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Aims: To estimate the overall health impact of transferring commuting trips from car to bicycle. Methods: In this study registry information on the location of home and work for residents in Stockholm County was used to obtain the shortest travel route on a network of bicycle paths and roads. Current modes of travel to work were based on travel survey data. The relation between duration of cycling and distance cycled was established as a basis for selecting the number of individuals that normally would drive a car to work, but have a distance to work that they could bicycle within 30 minutes. The change in traffic flows was estimated by a transport model (LuTrans) and effects on road traffic injuries and fatalities were estimated by using national hospital injury data. Effects on air pollution concentrations were modelled using dispersion models. Results: Within the scenario, 111,000 commuters would shift from car to bicycle. On average the increased physical activity reduced the one-year mortality risk by 12% among the additional bicyclists. Including the number of years lost due to morbidity, the total number of disability adjusted life-years gained was 696. The amount of disability adjusted life-years gained in the general population due to reduced air pollution exposure was 471. The number of disability adjusted life-years lost by traffic injuries was 176. Also including air pollution effects among bicyclists, the net benefit was 939 disability adjusted life-years per year. Conclusions: Large health benefits were estimated by transferring commuting by car to bicycle.

This study aims to use dispersion-modeled concentrations of nitrogen oxides (NOx) and black carbon (BC) to estimate bicyclist exposures along a network of roads and bicycle paths. Such modeling was also performed in a scenario with increased bicycling. Accumulated concentrations between home and work were thereafter calculated for both bicyclists and drivers of cars. A transport model was used to estimate traffic volumes and current commuting preferences in Stockholm County. The study used individuals&rsquo, home and work addresses, their age, sex, and an empirical model estimate of their expected physical capacity in order to establish realistic bicycle travel distances. If car commuters with estimated physical capacity to bicycle to their workplace within 30 min changed their mode of transport to bicycle, &gt, 110,000 additional bicyclists would be achieved. Time-weighted mean concentrations along paths were, among current bicyclists, reduced from 25.8 to 24.2 &mu, g/m3 for NOx and 1.14 to 1.08 &mu, g/m3 for BC. Among the additional bicyclists, the yearly mean NOx dose from commuting increased from 0.08 to 1.03 &mu, g/m3. This would be expected to yearly cause 0.10 fewer deaths for current bicycling levels and 1.7 more deaths for additional bicycling. This increased air pollution impact is much smaller than the decrease in the total population.

SUMMARYWorkplaces are one of the most important regular meeting places in society. The aim of this study was to use simulation experiments to examine the impact of different workplace cultures on influenza dissemination during pandemics. The impact is investigated by experiments with defined social-mixing patterns at workplaces using semi-virtual models based on authentic sociodemographic and geographical data from a North European community (population 136 000). A simulated pandemic outbreak was found to affect 33% of the total population in the community with the reference academic-creative workplace culture; virus transmission at the workplace accounted for 10·6% of the cases. A model with a prevailing industrial-administrative workplace culture generated 11% lower incidence than the reference model, while the model with a self-employed workplace culture (also corresponding to a hypothetical scenario with all workplaces closed) produced 20% fewer cases. The model representing an academic-creative workplace culture with restricted workplace interaction generated 12% lower cumulative incidence compared to the reference model. The results display important theoretical associations between workplace social-mixing cultures and community-level incidence rates during influenza pandemics. Social interaction patterns at workplaces should be taken into consideration when analysing virus transmission patterns during influenza pandemics.

SUMMARYThis study aims to develop a typology of generic meeting places based on social contact and mixing of relevance for infectious disease transmission. Data were collected by means of a contact diary survey conducted on a representative sample of the Swedish population. The typology is derived from a cluster analysis accounting for four dimensions associated with transmission risk: visit propensity and its characteristics in terms of duration, number of other persons present and likelihood of physical contact. In the analysis, we also study demographic, socio-economic and geographical differences in the propensity of visiting meeting places. The typology identifies the family venue, the fixed activity site, the family vehicle, the trading plaza and the social network hub as generic meeting places. The meeting place typology represents a spatially explicit account of social contact and mixing relevant to infectious disease modelling, where the social context of the outbreak can be highlighted in light of the actual infectious disease.

Pandemic simulation is a useful tool for analyzing outbreaks and exploring the impact of variations in disease, population, and intervention models. Unfortunately, this type of simulation can be quite time-consuming especially for large models and significant outbreaks, which makes it difficult to run the simulations interactively and to use simulation for decision support during ongoing outbreaks. Improved run-time performance enables new applications of pandemic simulations, and can potentially allow decision makers to explore different scenarios and intervention effects. Parallelization of infection-probability calculations and multicore architectures can take advantage of modern processors to achieve significant run-time performance improvements. However, because of the varying computational load during each simulation run, which originates from the changing number of infectious persons during the outbreak, it is not useful to us the same multicore setup during the simulation run. The best performance can be achieved by dynamically changing the use of the available processor cores to balance the overhead of multithreading with the performance gains of parallelization.

Our study is based on individual data on people's home and work addresses, as well as their age, sex and physical capacity, in order to establish realistic bicycle-travel distances. A transport model is used to single out data on commuting preferences in the County Stockholm. Our analysis shows there is a very large potential for reducing emissions and exposure if all car drivers living within a distance corresponding to a maximum of a 30min bicycle ride to work would change to commuting by bicycle. It would result in111,000 new cyclists, corresponding to an increase of 209% compared to the current situation. Mean population exposure would be reduced by about 7% for both NO

Simulation is an important resource for studying the dynamics of pandemic influenza and predicting the potential impact of interventions. However, there are several challenges for the design of such simulator architectures. Specifically, it is difficult to develop simulators that combine flexibility with run-time performance. This tradeoff is problematic in the pandemic-response setting because it makes it challenging to extend and adapt simulators for ongoing situations where rapid results are indispensable. Simulation architectures based on aspect-oriented programming can model specific concerns of the simulator and can allow developers to rapidly extend the simulator in new ways without sacrificing run-time performance. It is possible to use such aspects in conjunction with separate simulation models, which define community, disease, and intervention properties. The implication of this research for pandemic response is that aspects can add a novel layer of flexibility to simulation environments, which enables modelers to extend the simulator run-time component to new requirements that go beyond the original modeling framework.

Aim: To investigate celiac disease (CD) clustering at different geographical levels and to examine the association between neighborhood demographic and socioeconomic conditions and the risk of neighborhood CD. Methods: We included 2080 children diagnosed with CD between 1998 and 2003, identified from 43 of the 47 reporting hospitals in Sweden. A total of 8036 small area market statistics (SAMS) areas were included; these were nested in 253 municipalities that were further nested into eight ‘nomenclature of territorial units for statistics’ (NUTS) 2 regions. We performed multilevel logistic regression analyses. Results: We found the highest geographical variation in CD incidence at the municipality level, compared to the region level. The probability of having CD increased in the statistical areas of (SAMS) areas with higher average annual work income, with an odds ratio (OR) of 2.24 and 95% CI of 1.76–2.85. Reduced CD risk in neighborhoods was associated with higher average age (OR 0.96; 95% CI 0.95–0.97), higher proportion of residents with a university education (OR 0.98; 95% CI 0.97–0.99), and higher level of industrial and commercial activity (OR 0.59; 95% CI 0.44–0.82). We found no significant association between CD risk and population density, proportion of Nordic to non-Nordic inhabitants, nor share of the population with only a compulsory education. Conclusions: Neighborhood composition influences CD risk. This is one of the first attempts to identify factors explaining geographical variation in CD.

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.

The research reported in this article was made possible in part through the financial support of the Estonian Research Council (Institutional Research Grant IUT2-17 on Spatial Population Mobility and Geographical Changes in Urban Regions); the Estonian Science Foundation (Grant No. 8774 and 9247); the NORFACE research program Migration in Europe―Social, Economic, Cultural and Policy Dynamics (MIDI-REDIE); the EU Career Integration Grant (PCIG10-GA-2011-303728, call identifier FP7-PEOPLE-2011-CIG) (NBHCHOICE); and the Umeå SIMSAM Network―Register-based Research Program Connecting Childhood with Lifelong Health and Welfare funded by the Swedish Research Council (Grant No. 2008-28784-63564-191). Research on segregation of immigrant groups is increasingly turning its attention from residential areas toward other important places, such as the workplace, where immigrants can meet and interact with members of the native population. This article examines workplace segregation of immigrants. We use longitudinal, georeferenced Swedish population register data, which enables us to observe all immigrants in Sweden for the period 1990-2005 on an annual basis. We compare estimates from ordinary least squares with fixed-effects regressions to quantify the extent of immigrants' self-selection into specific workplaces, neighborhoods, and partnerships, which may bias more naïve ordinary least squares results. In line with previous research, we find lower levels of workplace segregation than residential segregation. The main finding is that low levels of residential segregation reduce workplace segregation, even after we take into account intermarriage with natives as well as unobserved characteristics of immigrants' such as willingness and ability to integrate into the host society. Being intermarried with a native reduces workplace segregation for immigrant men but not for immigrant women. Publisher PDF

OBJECTIVE: To investigate associations between exposure to air pollution and child and adolescent mental health. DESIGN: Observational study. SETTING: Swedish National Register data on dispensed medications for a broad range of psychiatric disorders, including sedative medications, sleeping pills and antipsychotic medications, together with socioeconomic and demographic data and a national land use regression model for air pollution concentrations for NO2, PM10 and PM2.5. PARTICIPANTS: The entire population under 18 years of age in 4 major counties. We excluded cohort members whose parents had dispensed a medication in the same medication group since the start date of the register. The cohort size was 552 221. MAIN OUTCOME MEASURES: Cox proportional hazards models to estimate HRs and their 95% CIs for the outcomes, adjusted for individual-level and group-level characteristics. RESULTS: The average length of follow-up was 3.5 years, with an average number of events per 1000 cohort members of ∼21. The mean annual level of NO2 was 9.8 µg/m(3). Children and adolescents living in areas with higher air pollution concentrations were more likely to have a dispensed medication for a psychiatric disorder during follow-up (HR=1.09, 95% CI 1.06 to 1.12, associated with a 10 µg/m(3) increase in NO2). The association with NO2 was clearly present in 3 out of 4 counties in the study area; however, no statistically significant heterogeneity was detected. CONCLUSION: There may be a link between exposure to air pollution and dispensed medications for certain psychiatric disorders in children and adolescents even at the relatively low levels of air pollution in the study regions. The findings should be corroborated by others.

Exposure misclassification in longitudinal studies of air pollution exposure and health effects can occur due to residential mobility in a study population over followup. The aim of this study was to investigate to what extent residential mobility during followup can be expected to cause exposure misclassification in such studies, where exposure at the baseline address is used as the main exposure assessment. The addresses for each participant in a large population-based study (N>25,000) were obtained via national registers. We used a Land Use Regression model to estimate theNOxconcentration for each participant's all addresses during the entire follow-up period (in average 14.6 years) and calculated an average concentration during followup. The Land Use Regression model explained 83% of the variation in measured levels. In summary, theNOxconcentration at the inclusion address was similar to the average concentration over followup with a correlation coefficient of 0.80, indicating that air pollution concentration at study inclusion address could be used as indicator of average air pollution concentrations over followup. The differences between an individual's inclusion and average follow-up mean concentration were small and seemed to be nondifferential with respect to a large range of factors and disease statuses, implying that bias due to residential mobility was small.

An understanding of the occurrence and comparative timing of influenza infections in different age groups is important for developing community response and disease control measures. This study uses data from a Scandinavian county (population 427,000) to investigate whether age was a determinant for being diagnosed with influenza 2005–2010 and to examine if age was associated with case timing during outbreaks. Aggregated demographic data were collected from Statistics Sweden, while influenza case data were collected from a county-wide electronic health record system. A logistic regression analysis was used to explore whether case risk was associated with age and outbreak. An analysis of variance was used to explore whether day for diagnosis was also associated to age and outbreak. The clinical case data were validated against case data from microbiological laboratories during one control year. The proportion of cases from the age groups 10–19 (p

Background: Advanced technical systems and analytic methods promise to provide policy makers with information to help them recognize the consequences of alternative courses of action during pandemics. Evaluations still show that response programs are insufficiently supported by information systems. This paper sets out to derive a protocol for implementation of integrated information infrastructures supporting regional and local pandemic response programs at the stage(s) when the outbreak no longer can be contained at its source. Methods: Nominal group methods for reaching consensus on complex problems were used to transform requirements data obtained from international experts into an implementation protocol. The analysis was performed in a cyclical process in which the experts first individually provided input to working documents and then discussed them in conferences calls. Argument-based representation in design patterns was used to define the protocol at technical, system, and pandemic evidence levels. Results: The Protocol for a Standardized information infrastructure for Pandemic and Emerging infectious disease Response (PROSPER) outlines the implementation of information infrastructure aligned with pandemic response programs. The protocol covers analyses of the community at risk, the response processes, and response impacts. For each of these, the protocol outlines the implementation of a supporting information infrastructure in hierarchical patterns ranging from technical components and system functions to pandemic evidence production. Conclusions: The PROSPER protocol provides guidelines for implementation of an information infrastructure for pandemic response programs both in settings where sophisticated health information systems already are used and in developing communities where there is limited access to financial and technical resources. The protocol is based on a generic health service model and its functions are adjusted for community-level analyses of outbreak detection and progress, and response program effectiveness. Scientifically grounded reporting principles need to be established for interpretation of information derived from outbreak detection algorithms and predictive modeling. Original Publication:Toomas Timpka, Henrik Eriksson, Elin A Gursky, Magnus Stromgren, Einar Holm, Joakim Ekberg, Olle Eriksson, Anders Grimvall, Lars Valter and James M Nyce, Requirements and Design of the PROSPER Protocol for Implementation of Information Infrastructures Supporting Pandemic Response: A Nominal Group Study, 2011, PLOS ONE, (6), 3, 0017941.http://dx.doi.org/10.1371/journal.pone.0017941Copyright: Public Library of Science (PLoS)http://www.plos.org/

Objective To examine the validity and usefulness of pandemic simulations aimed at informing practical decision-making in public health. Methods We recruited a multidisciplinary group of nine experts to assess a case-study simulation of influenza transmission in a Swedish county. We used a non-statistical nominal group technique to generate evaluations of the plausibility, formal validity (verification) and predictive validity of the simulation. A health-effect assessment structure was used as a framework for data collection. Findings The unpredictability of social order during disasters was not adequately addressed by simulation methods; even minor disruptions of the social order may invalidate key infrastructural assumptions underpinning current pandemic simulation models. Further, a direct relationship between model flexibility and computation time was noted. Consequently, simulation methods cannot, in practice, support integrated modifications of microbiological, epidemiological and spatial submodels or handle multiple parallel scenarios. Conclusion The combination of incomplete surveillance data and simulation methods that neglect social dynamics limits the ability of national public health agencies to provide policy-makers and the general public with the critical and timely information needed during a pandemic.

We studied the transfer of information during coordinated care planning between a university hospital and a local health care centre/social welfare department about 35 km away. During a seven-month study period, 10 sessions were conducted by videoconferencing and seven sessions were conducted by face-to-face conferencing. Videoconferencing reduced the time required for each coordinated care-planning session from an average of 60 to 45 min. There was also an increase in the number of participating professional categories. Travel time for the staff in the face-to-face group was 60–180 min each. Use of a care-planning report during the sessions resulted in improved quality of documentation, which contributed to better care following discharge. The technical problems that occurred did not detract from the beneficial experience of participating. Interviews with next of kin showed that they had been able to influence the content of the care during the care-planning sessions. Videoconferencing proved useful in coordinated care planning. It resulted in time saved due to reduced travel time, participation by more staff categories and an enhancement of the documentation quality.

BackgroundThere 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. ObjectiveThe 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. MethodsAn 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. ResultsLocal 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

Syndromic data sources have been sought to improve the timely detection of increased influenza transmission. This study set out to examine the prospective performance of telenursing chief complaints in predicting influenza activity. Data from two influenza seasons (2007/08 and 2008/09) were collected in a Swedish county (population 427,000) to retrospectively determine which grouping of telenursing chief complaints had the largest correlation with influenza case rates. This grouping was prospectively evaluated in the three subsequent seasons. The best performing telenursing complaint grouping in the retrospective algorithm calibration was fever (child, adult) and syncope (r=0.66; p