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Chan LYH, Rø G, Midtbø JE, Di Ruscio F, Watle SSV, Juvet LK, Littmann J, Aavitsland P, Nygård KM, Berg AS, Bukholm G, Kristoffersen AB, Engø-Monsen K, Engebretsen S, Swanson D, Palomares ADL, Lindstrøm JC, Frigessi A, de Blasio BF. Modeling geographic vaccination strategies for COVID-19 in Norway. PLoS Comput Biol 2024; 20:e1011426. [PMID: 38295111 PMCID: PMC10861074 DOI: 10.1371/journal.pcbi.1011426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 02/12/2024] [Accepted: 01/08/2024] [Indexed: 02/02/2024] Open
Abstract
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.
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Affiliation(s)
- Louis Yat Hin Chan
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
| | - Gunnar Rø
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
| | - Jørgen Eriksson Midtbø
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
| | - Francesco Di Ruscio
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
| | | | - Lene Kristine Juvet
- Department of Infection Control and Vaccines, Norwegian Institute of Public Health, Oslo, Norway
| | - Jasper Littmann
- Division of Infection Control, Norwegian Institute of Public Health, Oslo, Norway
- Bergen Centre for Ethics and Priority Setting (BCEPS), University of Bergen, Bergen, Norway
| | - Preben Aavitsland
- Division of Infection Control, Norwegian Institute of Public Health, Oslo, Norway
- Pandemic Centre, University of Bergen, Bergen, Norway
| | - Karin Maria Nygård
- Department of Infectious Diseases and Preparedness, Norwegian Institute of Public Health, Oslo, Norway
| | - Are Stuwitz Berg
- Department of Infection Control and Vaccines, Norwegian Institute of Public Health, Oslo, Norway
| | - Geir Bukholm
- Division of Infection Control, Norwegian Institute of Public Health, Oslo, Norway
- Faculty of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, Norway
| | | | | | | | - David Swanson
- Department of Biostatistics, MD Anderson Cancer Center, University of Texas, Houston, Texas, United States of America
| | | | | | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Birgitte Freiesleben de Blasio
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo and Oslo University Hospital, Oslo, Norway
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Kamineni M, Engø-Monsen K, Midtbø JE, Forland F, de Blasio BF, Frigessi A, Engebretsen S. Effects of non-compulsory and mandatory COVID-19 interventions on travel distance and time away from home, Norway, 2021. Euro Surveill 2023; 28. [PMID: 37103789 DOI: 10.2807/1560-7917.es.2023.28.17.2200382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2023] Open
Abstract
BackgroundGiven the societal, economic and health costs of COVID-19 non-pharmaceutical interventions (NPI), it is important to assess their effects. Human mobility serves as a surrogate measure for human contacts and compliance with NPI. In Nordic countries, NPI have mostly been advised and sometimes made mandatory. It is unclear if making NPI mandatory further reduced mobility.AimWe investigated the effect of non-compulsory and follow-up mandatory measures in major cities and rural regions on human mobility in Norway. We identified NPI categories that most affected mobility.MethodsWe used mobile phone mobility data from the largest Norwegian operator. We analysed non-compulsory and mandatory measures with before-after and synthetic difference-in-differences approaches. By regression, we investigated the impact of different NPI on mobility.ResultsNationally and in less populated regions, time travelled, but not distance, decreased after follow-up mandatory measures. In urban areas, however, distance decreased after follow-up mandates, and the reduction exceeded the decrease after initial non-compulsory measures. Stricter metre rules, gyms reopening, and restaurants and shops reopening were significantly associated with changes in mobility.ConclusionOverall, distance travelled from home decreased after non-compulsory measures, and in urban areas, distance further decreased after follow-up mandates. Time travelled reduced more after mandates than after non-compulsory measures for all regions and interventions. Stricter distancing and reopening of gyms, restaurants and shops were associated with changes in mobility.
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Affiliation(s)
- Meghana Kamineni
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | | | - Jørgen E Midtbø
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
| | - Frode Forland
- Division of Infection Control, Norwegian Institute of Public Health, Oslo, Norway
| | - Birgitte Freiesleben de Blasio
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo and Oslo University Hospital, Oslo, Norway
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Engebretsen S, Diz-Lois Palomares A, Rø G, Kristoffersen AB, Lindstrøm JC, Engø-Monsen K, Kamineni M, Hin Chan LY, Dale Ø, Midtbø JE, Stenerud KL, Di Ruscio F, White R, Frigessi A, de Blasio BF. A real-time regional model for COVID-19: Probabilistic situational awareness and forecasting. PLoS Comput Biol 2023; 19:e1010860. [PMID: 36689468 PMCID: PMC9894546 DOI: 10.1371/journal.pcbi.1010860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 02/02/2023] [Accepted: 01/08/2023] [Indexed: 01/24/2023] Open
Abstract
The COVID-19 pandemic is challenging nations with devastating health and economic consequences. The spread of the disease has revealed major geographical heterogeneity because of regionally varying individual behaviour and mobility patterns, unequal meteorological conditions, diverse viral variants, and locally implemented non-pharmaceutical interventions and vaccination roll-out. To support national and regional authorities in surveilling and controlling the pandemic in real-time as it unfolds, we here develop a new regional mathematical and statistical model. The model, which has been in use in Norway during the first two years of the pandemic, is informed by real-time mobility estimates from mobile phone data and laboratory-confirmed case and hospitalisation incidence. To estimate regional and time-varying transmissibility, case detection probabilities, and missed imported cases, we developed a novel sequential Approximate Bayesian Computation method allowing inference in useful time, despite the high parametric dimension. We test our approach on Norway and find that three-week-ahead predictions are precise and well-calibrated, enabling policy-relevant situational awareness at a local scale. By comparing the reproduction numbers before and after lockdowns, we identify spatially heterogeneous patterns in their effect on the transmissibility, with a stronger effect in the most populated regions compared to the national reduction estimated to be 85% (95% CI 78%-89%). Our approach is the first regional changepoint stochastic metapopulation model capable of real time spatially refined surveillance and forecasting during emergencies.
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Affiliation(s)
| | | | - Gunnar Rø
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
| | | | | | | | - Meghana Kamineni
- Oslo Centre for Biostatistics and Epidemiology. University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Louis Yat Hin Chan
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
| | | | - Jørgen Eriksson Midtbø
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
- Telenor Norge AS Fornebu, Norway
| | | | - Francesco Di Ruscio
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
| | - Richard White
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology. University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Birgitte Freiesleben de Blasio
- Department of Method Development and Analytics. Norwegian Institute of Public Health, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology. University of Oslo and Oslo University Hospital, Oslo, Norway
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Engebretsen S, Rø G, de Blasio BF. 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. BMC Med Res Methodol 2022; 22:146. [PMID: 35596137 PMCID: PMC9123765 DOI: 10.1186/s12874-022-01565-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 03/03/2022] [Indexed: 11/10/2022] Open
Abstract
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.
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Affiliation(s)
| | - Gunnar Rø
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
| | - Birgitte Freiesleben de Blasio
- Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
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Lindstrøm JC, Engebretsen S, Kristoffersen AB, Rø GØI, Palomares ADL, Engø-Monsen K, Madslien EH, Forland F, Nygård KM, Hagen F, Gantzel G, Wiklund O, Frigessi A, de Blasio BF. Increased transmissibility of the alpha SARS-CoV-2 variant: evidence from contact tracing data in Oslo, January to February 2021. Infect Dis (Lond) 2021; 54:72-77. [PMID: 34618665 DOI: 10.1080/23744235.2021.1977382] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
Abstract
BACKGROUND Information about the contagiousness of new SARS-CoV-2 variants, including the alpha lineage, and how they spread in various locations is essential. Country-specific estimates are needed because local interventions influence transmissibility. METHODS We analysed contact tracing data from Oslo municipality, reported from January through February 2021, when the alpha lineage became predominant in Norway and estimated the relative transmissibility of the alpha lineage with the use of Poisson regression. RESULTS Within households, we found an increase in the secondary attack rate by 60% (95% CI 20-114%) among cases infected with the alpha lineage compared to other variants; including all close contacts, the relative increase in the secondary attack rate was 24% (95% CI -6%-43%). There was a significantly higher risk of infecting household members in index cases aged 40-59 years who were infected with the alpha lineage; we found no association between transmission and household size. Overall, including all close contacts, we found that the reproduction number among cases with the alpha lineage was increased by 24% (95% CI 0%-52%), corresponding to an absolute increase of 0.19, compared to the group of index cases infected with other variants. CONCLUSION Our study suggests that households are the primary locations for rapid transmission of the new lineage alpha.
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Affiliation(s)
| | | | - Anja Bråthen Kristoffersen
- Division of Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Gunnar Øyvind Isaksson Rø
- Division of Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Alfonso Diz-Lois Palomares
- Division of Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | | | - Elisabeth Henie Madslien
- Division of Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Frode Forland
- Division of Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Karin Maria Nygård
- Division of Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Frode Hagen
- Oslo Municipality Health Service, Oslo, Norway
| | | | | | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Birgitte Freiesleben de Blasio
- Division of Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
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6
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Engebretsen S. S. Engebretsen responds. Tidsskr Nor Laegeforen 2021; 141:21-0021. [PMID: 33528133 DOI: 10.4045/tidsskr.21.0021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
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8
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Engebretsen S, Glad IK. Partially linear monotone methods with automatic variable selection and monotonicity direction discovery. Stat Med 2020; 39:3549-3568. [PMID: 32851696 DOI: 10.1002/sim.8680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Revised: 05/07/2020] [Accepted: 06/10/2020] [Indexed: 11/10/2022]
Abstract
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.
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Affiliation(s)
| | - Ingrid K Glad
- Department of Mathematics, University of Oslo, Oslo, Norway
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9
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Engebretsen S, Engø-Monsen K, Aleem MA, Gurley ES, Frigessi A, de Blasio BF. Time-aggregated mobile phone mobility data are sufficient for modelling influenza spread: the case of Bangladesh. J R Soc Interface 2020; 17:20190809. [PMID: 32546112 PMCID: PMC7328378 DOI: 10.1098/rsif.2019.0809] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Human mobility plays a major role in the spatial dissemination of infectious diseases. We develop a spatio-temporal stochastic model for influenza-like disease spread based on estimates of human mobility. The model is informed by mobile phone mobility data collected in Bangladesh. We compare predictions of models informed by daily mobility data (reference) with that of models informed by time-averaged mobility data, and mobility model approximations. We find that the gravity model overestimates the spatial synchrony, while the radiation model underestimates the spatial synchrony. Using time-averaged mobility resulted in spatial spreading patterns comparable to the daily mobility model. We fit the model to 2014–2017 influenza data from sentinel hospitals in Bangladesh, using a sequential version of approximate Bayesian computation. We find a good agreement between our estimated model and the case data. We estimate transmissibility and regional spread of influenza in Bangladesh, which are useful for policy planning. Time-averaged mobility appears to be a good proxy for human mobility when modelling infectious diseases. This motivates a more general use of the time-averaged mobility, with important implications for future studies and outbreak control. Moreover, time-averaged mobility is subject to less privacy concerns than daily mobility, containing less temporal information on individual movements.
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Affiliation(s)
- Solveig Engebretsen
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.,Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway.,Norwegian Computing Center, Oslo, Norway
| | | | - Mohammad Abdul Aleem
- International Centre for Diarrhoeal Disease Research, Bangladesh, ICDDR,B, Dhaka, Bangladesh
| | - Emily Suzanne Gurley
- International Centre for Diarrhoeal Disease Research, Bangladesh, ICDDR,B, Dhaka, Bangladesh.,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Birgitte Freiesleben de Blasio
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.,Department of Method Development and Analytics, Norwegian Institute of Public Health, Oslo, Norway
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10
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Abstract
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.
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Affiliation(s)
- Solveig Engebretsen
- Division for Infection Control and Environmental Health, Department of Infectious Disease Epidemiology and Modelling, Norwegian Institute of Public Health, Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway
| | - Jon Bohlin
- Division for Infection Control and Environmental Health, Department of Infectious Disease Epidemiology and Modelling, Norwegian Institute of Public Health, Oslo, Norway. .,Centre for Fertility and Health (CEFH), Norwegian Institute of Public Health, Oslo, Norway. .,Faculty of Veterinary Science, Department of Production Animals, Norwegian University of Life Science, Ås, Norway.
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Engebretsen S, Frigessi A, Engø-Monsen K, Furberg AS, Stubhaug A, de Blasio BF, Nielsen CS. The peer effect on pain tolerance. Scand J Pain 2019; 18:467-477. [PMID: 29794275 DOI: 10.1515/sjpain-2018-0060] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 04/04/2018] [Indexed: 11/15/2022]
Abstract
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.
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Affiliation(s)
- Solveig Engebretsen
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Post box 1122 Blindern, 0316 Oslo, Norway, Phone: +47 470 83 876.,Department of Infectious Disease Epidemiology and Modelling, Norwegian Institute of Public Health, Oslo, Norway
| | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | | | - Anne-Sofie Furberg
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway.,Department of Microbiology and Infection Control, University Hospital of North Norway, Tromsø, Norway
| | - Audun Stubhaug
- Department of Pain Management and Research, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Birgitte Freiesleben de Blasio
- Department of Infectious Disease Epidemiology and Modelling, Norwegian Institute of Public Health, Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway
| | - Christopher Sivert Nielsen
- Department of Pain Management and Research, Oslo University Hospital, Oslo, Norway.,Department of Ageing, Norwegian Institute of Public Health, Oslo, Norway
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Engebretsen S, Engø-Monsen K, Frigessi A, Freiesleben de Blasio B. A theoretical single-parameter model for urbanisation to study infectious disease spread and interventions. PLoS Comput Biol 2019; 15:e1006879. [PMID: 30845153 PMCID: PMC6424465 DOI: 10.1371/journal.pcbi.1006879] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 03/19/2019] [Accepted: 02/18/2019] [Indexed: 11/27/2022] Open
Abstract
The world is continuously urbanising, resulting in clusters of densely populated urban areas and more sparsely populated rural areas. We propose a method for generating spatial fields with controllable levels of clustering of the population. We build a synthetic country, and use this method to generate versions of the country with different clustering levels. Combined with a metapopulation model for infectious disease spread, this allows us to in silico explore how urbanisation affects infectious disease spread. In a baseline scenario with no interventions, the underlying population clustering seems to have little effect on the final size and timing of the epidemic. Under within-country restrictions on non-commuting travel, the final size decreases with increased population clustering. The effect of travel restrictions on reducing the final size is larger with higher clustering. The reduction is larger in the more rural areas. Within-country travel restrictions delay the epidemic, and the delay is largest for lower clustering levels. We implemented three different vaccination strategies-uniform vaccination (in space), preferentially vaccinating urban locations and preferentially vaccinating rural locations. The urban and uniform vaccination strategies were most effective in reducing the final size, while the rural vaccination strategy was clearly inferior. Visual inspection of some European countries shows that many countries already have high population clustering. In the future, they will likely become even more clustered. Hence, according to our model, within-country travel restrictions are likely to be less and less effective in delaying epidemics, while they will be more effective in decreasing final sizes. In addition, to minimise final sizes, it is important not to neglect urban locations when distributing vaccines. To our knowledge, this is the first study to systematically investigate the effect of urbanisation on infectious disease spread and in particular, to examine effectiveness of prevention measures as a function of urbanisation.
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Affiliation(s)
- Solveig Engebretsen
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Department of Infectious Disease Epidemiology and Modelling, Division for Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
| | | | - Arnoldo Frigessi
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway
| | - Birgitte Freiesleben de Blasio
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Department of Infectious Disease Epidemiology and Modelling, Division for Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
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14
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Engebretsen S, Sorrells R, Yi-Frazier JP, Early KB. Longitudinal quality of life improvement in underserved rural youth with obesity. Obes Sci Pract 2017; 2:444-455. [PMID: 28090350 PMCID: PMC5192546 DOI: 10.1002/osp4.82] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Revised: 09/30/2016] [Accepted: 10/21/2016] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVE ACT! (Actively Changing Together) is a family- and community-based intervention targeting youth with obesity. The objective of this study was to establish the longitudinal impact on Health-Related Quality of Life (HRQoL) as well as the relationship with anthropometric and demographic factors. METHODS Youth (n = 75) aged 8-14 years meeting criteria for overweight or obesity were referred to the programme. Twelve, 90-min classes in English and Spanish were held at the YMCA. Demographics and anthropometrics were assessed, as well as HRQoL that was measured with the child-reported Pediatric Quality of Life Inventory (PedsQL™) 4.0 Generic Core Scale. Data was collected at three follow-up points after completion of the intervention: initial follow-up (n = 65), 6 (n = 41) and 12 months (n = 25). Analysis included paired dependent t-tests between baseline and follow-up, and Pearson's correlations on HRQoL, anthropometric and demographic data. RESULTS PedsQL scores significantly improved from baseline to all follow-up timepoints (initial follow-up immediately following the intervention, and 6 and 12 months post intervention). Over time, body mass index Z-Score and per cent body fat displayed various points of significance and strengthening correlations. CONCLUSIONS Longitudinal improvements in HRQoL were sustained up to 12 months following a family- and community-based intervention in this underserved population. Anthropometric measures continuously correlated with and contributed to HRQoL outcomes.
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Affiliation(s)
- S Engebretsen
- College of Osteopathic Medicine Pacific Northwest University Yakima WA USA
| | - R Sorrells
- College of Osteopathic Medicine Pacific Northwest University Yakima WA USA
| | - J P Yi-Frazier
- Center for Clinical and Translational Research Seattle Children's Research Institute Seattle WA USA
| | - K Briggs Early
- College of Osteopathic Medicine Pacific Northwest University Yakima WA USA
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Abstract
Pigmented nevi are uncommon oral lesions. We report the clinical and microscopic features of three cases of combined nevus, characterized by the association of an intramucosal nevus and a common blue nevus. Two cases were clinically suspected to be melanoma. The lesions were located on the maxillary gingiva, the mandibular gingiva, and the mucosa of the left posterior portion of the hard palate. Combined nevi of the skin vary considerably in histologic appearance. The microscopic findings demonstrated by oral combined nevi also appear to exhibit substantial histologic variation. The oral combined nevus should be differentiated from malignant melanoma by histopathologic means for, as in the skin, it appears to be clinically benign.
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