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Forecasting of seasonal mortality patterns can provide useful information for planning health-care demand and capacity. Timely mortality forecasts are needed during severe winter spikes and/or pandemic waves to guide policy-making and public health decisions. In this article, we propose a flexible method for forecasting all-cause mortality in real time considering short-term changes in seasonal patterns within an epidemiologic year. All-cause mortality data have the advantage of being available with less delay than cause-specific mortality data. In this study, we use all-cause monthly death counts obtained from the national statistical offices of Denmark, France, Spain, and Sweden from epidemic seasons 2012-2013 through 2021-2022 to demonstrate the performance of the proposed approach. The method forecasts deaths 1 month ahead, based on their expected ratio to the next month. Prediction intervals are obtained via bootstrapping. The forecasts accurately predict the winter mortality peaks before the COVID-19 pandemic. Although the method predicts mortality less accurately during the first wave of the COVID-19 pandemic, it captures the aspects of later waves better than other traditional methods. The method is attractive for health researchers and governmental offices for aiding public health responses because it uses minimal input data, makes simple and intuitive assumptions, and provides accurate forecasts both during seasonal influenza epidemics and during novel virus pandemics., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

The state of pneumococcal carriage—that is, pneumococcal colonization in the nasopharynx of healthy persons—represents a reservoir for the spread of pneumococci among individuals. In light of the introduction of new pneumococcal conjugate vaccines, further knowledge on the dynamics of pneumococcal carriage is important. Different serotypes (strains) of pneumococcus are known to compete with each other in colonizing human hosts. Understanding the strength and mode of between-serotype competition is important because of its implications for vaccine-induced changes in the ecology of pneumococcal carriage. Competition may work through reduced acquisition of new serotypes, due to concurrent carriage in the individual, or through enhanced clearance of serotypes in carriers who harbor more than 1 serotype simultaneously. The authors employed longitudinal data (1999–2001) on pneumococcal carriage in Danish day-care children to analyze between-serotype competition. The data included observations of carriage in children who had not been vaccinated against pneumococcus, and the level of pneumococcal antibiotic resistance and antibiotic usage in the community was very low. Clearance of any single serotype was not affected by simultaneous carriage of other serotypes. In contrast, acquisition of other serotypes in already-colonized hosts was weak (relative rate of acquisition = 0.09, 95% credible interval: 0.05, 0.15). [ABSTRACT FROM PUBLISHER]

Life-course epidemiology is useful for describing and analyzing complex etiological mechanisms for disease development, but existing statistical methods are essentially confirmatory, because they rely on a priori model specification. This limits the scope of causal inquiries that can be made, because these methods are suited mostly to examine well-known hypotheses that do not question our established view of health, which could lead to confirmation bias. We propose an exploratory alternative. Instead of specifying a life-course model prior to data analysis, our method infers the life-course model directly from the data. Our proposed method extends the well-known Peter-Clark (PC) algorithm (named after its authors) for causal discovery, and it facilitates including temporal information for inferring a model from observational data. The extended algorithm is called temporal PC. The obtained life-course model can afterward be perused for interesting causal hypotheses. Our method complements classical confirmatory methods and guides researchers in expanding their models in new directions. We showcase the method using a data set encompassing almost 3,000 Danish men followed from birth until age 65 years. Using this data set, we inferred life-course models for the role of socioeconomic and health-related factors on development of depression., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: Early identification of the basic reproduction number (BRN) is imperative to political decision making during an epidemic. In this study, we estimated the BRN 7, 14, 21 and 28 days after societal lockdown of Denmark during the early stage of the COVID-19 epidemic., Method: We implemented the SEIR dynamical system for disease spread without vital dynamics. The BRN was modulated using a sigmoid function. Model parameters were estimated on number of admitted patients, number of patients in intensive care and cumulative number of deaths using the simulated annealing Monte Carlo algorithm. Results are presented with 95% prediction intervals (PI)., Results: We were unable to determine any reliable estimate of the BRN at 7 days following lockdown. The BRN had stabilised at day 14 throughout day 28, with the estimate ranging from 0.95 (95% PI: 0.92-0.98) at day 7 to 0.92 (95% PI: 0.92-0.93) at day 28. We estimated the BRN prior to lockdown to be 3.32 (95% PI: 3.31-3.33). The effect of the lockdown was occurring over a period of a few days centred at March 18th (95% PI: 17th-18th) 2020., Conclusion: We believe our model provides a valuable tool for decision makers to reliably estimate the effect of a politically determined lockdown during an epidemic., Competing Interests: The authors have declared that no competing interests exist.

BackgroundTimely monitoring of COVID-19 impact on mortality is critical for rapid risk assessment and public health action.AimBuilding upon well-established models to estimate influenza-related mortality, we propose a new statistical Attributable Mortality Model (AttMOMO), which estimates mortality attributable to one or more pathogens simultaneously (e.g. SARS-CoV-2 and seasonal influenza viruses), while adjusting for seasonality and excess temperatures.MethodsData from Nationwide Danish registers from 2014-week(W)W27 to 2020-W22 were used to exemplify utilities of the model, and to estimate COVID-19 and influenza attributable mortality from 2019-W40 to 2020-W20.ResultsSARS-CoV-2 was registered in Denmark from 2020-W09. Mortality attributable to COVID-19 in Denmark increased steeply, and peaked in 2020-W14. As preventive measures and national lockdown were implemented from 2020-W12, the attributable mortality started declining within a few weeks. Mortality attributable to COVID-19 from 2020-W09 to 2020-W20 was estimated to 16.2 (95% confidence interval (CI): 12.0 to 20.4) per 100,000 person-years. The 2019/20 influenza season was mild with few deaths attributable to influenza, 3.2 (95% CI: 1.1 to 5.4) per 100,000 person-years.ConclusionAttMOMO estimates mortality attributable to several pathogens simultaneously, providing a fuller picture of mortality by COVID-19 during the pandemic in the context of other seasonal diseases and mortality patterns. Using Danish data, we show that the model accurately estimates mortality attributable to COVID-19 and influenza, respectively. We propose using standardised indicators for pathogen circulation in the population, to make estimates comparable between countries and applicable for timely monitoring.

Alcoholic-related liver disease (ALD) is the cause of more than half of all liver-related deaths. Sustained excess drinking causes fatty liver and alcohol-related steatohepatitis, which may progress to alcoholic liver fibrosis (ALF) and eventually to alcohol-related liver cirrhosis (ALC). Unfortunately, it is difficult to identify patients with early-stage ALD, as these are largely asymptomatic. Consequently, the majority of ALD patients are only diagnosed by the time ALD has reached decompensated cirrhosis, a symptomatic phase marked by the development of complications as bleeding and ascites. The main goal of this study is to discover relevant upstream diagnoses helping to understand the development of ALD, and to highlight meaningful downstream diagnoses that represent its progression to liver failure. Here, we use data from the Danish health registries covering the entire population of Denmark during nineteen years (1996-2014), to examine if it is possible to identify patients likely to develop ALF or ALC based on their past medical history. To this end, we explore a knowledge discovery approach by using high-dimensional statistical and machine learning techniques to extract and analyze data from the Danish National Patient Registry. Consistent with the late diagnoses of ALD, we find that ALC is the most common form of ALD in the registry data and that ALC patients have a strong over-representation of diagnoses associated with liver dysfunction. By contrast, we identify a small number of patients diagnosed with ALF who appear to be much less sick than those with ALC. We perform a matched case-control study using the group of patients with ALC as cases and their matched patients with non-ALD as controls. Machine learning models (SVM, RF, LightGBM and NaiveBayes) trained and tested on the set of ALC patients achieve a high performance for data classification (AUC = 0.89). When testing the same trained models on the small set of ALF patients, their performance unsurprisingly drops a lot (AUC = 0.67 for NaiveBayes). The statistical and machine learning results underscore small groups of upstream and downstream comorbidities that accurately detect ALC patients and show promise in prediction of ALF. Some of these groups are conditions either caused by alcohol or caused by malnutrition associated with alcohol-overuse. Others are comorbidities either related to trauma and life-style or to complications to cirrhosis, such as oesophageal varices. Our findings highlight the potential of this approach to uncover knowledge in registry data related to ALD., Competing Interests: The authors have declared that no competing interests exist.

Introduction: Age assessment based on dental development is often requested in order to assess whether an individual is older or younger than 18 years of age. There are several statistical approaches to estimate age based upon third molar development. The aim of this study was to apply the principles of transition analysis (TA) to a Danish reference material and to evaluate whether it was indicated to include a model that allows for logistic non-linearity as opposed to applying a model only allowing for logistic linearity. For this we chose to use the generalized additive model (gam) and the generalized linear model (glm), respectively., Material and Method: A cross-sectional sample comprising 1302 panoramic radiographs of Danish subjects in the chronological age range of 13-25 years was included. All present third molars had been scored according to the 10-stage method of Gleiser and Hunt. Each transition from one stage to the subsequent stage was analyzed according to the statistical approach of TA and fitted with both the generalized linear model (glm) and the generalized additive model (gam). In order to assess whether gam or glm was more parsimonious for each transition individually, the Akaikon information criterion (AIC) was applied., Results: The results emphasized the importance of applying a statistical model that sufficiently captures the spread of the age estimate. The AIC values showed that some transitions were sufficiently described by glm whereas for others the gam curves fitted significantly better., Conclusion: We recommend that for an age assessment tool based on TA, both a fitting allowing for non-linearity and one allowing only for linearity should be included., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Importance: Suicide is a public health problem, with multiple causes that are poorly understood. The increased focus on combining health care data with machine-learning approaches in psychiatry may help advance the understanding of suicide risk., Objective: To examine sex-specific risk profiles for death from suicide using machine-learning methods and data from the population of Denmark., Design, Setting, and Participants: A case-cohort study nested within 8 national Danish health and social registries was conducted from January 1, 1995, through December 31, 2015. The source population was all persons born or residing in Denmark as of January 1, 1995. Data were analyzed from November 5, 2018, through May 13, 2019., Exposures: Exposures included 1339 variables spanning domains of suicide risk factors., Main Outcomes and Measures: Death from suicide from the Danish cause of death registry., Results: A total of 14 103 individuals died by suicide between 1995 and 2015 (10 152 men [72.0%]; mean [SD] age, 43.5 [18.8] years and 3951 women [28.0%]; age, 47.6 [18.8] years). The comparison subcohort was a 5% random sample (n = 265 183) of living individuals in Denmark on January 1, 1995 (130 591 men [49.2%]; age, 37.4 [21.8] years and 134 592 women [50.8%]; age, 39.9 [23.4] years). With use of classification trees and random forests, sex-specific differences were noted in risk for suicide, with physical health more important to men's suicide risk than women's suicide risk. Psychiatric disorders and possibly associated medications were important to suicide risk, with specific results that may increase clarity in the literature. Generally, diagnoses and medications measured 48 months before suicide were more important indicators of suicide risk than when measured 6 months earlier. Individuals in the top 5% of predicted suicide risk appeared to account for 32.0% of all suicide cases in men and 53.4% of all cases in women., Conclusions and Relevance: Despite decades of research on suicide risk factors, understanding of suicide remains poor. In this study, the first to date to develop risk profiles for suicide based on data from a full population, apparent consistency with what is known about suicide risk was noted, as well as potentially important, understudied risk factors with evidence of unique suicide risk profiles among specific subpopulations.

Hypothesis: We hypothesise that the validated HUNT Lung Cancer Risk Model would perform better than the NLST (USA) and the NELSON (Dutch-Belgian) criteria in the Danish Lung Cancer Screening Trial (DLCST)., Methods: The DLCST measured only five out of the seven variables included in validated HUNT Lung Cancer Model. Therefore a 'Reduced' model was retrained in the Norwegian HUNT2-cohort using the same statistical methodology as in the original HUNT model but based only on age, pack years, smoking intensity, quit time and body mass index (BMI), adjusted for sex. The model was applied on the DLCST-cohort and contrasted against the NLST and NELSON criteria., Results: Among the 4051 smokers in the DLCST with 10 years follow-up, median age was 57.6, BMI 24.75, pack years 33.8, cigarettes per day 20 and most were current smokers. For the same number of individuals selected for screening, the performance of the 'Reduced' HUNT was increased in all metrics compared with both the NLST and the NELSON criteria. In addition, to achieve the same sensitivity, one would need to screen fewer people by the 'Reduced' HUNT model versus using either the NLST or the NELSON criteria (709 vs 918, p=1.02e-11 and 1317 vs 1668, p=2.2e-16, respectively)., Conclusions: The 'Reduced' HUNT model is superior in predicting lung cancer to both the NLST and NELSON criteria in a cost-effective way. This study supports the use of the HUNT Lung Cancer Model for selection based on risk ranking rather than age, pack year and quit time cut-off values. When we know how to rank personal risk, it will be up to the medical community and lawmakers to decide which risk threshold will be set for screening., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: To externally validate the accuracy of the Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) model against existing risk scores for stroke and major bleeding risk in patients with non-valvular AF in a population-based cohort., Design: Retrospective cohort study., Setting: Danish nationwide registries., Participants: 90 693 patients with newly diagnosed non-valvular AF were included between 2010 and 2016, with follow-up censored at 1 year., Primary and Secondary Outcome Measures: External validation was performed using discrimination and calibration plots. C-statistics were compared with CHA 2 DS 2 VASc score for ischaemic stroke/systemic embolism (SE) and HAS-BLED score for major bleeding/haemorrhagic stroke outcomes., Results: Of the 90 693 included, 51 180 patients received oral anticoagulants (OAC). Overall median age (Q1, Q3) were 75 (66-83) years and 48 486 (53.5%) were male. At 1-year follow-up, a total of 2094 (2.3%) strokes/SE, 2642 (2.9%) major bleedings and 10 915 (12.0%) deaths occurred. The GARFIELD-AF model was well calibrated with the predicted risk for stroke/SE and major bleeding. The discriminatory value of GARFIELD-AF risk model was superior to CHA 2 DS 2 VASc for predicting stroke in the overall cohort (C-index: 0.71, 95% CI: 0.70 to 0.72 vs C-index: 0.67, 95% CI: 0.66 to 0.68, p<0.001) as well as in low-risk patients (C-index: 0.64, 95% CI: 0.59 to 0.69 vs C-index: 0.57, 95% CI: 0.53 to 0.61, p=0.007). The GARFIELD-AF model was comparable to HAS-BLED in predicting the risk of major bleeding in patients on OAC therapy (C-index: 0.64, 95% CI: 0.63 to 0.66 vs C-index: 0.64, 95% CI: 0.63 to 0.65, p=0.60)., Conclusion: In a nationwide Danish cohort with non-valvular AF, the GARFIELD-AF model adequately predicted the risk of ischaemic stroke/SE and major bleeding. Our external validation confirms that the GARFIELD-AF model was superior to CHA 2 DS 2 VASc in predicting stroke/SE and comparable with HAS-BLED for predicting major bleeding., Competing Interests: Competing interests: KF has received research grants Bayer/Janssen and AstraZeneca and Consulting/Fees Bayer, Sanofi/Regeneron and Verseon. FV has received honoraria for consulting and presentations from Bayer HealthCare, Boehringer Ibgelheimy, BMSPfizer and Daiichi-Sankyo. GHG has ownership of stocks in Novo Nordisk Pharmaceuticals and reports research grants from Pfizer, Bristol Myers Squibb, Boehringer Ingelheim and Bayer. CTP declares grants for studies from Bayer. Other authors have no conflicts of interest to declare., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

The research literature reveals an ongoing debate regarding the most appropriate conceptualization of psychopathic personality disorder. Specifically, it is discussed to what degree antisocial behavior is part of the conceptualization of the psychopathy construct and what constitutes the best factor model of the Psychopathy Checklist scales. The aim of the present study is to consider the underlying factor structure of the PCL:SV (Psychopathy Checklist Screening Version) in a Danish sample as well as considering the role of antisocial behavior in the psychopathy construct. Data from a Danish forensic patient sample (N = 225) was used and item response theory (IRT), aonfirmatory factor analyses (CFA) and structural equations model (SEM) analyses were carried out. Overall, the findings suggest appropriate item and model fit for the PCL:SV as well as superiority of the three-factor model over the four-factor model. The results are discussed in relation to the broader concept of personality disorder as well as clinical practice in regards to violence risk assessments and treatment of psychopathy., (© 2019 Scandinavian Psychological Associations and John Wiley & Sons Ltd.)

Frailty models have been developed to quantify both heterogeneity as well as association in multivariate time-to-event data. In recent years, numerous shared and correlated frailty models have been proposed in the survival literature allowing for different association structures and frailty distributions. A bivariate correlated gamma frailty model with an additive decomposition of the frailty variables into a sum of independent gamma components was introduced before. Although this model has a very convenient closed-form representation for the bivariate survival function, the correlation among event- or subject-specific frailties is bounded above which becomes a severe limitation when the values of the two frailty variances differ substantially. In this article, we review existing correlated gamma frailty models and propose novel ones based on bivariate gamma frailty distributions. Such models are found to be useful for the analysis of bivariate survival time data regardless of the censoring type involved. The frailty methodology was applied to right-censored and left-truncated Danish twins mortality data and serological survey current status data on varicella zoster virus and parvovirus B19 infections in Belgium. From our analyses, it has been shown that fitting more flexible correlated gamma frailty models in terms of the imposed association and correlation structure outperforms existing frailty models including the one with an additive decomposition.

Objectives: A recent study from Sweden showed that European hedgehogs may constitute a reservoir for methicillin-resistant Staphylococcus aureus (MRSA), but this host-parasite relationship remains to be investigated in other countries. In this study, we therefore sought to: 1) determine the dissemination of MRSA in European hedgehogs throughout Denmark; 2) investigate determinants of MRSA carriage in hedgehogs; 3) determine the potential for zoonotic transmission of MRSA from hedgehogs to humans; and 4) characterise the detected MRSA on both a phenotypic and molecular level., Methods: Nasal swabs were taken from 188 dead hedgehogs collected by volunteers throughout Denmark to determine the occurrence of MRSA. Additionally, 16 hedgehog rehabilitators were tested for potential zoonotic transmission of MRSA from hedgehogs to humans. The swabs were incubated in tryptic soy broth supplemented with 6.5% NaCl, followed by spread of 10 μl on Brilliance MRSA 2 agar. One presumptive MRSA colony from each plate was subcultured on 5% blood agar. All S. aureus subcultures were verified by a PCR assay detecting mecA, mecC, lukF-PV, scn, and spa, followed by spa typing., Results: A total of 114 (61%) hedgehogs carried mecC-MRSA, whereas none carried mecA-MRSA. The detected mecC-MRSA belonged to two genetic lineages CC130 (spa-types: t528, t843, t1048, t3256, t3570, t6220, t17133) and CC1943 (spa-types: t978, t2345, t3391, t8835, t16868), 52% of which were spa-type t843 (CC130).The detection rate of mecC-MRSA in the hedgehogs was similar regardless of cause of death, sex, region and habitat type. None of the hedgehog rehabilitators carried MRSA., Conclusions: This nationwide study confirms a high occurrence of mecC-MRSA in hedgehogs, which could serve as a natural reservoir for this specific type of MRSA. Furthermore, our study did not find signs of zoonotic transmission of mecC-MRSA to hedgehog rehabilitators., Competing Interests: The authors have declared that no competing interests exist.

Patients suffering a low-energy fracture are at high risk of subsequent fractures. Investigation of all fragility fracture patients above the age of 50 years is recommended in order to prevent further fractures. The aim of this study was to investigate alternative strategies including known risk factors (age, sex, and body weight) for selecting fracture patients for osteoporosis assessment and investigate how these strategies would affect the proportion of patients with osteoporosis identified and the number of patients referred for Dual X-ray Absorptiometry (DXA) compared with the original FLS strategy. From OFELIA; a cohort study comprising 794 fragility fracture patients we included 622 patients aged 18 + years (mean age 56 ± 17) with fragility fractures. We investigated the predictive value of clinical risk factors using ROC curves and AUC analyses. The revised strategies were compared by analyzing sensitivity and specificity of different strategies based on sex, age, BMI, and bodyweight. For patients 50 + years, a strategy investigating men and women with body weight ≤ 85 kg resulted in sensitivity and specificity of 94% and 25%, respectively, reducing the number of DXAs by 21%. For patients < 50 years, the prevalence of osteoporosis was low and it was, therefore, difficult to develop an acceptable strategy. We found that the original FLS strategy can be modified to include substantially fewer patients and still only miss the osteoporosis diagnosis in a very few patients. A modified strategy would potentially save costs and concerns in many patients.

We propose to model the cause-specific cumulative incidence function of multivariate competing risks data using a random effects model that allows for within-cluster dependence of both risk and timing. The model contains parameters that makes it possible to assess how the two are connected, e.g. if high-risk is related to early onset. Under the proposed model, the cumulative incidences of all failure causes are modeled and all cause-specific and cross-cause associations specified. Consequently, left-truncation and right-censoring are easily dealt with. The proposed model is assessed using simulation studies and applied in analysis of Danish register-based family data on breast cancer., (© The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Consumption of meat prepared by barbecuing is associated with risk of cancer due to formation of carcinogenic compounds including benzo[a]pyrene (BaP). Assessment of a population's risk of disease and people's individual probability of disease given specific consumer attributes may direct food safety strategies to where impact on public health is largest. The aim of this study was to propose a model that estimates the risk of cancer caused by exposure to BaP from barbecued meat in Denmark, and to estimate the probability of developing cancer in subgroups of the population given different barbecuing frequencies., Methods: We developed probabilistic models applying two dimensional Monte Carlo simulation to take into account the variation in exposure given age and sex and in the individuals' sensitivity to develop cancer after exposure to BaP, and the uncertainty in the dose response model. We used the Danish dietary consumption survey, monitoring data of chemical concentrations, data on consumer behavior of frequency of barbecuing, and animal dose response data., Findings: We estimated an average extra lifetime risk of cancer due to BaP from barbecued meat of 6.8 × 10-5 (95% uncertainty interval 2.6 × 10-7 - 7.0 × 10-4) in the Danish population. This corresponds to approximately one to 4,074 extra cancer cases over a lifetime, reflecting wide uncertainty. The impact per barbecuing event on the risk of cancer for men and women of low body weight was higher compared to higher bodyweight. However, the difference due to sex and bodyweight between subgroups are dwarfed by the uncertainty., Interpretation: This study proposes a model that can be applied to other substances and routes of exposure, and allows for deriving the change in risk following a specific change in behaviour. The presented methodology can serve as a valuable tool for risk management, allowing for the formulation of behaviour advice targeted to specific sub-groups in the population., Competing Interests: The authors have declared that no competing interests exist.

Objective: Type 1 diabetes is a complex disease, and development of multiple complications over time can be analyzed only with advanced statistical methods. This study describes the development of microvascular complications and explores the effect of complication burden and important concurrent risk factors by applying a multistate model., Research Design and Methods: We used a clinical cohort at the Steno Diabetes Center Copenhagen to study the development of diabetic kidney disease, retinopathy, and neuropathy. We extracted information from electronic patient records and estimated incidence rates of complications by concurrent complication burden. We explored the extent to which concurrent complications modify the effect of selected risk factors on the development of microvascular complications., Results: We included 3,586 individuals. Incidence rate ratios in individuals with two previous complications were 3.2 (95% CI 2.3-4.5) for diabetic kidney disease, 2.1 (1.5-3.1) for retinopathy, and 1.7 (1.2-2.4) for neuropathy compared with individuals without complications. The models included diabetes duration; calendar time and age as timescales; and sex, HbA 1c , lipid-lowering and antihypertensive treatment, systolic blood pressure, BMI, estimated glomerular filtration rate (eGFR), cardiovascular disease (CVD), LDL cholesterol, insulin dose (units/kg/day), and smoking status as covariates. Effects of HbA 1c , diabetes duration, systolic blood pressure, BMI, eGFR, and LDL cholesterol where not modified by concurrent complication burden, whereas the effect of sex and CVD were., Conclusions: The risk of microvascular complications highly depends on the concurrent complication burden and risk factor profile in individuals with type 1 diabetes. The results emphasize attention to risk factors, regardless of existing number of complications, to prevent development of further microvascular complications., (© 2018 by the American Diabetes Association.)

Aims: Mathematical optimization of automated external defibrillator (AED) placements has the potential to improve out-of-hospital cardiac arrest (OHCA) coverage and reverse the negative effects of limited AED accessibility. However, the generalizability of optimization approaches has not yet been investigated. Our goal is to examine the performance and generalizability of a spatiotemporal AED placement optimization methodology, initially developed for Toronto, Canada, to the new study setting of Copenhagen, Denmark., Methods: We identified all public OHCAs (1994-2016) and all registered AEDs (2016) in Copenhagen, Denmark. We calculated the coverage loss associated with limited temporal accessibility of registered AEDs, and used a spatiotemporal optimization model to quantify the potential coverage gain of optimized AED deployment. Coverage gain of spatiotemporal deployment over a spatial-only solution was quantified through 10-fold cross-validation. Statistical testing was performed using χ 2 and McNemar's tests., Results: We found 2149 public OHCAs and 1573 registered AED locations. Coverage loss was found to be 24.4% (1104 OHCAs covered under assumed 24/7 coverage, and 835 OHCAs under actual coverage). The coverage gain from using the spatiotemporal model over a spatial-only approach was 15.3%. Temporal and geographical trends in coverage gain were similar to Toronto., Conclusions: Without modification, a previously developed spatiotemporal AED optimization approach was applied to Copenhagen, resulting in similar OHCA coverage findings as Toronto, despite large geographic and cultural differences between the two cities. In addition to reinforcing the importance of temporal accessibility of AEDs, these similarities demonstrate the generalizability of optimization approaches to improve AED placement and accessibility., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: In temperate zones, all-cause mortality exhibits a marked seasonality, and influenza represents a major cause of winter excess mortality. We present a statistical model, FluMOMO, which estimate influenza-associated mortality from all-cause mortality data and apply it to Danish data from 2010/11 to 2016/17., Methods: We applied a multivariable time series model with all-cause mortality as outcome, influenza activity and extreme temperatures as explanatory variables while adjusting for time trend and seasonality. Three indicators of weekly influenza activity (IA) were explored: percentage of consultations for influenza-like illness (ILI) at primary health care, national percentage of influenza-positive samples, and the product of ILI percentage and percentage of influenza-positive specimens in a given week, that is, the Goldstein index., Results: Independent of the choice of parameter to represent influenza activity, the estimated influenza-associated mortality showed similar patterns with the Goldstein index being the most conservative. Over the 7 winter seasons, the median influenza-associated mortality per 100 000 population was 17.6 (range: 0.0-36.8), 14.1 (0.3-31.6) and 8.3 (0.0-25.0) for the 3 indicators, respectively, for all ages., Conclusion: The FluMOMO model fitted the Danish data well and has the potential to estimate all-cause influenza-associated mortality in near real time and could be used as a standardised method in other countries. We recommend using the Goldstein index as the influenza activity indicator in the FluMOMO model. Further work is needed to improve the interpretation of the estimated effects., (© 2018 The Authors. Influenza and Other Respiratory Viruses Published by John Wiley & Sons Ltd.)

Background: Survival predictions before surgery for metastatic bone disease in the extremities (based on statistical models and data of previous patients) are important for choosing an implant that will function for the remainder of the patient's life. The 2008-SPRING model, presented in 2016, enables the clinician to predict expected survival before surgery for metastatic bone disease in the extremities. However, to maximize the model's accuracy, it is necessary to maintain and update the patient database to refit the prediction models achieving more accurate calibration., Questions/purposes: The purposes of this study were (1) to refit the 2008-SPRING model for prediction of survival before surgery for metastatic bone disease in the extremities with a more modern cohort; and (2) to evaluate the performance of the refitted SPRING model in a population-based cohort of patients having surgery for metastatic bone disease in the extremities., Methods: We produced the 2013-SPRING model by adding to the 2008-SPRING model (n = 130) a cohort of patients from a consecutive institutional database of patients who underwent surgery for bone metastases in the extremities with bone resection and reconstruction between 2009 and 2013 at a highly specialized surgical center in Denmark (n = 140). Currently the model is only available as the nomogram fully available in the current article, which is sufficient to use in daily clinical work, but we are working on making the tool available online. As such, the 2013-SPRING model was produced using a consecutive cohort of patients (n = 270) treated during an 11-year period (2003-2013) called the training cohort, all treated with bone resection and reconstruction. We externally validated the 2008-SPRING and the 2013-SPRING models in a prospective cohort (n = 164) of patients who underwent surgery for metastatic bone disease in the extremities from May 2014 to May 2016, called the validation cohort. The validation cohort was identified from a cross-section of the Danish population who were treated for metastatic lesions (using endoprostheses and internal fixation) in the extremities at five secondary surgical centers and one highly specialized surgical center. This cross-section is representative of the Danish population and no patients were treated outside the included centers as a result of public healthcare settings. The indications for surgery for training and the validation cohort were pathologic fracture, impending fracture, or intractable pain despite radiation. Exact date of death was known for all patients as a result of the Danish Civil Registration System and no loss to followup existed. In the training cohort, 150 patients (out of 270 [56%]) and in the validation cohort 97 patients (out of 164 [59%]) died of disease within 1 year postoperatively. The 2013 model did not differ from the 2008 model and included hemoglobin, complete fracture/impending fracture, visceral and multiple bone metastases, Karnofsky Performance Status, and the American Society of Anesthesiologists score and primary cancer. The models were evaluated by area under the receiver operating characteristic curve (AUC ROC) and Brier score (the lower the better)., Results: The 2013-SPRING model was successfully refitted with a cohort using more patients than the 2008-SPRING model. Comparison of performance in external validation between the 2008 and 2013-SPRING models showed the AUC ROC was increased by 3% (95% confidence interval [CI], 0%-5%; p = 0.027) and 2% (95% CI, 0%-4%; p = 0.013) at 3-month and 6-month survival predictions, respectively, but not at 12 months at 1% (95% CI, 0%-3%; p = 0.112). Brier score was improved by -0.018 (95% CI, -0.032 to -0.004; p = 0.011) for 3-month, -0.028 (95% CI, -0.043 to -0.0123; p < 0.001) for 6-month, and -0.014 (95% CI, -0.025 to -0.002; p = 0.017) for 12-month survival prediction., Conclusions: We improved the SPRING model's ability to predict survival after surgery for metastatic bone disease in the extremities. As such, the refitted 2013-SPRING model gives the surgeon a tool to assist in the decision-making of a surgical implant that will serve the patient for the remainder of their life. The 2013-SPRING model may provide increased quality of life for patients with bone metastasis because potential implant failures can be minimized by precise survival prediction preoperatively and the model is freely available and ready to use from the current article., Level of Evidence: Level I, diagnostic study.

The effects of different environmental drivers on the changes in species' population abundances can be difficult to disentangle as they often act simultaneously. Researchers have built statistical models that include environmental variables (such as annual temperature) or species attributes (such as a species' temperature preference), which are assumed to detect the impacts of specific drivers (such as climate change). However, these approaches are often applied separately or, if combined, not explicitly compared. We show the complementary insights gained by applying both these approaches to a community dataset on Danish terrestrial birds. We use our analysis to compare the relative importance of climate change and agricultural land-use change for the abundance changes within the community between 1983 and 2013. Population models were fitted to the community data of species' annual abundances with predictors comprising: species attributes (species' temperature and habitat preferences), environmental variables (climatic and agricultural land-use change variables) or both. Relationships between species' abundances and environmental variables were used to identify the drivers associated with average abundance changes of species in the community. Relationships between species' abundances and their attributes were used to understand the drivers causing interspecific variation in abundance changes. Warmer winters were positively associated with community-level abundances, and warm-adapted species had more positive abundance changes than cold-adapted ones. Agricultural land-use area was negatively associated with community-level abundances, and birds using a high proportion of meadow and habitat specialists had more negative abundance changes than birds using other habitats and habitat generalists. Effect sizes of environmental variables were larger for agricultural land-use change while those of species attributes were larger for climate change. The environmental data approach suggested that agricultural land-use change has decreased the average abundances of species in the community, affecting total community size while the species attribute-based approach suggested that climate change has caused variation in abundance among species, affecting community composition. Environmental variables and species attributes that are hypothesized to link to specific drivers can be used together to provide complementary information on the impacts of different drivers on communities., (© 2018 The Authors. Journal of Animal Ecology © 2018 British Ecological Society.)

Arsenic is a risk factor for several noncommunicable diseases, even at low doses. Urinary arsenic (UAs) concentration is a good biomarker for internal dose, and demographic, dietary, and lifestyle factors are proposed predictors in nonoccupationally exposed populations. However, most predictor studies are limited in terms of size and number of predictors. We investigated demographic, dietary, and lifestyle determinants of UAs concentrations in 744 postmenopausal Danish women who had UAs measurements and questionnaire data on potential predictors. UAs concentrations were determined using mass spectrometry (ICP-MS), and determinants of the concentration were investigated using univariate and multivariate regression models. We used a forward selection procedure for model optimization. In all models, fish, alcohol, and poultry intake were associated with higher UAs concentration, and tap water, fruit, potato, and dairy intake with lower concentration. A forward regression model explained 35% ( R ²) of the variation in concentrations. Age, smoking, education, and area of residence did not predict concentration. The results were relatively robust across sensitivity analyses. The study suggested that UAs concentration in postmenopausal women was primarily determined by dietary factors, with fish consumption showing the strongest direct association. However, the majority of variation in UAs concentration in this study population is still unexplained.

Recurrent event data are frequently encountered in long-term follow-up studies. In many applications, the gap times between two successive recurrent events are natural outcomes of interest. Investigation on patterns of associations among recurrent gap times within subjects is an important inferential issue. In this paper, we introduce flexible functions of previous gap times to create a class of summary measures of serial associations for a sequence of recurrent gap times through Kendall's tau. Such a general class of serial association measures provides a useful tool to quantify the predictive abilities of event history with different aspects. Non-parametric estimators of the proposed measures of serial associations are developed by generalizing the existing estimator of Kendall's tau for two serial gap times, in which inverse probability of censoring weights is used to overcome the induced dependent censoring. Various tests are further constructed for testing the constancy of serial associations over different events. Our method is applied to Denmark schizophrenia data and the results show that association structures are different for distinct ages of onset of schizophrenia., (© The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: Hospitalisations for ambulatory care-sensitive conditions (ACSCs), a group of chronic and acute illnesses considered not to require inpatient treatment if timely and appropriate ambulatory care is received, and early rehospitalisations are common and costly. We sought to determine whether individuals with depression are at increased risk of hospitalisations for ACSCs, and rehospitalisation for the same or another ACSC, within 30 days., Design: National, population-based cohort study., Setting: Denmark., Participants: 5,049,353 individuals ≥ 18 years of age between 1 January 2005 and 31 December 2013., Measurements: Depression was ascertained via psychiatrist diagnoses in the Danish Psychiatric Central Register or antidepressant prescription redemption from the Danish National Prescription Registry. Hospitalisations for ACSCs and rehospitalisations within 30 days were identified using the Danish National Patient Register., Results: Overall, individuals with depression were 2.35 times more likely to be hospitalised for an ACSC (95% CI 2.32 to 2.37) versus those without depression after adjusting for age, sex and calendar period, and 1.45 times more likely after adjusting for socioeconomic factors, comorbidities and primary care utilisation (95% CI 1.43 to 1.46). After adjusting for ACSC-predisposing comorbidity, depression was associated with significantly greater risk of hospitalisations for all chronic (eg, angina, diabetes complications, congestive heart failure exacerbation) and acute ACSCs (eg, pneumonia) compared to those without depression. Compared to those without depression, persons with depression were 1.21 times more likely to be rehospitalised within 30 days for the same ACSC (95% CI 1.18 to 1.24) and 1.19 times more likely to be rehospitalised within 30 days for a different ACSC (95% CI 1.15 to 1.23)., Conclusions: Individuals with depression are at increased risk of hospitalisations for ACSCs, and once discharged are at elevated risk of rehospitalisations within 30 days for ACSCs., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/)

The main objective of the study was to find combinations of genetic variants significantly associated with bipolar disorder. In a previous study of bipolar disorder, combinations of three single nucleotide polymorphism (SNP) genotypes taken from 803 SNPs were analyzed, and four clusters of combinations were found to be significantly associated with bipolar disorder. In the present study, combinations of four SNP genotypes taken from the same 803 SNPs were analyzed, and one cluster of combinations was found to be significantly associated with bipolar disorder. Combinations from the new cluster and from the four previous clusters were identified in the genomes of 209 of the 607 patients in the study whereas none of the 1355 control participants had any of these combinations in their genome.

In this study, the temporal trends in the rates of multiple maternities in Denmark were analyzed and an association between the rates of multiple maternities and the socioeconomic variations in Denmark was obtained. The findings are compared with the results obtained in studies of Swedish rates. Obtained temporal differences in the twinning rates (TWRs) were similar to the results presented by Fellman and Eriksson (2014). Temporal variation in rates of multiple maternities in Sweden (1751-2000). JP Journal of Biostatistics, 11, 2, 143-156.

Background: Large population-based genome-wide association studies have identified several multiple sclerosis (MS) genetic risk variants, but the existing missing heritability warrants different strategies. Isolated populations offer an alternative way of searching for rare genetic variants and evaluating the possible role of consanguinity in the development of MS. Studies of consanguinity and MS risk have yielded conflicting results., Objectives: In this study we investigated the role of consanguinity on MS risk in the relatively isolated Faroe Islands, which have a presumed high level of inbreeding., Methods: A total of 29 cases and 28 matched controls were genotyped and assessed for inbreeding coefficients, number of runs of homozygosity (ROH) at different lengths and observed number of homozygotes as measures of relatedness. Parametric and non-parametric statistical models were applied., Results: Both cases and controls exhibited considerable relatedness demonstrated by very high inbreeding coefficients, large number of observed homozygotes and many long ROH. However, apart from the number of ROH ≥ 2.5 mega base pairs, no significant differences between the two groups were observed., Conclusions: Overall, no significant difference between cases and controls were found, indicating that consanguinity in itself does not appear to be an important risk factor for MS in the population of the Faroe Islands., (© The Author(s), 2015.)

Background: Many environmental factors have been shown to influence birth weight (BW) and one of these are season of birth., Aim: The aim of the present study was to investigate the seasonal variation in BW in Denmark during 1936-1989, and to see if the variation could be explained by sunshine exposure during pregnancy., Methods: The study population was selected from the Copenhagen School Health Records Register and included 276 339 children born between 1936 and 1989. Seasonal variation was modeled using a non-stationary sinusoidal model that allowed the underlying trend in BW and the amplitude and phase of the yearly cycles to change., Results: There was a clear seasonal pattern in BW which, however, changed gradually across the study period. The highest BWs were seen during fall (September - October) from 1936 to 1963, but a new peak gradually grew from the early 1940s during early summer (May - June) and became the highest from 1964 to 1989. The amplitude of the fall peak started at 25.5 (95%CI 24.6; 25.9) grams and gradually disappeared. The amplitude of the early summer peak gradually arose from nothing to a peak of 18.6 (95%CI 17.7; 19.6) grams in the mid 1980s where it started to decrease again. Sunshine did not explain the seasonal variation in BW., Conclusion: There was a clear seasonal pattern in BW in Denmark 1936-1989, which however changed across the study period. Throughout the study period we observed a peak in BW during the fall, but gradually, starting in the early 1940s, an additional early summer peak emerged and became the highest from 1964 and onwards., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

It is widely assumed that nutrition can improve school performance in children; however, evidence remains limited and inconclusive. In the present study, we investigated whether serving healthy school meals influenced concentration and school performance of 8- to 11-year-old Danish children. The OPUS (Optimal well-being, development and health for Danish children through a healthy New Nordic Diet) School Meal Study was a cluster-randomised, controlled, cross-over trial comparing a healthy school meal programme with the usual packed lunch from home (control) each for 3 months (NCT 01457794). The d2 test of attention, the Learning Rating Scale (LRS) and standard tests on reading and mathematics proficiency were administered at baseline and at the end of each study period. Intervention effects were evaluated using hierarchical mixed models. The school meal intervention did not influence concentration performance (CP; primary outcome, n 693) or processing speed; however, the decrease in error percentage was 0·18 points smaller (P<0·001) in the intervention period than in the control period (medians: baseline 2·03%; intervention 1·46%; control 1·37%). In contrast, the intervention increased reading speed (0·7 sentence, P=0·009) and the number of correct sentences (1·8 sentences, P<0·001), which corresponded to 11 and 25%, respectively, of the effect of one school year. The percentage of correct sentences also improved (P<0·001), indicating that the number correct improved relatively more than reading speed. There was no effect on overall math performance or outcomes from the LRS. In conclusion, school meals did not affect CP, but improved reading performance, which is a complex cognitive activity that involves inference, and increased errors related to impulsivity and inattention. These findings are worth examining in future trials.

Background: In geographical studies, population distribution is a key issue. An unequal distribution across units of analysis might entail extra-variation and produce misleading conclusions on healthcare performance variations. This article aims at assessing the impact of building more homogeneous units of analysis in the estimation of systematic variation in three countries., Methods: Hospital discharges for six conditions (congestive heart failure, short-term complications of diabetes, hip fracture, knee replacement, prostatectomy in prostate cancer and percutaneous coronary intervention) produced in Denmark, England and Portugal in 2008 and 2009 were allocated to both original geographical units and new ad hoc areas. New areas were built using Ward's minimum variance methods. The impact of the new areas on variability was assessed using Kernel distribution curves and different statistic of variation such as Extremal Quotient, Interquartile Interval ratio, Systematic Component of Variation and Empirical Bayes statistic., Results: Ward's method reduced the number of areas, allowing a more homogeneous population distribution, yet 20% of the areas in Portugal exhibited less than 100 000 inhabitants vs. 7% in Denmark and 5% in England. Point estimates for Extremal Quotient and Interquartile Interval Ratio were lower in the three countries, particularly in less prevalent conditions. In turn, the Systematic Component of Variation and Empirical Bayes statistic were slightly lower in more prevalent conditions., Conclusions: Building new geographical areas produced a reduction of the variation in hospitalization rates in several prevalent conditions mitigating random noise, particularly in the smallest areas and allowing a sounder interpretation of the variation across countries., (© The Author 2015. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.)

Background: With the increasing focus on obesity, growth patterns in infancy and early childhood have gained much attention. Although the adiposity rebound has been in focus because of a shown association with adult obesity, not much has been published about the infant peak in body mass index (BMI)., Objective: This study links age and BMI at infant peak to duration of breastfeeding and body composition at 3 y of age., Design: Frequent weight and height measurements for 311 Danish children in the SKOT (Complementary and Young Child Feeding - Impact on Short and Long Term Development and Health; in Danish) cohort were used to estimate BMI growth curves for the age span from 14 d to 19 mo by using a nonlinear mixed-effects model. BMI growth velocity before peak and age and BMI at peak were derived from the subject-specific models. Information about pregnancy and breastfeeding was assessed from background questionnaires. Assessment of body composition at age 3 y was made based on bioelectrical impedance, weight, and height., Results: A longer duration of exclusive breastfeeding was associated with an earlier peak in infant BMI (P = 0.0003) and a lower prepeak velocity (P < 0.0001). BMI level at peak and prepeak velocity was positively associated with fat and fat-free mass at age 3 y (all P < 0.0001), whereas a later age at peak was associated with a lower fat mass, fat mass index, and fat-free mass index at age 3 y (all P < 0.001)., Conclusions: BMI peak characteristics are strongly associated with both duration of exclusive breastfeeding and body composition at 3 y of age. Thus, a better knowledge of characteristics and determinants of the early BMI peak is likely to improve our understanding of early development of obesity., (© 2015 American Society for Nutrition.)

A standard assumption in the modelling of epidemic dynamics is that the population of interest is well mixed, and that no clusters of metapopulations exist. The well-known and oft-used SIR model, arguably the most important compartmental model in theoretical epidemiology, assumes that the disease being modelled is strongly immunizing, directly transmitted and has a well-defined period of infection, in addition to these population mixing assumptions. Childhood infections, such as measles, are prime examples of diseases that fit the SIR-like mechanism. These infections have been well studied for many systems with large, well-mixed populations with endemic infection. Here, we consider a setting where populations are small and isolated. The dynamics of infection are driven by stochastic extinction-recolonization events, producing large, sudden and short-lived epidemics before rapidly dying out from a lack of susceptible hosts. Using a TSIR model, we fit prevaccination measles incidence and demographic data in Bornholm, the Faroe Islands and four districts of Iceland, between 1901 and 1965. The datasets for each of these countries suffer from different levels of data heterogeneity and sparsity. We explore the potential for prediction of this model: given historical incidence data and up-to-date demographic information, and knowing that a new epidemic has just begun, can we predict how large it will be? We show that, despite a lack of significant seasonality in the incidence of measles cases, and potentially severe heterogeneity at the population level, we are able to estimate the size of upcoming epidemics, conditioned on the first time step, to within reasonable confidence. Our results have potential implications for possible control measures for the early stages of new epidemics in small populations.

A stochastic simulation model was developed to estimate the time from introduction of Bovine Viral Diarrhea Virus (BVDV) in a herd to detection of antibodies in bulk tank milk (BTM) samples using three ELISAs. We assumed that antibodies could be detected, after a fixed threshold prevalence of seroconverted milking cows was reached in the herd. Different thresholds were set for each ELISA, according to previous studies. For each test, antibody detection was simulated in small (70 cows), medium (150 cows) and large (320 cows) herds. The assays included were: (1) the Danish blocking ELISA, (2) the SVANOVIR(®)BVDV-Ab ELISA, and (3) the ELISA BVD/MD p80 Institute Pourquier. The validation of the model was mainly carried out by comparing the predicted incidence of persistently infected (PI) calves and the predicted detection time, with records from a BVD infected herd. Results showed that the SVANOVIR, which was the most efficient ELISA, could detect antibodies in the BTM of a large herd 280 days (95% prediction interval: 218; 568) after a transiently infected (TI) milking cow has been introduced into the herd. The estimated time to detection after introduction of one PI calf was 111 days (44; 605). With SVANOVIR ELISA the incidence of PIs and dead born calves could be limited and the impact of the disease on the animal welfare and income of farmers (before detection) could be minimized. The results from the simulation modeling can be used to improve the current Danish BVD surveillance program in detecting early infected herds., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Objective: To examine person, injury, and treatment characteristics associated with recovery trajectories of people with severe traumatic brain injury (TBI) during inpatient rehabilitation., Design: Observational prospective longitudinal study., Setting: TBI rehabilitation units., Participants: Adults (N=206) with severe nonpenetrating TBI admitted directly to inpatient rehabilitation from acute care. Participants were excluded for prior disability and intentional etiology of injury., Interventions: Naturally occurring treatments delivered within comprehensive multidisciplinary teams were recorded daily in 15-minute units provided to patients and family members, separately., Main Outcome Measures: Motor and cognitive FIM were measured on admission, discharge, and every 2 weeks in between and were analyzed with individual growth curve methodology., Results: Inpatient recovery was best modeled with linear, cubic, and quadratic components: relatively steep recovery was followed by deceleration of improvement, which attenuated prior to discharge. Slower recovery was associated with older age, longer coma, and interruptions to rehabilitation. Patients admitted at lower functional levels received more treatment, and more treatment was associated with slower recovery, presumably because treatment was allocated according to need. Therefore, effects of treatment on outcome could not be disentangled from effects of case mix factors., Conclusions: FIM gain during inpatient recovery from severe TBI is not a linear process. In observational studies, the specific effects of treatment on rehabilitation outcomes are difficult to separate from case mix factors that are associated with both outcome and allocation of treatment., (Copyright © 2014 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.)

The objectives of this study were to assess whether current surveillance capacity is sufficient to fulfill EU and Danish regulations to control a hypothetical foot-and-mouth disease (FMD) epidemic in Denmark, and whether enlarging the protection and/or surveillance zones could minimize economic losses. The stochastic spatial simulation model DTU-DADS was further developed to simulate clinical surveillance of herds within the protection and surveillance zones and used to model spread of FMD between herds. A queuing system was included in the model, and based on daily surveillance capacity, which was 450 herds per day, it was decided whether herds appointed for surveillance would be surveyed on the current day or added to the queue. The model was run with a basic scenario representing the EU and Danish regulations, which includes a 3 km protection and 10 km surveillance zone around detected herds. In alternative scenarios, the protection zone was enlarged to 5 km, the surveillance zone was enlarged to 15 or 20 km, or a combined enlargement of the protection and surveillance zones was modelled. Sensitivity analysis included changing surveillance capacity to 200, 350 or 600 herds per day, frequency of repeated visits for herds in overlapping surveillance zones from every 14 days to every 7, 21 and 30 days, and the size of the zones combined with a surveillance capacity increased to 600 herds per day. The results showed that the default surveillance capacity is sufficient to survey herds on time. Extra resources for surveillance did not improve the situation, but fewer resources could result in larger epidemics and costs. Enlarging the protection zone was a better strategy than the basic scenario. Despite that enlarging the surveillance zone might result in shorter epidemic duration, and lower number of affected herds, it resulted frequently in larger economic losses.

In studies of affective disorder, individuals are often observed to experience recurrent symptomatic exacerbations warranting hospitalization. Interest may lie in modeling the occurrence of such exacerbations over time and identifying associated risk factors. In some patients, recurrent exacerbations are temporally clustered following disease onset, but cease to occur after a period of time. We develop a dynamic Mover-Stayer model in which a canonical binary variable associated with each event indicates whether the underlying disease has resolved. An individual whose disease process has not resolved will experience events following a standard point process model governed by a latent intensity. When the disease process resolves, the complete data intensity becomes zero and no further event will occur. An expectation-maximization algorithm is described for parametric and semiparametric model fitting based on a discrete time dynamic Mover-Stayer model and a latent intensity-based model of the underlying point process.

Background: Preoperative airway assessment in Denmark is based on a non-specific clinical assessment left to the discretion of the responsible anesthesiologist. The DIFFICAIR trial compares the effect of using a systematic and consistent airway assessment versus a non-specific clinical assessment on the frequency of unanticipated difficult airway management.To prevent outcome bias and selective reporting, we hereby present a detailed statistical analysis plan as an amendment (update) to the previously published protocol for the DIFFICAIR trial., Method/design: The DIFFICAIR trial is a stratified, parallel group, cluster (cluster = department) randomized multicenter trial involving 28 departments of anesthesia in Denmark randomized to airway assessment either by the Simplified Airway Risk Index (SARI) or by a usual non-specific assessment. Data from patients' preoperative airway assessment are registered in the Danish Anesthesia Database. An objective score for intubation grading the severity, that is the severity of the intubations, as well as the frequency of unanticipated difficult intubation, is measured for each group.Primary outcome measures are the fraction of unanticipated difficult and easy intubations.The database is programmed so that the registration of the SARI is mandatory for the intervention group but invisible to controls.Data recruitment was commenced in October 2012 and ended in ultimo December 2013., Conclusion: We intend to increase the transparency of the data analyses regarding the DIFFICAIR trial by an a priori publication of a statistical analysis plan., Trial Registration: ClinicalTrials.gov: NCT01718561.

Background: We cross-validated three existing models for the prediction of non-sentinel node metastases in patients with micrometastases or isolated tumor cells (ITC) in the sentinel node, developed in Danish and Finnish cohorts of breast cancer patients, to find the best model to identify patients who might benefit from further axillary treatment., Material and Method: Based on 484 Finnish breast cancer patients with micrometastases or ITC in sentinel node a model has been developed for the prediction of non-sentinel node metastases. Likewise, two separate models have been developed in 1577 Danish patients with micrometastases and 304 Danish patients with ITC, respectively. The models were cross-validated in the opposite cohort., Results: The Danish model for micrometatases was accurate when tested in the Finnish cohort, with a slight change in AUC from 0.64 to 0.63. The AUC of the Finnish model decreased from 0.68 to 0.58 when tested in the Danish cohort, and the AUC of the Danish model for ITC decreased from 0.73 to 0.52, when tested in the Finnish cohort. The Danish micrometastatic model identified 14-22% of the patients as high-risk patients with over 30% risk of non-sentinel node metastases while less than 1% was identified by the Finish model. In contrast, the Finish model predicted a much larger proportion of patients being in the low-risk group with less than 10% risk of non-sentinel node metastases., Conclusion: The Danish model for micrometastases worked well in predicting high risk of non-sentinel node metastases and was accurate under external validation., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

For twin time-to-event data, we consider different concordance probabilities, such as the casewise concordance that are routinely computed as a measure of the lifetime dependence/correlation for specific diseases. The concordance probability here is the probability that both twins have experienced the event of interest. Under the assumption that both twins are censored at the same time, we show how to estimate this probability in the presence of right censoring, and as a consequence, we can then estimate the casewise twin concordance. In addition, we can model the magnitude of within pair dependence over time, and covariates may be further influential on the marginal risk and dependence structure. We establish the estimators large sample properties and suggest various tests, for example, for inferring familial influence. The method is demonstrated and motivated by specific twin data on cancer events with the competing risk death. We thus aim to quantify the degree of dependence through the casewise concordance function and show a significant genetic component., (Copyright © 2013 John Wiley & Sons, Ltd.)

Objective: To examine the direct and indirect impact of comorbidity on the survival., Design: A historical cohort study., Setting: Denmark., Participants: All patients with non-small cell lung cancer who were registered in the Danish Lung Cancer Registry in 2010., Main Outcome Measures: The influence of comorbidity on stage misclassification, probability of resection and survival., Results: It was estimated that the comorbidity influences the probability of resection with OR 0.65 and 95% credible interval (0.54; 0.79), the staging process with OR 1.08 and 95% credible interval (0.96; 1.20), and the survival process with HR 1.08 and 95% credible interval (1.02; 1.14)., Conclusions: We found that comorbidity has a significant indirect effect on survival mediated by the resection process and a slightly direct effect on mortality.

It has been hypothesized that, in aggregate, rare variants in coding regions of genes explain a substantial fraction of the heritability of common diseases. We sequenced the exomes of 1,000 Danish cases with common forms of type 2 diabetes (including body mass index > 27.5 kg/m(2) and hypertension) and 1,000 healthy controls to an average depth of 56×. Our simulations suggest that our study had the statistical power to detect at least one causal gene (a gene containing causal mutations) if the heritability of these common diseases was explained by rare variants in the coding regions of a limited number of genes. We applied a series of gene-based tests to detect such susceptibility genes. However, no gene showed a significant association with disease risk after we corrected for the number of genes analyzed. Thus, we could reject a model for the genetic architecture of type 2 diabetes where rare nonsynonymous variants clustered in a modest number of genes (fewer than 20) are responsible for the majority of disease risk., (Copyright © 2013 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

This paper examines the relationship between schizophrenia and employment. We use longitudinal register data and show a considerable drop in the employment rate for people with schizophrenia six years before the first treatment at a psychiatric facility. After the first treatment, the employment rate stabilizes at 18%. The difference in the employment rate in 2007 for siblings with and without schizophrenia is estimated at 67%. This difference is reduced to 62% when we include additional control variables. The results remain unchanged when we apply a sibling fixed effects approach that controls for the unobserved family specific characteristics that the siblings share., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Individuals who spent time in foster care as children fare on average worse than non-placed peers in early adult life. Recent research on the effect of foster care placement on early adult life outcomes provides mixed evidence. Some studies suggest negative effects of foster care placement on early adult outcomes, others find null effects. This study shows that differences in the average duration of foster care stays explain parts of these discordant findings and then test how foster care duration shapes later life outcomes using administrative data on 7220 children. The children experienced different average durations of foster care because of differences in exposure to a reform. Later born cohorts spent on average 3 months longer in foster care than earlier born cohorts. Isolating exogenous variation in duration of foster care, the study finds positive effects of increased duration of foster care on income and labor market participation., (Copyright © 2013 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.)

Parental education and maternal intelligence are well-known predictors of child IQ. However, the literature regarding other factors that may contribute to individual differences in IQ is inconclusive. The aim of this study was to examine the contribution of a number of variables whose predictive status remain unclarified, in a sample of basically healthy children with a low rate of pre- and postnatal complications. 1,782 5-year-old children sampled from the Danish National Birth Cohort (2003-2007) were assessed with a short form of the Wechsler Preschool and Primary Scale of Intelligence - Revised. Information on parental characteristics, pregnancy and birth factors, postnatal influences, and postnatal growth was collected during pregnancy and at follow-up. A model including study design variables and child's sex explained 7% of the variance in IQ, while parental education and maternal IQ increased the explained variance to 24%. Other predictors were parity, maternal BMI, birth weight, breastfeeding, and the child's head circumference and height at follow-up. These variables, however, only increased the explained variance to 29%. The results suggest that parental education and maternal IQ are major predictors of IQ and should be included routinely in studies of cognitive development. Obstetrical and postnatal factors also predict IQ, but their contribution may be of comparatively limited magnitude.

Agent-based simulation models provide a viable approach for developing applied models of species and systems for predictive management. However, there has been some reluctance to use these models for policy applications due to complexity and the need for improved testing and communication of the models. We present the development and testing of a comprehensive model for Skylark (Alauda arvensis) in Danish agricultural landscapes. The model is part of the ALMaSS system, which considers not only individual skylarks, but also the detailed dynamic environment from which they obtain the information necessary to simulate their behaviour. Population responses emerge from individuals interacting with each other and the environment. Model development and testing was carried out using pattern-oriented modelling. The testing procedure was based on the model's ability to represent detailed real world patterns of distribution and density, reproductive performance and seasonal changes in territory numbers. Data to support this was collected over a 13-year period and comprised detailed field observations of breeding birds and intensive surveys. The model was able to recreate the real world data patterns accurately; it was also able to simultaneously fit a number of other secondary system properties which were not formally a part of the testing procedure. The correspondence of model output to real world data and sensitivity analysis are presented and discussed, and the model's description is provided in ODdox format (a formal description inter-linked to the program code). Detailed and stringent tests for model performance were carried out, and standardised model description and open access to the source code were provided to open development of the skylark model to others. Over and above documenting the utility of the model, this open process is essential to engender the user trust and ensure continued development of these comprehensive systems for applied purposes.

Background: Sickness absence (SA) is a public health risk marker for morbidity and mortality. The aim of this study was to develop and validate prediction models to identify employees at risk of high SA., Methods: Two prediction models were developed using self-rated health (SRH) and prior SA as predictors. SRH was measured by the categories excellent, good, fair and poor in a convenience sample of 535 hospital employees. Prior SA was retrieved from the employer's register. The predictive performance of the models was assessed by logistic regression analysis with high (≥ 90 th percentile) vs. non-high (<90th percentile) SA days and SA episodes as outcome variables and by using bootstrapping techniques to validate the models., Results: The overall performance as reflected in the Nagelkerke's pseudo R(2) was 11.7% for the model identifying employees with high SA days and 31.8% for the model identifying employees with high SA episodes. The discriminative ability, represented by the area (AUC) under the receiver operating characteristic (ROC), was 0.729 (95% CI 0.667-0.809) for the model identifying employees with high SA days and 0.831 (95% CI 0.784-0.877) for the model identifying employees with high SA episodes. The Hosmer-Lemeshow test showed acceptable calibration for both models., Conclusions: The prediction models identified employees at risk of high SA, but need further external validation in other settings and working populations before applying them in public and occupational health research and care.

Narrowly defined personality facet scores are commonly reported and used for making decisions in clinical and organizational settings. Although these facets are typically related, scoring is usually carried out for a single facet at a time. This method can be ineffective and time consuming when personality tests contain many highly correlated facets. This article investigates the possibility of increasing the precision of the NEO PI-R facet scores by scoring items with multidimensional item response theory and by efficiently administering and scoring items with multidimensional computer adaptive testing (MCAT). The increase in the precision of personality facet scores is obtained from exploiting the correlations between the facets. Results indicate that the NEO PI-R could be substantially shorter without attenuating precision when the MCAT methodology is used. Furthermore, the study shows that the MCAT methodology is particularly appropriate for constructs that have many highly correlated facets.

Background: Otitis media (OM) is a very common condition in children and occurs during years that are critical to the development of learning, literacy, and math skills. Therefore, among a large cohort of Danish conscripts, we aimed to examine the association between hospitalisation with OM in early childhood and cognitive function and educational level in early adulthood., Methods: We conducted a population-based prevalence study using linked data from healthcare databases and conscription records of Danish men born between 1977 and 1983. We identified all hospitalisations with OM before 8 years of age. Cognitive function was measured by the Boerge Prien validated group intelligence test (Danish Børge Prien Prøve, BPP). We adjusted for potential confounders with and without stratification by hearing impairment. Furthermore, we examined the association between hospitalisation with OM and the prevalence of having achieved a General Certificate of Secondary Education (GCSE), stratified by quartiles of BPP scores., Results: Of the 18 412 eligible conscripts aged 18-25 years, 1000 (5.5%) had been hospitalised with OM before age 8. Compared with conscripts without such a record, the adjusted prevalence ratio (PR) for a BPP score in the bottom quartile was 1.20 (95% confidence interval [CI]: 1.09-1.33). There was no major difference in the proportion of men with a GCSE and those without among those hospitalised with OM in early childhood. For men in the bottom and upper quartiles of BPP scores, the PRs for early childhood hospitalisation with OM were 0.89 (95% CI: 0.59-1.33) and 0.96 (95% CI, 0.88-1.05), respectively. Among men with severe hearing impairment, the proportion with a BPP score in the bottom quartile did not differ between those with and without an OM hospitalisation [PR = 1.01 (95% CI: 0.78-1.34)]., Conclusions: Overall, we found that hospitalisation with OM in early childhood was associated with a slightly lower cognitive function in early adulthood. Hospitalisation for OM did not seem to influence the prevalence of GSCE when level of BPP was taken into account.

Neighbourhood risk factors for Campylobacter infection in Danish broilers were evaluated. Campylobacter infection status of a flock was identified by PCR analysis of cloacal swab samples collected as a part of national surveillance program. Included into the study were, in total, 10,876 broiler flocks sent by 226 farms to processing plants from 2007 to 2009. A multivariable logistic regression model with autocorrelation structure was used to model the effect of exposure variables on the probability of being tested positive to Campylobacter. Results showed a significant protective effect with the absence of infected neighbours within a distance of 2 km. The analysis was adjusted for potential confounding factors. Seasonal cyclic patterns of the Campylobacter infection was accounted for by using sine and cosine function. Predicted probability maps showed a heterogeneous spatial and temporal risk of Campylobacter infection in Danish broiler., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Few investigations of health event clustering have evaluated residential mobility, though causative exposures for chronic diseases such as cancer often occur long before diagnosis. Recently developed Q-statistics incorporate human mobility into disease cluster investigations by quantifying space- and time-dependent nearest neighbor relationships. Using residential histories from two cancer case-control studies, we created simulated clusters to examine Q-statistic performance. Results suggest the intersection of cases with significant clustering over their life course, Q(i), with cases who are constituents of significant local clusters at given times, Q(it), yielded the best performance, which improved with increasing cluster size. Upon comparison, a larger proportion of true positives were detected with Kulldorf's spatial scan method if the time of clustering was provided. We recommend using Q-statistics to identify when and where clustering may have occurred, followed by the scan method to localize the candidate clusters. Future work should investigate the generalizability of these findings., (Copyright © 2012 Elsevier Ltd. All rights reserved.)