The peer effect on pain tolerance

Accepted manuscript version, licensed CC BY-NC-ND 4.0. Published version available at https://doi.org/10.1515/sjpain-2018-0060 .Background and aims: Twin studies have found that approximately half of the variance in pain tolerance can be explained by genetic factors, while shared family environment has a negligible effect. Hence, a large proportion of the variance in pain tolerance is explained by the (non-shared) unique environment. The social environment beyond the family is a potential candidate for explaining some of the variance in pain tolerance. Numerous individual traits have previously shown to be associated with friendship ties. In this study, we investigate whether pain tolerance is associated with friendship ties. Methods: We study the friendship effect on pain tolerance by considering data from the Tromsø Study: Fit Futures I, which contains pain tolerance measurements and social network information for adolescents attending first year of upper secondary school in the Tromsø area in Northern Norway. Pain tolerance was measured with the cold-pressor test (primary outcome), contact heat and pressure algometry. We analyse the data by using statistical methods from social network analysis. Specifically, we compute pairwise correlations in pain tolerance among friends. We also fit network autocorrelation models to the data, where the pain tolerance of an individual is explained by (among other factors) the average pain tolerance of the individual’s friends. Results: We find a significant and positive relationship between the pain tolerance of an individual and the pain tolerance of their friends. The estimated effect is that for every 1 s increase in friends’ average cold-pressor tolerance time, the expected cold-pressor pain tolerance of the individual increases by 0.21 s (p-value: 0.0049, sample size n=997). This estimated effect is controlled for sex. The friendship effect remains significant when controlling for potential confounders such as lifestyle factors and test sequence among the students. Further investigating the role of sex on this friendship effect, we only find a significant peer effect of male friends on males, while there is no significant effect of friends’ average pain tolerance on females in stratified analyses. Similar, but somewhat lower estimates were obtained for the other pain modalities. Conclusions: We find a positive and significant peer effect in pain tolerance. Hence, there is a significant tendency for students to be friends with others with similar pain tolerance. Sex-stratified analyses show that the only significant effect is the effect of male friends on males. Implications: Two different processes can explain the friendship effect in pain tolerance, selection and social transmission. Individuals might select friends directly due to similarity in pain tolerance, or indirectly through similarity in other confounding variables that affect pain tolerance. Alternatively, there is an influence effect among friends either directly in pain tolerance, or indirectly through other variables that affect pain tolerance. If there is indeed a social influence effect in pain tolerance, then the social environment can account for some of the unique environmental variance in pain tolerance. If so, it is possible to therapeutically affect pain tolerance through alteration of the social environment

Salmon lice (Lepeophtheirus salmonis) in the stomach contents of lumpfish (Cyclopterus lumpus) sampled from Norwegian fish farms: Relationship between lice grazing and operational conditions

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Modeling geographic vaccination strategies for COVID-19 in Norway.

Vaccination was a key intervention in controlling the COVID-19 pandemic globally. In early 2021, Norway faced significant regional variations in COVID-19 incidence and prevalence, with large differences in population density, necessitating efficient vaccine allocation to reduce infections and severe outcomes. This study explored alternative vaccination strategies to minimize health outcomes (infections, hospitalizations, ICU admissions, deaths) by varying regions prioritized, extra doses prioritized, and implementation start time. Using two models (individual-based and meta-population), we simulated COVID-19 transmission during the primary vaccination period in Norway, covering the first 7 months of 2021. We investigated alternative strategies to allocate more vaccine doses to regions with a higher force of infection. We also examined the robustness of our results and highlighted potential structural differences between the two models. Our findings suggest that early vaccine prioritization could reduce COVID-19 related health outcomes by 8% to 20% compared to a baseline strategy without geographic prioritization. For minimizing infections, hospitalizations, or ICU admissions, the best strategy was to initially allocate all available vaccine doses to fewer high-risk municipalities, comprising approximately one-fourth of the population. For minimizing deaths, a moderate level of geographic prioritization, with approximately one-third of the population receiving doubled doses, gave the best outcomes by balancing the trade-off between vaccinating younger people in high-risk areas and older people in low-risk areas. The actual strategy implemented in Norway was a two-step moderate level aimed at maintaining the balance and ensuring ethical considerations and public trust. However, it did not offer significant advantages over the baseline strategy without geographic prioritization. Earlier implementation of geographic prioritization could have more effectively addressed the main wave of infections, substantially reducing the national burden of the pandemic

Monotone regression in high (and lower) dimensions

In this thesis, we first present an overview of monotone regression, both in the classical setting and in the high dimensional setting. High dimensional data means that the number of covariates, p, exceeds the number of observations, n. It is often reasonable to assume a monotone relationship between a predictor variable and the response, especially in medicine and biology. The monotone regression methods for the high dimensional data setting that are considered are the liso regression method and the monotone splines lasso regression method (to our knowledge, the only two methods). Both these methods are special forms of penalised regression. The performances of these two high dimensional methods in the classical setting are studied and compared to the performances of existing methods for monotone regression developed for the classical setting, known as MonBoost, scam and scar. The two high dimensional methods work well also in the classical setting, but they do not outperform the existing methods. The two methods can still be useful in the classical setting, since they can be used in situations where the monotonicity directions of the effects are not known, in contrast to the existing methods and also perform automatic variable selection. Furthermore, we investigate the robustness of the monotone splines lasso method to the number of interior knots used to fit the monotone splines and find that it is very robust. In addition, two new methods for fitting a partially linear model where the non-linear covariates are assumed to have a monotone effect on the response are developed. These two methods can be used in the setting where p > n as well as in the classical setting. To our knowledge, no such methods have been developed in the past. The first method, PLAMM-1, is a straight forward extension of the monotone splines lasso method to the partially linear setting. The second method developed, PLAMM-2, is a method with two penal- ties, one on the linear parameters and one on the non-linear parameters. In this last case, estimation has to be performed iteratively, and we prove convergence of the iterative scheme. The estimation, selection and prediction performances of the methods are investigated by simulation experiments in different settings. Through the simulation experiments, the methods are shown to work well in both the classical settings and in the high dimensional setting where the number of observations is not too small. We also apply the partially linear monotone model to a medical dataset where clinical covariates enter the linear part and genomic covariates are assumed to have a monotone effect on the outcome

Contributions to network science in public health

This thesis develops and exploits methodology within network science, driven by important applications. By use of large-scale simulations and high-quality data, we demonstrate how network science can contribute to understanding and predicting various phenomena in public health. We study peer effects of pain tolerance in a friendship network, and we use mobility networks between locations to develop spatio-temporal mathematical models of influenza. Pain tolerance is measured by how long you can hold your hand in cold water, and is censored. By extending social network methodology to handle censoring, pain tolerance among friends is found to be positively correlated. When the network is stratified on sex, we find that the peer effect is only present in males, and only through their male-male friendships. Transmission of airborne pathogens in humans on large scales is driven by the human mobility network. The mobility network is closely linked to the population density. As part of the urbanisation process, individuals cluster in geographical areas. We propose a method for generating spatial fields with controllable levels of population clustering, and simulate disease spread when population clustering is varied. Population clustering is found to be an important determinant for the effect of travel restrictions on infectious disease spread. We thus contribute to understanding how the effect of interventions can vary between countries. The spatio-temporal spread of influenza in Bangladesh is modelled by using a dynamic mobility network informed by daily mobile phone mobility data. Such data are particularly useful in low-income settings, due to scarce census data. When the model is informed by time-averaged mobility data, the results are very similar to the results with daily mobility. This is important for future studies and outbreak control. We apply the model to predict spatial spread and estimate transmissibility for influenza in Bangladesh

Statistical predictions with glmnet

Elastic net type regression methods have become very popular for prediction of certain outcomes in epigenome-wide association studies (EWAS). The methods considered accept biased coefficient estimates in return for lower variance thus obtaining improved prediction accuracy. We provide guidelines on how to obtain parsimonious models with low mean squared error and include easy to follow walk-through examples for each step in R

Partially linear monotone methods with automatic variable selection and monotonicity direction discovery

In many statistical regression and prediction problems, it is reasonable to assume monotone relationships between certain predictor variables and the outcome. Genomic effects on phenotypes are, for instance, often assumed to be monotone. However, in some settings, it may be reasonable to assume a partially linear model, where some of the covariates can be assumed to have a linear effect. One example is a prediction model using both high‐dimensional gene expression data, and low‐dimensional clinical data, or when combining continuous and categorical covariates. We study methods for fitting the partially linear monotone model, where some covariates are assumed to have a linear effect on the response, and some are assumed to have a monotone (potentially nonlinear) effect. Most existing methods in the literature for fitting such models are subject to the limitation that they have to be provided the monotonicity directions a priori for the different monotone effects. We here present methods for fitting partially linear monotone models which perform both automatic variable selection, and monotonicity direction discovery. The proposed methods perform comparably to, or better than, existing methods, in terms of estimation, prediction, and variable selection performance, in simulation experiments in both classical and high‐dimensional data settings

A compelling demonstration of why traditional statistical regression models cannot be used to identify risk factors from case data on infectious diseases: a simulation study

Background Regression models are often used to explain the relative risk of infectious diseases among groups. For example, overrepresentation of immigrants among COVID-19 cases has been found in multiple countries. Several studies apply regression models to investigate whether different risk factors can explain this overrepresentation among immigrants without considering dependence between the cases. Methods We study the appropriateness of traditional statistical regression methods for identifying risk factors for infectious diseases, by a simulation study. We model infectious disease spread by a simple, population-structured version of an SIR (susceptible-infected-recovered)-model, which is one of the most famous and well-established models for infectious disease spread. The population is thus divided into different sub-groups. We vary the contact structure between the sub-groups of the population. We analyse the relation between individual-level risk of infection and group-level relative risk. We analyse whether Poisson regression estimators can capture the true, underlying parameters of transmission. We assess both the quantitative and qualitative accuracy of the estimated regression coefficients. Results We illustrate that there is no clear relationship between differences in individual characteristics and group-level overrepresentation —small differences on the individual level can result in arbitrarily high overrepresentation. We demonstrate that individual risk of infection cannot be properly defined without simultaneous specification of the infection level of the population. We argue that the estimated regression coefficients are not interpretable and show that it is not possible to adjust for other variables by standard regression methods. Finally, we illustrate that regression models can result in the significance of variables unrelated to infection risk in the constructed simulation example (e.g. ethnicity), particularly when a large proportion of contacts is within the same group. Conclusions Traditional regression models which are valid for modelling risk between groups for non-communicable diseases are not valid for infectious diseases. By applying such methods to identify risk factors of infectious diseases, one risks ending up with wrong conclusions. Output from such analyses should therefore be treated with great caution