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In most societies, resources and opportunities are concentrated in neighborhoods and workplaces occupied by the host population. The spatial assimilation and place stratification theories propose trajectories (the sequences of events) leading to minority and migrant access to or exclusion from these advantageous places. However, most previous research on these theories did not ask whether such theorized trajectories occur. We apply sequence analysis to decade-long residence and workplace histories of newly arrived migrants in Sweden to identify a typology of combined residence-work trajectories. The seven types of trajectories in our typology are characterized by varying degrees of proximity to the host population in residential neighborhoods and workplaces and by different patterns of change in such proximity over time. The pivotal role of socioeconomic gains in spatial assimilation, posited by the namesake theory, is not supported, as we do not find that migrant employment precedes residence alongside the host population. The importance of housing-market discrimination for migrants’ exclusion from host-dominated spaces, posited by place stratification theory, is only weakly supported, as we find that migrants from less affluent countries accumulate disadvantage over time, likely due to discrimination in both the labor and housing markets. Our findings also underscore the need for new theories explaining migrant residential outcomes which apply to contexts where migrant-dense neighborhoods are still forming.

ObjectivesThe study aims to make use of individual data to estimate the impact on premature mortality due to both existing commuter bicycling and the potential impact due to increased physical activity through shifting transport mode from car commuting to bicycling.MethodsUsing registry data on home and work addresses for the population of Stockholm County the shortest bicycling route on a network of bicycle paths and roads was retrieved. Travel survey data were used to establish current modes of commuting. The relation between duration of bicycling and distance bicycled within the general population in 2015 was established as a basis for identifying individuals that currently drive a car to work but were estimated to have the physical capacity to bicycle to work within 30 min. Within this mode-shift scenario from car-to-bike the duration of bicycling per week was estimated, both among current and potential bicycle commuters. The health impact assessment (HIA) on mortality due to bicycle commuting physical activity was estimated using the same relative risk as within the WHO Health Economic Assessment Tool.ResultsThe current number of bicycle commuters were 53 000, and the scenario estimated an additional 111 000. Their mean bicycle distances were 4.5 and 3.4 km, respectively. On average these respective amounts of physical activity reduced the yearly mortality by 16% and 12%, resulting in 11.3 and 16.2 fewer preterm deaths per year.ConclusionThe HIA of transferring commuting by car to bicycle estimated large health benefits due to increased physical activity.

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.

Immigrant–native segregation is present in the spaces in which individuals from different ethnic/racial groups practice their everyday lives; interact with others and develop their ethnic, social and spatial networks. The overwhelming majority of academic research on immigrant segregation has focused on the residential domain, thus largely overlooking other arenas of daily interaction. The present study contributes to the emerging literature on immigrant residential and workplace segregation by examining changes in patterns of residential and workplace segregation over time. We draw our data from the Stockholm metropolitan region, Sweden’s main port of entry for immigrants. The results suggest a close association between residential and workplace segregation. Immigrant groups that are more segregated at home are also more segregated in workplace neighborhoods. More importantly, we found that a changing segregation level in one domain tends to involve a similar trend in the other domain.

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.

Ground level ozone [ozone] is considered a harmful air pollutant but there is a knowledge gap regarding its long term health effects. The main aim of this study is to develop local Land Use Regression [LUR] models that can be used to study long term health effects of ozone. The specific aim is to develop spatial LUR models for two Swedish cities, Umeå and Malmö, as well as a temporal model for Malmö in order to assess ozone exposure for long term epidemiological studies. For the spatial model we measured ozone, using Ogawa passive samplers, as weekly averages at 40 sites in each study area, during three seasons. This data was then inserted in the LUR-model with data on traffic, land use, population density and altitude to develop explanatory models of ozone variation. To develop the temporal model for Malmö, hourly ozone data was aggregated into daily means for two measurement stations in Malmö and one in a rural area outside Malmö. Using regression analyses we inserted meteorological variables into different temporal models and the one that performed best for all three stations was chosen. For Malmö the LUR-model had an adjusted model R2 of 0.40 and cross validation R2 of 0.17. For Umeå the model had an adjusted model R2 of 0.67 and cross validation adjusted R2 of 0.48. When restricting the model to only including measuring sites from urban areas, the Malmö model had adjusted model R2 of 0.51 (cross validation adjusted R2 0.33) and the Umeå model had adjusted model R2 of 0.81 (validation adjusted R2 of 0.73). The temporal model had adjusted model R2 0.54 and 0.61 for the two Malmö sites, the cross validation adjusted R2 was 0.42. In conclusion, we can with moderate accuracy, at least for Umeå, predict the spatial variability, and in Malmö the temporal variability in ozone variation.

During the 1960s in Sweden, socioeconomic differentials decreased sharply and both the labor force participation of married women and aggregate divorce rates increased more rapidly than during any other period of the twentieth century. The aim of this paper is to investigate how the socioeconomic composition of the couple influenced the probability of divorce during this period of rapid restructuring. The study uses a large data set covering the entire married population of Sweden in 1960 and applies a binary model whereby the couples are analyzed as units rather than separate individuals to model divorce during the period from 1960 to1965. The main results show that the equalization process between genders and social classes during this period contributed to the decrease in marital stability. Dual-provider families exhibit substantially higher probabilities of divorce as compared to traditional single-provider families. We also find that the socioeconomic gradient of divorce had become negative by the early 1960s and that couples with low socioeconomic status contributed more to the increase in divorce than did couples in the higher strata. A difference between the results reached in this study and those from divorce research covering later decades is that children do not reduce the probability of divorce when the wife's labor force participation is controlled for. The results indicate that the determinants of divorce have varied across different phases of the divorce transition during the twentieth century and that a historical perspective is necessary if we are to understand the long-term process that has produced current marital behavior.

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.

The research leading to these results has received funding from 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 European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 615159 (ERC Consolidator Grant DEPRIVEDHOODS, Socio-spatial inequality, deprived neighbourhoods, and neighbourhood effects); and the Marie Curie Programme under the European Union's Seventh Framework Programme (FP/2007-2013) / Career Integration Grant n. PCIG10-GA-2011-303728 (CIG Grant NBHCHOICE, Neighbourhood choice, neighbourhood sorting, and neighbourhood effects). Contemporary cities are becoming more and more diverse in population as a result of immigration. Research shows that while residential neighborhoods are becoming ethnically more diverse within cities, residential segregation from natives has overall remained persistently high. High levels of segregation are often seen as negative, preventing the integration of immigrants into their host society and having a negative impact on people's lives. Where as most studies of segregation deal with residential neighborhoods, this paper investigates segregation at workplaces for newly arrived immigrant men and women from the Global South to Sweden. By using the domain approach, we focus on the relationship between workplace segregation, residential segregation, and the ethnic composition of households. Using longitudinal register data from Sweden, we find that residential segregation is much weaker related to workplace segregation than revealed by studies using cross-sectional data. Furthermore, the residential context is not an important factor in explaining workplace segregation for immigrant men. The most important factors shaping workplace segregation pertain to economic sector and city size. Publisher PDF

Using Swedish register data, we analyse how life course preferences and sibling ties are related to the geographical dispersion of siblings. We argue that life course preferences and siblings ties ...

In recent decades, the share of traditional nuclear-family households has decreased in most Western countries, resulting in an increase in the proportion of children living in other forms of househ ...

Studies testing ‘spatial assimilation’ are no longer restricted to traditional suburbanisation movements correlated with high education, income and exposure to the ‘majority’ population. Instead, g ...

Drawing on a combination of register data and travel survey data, this research explores changes in the accessibility to different amenities for the Swedish population between 1995 and 2005, as wel ...

Factors influencing immigrant labour-market outcomes have received increased scholarly attention lately. A recent research focus has been the effects of residential setting on labour-market outcomes. This study brings a new dimension to this emerging body of research, introducing the role played by workplace composition, in addition to place of residence, in immigrant earnings. Based on Swedish longitudinal register data, ordinary least squares regression is used to examine effects of previous exposure to natives on earnings in three immigrant cohorts (1990, 1995, and 2000) five years after arrival. Besides controlling for individual characteristics and various labour-market attributes, a two-step Heckman correction procedure is applied to take into account the selectivity of entering the Swedish labour market. The main finding of the study is that exposure to the native population at the workplace is more important than residential exposure for predicting immigrant earnings.

Mathematical modelling of a heat exchanger in a carbon dioxide heat pump, an evaporator, is considered. A reduced model, called the the zero Mach-number limit, is derived from the Euler equations of compressible liquid flow through elimination of time scales associated with sound waves. The well-posedness of the resulting partial differential-algebraic equation (PDAE) is investigated by analysis of a frozen coefficient linearisation as well as by numerical experiments. The linear stability analysis is done through transformation to a canonical form with one hyperbolic component and one parabolic block of dimension 2. Using this canonical form it is seen how to prescribe boundary and initial data and an energy estimate is derived. Numerical experiments on the nonlinear PDAE using a finite difference spatial discretisation support the linear stability analysis.

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

Estimating within-city variability in air pollution concentrations is important. Land use regression (LUR) models are able to explain such small-scale within-city variations. Transparency in LUR model development methods is important to facilitate comparison of methods between different studies. We therefore developed LUR models in a standardized way in 36 study areas in Europe for the ESCAPE (European Study of Cohorts for Air Pollution Effects) project.Nitrogen dioxide (NO2) and nitrogen oxides (NOx) were measured with Ogawa passive samplers at 40 or 80 sites in each of the 36 study areas. The spatial variation in each area was explained by LUR modelling. Centrally and locally available Geographic Information System (GIS) variables were used as potential predictors. A leave-one out cross-validation procedure was used to evaluate the model performance.There was substantial contrast in annual average NO2 and NOx concentrations within the study areas. The model explained variances (R2) of the LUR models ranged from 55% to 92% (median 82%) for NO2 and from 49% to 91% (median 78%) for NOx. For most areas the cross-validation R2 was less than 10% lower than the model R2. Small-scale traffic and population/household density were the most common predictors. The magnitude of the explained variance depended on the contrast in measured concentrations as well as availability of GIS predictors, especially traffic intensity data were important. In an additional evaluation, models in which local traffic intensity was not offered had 10% lower R2 compared to models in the same areas in which these variables were offered.Within the ESCAPE project it was possible to develop LUR models that explained a large fraction of the spatial variance in measured annual average NO2 and NOx concentrations. These LUR models are being used to estimate outdoor concentrations at the home addresses of participants in over 30 cohort studies. © 2013 Elsevier Ltd.

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

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

We consider the mathematical model of a heat exchanger in a heat pumping system using carbon dioxide as refridgerant. From the Euler equations of compressible fluid flow we derive a reduced model, called the zero Mach-number limit, by eliminating time scales associated with soundwaves. The reduced model is a highly nonlinear partial differential-algebraic equation (PDAE). We analyse the stability of this model and show that it is possible to use simple spatial discretisations within the method of lines (MOL), a standard practice in many system simulation software, to simulate the system. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)