Low organisational justice and heavy drinking: a prospective cohort study

Objectives: To investigate whether low perceived organisational injustice predicts heavy drinking among employees. Methods: Data from the prospective occupational cohort study, the 10-Town Study, related to 15 290 Finnish public sector local government employees nested in 2432 work units, were used. Non-drinkers were excluded. Procedural, interactional and total organisational justice, heavy drinking (>=210 g of absolute alcohol per week) and other psychosocial factors were determined by means of questionnaire in 2000-2001 (phase 1) and 2004 (phase 2). Multilevel logistic regression analyses taking into account for the hierarchical structure of the data were conducted and adjustments were made for sex, age, socio-economic position, marital status, baseline heavy drinking, psychological distress and other psychosocial risk factors such as job strain and effort/reward imbalance. Results: After adjustments, participants who reported low procedural justice at phase 1 were about 1.2 times more likely to be heavy drinkers at phase 2 compared with their counterparts with high justice. Low perceived justice in interpersonal treatment and low perceived total organisational justice were associated with an elevated prevalence of heavy drinking only in the socio-demographics adjusted model. Conclusions: This is the first longitudinal study to show that low procedural justice is weakly associated with an increased likelihood of heavy drinking.

Effort-reward imbalance at work and the co-occurrence of lifestyle risk factors: cross-sectional survey in a sample of 36,127 public sector employees

Background In occupational life, a mismatch between high expenditure of effort and receiving few rewards may promote the co-occurrence of lifestyle risk factors, however, there is insufficient evidence to support or refute this hypothesis. The aim of this study is to examine the extent to which the dimensions of the Effort-Reward Imbalance (ERI) model – effort, rewards and ERI – are associated with the co-occurrence of lifestyle risk factors. Methods Based on data from the Finnish Public Sector Study, cross-sectional analyses were performed for 28,894 women and 7233 men. ERI was conceptualized as a ratio of effort and rewards. To control for individual differences in response styles, such as a personal disposition to answer negatively to questionnaires, occupational and organizational -level ecological ERI scores were constructed in addition to individual-level ERI scores. Risk factors included current smoking, heavy drinking, body mass index ≥25 kg/m2, and physical inactivity. Multinomial logistic regression models were used to estimate the likelihood of having one risk factor, two risk factors, and three or four risk factors. The associations between ERI and single risk factors were explored using binary logistic regression models. Results After adjustment for age, socioeconomic position, marital status, and type of job contract, women and men with high ecological ERI were 40% more likely to have simultaneously ≥3 lifestyle risk factors (vs. 0 risk factors) compared with their counterparts with low ERI. When examined separately, both low ecological effort and low ecological rewards were also associated with an elevated prevalence of risk factor co-occurrence. The results obtained with the individual-level scores were in the same direction. The associations of ecological ERI with single risk factors were generally less marked than the associations with the co-occurrence of risk factors. Conclusion This study suggests that a high ratio of occupational efforts relative to rewards may be associated with an elevated risk of having multiple lifestyle risk factors. However, an unexpected association between low effort and a higher likelihood of risk factor co-occurrence as well as the absence of data on overcommitment (and thereby a lack of full test of the ERI model) warrant caution in regard to the extent to which the entire ERI model is supported by our evidence.

Infección por virus sincitial respiratorio y su relación con valores de IGG en niños críticos

Antecedente: La infección por el virus sincitial respiratorio (VSR) representa una elevada morbimortalidad, y en algunos casos necesidad de manejo en unidades de cuidado intensivo pediátrico (UCIP). La respuesta inmunológica influye de manera directa en la expresión de la severidad y pronóstico de los pacientes con infección respiratoria. Metodología: Estudio de una cohorte retrospectiva de pacientes con infección respiratoria grave secundaria a VSR, sin historia de inmunodeficiencia, atendidos en la UCIP del Hospital Universitario Clínica San Rafael. Se realizó análisis descriptivoglobaly de acuerdo a la categorización de las prueba de IgG. Resultados: De 188 pacientes que ingresaron a la UCIP, 13% presentaron infección por VSR (24), con una edad promedio de 7,3 (DE=3,6) meses. Pertenecían al sexo masculino79,83%. Se encontró que 12,5% tenían un valor de IgGbajo para su edad, 58,33% tenían valores en límite inferior y el 29,17% dentro de rangos normales para su edad. En los pacientes con IgG baja, fue mayor la presentación de choque séptico que no responde a líquidos (100 vs 92 vs 86%), la mediana de días de ventilación mecánica fue mayor (8 vs 6 vs 5 respectivamente), así como la mortalidad (67 vs 7,1 vs 0%). Conclusión: Nuestra serie encontró que aquellos pacientes con niveles bajos o valores en el límite inferior de IgG sérica tuvieron mayor compromiso sistémico, mayor duración de ventilación mecánica y mayor mortalidad. Se necesitan estudios prospectivos que relaciones niveles bajos de IgG con severidad y pronostico en estos pacientes con infección grave por VSR.

Novel Machine-Learning Based IC Testing Strategy

The final goal of the thesis should be a real-world application in the production test data environment. This includes the pre-processing of the data, building models and visualizing the results. To do this, different machine learning models, outlier prediction oriented, should be investigated using a real dataset. Finally, the different outlier prediction algorithms should be compared, and their performance discussed.