Search-and-rescue in the Central Mediterranean Route does not induce migration: Predictive modeling to answer causal queries in migration research.

State- and private-led search-and-rescue are hypothesized to foster irregular migration (and thereby migrant fatalities) by altering the decision calculus associated with the journey. We here investigate this 'pull factor' claim by focusing on the Central Mediterranean route, the most frequented and deadly irregular migration route towards Europe during the past decade. Based on three intervention periods-(1) state-led Mare Nostrum, (2) private-led search-and-rescue, and (3) coordinated pushbacks by the Libyan Coast Guard-which correspond to substantial changes in laws, policies, and practices of search-and-rescue in the Mediterranean, we are able to test the 'pull factor' claim by employing an innovative machine learning method in combination with causal inference. We employ a Bayesian structural time-series model to estimate the effects of these three intervention periods on the migration flow as measured by crossing attempts (i.e., time-series aggregate counts of arrivals, pushbacks, and deaths), adjusting for various known drivers of irregular migration. We combine multiple sources of traditional and non-traditional data to build a synthetic, predicted counterfactual flow. Results show that our predictive modeling approach accurately captures the behavior of the target time-series during the various pre-intervention periods of interest. A comparison of the observed and predicted counterfactual time-series in the post-intervention periods suggest that pushback policies did affect the migration flow, but that the search-and-rescue periods did not yield a discernible difference between the observed and the predicted counterfactual number of crossing attempts. Hence we do not find support for search-and-rescue as a driver of irregular migration. In general, this modeling approach lends itself to forecasting migration flows with the goal of answering causal queries in migration research.

The impact of air travel on the precocity and severity of COVID-19 deaths in sub-national areas across 45 countries.

Human travel fed the worldwide spread of COVID-19, but it remains unclear whether the volume of incoming air passengers and the centrality of airports in the global airline network made some regions more vulnerable to earlier and higher mortality. We assess whether the precocity and severity of COVID-19 deaths were contingent on these measures of air travel intensity, adjusting for differences in local non-pharmaceutical interventions and pre-pandemic structural characteristics of 502 sub-national areas on five continents in April-October 2020. Ordinary least squares (OLS) models of precocity (i.e., the timing of the 1st and 10th death outbreaks) reveal that neither airport centrality nor the volume of incoming passengers are impactful once we consider pre-pandemic demographic characteristics of the areas. We assess severity (i.e., the weekly death incidence of COVID-19) through the estimation of a generalized linear mixed model, employing a negative binomial link function. Results suggest that COVID-19 death incidence was insensitive to airport centrality, with no substantial changes over time. Higher air passenger volume tends to coincide with more COVID-19 deaths, but this relation weakened as the pandemic proceeded. Different models prove that either the lack of airports in a region or total travel bans did reduce mortality significantly. We conclude that COVID-19 importation through air travel followed a 'travel as spark' principle, whereby the absence of air travel reduced epidemic risk drastically. However, once some travel occurred, its impact on the severity of the pandemic was only in part associated with the number of incoming passengers, and not at all with the position of airports in the global network of airline connections.

Fair comparisons: Life course selection bias and the effect of father absence on US children.

Studies have shown that father absence in opposite-gender couples has detrimental effects on children's wellbeing, net of selection bias. However, life course informed research suggests that the problem of selection bias may be more complex than currently thought. This paper shows the importance of nonparametrically adjusting for the trajectory of confounder covariates for the estimation of these effects. This paper uses data from the Fragile Families and Child Wellbeing Study to estimate the total effect of the departure of the biological father on children's wellbeing, as well as delayed or fade-out effects of this transition. The paper employs Bayesian additive regression trees, a machine learning and causal inference method suited for statistical models involving high-dimensional data sets. After adjusting for multiple time-invariant and -varying confounder covariates, as well as their history, estimates of father absence's effect on children's wellbeing are reduced substantially, a finding which may be referred to as life course selection bias. Results suggest early and middle childhood are not negatively affected by the departure of the biological father. Life course selection bias mostly affects estimates of this effect on adolescence, which is explained by children directly experiencing changes inparent's socioeconomic trajectories that lead to divorce or separation. This would not be the case when father absence is experienced in early childhood. Results suggest father absence is mostly a marker of life course cumulative socioeconomic disadvantage, not a cause of negative effects.

Determinants of Sickness Absence and Return to Work Among Employees with Common Mental Disorders: A Scoping Review.

Purpose To present an overview of the existing evidence on prognostic factors of (recurrent) sickness absence (SA) and return to work (RTW) among workers with a common mental disorder (CMD). This scoping review provides information about determinants for SA and RTW, which could be used to develop better interventions aimed at the prevention of SA and promotion of RTW among workers with a CMD. Methods Relevant articles were identified in PubMed, Embase, PsycINFO, PSYNDEX, and SINGLE up to October 2016. In order to be included, studies should provide insight into prognostic factors of SA or RTW of workers with a CMD. We classified all factors according to the domains of the International Classification of Functioning, Disability and Health. Results Our searches identified 2447 possible relevant articles, of which 71 were included for data extraction. There is consistent evidence in ≥3 studies that previous episodes of CMD, higher symptom severity, previous absenteeism, co-morbidity, high job demands, low job control, high job strain, female gender, lower educational level, smoking behavior, and low perceived general health are predictors of SA in people with CMDs. Earlier RTW is consistently predicted by lower symptom severity, having no previous absenteeism, younger age, and positive expectations concerning sick-leave duration or RTW. Conclusions The amount of research on determinants for SA and RTW in workers with CMD has increased dramatically in recent years, although most studies are from the Netherlands and Scandinavia. There are some research gaps identified in this scoping review that need further attention in primary and secondary studies. Based on the summary of the evidence, we provide guidance for policy, practice and research.

Análisis clínico de los niveles séricos de IgE en pacientes pediátricos asmáticos / Clinic analysis of Serum IgE levels in asthmatic patients

Antecedentes: con el paso del tiempo se ha acumulado una gran cantidad de conocimientos en relación con la síntesis, liberación y efectos de la IgE. Material y métodos: se reportan los niveles de concentración sérica de IgE y su relación con algunas variables en 78 pacientes asmáticos, menores de cinco años de edad (límites de 1 a 5 años), de uno y otro sexo. Resultados: el 66.7 por ciento de la población estudiada tenía más de un año de evolución con la enfermedad, 64 por ciento habían tenido más de dos cuadros de asma por mes y 82 por ciento había requerido manejo intrahospitalario para su estabilización. Por lo que se refiere a los resultados de laboratorio 52 pacientes tuvieron cifras de concentración sérica de IgE que rebasaban el límite superior de normalidad (límite: 8-12,000, media 687.27 UI/ml). De estos pacientes, 92 por ciento habían requerido manejo hintraospitalario, 96 por ciento tenían más de dos años de evolución con la enfermedad y 73 por ciento una concentración de eosinófilos circulantes superiores a lo normal. Conclusión: los resultados permitieron conocer algunas características de los pacientes con cifras de IgE sérica en nuestra región. Además, los datos obtenidos permitirán fundamentar el diseño de nuevos protocolos de investigación