BACKGROUND: Conventionally, capture-recapture techniques involving different lists such as police or hospitals are used for quantifying populations which are difficult to count, such as illicit drug user populations. Here, a novel approach is suggested based upon repeated entries in one list, which is less dependent on matching entries from different sources as in the conventional approach. METHODS: For this purpose, a population-based study was conducted that utilizes all data on treatment episodes of drug users from all 61 health treatment centers in the Bangkok metropolitan region to estimate the size of drug use in the Bangkok metropolitan region. The data stem from the drug treatment surveillance system of the Office of the Narcotics Control Board (ONCB) and cover the period from October 1 to December 31, 2001. Based upon the frequency of treatment episodes of each patient, a count distribution arose which could be modelled well by means of a Poisson mixture model. Using this count model, an estimate for the number of unobserved drug users could be constructed. RESULTS: From 11,222 drug users found during the period, 7063 (62.9%) were heroin users, 3346 (29.8%) metamphetamine users, and the remaining 813 (7.3%) distributed under 15 drug categories, none above 1%. The study concentrated on heroin and metamphetamine users who were predominantly male (96.2% for heroin and 91.8% for metamphetamine). Metamphetamine users were younger than heroin users (22.3 years 95% CI: 22.1-22.5 vs. 30.8 years 95% CI: 30.6-31.0). By using the truncated Poisson mixture model, an estimate of the unobserved frequency of drug users with zero treatment episodes could be constructed leading to an estimate of 11,296 (95% CI: 8,964-13,628) heroin users (completeness of identification: 38.42, 95% CI: 34.03-44.04%) and 32,105 (95% CI: 24,647-39,563) metamphetamine users (completeness of identification: 9.44, 95% CI: 7.79-11.97%) for the Bangkok metropolitan region. CONCLUSIONS: The proposed model showed excellent goodness-of-fit, unspecified for drug type and also if specified for the major drug types which allowed the prediction of the unobserved number of drug users in a realistic way, avoiding artefacts due to severe matching problems when using several, different sources. The technique is also easy to implement and can be used routinely to monitor drug user populations in space and time.
BACKGROUND: Conventionally, capture-recapture techniques involving different lists such as police or hospitals are used for quantifying populations which are difficult to count, such as illicit drug user populations. Here, a novel approach is suggested based upon repeated entries in one list, which is less dependent on matching entries from different sources as in the conventional approach. METHODS: For this purpose, a population-based study was conducted that utilizes all data on treatment episodes of drug users from all 61 health treatment centers in the Bangkok metropolitan region to estimate the size of drug use in the Bangkok metropolitan region. The data stem from the drug treatment surveillance system of the Office of the Narcotics Control Board (ONCB) and cover the period from October 1 to December 31, 2001. Based upon the frequency of treatment episodes of each patient, a count distribution arose which could be modelled well by means of a Poisson mixture model. Using this count model, an estimate for the number of unobserved drug users could be constructed. RESULTS: From 11,222 drug users found during the period, 7063 (62.9%) were heroin users, 3346 (29.8%) metamphetamine users, and the remaining 813 (7.3%) distributed under 15 drug categories, none above 1%. The study concentrated on heroin and metamphetamine users who were predominantly male (96.2% for heroin and 91.8% for metamphetamine). Metamphetamine users were younger than heroin users (22.3 years 95% CI: 22.1-22.5 vs. 30.8 years 95% CI: 30.6-31.0). By using the truncated Poisson mixture model, an estimate of the unobserved frequency of drug users with zero treatment episodes could be constructed leading to an estimate of 11,296 (95% CI: 8,964-13,628) heroin users (completeness of identification: 38.42, 95% CI: 34.03-44.04%) and 32,105 (95% CI: 24,647-39,563) metamphetamine users (completeness of identification: 9.44, 95% CI: 7.79-11.97%) for the Bangkok metropolitan region. CONCLUSIONS: The proposed model showed excellent goodness-of-fit, unspecified for drug type and also if specified for the major drug types which allowed the prediction of the unobserved number of drug users in a realistic way, avoiding artefacts due to severe matching problems when using several, different sources. The technique is also easy to implement and can be used routinely to monitor drug user populations in space and time.
Authors: A Domingo-Salvany; R L Hartnoll; A Maguire; M T Brugal; P Albertín; J A Caylà; J Casabona; J M Suelves Journal: Am J Epidemiol Date: 1998-10-15 Impact factor: 4.897
Authors: C Debrock; P M Preux; D Houinato; M Druet-Cabanac; F Kassa; C Adjien; G Avode; F Denis; F Boutros-Toni; M Dumas Journal: Int J Epidemiol Date: 2000-04 Impact factor: 7.196
Authors: N A H van Hest; F Smit; H W M Baars; G De Vries; P E W De Haas; P J Westenend; N J D Nagelkerke; J H Richardus Journal: Epidemiol Infect Date: 2006-12-07 Impact factor: 2.451
Authors: Hayley E Jones; Nicky J Welton; A E Ades; Matthias Pierce; Wyn Davies; Barbara Coleman; Tim Millar; Matthew Hickman Journal: Addiction Date: 2015-12-28 Impact factor: 6.526