Correlation between shift work and psychological problems among hospitals personnel of Ardabil University of Medical science

Background Shift work can be propounded as a risk factor of psychological problems creation. This study has been executed to determine relation between shift work and psychological problems among hospital’s personnel of Ardabil University of Medical science. Materials and methods This study is a retrospective case-control one and has been executed on 388 person of personnel of hospitals of Ardabil University of Medical Science (223 as case group and 65 as control group), that has been selected randomly. Requisite information was collected with three questionnaires. Including General Health Questionnaire (GHQ), personal questionnaire and standardized sleep state evaluation questionnaire and were analyzed by SPSS statistical software. Chi-square and analysis of variance tests were used to test investigation’s hypothesis. Results Meaningful relation were found between these problems such as somatoform signs (p< 0.005) anxiety (p< 0.019) social action disorder (p< 0.001) and depression (p< 0.019) and shift work. Conclusions It is advised to reform irregular circulation of shift Work in hospitals and with due attention to work conditions of any hospital

Circadian clocks and breast cancer

Circadian clocks respond to environmental time cues to coordinate 24-hour oscillations in almost every tissue of the body. In the breast, circadian clocks regulate the rhythmic expression of numerous genes. Disrupted expression of circadian genes can alter breast biology and may promote cancer. Here we overview circadian mechanisms, and the connection between the molecular clock and breast biology. We describe how disruption of circadian genes contributes to cancer via multiple mechanisms, and link this to increased tumour risk in women who work irregular shift patterns. Understanding the influence of circadian rhythms on breast cancer could lead to more efficacious therapies, reformed public health policy and improved patient outcome

Self-reported bruxism : Associated factors among media personnel with or without irregular shift work

Tutkimuksen kohderyhmänä oli mediatyöntekijöitä, joiden toimenkuva on viime vuosina muuttunut yhä kuormittavammaksi epäsäännöllisen vuorotyön sekä jatkuvien teknisten, organisatoristen ja taloudellisten tekijöiden ristipaineessa. Väitöskirjatutkimus on osa laajempaa tutkimushanketta, joka suunniteltiin selvittämään epäsäännöllisen vuorotyön mahdollisia haittoja. Tutkimusta tukivat taloudellisesti Työsuojelurahasto ja Suomen Hammaslääkäriseura Apollonia sekä resurssipanostuksin Hammaslääketieteen laitos (HY), Työterveyslaitos ja Yleisradio Oy. Bruksismi on tahdosta riippumatonta hampaiden narskuttelua tai yhteenpuristamista. Hampaiden narskuttelu on rytmistä jaksoittain toistuvaa puremalihasten toimintaa, joka esiintyy nukkuessa -tavallisimmin kevyen unen ja havahtumisjaksojen yhteydessä. Valveilla ollessa bruksismi on terveillä ihmisillä lähinnä hampaiden yhteenpuristamista. Yleisen käsityksen mukaan toistuvaa unibruksismia esiintyy noin 10 %:lla ja valveilla tapahtuvaa hampaiden yhteenpuristamista noin 20 %:lla. Aiemmin bruksismi kuului kansainvälisen unihäiriöluokituksen (ICSD 1997) mukaan unen erityishäiriöihin, mutta tuorein luokitus (ICSD 2005) listaa sen unen liikehäiriöihin. Väitöstutkimuksen yleisenä tavoitteena oli kartoittaa koetun bruksismin ja uni- valvehäiriöiden yhteyttä. Tutkimus oli poikittainen vertailututkimus epäsäännöllistä vuorotyötä ja säännöllisiä päivävuoroja tekevien välillä. Mielenkiinto kohdistui myös bruksismin ja kasvojen alueen kivun mahdolliseen yhteyteen. Lisäksi tutkimuksessa selvitettiin joidenkin tunnetusti unen laatua huonontavien psykososiaalisten, neurologisten ja fysiologisten tekijöiden yhteyttä koettuun bruksismiin. Tutkimuksen kohderyhmän muodosti 750 Yleisradion epäsäännöllistä vuorotyötä tekevää työntekijää. Vertailuryhmänä käytettiin samansuuruista satunnaistetusti valittua kaltaistettua Yleisradion työntekijäjoukkoa, joka tekee samankaltaista työtä, mutta säännöllisenä päivätyönä. Kohderyhmälle lähetettiin kyselylomakkeet, jotka kartoittivat koetun bruksismin lisäksi mm. tutkittavien taustatiedot, yleisen terveydentilan, yleisiä koettuja stressioireita ja tuntemuksia, kipuoireita, sekä unen laatua. Lisäksi esitettiin jaksamista ja työympäristöä koskevia kysymyksiä. Kyselyyn vastasi kaikkiaan 874 henkilöä. Kokonaisvastausprosentti oli 58,3 % (53,7 % miehiä). Epäsäännöllistä vuorotyötä tekevien vastausprosentti oli 82,3 % ja säännöllistä päivätyötä tekevien ryhmässä 34,3 %. Työtehtävät sisälsivät ohjelmien toimitus- ja tuottamistyötä, teknistä tuotanto- ja tukityötä, sekä esimies- ja hallintotyötä. Miesten keski-ikä vuorotyöryhmässä oli 45,0 (± 10,6) vuotta ja naisten keski-ikä 42,6 (± 10,7) vuotta, vastaavat luvut päivätyötä tekeville olivat 47,4 (± 9,7) ja 45,5 (± 10,1) vuotta. Vuorotyötä tekevistä oli miehiä 56,6 %, päivätyöryhmässä miehien osuus oli 46,7 %. Usein koettua bruksismia havaittiin koko tutkimusjoukossa 10,6 %:lla. Bruksismin esiintyvyydessä ei ollut merkitsevää eroa epäsäännöllistä vuorotyötä ja päivätyötä tekevien välillä. Kun bruksismia ja stressiä arvioitiin suhteessa tyytyväisyyteen nykyiseen työaikamuotoon, molemmat olivat merkitsevästi vallitsevimpia niillä, jotka halusivat vaihtaa nykyistä työaikamuotoaan. Epäsäännöllistä vuorotyötä tekevät lisäksi ilmoittivat kokevansa enemmän stressiä kuin päivätyötä tekevät sekä olivat tyytymättömämpiä työaikamuotoonsa. Tutkittavista henkilöistä katkonaista unta esiintyi 43,6 %:lla sekä 36,2 % koki unensa virkistämättömäksi. Kasvokipua esiintyi 19,6 %:lla. Usein toistuva bruksaus sekä tyytymättömyys työaikamuotoon olivat erittäin merkitsevästi yhteydessä unihäiriöiden sekä riittämättömän unen oireiden kanssa. Bruksismi ja katkonainen uni osoittautuivat myös kasvokivun taustatekijöiksi. Tutkimus osoitti, että koetulla bruksismilla oli merkitsevä yhteys unihäiriöihin, kasvokipuun, koettuun stressiin ja ahdistuneisuuteen, nuorempaan ikään, runsaampiin hammaslääkäri- ja lääkärikäynteihin sekä siihen että oli tyytymätön työaikamuotoonsa (itse työaikamuoto ei ollut merkitsevä tekijä). Tutkimuksen yhtenä johtopäätöksenä todettiin, että koettu bruksismi voi terveillä työikäisillä henkilöillä olla osa stressaavaa tilannetta ja siihen liittyvää käyttäytymistä. Tämän tiedostaminen terveydenhuollossa voisi olla hyödyllistä.The present study was performed on media personnel who could be considered to be under sustained pressure at work due to intense on-going technological, organizational and economic changes. The study formed part of a comprehensive investigation of shift work and its sleep/awake consequences. The general aim was to examine the relationships of self-reported bruxism and sleep quality among employees with or without irregular shift work. The study also focused on the possible associations of bruxism and orofacial pain. Some psychological, neurological and physiological factors known to be detrimental to sleep were also studied. A questionnaire with several standard questions was mailed to all employees of the Finnish Broadcasting Company with irregular shift work (n=750; 57.0 % men) and to an equal number of randomly selected controls in the same company with regular eight-hour daytime work (42.4 % men). The mean age of invited subjects was 43.0 (SD 10.4) years in irregular shift work and 44.8 (SD 10.2) years in day work. The work duties of the present media personnel included journalism, broadcasting, programme production, technical support and administration. The questionnaire covered perceived bruxism (assessed with a five-point scale) and, among others, the following: demographic items, employment details, general health experience, physical status, pain symptoms, psychosomatic symptoms, psychosocial status, stress experience, work satisfaction and performance, perceptions of sleep and its awake consequences. The overall response rate was 58.3% (53.7% men). The response rate in the irregular shift work group was 82.3% (56.6% men) and in the regular daytime work group 34.3% (46.7% men). The invited subjects and respondents in both shift work and day work groups were similar as regards gender and age (NS). Frequent self-reported bruxism was found among 10.6 % of subjects overall. The bruxism scores were evenly distributed in the irregular shift work and regular day work groups (NS). Similarly, a total of 43.6 % reported disrupted sleep and 36.2 % perceived their as sleep non-restorative. Current orofacial pain was found overall in 19.6 % of the study population. Among those reporting current pain 88.3 % had experienced it for over six months. According to the multivariate analyses, self-reported bruxism and dissatisfaction with current work shift schedule were significantly associated with most studied sleep variables. More frequent bruxism (p<0.01) and more severe stress (p<0.001) tended to occur more often among those subjects dissatisfied with their work shift schedule. It was found that dissatisfaction with one’s work shift schedule and not merely irregular shift work may aggravate stress and bruxism. In addition, frequent self-reported bruxism was associated with increased numbers of health care visits. The results also revealed significant associations between self-reports of bruxism and anxiety, and bruxism and orofacial pain experience. Based on the multivariate analyses, it can be concluded that disrupted sleep and bruxism may be concomitantly involved in the development of orofacial pain. It may also be possible that self-reported bruxism indicates sleep problems and their adherent awake consequences in non-patient populations. It was suggested that subjectively conceptualized awareness of bruxism may be linked to stress-related states and behavior which could be useful knowledge for health care professionals

Self-reported bruxism mirrors anxiety and stress in adults

Objectives: The aims were to analyze whether the levels of self-reported bruxism and anxiety associate among otherwise healthy subjects, and to investigate the independent effects of anxiety and stress experience on the probability of self-reported bruxism. Study Design: As part of a study on irregular shift work, a questionnaire was mailed to all employees of the Finnish Broadcasting Company with irregular shift work (number of subjects: n=750) and to an equal number of randomly selected employees in the same company with regular eight-hour daytime work. Results: The response rates were 82.3% (56.6 % men) and 34.3 % (46.7 % men), respectively. Among the 874 respondents, those aware of more frequent bruxism reported significantly more severe anxiety (p<0.001). Adjusted by age and gender, frequent bruxers were more than two times more likely to report severe stress (odds ratio 2.5; 95% confidence interval 1.5-4.2) and anxiety (odds ratio 2.2; 95% confidence interval 1.3-3.6) than non-or-mild bruxers. Conclusions: Present findings suggest that self-reported bruxism and psychological states such as anxiety or stress may be related in working age subjects

On the Reduction of Biases in Big Data Sets for the Detection of Irregular Power Usage

In machine learning, a bias occurs whenever training sets are not representative for the test data, which results in unreliable models. The most common biases in data are arguably class imbalance and covariate shift. In this work, we aim to shed light on this topic in order to increase the overall attention to this issue in the field of machine learning. We propose a scalable novel framework for reducing multiple biases in high-dimensional data sets in order to train more reliable predictors. We apply our methodology to the detection of irregular power usage from real, noisy industrial data. In emerging markets, irregular power usage, and electricity theft in particular, may range up to 40% of the total electricity distributed. Biased data sets are of particular issue in this domain. We show that reducing these biases increases the accuracy of the trained predictors. Our models have the potential to generate significant economic value in a real world application, as they are being deployed in a commercial software for the detection of irregular power usage

Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?

Non-technical losses (NTL) occur during the distribution of electricity in power grids and include, but are not limited to, electricity theft and faulty meters. In emerging countries, they may range up to 40% of the total electricity distributed. In order to detect NTLs, machine learning methods are used that learn irregular consumption patterns from customer data and inspection results. The Big Data paradigm followed in modern machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at theory and models. However, the sample of inspected customers may be biased, i.e. it does not represent the population of all customers. As a consequence, machine learning models trained on these inspection results are biased as well and therefore lead to unreliable predictions of whether customers cause NTL or not. In machine learning, this issue is called covariate shift and has not been addressed in the literature on NTL detection yet. In this work, we present a novel framework for quantifying and visualizing covariate shift. We apply it to a commercial data set from Brazil that consists of 3.6M customers and 820K inspection results. We show that some features have a stronger covariate shift than others, making predictions less reliable. In particular, previous inspections were focused on certain neighborhoods or customer classes and that they were not sufficiently spread among the population of customers. This framework is about to be deployed in a commercial product for NTL detection.Comment: Proceedings of the 19th International Conference on Intelligent System Applications to Power Systems (ISAP 2017

Shift work and occupational accident absence in Belgium : findings from the sixth European Working Condition Survey

(1) Background: Irregular and non-standard work arrangements have become a serious determinant to the health and safety of workers. The aim of this study is to investigate the relationship between shift work and occupational accident absence. A representative Belgian sample considering several sociodemographic and work characteristics is used. (2) Methods: This study is based on the data of the sixth European Working Condition Survey (EWCS). The sample is restricted to 2169 respondents from Belgium. By using multivariate logistic regression modeling techniques and adjusting several confounders, the associations between shift work and occupational accident absence are studied. (3) Results: It is found that about 11.1% of the workers undergo an occupational accident absence. A multivariate regression model demonstrates an increased occupational accident absence risk for workers who have shift work (odds ratio, or OR, 1.92, 95% CI 1.06-3.46). Also, gender and biomechanical exposure were significantly associated with occupational accident absence ((OR 2.07, 95% CI 1.16-3.69) and (OR 2.03, 95% CI 1.14-3.63), respectively). No significant interaction effects are found with gender and age variables. (4) Conclusion: This study confirms that doing shift work is significantly associated with occupational accidents. In order to reduce the significance of occupational accidents, shift work should be limited through national-level policies

Impact of Biases in Big Data

The underlying paradigm of big data-driven machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at theory and models. Is having simply more data always helpful? In 1936, The Literary Digest collected 2.3M filled in questionnaires to predict the outcome of that year's US presidential election. The outcome of this big data prediction proved to be entirely wrong, whereas George Gallup only needed 3K handpicked people to make an accurate prediction. Generally, biases occur in machine learning whenever the distributions of training set and test set are different. In this work, we provide a review of different sorts of biases in (big) data sets in machine learning. We provide definitions and discussions of the most commonly appearing biases in machine learning: class imbalance and covariate shift. We also show how these biases can be quantified and corrected. This work is an introductory text for both researchers and practitioners to become more aware of this topic and thus to derive more reliable models for their learning problems