Acute and cumulative effects of scheduling on aircrew fatigue in ultra-short-haul operations.

Aircrew fatigue constitutes a safety hazard in aviation, which authorities attempt to mitigate through flight time limitations. Some gaps in knowledge exist, however. The purpose of the present study was to investigate the associations of schedule characteristics with fatigue and amount of sleep in the acute 24-h window, and as cumulative effects across the 7-day work period. One hundred and six aircrew (14% cabin crew) participated. They rated fatigue on the Karolinska Sleepiness Scale (KSS) three times per flight day for four 7-day work periods, with up to 7 days off between work periods. Mixed model regression was applied to the data. In the multivariable model, more sleep was associated with lower fatigue (p = .000)), corresponding to 0.26 KSS units less per hour of sleep. Very early, early and late duty types, as well as duty time, were associated with higher fatigue. For the 7-day work period, accumulation of very early duties and longer duty time were associated with increased fatigue, and more accumulated sleep was associated with lower fatigue in the adjusted model (0.08 KSS units per hour of sleep) (p = .000). Accumulated duty time was not significant when analysed as a single variable, but became so after adjustment for sleep. The results suggest that sleep, duty time and early starts are important predictors of fatigue in the 24-h window and that the number of very early starts and short sleep have cumulative effects on fatigue across a 7-day work period.

Solidarity or self-interest? Public opinion in relation to alcohol policies in Sweden.

AIM: The aim of this article is to study how people sometimes accept policies that could in a narrow sense be seen as in conflict with their own self-interest. DESIGN: The study is based on survey data relating to public opinion on alcohol policy in Sweden targeted at people aged 16-85 years 2016-2017. Among the 3400 people questioned, the response rate was 52%. RESULTS: The results show that people's perception of the problematic societal consequences of alcohol, in combination with ideological norms regarding the responsibility of individuals, is much more important in explaining public opinion than self-interest factors. It is the view that there is a problem at the societal level, rather than at the personal level, that is most essential for explaining opinions on alcohol restrictions. General knowledge of alcohol-related matters has some effect, whereas personal experiences of close affiliates excessive drinking does not seem to color the opinions expressed. CONCLUSION: Support for restrictive alcohol policies in Swedish public opinion is mainly founded on norms of solidarity and astute problem analyses at the societal level, and to a much lesser extent on egoism and personal experiences.

Electronic data capture on athletes' pre-participation health and in-competition injury and illness at major sports championships: An extended usability study in Athletics.

This study set out to identify factors critical for the usability of electronic data collection in association with championships in individual sports. A qualitative analysis of electronic data collection system usability for collection of data on pre-participation health from athletes and in-competition injury and illness from team physicians was performed during the 2013 European Athletics Indoor Championships. A total of 15 athletes and team physicians participated. Athletes were found to experience few problems interacting with the electronic data collection system, but reported concerns about having to reflect on injury and illness before competitions and the medical terminology used. Team physicians encountered problems when first navigating through the module for clinical reporting, but they were not subjected to motivational problems. We conclude that athletes' motivation to self-report health data and the design of the human-computer interface for team physicians are key issues for the usability of electronic data collection systems in association with championships in individual sports.

That's what friends are for: how intergroup friendships promote historically disadvantaged groups' substantive political representation.

The interests of historically disadvantaged groups risk being overlooked if they are not present in the decision-making process. However, a mere presence in politics does not guarantee political success. Often groups need allies to promote their interests successfully. We argue that one way to identify such allies is to judge politicians by whether they have friends in historically disadvantaged groups, as intergroup friendships have been shown to make people understand and feel empathy for outgroups. In other words, intergroup friendships may function as an important complement to descriptive representation. We test our argument with a unique survey that asks all elected political representatives in Sweden's 290 municipalities (response rate 79 per cent) about their friendship ties to, and their representation of, five historically disadvantaged groups: women, immigrants, youths, pensioners and blue-collar workers. We find a strong correlation between representatives' friendship ties to these groups and their commitment to represent them. The correlation is especially strong for youths and blue-collar workers, which likely can be explained by the fact that these groups usually lack crucial political resources (such as experience and education). We conclude that friendship ties function as an important complement to descriptive representation for achieving substantive representation.

Extending in-competition Athletics injury and illness surveillance with pre-participation risk factor screening: A pilot study.

OBJECTIVES: To explore the performance of retrospective health data collected from athletes before Athletics championships for the analysis of risk factors for in-competition injury and illness (I&I). METHODS: For the 2013 European Athletics Indoor Championships, a self-report questionnaire (PHQ) was developed to record the health status of 127 athletes during the 4 weeks prior to the championship. Physician-based surveillance of in-competition I&I among all 577 athletes registered to compete was pursued during the championships. RESULTS: 74 athletes (58.3%) from the sample submitted a complete PHQ. 21 (28%) of these athletes sustained at least one injury and/or illness during the championships. Training more than 12 h/week predisposed for sustaining an in-competition injury, and a recent health problem for in-competition illness. Among the 577 registered athletes, 60 injuries (104/1000 registered athletes) were reported. 31% of injuries were caused by the track, and 29% by overuse. 29 illnesses were reported (50/1000 registered athletes); upper respiratory tract infection and gastro-enteritis/diarrhoea were the most reported diagnoses. CONCLUSIONS: Pre-participation screening using athletes' self-report PHQ showed promising results with regard to identification of individuals at risk. Indoor injury types could be attributed to extrinsic factors, such as small track size, track inclination, and race tactics.

Validating and extending the three process model of alertness in airline operations.

Sleepiness and fatigue are important risk factors in the transport sector and bio-mathematical sleepiness, sleep and fatigue modeling is increasingly becoming a valuable tool for assessing safety of work schedules and rosters in Fatigue Risk Management Systems (FRMS). The present study sought to validate the inner workings of one such model, Three Process Model (TPM), on aircrews and extend the model with functions to model jetlag and to directly assess the risk of any sleepiness level in any shift schedule or roster with and without knowledge of sleep timings. We collected sleep and sleepiness data from 136 aircrews in a real life situation by means of an application running on a handheld touch screen computer device (iPhone, iPod or iPad) and used the TPM to predict sleepiness with varying level of complexity of model equations and data. The results based on multilevel linear and non-linear mixed effects models showed that the TPM predictions correlated with observed ratings of sleepiness, but explorative analyses suggest that the default model can be improved and reduced to include only two-processes (S+C), with adjusted phases of the circadian process based on a single question of circadian type. We also extended the model with a function to model jetlag acclimatization and with estimates of individual differences including reference limits accounting for 50%, 75% and 90% of the population as well as functions for predicting the probability of any level of sleepiness for ecological assessment of absolute and relative risk of sleepiness in shift systems for safety applications.

Performance of eHealth data sources in local influenza surveillance: a 5-year open cohort study.

BACKGROUND: There is abundant global interest in using syndromic data from population-wide health information systems--referred to as eHealth resources--to improve infectious disease surveillance. Recently, the necessity for these systems to achieve two potentially conflicting requirements has been emphasized. First, they must be evidence-based; second, they must be adjusted for the diversity of populations, lifestyles, and environments. OBJECTIVE: The primary objective was to examine correlations between data from Google Flu Trends (GFT), computer-supported telenursing centers, health service websites, and influenza case rates during seasonal and pandemic influenza outbreaks. The secondary objective was to investigate associations between eHealth data, media coverage, and the interaction between circulating influenza strain(s) and the age-related population immunity. METHODS: An open cohort design was used for a five-year study in a Swedish county (population 427,000). Syndromic eHealth data were collected from GFT, telenursing call centers, and local health service website visits at page level. Data on mass media coverage of influenza was collected from the major regional newspaper. The performance of eHealth data in surveillance was measured by correlation effect size and time lag to clinically diagnosed influenza cases. RESULTS: Local media coverage data and influenza case rates showed correlations with large effect sizes only for the influenza A (A) pH1N1 outbreak in 2009 (r=.74, 95% CI .42-.90; P<.001) and the severe seasonal A H3N2 outbreak in 2011-2012 (r=.79, 95% CI .42-.93; P=.001), with media coverage preceding case rates with one week. Correlations between GFT and influenza case data showed large effect sizes for all outbreaks, the largest being the seasonal A H3N2 outbreak in 2008-2009 (r=.96, 95% CI .88-.99; P<.001). The preceding time lag decreased from two weeks during the first outbreaks to one week from the 2009 A pH1N1 pandemic. Telenursing data and influenza case data showed correlations with large effect sizes for all outbreaks after the seasonal B and A H1 outbreak in 2007-2008, with a time lag decreasing from two weeks for the seasonal A H3N2 outbreak in 2008-2009 (r=.95, 95% CI .82-.98; P<.001) to none for the A p H1N1 outbreak in 2009 (r=.84, 95% CI .62-.94; P<.001). Large effect sizes were also observed between website visits and influenza case data. CONCLUSIONS: Correlations between the eHealth data and influenza case rates in a Swedish county showed large effect sizes throughout a five-year period, while the time lag between signals in eHealth data and influenza rates changed. Further research is needed on analytic methods for adjusting eHealth surveillance systems to shifts in media coverage and to variations in age-group related immunity between virus strains. The results can be used to inform the development of alert-generating eHealth surveillance systems that can be subject for prospective evaluations in routine public health practice.

Intentions to perform non-pharmaceutical protective behaviors during influenza outbreaks in Sweden: a cross-sectional study following a mass vaccination campaign.

Failure to incorporate the beliefs and attitudes of the public into theoretical models of preparedness has been identified as a weakness in strategies to mitigate infectious disease outbreaks. We administered a cross-sectional telephone survey to a representative sample (n = 443) of the Swedish adult population to examine whether self-reported intentions to improve personal hygiene and increase social distancing during influenza outbreaks could be explained by trust in official information, self-reported health (SF-8), sociodemographic factors, and determinants postulated in protection motivation theory, namely threat appraisal and coping appraisal. The interviewees were asked to make their appraisals for two scenarios: a) an influenza with low case fatality and mild lifestyle impact; b) severe influenza with high case fatality and serious disturbances of societal functions. Every second respondent (50.0%) reported high trust in official information about influenza. The proportion that reported intentions to take deliberate actions to improve personal hygiene during outbreaks ranged between 45-85%, while less than 25% said that they intended to increase social distancing. Multiple logistic regression models with coping appraisal as the explanatory factor most frequently contributing to the explanation of the variance in intentions showed strong discriminatory performance for staying home while not ill (mild outbreaks: Area under the curve [AUC] 0.85 (95% confidence interval 0.82;0.89), severe outbreaks AUC 0.82 (95% CI 0.77;0.85)) and acceptable performance with regard to avoiding public transportation (AUC 0.78 (0.74;0.82), AUC 0.77 (0.72;0.82)), using handwash products (AUC 0.70 (0.65;0.75), AUC 0.76 (0.71;0.80)), and frequently washing hands (AUC 0.71 (0.66;0.76), AUC 0.75 (0.71;0.80)). We conclude that coping appraisal was the explanatory factor most frequently included in statistical models explaining self-reported intentions to carry out non-pharmaceutical health actions in the Swedish outlined context, and that variations in threat appraisal played a smaller role in these models despite scientific uncertainties surrounding a recent mass vaccination campaign.

Neuropathologic assessment of dementia markers in identical and fraternal twins.

Twin studies are an incomparable source of investigation to shed light on genetic and non-genetic components of neurodegenerative diseases, as Alzheimer's disease (AD). Detailed clinicopathologic correlations using twin longitudinal data and post-mortem examinations are mostly missing. We describe clinical and pathologic findings of seven monozygotic (MZ) and dizygotic (DZ) twin pairs. Our findings show good agreement between clinical and pathologic diagnoses in the majority of the twin pairs, with greater neuropathologic concordance in MZ than DZ twins. Greater neuropathologic concordance was found for ß-amyloid than tau pathology within the pairs. ApoE4 was associated with higher ß-amyloid and earlier dementia onset, and importantly, higher frequency of other co-occurring brain pathologies, regardless of the zygosity. Dementia onset, dementia duration, difference between twins in age at dementia onset and at death, did not correlate with AD pathology. These clinicopathologic correlations of older identical and fraternal twins support the relevance of genetic factors in AD, but not their sufficiency to determine the pathology, and consequently the disease, even in monozygotic twins. It is the interaction among genetic and non-genetic risks which plays a major role in influencing, or probably determining, the degeneration of those brain circuits associated with pathology and cognitive deficits in AD.

Visualization of infectious disease outbreaks in routine practice.

Throughout the history of epidemiology, visualizations have been used as the interface between public-health professionals and epidemiological data. The aim of this study was to examine the impact of the level of abstraction when using visualizations on routine infectious disease control. We developed three interactive visualization prototypes at increasing levels of abstraction to communicate subsets of influenza outbreak surveillance information. The visualizations were assessed through workshops in an exploratory evaluation with infectious disease epidemiologists. The results show that despite the potential of processed, abstract, and information-dense representations, increased levels of abstraction decreased epidemiologists' understanding and confidence in visualizations. Highly abstract representations were deemed not applicable in routine practice without training. Infectious disease epidemiologists' work routines and decision-making need to be further studied in order to develop visualizations that meet both the quality requirements imposed by policy-makers and the contextual nature of work practice.

Using computer simulation to assist in the robustness analysis of an ion-exchange chromatography step.

This paper presents a methodology to gain process knowledge and assist in the robustness analysis of an ion-exchange step in a protein purification process using a model-based approach. Factorial experimental design is common practice in industry today to obtain robustness characterization of unit operations with respect to variations in process parameters. This work aims at providing a better insight into what process variations affect quality and to further reduce the experimental work to the regions of process variation that are of most interest. This methodology also greatly increases the ability to predict process performance and promotes process understanding. The model calibration part of the methodology involves three consecutive steps to calibrate a steric mass action (SMA) ion-exchange chromatography model. Firstly, a number of gradient elution experiments are performed. Secondly, experimental breakthrough curves have to be generated for the proteins if the adsorption capacity of the medium for each component is not known. Thirdly, a multi-component loading experiment is performed to calibrate the multi-component effects that cannot be determined from the single-component experiments. The separation process studied in this work is the separation of polyclonal IgG from a mixture containing IgG, myoglobin and BSA. The calibrated model is used to simulate six process variations in a full factorial experiment. The results of the simulations provide information about the importance of the different process variations and the simulations are also used to determine the crucial points for the process parameter variations. The methodology can be used to assist in the robustness analysis normally performed in the pharmaceutical industry today as it is able to predict the impact on process performance resulting from variations in salt concentration, column load, protein concentration and flow rate.

Model-based optimization of a preparative ion-exchange step for antibody purification.

A method using a model-based approach to design and optimize an ion-exchange step in a protein purification process is proposed for the separation of IgG from a mixture containing IgG, BSA and myoglobin. The method consists of three steps. In the first step, the model is calibrated against carefully designed experiments. The chromatographic model describes the convective and dispersive flow in the column, the diffusion in the adsorbent particles, and the protein adsorption using Langmuir kinetics with mobile phase modulators (MPM). In the second step, the model is validated against a validation experiment and analyzed. In the third and final step, the operating conditions are optimized. In the optimization step, the loading volume and the elution gradient are optimized with regard to the most important costs: the fixed costs and the feed cost. The optimization is achieved by maximizing the objective functions productivity (i.e. the production rate for a given amount of stationary phase) and product yield (i.e. the fraction of IgG recovered in the product stream). All optimization is conducted under the constraint of 99% purity of the IgG. The model calibration and the analysis show that this purification step is determined mainly by the kinetics, although as large a protein as IgG is used in the study. The two different optima resulting from this study are a productivity of 2.7 g IgG/(s m3) stationary phase and a yield of 90%. This model-based approach also gives information of the robustness of the chosen operating conditions. It is shown that the bead diameter could only be increased from 15 microm to 35 microm with maximum productivity and a 99% purity constraint due to increased diffusion hindrance in larger beads.

Methodologies for model calibration to assist the design of a preparative ion-exchange step for antibody purification.

This work proposes methodologies using a model-based approach to gain knowledge on and assist the development of an ion-exchange step in a protein purification process; the separation of IgG from a mixture containing IgG, insulin and transferrin. This approach is suitable for capture and intermediate steps in a process. Both methods involve four consecutive steps. Firstly, the retention of the different protein components is determined giving a retention map of the system. From this the optimal pH and buffer can be determined. Secondly, additional salt gradient experiments are performed at the selected pH. Thirdly, experimental breakthrough curves have to be generated for the protein if the adsorption capacity of the medium for each component is not known. Fourthly, a validation experiment is performed. In method 1, where the capacity for the medium is assumed to be known. the protein adsorption is described by Langmuir kinetics with a mobile phase modulator (MPM). In this description salt is considered to be inert. In method 2 the adsorption behavior is described by steric mass action (SMA), where the salt component competes with the proteins for the available binding sites. Both methods use a dispersion model to describe transport in the mobile phase in the column. The methods are able to predict the separation and loading behavior of the three components. The methods can, with reasonable accuracy, predict the breakthrough of transferrin in a mixture of insulin, IgG and transferrin. Method 1 requires fewer experiments and predicts the mean volume of breakthrough for the loading step in the validation experiment more accurately than method 2. On the other hand, method 2 has a better accuracy to predict the position of 10% breakthrough and the shape of the breakthrough curve. The methods suggested in this work are shown to be efficient in process development. Some additional experiments have to be performed to obtain the unknown parameters in the models. However, the predictability that is achieved results in less experimental work in the process design as a whole.

Electronic speckle pattern interferometry: a tool for determining diffusion and partition coefficients for proteins in gels.

The aim of this study was to demonstrate electronic speckle pattern interferometry (ESPI) as a powerful tool in determining diffusion coefficients and partition coefficients for proteins in gels. ESPI employs a CCD camera instead of a holographic plate as in conventional holographic interferometry. This gives the advantage of being able to choose the reference state freely. If a hologram at the reference state is taken and compared to a hologram during the diffusion process, an interferometric picture can be generated that describes the refraction index gradients and thus the concentration gradients in the gel as well as in the liquid. MATLAB is then used to fit Fick's law to the experimental data to obtain the diffusion coefficients in gel and liquid. The partition coefficient is obtained from the same experiment from the flux condition at the interface between gel and liquid. This makes the comparison between the different diffusants more reliable than when the measurements are performed in separate experiments. The diffusion and partitioning coefficients of lysozyme, BSA, and IgG in 4% agarose gel at pH 5.6 and in 0.1 M NaCl have been determined. In the gel the diffusion coefficients were 11.2 +/- 1.6, 4.8 +/- 0.6, and 3.0 +/- 0.3 m(2)/s for lysozyme, BSA, and IgG, respectively. The partition coefficients were determined to be 0.65 +/- 0.04, 0.44 +/- 0.06, and 0.51 +/- 0.04 for lysozyme, BSA, and IgG, respectively. The current study shows that ESPI is easy to use and gives diffusion coefficients and partition coefficients for proteins with sufficient accuracy from the same experiment.

A comparison of diagnosis capture from medical records, self-reports, and drug registrations: a study in individuals 80 years and older.

BACKGROUND AND AIMS: Reports of diseases and health problems vary as a function of the information source. In the present study we compared the capture of information on morbidity using medical records, self-reports, and drug registrations. METHODS: A concurrent review of medical records, self-reports, and registration of marker drugs was conducted to determine diagnosis for 44 common diseases. Diagnoses from the various sources were uniformly classified according to ICD-10. The study included a sample of 702 individuals, aged 80 and older, enrolled in population-based longitudinal studies. RESULTS: The morbidity rates differed considerably across the used sources. Although medical records captured most of the morbidity, self-reports offered supplemental information especially for less objective health problems. Marker drugs typically confirmed information in the records, but only for a limited number of diseases. DISCUSSION: In studies of aging and health, a thorough review of medical records and a concurrent evaluation of self-reports and marker drugs represent a valuable strategy for portraying morbidity. This strategy goes beyond the use of a single source like self-reports, and provides better estimates of health conditions in the elderly.