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1. Bootstrapping multiple systems estimates to account for model selection

Author

Silverman, Bernard W., Chan, Lax, and Vincent, Kyle

Subjects
Statistics - Methodology, Statistics - Applications, 62F40
Abstract

Multiple systems estimation using a Poisson loglinear model is a standard approach to quantifying hidden populations where data sources are based on lists of known cases. Information criteria are often used for selecting between the large number of possible models. Confidence intervals are often reported conditional on the model selected, providing an over-optimistic impression of estimation accuracy. A bootstrap approach is a natural way to account for the model selection. However, because the model selection step has to be carried out for every bootstrap replication, there may be a high or even prohibitive computational burden. We explore the merit of modifying the model selection procedure in the bootstrap to look only among a subset of models, chosen on the basis of their information criterion score on the original data. This provides large computational gains with little apparent effect on inference. We also incorporate rigorous and economical ways of approaching issues of the existence of estimators when applying the method to sparse data tables., Comment: 18 pages, 4 figures, 6 tables

Published
2023
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4. The vote Package: Single Transferable Vote and Other Electoral Systems in R

Author

Raftery, Adrian E., Ševčíková, Hana, and Silverman, Bernard W.

Subjects
Statistics - Computation, Computer Science - Computer Science and Game Theory
Abstract

We describe the vote package in R, which implements the plurality (or first-past-the-post), two-round runoff, score, approval and single transferable vote (STV) electoral systems, as well as methods for selecting the Condorcet winner and loser. We emphasize the STV system, which we have found to work well in practice for multi-winner elections with small electorates, such as committee and council elections, and the selection of multiple job candidates. For single-winner elections, the STV is also called instant runoff voting (IRV), ranked choice voting (RCV), or the alternative vote (AV) system. The package also implements the STV system with equal preferences, for the first time in a software package, to our knowledge. It also implements a new variant of STV, in which a minimum number of candidates from a specified group are required to be elected. We illustrate the package with several real examples.

Published
2021

5. Key Questions for Modelling COVID-19 Exit Strategies

Author

Thompson, Robin N, Hollingsworth, T Deirdre, Isham, Valerie, Arribas-Bel, Daniel, Ashby, Ben, Britton, Tom, Challoner, Peter, Chappell, Lauren H K, Clapham, Hannah, Cunniffe, Nik J, Dawid, A Philip, Donnelly, Christl A, Eggo, Rosalind, Funk, Sebastian, Gilbert, Nigel, Gog, Julia R, Glendinning, Paul, Hart, William S, Heesterbeek, Hans, House, Thomas, Keeling, Matt, Kiss, Istvan Z, Kretzschmar, Mirjam, Lloyd, Alun L, McBryde, Emma S, McCaw, James M, Miller, Joel C, McKinley, Trevelyan J, Morris, Martina, ONeill, Philip D, Pearson, Carl A B, Parag, Kris V, Pellis, Lorenzo, Pulliam, Juliet R C, Ross, Joshua V, Tildesley, Michael J, Tomba, Gianpaolo Scalia, Silverman, Bernard W, Struchiner, Claudio J, Trapman, Pieter, Webb, Cerian R, Mollison, Denis, and Restif, Olivier

Subjects
Quantitative Biology - Other Quantitative Biology, Quantitative Biology - Populations and Evolution
Abstract

Combinations of intense non-pharmaceutical interventions ('lockdowns') were introduced in countries worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement lockdown exit strategies that allow restrictions to be relaxed while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, will allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. The roadmap requires a global collaborative effort from the scientific community and policy-makers, and is made up of three parts: i) improve estimation of key epidemiological parameters; ii) understand sources of heterogeneity in populations; iii) focus on requirements for data collection, particularly in Low-to-Middle-Income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

Published
2020
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6. Model fitting in Multiple Systems Analysis for the quantification of Modern Slavery: Classical and Bayesian approaches

Author

Silverman, Bernard W.

Subjects
Statistics - Applications, Statistics - Methodology
Abstract

Multiple systems estimation is a key approach for quantifying hidden populations such as the number of victims of modern slavery. The UK Government published an estimate of 10,000 to 13,000 victims, constructed by the present author, as part of the strategy leading to the Modern Slavery Act 2015. This estimate was obtained by a stepwise multiple systems method based on six lists. Further investigation shows that a small proportion of the possible models give rather different answers, and that other model fitting approaches may choose one of these. Three data sets collected in the field of modern slavery, together with a data set about the death toll in the Kosovo conflict, are used to investigate the stability and robustness of various multiple systems estimate methods. The crucial aspect is the way that interactions between lists are modelled, because these can substantially affect the results. Model selection and Bayesian approaches are considered in detail, in particular to assess their stability and robustness when applied to real modern slavery data. A new Markov Chain Monte Carlo Bayesian approach is developed; overall, this gives robust and stable results at least for the examples considered. The software and datasets are freely and publicly available to facilitate wider implementation and further research., Comment: 31 pages. Version 3. (Original version July 2018)

Published
2019
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7. Multiple Systems Estimation for Sparse Capture Data: Inferential Challenges when there are Non-Overlapping Lists

Author

Chan, Lax, Silverman, Bernard W., and Vincent, Kyle

Subjects
Statistics - Methodology
Abstract

Multiple systems estimation strategies have recently been applied to quantify hard-to-reach populations, particularly when estimating the number of victims of human trafficking and modern slavery. In such contexts, it is not uncommon to see sparse or even no overlap between some of the lists on which the estimates are based. These create difficulties in model fitting and selection, and we develop inference procedures to address these challenges. The approach is based on Poisson log-linear regression modeling. Issues investigated in detail include taking proper account of data sparsity in the estimation procedure, as well as the existence and identifiability of maximum likelihood estimates. A stepwise method for choosing the most suitable parameters is developed, together with a bootstrap approach to finding confidence intervals for the total population size. We apply the strategy to two empirical data sets of trafficking in US regions, and find that the approach results in stable, reasonable estimates. An accompanying R software implementation has been made publicly available., Comment: 41 pages, 1 figure

Published
2019
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8. Julian Ernst Besag, 26 March 1945 -- 6 August 2010, a biographical memoir

Author

Diggle, Peter J., Green, Peter J., and Silverman, Bernard W.

Subjects
Statistics - Other Statistics, Statistics - Computation
Abstract

Julian Besag was an outstanding statistical scientist, distinguished for his pioneering work on the statistical theory and analysis of spatial processes, especially conditional lattice systems. His work has been seminal in statistical developments over the last several decades ranging from image analysis to Markov chain Monte Carlo methods. He clarified the role of auto-logistic and auto-normal models as instances of Markov random fields and paved the way for their use in diverse applications. Later work included investigations into the efficacy of nearest neighbour models to accommodate spatial dependence in the analysis of data from agricultural field trials, image restoration from noisy data, and texture generation using lattice models., Comment: 26 pages, 14 figures; minor revisions, omission of full bibliography

Published
2017
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10. Efficacy and safety of aripiprazole lauroxil in schizophrenic patients presenting with severe psychotic symptoms during an acute exacerbation

Author

Potkin, Steven G, Risinger, Robert, Du, Yangchun, Zummo, Jacqueline, Bose, Anjana, Silverman, Bernard, Stankovic, Srdjan, and Ehrich, Elliot

Subjects
Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Clinical Research, Schizophrenia, Mental Health, Clinical Trials and Supportive Activities, Brain Disorders, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Mental health, Adult, Antipsychotic Agents, Aripiprazole, Double-Blind Method, Female, Follow-Up Studies, Humans, Male, Psychiatric Status Rating Scales, Schizophrenic Psychology, Treatment Outcome, Acute, Psychotic symptom, Severe, Aripiprazole lauroxil, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences
Abstract

Aripiprazole lauroxil (AL), a new long-acting injectable antipsychotic, demonstrated safety and efficacy in treating acute exacerbation symptoms of schizophrenia in a 12-week placebo-controlled trial of two doses of AL (441mg and 882mg) administered every 4weeks. We performed a post hoc analysis of this trial to evaluate the efficacy of AL in the subgroup of patients with severe psychotic symptoms, defined as those with baseline Positive and Negative Syndrome Scale (PANSS) Total score above the median score of 92 (n=309). Change from baseline to Day 85 in PANSS Total score; Positive, Negative, and General Psychopathology subscale scores; and overall response rate were assessed. Statistically significant and clinically meaningful improvements in PANSS Total score were demonstrated with AL 441mg and AL 882mg, with placebo-adjusted differences of -14.7 (p5%) were schizophrenia, akathisia, headache, insomnia, and anxiety. AL demonstrated robust efficacy in treatment of the subgroup of patients experiencing severe psychotic symptoms. Both doses (441mg and 882mg) were effective, with numerically greater improvement in symptoms and proportion of responders favoring the higher dose arm. Both doses had a side effect profile consistent with the known safety profile of aripiprazole.

Published
2017

11. Key questions for modelling COVID-19 exit strategies

Author

Thompson, Robin N., Hollingsworth, T. Déirdre, Isham, Valerie, Arribas-Bel, Daniel, Ashby, Ben, Britton, Tom, Challenor, Peter, Chappell, Lauren H. K., Clapham, Hannah, Cunniffe, Nik J., Dawid, A. Philip, Donnelly, Christl A., Eggo, Rosalind M., Funk, Sebastian, Gilbert, Nigel, Glendinning, Paul, Gog, Julia R., Hart, William S., Heesterbeek, Hans, House, Thomas, Keeling, Matt, Kiss, István Z., Kretzschmar, Mirjam E., Lloyd, Alun L., McBryde, Emma S., McCaw, James M., McKinley, Trevelyan J., Miller, Joel C., Morris, Martina, O’Neill, Philip D., Parag, Kris V., Pearson, Carl A. B., Pellis, Lorenzo, Pulliam, Juliet R. C., Ross, Joshua V., Tomba, Gianpaolo Scalia, Silverman, Bernard W., Struchiner, Claudio J., Tildesley, Michael J., Trapman, Pieter, Webb, Cerian R., Mollison, Denis, and Restif, Olivier

Published
2020

13. Treating Opioid Dependence With Injectable Extended-Release Naltrexone (XR-NTX)

Author

Nunes, Edward V, Krupitsky, Evgeny, Ling, Walter, Zummo, Jacqueline, Memisoglu, Asli, Silverman, Bernard L, and Gastfriend, David R

Subjects
HIV/AIDS, Clinical Research, Brain Disorders, Neurosciences, Behavioral and Social Science, Substance Misuse, Clinical Trials and Supportive Activities, Prevention, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Good Health and Well Being, Adult, Delayed-Action Preparations, Double-Blind Method, Female, Humans, Injections, Intramuscular, Male, Middle Aged, Naltrexone, Narcotic Antagonists, Opioid-Related Disorders, Treatment Outcome, Young Adult, abstinence, clinical severity, extended-release naltrexone, opioid dependence, predictors, Public Health and Health Services, Substance Abuse
Abstract

ObjectivesOnce-monthly intramuscular extended-release naltrexone (XR-NTX) has demonstrated efficacy for the prevention of relapse in opioid dependence, providing an alternative to agonist or partial agonist maintenance (ie, methadone and buprenorphine). The question remains, for whom is this unique treatment most efficacious and can patient-treatment matching factors be identified?MethodsA moderator analysis was conducted on a previously reported 24-week, placebo-controlled, multisite, randomized controlled trial of XR-NTX (n = 126) versus placebo (n = 124) among recently detoxified opioid-dependent adults in Russia, which showed XR-NTX superior to placebo in proportion of opioid abstinent weeks. The moderator analysis examined a dichotomous indicator of good clinical response-achieving at least 90% of weeks abstinent over the 24-week trial. A series of logistic regression models were fit for this outcome as functions of treatment (XR-NTX vs placebo), each baseline moderator variable, and their interactions. The 25 baseline variables included demographics, clinical severity (Addiction Severity Index, SF-36, and Clinical Global Impression-Severity), functioning (EQ-5D), craving, and HIV serostatus (HIV+).ResultsMore XR-NTX patients achieved 90% abstinence (64/126, 51%) versus placebo (39/124, 31%; P = 0.002). There were no significant interactions between baseline variables and treatment. There was a significant main effect of Clinical Global Impression-Severity score (P = 0.02), such that higher severity score was associated with a lower rate of Good Clinical Response.ConclusionsThe absence of significant baseline by treatment interactions indicates that no patient-treatment matching variables could be identified. This suggests that XR-NTX was effective in promoting abstinence from opioids across a range of demographic and severity characteristics.

Published
2015

14. Perfect simulation using dominated coupling from the past with application to area-interaction point processes and wavelet thresholding

Author

Ambler, Graeme K. and Silverman, Bernard W.

Subjects
Statistics - Methodology, Statistics - Computation
Abstract

We consider perfect simulation algorithms for locally stable point processes based on dominated coupling from the past, and apply these methods in two different contexts. A new version of the algorithm is developed which is feasible for processes which are neither purely attractive nor purely repulsive. Such processes include multiscale area-interaction processes, which are capable of modelling point patterns whose clustering structure varies across scales. The other topic considered is nonparametric regression using wavelets, where we use a suitable area-interaction process on the discrete space of indices of wavelet coefficients to model the notion that if one wavelet coefficient is non-zero then it is more likely that neighbouring coefficients will be also. A method based on perfect simulation within this model shows promising results compared to the standard methods which threshold coefficients independently., Comment: 27 pages, 8 figures. Chapter 3 of "Probability and Mathematical Genetics: Papers in Honour of Sir John Kingman" (Editors N.H. Bingham and C.M. Goldie), Cambridge University Press, 2010

Published
2010

15. Comment: Bibliometrics in the Context of the UK Research Assessment Exercise

Author

Silverman, Bernard W.

Subjects
Statistics - Methodology, Computer Science - Digital Libraries, Physics - Physics and Society
Abstract

Research funding and reputation in the UK have, for over two decades, been increasingly dependent on a regular peer-review of all UK departments. This is to move to a system more based on bibliometrics. Assessment exercises of this kind influence the behavior of institutions, departments and individuals, and therefore bibliometrics will have effects beyond simple measurement. [arXiv:0910.3529], Comment: Published in at http://dx.doi.org/10.1214/09-STS285A the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Published
2009
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16. Perfect simulation for Bayesian wavelet thresholding with correlated coefficients

Author

Ambler, Graeme K. and Silverman, Bernard W.

Subjects
Statistics - Methodology, Statistics - Computation
Abstract

We introduce a new method of Bayesian wavelet shrinkage for reconstructing a signal when we observe a noisy version. Rather than making the common assumption that the wavelet coefficients of the signal are independent, we allow for the possibility that they are locally correlated in both location (time) and scale (frequency). This leads us to a prior structure which is analytically intractable, but it is possible to draw independent samples from a close approximation to the posterior distribution by an approach based on Coupling From The Past., Comment: 16 pages, originally Tech Report, University of Bristol, 2004

Published
2009

17. Perfect simulation of spatial point processes using dominated coupling from the past with application to a multiscale area-interaction point process

Author

Ambler, Graeme K. and Silverman, Bernard W.

Subjects
Statistics - Methodology, Statistics - Applications, Statistics - Computation
Abstract

We consider perfect simulation algorithms for locally stable point processes based on dominated coupling from the past. A version of the algorithm is developed which is feasible for processes which are neither purely attractive nor purely repulsive. Such processes include multiscale area-interaction processes, which are capable of modelling point patterns whose clustering structure varies across scales. We prove correctness of the algorithm and existence of these processes. An application to the redwood seedlings data is discussed., Comment: 15 pages, originally a Bristol University 2004 Tech Report

Published
2009

21. Empirical Bayes selection of wavelet thresholds

Author

Johnstone, Iain M. and Silverman, Bernard W.

Subjects
Mathematics - Statistics Theory, 62C12, 62G08 (Primary) 62G20, 62H35, 65T60 (Secondary)
Abstract

This paper explores a class of empirical Bayes methods for level-dependent threshold selection in wavelet shrinkage. The prior considered for each wavelet coefficient is a mixture of an atom of probability at zero and a heavy-tailed density. The mixing weight, or sparsity parameter, for each level of the transform is chosen by marginal maximum likelihood. If estimation is carried out using the posterior median, this is a random thresholding procedure; the estimation can also be carried out using other thresholding rules with the same threshold. Details of the calculations needed for implementing the procedure are included. In practice, the estimates are quick to compute and there is software available. Simulations on the standard model functions show excellent performance, and applications to data drawn from various fields of application are used to explore the practical performance of the approach. By using a general result on the risk of the corresponding marginal maximum likelihood approach for a single sequence, overall bounds on the risk of the method are found subject to membership of the unknown function in one of a wide range of Besov classes, covering also the case of f of bounded variation. The rates obtained are optimal for any value of the parameter p in (0,\infty], simultaneously for a wide range of loss functions, each dominating the L_q norm of the \sigmath derivative, with \sigma\ge0 and 0

Published
2005
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24. Needles and straw in haystacks: Empirical Bayes estimates of possibly sparse sequences

Author

Johnstone, Iain M. and Silverman, Bernard W.

Subjects
Mathematics - Statistics Theory, 62C12 (Primary) 62G08\sep 62G05 (Secondary)
Abstract

An empirical Bayes approach to the estimation of possibly sparse sequences observed in Gaussian white noise is set out and investigated. The prior considered is a mixture of an atom of probability at zero and a heavy-tailed density \gamma, with the mixing weight chosen by marginal maximum likelihood, in the hope of adapting between sparse and dense sequences. If estimation is then carried out using the posterior median, this is a random thresholding procedure. Other thresholding rules employing the same threshold can also be used. Probability bounds on the threshold chosen by the marginal maximum likelihood approach lead to overall risk bounds over classes of signal sequences of length n, allowing for sparsity of various kinds and degrees. The signal classes considered are ``nearly black'' sequences where only a proportion \eta is allowed to be nonzero, and sequences with normalized \ell_p norm bounded by \eta, for \eta >0 and 01. Simulations show excellent performance., Comment: Published by the Institute of Mathematical Statistics (http://www.imstat.org) in the Annals of Statistics (http://www.imstat.org/aos/) at http://dx.doi.org/10.1214/009053604000000030

Published
2004
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28. Change in non-abstinent WHO drinking risk levels and alcohol dependence: a 3 year follow-up study in the US general population

Author

Fertig, Joanne, Isenberg, Keith, McCann, David, Meulien, Didier, Meyer, Roger, O'Brien, Charles, Ryan, Megan, Silverman, Bernard, Trinquet, Francoise, Wong, Conrad, Zakine, Benjamine, Hasin, Deborah S, Wall, Melanie, Witkiewitz, Katie, Kranzler, Henry R, Falk, Daniel, Litten, Raye, Mann, Karl, O'Malley, Stephanie S, Scodes, Jennifer, Robinson, Rebecca L, and Anton, Raymond

Published
2017
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42. Child Criminal Exploitation: and County Lines drugs distribution: Understanding the impact of Covid-19. Report

Author

Brewster, Ben, Robinson, Grace, Silverman, Bernard, and Walsh, D.

Subjects
covid-19, law enforcement, child criminal exploitation, safeguarding, drug supply
Abstract

In March 2020, restrictions on movement and social contact were imposed across the UK following the spread of the Covid-19 virus. Many legitimate businesses either closed their doors entirely or made changes to enable their employees to work from home. Media reports also began to circulate suggesting that the illicit drugs trade was also making alternative arrangements for the supply of illegal drugs.

Published
2022

43. Globalization and Modern Slavery

Author

Landman, Todd, Silverman, Bernard W., Landman, Todd, and Silverman, Bernard W.

Abstract

This article presents a cross-national comparative analysis of the relationship between different dimensions of globalization and modern slavery. It argues that both the economic and political dimensions of globalization are strongly associated with lower levels of slavery prevalence. Recent estimates suggest there are more than 40 million people in some form of slavery and the United Nations has committed the world to ending this problem by 2030. Some argue that a race to the bottom, and the structure of economic incentives associated with globalization have contributed to the problem of modern slavery. Others argue that increased openness and the diffusion of values, the spread of democratic forms of rule, and the advance of human rights that come with globalization limit modern slavery. This article presents a preliminary empirical analysis of these arguments using data on slavery prevalence across more than 60 countries and various measures of economic and political globalization. The analysis shows that economic measures of globalization and higher levels of democracy are significantly related to lower levels of slavery prevalence, even after controlling for armed conflict and regional differentiation. In order to support these findings, the article examines the international law on slavery, definitions and conceptions of modern slavery, and comparative data on slavery prevalence modeled across indicators of economic and political globalization. It concludes with a discussion of the implications of these findings for the trade-offs between globalization and modern slavery.

Published
2022

45. Discussion on the paper by Neumann, Evett and Skerrett

Author

Aitken, C. G. G., Balding, David J., Silverman, Bernard, Risinger, Michael, Cole, Simon A., Stoney, David A., Hunt, Ian, Butt, Leonard G., Chacón, José E., Fieller, Nick, Fienberg, Stephen E., Gastwirth, J. L., Pan, Q., Giraldo, Ramón, Malaver, Carlos Alonso, Porcu, Emilio, Hotz, Thomas, Munk, Axel, Jandhyala, Venkata K., Fotopoulos, Stergios B., Kadane, Joseph B., Kaye, David H., Lauritzen, Steffen, Cowell, Robert, Graversen, Therese, Llewelyn, Stephanie, Mateu, Jorge, Ramotowski, Robert S., Robertson, Bernard, Vignaux, G. A., Berger, Charles E. H., Saks, Michael J., Votruba, Ashley M., Stern, Hal, Thompson, William C., and Welling, Max

Published
2012
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46. Veteran Adherence to Oral Versus Injectable Alcohol Use Disorder Medication Treatment

Author

Dorflinger, Charles, Carey, Heather, Roche, Jennifer, Burant, Christopher, LeHew, Colleen, Stewart, Hayden, Mitchell, Brian, Ayanga, Daniel, Walder, Annette, Cooler, Jordan, Wu, Shuwen, Frey, Theresa, Wenthur, Cody, Burk, Bradley, Ward, Alex, Clark, Brooke, Leung, Jonathan G, Kutzke, Jade, May, Heather P, Maehler, Colin, Kummer, Bethany, Hamner, Jennifer, Ariefdjohan, Merlin, Stutzman, Danielle L, Kicklighter, Jackson, Musco, Shaina, Singson, Grace Marielle, Pamintuan, Claire, Francisco, Janelle, Phan, Tam, Sevak, Rajkumar J, Rogan, Edward L, Kehoe, William A, Halliwell, Robert F, Kanter, Joel, Ryan, Holly, Patel, Krupa, Moukaddam, Nidal, Saunders, John, Fernandez, Luis, Shah, Asim, Wong, Jerry, Erowele, Goldina, Ruppelt, Sara, Tillery, Erika E, Baker, Joni, Loflin, Bobbi Jo, Gray, Emily, Conley, Giulia, Hembree, Amanda, Sterling, Jacyntha, Buige, Ashley N, Nguyen, Mimi, Harris, Suzanne C, Bhavsar, Nisha, Shah, Niti, Lee, Bona, Jeong, Seoyoung, Maroney, Megan E, Whalen, Eric D, Maloney, Rebecca M, Hagemeier, Nicholas E, Takamura, Kay, Rice, Amanda G, Furbish, Amelia B, Hebbard, Amy M, Ross, Clint, Cumbie, Holly, Hickey, J. Scott, Lalani, Falak, Mucha, Hannah, Saklad, Stephen R, Ma, Connie, Lee, Kelly, O’Donnell, Carolyn, Jackson, Sarah, Albee, Jennifer Nelson, Yapel, Ann, Nash, Cynthia, MacDonald, Danielle, King, Amelia, Schneiderhan, Mark, Hager, Keri, Bell Lynum, Karimah S, Nash, Abigail, Fu, Dong Jing, Ionescu, Dawn F, Sliwa, Jennifer Kern, Turkoz, Ibrahim, Dorson, Peter G, Canuso, Carla, Hsia, Stephanie, Nguyen, Julie, La, Amenda, Gruenberg, Katherine, MacDougall, Conan, Brockbank, Josh, Wong, Kara, Schultz, Erica, Pinchinat, Jessica, Thibeaux, Taylor M, McCants, Tamara, Lam, Jason, Diaz-Luna, Jose, Nguyen, Jennifer, Yokoyama, Melissa, Albiar, Olivia, Bradley, Bridget, Popish, Sarah J, Bounthavong, Mark, Lau, Marcos K, Wells, Daina L, Kay, Chad L, Harvey, Michael A, Himstreet, Julianne E, Christopher, Melissa L.D, Weeda, Erin R, McGraw, Daniel J, Gonzalez, Misty L, Pardo-Pfeiffer, Carlos, Rodeghiero, Abigail, Zhang, Lusi, Brown, Sarah Jane, Shan, Yuting, Lee, Adam M, Allen, Josiah D, Eum, Seenae, Bishop, Jeffrey R, Gonzalez, Rolando, Wahl, Kimberly, Naglich, Andrew, Ayson, Marl, Thomas, Kelan, Renfro, Mandy L, Loera, Lindsey J, Hill, Lucas G, Tirado, Carlos, Delerme, Dante, Dean, Taylor, Cullen, Marissa, Straley, Craig M, Carr, Chelsea N, Sun, Wendy, Alzouby, Hiba, Harris, Suzanne, Banker, Christopher M, Cook, Sarah F, Muir, Justin, Brodie, Daniel, Cremers, Serge, Bies, Robert R, Dzierba, Amy L, Moton, Robert, Saklad, Stephen, Ferris, Ryan E, Noble, Brie N, Hartung, Daniel M, Tjia, Jennifer, Furuno, Jon P, Ibrahim, Hajer, Duong, Dat, Malcolm, Benjamin, Gogineni, Hyma P, Zahn, Amy L, Alibeckoff, Chloe C, Connor, Sharon, Buranosky, Raquel, Livezey, Sabrina N, Daniell, Jessica, DeClercq, Josh, Choi, Leena, Zuckerman, Autumn, Shah, Nisha B, Kang, Ashley, Anksorus, Heidi, Troglin, Courtney G, Bouldin, J. Brooke, Schreiner, Shannon, McKinney, Mariah, Brown, Stacy D, Pond, Brooks B, Nguyen, Thu, Chin, Hyun Ji, Dambly, Nicolle, Augsten, Alberto, Themas, Samantha, Vicencio, Claudia, Horner, Caroline, Covert, Kelly, Lewis, Paul, Tharp, Jen, Sommi, Roger W, Bidollari, Ilda, Still, Daniel, Du, Yangchun, Yagoda, Sergey, Weiden, Peter J, Long, Katelyn, Vandiver, Emily, Johnson, Stephanie, Darazs, Brooks, Tillery, Erika, Silvia, Richard J, Lee, Kelly C, Bien, Ivy, Farahmand, Khodayar, Goldsmith, Hannah, Farrar, Mallory, Le, Hadley, Huynh, Quynh, Kim, Jane, McCausland, Kristen, Haider, Batool, White, Michelle K, O’Sullivan, Amy K, Rychlec, Kaitlin, Akerman, Sarah, Fratantonio, James, Saxon, Andrew, Thase, Michael E, Parikh, Sagar V, Maheshwari, Priya, Rothschild, Anthony J, Dunlop, Boadie W, DeBattista, Charles, Conway, Charles R, Forester, Brent P, Shelton, Richard C, Macaluso, Matthew, Brown, Krystal, Li, James, Jablonski, Michael R, Greden, John F, Gidal, Barry, Patsalos, Philip, Szaflarski, Jerzy, VanLandingham, Kevan, Critchley, David, Morrison, Gilmour, Weiss, Catherine, Meehan, Stine Rasmussen, Ouyang, John, Hobart, Mary, Rowe, William, Vanover, Kimberly E, Kane, John M, Satlin, Andrew, Durgam, Suresh, Davis, Robert E, Mates, Sharon, Correll, Christoph U, Tamminga, Carol, Rabon, Hannah, Smith, Jordan, Squires, Karrie, VanDaele, Madeline, Logan, Linda D, Zhang, Feng, DeSousa, Norberto J, Sallee, F. Randy, Lickrish, David, Incledon, Bev, Smith, Joseph L, Candon, Molly, Fadeyibi, Oluwatoyin, Connolly, K. Ryan, Lim, Suet, Neimark, Geoffrey, Mandell, David, Newcomer, John W, Silverman, Bernard, DiPetrillo, Lauren, Graham, Christine, Jiang, Ying, Simmons, Adam, Hopkinson, Craig, McDonnell, David, Kahn, Rene, King, Thomas R, Kando, Judith C, Pardo, Antonio, King, Thomas, Edwards, John B, Chen, Richard, Huo, Jason, Cutler, Andrew J, Fawver, Jay, Flanagan, Mindy, Smith, Thomas, Drouin, Michelle, Mirro, Michael, Yatham, Lakshmi N, Vieta, Eduard, McIntyre, Roger S, Jain, Rakesh, Earley, Willie R, Patel, Mehul, Brown, Lisa, Sehgal, Rahul, Yu, Ken, Al Habbab, Talal, Johnson, Holly, Dechairo, Bryan, Chen, Jack J, Singer, Carlos, Marder, Stephen R, Comella, Cynthia L, Shah, Chirag, Jimenez, Roland, Goodwin, Heather, Morrow, Gina, Tewksbury, Ashley, Waters, Kristin, Eggert, Robyn, Tallian, Kimberly, Sepulveda, Joe A, Rojas, Sarah, Sikand, Harminder, Sutera, Nathan, Iuppa, Courtney A, Nelson, Leigh Anne, Kriz, Carrie R, Burns, Nicole A, Lang, Shelby E, Elliott, Ellie S.R, Luong, Uyen, Elsobky, Teresa, Abelleira, Audrey, Awan, Sundus, Dume, Roberta, Dilich, Adam, Anderson, Mikaela, Park, Priscilla, Moehnke, Austin, Aebi, Cydreese, Feighner, Lydia, Vernon, Martein, Streit, Jessica, Kauer, Jill, Lemons, Amber, Diefenderfer, Lauren A, Moon, Joseph, Smith, Jordan O, Franck, Hugh A, Randle, C. Hope, Hedgepeth Kennedy, M. Lindsey, James, Shannon, Coplan, Benjamin, Ruekert, Laura, Puri, Shilpa, Yeleti, Ramya, Cathright, Sela, Coveart, Stephanie, Regen, Sloan, Stewart, Shannon, Stratton, Miranda, Haight, Robert, Hoeft, Dawn, Bishop, Danielle, De La Cruz, Austin, Aurelio, Mariellee, Prestenback, Autumn, Price, Paul L, Makuch, Christian X, Salisbury, Nicole M, Koch, Jessa, Palmer, Melissa C, Chace, Ashley, Price, Cristofer, Putney, Jessica, Khan, Fatima, Huntsman, Amanda, Hutcheson, Brooklyn, Hightower, Sharae, Ott, Carol A, Scott, Catherine, Balser, Amanda, Karas, Andrew, Hsu, Michael, Malin, Maureen, Robert, Sophie, Leung, Edwina, Mierzwa, Mark, Kay, Lehua, Bowling, Dalia, Allen, Sabrina, Weingartner, Melissa, Ventricelli, Daniel, Santa, Heather, Nowalk, Alexandra, Downs, George, Le, N, Mospan, C, Burton, Caitlin, Mathys, Monica, Gutierrez, Erika, Ali, Sarah, Chang, Joni, Holzum, Dorothy N, Griffith, June A, Morgan, Katherine T, Laguado, S. Andrea, Steavenson, Rosana, Mehvar, Mina, Bechtold, Carson L, Kim, Heesoo, Dopheide, Julie Ann, Anderson, Lindsey, Kleyn, Thomas, DiRenzo, Baely, Walroth, Todd, Adamczyk, Raechel, Kendrick, Jenna, Thomas, Christopher, Mailloux, Lindsay M, Haas, Matthew T, Larew, Janel M, Stohs, Matthew E, DeJongh, Beth M, Satow, Britney A, Titus-Lay, Erika, Clark, Suzanne, Dike, Dozie, Pinsonnault, John, Gamber, Joy, Roseberry, Samantha, Mitchell, Brian G, Nguyen, Mai, Willborn, Robert, Roche-Desilets, Jennifer, Dailey, Alison, Ignatowski, Michael, Routhieaux, Melanie, Kawsky, Jaclyn, Stratman, Jennifer, Corboy, Alexander, Norberte, Cherisse, Gross, Tonya, Daniel, Jeremy, Tran, Aaron N, Wary, Megan, Kostric, Anna, Schaller, Samantha, Shin, Yunjung, Maroney, Megan, Hampton, Chanese, Krieger, Kelly, Hartwig, Rachel, Thamawatanakul, Rani, Wilbur, Eileen, Shofner, Steven, Butler, Justin, Moody, Breanna, Davis, Elizabeth, Hofammann, Elizabeth, Liveoak, Katie, Mitchell, McKinley, Lebrecht, Morgan, Hawley, Joanne, Smigiel, Joe, Radtke, Michelle, Walkerly, Autumn, King, Morgan, Wakefield, Andrew, Downs, Mona, Hudson, Nancy, Burghart, Steven, Reynoldson, Jill, Booth, Macey, Ishino, Risa, Lee, Tammie, Wright, Melissa, Boggs, Angela, Kaur, Rajandeep, Patel, Parth, Smith, Thomas R, Theriault, Sarah, Okusaga, Olaoluwa, Reinstatler, Kristina, Hawk, David, Rush, Stephen, Vachuska, Michaela, Powell, Elizabeth, Lacro, Jonathan, Chehovich, Charisse, Demler, Tammie Lee, deRosas, Andrea, Trigoboff, Eileen, Taylor, Aminah, Singh, Divita, Burdge, Gary, Reynolds, Jamie, Kriz, Carrie, Burns, Nicole, Sommi, Roger, Yabs, Melanie, Sarpal, Deepak, Fabian, Tanya, Bartos, Lindsey, Tallman, Kristin, Johnson, Dara, Taylor, Daniel, Hogan, Brendon, Kwan, Peter, Bast, Aubree, Sanchez, Apolinar, Maglalang, Patricia D, Scheible, Mahli M, Skurat, McKenzie K, Jackson, Sarah K, Belknap, Jazmin N, Ostfeld, Larissa, Fahy, Megan, Nguyen, Tuyen, Bui, Hoa, Little, Meg M, Seifert, Randall, Maister, Ashley, McCarthy, Caitlin, Burger, Emily, Mullen, Sandy, Cusimano, Joseph, VandenBerg, Amy, Duquette, Meghan, Guttenberg, Viktoria, Matsunaga, Candace, White, Shelby, Richard, Andrea, Jones, Jessica, Morgan, Kaitlyn, Kelsey, Taylor, Sartin, Carrie J, Rickert, Leah, Ferrill, Laine, Vallabh, Anuja, Jhawar, Archana, Barrett, Monica, Ward, Sarah, Colvard, Michelle, Phan, Joann, Preinitz, Jennifer, Ellis, Marcus, Gibu, Matthew, Kivlehan, Audrey, Chiulli, Dana, Rhodes, Kirby, Brewer, T. 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Greg, Jenne, Victoria, Wood, Joshua, Trout, Megan, Beck, Niels C, Pals, Hale, Frazier, Erica, Cavaliere, Vincent S, DiPaula, Bethany A, Glassman, Matthew, Wehring, Heidi J, Mackowick, Marie, Park, Jaeboon, Love, Raymond C, Kelly, Deanna L, Newbold, Mervan, Fioravanti, Nicole, Brown, Matthew J, Casey, Emily R, Nadkarni, Neha, Oh, Sarah, Patel, Romi, Riedley, Taylor, Mathis, Erin, Fadden, Patrick, Madden, Cristina, Schneiderhan, Mark E, Hintsala, Madelynn, Stenehjem, David, Rufus, Krystal M, Elhakim, Athar, Zolezzi, Monica, Elamin, Waad M, Homs, Shorouk, Qubaiah, Iman K, Mahmoud, Doaa A, Tawfik, Enge M, Fritz, Merideth, Alman, Brianne, Wartman, Carolanne, Clark, Megan, Ott, Carol, Rainey, Carly A, Mann, Elise, Modany, Madison, Schoettmer, Amanda, Buckley, Tiffany, Patwardhan, Vidisha, Boyle, Cynthia J, Gardner, Kristen N, Hamby, Kerri, Ngo, Nichol, Kim, Lila Q, Chen, Steven W, Mosley, Scott, Dadiomov, David, Goldstone, Lisa W, DellaVecchia, Matthew J, Cauffield, Jacintha, Grim, Hannah, Eschler, Anna, Aladeen, Traci, Blondell, Richard, Capote, Horacio, Rainka, Michelle, Lee, Ashley, Shelley, Maria, Stoncius, Kristina, Berry, Jonathan, Doroudgar, Shadi, Johnson, Kayla, Pouliot, Jonathon, Rarrick, Christine, Hebbard, Amy, Shikwana, Sara, Koller, Katherine, Reed, Ellen, Hoefling, Austin D, Miles, Talia M, Ansara, Elayne D, Titova, Viktoriya, Kandela, Dalea, Douglass, Amber R, Ehrhard, Kimberly, Stanley, Madison, Ganihong, Carissa, Liu, Mei T, Bohnenberger, Kristin, Zuber, Nicole, Plunkett, McKenzie, Axelrod, Chelsey, Lightfoot, Myaa, Moore, Miranda, Crouse, Ericka, Olazo, Muriel, Taormina, Stephanie, Reiss, Rebecca, Denno, Nora, Johnson, Braniesha, Wilkening, Gwendolyn Lucy, Andrews, Haley, Neumeister, Kristen, Murad, Muhammad Ubaidullah, Reid, Bailey, Franco, Ashley, Stutzman, Danielle, Jorgensen, Jennifer, Basit, Saadia, Jensen, Aimee, Nadal, Celeste N, Truong, Quynh-Nhu, Jameson, Melanie, Knox, Erin D, Bota, Robert G, Wieruszewski, Patrick M, and Leung, Sarah B

Subjects
CPNP Foundation Strategic Goals Award Finalists, Therapeutic Case Report Abstracts, Original Research Abstracts, Therapeutic Case Report Award Finalists, Article, Encore Presentation Abstracts, Work in Progress Abstracts, Neuropsychology and Physiological Psychology, Original Research Award Finalists, Research Trainee Award Finalists, Pharmacology (medical), Neurology (clinical), General Pharmacology, Toxicology and Pharmaceutics, Innovative Practices Abstracts, Innovative Practices Award Finalists, Scientific Posters
Published
2020

47. Child Criminal Exploitation and County Lines Drug Distribution: Understanding the Impact of Covid-19

Author

Brewster, Ben, Robinson, Grace, Silverman, Bernard, and Walsh, D.

Subjects
Drug Offending, County Lines, Covid-19, Crime Victims
Abstract

Executive summary In March 2020, restrictions on movement and social contact were imposed across the UK following the spread of the Covid-19 virus. Many legitimate businesses either closed their doors entirely or made changes to enable their employees to work from home. Media reports also began to circulate suggesting that the illicit drugs trade was also making alternative arrangements for the supply of illegal drugs. Research conducted for this UKRI Rapid Response funded project COVID-19 and Child Criminal Exploitation: Closing Urgent Knowledge and Data Gaps on the Implications of Pandemic for County Lines sought to understand the impact of the Covid-19 pandemic on the County Lines model of illegal drug supply, and in particular on the resulting criminal exploitation of children. In this report we highlight [1] practitioner-perceived changes and shifts in the County Lines drug supply model, [2] the impact of pandemic restrictions on law enforcement activities aimed at tackling County Lines activity, and [3] the pandemic’s effect on efforts to support and safeguard children and young people criminally exploited through County Lines, or at risk of it. Overall, our research participants indicated that they believed that the pandemic had induced shifts to County Lines that reflected the ongoing evolution of the drug supply model, in addition to responsive shifts directly resulting from pandemic restrictions on travel. Our participants reflected that the pandemic had caused them to reconsider their understandings of the modus operandi of offending and behaviour linked to County Lines, as the pandemic in some cases foregrounded tactics and practices that were different from those considered to be (stereo)typical. In our study, practitioners clearly articulated the impact of Covid-19 restrictions on some young people’s vulnerability to exploitation, on the way in which police and frontline practitioners respond to County Lines and CCE, and on the way in which illegal drugs were being moved and sold. The research methodology included ! A review of published sources (including academic publications, media articles and grey literature) ! Qualitative interviews with 46 practitioners across England. Participants included: ! practitioners working in frontline (statutory or non-governmental) service provision with young people currently or previously exploited in County Lines, or considered at-risk of exploitation; ! law enforcement officers with portfolio responsibility for policing County Lines and the illegal supply of drugs; ! practitioners from law enforcement, non-governmental or other statutory bodies working in analytical roles with responsibility for County Lines and illegal drug supply. Interviews conducted with key frontline and analytical practitioners aimed to understand their experiences and the way they perceived the impact of the pandemic. Through our research we aimed to investigate possible and sudden shifts in perpetrator behaviours, and the development of new safeguarding and support challenges related to County Lines and Child Criminal Exploitation (CCE). Data collection took place between July 2020 and September 2021. We identified several challenges County Lines supply methods Law enforcement agencies asserted that while they believed that the overall volume of County Lines activity was consistent with pre-pandemic levels, they had perceived adaptations to the actual supply methods used by County Lines networks. For instance, significant reductions in public footfall meant that it was no longer possible to hide in plain sight on the country’s rail networks, increasing County Lines’ reliance on moving product by road. Police also highlighted to us that increased enforcement activity on the rail networks may have been a factor that drove this change during the initial months of lockdown. There were also suggestions that restrictions on travel may also have increased the risk of some vulnerable adults having their properties cuckooed and taken over. Others suggested that exploited young people were being required to remain in drug market locations for longer as drugs were being moved at lower frequency, but in higher quantities. There were also indications of shifts towards the recruitment and exploitation of young people already in drug market areas, further reducing the need for frequent travel. Other tactics were also highlighted as mechanisms to avoid detection by law enforcement, such as the use of supermarket carparks as deal locations, enabling dealers to co-locate with customer shopping routines and use keyworker disguises. Safeguarding capacity Face-to-face contact between young people and various professionals was reduced, caused both by a lack of resources and social distancing restrictions (imposed to suppress infection rates), often being replaced either by doorstep or telephone communications. This had the effect of diminishing professionals’ ability to easily identify signs of exploitation. School closures also created challenges, contributing to overall reductions in referrals for young people potentially at risk of exploitation, heightening concerns that many young people were confined to dangerous and exploitative situations both within and away from their homes. Exploitation risk Participants believed that drug supply lines persisted at pre-pandemic levels as Covid-19 did little to affect the demand for class-A substances such as heroin and crack cocaine. This ensured continued demand for young people to move and distribute drugs on behalf of those organising the drug supply. Safeguarding practitioners emphasised these concerns, and suggested young people were at increased risk of grooming through social media, as they spent longer online due to school closures and restrictions on face-to-face social activity. Concern was also expressed regarding young people’s mental health, as practitioners began to receive reports of substance misuse, isolation, and self-harm.

Published
2022

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