohol drinking, family history of mental health disorder, subjective health, and subjective sleep quality. The model showed acceptable overall discrimination (AUCtrain = 0.836, 95%CI 0.818 to 0.854; AUCvalidation = 0.840, 95%CI 0.811 to 0.868) and calibration (Brier scoretrain = 0.048; Brier scorevalidation = 0.051). The decision curve analysis showed that the net benefit was superior to intervening on all participants or not intervening on all participants. Conclusions: This study provides a reliable tool for early prediction and customized management of depressive symptoms among commercial airline pilots. This approach promotes the development of the field by transitioning from passive mental health care to active mental health prevention, emphasizing personalized prevention strategies.

IMPORTANCE: Misaligned dietary rhythmicity has been associated with metabolic diseases; however, its association with mental health remains largely unexplored.

OBJECTIVE: To examine the association between dietary rhythms and the mental health condition of shift workers, specifically airline crew members.

DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study analyzed data collected from the Civil Aviation Health Cohort of China, an ongoing large-scale health survey of pilots, flight attendants, and air security officers employed by major airline companies in China. Participants aged 18 to 60 years were invited through text messages to complete a web-based survey. The data collection period was December 2022 to March 2023. Statistical analysis was performed from July 24, 2023, to April 12, 2024.

EXPOSURE: Data on timing of breakfast and dinner on workdays and rest days, daily time windows for food intake, and meal and eating jet lags were collected and calculated.

MAIN OUTCOMES AND MEASURES: Anxiety and depressive symptoms were measured using the 7-item Generalized Anxiety Disorder Assessment and the 9-item Patient Health Questionnaire. Multivariate logistic regressions were performed to evaluate the associations of anxiety and depression with meal timing, eating window time, meal jet lag (ie, delayed meals), and eating jet lag (ie, delayed eating). All models were adjusted for individual socioeconomic, demographic, and lifestyle characteristics.

RESULTS: Of the 22 617 participants (median [IQR] age, 29.1 [26.3-33.7] years; 13 712 males [60.6%]), 1755 (7.8%) had anxiety and 2768 (12.2%) had depression. After controlling for confounding factors, having dinner after 8 pm on morning-shift days was associated with increased odds of anxiety (adjusted odds ratio [AOR], 1.78; 95% CI, 1.53-2.05) and depression (AOR, 2.01; 95% CI, 1.78-2.27), compared with consuming dinner before 8 pm. Similar results were observed on night-shift days and rest days. An eating window of less than 12 hours was associated with reduced odds of anxiety (AOR, 0.84; 95% CI, 0.75-0.93) and depression (AOR, 0.81; 95% CI, 0.75-0.89) on morning-shift days; the results remained significant on rest days. Delayed dinner on morning-shift days was associated with increased odds of anxiety (AOR, 1.32; 95% CI, 1.13-1.54) and depression (AOR, 1.39; 95% CI, 1.22-1.58). On night-shift days, delayed dinner was associated with higher odds of anxiety (AOR, 1.22; 95% CI, 1.06-1.39) and depression (AOR, 1.21; 95% CI, 1.08-1.36). On morning-shift days, delayed eating rhythms were associated with higher odds of depression (AOR, 1.35; 95% CI, 1.13-1.61), whereas advanced eating rhythms were associated with lower odds of anxiety (AOR, 0.78; 95% CI, 0.70-0.87).

CONCLUSIONS AND RELEVANCE: This cross-sectional study found that meal timing, long eating window, and meal jet lags were associated with increased odds of depression and anxiety. These findings underscore the need for interventions and supportive policies that help mitigate the adverse implications of shift work and irregular working hours for the mental health of shift workers.

This article seeks to fill a research gap by analysing refugees’ voting behavior, using Sweden (known for high refugee immigration, relatively liberal enfranchisement rules, and comprehensive electoral data) as a case study. Relying on register data on turnout from Swedish municipal elections, the article sheds new light on how the political integration of refugees varies. We test theories of resocialization and examine the extent to which a refugee’s political integration is affected by the surrounding environment, focusing on the political culture of the areas surrounding their neighborhoods. The results show that two major factors strongly affect refugee turnout rates: the acquisition of citizenship, and the degree of diversity of nationality in the districts in which refugees live, based on different experiences of “bonding” and “bridging” with the surrounding environment.

Using bibliometrics, this study explores the land-scape of social computing sciences within the Turkish academic sphere. The analysis conducted at both the country and organizational levels reveals that collaborations in the field of social computing are not solely influenced by geographical factors. Through network analysis of keyword co-occurrences, distinct thematic patterns emerge, highlighting areas such as health-related applications, the social implications of computational approaches, the intersection of computational methods with the economy and agriculture, machine learning techniques, and the utilization of big data analytics for business applications. These findings provide evidence of the multidisciplinary nature inherent in social computing research.

Google Trends (GT) collate the volumes of search keywords over time and by geographical location. Such data could, in theory, provide insights into people’s ex ante intentions to migrate, and hence be useful for predictive analysis of future migration. Empirically, however, the predictive power of GT is sensitive, it may vary depending on geographical context, the search keywords selected for analysis, as well as Google’s market share and its users’ characteristics and search behavior, among others. Unlike most previous studies attempting to demonstrate the benefit of using GT for forecasting migration flows, this article addresses a critical but less discussed issue: when GT cannot enhance the performances of migration models. Using EUROSTAT statistics on first-time asylum applications and a set of push-pull indicators gathered from various data sources, we train three classes of gravity models that are commonly used in the migration literature, and examine how the inclusion of GT may affect models’ abilities to predict refugees’ destination choices. The results suggest that the effects of including GT are highly contingent on the complexity of different models. Specifically, GT can only improve the performance of relatively simple models, but not of those augmented by flow Fixed-Effects or by Auto-Regressive effects. These findings call for a more comprehensive analysis of the strengths and limitations of using GT, as well as other digital trace data, in the context of modeling and forecasting migration. It is our hope that this nuanced perspective can spur further innovations in the field, and ultimately bring us closer to a comprehensive modeling framework of human migration.

Migration models have evolved significantly during the last decade, most notably the so-called flow Fixed-Effects (FE) gravity models. Such models attempt to infer how human mobility may be driven by changing economy, geopolitics, and the environment among other things. They are also increasingly used for migration projections and forecasts. However, recent research shows that this class of models can neither explain, nor predict the temporal dynamics of human movement. This shortcoming is even more apparent in the context of forced migration, in which the processes and drivers tend to be heterogeneous and complex. In this article, we derived a Flow-Specific Temporal Gravity (FTG) model which, compared to the FE models, is theoretically similar (informed by the random utility framework), but empirically less restrictive. Using EUROSTAT data with climate, economic, and conflict indicators, we trained both models and compared their performances. The results suggest that the predictive power of these models is highly dependent on the length of training data. Specifically, as time-series migration data lengthens, FTG's predictions can be increasingly accurate, whereas the FE model becomes less predictive.

Population aging presents a looming challenge for sustaining intergenerational transfers from the economically productive population to dependent elderly. This chapter offers an interdisciplinary perspective on whether immigration may alleviate this economic challenge for an aging welfare state. Specifically, it demonstrates how immigration may be beneficial from a pure demographic perspective, but less so or costly from an economic perspective.

In this paper, we investigate whether integration policy improves refugees' economic performance, specifically examining the effects on refugees' income of Sweden's 2010 reform of the introduction programme (or IP). We also evaluate how the reform effects vary depending on refugees' gender and educational attainment. Our key finding shows a strong positive effect of the reform on refugees' income, immediately after the completion of the IP. More importantly, this positive effect intensifies over time, with no signs of diminishing, which implies a longer-term effect of the reform. Furthermore, the effects of the reform do not significantly vary between men and women or between the highly educated and the less-educated, suggesting that the new Swedish IP benefits refugees to the same extent, regardless of their gender and educational attainment.

This report analyzes the economic impact of forced migration on the host country. Specifically, it quantifies the magnitude of the costs associated with the Syrian refugees in Sweden. The results suggest that Syrians tend to accumulate large and persistent deficits over the life cycle. However, the aggregated impact of these deficits is negligible, largely because the Syrian population is much smaller, compared the native-born Swedes. The analysis further demonstrates that only half of the deficits are channeled through the public sector, implying that the fiscal costs of hosting Syrian refugees is quite modest, which is equivalent to about 0.4% of Sweden’s annual GDP.