Consequences of reduced effectiveness of salmon lice treatments for lice control.

The effective control of ectoparasitic salmon lice, Lepeophtheirus salmonis, in fish farms is challenged by the salmon lice having developed resistance towards several antiparasitic drugs and by the effectiveness of non-medicinal treatments being limited by considerations of fish welfare. When new antiparasitics are introduced to the market, these should be used sparingly to slow resistance development. Using a population model for salmon lice parameterised for salmonid fish farms in Norway, we quantified how reduced treatment effectiveness influences treatment frequency and lice abundance. Furthermore, we investigated when in the production cycle a highly effective lice treatment leads to the largest reduction in the total number of treatments, mean lice abundance and lice larvae production. Results showed that reductions in treatment effectiveness to lower than 50% led to the steepest increases in treatment frequency and mean lice abundance, as well as to increased risk that lice abundance increased beyond control. The timing of the most effective treatment had only moderate effects on the total treatment need and the mean number of adult female lice through the production cycle, but large effect on the production of lice larvae in spring. These findings imply that farmers can optimise the timing of the most effective treatment to reduce the release of lice larvae in the period of year when wild salmonids are in coastal waters, without compromising total treatment need or mean lice levels.

Towards better survival: Modeling drivers for daily mortality in Norwegian Atlantic salmon farming.

Mortality in the production of farmed salmonids is a major constraint to the sustainability of this form of animal husbandry. We have developed a model for the daily mortality in salmon farming over a full production cycle from stocking to harvest, considering different environmental and production factors. These factors included sea temperature, salinity, day within year, fish weight at stocking, stocking day, four types of lice treatments and the possible occurrence of pancreas disease (PD). We considered a generalized additive model following full production cycles, allowing for non-linear descriptions of how relevant factors relate to the daily mortality. We saw a high overall mortality rate immediately after stocking, which decreased the first three months in the cycle and thereafter increased. We found that the total mortality could be reduced by 21% if avoiding all lice treatments, and similarly reduced by 20% if no PD infections occurred. If avoiding jointly PD and all lice treatments, the accumulated mortality could be reduced by 34%. A single thermal or hydrogen peroxide treatment was associated with a mortality of around 1.6% and 1.3%, respectively. This modeling approach gave a unique opportunity to model how different factors interact on the overall global mortality and can easily be extended by other factors, such as additional fish diseases.

Simulated effects of increasing salmonid production on sea lice populations in Norway.

Norway produces more than one million tonnes of salmonids every year, almost exclusively in open-water net pens. In 2014, the Norwegian government announced plans to increase salmonid production. However, increasing the number of farmed salmonids can have negative effects on the marine environment that threaten the industry's sustainability. In particular, production growth can lead to an increase in density-dependent diseases, including parasitic sea lice. The aim of this study was to simulate the effects of increased salmonid production on sea lice abundance using different scenarios for increasing the number of fish and for the management of sea lice. We used a previously developed, partly stage-structured model based on Norwegian production and environmental data to simulate the different scenarios. Our results show that increasing the marine farmed salmonid population at a national level by two or five times the current production leads to an increase in the sea lice abundance by 3.5% and 7.1%, respectively. We also found that by lowering the maximum allowable level of sea lice to an average of 0.049 adult females per fish, weekly treatments can be used to control sea lice population growth with a five times increase in production. However, this increases the number of farms treating per week by as much as 281.3%, which can lead to high costs and increased mortality among farmed salmonids. Overall, the results from our study shed light on the effects of increasing salmonid production in Norway with respect to the ongoing threat of sea lice infestations.

Realtime case study simulations of transmission of Pancreas Disease (PD) in Norwegian salmonid farming for disease control purposes.

Pancreas Disease (PD) is a viral disease caused by Salmonid Alphavirus (SAV). It affects farmed salmonids in the North Atlantic, and leads to reduced feed intake and increased mortality with reduced production and welfare as a consequence. In 2013, the estimated cost of an outbreak on an average salmon farm was about 6.6 mil €. In Norway, PD has been notifiable since 2008, and regulations to mitigate disease spread are in place. However, despite the regulations, 140-170 farms are affected by PD every year. The aquaculture industry is growing continuously, introducing farms in new geographical areas, and fish are moved between hydrographically separated zones for trade and slaughter. All such movements and relocations need to be approved by the competent authorities. Thus, there is a demand for support to farmers and competent authorities when making decisions on disease management and especially on the effect of moving infected fish. We have used a disease-transmission model for outbreak-simulation in real time for assessing the probability of disease transmission from a farm that gets infected with PD. We have also simulated the effects of three different control-regimes: no stamping-out, delayed stamping-out or immediate stamping-out, on the transmission of PD to surrounding farms. Simulations showed that the immediate stamping out of an infected farm led to effective containment of an outbreak. No stamping out led to up to 32.1% of farms within 100 km of the index farm to become effected. We have used real production data for the model building and the scenario simulations, and the results illustrate that a risk assessment of horizontal disease transmission must be undertaken on a case-by-case basis, because the time and place of the outbreak has a large influence on the risk of transmission.

Determination of safety margins for whole blood concentrations of alcohol and nineteen drugs in driving under the influence cases.

Legislative limits for driving under the influence of 20 non-alcohol drugs were introduced in Norway in February 2012. Per se limits corresponding to blood alcohol concentrations (BAC) of 0.2g/kg were established for 20 psychoactive drugs, and limits for graded sanctions corresponding to BACs of 0.5 and 1.2g/kg were determined for 13 of these drugs. This new legislation made it possible for the courts to make sentences based on the analytical results, similar to the situation for alcohol. To ensure that the reported concentration is as least as high as the true concentration, with a 99% safety level, safety margins had to be calculated for each of the substances. Diazepam, tetrahydrocannabinol (THC) and alcohol were used as model substances to establish a new model for estimating the safety margins. The model was compared with a previous used model established several years ago, by a similar yet much simpler model, and they were found to be in agreement. The measurement uncertainties depend on the standard batch used, the work list and the measurements' replicate. A Bayesian modelling approach was used to determine the parameters in the model, using a dataset of 4700 diazepam positive specimens and 5400 THC positive specimens. Different safety margins were considered for low and high concentration levels of diazepam (≤2µM (0.6mg/L) and >2µM) and THC (≤0.01µM (0.003mg/L) and >0.01µM). The safety margins were for diazepam 19.5% (≤2µM) and 34% (>2µM), for THC 19.5% (≤0.01µM) and 24.9% (>0.01µM). Concentration dependent safety margins for BAC were based on a dataset of 29500 alcohol positive specimens, and were in the range 10.4% (0.1g/kg) to 4.0% (4.0g/kg) at a 99% safety level. A simplified approach was used to establish safety margins for the compounds amphetamine, MDMA, methamphetamine, alprazolam, phenazepam, flunitrazepam, clonazepam, nitrazepam, oxazepam, buprenorphine, GHB, methadone, ketamine, cocaine, morphine, zolpidem and zopiclone. The safety margins for these drugs were in the range 34-41%.

Space-time modelling of the spread of salmon lice between and within Norwegian marine salmon farms.

Parasitic salmon lice are potentially harmful to salmonid hosts and farm produced lice pose a threat to wild salmonids. To control salmon lice infections in Norwegian salmonid farming, numbers of lice are regularly counted and lice abundance is reported from all salmonid farms every month. We have developed a stochastic space-time model where monthly lice abundance is modelled simultaneously for all farms. The set of farms is regarded as a network where the degree of contact between farms depends on their seaway distance. The expected lice abundance at each farm is modelled as a function of i) lice abundance in previous months at the same farm, ii) at neighbourhood farms, and iii) other, unspecified sources. In addition, the model includes explanatory variables such as seawater temperature and farm-numbers of fish. The model gives insight into factors that affect salmon lice abundance and contributing sources of infection. New findings in this study were that 66% of the expected salmon lice abundance was attributed to infection within farms, 28% was attributed to infection from neighbourhood farms and 6% to non-specified sources of infection. Furthermore, we present the relative risk of infection between neighbourhood farms as a function of seaway distance, which can be viewed as a between farm transmission kernel for salmon lice. The present modelling framework lays the foundation for development of future scenario simulation tools for examining the spread and abundance of salmon lice on farmed salmonids under different control regimes.

Antibiotic resistance in hospitals: a ward-specific random effect model in a low antibiotic consumption environment.

Association between previous antibiotic use and emergence of antibiotic resistance has been reported for several microorganisms. The relationship has been extensively studied, and although the causes of antibiotic resistance are multi-factorial, clear evidence of antibiotic use as a major risk factor exists. Most studies are carried out in countries with high consumption of antibiotics and corresponding high levels of antibiotic resistance, and currently, little is known whether and at what level the associations are detectable in a low antibiotic consumption environment. We conduct an ecological, retrospective study aimed at determining the impact of antibiotic consumption on antibiotic-resistant Pseudomonas aeruginosa in three hospitals in Norway, a country with low levels of antibiotic use. We construct a sophisticated statistical model to capture such low signals. To reduce noise, we conduct our study at hospital ward level. We propose a random effect Poisson or binomial regression model, with a reparametrisation that allows us to reduce the number of parameters. Inference is likelihood based. Through scenario simulation, we study the potential effects of reduced or increased antibiotic use. Results clearly indicate that the effects of consumption on resistance are present under conditions with relatively low use of antibiotic agents. This strengthens the recommendation on prudent use of antibiotics, even when consumption is relatively low.

Sea lice as a density-dependent constraint to salmonid farming.

Fisheries catches worldwide have shown no increase over the last two decades, while aquaculture has been booming. To cover the demand for fish in the growing human population, continued high growth rates in aquaculture are needed. A potential constraint to such growth is infectious diseases, as disease transmission rates are expected to increase with increasing densities of farmed fish. Using an extensive dataset from all farms growing salmonids along the Norwegian coast, we document that densities of farmed salmonids surrounding individual farms have a strong effect on farm levels of parasitic sea lice and efforts to control sea lice infections. Furthermore, increased intervention efforts have been unsuccessful in controlling elevated infection levels in high salmonid density areas in 2009-2010. Our results emphasize host density effects of farmed salmonids on the population dynamics of sea lice and suggest that parasitic sea lice represent a potent negative feedback mechanism that may limit sustainable spatial densities of farmed salmonids.

An approach to combining parallel and cross-over trials with and without run-in periods using individual patient data.

BACKGROUND: In active run-in trials, where patients may be excluded after a run-in period based on their response to the treatment, it is implicitly assumed that patients have individual treatment effects. If individual patient data are available, active run-in trials can be modelled using patient-specific random effects. With more than one trial on the same medication available, one can obtain a more precise overall treatment effect estimate. METHODS: We present a model for joint analysis of a two-sequence, four-period cross-over trial (AABB/BBAA) and a three-sequence, two-period active run-in trial (AB/AA/A), where the aim is to investigate the effect of a new treatment for patients with pain due to osteoarthritis. RESULTS: Our approach enables us to separately estimate the direct treatment effect for all patients, for the patients excluded after the active run-in trial prior to randomisation, and for the patients who completed the active run-in trial. A similar model approach can be used to analyse other types of run-in trials, but this depends on the data and type of other trials available. LIMITATIONS: We assume equality of the various carry-over effects over time. CONCLUSIONS: The proposed approach is flexible and can be modified to handle other designs. Our results should be encouraging for those responsible for planning cost-efficient clinical development programmes.

A stochastic model for the assessment of the transmission pathways of heart and skeleton muscle inflammation, pancreas disease and infectious salmon anaemia in marine fish farms in Norway.

Salmon farming is threatened, economically and ecologically, by infectious diseases. To reduce the risk of epidemics, authorities have developed regulations. These are based on quantitative understanding of pathways of infection, representing disease specific risks. A stochastic model was fitted to historical data, to estimate risk factors associated with competing spread mechanisms. Three infectious diseases were compared, heart and skeletal muscle inflammation (HSMI), pancreas disease (PD) and infectious salmon anaemia (ISA). This study was based on space-time data, from Norway from 2003 to 2007, describing the susceptible fish cohorts and the reported infections. Particular interest was given to seaway distances between farms and their local management organisation. The parameter measuring the effect of distance to an infectious fish farm was positive and significant for all diseases, implying that the risk involved with proximate infectious fish farms increased with decreasing distance. For HSMI and PD there was a significant effect of sharing a contact network with an infectious farm. For HSMI, but not for PD or ISA, there was a significant effect of previous infected cohorts on the same farm. The relative contribution of each transmission pathway was dominated by seaway distance for PD and HSMI, while other non-defined pathways dominated for ISA. This comparative study highlights that the three diseases have different patterns of spread, with important consequences for disease prevention and management.

Predictors of sub-clinical enterovirus infections in infants: a prospective cohort study.

BACKGROUND: Enterovirus infections are common, although most often sub-clinical. The present purpose was to assess the impact of breastfeeding and other factors on enterovirus infections in infancy. METHODS: A prospective observational study was carried out on a population-based cohort of 639 Norwegian infants aged 3-12 months. The outcome was enterovirus RNA measured in monthly stool samples. Data on underlying determinants, such as dietary feeding and household factors, were reported in parental questionnaires. Multivariable logistic regression was performed to allow for common confounders. Statistical analyses were performed by GLLAMM using Stata 9.2, which corrects for subject-specific random effects. RESULTS: The prevalence of enterovirus in stools was 11.1% (475/4279). Risk of enterovirus infection decreased with increasing number of daily breastfeeds; the effect was most pronounced at the age of 3 months [odds ratio (OR), 0.85; 95% confidence interval (CI) 0.8-0.9, P < 0.001], gradually declining thereafter, reaching no effect at 11 months. Increased risk was associated with having one or more sibling(s) (OR 1.89; 95% CI 1.2-3.0), particularly if they attended daycare (OR 2.46; 95% CI 1.4-4.2), and with increasing exposure to other children (OR 1.04; 95% CI 1.0-1.1). There was a tendency towards higher prevalence of infection when a household's drinking water came from a well, and a protective effect of owning a dog or cat. CONCLUSIONS: Several factors may modify the risk for enterovirus infections in the first year of life. This study supports the protective effect of breastfeeding. The protection decreased with age and increased with dose of ingested milk.

Levels of hexachlorobenzene (HCB) in breast milk in relation to birth weight in a Norwegian cohort.

BACKGROUND: Hexachlorobenzene (HCB) is a ubiquitous environmental contaminant that, even at low doses, causes destruction of ovarian primordial germ cells in experimental studies. However, its potential for reproductive toxicity in humans exposed to background levels has not been fully evaluated. Here we examined the association between maternal levels of HCB and their infants' birth weight. METHODS: HCB was measured in milk samples from a subset of women in the Norwegian Human Milk Study (HUMIS), 2003-2006; 300 subjects were randomly chosen from the cohort and 26 from all small for gestational age (SGA) children. Additional information was obtained through questionnaires and the Medical Birth Registry. RESULTS: Overall, HCB was associated with birth weight (adjusted b = -90 g per 8 microg/kg milk fat, 95% CI-275 to 8) and with SGA (odds ratio 1.8, 95% CI 0.9-3.7 per 8 microg/kg milk fat (difference between the 10th and the 90th percentile)). After stratification, however, the association was present only among smokers. For birth weight for past or current smokers: b = -282, CI -467 to -98; for never smokers: b = 0.5, CI -149 to 150, p-value for interaction: 0.01. Similar results were observed for head circumference, crown-heel length, and SGA. CONCLUSIONS: We saw a moderate association between HCB and markers of impaired fetal growth among past and current smokers. This finding may be non-causal and due to underlying genetic variants tied to both growth and breakdown of HCB or to confounding by unmeasured toxicants that coexist in exposure sources. It may, however, also result from HCB exposure.

Prevalence of alcohol and drugs among Norwegian motor vehicle drivers: a roadside survey.

The objective of the study was to determine the prevalence of alcohol, psychoactive medicinal drugs and illegal drugs among drivers in Norwegian road traffic. Drivers of motor vehicles were selected from April 2005 to April 2006 in the south-eastern part of Norway, surrounding, but not including the capital, Oslo. A stratified two-stage cluster sampling procedure was used. In the first stage, random road sites and time intervals were selected, and in the second stage, drivers were stopped by random at those sites and time intervals. Altogether about 12,000 drivers were asked to provide a sample of oral fluid (saliva) and answer a few questions. Samples of oral fluid were obtained from 88% of the drivers, of whom 30% were females and 70% males. The prevalence of each drug was estimated by a weighted average using weights adjusted for under- or over-sampling compared to traffic statistics. Alcohol or drugs were found in oral fluid samples of 4.5% of the drivers; alcohol in 0.4%, psychoactive medicinal drugs in 3.4%, and illegal drugs in 1.0%. Illegal drugs were found more frequently in samples from younger drivers, while psychoactive medicinal drugs were more frequently found in samples from older drivers. Psychoactive medicinal drugs were more prevalent among females than males, among drivers stopped on working days rather than weekends, and among those who reported annual driving distances less than 16,000 km. The drugs found most frequently were zopiclone (1.4%), benzodiazepines (1.4%), codeine (0.8%), tetrahydrocannabinol (0.6%) and amphetamines (0.3%). Two or more drugs were found in 0.6% of the samples, corresponding to 15% of the drug-positive drivers.

A stochastic model for infectious salmon anemia (ISA) in Atlantic salmon farming.

Infectious salmon anemia (ISA) is one of the main infectious diseases in Atlantic salmon farming with major economical implications. Despite the strong regulatory interventions, the ISA epidemic is not under control, worldwide. We study the data covering salmon farming in Norway from 2002 to 2005 and propose a stochastic space-time model for the transmission of the virus. We model seaway transmission between farm sites, transmission through shared management and infrastructure, biomass effects and other potential pathways within the farming industry. We find that biomass has an effect on infectiousness, the local contact network and seaway distance of 5 km represent similar risks, but a large component of risk originates from other sources, among which are possibly infected salmon smolt and boat traffic.

Forecasting acidification effects using a Bayesian calibration and uncertainty propagation approach.

We present a statistical framework for model calibration and uncertainty estimation for complex deterministic models. A Bayesian approach is used to combine data from observations, the deterministic model, and prior parameter distributions to obtain forecast distributions. A case study is presented in which the statistical framework is applied using the hydrogeochemical model (MAGIC) for an assessment of recovery from acidification of soils and surface waters at a long-term study site in Norway under different future acid deposition conditions. The water quality parameters are coupled with a simple dose-response model for trout population health. Uncertainties in model output parameters are estimated and forecast results are presented as probability distributions for future water chemistry and as probability distributions of future healthy trout populations. The forecast results are examined for three different scenarios of future acid deposition corresponding to three different emissions control strategies for Europe. Despite the explicit consideration of uncertainties propagated into the future forecasts, there are clear differences among the scenarios. The case study illustrates how inclusion of uncertainties in model predictions can strengthen the inferences drawn from model results in support of decision making and assessments.

The influence of missing value imputation on detection of differentially expressed genes from microarray data.

MOTIVATION: Missing values are problematic for the analysis of microarray data. Imputation methods have been compared in terms of the similarity between imputed and true values in simulation experiments and not of their influence on the final analysis. The focus has been on missing at random, while entries are missing also not at random. RESULTS: We investigate the influence of imputation on the detection of differentially expressed genes from cDNA microarray data. We apply ANOVA for microarrays and SAM and look to the differentially expressed genes that are lost because of imputation. We show that this new measure provides useful information that the traditional root mean squared error cannot capture. We also show that the type of missingness matters: imputing 5% missing not at random has the same effect as imputing 10-30% missing at random. We propose a new method for imputation (LinImp), fitting a simple linear model for each channel separately, and compare it with the widely used KNNimpute method. For 10% missing at random, KNNimpute leads to twice as many lost differentially expressed genes as LinImp. AVAILABILITY: The R package for LinImp is available at http://folk.uio.no/idasch/imp.

Predicting blood donor arrival.

BACKGROUND: Keeping waiting time at blood donation short is important for making donation a good experience for the donors and hence to motivate for repeat donations. At the Blood Bank of Oslo, fixed appointments are used, and few donors arrive without appointments. On average, 59 percent of scheduled donors arrive, but day-to-day variations are large. Methods for predicting the number of donors that will arrive on a given day would be valuable in reducing waiting times. STUDY DESIGN AND METHODS: Information about candidate explanatory variables was collected for all appointments made in a 971-day period (179,121 appointments). A logistic regression model for the prediction of blood donor arrival was fitted. RESULTS: Among 18 explanatory variables, the most important were the time from appointment making to appointment date; the contact medium used; the donor age and total number of donations; and the number of no-shows, arrivals, and deferrals during the preceding 2 years. Compared to taking only the average arrival rate into account, prediction intervals were reduced by 43 percent. CONCLUSION: Statistical modeling can provide useful estimates of blood donor arrival, allowing for better planning of donation sessions.

Analysis of the humoral immune response to immunoselected phage-displayed peptides by a microarray-based method.

We describe a novel approach for high-throughput analysis of the immune response in cancer patients using phage-based microarray technology. The recombinant phages used for fabricating phage arrays were initially selected via the use of random peptide phage libraries and breast cancer patient serum antibodies. The peptides displayed by the phages retained their ability to be recognized by serum antibodies after immobilization. The recombinant phage microarrays were screened against either breast cancer or healthy donor serum antibodies. A model-based statistical method is proposed to estimate significant differences in serum antibody reactivity between patients and normals. A significant tumor effect was found with most of the selected phage-displayed peptides, suggesting that recombinant phage microarrays can serve as a tool in monitoring humoral responses towards phage-displayed peptides.

Comment on Cowling's "Spatial methods for line transect surveys".

We consider the problem of estimating the parameters of a two-dimensional Neyman-Scott process, from data collected through a line transect survey. Cowling (1998, Biometrics 54, 828-839) suggested an estimation method based on a one-dimensional K-function along the transect line. However, her expression for the theoretical K-function is wrong. In this article, we correct her K-function.