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Mixed methods to assess the use of rare illicit psychoactive substances: a case study

  • Eric Janssen ORCID logo EMAIL logo , Mike Vuolo , Clément Gérome and Agnès Cadet-Taïrou
Published/Copyright: February 24, 2021
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Epidemiologic Methods
From the journal Epidemiologic Methods Volume 10 Issue 1

Abstract

This article presents original mixed method research to describe the use of rare illicit psychoactive substances, with special emphasis on crack cocaine in France. We first introduce a unique monitoring system committed to the observation of hard-to-reach populations. Qualitative findings rely, among others, on perennial ethnographic studies and field professionals’ knowledge to provide guidance to estimate the number of crack cocaine users. We then rely on a set of multilevel capture-recapture estimators, a statistical procedure to indirectly estimate the size of elusive populations. Since prior field evidence suggests an increasing diversity in crack cocaine users’ profiles, we provide a measure of heterogeneity to assess which estimator better fits the data. The calculated estimates are then critically reviewed and debated in light of the previously gathered information. Our results uncover both individual and institutional heterogeneity and suggest that the spread of crack cocaine in France initiated earlier than originally thought. Our case study underlines the need for field-driven assessments to put quantitative results into perspective, a necessary step to tailor efficient health policy responses.

Keywords: capture-recapture; crack cocaine; heterogeneity; mixed methods; multilevel models
Corresponding author: Eric Janssen, PhD, French Monitoring Centre on Drugs and Drug Addiction (Observatoire Français des Drogues et Toxicomanies - OFDT), 69 rue de Varenne, 75007 Paris, France, Phone: +33 1 41 62 77 44, Fax: +33 1 41 62 77 00, E-mail:

Acknowledgments

We wish to thank all the TREND coordinators and their staff, health professionals and interviewees who willingly participated to the study. We are grateful to Christophe Palle (French Monitoring Centre on Drugs and Drug Addictions, OFDT) for updating the RECAP databases, and to Thomas Seyler (European Monitoring Centre on Drugs and Drug Addictions, EMCDDA) for providing the opportunity to expose a first draft of our research during the PDU Expert Meeting held in Lisbon, 2019. We are grateful to an anonymous referee and the Editor for their valuable comments to improve the manuscript.

  1. Research funding: None declared.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Not applicable.

  5. Ethical approval: The treatment centers survey has gained approval of the National Data Protection Authority.

References

Anderson, D., K. Burnham, and G. White. 1994. “AIC Model Selection in Overdispersed Capture-Recapture Data.” Ecology 75 (6): 1780–93. https://doi.org/10.2307/1939637.Search in Google Scholar

Bao, L., A. E. Raftery, and A. Reddy. 2015. “Estimating the Sizes of Populations at Risk of HIV Infection from Multiple Data Sources Using a Bayesian Hierarchical Model.” Statistics and Its Interface 8 (2): 125–36. https://doi.org/10.4310/SII.2015.v8.n2.a1.Search in Google Scholar

Beck, N., and J. N. Katz. 2017. “Random Coefficient Models for Time-Series—Cross-Section Data: Monte Carlo Experiments.” Political Analysis 15 (2): 182–95. https://doi.org/10.1093/pan/mpl001.Search in Google Scholar

Beletsky, L., K. D. Wagner, J. Arredondo, L. Palinkas, C. M. Rodríguez, N. Kalic, Natasha-Ludwig-Barron, and S. A. Strathdee. 2016. “Implementing Mexico’s “Narcomenudeo” Drug Law Reform: a Mixed Methods Assessment of Early Experiences Among People Who Inject Drugs.” Journal of Mixed Methods Research 10 (4): 384–401. https://doi.org/10.1177/1558689815575862.Search in Google Scholar

Bell, A., M. Fairbrother, and K. Jones. 2019. “Fixed and Random Effects Models: Making an Informed Choice.” Quality and Quantity 53 (2): 1051–74. https://doi.org/10.1007/s11135-018-0802-x.Search in Google Scholar

Biemer, P. P. 2010. “Total Survey Error: Design, Implementation, and Evaluation.” Public Opinion Quarterly 74 (5): 817–48. https://doi.org/10.1093/poq/nfq058.Search in Google Scholar

Bishop, Y. M. M., S. E. Fienberg, and P. W. Holland 1975. “Estimating the Size of a Closed Population” In Discrete Multivariate Analysis: Theory and Practice, 227–56. Cambridge, MA: MIT Press.Search in Google Scholar

Böhning, D., and P. G. M. van der Heijden. 2009. “A Covariate Adjustment for Zero-Truncated Approaches to Estimating the Size of Hidden and Elusive Populations.” Annals of Applied Statistics 3 (2): 595–610. https://doi.org/10.1214/08-AOAS214.Search in Google Scholar

Bouchard, M., and P. Tremblay. 2005. “Risks of Arrest across Drug Markets: a Capture-Recapture Analysis of “Hidden” Dealer and User Populations.” Journal of Drug Issues 35 (4): 733–54. https://doi.org/10.1177/002204260503500404.Search in Google Scholar

Box, G. E. P., and N. R. Draper 1987. Empirical Model-Building and Response Surfaces, 2nd ed. Probability & Mathematical Statistics. New York: John Wiley & Sons.Search in Google Scholar

Burnham, K. P., D. R. Anderson, G. C. White, C. Brownie, and K. H. Pollock 1987. “Design and Analysis Methods for Fish Survival Experiments Based on Release‐Capture.” In American Fisheries Society Monograph 5. Maryland, U.S.A.: American Fisheries Society.Search in Google Scholar

Cadet-Taïrou, A., C. Reynaud-Maurupt, J. M. Costes, and C. Palle. 2010. “Quantitative surveys in hard-to-reach populations: the experience of the ‘Observatoire français des drogues et des toxicomanies’.” Methodological Innovations Online 5 (2): 76–92. https://doi.org/10.4256/mio.2010.0020.Search in Google Scholar

Charmaz, K. 2006. Constructing Grounded Theory: A Practical Guide through Qualitative Analysis, In 208. Thousand Oaks: Sage.Search in Google Scholar

Cox, D. R., and E. J. Snell. 1989. Analysis of Binary Data, 2nd ed. Boca Raton: Chapman & Hall/CRC.Search in Google Scholar

De Oliveira, L. G., L. Pereira Barroso, C. Magalhães Silveira, Z. Van Der Meer Sanchez, J. De Carvalho Ponce, L. José Vaz, and S. Aparecida Nappo. 2009. “Neuropsychological Assessment of Current and Past Crack Cocaine Users.” Substance Use & Misuse 44 (13): 1941–57. https://doi.org/10.3109/10826080902848897.Search in Google Scholar

Deeks, J. J., J. P. T. Higgins, and D. G. Altman. 2019. “Chapter 10: Analysing Data and Undertaking Meta-Analyses.” In Cochrane Handbook for Systematic Reviews of Interventions Version 6.0 (Updated July 2019), edited by J. P. T. Higgins, J. Thomas, J. Chandler, M. Cumpston, T. Li, M. J. Page, and V. A. Welch. Chichester, UK: John Wiley & Sons.10.1002/9781119536604.ch10Search in Google Scholar

EMCDDA. 2019. European Drug Report 2018: Trends and Developments. Luxembourg: Publications Office of the European Union.Search in Google Scholar

Evertsson, N. 2017. “A Nested Analysis of Electoral Donations.” Journal of Mixed Methods Research 11 (1): 77–98. https://doi.org/10.1177/1558689815585208.Search in Google Scholar

Falck, R. S., J. Wang, R. G. Carlson, and H. A. Siegal. 2000. “Crack-cocaine Use and Health Status as Defined by the SF-36.” Addictive Behaviors 25 (4): 579–84. https://doi.org/10.1016/S0306-4603(99)00040-4.Search in Google Scholar

Falck, R. S., J. Wang, H. A. Siegal, and R. G. Carlson. 2004. “The Prevalence of Psychiatric Disorder Among a Community Sample of Crack Cocaine Users: An Exploratory Study with Practical Implications.” The Journal of Nervous and Mental Disease 192 (7): 503–7. https://doi.org/10.1097/01.nmd.0000131913.94916.d5.Search in Google Scholar

Feldman, J. M., S. Gruskin, B. A. Coull, and N. Krieger. 2017. “Quantifying Underreporting of Law-Enforcement-Related Deaths in United States Vital Statistics and News-Media-Based Data Sources: A Capture-Recapture Analysis.” PLoS Medicine 14 (10): e1002399. https://doi.org/10.1371/journal.pmed.1002399.Search in Google Scholar

Franklin, A., D. Anderson, and K. Burnham. 2002. “Estimation of Long-Term Trends and Variation in Avian Survival Probabilities.” Journal of Applied Statistics 29: 267–87. https://doi.org/10.1080/02664760120108719.Search in Google Scholar

Gimenez, O., E. Cam, and J.-M. Gaillard. 2018. “Individual Heterogeneity and Capture–Recapture Models: What, Why and How?” Oikos 127 (5): 664–86. https://doi.org/10.1111/oik.04532.Search in Google Scholar

Hajat, C. 2011. “An Introduction to Epidemiology.” Methods in Molecular Biology 713: 27–39. https://doi.org/10.1007/978-1-60327-416-6_3.Search in Google Scholar

Hay, G., A. Rael dos Santos, H. Reed, and V. Hope. 2019. “Estimates of the Prevalence of Opiate Use And/or Crack Cocaine Use, 2016/17: Sweep 13 Report.” In Liverpool: Public Health England. Liverpool: Liverpool John Moores University.Search in Google Scholar

Hay, G., and F. Smit. 2003. “Estimating the Number of Drug Injectors from Needle Exchange Data.” Addiction Research and Theory 11 (4): 235–43. https://doi.org/10.1080/1606635031000135622.Search in Google Scholar

Headley, M. G., and V. L. P. Clark. 2020. “Multilevel Mixed Methods Research Designs: Advancing a Refined Definition.” Journal of Mixed Methods Research 14 (2): 145–63. https://doi.org/10.1177/1558689819844417.Search in Google Scholar

Higgins, J. P., and S. G. Thompson. 2002. “Quantifying Heterogeneity in a Meta-Analysis.” Statistics in Medicine 21 (11): 1539–58. https://doi.org/10.1002/sim.1186.Search in Google Scholar

Higgins, J. P., S. G. Thompson, J. J. Deeks, and D. G. Altman. 2003. “Measuring Inconsistency in Meta-Analyses.” BMJ 327 (7414): 557–60. https://doi.org/10.1136/bmj.327.7414.557.Search in Google Scholar

Hook, E. B., and R. R. Regal. 1995. “Capture–Recapture Methods in Epidemiology: Methods and Limitations.” Epidemiologic Reviews 17 (2): 243–64. https://doi.org/10.1093/oxfordjournals.epirev.a036192.Search in Google Scholar

Hox, J. 2010. Multilevel Analysis. Techniques and Applications, 2nd ed. London: Lawrence Erlbaum.10.4324/9780203852279Search in Google Scholar

Janssen, E. 2016. “Estimating the Number of Heroin Users in Metropolitan France Using Treatment Centres Data. An Exploratory Analysis.” Substance Use & Misuse 52 (5): 683–7. https://doi.org/10.1080/10826084.2016.1245340.Search in Google Scholar

Janssen, E., A. Cadet-Taïrou, C. Gérome, and M. Vuolo. 2020. “Estimating the Size of Crack Cocaine Users in France: Methods for an Elusive Population with High Heterogeneity.” International Journal of Drug Policy 76 (2): e102637. https://doi.org/10.1016/j.drugpo.2019.102637.Search in Google Scholar

Jeyam, A., R. S. McCrea, T. Bregnballe, M. Frederiksen, and R. Pradel. 2018. “A Test of Positive Association for Detecting Heterogeneity in Capture for Capture–Recapture Data.” Journal of Agricultural, Biological, and Environmental Statistics 23 (1): 1–19. https://doi.org/10.1007/s13253-017-0315-4.Search in Google Scholar

Jones, H. E., N. J. Welton, A. E. Ades, M. Pierce, W. Davies, B. Coleman, T. Millar, and M. Hickman. 2016. “Problem Drug Use Prevalence Estimation Revisited: Heterogeneity in Capture–Recapture and the Role of External Evidence.” Addiction 111 (3): 438–47. https://doi.org/10.1111/add.13222.Search in Google Scholar

Kopp, P. and P. Fenoglio. 2001. Calculating the Social Cost of Illicit Drugs: Methods and Tools for Estimating the Social Cost of the Use of Psychotropic Substances. Strasbourg: Council of Europe.Search in Google Scholar

Larsen, K. and J. Merlo. 2005. “Appropriate Assessment of Neighborhood Effects on Individual Health: Integrating Random and Fixed Effects in Multilevel Logistic Regression.” American Journal of Epidemiology 161 (1): 81–8. https://doi.org/10.1093/aje/kwi017.Search in Google Scholar

Lucas, S. R. 2014. “An Inconvenient Dataset: Bias and Inappropriate Inference with the Multilevel Model.” Quality and Quantity 48 (3): 1619–49. https://doi.org/10.1007/s11135-013-9865-x.Search in Google Scholar

Melsen, W. G., M. C. Bootsma, M. M. Rovers, and M. J. Bonten. 2014. “The Effects of Clinical and Statistical Heterogeneity on the Predictive Values of Results from Meta-Analyses.” Clinical Microbiology and Infection 20 (2): 123–9. https://doi.org/10.1111/1469-0691.12494.Search in Google Scholar

Merlo, J., M. Yang, B. Chaix, J. Lynch, and L. Rastam. 2005. “A Brief Conceptual Tutorial on Multilevel Analysis in Social Epidemiology: Investigating Contextual Phenomena in Different Groups of People.” Journal of Epidemiology & Community Health 59 (9): 729–36. https://doi.org/10.1136/jech.2004.023929.Search in Google Scholar

Miles, M. B., A. M. Huberman, and J. Saldaña. 2019. Qualitative Data Analysis: A Methods Sourcebook, 4th ed. Thousand Oaks: Sage.Search in Google Scholar

Mittlböck, M., and H. Heinzl. 2006. “A Simulation Study Comparing Properties of Heterogeneity Measures in Meta-Analyses.” Statistics in Medicine 25 (24): 4321–33. https://doi.org/10.1002/sim.2692.Search in Google Scholar

Morgan, D. L. 2007. “Paradigms Lost and Pragmatism Regained: Methodological Implications of Combining Qualitative and Quantitative Methods.” Journal of Mixed Methods Research 1 (1): 48–76. https://doi.org/10.1177/2345678906292462.Search in Google Scholar

Papadatou, E., R. Pradel, M. Schaub, D. Dietrich, H. Geiger, C. Ibañez, G. Kerth, A. Popa-Lisseanu, W. Schorcht, T. Jana, and O. Gimenez. 2012. “Comparing Survival Among Species with Imperfect Detection Using Multilevel Analysis of Mark—Recapture Data: A Case Study on Bats.” Ecography 35 (2): 153–61. https://doi.org/10.1111/j.1600-0587.2011.07084.x.Search in Google Scholar

Patsopoulos, N. A., E. Evangelou, and J. P. A. Ioannidis. 2008. “Sensitivity of Between-Study Heterogeneity in Meta-Analysis: Proposed Metrics and Empirical Evaluation.” International Journal of Epidemiology 37 (5): 1148–57. https://doi.org/10.1093/ije/dyn065.Search in Google Scholar

Payne, E. H., M. Gebregziabher, J. W. Hardin, V. Ramakrishnan, and L. E. Egede. 2018. “An Empirical Approach to Determine a Threshold for Assessing Overdispersion in Poisson and Negative Binomial Models for Count Data.” Communications in Statistics - Simulation and Computation 47 (6): 1722–38. https://doi.org/10.1080/03610918.2017.1323223.Search in Google Scholar

Pearce, L. D. 2002. “3. Integrating Survey and Ethnographic Methods for Systematic Anomalous Case Analysis.” Sociological Methodology 32 (1): 103–32. https://doi.org/10.1111/1467-9531.00113.Search in Google Scholar

Pearce, L. D. 2012. “Mixed Methods Inquiry in Sociology.” American Behavioral Scientist 56 (6): 829–48. https://doi.org/10.1177/0002764211433798.Search in Google Scholar

Pluye, P., and Q. N. Hong. 2014. “Combining the Power of Stories and the Power of Numbers: Mixed Ethods Research and Mixed Studies Reviews.” Annual Review of Public Health 35 (1): 29–45. https://doi.org/10.1146/annurev-publhealth-032013-182440.Search in Google Scholar

Prangnell, A., H. Dong, P. Daly, M. J. Milloy, T. Kerr, and K. Hayashi. 2017. “Declining Rates of Health Problems Associated with Crack Smoking during the Expansion of Crack Pipe Distribution in Vancouver, Canada.” BMC Public Health 17 (1): 163. https://doi.org/10.1186/s12889-017-4099-9.Search in Google Scholar

Rabe-Hesketh, S., and A. Skrondal. 2012. Multilevel and Longitudinal Modeling Using Stata, volume 1: Continuous Responses, 3rd ed. College Station, TX: Stata Press.Search in Google Scholar

Raudenbush, S. W., and A. S. Bryk. 2002. Hierarchical Linear Models: Applications and Data Analysis Methods Advanced Quantitative Techniques in the Social Sciences. Thousand Oaks: Sage.Search in Google Scholar

Rose, J. P., G. D. Wylie, M. L. Casazza, and B. J. Halstead. 2018. “Integrating Growth and Capture–Mark–Recapture Models Reveals Size-dependent Survival in an Elusive Species.” Ecosphere 9 (8): e02384. https://doi.org/10.1002/ecs2.2384.Search in Google Scholar

Roy, E., P. Leclerc, C. Morissette, N. Arruda, C. Blanchette, K. Blouin, and M. Alary. 2013. “Prevalence and Temporal Trends of Crack Injection Among Injection Drug Users in Eastern Central Canada.” Drug and Alcohol Dependence 133 (1): 275–8. https://doi.org/10.1016/j.drugalcdep.2013.05.013.Search in Google Scholar

Schofield, M. R., and R. J. Barker. 2014. “Hierarchical Modeling of Abundance in Closed Population Capture–Recapture Models under Heterogeneity.” Environmental and Ecological Statistics 21 (3): 435–51. https://doi.org/10.1007/s10651-013-0262-3.Search in Google Scholar

Simeone, R., W. Nottingham, and L. Holland. 1993. “Estimating the Size of a Heroin Using Population: an Examination of the Use of Treatment Admissions Data.” International Journal of the Addictions 28 (2): 107–28. https://doi.org/10.3109/10826089309039618.Search in Google Scholar

Small, M. L. 2011. “How to Conduct a Mixed Ethods Study: Recent Trends in a Rapidly Growing Literature.” Annual Review of Sociology 37 (1): 57–86. https://doi.org/10.1146/annurev.soc.012809.102657.Search in Google Scholar

Snijders, T. A. B., and R. J. Bosker. 2012. Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling, 2nd ed. Thousand Oaks, CA: Sage.Search in Google Scholar

Story, A., G. Bothamley, and A. Hayward. 2008. “Crack Cocaine and Infectious Tuberculosis.” Emerging Infectious Diseases 14 (9): 1466–9. https://doi.org/10.3201/eid1409.070654.10.3201/eid1409.070654Search in Google Scholar PubMed PubMed Central

Tashakkori, A., and J. W. Creswell. 2007. “Editorial: The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (1): 3–7. https://doi.org/10.1177/2345678906293042.Search in Google Scholar

Tutz, G. 2018. “Binary Response Models with Underlying Heterogeneity: Identification and Interpretation of Effects.” European Sociological Review 34 (2): 211–21. https://doi.org/10.1093/esr/jcy001.Search in Google Scholar

Vaissade, L., and S. Legleye. 2009. “Capture-Recapture Estimates of the Local Prevalence of Problem Drug Use in Six French Cities.” The European Journal of Public Health 19 (1): 32–7. https://doi.org/10.1093/eurpub/ckn126.Search in Google Scholar

Wright, D. 2007. “Extra-Binomial Variation in Multilevel Logistic Models with Sparse Structure.” British Journal of Mathematical and Statistical Psychology 50: 21–9. https://doi.org/10.1111/j.2044-8317.1997.tb01099.x.Search in Google Scholar

Zelterman, D. 1988. “Robust Estimation in Truncated Discrete Distribution with Application to Capture–Recapture Experiments.” Journal of Statistical Planning and Inference 18 (2): 225–37. http://doi.org/10.1016/0378-3758(88)90007-9.10.1016/0378-3758(88)90007-9Search in Google Scholar
Received: 2020-08-17
Revised: 2021-01-29
Accepted: 2021-02-03
Published Online: 2021-02-24

© 2021 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/em-2020-0031
Keywords for this article
capture-recapture; crack cocaine; heterogeneity; mixed methods; multilevel models

Articles in the same Issue

  1. Research Articles
  2. Determinants of birth-intervals in Algeria: a semi-Markov model analysis
  3. A simplified approach to bias estimation for correlations
  4. Gamma frailty model for survival risk estimation: an application to cancer data
  5. Analysis for transmission of dengue disease with different class of human population
  6. Quantifying the influence of location of residence on blood pressure in urbanising South India: a path analysis with multiple mediators
  7. Mixed methods to assess the use of rare illicit psychoactive substances: a case study
  8. Reliability of fetal–infant mortality rates in perinatal periods of risk (PPOR) analysis
  9. Sampling from networks: respondent-driven sampling
  10. Reviewer Acknowledgment
  11. Reviewer acknowledgment
  12. Tutorial
  13. A guide to value of information methods for prioritising research in health impact modelling
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