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.

Eiko Jürgens / Susanne Miller (Hrsg.): Ungleichheit in der Gesellschaft und Ungleichheit in der Schule. Eine interdisziplinäre Sicht auf Inklusions- und Exklusionsprozesse. Weinheim und Basel: Beltz Juventa 2013 (282 S.) [Rezension]

Rezension von: Eiko Jürgens / Susanne Miller (Hrsg.): Ungleichheit in der Gesellschaft und Ungleichheit in der Schule. Eine interdisziplinäre Sicht auf Inklusions- und Exklusionsprozesse. Weinheim und Basel: Beltz Juventa 2013 (282 S.; ISBN 978-3-7799-2806-5)

Choix en situation d’incertitude: distinction entre les méthodes de calculs utilisées par les individus lors d’ajout d’effet de corrélation

Méthodologie: Équation de Bellman (Programmation dynamique) ; Méthode de la valeur optionnelle

Factores asociados a respuesta de abatacept subcutaneo en artritis reumatoide

INTRODUCCION Dado que la artritis reumatoide es la artropatía inflamatoria más frecuente en el mundo, siendo altamente discapacitante y causando gran impacto de alto costo, se busca ofrecer al paciente opciones terapéuticas y calidad de vida a través del establecimiento de un tratamiento oportuno y eficaz, teniendo presentes aquellos predictores de respuesta previo a instaurar determinada terapia. Existen pocos estudios que permitan establecer aquellos factores de adecuada respuesta para inicio de terapia biológica con abatacept, por lo cual en este estudio se busca determinar cuáles son esos posibles factores. METODOLOGIA Estudio analítico de tipo corte transversal de 94 pacientes con diagnóstico de AR, evaluados para determinar las posibles variables que influyen en la respuesta a terapia biológica con abatacept. Se incluyeron 67 de los 94 pacientes al modelo de regresión logística, que son aquellos pacientes en que fue posible medir la respuesta al tratamiento (respuesta EULAR) a través de la determinación del DAS 28 y así discriminar en dos grupos de comparación (respuesta y no respuesta). DISCUSION DE RESULTADOS La presencia de alta actividad de la enfermedad al inicio de la terapia biológica, aumenta la probabilidad de respuesta al tratamiento respecto al grupo con baja/moderada actividad de la enfermedad; OR 4,19 - IC 95%(1,18 – 14.9), (p 0,027). La ausencia de erosiones óseas aumenta la probabilidad de presentar adecuada respuesta a la terapia biológica respecto aquellos con erosiones, con un OR 3,1 (1,01-9,55), (p 0,048). Niveles de VSG y presencia de manifestaciones extra-articulares son otros datos de interés encontrados en el análisis bivariado. Respecto a las variables o características como predictores de respuesta al tratamiento con abatacept, se encuentran estudios que corroboran los hallazgos de este estudio, respecto al alto puntaje del DAS 28 al inicio de la terapia (9, 12). CONCLUSIONES Existen distintas variables que determinan la respuesta a los diferentes biológicos para manejo de AR. Es imprescindible evaluar dichos factores de manera individual con el fin de lograr de manera efectiva el control de la enfermedad y así mejorar la calidad de vida del individuo (medicina personalizada). Existen variables tales como la alta actividad de la enfermedad y la ausencia de erosiones como predictores de respuesta en la terapia con abatacept.

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.